Abstract:
  Performance of recommender systems (RS) relies heavily on the amount of
training data available. This poses a chicken-and-egg problem for early-stage
products, whose amount of data, in turn, relies on the performance of their RS.
On the other hand, zero-shot learning promises some degree of generalization
from an old dataset to an entirely new dataset. In this paper, we explore the
possibility of zero-shot learning in RS. We develop an algorithm, dubbed
ZEro-Shot Recommenders (ZESRec), that is trained on an old dataset and
generalize to a new one where there are neither overlapping users nor
overlapping items, a setting that contrasts typical cross-domain RS that has
either overlapping users or items. Different from categorical item indices,
i.e., item ID, in previous methods, ZESRec uses items' natural-language
descriptions (or description embeddings) as their continuous indices, and
therefore naturally generalize to any unseen items. In terms of users, ZESRec
builds upon recent advances on sequential RS to represent users using their
interactions with items, thereby generalizing to unseen users as well. We study
three pairs of real-world RS datasets and demonstrate that ZESRec can
successfully enable recommendations in such a zero-shot setting, opening up new
opportunities for resolving the chicken-and-egg problem for data-scarce
startups or early-stage products.


Abstract:
  We revisit gravitational particle production from the Stokes phenomenon
viewpoint, which helps us make a systematic way to understand asymptotic
behavior of mode functions in time-dependent background. One of our purposes of
this work is to make the method more practical for evaluation of
non-perturbative particle production rate. In particular, with several examples
of time-dependent backgrounds, we introduce some approximation methods that
make the analysis more practical. Specifically, we consider particle production
in simple expanding backgrounds, preheating after $R^2$ inflation, and a
transition model with smoothly changing mass. As we find several technical
issues in analyzing the Stokes phenomenon of each example, we discuss how to
simplify the problems while showing the accuracy of analytic estimation under
the approximations we make.


Abstract:
  We present new BVR band photometric light curves of BO Aries obtained in 2020
and combined them with the Transiting Exoplanet Survey Satellite (TESS) light
curves. We obtained times of minima based on Gaussian and Cauchy distributions
and then applied the Monte Carlo Markov Chain (MCMC) method to measure the
amount of uncertainty from our CCD photometry and TESS data. A new ephemeris of
the binary system was computed employing 204 times of minimum. The light curves
were analyzed using the Wilson-Devinney binary code combined with the Monte
Carlo (MC) simulation. For this light curve solution, we considered a dark spot
on the primary component. We conclude that this binary is an A-type system with
a mass ratio of q=0.2074+-0.0001, an orbital inclination of i=82.18+-0.02 deg,
and a fillout factor of f=75.7+-0.8%. Our results for the a(Rsun) and q
parameters are consistent with the results of the Xu-Dong Zhang and Sheng-Bang
Qian (2020) model. The absolute parameters of the two components were
calculated and the distance estimate of the binary system was found to be
142+-9 pc.


Abstract:
  For Dirichlet $L$-functions in $\mathbb{F}_q [T]$ we obtain a hybrid
Euler-Hadamard product formula. We make a splitting conjecture, namely that the
$2k$-th moment of the Dirichlet $L$-functions at $\frac{1}{2}$, averaged over
primitive characters of modulus $R$, is asymptotic to (as $\mathrm{deg} R
\longrightarrow \infty$) the $2k$-th moment of the Euler product multiplied by
the $2k$-th moment of the Hadamard product. We explicitly obtain the main term
of the $2k$-th moment of the Euler product, and we conjecture via random matrix
theory the main term of the $2k$-th moment of the Hadamard product. With the
splitting conjecture, this directly leads to a conjecture for the $2k$-th
moment of Dirichlet $L$-functions. Finally, we lend support for the splitting
conjecture by proving the cases $k=1,2$. This work is the function field
analogue of the work of Bui and Keating. A notable difference in the function
field setting is that the Euler-Hadamard product formula is exact, in that
there is no error term.


Abstract:
  Nonadiabatic holonomic quantum computation (NHQC) provides a method to
implement error resilient gates and that has attracted considerable attention
recently. Since it was proposed, three-level {\Lambda} systems have become the
typical building block for NHQC and a number of NHQC schemes have been
developed based on such systems. In this paper, we investigate the realization
of NHQC beyond the standard three-level setting. The central idea of our
proposal is to improve NHQC by enlarging the Hilbert space of the building
block system and letting it have a bipartite graph structure in order to ensure
purely holonomic evolution. Our proposal not only improves conventional
qubit-based NHQC by efficiently reducing its duration, but also provides
implementations of qudit-based NHQC. Therefore, our proposal provides a further
development of NHQC that can contribute significantly to the physical
realization of efficient quantum information processors.


Abstract:
  Bilateral trade, a fundamental topic in economics, models the problem of
intermediating between two strategic agents, a seller and a buyer, willing to
trade a good for which they hold private valuations. Despite the simplicity of
this problem, a classical result by Myerson and Satterthwaite (1983) affirms
the impossibility of designing a mechanism which is simultaneously efficient,
incentive compatible, individually rational, and budget balanced. This
impossibility result fostered an intense investigation of meaningful trade-offs
between these desired properties. Much work has focused on approximately
efficient fixed-price mechanisms, i.e., Blumrosen and Dobzinski (2014; 2016),
Colini-Baldeschi et al. (2016), which have been shown to fully characterize
strong budget balanced and ex-post individually rational direct revelation
mechanisms. All these results, however, either assume some knowledge on the
priors of the seller/buyer valuations, or a black box access to some samples of
the distributions, as in D{\"u}tting et al. (2021). In this paper, we cast for
the first time the bilateral trade problem in a regret minimization framework
over rounds of seller/buyer interactions, with no prior knowledge on the
private seller/buyer valuations. Our main contribution is a complete
characterization of the regret regimes for fixed-price mechanisms with
different models of feedback and private valuations, using as benchmark the
best fixed price in hindsight. More precisely, we prove the following bounds on
the regret:
  $\bullet$ $\widetilde{\Theta}(\sqrt{T})$ for full-feedback (i.e., direct
revelation mechanisms);
  $\bullet$ $\widetilde{\Theta}(T^{2/3})$ for realistic feedback (i.e.,
posted-price mechanisms) and independent seller/buyer valuations with bounded
densities;
  $\bullet$ $\Theta(T)$ for realistic feedback and seller/buyer valuations with
bounded densities;
  $\bullet$ $\Theta(T)$ for realistic feedback and independent seller/buyer
valuations;
  $\bullet$ $\Theta(T)$ for the adversarial setting.


Abstract:
  Spontaneous synchronization is a general phenomenon in which a large
population of coupled oscillators of diverse natural frequencies self-organize
to operate in unison. The phenomenon occurs in physical and biological systems
over a wide range of spatial and temporal scales, e.g., in electrochemical and
electronic oscillators, Josephson junctions, laser arrays, animal flocking,
pedestrians on footbridges, audience clapping, etc. Besides the obvious
necessity of the synchronous firings of cardiac cells to keep the heart
beating, synchrony is desired in many man-made systems such as parallel
computing, electrical power-grids. On the contrary, synchrony could also be
hazardous, e.g., in neurons, leading to impaired brain function in Parkinson's
disease and epilepsy. Due to this wide range of applications, collective
synchrony in networks of oscillators has attracted the attention of physicists,
applied mathematicians and researchers from many other fields. An essential
aspect of synchronizing systems is that long-range order naturally appear in
these systems, which questions the fact whether long-range interactions may be
particular suitable to synchronization. In this context, it is interesting to
remind that long-range interacting system required several adaptations from
statistical mechanics \`a la Gibbs Boltzmann, in order to deal with the
peculiarities of these systems: negative specific heat, breaking of ergodicity
or lack of extensivity. As for synchrony, it is still lacking a theoretical
framework to use the tools from statistical mechanics. The present issue
presents a collection of exciting recent theoretical developments in the field
of synchronization and long-range interactions, in order to highlight the
mutual progresses of these twin areas.


Abstract:
  We construct a toy model for the harmonic oscillator that is neither
classical nor quantum. The model features a discrete energy spectrum, a ground
state with sharp position and momentum, an eigenstate with non-positive Wigner
function as well as a state that has tunneling properties. The underlying
formalism exploits that the Wigner-Weyl approach to quantum theory and the
Hamilton formalism in classical theory can be formulated in the same
operational language, which we then use to construct generalized theories with
well-defined phase space. The toy model demonstrates that operational theories
are a viable alternative to operator-based approaches for building physical
theories.


Abstract:
  Extending on ideas of Lewin, Lieb and Seiringer (Phys Rev B, 100, 035127,
(2019)) we present a modified "floating crystal" trial state for Jellium (also
known as the classical homogeneous electron gas) with density equal to a
characteristic function. This allows us to show that three definitions of the
Jellium energy coincide in dimensions $d\geq 2$, thus extending the result of
Cotar and Petrache (arXiv: 1707.07664) and Lewin, Lieb and Seiringer (Phys Rev
B, 100, 035127, (2019)) that the three definitions coincide in dimension $d
\geq 3$. We show that the Jellium energy is also equivalent to a "renormalized
energy" studied in a series of papers by Serfaty and others and thus, by work
of B\'etermin and Sandier (Constr Approx, 47:39-74, (2018)), we relate the
Jellium energy to the order $n$ term in the logarithmic energy of $n$ points on
the unit 2-sphere. We improve upon known lower bounds for this renormalized
energy. Additionally, we derive formulas for the Jellium energy of periodic
configurations.


Abstract:
  Automated planning enables robots to find plans to achieve complex,
long-horizon tasks, given a planning domain. This planning domain consists of a
list of actions, with their associated preconditions and effects, and is
usually manually defined by a human expert, which is very time-consuming or
even infeasible. In this paper, we introduce a novel method for generating this
domain automatically from human demonstrations. First, we automatically segment
and recognize the different observed actions from human demonstrations. From
these demonstrations, the relevant preconditions and effects are obtained, and
the associated planning operators are generated. Finally, a sequence of actions
that satisfies a user-defined goal can be planned using a symbolic planner. The
generated plan is executed in a simulated environment by the TIAGo robot. We
tested our method on a dataset of 12 demonstrations collected from three
different participants. The results show that our method is able to generate
executable plans from using one single demonstration with a 92% success rate,
and 100% when the information from all demonstrations are included, even for
previously unknown stacking goals.


