title:
Ultimate precision of joint parameter estimation under noisy Gaussian
  environment

abstract:
  The major problem of multiparameter quantum estimation theory is to find an
ultimate measurement scheme to go beyond the standard quantum limits that each
quasi-classical estimation measurement is limited by. Although, in some
specifics quantum protocols without environmental noise, the ultimate
sensitivity of a multiparameter quantum estimation can beat the standard
quantum limit. However, the presence of noise imposes limitations on the
enhancement of precision due to the inevitable existence of environmental
fluctuations. Here, we address the motivation behind the usage of Gaussian
quantum resources and their advantages in reaching the standard quantum limits
under realistic noise. In this context, our work aims to explore the ultimate
limits of precision for the simultaneous estimation of a pair of parameters
that characterize the displacement channel acting on Gaussian probes and
subjected to open dynamics. More precisely, we focus on a general two-mode
mixed squeezed displaced thermal state, after reducing it to various Gaussian
probes states, like; a two-mode pure squeezed vacuum, two-mode pure displaced
vacuum, two-mode mixed displaced thermal, two-mode mixed squeezed thermal. To
study the ultimate estimation precision, we evaluate the upper and bottom bound
of HCRB in various cases. We find that when the entangled states, two-mode pure
squeezed vacuum and two-mode mixed squeezed thermal, are employed as probes
states, the upper and bottom bound of HCRB beats the standard quantum limit in
the presence of a noisy environment.


title:
Gender classification by means of online uppercase handwriting: A
  text-dependent allographic approach

abstract:
  This paper presents a gender classification schema based on online
handwriting. Using samples acquired with a digital tablet that captures the
dynamics of the writing, it classifies the writer as a male or a female. The
method proposed is allographic, regarding strokes as the structural units of
handwriting. Strokes performed while the writing device is not exerting any
pressure on the writing surface, pen-up (in-air) strokes, are also taken into
account. The method is also text-dependent meaning that training and testing is
done with exactly the same text. Text-dependency allows classification be
performed with very small amounts of text. Experimentation, performed with
samples from the BiosecurID database, yields results that fall in the range of
the classification averages expected from human judges. With only four
repetitions of a single uppercase word, the average rate of well classified
writers is 68%; with sixteen words, the rate rises to an average 72.6%.
Statistical analysis reveals that the aforementioned rates are highly
significant. In order to explore the classification potential of the pen-up
strokes, these are also considered. Although in this case results are not
conclusive, an outstanding average of 74% of well classified writers is
obtained when information from pen-up strokes is combined with information from
pen-down ones.


title: Neural Predictor for Black-Box Adversarial Attacks on Speech Recognition

abstract:
  Recent works have revealed the vulnerability of automatic speech recognition
(ASR) models to adversarial examples (AEs), i.e., small perturbations that
cause an error in the transcription of the audio signal. Studying audio
adversarial attacks is therefore the first step towards robust ASR. Despite the
significant progress made in attacking audio examples, the black-box attack
remains challenging because only the hard-label information of transcriptions
is provided. Due to this limited information, existing black-box methods often
require an excessive number of queries to attack a single audio example. In
this paper, we introduce NP-Attack, a neural predictor-based method, which
progressively evolves the search towards a small adversarial perturbation.
Given a perturbation direction, our neural predictor directly estimates the
smallest perturbation that causes a mistranscription. In particular, it enables
NP-Attack to accurately learn promising perturbation directions via
gradient-based optimization. Experimental results show that NP-Attack achieves
competitive results with other state-of-the-art black-box adversarial attacks
while requiring a significantly smaller number of queries. The code of
NP-Attack is available online.


title:
Non-local heat equation with moving boundary and curve-crossing of
  delayed Brownian motion

abstract:
  In this paper we consider a non-local (in time) heat equation on a
time-increasing parabolic set whose boundary is determined by a suitable curve.
We provide a notion of solution for this equation and we study well-posedness
under Dirichlet conditions outside the domain. A maximum principle is proved
and used to derive uniqueness and continuity with respect to the initial datum
of the solutions of the Dirichlet problem. Existence is proved by showing a
stochastic representation based on the delayed Brownian motion killed on the
boundary. Related properties on crossing probabilities of the delayed Brownian
motion are obtained. The asymptotic behaviour of the mean square displacement
of the process is determined, showing that the diffusive behaviour is
anomalous.


title:
Convergence of a finite-volume scheme for a heat equation with a
  multiplicative Lipschitz noise

abstract:
  We study here the approximation by a finite-volume scheme of a heat equation
forced by a Lipschitz continuous multiplicative noise in the sense of It\^o.
More precisely, we consider a discretization which is semi-implicit in time and
a two-point flux approximation scheme (TPFA) in space. Since the nonlinearity
in the stochastic integral is not compatible with the weak convergence obtained
by the a priori estimates, we adapt the method based on the theorem of
Prokhorov and on Skorokhod's representation theorem in order to show
stochastically strong convergence of the scheme towards the unique variational
solution of our parabolic problem.


title: Decision-Making under Miscalibration

abstract:
  ML-based predictions are used to inform consequential decisions about
individuals. How should we use predictions (e.g., risk of heart attack) to
inform downstream binary classification decisions (e.g., undergoing a medical
procedure)? When the risk estimates are perfectly calibrated, the answer is
well understood: a classification problem's cost structure induces an optimal
treatment threshold $j^{\star}$. In practice, however, some amount of
miscalibration is unavoidable, raising a fundamental question: how should one
use potentially miscalibrated predictions to inform binary decisions? We
formalize a natural (distribution-free) solution concept: given anticipated
miscalibration of $\alpha$, we propose using the threshold $j$ that minimizes
the worst-case regret over all $\alpha$-miscalibrated predictors, where the
regret is the difference in clinical utility between using the threshold in
question and using the optimal threshold in hindsight. We provide closed form
expressions for $j$ when miscalibration is measured using both expected and
maximum calibration error, which reveal that it indeed differs from $j^{\star}$
(the optimal threshold under perfect calibration). We validate our theoretical
findings on real data, demonstrating that there are natural cases in which
making decisions using $j$ improves the clinical utility.


title: Relating Siegel cusp forms to Siegel-Maa{\ss} forms

abstract:
  In this paper we generalize a well-known isomorphism between the space of
cusp forms of weight $k$ for a Fuchsian subgroup of the first kind $\Gamma
\subset\mathrm{SL}_{2}(\mathbb{R})$ and the space of certain Maa{\ss} forms of
weight $k$ for $\Gamma$ to an isomorphism between the space of Siegel cusp
forms of weight $k$ for a subgroup $\Gamma\subset\mathrm{Sp}_{n}(\mathbb{R})$,
which is commensurable with the Siegel modular group
$\mathrm{Sp}_{n}(\mathbb{Z})$, and a suitable space of Siegel-Maa{\ss} forms of
weight $k$ for $\Gamma$.


title:
Sunbathing under white light -- 3D modelling of brown dwarf - white
  dwarf atmospheres with strong UV irradiation

abstract:
  The atmospheres of brown dwarfs orbiting in close proximity to their parent
white dwarf represent some of the most extreme irradiated environments known.
Understanding their complex dynamical mechanisms pushes the limits of
theoretical and modelling efforts, making them valuable objets to study to test
contemporary understanding of irradiated atmospheres. We use the Exo-FMS GCM to
simulate the brown dwarfs WD0137-349B, SDSS J141126.20+200911.1B and
EPIC212235321B, first coupled to a multi-banded grey radiative-transfer scheme
then a spectral correlated-k scheme with high temperature opacity tables. We
then post-process the GCM results using gCMCRT to compare to available
observational data. Our GCM models predict strongly temperature inverted
atmospheres, spanning many decades in pressure due to impact of UV band
heating. Post-processing of our models suggest that the day-night contrast is
too small in the GCM results. We therefore suggest that the formation of cloud
particles as well as atmospheric drag effects such as magnetic drag are
important considerations in setting the day-night temperature contrast for
these objects.


title: Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion

abstract:
  In this paper, we formulate a potentially valuable panoramic depth completion
(PDC) task as panoramic 3D cameras often produce 360{\deg} depth with missing
data in complex scenes. Its goal is to recover dense panoramic depths from raw
sparse ones and panoramic RGB images. To deal with the PDC task, we train a
deep network that takes both depth and image as inputs for the dense panoramic
depth recovery. However, it needs to face a challenging optimization problem of
the network parameters due to its non-convex objective function. To address
this problem, we propose a simple yet effective approach termed M{^3}PT:
multi-modal masked pre-training. Specifically, during pre-training, we
simultaneously cover up patches of the panoramic RGB image and sparse depth by
shared random mask, then reconstruct the sparse depth in the masked regions. To
our best knowledge, it is the first time that we show the effectiveness of
masked pre-training in a multi-modal vision task, instead of the single-modal
task resolved by masked autoencoders (MAE). Different from MAE where
fine-tuning completely discards the decoder part of pre-training, there is no
architectural difference between the pre-training and fine-tuning stages in our
M$^{3}$PT as they only differ in the prediction density, which potentially
makes the transfer learning more convenient and effective. Extensive
experiments verify the effectiveness of M{^3}PT on three panoramic datasets.
Notably, we improve the state-of-the-art baselines by averagely 26.2% in RMSE,
51.7% in MRE, 49.7% in MAE, and 37.5% in RMSElog on three benchmark datasets.
Codes and pre-trained models are available at
https://github.com/anonymoustbd/MMMPT.


title: On distance magic circulants of valency 6

abstract:
  A graph $\Gamma = (V,E)$ of order $n$ is {\em distance magic} if it admits a
bijective labeling $\ell \colon V \to \{1,2, \ldots, n\}$ of its vertices for
which there exists a positive integer $\kappa$ such that $\sum_{u \in N(v)}
\ell(u) = \kappa$ for all vertices $v \in V$, where $N(v)$ is the neighborhood
of $v$. %It is well known that a regular distance magic graph is necessarily of
even valency.
  A {\em circulant} is a graph admitting an automorphism cyclically permuting
its vertices. In this paper we study distance magic circulants of valency $6$.
We obtain some necessary and some sufficient conditions for a circulant of
valency $6$ to be distance magic, thereby finding several infinite families of
examples. The combined results of this paper provide a partial classification
of all distance magic circulants of valency $6$. In particular, we classify
distance magic circulants of valency $6$, whose order is not divisible by $12$.


