title: Semiring identities of finite inverse semigroups

abstract:
  We study the Finite Basis Problem for finite additively idempotent semirings
whose multiplicative reducts are inverse semigroups. In particular, we show
that each additively idempotent semiring whose multiplicative reduct is a
nontrivial rook monoid admits no finite identity basis, and so do almost all
additively idempotent semirings whose multiplicative reducts are combinatorial
inverse semigroups.


title: Modulating quantum evolution of moving-qubit by using classical driving

abstract:
  In this work, we study quantum evolution of an open moving-qubit modulated by
a classical driving field. We obtain the density operator of qubit at zero
temperature and analyze its quantum evolution dynamics by using the quantum
speed limit time (QSLT) and the non-Markovianity. The results show that both
the non-Markovian environment and the classical driving can speed up the
evolution process, this quantum speed up process is induced by the
non-Markovianity and the critical points only depend on the qubit velocity.
Moreover, the qubit motion will delay the evolution process, but this negative
effect of the qubit velocity on the quantum speedup can be suppressed by the
classical driving. Finally, we give the corresponding physical explanation by
using the decoherence rates.


title: Implicit Object Mapping With Noisy Data

abstract:
  Modelling individual objects as Neural Radiance Fields (NeRFs) within a
robotic context can benefit many downstream tasks such as scene understanding
and object manipulation. However, real-world training data collected by a robot
deviate from the ideal in several key aspects. (i) The trajectories are
constrained and full visual coverage is not guaranteed - especially when
obstructions are present. (ii) The poses associated with the images are noisy.
(iii) The objects are not easily isolated from the background. This paper
addresses the above three points and uses the outputs of an object-based SLAM
system to bound objects in the scene with coarse primitives and - in concert
with instance masks - identify obstructions in the training images. Objects are
therefore automatically bounded, and non-relevant geometry is excluded from the
NeRF representation. The method's performance is benchmarked under ideal
conditions and tested against errors in the poses and instance masks. Our
results show that object-based NeRFs are robust to pose variations but
sensitive to the quality of the instance masks.


title: Asymptotic Analysis of a General Multi-Structured Population Model

abstract:
  Structured populations are ubiquitous across the biological sciences.
Mathematical models of these populations allow us to understand how individual
physiological traits drive the overall dynamics in aggregate. For example,
linear age- or age-and-size-structured models establish constraints on
individual growth under which the age- or age-and-size- distribution
stabilizes, even as the population continues to grow without bound. However,
individuals in real-world populations exhibit far more structural features than
simply age and size. Notably, cyanobacteria contain carboxysome organelles
which are central to carbon fixation and can be older (if inherited from parent
cells) or younger (if created after division) than the enveloping cell.
Motivated by a desire to understand how carboxysome age impacts growth at the
colony level, we develop a multi-structured model which allows for an arbitrary
(but finite) number of structure variables. We then derive and solve the
renewal equation for cell division to obtain an asymptotic solution, and show
that, under certain conditions, a stable structural distribution is reached.


title: Centrally Essential Semigroup Algebras

abstract:
  For a cancellative semigroup S and a field F, it is proved that the semigroup
algebra FS is centrally essential if and only if the group of fractions $G_S$
of the semigroup $S$ exists and the group algebra $FG_S$ of $G_S$ is centrally
essential. The semigroup algebra of a cancellative semigroup is centrally
essential if and only if it has the classical right ring of fractions which is
a centrally essential ring. There exist non-commutative centrally essential
semigroup algebras over fields of zero characteristic (this contrasts with the
known fact that centrally essential group algebras over fields of zero
characteristic are commutative).


title:
Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting
  Patronising and Condescending Language

abstract:
  This work describes the development of different models to detect patronising
and condescending language within extracts of news articles as part of the
SemEval 2022 competition (Task-4). This work explores different models based on
the pre-trained RoBERTa language model coupled with LSTM and CNN layers. The
best models achieved 15$^{th}$ rank with an F1-score of 0.5924 for subtask-A
and 12$^{th}$ in subtask-B with a macro-F1 score of 0.3763.


title:
High rectifying performance of heterojunctions with interface between
  armchair C$_3$N nanoribbons with and without edge H-passivation

abstract:
  Two-dimensional polyaniline with C$_3$N stoichiometry, is a newly fabricated
layered material that has been expected to possess fascinating electronic,
thermal, mechanical and chemical properties. The nature of its counterpart
nano-ribbons/structures offering even more tunability in property because of
the unique quantum confinement and edge effect, however, has not been revealed
sufficiently. Here, using the first-principles calculation based on density
functional theory and nonequilibrium Green's function technique, we first
perform a study on the electron band structure of armchair C$_3$N nanoribbons
(AC$_3$NNRs) without and with H-passivation. The calculated results show that
the pristine AC$_3$NNRs are metal, while the H-passivated ones are either
direct or indirect band gap semiconductors depending on the detailed edge
atomic configurations. Then we propose a lateral planar homogenous junction
with an interface between the pristine and H-passivated AC$_3$NNRs, in which
forms a Schottky-like barrier. Interestingly, our further transport calculation
demonstrates that this AC$_3$NNRs-based heterojunction exhibits a good
rectification behavior. In specification, the average rectification ratio (RR)
can reach up to $10^3$ in the bias regime from 0.2 to 0.4 V. Particularly,
extending the length of semiconductor part in the heterojunction leads to the
decrease of the current through the junction, but the RR can be enlarged
obviously. The average RR increases to the order of $10^4$ in the bias from
0.25 to 0.40 V, with the boosted maximum up to $10^5$ at 0.35 V. The findings
of this work may be serviceable for the design of functional nanodevices based
on AC$_3$NNRs in the future.


title: Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem

abstract:
  We introduce the task of implicit offensive text detection in dialogues,
where a statement may have either an offensive or non-offensive interpretation,
depending on the listener and context. We argue that reasoning is crucial for
understanding this broader class of offensive utterances and release SLIGHT, a
dataset to support research on this task. Experiments using the data show that
state-of-the-art methods of offense detection perform poorly when asked to
detect implicitly offensive statements, achieving only ${\sim} 11\%$ accuracy.
  In contrast to existing offensive text detection datasets, SLIGHT features
human-annotated chains of reasoning which describe the mental process by which
an offensive interpretation can be reached from each ambiguous statement. We
explore the potential for a multi-hop reasoning approach by utilizing existing
entailment models to score the probability of these chains and show that even
naive reasoning models can yield improved performance in most situations.
Furthermore, analysis of the chains provides insight into the human
interpretation process and emphasizes the importance of incorporating
additional commonsense knowledge.


title:
Coherent interaction of a-few-electron quantum dot with a terahertz
  optical resonator in the ultrastrong coupling regime

abstract:
  Hybrid excitations of light and matter, namely, polaritons, in the
ultrastrong coupling regime have been intensively investigated to explore novel
material functions and realize coherent control of material properties by
optical means. However, realization of ultrastrong coupling in a-few-electron
systems has been challenging, because the electronic dipole moment decreases
with decreasing electron numbers in the system. Here, we fabricate a
gate-defined quantum dot (QD) in the vicinity of a gap of a terahertz (THz)
split-ring resonator (SRR). By illuminating the system with external THz
radiation, the QD shows a current change whose spectrum exhibits anti-crossing
behavior between the resonant excitation of the quantized electronic states and
the resonance mode of the SRR. Our result indicates that, owing to the field
enhancement by the THz SRR, the system enters the ultrastrong coupling regime
even when only a few electrons reside in the QD.


title: Unifying Cosine and PLDA Back-ends for Speaker Verification

abstract:
  State-of-art speaker verification (SV) systems use a back-end model to score
the similarity of speaker embeddings extracted from a neural network model. The
commonly used back-end models are the cosine scoring and the probabilistic
linear discriminant analysis (PLDA) scoring. With the recently developed neural
embeddings, the theoretically more appealing PLDA approach is found to have no
advantage against or even be inferior the simple cosine scoring in terms of SV
system performance. This paper presents an investigation on the relation
between the two scoring approaches, aiming to explain the above
counter-intuitive observation. It is shown that the cosine scoring is
essentially a special case of PLDA scoring. In other words, by properly setting
the parameters of PLDA, the two back-ends become equivalent. As a consequence,
the cosine scoring not only inherits the basic assumptions for the PLDA but
also introduces additional assumptions on the properties of input embeddings.
Experiments show that the dimensional independence assumption required by the
cosine scoring contributes most to the performance gap between the two methods
under the domain-matched condition. When there is severe domain mismatch and
the dimensional independence assumption does not hold, the PLDA would perform
better than the cosine for domain adaptation.


title:
Proximity effects in graphene on monolayers of transition-metal
  phosphorus trichalcogenides MPX$_3$

abstract:
  We investigate the electronic band structure of graphene on a series of
two-dimensional magnetic transition-metal phosphorus trichalcogenide
monolayers, MPX$_3$ with M={Mn,Fe,Ni,Co} and X={S,Se}, with first-principles
calculations. A symmetry-based model Hamiltonian is employed to extract orbital
parameters and sublattice resolved proximity-induced exchange couplings
($\lambda_{\textrm{ex}}^\textrm{A}$ and $\lambda_{\textrm{ex}}^\textrm{B}$)
from the low-energy Dirac bands of the proximitized graphene. Depending on the
magnetic phase of the MPX$_3$ layer (ferromagnetic and three antiferromagnetic
ones), completely different Dirac dispersions can be realized with exchange
splittings ranging from 0 to 10~meV. Surprisingly, not only the magnitude of
the exchange couplings depends on the magnetic phase, but also the global sign
and the type. Important, one can realize uniform
($\lambda_{\textrm{ex}}^\textrm{A} \approx \lambda_{\textrm{ex}}^\textrm{B}$)
and staggered ($\lambda_{\textrm{ex}}^\textrm{A} \approx
-\lambda_{\textrm{ex}}^\textrm{B}$) exchange couplings in graphene. From
selected cases, we find that the interlayer distance, as well as a transverse
electric field are efficient tuning knobs for the exchange splittings of the
Dirac bands. More specifically, decreasing the interlayer distance by only
about 10\%, a giant 5-fold enhancement of proximity exchange is found, while
applying few V/nm of electric field, provides tunability of proximity exchange
by tens of percent. We have also studied the dependence on the Hubbard $U$
parameter and find it to be weak. Moreover, we find that the effect of SOC on
the proximitized Dirac dispersion is negligible compared to the exchange
coupling.


