  It is a rather universal tacit and unquestioned belief - and even more so
among physicists - that there is one and only one set of real scalars, namely,
the one given by the usual field $\mathbb{R}$ of real numbers, with its usual
linear order structure on the geometric line. Such a dramatically limiting and
thus harmful belief comes, unknown to equally many, from the similarly tacit
acceptance of the ancient Archimedean Axiom in Euclid's Geometry. The
consequence of that belief is a similar belief in the uniqueness of the field
$\mathbb{C}$ of complex numbers, and therefore, of the various spaces,
manifolds, etc., be they finite or infinite dimensional, constructed upon the
real or complex numbers, including the Hilbert spaces used in Quantum
Mechanics. An near total lack of awareness follows about the {\it rich
self-similar} structure of various linearly ordered scalar fields obtained
through the {\it ultrapower} construction which extend the usual field
$\mathbb{R}$ of real numbers. Such {\it ultrapower field} extensions contain as
a rather small subset the usual field $\mathbb{R}$ of real numbers. The rich
self-similar structure of such field extensions is due to {\it infinitesimals},
and thus also of {\it infinitely large} elements in such fields, which make
such fields {\it non-Archimedean}. With the concept of {\it walkable world},
which has highly intuitive and pragmatic algebraic and geometric meaning, the
mentioned rich self-similar structure is illustrated. The ultrapower fields
presented can have a wide ranging relevance in Physics, among others, for a
proper treatment of what are usually called the "infinities in Physics". The
ultrapower construction which gives such non-Archimedean fields is rather
simple and elementary, requiring only 101 Algebra.


  Plasmonic devices and circuits, bridging the gap between integrated photonic
and microelectronic technology, are promising candidates to realize on-chip
ultrawide-band and ultrahigh-speed information processing. Unfortunately, the
wideband surface plasmon source, one of the most important core components of
integrated plasmonic circuits, is still unavailable up to now. This has
seriously restricted the practical applications of plasmonic circuits. Here, we
report an ultrawide-band unidirectional surface plasmon polariton launcher with
high launching efficiency ratio and large extinction ratio, realized by
combining plasmonic bandgap engineering and linear interference effect. This
device offers excellent performances over an ultrabroad wavelength range from
690 to 900 nm, together with a high average launching efficiency ratio of 1.25,
large average extinction ratio of 30 dB, and ultracompact lateral dimension of
less than 4 um. Compared with previous reports, the operating bandwidth is
enlarged 210 folds, while the largest launching efficiency ratio, largest
extinction ratio, and small feature size are maintained simultaneously. This
provides a strategy for constructing on-chip surface plasmon source, and also
paving the way for the study of integrated plasmonic circuits.


  The present article deals with properties of one map between two expansions
of real numbers of the Salem type. Differential, integral, and other properties
of the function were considered.


  We study voting games on binary issues, where voters hold an objective over
the outcome of the collective decision and are allowed, before the vote takes
place, to negotiate their voting strategy with the other participants. We
analyse the voters' rational behaviour in the resulting two-phase game, showing
under what conditions undesirable equilibria can be removed and desirable ones
sustained as a consequence of the pre-vote phase.


  In this paper, we gave some explicit relations between absolute and relative
Gromov-Witten invariants. We proved that a symplectic manifold is symplectic
rationally connected if it contains a positive divisor symplectomorphic to
$P^n$.


  Structured illumination microscopy (SIM) is one of the most versatile
super-resolution techniques. Yet, its application to live imaging has been so
far mainly limited to fluorescent and stationary specimens. Here, we present
advancements in SIM to jointly tackle all the challenges of imaging living
samples, i.e., obtaining super-resolution over an undistorted wide-field while
dealing with sample motion, scattering, sample-induced optical aberrations and
low signal-to-noise ratio. By using adaptive optics to compensate for optical
aberrations and a reconstruction algorithm tailored for a moving and thick
tissue, we successfully applied SIM to in vivo retinal imaging and demonstrated
structured illumination ophthalmoscopy for high contrast super-resolution in
vivo imaging of the human retina.


  Given a first-order autonomous algebraic ordinary differential equation, we
present a method for computing formal power series solutions by means of
places. We provide an algorithm for computing a full characterization of
possible initial values, classified in terms of the number of distinct formal
power series solutions extending them. In addition, if a particular initial
value is given, we present a second algorithm that computes all the formal
power series solutions, up to a suitable degree, corresponding to it.
Furthermore, when the ground field is the field of the complex numbers, we
prove that the computed formal power series solutions are all convergent in
suitable neighborhoods.


  The \emph{minimum positive co-degree} $\delta^{+}_{r-1}(H)$ of a non-empty
$r$-graph $H$ is the maximum $k$ such that if $S$ is an $(r-1)$-set contained
in a hyperedge of $H$, then $S$ is contained in at least $k$ hyperedges of $H$.
For any $r$-graph $F$, the \emph{positive degree Tur\'an number}
$\mathrm{co}^{+}\mathrm{ex}(n,F)$ is defined as the maximum value of
$\delta^{+}_{r-1}(H)$ over all $n$-vertex $F$-free non-empty $r$-graphs $H$. In
this paper, we determine the positive degree Tur\'an number for $C_5$ and
$C_5^{-}$.


  To obtain a simple description of a geometrically thin magnetic accretion
disk, we apply the method of asymptotic expansion. For the first time we write
a full set of stationary asymptotic approximation equations of a thin magnetic
accretion disk. As the obtained equations cannot be solved without knowledge of
the solutions at the disk surface, we combine the results from numerical
simulations and from analytical equations to find a simple set of functional
expressions describing the radial and vertical dependence of physical
quantities in the disk. Except very close to the star, the functional form of
the disk variables is quite similar in the HD and MHD cases, with the overall
scale of density, and the vertical and radial velocity components modified by
the stellar magnetic field.


  How did the dwarf galaxy population of present-day galaxy clusters form and
grow over time? We address this question by analysing the history of dark
matter subhaloes in the Millennium-II cosmological simulation. A semi-analytic
model serves as the link to observations. We argue that a reasonable analogue
to early morphological types or red-sequence dwarf galaxies are those subhaloes
that experienced strong mass loss, or alternatively those that have spent a
long time in massive haloes. This approach reproduces well the observed
morphology-distance relation of dwarf galaxies in the Virgo and Coma clusters,
and thus provides insight into their history. Over their lifetime, present-day
late types have experienced an amount of environmental influence similar to
what the progenitors of dwarf ellipticals had already experienced at redshifts
above two. Therefore, dwarf ellipticals are more likely to be a result of early
and continuous environmental influence in group- and cluster-size haloes,
rather than a recent transformation product. The observed morphological
sequences of late-type and early-type galaxies have developed in parallel, not
consecutively. Consequently, the characteristics of today's late-type galaxies
are not necessarily representative for the progenitors of today's dwarf
ellipticals. Studies aiming to reproduce the present-day dwarf population thus
need to start at early epochs, model the influence of various environments, and
also take into account the evolution of the environments themselves.


  Dynamic graph theory is a novel, growing area that deals with graphs that
change over time and is of great utility in modelling modern wireless, mobile
and dynamic environments. As a graph evolves, possibly arbitrarily, it is
challenging to identify the graph properties that can be preserved over time
and understand their respective computability.
  In this paper we are concerned with the treewidth of dynamic graphs. We focus
on metatheorems, which allow the generation of a series of results based on
general properties of classes of structures. In graph theory two major
metatheorems on treewidth provide complexity classifications by employing
structural graph measures and finite model theory. Courcelle's Theorem gives a
general tractability result for problems expressible in monadic second order
logic on graphs of bounded treewidth, and Frick & Grohe demonstrate a similar
result for first order logic and graphs of bounded local treewidth.
  We extend these theorems by showing that dynamic graphs of bounded (local)
treewidth where the length of time over which the graph evolves and is observed
is finite and bounded can be modelled in such a way that the (local) treewidth
of the underlying graph is maintained. We show the application of these results
to problems in dynamic graph theory and dynamic extensions to static problems.
In addition we demonstrate that certain widely used dynamic graph classes
naturally have bounded local treewidth.


  Recommender systems aim to enhance the overall user experience by providing
tailored recommendations for a variety of products and services. These systems
help users make more informed decisions, leading to greater user engagement
with the platform. However, the implementation of these systems largely depends
on the context, which can vary from recommending an item or package to a user
or a group. This requires careful exploration of several models during the
deployment, as there is no comprehensive and unified approach that deals with
recommendations at different levels. Furthermore, these individual models must
be closely attuned to their generated recommendations depending on the context
to prevent significant variation in their generated recommendations. In this
paper, we propose a novel unified recommendation framework that addresses all
four recommendation tasks, namely, personalized, group, package, and
package-to-group recommendation, filling the gap in the current research
landscape. The proposed framework can be integrated with most of the
traditional matrix factorization-based collaborative filtering (CF) models.
This research underscores the significance of including group and package
information while learning latent representations of users and items for
personalized recommendations. These components help in exploiting a rich latent
representation of the user/item by enforcing them to align closely with their
corresponding group/package representation. We consider two prominent CF
techniques, namely Regularized Matrix Factorization and Maximum Margin Matrix
factorization, as the baseline models and demonstrate their customization to
various recommendation tasks. Experimental results on two publicly available
datasets are reported, comparing them to other baseline approaches for various
recommendation tasks.


  It is demonstrated that the time evolution of the electron-nuclear
polarization transfer process during pulsed dynamic nuclear polarization (DNP)
can be reversed on a microsecond timescale, leading to the observation of DNP
echoes. The DNP echoes are induced by consecutive application of two pulse
trains that produce effective Hamiltonians that differ only in the sign of the
effective hyperfine coupling. The experiments have been performed on a frozen
solution of trityl radicals in water/glycerol on a home-built X-band EPR/DNP
spectrometer at 80 K. We envisage that DNP echoes will play an important role
in future development of pulsed DNP for sensitivity-enhanced NMR, hyperfine
spectroscopy, and quantum sensing.


  This paper investigates how Natural Language Understanding (NLU) could be
applied in Emotion Recognition, a specific task in affective computing. We
finetuned different transformers language models (BERT, DistilBERT, RoBERTa,
XLNet, and ELECTRA) using a fine-grained emotion dataset and evaluating them in
terms of performance (f1-score) and time to complete.


