Abstract:
  This work presents an in-depth analysis of four gravitationally lensed red
galaxies at z = 1.6-3.2. The sources are magnified by factors of 2.7-30 by
foreground clusters, enabling spectral and morphological measurements that are
otherwise challenging. Our sample extends below the characteristic mass of the
stellar mass function and is thus more representative of the quiescent galaxy
population at z > 1 than previous spectroscopic studies. We analyze deep
VLT/X-SHOOTER spectra and multi-band Hubble Space Telescope photometry that
cover the rest-frame UV-to-optical regime. The entire sample resembles stellar
disks as inferred from lensing-reconstructed images. Through stellar population
synthesis analysis we infer that the targets are young (median age = 0.1-1.2
Gyr) and formed 80% of their stellar masses within 0.07-0.47 Gyr. Mg II
$\lambda\lambda 2796,2803$ absorption is detected across the sample.
Blue-shifted absorption and/or redshifted emission of Mg II is found in the two
youngest sources, indicative of a galactic-scale outflow of warm ($T\sim10^{4}$
K) gas. The [O III] $\lambda5007$ luminosity is higher for the two young
sources (median age less than 0.4 Gyr) than the two older ones, perhaps
suggesting a decline in nuclear activity as quenching proceeds. Despite
high-velocity ($v\approx1500$ km s$^{-1}$) galactic-scale outflows seen in the
most recently quenched galaxies, warm gas is still present to some extent long
after quenching. Altogether our results indicate that star formation quenching
at high redshift must have been a rapid process (< 1 Gyr) that does not
synchronize with bulge formation or complete gas removal. Substantial bulge
growth is required if they are to evolve into the metal-rich cores of
present-day slow-rotators.


Abstract:
  ATLAS is the first Portuguese radar system that aims to detect space debris.
The article introduces the system and provides a brief description of its
capabilities. The system is capable of synthesizing arbitrary amplitude
modulated pulse shapes with a resolution of 10 ns. Given that degree of freedom
we decided to test an amplitude modulated chirp signal developed by us and a
nested barker code. These waveforms are explained as well as their advantages
and drawbacks for space debris detection. An experimental setup was developed
to test the system receiver and waveforms are processed by digital matched
filtering. The experiments test the system using different waveform shapes and
noise levels. Experimental results are in agreement with simulation and show
that the chirp signal is more resilient to Doppler shifts, has higher range
resolution and lower peak-to-sidelobe ratio in comparison with the nested
barker code. Future work in order to increase detection capabilities is
discussed at the end.


Abstract:
  Metaphor is widely used in human communication. The cohort of scholars
studying metaphor in various fields is continuously growing, but very few work
has been done in bibliographical analysis of metaphor research. This paper
examines the advancements in metaphor research from 2000 to 2017. Using data
retrieved from Microsoft Academic Graph and Web of Science, this paper makes a
macro analysis of metaphor research, and expounds the underlying patterns of
its development. Taking into consideration sub-fields of metaphor research, the
internal analysis of metaphor research is carried out from a micro perspective
to reveal the evolution of research topics and the inherent relationships among
them. This paper provides novel insights into the current state of the art of
metaphor research as well as future trends in this field, which may spark new
research interests in metaphor from both linguistic and interdisciplinary
perspectives.


Abstract:
  We prove Sklar's theorem in infinite dimensions via a topological argument
and the notion of inverse systems.


Abstract:
  Cross-resolution face recognition (CRFR), which is important in intelligent
surveillance and biometric forensics, refers to the problem of matching a
low-resolution (LR) probe face image against high-resolution (HR) gallery face
images. Existing shallow learning-based and deep learning-based methods focus
on mapping the HR-LR face pairs into a joint feature space where the resolution
discrepancy is mitigated. However, little works consider how to extract and
utilize the intermediate discriminative features from the noisy LR query faces
to further mitigate the resolution discrepancy due to the resolution
limitations. In this study, we desire to fully exploit the multi-level deep
convolutional neural network (CNN) feature set for robust CRFR. In particular,
our contributions are threefold. (i) To learn more robust and discriminative
features, we desire to adaptively fuse the contextual features from different
layers. (ii) To fully exploit these contextual features, we design a feature
set-based representation learning (FSRL) scheme to collaboratively represent
the hierarchical features for more accurate recognition. Moreover, FSRL
utilizes the primitive form of feature maps to keep the latent structural
information, especially in noisy cases. (iii) To further promote the
recognition performance, we desire to fuse the hierarchical recognition outputs
from different stages. Meanwhile, the discriminability from different scales
can also be fully integrated. By exploiting these advantages, the efficiency of
the proposed method can be delivered. Experimental results on several face
datasets have verified the superiority of the presented algorithm to the other
competitive CRFR approaches.


Abstract:
  The memorization effect of deep neural network (DNN) plays a pivotal role in
many state-of-the-art label-noise learning methods. To exploit this property,
the early stopping trick, which stops the optimization at the early stage of
training, is usually adopted. Current methods generally decide the early
stopping point by considering a DNN as a whole. However, a DNN can be
considered as a composition of a series of layers, and we find that the latter
layers in a DNN are much more sensitive to label noise, while their former
counterparts are quite robust. Therefore, selecting a stopping point for the
whole network may make different DNN layers antagonistically affected each
other, thus degrading the final performance. In this paper, we propose to
separate a DNN into different parts and progressively train them to address
this problem. Instead of the early stopping, which trains a whole DNN all at
once, we initially train former DNN layers by optimizing the DNN with a
relatively large number of epochs. During training, we progressively train the
latter DNN layers by using a smaller number of epochs with the preceding layers
fixed to counteract the impact of noisy labels. We term the proposed method as
progressive early stopping (PES). Despite its simplicity, compared with the
early stopping, PES can help to obtain more promising and stable results.
Furthermore, by combining PES with existing approaches on noisy label training,
we achieve state-of-the-art performance on image classification benchmarks.


Abstract:
  The problem of causal inference with panel data is a central econometric
question. The following is a fundamental version of this problem: Let $M^*$ be
a low rank matrix and $E$ be a zero-mean noise matrix. For a `treatment' matrix
$Z$ with entries in $\{0,1\}$ we observe the matrix $O$ with entries $O_{ij} :=
M^*_{ij} + E_{ij} + \mathcal{T}_{ij} Z_{ij}$ where $\mathcal{T}_{ij} $ are
unknown, heterogenous treatment effects. The problem requires we estimate the
average treatment effect $\tau^* := \sum_{ij} \mathcal{T}_{ij} Z_{ij} /
\sum_{ij} Z_{ij}$. The synthetic control paradigm provides an approach to
estimating $\tau^*$ when $Z$ places support on a single row. This paper extends
that framework to allow rate-optimal recovery of $\tau^*$ for general $Z$, thus
broadly expanding its applicability. Our guarantees are the first of their type
in this general setting. Computational experiments on synthetic and real-world
data show a substantial advantage over competing estimators.


Abstract:
  The recognition of personalized content, such as contact names, remains a
challenging problem for end-to-end speech recognition systems. In this work, we
demonstrate how first and second-pass rescoring strategies can be leveraged
together to improve the recognition of such words. Following previous work, we
use a shallow fusion approach to bias towards recognition of personalized
content in the first-pass decoding. We show that such an approach can improve
personalized content recognition by up to 16% with minimum degradation on the
general use case. We describe a fast and scalable algorithm that enables our
biasing models to remain at the word-level, while applying the biasing at the
subword level. This has the advantage of not requiring the biasing models to be
dependent on any subword symbol table. We also describe a novel second-pass
de-biasing approach: used in conjunction with a first-pass shallow fusion that
optimizes on oracle WER, we can achieve an additional 14% improvement on
personalized content recognition, and even improve accuracy for the general use
case by up to 2.5%.


Abstract:
  Designing feasible and effective architectures under diverse computation
budgets incurred by different applications/devices is essential for deploying
deep models in practice. Existing methods often perform an independent
architecture search for each target budget, which is very inefficient yet
unnecessary. Moreover, the repeated independent search manner would inevitably
ignore the common knowledge among different search processes and hamper the
search performance. To address these issues, we seek to train a general
architecture generator that automatically produces effective architectures for
an arbitrary budget merely via model inference. To this end, we propose a
Pareto-Frontier-aware Neural Architecture Generator (NAG) which takes an
arbitrary budget as input and produces the Pareto optimal architecture for the
target budget. We train NAG by learning the Pareto frontier (i.e., the set of
Pareto optimal architectures) over model performance and computational cost
(e.g., latency). Extensive experiments on three platforms (i.e., mobile, CPU,
and GPU) show the superiority of the proposed method over existing NAS methods.


Abstract:
  In oxide heterostructures, different materials are integrated into a single
artificial crystal, resulting in a breaking of inversion-symmetry across the
heterointerfaces. A notable example is the interface between polar and
non-polar materials, where valence discontinuities lead to otherwise
inaccessible charge and spin states. This approach paved the way to the
discovery of numerous unconventional properties absent in the bulk
constituents. However, control of the geometric structure of the electronic
wavefunctions in correlated oxides remains an open challenge. Here, we create
heterostructures consisting of ultrathin SrRuO$_3$, an itinerant ferromagnet
hosting momentum-space sources of Berry curvature, and LaAlO$_3$, a polar
wide-bandgap insulator. Transmission electron microscopy reveals an atomically
sharp LaO/RuO$_2$/SrO interface configuration, leading to excess charge being
pinned near the LaAlO$_3$/SrRuO$_3$ interface. We demonstrate through
magneto-optical characterization, theoretical calculations and transport
measurements that the real-space charge reconstruction modifies the
momentum-space Berry curvature in SrRuO$_3$, driving a reorganization of the
topological charges in the band structure. Our results illustrate how the
topological and magnetic features of oxides can be manipulated by engineering
charge discontinuities at oxide interfaces.


Abstract:
  Score-based generative models and diffusion probabilistic models have been
successful at generating high-quality samples in continuous domains such as
images and audio. However, due to their Langevin-inspired sampling mechanisms,
their application to discrete and sequential data has been limited. In this
work, we present a technique for training diffusion models on sequential data
by parameterizing the discrete domain in the continuous latent space of a
pre-trained variational autoencoder. Our method is non-autoregressive and
learns to generate sequences of latent embeddings through the reverse process
and offers parallel generation with a constant number of iterative refinement
steps. We apply this technique to modeling symbolic music and show strong
unconditional generation and post-hoc conditional infilling results compared to
autoregressive language models operating over the same continuous embeddings.