title:
Research on spatial information transmission efficiency and capability
  of safe evacuation signs

abstract:
  As an indispensable spatial direction information indicator for emergency
evacuation, the spatial relationship between safety evacuation signs and
evacuees will affect the response time of evacuees and the evacuation
efficiency. This paper takes 2 kinds of common safety evacuation signs,
hangtag-type and embedded, as the research object and designs space direction
information transmission efficiency and capability simulation experiment and
fire drill, the efficiency and capability of spatial direction information
transmission of safety evacuation signs are studied. The results show that the
space angle of the hangtag-type safety evacuation sign is inversely
proportional to the information transmission efficiency and capability of the
space direction, and the fire drill also confirms this conclusion. When the
spatial angle of the embedded safety evacuation sign is 5{\deg}, the spatial
direction information transmission efficiency and capability increase.
Simultaneously, the average escape time of the participants in the fire drill
was lower, and the percentage of choosing unfamiliarity exports increased. The
evolution of spatial angle has no significant effect on the intention of the
response of subjects of different genders; when choosing the direction, males
are more easily affected by the change of spatial angle than females; the
confidence level of females' choice is more easily affected by spatial angle.
In addition, according to the research results, the corresponding
three-dimensional structure safety evacuation signs are designed. The
functional structure of the safety evacuation signs is perfected, which can
effectively improve the efficiency of fire emergency evacuation.


title:
A survey on the parameterized complexity of the independent set and
  (connected) dominating set reconfiguration problems

abstract:
  A graph vertex-subset problem defines which subsets of the vertices of an
input graph are feasible solutions. We view a feasible solution as a set of
tokens placed on the vertices of the graph. A reconfiguration variant of a
vertex-subset problem asks, given two feasible solutions of size $k$, whether
it is possible to transform one into the other by a sequence of token slides
(along edges of the graph) or token jumps (between arbitrary vertices of the
graph) such that each intermediate set remains a feasible solution of size $k$.
Many algorithmic questions present themselves in the form of reconfiguration
problems: Given the description of an initial system state and the description
of a target state, is it possible to transform the system from its initial
state into the target one while preserving certain properties of the system in
the process? Such questions have received a substantial amount of attention
under the so-called combinatorial reconfiguration framework. We consider
reconfiguration variants of three fundamental underlying graph vertex-subset
problems, namely Independent Set, Dominating Set, and Connected Dominating Set.
We survey both older and more recent work on the parameterized complexity of
all three problems when parameterized by the number of tokens $k$. The emphasis
will be on positive results and the most common techniques for the design of
fixed-parameter tractable algorithms.


title: Few-Shot Object Detection with Proposal Balance Refinement

abstract:
  Few-shot object detection has gained significant attention in recent years as
it has the potential to greatly reduce the reliance on large amounts of
manually annotated bounding boxes. While most existing few-shot object
detection literature primarily focuses on bounding box classification by
obtaining as discriminative feature embeddings as possible, we emphasize the
necessity of handling the lack of intersection-over-union (IoU) variations
induced by a biased distribution of novel samples. In this paper, we analyze
the IoU imbalance that is caused by the relatively high number of low-quality
region proposals, and reveal that it plays a critical role in improving
few-shot learning capabilities. The well-known two stage fine-tuning technique
causes insufficient quality and quantity of the novel positive samples, which
hinders the effective object detection of unseen novel classes. To alleviate
this issue, we present a few-shot object detection model with proposal balance
refinement, a simple yet effective approach in learning object proposals using
an auxiliary sequential bounding box refinement process. This process enables
the detector to be optimized on the various IoU scores through additional novel
class samples. To fully exploit our sequential stage architecture, we revise
the fine-tuning strategy and expose the Region Proposal Network to the novel
classes in order to provide increased learning opportunities for the
region-of-interest (RoI) classifiers and regressors. Our extensive assessments
on PASCAL VOC and COCO demonstrate that our framework substantially outperforms
other existing few-shot object detection approaches.


title:
Spectral Theorem approach to the Characteristic Function of Quantum
  Observables II

abstract:
  We compute the resolvent of the anti-commutator operator $XP+PX$ and of the
quantum harmonic oscillator Hamiltonian operator $\frac{1}{2}(X^2+P^2)$. Using
Stone's formula for finding the spectral resolution of an, either bounded or
unbounded, self-adjoint operator on a Hilbert space, we also compute their
Vacuum Characteristic Function (Quantum Fourier Transform). We also show how
Stone's formula is applied to the computation of the Vacuum Characteristic
Function of finite dimensional quantum observables. The method is proposed as
an analytical alternative to the algebraic (or Heisenberg) approach relying on
the associated Lie algebra commutation relations.


title: Exploring Hidden Semantics in Neural Networks with Symbolic Regression

abstract:
  Many recent studies focus on developing mechanisms to explain the black-box
behaviors of neural networks (NNs). However, little work has been done to
extract the potential hidden semantics (mathematical representation) of a
neural network. A succinct and explicit mathematical representation of a NN
model could improve the understanding and interpretation of its behaviors. To
address this need, we propose a novel symbolic regression method for neural
works (called SRNet) to discover the mathematical expressions of a NN. SRNet
creates a Cartesian genetic programming (NNCGP) to represent the hidden
semantics of a single layer in a NN. It then leverages a multi-chromosome NNCGP
to represent hidden semantics of all layers of the NN. The method uses a
(1+$\lambda$) evolutionary strategy (called MNNCGP-ES) to extract the final
mathematical expressions of all layers in the NN. Experiments on 12 symbolic
regression benchmarks and 5 classification benchmarks show that SRNet not only
can reveal the complex relationships between each layer of a NN but also can
extract the mathematical representation of the whole NN. Compared with LIME and
MAPLE, SRNet has higher interpolation accuracy and trends to approximate the
real model on the practical dataset.


title: Multi-view Information Bottleneck Without Variational Approximation

abstract:
  By "intelligently" fusing the complementary information across different
views, multi-view learning is able to improve the performance of classification
tasks. In this work, we extend the information bottleneck principle to a
supervised multi-view learning scenario and use the recently proposed
matrix-based R{\'e}nyi's $\alpha$-order entropy functional to optimize the
resulting objective directly, without the necessity of variational
approximation or adversarial training. Empirical results in both synthetic and
real-world datasets suggest that our method enjoys improved robustness to noise
and redundant information in each view, especially given limited training
samples. Code is available at~\url{https://github.com/archy666/MEIB}.


title:
Unsteadiness characterisation of shock wave/turbulent boundary-layer
  interaction at moderate Reynolds number

abstract:
  A direct numerical simulation of an oblique shock wave impinging on a
turbulent boundary layer at Mach number 2.28 is carried out at moderate
Reynolds number, matching the full conditions of the reference experiment
(Dupont et al., 2006). The low-frequency shock unsteadiness, whose
characteristics have been the focus of considerable research efforts, is here
investigated via the Morlet wavelet transform. Owing to its compact support in
both physical and Fourier spaces, the wavelet transformation makes it possible
to track the time evolution of the various scales of the wall-pressure
fluctuations. This property also allows us to define a local intermittency
measure, representing a frequency-dependent flatness factor, to pinpoint the
bursts of energy that characterise the shock intermittency scale by scale. As a
major result, we show that the broadband shock movement is actually the result
of a collection of sparse events in time, each characterised by its own
temporal scale. This feature is hidden by the classical Fourier analysis, which
can only show the time-averaged behaviour. Then, we propose a procedure to
process any relevant time series, such as the time history of the wall-pressure
or that of the separation bubble extent,in which we use a condition based on
the local intermittency measure to filter out the turbulent content in the
proximity of the shock foot and to isolate only the intermittent component of
the signal.In addition, wavelet analysis reveals the intermittent behaviour
also of the breathing motion of the recirculation bubble behind the reflected
shock, and allows us to detect a direct correspondence between the most
significant intermittent events of the separation region and those of the
wall-pressure at the foot of the shock.


title: End-to-end symbolic regression with transformers

abstract:
  Symbolic regression, the task of predicting the mathematical expression of a
function from the observation of its values, is a difficult task which usually
involves a two-step procedure: predicting the "skeleton" of the expression up
to the choice of numerical constants, then fitting the constants by optimizing
a non-convex loss function. The dominant approach is genetic programming, which
evolves candidates by iterating this subroutine a large number of times. Neural
networks have recently been tasked to predict the correct skeleton in a single
try, but remain much less powerful. In this paper, we challenge this two-step
procedure, and task a Transformer to directly predict the full mathematical
expression, constants included. One can subsequently refine the predicted
constants by feeding them to the non-convex optimizer as an informed
initialization. We present ablations to show that this end-to-end approach
yields better results, sometimes even without the refinement step. We evaluate
our model on problems from the SRBench benchmark and show that our model
approaches the performance of state-of-the-art genetic programming with several
orders of magnitude faster inference.


title:
Fourier Imager Network (FIN): A deep neural network for hologram
  reconstruction with superior external generalization

abstract:
  Deep learning-based image reconstruction methods have achieved remarkable
success in phase recovery and holographic imaging. However, the generalization
of their image reconstruction performance to new types of samples never seen by
the network remains a challenge. Here we introduce a deep learning framework,
termed Fourier Imager Network (FIN), that can perform end-to-end phase recovery
and image reconstruction from raw holograms of new types of samples, exhibiting
unprecedented success in external generalization. FIN architecture is based on
spatial Fourier transform modules that process the spatial frequencies of its
inputs using learnable filters and a global receptive field. Compared with
existing convolutional deep neural networks used for hologram reconstruction,
FIN exhibits superior generalization to new types of samples, while also being
much faster in its image inference speed, completing the hologram
reconstruction task in ~0.04 s per 1 mm^2 of the sample area. We experimentally
validated the performance of FIN by training it using human lung tissue samples
and blindly testing it on human prostate, salivary gland tissue and Pap smear
samples, proving its superior external generalization and image reconstruction
speed. Beyond holographic microscopy and quantitative phase imaging, FIN and
the underlying neural network architecture might open up various new
opportunities to design broadly generalizable deep learning models in
computational imaging and machine vision fields.


