title:
Combining Visual Saliency Methods and Sparse Keypoint Annotations to
  Providently Detect Vehicles at Night

abstract:
  Provident detection of other road users at night has the potential for
increasing road safety. For this purpose, humans intuitively use visual cues,
such as light cones and light reflections emitted by other road users to be
able to react to oncoming traffic at an early stage. This behavior can be
imitated by computer vision methods by predicting the appearance of vehicles
based on emitted light reflections caused by the vehicle's headlights. Since
current object detection algorithms are mainly based on detecting directly
visible objects annotated via bounding boxes, the detection and annotation of
light reflections without sharp boundaries is challenging. For this reason, the
extensive open-source dataset PVDN (Provident Vehicle Detection at Night) was
published, which includes traffic scenarios at night with light reflections
annotated via keypoints. In this paper, we explore the potential of
saliency-based approaches to create different object representations based on
the visual saliency and sparse keypoint annotations of the PVDN dataset. For
that, we extend the general idea of Boolean map saliency towards a
context-aware approach by taking into consideration sparse keypoint annotations
by humans. We show that this approach allows for an automated derivation of
different object representations, such as binary maps or bounding boxes so that
detection models can be trained on different annotation variants and the
problem of providently detecting vehicles at night can be tackled from
different perspectives. With that, we provide further powerful tools and
methods to study the problem of detecting vehicles at night before they are
actually visible.


title:
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning
  Using Shared Data on the Server

abstract:
  Despite achieving remarkable performance, Federated Learning (FL) suffers
from two critical challenges, i.e., limited computational resources and low
training efficiency. In this paper, we propose a novel FL framework, i.e.,
FedDUAP, with two original contributions, to exploit the insensitive data on
the server and the decentralized data in edge devices to further improve the
training efficiency. First, a dynamic server update algorithm is designed to
exploit the insensitive data on the server, in order to dynamically determine
the optimal steps of the server update for improving the convergence and
accuracy of the global model. Second, a layer-adaptive model pruning method is
developed to perform unique pruning operations adapted to the different
dimensions and importance of multiple layers, to achieve a good balance between
efficiency and effectiveness. By integrating the two original techniques
together, our proposed FL model, FedDUAP, significantly outperforms baseline
approaches in terms of accuracy (up to 4.8% higher), efficiency (up to 2.8
times faster), and computational cost (up to 61.9% smaller).


title: Geometrical aspects of contact mechanical systems and field theories

abstract:
  Many important theories in modern physics can be stated using differential
geometry. Symplectic geometry is the natural framework to deal with autonomous
Hamiltonian mechanics. This admits several generalizations for nonautonomous
systems, both regular and singular. Some of these extensions are the subject of
this thesis.
  Recently there has been a growing interest in studying dissipative mechanical
systems from a geometric perspective using contact geometry. In this thesis we
review what has been done in this topic and go deeper, studying symmetries and
dissipated quantities of contact systems, and developing the Skinner-Rusk
formalism for these systems.
  With regard to classical field theory, we introduce the notion of
k-precosymplectic manifold and use it to give a geometric description of
singular nonautonomous field theories. We also devise a constraint algorithm
for these systems.
  Field theories with damping are described through a modification of the De
Donder-Weyl Hamiltonian field theory. This is achieved by combining contact
geometry and k-symplectic structures, resulting in the k-contact formalism. We
introduce two notions of dissipation laws, generalizing the concept of
dissipated quantity. These developments are also applied to Lagrangian field
theory. The Skinner-Rusk formulation for k-contact systems is described in
detail and we show how to recover the Lagrangian and Hamiltonian formalisms
from it.
  Throughout the thesis we present several examples in mechanics and field
theory. The most remarkable mechanical examples are the damped harmonic
oscillator, the motion in a gravitational field with friction, the parachute
equation and the damped simple pendulum. In field theory, we study the damped
vibrating string, the Burgers' equation, the Klein-Gordon equation and its
relation with the telegrapher's equation, and the Maxwell's equations with
dissipation.


title:
Leveraging RIS-Enabled Smart Signal Propagation for Solving Infeasible
  Localization Problems

abstract:
  Reconfigurable intelligent surfaces (RISs) have tremendous potential for both
communication and localization. While communication benefits are now
well-understood, the breakthrough nature of the technology may well lie in its
capability to provide location estimates when conventional approaches fail,
(e.g., due to insufficient available infrastructure). A limited number of
example scenarios have been identified, but an overview of possible RIS-enabled
localization scenarios is still missing from the literature. In this article,
we present such an overview and extend localization to include even user
orientation or velocity. In particular, we consider localization scenarios with
various numbers of RISs, single- or multi-antenna base stations, narrowband or
wideband transmissions, and near- and farfield operation. Furthermore, we
provide a short description of the general RIS operation together with radio
localization fundamentals, experimental validation of a localization scheme
with two RISs, as well as key research directions and open challenges specific
to RIS-enabled localization and sensing.


title:
Regularized 3D spectroscopy with CubeFit: method and application to the
  Galactic Center Circumnuclear disk

abstract:
  The Galactic Center black hole and the nuclear star cluster are surrounded by
a clumpy ring of gas and dust (the circumnuclear disk, CND) that rotates about
them at a standoff distance of ~1.5 pc. The mass and density of individual
clumps in the CND are disputed. We seek to use H$_2$ to characterize the clump
size distribution and to investigate the morphology and dynamics of the
interface between the ionized interior layer of the CND and the molecular
reservoir lying further out (corresponding to the inner rim of the CND,
illuminated in ultraviolet light by the central star cluster). We have observed
two fields of approximately 20"x20" in the CND at near-infrared wavelengths
with the OSIRIS spectro-imager at the Keck Observatory. These two fields,
located at the approaching and receding nodes of the CND, best display this
interface. Our data cover two H$_2$ lines as well as the Br$\gamma$ line
(tracing H ii). We have developed the tool CubeFit, an original method to
extract maps of continuous physical parameters (such as velocity field and
velocity dispersion) from integral-field spectroscopy data, using
regularization to largely preserve spatial resolution in regions of low
signal-to-noise ratio. This original method enables us to isolate compact,
bright features in the interstellar medium of the CND. Several clumps in the
southwestern field assume the appearance of filaments, many of which are
parallel to each other. We conclude that these clumps cannot be
self-gravitating.


title: Research Status of Deep Learning Methods for Rumor Detection

abstract:
  To manage the rumors in social media to reduce the harm of rumors in society.
Many studies used methods of deep learning to detect rumors in open networks.
To comprehensively sort out the research status of rumor detection from
multiple perspectives, this paper analyzes the highly focused work from three
perspectives: Feature Selection, Model Structure, and Research Methods. From
the perspective of feature selection, we divide methods into content feature,
social feature, and propagation structure feature of the rumors. Then, this
work divides deep learning models of rumor detection into CNN, RNN, GNN,
Transformer based on the model structure, which is convenient for comparison.
Besides, this work summarizes 30 works into 7 rumor detection methods such as
propagation trees, adversarial learning, cross-domain methods, multi-task
learning, unsupervised and semi-supervised methods, based knowledge graph, and
other methods for the first time. And compare the advantages of different
methods to detect rumors. In addition, this review enumerate datasets available
and discusses the potential issues and future work to help researchers advance
the development of field.


title:
Mean value formulas for classical solutions to some degenerate elliptic
  equations in Carnot groups

abstract:
  We prove surface and volume mean value formulas for classical solutions to
uniformly elliptic equations in divergence form with H\"{o}lder continuous
coefficients. The kernels appearing in the integrals are supported on the level
and superlevel sets of the fundamental solution relevant the adjoint
differential operator. We then extend the aforementioned formulas to some
subelliptic operators on Carnot groups. In this case we rely on the theory of
finite perimeters on stratified Lie groups.


title: A Discrete Element Method model for frictional fibers

abstract:
  We present a Discrete Element Method algorithm for the simulation of elastic
fibers in frictional contacts. The fibers are modeled as chains of cylindrical
segments connected to each other by springs taking into account elongation,
bending and torsion forces. The frictional contacts between the cylinders are
modeled using a Cundall and Strack model routinely used in granular material
simulations. The physical scales for simulations, the determination and the
tracking of contacts, and the algorithm are discussed. Tests on different
situations involving few or many contact points are presented and compared to
experiments or to theoretical predictions.


title:
Massive particle acceleration on a photonic chip via spatial-temporal
  modulation

abstract:
  Recently, the spectral manipulation of single photons has been achieved
through spatial-temporal modulation of the optical refractive index. Here, we
generalize this mechanism to massive particles, i.e. realizing the acceleration
or deceleration of particles through the spatial-temporal modulation of
potential induced by lasers. On a photonic integrated chip, we propose a
MeV-magnitude acceleration by distributed modulation units driven by lasers.
The mechanism could also be applied to atom trapping, which promises a
millimeter-scale decelerator to trap atoms. The spatial-temporal modulation
approach is universal and could be generalized to other systems, which may play
a significant role in hybrid photonic chip and microscale particle
manipulation.


title:
Rethinking Multi-Modal Alignment in Video Question Answering from
  Feature and Sample Perspectives

abstract:
  Reasoning about causal and temporal event relations in videos is a new
destination of Video Question Answering (VideoQA).The major stumbling block to
achieve this purpose is the semantic gap between language and video since they
are at different levels of abstraction. Existing efforts mainly focus on
designing sophisticated architectures while utilizing frame- or object-level
visual representations. In this paper, we reconsider the multi-modal alignment
problem in VideoQA from feature and sample perspectives to achieve better
performance. From the view of feature,we break down the video into trajectories
and first leverage trajectory feature in VideoQA to enhance the alignment
between two modalities. Moreover, we adopt a heterogeneous graph architecture
and design a hierarchical framework to align both trajectory-level and
frame-level visual feature with language feature. In addition, we found that
VideoQA models are largely dependent on language priors and always neglect
visual-language interactions. Thus, two effective yet portable training
augmentation strategies are designed to strengthen the cross-modal
correspondence ability of our model from the view of sample. Extensive results
show that our method outperforms all the state-of-the-art models on the
challenging NExT-QA benchmark, which demonstrates the effectiveness of the
proposed method.