  In this article, an experiment to measure air velocity with the barometer of
a smartphone is presented utilizing the concept of a Pitot tube. For the
experiment, the app phyphox is used which has a set of useful features for
performing smartphone-based experiments 9,10 . Both the data analysis tools
included in the app phyphox and the remote access function to smartphones in
experimental setups are used in the presented experiment. This experiment is
also an example for designing customized experiments within phyphox which can
be realized by any user via a web-based editor 9 . In addition, it is shown how
an external Bluetooth pressure sensor can be used to extend an experiment,
which is used here to measure the speed of a vehicle. Such an integration of
external Bluetooth Low Energy sensors into smartphone experiments became
available with phyphox version 1.1.0.


  An index for the characterisation of the Perovskite type Superconductors is
introduced. The index, denominated as Structural Parameter of High Tc
Superconductivity (PESATc from its name in Spanish), is calculated using a
Moebius Orbital defined as the length of an Octahedral Moebius Strip overlaying
the octahedra created by the CuO planes and the apical Oxygen atoms, O(4).
Results for structures of YBCO:123 type compounds are presented; their atomic
positions were measured by neutron diffraction and x-ray diffraction techniques
as reported in the literature. It has been found that critical temperature
depends on the value of the measured structural parameter; furthermore, for a
set of RBCO compounds there is a lineal response between Tc and PESATc. In the
former case, a specific range for the superconducting behaviour is established
by PESATc values, and a well-defined point for the maximum Tc is found. In the
latter one, a slope of 4.515 with correlation coefficient of 0.9402 was found
when seven Rare Earth compounds were considered. The only compound that goes
out from this trend, is the La based compound.


  We regard the non-relativistic Schrodinger equation as an ensemble mean
representation of the stochastic motion of a single particle in a vacuum,
subject to an undefined stochastic quantum force. The local mean of the quantum
force is found to be proportional to the third spatial derivative of the
probability density function, while its associated pressure is proportional to
the second spatial derivative. The latter arises from the single particle
diluted gas pressure, and this observation allows to interpret the quantum Bohm
potential as the energy required to put a particle in a bath of fluctuating
vacuum at constant entropy and volume. The stochastic force expectation value
is zero and is uncorrelated with the particle location, thus does not perform
work on average. Nonetheless it is anti-correlated with volume and this
anti-correlation leads to an uncertainty relation. We analyze the dynamic
Gaussian solution to the Schrodinger equation as a simple example for exploring
the mean properties of this quantum force. We conclude with a few possible
interpretations as to the origins of quantum stochasticity.


  One key challenge in talent search is to translate complex criteria of a
hiring position into a search query, while it is relatively easy for a searcher
to list examples of suitable candidates for a given position. To improve search
efficiency, we propose the next generation of talent search at LinkedIn, also
referred to as Search By Ideal Candidates. In this system, a searcher provides
one or several ideal candidates as the input to hire for a given position. The
system then generates a query based on the ideal candidates and uses it to
retrieve and rank results. Shifting from the traditional Query-By-Keyword to
this new Query-By-Example system poses a number of challenges: How to generate
a query that best describes the candidates? When moving to a completely
different paradigm, how does one leverage previous product logs to learn
ranking models and/or evaluate the new system with no existing usage logs?
Finally, given the different nature between the two search paradigms, the
ranking features typically used for Query-By-Keyword systems might not be
optimal for Query-By-Example. This paper describes our approach to solving
these challenges. We present experimental results confirming the effectiveness
of the proposed solution, particularly on query building and search ranking
tasks. As of writing this paper, the new system has been available to all
LinkedIn members.


  Kira Adaricheva and Madina Bolat have recently proved that if $U_0$ and $U_1$
are circles in a triangle with vertices $A_0,A_1,A_2$, then there exist $j\in
\{0,1,2\}$ and $k\in\{0,1\}$ such that $U_{1-k}$ is included in the convex hull
of $U_k\cup(\{A_0,A_1, A_2\}\setminus\{A_j\})$. We give a short new proof for
this result, and we point out that a straightforward generalizaton for shperes
fails.


  Detection of arbitrarily rotated objects is a challenging task due to the
difficulties of locating the multi-angle objects and separating them
effectively from the background. The existing methods are not robust to angle
varies of the objects because of the use of traditional bounding box, which is
a rotation variant structure for locating rotated objects. In this article, a
new detection method is proposed which applies the newly defined rotatable
bounding box (RBox). The proposed detector (DRBox) can effectively handle the
situation where the orientation angles of the objects are arbitrary. The
training of DRBox forces the detection networks to learn the correct
orientation angle of the objects, so that the rotation invariant property can
be achieved. DRBox is tested to detect vehicles, ships and airplanes on
satellite images, compared with Faster R-CNN and SSD, which are chosen as the
benchmark of the traditional bounding box based methods. The results shows that
DRBox performs much better than traditional bounding box based methods do on
the given tasks, and is more robust against rotation of input image and target
objects. Besides, results show that DRBox correctly outputs the orientation
angles of the objects, which is very useful for locating multi-angle objects
efficiently. The code and models are available at
https://github.com/liulei01/DRBox.


  We present several series of synchronizing automata with multiple parameters,
generalizing previously known results. Let p and q be two arbitrary co-prime
positive integers, q > p. We describe reset thresholds of the colorings of
primitive digraphs with exactly one cycle of length p and one cycle of length
q. Also, we study reset thresholds of the colorings of primitive digraphs with
exactly one cycle of length q and two cycles of length p.


  Recent experiments suggest that certain B -> pi K branching ratios are
inconsistent with the standard model expectations. We show that a
flavor-changing Z' provides a solution to the problem. Electroweak penguin
amplitudes are enhanced by the Z' boson for select parameters. We discuss
implications for the Z' mass and its couplings to the standard model fermions.
We also show that the solution is consistent with constraints from the CP
asymmetries of the B -> phi K_S decay.


  In the ever-evolving landscape of technology, product innovation thrives on
replacing outdated technologies with groundbreaking ones or through the
ingenious recombination of existing technologies. Our study embarks on a
revolutionary journey by genetically representing products, extracting their
chromosomal data, and constructing a comprehensive phylogenetic network of
automobiles. We delve deep into the technological features that shape
innovation, pinpointing the ancestral roots of products and mapping out
intricate product-family triangles. By leveraging the similarities within these
triangles, we introduce a pioneering "Product Disruption Index"-inspired by the
CD index (Funk and Owen-Smith, 2017)-to quantify a product's disruptiveness.
Our approach is rigorously validated against the scientifically recognized
trend of decreasing disruptiveness over time (Park et al., 2023) and through
compelling case studies. Our statistical analysis reveals a fascinating
insight: disruptive product innovations often stem from minor, yet crucial,
modifications.


  Wave self-focusing in molecular systems subject to thermal effects, such as
thin molecular films and long biomolecules, can be modeled by stochastic
versions of the Discrete Self-Trapping equation of Eilbeck, Lomdahl and Scott,
and this can be approximated by continuum limits in the form of stochastic
nonlinear Schroedinger equations.
  Previous studies directed at the SNLS approximations have indicated that the
self-focusing of wave energy to highly localized states can be inhibited by
phase noise (modeling thermal effects) and can be restored by phase damping
(modeling heat radiation).
  We show that the continuum limit is probably ill-posed in the presence of
spatially uncorrelated noise, at least with little or no damping, so that
discrete models need to be addressed directly. Also, as has been noted by other
authors, omission of damping produces highly unphysical results.
  Numerical results are presented for the first time for the discrete models
including the highly nonlinear damping term, and new numerical methods are
introduced for this purpose. Previous conjectures are in general confirmed, and
the damping is shown to strongly stabilize the highly localized states of the
discrete models. It appears that the previously noted inhibition of nonlinear
wave phenomena by noise is an artifact of modeling that includes the effects of
heat, but not of heat loss.


  Trumpler 20 is an open cluster located at low Galactic longitude, just beyond
the great Carina spiral arm, and whose metallicity and fundamental parameters
were very poorly known until now. As it is most likely a rare example of an
old, rich open cluster -- possibly a twin of NGC 7789 -- it is useful to
characterize it. To this end, we determine here the abundance of several
elements and their ratios in a sample of stars in the clump of Trumpler 20. The
primary goal is to measure Trumpler 20 metallicity, so far very poorly
constrained, and revise the cluster's fundamental parameters. We present
high-resolution spectroscopy of eight clump stars. Based on their radial
velocities, we identify six bona fide cluster members, and for five of them
(the sixth being a fast rotator) we perform a detailed abundance analysis.
  We find that Trumpler 20 is slightly more metal-rich than the Sun, having
[Fe/H]=+0.09$\pm$0.10. The abundance ratios of alpha-elements are generally
solar. In line with recent studies of clusters as old as Trumpler 20, Ba is
overabundant compared to the Sun. Our analysis of the iron-peak elements (Cr
and Ni) does not reveal anything anomalous. Based on these results, we
re-estimate the cluster age to be 1.5$^{+0.2}_{-0.1}$ Gyr. Its distance to the
Galactic centre turns out to be 7.3 kpc. With this distance and metallicity,
Trumpler 20 fits fairly well in the metallicity gradient for the galactic inner
disc. With this new study, the characterization of Trumpler~20 is now on much
more solid ground. Further studies should focus on the estimate of the binary
fraction and on its main sequence membership.


  In this paper, we investigate the controller design problem for linear
disturbed systems under signal temporal logic (STL) specifications imposing
both spatial and temporal constraints on system behavior. We first implement
zonotope-based techniques to partition the state space into finite cells, then
propose an evaluation mechanism to rearrange the time constraints of the STL
specification, and finally decompose the global STL formula into finite local
STL formulas. In this way, each cell has a local control design problem, which
is further formulated into a local optimization problem. To deal with each
local optimization problem, we take advantage of the properties of zonotopes
and reachability analysis to design local controller consisting of feedforward
and feedback parts. By solving all local optimization problems, all local
controllers are combined to guarantee the global STL specification. Finally, a
numerical example is presented to illustrate the derived results.