Abstract:
  Over the last several decades, entangled photon pairs generated by
spontaneous parametric down conversion processes in both second-order and
third-order nonlinear optical materials have been intensively studied for
various quantum features such as Bell inequality violation and anticorrelation.
In an interferometric scheme, anticorrelation results from photon bunching
based on randomness when entangled photon pairs coincidently impinge on a beam
splitter. Compared with post-measurement-based probabilistic confirmation, a
coherence version has been recently proposed using the wave nature of photons.
Here, the origin of quantum features in a coupled interferometric scheme is
investigated using pure coherence optics. In addition, a deterministic method
of entangled photon-pair generation is proposed for on-demand coherence control
of quantum processing.


Abstract:
  Developing novel techniques for depositing transition metal dichalcogenides
is crucial for the industrial adoption of 2D materials in optoelectronics. In
this work, the lateral growth of molybdenum disulfide (MoS2) over an insulating
surface is demonstrated using electrochemical deposition. By fabricating a new
type of microelectrodes, MoS2 2D films grown from TiN electrodes across
opposite sides have been connected over an insulating substrate, hence, forming
a lateral device structure through only one lithography and deposition step.
Using a variety of characterization techniques, the growth rate of MoS2 has
been shown to be highly anisotropic with lateral to vertical growth ratios
exceeding 20-fold. Electronic and photo-response measurements on the device
structures demonstrate that the electrodeposited MoS2 layers behave like
semiconductors, confirming their potential for photodetection applications.
This lateral growth technique paves the way towards room temperature, scalable
and site-selective production of various transition metal dichalcogenides and
their lateral heterostructures for 2D materials-based fabricated devices.


Abstract:
  The sea-ice cover of the Arctic Ocean is an important element of the climate
and ocean system in the Northern Hemisphere as it impacts albedo, atmospheric
pressure regimes, CO2-exchange at the ocean/atmosphere interface as well as the
North Atlantic freshwater budget and thermohaline circulation [1]. Due to
global warming, the Arctic sea-ice cover is presently evolving at an
unprecedent rate towards full melt during the summer season, driving the
so-called "Arctic amplification" [2]. However, the Arctic sea-ice has also
experienced large amplitude variations, from seasonal to orbital (Milankovitch)
time scales, in the past. Recent studies led to suggest that whereas insolation
has been a major driver of Arctic sea-ice variability through time, sea-level
changes governed the development of "sea-ice factories" over shelves (Figure
1), thus fine-tuning the response of the Arctic Ocean to glacial/interglacial
oscillations that is slightly out of phase compared to lower latitudes [3,4].
We discuss below how insolation and sea-level changes may have interacted and
controlled the sea-ice cover of the Arctic Ocean during warm past intervals and
how they could still interfere in the future.


Abstract:
  The increase in the resistivity with decreasing temperature followed by a
drop by more than one order of magnitude is observed on the metallic side near
the zero-magnetic-field metal-insulator transition in a strongly interacting
two-dimensional electron system in ultra-clean SiGe/Si/SiGe quantum wells. We
find that the temperature $T_{\text{max}}$, at which the resistivity exhibits a
maximum, is close to the renormalized Fermi temperature, in agreement with the
dynamical mean-field theory. However, rather than increasing along with the
Fermi temperature, the value $T_{\text{max}}$ decreases appreciably for
spinless electrons in spin-polarizing magnetic fields, which is in
contradiction with the theory in its current form. Remarkably, the
characteristic scaling of the resistivity, predicted by the theory, holds in
both spin-unpolarized and completely spin-polarized systems.


Abstract:
  We initiate the study of the $C^0$ symplectic mapping class group, i.e. the
group of isotopy classes of symplectic homeomorphisms. We prove that none of
the different powers of the square of the Dehn-Seidel twist belong to the same
connected component of the group of symplectic homeomorphisms of certain
Liouville domains. This generalizes to the $C^0$ setting a celebrated result of
Seidel. In other words, we obtain the non-triviality of the $C^0$ symplectic
mapping class group in these domains and in fact an element of infinite order.
For that purpose, we develop a method coming from Floer theory and the theory
of barcodes. This builds on recent developments of $C^0$-symplectic topology.
In particular, we adapt and generalize to our context results by
Buhovsky-Humili\`ere-Seyfaddini and Kislev-Shelukhin.


Abstract:
  In this paper, we present a qualitative study on speleology that aims to
widen the current understanding of people's practices in Nature and identify a
design space for technology that supports such practices. Speleology is a
practice based on the discovery, study, and dissemination of natural cavities.
Speleologists are amateur experts who often collaborate with scientists and
local institutions to understand the geology, hydrology, and biology of a
territory. Their skills are at the same time physical, technical, and
theoretical; this is why speleology is defined as a 'sporting science'. Being
at the boundary between outdoor adventure sports and citizen science,
speleology is an interesting case study for investigating the variety and
complexity of activities carried out in the natural context. We interviewed 15
experienced speleologists to explore their goals, routines, vision of the
outdoors, and attitude towards technology. From our study, it emerged that i)
the excitement of discovery and the unpredictability of an explorative trip are
the strongest motivations for people to engage in speleology; ii) physical
skilfulness is a means for knowledge generation; iii) the practice is
necessarily collective and requires group coordination. From these findings, an
ambivalent attitude towards technology emerged: on the one hand, the scientific
vocation of speleology welcomes technology supporting the development of
knowledge; on the other hand, aspects typical of adventure sports lead to
resistance to technology facilitating the physical performance. We conclude the
article by presenting design considerations for devices supporting speleology,
as well as a few reflections on how communities of speleologists can inspire
citizen science projects.


Abstract:
  We perform a feasibility study to search for axion-like particles (ALPs)
using vector boson fusion (VBF) processes at the LHC. We work in an effective
field theory framework with cutoff scale $\Lambda$ and ALP mass $m_{a}$, and
assume that ALPs couple to photons with strength $\propto 1/\Lambda$. Assuming
proton-proton collisions at $\sqrt{s} = 13$ TeV, we present the total VBF ALP
production cross sections, ALP decay widths and lifetimes, and relevant
kinematic distributions as a function of $m_{a}$ and $\Lambda$. We consider the
$a\to\gamma\gamma$ decay mode to show that the requirement of an energetic
diphoton pair combined with two forward jets with large dijet mass and
pseudorapidity separation can significantly reduce the Standard Model
backgrounds, leading to a $5\sigma$ discovery reach for $10 \text{ MeV}
\lesssim m_{a} \lesssim 1$ TeV with $\Lambda \lesssim 2$ TeV, assuming an
integrated luminosity of 3000 fb$^{-1}$. In particular, this extends the LHC
sensitivity to a previously unstudied region of the ALP parameter space.


Abstract:
  Many chemical concepts can be well defined in the context of quantum chemical
theories. Examples are the electronegativity scale of Mulliken and Jaffe and
the hard and soft acids and bases concept of Pearson. The sound theoretical
basis allows for a systematic definition of such concepts. However, while they
are often used to describe and compare chemical processes in terms of
reactivity, their predictive power remains unclear. In this work, we elaborate
on the predictive potential of chemical reactivity concepts, which can be
crucial for autonomous reaction exploration protocols to guide them by
first-principles heuristics that expoit these concepts.


Abstract:
  Embedding entities and relations of a knowledge graph in a low-dimensional
space has shown impressive performance in predicting missing links between
entities. Although progresses have been achieved, existing methods are
heuristically motivated and theoretical understanding of such embeddings is
comparatively underdeveloped. This paper extends the random walk model (Arora
et al., 2016a) of word embeddings to Knowledge Graph Embeddings (KGEs) to
derive a scoring function that evaluates the strength of a relation R between
two entities h (head) and t (tail). Moreover, we show that marginal loss
minimisation, a popular objective used in much prior work in KGE, follows
naturally from the log-likelihood ratio maximisation under the probabilities
estimated from the KGEs according to our theoretical relationship. We propose a
learning objective motivated by the theoretical analysis to learn KGEs from a
given knowledge graph. Using the derived objective, accurate KGEs are learnt
from FB15K237 and WN18RR benchmark datasets, providing empirical evidence in
support of the theory.


Abstract:
  This paper introduces an approach for formally verifying the safety of the
flight controller of an octorotor platform. Our method involves finding regions
of the octorotor's state space that are considered safe, and which can be
proven to be invariant with respect to the dynamics. Specifically, exponential
barrier functions are used to construct candidate invariant regions near
desired commanded states. The proof that these regions are invariant is
discovered automatically using the dReal SMT solver, which ensures the accurate
command tracking of the octorotor to within a certain margin of error. Rotor
failures in which rotor thrusts become stuck at fixed values are considered and
accounted for via a pseudo-inverse control allocator. The safety of the control
allocator is verified in dReal by checking that the thrusts demanded by the
allocator never exceed the capability of the rotors. We apply our approach on a
specific octorotor example and verify the desired command tracking properties
of the controller under normal conditions and various combinations of rotor
failures.


Abstract:
  Lower extremity amputees face challenges in natural locomotion, which is
partially compensated using powered assistive systems, e.g., micro-processor
controlled prosthetic leg. In this paper, a radar-based perception system is
proposed to assist prosthetic legs for autonomous obstacle traversing, focusing
on multiple-step staircases. The presented perception system is composed of a
radar module operating with a multiple-input-multiple-output (MIMO)
configuration to localize consecutive stair corners. An inertial measurement
unit (IMU) is integrated for coordinates correction due to the angular
dis-positioning that occurs because of the knee angular motion. The captured
information from both sensors is used for staircase dimensioning (depth and
height). A shallow neural network (NN) is proposed to model the error due to
the hardware limitations and enhance the dimension estimation accuracy (1 cm).
The algorithm is implemented on a microcontroller subsystem of the radar kit to
qualify the perception system for embedded integration in powered prosthetic
legs.