title:
LoL: A Comparative Regularization Loss over Query Reformulation Losses
  for Pseudo-Relevance Feedback

abstract:
  Pseudo-relevance feedback (PRF) has proven to be an effective query
reformulation technique to improve retrieval accuracy. It aims to alleviate the
mismatch of linguistic expressions between a query and its potential relevant
documents. Existing PRF methods independently treat revised queries originating
from the same query but using different numbers of feedback documents,
resulting in severe query drift. Without comparing the effects of two different
revisions from the same query, a PRF model may incorrectly focus on the
additional irrelevant information increased in the more feedback, and thus
reformulate a query that is less effective than the revision using the less
feedback. Ideally, if a PRF model can distinguish between irrelevant and
relevant information in the feedback, the more feedback documents there are,
the better the revised query will be. To bridge this gap, we propose the
Loss-over-Loss (LoL) framework to compare the reformulation losses between
different revisions of the same query during training. Concretely, we revise an
original query multiple times in parallel using different amounts of feedback
and compute their reformulation losses. Then, we introduce an additional
regularization loss on these reformulation losses to penalize revisions that
use more feedback but gain larger losses. With such comparative regularization,
the PRF model is expected to learn to suppress the extra increased irrelevant
information by comparing the effects of different revised queries. Further, we
present a differentiable query reformulation method to implement this
framework. This method revises queries in the vector space and directly
optimizes the retrieval performance of query vectors, applicable for both
sparse and dense retrieval models. Empirical evaluation demonstrates the
effectiveness and robustness of our method for two typical sparse and dense
retrieval models.


title: Symmetrized non-commutative tori revisited

abstract:
  For the flip action of $\mathbb{Z}_2$ on an $n$-dimensional noncommutative
torus $A_\theta,$ where $\theta$ is an $n \times n$ antisymmetric matrix, we
show that the map $i_* : \mathrm{K}_0(A_\theta) \rightarrow
\mathrm{K}_0(A_\theta \rtimes \mathbb{Z}_2)$ induced by the natural inclusion
map sends generators to a set of elements which can be extended to provide a
set of generators of $\mathrm{K}_0(A_\theta \rtimes \mathbb{Z}_2)$. This
clarifies one crucial step in the computation of the K-theory of the
C$^*$-algebra $A_\theta \rtimes \mathbb{Z}_2$ by Farsi and Watling in [FW93],
and proves $\mathrm{K}_{0}(A_{\theta}\rtimes \mathbb{Z}_2) \cong \mathbb{Z}^{3
\cdot 2^{n-1}},$ $\mathrm{K}_{1}(A_{\theta}\rtimes \mathbb{Z}_2) = 0.$ Our
methods also provide an explicit basis of $\mathrm{K}_{0}(A_{\theta}\rtimes
\mathbb{Z}_2).$ As an application, for a simple $n$-dimensional torus
$A_{\theta},$ using classification techniques, we determine the isomorphism
class of $A_{\theta}\rtimes \mathbb{Z}_2$ in terms of the isomorphism class of
$A_{\theta}.$


title: Coupling Matrix-based Beamforming for Superdirective Antenna Arrays

abstract:
  In most multiple-input multiple-output (MIMO) communication systems, e.g.,
Massive MIMO, the antenna spacing is generally no less than half a wavelength.
It helps to reduce the mutual coupling and therefore facilitate the system
design. The maximum array gain is the number of antennas in this settings.
However, when the antenna spacing is made very small, the array gain of a
compact array can be proportional to the square of the number of antennas - a
value much larger than the traditional array. To achieve this so-called
"superdirectivity" however, the calculation of the excitation coefficients
(beamforming vector) is known to be a challenging problem. In this paper, we
derive the beamforming vector of superdirective arrays based on a novel
coupling matrix-enabled method. We also propose an approach to obtain the
coupling matrix, which is derived by the spherical wave expansion method and
active element pattern. The full-wave electromagnetic simulations are conducted
to validate the effectiveness of our proposed method. Simulation results show
that when the beamforming vector obtained by our method is applied, the
directivity of the designed dipole antenna array has a good agreement with the
theoretical values.


title:
PedRecNet: Multi-task deep neural network for full 3D human pose and
  orientation estimation

abstract:
  We present a multitask network that supports various deep neural network
based pedestrian detection functions. Besides 2D and 3D human pose, it also
supports body and head orientation estimation based on full body bounding box
input. This eliminates the need for explicit face recognition. We show that the
performance of 3D human pose estimation and orientation estimation is
comparable to the state-of-the-art. Since very few data sets exist for 3D human
pose and in particular body and head orientation estimation based on full body
data, we further show the benefit of particular simulation data to train the
network. The network architecture is relatively simple, yet powerful, and
easily adaptable for further research and applications.


title: MathPartner Computer Algebra

abstract:
  In this paper, we describe general characteristics of the MathPartner
computer algebra system (CAS) and Mathpar programming language thereof.
MathPartner can be used for scientific and engineering calculations, as well as
in high schools and universities. It allows one to carry out both simple
calculations (acting as a scientific calculator) and complex calculations with
large-scale mathematical objects. Mathpar is a procedural language; it supports
a large number of elementary and special functions, as well as matrix and
polynomial operators. This service allows one to build function images and
animate them. MathPartner also makes it possible to solve some symbolic
computation problems on supercomputers with distributed memory. We highlight
main differences of MathPartner from other CASs and describe the Mathpar
language along with the user service provided.


title:
Speech Detection For Child-Clinician Conversations In Danish For
  Low-Resource In-The-Wild Conditions: A Case Study

abstract:
  Use of speech models for automatic speech processing tasks can improve
efficiency in the screening, analysis, diagnosis and treatment in medicine and
psychiatry. However, the performance of pre-processing speech tasks like
segmentation and diarization can drop considerably on in-the-wild clinical
data, specifically when the target dataset comprises of atypical speech. In
this paper we study the performance of a pre-trained speech model on a dataset
comprising of child-clinician conversations in Danish with respect to the
classification threshold. Since we do not have access to sufficient labelled
data, we propose few-instance threshold adaptation, wherein we employ the first
minutes of the speech conversation to obtain the optimum classification
threshold. Through our work in this paper, we learned that the model with
default classification threshold performs worse on children from the patient
group. Furthermore, the error rates of the model is directly correlated to the
severity of diagnosis in the patients. Lastly, our study on few-instance
adaptation shows that three-minutes of clinician-child conversation is
sufficient to obtain the optimum classification threshold.


title: Numerically analyzing self-interacting dark matter

abstract:
  We consider the scenario of self-interacting dark matter(SIDM) with a light
mediator in a model-independent way, which can alleviate two long-standing
issues of the small scale cosmology namely cusp vs. core and too-big-to-fail. A
Yukawa potential is chosen to achieve mediator exchange between DM particles as
part of their self-interactions. The dynamics of self-interacting transfer
cross-section are studied for a range of mediator mass($m_Z'$). Also, a
relationship is established between the cross-section and DM particles'
relative velocity, which ensures the solution to the DM crisis at small scales.
Our obtained numerical results are efficient compared to the earlier works in
the context that a lesser number of $\ell$ modes have been used by us to
achieve the same level of accuracy in the cross-section calculations. For a
better understanding of the SIDM parameter space, we perform an analytical
analysis on the dependence of transfer cross-section over the other important
SIDM parameters using a Hulth\'{e}n potential which is similar in its behavior
to Yukawa potential. A detailed evolution of particle dynamics using the
Boltzmann equation and the effect of Sommerfeld enhancement on such
calculations has also been studied here. We also provide a minimal anomaly-free
leptophilic extension of the standard model, that can incorporate SIDM and its
mediator candidate in the framework.


title:
Experimental demonstration of remotely creating Wigner negativity via
  quantum steering

abstract:
  Non-Gaussian states with Wigner negativity are of particular interest in
quantum technology due to their potential applications in quantum computing and
quantum metrology. However, how to create such states at a remote location
remains a challenge, which is important for efficiently distributing quantum
resource between distant nodes in a network. Here, we experimentally prepare
optical non-Gaussian state with negative Wigner function at a remote node via
local non-Gaussian operation and shared Gaussian entangled state existing
quantum steering. By performing photon subtraction on one mode, Wigner
negativity is created in the remote target mode. We show that the Wigner
negativity is sensitive to loss on the target mode, but robust to loss on the
mode performing photon subtraction. This experiment confirms the connection
between the remotely created Wigner negativity and quantum steering. As an
application, we present that the generated non-Gaussian state exhibits
metrological power in quantum phase estimation.


title:
Dynamics of economic unions: an agent-based model to investigate the
  economic and social drivers of withdrawals

abstract:
  Economic unions are international agreements oriented to increase economic
efficiency and establishing political and cultural ties between the member
countries. Becoming a member of an existing union usually requires the approval
of both the candidate and members, while leaving it may require only the
unilateral will of the exiting country. There are many examples of accession of
states to previously consolidated economic unions, and a recent example of
leaving is the withdrawal of the United Kingdom from the European Union.
Motivated by the Brexit process, in this paper we propose an agent-based model
to study the determinant factors driving withdrawals from an economic union. We
show that both Union and local taxes promote the exits, whereas customs fees
out of the Union boost cohesion. Furthermore, heterogeneity in both business
conditions and wealth distribution promotes withdrawals, while countries' size
diversity does not have a significant effect on them. We also deep into the
individual causes that lead to dissatisfaction and, ultimately, to exits. We
found that, for low Union taxes, the wealth inequality within the country is
the leading cause of anti-Union opinion spreading. Conversely, for high Union
taxes, the country's performance turns out to be the main driving force,
resulting in a risk of wealthier countries leaving the Union. These findings
will be helpful for the design of economic policies and effective informative
campaigns.


title: CVA in fractional and rough volatility models

abstract:
  In this work we present a general representation formula for the price of a
vulnerable European option, and the related CVA in stochastic (either rough or
not) volatility models for the underlying's price, when admitting correlation
with the default event. We specialize it for some volatility models and we
provide price approximations, based on the representation formula. We study
numerically their accuracy, comparing the results with Monte Carlo simulations,
and we run a theoretical study of the error. We also introduce a seminal study
of roughness influence on the claim's price.