Abstract:
  We investigate the upscaling of diffusive transport parameters as function of
pore scale material structure using a stochastic framework. We focus on sub-REV
(representative elementary volume) scale where the complexity of pore space
geometry leads to a significant scatter of transport observations. We study a
large data set of sub-REV measurements on porosity and transport ability being
a dimensionless parameter representing the ratio of diffusive flow through the
porous volume and through an empty volume. We characterize transport ability as
probability distribution functions (PDFs) of porosity capturing the effect of
pore structure differences among samples. We then investigate domain size
effects and predict the REV scale. While scatter in porosity observation
decrease linearly with increasing sample size, the observed scatter in
transport ability converges towards a constant value larger zero. Our results
confirm that differences in pore structure topology impact transport parameters
at all scales. Consequently, the use of PDFs to describe the relationship of
effective transport coefficients to porosity is advantageous to deterministic
semi-empirical functions. We discuss the consequences and advocate the use of
PDFs for effective parameters in both continuum equations and data
interpretation of experimental or computational work. We believe that the
presented statistics-based upscaling technique of sub-REV microscopy data
provides a new tool in understanding, describing and predicting macroscopic
transport behavior of micro-porous media.


Abstract:
  The linear noise approximation models the random fluctuations from the mean
field model of a chemical reaction that unfolds near the thermodynamic limit.
Specifically, the fluctuations obey a linear Langevin equation up to order
$\Omega^{-1/2}$, where $\Omega$ is the size of the chemical system (usually the
volume). In the presence of disparate timescales, the linear noise
approximation admits a quasi-steady-state reduction referred to as the
\textit{slow scale} linear noise approximation. However, the slow scale linear
approximation has only been derived for fast/slow systems that are in Tikhonov
standard form. In this work, we derive, for the first time, the slow scale
linear noise approximation directly from Fenichel theory, without the need for
a priori scaling and dimensional analysis. In so doing, we also apply, for the
first time, the slow scale linear noise approximation to fast/slow systems that
are not in standard form. Furthermore, we demonstrate how a priori scaling
analysis can in some cases distort the structure of the mean field singular
perturbation, and this distortion can lead to the incomplete reduction of the
linear Langevin equation. In its entirety, our paper not only presents new
mathematical results to the fields of mathematical biology and physical
chemistry, it also unifies the several important research results in
deterministic and stochastic chemical kinetics.


Abstract:
  Multilingual transformers (XLM, mT5) have been shown to have remarkable
transfer skills in zero-shot settings. Most transfer studies, however, rely on
automatically translated resources (XNLI, XQuAD), making it hard to discern the
particular linguistic knowledge that is being transferred, and the role of
expert annotated monolingual datasets when developing task-specific models. We
investigate the cross-lingual transfer abilities of XLM-R for Chinese and
English natural language inference (NLI), with a focus on the recent
large-scale Chinese dataset OCNLI. To better understand linguistic transfer, we
created 4 categories of challenge and adversarial tasks (totaling 17 new
datasets) for Chinese that build on several well-known resources for English
(e.g., HANS, NLI stress-tests). We find that cross-lingual models trained on
English NLI do transfer well across our Chinese tasks (e.g., in 3/4 of our
challenge categories, they perform as well/better than the best monolingual
models, even on 3/5 uniquely Chinese linguistic phenomena such as idioms, pro
drop). These results, however, come with important caveats: cross-lingual
models often perform best when trained on a mixture of English and high-quality
monolingual NLI data (OCNLI), and are often hindered by automatically
translated resources (XNLI-zh). For many phenomena, all models continue to
struggle, highlighting the need for our new diagnostics to help benchmark
Chinese and cross-lingual models. All new datasets/code are released at
https://github.com/huhailinguist/ChineseNLIProbing.


Abstract:
  For lower arm amputees, robotic prosthetic hands offer the promise to regain
the capability to perform fine object manipulation in activities of daily
living. Current control methods based on physiological signals such as EEG and
EMG are prone to poor inference outcomes due to motion artifacts, variability
of skin electrode junction impedance over time, muscle fatigue, and other
factors. Visual evidence is also susceptible to its own artifacts, most often
due to object occlusion, lighting changes, variable shapes of objects depending
on view-angle, among other factors. Multimodal evidence fusion using
physiological and vision sensor measurements is a natural approach due to the
complementary strengths of these modalities.
  In this paper, we present a Bayesian evidence fusion framework for grasp
intent inference using eye-view video, gaze, and EMG from the forearm processed
by neural network models. We analyze individual and fused performance as a
function of time as the hand approaches the object to grasp it. For this
purpose, we have also developed novel data processing and augmentation
techniques to train neural network components. Our experimental data analyses
demonstrate that EMG and visual evidence show complementary strengths, and as a
consequence, fusion of multimodal evidence can outperform each individual
evidence modality at any given time. Specifically, results indicate that, on
average, fusion improves the instantaneous upcoming grasp type classification
accuracy while in the reaching phase by 13.66% and 14.8%, relative to EMG and
visual evidence individually. An overall fusion accuracy of 95.3% among 13
labels (compared to a chance level of 7.7%) is achieved, and more detailed
analysis indicate that the correct grasp is inferred sufficiently early and
with high confidence compared to the top contender, in order to allow
successful robot actuation to close the loop.


Abstract:
  The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic
factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis.
Computer-aided nuclei grading aims to improve pathologists' work efficiency
while reducing their misdiagnosis rate by automatically identifying the grades
of tumor nuclei within histopathological images. Such a task requires precisely
segment and accurately classify the nuclei. However, most of the existing
nuclei segmentation and classification methods can not handle the inter-class
similarity property of nuclei grading, thus can not be directly applied to the
ccRCC grading task. In this paper, we propose a Composite High-Resolution
Network for ccRCC nuclei grading. Specifically, we propose a segmentation
network called W-Net that can separate the clustered nuclei. Then, we recast
the fine-grained classification of nuclei to two cross-category classification
tasks, based on two high-resolution feature extractors (HRFEs) which are
proposed for learning these two tasks. The two HRFEs share the same backbone
encoder with W-Net by a composite connection so that meaningful features for
the segmentation task can be inherited for the classification task. Last, a
head-fusion block is applied to generate the predicted label of each nucleus.
Furthermore, we introduce a dataset for ccRCC nuclei grading, containing 1000
image patches with 70945 annotated nuclei. We demonstrate that our proposed
method achieves state-of-the-art performance compared to existing methods on
this large ccRCC grading dataset.


Abstract:
  Heavy ion collisions provide a unique opportunity to study the nature of
X(3872) compared with electron-positron and proton-proton (antiproton)
collisions. With the abundant charm pairs produced in heavy-ion collisions, the
production of multicharm hadrons and molecules can be enhanced by the
combination of charm and anticharm quarks in the medium. We investigate the
centrality and momentum dependence of X(3872) in heavy-ion collisions via the
Langevin equation and instant coalescence model (LICM). When X(3872) is treated
as a compact tetraquark state, the tetraquarks are produced via the coalescence
of heavy and light quarks near the quantum chromodynamic (QCD) phase transition
due to the restoration of the heavy quark potential at $T\rightarrow T_c$. In
the molecular scenario, loosely bound X(3872) is produced via the coalescence
of $D^0$-$\bar D^{*0}$ mesons in a hadronic medium after kinetic freeze-out.
The phase space distributions of the charm quarks and D mesons in a bulk medium
are studied with the Langevin equation, while the coalescence probability
between constituent particles is controlled by the Wigner function, which
encodes the internal structure of the formed particle.
  First, we employ the LICM to explain both $D^0$ and $J/\psi$ production as a
benchmark. Then, we give predictions regarding X(3872) production. We find that
the total yield of tetraquark is several times larger than the molecular
production in Pb-Pb collisions. Although the geometric size of the molecule is
huge, the coalescence probability is small due to strict constraints on the
relative momentum between $D^0$ and $\bar D^{*0}$ in the molecular Wigner
function, which significantly suppresses the molecular yield.


Abstract:
  With a model of a geometric theory in an arbitrary topos, we associate a site
obtained by endowing a category of generalized elements of the model with a
Grothendieck topology, which we call the antecedent topology. Then we show that
the associated sheaf topos, which we call the over-topos at the given model,
admits a canonical totally connected morphism to the given base topos and
satisfies a universal property generalizing that of the colocalization of a
topos at a point. We first treat the case of the base topos of sets, where
global elements are sufficient to describe our site of definition; in this
context, we also introduce a geometric theory classified by the over-topos,
whose models can be identified with the model homomorphisms towards the
(internalizations of the) model. Then we formulate and prove the general
statement over an arbitrary topos, which involves the stack of generalized
elements of the model. Lastly, we investigate the geometric and 2-categorical
aspects of the over-topos construction, exhibiting it as a bilimit in the
bicategory of Grothendieck toposes.


Abstract:
  Let F be a non-Archimedean locally compact field of residue characteristic p,
let G be an inner form of GL(n,F) with n>0, and let l be a prime number
different from p. We describe the block decomposition of the category of finite
length smooth representations of G with coefficients in an algebraically closed
field of characteristic l. Unlike the case of complex representations of an
arbitrary p-adic reductive group and that of l-modular representations of
GL(n,F), several non-isomorphic supercuspidal supports may correspond to the
same block. We describe the (finitely many) supercuspidal supports
corresponding to a given block. We also prove that a supercuspidal block is
equivalent to the principal (that is, the one which contains the trivial
character) block of the multiplicative group of a suitable division algebra,
and we determine those irreducible representations having a nontrivial
extension with a given supercuspidal representation of G.


Abstract:
  Unmanned aerial vehicles (UAVs) are expected to be an integral part of
wireless networks, and determining collision-free trajectories for multiple
UAVs while satisfying requirements of connectivity with ground base stations
(GBSs) is a challenging task. In this paper, we first reformulate the multi-UAV
trajectory optimization problem with collision avoidance and wireless
connectivity constraints as a sequential decision making problem in the
discrete time domain. We, then, propose a decentralized deep reinforcement
learning approach to solve the problem. More specifically, a value network is
developed to encode the expected time to destination given the agent's joint
state (including the agent's information, the nearby agents' observable
information, and the locations of the nearby GBSs). A
signal-to-interference-plus-noise ratio (SINR)-prediction neural network is
also designed, using accumulated SINR measurements obtained when interacting
with the cellular network, to map the GBSs' locations into the SINR levels in
order to predict the UAV's SINR. Numerical results show that with the value
network and SINR-prediction network, real-time navigation for multi-UAVs can be
efficiently performed in various environments with high success rate.