title:
A Framework for Formal Specification and Verification of Security
  Properties of the Android Permissions System

abstract:
  Android is a widely deployed operating system that employs a permission-based
access control model. The Android Permissions System (APS) is responsible for
mediating resource requests from applications. APS is a critical component of
the Android security mechanism. A failure in the design of APS can potentially
lead to vulnerabilities that grant unauthorized access to resources by
malicious applications. Researchers have employed formal methods for analyzing
the security properties of APS. Since Android is constantly evolving, we intend
to design and implement a framework for formal specification and verification
of the security properties of APS. In particular, we intend to present a
behavioral model of APS that represents the non-binary, context dependent
permissions introduced in Android 10 and temporal permissions introduced in
Android 11.


title: Non-invariance of weak approximation with Brauer--Manin obstruction

abstract:
  In this paper, we study weak approximation with Brauer--Manin obstruction
with respect to extensions of number fields. For any nontrivial extension
$L/K,$ assuming a conjecture of M. Stoll, we prove that there exists a
$K$-threefold satisfying weak approximation with Brauer--Manin obstruction off
all archimedean places, while its base change to $L$ fails. Then we illustrate
this construction with an explicit unconditional example.


title:
Promoting Ethical Awareness in Communication Analysis: Investigating
  Potentials and Limits of Visual Analytics for Intelligence Applications

abstract:
  Digital systems for analyzing human communication data have become prevalent
in recent years. Intelligence analysis of communications data in investigative
journalism, criminal intelligence, and law present particularly interesting
cases, as they must take into account the often highly sensitive properties of
the underlying operations and data. At the same time, these are areas where
increasingly automated, sophisticated approaches systems can be particularly
relevant, especially in terms of Big Data manageability. However, by the
shifting of responsibilities, this also poses dangers. In addition to privacy
concerns, these dangers relate to uncertain or poor data quality, leading to
discrimination and potentially misleading insights. Visual analytics combines
machine learning methods with interactive visual interfaces to enable human
sense- and decision-making. This technique can be key for designing and
operating meaningful interactive communication analysis systems that consider
these ethical challenges. In this interdisciplinary work, a joint endeavor of
computer scientists, ethicists, and scholars in Science & Technology Studies,
we investigate and evaluate opportunities and risks involved in using Visual
analytics approaches for communication analysis in intelligence applications in
particular. We introduce, at first, the common technological systems used in
communication analysis, further discussing the domain-specific ethical
implications, tensions, and risks involved. We then make the case of how
tailored Visual Analytics approaches may reduce and mitigate the described
problems, both theoretically and through practical examples. We show that
finding Visual Analytics design solutions for ethical issues is not a mere
optimization task, but balancing out and negotiating these trade-offs has, as
we argue, to be an integral aspect of the system design process from the
outset.


title: Pseudo Bias-Balanced Learning for Debiased Chest X-ray Classification

abstract:
  Deep learning models were frequently reported to learn from shortcuts like
dataset biases. As deep learning is playing an increasingly important role in
the modern healthcare system, it is of great need to combat shortcut learning
in medical data as well as develop unbiased and trustworthy models. In this
paper, we study the problem of developing debiased chest X-ray diagnosis models
from the biased training data without knowing exactly the bias labels. We start
with the observations that the imbalance of bias distribution is one of the key
reasons causing shortcut learning, and the dataset biases are preferred by the
model if they were easier to be learned than the intended features. Based on
these observations, we propose a novel algorithm, pseudo bias-balanced
learning, which first captures and predicts per-sample bias labels via
generalized cross entropy loss and then trains a debiased model using pseudo
bias labels and bias-balanced softmax function. To our best knowledge, we are
pioneered in tackling dataset biases in medical images without explicit
labeling on the bias attributes. We constructed several chest X-ray datasets
with various dataset bias situations and demonstrated with extensive
experiments that our proposed method achieved consistent improvements over
other state-of-the-art approaches.


title:
The $C^\infty$-isomorphism property for a class of singularly-weighted
  X-ray transforms

abstract:
  We study various self-adjoint realizations of the X-ray transform on the
Euclidean disk $\mathbb{D}$, obtained by considering specific singularly
weighted $L^2$ topologies. We first recover the well-known Singular Value
Decompositions in terms of disk orthogonal polynomials, then prove that each
such realization is an isomorphism of $C^\infty(\mathbb{D})$. As corollaries:
we give some range characterizations; we explain how for such choices, these
normal operators are in the functional calculus of two distinguished
differential operators; we show that the isomorphism property also holds on a
class of constant-curvature, circularly symmetric simple surfaces.


title:
Finite-sample analysis of identification of switched linear systems with
  arbitrary or restricted switching

abstract:
  This work aims to derive a data-independent finite-sample error bound for the
least-squares (LS) estimation error of switched linear systems when the state
and the switching signal are measured. While the existing finite-sample bounds
for linear system identification extend to the problem under consideration, the
Gramian of the switched system, an essential term in the error bound, depends
on the measured switching signal. Therefore, data-independent bounds on the
spectrum of the Gramian are developed for globally asymptotically and
marginally stable switched systems when the switching is arbitrary or subject
to an average dwell time constraint. Combining the bounds on the spectrum of
the Gramian and the preliminary error bound extended from linear system
identification leads to the error bound for the LS estimate of the switched
system.


title: A simulation study of tau neutrino events at the ICAL detector in INO

abstract:
  We present the first detailed simulations study of tau neutrino-induced
charged current (CC) events from atmospheric neutrino interactions in the Iron
Calorimeter (ICAL) detector at the proposed India-based Neutrino Observatory
(INO) laboratory. Since the intrinsic atmospheric neutrino flux at few to 10s
of GeV energy comprises only electron and muon neutrinos (and anti-neutrinos)
with negligible tau neutrino component, any signature of atmospheric tau
neutrinos is a signal for neutrino oscillations. We study the tau leptons
produced through these CC interactions via their hadronic decay. These events
appear as an excess over the neutral current (NC) background where hadrons are
the only observable component. We find that the presence of tau neutrinos in
the atmospheric neutrino flux can be demonstrated to nearly $4\sigma$
confidence with 10 years data; in addition, these events are sensitive to the
neutrino oscillation parameters, $\sin^2\theta_{23}$ and $|\Delta m_{31}^2 |$
(or $|\Delta m_{32}^2 |$), in the 2--3 sector. Finally, we show that combining
these events with the standard muon analysis which is the core goal of ICAL
further improves the precision with which these parameters, especially the
octant of $\theta_{23}$, can be measured.


title: Generalized Score Matching for Regression

abstract:
  Many probabilistic models that have an intractable normalizing constant may
be extended to contain covariates. Since the evaluation of the exact likelihood
is difficult or even impossible for these models, score matching was proposed
to avoid explicit computation of the normalizing constant. In the literature,
score matching has so far only been developed for models in which the
observations are independent and identically distributed (IID). However, the
IID assumption does not hold in the traditional fixed design setting for
regression-type models. To deal with the estimation of these
covariate-dependent models, this paper presents a new score matching approach
for independent but not necessarily identically distributed data under a
general framework for both continuous and discrete responses, which includes a
novel generalized score matching method for count response regression. We prove
that our proposed score matching estimators are consistent and asymptotically
normal under mild regularity conditions. The theoretical results are supported
by simulation studies and a real-data example. Additionally, our simulation
results indicate that, compared to approximate maximum likelihood estimation,
the generalized score matching produces estimates with substantially smaller
biases in an application to doctoral publication data.


title:
Weak lensing magnification of Type Ia Supernovae from the Pantheon
  sample

abstract:
  Using data from the Pantheon SN Ia compilation and the Sloan Digital Sky
Survey (SDSS), we propose an estimator for weak lensing convergence
incorporating positional and photometric data of foreground galaxies. The
correlation between this and the Hubble diagram residuals of the supernovae is
$3.7\sigma$, consistent with weak lensing magnification due to dark matter
halos centered on galaxies. We additionally constrain the properties of the
galactic haloes, such as the mass-to-light ratio $\Gamma$ and radial profile of
the halo matter density $\rho(r)$. We derive a new relationship for the
additional r.m.s. scatter in magnitudes caused by lensing, finding $\sigma_{\rm
lens} = (0.06 \pm 0.017) (d_{\rm M}(z)/ d_{\rm M}(z=1))^{3/2}$ where $d_{\rm
M}(z)$ is the comoving distance to redshift $z$. We therefore find that the
scatter in flux caused by lensing will be of a similar size as the intrinsic
scatter (after standardisation of the magnitude) of modern SN Ia surveys by $z
\sim 1.2$, which is closer than generally assumed. We propose a modification of
the distance modulus estimator for SN Ia to incorporate lensing, which we
anticipate will improve the accuracy of cosmological parameter estimation using
high-redshift SN Ia data.


title:
Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias
  in Speech Translation

abstract:
  Gender bias is largely recognized as a problematic phenomenon affecting
language technologies, with recent studies underscoring that it might surface
differently across languages. However, most of current evaluation practices
adopt a word-level focus on a narrow set of occupational nouns under synthetic
conditions. Such protocols overlook key features of grammatical gender
languages, which are characterized by morphosyntactic chains of gender
agreement, marked on a variety of lexical items and parts-of-speech (POS). To
overcome this limitation, we enrich the natural, gender-sensitive MuST-SHE
corpus (Bentivogli et al., 2020) with two new linguistic annotation layers (POS
and agreement chains), and explore to what extent different lexical categories
and agreement phenomena are impacted by gender skews. Focusing on speech
translation, we conduct a multifaceted evaluation on three language directions
(English-French/Italian/Spanish), with models trained on varying amounts of
data and different word segmentation techniques. By shedding light on model
behaviours, gender bias, and its detection at several levels of granularity,
our findings emphasize the value of dedicated analyses beyond aggregated
overall results.