title:
General Time-Dependent Configuration-Interaction Singles II: The Atomic
  Case

abstract:
  We present a specialization of the grid-based implementation of the
time-dependent configuration-interaction singles described in the preceding
paper (arXiv:2204.09966) to the case of spherical symmetry. We describe the
intricate time propagator in detail, and conclude with a few example
calculations. Among these, of note are high-resolution photoelectron spectra in
the vicinity of the Fano resonances in photoionization of neon, and
spin-polarized photoelectrons from xenon, in agreement with recent experiments.


title: Alleviating Representational Shift for Continual Fine-tuning

abstract:
  We study a practical setting of continual learning: fine-tuning on a
pre-trained model continually. Previous work has found that, when training on
new tasks, the features (penultimate layer representations) of previous data
will change, called representational shift. Besides the shift of features, we
reveal that the intermediate layers' representational shift (IRS) also matters
since it disrupts batch normalization, which is another crucial cause of
catastrophic forgetting. Motivated by this, we propose ConFiT, a fine-tuning
method incorporating two components, cross-convolution batch normalization
(Xconv BN) and hierarchical fine-tuning. Xconv BN maintains pre-convolution
running means instead of post-convolution, and recovers post-convolution ones
before testing, which corrects the inaccurate estimates of means under IRS.
Hierarchical fine-tuning leverages a multi-stage strategy to fine-tune the
pre-trained network, preventing massive changes in Conv layers and thus
alleviating IRS. Experimental results on four datasets show that our method
remarkably outperforms several state-of-the-art methods with lower storage
overhead.


title: Sharper Utility Bounds for Differentially Private Models

abstract:
  In this paper, by introducing Generalized Bernstein condition, we propose the
first $\mathcal{O}\big(\frac{\sqrt{p}}{n\epsilon}\big)$ high probability excess
population risk bound for differentially private algorithms under the
assumptions $G$-Lipschitz, $L$-smooth, and Polyak-{\L}ojasiewicz condition,
based on gradient perturbation method. If we replace the properties
$G$-Lipschitz and $L$-smooth by $\alpha$-H{\"o}lder smoothness (which can be
used in non-smooth setting), the high probability bound comes to
$\mathcal{O}\big(n^{-\frac{\alpha}{1+2\alpha}}\big)$ w.r.t $n$, which cannot
achieve $\mathcal{O}\left(1/n\right)$ when $\alpha\in(0,1]$. To solve this
problem, we propose a variant of gradient perturbation method,
\textbf{max$\{1,g\}$-Normalized Gradient Perturbation} (m-NGP). We further show
that by normalization, the high probability excess population risk bound under
assumptions $\alpha$-H{\"o}lder smooth and Polyak-{\L}ojasiewicz condition can
achieve $\mathcal{O}\big(\frac{\sqrt{p}}{n\epsilon}\big)$, which is the first
$\mathcal{O}\left(1/n\right)$ high probability excess population risk bound
w.r.t $n$ for differentially private algorithms under non-smooth conditions.
Moreover, we evaluate the performance of the new proposed algorithm m-NGP, the
experimental results show that m-NGP improves the performance of the
differentially private model over real datasets. It demonstrates that m-NGP
improves the utility bound and the accuracy of the DP model on real datasets
simultaneously.


title:
De-channeling in terms of instantaneous transition rates -- Computer
  simulations for 855 MeV electrons at (110) planes of diamond

abstract:
  Monte-Carlo simulation calculation have been performed for 855 MeV electrons
channeling in (110) planes of a diamond single crystal. The continuum potential
picture has been utilized. Both, the transverse potential and the angular
distributions of the scattered electrons at screened atoms are based on the
Doyle-Turner scattering factors which were extrapolated with the functional
dependence of the Moli\`{e}re representation to large momentum transfers.
Scattering cross-sections at bound electrons have been derived for energies
less than 30 keV from the double differential cross-section as function of
both, energy and momentum transfer, taking into account also longitudinal and
transverse excitations. For energies above 30 keV the M{\o}ller cross-section
is used. The dynamics of the particle in the continuum transverse potential has
been described classically. Results of the channeling process are presented in
terms of instantaneous transition rates as function of the penetration depth,
indicating that channeling can be described by a single exponential function
only after the equilibration phase has been reached after about 15 $\mu$m. As a
byproduct, improved drift and diffusion coefficients entering the Fokker-Planck
equation have been derived with which its predictive power can be improved.


title:
Variational problems for integral invariants of the second fundamental
  form of a map between pseudo-Riemannian manifolds

abstract:
  We study variational problems for integral invariants, which are defined as
integrations of invariant functions of the second fundamental form, of a smooth
map between pseudo-Riemannian manifolds. We derive the first variational
formulae for integral invariants defined from invariant homogeneous polynomials
of degree two. Among these integral invariants, we show that the Euler-Lagrange
equation of the Chern-Federer energy functional is reduced to a second order
PDE. Then we give some examples of Chern-Federer submanifolds in Riemannian
space forms.


title:
Energy-efficient and Privacy-aware Social Distance Monitoring with
  Low-resolution Infrared Sensors and Adaptive Inference

abstract:
  Low-resolution infrared (IR) Sensors combined with machine learning (ML) can
be leveraged to implement privacy-preserving social distance monitoring
solutions in indoor spaces. However, the need of executing these applications
on Internet of Things (IoT) edge nodes makes energy consumption critical. In
this work, we propose an energy-efficient adaptive inference solution
consisting of the cascade of a simple wake-up trigger and a 8-bit quantized
Convolutional Neural Network (CNN), which is only invoked for
difficult-to-classify frames. Deploying such adaptive system on a IoT
Microcontroller, we show that, when processing the output of a 8x8
low-resolution IR sensor, we are able to reduce the energy consumption by
37-57% with respect to a static CNN-based approach, with an accuracy drop of
less than 2% (83% balanced accuracy).


title:
Following Closely: A Robust Monocular Person Following System for Mobile
  Robot

abstract:
  Monocular person following (MPF) is a capability that supports many useful
applications of a mobile robot. However, existing MPF solutions are not
completely satisfactory. Firstly, they often fail to track the target at a
close distance either because they are based on a visual servo or they need the
observation of the full body by the robot. Secondly, their target
Re-IDentification (Re-ID) abilities are weak in cases of target appearance
change and highly similar appearance of distracting people. To remove the
assumption of full-body observation, we propose a width-based tracking module,
which relies on the target width, which can be observed even at a close
distance. For handling issues related to appearance variation, we use a global
CNN (convolutional neural network) descriptor to represent the target and a
ridge regression model to learn a target appearance model online. We adopt a
sampling strategy for online classifier learning, in which both long-term and
short-term samples are involved. We evaluate our method in two datasets
including a public person following dataset and a custom-built one with
challenging target appearance and target distance. Our method achieves
state-of-the-art (SOTA) results on both datasets. For the benefit of the
community, we make public the dataset and the source code.


title:
Privacy-preserving Social Distance Monitoring on Microcontrollers with
  Low-Resolution Infrared Sensors and CNNs

abstract:
  Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and
privacy-preserving alternative to optical cameras and smartphones/wearables for
social distance monitoring in indoor spaces, permitting the recognition of
basic shapes, without revealing the personal details of individuals. In this
work, we demonstrate that an accurate detection of social distance violations
can be achieved processing the raw output of a 8x8 IR array sensor with a
small-sized Convolutional Neural Network (CNN). Furthermore, the CNN can be
executed directly on a Microcontroller (MCU)-based sensor node.
  With results on a newly collected open dataset, we show that our best CNN
achieves 86.3% balanced accuracy, significantly outperforming the 61% achieved
by a state-of-the-art deterministic algorithm. Changing the architectural
parameters of the CNN, we obtain a rich Pareto set of models, spanning
70.5-86.3% accuracy and 0.18-75k parameters. Deployed on a STM32L476RG MCU,
these models have a latency of 0.73-5.33ms, with an energy consumption per
inference of 9.38-68.57{\mu}J.


title: Introducing smooth amnesia to the memory of the Elephant Random Walk

abstract:
  This paper is devoted to the asymptotic analysis of the amnesic elephant
random walk (AERW) using a martingale approach. More precisely, our analysis
relies on asymptotic results for multidimensional martingales with matrix
normalization. In the diffusive and critical regimes, we establish the almost
sure convergence and the quadratic strong law for the position of the AERW. The
law of iterated logarithm is given in the critical regime. The distributional
convergences of the AERW to Gaussian processes are also provided. In the
superdiffusive regime, we prove the distributional convergence as well as the
mean square convergence of the AERW.


title: Zero and Few-shot Learning for Author Profiling

abstract:
  Author profiling classifies author characteristics by analyzing how language
is shared among people. In this work, we study that task from a low-resource
viewpoint: using little or no training data. We explore different zero and
few-shot models based on entailment and evaluate our systems on several
profiling tasks in Spanish and English. In addition, we study the effect of
both the entailment hypothesis and the size of the few-shot training sample. We
find that entailment-based models out-perform supervised text classifiers based
on roberta-XLM and that we can reach 80% of the accuracy of previous approaches
using less than 50\% of the training data on average.


title:
Surfaces in the flag threefold containing smooth conics and twistor
  fibers

abstract:
  We study smooth integral curves of bidegree $(1,1)$, called \textit{smooth
conics}, in the flag threefold $\mathbb{F}$. The study is motivated by the fact
that the family of smooth conics contains the set of fibers of the twistor
projection $\mathbb{F}\to\mathbb{CP}^{2}$. We give a bound on the maximum
number of smooth conics contained in a smooth surface $S\subset\mathbb{F}$.
Then, we show qualitative properties of algebraic surfaces containing a
prescribed number of smooth conics. Lastly, we study surfaces containing
infinitely many twistor fibers. We show that the only smooth cases are surfaces
of bidegree $(1,1)$. Then, for any integer $a>1$, we exhibit a method to
construct an integral surface of bidegree $(a,a)$ containing infinitely many
twistor fibers.


