title: Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation

abstract:
  6D object pose estimation is a fundamental yet challenging problem in
computer vision. Convolutional Neural Networks (CNNs) have recently proven to
be capable of predicting reliable 6D pose estimates even under monocular
settings. Nonetheless, CNNs are identified as being extremely data-driven, and
acquiring adequate annotations is oftentimes very time-consuming and labor
intensive. To overcome this limitation, we propose a novel monocular 6D pose
estimation approach by means of self-supervised learning, removing the need for
real annotations. After training our proposed network fully supervised with
synthetic RGB data, we leverage current trends in noisy student training and
differentiable rendering to further self-supervise the model on these
unsupervised real RGB(-D) samples, seeking for a visually and geometrically
optimal alignment. Moreover, employing both visible and amodal mask
information, our self-supervision becomes very robust towards challenging
scenarios such as occlusion. Extensive evaluations demonstrate that our
proposed self-supervision outperforms all other methods relying on synthetic
data or employing elaborate techniques from the domain adaptation realm.
Noteworthy, our self-supervised approach consistently improves over its
synthetically trained baseline and often almost closes the gap towards its
fully supervised counterpart. The code and models are publicly available at
https://github.com/THU-DA-6D-Pose-Group/self6dpp.git.


title:
Equilibrium Validation for Triblock Copolymers via Inverse Norm Bounds
  for Fourth-Order Elliptic Operators

abstract:
  Block copolymers play an important role in materials sciences and have found
widespread use in many applications. From a mathematical perspective, they are
governed by a nonlinear fourth-order partial differential equation which is a
suitable gradient of the Ohta-Kawasaki energy. While the equilibrium states
associated with this equation are of central importance for the description of
the dynamics of block copolymers, their mathematical study remains challenging.
In the current paper, we develop computer-assisted proof methods which can be
used to study equilibrium solutions in block copolymers consisting of more than
two monomer chains, with a focus on triblock copolymers. This is achieved by
establishing a computer-assisted proof technique for bounding the norm of the
inverses of certain fourth-order elliptic operators, in combination with an
application of a constructive version of the implicit function theorem. While
these results are only applied to the triblock copolymer case, we demonstrate
that the obtained norm estimates can also be directly used in other contexts
such as the rigorous verification of bifurcation points, or pseudo-arclength
continuation in fourth-order parabolic problems.


title:
Dynamics of scalar hair with self-interactions around Schwarzchild black
  hole

abstract:
  In this paper, we study the dynamics of scalar hair around a Schwarzchild
black hole. The scalar hair is sourced by the Gauss-Bonnet invariant with a
linear coupling. We work perturbatively in the coupling constant and ignore the
back-reaction of the scalar hair and the Gauss-Bonnet invariant. We evolve the
scalar field in the background of a Schwarzchild black hole and study the
dynamical formation of scalar hair with different self-interactions. We
integrate the energy and compute the energy flux of the scalar hair in terms of
the canonical energy-momentum tensor and give the corresponding dependence on
the self-interactions. Our results allow us to estimate the radiation and the
condensation of scalar field with different self-interactions, these would
improve our understanding for the dynamical scalarization of black holes and
the possible configuration of the scalar hair in scalar Gauss-Bonnet gravity.


title: Delta and Theta Operator Expansions

abstract:
  We give an elementary symmetric function expansion for $M\Delta_{m_\gamma
e_1}\Pi e_\lambda^{\ast}$ and $M\Delta_{m_\gamma e_1}\Pi s_\lambda^{\ast}$ when
$t=1$ in terms of what we call $\gamma$-parking functions and lattice
$\gamma$-parking functions. Here, $\Delta_F$ and $\Pi$ are certain
eigenoperators of the modified Macdonald basis and $M=(1-q)(1-t)$. Our main
results in turn give an elementary basis expansion at $t=1$ for symmetric
functions of the form $M \Delta_{Fe_1} \Theta_{G} J$ whenever $F$ is expanded
in terms of monomials, $G$ is expanded in terms of the elementary basis, and
$J$ is expanded in terms of the modified elementary basis $\{\Pi
e_\lambda^\ast\}_\lambda$. Even the most special cases of this general Delta
and Theta operator expression are significant; we highlight a few of these
special cases. We end by giving an $e$-positivity conjecture for when $t$ is
not specialized, proposing that our objects can also give the elementary basis
expansion in the unspecialized symmetric function.


title: Automatic Detection of Entity-Manipulated Text using Factual Knowledge

abstract:
  In this work, we focus on the problem of distinguishing a human written news
article from a news article that is created by manipulating entities in a human
written news article (e.g., replacing entities with factually incorrect
entities). Such manipulated articles can mislead the reader by posing as a
human written news article. We propose a neural network based detector that
detects manipulated news articles by reasoning about the facts mentioned in the
article. Our proposed detector exploits factual knowledge via graph
convolutional neural network along with the textual information in the news
article. We also create challenging datasets for this task by considering
various strategies to generate the new replacement entity (e.g., entity
generation from GPT-2). In all the settings, our proposed model either matches
or outperforms the state-of-the-art detector in terms of accuracy. Our code and
data are available at https://github.com/UBC-NLP/manipulated_entity_detection.


title:
No Shifted Augmentations (NSA): compact distributions for robust
  self-supervised Anomaly Detection

abstract:
  Unsupervised Anomaly detection (AD) requires building a notion of normalcy,
distinguishing in-distribution (ID) and out-of-distribution (OOD) data, using
only available ID samples. Recently, large gains were made on this task for the
domain of natural images using self-supervised contrastive feature learning as
a first step followed by kNN or traditional one-class classifiers for feature
scoring. Learned representations that are non-uniformly distributed on the unit
hypersphere have been shown to be beneficial for this task. We go a step
further and investigate how the \emph {geometrical compactness} of the ID
feature distribution makes isolating and detecting outliers easier, especially
in the realistic situation when ID training data is polluted (i.e. ID data
contains some OOD data that is used for learning the feature extractor
parameters). We propose novel architectural modifications to the
self-supervised feature learning step, that enable such compact distributions
for ID data to be learned. We show that the proposed modifications can be
effectively applied to most existing self-supervised objectives, with large
gains in performance. Furthermore, this improved OOD performance is obtained
without resorting to tricks such as using strongly augmented ID images (e.g. by
90 degree rotations) as proxies for the unseen OOD data, as these impose overly
prescriptive assumptions about ID data and its invariances. We perform
extensive studies on benchmark datasets for one-class OOD detection and show
state-of-the-art performance in the presence of pollution in the ID data, and
comparable performance otherwise. We also propose and extensively evaluate a
novel feature scoring technique based on the angular Mahalanobis distance, and
propose a simple and novel technique for feature ensembling during evaluation
that enables a big boost in performance at nearly zero run-time cost.


title:
Spin wave excitation and directional propagation in presence of magnetic
  charges in square artificial spin ice

abstract:
  Artificial spin ice is a special class of engineered lattice of highly shape
anisotropic single domain magnetic nanostructures which is used as one of the
model systems to study the spin ice behavior observed in pyrochlore oxides. The
nanomagnets interact via dipolar interaction which results in correlated
magnetization dynamics exhibiting macroscopic spin configuration states. Here,
we exploit the interplay of underlying magnetic state and external bias field
orientation to study controlled spin wave propagation in square Artificial Spin
Ice (sASI) by performing detailed micromagnetic simulations. We report that
careful selection of vertices with local magnetic charges can effectively
direct the anisotropic spin wave in presence of an external field. Further, we
explore the influence of local charges due to the excited state in
even-coordinated vertices as well as uncompensated charges due to
odd-coordinated vertices on spin wave behavior. Our studies suggest that there
is no perceptible difference on spin wave dynamical behavior due to the origin
of local magnetic charge in sASI. Our results of controlled and directional
spin wave propagation in sASI system may be useful for low-power consumption
based all magnonic on-chip devices.


title:
Perturbations in the Wild: Leveraging Human-Written Text Perturbations
  for Realistic Adversarial Attack and Defense

abstract:
  We proposes a novel algorithm, ANTHRO, that inductively extracts over 600K
human-written text perturbations in the wild and leverages them for realistic
adversarial attack. Unlike existing character-based attacks which often
deductively hypothesize a set of manipulation strategies, our work is grounded
on actual observations from real-world texts. We find that adversarial texts
generated by ANTHRO achieve the best trade-off between (1) attack success rate,
(2) semantic preservation of the original text, and (3) stealthiness--i.e.
indistinguishable from human writings hence harder to be flagged as suspicious.
Specifically, our attacks accomplished around 83% and 91% attack success rates
on BERT and RoBERTa, respectively. Moreover, it outperformed the TextBugger
baseline with an increase of 50% and 40% in terms of semantic preservation and
stealthiness when evaluated by both layperson and professional human workers.
ANTHRO can further enhance a BERT classifier's performance in understanding
different variations of human-written toxic texts via adversarial training when
compared to the Perspective API.


title:
Discovery of a thin lithium plateau among metal-poor red giant branch
  stars

abstract:
  The surface lithium abundance A(Li) of warm metal-poor dwarf stars exhibits a
narrow plateau down to [Fe/H]~-2.8 dex, while at lower metallicities the
average value drops by 0.3 dex with a significant star-by-star scatter (called
lithium meltdown). This behaviour is in conflict with predictions of standard
stellar evolution models calculated with the initial A(Li) provided by the
standard Big Bang nucleosynthesis. The lower red giant branch (LRGB) stars
provide a complementary tool to understand the initial A(Li) distribution in
metal-poor stars. We have collected a sample of high-resolution spectra of 58
LRGB stars spanning a range of [Fe/H] between ~ -7.0 dex and ~ -1.3 dex. The
LRGB stars display an A(Li) distribution clearly different from that of the
dwarfs, without signatures of a meltdown and with two distinct components: (a)
a thin A(Li) plateau with an average A(Li)=1.09+-0.01 dex (sigma=0.07 dex), and
(b) a small fraction of Li-poor stars with A(Li) lower than ~0.7 dex. The A(Li)
distribution observed in LRGB stars can be reconciled with an initial abundance
close to the cosmological value, by including an additional chemical element
transport in stellar evolution models. The required efficiency of this
transport allows us to match also the Spite plateau lithium abundance measured
in the dwarfs. The emerging scenario is that all metal-poor stars formed with
the same initial A(Li) but those that are likely the product of coalescence or
that experienced binary mass transfer and show lower A(Li) . We conclude that
A(Li) in LRGB stars is qualitatively compatible with the cosmological A(Li)
value and that the meltdown observed in dwarf stars does not reflect a real
drop of the abundance at birth.


