  Lip motion accuracy is important for speech intelligibility, especially for
users who are hard of hearing or second language learners. A high level of
realism in lip movements is also required for the game and film production
industries. 3D morphable models (3DMMs) have been widely used for facial
analysis and animation. However, factors that could influence their use in
facial animation, such as the differences in facial features between recorded
real faces and animated synthetic faces, have not been given adequate
attention. This paper investigates the mapping between real speakers and
similar and non-similar 3DMMs and the impact on the resulting 3D lip motion.
Mouth height and mouth width are used to determine face similarity. The results
show that mapping 2D videos of real speakers with low mouth heights to 3D heads
that correspond to real speakers with high mouth heights, or vice versa,
generates less good 3D lip motion. It is thus important that such a mismatch is
considered when using a 2D recording of a real actor's lip movements to control
a 3D synthetic character.


  Transformers have become a standard neural network architecture for many NLP
problems, motivating theoretical analysis of their power in terms of formal
languages. Recent work has shown that transformers with hard attention are
quite limited in power (Hahn, 2020), as they can be simulated by constant-depth
AND/OR circuits (Hao et al. 2021). However, hard attention is a strong
assumption, which may complicate the relevance of these results in practice. In
this work, we analyze the circuit complexity of transformers with saturated
attention: a generalization of hard attention that more closely captures the
attention patterns learnable in practical transformers. We first show that
saturated transformers transcend the known limitations of hard-attention
transformers. We then prove saturated transformers with floating-point values
can be simulated by constant-depth threshold circuits, giving the class
$\mathsf{TC}^0$ as an upper bound on the formal languages they recognize.


  We carry out molecular statics (MS) simulations to study the indentation
process of Pt (111) surfaces using an indenter with the radius of 5-20 nm. The
substrate and indenter surfaces are either bare or graphene-covered. Our
simulations show that the influence of the adhesion between the bare substrate
and indenter tip can be significantly reduced by decreasing the adhesion
strength and adhesion range between the atoms on the substrate and indenter, or
by enhancing the substrate stiffness. Our results suggest that the elastic
response of the substrate exhibits weaker adhesion after the coating of
graphene layers on either side of the contacting interface, which is attributed
to the weak interaction between the graphene layers. Based on these principles
obtained for the bare substrate, the nanoscale contact behavior of the
substrate can be tuned into a near-ideal Hertzian state by increasing the
number of graphene layers, applying pre-strains to graphene on substrate, or
using large indenters. Our research provides theoretical guidance for designing
adhesion-less coatings for AFM probes and MEMS/NEMS systems.


  We explore the effects of nonstandard neutrino interactions in the lower
components of the solar neutrino spectrum which are predominant by the vacuum
oscillations. The recent measurements of Borexino experiment between 2011 and
2015 provide a clean test to study the nonstandard neutrino interactions at the
source (sun) and the at solar detector. In this work, first the possible
standard model parameters are estimated from the combined data of the low
energy regime and then the nonstandard effects at the source, at the detector,
and from the interplay between source and detector parameters are bounded. The
same effects are also investigated for the proposed experiments like LENA and
Jinpin Neutrino Experiment with their projected sensitivities.


  This note constructs a compact, real-analytic, riemannian 4-manifold
({\Sigma}, g) with the properties that: (1) its geodesic flow is completely
integrable with smooth but not real-analytic integrals; (2) {\Sigma} is
diffeomorphic to $T^2 \times S^2$ ; and (3) the limit set of the geodesic flow
on the universal cover is dense. This shows there are obstructions to
realanalytic integrability beyond the topology of the configuration space.


  We report here the observation of anomalous paramagnetic signal(s) in the
isofield field-cooled cool-down magnetization scans (MFCC(T)) recorded for a
single crystal of a low Tc superconductor Ca3Ir4Sn13. Novel features emanating
from the MFCC(T) response include an oscillatory magnetization behaviour below
Tc and a rich multiplicity (non-uniqueness) in magnetization ranging from
diamagnetism to paramagnetism at a given H, T value. The metastability in
MFCC(T) has been ascribed to non-unique coexistence of multi-quanta vortex
states and single quantum (Abrikosov) vortices. Additionally, the isothermal
M(H) scans recorded across a short window of temperature just below Tc show
evidence for only the multi-quanta vortex states in the domain of surface
superconductivity, with no fingerprint(s) of pinned Abrikosov lattice.


  Skin cancer is the most common cancer type. Usually, patients with suspicion
of cancer are treated by doctors without any aided visual inspection. At this
point, dermoscopy has become a suitable tool to support physicians in their
decision-making. However, clinicians need years of expertise to classify
possibly malicious skin lesions correctly. Therefore, research has applied
image processing and analysis tools to improve the treatment process. In order
to perform image analysis and train a model on dermoscopic images data needs to
be centralized. Nevertheless, data centralization does not often comply with
local data protection regulations due to its sensitive nature and due to the
loss of sovereignty if data providers allow unlimited access to the data. A
method to circumvent all privacy-related challenges of data centralization is
Distributed Analytics (DA) approaches, which bring the analysis to the data
instead of vice versa. This paradigm shift enables data analyses - in our case,
image analysis - with data remaining inside institutional borders, i.e., the
origin. In this documentation, we describe a straightforward use case including
a model training for skin lesion classification based on decentralised data.