title:
Bottom-Up Reconstruction of Viable GW170817 Compatible
  Einstein-Gauss-Bonnet Theories

abstract:
  In this work we shall use a bottom-up approach for obtaining viable
inflationary Einstein-Gauss-Bonnet models which are also compatible with the
GW170817 event. Specifically, we shall use a recently developed theoretical
framework in which we shall specify only the tensor-to-scalar ratio, in terms
of the $e$-foldings number. Starting from the tensor-to-scalar ratio, we shall
reconstruct from it the Einstein-Gauss-Bonnet theory which can yield such a
tensor-to-scalar ratio, finding the scalar potential and the Gauss-Bonnet
coupling scalar function as functions of the $e$-foldings number. Accordingly,
the calculation of the spectral index of the primordial scalar perturbations,
and of the tensor spectral index easily is greatly simplified and these
observational indices can easily be found. After presenting the general
formalism for the bottom-up reconstruction, we exemplify our findings by
presenting several Einstein-Gauss-Bonnet models of interest which yield a
viable inflationary phenomenology. These models have also an interesting common
characteristic, which is a blue tilted tensor spectral index. We also
investigate the predicted energy spectrum of the primordial gravitational waves
for these Einstein-Gauss-Bonnet models, and as we show, all the models yield a
detectable primordial wave energy power spectrum.


title: Exact approaches for the Connected Vertex Cover problem

abstract:
  Given a graph $G$, the Connected Vertex Cover problem (CVC) asks to find a
minimum cardinality vertex cover of $G$ that induces a connected subgraph. In
this paper we describe some approaches to solve the CVC problem exactly. First,
we give compact mixed-integer extended formulations for CVC: these are the
first formulations proposed for this problem, and can be easily adapted to
variations of the problem such as Tree Cover. Second, we describe a simple
branch and bound algorithm for the CVC problem. Finally, we implement our
algorithm and compare its performance against our best formulation: contrary to
what usually happens for the classical Vertex Cover problem, our formulation
outperforms the branch and bound algorithm.


title:
Electromagnetically induced transparency in inhomogeneously broadened
  divacancy defect ensembles in SiC

abstract:
  Electromagnetically induced transparency (EIT) is a phenomenon that can
provide strong and robust interfacing between optical signals and quantum
coherence of electronic spins. In its archetypical form, mainly explored with
atomic media, it uses a (near-)homogeneous ensemble of three-level systems, in
which two low-energy spin-1/2 levels are coupled to a common optically excited
state. We investigate the implementation of EIT with c-axis divacancy color
centers in silicon carbide. While this material has attractive properties for
quantum device technologies with near-IR optics, implementing EIT is
complicated by the inhomogeneous broadening of the optical transitions
throughout the ensemble and the presence of multiple ground-state levels. These
may lead to darkening of the ensemble upon resonant optical excitation. Here,
we show that EIT can be established with high visibility also in this material
platform upon careful design of the measurement geometry. Comparison of our
experimental results with a model based on the Lindblad equations indicates
that we can create coherences between different sets of two levels
all-optically in these systems, with potential impact for RF-free quantum
sensing applications. Our work provides an understanding of EIT in multi-level
systems with significant inhomogeneities, and our considerations are valid for
a wide array of defects in semiconductors.


title:
Open-source finite volume solvers for multiphase (n-phase) flows
  involving either Newtonian or non-Newtonian complex fluids

abstract:
  Two new control volume solvers multiFluidInterFoam and
rheoMultiFluidInterFoam are presented for the simulation of Newtonian and
non-Newtonian n-phase flows, respectively, fully accounting for interfacial
tension and contact-angle effects for each phase. The multiFluidInterFoam
solver modifies certain crucial aspects of the regular multiphaseInterFoam
solver provided by OpenFOAM for Newtonian flows, improving its efficiency and
robustness, but most importantly improving considerably its accuracy for
surface tension driven flows. The new solver uses the volume-of-fluid (VOF)
method based on the MULES method and artificial interface compression for the
interface capturing, in combination with a suitable continuum surface force
model; i.e. the interfacial tension coefficient decomposition method is
employed to treat pairings of interfacial tension between the phases. VOF
smoothing is also implemented by applying a Laplacian filter on the
distribution of volume fractions. The rheoMultiFluidInterFoam solver is capable
of fully taking into account complex non-Newtonian effects (e.g. yield stress,
viscoelasticity, etc.) of the involved liquids. To this end, the developed
solver incorporates the RheoTool toolbox (Pimenta and Alves (2017) J.
Non-Newtonian Fluid Mech. 239, 85-104), utilizing a wealth of constitutive
equations suitable for modelling different types of fluids with complex
rheology. Our solvers are tested for a wide range of different flow setups,
considering typical benchmark two-phase and three-phase flows; flows involving
Newtonian, viscoplastic and viscoelastic liquids have been considered.
Comparisons against analytical solutions or previously published numerical data
are performed clearly demonstrating the capability of the new solvers to
efficiently and accurately simulate interfacial tension dominated three-phase
flows for both simple and complex liquids.


title: On Robust Regulation of PDEs: from Abstract Methods to PDE Controllers

abstract:
  In this paper we study robust output tracking and disturbance rejection of
linear partial differential equation (PDE) models. We focus on demonstrating
how the abstract internal model based controller design methods developed for
"regular linear systems" are applied in controller design for concrete PDE
systems. We show that when implemented for PDE systems, the abstract control
design methods lead in a natural way to controllers with "PDE parts". Moreover,
we formulate the controller construction in a way which utilises minimal
knowledge of the abstract system representation and is instead solely based on
natural properties of the original PDE. We also discuss computation and
approximation of the controller parameters, and illustrate the results with an
example on control design for a boundary controlled diffusion equation.


title:
Effect of Active and Passive Protective Soft Skins on Collision Forces
  in Human-robot Collaboration

abstract:
  Soft electronic skins are one of the means to turn an industrial manipulator
into a collaborative robot. For manipulators that are already fit for physical
human-robot collaboration, soft skins can make them safer. In this work, we
study the after impact behavior of two collaborative manipulators (UR10e and
KUKA LBR iiwa) and one classical industrial manipulator (KUKA Cybertech), in
the presence or absence of an industrial protective skin (AIRSKIN). In
addition, we isolate the effects of the passive padding and the active
contribution of the sensor to robot reaction. We present a total of 2250
collision measurements and study the impact force, contact duration, clamping
force, and impulse. The dataset is publicly available. We summarize our results
as follows. For transient collisions, the passive skin properties lowered the
impact forces by about 40 %. During quasi-static contact, the effect of skin
covers -- active or passive -- cannot be isolated from the collision detection
and reaction by the collaborative robots. Important effects of the stop
categories triggered by the active protective skin were found. We
systematically compare the different settings and the empirically established
safe velocities with prescriptions by the ISO/TS 15066. In some cases, up to
the quadruple of the ISO/TS 15066 prescribed velocity can comply with the
impact force limits and thus be considered safe. We propose an extension of the
formulas relating impact force and permissible velocity that take into account
the stiffness and compressible thickness of the protective cover, leading to
better predictions of the collision forces. At the same time, this work
emphasizes the need for in situ measurements as all the factors we studied --
presence of active/passive skin, safety stop settings, robot collision
reaction, impact direction, and, of course, velocity -- have effects on the
force evolution after impact.


title: Magnetization and chemical bond in alpha-RuCl3

abstract:
  A previous detailed study of the three-dimensional chemical and magnetic
structures of an untwined alpha-RuCl3 crystal revealed a monoclinic unit cell
(C2/m) and a zigzag antiferromagnetic ground state (Cc2/m) [R. D. Johnson et
al., Phys. Rev. B 92, 235119 (2015)]. Here, we demonstrate that the proposals
infer the existence of Ru Dirac multipoles, e.g., anapoles, also known as
toroidal dipoles, that are parity-odd and time-odd. Notably, Dirac multipoles
are predicted to be sole contributors to specific magnetic Bragg spots in the
alpha-RuCl3 diffraction pattern, akin to exhaustively studied basis-forbidden
(2, 2, 2) Bragg spots of diamond-like structures. Dirac multipoles contribute
to all magnetic Bragg spots from the zigzag antiferromagnetic ground state,
although they are absent from the published analysis of neutron diffraction
patterns that supports the current ground-state assignment. They are visible in
x-ray Bragg diffraction enhanced by an E1-E2 absorption event, and a magnetic
amplitude appropriate for Ru anapoles and quadrupoles is presented. Like
quadrupoles are candidates for magnetism in the pseudo-gap phases of cuprate
superconductors YBCO and Hg1201. X-ray absorption spectra published for
alpha-RuCl3 possess a significant low-energy feature [K. W. Plumb et al., Phys.
Rev. B 90, 041112(R) (2014)]. Many experimental studies of other Cl-metal
compounds concluded that identical features hallmark the chemical bond. Using a
monoclinic C2/m structure, we predict the contribution to Bragg diffraction at
the Cl K-edge absorption. Specifically, the variation of intensity of Bragg
spots with rotation of the crystal about the reflection vector.


title:
Existence for a nonlocal Penrose--Fife type phase field system with
  inertial term

abstract:
  This article deals with a nonlocal Penrose-Fife type phase field system with
inertial term. We do not know whether we can prove existence of solutions in
reference to Colli--Grasselli--Ito [Electron. J. Differential Equations 2002,
No. 100, 32 pp.] or not (see Remark 1.1). In this paper we introduce a time
discretization scheme (see Section 2), pass to the limit as the time step $h$
goes to $0$ and obtain an error estimate for the difference between continuous
and discrete solutions (see Section 5).


title: A WISE view on extreme AGB stars

abstract:
  (abridged) Variability is a key property of stars on the asymptotic giant
branch (AGB). Their pulsation period is related to the luminosity and mass-loss
rate (MLR) of the star. Long-period variables (LPVs) and Mira variables are the
most prominent of all types of variability of evolved stars. However, the
reddest, most obscured AGB stars are too faint in the optical and have eluded
large variability surveys. Our goal is to obtain a sample of LPVs with large
MLRs by analysing WISE W1 and W2 light curves (LCs) for about 2000 sources,
photometrically selected to include known C-stars with the 11.3 mu silicon
carbide dust feature in absorption, and Galactic O-stars with periods longer
than 1000 days. Epoch photometry was retrieved from the AllWISE and NEOWISE
database and fitted with a sinus curve. Photometry from other variability
surveys was also downloaded and fitted. For a subset of 316 of the reddest
stars, spectral energy distributions (SEDs) were constructed, and, together
with mid-infrared (MIR) spectra when available, fitted with a dust radiative
transfer programme in order to derive MLRs. WISE based LCs and fits to the data
are presented for all stars. Inspired by a recent paper, a number of
non-variable OH/IRs are identified. Based on a selection on amplitude, a sample
of about 750 (candidate) LPVs is selected of which 145 have periods over 1000
days, many of them being new. For the subset of the stars with the colours of
C-rich extremely red objects (EROs) the fitting of the SEDs (and available MIR
spectra) separates them into C- and O-rich objects. The number of Galactic EROs
appears to be complete up to about 5~kpc and a total dust return rate in the
solar neighbourhood for this class is determined. Based on the EROs in the
Magellanic Clouds, a bolometric period luminosity is derived.