Abstract:
  We report on building of a compact vacuum chamber for spectroscopy of
ultracold thulium and trapping of up to 13 million atoms. Compactness is
achieved by obviating a classical Zeeman slower section and placing an atomic
oven close to a magneto-optical trap (MOT), specifically at the distance of 11
cm. In this configuration, we significantly gained in solid angle of an atomic
beam, which is affected by MOT laser beams, and reached 1 million atoms loaded
directly in the MOT. By exploiting Zeeman-like deceleration of atoms with an
additional laser beam, we increased the number of trapped atoms to 6 million.
Then we gained an extra factor of 2 by tailoring the MOT magnetic field
gradient with an additional small magnetic coil. Demonstrated results show
great perspective of the developed setup for realizing a compact
high-performance optical atomic clock based on thulium atoms.


Abstract:
  Among the challenges in discriminating between theoretical approaches to the
glass transition is obtaining suitable data. In particular, particle--resolved
data in liquids supercooled past the mode--coupling crossover has until
recently been hard to obtain. Here we combine nano-particle resolved
experiments and GPU simulation data which addresses this and investigate the
predictions of differing theoretical approaches. We find support for both
dynamic facilitation and thermodynamic approaches. In particular, excitations,
the elementary units of relaxation in dynamic facilitation theory follow the
predicted scaling behaviour and the properties of cooperatively rearranging
regions (CRRs) are consistent with RFOT theory. At weak supercooling there is
limited correlation between particles identified in excitations and CRRs, but
this increases very substantially at deep supercooling. We identify a timescale
related to the CRRs which is coupled to the structural relaxation time and thus
decoupled from the excitation timescale, which remains microscopic.


Abstract:
  At a first glance the Theory of computation relies on potential infinity and
an organization aimed at solving a problem. Under such aspect it is like
Mendeleev theory of chemistry. Also its theoretical development reiterates that
of this scientific theory: it makes use of doubly negated propositions and its
reasoning proceeds through ad absurdum proofs; a final, universal predicate of
equivalence of all definitions of a computations is translated into an equality
one, and at the same time intuitionist logic into classical logic. Yet, the
last step of this development of current theory includes both a misleading
notion of thesis and intuitive notions (e.g. the partial computable function,
as stressed by some scholars). A program for a rational re-construction of the
theory according to the theoretical development of the above mentioned theories
is sketchy suggested.


Abstract:
  Gaussian Graphical models (GGM) are widely used to estimate the network
structures in many applications ranging from biology to finance. In practice,
data is often corrupted by latent confounders which biases inference of the
underlying true graphical structure. In this paper, we compare and contrast two
strategies for inference in graphical models with latent confounders: Gaussian
graphical models with latent variables (LVGGM) and PCA-based removal of
confounding (PCA+GGM). While these two approaches have similar goals, they are
motivated by different assumptions about confounding. In this paper, we explore
the connection between these two approaches and propose a new method, which
combines the strengths of these two approaches. We prove the consistency and
convergence rate for the PCA-based method and use these results to provide
guidance about when to use each method. We demonstrate the effectiveness of our
methodology using both simulations and in two real-world applications.


Abstract:
  We consider the model of the data broker selling information to a single
agent to maximize his revenue. The agent has private valuation for the
additional information, and upon receiving the signal from the data broker, the
agent can conduct her own experiment to refine her posterior belief on the
states with additional costs. In this paper, we show that in the optimal
mechanism, the agent has no incentive to acquire any additional costly
information under equilibrium. Still, the ability to acquire additional
information distorts the incentives of the agent, and reduces the optimal
revenue of the data broker. Moreover, we characterize the optimal mechanism
when the valuation function of the agent is separable. The optimal mechanism in
general may be complex and contain a continuum of menu entries. However, we
show that posting a deterministic price for revealing the states is optimal
when the prior distribution is sufficiently informative or the cost of
acquiring additional information is sufficiently high, and obtains at least
half of the optimal revenue for arbitrary prior and cost functions.


Abstract:
  Why does the vaccination rate remain low, even in countries where
long-established immunization programs exist, and vaccines are provided for
free? We study this lower vaccination paradox in the context of India- which
contributes to the largest pool of under-vaccinated children in the world and
about one-third of all vaccine-preventable deaths globally. We explore the
importance of historical events shaping current vaccination practices.
Combining historical records with survey datasets, we examine the Indian
government's forced sterilization policy implemented in 1976-77 and find that
greater exposure to forced sterilization has had a large negative effect on the
current vaccination completion rate. We explore the mechanism for this practice
and find that institutional delivery and antenatal care are low in states where
policy exposure was high. Finally, we examine the consequence of lower
vaccination, suggesting that child mortality is currently high in states with
greater sterilization exposure. Together, the evidence suggests that government
policies implemented in the past could have persistent impacts on adverse
demand for health-seeking behavior, even if the burden is exceedingly high.


Abstract:
  Accurate and robust prediction of patient's response to drug treatments is
critical for developing precision medicine. However, it is often difficult to
obtain a sufficient amount of coherent drug response data from patients
directly for training a generalized machine learning model. Although the
utilization of rich cell line data provides an alternative solution, it is
challenging to transfer the knowledge obtained from cell lines to patients due
to various confounding factors. Few existing transfer learning methods can
reliably disentangle common intrinsic biological signals from confounding
factors in the cell line and patient data. In this paper, we develop a Coherent
Deconfounding Autoencoder (CODE-AE) that can extract both common biological
signals shared by incoherent samples and private representations unique to each
data set, transfer knowledge learned from cell line data to tissue data, and
separate confounding factors from them. Extensive studies on multiple data sets
demonstrate that CODE-AE significantly improves the accuracy and robustness
over state-of-the-art methods in both predicting patient drug response and
de-confounding biological signals. Thus, CODE-AE provides a useful framework to
take advantage of in vitro omics data for developing generalized patient
predictive models. The source code is available at
https://github.com/XieResearchGroup/CODE-AE.


Abstract:
  In [M. Zych et al., Nat. Commun. 2, 505 (2011)], the authors predicted that
the interferometric visibility is affected by a gravitational field in way that
cannot be explained without the general relativistic notion of proper time. In
this work, we take a different route and start deriving the same effect using
the unitary representation of the local Lorentz transformation in the Newtonian
Limit. In addition, we show that the effect on the interferometric visibility
due to gravity persists in different spacetime geometries. However, the
influence is not necessarily due to the notion of proper time. For instance, by
constructing a `astronomical' Mach-Zehnder interferometer in the Schwarzschild
spacetime, the influence on the interferometric visibility can be due to
another general relativistic effect, the geodetic precession. Besides, by using
the unitary representation of the local Lorentz transformation, we show that
this behavior of the interferometric visibility is general for an arbitrary
spacetime, provided that we restrict the motion of the quanton to a
two-dimensional spacial plane.


Abstract:
  We define a universal state sum construction which specializes to most
previously known state sums (Turaev-Viro, Dijkgraaf-Witten, Crane-Yetter,
Douglas-Reutter, Witten-Reshetikhin-Turaev surgery formula, Brown-Arf). The
input data for the state sum is an n-category satisfying various conditions,
including finiteness, semisimplicity and n-pivotality. From this n-category one
constructs an n+1-dimensional TQFT, and applying the TQFT gluing rules to a
handle decomposition of an n+1-manifold produces the state sum.


Abstract:
  Transition metal dichalcogenide heterobilayers offer attractive opportunities
to realize lattices of interacting bosons with several degrees of freedom. Such
heterobilayers can feature moir\'e patterns that modulate their electronic band
structure, leading to spatial confinement of single interlayer excitons (IXs)
that act as quantum emitters with $C_3$ symmetry. However, the narrow emission
linewidths of the quantum emitters contrast with a broad ensemble IX emission
observed in nominally identical heterobilayers, opening a debate regarding the
origin of IX emission. Here we report the continuous evolution from a few
trapped IXs to an ensemble of IXs with both triplet and singlet spin
configurations in a gate-tunable $2H$-MoSe$_2$/WSe$_2$ heterobilayer. We
observe signatures of dipolar interactions in the IX ensemble regime which,
when combined with magneto-optical spectroscopy, reveal that the narrow
quantum-dot-like and broad ensemble emission originate from IXs trapped in
moir\'e potentials with the same atomic registry. Finally, electron doping
leads to the formation of three different species of localised negative trions
with contrasting spin-valley configurations, among which we observe both
intervalley and intravalley IX trions with spin-triplet optical transitions.
Our results identify the origin of IX emission in MoSe$_2$/WSe$_2$
heterobilayers and highlight the important role of exciton-exciton interactions
and Fermi-level control in these highly tunable quantum materials.


Abstract:
  Multifractal detrended fluctuation analysis (MFDFA) has become a central
method to characterise the variability and uncertainty in empiric time series.
Extracting the fluctuations on different temporal scales allows quantifying the
strength and correlations in the underlying stochastic properties, their
scaling behaviour, as well as the level of fractality. Several extensions to
the fundamental method have been developed over the years, vastly enhancing the
applicability of MFDFA, e.g. empirical mode decomposition for the study of
long-range correlations and persistence. In this article we introduce an
efficient, easy-to-use python library for MFDFA, incorporating the most common
extensions and harnessing the most of multi-threaded processing for very fast
calculations.


Abstract:
  The Imry-Ma phenomenon, predicted in 1975 by Imry and Ma and rigorously
established in 1989 by Aizenman and Wehr, states that first-order phase
transitions of low-dimensional spin systems are `rounded' by the addition of a
quenched random field to the quantity undergoing the transition. The phenomenon
applies to a wide class of spin systems in dimensions $d\le 2$ and to spin
systems possessing a continuous symmetry in dimensions $d\le 4$.
  This work provides quantitative estimates for the Imry--Ma phenomenon: In a
cubic domain of side length $L$, we study the effect of the boundary conditions
on the spatial and thermal average of the quantity coupled to the random field.
We show that the boundary effect diminishes at least as fast as an inverse
power of $\log\log L$ for general two-dimensional spin systems and for
four-dimensional spin systems with continuous symmetry, and at least as fast as
an inverse power of $L$ for two- and three-dimensional spin systems with
continuous symmetry. Specific models of interest for the obtained results
include the two-dimensional random-field $q$-state Potts and Edwards-Anderson
spin glass models, and the $d$-dimensional random-field spin $O(n)$ models
($n\ge 2$) in dimensions $d\le 4$.