Abstract:
  The increasing frequency, impact, consequence and sophistication of
cybersecurity attacks is becoming a strategic concern for boards and executive
management of organisations. Consequently, in addition to focusing on
productivity and performance, organisations are prioritizing Information
Security Management (ISM). However, research has revealed little or no
conceptualisation of a dynamic ISM capability and its link to organisational
performance. In this research, we set out to 1) define and describe an
organisational level dynamic ISM capability, 2) to develop a strategic model
that links resources with this dynamic capability, and then 3) empirically
demonstrate how dynamic ISM capability contributes to firm performance. By
drawing on Resource-Based Theory (RBT) and Dynamic Capabilities View (DCV), we
have developed the Dynamic ISM Capability model to address the identified gap.
As we develop this research, we will empirically test this model to demonstrate
causality between ISM capability and organisational performance.


Abstract:
  Topological lattices have recently generated a great deal of interest based
on the unique mechanical properties rooted in their topological polarization,
including the ability to support localized modes at certain floppy edges. The
study of these systems has been predominantly restricted to the realm of
in-plane mechanics, to which many topological effects are germane. In this
study, we stretch this paradigm by exploring the possibility to export certain
topological attributes to the flexural wave behavior of thin lattice sheets. To
couple the topological modes to the out-of-plane response, we assemble a
bilayer lattice by stacking a thick topological kagome layer onto a thin
twisted kagome lattice. The band diagram reveals the existence of modes whose
out-of-plane character is controlled by the edge modes of the topological
layer, a behavior elucidated via simulations and confirmed via laser vibrometer
experiments on a bilayer prototype specimen. These results open an alternative
direction for topological mechanics whereby flexural waves are controlled by
the in-plane topology, leading to potential applications for flexural wave
devices with engineered polarized response.


Abstract:
  The idea of using a Reconfigurable Intelligent Surface (RIS) consisting of a
large array of passive scattering elements to assist wireless communication
systems has recently attracted much attention from academia and industry. A
central issue with RIS is how much power they can effectively convey to the
target radio nodes. Regarding this question, several power level models exist
in the literature but few have been validated through experiments. In this
paper, we propose a radar cross section-based received power model for an
RIS-aided wireless communication system that is rooted in the physical
properties of RIS. Our proposed model follows the intuition that the received
power is related to the distances from the transmitter/receiver to the RIS, the
angles in the TX-RIS-RX triangle, the effective area of each element, and the
reflection coefficient of each element. To the best of our knowledge, this
paper is the first to model the angle-dependent phase shift of the reflection
coefficient, which is typically ignored in existing literature. We further
measure the received power with our experimental platform in different
scenarios to validate our model. The measurement results show that our model is
appropriate both in near field and far field and can characterize the impact of
angles well.


Abstract:
  Novae and supernovae play a key role in many fields of Astrophysics and
Cosmology. Despite their importance, an accurate description of which objects
explode and why and how they explode is still lacking. One of the main
characteristics of such explosions is that they are the main suppliers of newly
synthesized chemical elements in the Galaxy. Since some of these isotopes are
radioactive, it is possible to use the corresponding gamma-rays as a diagnostic
tool of the explosion thanks to their independence on the thermal state of the
debris. The drawback is the poor sensitivity of detectors in the MeV energy
domain. As a consequence, the radioactive lines have only been detected in one
core collapse supernova (SN 1987A), one Type Ia supernova (SN 2014J), and one
supernova remnant (Cas A). Nevertheless these observations have provided and
are providing important information about the explosion mechanisms.
Unfortunately, novae are still eluding detection. These results emphasize the
necessity to place as soon as possible a new instrument in orbit with enough
sensitivity to noticeably enlarge the sample of detected events


Abstract:
  Simplicial complexes form an important class of topological spaces that are
frequently used to in many applications areas such as computer-aided design,
computer graphics, and simulation. The representation learning on graphs, which
are just 1-d simplicial complexes, has witnessed a great attention and success
in the past few years. Due to the additional complexity higher dimensional
simplicial hold, there has not been enough effort to extend representation
learning to these objects especially when it comes to learn entire-simplicial
complex representation. In this work, we propose a method for simplicial
complex-level representation learning that embeds a simplicial complex to a
universal embedding space in a way that complex-to-complex proximity is
preserved. Our method utilizes a simplex-level embedding induced by a
pre-trained simplicial autoencoder to learn an entire simplicial complex
representation. To the best of our knowledge, this work presents the first
method for learning simplicial complex-level representation.


Abstract:
  Interaction-aware planning for autonomous driving requires an exploration of
a combinatorial solution space when using conventional search- or
optimization-based motion planners. With Deep Reinforcement Learning, optimal
driving strategies for such problems can be derived also for higher-dimensional
problems. However, these methods guarantee optimality of the resulting policy
only in a statistical sense, which impedes their usage in safety critical
systems, such as autonomous vehicles. Thus, we propose the
Experience-Based-Heuristic-Search algorithm, which overcomes the statistical
failure rate of a Deep-reinforcement-learning-based planner and still benefits
computationally from the pre-learned optimal policy. Specifically, we show how
experiences in the form of a Deep Q-Network can be integrated as heuristic into
a heuristic search algorithm. We benchmark our algorithm in the field of path
planning in semi-structured valet parking scenarios. There, we analyze the
accuracy of such estimates and demonstrate the computational advantages and
robustness of our method. Our method may encourage further investigation of the
applicability of reinforcement-learning-based planning in the field of
self-driving vehicles.


Abstract:
  Recent studies show that the inner Galactic regions host genuine bulge
globular clusters, but also halo intruders, complex remnants of primordial
building blocks, and objects likely accreted during major merging events. In
this study we focus on the properties of M 28, a very old and massive cluster
currently located in the Galactic bulge. We analysed wide-field infrared
photometry collected by the VVV survey, VVV proper motions, and
intermediate-resolution spectra in the calcium triplet range for 113 targets in
the cluster area. Our results in general confirm previous estimates of the
cluster properties available in the literature. We find no evidence of
differences in metallicity between cluster stars, setting an upper limit of
Delta[Fe/H]<0.08 dex to any internal inhomogeneity. We confirm that M 28 is one
of the oldest objects in the Galactic bulge (13-14 Gyr). From this result and
the literature data, we find evidence of a weak age-metallicity relation among
bulge globular clusters that suggests formation and chemical enrichment. In
addition, wide-field density maps show that M 28 is tidally stressed and that
it is losing mass into the general bulge field. Our study indicates that M 28
is a genuine bulge globular cluster, but its very old age and its mass loss
suggest that this cluster could be the remnant of a larger structure, possibly
a primeval bulge building block.


Abstract:
  The Laser Interferometer Space Antenna (LISA) mission, scheduled for launch
in the early 2030s, is a gravitational wave observatory in space designed to
detect sources emitting in the milli-Hertz band. In contrast to the present
ground based detectors, the LISA data are expected to be a signaldominated,
with strong and weak gravitational wave signals overlapping in time and in
frequency. Astrophysical population models predict a sufficient number of
signals in the LISA band to blend together and form an irresolvable foreground
noise. In this work, we present a generic method for characterizing the
foreground signals originating from a given astrophysical population of
coalescing compact binaries. Assuming idealized detector conditions and perfect
data analysis technique capable of identifying and removing the bright sources,
we apply an iterative procedure which allows us to predict the different levels
of foreground noise.


Abstract:
  Graphene is a material with enormous potential for numerous applications.
Therefore, significant efforts are dedicated to large-scale graphene production
using a chemical vapor deposition (CVD) technique. In addition, research is
directed at developing methods to incorporate graphene in established
production technologies and process flows. In this paper, we present a brief
review of available CVD methods for graphene synthesis. We also discuss
scalable methods to transfer graphene onto desired substrates. Finally, we
discuss potential applications that would benefit from a fully scaled,
semiconductor technology compatible production process.


Abstract:
  We consider the self-adjoint Schr\"odinger operator in $L^2(\mathbb{R}^d)$,
$d\geq 2$, with a $\delta$-potential supported on a hyperplane
$\Sigma\subseteq\mathbb{R}^d$ of strength $\alpha=\alpha_0+\alpha_1$, where
$\alpha_0\in\mathbb{R}$ is a constant and $\alpha_1\in L^p(\Sigma)$ is a
nonnegative function. As the main result, we prove that the lowest spectral
point of this operator is not smaller than that of the same operator with
potential strength $\alpha_0+\alpha_1^*$, where $\alpha_1^*$ is the symmetric
decreasing rearrangement of $\alpha_1$. The proof relies on the
Birman-Schwinger principle and the reduction to an analogue of the
P\'{o}lya-Szeg\H{o} inequality for the relativistic kinetic energy in
$\mathbb{R}^{d-1}$.


Abstract:
  Manual evaluation is essential to judge progress on automatic text
summarization. However, we conduct a survey on recent summarization system
papers that reveals little agreement on how to perform such evaluation studies.
We conduct two evaluation experiments on two aspects of summaries' linguistic
quality (coherence and repetitiveness) to compare Likert-type and ranking
annotations and show that best choice of evaluation method can vary from one
aspect to another. In our survey, we also find that study parameters such as
the overall number of annotators and distribution of annotators to annotation
items are often not fully reported and that subsequent statistical analysis
ignores grouping factors arising from one annotator judging multiple summaries.
Using our evaluation experiments, we show that the total number of annotators
can have a strong impact on study power and that current statistical analysis
methods can inflate type I error rates up to eight-fold. In addition, we
highlight that for the purpose of system comparison the current practice of
eliciting multiple judgements per summary leads to less powerful and reliable
annotations given a fixed study budget.


Abstract:
  We prove existence results of two solutions of the problem \[ \begin{cases}
L(u)+u^{m-1}=\lambda u^{p-1} & \text{ in $\Omega$}, \\ \quad u>0 &\text{ in
$\Omega$}, \\ \quad u=0 & \text{ on $\partial \Omega$}, \end{cases} \] where
$L(v)=-{\rm div}(M(x)\nabla v)$ is a linear operator, $p\in (2,2^{*}]$ and
$\lambda$ and $ m$ sufficiently large. Then their asymptotical limit as $m\to
+\infty$ is investigated showing different behaviors.