title: Iceline Variations Driven by Protoplanetary Disc Gaps

abstract:
  The composition of forming planets is strongly affected by the protoplanetary
disc's thermal structure. This thermal structure is predominantly set by dust
radiative transfer and viscous (accretional) heating and can be impacted by
gaps - regions of low dust and gas density that can occur when planets form.
The effect of variations in dust surface density on disc temperature has been
poorly understood until now. In this work, we use the radiative transfer code
MCMax to model the 2D dust thermal structure with individual gaps corresponding
to planets with masses of 0.1 M$_J$ - 5 M$_J$ and orbital radii of 3, 5, and 10
AU. Low dust opacity in the gap allows radiation to penetrate deeper into the
disc and warm the midplane by up to 16 K, but only for gaps located in the
region of the disc where stellar irradiation is the dominant source of heat
(here, a$\gtrsim$4 AU). In viscously-heated regions (a$\lesssim$4 AU), the
midplane of the gap is relatively cooler by up to 100 K. Outside of the gap,
broad radial oscillations in heating and cooling are present due to changes in
disc flaring. These thermal features affect local segregation of volatile
elements (H$_2$O, CH$_4$, CO2, CO) between the dust and gas. We find that
icelines experience dramatic shifts relative to gapless models: up to 6.5 AU
towards the star and 4.3 AU towards the midplane. While quantitative
predictions of iceline deviations will require more sophisticated models which
include transport and sublimation/condensation kinetics, our results provide
evidence that planet-induced iceline variations represent a potential feedback
from the planet onto the composition of material it is accreting.


title:
Design optimization of band-pass filter based on parity-time symmetry
  coupled-resonant

abstract:
  Integrated optical filter based on microring resonators plays a critical role
in many applications, ranging from wavelength division multiplexing and
switching to channel routing. Bandwidth tunable filters are capable of meeting
the on-demand flexible operations in complex situations, due to their
advantages of scalability, multi-function, and energy-saving. It has been
investigated recently that parity-time (PT) symmetry coupled-resonant systems
can be applied to the bandwidth-tunable filters. However, due to the trade-off
between the bandwidth-tunable contrast ratio and insertion loss of system, the
bandwidth-tunable contrast ratio of this method is severely limited. Here, the
bandwidth-tunable contrast ratio is defined as the maximum bandwidth divided by
the minimum bandwidth. In this work, we show that high bandwidth-tunable
contrast ratio and low insertion loss of system can be achieved simultaneously
by increasing the coupling strength between the input port and the resonant.
System characterizations under different coupling states reveal that the low
insertion loss can be obtained when the system initially operates at the
over-coupling condition. A high bandwidth-tunable contrast ratio PT-symmetry
band-pass filter with moderate insertion loss is shown on the Silicon platform.
Our scheme provides an effective method to reduce the insertion loss of on-chip
tunable filters, which is also applicable to the high-order cascaded microring
systems.


title:
Localisation of perturbations of a constant state in a traffic flow
  model

abstract:
  We consider, in the Aw-Rascle-Zhang traffic flow model, the problem of the
asymptotic stability of constant flows. By using a perturbative approach, we
show the stability in a larger space of perturbation than previous results.
Furthermore, we are able to compute where the perturbation is mainly localised
in space for a given time, based on the localisation of the perturbation
initially. These new ideas can be applied to various other models of hyperbolic
conservation laws with relaxations.


title: Thermodynamic stability of a new three dimensional regular black hole

abstract:
  A new model of the regular black hole in $(2+1)-$dimensions is introduced by
considering an appropriate matter field as the energy-momentum tensor. First,
we propose a novel model of $d$-dimensional energy density that in
$(2+1)-$dimensions leads to the existence of an upper bound on the radius of
the event horizon and a lower bound on the mass of the black hole which are
motivated by the features of astrophysical black holes. According to these
bounds, we introduce an admissible domain for the event horizon radius,
depending on the metric parameters. After investigation of geometric properties
of the obtained solutions, we study the thermal stability of the solution in
the canonical ensemble and find that the regular black hole is thermally stable
in the mentioned admissible domain. Besides, the Gibbs free energy is
calculated to examine the global stability of the solution.


title:
Modelling seagrass competition in the Mediterranean Sea in global
  warming scenarios

abstract:
  Temperature anomalies in the coastal water are expected to intensify in the
next years due to global climate change. Temperature elevation has been
observed to strongly affect the ecosystems of seagrass meadows worldwide,
decreasing the shoot density, biomass, and leaf productivity. This effect will
be particularly relevant in the Mediterranean Sea in which endemic seagrasses
{\it P. oceanica} and {\it C. nodosa} show a different response to the thermal
stress. The changes in the species competition will alter the growth dynamics,
their spatial distribution, and, consequently, the ecosystem functionality.
  In this article, we use a mathematical model based on the clonal growth of
seagrasses to study the competition between {\it P. oceanica} and {\it C.
nodosa}. One of our main findings is the identification of a region of
coexistence located at the borders between patches of mono-specific species.
This region has the shape of a stripe and the model predictions are found to be
in agreement with field observations performed in Ses Olles de Son Saura
(Balearic Islands, Western Mediterranean Sea). Such comparison allowed us to
evaluate the cross-species interaction parameters through the measurement of
the width of the coexistence region.
  Previous observations show that the mortality of {\it P. oceanica} alarmingly
increases with seawater temperature, while {\it C. nodosa} has a higher thermal
resilience. It is expected that in the following decades {\it C. nodosa} will
become the predominant seagrass in the Mediterranean Sea, replacing the
substrate previously occupied by {\it P. oceanica}. In a positively-moderate
scenario of carbon emissions and local disturbances, our model predicts a total
invasion of {\it C. nodosa} by the year 2072, and the functional extinction of
{\it P. oceanica} in the year 2076 in locations with similar environmental
conditions to the study site.


title:
Robust inference for non-destructive one-shot device testing under
  step-stress model with exponential lifetimes

abstract:
  One-shot devices analysis involves an extreme case of interval censoring,
wherein one can only know whether the failure time is either before or after
the test time. Some kind of one-shot devices do not get destroyed when tested,
and so can continue within the experiment, providing extra information for
inference, if they did not fail before an inspection time. In addition, their
reliability can be rapidly estimated via accelerated life tests (ALTs) by
running the tests at varying and higher stress levels than working conditions.
In particular, step-stress tests allow the experimenter to increase the stress
levels at pre-fixed times gradually during the life-testing experiment. The
cumulative exposure model is commonly assumed for step-stress models, relating
the lifetime distribution of units at one stress level to the lifetime
distributions at preceding stress levels. In this paper,vwe develop robust
estimators and Z-type test statistics based on the density power divergence
(DPD) for testing linear null hypothesis for non-destructive one-shot devices
under the step-stress ALTs with exponential lifetime distribution. We study
asymptotic and robustness properties of the estimators and test statistics,
yielding point estimation and confidence intervals for different lifetime
characteristic such as reliability, distribution quantiles and mean lifetime of
the devices. A simulation study is carried out to assess the performance of the
methods of inference developed here and some real-life data sets are analyzed
finally for illustrative purpose.


title: Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction

abstract:
  Human trajectory forecasting is a key component of autonomous vehicles,
social-aware robots and advanced video-surveillance applications. This
challenging task typically requires knowledge about past motion, the
environment and likely destination areas. In this context, multi-modality is a
fundamental aspect and its effective modeling can be beneficial to any
architecture. Inferring accurate trajectories is nevertheless challenging, due
to the inherently uncertain nature of the future. To overcome these
difficulties, recent models use different inputs and propose to model human
intentions using complex fusion mechanisms. In this respect, we propose a
lightweight attention-based recurrent backbone that acts solely on past
observed positions. Although this backbone already provides promising results,
we demonstrate that its prediction accuracy can be improved considerably when
combined with a scene-aware goal-estimation module. To this end, we employ a
common goal module, based on a U-Net architecture, which additionally extracts
semantic information to predict scene-compliant destinations. We conduct
extensive experiments on publicly-available datasets (i.e. SDD, inD, ETH/UCY)
and show that our approach performs on par with state-of-the-art techniques
while reducing model complexity.


title: Determinantal Point Process Likelihoods for Sequential Recommendation

abstract:
  Sequential recommendation is a popular task in academic research and close to
real-world application scenarios, where the goal is to predict the next
action(s) of the user based on his/her previous sequence of actions. In the
training process of recommender systems, the loss function plays an essential
role in guiding the optimization of recommendation models to generate accurate
suggestions for users. However, most existing sequential recommendation
techniques focus on designing algorithms or neural network architectures, and
few efforts have been made to tailor loss functions that fit naturally into the
practical application scenario of sequential recommender systems.
  Ranking-based losses, such as cross-entropy and Bayesian Personalized Ranking
(BPR) are widely used in the sequential recommendation area. We argue that such
objective functions suffer from two inherent drawbacks: i) the dependencies
among elements of a sequence are overlooked in these loss formulations; ii)
instead of balancing accuracy (quality) and diversity, only generating accurate
results has been over emphasized. We therefore propose two new loss functions
based on the Determinantal Point Process (DPP) likelihood, that can be
adaptively applied to estimate the subsequent item or items. The
DPP-distributed item set captures natural dependencies among temporal actions,
and a quality vs. diversity decomposition of the DPP kernel pushes us to go
beyond accuracy-oriented loss functions. Experimental results using the
proposed loss functions on three real-world datasets show marked improvements
over state-of-the-art sequential recommendation methods in both quality and
diversity metrics.