Abstract:
  We study the problem of fairly allocating indivisible items to agents with
different entitlements, which captures, for example, the distribution of
ministries among political parties in a coalition government. Our focus is on
picking sequences derived from common apportionment methods, including five
traditional divisor methods and the quota method. We paint a complete picture
of these methods in relation to known envy-freeness and proportionality
relaxations for indivisible items as well as monotonicity properties with
respect to the resource, population, and weights. In addition, we provide
characterizations of picking sequences satisfying each of the fairness notions,
and show that the well-studied maximum Nash welfare solution fails resource-
and population-monotonicity even in the unweighted setting. Our results serve
as an argument in favor of using picking sequences in weighted fair division
problems.


Abstract:
  In a surprising recent work, Lemke Oliver and Soundararajan noticed how
experimental data exhibits erratic distributions for consecutive pairs of
primes in arithmetic progressions, and proposed a heuristic model based on the
Hardy--Littlewood conjectures containing a large secondary term, which fits the
data very well. In this paper, we study consecutive pairs of sums of squares in
arithmetic progressions, and develop a similar heuristic model based on the
Hardy--Littlewood conjecture for sums of squares, which also explain the biases
in the experimental data. In the process, we prove several results related to
averages of the Hardy--Littlewood constant in the context of sums of two
squares.


Abstract:
  We investigate the problem of jointly testing multiple hypotheses and
estimating a random parameter of the underlying distribution in a sequential
setup. The aim is to jointly infer the true hypothesis and the true parameter
while using on average as few samples as possible and keeping the detection and
estimation errors below predefined levels. Based on mild assumptions on the
underlying model, we propose an asymptotically optimal procedure, i.e., a
procedure that becomes optimal when the tolerated detection and estimation
error levels tend to zero. The implementation of the resulting asymptotically
optimal stopping rule is computationally cheap and, hence, applicable for
high-dimensional data. We further propose a projected quasi-Newton method to
optimally chose the coefficients that parameterize the instantaneous cost
function such that the constraints are fulfilled with equality. The proposed
theory is validated by numerical examples.


Abstract:
  Background: Most of the stars in the Universe will end their evolution by
losing their envelope during the thermally pulsing asymptotic giant branch
(TP-AGB) phase, enriching the interstellar medium of galaxies with heavy
elements, partially condensed into dust grains formed in their extended
circumstellar envelopes. Among these stars, carbon-rich TP-AGB stars (C-stars)
are particularly relevant for the chemical enrichment of galaxies. We here
investigated the role of the metallicity in the dust formation process from a
theoretical viewpoint. Methods: We coupled an up-to-date description of dust
growth and dust-driven wind, which included the time-averaged effect of shocks,
with FRUITY stellar evolutionary tracks. We compared our predictions with
observations of C-stars in our Galaxy, in the Magellanic Clouds (LMC and SMC)
and in the Galactic Halo, characterised by metallicity between solar and 1/10
of solar. Results: Our models explained the variation of the gas and dust
content around C-stars derived from the IRS Spitzer spectra. The wind speed of
the C-stars at varying metallicity was well reproduced by our description. We
predicted the wind speed at metallicity down to 1/10 of solar in a wide range
of mass-loss rates.


Abstract:
  Lower-limb prosthesis wearers are more prone to fall than non-amputees.
Powered prostheses can reduce this instability of passive prostheses. While
shown to be more stable in practice, powered prostheses generally use
model-independent control methods that lack formal guarantees of stability and
rely on heuristic tuning. Recent work overcame one of the limitations of
model-based prosthesis control by developing a class of stable prosthesis
subsystem controllers independent of the human model, except for its
interaction forces with the prosthesis. Our work realizes the first
model-dependent prosthesis controller that uses in-the-loop on-board real-time
force sensing at the interface between the human and prosthesis and at the
ground, resulting in stable human-prosthesis walking and increasing the
validity of our formal guarantees of stability. Experimental results
demonstrate this controller using force sensors outperforms the controller when
not using force sensors with better tracking performance and more consistent
tracking performance across 4 types of terrain.


Abstract:
  In this paper, we apply a Bayesian perspective to the sampling of
alternatives for multinomial logit (MNL) and mixed multinomial logit (MMNL)
models. A sampling of alternatives reduces the computational challenge of
evaluating the denominator of the logit choice probability for large choice
sets by only using a smaller subset of sampled alternatives including the
chosen alternative. To correct for the resulting overestimation of the choice
probability, a correction factor has to be applied. McFadden (1978) proposes a
correction factor to the utility of each alternative which is based on the
probability of sampling the smaller subset of alternatives and that alternative
being chosen. McFadden's correction factor ensures consistency of parameter
estimates under a wide range of sampling protocols. A special sampling protocol
discussed by McFadden is uniform conditioning, which assigns the same sampling
probability and therefore the same correction factor to each alternative in the
sampled choice set. Since a constant is added to each alternative the
correction factor cancels out, but consistent estimates are still obtained.
Bayesian estimation is focused on describing the full posterior distributions
of the parameters of interest instead of the consistency of their point
estimates. We theoretically show that uniform conditioning is sufficient to
minimise the loss of information from a sampling of alternatives on the
parameters of interest over the full posterior distribution in Bayesian MNL
models. Minimum loss of information is, however, not guaranteed for other
sampling protocols. This result extends to Bayesian MMNL models estimated using
the principle of data augmentation. The application of uniform conditioning, a
more restrictive sampling protocol, is thus sufficient in a Bayesian estimation
context to achieve finite sample properties of MNL and MMNL parameter
estimates.


Abstract:
  A decision maker is choosing between an active action (e.g., purchase a
house, invest certain stock) and a passive action. The payoff of the active
action depends on the buyer's private type and also an unknown state of nature.
An information seller can design experiments to reveal information about the
realized state to the decision maker, and would like to maximize profit from
selling such information. We characterize, in closed-form, the revenue-optimal
information selling mechanism for the seller. After eliciting the buyer's type,
the optimal mechanism charges the buyer an upfront payment and then simply
reveals whether the realized state exceeds a certain threshold or not. The
optimal mechanism features both price discrimination and information
discrimination. The special buyer type who is a-priori indifferent between the
active and passive action benefits the most from participating in the
mechanism.


Abstract:
  Two-particle angular correlations are measured in high-multiplicity
proton-proton collisions at $\sqrt{s} =13$ TeV by the ALICE Collaboration. The
yields of particle pairs at short-($\Delta\eta$ $\sim$ 0) and long-range ($1.6
< |\Delta\eta| < 1.8$) in pseudorapidity are extracted on the near-side
($\Delta\varphi$ $\sim$ 0). They are reported as a function of transverse
momentum ($p_{\mathrm T}$) in the range $1 <p_{\mathrm T}< 4$ GeV/$c$.
Furthermore, the event-scale dependence is studied for the first time by
requiring the presence of high-$p_{\rm T}$ leading particles and jets for
varying $p_{\rm T}$ thresholds. The results demonstrate that the long-range
"ridge" yield, possibly related to the collective behavior of the system, is
present in events with high-$p_{\mathrm T}$ processes. The magnitudes of the
short- and long-range yields are found to grow with the event scale. The
results are compared to EPOS LHC and PYTHIA 8 calculations, with and without
string-shoving interactions. It is found that while both models describe the
qualitative trends in the data, calculations from EPOS LHC show a better
quantitative agreement, in particular for the $p_{\rm T}$ and event-scale
dependencies.


Abstract:
  Recent empirical evidence suggests that the Weston-Watkins support vector
machine is among the best performing multiclass extensions of the binary SVM.
Current state-of-the-art solvers repeatedly solve a particular subproblem
approximately using an iterative strategy. In this work, we propose an
algorithm that solves the subproblem exactly using a novel reparametrization of
the Weston-Watkins dual problem. For linear WW-SVMs, our solver shows
significant speed-up over the state-of-the-art solver when the number of
classes is large. Our exact subproblem solver also allows us to prove linear
convergence of the overall solver.


Abstract:
  Tetragonal Mo5PB2 compound, a recently discovered superconductor, belongs to
technologically important class of materials. It is quite surprising to note
that a large number of physical properties of Mo5PB2, including elastic
properties and their anisotropy, acoustic behavior, electronic (charge density
distribution, electron density difference), thermo-physical, bonding
characteristics, and optical properties have not been carried out at all. In
the present work we have explored all these properties in details for the first
time with density functional theory based first-principles method. Mo5PB2 is
found to be a mechanically stable, elastically anisotropic compound with
ductile character. Moreover, the chemical bonding is interpreted by calculating
the electronic energy density of states, electron density distribution, elastic
properties and Mulliken bond population analysis. Mo5PB2 has a combination of
mainly ionic, metallic, and some covalent bonding characteristics. The compound
possesses high level of machinability. The band structure along with a large
electronic density of states at the Fermi level reveals metallic character.
Calculated values of different thermal parameters of Mo5PB2 are closely related
to the elastic properties. The energy dependent optical parameters show close
assent to the underlying electronic band structure. The optical absorption and
reflectivity spectra and the low energy index of refraction of Mo5PB2 show that
the compound holds promise to be used in optoelectronic device sector. Unlike
the notable anisotropy found in elastic, mechanical properties and minimum
thermal conductivity, the optical parameters are found to be almost isotropic
with respect to the polarization direction of the incident electric field.