  It is prohibitively expensive to annotate a large-scale video-based person
re-identification (re-ID) dataset, which makes fully supervised methods
inapplicable to real-world deployment. How to maximally reduce the annotation
cost while retaining the re-ID performance becomes an interesting problem. In
this paper, we address this problem by integrating an active learning scheme
into a deep learning framework. Noticing that the truly matched tracklet-pairs,
also denoted as true positives (TP), are the most informative samples for our
re-ID model, we propose a sampling criterion to choose the most TP-likely
tracklet-pairs for annotation. A view-aware sampling strategy considering
view-specific biases is designed to facilitate candidate selection, followed by
an adaptive resampling step to leave out the selected candidates that are
unnecessary to annotate. Our method learns the re-ID model and updates the
annotation set iteratively. The re-ID model is supervised by the tracklets'
pesudo labels that are initialized by treating each tracklet as a distinct
class. With the gained annotations of the actively selected candidates, the
tracklets' pesudo labels are updated by label merging and further used to
re-train our re-ID model. While being simple, the proposed method demonstrates
its effectiveness on three video-based person re-ID datasets. Experimental
results show that less than 3\% pairwise annotations are needed for our method
to reach comparable performance with the fully-supervised setting.


  Temporal action localization is a challenging computer vision problem with
numerous real-world applications. Most existing methods require laborious
frame-level supervision to train action localization models. In this work, we
propose a framework, called 3C-Net, which only requires video-level supervision
(weak supervision) in the form of action category labels and the corresponding
count. We introduce a novel formulation to learn discriminative action features
with enhanced localization capabilities. Our joint formulation has three terms:
a classification term to ensure the separability of learned action features, an
adapted multi-label center loss term to enhance the action feature
discriminability and a counting loss term to delineate adjacent action
sequences, leading to improved localization. Comprehensive experiments are
performed on two challenging benchmarks: THUMOS14 and ActivityNet 1.2. Our
approach sets a new state-of-the-art for weakly-supervised temporal action
localization on both datasets. On the THUMOS14 dataset, the proposed method
achieves an absolute gain of 4.6% in terms of mean average precision (mAP),
compared to the state-of-the-art. Source code is available at
https://github.com/naraysa/3c-net.


  We propose an adiabatic magnetization process for cooling the Fermi electron
gas to ultra-low temperatures as an alternative to the known adiabatic
demagnetization mechanism. We show via a new adiabatic equation that at the
constant density the increase of the magnetic field leads to the temperature
decrease as $T\sim 1/H^2$.


  The Mu2e electromagnetic calorimeter has to provide precise information on
energy, time and position for $\sim$100 MeV electrons. It is composed of 1348
un-doped CsI crystals, each coupled to two large area Silicon Photomultipliers
(SiPMs). A modular and custom SiPM layout consisting of a 3$\times$2 array of
6$\times$6 mm$^2$ UV-extended monolithic SiPMs has been developed to fulfill
the Mu2e calorimeter requirements and a pre-production of 150 prototypes has
been procured by three international firms (Hamamatsu, SensL and Advansid). A
detailed quality assurance process has been carried out on this first batch of
photosensors: the breakdown voltage, the gain, the quenching time, the dark
current and the Photon Detection Efficiency (PDE) have been determined for each
monolithic cell of each SiPMs array. One sample for each vendor has been
exposed to a neutron fluency up to $\sim$8.5~$\times$~10$^{11}$ 1 MeV (Si) eq.
n/cm$^{2}$ and a linear increase of the dark current up to tens of mA has been
observed. Others 5 samples for each vendor have undergone an accelerated aging
in order to verify a Mean Time To Failure (MTTF) higher than $\sim$10$^{6}$
hours.


  We investigate D2-branes and D4-branes parallel to D8-branes. The low energy
world volume theory on the branes is non-supersymmetric Chern-Simons theory. We
identify the fundamental strings as the anyons of the 2+1 Chern-Simons theory
and the D0-branes as solitons. The Chern-Simons theory with a boundary is
modeled using NS 5-branes with ending D6-branes. The brane set-up provides for
a graphical description of anomaly inflow. We also model the 4+1 Chern-Simons
theory using branes and conjecture that D4-branes with a boundary describes a
supersymmetric version of Kaplan's theory of chiral fermions.


  Motivated by the appearance of associativity anomalies in the context of
superstring field theory, we give a generalized solution built from boundary
condition changing operators which can be associated to a generic tachyon
vacuum in the $KBc$ subalgebra of the Okawa form. We articulate sufficient
conditions on the choice of tachyon vacuum to ensure that ambiguous products do
not appear in the equations of motion.


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


  Motivated by investigation of black hole properties in the lab, some
interesting subjects such as analogue gravity and transformation optics are
generated. In this paper, we look for the analogies between the geometry of a
gravitating system and the optical medium. In addition, we recognize that
appropriate metamaterials can be used to mimic the propagation of light in the
curved spacetimes and behave like black holes. The resemblance of metamaterials
with Kerr and Reissner-Nordstr\"{o}m spacetimes is studied. At last, we compare
the full-wave numerical calculation of light with its optical limit of
geometry.


  We characterize the passage from nonlinear to linearized Griffith-fracture
theories under non-interpenetration constraints. In particular, sequences of
deformations satisfying a Ciarlet-Ne\v{c}as condition in SBV^2 and for which a
convergence of the energies is ensured, are shown to admit asymptotic
representations in GSBD^2 satisfying a suitable contact condition. With an
explicit counterexample, we prove that this result fails if convergence of the
energies does not hold. We further prove that each limiting displacement
satisfying the contact condition can be approximated by an energy-convergent
sequence of deformations fulfilling a Ciarlet-Ne\v{c}as condition. The proof
relies on a piecewise Korn-Poincar\'e inequality in GSBD2, on a careful blow-up
analysis around jump points, as well as on a refined GSBD^2-density result
guaranteeing enhanced contact conditions for the approximants.


  The success of deep learning comes at a tremendous computational and energy
cost, and the scalability of training massively overparametrized neural
networks is becoming a real barrier to the progress of artificial intelligence
(AI). Despite the popularity and low cost-per-iteration of traditional
backpropagation via gradient decent, stochastic gradient descent (SGD) has
prohibitive convergence rate in non-convex settings, both in theory and
practice.
  To mitigate this cost, recent works have proposed to employ alternative
(Newton-type) training methods with much faster convergence rate, albeit with
higher cost-per-iteration. For a typical neural network with
$m=\mathrm{poly}(n)$ parameters and input batch of $n$ datapoints in
$\mathbb{R}^d$, the previous work of [Brand, Peng, Song, and Weinstein,
ITCS'2021] requires $\sim mnd + n^3$ time per iteration. In this paper, we
present a novel training method that requires only $m^{1-\alpha} n d + n^3$
amortized time in the same overparametrized regime, where $\alpha \in (0.01,1)$
is some fixed constant. This method relies on a new and alternative view of
neural networks, as a set of binary search trees, where each iteration
corresponds to modifying a small subset of the nodes in the tree. We believe
this view would have further applications in the design and analysis of deep
neural networks (DNNs).


  Recent detection of sub-TeV emission from gamma-ray bursts (GRBs) represents
a breakthrough in the GRB study. The multi-wavelength data of the afterglows of
GRB 190114C support the synchrotron self-Compton (SSC) origin for its sub-TeV
emission. We present a comparative analysis on the SSC emission of GRB
afterglows in the homogeneous and wind environment in the framework of the
forward shock model. The $\gamma\gamma$ absorption of very high-energy photons
due to pair production within the source and the Klein-Nishina effect on the
inverse-Compton scattering are considered. Generally a higher SSC flux is
expected for a larger circum-burst density due to a larger Compton parameter,
but meanwhile the internal $\gamma\gamma$ absorption is more severer for
sub-TeV emission. The flux ratio between the SSC component and the synchrotron
component decreases more quickly with time in the wind medium case than that in
the homogenous-density medium case. The light curves of the SSC emission are
also different for the two types of media. We also calculate the cascade
emission resulted from the absorbed high-energy photons. In the ISM environment
with $n> 1\,\rm cm^{-3}$, the cascade synchrotron emission could be comparable
to the synchrotron emission of the primary electrons in the optical band, which
may flatten the optical afterglow light curve at early time ($t<1$ h). In the
wind medium with $A_{\ast}> 0.1$, the cascade emission in the eV-GeV band is
comparable or even larger than the emission of the primary electrons at early
time.


  We have used the Balmer and Paschen jumps to determine the temperatures of
the H+ zones of a total sample of 47 H II regions. The Balmer jump was used on
MMT spectrophotometric data of 22 low-metallicity H II regions in 18 blue
compact dwarf (BCD) galaxies and of one H II region in the spiral galaxy M101.
The Paschen jump was used on spectra of 24 H II emission-line galaxies selected
from the Data Release 3 of the Sloan Digital Sky Survey (SDSS). To derive the
temperatures, we have used a Monte Carlo technique varying the electron
temperature in the H+ zone, the extinction of the ionized gas and that of the
stellar population, the relative contribution of the ionized gas to the total
emission and the star formation history to fit the spectral energy distribution
of the galaxies. For the MMT spectra, the fit was done in the wavelength range
of 3200 - 5200A which includes the Balmer discontinuity, and for the SDSS
spectra, in the wavelength range of 3900 - 9200A which includes the Paschen
discontinuity. We find for our sample of H II regions that the temperatures of
the O+2 zones determined from the nebular to auroral line intensity ratio of
doubly ionized oxygen [O III] (4959+5007)/4363 do not differ, in a statistical
sense, from the temperatures of the H+ zones determined from fitting the Balmer
and Paschen jumps and the SEDs. We cannot rule out small temperature
differences of the order of 3-5%.


  We apply BERT to coreference resolution, achieving strong improvements on the
OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of
model predictions indicates that, compared to ELMo and BERT-base, BERT-large is
particularly better at distinguishing between related but distinct entities
(e.g., President and CEO). However, there is still room for improvement in
modeling document-level context, conversations, and mention paraphrasing. Our
code and models are publicly available.