title:
Thermal Alteration and Differential Sublimation Can Create Phaethons
  "Rock Comet" Activity and Blue Color

abstract:
  In 2010 Jewitt and Li published a paper examining the behavior of
comet-asteroid transition object 3200 Phaethon, arguing it was asteroid-like in
its behavior throughout most of its orbit, but that near its perihelion, at a
distance of only 0.165 AU from the sun, its dayside temperatures would be hot
enough to vaporize rock (>1000 K, Hanus et al. 2016). Thus it would act like a
"rock comet" as gases produced from evaporating rock were released from the
body, in a manner similar to the more familiar sublimation of water ice into
vacuum seen for comets coming within ~3 AU of the Sun. In this Note we predict
that the same thermal effects that would create "rock comet" behavior with Qgas
~ 10$^{22}$ mol/sec at perihelion would also help greatly bluen Phaethon's
surface via preferential thermal alteration and sublimative removal of surface
Fe and refractory organics, known reddening and darkening agents. These
predictions are testable by searching for signs of spectral bluening of the
surfaces of other objects in Phaethon-like small perihelion orbits, and by in
situ measurements of Phaethons surface and coma composition near perihelion
with the upcoming DESTINY+ mission to Phaethon by JAXA.


title:
Existence of flipped orthogonal conjugate symmetric Jordan canonical
  bases for real H-selfadjoint matrices

abstract:
  For real matrices selfadjoint in an indefinite inner product there are two
special canonical Jordan forms, that is (i) flipped orthogonal (FO) and (ii)
$\gamma$-conjugate symmetric (CS). These are the classical Jordan forms with
certain additional properties induced by the fact that they are
$H$-selfadjoint. In this paper we prove that for any real $H$-selfadjoint
matrix there is a $\gamma$-FOCS Jordan form that is simultaneously flipped
orthogonal and $\gamma$-conjugate symmetric.


title:
Automatic analysis of Categorical Verbal Fluency for Mild Cognitive
  Impartment detection: a non-linear language independent approach

abstract:
  Alzheimer's disease (AD) is one the main causes of dementia in the world and
the patients develop severe disability and sometime full dependence. In
previous stages Mild Cognitive Impairment (MCI) produces cognitive loss but not
severe enough to interfere with daily life. This work, on selection of
biomarkers from speech for the detection of AD, is part of a wide-ranging cross
study for the diagnosis of Alzheimer. Specifically in this work a task for
detection of MCI has been used. The task analyzes Categorical Verbal Fluency.
The automatic classification is carried out by SVM over classical linear
features, Castiglioni fractal dimension and Permutation Entropy. Finally the
most relevant features are selected by ANOVA test. The promising results are
over 50% for MCI


title:
Class-wise Classifier Design Capable of Continual Learning using
  Adaptive Resonance Theory-based Topological Clustering

abstract:
  This paper proposes a supervised classification algorithm capable of
continual learning by utilizing an Adaptive Resonance Theory (ART)-based
growing self-organizing clustering algorithm. The ART-based clustering
algorithm is theoretically capable of continual learning, and the proposed
algorithm independently applies it to each class of training data for
generating classifiers. Whenever an additional training data set from a new
class is given, a new ART-based clustering will be defined in a different
learning space. Thanks to the above-mentioned features, the proposed algorithm
realizes continual learning capability. Simulation experiments showed that the
proposed algorithm has superior classification performance compared with
state-of-the-art clustering-based classification algorithms capable of
continual learning.


title:
Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem
  Recitation

abstract:
  Up to 90 % of patients with Parkinson's disease (PD) suffer from hypokinetic
dysarthria (HD). In this work, we analysed the power of conventional speech
features quantifying imprecise articulation, dysprosody, speech dysfluency and
speech quality deterioration extracted from a specialized poem recitation task
to discriminate dysarthric and healthy speech. For this purpose, 152 speakers
(53 healthy speakers, 99 PD patients) were examined. Only mildly strong
correlation between speech features and clinical status of the speakers was
observed. In the case of univariate classification analysis, sensitivity of
62.63% (imprecise articulation), 61.62% (dysprosody), 71.72% (speech
dysfluency) and 59.60% (speech quality deterioration) was achieved.
Multivariate classification analysis improved the classification performance.
Sensitivity of 83.42% using only two features describing imprecise articulation
and speech quality deterioration in HD was achieved. We showed the promising
potential of the selected speech features and especially the use of poem
recitation task to quantify and identify HD in PD.


title: An Overview of Modest Models and Tools for Real Stochastic Timed Systems

abstract:
  We depend on the safe, reliable, and timely operation of cyber-physical
systems ranging from smart grids to avionics components. Many of them involve
time-dependent behaviours and are subject to randomness. Modelling languages
and verification tools thus need to support these quantitative aspects. In my
invited presentation at MARS 2022, I gave an introduction to quantitative
verification using the Modest modelling language and the Modest Toolset, and
highlighted three recent case studies with increasing demands on model
expressiveness and tool capabilities: A case of power supply noise in a
network-on-chip modelled as a Markov chain; a case of message routing in
satellite constellations that uses Markov decision processes with distributed
information; and a case of optimising an attack on Bitcoin via Markov automata
model checking. This paper summarises the presentation.


title: Advanced Models for the OSPF Routing Protocol

abstract:
  We present two formal models for the OSPF routing protocol, designed for the
model checker Uppaal. The first one is an optimised model of an existing model
that allows to check larger network topologies. The second one is a specialised
model for adjacency building, a complex subprocedure of OSPF, which is not part
of any existing model and which is known to be vulnerable to cyber attacks. We
illustrate how both models can be used to discover vulnerabilities in routing
protocols.


title:
Topological properties of two-dimensional photonic square lattice
  without $C_4$ and $M_{x(y)}$ symmetries

abstract:
  Rich topological phenomena, edge states and two types of corner states, are
unveiled in a two-dimensional square-lattice dielectric photonic crystal
without both $C_4$ and $M_{x(y)}$ symmetries. Specifically, non-trivial type-I
corner states, which do not exist in systems with $C_4$ and $M_{x(y)}$ since
the degeneracy, are protected by non-zero quadrupole moment, no longer
quantized to but less than $0.5$. Excellent properties, e.g. sub-wavelength
localization and air-concentrated field distribution, are presented. Type-II
corner states, induced by long-range interactions, are easier realized due to
asymmetry. This work broadens the topological physics for the symmetries-broken
systems and provides potential applications.


title: Modeling R$^3$ Needle Steering in Uppaal

abstract:
  Medical cyber-physical systems are safety-critical, and as such, require
ongoing verification of their correct behavior, as system failure during run
time may cause severe (or even fatal) personal damage. However, creating a
verifiable model often conflicts with other application requirements, most
notably regarding data precision and model accuracy, as efficient model
checking promotes discrete data (over continuous) and abstract models to reduce
the state space. In this paper, we approach the task of medical needle steering
in soft tissue around potential obstacles. We design a verifiable model of
needle motion (implemented in Uppaal Stratego) and a framework embedding the
model for online needle steering. We mitigate the conflict by imposing
boundedness on both the data types, reducing from R^3 to Z^3 when needed, and
the motion and environment models, reducing the set of allowed local actions
and global paths. In experiments, we successfully apply the static model alone,
as well as the dynamic framework in scenarios with varying environment
complexity and both a virtual and real needle setting, where up to 100% of
targets were reached depending on the scenario and needle.


title: Formally Modeling Autonomous Vehicles in LNT for Simulation and Testing

abstract:
  We present two behavioral models of an autonomous vehicle and its interaction
with the environment. Both models use the formal modeling language LNT provided
by the CADP toolbox. This paper discusses the modeling choices and the
challenges of our autonomous vehicle models, and also illustrates how formal
validation tools can be applied to a single component or the overall vehicle.


title: Analysis Description Language: A DSL for HEP Analysis

abstract:
  We propose to adopt a declarative domain specific language for describing the
physics algorithm of a high energy physics (HEP) analysis in a standard and
unambiguous way decoupled from analysis software frameworks, and argue that
this approach provides an accessible and sustainable environment for analysis
design, use and preservation. Prototype of such a language called Analysis
Description Language (ADL) and its associated tools are being developed and
applied in various HEP physics studies. We present the motivations for using a
DSL, design principles of ADL and its runtime interpreter CutLang, along with
current physics studies based on this approach. We also outline ideas and
prospects for the future. Recent physics studies, hands-on workshops and
surveys indicate that ADL is a feasible and effective approach with many
advantages and benefits, and offers a direction to which the HEP field should
give serious consideration.


title:
CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric
  Guidance

abstract:
  Transformers have gained much attention by outperforming convolutional neural
networks in many 2D vision tasks. However, they are known to have
generalization problems and rely on massive-scale pre-training and
sophisticated training techniques. When applying to 3D tasks, the irregular
data structure and limited data scale add to the difficulty of transformer's
application. We propose CodedVTR (Codebook-based Voxel TRansformer), which
improves data efficiency and generalization ability for 3D sparse voxel
transformers. On the one hand, we propose the codebook-based attention that
projects an attention space into its subspace represented by the combination of
"prototypes" in a learnable codebook. It regularizes attention learning and
improves generalization. On the other hand, we propose geometry-aware
self-attention that utilizes geometric information (geometric pattern, density)
to guide attention learning. CodedVTR could be embedded into existing sparse
convolution-based methods, and bring consistent performance improvements for
indoor and outdoor 3D semantic segmentation tasks


title:
Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut,
  Weighted Kernel $k$-means, and Heat Kernel

abstract:
  We propose a theoretical framework of multi-way similarity to model
real-valued data into hypergraphs for clustering via spectral embedding. For
graph cut based spectral clustering, it is common to model real-valued data
into graph by modeling pairwise similarities using kernel function. This is
because the kernel function has a theoretical connection to the graph cut. For
problems where using multi-way similarities are more suitable than pairwise
ones, it is natural to model as a hypergraph, which is generalization of a
graph. However, although the hypergraph cut is well-studied, there is not yet
established a hypergraph cut based framework to model multi-way similarity. In
this paper, we formulate multi-way similarities by exploiting the theoretical
foundation of kernel function. We show a theoretical connection between our
formulation and hypergraph cut in two ways, generalizing both weighted kernel
$k$-means and the heat kernel, by which we justify our formulation. We also
provide a fast algorithm for spectral clustering. Our algorithm empirically
shows better performance than existing graph and other heuristic modeling
methods.