Abstract:
  Point cloud upsampling is vital for the quality of the mesh in
three-dimensional reconstruction. Recent research on point cloud upsampling has
achieved great success due to the development of deep learning. However, the
existing methods regard point cloud upsampling of different scale factors as
independent tasks. Thus, the methods need to train a specific model for each
scale factor, which is both inefficient and impractical for storage and
computation in real applications. To address this limitation, in this work, we
propose a novel method called ``Meta-PU" to firstly support point cloud
upsampling of arbitrary scale factors with a single model. In the Meta-PU
method, besides the backbone network consisting of residual graph convolution
(RGC) blocks, a meta-subnetwork is learned to adjust the weights of the RGC
blocks dynamically, and a farthest sampling block is adopted to sample
different numbers of points. Together, these two blocks enable our Meta-PU to
continuously upsample the point cloud with arbitrary scale factors by using
only a single model. In addition, the experiments reveal that training on
multiple scales simultaneously is beneficial to each other. Thus, Meta-PU even
outperforms the existing methods trained for a specific scale factor only.


Abstract:
  We construct colored lattice models whose partition functions represent
symplectic and odd orthogonal Demazure characters and atoms. We show that our
lattice models are not solvable, but we are able to show the existence of
sufficiently many solutions of the Yang--Baxter equation that allows us to
compute functional equations for the corresponding partition functions. From
these functional equations, we determine that the partition function of our
models are the Demazure atoms and characters for the symplectic and odd
orthogonal Lie groups. We coin our lattice models as quasi-solvable. We use the
natural bijection of admissible states in our models with Proctor patterns to
give a right key algorithm for reverse King tableaux and Sundaram tableaux.


Abstract:
  The estimation of entropy rates for stationary discrete-valued stochastic
processes is a well studied problem in information theory. However, estimating
the entropy rate for stationary continuous-valued stochastic processes has not
received as much attention. In fact, many current techniques are not able to
accurately estimate or characterise the complexity of the differential entropy
rate for strongly correlated processes, such as Fractional Gaussian Noise and
ARFIMA(0,d,0). To the point that some cannot even detect the trend of the
entropy rate, e.g. when it increases/decreases, maximum, or asymptotic trends,
as a function of their Hurst parameter. However, a recently developed technique
provides accurate estimates at a high computational cost. In this paper, we
define a robust technique for non-parametrically estimating the differential
entropy rate of a continuous valued stochastic process from observed data, by
making an explicit link between the differential entropy rate and the Shannon
entropy rate of a quantised version of the original data. Estimation is
performed by a Shannon entropy rate estimator, and then converted to a
differential entropy rate estimate. We show that this technique inherits many
important statistical properties from the Shannon entropy rate estimator. The
estimator is able to provide better estimates than the defined relative
measures and much quicker estimates than known absolute measures, for strongly
correlated processes. Finally, we analyse the complexity of the estimation
technique and test the robustness to non-stationarity, and show that none of
the current techniques are robust to non-stationarity, even if they are robust
to strong correlations.


Abstract:
  Within the MOJAVE VLBA program (Monitoring of Jets in AGN with VLBA
Experiments), we have accumulated observational data at 15 GHz for hundreds of
jets in $\gamma$-ray bright active galactic nuclei since the beginning of the
Fermi scientific observations in August 2008. We investigated a time delay
between the flux density of AGN parsec-scale radio emission at 15 GHz and
0.1$-$300 GeV Fermi LAT photon flux, taken from constructed light curves using
weekly and adaptive binning. The correlation analysis shows that radio is
lagging $\gamma$-ray radiation by up to 8 months in the observer's frame, while
in the source frame, the typical delay is about 2-3 months. If the jet radio
emission, excluding the opaque core, is considered, significant correlation is
found at greater time lags. We supplement these results with VLBI kinematics
and core shift data to conclude that the dominant high-energy production zone
is typically located at a distance of several parsecs from the central nucleus.
We also found that quasars have on average more significant correlation peak,
more distant $\gamma$-ray emission region from the central engine and shorter
variability time scale compared to those of BL Lacertae objects.


Abstract:
  We investigate a cooperative federated learning framework among devices for
mobile edge computing, named CFLMEC, where devices co-exist in a shared
spectrum with interference. Keeping in view the time-average network throughput
of cooperative federated learning framework and spectrum scarcity, we focus on
maximize the admission data to the edge server or the near devices, which fills
the gap of communication resource allocation for devices with federated
learning. In CFLMEC, devices can transmit local models to the corresponding
devices or the edge server in a relay race manner, and we use a decomposition
approach to solve the resource optimization problem by considering maximum data
rate on sub-channel, channel reuse and wireless resource allocation in which
establishes a primal-dual learning framework and batch gradient decent to learn
the dynamic network with outdated information and predict the sub-channel
condition. With aim at maximizing throughput of devices, we propose
communication resource allocation algorithms with and without sufficient
sub-channels for strong reliance on edge servers (SRs) in cellular link, and
interference aware communication resource allocation algorithm for less
reliance on edge servers (LRs) in D2D link. Extensive simulation results
demonstrate the CFLMEC can achieve the highest throughput of local devices
comparing with existing works, meanwhile limiting the number of the
sub-channels.


Abstract:
  Underwater robots play an important role in oceanic geological exploration,
resource exploitation, ecological research, and other fields. However, the
visual perception of underwater robots is affected by various environmental
factors. The main challenge now is that images captured by underwater robots
are color-distorted. The hue of underwater images tends to be close to green
and blue. In addition, the contrast is low and the details are fuzzy. In this
paper, a new underwater image enhancement algorithm based on deep learning and
image formation model is proposed. Experimental results show that the
advantages of the proposed method are that it eliminates the influence of
underwater environmental factors, enriches the color, enhances details,
achieves higher scores in PSNR and SSIM metrics, and helps feature key-point
point matching get better results. Another significant advantage is that its
computation speed is much faster than other methods.


Abstract:
  In this paper, we compare single-photon qubits and hybrid qubits as
information carriers through quantum teleportation using a Gaussian
continuous-variable channel. A hybrid qubit in our study is in the form of
entanglement between a coherent state and a single photon. We find that hybrid
qubits outperform photonic qubits when coherent amplitudes of the hybrid qubits
are as low as $\alpha\lesssim 1$, while photonic qubits yield better results
for larger amplitudes. We analyze effects of photon losses, and observe that
the overall character of teleportation for different qubits remains the same
although the teleportation fidelities are degraded by photon losses. Our work
provides a comparative look at practical quantum information processing with
different types of qubits.


Abstract:
  Periodic quasars have been suggested as candidates for hosting binary
supermassive black holes (SMBHs), although alternative scenarios remain
possible to explain the optical light curve periodicity. To test the
alternative hypothesis of precessing radio jet, we present deep 6 GHz radio
imaging conducted with NSF's Karl G. Jansky Very Large Array (VLA) in its C
configuration for the three candidate periodic quasars, DES
J024703.24$-$010032.0, DES J024944.66$-$000036.8, and DES
J025214.67$-$002813.7. Our targets were selected based on their optical
variability using 20-yr long multi-color light curves from the Dark Energy
Survey (DES) and the Sloan Digital Sky Survey (SDSS). The new VLA observations
show that all three periodic quasars are radio-quiet with the radio loudness
parameters measured to be $R\equiv f_{6\,{\rm cm}}/f_{{\rm 2500}}$ of
$\lesssim$1.0$-$1.5 and the $k$-corrected luminosities $\nu L_\nu$[6 GHz] of
$\lesssim$5$-$21 $\times$ 10$^{39}$ erg s$^{-1}$. They are in stark contrast to
previously known periodic quasars proposed as binary SMBH candidates such as
the blazar OJ 287 and PG1302$-$102. Our results rule out optical emission
contributed from precessing radio jets as the origin of the optical periodicity
in the three DES$-$SDSS-selected candidate periodic quasars. Future continued
optical monitoring and complementary multi-wavelength observations are still
needed to further test the binary SMBH hypothesis as well as other competing
scenarios to explain the optical periodicity.


Abstract:
  As the Internet of Things (IoT) rolls out today to devices whose lifetime may
well exceed a decade, conservative threat models should consider attackers with
access to quantum computing power. The SUIT standard (specified by the IETF)
defines a security architecture for IoT software updates, standardizing the
metadata and the cryptographic tools-namely, digital signatures and hash
functions-that guarantee the legitimacy of software updates. While the
performance of SUIT has previously been evaluated in the pre-quantum context,
it has not yet been studied in a post-quantum context. Taking the open-source
implementation of SUIT available in RIOT as a case study, we overview
post-quantum considerations, and quantum-resistant digital signatures in
particular, focusing on lowpower, microcontroller-based IoT devices which have
stringent resource constraints in terms of memory, CPU, and energy consumption.
We benchmark a selection of proposed post-quantum signature schemes (LMS,
Falcon, and Dilithium) and compare them with current pre-quantum signature
schemes (Ed25519 and ECDSA). Our benchmarks are carried out on a variety of IoT
hardware including ARM Cortex-M, RISC-V, and Espressif (ESP32), which form the
bulk of modern 32-bit microcontroller architectures. We interpret our benchmark
results in the context of SUIT, and estimate the real-world impact of
post-quantum alternatives for a range of typical software update categories.
CCS CONCEPTS $\bullet$ Computer systems organization $\rightarrow$ Embedded
systems.


Abstract:
  Crowdsourcing is becoming an accepted method of software development for
different phases in the production lifecycle. Ideally, mass parallel production
through Crowdsourcing could be an option for rapid acquisition in software
engineering by leveraging infinite worker resource on the internet. It is
important to understand the patterns and strategies of decomposing and
uploading parallel tasks to maintain a stable worker supply as well as a
satisfactory task completion rate.
  This research report is an empirical analysis of the available tasks'
lifecycle patterns in crowdsourcing. Following the waterfall model in
Crowdsourced Software Development (CSD), this research identified four patterns
for the sequence of task arrival per project: 1) Prior Cycle, 2) Current Cycle,
3) Orbit Cycle, and 4) Fresh Cycle.


Abstract:
  For abelian groups $A, B$, $A$ is called $B$-small if the covariant functor
$Hom(A,-)$ commutes with all direct sums $B^{(\kappa)}$ and $A$ is self-small
provided it is $A$-small. The paper characterizes self-small products applying
developed closure properties of the classes of relatively small groups. As a
consequence, self-small products of finitely generated abelian groups are
described.