Abstract:
  Human language has been described as a system that makes \textit{use of
finite means to express an unlimited array of thoughts}. Of particular interest
is the aspect of compositionality, whereby, the meaning of a compound language
expression can be deduced from the meaning of its constituent parts. If
artificial agents can develop compositional communication protocols akin to
human language, they can be made to seamlessly generalize to unseen
combinations. Studies have recognized the role of curiosity in enabling
linguistic development in children. In this paper, we seek to use this
intrinsic feedback in inducing a systematic and unambiguous protolanguage. We
demonstrate how compositionality can enable agents to not only interact with
unseen objects but also transfer skills from one task to another in a zero-shot
setting: \textit{Can an agent, trained to `pull' and `push twice', `pull
twice'?}.


Abstract:
  We show that the property of being rationally $K$-stable passes from the
fibers of a continuous $C(X)$-algebra to the ambient algebra, under the
assumption that the underlying space $X$ is compact, metrizable, and of finite
covering dimension. As an application, we show that a crossed product
C*-algebra is (rationally) $K$-stable provided the underlying C*-algebra is
(rationally) $K$-stable, and the action has finite Rokhlin dimension with
commuting towers.


Abstract:
  A physically-based method to derive well-posed instances of the two-fluid
transport equations for two-phase flow, from the Hamilton principle, is
presented. The state of the two-fluid flow is represented by the superficial
velocity and the drift-flux, instead of the average velocities of each fluid.
This generates the conservation equations of the two principal motion modes
naturally: the global center-of-mass flow and the relative velocity between
fluids. Well-posed equations can be obtained by modelling the storage of
kinetic energy in fluctuations structures induced by the interaction between
fluids, like wakes and vortexes. In this way, the equations can be regularized
without losing in the process the instabilities responsible for flow-patterns
formation and transition. A specific case of vertical air-water flow is
analyzed showing the capability of the present model to predict the formation
of the slug flow regime as trains of non-linear waves.


Abstract:
  Modern risk modelling approaches deal with vectors of multiple components.
The components could be, for example, returns of financial instruments or
losses within an insurance portfolio concerning different lines of business.
One of the main problems is to decide if there is any type of dependence
between the components of the vector and, if so, what type of dependence
structure should be used for accurate modelling.
  We study a class of heavy-tailed multivariate random vectors under a
non-parametric shape constraint on the tail decay rate. This class contains,
for instance, elliptical distributions whose tail is in the intermediate
heavy-tailed regime, which includes Weibull and lognormal type tails. The study
derives asymptotic approximations for tail events of random walks.
Consequently, a full large deviations principle is obtained under, essentially,
minimal assumptions. As an application, an optimisation method for a large
class of Quota Share (QS) risk sharing schemes used in insurance and finance is
obtained.


Abstract:
  Convolutional neural networks (CNNs) have been used quite successfully for
semantic segmentation of brain tumors. However, current CNNs and attention
mechanisms are stochastic in nature and neglect the morphological indicators
used by radiologists to manually annotate regions of interest. In this paper,
we introduce a channel and spatial wise asymmetric attention (CASPIAN) by
leveraging the inherent structure of tumors to detect regions of saliency. To
demonstrate the efficacy of our proposed layer, we integrate this into a
well-established convolutional neural network (CNN) architecture to achieve
higher Dice scores, with less GPU resources. Also, we investigate the inclusion
of auxiliary multiscale and multiplanar attention branches to increase the
spatial context crucial in semantic segmentation tasks. The resulting
architecture is the new CASPIANET++, which achieves Dice Scores of 91.19% whole
tumor, 87.6% for tumor core and 81.03% for enhancing tumor. Furthermore, driven
by the scarcity of brain tumor data, we investigate the Noisy Student method
for segmentation tasks. Our new Noisy Student Curriculum Learning paradigm,
which infuses noise incrementally to increase the complexity of the training
images exposed to the network, further boosts the enhancing tumor region to
81.53%. Additional validation performed on the BraTS2020 data shows that the
Noisy Student Curriculum Learning method works well without any additional
training or finetuning.


Abstract:
  We present the first provable Least-Squares Value Iteration (LSVI) algorithms
that have runtime complexity sublinear in the number of actions. We formulate
the value function estimation procedure in value iteration as an approximate
maximum inner product search problem and propose a locality sensitive hashing
(LSH) [Indyk and Motwani STOC'98, Andoni and Razenshteyn STOC'15, Andoni,
Laarhoven, Razenshteyn and Waingarten SODA'17] type data structure to solve
this problem with sublinear time complexity. Moreover, we build the connections
between the theory of approximate maximum inner product search and the regret
analysis of reinforcement learning. We prove that, with our choice of
approximation factor, our Sublinear LSVI algorithms maintain the same regret as
the original LSVI algorithms while reducing the runtime complexity to sublinear
in the number of actions. To the best of our knowledge, this is the first work
that combines LSH with reinforcement learning resulting in provable
improvements. We hope that our novel way of combining data-structures and
iterative algorithm will open the door for further study into cost reduction in
optimization.


Abstract:
  The aim of this paper is to derive a new uncertainty principle for the
generalized $q$-Bessel wavelet transform studied earlier in \cite{Rezguietal}.
In this paper, an uncertainty principle associated with wavelet transforms in
the $q$-calculus framework has been established. A two-parameters extension of
the classical Bessel operator is applied to generate a wavelet function which
is exploited next to explore a wavelet uncertainty principle already in the
$q$-calculus framework.


Abstract:
  We perform high-fidelity, two-dimensional (2D), fluid-structure interaction
(FSI) simulations at a Reynolds number of $Re=200$ of uniform flow past an
inverted flag (i.e., clamped at its trailing edge). The inverted flag system
can exhibit large-amplitude flapping motions (on the order of the flag length)
that can be converted to electricity via, e.g., piezoelectric materials. We
investigate the effect of structural nonuniformity in altering the FSI dynamics
compared with the uniform-stiffness scenario that has been thoroughly
characterized. We consider linear, quadratic, and cubic stiffness
distributions, and demonstrate that the FSI dynamics mirror those of a
uniform-stiffness flag with an appropriately defined effective stiffness. We
show that this effective stiffness can be computed simply via analysis of an
\emph{in-vacuo} Euler-Bernoulli beam. When expressed in terms of the effective
stiffness, the FSI dynamics of the nonuniform-stiffness flag exhibit the same
regimes -- with many similarities in the detailed dynamics -- as a
uniform-stiffness flag. This study opens questions about (i) what the optimal
stiffness distribution is for, e.g., energy harvesting capacity, and (ii) how
to use nonuniform (and possibly time-varying) stiffness distributions to
control the flag dynamics towards a desired state.


Abstract:
  We report microscale friction experiments for diamond/metal and
diamond/silica contacts under gigapascal contact pressures. Using a new
nanoprobe technique which has sufficient dynamic range of force and stiffness,
we demonstrate the processes involved in the transition from purely interface
sliding at the nanoscale to the situation where at least one of the sliding
bodies undergoes some plastic deformation. For sliding of micrometer-scale
diamond spherical tips on metallic substrates, additional local plastic
yielding of the substrate resulting from tangential tractions causes the tip to
sink into the surface, increasing the contact area in the direction of loading
and resulting in a static friction coefficient higher than the kinetic during
ploughing. This sink-in is largely absent in fused silica, and no friction drop
is observed, along with lower friction in general. The transition from sinking
in within the static friction regime to ploughing in the sliding friction
regime is mediated by failure of the contact interface, indicated by a sharp
increase in energy dissipation. At lower contact pressures, the elastic
interfacial sliding behaviour characteristic of scanning probe or surface force
apparatus experiments is recovered, bridging the gap between the exotic realm
of nanotribology and plasticity dominated macroscale friction. We delineate the
material and geometric factors which determine the transition. We also find
that some unexpected light is cast on the origins of the difference between
static and dynamic friction.


Abstract:
  The Standard Model Extension (SME) is a generic parametrization for Lorentz
violation and the phenomenological consequences of the minimal gravity sector
of the SME are usually studied using a post-Newtonian expansion that requires
spacetime to be asymptotically flat. However, there is a term in this sector
for which these approximations are unable to make predictions; this is known as
the $t$ puzzle. The present paper studies a model of spontaneous Lorentz
violation in the minimal gravity sector of the SME in a static and spherically
symmetric situation, when no additional matter fields are present. It is shown
that, under the above mentioned assumptions, $t$ is the only term in the
minimal gravity sector for which no asymptotically flat solutions exist. This
stems from the fact that the $t$ term fixes the asymptotic behavior of all the
pieces of the curvature tensor.


Abstract:
  We present the first characterization of the circumgalactic medium of
Ly$\alpha$ emitters (LAEs), using a sample of 96 $z\approx3.3$ LAEs detected
with the VLT/MUSE in fields centered on 8 bright background quasars. The LAEs
have low Ly$\alpha$ luminosities ($\sim 10^{42}\,\text{erg}\,\text{s}^{-1}$)
and star formation rates (SFRs) $\sim 1~\text{M}_\odot\,\text{yr}^{-1}$, which
for main sequence galaxies corresponds to stellar masses of only $\sim
10^{8.6}\,\text{M}_\odot$. The median transverse distance between the LAEs and
the quasar sightlines is 165 proper kpc (pkpc). We stacked the high-resolution
quasar spectra and measured significant excess HI and CIV absorption near the
LAEs out to 500 $\text{km}\,\text{s}^{-1}$ and at least $\approx 250$ pkpc
(corresponding to $\approx 7$ virial radii). At $\lesssim
30~\text{km}\,\text{s}^{-1}$ from the galaxies the median HI and CIV optical
depths are enhanced by an order of magnitude. The absorption is significantly
stronger around the $\approx 1/3$ of our LAEs that are part of `groups', which
we attribute to the large-scale structures in which they are embedded. We do
not detect any strong dependence of either the HI or CIV absorption on
transverse distance (over the range $\approx 50-250$ pkpc), redshift, or the
properties of the Ly$\alpha$ emission line (luminosity, full width at half
maximum, or equivalent width). However, for HI, but not CIV, the absorption at
$\lesssim 100\,\text{km}\,\text{s}^{-1}$ from the LAE does increase with the
SFR. This suggests that LAEs surrounded by more HI tend to have higher SFRs.