title: Font Generation with Missing Impression Labels

abstract:
  Our goal is to generate fonts with specific impressions, by training a
generative adversarial network with a font dataset with impression labels. The
main difficulty is that font impression is ambiguous and the absence of an
impression label does not always mean that the font does not have the
impression. This paper proposes a font generation model that is robust against
missing impression labels. The key ideas of the proposed method are (1)a
co-occurrence-based missing label estimator and (2)an impression label space
compressor. The first is to interpolate missing impression labels based on the
co-occurrence of labels in the dataset and use them for training the model as
completed label conditions. The second is an encoder-decoder module to compress
the high-dimensional impression space into low-dimensional. We proved that the
proposed model generates high-quality font images using multi-label data with
missing labels through qualitative and quantitative evaluations.


title:
Machine learning for impurity charge-state transition levels in
  semiconductors from elemental properties using multi-fidelity datasets

abstract:
  Quantifying charge-state transition energy levels of impurities in
semiconductors is critical to understanding and engineering their
optoelectronic properties for applications ranging from solar photovoltaics to
infrared lasers. While these transition levels can be measured and calculated
accurately, such efforts are time-consuming and more rapid prediction methods
would be beneficial. Here, we significantly reduce the time typically required
to predict impurity transition levels using multi-fidelity datasets and a
machine learning approach employing features based on elemental properties and
impurity positions. We use transition levels obtained from low-fidelity (i.e.,
local-density approximation or generalized gradient approximation) density
functional theory (DFT) calculations, corrected using a recently proposed
modified band alignment scheme, which well-approximates transition levels from
high-fidelity DFT (i.e., hybrid HSE06). The model fit to the large
multi-fidelity database shows improved accuracy compared to the models trained
on the more limited high-fidelity values. Crucially, in our approach, when
using the multi-fidelity data, high-fidelity values are not required for model
training, significantly reducing the computational cost required for training
the model. Our machine learning model of transition levels has a root mean
squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid
functional values when averaged over 14 semiconductor systems from the II-VI
and III-V families. As a guide for use on other systems, we assessed the model
on simulated data to show the expected accuracy level as a function of bandgap
for new materials of interest. Finally, we use the model to predict a complete
space of impurity charge-state transition levels in all zinc blende III-V and
II-VI systems.


title: CLRNet: Cross Layer Refinement Network for Lane Detection

abstract:
  Lane is critical in the vision navigation system of the intelligent vehicle.
Naturally, lane is a traffic sign with high-level semantics, whereas it owns
the specific local pattern which needs detailed low-level features to localize
accurately. Using different feature levels is of great importance for accurate
lane detection, but it is still under-explored. In this work, we present Cross
Layer Refinement Network (CLRNet) aiming at fully utilizing both high-level and
low-level features in lane detection. In particular, it first detects lanes
with high-level semantic features then performs refinement based on low-level
features. In this way, we can exploit more contextual information to detect
lanes while leveraging local detailed lane features to improve localization
accuracy. We present ROIGather to gather global context, which further enhances
the feature representation of lanes. In addition to our novel network design,
we introduce Line IoU loss which regresses the lane line as a whole unit to
improve the localization accuracy. Experiments demonstrate that the proposed
method greatly outperforms the state-of-the-art lane detection approaches.


title: The Sandbox Environment for Generalizable Agent Research (SEGAR)

abstract:
  A broad challenge of research on generalization for sequential
decision-making tasks in interactive environments is designing benchmarks that
clearly landmark progress. While there has been notable headway, current
benchmarks either do not provide suitable exposure nor intuitive control of the
underlying factors, are not easy-to-implement, customizable, or extensible, or
are computationally expensive to run. We built the Sandbox Environment for
Generalizable Agent Research (SEGAR) with all of these things in mind. SEGAR
improves the ease and accountability of generalization research in RL, as
generalization objectives can be easy designed by specifying task
distributions, which in turns allows the researcher to measure the nature of
the generalization objective. We present an overview of SEGAR and how it
contributes to these goals, as well as experiments that demonstrate a few types
of research questions SEGAR can help answer.


title:
Revisiting the transition $\Xi^{+}_{cc}\to\Xi^{(')+}_c$ to understand
  the data from LHCb

abstract:
  The LHCb collaboration newly measured the decay rate of doubly charmed baryon
$\Xi^{++}_{cc}\to\Xi^{'+}\pi^+$ and a ratio of its branching fraction with
respect to that of the decay $\Xi^{++}_{cc}\to\Xi^{+}\pi^+$ is reported as
$1.41\pm 0.17\pm 0.10$. This result conflicts with the theoretical predictions
made by several groups. In our previous work, following the prescription given
in early literature where the $us$ diquark in $\Xi^{+}_{c}$ is assumed to be a
scalar whereas in $\Xi^{'+}_{c}$ is a vector i.e. the spin-flavor structure of
$\Xi^{+}_{c}$ is $[us]_0 c$ and that of $\Xi^{'+}_{c}$ is $[us]_1 c$, we
studied the case of $\Xi^{++}_{cc}\to\Xi^{(')+}$ with the light front quark
model. Numerically we obtained
$\Gamma(\Xi^{++}_{cc}\to\Xi^{'+}\pi^+)/\Gamma(\Xi^{++}_{cc}\to\Xi^{+}\pi^+)=0.56\pm0.18$
which is about half of the data. While abandoning the presupposition, we
suppose the spin-flavor structure of $us$ in $\Xi^{+}_{c}$ may be a mixture of
scalar and vector, namely the spin-flavor function of $\Xi^{+}_{c}$ could be
${\rm cos}\theta\, [us]_{0}[c]+{\rm sin}\theta\, [us]_{1}[c]$. An alternative
combination $-{\rm sin}\theta\,[us]_{0}[c]+{\rm cos}\theta\, [us]_{1}[c]$ would
correspond to $\Xi^{'+}_{c}$. Introducing the mixing mechanism the ratio
$\Gamma(\Xi^{++}_{cc}\to\Xi^{'+}\pi^+)/\Gamma(\Xi^{++}_{cc}\to\Xi^{+}\pi^+)$
depends on the mixing angle $\theta$. With the mixing scenario, the theoretical
prediction on the ratio between the transition rate of
$\Xi^{+}_{cc}\to\Xi^{'+}_c$ and that of $\Xi^{+}_{cc}\to\Xi^{+}_c$ can coincide
with the data as long as $\theta=16.27^\circ\pm2.30^\circ$ or
$85.54^\circ\pm2.30^\circ$ is set. Definitely, more precise measurements on
other decay portals of $\Xi^{+}_{cc}$ are badly needed for testing the mixing
mechanism and further determining the mixing angle.


title:
Number-of-constituent-quark scaling of elliptic flow: a quantitative
  study

abstract:
  The number-of-constituent-quark (NCQ) scaling behavior of the elliptic flow
of identified particles produced in A+A collisions is studied quantitatively
using an empirical function that fits the experimental $v_2$ data available
from the RHIC and LHC. The most common approach for NCQ scaling involves (1)
doing a scaling of the experimental $v_2$ data of an identified particle with
its NCQ, (2) doing the same to its transverse momentum or energy, then (3)
combining all the scaled data and identifying the NCQ behavior by intuitively
looking (since the measured experimental data are discrete). We define two
variables $(d_1, d_2)$ to describe NCQ scaling quantitatively and
simultaneously, and identify the approximate region where the NCQ scaling
holds. This approach could be applied to study NCQ or other scaling phenomena
in future experiments.


title: Meta-Learning for Online Update of Recommender Systems

abstract:
  Online recommender systems should be always aligned with users' current
interest to accurately suggest items that each user would like. Since user
interest usually evolves over time, the update strategy should be flexible to
quickly catch users' current interest from continuously generated new user-item
interactions. Existing update strategies focus either on the importance of each
user-item interaction or the learning rate for each recommender parameter, but
such one-directional flexibility is insufficient to adapt to varying
relationships between interactions and parameters. In this paper, we propose
MeLON, a meta-learning based novel online recommender update strategy that
supports two-directional flexibility. It is featured with an adaptive learning
rate for each parameter-interaction pair for inducing a recommender to quickly
learn users' up-to-date interest. The procedure of MeLON is optimized following
a meta-learning approach: it learns how a recommender learns to generate the
optimal learning rates for future updates. Specifically, MeLON first enriches
the meaning of each interaction based on previous interactions and identifies
the role of each parameter for the interaction; and then combines these two
pieces of information to generate an adaptive learning rate. Theoretical
analysis and extensive evaluation on three real-world online recommender
datasets validate the effectiveness of MeLON.


title: Natural annihilators and operators of constant rank over $\mathbb{C}$

abstract:
  Even if the Fourier symbols of two constant rank differential operators have
the same nullspace for each non-trivial phase space variable, the nullspaces of
those differential operators might differ by an infinite dimensional space.
Under the natural condition of constant rank over $\mathbb{C}$, we establish
that the equality of nullspaces on the Fourier symbol level already implies the
equality of the nullspaces of the differential operators in $\mathscr{D}'$
modulo polynomials of a fixed degree. In particular, this condition allows to
speak of natural annihilators within the framework of complexes of differential
operators. As an application, we establish a Poincar\'{e}-type lemma for
differential operators of constant complex rank in two dimensions.


title:
On Debugging the Performance of Configurable Software Systems: Developer
  Needs and Tailored Tool Support

abstract:
  Determining whether a configurable software system has a performance bug or
it was misconfigured is often challenging. While there are numerous debugging
techniques that can support developers in this task, there is limited empirical
evidence of how useful the techniques are to address the actual needs that
developers have when debugging the performance of configurable software
systems; most techniques are often evaluated in terms of technical accuracy
instead of their usability. In this paper, we take a human-centered approach to
identify, design, implement, and evaluate a solution to support developers in
the process of debugging the performance of configurable software systems. We
first conduct an exploratory study with 19 developers to identify the
information needs that developers have during this process. Subsequently, we
design and implement a tailored tool, adapting techniques from prior work, to
support those needs. Two user studies, with a total of 20 developers, validate
and confirm that the information that we provide helps developers debug the
performance of configurable software systems.