  We present a method to implement the idea of Jain & Taylor to constrain
cosmological parameters with weak gravitational lensing. Photometric redshift
information on foreground galaxies is used to produce templates of the mass
structure at foreground slices z_\ell, and the predicted distortion field is
cross-correlated with the measured shapes of sources at redshift z_s. The
variation of the cross-correlation with z_s depends purely on ratios of angular
diameter distances. We propose a formalism for such an analysis that makes use
of all foreground-background redshift pairs, and derive the Fisher
uncertainties on the dark energy parameters that would result from such a
survey. Surveys from the proposed SNAP satellite or the LSST observatory could
constrain the dark energy equation of state to 0.01 f_sky^{-1/2} in w_0 and
0.035 f_sky^{-1/2} in w_a after application of a practical prior on \Omega_m.
Advantages of this method over power-spectrum measurements are that it is
unaffected by residual PSF distortions, is not limited by sample-variance, and
can use non-linear mass structures to constrain cosmology. The signal is,
however, very small, amounting to a change of a few parts in 10^3 of the
lensing distortion. The calibration of lensing distortion must be independent
of redshift to comparable levels, and photometric redshifts must be similarly
free of bias. Both of these tasks require substantial advance over the present
state of the art, but we discuss how such accurate calibrations might be
achieved using internal consistency tests. Elimination of redshift bias would
require spectroscopic redshifts of 10^4-10^5 high redshift galaxies.


  After a brief introduction to neutrino oscillations and a review of the world
knowledge of neutrino oscillation parameters, we introduce two current neutrino
oscillation experiments, MINOS and MiniBooNE. MINOS makes precise measurements
of the oscillation parameters |\Delta m^{2}_{32}| and sin^{2} (2\theta_{23}).
MiniBooNE tests neutrino oscillations in the parameter region reported by the
LSND experiment, which would require a new neutrino state. We review recent
experimental results from both experiments and give an outlook on future
measurements.


  Current understanding of core collapse and thermonuclear supernovae is
reviewed. Recent progress in unveiling the nature of cosmic gamma-ray bursts
(GRB) is discussed, with the focus on the apparent link of several GRBs with an
energetic subclass of stellar explosions, type Ib/c core-collapse supernovae.
This relation provides the strong case that the GRB phenomenon is connected
with the final stages of massive star evolution and possibly with the formation
of neutron stars and black holes.


  Non-parallel data voice conversion (VC) have achieved considerable
breakthroughs recently through introducing bottleneck features (BNFs) extracted
by the automatic speech recognition(ASR) model. However, selection of BNFs have
a significant impact on VC result. For example, when extracting BNFs from ASR
trained with Cross Entropy loss (CE-BNFs) and feeding into neural network to
train a VC system, the timbre similarity of converted speech is significantly
degraded. If BNFs are extracted from ASR trained using Connectionist Temporal
Classification loss (CTC-BNFs), the naturalness of the converted speech may
decrease. This phenomenon is caused by the difference of information contained
in BNFs. In this paper, we proposed an any-to-one VC method using hybrid
bottleneck features extracted from CTC-BNFs and CE-BNFs to complement each
other advantages. Gradient reversal layer and instance normalization were used
to extract prosody information from CE-BNFs and content information from
CTC-BNFs. Auto-regressive decoder and Hifi-GAN vocoder were used to generate
high-quality waveform. Experimental results show that our proposed method
achieves higher similarity, naturalness, quality than baseline method and
reveals the differences between the information contained in CE-BNFs and
CTC-BNFs as well as the influence they have on the converted speech.


  The Damped and sub-Damped Lyman-alpha (DLA and sub-DLA) systems seen in the
spectra of QSOs offer a unique way to study the interstellar medium of high
redshift galaxies. In this paper we report on new abundance determinations in a
sample of 10 new systems, nine of the lesser studied sub-DLAs and one DLA,
along the line of sight to seven QSOs from spectra taken with the MIKE
spectrograph. Lines of Mg I, Mg II, Al II, Al III, Ca II, Mn II, Fe II, and Zn
II were detected. Here, we give the column densities and equivalent widths of
the observed absorption lines, as well as the abundances determined for these
systems. Zn, a relatively undepleted element in the local interstellar medium
(ISM) is detected in one system with a high metallicity of [Zn/H]=+0.27\pm0.18.
In one other system, a high abundance based on the more depleted element Fe is
seen with [Fe/H]=-0.37\pm0.13, although Zn is not detected. The N(HI)-weighted
mean metallicity of these sub-DLA systems based on Fe is <[Fe/H]>=-0.76\pm0.11,
nearly ~0.7 dex higher (a factor of 5) than what is seen in DLAs in this
redshift range. The relative abundance of [Mn/Fe] is also investigated. A clear
trend is visible for these systems as well as systems from the literature, with
[Mn/Fe] increasing with increasing metallicity in good agreement with with
Milky Way stellar abundances.


  We present preliminary results of study of the ethapi-, etha'pi- and b1pi
systems produced in the pi-Be-interaction at 28GeV/c. JPC=2++ and 1-+-waves
resulted from the PWA have been fitted in each system separately to establish
the nature of the 1-+ wave. A hypothesis of the 1-+ wave resonant nature in the
ethapi- and etha'pi- has no statistically significant preference over the
non-resonant one. The b1pi-system study confirms the results of the 37GeV/c
beam data analysis in favor of the resonant treatment of the bump at 1.6GeV.


  Scale invariance is considered in the context of a gravitational theory where
the action, in the first order formalism, is of the form S = \int L_{1} \Phi
d^4x + \int L_{2}\sqrt{-g}d^4x where \Phi is a density built out of degrees of
freedom independent of the metric. For global scale invariance, a "dilaton"
\phi has to be introduced, with non-trivial potentials
V(\phi)=f_{1}e^{\alpha\phi} in L_1 and U(\phi) = f_{2}e^{2\alpha\phi} in L_2.
In the effective Einstein frame, this leads to a non-trivial \phi potential (of
the Morse type) which has a flat region with energy density f_{1}^{2}/4f_{2} as
\phi\to\infty. The addition of an R^{2} term produces an effective potential
with two connected flat regions: one of the Planck scale, that can be
responsible for early inflation, and another for the description of the present
universe.


  We analyze the possibility that the lighter stop {\tilde t_1} could be the
next-to-lightest supersymmetric particle (NLSP) in models where the gravitino
is the lightest supersymmetric particle (LSP). We do not find any possibility
for a stop NLSP in the constrained MSSM with universal input soft
supersymmetry-breaking masses at the GUT scale (CMSSM), but do find small
allowed regions in models with non-universal Higgs masses (NUHM). We discuss
the cosmological evolution of stop hadrons. Most {\tilde t_1}qq `sbaryons' and
the corresponding `antisbaryons' annihilate with conventional antibaryons and
baryons into {\tilde t_1}{\bar q} `mesinos' and the corresponding
`antimesinos', respectively, shortly after the quark-hadron transition in the
early Universe, and most mesinos and antimesinos subsequently annihilate. As a
result, insufficient metastable charged stop hadrons survive to alter Big Bang
nucleosynthesis.


  Most control synthesis methods under temporal logic properties require a
model of the system, however, identifying such a model can be a challenging
task. In this work, we develop a direct data-driven control synthesis method
for temporal logic specifications, which does not require this explicit
modeling step, capable of providing certificates for the general class of
linear systems. After collecting a single sequence of input-output data from
the system, we construct a data-driven characterization of the behavior. Using
this characterization, we synthesize a controller, such that the controlled
system satisfies a (possibly unbounded) temporal logic specification. The
underlying optimization problem is solved by mixed-integer linear programming.
We demonstrate the applicability of the results through simulation examples.


  SModelS is an automatised tool enabling the fast interpretation of simplified
model results from the LHC within any model of new physics respecting a
$\mathbb{Z}_2$ symmetry. With the version 1.2 we announce several new features.
First, previous versions were restricted to missing energy signatures and
assumed prompt decays within each decay chain. SModelS v1.2 considers the
lifetime of each $\mathbb{Z}_2$-odd particle and appropriately takes into
account missing energy, heavy stable charge particle and R-hadron signatures.
Second, the current version allows for a combination of signal regions in
efficiency map results whenever a covariance matrix is available from the
experiment. This is an important step towards fully exploiting the constraining
power of efficiency map results. Several other improvements increase the
user-friendliness, such as the use of wildcards in the selection of
experimental results, and a faster database which can be given as a URL.
Finally, smodelsTools provides an interactive plots maker to conveniently
visualize the results of a model scan.


  We prove existence of solutions to problems whose model is
  $$\begin{cases}
  \displaystyle -\Delta_p u + u^q = \frac{f}{u^\gamma} & \text{in}\ \Omega,
\newline
  u\ge0 &\text{in}\ \Omega,\newline
  u=0 &\text{on}\ \partial\Omega,
  \end{cases}$$
  where $\Omega$ is an open bounded subset of $\mathbb{R}^N$ ($N\ge2$),
$\Delta_p u$ is the $p$-laplacian operator for $1\le p <N$, $q>0$, $\gamma\ge
0$ and $f$ is a nonnegative function in $L^m(\Omega)$ for some $m\ge1$. In
particular we analyze the regularizing effect produced by the absorption term
in order to infer the existence of finite energy solutions in case $\gamma\le
1$. We also study uniqueness of these solutions as well as examples which show
the optimality of the results. Finally, we find local $W^{1,p}$-solutions in
case $\gamma>1$.


  The composition-independent virial coefficients of a $d$-dimensional binary
mixture of (additive) hard hyperspheres following from a recent proposal for
the equation of state of the mixture [Santos, A., Yuste, S. B., and L\'opez de
Haro, M., 1999, Molec. Phys., 96, 1] are examined. Good agreement between
theoretical estimates and available exact or numerical results is found for
$d=2$, 3, 4 and 5, except for mixtures whose components are very disparate in
size. A slight modification that remedies this deficiency is introduced and the
resummation of the associated virial series is carried out, leading to a new
proposal for the equation of state. The case of binary hard-sphere mixtures
($d=3$) is analyzed in some detail.


  This study analyzes Walt Whitman's stylistic changes in his phenomenal work
Leaves of Grass from a computational perspective and relates findings to
standard literary criticism on Whitman. The corpus consists of all 7 editions
of Leaves of Grass, ranging from the earliest 1855 edition to the 1891-92
"deathbed" edition. Starting from counting word frequencies, the simplest
stylometry technique, we find consistent shifts in word choice.
Macro-etymological analysis reveals Whitman's increasing preference for words
of specific origins, which is correlated to the increasing lexical complexity
in Leaves of Grass. Principal component analysis, an unsupervised learning
algorithm, reduces the dimensionality of tf-idf vectors to 2 dimensions,
providing a straightforward view of stylistic changes. Finally, sentiment
analysis shows the evolution of Whitman's emotional state throughout his
writing career.