title:
The Quasinormal Modes and Greybody Bounds of Rotating Black Holes in a
  Dark Matter Halo

abstract:
  The quasinormal mode (QNM) is a characteristic "sound" for a black hole which
can provide us with a new method to identify black holes in our universe. Based
on this, we study the quasinormal modes (QNMs) and greybody factors (GFs) of
axisymmetric black holes in a dark matter halo, and make a comparison with the
Kerr black hole. The main results we obtained are as follows: 1, The peak value
of the effective potential of a black hole in a dark matter halo is larger than
that of the Kerr black hole. The effective potential increases with the
increase of the angular quantum number $L$, and decreases with the increase of
the rotation parameter $a$. 2, In the case of slowly rotation, the oscillation
frequencies (real part) of black holes both in a dark matter halo and the Kerr
spacetime increases with the angular quantum number $L$ and the magnetic
quantum number $m$. Their decay rate (imaginary part) is relatively constant
throughout the field perturbation. The decay rate of a black hole in a dark
matter halo is lower than that of the Kerr black hole because of the
interaction of dark matter with a black hole. 3, The rotation parameters $a$
and angular quantum number $L$ of the black hole both in a dark matter halo and
the Kerr spacetime have a positive contribution to the greybody bounds. The
values of greybody bounds decreases with both rotation parameter $a$ and the
angular quantum number $L$ increase. 4, The quasinormal frequencies (QNFs) of
black holes fitted by the P{\"o}schl-Teller approximation and the WKB method
are in good agreement.


title:
Maximum Mean Discrepancy Distributionally Robust Nonlinear
  Chance-Constrained Optimization with Finite-Sample Guarantee

abstract:
  This paper is motivated by addressing open questions in distributionally
robust chance-constrained programs (DRCCP) using the popular Wasserstein
ambiguity sets. Specifically, the computational techniques for those programs
typically place restrictive assumptions on the constraint functions and the
size of the Wasserstein ambiguity sets is often set using costly
cross-validation (CV) procedures or conservative measure concentration bounds.
In contrast, we propose a practical DRCCP algorithm using kernel maximum mean
discrepancy (MMD) ambiguity sets, which we term MMD-DRCCP, to treat general
nonlinear constraints without using ad-hoc reformulation techniques. MMD-DRCCP
can handle general nonlinear and non-convex constraints with a proven
finite-sample constraint satisfaction guarantee of a dimension-independent
$\mathcal{O}(\frac{1}{\sqrt{N}})$ rate, achievable by a practical algorithm. We
further propose an efficient bootstrap scheme for constructing sharp MMD
ambiguity sets in practice without resorting to CV. Our algorithm is validated
numerically on a portfolio optimization problem and a tube-based
distributionally robust model predictive control problem with non-convex
constraints.


title:
Value of Optimal Trip and Charging Scheduling of Commercial Electric
  Vehicle Fleets with Vehicle-to-Grid in Future Low Inertia Systems

abstract:
  The electrification of transport is seen as an important step in the global
decarbonisation agenda. With such a large expected load on the power system
from electric vehicles (EVs), it is important to coordinate charging in order
to balance the supply and demand for electricity. Bidirectional charging,
enabled through Vehicle-to-Grid (V2G) technology, will unlock significant
storage capacity from stationary EVs that are plugged in. To take this concept
a step further, this paper quantifies the potential revenues to be gained by a
commercial EV fleet operator from simultaneously scheduling its trips on a
day-ahead basis, as well as its charging. This allows the fleet to complete its
trips (with user defined trip length and distance), while taking advantage of
fluctuating energy and ancillary services prices. A mathematical framework for
optimal trip scheduling is proposed, formulated as a mixed-integer linear
program, and is applied to several relevant scenarios of the present and future
British electricity system. It is demonstrated that an optimal journey start
time can increase the revenue of commercial fleets by up to 38% in summer and
12% in winter. This means a single EV from the maintenance fleet can make
additional annual revenue of up to {\pounds}729. Flexible trip schedules are
more valuable in the summer because keeping EVs plugged in during peak solar
output will benefit the grid and the fleet operators the most. It was also
found that a fleet of 5,000 EVs would result in the equivalent $\textrm{CO}_2$
of removing one Combined Cycle Gas Turbine from the system. This significant
increase in revenue and carbon savings show this approach is worth
investigating for potential future application.


title: Composition operators on weighted Hilbert spaces of Dirichlet series

abstract:
  We study composition operators of characteristic zero on weighted Hilbert
spaces of Dirichlet series. For this purpose we demonstrate the existence of
weighted mean counting functions associated with the Dirichlet series symbol,
and provide a corresponding change of variables formula for the composition
operator. This leads to natural necessary conditions for the boundedness and
compactness. For Bergman-type spaces, we are able to show that the compactness
condition is also sufficient, by employing a Schwarz-type lemma for Dirichlet
series.


title:
Deep CSI Compression for Massive MIMO: A Self-information Model-driven
  Neural Network

abstract:
  In order to fully exploit the advantages of massive multiple-input
multiple-output (mMIMO), it is critical for the transmitter to accurately
acquire the channel state information (CSI). Deep learning (DL)-based methods
have been proposed for CSI compression and feedback to the transmitter.
Although most existing DL-based methods consider the CSI matrix as an image,
structural features of the CSI image are rarely exploited in neural network
design. As such, we propose a model of self-information that dynamically
measures the amount of information contained in each patch of a CSI image from
the perspective of structural features. Then, by applying the self-information
model, we propose a model-and-data-driven network for CSI compression and
feedback, namely IdasNet. The IdasNet includes the design of a module of
self-information deletion and selection (IDAS), an encoder of informative
feature compression (IFC), and a decoder of informative feature recovery (IFR).
In particular, the model-driven module of IDAS pre-compresses the CSI image by
removing informative redundancy in terms of the self-information. The encoder
of IFC then conducts feature compression to the pre-compressed CSI image and
generates a feature codeword which contains two components, i.e., codeword
values and position indices of the codeword values. Subsequently, the IFR
decoder decouples the codeword values as well as position indices to recover
the CSI image. Experimental results verify that the proposed IdasNet noticeably
outperforms existing DL-based networks under various compression ratios while
it has the number of network parameters reduced by orders-of-magnitude compared
with various existing methods.


title: Relevance Models Based on the Knowledge Gap

abstract:
  Search systems are increasingly used for gaining knowledge through accessing
relevant resources from a vast volume of content. However, search systems
provide only limited support to users in knowledge acquisition contexts.
Specifically, they do not fully consider the knowledge gap which we define as
the gap existing between what the user knows and what the user intends to
learn. The effects of considering the knowledge gap for knowledge acquisition
tasks remain largely unexplored in search systems. We propose to model and
incorporate the knowledge gap into search algorithms. We plan to explore to
what extent the incorporation of the knowledge gap leads to an improvement in
the performance of search systems in knowledge acquisition tasks. Furthermore,
we aim to investigate and design a metric for the evaluation of the search
systems' performance in the context of knowledge acquisition tasks.


title: Pre-acceleration in the Electron Foreshock I: Electron Acoustic Waves

abstract:
  To undergo diffusive shock acceleration, electrons need to be pre-accelerated
to increase their energies by several orders of magnitude, else their
gyro-radii are smaller than the finite width of the shock. In oblique shocks,
where the upstream magnetic field orientation is neither parallel or
perpendicular to the shock normal, electrons can escape to the shock upstream,
modifying the shock foot to a region called the electron foreshock. To
determine the pre-acceleration in this region, we undertake PIC simulations of
oblique shocks while varying the obliquity and in-plane angles. We show that
while the proportion of reflected electrons is negligible for $\theta_{\rm Bn}
= 74.3^\circ$, it increases to $R \sim 5\%$ for $\theta_{\rm Bn} = 30^\circ$,
and that, via the electron acoustic instability, these electrons power
electrostatic waves upstream with energy density proportional to $R^{0.6}$ and
a wavelength $\approx 2 \lambda_{\rm{se}}$, where $\lambda_{\rm{se}}$ is the
electron skin length. While the initial reflection mechanism is typically a
combination of shock surfing acceleration and magnetic mirroring, we show that
once the electrostatic waves have been generated upstream they themselves can
increase the momenta of upstream electrons parallel to the magnetic field. In
$\lesssim 1\%$ of cases, upstream electrons are prematurely turned away from
the shock and never injected downstream. In contrast, a similar fraction are
re-scattered back towards the shock after reflection, re-interact with the
shock with energies much greater than thermal, and cross into the downstream.


title:
Corrections to electroweak precision observables from mixings of an
  exotic vector boson in light of the CDF $W$-mass anomaly

abstract:
  We enumerate various effective couplings that contribute to the mixings
between an exotic vector boson $Z^{\prime}$ and the neutral electroweak vector
bosons. The miscellaneous mixing patterns can be evaluated perturbatively. The
oblique parameters $S$, $T$, and $U$ are calculated to compare with the
electroweak precision test results. With the contributions to the
non-negligible $U$ parameter from the $\epsilon_{B,W}$ parameters and the aid
of some other parameters to cancel the negative $T$, the recent CDF $W$-mass
anomaly can therefore be explained.


title:
Microscopic field theory for structure formation in systems of
  self-propelled particles with generic torques

abstract:
  We derive a dynamical field theory for self-propelled particles subjected to
generic torques and forces by explicitly coarse-graining their microscopic
dynamics, described by a many-body Fokker-Planck equation. The model includes
both intrinsic torques inducing self-rotation, as well as interparticle torques
leading to, for instance, the local alignment of particles' orientations.
Within this approach, although the functional form of the pairwise interactions
does not need to be specified, one can directly map the parameters of the field
theory onto the parameters of particle-based models. We perform a linear
stability analysis of the homogeneous solution of the field equations and find
both long-wavelength and short-wavelength instabilities. The former signals the
emergence of a macroscopic structure, which we associate with motility-induced
phase separation, while the second one signals the growth of a finite structure
with a characteristic size. Intrinsic torques hinder phase separation, pushing
the onset of the long-wavelength instability to higher activities. Further,
they generate finite-sized structures with a characteristic size proportional
to both the self-propulsion velocity and the inverse of the self-rotation
frequency. Our results show that a general mechanism might explain why
chirality tends to suppress motility-induced phase separation but instead
promotes the formation of non-equilibrium patterns.