title:
A consistent co-rotational formulation for aerodynamic nonlinear
  analysis of flexible frame structures

abstract:
  The design of structures submitted to aerodynamic loads usually requires the
development of specific computational models considering fluid-structure
interactions. Models using structural frame elements are developed in several
relevant applications such as the design of advanced aircraft wings, wind
turbine blades or power transmission lines. In the case of flexible frame
structures submitted to fluid flows, the consistent computation of inertial and
aerodynamic forces for large displacements is a challenging task. In this
article we present a novel formulation for the accurate computation of
aerodynamic forces for large displacements and rotations using the
co-rotational approach, the quasi-steady theory and the principle of virtual
work. This formulation is coupled with a reference consistent co-rotational
formulation for computing internal and inertial forces, providing a unified set
of nonlinear balance equations. A numerical resolution procedure is proposed
and implemented within the open-source library ONSAS. The proposed formulation
and its implementation are validated through the resolution of five examples,
including a realistic wind turbine analysis problem. The results provided by
the proposed formulation are compared with analytic solutions and solutions
obtained using a lumped mass approach. The proposed formulation provides
accurate solutions for challenging numerical problems with large displacements
and rotations.


title: Depth Pruning with Auxiliary Networks for TinyML

abstract:
  Pruning is a neural network optimization technique that sacrifices accuracy
in exchange for lower computational requirements. Pruning has been useful when
working with extremely constrained environments in tinyML. Unfortunately,
special hardware requirements and limited study on its effectiveness on already
compact models prevent its wider adoption. Depth pruning is a form of pruning
that requires no specialized hardware but suffers from a large accuracy
falloff. To improve this, we propose a modification that utilizes a highly
efficient auxiliary network as an effective interpreter of intermediate feature
maps. Our results show a parameter reduction of 93% on the MLPerfTiny Visual
Wakewords (VWW) task and 28% on the Keyword Spotting (KWS) task with accuracy
cost of 0.65% and 1.06% respectively. When evaluated on a Cortex-M0
microcontroller, our proposed method reduces the VWW model size by 4.7x and
latency by 1.6x while counter intuitively gaining 1% accuracy. KWS model size
on Cortex-M0 was also reduced by 1.2x and latency by 1.2x at the cost of 2.21%
accuracy.


title:
Versatile coating platform for metal oxide nanoparticles: applications
  to materials and biological science

abstract:
  In this feature article, we provide an overview of our research on
statistical copolymers as a coating material for metal oxide nanoparticles and
surfaces. These copolymers contain functional groups enabling non-covalent
binding to oxide surfaces and poly(ethylene glycol) (PEG) polymers for
colloidal stability and stealthiness. The functional groups are organic
derivatives of phosphorous acid compounds R-H$_2$PO$_3$, also known as
phosphonic acids that have been screened for their strong affinity to metals
and their ability to build multidentate binding. Herein we develop a
polymer-based coating platform that shares features with the techniques of
self-assembled monolayers (SAM) and Layer-by-Layer (L-b-L) deposition. The
milestones of this endeavor are the synthesis of PEG-based copolymers
containing multiple phosphonic acid groups, the implementation of simple
protocols combining versatility with high particle production yields and the
experimental demonstration of the colloidal stability of the coated particles.
As a demonstration, coating studies are conducted on cerium (CeO$_2$), iron
($\gamma$-Fe$_2$O$_3$), aluminum (Al$_2$O$_3$) and titanium (TiO$_2$) oxides of
different sizes and morphologies. We finally discuss applications in the domain
of nanomaterials and nanomedicine. We evaluate the beneficial effects of
coating on redispersible nanopowders, contrast agents for In Vitro/Vivo assays
and stimuli-responsive particles.


title:
Extraction of different temperatures and kinetic freeze-out volume in
  high energy collisions

abstract:
  We analyze the transverse momentum ($p_T$) spectra,
$1/N_{ev}$[(1/2$\pi$$p_T$) $d^2$$N$/$dyd$$p_T$], of kaon, proton, deuteron and
triton in different centrality events in gold-gold (Au-Au) collisions at
Relativistic Heavy Ion Collisions (RHIC) by Hagedorn thermal model and
extracted the excitation function of effective temperature, kinetic freeze-out
volume, initial temperature and kinetic freeze-out temperature. We perceived
that the effective temperature, initial temperature and kinetic freeze-out
temperature sharply increases from 7.7 GeV to 14.5 GeV and then remain static
from 14.5-39 GeV, and this consistency may disclose that the onset energy of
the phase transition of partial deconfinement and the whole deconfinement are
14.5 and 39 GeV respectively. The kinetic freeze-out volume and mean transverse
momentum grows with the rise of collision energy. Furthermore, the different
extracted temperatures are observed in the order of time evolution of the
interacting system, and they (as well as kinetic freeze-out volume) have an
increasing trend from peripheral to central collisions. We also observed the
mass dependence of the effective temperature and kinetic freeze-out volume
where former increases while the later decreases for heavier particles, which
indicates the early freeze-out of the heavier particles.


title:
JIFF: Jointly-aligned Implicit Face Function for High Quality Single
  View Clothed Human Reconstruction

abstract:
  This paper addresses the problem of single view 3D human reconstruction.
Recent implicit function based methods have shown impressive results, but they
fail to recover fine face details in their reconstructions. This largely
degrades user experience in applications like 3D telepresence. In this paper,
we focus on improving the quality of face in the reconstruction and propose a
novel Jointly-aligned Implicit Face Function (JIFF) that combines the merits of
the implicit function based approach and model based approach. We employ a 3D
morphable face model as our shape prior and compute space-aligned 3D features
that capture detailed face geometry information. Such space-aligned 3D features
are combined with pixel-aligned 2D features to jointly predict an implicit face
function for high quality face reconstruction. We further extend our pipeline
and introduce a coarse-to-fine architecture to predict high quality texture for
our detailed face model. Extensive evaluations have been carried out on public
datasets and our proposed JIFF has demonstrates superior performance (both
quantitatively and qualitatively) over existing state-of-the-arts.


title:
Quantum stabilized fluctuating phase and critical dynamics in a
  kinetically-constrained open many-body system with two absorbing states

abstract:
  We investigate an open many-body quantum system in which kinetically
constrained coherent and dissipative processes compete. The form of the
incoherent dissipative dynamics is inspired by that of epidemic spreading or
cellular-automaton-based computation related to the density-classification
problem. It features two non-fluctuating absorbing states as well as a
$\mathcal{Z}_2$-symmetric point in parameter space. The coherent evolution is
governed by a kinetically constrained $\mathcal{Z}_2$-symmetric many-body
Hamiltonian which is related to the quantum XOR-Fredrickson-Andersen model. We
show that the quantum coherent dynamics can stabilize a fluctuating state and
we characterize the transition between this active phase and the absorbing
states. We also identify a rather peculiar behavior at the
$\mathcal{Z}_2$-symmetric point. Here the system approaches the absorbing-state
manifold with a dynamics that follows a power-law whose exponent continuously
varies with the relative strength of the coherent dynamics. Our work shows how
the interplay between coherent and dissipative processes as well as symmetry
constraints may lead to a highly intricate non-equilibrium evolution and may
stabilize phases that are absent in related classical problems.


title:
Compactness property of the linearized Boltzmann operator for a
  polyatomic gas undergoing resonant collisions

abstract:
  In this paper, we investigate a compactness property of the linearized
Boltzmann operator in the context of a polyatomic gas whose molecules undergo
resonant collisions. The peculiar structure of resonant collision rules allows
to tensorize the problem into a velocity-related one, neighbouring the
monatomic case, and an internal energy-related one. Our analysis is based on a
specific treatment of the contributions due to the internal energy of the
molecules. We also propose a geometric variant of Grad's proof of the same
compactness property in the monatomic case.


title:
Making Parameterization and Constrains of Object Landmark Globally
  Consistent via SPD(3) Manifold and Improved Cost Functions

abstract:
  Object-level SLAM introduces semantic meaningful and compact object landmarks
that help both indoor robot applications and outdoor autonomous driving tasks.
However, the back end of object-level SLAM suffers from singularity problems
because existing methods parameterize object landmark separately by their
scales and poses. Under that parameterization method, the same abstract object
can be represented by rotating the object coordinate frame by 90 deg and
swapping its length with width value, making the pose of the same object
landmark not globally consistent. To avoid the singularity problem, we first
introduce the symmetric positive-definite (SPD) matrix manifold as an improved
object-level landmark representation and further improve the cost functions in
the back end to make them compatible with the representation. Our method
demonstrates a faster convergence rate and more robustness in simulation
experiments. Experiments on real datasets also reveal that using the same
front-end data, our strategy improves the mapping accuracy by 22% on average.


title:
Mutational paths in protein-sequence landscapes: from sampling to
  low-dimensional characterization

abstract:
  Understanding how protein functionalities vary along mutational paths is an
important issue in evolutionary biology and in bioengineering. We here propose
an algorithm to sample mutational paths in the sequence space, realizing a
trade-off between protein optimality and path stiffness. The algorithm is
benchmarked on exactly solvable models of proteins in silico, and applied to
data-driven models of natural proteins learned from sequence data. Using
mean-field theory, we monitor the projections of the sequence on relevant modes
along the path, allowing for an interpretation of the protein sequence
trajectory. Qualitative changes observed in paths as their lengths are varied
can be explained by the existence of a phase transition in infinitely-long
strings of strongly coupled Hopfield models.


title: Asymptotic dimensions of the arc graphs and disk graphs

abstract:
  We give quadratic upper bounds for the asymptotic dimensions of the arc
graphs and disk graphs.


title: KALA: Knowledge-Augmented Language Model Adaptation

abstract:
  Pre-trained language models (PLMs) have achieved remarkable success on
various natural language understanding tasks. Simple fine-tuning of PLMs, on
the other hand, might be suboptimal for domain-specific tasks because they
cannot possibly cover knowledge from all domains. While adaptive pre-training
of PLMs can help them obtain domain-specific knowledge, it requires a large
training cost. Moreover, adaptive pre-training can harm the PLM's performance
on the downstream task by causing catastrophic forgetting of its general
knowledge. To overcome such limitations of adaptive pre-training for PLM
adaption, we propose a novel domain adaption framework for PLMs coined as
Knowledge-Augmented Language model Adaptation (KALA), which modulates the
intermediate hidden representations of PLMs with domain knowledge, consisting
of entities and their relational facts. We validate the performance of our KALA
on question answering and named entity recognition tasks on multiple datasets
across various domains. The results show that, despite being computationally
efficient, our KALA largely outperforms adaptive pre-training. Code is
available at: https://github.com/Nardien/KALA/.