title: Entry-exit in the halo of a slow semi-stable curve

abstract:
  We consider a slow-fast differential system (SF) in dimension two which
appears in the study of some linear model (LM) with periodic coefficients in
population dynamics. We show existence of "canard solutions" of (SF) along
semi-stable slow curve which explains some stability properties of (LM) when
the period tends to infinity.


title: Multi-Domain Multi-Definition Landmark Localization for Small Datasets

abstract:
  We present a novel method for multi image domain and multi-landmark
definition learning for small dataset facial localization. Training a small
dataset alongside a large(r) dataset helps with robust learning for the former,
and provides a universal mechanism for facial landmark localization for new
and/or smaller standard datasets. To this end, we propose a Vision Transformer
encoder with a novel decoder with a definition agnostic shared landmark
semantic group structured prior, that is learnt, as we train on more than one
dataset concurrently. Due to our novel definition agnostic group prior the
datasets may vary in landmark definitions and domains. During the decoder stage
we use cross- and self-attention, whose output is later fed into
domain/definition specific heads that minimize a Laplacian-log-likelihood loss.
We achieve state-of-the-art performance on standard landmark localization
datasets such as COFW and WFLW, when trained with a bigger dataset. We also
show state-of-the-art performance on several varied image domain small datasets
for animals, caricatures, and facial portrait paintings. Further, we contribute
a small dataset (150 images) of pareidolias to show efficacy of our method.
Finally, we provide several analysis and ablation studies to justify our
claims.


title: FPGA-extended Modified Harvard Architecture

abstract:
  This paper introduces a computer architecture, where part of the instruction
set architecture (ISA) is implemented on small highly-integrated
field-programmable gate arrays (FPGAs). It has already been demonstrated that
small FPGAs inside a general-purpose processor (CPU) can be used effectively to
implement custom instructions and, in some cases, approach accelerator-level of
performance. Our proposed architecture goes one step further to directly
address some related challenges for high-end CPUs, where such highly-integrated
FPGAs would have the highest impact, including access to the memory hierarchy
with the highest bandwidth available. The main contribution is the introduction
of the "FPGA-extended modified Harvard architecture" model to enable
software-transparent context-switching between processes with a different
distribution of instructions. The simulation-based evaluation of a dynamically
reconfigurable core shows promising results for single and multi-processing,
approaching the performance of an equivalent core with all enabled
instructions, and better performance than when featuring a fixed subset of the
supported instructions. Finally, the feasibility of adopting the proposed
architecture in today's CPUs is studied through the prototyping of
fast-reconfigurable FPGAs and studying the miss behaviour of opcodes.


title: Measuring the severity of multi-collinearity in high dimensions

abstract:
  Multi-collinearity is a wide-spread phenomenon in modern statistical
applications and when ignored, can negatively impact model selection and
statistical inference. Classic tools and measures that were developed for
"$n>p$" data are not applicable nor interpretable in the high-dimensional
regime. Here we propose 1) new individualized measures that can be used to
visualize patterns of multi-collinearity, and subsequently 2) global measures
to assess the overall burden of multi-collinearity without limiting the
observed data dimensions. We applied these measures to genomic applications to
investigate patterns of multi-collinearity in genetic variations across
individuals with diverse ancestral backgrounds. The measures were able to
visually distinguish genomic regions of excessive multi-collinearity and
contrast the level of multi-collinearity between different continental
populations.


title:
The effect of isovalent doping on the electronic band structure of group
  IV semiconductors

abstract:
  The band gap engineering of group IV semiconductors has not been well
explored theoretically and experimentally, except for SiGe. Recently, GeSn has
attracted much attention due to the possibility of obtaining a direct band gap
in this alloy, thereby making it suitable for light emitters. Other group IV
alloys may also potentially exhibit material properties useful for device
applications, expanding the space for band gap engineering in group IV. In this
work the electronic band structure of all group IV semiconductor alloys is
investigated. Twelve possible A:B alloys, where A is a semiconducting host (A =
C, Si, and Ge) and B is an isovalent dopant (B = C, Si, Ge, Sn, and Pb), were
studied in the dilute regime (0.8%) of the isovalent dopant in the entire
Brillouin zone (BZ), and the chemical trends in the evolution of their
electronic band structure were carefully analyzed. Density functional theory
with state-of-the-art methods such as meta-GGA functionals and a spectral
weight approach to band unfolding from large supercells was used to obtain
dopant-related changes in the band structure, in particular the direct band gap
at the {\Gamma} point and indirect band gaps at the L(X) points of the BZ.
Analysis of contributions from geometry distortion and electronic interaction
was also performed. Moreover, the obtained results are discussed in the context
of obtaining a direct fundamental gap in Ge:B (B = C, Sn, and Pb) alloys, and
intermediate band formation in C:B (B = Sn and Pb) and Ge:C. An increase in
localization effects is also observed: a strong hole localization for alloys
diluted with a dopant of a larger covalent radius and a strong electron
localization for alloys with a dopant of smaller radius. Finally, it is shown
that alloying Si and Ge with other elements from group IV is a promising way to
enhance the functionality of group IV semiconductors.


title: Interaction potential for NaCs for ultracold scattering and spectroscopy

abstract:
  We obtain the interaction potential for NaCs by fitting to experiments on
ultracold scattering and spectroscopy in optical tweezers. The central region
of the potential has been accurately determined from Fourier-Transform
spectroscopy at higher temperatures, so we focus on adjusting the long-range
and short-range parts. We use coupled-channel calculations of binding energies
and wave functions to understand the nature of the molecular states observed in
ultracold spectroscopy, and of the state that causes the Feshbach resonance
used to create ultracold NaCs molecules. We elucidate the relationships between
the experimental quantities and features of the interaction potential. We
establish the combinations of experimental quantities that determine particular
features of the potential. We find that the long-range dispersion coefficient
$C_6$ must be increased by about 0.9% to 3256(1) $E_\textrm{h} a_0^6$ to fit
the experimental results. We use coupled-channel calculations on the final
potential to predict bound-state energies and resonance positions.


title: ALAP-AE: As-Lite-as-Possible Auto-Encoder

abstract:
  We present a novel algorithm to reduce tensor compute required by a
conditional image generation autoencoder and make it as-lite-as-possible,
without sacrificing quality of photo-realistic image generation. Our method is
device agnostic, and can optimize an autoencoder for a given CPU-only, GPU
compute device(s) in about normal time it takes to train an autoencoder on a
generic workstation. We achieve this via a two-stage novel strategy where,
first, we condense the channel weights, such that, as few as possible channels
are used. Then, we prune the nearly zeroed out weight activations, and
fine-tune this lite autoencoder. To maintain image quality, fine-tuning is done
via student-teacher training, where we reuse the condensed autoencoder as the
teacher. We show performance gains for various conditional image generation
tasks: segmentation mask to face images, face images to cartoonization, and
finally CycleGAN-based model on horse to zebra dataset over multiple compute
devices. We perform various ablation studies to justify the claims and design
choices, and achieve real-time versions of various autoencoders on CPU-only
devices while maintaining image quality, thus enabling at-scale deployment of
such autoencoders.


title: On Practical Nearest Sub-Trajectory Queries under the Fr\'echet Distance

abstract:
  We study the problem of sub-trajectory nearest-neighbor queries on polygonal
curves under the continuous Fr\'echet distance. Given an $n$ vertex trajectory
$P$ and an $m$ vertex query trajectory $Q$, we seek to report a vertex-aligned
sub-trajectory $P'$ of $P$ that is closest to $Q$, i.e. $P'$ must start and end
on contiguous vertices of $P$. Since in real data $P$ typically contains a very
large number of vertices, we focus on answering queries, without restrictions
on $P$ or $Q$, using only precomputed structures of ${\mathcal{O}}(n)$ size.
  We use three baseline algorithms from straightforward extensions of known
work, however they have impractical performance on realistic inputs. Therefore,
we propose a new Hierarchical Simplification Tree data structure and an
adaptive clustering based query algorithm that efficiently explores relevant
parts of $P$. The core of our query methods is a novel greedy-backtracking
algorithm that solves the Fr\'echet decision problem using ${\cal O}(n+m)$
space and ${\cal O}(nm)$ time in the worst case.
  Experiments on real and synthetic data show that our heuristic effectively
prunes the search space and greatly reduces computations compared to baseline
approaches.


title: Domain Representative Keywords Selection: A Probabilistic Approach

abstract:
  We propose a probabilistic approach to select a subset of a \textit{target
domain representative keywords} from a candidate set, contrasting with a
context domain. Such a task is crucial for many downstream tasks in natural
language processing. To contrast the target domain and the context domain, we
adapt the \textit{two-component mixture model} concept to generate a
distribution of candidate keywords. It provides more importance to the
\textit{distinctive} keywords of the target domain than common keywords
contrasting with the context domain. To support the \textit{representativeness}
of the selected keywords towards the target domain, we introduce an
\textit{optimization algorithm} for selecting the subset from the generated
candidate distribution. We have shown that the optimization algorithm can be
efficiently implemented with a near-optimal approximation guarantee. Finally,
extensive experiments on multiple domains demonstrate the superiority of our
approach over other baselines for the tasks of keyword summary generation and
trending keywords selection.


title: Deep Learning Generalization, Extrapolation, and Over-parameterization

abstract:
  We study the generalization of over-parameterized deep networks (for image
classification) in relation to the convex hull of their training sets. Despite
their great success, generalization of deep networks is considered a mystery.
These models have orders of magnitude more parameters than their training
samples, and they can achieve perfect accuracy on their training sets, even
when training images are randomly labeled, or the contents of images are
replaced with random noise. The training loss function of these models has
infinite number of near zero minimizers, where only a small subset of those
minimizers generalize well. Overall, it is not clear why models need to be
over-parameterized, why we should use a very specific training regime to train
them, and why their classifications are so susceptible to imperceivable
adversarial perturbations (phenomenon known as adversarial vulnerability)
\cite{papernot2016limitations,shafahi2018adversarial,tsipras2018robustness}.
Some recent studies have made advances in answering these questions, however,
they only consider interpolation. We show that interpolation is not adequate to
understand generalization of deep networks and we should broaden our
perspective.