title: Upgrade of ASACUSA's Antihydrogen Detector

abstract:
  The goal of the ASACUSA (Atomic Spectroscopy And Collisions Using Slow
Antiprotons) CUSP experiment at CERN's Antiproton Decelerator is to measure the
ground state hyperfine splitting of antihydrogen in order to test whether CPT
invariance is broken.
  The ASACUSA hodoscope is a detector consisting of two layers of 32 plastic
scintillator bars individually read out by two serially connected silicon photo
multipliers (SiPMs) on each end. Two additional layers for position resolution
along the beam axis were scintillator fibres, which will now be replaced by
scintillating tiles placed onto the existing bars and also read out by SiPMs.
If the antiproton of antihydrogen annihilates in the center of the hodoscope,
particles (mostly pions) are produced and travel through the various layers of
the detector and produce signals.
  The hodoscope was successfully used during the last data taking period at
CERN. The necessary time resolution to discriminate between particles
travelling through the detector from outside and particles produced in the
center of the detector was achieved by the use of waveform digitisers and
software constant fraction discrimination. The disadvantage of this readout
scheme was the slow readout speed, which was improved by two orders of
magnitude. This was done by omitting the digitisers and replacing them with
TDCs reading out the digital time-over-threshold (ToT) signal using leading
edge discrimination.


title:
Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video
  Parsing

abstract:
  This paper focuses on the weakly-supervised audio-visual video parsing task,
which aims to recognize all events belonging to each modality and localize
their temporal boundaries. This task is challenging because only overall labels
indicating the video events are provided for training. However, an event might
be labeled but not appear in one of the modalities, which results in a
modality-specific noisy label problem. Motivated by two observations that
networks tend to learn clean samples first and that a labeled event would
appear in at least one modality, we propose a training strategy to identify and
remove modality-specific noisy labels dynamically. Specifically, we sort the
losses of all instances within a mini-batch individually in each modality, then
select noisy samples according to relationships between intra-modal and
inter-modal losses. Besides, we also propose a simple but valid noise ratio
estimation method by calculating the proportion of instances whose confidence
is below a preset threshold. Our method makes large improvements over the
previous state of the arts (e.g., from 60.0% to 63.8% in segment-level visual
metric), which demonstrates the effectiveness of our approach.


title:
A global analysis of metrics used for measuring performance in natural
  language processing

abstract:
  Measuring the performance of natural language processing models is
challenging. Traditionally used metrics, such as BLEU and ROUGE, originally
devised for machine translation and summarization, have been shown to suffer
from low correlation with human judgment and a lack of transferability to other
tasks and languages. In the past 15 years, a wide range of alternative metrics
have been proposed. However, it is unclear to what extent this has had an
impact on NLP benchmarking efforts. Here we provide the first large-scale
cross-sectional analysis of metrics used for measuring performance in natural
language processing. We curated, mapped and systematized more than 3500 machine
learning model performance results from the open repository 'Papers with Code'
to enable a global and comprehensive analysis. Our results suggest that the
large majority of natural language processing metrics currently used have
properties that may result in an inadequate reflection of a models'
performance. Furthermore, we found that ambiguities and inconsistencies in the
reporting of metrics may lead to difficulties in interpreting and comparing
model performances, impairing transparency and reproducibility in NLP research.


title:
Deep Reinforcement Learning for Orienteering Problems Based on
  Decomposition

abstract:
  This paper presents a new method for solving an orienteering problem (OP) by
breaking it down into two parts: a knapsack problem (KP) and a traveling
salesman problem (TSP). A KP solver is responsible for picking nodes, while a
TSP solver is responsible for designing the proper path and assisting the KP
solver in judging constraint violations. To address constraints, we propose a
dual-population coevolutionary algorithm (DPCA) as the KP solver, which
simultaneously maintains both feasible and infeasible populations. A dynamic
pointer network (DYPN) is introduced as the TSP solver, which takes city
locations as inputs and immediately outputs a permutation of nodes. The model,
which is trained by reinforcement learning, can capture both the structural and
dynamic patterns of the given problem. The model can generalize to other
instances with different scales and distributions. Experimental results show
that the proposed algorithm can outperform conventional approaches in terms of
training, inference, and generalization ability.


title:
Optimal Control Measures Based on the Reconstruction of the COVID-19
  Interlocalilty Transmission Network in Lebanon

abstract:
  In this paper, we study the evolution of COVID-19 in Lebanon using the data
collected by the Epidemiological Surveillance Program of Lebanon's Ministry of
Public Health. We develop an autoregressive model that allows us to decompose
the mean number of infections into three components that describe:
intra-locality infections, inter-locality infections, and infections from other
sources such as travelers arriving from abroad. We observe that the
inter-locality term, which we identify as a time-evolving network, drives the
dynamics of the disease. Tools from network analysis are then employed to get
insight into its topology. Building on this, and particularly on the centrality
of the nodes of the identified network, a strategy for intervention and disease
control is devised.


title: The $(n+1)$-centered operator on a Hilbert $C^*$-module

abstract:
  Let $T$ be an adjointable operator on a Hilbert $C^*$-module such that $T$
has the polar decomposition $T=UT|$. For each natural number $n$, $T$ is called
an $(n+1)$-centered operator if $T^k=U^k|T^k|$ is the polar decomposition for
$1\le k\le n+1$. This paper initiates the study of the $(n+1)$-centered
operator via the generalized Aluthge transform and the generalized iterative
Aluthge transform. Some new characterizations of the $(n+1)$-centered operator
are provided.


title: Scattering phenomena for spin transport in Kitaev spin liquid

abstract:
  The Kitaev model exhibits a canonical quantum spin liquid as a ground state
and hosts two fractional quasiparticles, itinerant Majorana fermion and
localized flux excitation. The former can carry heat and spin modulations in
the quantum spin liquid, but the role of the latter remains unknown for the
transport phenomena. Here, we focus on spin transport in the presence of
excited fluxes and report that they yield strong interference in the
propagation of the Majorana fermions, which feel gauge-like potential emergent
around the fluxes. We examine the transient spin dynamics triggered by a pulsed
magnetic field at an edge. In the absence of excited fluxes, the magnetic-field
pulse creates the plane wave of the Majorana fermions, which flows in the
quantum spin liquid. Although this wave does not accompany the change of local
spin moments in bulk, it induces local moments at the side opposite to the edge
under the magnetic-field pulse. We observe the spatial modulation of induced
spin moments when fluxes are excited in the bulk region. This behavior is more
striking than the case of lattice defects. Moreover, we find that, although the
amplitude of the spatial change is almost independent of the distance between
lattice defects, it is strongly enhanced by increasing the distance for the
case of excited fluxes. The difference is understood from the influence on the
itinerant Majorana fermions; the lattice defects change the system locally, but
flux excitations alter all the transfer integrals on the string connecting
them. The present results will provide another route to observing intrinsic
flux excitations distinguished from extrinsic effects such as lattice defects.


title: NIHAO XXVII: Crossing the green valley

abstract:
  The transition of high-mass galaxies from being blue and star forming to
being red and dead is a crucial step in galaxy evolution, yet not fully
understood. In this work, we use the NIHAO suite of galaxy simulations to
investigate the relation between the transition time through the green valley
and other galaxy properties. The typical green valley crossing time of our
galaxies is approximately 400 Myr, somewhat shorter than observational
estimates. The crossing of the green valley is triggered by the onset of AGN
feedback and the subsequent shut down of star formation. Interestingly the time
spent in the green valley is not related to any other galaxy properties, such
as stellar age or metallicity, or the time at which the star formation
quenching takes place. The crossing time is set by two main contributions: the
ageing of the current stellar population and the residual star formation in the
green valley. These effects are of comparable magnitude, while major and minor
mergers have a negligible contribution. Most interestingly, we find the time
that a galaxy spends to travel through the green valley is twice the
$e$-folding time of the star formation quenching. This result is stable against
galaxy properties and the exact numerical implementation of AGN feedback in the
simulation. Assuming a typical crossing time of about one Gyr inferred from
observations, our results imply that any mechanism or process aiming to quench
star formation, must do it on a typical timescale of 500 Myr.


title:
Novel non-involutive solutions of the Yang-Baxter equation from (skew)
  braces

abstract:
  We produce novel non-involutive solutions of the Yang-Baxter equation coming
from (skew) braces. These solutions are generalisations of the known ones
coming from the standard braces and skew braces, and surprisingly in the case
of braces they are not necessarily involutive. In the case of two-sided (skew)
braces one can assign such solutions to every element of the set. Novel
bijective maps associated to the inverse solutions are also introduced.
Moreover, we show that the recently derived Drinfeld twists of the involutive
case are still admissible in the non-involutive frame and we identify the
twisted r-matrices and twisted copoducts. We observe that as in the involutive
case the underlying quantum algebra is not a quasi-triangular bialgebra, as one
would expect, but a quasi-triangular quasi-bialgebra. The same applies to the
quantum algebra of the twisted r-matrices, contrary to the involutive case.


title: The Left Adjoint of Derived Parabolic Induction

abstract:
  We prove that the derived parabolic induction functor, defined on the
unbounded derived category of smooth mod $p$ representations of a $p$-adic
reductive group, admits a left adjoint $\mathrm{L}(U,-)$. We study the
cohomology functors $\mathrm{H}^i\circ \mathrm{L}(U,-)$ in some detail and
deduce that $\mathrm{L}(U,-)$ preserves bounded complexes and global
admissibility in the sense of Schneider--Sorensen. Using $\mathrm{L}(U,-)$ we
define a derived Satake homomorphism und prove that it encodes the mod $p$
Satake homomorphisms defined explicitly by Herzig.


title:
Graph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object
  Detection

abstract:
  3D object detection from multiple image views is a fundamental and
challenging task for visual scene understanding. Due to its low cost and high
efficiency, multi-view 3D object detection has demonstrated promising
application prospects. However, accurately detecting objects through
perspective views in the 3D space is extremely difficult due to the lack of
depth information. Recently, DETR3D introduces a novel 3D-2D query paradigm in
aggregating multi-view images for 3D object detection and achieves
state-of-the-art performance. In this paper, with intensive pilot experiments,
we quantify the objects located at different regions and find that the
"truncated instances" (i.e., at the border regions of each image) are the main
bottleneck hindering the performance of DETR3D. Although it merges multiple
features from two adjacent views in the overlapping regions, DETR3D still
suffers from insufficient feature aggregation, thus missing the chance to fully
boost the detection performance. In an effort to tackle the problem, we propose
Graph-DETR3D to automatically aggregate multi-view imagery information through
graph structure learning (GSL). It constructs a dynamic 3D graph between each
object query and 2D feature maps to enhance the object representations,
especially at the border regions. Besides, Graph-DETR3D benefits from a novel
depth-invariant multi-scale training strategy, which maintains the visual depth
consistency by simultaneously scaling the image size and the object depth.
Extensive experiments on the nuScenes dataset demonstrate the effectiveness and
efficiency of our Graph-DETR3D. Notably, our best model achieves 49.5 NDS on
the nuScenes test leaderboard, achieving new state-of-the-art in comparison
with various published image-view 3D object detectors.