Abstract:
  Boundary samples are special inputs to artificial neural networks crafted to
identify the execution environment used for inference by the resulting output
label. The paper presents and evaluates algorithms to generate transparent
boundary samples. Transparency refers to a small perceptual distortion of the
host signal (i.e., a natural input sample). For two established image
classifiers, ResNet on FMNIST and CIFAR10, we show that it is possible to
generate sets of boundary samples which can identify any of four tested
microarchitectures. These sets can be built to not contain any sample with a
worse peak signal-to-noise ratio than 70dB. We analyze the relationship between
search complexity and resulting transparency.


Abstract:
  The rapid progress of noisy intermediate-scale quantum (NISQ) computing
underscores the need to test and evaluate new devices and applications. Quantum
chemistry is a key application area for these devices, and therefore serves as
an important benchmark for current and future quantum computer performance.
Previous benchmarks in this field have focused on variational methods for
computing ground and excited states of various molecules, including a
benchmarking suite focused on performance of computing ground states for
alkali-hydrides under an array of error mitigation methods. Here, we outline
state of the art methods to reach chemical accuracy in hybrid quantum-classical
electronic structure calculations of alkali hydride molecules on NISQ devices
from IBM. We demonstrate how to extend the reach of variational eigensolvers
with new symmetry preserving Ans\"atze. Next, we outline how to use quantum
imaginary time evolution and Lanczos as a complementary method to variational
techniques, highlighting the advantages of each approach. Finally, we
demonstrate a new error mitigation method which uses systematic error
cancellation via hidden inverse gate constructions, improving the performance
of typical variational algorithms. These results show that electronic structure
calculations have advanced rapidly, to routine chemical accuracy for simple
molecules, from their inception on quantum computers a few short years ago, and
they point to further rapid progress to larger molecules as the power of NISQ
devices grows.


Abstract:
  We give a non-asymptotic bound on the spectral norm of a $d\times d$ matrix
$X$ with centered jointly Gaussian entries in terms of the covariance matrix of
the entries. In some cases, this estimate is sharp and removes the $\sqrt{\log
d}$ factor in the noncommutative Khintchine inequality.


Abstract:
  Regulatory compliance is a well-studied area, including research on how to
model, check, analyse, enact, and verify compliance of software. However, while
the theoretical body of knowledge is vast, empirical evidence on challenges
with regulatory compliance, as faced by industrial practitioners particularly
in the Software Engineering domain, is still lacking. In this paper, we report
on an industrial case study which aims at providing insights into common
practices and challenges with checking and analysing regulatory compliance, and
we discuss our insights in direct relation to the state of reported evidence.
Our study is performed at Ericsson AB, a large telecommunications company,
which must comply to both locally and internationally governing regulatory
entities and standards such as GDPR. The main contributions of this work are
empirical evidence on challenges experienced by Ericsson that complement the
existing body of knowledge on regulatory compliance.


Abstract:
  Layer transfer offers enormous potential for the industrial implementation of
2D material technology platforms. However, the transfer method used must retain
as-grown uniformity and cleanliness in the transferred films for the
fabrication of 2D material-based. Additionally, the method used must be capable
of large-area transfer to maintain wafer-scale fabrication standards. Here, a
facile route to transfer centimeter-scale synthesized 2D TMDCs (3L MoS2, 1L
WS2) onto various substrates such as sapphire, SiO2/Si, and flexible substrates
(mica, polyimide) has been developed using a water-soluble layer (Na2S/Na2SO4)
underneath the as-grown film. The developed transfer process represents a fast,
clean, generic, and scalable technique to transfer 2D atomic layers. The key
strategy used in this process includes the dissolution of Na2S/Na2SO4 layer due
to the penetration of NaOH solution between the growth substrate and
hydrophobic 2D TMDC film. As a proof-of-concept device, a broadband
photodetector has been fabricated onto transferred 3L MoS2, which shows
photoresponse behavior for a wide range of wavelength ranging from NIR to UV.
The enhancement in photocurrent was found to be 100 times and 10 times to dark
current in the UV and visible region, respectively. This work opens up the
pathway towards flexible electronics and optoelectronics.


Abstract:
  We derive two new forms of the K\'arm\'an-Howarth-Monin equation for decaying
compressible Hall magnetohydrodynamic (MHD) turbulence. We test them on results
of a weakly-compressible, two-dimensional, moderate-Reynolds-number Hall MHD
simulation and compare them with an isotropic spectral transfer (ST) equation.
The KHM and ST equations are automatically satisfied during the whole
simulation owing to the periodic boundary conditions and have complementary
cumulative behavior. They are used here to analyze the onset of turbulence and
its properties when it is fully developed. These approaches give equivalent
results characterizing: the decay of the kinetic + magnetic energy at large
scales, the MHD and Hall cross-scale energy transfer/cascade, the pressure
dilatation, and the dissipation. The Hall cascade appears when the MHD one
brings the energy close to the ion inertial range and is related to the
formation of reconnecting current sheets. At later times, the pressure-dilation
energy-exchange rate oscillates around zero with no net effect on the
cross-scale energy transfer when averaged over a period of its oscillations. A
reduced one-dimensional analysis suggests that all three methods may be useful
to estimate the energy cascade rate from in situ observations.


Abstract:
  In this paper we study the problem of model reduction by moment matching for
stochastic systems. We characterize the mathematical object which generalizes
the notion of moment to stochastic differential equations and we find a class
of models which achieve moment matching. However, differently from the
deterministic case, these reduced-order models cannot be considered "simpler"
because of the high computational cost paid to determine the moment. To
overcome this difficulty, we relax the moment matching problem in two different
ways and we present two classes of reduced-order models which, approximately
matching the stochastic moment, are computationally tractable.


Abstract:
  Neural image compression (NIC) is a new coding paradigm where coding
capabilities are captured by deep models learned from data. This data-driven
nature enables new potential functionalities. In this paper, we study the
adaptability of codecs to custom domains of interest. We show that NIC codecs
are transferable and that they can be adapted with relatively few target domain
images. However, naive adaptation interferes with the solution optimized for
the original source domain, resulting in forgetting the original coding
capabilities in that domain, and may even break the compatibility with
previously encoded bitstreams. Addressing these problems, we propose Codec
Adaptation without Forgetting (CAwF), a framework that can avoid these problems
by adding a small amount of custom parameters, where the source codec remains
embedded and unchanged during the adaptation process. Experiments demonstrate
its effectiveness and provide useful insights on the characteristics of
catastrophic interference in NIC.


Abstract:
  We present a graph neural network to learn graph coloring heuristics using
reinforcement learning. Our learned deterministic heuristics give better
solutions than classical degree-based greedy heuristics and only take seconds
to evaluate on graphs with tens of thousands of vertices. As our approach is
based on policy-gradients, it also learns a probabilistic policy as well. These
probabilistic policies outperform all greedy coloring baselines and a machine
learning baseline. Our approach generalizes several previous machine-learning
frameworks, which applied to problems like minimum vertex cover. We also
demonstrate that our approach outperforms two greedy heuristics on minimum
vertex cover.


Abstract:
  Expressing attack-defence trees in a multi-agent setting allows for studying
a new aspect of security scenarios, namely how the number of agents and their
task assignment impact the performance, e.g. attack time, of strategies
executed by opposing coalitions. Optimal scheduling of agents' actions, a
non-trivial problem, is thus vital. We discuss associated caveats and propose
an algorithm that synthesises such an assignment, targeting minimal attack time
and using minimal number of agents for a given attack-defence tree.


Abstract:
  Diferentially private (DP) synthetic datasets are a powerful approach for
training machine learning models while respecting the privacy of individual
data providers. The effect of DP on the fairness of the resulting trained
models is not yet well understood. In this contribution, we systematically
study the effects of differentially private synthetic data generation on
classification. We analyze disparities in model utility and bias caused by the
synthetic dataset, measured through algorithmic fairness metrics. Our first set
of results show that although there seems to be a clear negative correlation
between privacy and utility (the more private, the less accurate) across all
data synthesizers we evaluated, more privacy does not necessarily imply more
bias. Additionally, we assess the effects of utilizing synthetic datasets for
model training and model evaluation. We show that results obtained on synthetic
data can misestimate the actual model performance when it is deployed on real
data. We hence advocate on the need for defining proper testing protocols in
scenarios where differentially private synthetic datasets are utilized for
model training and evaluation.


Abstract:
  In this simple paper, we exhibit a Schur partition giving rise to a
triangle-free linear colouring of $K_{1697}$ in 7 colours. Thus we show that
the Schur number $S(7) \ge 1696$ and the multicolour Ramsey number $R_{7}(3)
\ge 1698$.
  We also demonstrate a specific partition of the closed integer interval $[1,
376]$ which, through repeated use of a specific construction, allows us to
conclude that $S(k+5) \ge 376.S(k)+160$ for any positive integer $k$. The
existence of this partition, with its specific properties, implies that
$\lim_{\substack{r \rightarrow \infty}} R{_r}(3)^{1/r} > 3.273$.


Abstract:
  An square matrix is $k$-Toeplitz if its diagonals are periodic sequences of
period $k$. We find rational formulas for the determinant, the characteristic
polynomial, and the elements of the inverse of a tridiagonal $k$-Toeplitz
matrix (in particular, of any tridiagonal matrix) over any commutative unital
ring, using only elementary linear algebra.


Abstract:
  The increasing availability of data presents an opportunity to calibrate
unknown parameters which appear in complex models of phenomena in the
biomedical, physical and social sciences. However, model complexity often leads
to parameter-to-data maps which are expensive to evaluate and are only
available through noisy approximations. This paper is concerned with the use of
interacting particle systems for the solution of the resulting inverse problems
for parameters. Of particular interest is the case where the available forward
model evaluations are subject to rapid fluctuations, in parameter space,
superimposed on the smoothly varying large scale parametric structure of
interest. Multiscale analysis is used to study the behaviour of interacting
particle system algorithms when such rapid fluctuations, which we refer to as
noise, pollute the large scale parametric dependence of the parameter-to-data
map. Ensemble Kalman methods (which are derivative-free) and Langevin-based
methods (which use the derivative of the parameter-to-data map) are compared in
this light. The ensemble Kalman methods are shown to behave favourably in the
presence of noise in the parameter-to-data map, whereas Langevin methods are
adversely affected. On the other hand, Langevin methods have the correct
equilibrium distribution in the setting of noise-free forward models, whilst
ensemble Kalman methods only provide an uncontrolled approximation, except in
the linear case. Therefore a new class of algorithms, ensemble Gaussian process
samplers, which combine the benefits of both ensemble Kalman and Langevin
methods, are introduced and shown to perform favourably.