title:
Magnetic band representations, Fu-Kane-like symmetry indicators and
  magnetic topological materials

abstract:
  In order to realize novel topological phases and to pursue potential
applications in low-energy consumption spintronics, the study of magnetic
topological materials is of great interest. Starting from the theory of
nonmagnetic topological quantum chemistry [TQC; Bradlyn et al., Nature 547, 298
(2017)], we have obtained irreducible (co)representations and compatibility
relations (CRs) in momentum space and constructed a complete list of magnetic
band (co)representations (MBRs) in real space for other MSGs with anti-unitary
symmetries (i.e., type-III and type-IV MSGs). The results are consistent with
the magnetic topological quantum chemistry [MTQC; Elcoro et al.,Nat. Comm. 12,
5965 (2021)]. Using the CRs and MBRs, we reproduce the symmetry-based
classifications for MSGs and obtain a set of Fu-Kane-like formulas of symmetry
indicators (SIs) in both spinless (bosonic) and spinful (fermionic) systems,
which are implimented in an automatic code - TopMat - to diagnose topological
magnetic materials. The magnetic topological materials, whose occupied states
can not be decomposed into a sum of MBRs, are consistent with nonzero SIs.
Lastly, using our online code, we have performed spin-polarized calculations
for magnetic compounds in the materials database and find many magnetic
topological compounds.


title:
Comment on Two-dimensional porous graphitic carbon nitride C6N7
  monolayer: First-principles calculations [Appl. Phys. Lett. 2021, 119,
  142102]

abstract:
  Recently, Bafekry et al. [Appl. Phys. Lett. 119, 142102 (2021)] reported
their density functional theory (DFT) results on the elastic constants of a
novel C6N7 monolayer. They predicted a very soft elastic modulus of 36.29 GPa
for the C6N7 monolayer, which is remarkably low for carbon-nitride 2D lattices.
Using DFT calculations, we predict a remarkably higher elastic modulus of 267
GPa for this monolayer. The maximum tensile strength is also predicted to be
20.5 GPa, revealing the outstanding mechanical properties of the C6N7
monolayer.


title:
Sparse and Dense Approaches for the Full-rank Retrieval of Responses for
  Dialogues

abstract:
  Ranking responses for a given dialogue context is a popular benchmark in
which the setup is to re-rank the ground-truth response over a limited set of
$n$ responses, where $n$ is typically 10. The predominance of this setup in
conversation response ranking has lead to a great deal of attention to building
neural re-rankers, while the first-stage retrieval step has been overlooked.
Since the correct answer is always available in the candidate list of $n$
responses, this artificial evaluation setup assumes that there is a first-stage
retrieval step which is always able to rank the correct response in its top-$n$
list. In this paper we focus on the more realistic task of full-rank retrieval
of responses, where $n$ can be up to millions of responses. We investigate both
dialogue context and response expansion techniques for sparse retrieval, as
well as zero-shot and fine-tuned dense retrieval approaches. Our findings based
on three different information-seeking dialogue datasets reveal that a learned
response expansion technique is a solid baseline for sparse retrieval. We find
the best performing method overall to be dense retrieval with intermediate
training, i.e. a step after the language model pre-training where sentence
representations are learned, followed by fine-tuning on the target
conversational data. We also investigate the intriguing phenomena that harder
negatives sampling techniques lead to worse results for the fine-tuned dense
retrieval models. The code and datasets are available at
https://github.com/Guzpenha/transformer_rankers/tree/full_rank_retrieval_dialogues.


title: Confined vacuum resonances as artificial atoms with tunable lifetime

abstract:
  Atomically engineered artificial lattices are a useful tool for simulating
complex quantum phenomena, but have so far been limited to the study of
Hamiltonians where electron-electron interactions do not play a role -- but
it's precisely the regime in which these interactions do matter where
computational times lend simulations a critical advantage over numerical
methods. Here, we propose a new platform for constructing artificial matter
that relies on the confinement of field-emission resonances, a class of
vacuum-localized discretized electronic states. We use atom manipulation of
surface vacancies in a chlorine-terminated Cu(100) surface to reveal square
patches of the underlying metal, thereby creating atomically-precise potential
wells that host particle-in-a-box modes. By adjusting the shape and size of the
confining potential, we can access states with different quantum numbers,
making these patches attractive candidates as quantum dots or artificial atoms.
We demonstrate that the lifetime of electrons in these engineered states can be
extended and tuned through modification of the confining potential, either via
atomic assembly or by changing the tip-sample distance. We also demonstrate
control over a finite range of state-filling, a parameter which plays a key
role in the evolution of quantum many-body states. We model the transport
through the localized state to disentangle and quantify the lifetime-limiting
processes, illustrating the critical dependency of the electron lifetime on the
properties of the underlying bulk band structure. The interplay with the bulk
bands also gives rise to negative differential resistance, opening possible
avenues for engineering custom atomic-scale resonant tunnelling diodes, which
exhibit similar current-voltage characteristics.


title:
Development of an algorithm for medical image segmentation of bone
  tissue in interaction with metallic implants

abstract:
  This preliminary study focuses on the development of a medical image
segmentation algorithm based on artificial intelligence for calculating bone
growth in contact with metallic implants. %as a result of the problem of
estimating the growth of new bone tissue due to artifacts. %the presence of
various types of distortions and errors, known as artifacts.
  Two databases consisting of computerized microtomography images have been
used throughout this work: 100 images for training and 196 images for testing.
Both bone and implant tissue were manually segmented in the training data set.
The type of network constructed follows the U-Net architecture, a convolutional
neural network explicitly used for medical image segmentation.
  In terms of network accuracy, the model reached around 98\%. Once the
prediction was obtained from the new data set (test set), the total number of
pixels belonging to bone tissue was calculated. This volume is around 15\% of
the volume estimated by conventional techniques, which are usually
overestimated. This method has shown its good performance and results, although
it has a wide margin for improvement, modifying various parameters of the
networks or using larger databases to improve training.


title: Speaking-Rate-Controllable HiFi-GAN Using Feature Interpolation

abstract:
  This paper presents a speaking-rate-controllable HiFi-GAN neural vocoder.
Original HiFi-GAN is a high-fidelity, computationally efficient, and
tiny-footprint neural vocoder. We attempt to incorporate a speaking rate
control function into HiFi-GAN for improving the accessibility of synthetic
speech. The proposed method inserts a differentiable interpolation layer into
the HiFi-GAN architecture. A signal resampling method and an image scaling
method are implemented in the proposed method to warp the mel-spectrograms or
hidden features of the neural vocoder. We also design and open-source a
Japanese speech corpus containing three kinds of speaking rates to evaluate the
proposed speaking rate control method. Experimental results of comprehensive
objective and subjective evaluations demonstrate that 1) the proposed method
outperforms a baseline time-scale modification algorithm in speech naturalness,
2) warping mel-spectrograms by image scaling obtained the best performance
among all proposed methods, and 3) the proposed speaking rate control method
can be incorporated into HiFi-GAN without losing computational efficiency.


title: Efficient Pipeline Planning for Expedited Distributed DNN Training

abstract:
  To train modern large DNN models, pipeline parallelism has recently emerged,
which distributes the model across GPUs and enables different devices to
process different microbatches in pipeline. Earlier pipeline designs allow
multiple versions of model parameters to co-exist (similar to asynchronous
training), and cannot ensure the same model convergence and accuracy
performance as without pipelining. Synchronous pipelining has recently been
proposed which ensures model performance by enforcing a synchronization barrier
between training iterations. Nonetheless, the synchronization barrier requires
waiting for gradient aggregation from all microbatches and thus delays the
training progress. Optimized pipeline planning is needed to minimize such wait
and hence the training time, which has not been well studied in the literature.
This paper designs efficient, near-optimal algorithms for expediting
synchronous pipeline-parallel training of modern large DNNs over arbitrary
inter-GPU connectivity. Our algorithm framework comprises two components: a
pipeline partition and device mapping algorithm, and a pipeline scheduler that
decides processing order of microbatches over the partitions, which together
minimize the per-iteration training time. We conduct thorough theoretical
analysis, extensive testbed experiments and trace-driven simulation, and
demonstrate our scheme can accelerate training up to 157% compared with
state-of-the-art designs.


title:
Wormholes satisfying the Energy Conditions in f(R) Gravity and
  Non-commutative Geometry

abstract:
  Traversable wormhole solutions in general relativity (GR) require exotic
matter sources that violate the null energy condition, and such exotic behavior
may be avoided in $f(R)$ modified gravity. Moreover, the concept of
non-commutative geometry as a gravitational source can be leveraged both in GR
and modified gravity to realize non-trivial space-time configurations. In this
study, we investigate a viable $f(R)$ gravity model and non-commutative
geometry to analyze spherically symmetric traversable Morris-Thorne wormhole
solutions. We analyze the different energy conditions, and demonstrate that
wormholes satisfying the energy conditions can be realized in both frameworks.


title:
Expansion of landing areas on the Venus surface using resonant orbits in
  the Venera-D project

abstract:
  A problem of determining attainable landing sites on the surface of Venus is
an essential part of the Venera-D project aimed to explore the planet using a
lander. This problem appears due to the inability for the descent module to
land at any point on the surface of Venus because of the short duration of the
launch window (about 2 weeks from the optimal launch date), as well as
restrictions on the maximum permissible overload. An additional factor
affecting the reduction of attainable landing sites is the low angular velocity
of Venus own rotation. This study proposes a new approach to expand the
attainable landing areas. The approach is based on the use of the gravitational
field of Venus to transfer the spacecraft to an orbit resonant to the Venusian
one with a ratio of periods of 1:1. All the simulations were performed at the
patched conic approximation. As an example, we considered a flight to Venus at
launch in 2029 or 2031. For both cases maps of attainable landing areas on the
surface were constructed. It has been demonstrated that there is always at
least one launch date within the launch window, allowing the spacecraft to
reach almost any point on the surface of Venus. It is shown that the
application of the proposed approach makes it possible to achieve a significant
expansion of the attainable landing areas (over 70\% of the surface) and, in
some cases, provide access to any point on the surface of Venus. However, the
price of this advantage is an increase in the flight duration by one Venusian
year.