Abstract:
  We consider the $k$-clustering problem with $\ell_p$-norm cost, which
includes $k$-median, $k$-means and $k$-center cost functions, under an
individual notion of fairness proposed by Jung et al. [2020]: given a set of
points $P$ of size $n$, a set of $k$ centers induces a fair clustering if for
every point $v\in P$, $v$ can find a center among its $n/k$ closest neighbors.
Recently, Mahabadi and Vakilian [2020] showed how to get a
$(p^{O(p)},7)$-bicriteria approximation for the problem of fair $k$-clustering
with $\ell_p$-norm cost: every point finds a center within distance at most $7$
times its distance to its $(n/k)$-th closest neighbor and the $\ell_p$-norm
cost of the solution is at most $p^{O(p)}$ times the cost of an optimal fair
solution. In this work, for any $\varepsilon>0$, we present an improved $(16^p
+\varepsilon,3)$-bicriteria approximation for the fair $k$-clustering with
$\ell_p$-norm cost. To achieve our guarantees, we extend the framework of
[Charikar et al., 2002, Swamy, 2016] and devise a $16^p$-approximation
algorithm for the facility location with $\ell_p$-norm cost under matroid
constraint which might be of an independent interest. Besides, our approach
suggests a reduction from our individually fair clustering to a clustering with
a group fairness requirement proposed by Kleindessner et al. [2019], which is
essentially the median matroid problem [Krishnaswamy et al., 2011].


Abstract:
  Autonomous driving vehicles and robotic systems rely on accurate perception
of their surroundings. Scene understanding is one of the crucial components of
perception modules. Among all available sensors, LiDARs are one of the
essential sensing modalities of autonomous driving systems due to their active
sensing nature with high resolution of sensor readings. Accurate and fast
semantic segmentation methods are needed to fully utilize LiDAR sensors for
scene understanding. In this paper, we present Lite-HDSeg, a novel real-time
convolutional neural network for semantic segmentation of full $3$D LiDAR point
clouds. Lite-HDSeg can achieve the best accuracy vs. computational complexity
trade-off in SemanticKitti benchmark and is designed on the basis of a new
encoder-decoder architecture with light-weight harmonic dense convolutions as
its core. Moreover, we introduce ICM, an improved global contextual module to
capture multi-scale contextual features, and MCSPN, a multi-class Spatial
Propagation Network to further refine the semantic boundaries. Our experimental
results show that the proposed method outperforms state-of-the-art semantic
segmentation approaches which can run real-time, thus is suitable for robotic
and autonomous driving applications.


Abstract:
  Automotive engineering is recognized as a combination of software and
mechanical engineering due to the ever-increasing number of software-based
components in vehicles. Since vehicles have become more sophisticated than
before to ensure robustness, testing of automotive electronics is performed in
high volume, producing immense test-related data.
  This study investigates how unstructured and decentralized test-related data
from testing of automotive electronics creates issues in decision making during
the testing and analysis process of test artifacts by performing an exploratory
case-study at one of the leading automotive companies, Volvo Cars. From the
findings of the exploratory study, a prototype was designed to improve the data
and information structure and presentation for test analysis and diagnostics
for automotive electronics. The prototype's results showed that providing
better data and information structure significantly increases the efficiency
and reduces the workload for testers when conducting test analysis and
diagnostics. Testers showed a decrease in task load for tasks related to
testing due to better information structure, presentation, correctness and
accessibility. Hence, the improvements aided the testers to arrive at decisions
regarding root cause analysis of failed tests efficiently. The findings of this
study can assist automotive companies in systematically investigating and
improving the testing process of automotive electronics in regards to managing
and structuring test-related data.
  Keywords: Testing, Automotive Electronics, Electronic Control Unit, ECU,
Unstructured Data.


Abstract:
  We study Birkhoff sums as distributions. We obtain regularity results on such
distributions for various dynamical systems with hyperbolicity, as hyperbolic
linear maps on the torus and piecewise expanding maps on the interval. We also
give some applications, as the study of advection in discrete dynamical
systems.


Abstract:
  Although several impurity solvers in the dynamical mean field theory (DMFT)
have been proposed, especially in multi-band systems, there are practical
difficulties arising from a trade-off between numerical costs and reliability.
In this study, we re-interpret the iterative perturbation theory (IPT) as an
approximation which captures the strong correlation effects by mimicking the
particular frequency structures of the exact full vertex, and extend it such
that it can have efficiency and reliability simultaneously by modifying IPT
vertex using the parquet equations. We apply this method to several models to
evaluate their validity. We confirm that our method shows good agreements with
the numerically exact continuous-time quantum Monte Carlo method in the
single-site DMFT calculation.


Abstract:
  With the large-scale deployment of industrial internet of things (IIoT)
devices, the number of vulnerabilities that threaten IIoT security is also
growing dramatically, including a mass of undisclosed IIoT vulnerabilities that
lack mitigation measures. Coordination Vulnerabilities Disclosure (CVD) is one
of the most popular vulnerabilities sharing solutions, in which some security
workers (SWs) can develop undisclosed vulnerabilities patches together.
However, CVD assumes that sharing participants (SWs) are all honest, and thus
offering chances for dishonest SWs to leak undisclosed IIoT vulnerabilities. To
combat such threats, we propose an Undisclosed IIoT Vulnerabilities Trusted
Sharing Protection (UIV-TSP) scheme with dynamic token. In this article, a
dynamic token is an implicit access credential for an SW to acquire an
undisclosed vulnerability information, which is only held by the system and
constantly updated as the SW access. Meanwhile, the latest updated token can be
stealthily sneaked into the acquired information as the traceability token.
Once the undisclosed vulnerability information leaves the SW host, the embedded
self-destruct program will be automatically triggered to prevent leaks since
the destination MAC address in the traceability token has changed. To quickly
distinguish dishonest SWs, trust mechanism is adopted to evaluate the trust
value of SWs. Moreover, we design a blockchain-assisted continuous logs storage
method to achieve the tamper-proofing of dynamic token and the transparency of
undisclosed IIoT vulnerabilities sharing. The simulation results indicate that
our proposed scheme is resilient to suppress dishonest SWs and protect the IoT
undisclosed vulnerabilities effectively.


Abstract:
  In this work, we introduce X-FACT: the largest publicly available
multilingual dataset for factual verification of naturally existing real-world
claims. The dataset contains short statements in 25 languages and is labeled
for veracity by expert fact-checkers. The dataset includes a multilingual
evaluation benchmark that measures both out-of-domain generalization, and
zero-shot capabilities of the multilingual models. Using state-of-the-art
multilingual transformer-based models, we develop several automated
fact-checking models that, along with textual claims, make use of additional
metadata and evidence from news stories retrieved using a search engine.
Empirically, our best model attains an F-score of around 40%, suggesting that
our dataset is a challenging benchmark for evaluation of multilingual
fact-checking models.


Abstract:
  We describe computer searches that prove the graph reconstruction conjecture
for graphs with up to 13 vertices and some limited classes on larger sizes. We
also investigate the reconstructibility of tournaments up to 13 vertices,
digraphs up to 9 vertices, and posets up to 13 points. In all cases, our
results also apply to the set reconstruction problem that uses the
isomorph-reduced deck.


Abstract:
  In atomic physics, the Hund rule says that the largest spin and orbital state
is realized due to the interplay of the spin-orbit coupling (SOC) and the
Coulomb interactions. Here, we show that in ferromagnetic solids the effective
SOC and the orbital magnetic moment can be dramatically enhanced by a factor of
$1/[1-(2U^\prime-U-J_H)\rho_0]$, where $U$ and $U^\prime$ are the on-site
Coulomb interaction within the same oribtals and between different orbitals,
respectively, $J_H$ is the Hund coupling, and $\rho_0$ is the average density
of states. This factor is obtained by using the two-orbital as well as
five-orbital Hubbard models with SOC. We also find that the spin polarization
is more favorable than the orbital polarization, being consistent with
experimental observations. This present work provides a fundamental basis for
understanding the enhancements of SOC and orbital moment by Coulomb
interactions in ferromagnets, which would have wide applications in
spintronics.


Abstract:
  The possibility of formation of Bose-Einstein Condensation (BEC) is studied
in $pp$ collisions at $\sqrt s$ = 7 TeV at the Large Hadron Collider. A
thermodynamically consistent non-extensive formulation of the identified hadron
transverse momentum distributions is used to estimate the critical temperature
required to form BEC of charged pions, which are the most abundant species in a
multi-particle production process in hadronic and nuclear collisions. The
obtained results have been contrasted with the systems produced in Pb-Pb
collisions to have a better understanding. We observe an explicit dependency of
BEC critical temperature and number of particles in the pion condensates on the
non-extensive parameter $q$, which is a measure of degree of non-equilibrium --
as $q$ decreases, the critical temperature increases and approaches to the
critical temperature obtained from Bose-Einstein statistics without
non-extensivity. Studies are performed on the final state multiplicity
dependence of number of particles in the pion condensates in a wide range of
multiplicity covering hadronic and heavy-ion collisions, using the inputs from
experimental transverse momentum spectra.


Abstract:
  One of the last unexplored windows to the cosmos, the Dark Ages and Cosmic
Dawn, can be opened using a simple low frequency radio telescope from the
stable, quiet lunar farside to measure the Global 21-cm spectrum. This frontier
remains an enormous gap in our knowledge of the Universe. Standard models of
physics and cosmology are untested during this critical epoch. The messenger of
information about this period is the 1420 MHz (21-cm) radiation from the
hyperfine transition of neutral hydrogen, Doppler-shifted to low radio
astronomy frequencies by the expansion of the Universe. The Global 21-cm
spectrum uniquely probes the cosmological model during the Dark Ages plus the
evolving astrophysics during Cosmic Dawn, yielding constraints on the first
stars, on accreting black holes, and on exotic physics such as dark
matter-baryon interactions. A single low frequency radio telescope can measure
the Global spectrum between ~10-110 MHz because of the ubiquity of neutral
hydrogen. Precise characterizations of the telescope and its surroundings are
required to detect this weak, isotropic emission of hydrogen amidst the bright
foreground Galactic radiation. We describe how two antennas will permit
observations over the full frequency band: a pair of orthogonal wire antennas
and a 0.3-m$^3$ patch antenna. A four-channel correlation spectropolarimeter
forms the core of the detector electronics. Technology challenges include
advanced calibration techniques to disentangle covariances between a bright
foreground and a weak 21-cm signal, using techniques similar to those for the
CMB, thermal management for temperature swings of >250C, and efficient power to
allow operations through a two-week lunar night. This simple telescope sets the
stage for a lunar farside interferometric array to measure the Dark Ages power
spectrum.