Abstract:
  Scenario-based question answering (SQA) has attracted an increasing research
interest. Compared with the well-studied machine reading comprehension (MRC),
SQA is a more challenging task: a scenario may contain not only a textual
passage to read but also structured data like tables, i.e., tabular scenario
based question answering (TSQA). AI applications of TSQA such as answering
multiple-choice questions in high-school exams require synthesizing data in
multiple cells and combining tables with texts and domain knowledge to infer
answers. To support the study of this task, we construct GeoTSQA. This dataset
contains 1k real questions contextualized by tabular scenarios in the geography
domain. To solve the task, we extend state-of-the-art MRC methods with TTGen, a
novel table-to-text generator. It generates sentences from variously
synthesized tabular data and feeds the downstream MRC method with the most
useful sentences. Its sentence ranking model fuses the information in the
scenario, question, and domain knowledge. Our approach outperforms a variety of
strong baseline methods on GeoTSQA.


Abstract:
  As a novel layered noble metal dichalcogenide material, palladium diselenide
(PdSe2) has attracted wide interest due to its excellent optical and electronic
properties. In this work, a strong third-order nonlinear optical response of 2D
PdSe2 films is reported. We conduct both open aperture (OA) and closed-aperture
(CA) Z scan measurements with a femtosecond pulsed laser at 800 nm to
investigate the nonlinear absorption and nonlinear refraction, respectively. In
the OA experiment, we observe optical limiting behaviour originating from large
two photo absorption (TPA) in the PdSe2 film of \b{eta} = 3.26 x 10-8 m/W. In
the CA experiment, we measure a peak-valley response corresponding to a large
and negative Kerr nonlinearity of n2 = -1.33 x 10-15 m2/W, two orders of
magnitude larger than bulk silicon. In addition, the variation of n2 as a
function of laser intensity is also characterized, with n2 decreasing in
magnitude when increasing incident laser intensity, becoming saturated at n2 =
-9.96 x 10-16 m2/W at high intensities. Our results show that the extraordinary
third order nonlinear optical properties of PdSe2 have strong potential for
high-performance nonlinear photonic devices.


Abstract:
  We employ a non-compact gauging of four-dimensional maximal supergravity to
construct a two-parameter family of AdS$_4$ J-fold solutions preserving
$\mathcal{N}=2$ supersymmetry. All solutions preserve
$\mathfrak{u}(1)\times\mathfrak{u}(1)$ global symmetry and in special limits we
recover the previously known $\mathfrak{su}(2)\times\mathfrak{u}(1)$ invariant
$\mathcal{N}=2$ and $\mathfrak{su}(2)\times\mathfrak{su}(2)$ invariant
$\mathcal{N}=4$ J-fold solutions. This family of AdS$_4$ backgrounds can be
uplifted to type IIB string theory and is holographically dual to the conformal
manifold of a class of three-dimensional $S$-fold SCFTs obtained from the
$\mathcal{N}=4$ $T[\text{U}(N)]$ theory of Gaiotto-Witten. We find the spectrum
of supergravity excitations of the AdS$_4$ solutions and use it to study how
the operator spectrum of the three-dimensional SCFT depends on the exactly
marginal couplings.


Abstract:
  Whistler mode chorus waves are quasi-coherent electromagnetic emissions with
frequency chirping. Various models have been proposed to understand the
chirping mechanism, which is a long-standing problem in space plasmas. Based on
analysis of effective wave growth rate and electron phase space dynamics in a
self-consistent particle simulation, we propose here a phenomenological model
called the "Trap-Release-Amplify" (TaRA) model for chorus. In this model, phase
space structures of correlated electrons are formed by nonlinear wave particle
interactions, which mainly occur in the downstream. When released from the wave
packet in the upstream, these electrons selectively amplify new emissions which
satisfy the phase-locking condition to maximize wave power transfer, leading to
frequency chirping. The phase-locking condition at the release point gives a
frequency chirping rate that is fully consistent with the one by Helliwell in
case of a nonuniform background magnetic field. The nonlinear wave particle
interaction part of the TaRA model results in a chirping rate that is
proportional to wave amplitude, a conclusion originally reached by Vomvoridis
et al. Therefore, the TaRA model unifies two different results from seemingly
unrelated studies. Furthermore, the TaRA model naturally explains fine
structures of chorus waves, including subpackets and bandwidth, and their
evolution through dynamics of phase-trapped electrons. Finally, we suggest that
this model could be applied to explain other related phenomena, including
frequency chirping of chorus in a uniform background magnetic field and of
electromagnetic ion cyclotron waves in the magnetosphere.


Abstract:
  Probabilistic linear discriminant analysis (PLDA) or cosine similarity have
been widely used in traditional speaker verification systems as back-end
techniques to measure pairwise similarities. To make better use of multiple
enrollment utterances, we propose a novel attention back-end model, which can
be used for both text-independent (TI) and text-dependent (TD) speaker
verification, and employ scaled-dot self-attention and feed-forward
self-attention networks as architectures that learn the intra-relationships of
the enrollment utterances. In order to verify the proposed attention back-end,
we conduct a series of experiments on CNCeleb and VoxCeleb datasets by
combining it with several sate-of-the-art speaker encoders including TDNN and
ResNet. Experimental results using multiple enrollment utterances on CNCeleb
show that the proposed attention back-end model leads to lower EER and minDCF
score than the PLDA and cosine similarity counterparts for each speaker encoder
and an experiment on VoxCeleb indicate that our model can be used even for
single enrollment case.


Abstract:
  Atmospheric retrievals of exoplanetary transmission spectra provide important
constraints on various properties such as chemical abundances, cloud/haze
properties, and characteristic temperatures, at the day-night atmospheric
terminator. To date, most spectra have been observed for giant exoplanets due
to which retrievals typically assume H-rich atmospheres. However, recent
observations of mini-Neptunes/super-Earths, and the promise of upcoming
facilities including JWST, call for a new generation of retrievals that can
address a wide range of atmospheric compositions and related complexities. Here
we report Aurora, a next-generation atmospheric retrieval framework that builds
upon state-of-the-art architectures and incorporates the following key
advancements: a) a generalised compositional retrieval allowing for H-rich and
H-poor atmospheres, b) a generalised prescription for inhomogeneous
clouds/hazes, c) multiple Bayesian inference algorithms for high-dimensional
retrievals, d) modular considerations for refraction, forward scattering, and
Mie-scattering, and e) noise modeling functionalities. We demonstrate Aurora on
current and/or synthetic observations of hot Jupiter HD209458b, mini-Neptune
K218b, and rocky exoplanet TRAPPIST1d. Using current HD209458b spectra, we
demonstrate the robustness of our framework and cloud/haze prescription against
assumptions of H-rich/H-poor atmospheres, improving on previous treatments.
Using real and synthetic spectra of K218b, we demonstrate the agnostic approach
to confidently constrain its bulk atmospheric composition and obtain precise
abundance estimates. For TRAPPIST1d, 10 JWST NIRSpec transits can enable
identification of the main atmospheric component for cloud-free CO$_2$-rich and
N$_2$-rich atmospheres, and abundance constraints on trace gases including
initial indications of O$_3$ if present at enhanced levels ($\sim$10-100x Earth
levels).


Abstract:
  Many autonomous systems, such as robots and self-driving cars, involve
real-time decision making in complex environments, and require prediction of
future outcomes from limited data. Moreover, their decisions are increasingly
required to be interpretable to humans for safe and trustworthy co-existence.
This paper is a first step towards interpretable learning-based robot control.
We introduce a novel learning problem, called incremental formula and predictor
learning, to generate binary classifiers with temporal logic structure from
time-series data. The classifiers are represented as pairs of Signal Temporal
Logic (STL) formulae and predictors for their satisfaction. The incremental
property provides prediction of labels for prefix signals that are revealed
over time. We propose a boosted decision-tree algorithm that leverages weak,
but computationally inexpensive, learners to increase prediction and runtime
performance. The effectiveness and classification accuracy of our algorithms
are evaluated on autonomous-driving and naval surveillance case studies.


Abstract:
  Photoacoustic tomography (PAT) has the potential to recover morphological and
functional tissue properties such as blood oxygenation with high spatial
resolution and in an interventional setting. However, decades of research
invested in solving the inverse problem of recovering clinically relevant
tissue properties from spectral measurements have failed to produce solutions
that can quantify tissue parameters robustly in a clinical setting. Previous
attempts to address the limitations of model-based approaches with machine
learning were hampered by the absence of labeled reference data needed for
supervised algorithm training. While this bottleneck has been tackled by
simulating training data, the domain gap between real and simulated images
remains a huge unsolved challenge. As a first step to address this bottleneck,
we propose a novel approach to PAT data simulation, which we refer to as
"learning to simulate". Our approach involves subdividing the challenge of
generating plausible simulations into two disjoint problems: (1) Probabilistic
generation of realistic tissue morphology, represented by semantic segmentation
maps and (2) pixel-wise assignment of corresponding optical and acoustic
properties. In the present work, we focus on the first challenge. Specifically,
we leverage the concept of Generative Adversarial Networks (GANs) trained on
semantically annotated medical imaging data to generate plausible tissue
geometries. According to an initial in silico feasibility study our approach is
well-suited for contributing to realistic PAT image synthesis and could thus
become a fundamental step for deep learning-based quantitative PAT.


Abstract:
  A dramatic slowing down of acoustic wave transport in dense fish shoals is
observed in open-sea fish cages. By employing a multi-beam ultrasonic antenna,
we observe the coherent backscattering (CBS) phenomenon. We extract key
parameters of wave transport such as the transport mean free path and the
energy transport velocity of diffusive waves from diffusion theory fits to the
experimental data. The energy transport velocity is found to be about 10 times
smaller than the speed of sound in water, a value that is exceptionally low
compared with most observations in acoustics. By studying different models of
the fish body, we explain the basic mechanism responsible for the observed very
slow transport of ultrasonic waves in dense fish shoals. Our results show that,
while the fish swim bladder plays an important role in wave scattering, other
organs have to be considered to explain ultra-low energy transport velocities.