title: Battle royale optimizer with a new movement strategy

abstract:
  Gamed-based is a new stochastic metaheuristics optimization category that is
inspired by traditional or digital game genres. Unlike SI-based algorithms,
in-dividuals do not work together with the goal of defeating other individuals
and winning the game. Battle royale optimizer (BRO) is a Gamed-based
me-taheuristic optimization algorithm that has been recently proposed for the
task of continuous problems. This paper proposes a modified BRO (M-BRO) in
order to improve balance between exploration and exploitation. For this matter,
an additional movement operator has been used in the movement strategy.
Moreover, no extra parameters are required for the proposed ap-proach.
Furthermore, the complexity of this modified algorithm is the same as the
original one. Experiments are performed on a set of 19 (unimodal and
multimodal) benchmark functions (CEC 2010). The proposed method has been
compared with the original BRO alongside six well-known/recently proposed
optimization algorithms. The results show that BRO with additional movement
operator performs well to solve complex numerical optimization problems
compared to the original BRO and other competitors.


title:
Electron removal energies in noble gas atoms up to 100 keV: ab initio GW
  vs XPS

abstract:
  X-ray photoelectron spectroscopy (XPS) measures electron removal energies,
providing direct access to core and valence electron binding energies, hence
probing the electronic structure. In this work, we benchmark for the first time
the ab initio many-body GW approximation on the complete electron binding
energies of noble gas atoms (He-Rn), which spans 100~keV. Our results
demonstrate that GW achieves an accuracy within 1.2% in XPS binding energies,
by systematically restoring the underestimation from density-functional theory
(DFT, error of 14%) or the overestimation from Hartree-Fock (HF, error of
4.7%). Such results also imply the correlations of $d$ electrons are very well
described by GW.

categories: cond-mat.other cond-mat.str-el nucl-th physics.atom-ph physics.chem-ph


title: Zero-range potentials for Dirac particles: Bound-state problems

abstract:
  A model in which a Dirac particle in $\mathbb{R}^{3}$ is bound by
$N\geqslant1$ spatially distributed zero-range potentials is presented.
Interactions between the particle and the potentials are modeled by subjecting
a particle's bispinor wave function to certain limiting conditions at the
potential centers. Each of these conditions is parametrized by a $2\times2$
Hermitian matrix (or, equivalently, a real scalar and a real vector) and mixes
the upper and the lower components of the wave function. The problem of
determining particle's bound-state eigenenergies is reduced to the problem of
finding real zeroes of a determinant of a certain $2N\times2N$ matrix. As the
lower component of the particle's wave function is inverse-square singular at
each of the potential centers, the wave function itself is not
square-integrable. Nevertheless, one can define a scalar pseudo-product with
the property that wave functions belonging to different eigenenergies are
orthogonal with respect to it. The wave functions may then be normalized so
that their self-pseudo-products are plus one, minus one or zero. An auxiliary
set of Sturmian functions is constructed and used to derive an explicit
representation of particle's matrix Green's function. For illustration
purposes, two particular systems are studied in detail: 1) a particle bound in
a field of a single zero-range potential, 2) a particle bound in a field of two
identical zero-range potentials.


title:
SCoT: Sense Clustering over Time: a tool for the analysis of lexical
  change

abstract:
  We present Sense Clustering over Time (SCoT), a novel network-based tool for
analysing lexical change. SCoT represents the meanings of a word as clusters of
similar words. It visualises their formation, change, and demise. There are two
main approaches to the exploration of dynamic networks: the discrete one
compares a series of clustered graphs from separate points in time. The
continuous one analyses the changes of one dynamic network over a time-span.
SCoT offers a new hybrid solution. First, it aggregates time-stamped documents
into intervals and calculates one sense graph per discrete interval. Then, it
merges the static graphs to a new type of dynamic semantic neighbourhood graph
over time. The resulting sense clusters offer uniquely detailed insights into
lexical change over continuous intervals with model transparency and
provenance. SCoT has been successfully used in a European study on the changing
meaning of `crisis'.


title:
A Lightweight Instrument-Agnostic Model for Polyphonic Note
  Transcription and Multipitch Estimation

abstract:
  Automatic Music Transcription (AMT) has been recognized as a key enabling
technology with a wide range of applications. Given the task's complexity, best
results have typically been reported for systems focusing on specific settings,
e.g. instrument-specific systems tend to yield improved results over
instrument-agnostic methods. Similarly, higher accuracy can be obtained when
only estimating frame-wise $f_0$ values and neglecting the harder note event
detection. Despite their high accuracy, such specialized systems often cannot
be deployed in the real-world. Storage and network constraints prohibit the use
of multiple specialized models, while memory and run-time constraints limit
their complexity. In this paper, we propose a lightweight neural network for
musical instrument transcription, which supports polyphonic outputs and
generalizes to a wide variety of instruments (including vocals). Our model is
trained to jointly predict frame-wise onsets, multipitch and note activations,
and we experimentally show that this multi-output structure improves the
resulting frame-level note accuracy. Despite its simplicity, benchmark results
show our system's note estimation to be substantially better than a comparable
baseline, and its frame-level accuracy to be only marginally below those of
specialized state-of-the-art AMT systems. With this work we hope to encourage
the community to further investigate low-resource, instrument-agnostic AMT
systems.


title:
Measurements of second-harmonic Fourier coefficients from azimuthal
  anisotropies in $p$$+$$p$, $p$$+$Au, $d$$+$Au, and $^3$He$+$Au collisions at
  $\sqrt{s_{_{NN}}}=200$ GeV

abstract:
  Recently, the PHENIX Collaboration has published second- and third-harmonic
Fourier coefficients $v_2$ and $v_3$ for midrapidity ($|\eta|<0.35$) charged
hadrons in 0\%--5\% central $p$$+$Au, $d$$+$Au, and $^3$He$+$Au collisions at
$\sqrt{s_{_{NN}}}=200$ GeV utilizing three sets of two-particle correlations
for two detector combinations with different pseudorapidity acceptance [Phys.
Rev. C {\bf 105}, 024901 (2022)]. This paper extends these measurements of
$v_2$ to all centralities in $p$$+$Au, $d$$+$Au, and $^3$He$+$Au collisions, as
well as $p$$+$$p$ collisions, as a function of transverse momentum ($p_T$) and
event multiplicity. The kinematic dependence of $v_2$ is quantified as the
ratio $R$ of $v_2$ between the two detector combinations as a function of event
multiplicity for $0.5$$<$$p_T$$<$$1$ and $2$$<$$p_T$$<$$2.5$ GeV/$c$. A
multiphase-transport (AMPT) model can reproduce the observed $v_2$ in
most-central to midcentral $d$$+$Au and $^3$He$+$Au collisions. However, the
AMPT model systematically overestimates the measurements in $p$$+$$p$,
$p$$+$Au, and peripheral $d$$+$Au and $^3$He$+$Au collisions, indicating a
higher nonflow contribution in AMPT than in the experimental data. The AMPT
model fails to describe the observed $R$ for $0.5$$<$$p_T$$<$$1$ GeV/$c$, but
there is qualitative agreement with the measurements for $2$$<$$p_T$$<$$2.5$
GeV/$c$.


title:
Bayesian Spectral Deconvolution of X-Ray Absorption Near Edge Structure
  Discriminating High- and Low-Energy Domain

abstract:
  In this paper, we propose a Bayesian spectral deconvolution considering the
properties of peaks in different energy domains. Bayesian spectral
deconvolution regresses spectral data into the sum of multiple basis functions.
Conventional methods use a model that treats all peaks equally. However, in
X-ray absorption near edge structure (XANES) spectra, the properties of the
peaks differ depending on the energy domain, and the specific energy domain of
XANES is essential in condensed matter physics. We propose a model that
discriminates the energy domain and a prior distribution that reflects the
physical properties. We compare the conventional and proposed models in
computational efficiency, estimation accuracy, and model evidence. We
demonstrate that our method effectively estimates the number of transition
components in the important energy domain, on which the material scientists
focus for mapping to the electronic transition analysis by first-principles
simulation.


title:
Experimental observation of a dissipative phase transition in a
  multi-mode many-body quantum system

abstract:
  We characterize the dissipative phase transition in a driven dissipative
Bose-Einstein condensate of neutral atoms. Our results generalize the work on
dissipative nonlinear Kerr resonators towards many modes and stronger
interactions. We measure the effective Liouvillian gap and analyze the
microscopic system dynamics, where we identify a non-equilibrium condensation
process.


title: Cartier transform and prismatic crystals

abstract:
  We show that the abstract equivalence of categories, called Cartier
transform, between crystals on the q-crystalline and prismatic sites can be
locally identified with the explicit local q-twisted Simpson correspondence.
This establishes four equivalences that are all compatible with the relevant
cohomology theories. We restrict ourselves for simplicity to the dimension one
situation.


title:
Development Effort Estimation in Free/Open Source Software from Activity
  in Version Control Systems

abstract:
  Effort estimation models are a fundamental tool in software management, and
used as a forecast for resources, constraints and costs associated to software
development. For Free/Open Source Software (FOSS) projects, effort estimation
is especially complex: professional developers work alongside occasional,
volunteer developers, so the overall effort (in person-months) becomes
non-trivial to determine.
  The objective of this work it to develop a simple effort estimation model for
FOSS projects, based on the historic data of developers' effort. The model is
fed with direct developer feedback to ensure its accuracy.
  After extracting the personal development profiles of several thousands of
developers from 6 large FOSS projects, we asked them to fill in a questionnaire
to determine if they should be considered as full-time developers in the
project that they work in. Their feedback was used to fine-tune the value of an
effort threshold, above which developers can be considered as full-time.
  With the help of the over 1,000 questionnaires received, we were able to
determine, for every project in our sample, the threshold of commits that
separates full-time from non-full-time developers.%, and that minimizes the
type I and type II errors. We finally offer guidelines and a tool to apply our
model to FOSS projects that use a version control system.