Abstract:
  Pre-trained language models (PrLM) has been shown powerful in enhancing a
broad range of downstream tasks including various dialogue related ones.
However, PrLMs are usually trained on general plain text with common language
model (LM) training objectives, which cannot sufficiently capture dialogue
exclusive features due to the limitation of such training setting, so that
there is an immediate need to fill the gap between a specific dialogue task and
the LM task. As it is unlikely to collect huge dialogue data for
dialogue-oriented pre-training, in this paper, we propose three strategies to
simulate the conversation features on general plain text. Our proposed method
differs from existing post-training methods that it may yield a general-purpose
PrLM and does not individualize to any detailed task while keeping the
capability of learning dialogue related features including speaker awareness,
continuity and consistency. The resulted Dialog-PrLM is fine-tuned on three
public multi-turn dialogue datasets and helps achieve significant and
consistent improvement over the plain PrLMs.


Abstract:
  A prevalent theory circulating among the non-scientific community is that the
intensive deployment of base stations over the territory significantly
increases the level of electromagnetic field (EMF) exposure and affects
population health. To alleviate this concern, in this work, we propose a
network architecture that introduces tethered unmanned aerial vehicles (TUAVs)
carrying green antennas to minimize the EMF exposure while guaranteeing a high
data rate for users. In particular, each TUAV can attach itself to one of the
possible ground stations at the top of some buildings. The location of the
TUAVs, transmit power of user equipment and association policy are optimized to
minimize the EMF exposure. Unfortunately, the problem turns out to be
mixed-integer non-linear programming (MINLP), which is non-deterministic
polynomial-time (NP) hard. We propose an efficient low-complexity algorithm
composed of three submodules. Firstly, we propose an algorithm based on the
greedy principle to determine the optimal association matrix between the users
and base stations. Then, we offer two approaches, a modified K-mean and shrink
and realign (SR) process, to associate each TUAV with a ground station.
Finally, we put forward two algorithms based on the golden search and SR
process to adjust the TUAV's position within the hovering area over the
building. After that, we consider the dual problem that maximizes the sum rate
while keeping the exposure below a predefined value, such as the level enforced
by the regulation. Next, we perform extensive simulations to show the
effectiveness of the proposed TUAVs to reduce the exposure compared to various
architectures. Eventually, we show that TUAVs with green antennas can
effectively mitigate the EMF exposure by more than 20% compared to fixed green
small cells while achieving a higher data rate.


Abstract:
  Astronomy has always been propelled by the discovery of new phenomena lacking
precedent, often followed by new theories to explain their existence and
properties. In the modern era of large surveys tiling the sky at ever high
precision and sampling rates, these serendipitous discoveries look set to
continue, with recent examples including Boyajian's Star, Fast Radio Bursts and
`Oumuamua. Accordingly, we here look ahead and aim to provide a statistical
framework for interpreting such events and providing guidance to future
observations, under the basic premise that the phenomenon in question
stochastically repeat at some unknown, constant rate, $\lambda$. Specifically,
expressions are derived for 1) the a-posteriori distribution for $\lambda$, 2)
the a-posteriori distribution for the recurrence time, and, 3) the
benefit-to-cost ratio of further observations relative to that of the inaugural
event. Some rule-of-thumb results for each of these are found to be 1) $\lambda
< \{0.7, 2.3, 4.6\}\,t_1^{-1}$ to $\{50, 90, 95\}\%$ confidence (where $t_1=$
time to obtain the first detection), 2) the recurrence time is $t_2 < \{1, 9,
99\}\,t_1$ to $\{50, 90, 95\}\%$ confidence, with a lack of repetition by time
$t_2$ yielding a $p$-value of $1/[1+(t_2/t_1)]$, and, 3) follow-up for
$\lesssim 10\,t_1$ is expected to be scientifically worthwhile under an array
of differing assumptions about the object's intrinsic scientific value. We
apply these methods to the Breakthrough Listen Candidate 1 signal and tidal
disruption events observed by TESS.


Abstract:
  More than 50 years after the discovery of pulsars and confirmation of their
association with supernova explosions, the origin of the initial spin and
velocity of pulsars remains largely a mystery. The typical space velocities of
several hundred km/s have been attributed to "kicks" resulting from asymmetries
either in the supernova ejecta or in the neutrino emission. Observations have
shown a strong tendency for alignment of the pulsar space velocity and spin
axis in young pulsars but, up to now, these comparisons have been restricted to
two dimensions. We report here the first evidence for three-dimensional
alignment between the spin and velocity vectors, largely based on observations
made with the Five-hundred-meter Aperture Spherical radio Telescope of the
pulsar PSR~J0538+2817 and its associated supernova remnant S147. Analysis of
these and related observations has enabled us to determine the location of the
pulsar within the supernova remnant and hence its radial velocity. Current
simulations of supernova explosions have difficulty producing such
three-dimensional alignment. Our results, which depend on the unprecedented
sensitivity of the new observations, add another dimension to the intriguing
correlation between pulsar spin-axis and birth-kick directions, thus deepening
the mysteries surrounding the birth of neutron stars.


Abstract:
  By specializing the parameters in the partition function of the 8VSOS model
with domain wall boundary conditions and diagonal reflecting end, we find
connections between the three-color model and certain polynomials $p_n(z)$ of
Bazhanov and Mangazeev appearing in the eigenvectors of the Hamiltonian of the
XYZ spin chain. This work is a continuation of a previous paper where we
investigated the related polynomials $q_n(z)$ of Bazhanov and Mangazeev, also
appearing in the eigenvectors of the XYZ spin chain.


Abstract:
  #StopAsianHate and #StopAAPIHate are two of the most commonly used hashtags
that represent the current movement to end hate crimes against the Asian
American and Pacific Islander community. We conduct a social media study of
public opinion on the #StopAsianHate and #StopAAPIHate movement based on 46,058
Twitter users across 30 states in the United States ranging from March 18 to
April 11, 2021. The movement attracts more participation from women, younger
adults, Asian and Black communities. 51.56% of the Twitter users show direct
support, 18.38% are news about anti-Asian hate crimes, while 5.43% show a
negative attitude towards the movement. Public opinion varies across user
characteristics. Furthermore, among the states with most racial bias motivated
hate crimes, the negative attitude towards the #StopAsianHate and #StopAAPIHate
movement is the weakest. To our best knowledge, this is the first large-scale
social media-based study to understand public opinion on the #StopAsianHate and
#StopAAPIHate movement. We hope our study can provide insights and promote
research on anti-Asian hate crimes, and ultimately help address such a serious
societal issue for the common benefits of all communities.


Abstract:
  We gave a classification of P and Q with a finite number of K-orbits of a
double flag variety G/P*K/Q for a symmetric pair (G, K) when G=GL_{m+n} and
K=GL_{m}*GL_{n}, and a description of K-orbits when the number of K-orbits of
G/P*K/Q is finite. We solved the problem by providing a correspondence between
the K-orbits and the quiver representations.


Abstract:
  Abnormal event detection is a challenging task that requires effectively
handling intricate features of appearance and motion. In this paper, we present
an approach of detecting anomalies in videos by learning a novel LSTM based
self-contained network on normal dense optical flow. Due to their sigmoid
implementations, standard LSTM's forget gate is susceptible to overlooking and
dismissing relevant content in long sequence tasks like abnormality detection.
The forget gate mitigates participation of previous hidden state for
computation of cell state prioritizing current input. In addition, the
hyperbolic tangent activation of standard LSTMs sacrifices performance when a
network gets deeper. To tackle these two limitations, we introduce a bi-gated,
light LSTM cell by discarding the forget gate and introducing sigmoid
activation. Specifically, the LSTM architecture we come up with fully sustains
content from previous hidden state thereby enabling the trained model to be
robust and make context-independent decision during evaluation. Removing the
forget gate results in a simplified and undemanding LSTM cell with improved
performance effectiveness and computational efficiency. Empirical evaluations
show that the proposed bi-gated LSTM based network outperforms various LSTM
based models verifying its effectiveness for abnormality detection and
generalization tasks on CUHK Avenue and UCSD datasets.


Abstract:
  This papers presents a novel quantised transform (the Sinclair-Town or ST
transform for short) that subsumes the rolls of both edge-detector, MSER style
region detector and corner detector. The transform is similar to the $unsharp$
transform but the difference from the local mean is quantised to 3 values
(dark-neutral-light). The transform naturally leads to the definition of an
appropriate local scale. A range of methods for extracting shape features form
the transformed image are presented. The generalized feature provides a robust
basis for establishing correspondence between images. The transform readily
admits more complicated kernel behaviour including multi-scale and asymmetric
elements to prefer shorter scale or oriented local features.


Abstract:
  Technologies play an important role in the hiring process for software
professionals. Within this process, several studies revealed misconceptions and
bad practices which lead to suboptimal recruitment experiences. In the same
context, grey literature anecdotally coined the term R\'esum\'e-Driven
Development (RDD), a phenomenon describing the overemphasis of trending
technologies in both job offerings and resumes as an interaction between
employers and applicants. While RDD has been sporadically mentioned in books
and online discussions, there are so far no scientific studies on the topic,
despite its potential negative consequences. We therefore empirically
investigated this phenomenon by surveying 591 software professionals in both
hiring (130) and technical (558) roles and identified RDD facets in substantial
parts of our sample: 60% of our hiring professionals agreed that trends
influence their job offerings, while 82% of our software professionals believed
that using trending technologies in their daily work makes them more attractive
for prospective employers. Grounded in the survey results, we conceptualize a
theory to frame and explain R\'esum\'e-Driven Development. Finally, we discuss
influencing factors and consequences and propose a definition of the term. Our
contribution provides a foundation for future research and raises awareness for
a potentially systemic trend that may broadly affect the software industry.


Abstract:
  Deploying distributed renewable energy at the demand side is an important
measure to implement a sustainable society. However, the massive small solar
and wind generation units are beyond the control of a central operator. To
encourage users to participate in energy management and reduce the dependence
on dispatchable resources, a peer-to-peer energy sharing scheme is proposed
which releases the flexibility at the demand side. Every user makes decision
individually considering only local constraints; the microgrid operator
announces the sharing prices subjective to the coupling constraints without
knowing users' local constraints. This can help protect privacy. We prove that
the proposed mechanism can achieve the same disutility and flexibility as
centralized dispatch, and develop an effective modified best response based
algorithm for reaching the market equilibrium. The concept of absorbable region
is presented to measure the operating flexibility under the proposed energy
sharing mechanism. A linear programming based polyhedral projection algorithm
is developed to compute that region. Case studies validate the theoretical
results and show that the proposed method is scalable.