  The theory for current fluctuations in ac-driven transport through nanoscale
systems is put forward. By use of a generalized, non-Hermitian Floquet theory
we derive novel explicit expressions for the time-averaged current and the
zero-frequency component of the power spectrum of current fluctuations. A
distinct suppression of both the zero-frequency noise and the dc-current occurs
for suitably tailored ac-fields. The relative level of transport noise, being
characterized by a Fano factor, can selectively be manipulated by ac-sources;
in particular, it exhibits both characteristic maxima and minima near current
suppression.


  In this paper we distinguish between top-performance and lower performance
groups in the analysis of statistical properties of bibliometric
characteristics of two large sets of research groups. We find intriguing
differences between top-performance and lower performance groups, but also
between the two sets of research groups. Particularly these latter differences
are interesting, as they may indicate the influence of research management
strategies. Lower performance groups have a larger scale-dependent cumulative
advantage than top-performance groups. We also find that regardless of
performance, larger groups have less not-cited publications. We introduce a
simple model in which processes at the micro level lead to the observed
phenomena at the macro level. Top-performance groups are, on average, more
successful in the entire range of journal impact. We fit our findings into a
concept of hierarchically layered networks. In this concept, the network of
research groups constitutes a layer of one hierarchical step higher than the
basic network of publications connected by citations. The cumulative
size-advantage of citations received by a group looks like preferential
attachment in the basic network in which highly connected nodes (publications)
increase their connectivity faster than less connected nodes. But in our study
it is size that causes an advantage. In general, the larger a group (node in
the research group network), the more incoming links this group acquires in a
non-linear, cumulative way. Moreover, top-performance groups are about an order
of magnitude more efficient in creating linkages (i.e., receiving citations)
than the lower performance groups.


  We develop the theory of halving spaces to obtain lower bounds in real
enumerative geometry. Halving spaces are topological spaces with an action of a
Lie group $\Gamma$ with additional cohomological properties. For
$\Gamma=\mathbb{Z}_2$ we recover the conjugation spaces of Hausmann, Holm and
Puppe. For $\Gamma=\mathrm{U}(1)$ we obtain the circle spaces. We show that
real even and quaternionic partial flag manifolds are circle spaces leading to
non-trivial lower bounds for even real and quaternionic Schubert problems. To
prove that a given space is a halving space, we generalize results of Borel and
Haefliger on the cohomology classes of real subvarieties and their
complexifications. The novelty is that we are able to obtain results in
rational cohomology instead of modulo 2. The equivariant extension of the
theory of circle spaces leads to generalizations of the results of Borel and
Haefliger on Thom polynomials.


  This paper presents a search for Higgs bosons decaying to four leptons,
either electrons or muons, via one or two light exotic gauge bosons $Z_d$,
$H\to Z Z_d \to 4\ell$ or $H\to Z_d Z_d \to 4\ell$. The search was performed
using $pp$ collision data corresponding to an integrated luminosity of about 20
fb$^{-1}$ at the center-of-mass energy of $\sqrt{s}=8 $TeV recorded with the
ATLAS detector at the Large Hadron Collider. The observed data are well
described by the Standard Model prediction. Upper bounds on the branching ratio
of $H\to Z Z_d \to 4\ell$ and on the kinetic mixing parameter between the $Z_d$
and the Standard Model hypercharge gauge boson are set in the range
$(1$--$9)\times10^{-5}$ and $(4$--$17)\times10^{-2}$ respectively, at 95%
confidence level assuming the Standard Model branching ratio of $H\to Z Z^* \to
4\ell$, for $Z_d$ masses between 15 and 55 GeV. Upper bounds on the effective
mass mixing parameter between the $Z$ and the $Z_d$ are also set using the
branching ratio limits in the $H \to ZZ_d\to 4\ell$ search, and are in the
range $(1.5$--$8.7)\times 10^{-4}$ for $15<m_{Z_d} < 35$ GeV. Upper bounds on
the branching ratio of $H\rightarrow Z_dZ_d \to 4\ell$ and on Higgs portal
coupling parameter, controlling the strength of the coupling of the Higgs boson
to dark vector bosons, are set in the range $(2$--$3)\times10^{-5}$ and
$(1$--$10)\times 10^{-4}$ respectively, at 95% confidence level assuming the
Standard Model Higgs boson production cross sections, for $Z_d$ masses between
15 and 60 GeV.


  MIMO processing enables jammer mitigation through spatial filtering, provided
that the receiver knows the spatial signature of the jammer interference.
Estimating this signature is easy for barrage jammers that transmit
continuously and with static signature, but difficult for more sophisticated
jammers: Smart jammers may deliberately suspend transmission when the receiver
tries to estimate their spatial signature, they may use time-varying
beamforming to continuously change their spatial signature, or they may stay
mostly silent and jam only specific instants (e.g., transmission of control
signals). To deal with such smart jammers, we propose MASH, the first method
that indiscriminately mitigates all types of jammers: Assume that the
transmitter and receiver share a common secret. Based on this secret, the
transmitter embeds (with a linear time-domain transform) its signal in a secret
subspace of a higher-dimensional space. The receiver applies a reciprocal
linear transform to the receive signal, which (i) raises the legitimate
transmit signal from its secret subspace and (ii) provably transforms any
jammer into a barrage jammer, which makes estimation and mitigation via MIMO
processing straightforward. We show the efficacy of MASH for data transmission
in the massive multi-user MIMO uplink.


  We compute the two-loop renormalization functions, in the RI $^\prime$
scheme, of local bilinear quark operators $\bar{\psi}\Gamma\psi$, where
$\Gamma$ denotes the Scalar and Pseudoscalar Dirac matrices, in the lattice
formulation of QCD. We consider both the flavor non-singlet and singlet
operators; the latter, in the scalar case, leads directly to the two-loop
fermion mass renormalization, $Z_m$.
  As a prerequisite for the above, we also compute the quark field
renormalization, $Z_{\psi}$, up to two loops.
  We use the clover action for fermions and the Wilson action for gluons. Our
results are given as a polynomial in $c_{SW}$, in terms of both the
renormalized and bare coupling constant, in the renormalized Feynman gauge. We
also confirm the 1-loop renormalization functions, for generic gauge.
  Finally, we present our results in the $\bar{MS}$ scheme, for easier
comparison with calculations in the continuum.
  The corresponding results, for fermions in an arbitrary representation, are
included in an Appendix.


  In bioinformatics, the rapid development of sequencing technology has enabled
us to collect an increasing amount of omics data. Classification based on omics
data is one of the central problems in biomedical research. However, omics data
usually has a limited sample size but high feature dimensions, and it is
assumed that only a few features (biomarkers) are active, i.e. informative to
discriminate between different categories (cancer subtypes,
responder/non-responder to treatment, for example). Identifying active
biomarkers for classification has therefore become fundamental for omics data
analysis. Focusing on binary classification, we propose an innovative feature
selection method aiming at dealing with the high correlations between the
biomarkers. Various research has shown the notorious influence of correlated
biomarkers and the difficulty of accurately identifying active ones. Our
method, WLogit, consists in whitening the design matrix to remove the
correlations between biomarkers, then using a penalized criterion adapted to
the logistic regression model to select features. The performance of WLogit is
assessed using synthetic data in several scenarios and compared with other
approaches. The results suggest that WLogit can identify almost all active
biomarkers even in the cases where the biomarkers are highly correlated, while
the other methods fail, which consequently leads to higher classification
accuracy. The performance is also evaluated on the classification of two
Lymphoma subtypes, and the obtained classifier also outperformed other methods.
Our method is implemented in the \texttt{WLogit} R package available from the
Comprehensive R Archive Network (CRAN).


  We consider the simplest geometrical particle model associated with
light-like curves in (2+1)-dimensions. The action is proportional to the
pseudo-arc length of the particle's path. We show that under quantization it
yields the (2+1)-dimensional anyonic field equation supplemented with a
Majorana-like relation on mass and spin, i.e., $mass \times spin =\alpha^2$,
with $\alpha$ the coupling constant in front of the action.


  With an action $\alpha$ of $\mathbb{R}^n$ on a $C^*$-algebra $A$ and a
skew-symmetric $n\times n$ matrix $\Theta$ one can consider the Rieffel
deformation $A_\Theta$ of $A$, which is a $C^*$-algebra generated by the
$\alpha$-smooth elements of $A$ with a new multiplication. The purpose of this
paper is to obtain explicit formulas for $K$-theoretical quantities defined by
elements of $A_\Theta$. We assume that there is a densely defined trace on $A$,
invariant under the action. We give an explicit realization of Thom class in
$KK$ in any dimension $n$, and use it in the index pairings. When $n$ is odd,
for example, we give a formula for the index of operators of the form
$P\pi^\Theta(u)P$, where $\pi^\Theta(u)$ is the operator of left Rieffel
multiplication by an invertible element $u$ over the unitization of $A$, and
$P$ is projection onto the nonnegative eigenspace of a Dirac operator
constructed from the action $\alpha$. The results are new also for the
undeformed case $\Theta=0$. The construction relies on two approaches to
Rieffel deformations in addition to Rieffel's original one: "Kasprzak
deformation" and "warped convolution". We end by outlining potential
applications in mathematical physics.


  This paper is concerned with the efficient numerical treatment of 1D
stationary Schr\"odinger equations in the semi-classical limit when including a
turning point of first order. For the considered scattering problems we show
that the wave function asymptotically blows up at the turning point as the
scaled Planck constant $\varepsilon\to0$, which is a key challenge for the
analysis. Assuming that the given potential is linear or quadratic in a small
neighborhood of the turning point, the problem is analytically solvable on that
subinterval in terms of Airy or parabolic cylinder functions, respectively.
Away from the turning point, the analytical solution is coupled to a numerical
solution that is based on a WKB-marching method -- using a coarse grid even for
highly oscillatory solutions. We provide an error analysis for the hybrid
analytic-numerical problem up to the turning point (where the solution is
asymptotically unbounded) and illustrate it in numerical experiments: If the
phase of the problem is explicitly computable, the hybrid scheme is
asymptotically correct w.r.t.\ $\varepsilon$. If the phase is obtained with a
quadrature rule of, e.g., order 4, then the spatial grid size has the
limitation $h={\cal O}(\varepsilon^{7/12})$ which is slightly worse than the
$h={\cal O}(\varepsilon^{1/2})$ restriction in the case without a turning
point.