Abstract:
  In recent decades, with the emergence of numerous novel intelligent
optimization algorithms, many optimization researchers have begun to look for a
basic search mechanism for their schemes that provides a more essential
explanation of their studies. This paper aims to study the basic mechanism of
an algorithm for black-box optimization with quantum theory. To achieve this
goal, the Schroedinger equation is employed to establish the relationship
between the optimization problem and the quantum system, which makes it
possible to study the dynamic search behaviors in the evolution process with
quantum theory. Moreover, to explore the basic behavior of the optimization
system, the optimization problem is assumed to be decomposed and approximated.
Then, a multilevel approximation quantum dynamics model of the optimization
algorithm is established, which provides a mathematical and physical framework
for the analysis of the optimization algorithm. Correspondingly, the basic
search behavior based on this model is derived, which is governed by quantum
theory. Comparison experiments and analysis between different bare-bones
algorithms confirm the existence of the quantum mechanic based basic search
mechanism of the algorithm on black-box optimization.


Abstract:
  Psycholinguistic studies of human word processing and lexical access provide
ample evidence of the preferred nature of word-initial versus word-final
segments, e.g., in terms of attention paid by listeners (greater) or the
likelihood of reduction by speakers (lower). This has led to the conjecture --
as in Wedel et al. (2019b), but common elsewhere -- that languages have evolved
to provide more information earlier in words than later. Information-theoretic
methods to establish such tendencies in lexicons have suffered from several
methodological shortcomings that leave open the question of whether this high
word-initial informativeness is actually a property of the lexicon or simply an
artefact of the incremental nature of recognition. In this paper, we point out
the confounds in existing methods for comparing the informativeness of segments
early in the word versus later in the word, and present several new measures
that avoid these confounds. When controlling for these confounds, we still find
evidence across hundreds of languages that indeed there is a cross-linguistic
tendency to front-load information in words.


Abstract:
  Lentil seeds have been packed in a dielectric barrier device and exposed for
several minutes to a cold atmospheric plasma generated in helium with/without a
reactive gas (nitrogen or oxygen). While no impact is evidenced on germination
rates (caping nearly at 100% with/without plasma exposure), seeds vigor is
clearly improved with a median germination time decreasing from 1850 min (31h)
to 1500 min (26 h), hence representing a time saving of at least 5 hours. We
show that the admixture of nitrogen to helium can further increase this time
saving up to 8 hours. Contrarily, we demonstrate that the addition of molecular
oxygen to the helium discharge does not promote seeds vigor. Whatever the
plasma chemistry utilized, these biological effects are accompanied with strong
hydrophilization of the seed coating (with a decrease in contact angles from
118{\deg} to 25{\deg}) as well as increased water absorption (water uptakes
measured 8 hours after imbibition are close to 50% for plasma-treated seeds
instead of 37% for seeds from the control group). A follow-up of the seeds over
a 45-days ageing period shows the sustainability of the plasma-triggered
biological effects: whatever the plasma treatment, seeds vigor remains stable
and much higher than for seeds unexposed to plasma). For these reasons, the
seed-packed dielectric barrier device (SP-DBD) supplied with a He-N2 gas
mixture can be considered as a relevant dry atmospheric priming plasma (DAPP)
in the same way as those used in routine by seed companies.


Abstract:
  Photoacoustic (PA) imaging is a biomedical imaging modality capable of
acquiring high-contrast images of optical absorption at depths much greater
than traditional optical imaging techniques. However, practical instrumentation
and geometry limit the number of available acoustic sensors surrounding the
imaging target, which results in the sparsity of sensor data. Conventional PA
image reconstruction methods give severe artifacts when they are applied
directly to the sparse PA data. In this paper, we firstly propose to employ a
novel signal processing method to make sparse PA raw data more suitable for the
neural network, concurrently speeding up image reconstruction. Then we propose
Attention Steered Network (AS-Net) for PA reconstruction with multi-feature
fusion. AS-Net is validated on different datasets, including simulated
photoacoustic data from fundus vasculature phantoms and experimental data from
in vivo fish and mice. Notably, the method is also able to eliminate some
artifacts present in the ground truth for in vivo data. Results demonstrated
that our method provides superior reconstructions at a faster speed.


Abstract:
  The current methods for realization of the second with the highest accuracy
through the frequency of the hyperfine transition between two levels of the
ground state of the cesium 133 atom are considered. It is discussed possible
ways for the future redefinition of the SI time unit with the help of optical
frequency standards, as well as thorium and super-high frequency standards.
Several constants which can be also used for definitions of the time, mass and
electric current units are analyzed.


Abstract:
  Using Infrastructure-to-Vehicle (I2V) information can be of great benefit
when driving autonomously in high-density traffic situations with limited
visibility, since the sensing capabilities of the vehicle are enhanced by
external sensors. In this research, a method is introduced to increase the
vehicle's self-awareness in intersections for one of the largest foreseen
challenges when using I2V communication: cyber security. The introduced anomaly
detection algorithm, running on the automated vehicle, assesses the health of
the I2V communication against multiple cyber security attacks. The analysis is
done in a simulation environment, using cyber-attack scenarios from the
Secredas Project (Cyber Security for Cross Domain Reliable Dependable Automated
Systems) and provides insights into the limitations the vehicle has when facing
I2V cyber attacks of different types and amplitudes and when sensor redundancy
is lost. The results demonstrate that anomalies injected can be robustly
detected and mitigated by the autonomous vehicle, allowing it to react more
safely and comfortably and maintaining correct object tracking in
intersections.


Abstract:
  Although close contact represents an important contagion route, the mechanism
of exposure to exhaled droplets remains insufficiently characterized. In this
study, an integrated risk assessment is presented for SARS-CoV-2 close contact
exposure between a speaking infectious subject and a susceptible subject. It is
based on a three-dimensional transient numerical model for the description of
exhaled droplet spread once emitted by a speaking person, coupled with a
recently proposed SARS-CoV-2 emission approach. Particle image velocimetry
measurements were conducted to validate the numerical model. The contribution
of large droplets to infection risk is dominant for distances < 0.6 m, whereas
for longer distances, the exposure risk depends only on airborne droplets. In
particular, for short exposures (10 s) a minimum safety distance of 0.75 m
should be maintained to lower the risk below 0.1%; for exposures of 1 and 15
min this distance increases to about 1.0 and 1.5 m, respectively. Based on the
interpersonal distances across countries reported as a function of interacting
individuals, cultural differences, and environmental and sociopsychological
factors, the approach presented here revealed that, in addition to intimate and
personal distances, particular attention must be paid to exposures longer than
1 min within social distances (of about 1 m).


Abstract:
  It is possible to implement a certain form of modified gravity inspired by
loop quantization through non-bijective canonical transformations. The
canonical nature might suggest that such modifications are guaranteed to
preserve general covariance. Here, however, we show that a dedicated space-time
analysis is still required, even in the case of a bijective canonical
transformation. In addition, a complete global analysis is presented for a
recent proposal of a non-bijective transformation, showing that it does not
preserve general covariance and that the only novel physical effect introduced
by the modification is the presence of certain time-reversal hypersurfaces
between classical space-time regions. These results provide further insights
into the physical interpretation of modified dynamics in models of loop quantum
gravity.


Abstract:
  We discuss whether the bound nature of multiquark states in quark models
could benefit from relativistic effects on the kinetic energy operator. For
mesons and baryons, relativistic corrections to the kinetic energy lead to
lower energies, and thus call for a retuning of the parameters of the model.
For multiquark states, as well as their respective thresholds, a comparison is
made of the results obtained with non-relativistic and relativistic kinetic
energy. It is found that the binding energy is lower in the relativistic case.
In particular, $QQ\bar q\bar q$ tetraquarks with double heavy flavor become
stable for a larger ratio of the heavy to light quark masses; and the all-heavy
tetraquarks $QQ\bar Q\bar Q$ that are not stable in standard non-relativistic
quark models remain unstable when a relativistic form of kinetic energy is
adopted


Abstract:
  We report on precise Doppler measurements of L231-32 (TOI-270), a nearby M
dwarf ($d=22$ pc, $M_\star = 0.39$ M$_\odot$, $R_\star = 0.38$ R$_\odot$),
which hosts three transiting planets that were recently discovered using data
from the Transiting Exoplanet Survey Satellite (TESS). The three planets are
1.2, 2.4, and 2.1 times the size of Earth and have orbital periods of 3.4, 5.7,
and 11.4 days. We obtained 29 high-resolution optical spectra with the newly
commissioned Echelle Spectrograph for Rocky Exoplanet and Stable Spectroscopic
Observations (ESPRESSO) and 58 spectra using the High Accuracy Radial velocity
Planet Searcher (HARPS). From these observations, we find the masses of the
planets to be $1.58 \pm 0.26$, $6.15 \pm 0.37$, and $4.78 \pm 0.43$ M$_\oplus$,
respectively. The combination of radius and mass measurements suggests that the
innermost planet has a rocky composition similar to that of Earth, while the
outer two planets have lower densities. Thus, the inner planet and the outer
planets are on opposite sides of the `radius valley' -- a region in the
radius-period diagram with relatively few members, which has been interpreted
as a consequence of atmospheric photo-evaporation. We place these findings into
the context of other small close-in planets orbiting M dwarf stars, and use
support vector machines to determine the location and slope of the M dwarf
($T_\mathrm{eff} < 4000$ K) radius valley as a function of orbital period. We
compare the location of the M dwarf radius valley to the radius valley observed
for FGK stars, and find that its location is a good match to photo-evaporation
and core-powered mass loss models. Finally, we show that planets below the M
dwarf radius valley have compositions consistent with stripped rocky cores,
whereas most planets above have a lower density consistent with the presence of
a H-He atmosphere.


Abstract:
  We propose a definition for the average indirect effect of a binary treatment
in the potential outcomes model for causal inference under cross-unit
interference. Our definition is analogous to the standard definition of the
average direct effect, and can be expressed without needing to compare outcomes
across multiple randomized experiments. We show that the proposed indirect
effect satisfies a decomposition theorem whereby, in a Bernoulli trial, the sum
of the average direct and indirect effects always corresponds to the effect of
a policy intervention that infinitesimally increases treatment probabilities.
We also consider a number of parametric models for interference, and find that
our (non-parametric) indirect effect remains a natural estimand when
re-expressed in the context of these models.