title:
Density results and trace operator in weighted Sobolev Spaces defined on
  the half line equipped with power weights

abstract:
  We study properties of $W_0^{1,p}(\mathbb{R}_+,t^\beta)$ - the completion of
$C_0^\infty(\mathbb{R}_+)$ in the power-weighted Sobolev spaces
$W^{1,p}(\mathbb{R}_+,t^\beta)$, where $\beta\in\mathbb{R}$. Among other
results, we obtain the analytic characterization of
$W_0^{1,p}(\mathbb{R}_+,t^\beta)$ for all $\beta\in \mathbb{R}$. Our analysis
is based on the precise study the two trace operators: $Tr^{0}(u):= \lim_{t\to
0} u(t)$ and $Tr^{\infty}(u):= \lim_{t\to \infty} u(t)$, which leads to the
analysis of the asymptotic behavior of functions from
$W_0^{1,p}(\mathbb{R}_+,t^\beta)$ near zero or infinity. The obtained
statements can contribute to the proper formulation of Boundary Value Problems
in ODE's, or PDE's with the radial symmetries. We can also apply our results to
some questions in the complex interpolation theory, raised by M. Cwikel and A.
Einav in 2019, which we discuss within the particular case of Sobolev spaces
$W^{1,p}(\mathbb{R}_+,t^\beta)$.


title:
NVM-ESR: Using Non-Volatile Memory in Exact State Reconstruction of
  Preconditioned Conjugate Gradient

abstract:
  HPC systems are a critical resource for scientific research and advanced
industries. The demand for computational power and memory is increasing and
ushers in the exascale era, in which supercomputers are designed to provide
enormous computing power to meet these needs. These complex supercomputers
consist of many compute nodes and are consequently expected to experience
frequent faults and crashes. Exact state reconstruction (ESR) has been proposed
as a mechanism to alleviate the impact of frequent failures on long-term
computations. ESR has shown great potential in the context of iterative linear
algebra solvers, a key building block in numerous scientific applications.
Recent designs of supercomputers feature the emerging nonvolatile memory (NVM)
technology. For example, the Exascale Aurora supercomputer is planned to
integrate Intel Optane DCPMM. This work investigates how NVM can be used to
improve ESR so that it can scale to future exascale systems such as Aurora and
provide enhanced resilience. We propose the non-volatile memory ESR (NVM-ESR)
mechanism. NVM-ESR demonstrates how NVM can be utilized in supercomputers for
enabling efficient recovery from faults while requiring significantly smaller
memory footprint and time overheads in comparison to ESR. We focus on the
preconditioned conjugate gradient (PCG) iterative solver also studied in prior
ESR research, because it is employed by the representative HPCG scientific
benchmark.


title:
Will claim history become a deprecated rating factor? An optimal design
  method for the real-time road risk model

abstract:
  With the popularity of Telematics and Self-driving, more and more rating
factors, such as mileage, route, driving behavior, etc., are introduced into
actuarial models. There are quite a few doubts and disputes on the rationality
and accuracy of the selection of rating variables, but it does not involve the
widely accepted historical claim records. Recently, Tesla Insurance released a
new generation of Safety Score-based insurance, irrespective of accident
history. Forward-looking experts and scholars began to discuss whether claim
history will disappear in the future auto insurance rate-making system.
Therefore, this paper proposes a new risk variable elimination method as well
as a real-time road risk model design framework and concludes that claim
history will be regarded as a "noise" factor and deprecated in the
Pay-How-You-Drive model.


title: Which Discriminator for Cooperative Text Generation?

abstract:
  Language models generate texts by successively predicting probability
distributions for next tokens given past ones. A growing field of interest
tries to leverage external information in the decoding process so that the
generated texts have desired properties, such as being more natural, non toxic,
faithful, or having a specific writing style. A solution is to use a classifier
at each generation step, resulting in a cooperative environment where the
classifier guides the decoding of the language model distribution towards
relevant texts for the task at hand. In this paper, we examine three families
of (transformer-based) discriminators for this specific task of cooperative
decoding: bidirectional, left-to-right and generative ones. We evaluate the
pros and cons of these different types of discriminators for cooperative
generation, exploring respective accuracy on classification tasks along with
their impact on the resulting sample quality and computational performances. We
also provide the code of a batched implementation of the powerful cooperative
decoding strategy used for our experiments, the Monte Carlo Tree Search,
working with each discriminator for Natural Language Generation.


title:
Adversarial Filtering Modeling on Long-term User Behavior Sequences for
  Click-Through Rate Prediction

abstract:
  Rich user behavior information is of great importance for capturing and
understanding user interest in click-through rate (CTR) prediction. To improve
the richness, collecting long-term behaviors becomes a typical approach in
academy and industry but at the cost of increasing online storage and latency.
Recently, researchers have proposed several approaches to shorten long-term
behavior sequence and then model user interests. These approaches reduce online
cost efficiently but do not well handle the noisy information in long-term user
behavior, which may deteriorate the performance of CTR prediction
significantly. To obtain better cost/performance trade-off, we propose a novel
Adversarial Filtering Model (ADFM) to model long-term user behavior. ADFM uses
a hierarchical aggregation representation to compress raw behavior sequence and
then learns to remove useless behavior information with an adversarial
filtering mechanism. The selected user behaviors are fed into interest
extraction module for CTR prediction. Experimental results on public datasets
and industrial dataset demonstrate that our method achieves significant
improvements over state-of-the-art models.


title:
Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task
  Neural Survival Networks

abstract:
  Discontinuing ad creatives at an appropriate time is one of the most
important ad operations that can have a significant impact on sales. Such
operational support for ineffective ads has been less explored than that for
effective ads. After pre-analyzing 1,000,000 real-world ad creatives, we found
that there are two types of discontinuation: short-term (i.e., cut-out) and
long-term (i.e., wear-out). In this paper, we propose a practical prediction
framework for the discontinuation of ad creatives with a hazard function-based
loss function inspired by survival analysis. Our framework predicts the
discontinuations with a multi-modal deep neural network that takes as input the
ad creative (e.g., text, categorical, image, numerical features). To improve
the prediction performance for the two different types of discontinuations and
for the ad creatives that contribute to sales, we introduce two new techniques:
(1) a two-term estimation technique with multi-task learning and (2) a
click-through rate-weighting technique for the loss function. We evaluated our
framework using the large-scale ad creative dataset, including 10 billion scale
impressions. In terms of the concordance index (short: 0.896, long: 0.939, and
overall: 0.792), our framework achieved significantly better performance than
the conventional method (0.531). Additionally, we confirmed that our framework
(i) demonstrated the same degree of discontinuation effect as manual operations
for short-term cases, and (ii) accurately predicted the ad discontinuation
order, which is important for long-running ad creatives for long-term cases.


title: Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation

abstract:
  Ads allocation, that allocates ads and organic items to limited slots in feed
with the purpose of maximizing platform revenue, has become a popular problem.
However, e-commerce platforms usually have multiple entrances for different
categories and some entrances have few visits. Data accumulated on these
entrances can hardly support the learning of a good agent. To address this
challenge, we present Similarity-based Hybrid Transfer for Ads Allocation
(SHTAA), which can effectively transfer the samples as well as the knowledge
from data-rich entrance to other data-poor entrance. Specifically, we define an
uncertainty-aware Markov Decision Process (MDP) similarity which can estimate
the MDP similarity of different entrances. Based on the MDP similarity, we
design a hybrid transfer method (consisting of instance transfer and strategy
transfer) to efficiently transfer the samples and the knowledge from one
entrance to another. Both offline and online experiments on Meituan food
delivery platform demonstrate that our method can help to learn better agent
for data-poor entrance and increase the revenue for the platform.


title:
Unsupervised Domain Adaptation for Monocular 3D Object Detection via
  Self-Training

abstract:
  Monocular 3D object detection (Mono3D) has achieved unprecedented success
with the advent of deep learning techniques and emerging large-scale autonomous
driving datasets. However, drastic performance degradation remains an
unwell-studied challenge for practical cross-domain deployment as the lack of
labels on the target domain. In this paper, we first comprehensively
investigate the significant underlying factor of the domain gap in Mono3D,
where the critical observation is a depth-shift issue caused by the geometric
misalignment of domains. Then, we propose STMono3D, a new self-teaching
framework for unsupervised domain adaptation on Mono3D. To mitigate the
depth-shift, we introduce the geometry-aligned multi-scale training strategy to
disentangle the camera parameters and guarantee the geometry consistency of
domains. Based on this, we develop a teacher-student paradigm to generate
adaptive pseudo labels on the target domain. Benefiting from the end-to-end
framework that provides richer information of the pseudo labels, we propose the
quality-aware supervision strategy to take instance-level pseudo confidences
into account and improve the effectiveness of the target-domain training
process. Moreover, the positive focusing training strategy and dynamic
threshold are proposed to handle tremendous FN and FP pseudo samples. STMono3D
achieves remarkable performance on all evaluated datasets and even surpasses
fully supervised results on the KITTI 3D object detection dataset. To the best
of our knowledge, this is the first study to explore effective UDA methods for
Mono3D.


title:
Ocean Surface Roughness from Satellite Observations and Spectrum
  Modeling of Wind Waves

abstract:
  Many wind wave spectrum models provide excellent wave height prediction given
the input of wind speed and wave age. Their quantification of the surface
roughness, on the other hand, varies considerably. The ocean surface roughness
is generally represented by the mean square slope, its direct measurement in
open ocean remains a challenging task. Microwave ocean remote sensing from
space delivers ocean surface roughness information. Satellite platforms offer
global coverage in a broad range of environmental conditions. This paper
presents lowpass mean square slope (LPMSS) data obtained by spaceborne
microwave altimeters and reflectometers operating at L, Ku, and Ka bands (about
1.6, 14, and 36 GHz). The LPMSS data represent the spectrally integrated ocean
surface roughness with 11, 95, and 250 rad/m upper cutoff wave numbers, the
maximum wind speeds are 80, 29, and 25 m/s, respectively. A better
understanding of the ocean surface roughness is important to the goal of
improving wind wave spectrum modeling. The analysis presented in this paper
shows that over two orders of magnitude of the wave number range (0.3 to 30
rad/m), the spectral components follow a power function relating the
dimensionless spectrum and the ratio between wind friction velocity and wave
phase speed. The power function exponent is 0.38, which is considerable smaller
than unity as expected from the classical equilibrium spectrum function. It may
suggest that wave breaking is not only an energy sink but also a source of
roughness generation covering a wideband of wavelengths about 20 m and shorter.