title:
Localized States in Local Isomorphism Classes of Pentagonal
  Quasicrystals

abstract:
  A family of pentagonal quasicrystals can be defined by projecting a section
of the five-dimensional cubic lattice to two dimensions. A single parameter,
the sum of intercepts $\Gamma=\sum_j \gamma_j$, describes this family. Each
value of $0\le \Gamma \le \frac{1}{2}$ defines a unique local isomorphism class
for these quasicrystals, with $\Gamma=0$ giving the Penrose lattice. Except for
a few special values of $\Gamma$, these lattices lack simple
inflation-deflation rules making it hard to count how frequently a given local
configuration is repeated. We consider the vertex-tight-binding model on these
quasicrystals and investigate the strictly localized states (LS) for all values
of $\Gamma$. We count the frequency of localized states both by numerical exact
diagonalization on lattices of $~10^5$ sites and by identifying localized state
types and calculating their perpendicular space images. While the imbalance
between the number of sites of the sublattices of the bipartite quasicrystal
grows monotonically with $\Gamma$, we find that the localized state fraction
first decreases and then increases as the distance from the Penrose lattice
grows. The highest LS fraction of $~10.17\%$ is attained at $\Gamma=0.5$ while
the minimum is $~4.5\%$ at $\Gamma \simeq 0.12$. The LS on the even sublattice
are generally concentrated near sites with high symmetry, while the LS on the
odd sublattice are more uniformly distributed. The odd sublattice has a higher
LS fraction, having almost three times the LS frequency of the even sublattice
at $\Gamma=0.5$. We identify 20 LS types on the even sublattice, and their
total frequency agrees well with the numerical exact diagonalization result for
all values of $\Gamma$. For the odd sublattice, we identify 45 LS types.
However, their total frequency remains significantly below the numerical
calculation, indicating the possibility of more independent LS types.


title: Runaway electron generation during tokamak start-up

abstract:
  Tokamak start-up is characterized by low electron densities and strong
electric fields, in order to quickly raise the plasma current and temperature,
allowing the plasma to fully ionize and magnetic flux surfaces to form. Such
conditions are ideal for the formation of superthermal electrons, which may
reduce the efficiency of ohmic heating and prevent the formation of a healthy
thermal fusion plasma. This is of particular concern in ITER where engineering
limitations put restrictions on the allowable electric fields and limit the
prefill densities during start-up. In this study, we present a new 0D
burn-through simulation tool called STREAM (STart-up Runaway Electron Analysis
Model), which self-consistently evolves the plasma density, temperature and
electric field, while accounting for the generation and loss of relativistic
runaway electrons. After verifying the burn-through model, we investigate
conditions under which runaway electrons can form during tokamak start-up as
well as their effects on the plasma initiation. We find that Dreicer generation
plays a crucial role in determining whether a discharge becomes
runaway-dominated or not, and that a large number of runaway electrons could
limit the ohmic heating of the plasma, thus preventing successful burn-through
or further ramp-up of the plasma current. The runaway generation can be
suppressed by raising the density via gas fuelling, but only if done
sufficiently early. Otherwise a large runaway seed may have already been built
up, which can avalanche even at relatively low electric fields and high
densities.


title: BCEA: An R Package for Cost-Effectiveness Analysis

abstract:
  We describe in detail how to perform health economic cost-effectiveness
analyses (CEA) using the R package $\textbf{BCEA}$ (Bayesian Cost-Effectiveness
Analysis). CEA consist of analytic approaches for combining costs and health
consequences of intervention(s). These help to understand how much an
intervention may cost (per unit of health gained) compared to an alternative
intervention, such as a control or status quo. For resource allocation, a
decision maker may wish to know if an intervention is cost saving, and if not
then how much more would it cost to implement it compared to a less effective
intervention.
  Current guidance for cost-effectiveness analyses advocates the quantification
of uncertainties which can be represented by random samples obtained from a
probability sensitivity analysis or, more efficiently, a Bayesian model.
$\textbf{BCEA}$ can be used to post-process the sampled costs and health
impacts to perform advanced analyses producing standardised and highly
customisable outputs. We present the features of the package, including its
many functions and their practical application. $\textbf{BCEA}$ is valuable for
statisticians and practitioners working in the field of health economic
modelling wanting to simplify and standardise their workflow, for example in
the preparation of dossiers in support of marketing authorisation, or academic
and scientific publications.


title: Frobenius methods in combinatorics

abstract:
  We survey results produced from the interaction between methods in prime
characteristic and combinatorial commutative algebra. We showcase results for
edge ideals, toric varieties, Stanley-Reisner rings, and initial ideals that
were proven via Frobenius. We also discuss results for monomial ideals obtained
using Frobenius-like maps. Finally, we present results for $F$-pure rings that
were inspired by work done for Stanley-Reisner rings.


title: Configurable Per-Query Data Minimization for Privacy-Compliant Web APIs

abstract:
  The purpose of regulatory data minimization obligations is to limit personal
data to the absolute minimum necessary for a given context. Beyond the initial
data collection, storage, and processing, data minimization is also required
for subsequent data releases, as it is the case when data are provided using
query-capable Web APIs. Data-providing Web APIs, however, typically lack
sophisticated data minimization features, leaving the task open to manual and
all too often missing implementations. In this paper, we address the problem of
data minimization for data-providing, query-capable Web APIs. Based on a
careful analysis of functional and non-functional requirements, we introduce
Janus, an easy-to-use, highly configurable solution for implementing legally
compliant data minimization in GraphQL Web APIs. Janus provides a rich set of
information reduction functionalities that can be configured for different
client roles accessing the API. We present a technical proof-of-concept along
with experimental measurements that indicate reasonable overheads. Janus is
thus a practical solution for implementing GraphQL APIs in line with the
regulatory principle of data minimization.


title: Do Multilingual Language Models Capture Differing Moral Norms?

abstract:
  Massively multilingual sentence representations are trained on large corpora
of uncurated data, with a very imbalanced proportion of languages included in
the training. This may cause the models to grasp cultural values including
moral judgments from the high-resource languages and impose them on the
low-resource languages. The lack of data in certain languages can also lead to
developing random and thus potentially harmful beliefs. Both these issues can
negatively influence zero-shot cross-lingual model transfer and potentially
lead to harmful outcomes. Therefore, we aim to (1) detect and quantify these
issues by comparing different models in different languages, (2) develop
methods for improving undesirable properties of the models. Our initial
experiments using the multilingual model XLM-R show that indeed multilingual
LMs capture moral norms, even with potentially higher human-agreement than
monolingual ones. However, it is not yet clear to what extent these moral norms
differ between languages.


title: Learning Affordance Grounding from Exocentric Images

abstract:
  Affordance grounding, a task to ground (i.e., localize) action possibility
region in objects, which faces the challenge of establishing an explicit link
with object parts due to the diversity of interactive affordance. Human has the
ability that transform the various exocentric interactions to invariant
egocentric affordance so as to counter the impact of interactive diversity. To
empower an agent with such ability, this paper proposes a task of affordance
grounding from exocentric view, i.e., given exocentric human-object interaction
and egocentric object images, learning the affordance knowledge of the object
and transferring it to the egocentric image using only the affordance label as
supervision. To this end, we devise a cross-view knowledge transfer framework
that extracts affordance-specific features from exocentric interactions and
enhances the perception of affordance regions by preserving affordance
correlation. Specifically, an Affordance Invariance Mining module is devised to
extract specific clues by minimizing the intra-class differences originated
from interaction habits in exocentric images. Besides, an Affordance
Co-relation Preserving strategy is presented to perceive and localize
affordance by aligning the co-relation matrix of predicted results between the
two views. Particularly, an affordance grounding dataset named AGD20K is
constructed by collecting and labeling over 20K images from 36 affordance
categories. Experimental results demonstrate that our method outperforms the
representative models in terms of objective metrics and visual quality. Code:
github.com/lhc1224/Cross-View-AG.


title: On local antimagic chromatic number of a corona product graph

abstract:
  In this paper, we provide a correct proof for the lower bounds of the local
antimagic chromatic number of the corona product of friendship and fan graphs
with null graph respectively as in [On local antimagic vertex coloring of
corona products related to friendship and fan graph, {\it Indon. J. Combin.},
5(2) (2021) 110--121]. Consequently, we obtained a sharp lower bound that gives
the exact local antimagic chromatic number of the corona product of friendship
and null graph.


title: Learning to Reduce False Positives in Analytic Bug Detectors

abstract:
  Due to increasingly complex software design and rapid iterative development,
code defects and security vulnerabilities are prevalent in modern software. In
response, programmers rely on static analysis tools to regularly scan their
codebases and find potential bugs. In order to maximize coverage, however,
these tools generally tend to report a significant number of false positives,
requiring developers to manually verify each warning. To address this problem,
we propose a Transformer-based learning approach to identify false positive bug
warnings. We demonstrate that our models can improve the precision of static
analysis by 17.5%. In addition, we validated the generalizability of this
approach across two major bug types: null dereference and resource leak.


title:
Identifying Transients in the Dark Energy Survey using Convolutional
  Neural Networks

abstract:
  The ability to discover new transients via image differencing without direct
human intervention is an important task in observational astronomy. For these
kind of image classification problems, machine Learning techniques such as
Convolutional Neural Networks (CNNs) have shown remarkable success. In this
work, we present the results of an automated transient identification on images
with CNNs for an extant dataset from the Dark Energy Survey Supernova program
(DES-SN), whose main focus was on using Type Ia supernovae for cosmology. By
performing an architecture search of CNNs, we identify networks that
efficiently select non-artifacts (e.g. supernovae, variable stars, AGN, etc.)
from artifacts (image defects, mis-subtractions, etc.), achieving the
efficiency of previous work performed with random Forests, without the need to
expend any effort in feature identification. The CNNs also help us identify a
subset of mislabeled images. Performing a relabeling of the images in this
subset, the resulting classification with CNNs is significantly better than
previous results.


title: Arkady Onishchik: on his life and work on supersymmetry

abstract:
  Selected stories about the life of A. L. Onishchik, and a review of his
contribution to the classification of non-split supermanifolds, in particular,
supercurves a.k.a. superstrings; his editorial and educational work. A brief
overview of his and his students' results in supersymmetry, and their impact on
other researchers.
  Several open problems growing out of Onishchik's research are presented, some
of them are related with odd parameters of deformations and non-holonomic
structures of supermanifolds important in physical models, such as Minkowski
superspaces and certain superstrings.