title:
Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial
  Intelligence Approach

abstract:
  We perform a comprehensive analysis of complete fusion cross section data
with the aim to derive, in a completely data-driven way, a model suitable to
predict the integrated cross section of the fusion between light to medium mass
nuclei at above barrier energies. To this end, we adopted a novel artificial
intelligence approach, based on a hybridization of genetic programming and
artificial neural networks, capable to derive an analytical model for the
description of experimental data. The approach enables, for the first time, to
perform a global search for computationally simple models over several
variables and a considerable body of nuclear data. The derived phenomenological
formula can serve to reproduce the trend of fusion cross section for a large
variety of light to intermediate mass collision systems in an energy domain
ranging approximately from the Coulomb barrier to the onset of
multi-fragmentation phenomena.


title:
Remarks on the Small Cohen-Macaulay conjecture and new instances of
  maximal Cohen-Macaulay modules

abstract:
  We show that any quasi-Gorenstein deformation of a $3$-dimensional
quasi-Gorenstein Buchsbaum local ring with $I$-invariant $1$ admits a maximal
Cohen-Macaulay module, provided it is a quotient of a Gorenstein ring. Such a
class of rings includes two instances of unique factorization domains
constructed by Marcel-Schenzel and by Imtiaz-Schenzel, respectively. Apart from
this result, motivated by the small Cohen-Macaulay conjecture in prime
characteristic, we examine a question about when the Frobenius pushforward
$F^e_*(M)$ of an $R$-module $M$ comprises a maximal Cohen-Macaulay direct
summand in both local and graded cases.


title: The Online Behaviour of the Algerian Abusers in Social Media Networks

abstract:
  Connecting to social media networks becomes a daily task for the majority of
people around the world, and the amount of shared information is growing
exponentially. Thus, controlling the way in which people communicate is
necessary, in order to protect them from disorientation, conflicts,
aggressions, etc. In this paper, we conduct a statistical study on the
cyber-bullying and the abusive content in social media (i.e. Facebook), where
we try to spot the online behaviour of the abusers in the Algerian community.
More specifically, we have involved 200 Facebook users from different regions
among 600 to carry out this study. The aim of this investigation is to aid
automatic systems of abuse detection to take decision by incorporating the
online activity. Abuse detection systems require a large amount of data to
perform better on such kind of texts (i.e. unstructured and informal texts),
and this is due to the lack of standard orthography, where there are various
Algerian dialects and languages spoken.


title: Multiscale simulation of powder-bed fusion processing of metallic alloys

abstract:
  We present a computational framework for the simulations of powder-bed fusion
of metallic alloys, which combines: (1) CalPhaD calculations of
temperature-dependent alloy properties and phase diagrams, (2) macroscale
finite element (FE) thermal simulations of the material addition and fusion,
and (3) microscopic phase-field (PF) simulations of solidification in the melt
pool. The methodology is applied to simulate the selective laser melting (SLM)
of an Inconel 718 alloy using realistic processing parameters. We discuss the
effect of temperature-dependent properties and the importance of accounting for
different properties between the powder bed and the dense material in the
macroscale thermal simulations. Using a two-dimensional longitudinal slice of
the thermal field calculated via FE simulations, we perform an
appropriately-converged PF solidification simulation at the scale of the entire
melt pool, resulting in a calculation with over one billion grid points, yet
performed on a single cluster node with eight graphics processing units (GPUs).
These microscale simulations provide new insight into the grain texture
selection via polycrystalline growth competition under realistic SLM
conditions, with a level of detail down to individual dendrites.


abstract:
  The Hopf degree theorem states that homotopy classes of continuous maps from
a smooth connected closed $n$-manifold $M$ to the $n$-sphere are classified by
their degree when $M$ is oriented and by their mod $2$ degree when $M$ is
nonorientable. Such a map is equivalent to a section of the trivial $n$-sphere
bundle over $M$. A generalization of the Hopf degree theorem is obtained for
the case that the sphere bundle over $M$ is nontrivial.


title:
Application limit of the photocentre displacement to fundamental stellar
  parameters of fast rotators -- Illustration on the edge-on fast rotator
  Regulus

abstract:
  Differential Interferometry allows to obtain the differential visibility and
phase, in addition to the spectrum. The differential phase contains important
information about the structure and motion of stellar photosphere such as
stellar spots and non-radial pulsations, and particularly the rotation. Thus,
this interferometric observable strongly helps to constrain the stellar
fundamental parameters of fast rotators. The spectro-astrometry mainly uses the
photocentre displacements, which is a first approximation of the differential
phase, and is applicable only for unresolved or marginally objects. We study
here the sensitivity of relevant stellar parameters to the simulated
photocentres using the SCIROCCO code: a semi-analytical algorithm dedicated to
fast rotators, applied to two theoretical modeling stars based on Achernar and
Regulus, in order to classify the importance of these parameters and their
impact on the modeling. We compare our simulations with published VLTI/AMBER
data. This current work sets the limits of application of photocentre
displacements to fast rotators, and under which conditions we can use the
photocentres and/or the differential phase, through a pre-established physical
criterion. To validate our theoretical study, we apply our method of analysis
on observed data of the edge-on fast rotator Regulus. For unresolved targets,
with a visibility $V\sim 1$, the photocentre can constrain the main stellar
fundamental parameters of fast rotators, whereas from marginally resolved
objects ($0.8 \leq V < 1$), mainly the rotation axis position angle ($\rm
PA_{\rm rot}$) can be directly deduced from the vectorial photocentre
displacement, which is very important for young cluster studies.


title: A naive method to discover directions in the StyleGAN2 latent space

abstract:
  Several research groups have shown that Generative Adversarial Networks
(GANs) can generate photo-realistic images in recent years. Using the GANs, a
map is created between a latent code and a photo-realistic image. This process
can also be reversed: given a photo as input, it is possible to obtain the
corresponding latent code. In this paper, we will show how the inversion
process can be easily exploited to interpret the latent space and control the
output of StyleGAN2, a GAN architecture capable of generating photo-realistic
faces. From a biological perspective, facial features such as nose size depend
on important genetic factors, and we explore the latent spaces that correspond
to such biological features, including masculinity and eye colour. We show the
results obtained by applying the proposed method to a set of photos extracted
from the CelebA-HQ database. We quantify some of these measures by utilizing
two landmarking protocols, and evaluate their robustness through statistical
analysis. Finally we correlate these measures with the input parameters used to
perturb the latent spaces along those interpretable directions. Our results
contribute towards building the groundwork of using such GAN architecture in
forensics to generate photo-realistic faces that satisfy certain biological
attributes.


title:
Gravitational transitions via the explicitly broken symmetron screening
  mechanism

abstract:
  We generalize the symmetron screening mechanism by allowing for an explicit
symmetry breaking of the symmetron $\phi^4$ potential. A coupling to matter of
the form $A(\phi)=1+\frac{\phi^2}{M^2}$ leads to an explicitly broken symmetry
with effective potential $V_{eff}(\phi)=-\mu^2 (1-\frac{\rho}{\mu^2 M^2})\phi^2
+\frac{\lambda}{2}\phi^4 + 2 \varepsilon \phi^3+\frac{\lambda}{2}\eta^4$. Due
to the explicit symmetry breaking induced by the cubic term we call this field
the 'asymmetron'. For large matter density $\rho>\rho_*\equiv
\mu^2M^2+\frac{9}{4}\varepsilon\eta M^2$ the effective potential has a single
minimum at $\phi=0$ leading to restoration of General Relativity as in the
usual symmetron screening mechanism. For low matter density however, there is a
false vacuum and a single true vacuum due to the explicit symmetry breaking.
This is expected to lead to an unstable network of domain walls with slightly
different value of the gravitational constant $G$ on each side of the wall.
This network would be in constant interaction with matter overdensities and
would lead to interesting observational signatures which could be detected as
gravitational and expansion rate transitions in redshift space. Such a
gravitational transition has been recently proposed for the resolution of the
Hubble tension.


title:
Design and Development of a Research Oriented Low Cost Robotics Platform
  with a Novel Dynamic Global Path Planning Approach

abstract:
  Autonomous navigation systems based on computer vision sensors often require
sophisticated robotics platforms which are very expensive. This poses a barrier
for the implementation and testing of complex localization, mapping, and
navigation algorithms that are vital in robotics applications. Addressing this
issue, in this work, Robot Operating System (ROS) supported mobile robotics
platforms are compared and an end-to-end implementation of an autonomous
navigation system based on a low-cost educational robotics platform, AlphaBot2
is presented, while integrating the Intel RealSense D435 camera. Furthermore, a
novel approach to implement dynamic path planners as global path planners in
the ROS framework is presented. We evaluate the performance of this approach
and highlight the improvements that could be achieved through a dynamic global
path planner. This low-cost modified AlphaBot2 robotics platform along with the
proposed dynamic global path planning approach will be useful for researchers
and students for getting hands-on experience with computer vision-based
navigation systems.


title:
On sets related to integer partitions with quasi-required elements and
  disallowed elements

abstract:
  Given a set A of non-negative integers and a set B of positive integers,we
are interested in computing all sets C (of positive integers) that are minimal
in the family of sets K (of positive integers) such that (i) K contains no
elements generated by integer linear combinations of elements in A and (ii) for
any partition of an element in B there is at least one summand that belongs to
K. To solve this question, we translate it into a numerical semigroups problem.


title: Nurturing the Industrial Accelerator Technology Base in the US

abstract:
  The purpose of this white paper is to discuss the importance of having a
world class domestic industrial vendor base, capable of supporting the needs of
the particle accelerator facilities, and the necessary steps to support and
develop such a base in the United States. The paper focuses on economic,
regulatory, and policy-driven barriers and hurdles, which presently limit the
depth and scope of broader industrial participation in US accelerator
facilities. It discusses the international competition landscape and proposes
steps to improve the strength and vitality of US industry.