Abstract:
  Interstellar dust grains are non-spherical and, in some environments,
partially aligned along the direction of the interstellar magnetic field.
Numerous alignment theories have been proposed, all of which examine the grain
rotational dynamics. In 1999, Lazarian & Draine introduced the important
concept of thermal flipping, in which internal relaxation processes induce the
grain body to flip while its angular momentum remains fixed. Through detailed
numerical simulations, we study the role of thermal flipping on the grain
dynamics during periods of relatively slow rotation, known as `crossovers', for
the special case of a spheroidal grain with a non-uniform mass distribution.
Lazarian & Draine proposed that rapid flipping during a crossover would lead to
`thermal trapping', in which a systematic torque, fixed relative to the grain
body, would time average to zero, delaying spin-up to larger rotational speeds.
We find that the time-averaged systematic torque is not zero during the
crossover and that thermal trapping is not prevalent. As an application, we
examine whether the classic Davis-Greenstein alignment mechanism is viable, for
grains residing in the cold neutral medium and lacking superparamagnetic
inclusions. We find that Davis-Greenstein alignment is not hindered by thermal
trapping, but argue that it is, nevertheless, too inefficient to yield the
alignment of large grains responsible for optical and infrared starlight
polarization. Davis-Greenstein alignment of small grains could potentially
contribute to the observed ultraviolet polarization. The theoretical and
computational tools developed here can also be applied to analyses of alignment
via radiative torques and rotational disruption of grains.


Abstract:
  Biopharmaceutical manufacturing is a rapidly growing industry with impact in
virtually all branches of medicine. Biomanufacturing processes require close
monitoring and control, in the presence of complex bioprocess dynamics with
many interdependent factors, as well as extremely limited data due to the high
cost and long duration of experiments. We develop a novel model-based
reinforcement learning framework that can achieve human-level control in
low-data environments. The model uses a probabilistic knowledge graph to
capture causal interdependencies between factors in the underlying stochastic
decision process, leveraging information from existing kinetic models from
different unit operations while incorporating real-world experimental data. We
then present a computationally efficient, provably convergent stochastic
gradient method for policy optimization. Validation is conducted on a realistic
application with a multi-dimensional, continuous state variable.


Abstract:
  A key challenge in scaling Gaussian Process (GP) regression to massive
datasets is that exact inference requires computation with a dense n x n kernel
matrix, where n is the number of data points. Significant work focuses on
approximating the kernel matrix via interpolation using a smaller set of m
inducing points. Structured kernel interpolation (SKI) is among the most
scalable methods: by placing inducing points on a dense grid and using
structured matrix algebra, SKI achieves per-iteration time of O(n + m log m)
for approximate inference. This linear scaling in n enables inference for very
large data sets; however the cost is per-iteration, which remains a limitation
for extremely large n. We show that the SKI per-iteration time can be reduced
to O(m log m) after a single O(n) time precomputation step by reframing SKI as
solving a natural Bayesian linear regression problem with a fixed set of m
compact basis functions. With per-iteration complexity independent of the
dataset size n for a fixed grid, our method scales to truly massive data sets.
We demonstrate speedups in practice for a wide range of m and n and apply the
method to GP inference on a three-dimensional weather radar dataset with over
100 million points.


Abstract:
  Alkaline-earth-like transition-metal atoms such as Zn and Cd are promising
candidates for precision measurements and quantum many-body physics
experiments. Here, we theoretically investigate the properties of diatomic
molecules containing these closed-shell atoms. We calculate potential energy
curves, permanent electric dipole moments, and spectroscopic constants for
molecules consisting of either a Zn or Cd atom interacting with an alkali-metal
(Li, Na, K, Rb, Cs, Fr) or alkaline-earth-metal (Be, Mg, Ca, Sr, Ba, Ra) atom.
We use the ab initio electronic structure coupled cluster method with single,
double, and triple excitations combined with large Gaussian basis sets and
small-core relativistic energy-consistent pseudopotentials for heavier atoms.
We predict that the studied molecules in the ground electronic state are
chemically reactive weakly bound van der Waals complexes with small permanent
electric dipole moments. The present results may be useful for spectroscopy and
application of the studied molecules in modern ultracold physics and chemistry
experiments.


Abstract:
  The long-tailed distribution datasets poses great challenges for deep
learning based classification models on how to handle the class imbalance
problem. Existing solutions usually involve class-balacing strategies or
transfer learing from head- to tail-classes or use two-stages learning strategy
to re-train the classifier. However, the existing methods are difficult to
solve the low quality problem when images are obtained by SAR. To address this
problem, we establish a novel three-stages training strategy, which has
excellent results for processing SAR image datasets with long-tailed
distribution. Specifically, we divide training procedure into three stages. The
first stage is to use all kinds of images for rough-training, so as to get the
rough-training model with rich content. The second stage is to make the rough
model learn the feature expression by using the residual dataset with the class
0 removed. The third stage is to fine tune the model using class-balanced
datasets with all 10 classes (including the overall model fine tuning and
classifier re-optimization). Through this new training strategy, we only use
the information of SAR image dataset and the network model with very small
parameters to achieve the top 1 accuracy of 22.34 in development phase.


Abstract:
  Video-based human pose estimation (HPE) is a vital yet challenging task.
While deep learning methods have made significant progress for the HPE, most
approaches to this task detect each joint independently, damaging the pose
structural information. In this paper, unlike the prior methods, we propose a
Relation-based Pose Semantics Transfer Network (RPSTN) to locate joints
associatively. Specifically, we design a lightweight joint relation extractor
(JRE) to model the pose structural features and associatively generate heatmaps
for joints by modeling the relation between any two joints heuristically
instead of building each joint heatmap independently. Actually, the proposed
JRE module models the spatial configuration of human poses through the
relationship between any two joints. Moreover, considering the temporal
semantic continuity of videos, the pose semantic information in the current
frame is beneficial for guiding the location of joints in the next frame.
Therefore, we use the idea of knowledge reuse to propagate the pose semantic
information between consecutive frames. In this way, the proposed RPSTN
captures temporal dynamics of poses. On the one hand, the JRE module can infer
invisible joints according to the relationship between the invisible joints and
other visible joints in space. On the other hand, in the time, the propose
model can transfer the pose semantic features from the non-occluded frame to
the occluded frame to locate occluded joints. Therefore, our method is robust
to the occlusion and achieves state-of-the-art results on the two challenging
datasets, which demonstrates its effectiveness for video-based human pose
estimation. We will release the code and models publicly.


Abstract:
  A subset $A$ of the integers is a $B_k[g]$ set if the number of multisets
from $A$ that sum to any fixed integer is at most $g$. Let $F_{k,g}(n)$ denote
the maximum size of a $B_k[g]$ set in $\{1,\dots, n\}$. In this paper we
improve the best-known upper bounds on $F_{k,g}(n)$ for $g>1$ and $k$ large.
When $g=1$ we match the best upper bound of Green with an improved error term.
Additionally, we give a lower bound on $F_{k,g}(n)$ that matches a construction
of Lindstr\"om while removing one of the hypotheses.


Abstract:
  A theory of local convexity for a second order differential equation (SODE)
on a Lie algebroid is developed. The particular case when the SODE is
homogeneous quadratic is extensively discussed.


Abstract:
  Tidal perturbations play an important role in the study of the dynamics in
the classical two-body system. Understanding tidal effects in strong-field
regions may allow one to use gravitational-wave or electromagnetic observations
to locate or constraint the location of possible companions. Here, we
investigate how timelike and null geodesics of a Schwarzschild black hole are
affected in the presence of a companion. There is a panoply of new effects. In
some limiting cases, we find analytical solutions for closed null or timelike
geodesics. Our results show that light ring period as measured by a far-away
observer can be eiter shorter or longer, depending on the location of the
companion. We also show that there are closed lightlike trajectories which are
elliptic (for equatorial companions), and that timelike particles are affected
in a similar manner. Finally, we attempt at estimating the ringdown from
tidally perturbed geometries. Our results indicate that there are two stages in
the relaxation of such geometries, one associated with a prompt decay of waves
around the deformed photonsphere, and a later relaxation of the global
geometry. These results are consistent with previous, full numerical studies.


Abstract:
  Solar cycle 24 is one of the weakest solar cycles recorded, but surprisingly
the declining phase of it had a slow CME which evolved without any low coronal
signature and is classified as a stealth CME which was responsible for an
intense geomagnetic storm at Earth (Dst = -176 nT). The impact of this space
weather event on the terrestrial ionosphere has been reported. However,the
propagation of this CME beyond 1 au and the impact of this CME on other
planetary environments have not been studied so far. In this paper, we analyse
the data from Sun-Earth L1 point as well as from the Martian orbit (near 1.5
au) to understand the characteristics of the stealth CME as observed beyond 1
au. The observations near Earth are using data from the Solar Dynamics
Observatory (SDO) and the Advanced Composition Explorer (ACE) satellite located
at L1 point whereas those near Mars are from the instruments for plasma and
magnetic field measurements on board Mars Atmosphere and Volatile EvolutioN
(MAVEN) mission. The observations show that the stealth CME has reached 1.5 au
after 7 days of its initial observations at the Sun and caused depletion in the
nightside topside ionosphere of Mars, as observed during the inbound phase
measurements of the Langmuir Probe and Waves (LPW) instrument on board MAVEN.
These observations have implications on the ion escape rates from the Martian
upper atmosphere.