title:
Fourier Document Restoration for Robust Document Dewarping and
  Recognition

abstract:
  State-of-the-art document dewarping techniques learn to predict 3-dimensional
information of documents which are prone to errors while dealing with documents
with irregular distortions or large variations in depth. This paper presents
FDRNet, a Fourier Document Restoration Network that can restore documents with
different distortions and improve document recognition in a reliable and
simpler manner. FDRNet focuses on high-frequency components in the Fourier
space that capture most structural information but are largely free of
degradation in appearance. It dewarps documents by a flexible Thin-Plate Spline
transformation which can handle various deformations effectively without
requiring deformation annotations in training. These features allow FDRNet to
learn from a small amount of simply labeled training images, and the learned
model can dewarp documents with complex geometric distortion and recognize the
restored texts accurately. To facilitate document restoration research, we
create a benchmark dataset consisting of over one thousand camera documents
with different types of geometric and photometric distortion. Extensive
experiments show that FDRNet outperforms the state-of-the-art by large margins
on both dewarping and text recognition tasks. In addition, FDRNet requires a
small amount of simply labeled training data and is easy to deploy.


title:
Why we need biased AI -- How including cognitive and ethical machine
  biases can enhance AI systems

abstract:
  This paper stresses the importance of biases in the field of artificial
intelligence (AI) in two regards. First, in order to foster efficient
algorithmic decision-making in complex, unstable, and uncertain real-world
environments, we argue for the structurewise implementation of human cognitive
biases in learning algorithms. Secondly, we argue that in order to achieve
ethical machine behavior, filter mechanisms have to be applied for selecting
biased training stimuli that represent social or behavioral traits that are
ethically desirable. We use insights from cognitive science as well as ethics
and apply them to the AI field, combining theoretical considerations with seven
case studies depicting tangible bias implementation scenarios. Ultimately, this
paper is the first tentative step to explicitly pursue the idea of a
re-evaluation of the ethical significance of machine biases, as well as putting
the idea forth to implement cognitive biases into machines.


title:
On Weak Annihilators and Nilpotent Associated Primes of Skew PBW
  Extensions

abstract:
  We investigate the notions of weak annihilator and nilpotent associated prime
defined by Ouyang and Birkenmeier \cite{OuyangBirkenmeier2012} in the setting
of noncommutative rings having PBW bases. We extend several results formulated
in the literature concerning annihilators and associated primes of commutative
rings and skew polynomial rings to a more general setting of algebras not
considered before. We exemplify our results with families of algebras appearing
in the theory of enveloping algebras, differential operators on noncommutative
spaces, noncommutative algebraic geometry, and theoretical physics. Finally, we
present some ideas for future research.


title:
Convolutional Simultaneous Sparse Approximation with Applications to
  RGB-NIR Image Fusion

abstract:
  Simultaneous sparse approximation (SSA) seeks to represent a set of dependent
signals using sparse vectors with identical supports. The SSA model has been
used in various signal and image processing applications involving multiple
correlated input signals. In this paper, we propose algorithms for
convolutional SSA (CSSA) based on the alternating direction method of
multipliers. Specifically, we address the CSSA problem with different sparsity
structures and the convolutional feature learning problem in multimodal
data/signals based on the SSA model. We evaluate the proposed algorithms by
applying them to multimodal and multifocus image fusion problems.


title:
Comparing SONN Types for Efficient Robot Motion Planning in the
  Configuration Space

abstract:
  Motion planning in the configuration space (C-space) induces benefits, such
as smooth trajectories. It becomes more complex as the degrees of freedom (DOF)
increase. This is due to the direct relation between the dimensionality of the
search space and the DOF. Self-organizing neural networks (SONN) and their
famous candidate, the Self-Organizing Map, have been proven to be useful tools
for C-space reduction while preserving its underlying topology, as presented in
[29]. In this work, we extend our previous study with additional models and
adapt the approach from human motion data towards robots' kinematics. The
evaluation includes the best performant models from [29] and three additional
SONN architectures, representing the consequent continuation of this previous
work. Generated Trajectories, planned with the different SONN models, were
successfully tested in a robot simulation.


title:
Collaborative Driving: Learning- Aided Joint Topology Formulation and
  Beamforming

abstract:
  Currently, autonomous vehicles are able to drive more naturally based on the
driving policies learned from millions of driving miles in real environments.
However, to further improve the automation level of vehicles is a challenging
task, especially in the case of multi-vehicle cooperation. In recent heated
discussions of 6G, millimeter-wave (mmWave) and terahertz (THz) bands are
deemed to play important roles in new radio communication architectures and
algorithms. To enable reliable autonomous driving in 6G, in this paper, we
envision collaborative autonomous driving, a new framework that jointly
controls driving topology and formulate vehicular networks in the mmWave/THz
bands. As a swarm intelligence system, the collaborative driving scheme goes
beyond existing autonomous driving patterns based on single-vehicle
intelligence in terms of safety and efficiency. With efficient data sharing,
the proposed framework is able to achieve cooperative sensing and load
balancing so that improve sensing efficiency with saved computational
resources. To deal with the new challenges in the collaborative driving
framework, we further illustrate two promising approaches for mmWave/THz-based
vehicle-to-vehicle (V2V) communications. Finally, we discuss several potential
open research problems for the proposed collaborative driving scheme.


title:
Shape Optimization for the Mitigation of Coastal Erosion via Smoothed
  Particle Hydrodynamics

abstract:
  Adjoint-based shape optimization most often relies on Eulerian flow field
formulations. However, since Lagrangian particle methods are the natural choice
for solving sedimentation problems in oceanography, extensions to the
Lagrangian framework are desirable. For the mitigation of coastal erosion, we
perform shape optimization for fluid flows, that are described by
incompressible Navier-Stokes equations and discretized via Smoothed Particle
Hydrodynamics. The obstacle's shape is hereby optimized over an appropriate
cost function to minimize the height of water waves along the shoreline based
on shape calculus. Theoretical results will be numerically verified exploring
different scenarios.


title: A study of eccentric binaries in Horndeski theory

abstract:
  We study the orbital evolution of eccentric binary systems in Horndeski
theory. This particular theory provides a testbed to give insightful
comparisons with data. We compute the rate of energy loss and the rate of
change of angular momentum for the binaries by calculating the multipole
moments of the radiation fields. We have used appropriate parameters for the
eccentric binaries to compute the decay rates of its orbital eccentricity and
semi-major axis. We then compare this decay rate with that of GR.


title:
Distance-layer structure of the De Bruijn and Kautz digraphs: analysis
  and application to deflection routing (with examples and remarks)

abstract:
  In this paper, we present a detailed study of the reach distance-layer
structure of the De Bruijn and Kautz digraphs, and we apply our analysis to the
performance evaluation of deflection routing in De Bruijn and Kautz networks.
Concerning the distance-layer structure, we provide explicit polynomial
expressions, in terms of the degree of the digraph, for the cardinalities of
some relevant sets of this structure. Regarding the application to defection
routing, and as a consequence of our polynomial description of the
distance-layer structure, we formulate explicit rational expressions, in terms
of the degree of the digraph, for some probabilities of interest in the
analysis of this type of routing.
  De Bruijn and Kautz digraphs are fundamental examples of digraphs on alphabet
and iterated line digraphs. If the topology of the network under consideration
corresponds to a digraph of this type, we can perform, in principle, a similar
vertex layer description.


title:
Path integral molecular dynamics simulations for Green's function in a
  system of identical bosons

abstract:
  Path integral molecular dynamics (PIMD) has been successfully applied to
perform simulations of large bosonic systems in a recent work (Hirshberg et
al., PNAS, 116, 21445 (2019)). In this work we extend PIMD techniques to study
Green's function for bosonic systems. We demonstrate that the development of
the original PIMD method enables us to calculate Green's function and extract
momentum distribution from our simulations. We also apply our method to systems
of identical interacting bosons to study Berezinskii-Kosterlitz-Thouless
transition around its critical temperature.

categories: quant-ph cond-mat.quant-gas cond-mat.stat-mech physics.chem-ph physics.comp-ph


title:
A Statistical Framework to Investigate the Optimality of Neural Networks
  for Inverse Problems

abstract:
  We present a statistical framework to benchmark the performance of
neural-network-based reconstruction algorithms for linear inverse problems. The
underlying signals in our framework are realizations of sparse stochastic
processes and are ideally matched to variational sparsity-promoting techniques,
some of which can be reinterpreted as their maximum a posteriori (MAP)
estimators. We derive Gibbs sampling schemes to compute the minimum mean square
error (MMSE) estimators for processes with Laplace, Student's t and
Bernoulli-Laplace innovations. These allow our framework to provide
quantitative measures of the degree of optimality (in the mean-square-error
sense) for any given reconstruction method. We showcase the use of our
framework by benchmarking the performance of CNN architectures for
deconvolution and Fourier sampling problems. Our experimental results suggest
that while these architectures achieve near-optimal results in many settings,
their performance deteriorates severely for signals associated with
heavy-tailed distributions.


title:
The optimal fidelity estimation operation for entanglement distribution
  networks with arbitrary noise

abstract:
  Fidelity estimation is essential for the quality control of entanglement
distribution networks. In this paper, we consider a setup in which nodes
randomly sample a subset of the entangled qubit pairs for measurement and then
estimate the fidelity of the unsampled pairs conditioned on the measurement
outcome. The proposed estimation protocol, which performs local Pauli operators
according to a predefined sequence, is implementation friendly. Despite its
simplicity, this protocol achieves the lowest estimation error in the difficult
scenario with arbitrary noise and no prior information. The analysis reveals
the issue of excessive measurements, a counterintuitive phenomenon in which
more measurements lead to less accurate estimation when the sampling ratio
exceeds 0.5 in scenarios with arbitrary noise.


title: Bose-Einstein condensate in an elliptical waveguide

abstract:
  We investigate the effects of spatial curvature for an atomic Bose-Einstein
condensate confined in an elliptical waveguide. The system is well described by
an effective 1D Gross-Pitaevskii equation with a quantum-curvature potential,
which has the shape of a double-well but crucially depends on the eccentricity
of the ellipse. The ground state of the system displays a quantum phase
transition from a two-peak configuration to a one-peak configuration at a
critical attractive interaction strength. In correspondence of this phase
transition the superfluid fraction strongly reduces and goes to zero for a
sufficiently attractive Bose-Bose interaction.