title:
Generalised Score Distribution: Underdispersed Continuation of the
  Beta-Binomial Distribution

abstract:
  A class of discrete probability distributions contains distributions with
limited support. A typical example is some variant of a Likert scale, with
response mapped to either the $\{1, 2, \ldots, 5\}$ or $\{-3, -2, \ldots, 2,
3\}$ set. An interesting subclass of discrete distributions with finite support
are distributions limited to two parameters and having no more than one change
in probability monotonicity. The main contribution of this paper is to propose
a family of distributions fitting the above description, which we call the
Generalised Score Distribution (GSD) class. The proposed GSD class covers the
whole set of possible mean and variances, for any fixed and finite support.
Furthermore, the GSD class can be treated as an underdispersed continuation of
a reparametrized beta-binomial distribution. The GSD class parameters are
intuitive and can be easily estimated by the method of moments. We also offer a
Maximum Likelihood Estimation (MLE) algorithm for the GSD class and evidence
that the class properly describes response distributions coming from 24
Multimedia Quality Assessment experiments. At last, we show that the GSD class
can be represented as a sum of dichotomous zero-one random variables, which
points to an interesting interpretation of the class.


title: Simulation of the FDA Nozzle Benchmark: A Lattice Boltzmann Study

abstract:
  Background and objective: Contrary to flows in small intracranial vessels,
many blood flow configurations such as those found in aortic vessels and
aneurysms involve larger Reynolds numbers and, therefore, transitional or
turbulent conditions. Dealing with such systems require both robust and
efficient numerical methods. Methods: We assess here the performance of a
lattice Boltzmann solver with full Hermite expansion of the equilibrium and
central Hermite moments collision operator at higher Reynolds numbers,
especially for under-resolved simulations. To that end the food and drug
administration's benchmark nozzle is considered at three different Reynolds
numbers covering all regimes: 1) laminar at a Reynolds number of 500, 2)
transitional at a Reynolds number of $3500$, and 3) low-level turbulence at a
Reynolds number of 6500. Results: The lattice Boltzmann results are compared
with previously published inter-laboratory experimental data obtained by
particle image velocimetry. Our results show good agreement with the
experimental measurements throughout the nozzle, demonstrating the good
performance of the solver even in under-resolved simulations. Conclusion: In
this manner, fast but sufficiently accurate numerical predictions can be
achieved for flow configurations of practical interest regarding medical
applications.


title: Non-isogenous elliptic curves and hyperelliptic jacobians II

abstract:
  Let $K$ be a field of characteristic different from $2$, $\bar{K}$ its
algebraic closure. Let $n \ge 3$ be an odd integer. Let $f(x)$ and $h(x)$ be
degree $n$ polynomials with coefficients in $K$ and without repeated roots. Let
us consider genus $(n-1)/2$ hyperelliptic curves $C_f: y^2=f(x)$ and $C_h:
y^2=h(x)$, and their jacobians $J(C_f)$ and $J(C_h)$, which are
$(n-1)/2$-dimensional abelian varieties defined over $K$.
  Suppose that one of the polynomials is irreducible and the other splits
completely over $K$. We prove that if $J(C_f)$ and $J(C_h)$ are isogenous over
$\bar{K}$ then there is an (odd) prime $\ell$ dividing $n$ such that the
endomorphism algebras of both $J(C_f)$ and $J(C_h)$ contain a subfield that is
isomorphic to the field of $\ell$th roots of $1$.


title:
A Linear Time Algorithm for Computing Max-Flow Vitality in Undirected
  Unweighted Planar Graphs

abstract:
  The vitality of an edge in a graph with respect to the maximum flow between
two fixed vertices $s$ and $t$ is defined as the reduction of the maximum flow
value caused by the removal of that edge.
  The max-flow vitality problem has already been efficiently solved for
$st$-planar graphs but has remained open for general planar graphs. For the
first time our result provides an optimal solution for general planar graphs
although restricted to the case of unweighted planar graphs.


title:
Comparison of different sensor thicknesses and substrate materials for
  the monolithic small collection-electrode technology demonstrator CLICTD

abstract:
  Small collection-electrode monolithic CMOS sensors profit from a high
signal-to-noise ratio and a small power consumption, but have a limited active
sensor volume due to the fabrication process based on thin high-resistivity
epitaxial layers. In this paper, the active sensor depth is investigated in the
monolithic small collection-electrode technology demonstrator CLICTD. Charged
particle beams are used to study the charge-collection properties and the
performance of devices with different thicknesses both for perpendicular and
inclined particle incidence. In CMOS sensors with a high-resistivity
Czochralski substrate, the depth of the sensitive volume is found to increase
by a factor two in comparison with standard epitaxial material and leads to
significant improvements in the hit-detection efficiency and the spatial and
time resolution.


title:
Stellar populations of a sample of far-IR AGN and non-AGN green valley
  galaxies

abstract:
  We present a study on the stellar populations and stellar ages of sub-sample
of far-infrared AGN and non-AGN green valley analysed in Mahoro et al.
(2017,2019) at 0.6 < z < 1.0 using the data from the COSMOS field. We used
long-slit spectroscopy and derived stellar populations and stellar ages using
the stellar population synthesis code "STARLIGHT" and analysed the available
Lick/IDS indices, such as Dn4000 and $\rm{H\delta_{A}}$. We find that both FIR
AGN and non-AGN green valley galaxies are dominated by intermediate stellar
populations 67 % and 53 %, respectively. The median stellar ages for AGN and
non-AGN are log t = 8.5 [yr] and log t = 8.4 [yr], respectively. We found that
majority of our sources (62 % of AGN and 66 % non-AGN) could have experienced
bursts and continuous star formation. In addition, most of our FIR AGN (38 %)
compared to FIR non-AGN (27 %) might have experienced a burst of SF more than
0.1 Gyr ago. We also found that our FIR AGN and non-AGN green valley galaxies
have similar quenching time-scales of ~ 70 Myr. Therefore, the results obtained
here are in line with our previous results where we do not find that our sample
of FIR AGN in the green valley shows signs of negative AGN feedback, as has
been suggested previously in optical studies.


title:
Distributing Polarization Entangled Photon Pairs with High Rate over
  Long Distance through Standard Telecommunication Fiber

abstract:
  Entanglement distribution over long distances is essential for many quantum
communication schemes like quantum teleportation, some variants of quantum key
distribution, or implementations of a quantum internet. Distributing
entanglement through standard telecommunication fiber is particularly important
for quantum key distribution protocols with low vulnerability over metropolitan
distances. However, entanglement distribution over long distance through
optical fiber so far could only be accomplished with moderate photon pair
rates. In this work, we present entanglement distribution over 50km of standard
telecommunication fiber with pair rate more than 10,000 s$^{-1}$ using a bright
non-degenerate photon pair source. Signal and idler wavelengths of this source
are optimized for low dispersion in optical fiber and high efficiency for
single-photon avalanche diode detectors, respectively. The resulting modest
hardware requirement and high rate of detected entangled photon pairs could
significantly enhance practical entanglement-based quantum key distribution in
existing metropolitan fiber networks.


title:
Notip: Non-parametric True Discovery Proportion estimation for brain
  imaging

abstract:
  Cluster-level inference procedures are widely used for brain mapping. These
methods compare the size of clusters obtained by thresholding brain maps to an
upper bound under the global null hypothesis, computed using Random Field
Theory or permutations. However, the guarantees obtained by this type of
inference - i.e. at least one voxel is truly activated in the cluster - are not
informative with regards to the strength of the signal therein. There is thus a
need for methods to assess the amount of signal within clusters; yet such
methods have to take into account that clusters are defined based on the data,
which creates circularity in the inference scheme. This has motivated the use
of post hoc estimates that allow statistically valid estimation of the
proportion of activated voxels in clusters. In the context of fMRI data, the
All-Resolutions Inference framework introduced in [24] provides post hoc
estimates of the proportion of activated voxels. However, this method relies on
parametric threshold families, which results in conservative inference. In this
paper, we leverage randomization methods to adapt to data characteristics and
obtain tighter false discovery control. We obtain Notip: a powerful,
non-parametric method that yields statistically valid estimation of the
proportion of activated voxels in data-derived clusters. Numerical experiments
demonstrate substantial power gains compared with state-of-the-art methods on
36 fMRI datasets. The conditions under which the proposed method brings
benefits are also discussed.


title:
Energy estimates and hypocoercivity analysis for a multi-phase
  Navier-Stokes-Vlasov-Fokker-Planck system with uncertainty

abstract:
  This paper is concerned with a kineitc-fluid model with random initial inputs
in the fine particle regime, which is a system coupling the incompressible
Navier-Stokes equations and the Vlasov-Fokker-Planck equations that model
dispersed particles of different sizes. A uniform regularity for random initial
data near the global equilibrium is established in some suitable Sobolev spaces
by using energy estimates, and we also prove the energy decays exponentially in
time by hypocoercivity arguments, which means that the long time behavior of
the solution is insensitive to the random perturbation in the initial data. For
the generalized polynomial chaos stochastic Galerkin method (gPC-sG) for the
model, with initial data near the global equilibrium and smooth enough in the
physical and random spaces, we prove that the gPC-sG method has spectral
accuracy, uniformly in time and the Knudsen number, and the error decays
exponentially in time.


title:
Controlling magnetism with light in a zero orbital angular momentum
  antiferromagnet

abstract:
  Antiferromagnetic materials feature intrinsic ultrafast spin dynamics, making
them ideal candidates for future magnonic devices operating at THz frequencies.
A major focus of current research is the investigation of optical methods for
the efficient generation of coherent magnons in antiferromagnetic insulators.
In magnetic lattices endowed with orbital angular momentum, spin-orbit coupling
enables spin dynamics through the resonant excitation of low-energy electric
dipoles such as phonons and orbital resonances which interact with spins.
However, in magnetic systems with zero orbital angular momentum, microscopic
pathways for the resonant and low-energy optical excitation of coherent spin
dynamics are lacking. Here, we consider experimentally the relative merits of
electronic and vibrational excitations for the optical control of zero orbital
angular momentum magnets, focusing on a limit case: the antiferromagnet
manganese thiophoshate (MnPS3), constituted by orbital singlet Mn2+ ions. We
study the correlation of spins with two types of excitations within its band
gap: a bound electron orbital excitation from the singlet orbital ground state
of Mn2+ into an orbital triplet state, which causes coherent spin precession,
and a vibrational excitation of the crystal field that causes thermal spin
disorder. Our findings cast orbital transitions as key targets for magnetic
control in insulators constituted by magnetic centers of zero orbital angular
momentum.