  We discuss the singularity structure of Kahan discretizations of a class of
quadratric vector fields and provide a classification of the parameter values
such that the corresponding Kahan map is integrable, in particular, admits an
invariant pencil of elliptic curves.


  Deep learning and reinforcement learning methods have recently been used to
solve a variety of problems in continuous control domains. An obvious
application of these techniques is dexterous manipulation tasks in robotics
which are difficult to solve using traditional control theory or
hand-engineered approaches. One example of such a task is to grasp an object
and precisely stack it on another. Solving this difficult and practically
relevant problem in the real world is an important long-term goal for the field
of robotics. Here we take a step towards this goal by examining the problem in
simulation and providing models and techniques aimed at solving it. We
introduce two extensions to the Deep Deterministic Policy Gradient algorithm
(DDPG), a model-free Q-learning based method, which make it significantly more
data-efficient and scalable. Our results show that by making extensive use of
off-policy data and replay, it is possible to find control policies that
robustly grasp objects and stack them. Further, our results hint that it may
soon be feasible to train successful stacking policies by collecting
interactions on real robots.


  In this letter we introduced a new scaling method based on Adam-Gibbs model.
Moreover, generalised critical-like expression of configurational entropy S_C
(T)=S_0 (1-T_K/T)^n is used. Obtained values of pseudocritical exponent n seem
to be universal for studied systems and corresponds well with thermodynamic
data. Relation between dynamic portrayal represented by primary relaxation time
and thermodynamic (specific heat capacity) is in a good agreement.


  Since the landmarks established by the Cremonese school in the 16th century,
the history of violin design has been marked by experimentation. While great
effort has been invested since the early 19th century by the scientific
community on researching violin acoustics, substantially less attention has
been given to the statistical characterization of how the violin shape evolved
over time. In this paper we study the morphology of violins retrieved from the
Musical Instrument Museums Online (MIMO) database -- the largest freely
accessible platform providing information about instruments held in public
museums. From the violin images, we derive a set of measurements that reflect
relevant geometrical features of the instruments. The application of Principal
Component Analysis (PCA) uncovered similarities between violin makers and their
respective copyists, as well as among luthiers belonging to the same family
lineage, in the context of historical narrative. Combined with a time-windowed
approach, thin plate splines visualizations revealed that the average violin
outline has remained mostly stable over time, not adhering to any particular
trends of design across different periods in music history.


  Point clouds acquired from 3D sensors are usually sparse and noisy. Point
cloud upsampling is an approach to increase the density of the point cloud so
that detailed geometric information can be restored. In this paper, we propose
a Dual Back-Projection network for point cloud upsampling (DBPnet). A Dual
Back-Projection is formulated in an up-down-up manner for point cloud
upsampling. It not only back projects feature residues but also coordinates
residues so that the network better captures the point correlations in the
feature and space domains, achieving lower reconstruction errors on both
uniform and non-uniform sparse point clouds. Our proposed method is also
generalizable for arbitrary upsampling tasks (e.g. 4x, 5.5x). Experimental
results show that the proposed method achieves the lowest point set matching
losses with respect to the benchmark. In addition, the success of our approach
demonstrates that generative networks are not necessarily needed for
non-uniform point clouds.


  The differential rotation of the solar corona for the period 1976-2004 was
studied as a function of the distance from the center of the Sun. For this
study, we developed a method using the coronal magnetic field as a tracer. The
field in a spherical layer from the base of the corona up to the source surface
was determined from photospheric measurements. Calculations were performed for
14 heliocentric distances from the base of the corona up to 2.45 solar radii
and from the equator to about 75 degrees of latitude at 5 degrees steps. For
each day, we calculated three components, which were then used to obtain the
field strength. The coronal rotation periods were determined by the periodogram
method for all distances and latitudes under consideration. The variations in
the coronal rotation during the time interval 1976-2004 were as follows: the
gradient of differential rotation decreased with the increase of heliocentric
distance; the rotation remaining differential even in the vicinity of the
source surface. The largest rotation rates were recorded at the cycle minimum
at small heights in the corona. The lowest rotation rate was observed at the
middle of the ascending branch at large distances. At the minimum of the cycle,
the differential rotation is most clearly pronounced, especially at small
heliocentric distances. As the distance increases, the differential gradient
decreases in all phases. The results based on magnetic data and on the
brightness of the coronal green line 530.3nm FeXIV used earlier show a
satisfactory agreement. Since the rotation of the magnetic field at the
corresponding heights in the corona is probably determined by the conditions in
the field generation region, an opportunity arises to use this method for
diagnostics of differential rotation in the subphotospheric layers.


  The Athena+ mission concept is designed to implement the Hot and Energetic
Universe science theme submitted to the European Space Agency in response to
the call for White Papers for the definition of the L2 and L3 missions of its
science program. The Athena+ science payload consists of a large aperture high
angular resolution X-ray optics and twelve meters away, two interchangeable
focal plane instruments: the X-ray Integral Field Unit (X-IFU) and the Wide
Field Imager (WFI). The X-IFU is a cryogenic X-ray spectrometer, based on a
large array of Transition Edge Sensors (TES), offering 2.5 eV spectral
resolution, with ~5" pixels, over a field of view of 5 arc minutes in diameter.
In this paper, we briefly describe the Athena+ mission concept and the X-IFU
performance requirements. We then present the X-IFU detector and readout
electronics principles, the current design of the focal plane assembly, the
cooling chain and review the global architecture design. Finally, we describe
the current performance estimates, in terms of effective area, particle
background rejection, count rate capability and velocity measurements. Finally,
we emphasize on the latest technology developments concerning TES array
fabrication, spectral resolution and readout performance achieved to show that
significant progresses are being accomplished towards the demanding X-IFU
requirements.


  This paper describes a decentralized control strategy for the automation of
road intersections and studies its impact on traffic in a realistic urban road
network. The controller incorporates a consensus-based auction algorithm
(CBAA-M), which allows vehicles to agree on a crossing order at each road
intersection, and an on-board model predictive controller that avoids
collisions with other traffic participants, while trying to satisfy performance
metrics over time. Randomized simulations show that this decentralized control
approach guarantees efficiency, safety, and a higher throughput than
traditional solutions.


  A stability analysis is performed on high-order schemes formulated using the
Flux Reconstruction (FR) approach. The one-dimensional advection model equation
is used for the assessment of the stability region of these schemes when
coupled with Runge-Kutta-type time-march procedures. Schemes are created using
different numbers of internal points for each cell of the discrete domain, in
such a way that a broad spectrum of spatial discretization orders can be
analyzed. Multiple correction functions are employed so that well-known
schemes, for instance the Discontinuous Galerkin (DG) and the Staggered-Grid
schemes, may also be included in the study. The stability analysis is performed
by identifying the behavior, in the complex plane, of the eigenvalues
associated with each one of the considered cases. It is observed that, as the
order of the scheme increases, a significant decrease in its domain of
stability occurs, followed by a significant reduction in the amount of
artificial dissipation that is introduced to the solution.


  This paper proposes a notion of viscosity weak supersolutions to build a
bridge between stochastic Lyapunov stability theory and viscosity solution
theory. Different from ordinary differential equations, stochastic differential
equations can have the origins being stable despite having no smooth stochastic
Lyapunov functions (SLFs). The feature naturally requires that the related
Lyapunov equations are illustrated via viscosity solution theory, which deals
with non-smooth solutions to partial differential equations. This paper claims
that stochastic Lyapunov stability theory needs a weak extension of viscosity
supersolutions, and the proposed viscosity weak supersolutions describe
non-smooth SLFs ensuring a large class of the origins being noisily
(asymptotically) stable and (asymptotically) stable in probability. The
contribution of the non-smooth SLFs are confirmed by a few examples;
especially, they ensure that all the linear-quadratic-Gaussian (LQG) controlled
systems have the origins being noisily asymptotically stable for any additive
noises.


  In this manuscript, the concept of dual and approximate dual for continuous
frames in Hilbert spaces will be introduced. Some of its properties will be
studied. Also, the relations between two continuous Riesz bases in Hilbert
spaces will be clarified through examples.


  Increasing Internet of Things (IoT) deployments present a growing surface
over which villainous actors can carry out attacks. This disturbing revelation
is amplified by the fact that a majority of IoT devices use weak or no
encryption at all. Specific Emitter Identification (SEI) is an approach
intended to address this IoT security weakness. This work provides the first
Deep Learning (DL) driven SEI approach that upsamples the signals after
collection to improve performance while simultaneously reducing the hardware
requirements of the IoT devices that collect them. DL-driven upsampling results
in superior SEI performance versus two traditional upsampling approaches and a
convolutional neural network only approach.


  In this work we focus on efficient heuristics for solving a class of
stochastic planning problems that arise in a variety of business, investment,
and industrial applications. The problem is best described in terms of future
buy and sell contracts. By buying less reliable, but less expensive, buy
(supply) contracts, a company or a trader can cover a position of more reliable
and more expensive sell contracts. The goal is to maximize the expected net
gain (profit) by constructing a dose to optimum portfolio out of the available
buy and sell contracts. This stochastic planning problem can be formulated as a
two-stage stochastic linear programming problem with recourse. However, this
formalization leads to solutions that are exponential in the number of possible
failure combinations. Thus, this approach is not feasible for large scale
problems. In this work we investigate heuristic approximation techniques
alleviating the efficiency problem. We primarily focus on the clustering
approach and devise heuristics for finding clusterings leading to good
approximations. We illustrate the quality and feasibility of the approach
through experimental data.


  In this paper we justify the hydrostatic approximation of the primitive
equations in the maximal $L^p$-$L^q$-setting in the three-dimensional layer
domain $\Omega = \Torus^2 \times (-1, 1)$ under the no-slip (Dirichlet)
boundary condition in any time interval $(0, T)$ for $T>0$. We show that the
solution to the scaled Navier-Stokes equations with Besov initial data $u_0 \in
B^{s}_{q,p}(\Omega)$ for $s > 2 - 2/p + 1/ q$ converges to the solution to the
primitive equations with the same initial data in $\mathbb{E}_1 (T) = W^{1,
p}(0, T ; L^q (\Omega)) \cap L^p(0, T ; W^{2, q} (\Omega)) $ with order
$O(\epsilon)$ where $(p,q) \in (1,\infty)^2$ satisfies $ \frac{1}{p} \leq \min
\bracket{ 1 - 1/q, 3/2 - 2/q }$. The global well-posedness of the scaled
Navier-Stokes equations in $\mathbb{E}_1 (T)$ is also proved for sufficiently
small $\epsilon>0$. Note that $T = \infty$ is included.