title:
Implications of phonon anisotropy on thermal conductivity of fluorite
  oxides

abstract:
  Fluorite oxides are attractive ionic compounds for a range of applications
with critical thermal management requirements. In view of recent reports
alluding to anisotropic thermal conductivity in face-centered cubic crystalline
systems, we perform a detailed analysis of the impact of direction-dependent
phonon group velocities and lifetimes on thermal transport (i.e. phonon
anisotropy). In this article, we aim to investigate the implication of the
anisotropic features on the mode resolved contribution to thermal conductivity.
We observe that for accurate determination of thermal conductivity it is not
adequate to only consider phonon properties along high symmetry directions.
While the bulk thermal conductivity of this class of materials is isotropic, we
show that breaking the symmetry of the phonon lifetime under external stimuli
such as boundary scattering gives rise to anisotropic thermal conductivity.


title:
Probing neutron-skin thickness with free spectator neutrons in
  ultracentral high-energy isobaric collisions

abstract:
  We show that the yield ratio of free spectator neutrons produced in
high-energy $^{96}$Zr+$^{96}$Zr to $^{96}$Ru+$^{96}$Ru collisions is a clean
probe of the neutron-skin thickness of colliding nuclei and the slope parameter
$L$ of symmetry energy. The idea is demonstrated based on the proton and
neutron density distributions via a state-of-the-art Skyrme-Hartree-Fock
calculation, and the production of free spectator neutrons via a coalescence
approach and the popular GEMINI model to describe, respectively, the cluster
formation and deexcitation among spectator nucleons. More free neutrons are
produced in collisions of $^{96}$Zr nucleus due to its larger neutron skin,
leading to an enhanced ratio of neutron multiplicities in $^{96}$Zr+$^{96}$Zr
to $^{96}$Ru+$^{96}$Ru collisions. This ratio is further increased with the
increasing difference of the neutron-skin thickness between $^{96}$Zr and
$^{96}$Ru with a larger $L$ value, and the increase in ultracentral collisions
is particularly insensitive to model details and experimental uncertainties.
Therefore the ratio of free spectator neutron multiplicity in ultracentral
isobaric collisions is a precision observable for constraining the neutron skin
and $L$ value.


title: Exponential meshes and $\mathcal{H}$-matrices

abstract:
  In our previous works, we proved that the inverse of the stiffness matrix of
an $h$-version finite element method (FEM) applied to scalar second order
elliptic boundary value problems can be approximated at an exponential rate in
the block rank by $\mathcal{H}$-matrices. Here, we improve on this result in
multiple ways: (1) The class of meshes is significantly enlarged and includes
certain exponentially graded meshes. (2) The dependence on the polynomial
degree $p$ of the discrete ansatz space is made explicit in our analysis. (3)
The bound for the approximation error is sharpened, and (4) the proof is
simplified.


title:
CITS: Coherent Ising Tree Search Algorithm Towards Solving Combinatorial
  Optimization Problems

abstract:
  Simulated annealing (SA) attracts more attention among classical heuristic
algorithms because the solution of the combinatorial optimization problem can
be naturally mapped to the ground state of the Ising Hamiltonian. However, in
practical implementation, the annealing process cannot be arbitrarily slow and
hence, it may deviate from the expected stationary Boltzmann distribution and
become trapped in a local energy minimum. To overcome this problem, this paper
proposes a heuristic search algorithm by expanding search space from a Markov
chain to a recursive depth limited tree based on SA, where the parent and child
nodes represent the current and future spin states. At each iteration, the
algorithm will select the best near-optimal solution within the feasible search
space by exploring along the tree in the sense of `look ahead'. Furthermore,
motivated by coherent Ising machine (CIM), we relax the discrete representation
of spin states to continuous representation with a regularization term and
utilize the reduced dynamics of the oscillators to explore the surrounding
neighborhood of the selected tree nodes. We tested our algorithm on a
representative NP-hard problem (MAX-CUT) to illustrate the effectiveness of
this algorithm compared to semi-definite programming (SDP), SA, and simulated
CIM. Our results show that above the primal heuristics SA and CIM, our
high-level tree search strategy is able to provide solutions within fewer
epochs for Ising formulated NP-optimization problems.


title: Revealing Reliable Signatures by Learning Top-Rank Pairs

abstract:
  Signature verification, as a crucial practical documentation analysis task,
has been continuously studied by researchers in machine learning and pattern
recognition fields. In specific scenarios like confirming financial documents
and legal instruments, ensuring the absolute reliability of signatures is of
top priority. In this work, we proposed a new method to learn "top-rank pairs"
for writer-independent offline signature verification tasks. By this scheme, it
is possible to maximize the number of absolutely reliable signatures. More
precisely, our method to learn top-rank pairs aims at pushing positive samples
beyond negative samples, after pairing each of them with a genuine reference
signature. In the experiment, BHSig-B and BHSig-H datasets are used for
evaluation, on which the proposed model achieves overwhelming better pos@top
(the ratio of absolute top positive samples to all of the positive samples)
while showing encouraging performance on both Area Under the Curve (AUC) and
accuracy.


title:
Deepfake Style Transfer Mixture: a First Forensic Ballistics Study on
  Synthetic Images

abstract:
  Most recent style-transfer techniques based on generative architectures are
able to obtain synthetic multimedia contents, or commonly called deepfakes,
with almost no artifacts. Researchers already demonstrated that synthetic
images contain patterns that can determine not only if it is a deepfake but
also the generative architecture employed to create the image data itself.
These traces can be exploited to study problems that have never been addressed
in the context of deepfakes. To this aim, in this paper a first approach to
investigate the image ballistics on deepfake images subject to style-transfer
manipulations is proposed. Specifically, this paper describes a study on
detecting how many times a digital image has been processed by a generative
architecture for style transfer. Moreover, in order to address and study
accurately forensic ballistics on deepfake images, some mathematical properties
of style-transfer operations were investigated.


title: Single Pixel MEMS Spectrometer using Electrothermal Tunable Grating

abstract:
  Miniaturized spectrometers are widely used for non-destructive and on-field
spectral analysis. Here we report a tunable grating-based MEMS spectrometer for
visible to near-infrared (VIS-NIR) spectroscopy. The MEMS spectrometer consists
of a spherical mirror and an electrothermally actuated tunable grating. The
spectrometer detects the dispersed spectral signal with a single-pixel detector
by tilting the diffraction grating. The large tilting angle from electrothermal
actuation and highly dispersive diffraction grating improves the spectral range
and resolution, respectively. The MEMS spectrometer was fully packaged within
1.7 cm3 and provides a measurable spectral range up to 800 nm with an average
1.96 nm spectral resolution. This miniaturized single-pixel spectrometer can
provide diverse applications for advanced mobile spectral analysis in
agricultural, industrial, or medical fields.


title:
Demographics of young stars and their protoplanetary disks: lessons
  learned on disk evolution and its connection to planet formation

abstract:
  Since Protostars and Planets VI (PPVI), our knowledge of the global
properties of protoplanetary and debris disks, as well as of young stars, has
dramatically improved. At the time of PPVI, mm-observations and optical to
near-infrared spectroscopic surveys were largely limited to the Taurus
star-forming region, especially of its most massive disk and stellar
population. Now, near-complete surveys of multiple star-forming regions cover
both spectroscopy of young stars and mm interferometry of their protoplanetary
disks. This provides an unprecedented statistical sample of stellar masses and
mass accretion rates, as well as disk masses and radii, for almost 1000 young
stellar objects within 300 pc from us, while also sampling different
evolutionary stages, ages, and environments. At the same time, surveys of
debris disks are revealing the bulk properties of this class of more evolved
objects. This chapter reviews the statistics of these measured global star and
disk properties and discusses their constraints on theoretical models
describing global disk evolution. Our comparisons of observations to
theoretical model predictions extends beyond the traditional viscous evolution
framework to include analytical descriptions of magnetic wind effects. Finally,
we discuss how recent observational results can provide a framework for models
of planet population synthesis and planet formation.


title: 3DAC: Learning Attribute Compression for Point Clouds

abstract:
  We study the problem of attribute compression for large-scale unstructured 3D
point clouds. Through an in-depth exploration of the relationships between
different encoding steps and different attribute channels, we introduce a deep
compression network, termed 3DAC, to explicitly compress the attributes of 3D
point clouds and reduce storage usage in this paper. Specifically, the point
cloud attributes such as color and reflectance are firstly converted to
transform coefficients. We then propose a deep entropy model to model the
probabilities of these coefficients by considering information hidden in
attribute transforms and previous encoded attributes. Finally, the estimated
probabilities are used to further compress these transform coefficients to a
final attributes bitstream. Extensive experiments conducted on both indoor and
outdoor large-scale open point cloud datasets, including ScanNet and
SemanticKITTI, demonstrated the superior compression rates and reconstruction
quality of the proposed 3DAC.


title: Efficient FFT Computation in IFDMA Transceivers

abstract:
  Interleaved Frequency Division Multiple Access (IFDMA) has the salient
advantage of lower Peak-to-Average Power Ratio (PAPR) than its competitors like
Orthogonal FDMA (OFDMA). A recent research effort put forth a new IFDMA
transceiver design significantly less complex than conventional IFDMA
transceivers. The new IFDMA transceiver design reduces the complexity by
exploiting a certain correspondence between the IFDMA signal processing and the
Cooley-Tukey IFFT/FFT algorithmic structure so that IFDMA streams can be
inserted/extracted at different stages of an IFFT/FFT module according to the
sizes of the streams. Although the prior work has laid down the theoretical
foundation for the new IFDMA transceiver's structure, the practical realization
of the transceiver on specific hardware with resource constraints has not been
carefully investigated. This paper is an attempt to fill the gap. Specifically,
this paper puts forth a heuristic algorithm called multi-priority scheduling
(MPS) to schedule the execution of the butterfly computations in the IFDMA
transceiver with the constraint of a limited number of hardware processors. The
resulting FFT computation, referred to as MPS-FFT, has a much lower computation
time than conventional FFT computation when applied to the IFDMA signal
processing. Importantly, we derive a lower bound for the optimal IFDMA FFT
computation time to benchmark MPS-FFT. Our experimental results indicate that
when the number of hardware processors is a power of two: 1) MPS-FFT has
near-optimal computation time; 2) MPS-FFT incurs less than 44.13\% of the
computation time of the conventional pipelined FFT.