title:
A piece-wise constant approximation for non-conjugate Gaussian Process
  models

abstract:
  Gaussian Processes (GPs) are a versatile and popular method in Bayesian
Machine Learning. A common modification are Sparse Variational Gaussian
Processes (SVGPs) which are well suited to deal with large datasets. While GPs
allow to elegantly deal with Gaussian-distributed target variables in closed
form, their applicability can be extended to non-Gaussian data as well. These
extensions are usually impossible to treat in closed form and hence require
approximate solutions. This paper proposes to approximate the inverse-link
function, which is necessary when working with non-Gaussian likelihoods, by a
piece-wise constant function. It will be shown that this yields a closed form
solution for the corresponding SVGP lower bound. In addition, it is
demonstrated how the piece-wise constant function itself can be optimized,
resulting in an inverse-link function that can be learnt from the data at hand.


title:
Comparison of numerical treatments for the pseudo-differential term in
  the Wigner equation

abstract:
  Effectively handling the nonlocal pseudo-differential term plays an
impor?tant role in solving the Wigner equation with high accuracy. This paper
systematically analyzes and compares numerical treatments of the
pseudo-differential term under different types of potentials.


title: Exploring Contur beyond its default mode: a case study

abstract:
  We discuss Contur's different modes by studying a leptophobic Top-Colour (TC)
model. We use, for the first time, higher order calculations for both the
signal (NLO) and the background (up to NNLO). We compare the results between
the different approaches of Contur. Furthermore, we compare these results to
the ones coming from a direct search.


title:
On Leray's problem in an infinite-long pipe with the Navier-slip
  boundary condition

abstract:
  The original Leray's problem concerns the well-posedness of weak solutions to
the steady incompressible Navier-Stokes equations in a distorted pipe, which
approach to the Poiseuille flow subject to the no-slip boundary condition at
spacial infinity. In this paper, the same problem with the Navier-slip boundary
condition instead of the no-slip boundary condition, is addressed. Due to the
complexity of the boundary condition, some new ideas, presented as follows, are
introduced to handle the extra difficulties caused by boundary terms.
  First, the Poiseuille flow in the semi-infinite straight pipe with the
Navier-slip boundary condition will be introduced, which will be served as the
asymptotic profile of the solution to the generalized Leray's problem at
spacial infinity. Second, a solenoidal vector function defined in the whole
pipe, satisfying the Navier-slip boundary condition, having the designated flux
and equalling to the Poiseuille flow at large distance, will be carefully
constructed. This plays an important role in reformulating our problem. Third,
the energy estimates depend on a combined $L^2$ estimate of the gradient and
the stress tensor of the velocity.


title:
Lossy compression of matrices by black-box optimisation of mixed-integer
  non-linear programming

abstract:
  In edge computing, suppressing data size is a challenge for machine learning
models that perform complex tasks such as autonomous driving, in which
computational resources (speed, memory size and power) are limited. Efficient
lossy compression of matrix data has been introduced by decomposing it into the
product of an integer and real matrices. However, its optimisation is difficult
as it requires simultaneous optimisation of an integer and real variables. In
this paper, we improve this optimisation by utilising recently developed
black-box optimisation (BBO) algorithms with an Ising solver for integer
variables. In addition, the algorithm can be used to solve mixed-integer
programming problems that are linear and non-linear in terms of real and
integer variables, respectively. The differences between the choice of Ising
solvers (simulated annealing (SA), quantum annealing (QA) and simulated
quenching (SQ)) and the strategies of the BBO algorithms (BOCS, FMQA and their
variations) are discussed for further development of the BBO techniques.


title:
Multi-scale VLBI observations of the candidate host galaxy of GRB
  200716C

abstract:
  In this Letter we present the analysis of multi-band public data and high
sensitivity EVN and e-MERLIN follow-up observations of the candidate host
galaxy of GRB 200716C, namely J1304+2938. GRB 200716C is a long-duration
$\gamma$-ray burst (GRB), which surprisingly lies among the short-duration GRBs
in the E$_{iso}$-E$_{p,z}$ Amati relation. Because of this property and the
presence of two peaks in the $\gamma$-ray light curve, it has been recently
suggested that GRB 200716C might be a short-GRB gravitationally lensed by an
intermediate-mass black hole (IMBH) hosted by J1304+2938 at z = 0.341. While no
detections of the afterglow have been reported in radio hitherto, we present
here the discovery of significant emission in survey data between 130 MHz and 3
GHz, together with the results of deep, dedicated high-angular-resolution
observations carried out with EVN+e-MERLIN, which do not reveal any significant
emission down to the $\sim$10 uJy beam$^{-1}$ level. We associate the radio
emission to J1304+2938, and we derive a luminosity larger than (2.9$\pm$0.4)
$\times$ 10$^{30}$ erg s$^{-1}$ Hz$^{-1}$: if GRB 200716C belongs to
J1304+2938, the latter would be one of the brightest host galaxy detected so
far in the radio. As a matter of fact, altogether the properties of J1304+2938
are consistent with a highly star forming galaxy (SFR > 186 M$_{\odot}$
yr$^{-1}$), which is the typical environment expected for long-GRBs.
Conversely, neither the public data nor our VLBI observations can confirm or
rule out the presence of an IMBH acting as a (milli-)lens, and thus the nature
of the burst still remains unknown.


title: FAIR4Cov: Fused Audio Instance and Representation for COVID-19 Detection

abstract:
  Audio-based classification techniques on body sounds have long been studied
to support diagnostic decisions, particularly in pulmonary diseases. In
response to the urgency of the COVID-19 pandemic, a growing number of models
are developed to identify COVID-19 patients based on acoustic input. Most
models focus on cough because the dry cough is the best-known symptom of
COVID-19. However, other body sounds, such as breath and speech, have also been
revealed to correlate with COVID-19 as well. In this work, rather than relying
on a specific body sound, we propose Fused Audio Instance and Representation
for COVID-19 Detection (FAIR4Cov). It relies on constructing a joint feature
vector obtained from a plurality of body sounds in waveform and spectrogram
representation. The core component of FAIR4Cov is a self-attention fusion unit
that is trained to establish the relation of multiple body sounds and audio
representations and integrate it into a compact feature vector. We set up our
experiments on different combinations of body sounds using only waveform,
spectrogram, and a joint representation of waveform and spectrogram. Our
findings show that the use of self-attention to combine extracted features from
cough, breath, and speech sounds leads to the best performance with an Area
Under the Receiver Operating Characteristic Curve (AUC) score of 0.8658, a
sensitivity of 0.8057, and a specificity of 0.7958. This AUC is 0.0227 higher
than the one of the models trained on spectrograms only and 0.0847 higher than
the one of the models trained on waveforms only. The results demonstrate that
the combination of spectrogram with waveform representation helps to enrich the
extracted features and outperforms the models with single representation.


title:
Dimensional regularization, Wilsonian RG, and the Naturalness/Hierarchy
  problem

abstract:
  While it is usually stated that dimensional regularization (DR) has no direct
physical interpretation, consensus has recently grown on the idea that it might
be endowed with special physical properties that would provide the mechanism
that solves the naturalness/hierarchy problem. Comparing direct Wilsonian
calculations with the corresponding DR ones, we find that DR indeed has a
well-defined physical meaning, and we point out its limitations. In particular,
our results show that DR cannot provide the solution to the
naturalness/hierarchy problem. The absence of too large corrections to the
Higgs boson mass is due to a secretly realized fine-tuning, rather than special
physical properties of DR. We also investigate these issues within the
Wilsonian RG framework and, by comparison with the usual perturbative RG
analysis, we show that several popular proposals for the resolution of the
problem, commonly considered as physical mechanisms free of fine-tuning, again
secretly implement the tuning.


title:
Compactness and existence results of the prescribing fractional
  $Q$-curvatures problem on $\mathbb{S}^n$

abstract:
  This paper is devoted to establishing the compactness and existence results
of the solutions to the prescribing fractional $Q$-curvatures problem of order
$2\sigma$ on $n$-dimensional standard sphere when $ n-2\sigma=2$,
$\sigma=1+m/2,$ $m\in \mathbb{N}_{+}.$ The compactness results are novel and
optimal. In addition, we prove a degree-counting formula of all solutions to
achieve the existence. From our results, we can know where blow up occur.
Furthermore, the sequence of solutions that blow up precisely at any finite
distinct location can be constructed. It is worth noting that our results
include the case of multiple harmonic.


title: Non-Uniformly Terminating Chase: Size and Complexity

abstract:
  The chase procedure, originally introduced for checking implication of
database constraints, and later on used for computing data exchange solutions,
has recently become a central algorithmic tool in rule-based ontological
reasoning. In this context, a key problem is non-uniform chase termination:
does the chase of a database w.r.t. a rule-based ontology terminate? And if
this is the case, what is the size of the result of the chase? We focus on
guarded tuple-generating dependencies (TGDs), which form a robust rule-based
ontology language, and study the above central questions for the semi-oblivious
version of the chase. One of our main findings is that non-uniform
semi-oblivious chase termination for guarded TGDs is feasible in polynomial
time w.r.t. the database, and the size of the result of the chase (whenever is
finite) is linear w.r.t. the database. Towards our results concerning
non-uniform chase termination, we show that basic techniques such as
simplification and linearization, originally introduced in the context of
ontological query answering, can be safely applied to the chase termination
problem.


title: Data Clustering as an Emergent Consensus of Autonomous Agents

abstract:
  We present a data segmentation method based on a first-order density-induced
consensus protocol. We provide a mathematically rigorous analysis of the
consensus model leading to the stopping criteria of the data segmentation
algorithm. To illustrate our method, the algorithm is applied to
two-dimensional shape datasets and selected images from Berkeley Segmentation
Dataset. The method can be seen as an augmentation of classical clustering
techniques for multimodal feature space, such as DBSCAN. It showcases a curious
connection between data clustering and collective behavior.


title: Efficient Training of Neural Transducer for Speech Recognition

abstract:
  As one of the most popular sequence-to-sequence modeling approaches for
speech recognition, the RNN-Transducer has achieved evolving performance with
more and more sophisticated neural network models of growing size and
increasing training epochs. While strong computation resources seem to be the
prerequisite of training superior models, we try to overcome it by carefully
designing a more efficient training pipeline. In this work, we propose an
efficient 3-stage progressive training pipeline to build highly-performing
neural transducer models from scratch with very limited computation resources
in a reasonable short time period. The effectiveness of each stage is
experimentally verified on both Librispeech and Switchboard corpora. The
proposed pipeline is able to train transducer models approaching
state-of-the-art performance with a single GPU in just 2-3 weeks. Our best
conformer transducer achieves 4.1% WER on Librispeech test-other with only 35
epochs of training.