title: Deflectometry for specular surfaces: an overview

abstract:
  Deflectometry as a technical approach to assessing reflective surfaces has
now existed for almost 40 years. Different aspects and variations of the method
have been studied in multiple theses and research articles, and reviews are
also becoming available for certain subtopics. Still a field of active
development with many unsolved problems, deflectometry now encompasses a large
variety of application domains, hardware setup types, and processing workflows
designed for different purposes, and spans a range from qualitative defect
inspection of large vehicles to precision measurements of microscopic optics.
Over these years, many exciting developments have accumulated in the underlying
theory, in the systems design, and in the implementation specifics. This
diversity of topics is difficult to grasp for experts and non-experts alike and
may present an obstacle to a wider acceptance of deflectometry as a useful tool
in other research fields and in the industry.
  This paper presents an attempt to summarize the status of deflectometry, and
to map relations between its notable "spin-off" branches. The intention of the
paper is to provide a common communication basis for practitioners and at the
same time to offer a convenient entry point for those interested in learning
and using the method. The list of references is extensive but definitely not
exhaustive, introducing some prominent trends and established research groups
in order to facilitate further self-directed exploration by the reader.


title:
Scaling Cross-Domain Content-Based Image Retrieval for E-commerce Snap
  and Search Application

abstract:
  In this industry talk at ECIR 2022, we illustrate how we approach the main
challenges from large scale cross-domain content-based image retrieval using a
cascade method and a combination of our visual search and classification
capabilities. Specifically, we present a system that is able to handle the
scale of the data for e-commerce usage and the cross-domain nature of the query
and gallery image pools. We showcase the approach applied in real-world
e-commerce snap and search use case and its impact on ranking and latency
performance.


title: Addressing Leakage in Self-Supervised Contextualized Code Retrieval

abstract:
  We address contextualized code retrieval, the search for code snippets
helpful to fill gaps in a partial input program. Our approach facilitates a
large-scale self-supervised contrastive training by splitting source code
randomly into contexts and targets. To combat leakage between the two, we
suggest a novel approach based on mutual identifier masking, dedentation, and
the selection of syntax-aligned targets. Our second contribution is a new
dataset for direct evaluation of contextualized code retrieval, based on a
dataset of manually aligned subpassages of code clones. Our experiments
demonstrate that our approach improves retrieval substantially, and yields new
state-of-the-art results for code clone and defect detection.


title:
Ultrafast racetrack based on compensated Co/Gd-based synthetic
  ferrimagnet with all-optical switching

abstract:
  Spin-orbitronics and single pulse all-optical switching (AOS) of
magnetization are two major successes of the rapidly advancing field of
nanomagnetism in recent years, with high potential for enabling novel, fast and
energy-efficient memory and logic platforms. Fast current-induced domain wall
motion (CIDWM) and single shot AOS have been individually demonstrated in
different ferrimagnetic alloys. However, the stringent requirement for their
composition control makes these alloys challenging materials for wafer-scale
production. Here, we simultaneously demonstrate fast CIDWM and energy efficient
AOS in a synthetic ferrimagnetic system based on multilayered [Co/Gd/Co/Gd]. We
firstly show that AOS is present in its full composition range. We find that
current-driven domain wall velocities over 2000 m/s at room temperature,
achieved by compensating the total angular momentum through layer thickness
tuning. Furthermore, analytical modeling of the CIDWM reveals that Joule
heating needs to be treated transiently to properly describe the CIDWM for our
sub-ns current pulses. Our studies establish [Co/Gd]-based synthetic
ferrimagnets to be a unique materials platform for domain wall devices with
access to ultrafast single pulse AOS.


title: A Simple Structure For Building A Robust Model

abstract:
  As deep learning applications, especially programs of computer vision, are
increasingly deployed in our lives, we have to think more urgently about the
security of these applications.One effective way to improve the security of
deep learning models is to perform adversarial training, which allows the model
to be compatible with samples that are deliberately created for use in
attacking the model.Based on this, we propose a simple architecture to build a
model with a certain degree of robustness, which improves the robustness of the
trained network by adding an adversarial sample detection network for
cooperative training.At the same time, we design a new data sampling strategy
that incorporates multiple existing attacks, allowing the model to adapt to
many different adversarial attacks with a single training.We conducted some
experiments to test the effectiveness of this design based on Cifar10 dataset,
and the results indicate that it has some degree of positive effect on the
robustness of the model.Our code could be found at
https://github.com/dowdyboy/simple_structure_for_robust_model.


title: Holey Schr\"oder Designs of Type $\bf 3^n u^1$

abstract:
  A holey Schr\"oder design of type $h_1^{n_1}h_4^{n_2}\cdots h^{n_k}_k$
(HSD$(h_1^{n_1}h_4^{n_2}\cdots h^{n_k}_k))$ is equivalent to a frame idempotent
Schr\"oder quasigroup (FISQ$(h_1^{n_1}h_4^{n_2}\cdots h^{n_k}_k))$ of order $n$
with $n_i$ missing subquasigroups (holes) of order $h_i, 1 \le i \le k$, which
are disjoint and spanning (i.e., $\sum_{1\le i \le k}n_ih_i = n$). The
existence of HSD$(h^nu^1)$ for $h=1, 2, 4$ has been known. In this paper, we
consider the existence of HSD$(3^nu^1)$ and show that for $0\le u \le 15$, an
HSD$(3^nu^1)$ exists if and only if $n(n + 2u -1) \equiv 0~(mod~4)$, $n\ge 4$
and $n\ge 1+2u/3$. For $0 \le u \le n$, an HSD$(3^nu^1)$ exists if and only if
$n(n + 2u -1) \equiv 0~(mod~4)$ and $n \ge 4$, with possible exceptions of $n =
29, 43$. We have also found six new HSDs of type $(4^nu^1)$.


title:
Mining Root Cause Knowledge from Cloud Service Incident Investigations
  for AIOps

abstract:
  Root Cause Analysis (RCA) of any service-disrupting incident is one of the
most critical as well as complex tasks in IT processes, especially for cloud
industry leaders like Salesforce. Typically RCA investigation leverages
data-sources like application error logs or service call traces. However a rich
goldmine of root cause information is also hidden in the natural language
documentation of the past incidents investigations by domain experts. This is
generally termed as Problem Review Board (PRB) Data which constitute a core
component of IT Incident Management. However, owing to the raw unstructured
nature of PRBs, such root cause knowledge is not directly reusable by manual or
automated pipelines for RCA of new incidents. This motivates us to leverage
this widely-available data-source to build an Incident Causation Analysis (ICA)
engine, using SoTA neural NLP techniques to extract targeted information and
construct a structured Causal Knowledge Graph from PRB documents. ICA forms the
backbone of a simple-yet-effective Retrieval based RCA for new incidents,
through an Information Retrieval system to search and rank past incidents and
detect likely root causes from them, given the incident symptom. In this work,
we present ICA and the downstream Incident Search and Retrieval based RCA
pipeline, built at Salesforce, over 2K documented cloud service incident
investigations collected over a few years. We also establish the effectiveness
of ICA and the downstream tasks through various quantitative benchmarks,
qualitative analysis as well as domain expert's validation and real incident
case studies after deployment.


title:
Decays $B\rightarrow D_{\left(s\right)}^{\left(*\right)}h$
  ($h=\pi,\rho$) in confined covariant quark model

abstract:
  Decay processes $B\rightarrow D_{\left(s\right)}^{\left(*\right)}h$
($h=\pi,\rho$) are studied in the framework of the confined covariant quark
model using the na\"{i}ve factorization assumption. We observe that the
theoretical results on branching fractions have tendency to systematically
exceed the experimental numbers. Such a behavior has already been seen for
similar processes by other authors.


title:
Rational design of carbon-based materials for purification and storage
  of energy carrier gases of methane and hydrogen

abstract:
  Today, fast-growing energy demands and fuel resource depletion are among the
hottest concerning issues that treating our world. So, a huge need is felt to
find efficient, affordable and eco-friendly energy storage and production
systems. Much current research effort proved that gaseous energy carriers such
as CH4 and H2 seem to be the right choice for alternative fuel resources.
However, the most important challenge with this new-faced resource is the
comparatively low volumetric energy storage density. Fortunately, the
high-pressure gas storage technique inside the porous media of solid adsorbent
is considered as one best way to tackle the energy density problem. Famous
family of porous carbon materials, with a suitable pore size distribution
centred in the micropore range and a large number of adsorption sites per
volume of solid, open up a great scope for gas storing applications. This
review article represents the state-of-the-art with a precise focus on what has
and can be done to improve/enhance the gas/energy storage capacity of
traditional and novel structures of low-cost carbon-based adsorbents. We review
a wide variety of design strategies to synthesis carbonaceous adsorbents, with
a strong focus on creating the connection between structural properties and gas
adsorption performance. In this regard, various synthesis techniques have been
studied with emphasis on the more interesting recent progress that allows
better control and optimisation of porosity of porous carbons for maxing gas
storage capacity. We will also show that carbon-based adsorbents, particularly
activated carbons, have been extensively studied and remain a powerful
candidate in the search for an energy carrier economy. In the end, a
perspective is provided to forecast the future development of carbon-based
materials.