Abstract:
  This work presents a system identification procedure based on Convolutional
Neural Networks (CNN) for human posture control using the DEC (Disturbance
Estimation and Compensation) parametric model. The modular structure of the
proposed control model inspired the design of a modular identification
procedure, in the sense that the same neural network is used to identify the
parameters of the modules controlling different degrees of freedom. In this way
the presented examples of body sway induced by external stimuli provide several
training samples at once


Abstract:
  Aerosolized droplets play a central role in the transmission of various
infectious diseases, including Legionnaire's disease, gastroenteritis-causing
norovirus, and most recently COVID-19. Respiratory droplets are known to be the
most prominent source of transmission for COVID-19, however, alternative routes
may exist given the discovery of small numbers of viable viruses in urine and
stool samples. Flushing biomatter can lead to the aerosolization of
microorganisms, thus, there is a likelihood that bioaerosols generated in
public restrooms may pose a concern for the transmission of COVID-19,
especially since these areas are relatively confined, experience heavy foot
traffic, and may suffer from inadequate ventilation. To quantify the extent of
aerosolization, we measure the size and number of droplets generated by
flushing toilets and urinals in a public restroom. The results indicate that
the particular designs tested in the study generate a large number of droplets
in the size range 0.3$\mu m$ to 3$\mu m$, which can reach heights of at least
1.52m. Covering the toilet reduced aerosol levels but did not eliminate them
completely, suggesting that aerosolized droplets escaped through small gaps
between the cover and the seat. In addition to consistent increases in aerosol
levels immediately after flushing, there was a notable rise in ambient aerosol
levels due to the accumulation of droplets from multiple flushes conducted
during the tests. This highlights the need for incorporating adequate
ventilation in the design and operation of public spaces, which can help
prevent aerosol accumulation in high occupancy areas and mitigate the risk of
airborne disease transmission.


Abstract:
  Einstein claimed that the fundamental dynamical insight of special relativity
was the equivalence of mass and energy. I disagree. Not only are mass and
energy not equivalent (whatever exactly that means) but talk of such
equivalence obscures the real dynamical insight of special relativity, which
concerns the nature of 4-forces and interactions more generally. In this paper
I present and defend a new ontology of special relativistic particle dynamics
that makes this insight perspicuous and I explain how alleged cases of
mass-energy conversion can be accommodated within that ontology.


Abstract:
  Triangles with integer length sides and integer area are known as Heron
triangles. Taking rescaling freedom into account, one can apply the same name
when all sides and the area are rational numbers. A perfect triangle is a Heron
triangle with all three medians being rational, and it is a longstanding
conjecture that no such triangle exists. However, Buchholz and Rathbun showed
that there are infinitely many Heron triangles with two rational medians, an
infinite subset of which are associated with rational points on an elliptic
curve $E(\mathbb{Q})$ with Mordell-Weil group $\mathbb{Z}\times
\mathbb{Z}/2\mathbb{Z}$, and they observed a connection with a pair of Somos-5
sequences. Here we make the latter connection more precise by providing
explicit formulae for the integer side lengths, the two rational medians, and
the area in this infinite family of Heron triangles. The proof uses a combined
approach to Somos-5 sequences and associated Quispel-Roberts-Thompson (QRT)
maps in the plane, from several different viewpoints: complex analysis, real
dynamics, and reduction modulo a prime.


Abstract:
  This paper studies the convergence of the tamed Euler-Maruyama (EM) scheme
for a class of McKean-Vlasov neutral stochastic differential delay equations
(MV-NSDDEs) that the drift coefficients satisfy the super-linear growth
condition. We provide the existence and uniqueness of strong solutions to
MV-NSDDEs. Then, we use a stochastic particle method, which is based upon the
theory of the propagation of chaos between particle system and the original
MV-NSDDE, to deal with the approximation of the law. Moreover, we obtain the
convergence rate of tamed EM scheme with respect to the corresponding particle
system. Combining the result of propagation of chaos and the convergence rate
of the numerical solution to the particle system, we get a convergence error
between the numerical solution and exact solution of the original MV-NSDDE in
the stepsize and number of particles.


Abstract:
  We investigate the prospects of next-generation neutrino oscillation
experiments DUNE, T2HK and JUNO including TAO within Standard Model Effective
Field Theory (SMEFT). We also re-interpret COHERENT data in this framework.
Considering both charged and neutral current neutrino Non-Standard Interactions
(NSIs), we analyse dimension-6 SMEFT operators and derive lower bounds to UV
scale $\Lambda$. The most powerful probe is obtained on ${\cal
O}_{{ledq}_{1211}}$ with $\Lambda \gtrsim$ 450 TeV due to the electron neutrino
sample in T2HK near detector. We find DUNE and JUNO to be complementary to T2HK
in exploring different subsets of SMEFT operators at about 25 TeV. We conclude
that near detectors play a significant role in each experiment. We also find
COHERENT with CsI and LAr targets to be sensitive to new physics up to
$\sim$900 GeV.


Abstract:
  Mutation testing is used to evaluate the effectiveness of test suites. In
recent years, a promising variation called extreme mutation testing emerged
that is computationally less expensive. It identifies methods where their
functionality can be entirely removed, and the test suite would not notice it,
despite having coverage. These methods are called pseudo-tested. In this paper,
we compare the execution and analysis times for traditional and extreme
mutation testing and discuss what they mean in practice. We look at how extreme
mutation testing impacts current software development practices and discuss
open challenges that need to be addressed to foster industry adoption. For
that, we conducted an industrial case study consisting of running traditional
and extreme mutation testing in a large software project from the semiconductor
industry that is covered by a test suite of more than 11,000 unit tests. In
addition to that, we did a qualitative analysis of 25 pseudo-tested methods and
interviewed two experienced developers to see how they write unit tests and
gathered opinions on how useful the findings of extreme mutation testing are.
Our results include execution times, scores, numbers of executed tests and
mutators, reasons why methods are pseudo-tested, and an interview summary. We
conclude that the shorter execution and analysis times are well noticeable in
practice and show that extreme mutation testing supplements writing unit tests
in conjunction with code coverage tools. We propose that pseudo-tested code
should be highlighted in code coverage reports and that extreme mutation
testing should be performed when writing unit tests rather than in a decoupled
session. Future research should investigate how to perform extreme mutation
testing while writing unit tests such that the results are available fast
enough but still meaningful.


Abstract:
  In this paper, we interpret the theta rank of an irreducible character of a
finite classical group in terms of the data from the Lusztig classification.
Then we prove the following two results: (1) the agreement of the $U$-rank and
the theta rank for any irreducible character of low Theta rank; (2) the
agreement of the parabolic asymptotic rank and the theta rank for any
irreducible character.


Abstract:
  The IDIA Visualisation Laboratory based at the University of Cape Town is
exploring the use of virtual reality technology to visualise and analyse
astronomical data. The iDaVIE software suite currently under development reads
from both volumetric data cubes and sparse multi-dimensional catalogs,
rendering them in a room-scale immersive environment that allows the user to
intuitively view, navigate around and interact with features in three
dimensions. This paper will highlight how the software imports from common
astronomy data formats and processes the information for loading into the Unity
game engine. It will also describe what tools are currently available to the
user and the various performance optimisations made for seamless use.
Applications by astronomers will be reviewed in addition to the features we
plan to include in future releases.


Abstract:
  We study robustly optimal mechanisms for selling multiple items. The seller
maximizes revenue against a worst-case distribution of a buyer's valuations
within a set of distributions, called an "ambiguity" set. We identify the exact
forms of robustly optimal selling mechanisms and the worst-case distributions
when the ambiguity set satisfies a variety of moment conditions on the values
of subsets of goods. We also identify general properties of the ambiguity set
that lead to the robust optimality of partial bundling which includes separate
sales and pure bundling as special cases.


Abstract:
  Multi-scale structures are prevalent in both natural and artificial systems,
as they can handle increasing complexity. Several terms are employed almost
interchangeably across various application domains to refer to the multi-scale
concept - e.g., hierarchy, holarchy, multi-level, multi-layer, nested,
embedded, micro-macro or coarse graining. While the concrete meanings behind
these terms may differ slightly, several core commonalities persist across all
cases. In this position paper we aim to highlight these common features of the
multi-scale concept, as a preliminary basis for a generic theory of multi-scale
systems. We discuss the concepts of scale and multi-scale systems in general,
and then of multi-scale feedback systems in particular, focusing on the role
played by information in such systems. Our long-term objective is to develop a
general theory of multi-scale feedback systems, applicable across all domains
dealing with complex systems.


Abstract:
  The tightest and most robust cosmological results of the next decade will be
achieved by bringing together multiple surveys of the Universe. This endeavor
has to happen across multiple layers of the data processing and analysis, e.g.,
enhancements are expected from combining Euclid, Rubin, and Roman (as well as
other surveys) not only at the level of joint processing and catalog
combination, but also during the post-catalog parts of the analysis such as the
cosmological inference process. While every experiment builds their own
analysis and inference framework and creates their own set of simulations,
cross-survey work that homogenizes these efforts, exchanges information from
numerical simulations, and coordinates details in the modeling of astrophysical
and observational systematics of the corresponding datasets is crucial.


Abstract:
  A rigorous approach for solving canonical circular open-ended
dielectric-lined waveguide diffraction problems is presented. This is
continuation of our recent paper [1] where a simpler case of uniform dielectric
filling has been considered. Here we deal with the case of an open-ended
circular waveguide with layered dielectric filling which is closer to potential
applications. The presented method uses the solution of corresponding
Wiener-Hopf-Fock equation and leads to an infinite linear system for reflection
coefficients (S-parameters) of the waveguide, the latter can be efficiently
solved numerically using the reducing technique. As a specific example directly
applicable to beam-driven radiation sources based on dielectric-lined
capillaries, diffraction of a slow TM symmetrical mode at the open end of the
described waveguide is considered. A series of such modes forms the wakefield
(Cherenkov radiation field) generated by a charged particle bunch during its
passage along the vacuum channel axis. Calculated S-parameters were compared
with those obtained from COMSOL simulation and an excellent agreement was
shown. This method is expected to be very convenient for analytical
investigation of various electromagnetic interactions of Terahertz (THz) waves
(both free and guided) and charged particle bunches with slow-wave structures
prospective in context of modern beam-driven THz emitters, THz accererators and
THz-based bunch manipulation and bunch diagnostic systems.


Abstract:
  The concept of smart cities is driven by the need to enhance citizens'
quality of life. It is estimated that 70% of the world population will live in
urban areas by 2050. The electric grid is the energy backbone of smart city
deployments. An electric energy system immune to adverse events, both cyber and
physical risks, and able to support the integration of renewable sources will
drive a transformational development approach for future smart cities. This
article describes how the future electric energy system with 100% electricity
supply from renewable energy sources requires the "birth of security and
resiliency" incorporated with its ecosystem.