title:
Adaptive hyperspectral imaging using structured illumination in a
  spatial light modulator-based interferometer

abstract:
  We develop a novel hyperspectral imaging system using structured illumination
in an SLM-based Michelson interferometer. In our design, we use a reflective
SLM as a mirror in one of the arms of a Michelson interferometer, and scan the
interferometer by varying the phase across the SLM display. For achieving the
latter, we apply a checkerboard phase mask on the SLM display where the gray
value varies between 0-255, thereby imparting a dynamic phase of up to
262{\deg} to the incident light beam. We couple a supercontinuum source into
the interferometer in order to mimic an astronomical object such as the Sun,
and choose a central wavelength of 637.4 nm akin to the strong emission line of
Fe X present in the solar spectrum. We use a bandwidth of 30 nm, and extract
fringes corresponding to a spectral resolution of 3.8 nm which is limited by
the reflectivity of the SLM. We also demonstrate a maximum wavelength
tunability of ~8 nm by varying the phase over the phase mask with a spectral
sampling of around 0.03 nm between intermediate fringes. The checkerboard phase
mask can be adapted close to real time on time-scales of a few tens of
milliseconds to obtain spectral information for other near-contiguous
wavelengths. The compactness, potential low cost, low power requirements,
real-time tunability and lack of moving mechanical parts in the setup implies
that it can have very useful applications in settings which require near
real-time, multi-wavelength spectroscopic applications, and is especially
relevant in space astronomy.


title:
A Note on the Regularity of Images Generated by Convolutional Neural
  Networks

abstract:
  The regularity of images generated by convolutional neural networks, such as
the U-net, generative adversarial networks, or the deep image prior, is
analyzed. In a resolution-independent, infinite dimensional setting, it is
shown that such images, represented as functions, are always continuous and, in
some circumstances, even continuously differentiable, contradicting the widely
accepted modeling of sharp edges in images via jump discontinuities. While such
statements require an infinite dimensional setting, the connection to
(discretized) neural networks used in practice is made by considering the limit
as the resolution approaches infinity. As practical consequence, the results of
this paper suggest to refrain from basic L2 regularization of network weights
in case of images being the network output.


title:
Linear-Algebraic Models of Linear Logic as Categories of Modules over
  Sigma-Semirings

abstract:
  A number of models of linear logic are based on or closely related to linear
algebra, in the sense that morphisms are "matrices" over appropriate
coefficient sets. Examples include models based on coherence spaces, finiteness
spaces and probabilistic coherence spaces, as well as the relational and
weighted relational models. This paper introduces a unified framework based on
module theory, making the linear algebraic aspect of the above models more
explicit. Specifically we consider modules over Sigma-semirings $R$, which are
ring-like structures with partially-defined countable sums, and show that
morphisms in the above models are actually $R$-linear maps in the standard
algebraic sense for appropriate $R$. An advantage of our algebraic treatment is
that the category of $R$-modules is locally presentable, from which it easily
follows that this category becomes a model of intuitionistic linear logic with
the cofree exponential. We then discuss constructions of classical models and
show that the above-mentioned models are examples of our constructions.


title:
Emergent Communication for Understanding Human Language Evolution:
  What's Missing?

abstract:
  Emergent communication protocols among humans and artificial neural network
agents do not yet share the same properties and show some critical mismatches
in results. We describe three important phenomena with respect to the emergence
and benefits of compositionality: ease-of-learning, generalization, and group
size effects (i.e., larger groups create more systematic languages). The latter
two are not fully replicated with neural agents, which hinders the use of
neural emergent communication for language evolution research. We argue that
one possible reason for these mismatches is that key cognitive and
communicative constraints of humans are not yet integrated. Specifically, in
humans, memory constraints and the alternation between the roles of speaker and
listener underlie the emergence of linguistic structure, yet these constraints
are typically absent in neural simulations. We suggest that introducing such
communicative and cognitive constraints would promote more linguistically
plausible behaviors with neural agents.


title: SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues

abstract:
  Dialogue systems are usually categorized into two types, open-domain and
task-oriented. The first one focuses on chatting with users and making them
engage in the conversations, where selecting a proper topic to fit the dialogue
context is essential for a successful dialogue. The other one focuses on a
specific task instead of casual talks, e.g., finding a movie on Friday night,
or playing a song. These two directions have been studied separately due to
their different purposes. However, how smoothly transitioning from social
chatting to task-oriented dialogues is important for triggering business
opportunities, and there is no public data focusing on such scenarios. Hence,
this paper focuses on investigating the conversations starting from open-domain
social chatting and then gradually transitioning to task-oriented purposes, and
releases a large-scale dataset with detailed annotations for encouraging this
research direction. To achieve this goal, this paper proposes a framework to
automatically generate many dialogues without human involvement, in which any
powerful open-domain dialogue generation model can be easily leveraged. The
human evaluation shows that our generated dialogue data has a natural flow at a
reasonable quality, showing that our released data has a great potential of
guiding future research directions and commercial activities. Furthermore, the
released models allow researchers to automatically generate unlimited dialogues
in the target scenarios, which can greatly benefit semi-supervised and
unsupervised approaches.


title: Uniform Operational Consistent Query Answering

abstract:
  Operational consistent query answering (CQA) is a recent framework for CQA,
based on revised definitions of repairs and consistent answers, which opens up
the possibility of efficient approximations with explicit error guarantees. The
main idea is to iteratively apply operations (e.g., fact deletions), starting
from an inconsistent database, until we reach a database that is consistent
w.r.t. the given set of constraints. This gives us the flexibility of choosing
the probability with which we apply an operation, which in turn allows us to
calculate the probability of an operational repair, and thus, the probability
with which a consistent answer is entailed. A natural way of assigning
probabilities to operations is by targeting the uniform probability
distribution over a reasonable space such as the set of operational repairs,
the set of sequences of operations that lead to an operational repair, and the
set of available operations at a certain step of the repairing process. This
leads to what we generally call uniform operational CQA. The goal of this work
is to perform a data complexity analysis of both exact and approximate uniform
operational CQA, focusing on functional dependencies (and subclasses thereof),
and conjunctive queries. The main outcome of our analysis (among other positive
and negative results), is that uniform operational CQA pushes the efficiency
boundaries further by ensuring the existence of efficient approximation schemes
in scenarios that go beyond the simple case of primary keys, which seems to be
the limit of the classical approach to CQA.


title: LibriS2S: A German-English Speech-to-Speech Translation Corpus

abstract:
  Recently, we have seen an increasing interest in the area of speech-to-text
translation. This has led to astonishing improvements in this area. In
contrast, the activities in the area of speech-to-speech translation is still
limited, although it is essential to overcome the language barrier. We believe
that one of the limiting factors is the availability of appropriate training
data. We address this issue by creating LibriS2S, to our knowledge the first
publicly available speech-to-speech training corpus between German and English.
For this corpus, we used independently created audio for German and English
leading to an unbiased pronunciation of the text in both languages. This allows
the creation of a new text-to-speech and speech-to-speech translation model
that directly learns to generate the speech signal based on the pronunciation
of the source language. Using this created corpus, we propose Text-to-Speech
models based on the example of the recently proposed FastSpeech 2 model that
integrates source language information. We do this by adapting the model to
take information such as the pitch, energy or transcript from the source speech
as additional input.


title: The Fundamental Theorem of Affine and Projective Geometries

abstract:
  We deal with two natural generalizations of the Fundamental Theorem of Affine
Geometry to the case of non bijective maps. These extensions were obtained by
W. Zick in 1981, but his results were not published. Our aim is to expose
simplified or new proofs of these extensions and discuss their context and
naturality.


title: Spacing Loss for Discovering Novel Categories

abstract:
  Novel Class Discovery (NCD) is a learning paradigm, where a machine learning
model is tasked to semantically group instances from unlabeled data, by
utilizing labeled instances from a disjoint set of classes. In this work, we
first characterize existing NCD approaches into single-stage and two-stage
methods based on whether they require access to labeled and unlabeled data
together while discovering new classes. Next, we devise a simple yet powerful
loss function that enforces separability in the latent space using cues from
multi-dimensional scaling, which we refer to as Spacing Loss. Our proposed
formulation can either operate as a standalone method or can be plugged into
existing methods to enhance them. We validate the efficacy of Spacing Loss with
thorough experimental evaluation across multiple settings on CIFAR-10 and
CIFAR-100 datasets.


title:
A short-circuited coplanar waveguide for low-temperature single-port
  VNA-FMR spectroscopy set-up to probe the magnetic properties of ferromagnetic
  thin films

abstract:
  A coplanar waveguide(CPW) shorted in one end is proposed, designed, and
implemented successfully to measure the properties of magnetic thin films as a
part of the VNA-FMR spectroscopy setup. Its simple structure, potential
applications and easy installation inside the cryostat chamber made it
advantageous especially for low-temperature measurements. It provides a wide
band of frequencies in the gigahertz range essential for FMR measurements. Our
spectroscopy setup with short-circuited CPW has been used to extract Gilbert
damping coefficient and effective magnetization values for the standard
ferromagnet, Permalloy thin films. The thickness and temperature dependent
studies of those magnetic parameters have also been done here for the afore
mentioned magnetic samples.


title: Cosmological joint analysis with cosmic growth and expansion rate

abstract:
  The measurements of expansion rate $H(z)$ and the growth rate $f\sigma_8(z)$
describe the evolution of the universe, and both of them can constrain the
cosmological models through data analysis. Due to the lack of data points,
combining those datasets through the traditional combined method ($\chi^2$
method) to select a best-fitting cosmological model. In 2017, Linder proposed a
joint method, which describes the evolution of the universe through
$H(z)-f\sigma_8$ diagram instead of the redshift z. And Linder showed the
advantages of distinguishing cosmologies of the joint method compared to the
individual dataset. In this paper, we compare the significance between the
traditional combined method and Linder's joint method by constraining the
density parameter $\Omega_M$ using Akaike Information Criterion (AIC) and
Bayesian Information Criterion (BIC). The result shows that the joint method is
more significant than the traditional combined method.