title: On Robust Prefix-Tuning for Text Classification

abstract:
  Recently, prefix-tuning has gained increasing attention as a
parameter-efficient finetuning method for large-scale pretrained language
models. The method keeps the pretrained models fixed and only updates the
prefix token parameters for each downstream task. Despite being lightweight and
modular, prefix-tuning still lacks robustness to textual adversarial attacks.
However, most currently developed defense techniques necessitate auxiliary
model update and storage, which inevitably hamper the modularity and low
storage of prefix-tuning. In this work, we propose a robust prefix-tuning
framework that preserves the efficiency and modularity of prefix-tuning. The
core idea of our framework is leveraging the layerwise activations of the
language model by correctly-classified training data as the standard for
additional prefix finetuning. During the test phase, an extra batch-level
prefix is tuned for each batch and added to the original prefix for robustness
enhancement. Extensive experiments on three text classification benchmarks show
that our framework substantially improves robustness over several strong
baselines against five textual attacks of different types while maintaining
comparable accuracy on clean texts. We also interpret our robust prefix-tuning
framework from the optimal control perspective and pose several directions for
future research.


title: Lazy Rearrangement Planning in Confined Spaces

abstract:
  Object rearrangement is important for many applications but remains
challenging, especially in confined spaces, such as shelves, where objects
cannot be accessed from above and they block reachability to each other. Such
constraints require many motion planning and collision checking calls, which
are computationally expensive. In addition, the arrangement space grows
exponentially with the number of objects. To address these issues, this work
introduces a lazy evaluation framework with a local monotone solver and a
global planner. Monotone instances are those that can be solved by moving each
object at most once. A key insight is that reachability constraints at the
grasps for objects' starts and goals can quickly reveal dependencies between
objects without having to execute expensive motion planning queries. Given
that, the local solver builds lazily a search tree that respects these
reachability constraints without verifying that the arm paths are collision
free. It only collision checks when a promising solution is found. If a
monotone solution is not found, the non-monotone planner loads the lazy search
tree and explores ways to move objects to intermediate locations from where
monotone solutions to the goal can be found. Results show that the proposed
framework can solve difficult instances in confined spaces with up to 16
objects, which state-of-the-art methods fail to solve. It also solves problems
faster than alternatives, when the alternatives find a solution. It also
achieves high-quality solutions, i.e., only 1.8 additional actions on average
are needed for non-monotone instances.


title: Counting multiplicative approximations

abstract:
  A famous conjecture of Littlewood (c. 1930) concerns approximating two real
numbers by rationals of the same denominator, multiplying the errors. In a
lesser-known paper, Wang and Yu (1981) established an asymptotic formula for
the number of such approximations, valid almost always. Using the quantitative
Koukoulopoulos--Maynard theorem of Aistleitner--Borda--Hauke, together with
bounds arising from the theory of Bohr sets, we deduce lower bounds of the
expected order of magnitude for inhomogeneous and fibre refinements of the
problem.


title: Small-World Disordered Lattices: Spectral Gaps and Diffusive Transport

abstract:
  We investigate the dynamic behavior of lattices with disorder introduced
through non-local network connections. Inspired by the Watts-Strogatz
small-world model, we employ a single parameter to determine the probability of
local connections being re-wired, and to induce transitions between regular and
disordered lattices. These connections are added as non-local springs to
underlying periodic one-dimensional (1D) and two-dimensional (2D) square,
triangular and hexagonal lattices. Eigenmode computations illustrate the
emergence of spectral gaps in various representative lattices for increasing
degrees of disorder. These gaps manifest themselves as frequency ranges where
the modal density goes to zero, or that are populated only by localized modes.
In both cases, we observe low transmission levels of vibrations across the
lattice. Overall, we find that these gaps are more pronounced for lattice
topologies with lower connectivity, such as the 1D lattice or the 2D hexagonal
lattice. We then illustrate that the disordered lattices undergo transitions
from ballistic to super-diffusive or diffusive transport for increasing levels
of disorder. These properties, illustrated through numerical simulations,
unveil the potential for disorder in the form of non-local connections to
enable additional functionalities for metamaterials. These include the
occurrence of disorder-induced spectral gaps, which is relevant to frequency
filtering devices, as well as the possibility to induce diffusive-type
transport which does not occur in regular periodic materials, and that may find
applications in dynamic stress mitigation.


title:
Investigating End-Users' Values in Agriculture Mobile Applications
  Development: An Empirical Study on Bangladeshi Female Farmers

abstract:
  The omnipresent nature of mobile applications (apps) in all aspects of daily
lives raises the necessity of reflecting end-users values (e.g., fairness,
honesty, etc.) in apps. However, there are limited considerations of end-users
values in apps development. Value violations by apps have been reported in the
media and are responsible for end-users dissatisfaction and negative
socio-economic consequences. Value violations may bring more severe and lasting
problems for marginalized and vulnerable end-users of apps, which have been
explored less (if at all) in the software engineering community. However,
understanding the values of the end-users of apps is the essential first step
towards values-based apps development. This research aims to fill this gap by
investigating the human values of Bangladeshi female farmers as a marginalized
and vulnerable group of end-users of Bangladeshi agriculture apps. We conducted
an empirical study that collected and analyzed data from a survey with 193
Bangladeshi female farmers to explore the underlying factor structure of the
values of Bangladeshi female farmers and the significance of demographics on
their values. The results identified three underlying factors of Bangladeshi
female farmers. The first factor comprises of five values: benevolence,
security, conformity, universalism, and tradition. The second factor consists
of two values: self-direction and stimulation. The third factor includes three
values: power, achievement, and hedonism. We also identified strong influences
of demographics on some of the values of Bangladeshi female farmers. For
example, area has significant impacts on three values: hedonism, achievement,
and tradition. Similarly, there are also strong influences of household income
on power and security.


title: Meshfree One-Fluid Modelling of Liquid-Vapor Phase Transitions

abstract:
  We introduce a meshfree collocation framework to model the phase change from
liquid to vapor at or above the boiling point. While typical vaporization or
boiling simulations focus on the vaporization from the bulk of the fluid, here
we include the possibility of vaporization from the free surface, when a moving
fluid comes into contact with a superheated surface. We present a continuum,
one-fluid approach in which the liquid and vapor phases are modelled with the
same constitutive equations, with different material properties. The novelty
here is a monolithic approach without explicit modelling of the interface
between the phases, neither in a sharp nor diffuse sense. Furthermore, no
interface boundary conditions or source terms are needed between the liquid and
vapour phases. Instead, the phase transition is modelled only using material
properties varying with temperature. Towards this end, we also present an
enrichment of strong form meshfree generelized finite difference methods (GFDM)
to be able to accurately capture derivatives in the presence of jumps in
density, viscosity and other physical properties. The numerical results show
the ability of our proposed framework to model phase changes with large jumps.


title:
Data Smells: Categories, Causes and Consequences, and Detection of
  Suspicious Data in AI-based Systems

abstract:
  High data quality is fundamental for today's AI-based systems. However,
although data quality has been an object of research for decades, there is a
clear lack of research on potential data quality issues (e.g., ambiguous,
extraneous values). These kinds of issues are latent in nature and thus often
not obvious. Nevertheless, they can be associated with an increased risk of
future problems in AI-based systems (e.g., technical debt, data-induced
faults). As a counterpart to code smells in software engineering, we refer to
such issues as Data Smells. This article conceptualizes data smells and
elaborates on their causes, consequences, detection, and use in the context of
AI-based systems. In addition, a catalogue of 36 data smells divided into three
categories (i.e., Believability Smells, Understandability Smells, Consistency
Smells) is presented. Moreover, the article outlines tool support for detecting
data smells and presents the result of an initial smell detection on more than
240 real-world datasets.


title: PressureVision: Estimating Hand Pressure from a Single RGB Image

abstract:
  People often interact with their surroundings by applying pressure with their
hands. Machine perception of hand pressure has been limited by the challenges
of placing sensors between the hand and the contact surface. We explore the
possibility of using a conventional RGB camera to infer hand pressure. The
central insight is that the application of pressure by a hand results in
informative appearance changes. Hands share biomechanical properties that
result in similar observable phenomena, such as soft-tissue deformation, blood
distribution, hand pose, and cast shadows. We collected videos of 36
participants with diverse skin tone applying pressure to an instrumented planar
surface. We then trained a deep model (PressureVisionNet) to infer a pressure
image from a single RGB image. Our model infers pressure for participants
outside of the training data and outperforms baselines. We also show that the
output of our model depends on the appearance of the hand and cast shadows near
contact regions. Overall, our results suggest the appearance of a previously
unobserved human hand can be used to accurately infer applied pressure.


title: A remark about amalgamation of union of theories

abstract:
  We provide conditions under which the union of two first-order theories has
the amalgamation property.


title: New algorithms for computing the least trimmed squares estimator

abstract:
  Instead of minimizing the sum of all $n$ squared residuals as the classical
least squares (LS) does, Rousseeuw (1984) proposed to minimize the sum of $h$
($n/2 \leq h < n$) smallest squared residuals, the resulting estimator is
called least trimmed squares (LTS). The idea of the LTS is simple but its
computation is challenging since no LS-type analytical computation formula
exists anymore. Attempts had been made since its presence, the feasible
solution algorithm (Hawkins (1994)), fastlts.f (Rousseeuw and Van Driessen
(1999)), and FAST-LTS (Rousseeuw and Van Driessen (2006)), among others, are
promising approximate algorithms. The latter two have been incorporated into R
function ltsReg by Valentin Todorov. These algorithms utilize combinatorial- or
subsampling- approaches. With the great software accessibility and fast speed,
the LTS, enjoying many desired properties, has become one of the most popular
robust regression estimators across multiple disciplines. This article proposes
analytic approaches -- employing first-order derivative (gradient) and
second-order derivative (Hessian matrix) of the objective function. Our
approximate algorithms for the LTS are vetted in synthetic and real data
examples. Compared with ltsReg -- the benchmark in robust regression and
well-known for its speed, our algorithms are comparable (and sometimes even
favorable) with respect to both speed and accuracy criteria. Other major
contributions include (i) originating the uniqueness and the strong and Fisher
consistency at empirical and population settings respectively; (ii) deriving
the influence function in a general setting; (iii) re-establishing the
asymptotic normality (consequently root-n consistency) of the estimator with a
neat and general approach.