  This paper concerns with the hydrodynamic limit of the Kob-Andersen model, an
interacting particle system that has been introduced by physicists in order to
explain glassy behavior, and widely studies since. We will see that the density
profile evolves in the hydrodynamic limit according to a non-degenerate
hydrodynamic equation, and see how the diffusion coefficient decays as density
grows.


  Can a Bell test with no detection loophole be demonstrated for multi-photon
entangled states of light within the current technology? We examine the
possibility of a postselection-free CHSH-Bell inequality test wih an
unsymmetrical polarization singlet. To that end we employ a preselection
procedure which is performed prior to the test. It allows using imperfect
(coarse-grained) binary photodetection in the test. We show an example of
preselection scheme which improves violation of the CHSH inequality with the
micro-macro polarization singlet produced by the optimal quantum cloning. The
preselection is realized by a quantum filter which is believed to be not useful
for this purpose.


  In this article, we extend previous studies based on CMB spherical harmonic
phases (SHP) to examine the validity of the hypothesis that the temperature
field of the CMB is consistent with a Gaussian random field (GRF). The null
hypothesis is that the corresponding CMB SHP are independent and identically
distributed in terms of a uniform distribution in the interval [0, 2$\pi$]
\citep{1986ApJ...304...15B,2013rossmanith}. We devise a new model-independent
method where we use ordered and non-parametric Rao's statistic, based on sample
arc-lengths to comprehensively test uniformity and independence of SHP. We
performed our analysis on the scales limited by spherical harmonic modes $\le$
128, to restrict ourselves to signal-dominated regions. To find the non-uniform
or dependent sets of SHP, we calculate the statistic for the data and 10000
Monte Carlo simulated uniformly random sets of SHP and use 0.05 and 0.001
$\alpha$ levels to distinguish between statistically significant and highly
significant detections. We first establish the performance of our method using
simulated Gaussian, non-Gaussian CMB temperature maps, along with observed
non-Gaussian 100 and 143 GHz Planck channel maps. We find that our method
performs efficiently and accurately in detecting phase correlations generated
in all of the non-Gaussian simulations and observed foreground contaminated 100
and 143 GHz Planck channel temperature maps. We apply our method on the Planck
satellite mission's final released CMB temperature anisotropy maps- COMMANDER,
SMICA, NILC, and SEVEM along with WMAP 9 year released ILC map. We report that
SHP corresponding to some of the $m$-modes is non-uniform, some of the $\ell$
mode SHP and neighboring mode pair SHP are correlated in cleaned CMB maps. The
detection of non-uniformity or correlation in the SHP indicates the presence of
non-Gaussian signals in the foreground minimized CMB maps.


  In the first three years since the discovery of Fe-based high Tc
superconductors, scanning tunneling microscopy (STM) and spectroscopy have shed
light on three important questions. First, STM has demonstrated the complexity
of the pairing symmetry in Fe-based materials. Phase-sensitive quasiparticle
interference (QPI) imaging and low temperature spectroscopy have shown that the
pairing order parameter varies from nodal to nodeless s\pm within a single
family, FeTe1-xSex. Second, STM has imaged C4 -> C2 symmetry breaking in the
electronic states of both parent and superconducting materials. As a local
probe, STM is in a strong position to understand the interactions between these
broken symmetry states and superconductivity. Finally, STM has been used to
image the vortex state, giving insights into the technical problem of vortex
pinning, and the fundamental problem of the competing states introduced when
superconductivity is locally quenched by a magnetic field. Here we give a
pedagogical introduction to STM and QPI imaging, discuss the specific
challenges associated with extracting bulk properties from the study of
surfaces, and report on progress made in understanding Fe-based superconductors
using STM techniques.


  It is well-known that compositions of Markov processes with inverse
subordinators are governed by integro-differential equations of generalized
fractional type. This kind of processes are of wide interest in statistical
physics as they are connected to anomalous diffusions. In this paper we
consider a generalization; more precisely we mean componentwise compositions of
$\mathbb{R}^d$-valued Markov processes with the components of an independent
multivariate inverse subordinator. As a possible application, we present a
model of anomalous diffusion in anisotropic medium, which is obtained as a weak
limit of suitable continuous-time random walks.


  We give a polynomial-time algorithm for learning neural networks with one
layer of sigmoids feeding into any Lipschitz, monotone activation function
(e.g., sigmoid or ReLU). We make no assumptions on the structure of the
network, and the algorithm succeeds with respect to {\em any} distribution on
the unit ball in $n$ dimensions (hidden weight vectors also have unit norm).
This is the first assumption-free, provably efficient algorithm for learning
neural networks with two nonlinear layers.
  Our algorithm-- {\em Alphatron}-- is a simple, iterative update rule that
combines isotonic regression with kernel methods. It outputs a hypothesis that
yields efficient oracle access to interpretable features. It also suggests a
new approach to Boolean learning problems via real-valued conditional-mean
functions, sidestepping traditional hardness results from computational
learning theory.
  Along these lines, we subsume and improve many longstanding results for PAC
learning Boolean functions to the more general, real-valued setting of {\em
probabilistic concepts}, a model that (unlike PAC learning) requires non-i.i.d.
noise-tolerance.


  Tissue plasminogen activator (tPA) is the sole approved therapeutic molecule
for the treatment of acute ischemic stroke. Yet, only a small percentage of
patients could benefit from this life-saving treatment because of medical
contraindications and severe side effects, including brain hemorrhage,
associated with delayed administration. Here, a nano therapeutic agent is
realized by directly associating the clinical formulation of tPA to the porous
structure of soft discoidal polymeric nanoconstructs (tPA-DPNs). The porous
matrix of DPNs protects tPA from rapid degradation, allowing tPA-DPNs to
preserve over 70 % of the tPA original activity after 3 h of exposure to serum
proteins. Under dynamic conditions, tPA-DPNs dissolve clots more efficiently
than free tPA, as demonstrated in a microfluidic chip where clots are formed
mimicking in vivo conditions. At 60 min post treatment initiation, the clot
area reduces by half (57 + 8 %) with tPA-DPNs, whereas a similar result (56 +
21 %) is obtained only after 90 min for free tPA. In murine mesentery venules,
the intravenous administration of 2.5 mg/kg of tPA-DPNs resolves almost 90 % of
the blood clots, whereas a similar dose of free tPA successfully recanalize
only about 40 % of the treated vessels. At about 1/10 of the clinical dose (1.0
mg/kg), tPA-DPNs still effectively dissolve 70 % of the clots, whereas free tPA
works efficiently only on 16 % of the vessels. In vivo, discoidal tPA-DPNs
outperform the lytic activity of 200 nm spherical tPA-coated nanoconstructs in
terms of both percentage of successful recanalization events and clot area
reduction. The conjugation of tPA with preserved lytic activity, the
deformability and blood circulating time of DPNs together with the faster blood
clot dissolution would make tPA-DPNs a promising nanotool for enhancing both
potency and safety of thrombolytic therapies.


  Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission
(Taiji and Tianqin), will offer novel perspectives for exploring our Universe
while introduce new challenges, especially in data analysis. Aside from the
known challenges like high parameter space dimension, superposition of large
number of signals and etc., gravitational wave detections in space would be
more seriously affected by anomalies or non-stationarities in the science
measurements. Considering the three types of foreseeable non-stationarities
including data gaps, transients (glitches), and time-varying noise
auto-correlations, which may come from routine maintenance or unexpected
disturbances during science operations, we developed a deep learning model for
accurate signal extractions confronted with such anomalous scenarios. Our model
exhibits the same performance as the current state-of-the-art models do for the
ideal and anomaly free scenario, while shows remarkable adaptability in
extractions of coalescing massive black hole binary signal against all three
types of non-stationarities and even their mixtures. This also provide new
explorations into the robustness studies of deep learning models for data
processing in space-borne gravitational wave missions.


  We present a novel discontinuous Galerkin finite element method for numerical
simulations of the rotating thermal shallow water equations in complex
geometries using curvilinear meshes, with arbitrary accuracy. We derive an
entropy functional which is convex, and which must be preserved in order to
preserve model stability at the discrete level. The numerical method is
provably entropy stable and conserves mass, buoyancy, vorticity, and energy.
This is achieved by using novel entropy stable numerical fluxes,
summation-by-parts principle, and splitting the pressure and convection
operators so that we can circumvent the use of chain rule at the discrete
level. Numerical simulations on a cubed sphere mesh are presented to verify the
theoretical results. The numerical experiments demonstrate the robustness of
the method for a regime of well developed turbulence, where it can be run
stably without any dissipation. The entropy stable fluxes are sufficient to
control the grid scale noise generated by geostrophic turbulence, eliminating
the need for artificial stabilisation.


  We have investigated the pulse shape evolution of the Crab pulsar emission in
the hard X-ray domain of the electromagnetic spectrum. In particular, we have
studied the alignment of the Crab pulsar phase profiles measured in the hard
X-rays and in other wavebands. To obtain the hard X-ray pulse profiles, we have
used six year (2003-2009, with a total exposure of about 4 Ms) of publicly
available data of the SPI telescope on-board of the INTEGRAL observatory,
folded with the pulsar time solution derived from the Jodrell Bank Crab Pulsar
Monthly Ephemeris. We found that the main pulse in the hard X-ray 20-100 keV
energy band is leading the radio one by $8.18\pm0.46$ milliperiods in phase, or
$275\pm15 \mu s$ in time. Quoted errors represent only statistical
uncertainties.Our systematic error is estimated to be $\sim 40 \mu s$ and is
mainly caused by the radio measurement uncertainties. In hard X-rays, the
average distance between the main pulse and interpulse on the phase plane is
$0.3989\pm0.0009$. To compare our findings in hard X-rays with the soft 2-20
keV X-ray band, we have used data of quasi-simultaneous Crab observations with
the PCA monitor on-board the Rossi X-Ray Timing Explorer (RXTE) mission. The
time lag and the pulses separation values measured in the 3-20 keV band are
$0.00933\pm0.00016$ (corresponding to $310\pm6 \mu s$) and $0.40016\pm0.00028$
parts of the cycle, respectively. While the pulse separation values measured in
soft X-rays and hard X-rays agree, the time lags are statistically different.
Additional analysis show that the delay between the radio and X-ray signals
varies with energy in the 2 - 300 keV energy range. We explain such a behaviour
as due to the superposition of two independent components responsible for the
Crab pulsed emission in this energy band.