title: Non-Hermitian Topology and Exceptional-Point Geometries

abstract:
  Non-Hermitian theory is a theoretical framework that excels at describing
open systems. It offers a powerful tool in the characterization of both the
intrinsic degrees of freedom (DOFs) of a system and the interactions with the
external environment. The non-Hermitian framework consists of mathematical
structures that are fundamentally different from those of Hermitian theories.
These structures not only underpin novel approaches for precisely tailoring
non-Hermitian systems for applications but also give rise to topologies not
found in Hermitian systems. In this paper, we comprehensively review
non-Hermitian topology by establishing its relationship with the behaviors of
complex eigenvalues and biorthogonal eigenvectors. Special attentions are given
to exceptional points - branch-point singularities on the complex eigenvalue
manifolds that exhibit non-trivial topological properties. We also discuss
recent developments in non-Hermitian band topology, such as the non-Hermitian
skin effect and non-Hermitian topological classifications.


title:
Broad Recommender System: An Efficient Nonlinear Collaborative Filtering
  Approach

abstract:
  Recently, Deep Neural Networks (DNNs) have been widely introduced into
Collaborative Filtering (CF) to produce more accurate recommendation results
due to their capability of capturing the complex nonlinear relationships
between items and users.However, the DNNs-based models usually suffer from high
computational complexity, i.e., consuming very long training time and storing
huge amount of trainable parameters. To address these problems, we propose a
new broad recommender system called Broad Collaborative Filtering (BroadCF),
which is an efficient nonlinear collaborative filtering approach. Instead of
DNNs, Broad Learning System (BLS) is used as a mapping function to learn the
complex nonlinear relationships between users and items, which can avoid the
above issues while achieving very satisfactory recommendation performance.
However, it is not feasible to directly feed the original rating data into BLS.
To this end, we propose a user-item rating collaborative vector preprocessing
procedure to generate low-dimensional user-item input data, which is able to
harness quality judgments of the most similar users/items. Extensive
experiments conducted on seven benchmark datasets have confirmed the
effectiveness of the proposed BroadCF algorithm


title:
The Malliavin-Rubel theorem on small entire functions of exponential
  type with given zeros: 60 years later

abstract:
  Let $Z$ and $W$ are distributions of points on the complex plane $\mathbb C$.
The following problem goes back to the studies of F. Carlson, T. Carleman, L.
Schwartz, A. F. Leont'ev, B. Ya. Levin, J.-P. Kahane and others. For which $Z$
and $W$ for an entire function $g\neq 0$ of exponential type vanishing on $W$,
there is an entire function $f\neq 0$ of exponential type vanishing on $Z$ such
that $|f|\leq |g|$ on the imaginary axis? The classical Malliavin-Rubel theorem
of the early 1960s completely solves this problem for "positive" $Z$ and $W$
lying only on the positive semiaxis. A number of generalizations of this
criterion were established by us in the late 1980s for "complex"
$Z\subset\mathbb C$ and $W\subset\mathbb C$ separated by angles from the
imaginary axis, with some advances in the 2020s. In this paper, tougher
problems are solved in a more general subharmonic framework for distributions
of masses on $\mathbb C$. All the previously mentioned results can be obtained
from the main results of the paper in a much stronger form even in the initial
formulation for distributions of points $Z$ and $W$ and entire functions $f$
and $g$ of exponential type. Some of the results of the article are closely
related to the famous Beurling-Malliavin theorems on the radius of completeness
and the multiplier.


title: Thermalization and prethermalization in the soft-wall AdS/QCD model

abstract:
  The real-time dynamics of chiral phase transition is investigated in a
two-flavor ($N_f=2$) soft-wall AdS/QCD model. To understand the dynamics of
thermalization, we quench the system from initial states deviating from the
equilibrium states. Then, we solve the nonequilibrium evolution of the order
parameter (chiral condensate $\langle \sigma\equiv\bar{q}q\rangle$). It is
shown that the system undergoes an exponential relaxation at temperatures away
from the critical temperature $T_c$. The relaxation time diverges at $T_c$,
presenting a typical behavior of critical slowing down. Numerically, we extract
the dynamic exponent $z$, and get $z\approx 2$ by fitting the scaling behavior
$\sigma\simeq t^{-\beta/(\nu z)}$, where the mean-field equilibrium critical
exponents ($\beta=1/2, \nu=1/2$ and $\delta=3$) have been applied. More
interestingly, it is remarked that, for a large class of initial states, the
system would linger over a quasi-steady state for a certain period of time
before the thermalization. It is suggested that the interesting phenomenon,
known as prethermalization, has been observed in the framework of holographic
models. In such prethermal stage, we verify that the system is characterized by
a universal dynamical scaling law and described by the initial-slip exponent
$\theta=0$.


title: Designing Multi-functional Metamaterials

abstract:
  The ability to design passive structures that perform different operations on
different electromagnetic fields is key to many technologies, from
beam-steering to optical computing. While many techniques have been developed
to optimise structure to achieve specific functionality through inverse design,
designing multi-function materials remains challenging. We present a
semi-analytic method, based on the discrete dipole approximation, to design
multi-functional metamaterials. To demonstrate the generality of our method, we
design a device that operates at optical wavelengths and beams light into
different directions depending on the source polarisation and a device that
works at microwave wavelengths and sorts plane waves by their angle of
incidence.


title: Hunting stabilization effects of the high-energy resummation at the LHC

abstract:
  Studying semi-hard processes in the large center-of-mass energy limit gives
us an opportunity to further test perturbative QCD in an unexplored kinematical
configuration, contributing to a better understanding of the dynamics of strong
interactions. For semi-hard reactions in kinematics at large center-of-mass
energy $\sqrt{s}$, the BFKL resummation of energy logarithms comes into play,
since large energy logarithms compensate the smallness of QCD coupling
$\alpha_s$ and must therefore be accounted for to all perturbative orders.
Tracing the path toward performing precision calculations via BFKL resummation
of high-energy logarithms, in this thesis we present phenomenological analyses
for distinct inclusive processes, highlighting the recognized problem of
instabilities under higher-order corrections and energy-scales variations, that
would abort any possibility to investigate semi-hard reactions with
high-precision at natural energy-scales. At the same time, we present new
reactions that seem to act as fair stabilizers of the high-energy series.
First, the inclusive production at the LHC of a charged light hadron and of a
jet, featuring a wide separation in rapidity, is presented making use of
optimization methods to fix energy-scale. We report some predictions, tailored
on the CMS and CASTOR acceptances. Then, we propose as a novel probe channel
for the manifestation of the BFKL dynamics, the inclusive hadroproduction of a
Higgs boson and of a jet, featuring large transverse momenta and well separated
in rapidity. Finlay, we propose the inclusive semi-hard production of two
bottom-flavored hadrons, as well as of a single bottom-flavored hadron
accompanied by a light jet, as novel channels for targeting stabilization
effects of the high-energy resummation under higher-order corrections.


title: Rational points close to non-singular algebraic curves

abstract:
  We study the density of solutions to Diophantine inequalities involving
non-singular ternary forms, or equivalently, the density of rational points
close to non-singular plane algebraic curves.


title: How the Big Bang Ends up Inside a Black Hole

abstract:
  The standard model of cosmology assumes that our Universe began 14 Gyrs
(billion years) ago from a singular Big Bang creation. This can explain a vast
range of different astrophysical data from a handful of free cosmological
parameters. However, we have no direct evidence or fundamental understanding of
some key assumptions: Inflation, Dark Matter and Dark Energy. Here we review
the idea that cosmic expansion originates instead from gravitational collapse
and bounce. The collapse generates a Black Hole (BH) of mass $ M \simeq 5
\times 10^{22} M_{\odot}$ that formed 25~Gyrs ago. As there is no pressure
support, the cold collapse can continue inside in free fall until it reaches
atomic nuclear saturation (GeV), when is halted by Quantum Mechanics, as two
particles cannot occupy the same quantum state. The collapse then bounces like
a core-collapse supernovae, producing the Big Bang expansion. Cosmic
acceleration results from the BH event horizon. During collapse, perturbations
exit the horizon to re-enter during expansion, giving rise to the observed
universe without the need for Inflation or Dark Energy. Using Ockham's razor,
this makes the BH Universe (BHU) model more compelling than the standard
singular Big Bang creation.


title:
Cosmology in holographic non-minimal derivative coupling theory:
  constraints from inflation and variation of gravitational constant

abstract:
  We consider a cosmological model of non-minimal derivative coupling (NMDC) to
gravity with holographic effect from Bekenstein-Hawking entropy using Hubble
horizon IR cutoff. Holographic parameter $c$ is constant in a range, $0 \leq c
< 1$. NMDC effect allows gravitational constant to be time-varying. Definition
of holographic density include time-varying part of the gravitational constant.
NMDC part reduces strength of gravitational constant for $\k > 0$ and opposite
for $\k < 0$. The holographic part enhances gravitational strength. We use
spectral index and tensor-to-scalar ratio to test the model against CMB
constraint. Number of e-folding is chosen to be $N \geq 60$. Potentials, $V =
V_0 \phi^n $ with $n = 2, 4$, and $V = V_0 \exp{(-\beta \phi)}$ are considered.
Combined parametric plots of $\k$ and $\phi$ show that the allowed regions of
the power spectrum index and of the tensor-to-scalar ratio are not overlapping.
NMDC inflation is ruled out and the holographic NMDC inflation is also ruled
out for $0 < c < 1$. NMDC significantly changes major anatomy of the dynamics,
i.e. it gives new late-time attractor trajectories in acceleration regions. The
holographic part clearly affects pattern of trajectories. However, for the
holographic part to affect shape of the acceleration region, the NMDC field
must be in presence. To constrain the model at late time, variation of
gravitational constant is considered. Gravitational-wave standard sirens and
supernovae data give a constraint, $\dot{G}/G|_{t_0} \lesssim 3\times10^{-12}
\, \text{year}^{-1}$ \cite{Zhao:2018gwk} which, for this model, results in $
10^{-12} \, \text{year}^{-1} \, \gtrsim \, {- \kappa}
\dot{\phi}\ddot{\phi}/{M^2_{\p}}\,. $ Positive $\k$ is favored and greater
$c^2$ results in lifting up lower bound of $\k$.


title: The sound of the udu

abstract:
  The udu is a traditional Nigerian percussion instrument that can be thought
of as a Helmholtz resonator with two apertures. We use a simple two-particle
mechanical analogue to study the behaviour of such a resonator and find that
the model describes quite well the main features of udu acoustics.