title:
Water-to-air transfer of nano/micro-sized particulates: enrichment
  effect in bubble bursting jet drops

abstract:
  Bubbles dispersed in liquids are widely present in many natural and
industrial processes, and play a key role in mediating mass transfer during
their lifetime from formation to rising to bursting. In particular,
nano/micro-sized particulates and organisms, present in the bulk water can be
highly enriched in the jet drops ejected during bubble bursting, impacting
global climate and public health. However, the detailed mechanism of this
enrichment remains obscure, with the enrichment factor being difficult to
predict. Here, we experimentally investigate the enrichment of nano/micro-sized
particles in bubble bursting jet drops and highlight the underlying
hydrodynamic mechanism, combining the effects of bubble scavenge and bursting
on the transport of particles. Scaling laws for the enrichment factor are
subsequently proposed that describe both our and prior experimental results
reasonably well. Our study may provide new insights for water-to-air transfer
of microbes related to bubble bursting.


title: On m-quasi-ideals in m-regular ordered semigroups

abstract:
  In this paper we characterize left(right) ideals, bi-ideals and quasi-ideals
of an ordered semigroup by an index $m$ and give some important interplays
between these ideals. The concept of m-regularity of an ordered semigroups has
been introduced. Moreover m-regular ordered semigroups are characterized by
their m-quasi-ideals and the fact that for any m-regular ordered semigroups
$A$, the set $Q_{A}$ of all m-quasi-ideals of $A$, with multiplication defined
by: $Q_{1}\circ Q_{2}=(Q_{1}Q_{2}]$, for all $Q_{1},Q_{2}\in Q_{A}$, is a
m-regular semigroup is obtained here.


title: RT-WiFi on Software-Defined Radio: Design and Implementation

abstract:
  Applying high-speed real-time wireless technologies in industrial
applications has the great potential to reduce the deployment and maintenance
costs compared to their wired counterparts. Wireless technologies enhance the
mobility and reduce the communication jitter and delay for mobile industrial
equipment, such as mobile collaborative robots. Unfortunately, most existing
wireless solutions employed in industrial fields either cannot support the
desired high-speed communications or cannot guarantee deterministic, real-time
performance. A more recent wireless technology, RT-WiFi, achieves a good
balance between high-speed data rates and deterministic communication
performance. It is however developed on commercial-of-the-shelf (COTS)
hardware, and takes considerable effort and hardware expertise to maintain and
upgrade. To address these problems, this paper introduces the software-defined
radio (SDR)-based RT-WiFi solution which we call SRT-WiFi. SRT-WiFi provides
full-stack configurability for high-speed real-time wireless communications. We
present the overall system architecture of SRT-WiFi and discuss its key
functions which achieve better timing performance and solve the queue
management and rate adaptation issues compared to COTS hardware-based RT-WiFi.
To achieve effective network management with rate adaptation in multi-cluster
SRT-WiFi, a novel scheduling problem is formulated and an effective algorithm
is proposed to solve the problem. A multi-cluster SRT-WiFi testbed is developed
to validate the design, and extensive experiments are performed to evaluate the
performance at both device and system levels.


title: Unpolarized Shafarevich conjectures for hyper-K\"ahler varieties

abstract:
  The Shafarevich conjecture/problem is about the finiteness of isomorphism
classes of a family of varieties defined over a number field with good
reduction outside a finite collection of places. For K3 surfaces, such a
finiteness result was proved by Y. She. For hyper-K\"ahler varieties, which are
higher-dimensional analogs of K3 surfaces, Y. Andr\'e proved the Shafarevich
conjecture for hyper-K\"ahler varieties of a given dimension and admitting a
very ample polarization of bounded degree. In this paper, we provide a
unification of both results by proving the (unpolarized) Shafarevich conjecture
for hyper-K\"ahler varieties in a given deformation type. We also discuss the
cohomological generalization of the Shafarevich conjecture by replacing the
good reduction condition by the unramifiedness of the cohomology, where our
results are subject to a certain necessary assumption on the faithfulness of
the action of the automorphism group on cohomology. In a similar fashion,
generalizing a result of Orr and Skorobogatov on K3 surfaces, we prove the
finiteness of geometric isomorphism classes of hyper-K\"ahler varieties of CM
type in a given deformation type defined over a number field with bounded
degree. A key to our approach to these results is a uniform Kuga--Satake map,
inspired by She's work, and we study its arithmetic properties, which are of
independent interest.


title:
Minimum effort decentralized control design for contracting network
  systems

abstract:
  We consider the problem of making a networked system contracting by designing
minimal effort local controllers. Our method combines a hierarchical
contraction characterization and a matrix-balancing approach to stabilizing a
Metzler matrix via minimal diagonal perturbations. We demonstrate our approach
by designing local controllers that render contractive a network of
FitzHugh-Nagumo neurons with a general topology of interactions.


title: In Search of Excellence and Equity in Physics

abstract:
  Equal opportunity is central to the concept of meritocracy. Opportunity and
leadership should go to the people most qualified by performance, and not on
the basis of arbitrary or irrelevant attributes. This principle is arguably
most important for high-level leadership due to their outsized impact on the
field. At the moment, many in the community perceive that the choice of leaders
is infused with a lack of meritocracy and too often driven by cronyism. This is
possibly a reason why far worse underrepresentation persists than could be
expected from a functioning meritocracy. If we want to change this, we need to
change our behavior, i.e., practices.


title: Abstract entropy and expansiveness

abstract:
  We give a general definition of entropy in the setting of pre-ordered
semigroups following the method of the seminal paper of Adler, Konheim and
McAndrew, where the notion of topological entropy for continuous maps is
introduced in 1965. From our definition we obtain the basic properties
exhibited by various entropy-like theories encountered in the literature,
several of them being particular cases of our scheme. We also introduce a
notion of expansiveness in this general context extending the notion of
expansive dynamical system, which is related to the entropy in a similar
fashion as in the case of the topological entropy of an expansive system.


title:
Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source
  Mixed Sampling and Meta-Learning

abstract:
  Autonomous vehicles utilize urban scene segmentation to understand the real
world like a human and react accordingly. Semantic segmentation of normal
scenes has experienced a remarkable rise in accuracy on conventional
benchmarks. However, a significant portion of real-life accidents features
abnormal scenes, such as those with object deformations, overturns, and
unexpected traffic behaviors. Since even small mis-segmentation of driving
scenes can lead to serious threats to human lives, the robustness of such
models in accident scenarios is an extremely important factor in ensuring
safety of intelligent transportation systems.
  In this paper, we propose a Multi-source Meta-learning Unsupervised Domain
Adaptation (MMUDA) framework, to improve the generalization of segmentation
transformers to extreme accident scenes. In MMUDA, we make use of Multi-Domain
Mixed Sampling to augment the images of multiple-source domains (normal scenes)
with the target data appearances (abnormal scenes). To train our model, we
intertwine and study a meta-learning strategy in the multi-source setting for
robustifying the segmentation results. We further enhance the segmentation
backbone (SegFormer) with a HybridASPP decoder design, featuring large window
attention spatial pyramid pooling and strip pooling, to efficiently aggregate
long-range contextual dependencies. Our approach achieves a mIoU score of
46.97% on the DADA-seg benchmark, surpassing the previous state-of-the-art
model by more than 7.50%. Code will be made publicly available at
https://github.com/xinyu-laura/MMUDA.


title: Countable Ramsey

abstract:
  The celebrated Erd\H{o}s-Hajnal Conjecture says that in any proper hereditary
class of finite graphs we are guaranteed to have a clique or anti-clique of
size $n^c$, which is a much better bound than the logarithmic size that is
provided by Ramsey's Theorem in general. On the other hand, in uncountable
cardinalities, the model-theoretic property of stability guarantees a uniform
set much larger than the bound provided by the Erd\H{o}s-Rado Theorem in
general.
  Even though the consequences of stability in the finite have been much
studied in the literature, the countable setting seems a priori quite
different, namely, in the countably infinite the notion of largeness based on
cardinality alone does not reveal any structure as Ramsey's Theorem already
provides a countably infinite uniform set in general. In this paper, we show
that the natural notion of largeness given by upper density reveals that these
phenomena meet in the countable: a countable graph has an almost clique or
anti-clique of positive upper density if and only if it has a positive upper
density almost stable set. Moreover, this result also extends naturally to
countable models of a universal theory in a finite relational language.
  Our methods explore a connection with the notion of convergence in the theory
of limits of dense combinatorial objects, introducing and studying a natural
approximate version of the Erd\H{o}s-Hajnal property that allows for a
negligible error in the edges (in general, predicates) but requires
linear-sized uniform sets in convergent sequences of models (this is much
stronger than what stable regularity can provide as the error is required to go
to zero). Finally, surprisingly, we completely characterize all hereditary
classes of finite graphs that have this approximate Erd\H{o}s-Hajnal property.
The proof highlights both differences and similarities with the original
conjecture.


title:
Mechanism, measurement, and quantification of stress in decision
  process: a model based systematic-review protocol

abstract:
  Every human action begins with decision-making. Stress is a significant
source of biases that can influence human decision-making. In order to
understand the relationship between stress and decision-making, stress
quantification is fundamental. Different methods of measuring and quantifying
stress in decision-making have been described in the literature while an
up-to-date systematic review of the existing methods is lacking. Moreover,
mental stress, mental effort, cognitive workload, and workload are often used
interchangeably but should be distinguished to enable in-depth investigations
of decision-mechanisms. Our objectives are to clarify stress related concepts
and review the measurement, quantification, and application of stress during
decision making activities.