  The synthetic control method offers a way to quantify the effect of an
intervention using weighted averages of untreated units to approximate the
counterfactual outcome that the treated unit(s) would have experienced in the
absence of the intervention. This method is useful for program evaluation and
causal inference in observational studies. We introduce the software package
scpi for prediction and inference using synthetic controls, implemented in
Python, R, and Stata. For point estimation or prediction of treatment effects,
the package offers an array of (possibly penalized) approaches leveraging the
latest optimization methods. For uncertainty quantification, the package offers
the prediction interval methods introduced by Cattaneo, Feng and Titiunik
(2021) and Cattaneo, Feng, Palomba and Titiunik (2022). The paper includes
numerical illustrations and a comparison with other synthetic control software.


  We present a description of the Radio Frequency Quadrupole (RFQ) ion trap
built as part of the TITAN facility. It consists of a gas-filled, segmented,
linear Paul trap and is the first stage of the TITAN setup with the purpose of
cooling and bunching radioactive ion beams delivered from ISAC-TRIUMF. This is
the first such device to be driven digitally, i.e., using a high voltage
($V_{pp} = \rm{400 \, V}$), wide bandwidth ($0.2 < f < 1.2 \, \rm{MHz}$)
square-wave as compared to the typical sinusoidal wave form. Results from the
commissioning of the device as well as systematic studies with stable and
radioactive ions are presented including efficiency measurements with stable
$^{133}$Cs and radioactive $^{124, 126}$Cs. A novel and unique mode of
operation of this device is also demonstrated where the cooled ion bunches are
extracted in reverse mode, i.e., in the same direction as previously injected.


  Recent measurements of the branching fraction for D_s -> l nu disagree with
the Standard Model expectation, which relies on calculations of $f_{D_s}$ from
lattice QCD. This paper uses recent preliminary measurements from CLEO and a
new preliminary lattice-QCD result from this conference to update the
significance of the discrepancy. The "f_{D_s} puzzle" stands now at 3.5sigma,
with sigma predominantly from the statistical uncertainty of the experiments.
New physics scenarios that could solve the puzzle would also lead to
non-Standard amplitudes mediating the semileptonic decays D -> K l nu. This
paper shows where the new amplitudes enter the differential rate and outlines
where lattice QCD calculations are needed to confront recent and forthcoming
measurements.


  Encoding information in light with orbital angular momentum (OAM) enables
networks to increase channel capacity significantly. However, light in only the
fundamental Gaussian mode is suitable for fibre transmission, and not higher
order Laguerre Gaussian modes, which carry OAM. Therefore, building a bridge to
interface light with OAM and Gaussian mode time-binning is crucially important.
Here, we report the realization of a photonic space-time transcoder, by which
light with an arbitrary OAM superposition is experimentally converted into a
time-bin Gaussian pulse, and vice versa. Furthermore, we clearly demonstrate
that coherence is well conserved and there is no cross-talk between orthogonal
modes. This photonic device is simple and can be built with scalable
architecture. Our experimental demonstration paves the way towards a mixed
optical communication in free-space and optical fibre.


  Micro-expression has emerged as a promising modality in affective computing
due to its high objectivity in emotion detection. Despite the higher
recognition accuracy provided by the deep learning models, there are still
significant scope for improvements in micro-expression recognition techniques.
The presence of micro-expressions in small-local regions of the face, as well
as the limited size of available databases, continue to limit the accuracy in
recognizing micro-expressions. In this work, we propose a facial
micro-expression recognition model using 3D residual attention network named
MERANet to tackle such challenges. The proposed model takes advantage of
spatial-temporal attention and channel attention together, to learn deeper
fine-grained subtle features for classification of emotions. Further, the
proposed model encompasses both spatial and temporal information simultaneously
using the 3D kernels and residual connections. Moreover, the channel features
and spatio-temporal features are re-calibrated using the channel and
spatio-temporal attentions, respectively in each residual module. Our attention
mechanism enables the model to learn to focus on different facial areas of
interest. The experiments are conducted on benchmark facial micro-expression
datasets. A superior performance is observed as compared to the
state-of-the-art for facial micro-expression recognition on benchmark data.


  We explore how nuclear modifications to the nucleon parton distributions
affect production of high transverse momentum hadrons in deuteron-nucleus
collisions. We calculate the charged hadron spectra to leading order using
standard fragmentation functions and shadowing parameterizations. We obtain the
d+Au to pp ratio both in minimum bias collisions and as a function of
centrality. The minimum bias results agree reasonably well with the BRAHMS data
while the calculated centrality dependence underestimates the data and is a
stronger function of p_T than the data indicate.


  The pairing correlations in superfluid He-3 are strongly modified by
quasiparticle scattering off a surface or an interface. We present theoretical
results and predictions for the order parameter, the quasiparticle excitation
spectrum and the free energy for thin films of superfluid He-3. Both specular
and diffuse scattering by a substrate are considered, while the free surface is
assumed to be a perfectly reflecting specular boundary. The results are based
on self-consistent calculations of the order parameter and quasiparticle
excitation spectrum at zero pressure. We obtain new results for the phase
diagram, free energy, entropy and specific heat of thin films of superfluid
He-3.


  The HOM problem, which asks whether the image of a regular tree language
under a given tree homomorphism is again regular, is known to be decidable
[Godoy & Gim\'enez: The HOM problem is decidable. JACM 60(4), 2013]. However,
the problem remains open for regular weighted tree languages. It is
demonstrated that the main notion used in the unweighted setting, the tree
automaton with equality and inequality constraints, can straightforwardly be
generalized to the weighted setting and can represent the image of any regular
weighted tree language under any nondeleting and nonerasing tree homomorphism.
Several closure properties as well as decision problems are also investigated
for the weighted tree languages generated by weighted tree automata with
constraints.


  The mass splitting of elliptic anisotropy ($v_2$) at low transverse momentum
is considered as a hallmark of hydrodynamic collective flow. We investigate a
multiphase transport (AMPT) model where the $v_2$ is mainly generated by an
anisotropic escape mechanism, not of the hydrodynamic flow nature, and where
mass splitting is also observed. We demonstrate that the $v_2$ mass splitting
in AMPT is small right after hadronization (especially when resonance decays
are included); the mass splitting mainly comes from hadronic rescatterings,
even though their contribution to the overall charged hadron $v_2$ is small.
These findings are qualitatively the same as those from hybrid models that
combine hydrodynamics with a hadron cascade. We further show that there is no
qualitative difference between heavy ion collisions and small system
collisions. Our results indicate that the $v_2$ mass splitting is not a unique
signature of hydrodynamic collective flow and thus cannot distinguish whether
the elliptic flow is generated mainly from hydrodynamics or the anisotropic
parton escape.


  Airtime interference is a key performance indicator for WLANs, measuring, for
a given time period, the percentage of time during which a node is forced to
wait for other transmissions before to transmitting or receiving. Being able to
accurately estimate interference resulting from a given state change (e.g.,
channel, bandwidth, power) would allow a better control of WLAN resources,
assessing the impact of a given configuration before actually implementing it.
  In this paper, we adopt a principled approach to interference estimation in
WLANs. We first use real data to characterize the factors that impact it, and
derive a set of relevant synthetic workloads for a controlled comparison of
various deep learning architectures in terms of accuracy, generalization and
robustness to outlier data. We find, unsurprisingly, that Graph Convolutional
Networks (GCNs) yield the best performance overall, leveraging the graph
structure inherent to campus WLANs. We notice that, unlike e.g. LSTMs, they
struggle to learn the behavior of specific nodes, unless given the node indexes
in addition. We finally verify GCN model generalization capabilities, by
applying trained models on operational deployments unseen at training time.


  Along with the rapid expansion of information technology and digitalization
of health data, there is an increasing concern on maintaining data privacy
while garnering the benefits in medical field. Two critical challenges are
identified: Firstly, medical data is naturally distributed across multiple
local sites, making it difficult to collectively train machine learning models
without data leakage. Secondly, in medical applications, data are often
collected from different sources and views, resulting in heterogeneity and
complexity that requires reconciliation. This paper aims to provide a generic
Federated Multi-View Learning (FedMV) framework for multi-view data leakage
prevention, which is based on different types of local data availability and
enables to accommodate two types of problems: Vertical Federated Multi-View
Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV). We
experimented with real-world keyboard data collected from BiAffect study. The
results demonstrated that the proposed FedMV approach can make full use of
multi-view data in a privacy-preserving way, and both V-FedMV and H-FedMV
methods perform better than their single-view and pairwise counterparts.
Besides, the proposed model can be easily adapted to deal with multi-view
sequential data in a federated environment, which has been modeled and
experimentally studied. To the best of our knowledge, this framework is the
first to consider both vertical and horizontal diversification in the
multi-view setting, as well as their sequential federated learning.


  In order to address the limitations of gestural capabilities in physical
robots, researchers in Virtual, Augmented, Mixed Reality Human-Robot
Interaction (VAM-HRI) have been using augmented-reality visualizations that
increase robot expressivity and improve user perception (e.g., social
presence). While a multitude of virtual robot deictic gestures (e.g., pointing
to an object) have been implemented to improve interactions within VAM-HRI,
such systems are often reported to have tradeoffs between functional and social
user perceptions of robots, creating a need for a unified approach that
considers both attributes. We performed a literature analysis that selected
factors that were noted to significantly influence either user perception or
task efficiency and propose a set of design considerations and recommendations
that address those factors by combining anthropomorphic and non-anthropomorphic
virtual gestures based on the motivation of the interaction, visibility of the
target and robot, salience of the target, and distance between the target and
robot. The proposed recommendations provide the VAM-HRI community with starting
points for selecting appropriate gesture types for a multitude of interaction
contexts.