title:
Ethical Considerations When Constructing Participatory Design Protocols
  for Social Robots

abstract:
  Participatory design has emerged as a popular approach to foreground ethical
considerations in social robots by incorporating anticipated users and
stakeholders as designers. Here we draw attention to the ethics of
participatory design as a method, distinct from the ethical considerations of
the social robot being co-designed. More specifically, we consider the ethical
concerns posed by the act of stakeholder participation - the morals and values
that should be explicitly considered when we, as researchers or practitioners,
devise protocols for participatory design of social robots ("how" stakeholders
participate). We use the case of robot-assisted sexual violence mitigation to
exemplify ethical considerations of participatory design protocols such as risk
of harm, exploitation, and reduction of stakeholder agency. To incorporate
these and other ethical considerations in the creation of social robot
participatory design protocols, we advocate letting stakeholders design their
own form of participation by including them in the creation of participatory
design sessions, structures, and processes.


title: Network analysis of the Kyiv bylyny cycle -- east Slavic epic narratives

abstract:
  Since the pioneering work of Joseph Campbell in the 1960's, universality
emerged as an important qualitative notion in the field of comparative
mythology. In recent times, the advent of network science permitted new
quantitative approaches to literary studies. Here we bring the Kyiv bylyny
cycle into the field -- East Slavic epic narratives originating in modern-day
Ukraine. By comparing them to other prominent European epics, we identify
universal and distinguishing properties of the social networks in bylyny. We
analyse community structures and rank most important characters. The method
allows to bolster hypotheses from humanities literature -- such as the solar
position of Prince Volodymyr -- and to generate new ones. We show how the Kyiv
cycle of bylyny fits very well with narrative networks from other nations --
especially heroic ones. We anticipate that, besides delivering new
narratological insights, this study will aid future scholars and interested
public to navigate their way through Ukraine's epic story and identify its
heroes.


title:
Status Updating with an Energy Harvesting Sensor under Partial Battery
  Knowledge

abstract:
  We consider status updating under inexact knowledge of the battery level of
an energy harvesting (EH) sensor that sends status updates about a random
process to users via a cache-enabled edge node. More precisely, the control
decisions are performed by relying only on the battery level knowledge captured
from the last received status update packet. Upon receiving on-demand requests
for fresh information from the users, the edge node uses the available
information to decide whether to command the sensor to send a status update or
to retrieve the most recently received measurement from the cache. We seek for
the best actions of the edge node to minimize the average AoI of the served
measurements, i.e., average on-demand AoI. Accounting for the partial battery
knowledge, we model the problem as a partially observable Markov decision
process (POMDP), and, through characterizing its key structures, develop a
dynamic programming algorithm to obtain an optimal policy. Simulation results
illustrate the threshold-based structure of an optimal policy and show the
gains obtained by the proposed optimal POMDP-based policy compared to a
request-aware greedy (myopic) policy.


title:
Non-monotonic dc Stark shifts in the rapidly ionizing orbitals of the
  water molecule

abstract:
  We extend a previously developed model for the Stark resonances of the water
molecule. The method employs a partial-wave expansion of the single-particle
orbitals using spherical harmonics. To find the resonance positions and decay
rates, we use the exterior complex scaling approach which involves the analytic
continuation of the radial variable into the complex plane and yields a
non-hermitian Hamiltonian matrix. The real part of the eigenvalues provides the
resonance positions (and thus the Stark shifts), while the imaginary parts
$-\Gamma/2$ are related to the decay rates $\Gamma$, i.e., the full-widths at
half-maximum of the Breit-Wigner resonances. We focus on the three outermost
(valence) orbitals, as they are dominating the ionization process. We find that
for forces directed in the three Cartesian co-ordinates, the fastest ionizing
orbital always displays a non-monotonic Stark shift. For the case of fields
along the molecular axis we also compare results as a function of the number of
spherical harmonics included ($\ell_{\max}=3,4$). We also compare our results
to the total molecular Stark shifts for the Hartree-Fock and coupled cluster
methods.


title: Odd colourings, conflict-free colourings and strong colouring numbers

abstract:
  The odd chromatic number and the conflict-free chromatic number are new graph
parameters introduced by Petru\v{s}evski and \v{S}krekovski [2021] and Fabrici,
Lu\v{z}ar, Rindo\v{s}ov\'a and Sot\'ak [2022] respectively. In this note, we
show that graphs with bounded $2$-strong colouring number have bounded odd
chromatic number and bounded conflict-free chromatic number. This implies that
graph classes with bounded expansion have bounded odd chromatic number and
bounded conflict-free chromatic number. Moreover, it follows by known results
that the odd chromatic number and the conflict-free chromatic number of
$k$-planar graphs is $O(k)$ which improves a recent result of Dujmovi\'{c},
Morin and Odak [2022].


title:
An Exploratory Study into Vulnerability Chaining Blindness Terminology
  and Viability

abstract:
  To tie together the concepts of linkage blindness and the inability to link
vulnerabilities together in a Vulnerability Management Program (VMP), the
researcher postulated new terminology. The terminology of vulnerability
chaining blindness is proposed to understand the underlying issues behind
vulnerability management and vulnerabilities that can be used in combination.
The general problem is that IT and cybersecurity professionals have a difficult
time identifying chained vulnerabilities due to the complexity of vulnerability
prioritization and remediation (Abomhara & K{\o}ien, 2015; Felmetsger et al.,
2010). The specific problem is the inability to link and view multiple
vulnerabilities in combination based on limited expertise and awareness of
vulnerability chaining (Tang et al., 2017). The population of this study was
limited to one focus group, within the IT and Security fields, within the
United States. The sample size consisted of one focus group comprised of 8-10
IT and cybersecurity professionals. The research questions focused on if
participants were aware of linkage blindness or vulnerability chaining, as well
as if vulnerability chaining blindness would be applicable to describe the
phenomenon. Several themes emerged through top-level, eclectic, and
second-level coding data analysis. These themes included complexity in
cybersecurity programs, new concepts in vulnerability management, as well as
fear of the unknown and where security meets technology.
  Keywords: linkage blindness, vulnerability chaining, vulnerability chaining
blindness, vulnerability management


title:
Intensity $g^{(2)}$-correlations in random fiber lasers: A random matrix
  theory approach

abstract:
  We propose a new approach based on random matrix theory to calculate the
temporal second-order intensity correlation function $g^{(2)}(t)$ of the
radiation emitted by random lasers and random fiber lasers. The multimode
character of these systems, with a relevant degree of disorder in the active
medium, and large number of random scattering centers substantially hinder the
calculation of $g^{(2)}(t)$. Here we apply for the first time in a photonic
system the universal statistical properties of Ginibre's non-Hermitian random
matrix ensemble to obtain $g^{(2)}(t)$. Excellent agreement is found with
time-resolved measurements for several excitation powers of an erbium-based
random fiber laser. We also discuss the extension of the random matrix approach
to address the statistical properties of general disordered photonic systems
with various Hamiltonian symmetries.


title: IID Time Series Testing

abstract:
  Traditional white noise testing, for example the Ljung-Box test, studies only
the autocorrelation function (ACF). Time series can be heteroscedastic and
therefore not i.i.d. but still white noise (that is, with zero ACF). An example
of heteroscedasticity is financial time series: times of high variance
(financial crises) can alternate with times of low variance (calm times). Here,
absolute values of time series terms are not white noise. We could test for
white noise separately for original and absolute values, for example using
Ljung-Box tests for both. In this article, we create an omnibus test which
combines these two tests. Moreover, we create a general framework to create
various i.i.d. tests. We apply tests to simulated data, both autoregressive
linear and heteroscedastic.


title:
Direct Participation of Dynamic Virtual Power Plants in Secondary
  Frequency Control

abstract:
  This paper proposes a novel control strategy in which Renewable Energy
Sources (RES) considered in a new Dynamic Virtual Power Plant (DVPP) concept
directly participate to Secondary Frequency Control (SFC). This allows full
participation of these generators to SFC, i.e., in the same manner as classic
synchronous generators by fulfilling identical specifications from both control
and contractual points of view. An internal real-time redispatch has been
proposed to account in DVPP in order to determine the amount of active power
injection by each RES unit for provision of frequency support in secondary
level. The whole control scheme is designed to take into account both rapid and
slow dynamics of modern power systems which contain both classic synchronous
generators and rapid power electronics for renawable energy sources in which
DVPP is supposed to be inserted. The performance of secondary frequency control
strategy has been validated through simulation studies on a two-area benchmark
with mixed wind power plants and classic synchronous generators. This work is
part of the H2020 POSYTYF project (https://posytyf-h2020.eu/).


title:
Investigating the Effects of Robot Proficiency Self-Assessment on Trust
  and Performance

abstract:
  Human-robot teams will soon be expected to accomplish complex tasks in
high-risk and uncertain environments. Here, the human may not necessarily be a
robotics expert, but will need to establish a baseline understanding of the
robot's abilities in order to appropriately utilize and rely on the robot. This
willingness to rely, also known as trust, is based partly on the human's belief
in the robot's proficiency at a given task. If trust is too high, the human may
push the robot beyond its capabilities. If trust is too low, the human may not
utilize it when they otherwise could have, wasting precious resources. In this
work, we develop and execute an online human-subjects study to investigate how
robot proficiency self-assessment reports based on Factorized Machine
Self-Confidence affect operator trust and task performance in a grid world
navigation task. Additionally we present and analyze a metric for trust level
assessment, which measures the allocation of control between an operator and
robot when the human teammate is free to switch between teleportation and
autonomous control. Our results show that an a priori robot self-assessment
report aligns operator trust with robot proficiency, and leads to performance
improvements and small increases in self-reported trust.


title:
Anomaly Detection in Emails using Machine Learning and Header
  Information

abstract:
  Anomalies in emails such as phishing and spam present major security risks
such as the loss of privacy, money, and brand reputation to both individuals
and organizations. Previous studies on email anomaly detection relied on a
single type of anomaly and the analysis of the email body and subject content.
A drawback of this approach is that it takes into account the written language
of the email content. To overcome this deficit, this study conducted feature
extraction and selection on email header datasets and leveraged both multi and
one-class anomaly detection approaches. Experimental analysis results obtained
demonstrate that email header information only is enough to reliably detect
spam and phishing emails. Supervised learning algorithms such as Random Forest,
SVM, MLP, KNN, and their stacked ensembles were found to be very successful,
achieving high accuracy scores of 97% for phishing and 99% for spam emails.
One-class classification with One-Class SVM achieved accuracy scores of 87% and
89% with spam and phishing emails, respectively. Real-world email filtering
applications will benefit from the use of only the header information in terms
of resources utilization and efficiency.


title: Pulsar scintillation studies with LOFAR. I. The census

abstract:
  Context. Interstellar scintillation (ISS) of pulsar emission can be used both
as a probe of the ionised interstellar medium (IISM) and cause corruptions in
pulsar timing experiments. Of particular interest are so-called scintillation
arcs which can be used to measure time-variable interstellar scattering delays
directly, potentially allowing high-precision improvements to timing precision.
  Aims. The primary aim of this study is to carry out the first sizeable and
self-consistent census of diffractive pulsar scintillation and
scintillation-arc detectability at low frequencies, as a primer for
larger-scale IISM studies and pulsar-timing related propagation studies with
the LOw-Frequency ARray (LOFAR) High Band Antennae (HBA).
  Results. In this initial set of 31 sources, 15 allow full determination of
the scintillation properties; nine of these show detectable scintillation arcs
at 120-180 MHz. Eight of the observed sources show unresolved scintillation;
and the final eight don't display diffractive scintillation. Some correlation
between scintillation detectability and pulsar brightness and dispersion
measure is apparent, although no clear cut-off values can be determined. Our
measurements across a large fractional bandwidth allow a meaningful test of the
frequency scaling of scintillation parameters, uncorrupted by influences from
refractive scintillation variations.
  Conclusions. Our results indicate the powerful advantage and great potential
of ISS studies at low frequencies and the complex dependence of scintillation
detectability on parameters like pulsar brightness and interstellar dispersion.
This work provides the first installment of a larger-scale census and
longer-term monitoring of interstellar scintillation effects at low
frequencies.


title: Language-Preserving Reduction Rules for Block-Structured Workflow Nets

abstract:
  Process models are used by human analysts to model and analyse behaviour, and
by machines to verify properties such as soundness, liveness or other
reachability properties, and to compare their expressed behaviour with recorded
behaviour within business processes of organisations. For both human and
machine use, small models are preferable over large and complex models: for
ease of human understanding and to reduce the time spent by machines in state
space explorations. Reduction rules that preserve the behaviour of models have
been defined for Petri nets, however in this paper we show that a subclass of
Petri nets returned by process discovery techniques, that is, block-structured
workflow nets, can be further reduced by considering their block structure in
process trees. We revisit an existing set of reduction rules for process trees
and show that the rules are correct, terminating, confluent and complete, and
for which classes of process trees they are and are not complete. In a
real-life experiment, we show that these rules can reduce process models
discovered from real-life event logs further compared with rules that consider
only Petri net structures.


title: Birth-Death Processes in Interactive Random Environments

abstract:
  We study birth-death processes in interactive random environments, where not
only the birth and death rates depend on the state of the environment, but also
the evolution dynamics of the environment depends on the state of the
birth-death process. Two types of random environments are considered, either
pure jump (finite or infinite countable) Markov process or a reflected (jump)
diffusion process. The joint Markov process is constructed in such a way that
the interactive impact is explicitly characterized, and the joint invariant
measure takes an explicit form which may be regarded as a type of product form.
We provide a few examples in queueing (multi-server and infinite-server models)
and population growth models, to illustrate the construction of the joint
Markov process and the conditions under which the explicit product-form
invariant measure can be derived.
  We then study the rate of convergence to stationarity of these models. We
consider two settings which lead to either an exponential rate or a polynomial
rate. In both settings, we assume that the underlying environmental Markov
process has an exponential rate of convergence, however, the convergence rates
of the joint Markov process depend on the certain conditions on the birth and
death rates. We use a coupling approach to prove these results on the rates of
convergence.


title:
Computer experiments and visualization in mathematics and physics. A
  subjective short walk among some historical examples

abstract:
  In this short essay, we show how computer experiments, and especially
visualization, allowed for the investigation and discovery of phenomena which
would have passed unnoticed. We shall also highlight the importance of
interactivity between the computer and the user. We do this by surveying
several historical examples from mathematics and the physical sciences. Many
pictures, and even hyperlinks to online interactive numerical experiments, are
provided. Needless to say that we do not claim to be exhaustive, and that the
chosen examples reflect our taste as well as our limited knowledge. Hyperlinks
to several online interactive digital experiments are provided.


title: On monogenity of certain number fields defined by $x^{2^r}+ax^m+b$

abstract:
  Let $K = \mathbb{Q} (\th) $ be a number field generated by a complex root
$\th$ of a monic irreducible trinomial of type $ F(x)= x^{2^r}+ax^m+b \in
\Z[x]$ and $\Z_K$ its ring of integers. In this paper, we study the monogenity
of $K$. More precisely, when $m=1$, we provide some explicit conditions on $a$,
$b$, and $r$ for which $K$ is not monogenic. We also construct a family of
irreducible trinomials which are not monogenic, but their roots generate
monogenic number fields. To illustrate our results, we give some computational
examples.


title:
Unique continuation results for abstract quasi-linear evolution
  equations in Banach spaces

abstract:
  Unique continuation properties for a class of evolution equations defined on
Banach spaces are considered from two different point of views: the first one
is based on the existence of conserved quantities, which very often translates
into the conservation of some norm of the solutions of the system in a suitable
Banach space. The second one is regarded to well-posed problems in which the
data-to-solution map is continuous. Our results are then applied to some
equations, most of them describing physical processes like wave propagation,
hydrodynamics and integrable systems, such as the $b-$; Fornberg-Whitham;
potential and $\pi-$Camassa-Holm; and generalised Boussinesq equations.


title:
How does the pre-training objective affect what large language models
  learn about linguistic properties?

abstract:
  Several pre-training objectives, such as masked language modeling (MLM), have
been proposed to pre-train language models (e.g. BERT) with the aim of learning
better language representations. However, to the best of our knowledge, no
previous work so far has investigated how different pre-training objectives
affect what BERT learns about linguistics properties. We hypothesize that
linguistically motivated objectives such as MLM should help BERT to acquire
better linguistic knowledge compared to other non-linguistically motivated
objectives that are not intuitive or hard for humans to guess the association
between the input and the label to be predicted. To this end, we pre-train BERT
with two linguistically motivated objectives and three non-linguistically
motivated ones. We then probe for linguistic characteristics encoded in the
representation of the resulting models. We find strong evidence that there are
only small differences in probing performance between the representations
learned by the two different types of objectives. These surprising results
question the dominant narrative of linguistically informed pre-training.


title: Methodology for Testing and Evaluation of Safety Analytics Approaches

abstract:
  There has been a significant increase in the development of data-driven
safety analytics approaches in recent years. In light of these advances it has
become imperative to evaluate such approaches in a principled way to determine
their merits and limitations. To that end, we propose an evaluation methodology
underpinned by a simulated environment that allows for a comprehensive
assessment of safety analytics approaches. While assessing those approaches
with historical field data is undoubtedly important, such an assessment has
limited statistical power because it corresponds to only one realization. The
proposed methodology enables validation over a large number of realizations,
thereby circumventing the statistical limitations of evaluation on historical
data. Moreover, by using a simulated environment one is able to clearly
distinguish between the variability in the observed data and differences in
performance between safety approaches. A simulated environment does this by
comparing the approaches under controlled circumstances, resulting in a fair
and systematic evaluation of the potential long-term benefits. We demonstrate
the utility of the proposed methodology via a case study that compares a few
candidate safety analytics approaches. These approaches differ in how they
assimilate field safety data to assess safety risk and suggest mitigative
actions. We show that the proposed methodology indeed reveals useful insights
and quantifies the relative merits and drawbacks of the different approaches,
which would be otherwise difficult to objectively determine in a real-world
scenario.


title:
Attri-VAE: attribute-based, disentangled and interpretable
  representations of medical images with variational autoencoders

abstract:
  Deep learning (DL) methods where interpretability is intrinsically considered
as part of the model are required to better understand the relationship of
clinical and imaging-based attributes with DL outcomes, thus facilitating their
use in reasoning medical decisions. Latent space representations built with
variational autoencoders (VAE) do not ensure individual control of data
attributes. Attribute-based methods enforcing attribute disentanglement have
been proposed in the literature for classical computer vision tasks in
benchmark data. In this paper, we propose a VAE approach, the Attri-VAE, that
includes an attribute regularization term to associate clinical and medical
imaging attributes with different regularized dimensions in the generated
latent space, enabling a better disentangled interpretation of the attributes.
Furthermore, the generated attention maps explained the attribute encoding in
the regularized latent space dimensions. The Attri-VAE approach analyzed
healthy and myocardial infarction patients with clinical, cardiac morphology,
and radiomics attributes. The proposed model provided an excellent trade-off
between reconstruction fidelity, disentanglement, and interpretability,
outperforming state-of-the-art VAE approaches according to several quantitative
metrics. The resulting latent space allowed the generation of realistic
synthetic data in the trajectory between two distinct input samples or along a
specific attribute dimension to better interpret changes between different
cardiac conditions.


title: How do noise tails impact on deep ReLU networks?

abstract:
  This paper investigates the stability of deep ReLU neural networks for
nonparametric regression under the assumption that the noise has only a finite
p-th moment. We unveil how the optimal rate of convergence depends on p, the
degree of smoothness and the intrinsic dimension in a class of nonparametric
regression functions with hierarchical composition structure when both the
adaptive Huber loss and deep ReLU neural networks are used. This optimal rate
of convergence cannot be obtained by the ordinary least squares but can be
achieved by the Huber loss with a properly chosen parameter that adapts to the
sample size, smoothness, and moment parameters. A concentration inequality for
the adaptive Huber ReLU neural network estimators with allowable optimization
errors is also derived. To establish a matching lower bound within the class of
neural network estimators using the Huber loss, we employ a different strategy
from the traditional route: constructing a deep ReLU network estimator that has
a better empirical loss than the true function and the difference between these
two functions furnishes a low bound. This step is related to the Huberization
bias, yet more critically to the approximability of deep ReLU networks. As a
result, we also contribute some new results on the approximation theory of deep
ReLU neural networks.