  The projectability of Poincar\'e-Cartan forms in a third-order jet bundle
$J^3\pi$ onto a lower-order jet bundle is a consequence of the degenerate
character of the corresponding Lagrangian. This fact is analyzed using the
constraint algorithm for the associated Euler-Lagrange equations in $J^3\pi$.
The results are applied to study the Hilbert Lagrangian for the Einstein
equations (in vacuum) from a multisymplectic point of view. Thus we show how
these equations are a consequence of the application of the constraint
algorithm to the geometric field equations, meanwhile the other constraints are
related with the fact that this second-order theory is equivalent to a
first-order theory. Furthermore, the case of higher-order mechanics is also
studied as a particular situation.


  Physics-informed extreme learning machine (PIELM) has recently received
significant attention as a rapid version of physics-informed neural network
(PINN) for solving partial differential equations (PDEs). The key
characteristic is to fix the input layer weights with random values and use
Moore-Penrose generalized inverse for the output layer weights. The framework
is effective, but it easily suffers from overfitting noisy data and lacks
uncertainty quantification for the solution under noise scenarios.To this end,
we develop the Bayesian physics-informed extreme learning machine (BPIELM) to
solve both forward and inverse linear PDE problems with noisy data in a unified
framework. In our framework, a prior probability distribution is introduced in
the output layer for extreme learning machine with physic laws and the Bayesian
method is used to estimate the posterior of parameters. Besides, for inverse
PDE problems, problem parameters considered as new output layer weights are
unified in a framework with forward PDE problems. Finally, we demonstrate
BPIELM considering both forward problems, including Poisson, advection, and
diffusion equations, as well as inverse problems, where unknown problem
parameters are estimated. The results show that, compared with PIELM, BPIELM
quantifies uncertainty arising from noisy data and provides more accurate
predictions. In addition, BPIELM is considerably cheaper than PINN in terms of
the computational cost.


  Some mathematical models of applied problems lead to the need of solving
boundary value problems with a fractional power of an elliptic operator. In a
number of works, approximations of such a nonlocal operator are constructed on
the basis of an integral representation with a singular integrand. In the
present paper, new and more convenient integral representations are proposed
for operators with fractional powers. Approximations are based on the classical
quadrature formulas. The results of numerical experiments on the accuracy of
quadrature formulas are presented. The proposed approximations are used for
numerical solving a model two-dimensional boundary value problem for fractional
diffusion.


  We consider the linear representations of the mapping class group of an
n-punctured 2-sphere constructed by V. F. R. Jones using Iwahori-Hecke algebras
of type A. We show that their faithfulness is equivalent to that of certain
related Iwahori-Hecke algebra representation of Artin's braid group of n-1
strands. In the case of n=6, we provide a further restriction for the kernel
using our previous result, as well as a certain relation to the Burau
representation of degree 4.


  The assembly of the Milky Way bulge is an old topic in astronomy, one now in
a period of renewed and rapid development. The dominant scenario for bulge
formation is that of the Milky Way as a nearly pure disk galaxy, with the inner
disk having formed a bar and buckled. This can potentially explain virtually
all bulge stars with [Fe/H] <~ -1.0, comprising 95% of the stellar population.
The evidence is the incredible success in N-body models of this type in making
meaningful predictions, such as the rotation curve and velocity dispersion
measured from radial velocities, and the spatial morphologies of the
peanut/X-shape and the long bar. The classical bulge scenario remains viable
for stars with [Fe/H] <~ -1.0 and potentially a minority of the other stars. A
classical bulge is expected from Lambda-CDM cosmological simulations, can
accentuate the properties of an existing bar in a hybrid system, and is most
consistent with the bulge abundance trends such as [Mg/Fe], which are elevated
relative to both the thin and thick disks. Finally, the clumpy-galaxy scenario
is considered, as it is the correct description of most Milky Way precursors
given observations of high-redshift galaxies. Simulations predict that these
star-forming clumps will sometimes migrate to the centres of galaxies where
they may form a bulge, and galaxies often include a bulge clump as well. They
will possibly form a bar with properties consistent with those of the Milky
Way, such as the exponential profile and metallicity gradient. Given the
relative successes of these scenarios, the Milky Way bulge is plausibly of
composite origin, with a classical bulge and/or inner halo numerically dominant
for stars with [Fe/H] <~ -1.0, a buckling thick disk for stars with -1.0 <~
[Fe/H] <~ -0.50 perhaps descended from the clumpy galaxy phase, and a buckling
thin disk for stars with [Fe/H] >~ -0.50.


  Motivated by the problem of sorting, we introduce two simple combinatorial
models with distinct Hamiltonians yet identical spectra (and hence partition
function) and show that the local dynamics of these models are very different.
After a deep quench, one model slowly relaxes to the sorted state whereas the
other model becomes blocked by the presence of stable local minima.


  Hexagonal metals have anisotropic coefficients of thermal expansion causing
grain-level internal stresses during heating. High energy x-ray diffraction
microscopy, a non-destructive, in situ, micromechanical and microstructural
characterization technique, has been used to determine the anisotropic
coefficients of thermal expansion (CTEs) for Ti-7Al. Two samples of
polycrystalline $\alpha$-phase Ti-7Al were continuously heated from room
temperature to 850 $^\circ$C while far-field HEDM scans were collected. The
results showed a change in the ratio of the CTEs in the 'a' and 'c' directions
which explains discrepancies found in the literature. The CTE additionally
appears to be affected by the dissolution of $\alpha_2$ precipitates. Analysis
of the grain-resolved micromechanical data also shows reconfiguration of the
grain scale stresses likely due to anisotropic expansion driving
crystallographic slip.


  Summary sentences produced by abstractive summarization models may be
coherent and comprehensive, but they lack control and rely heavily on reference
summaries. The BRIO training paradigm assumes a non-deterministic distribution
to reduce the model's dependence on reference summaries, and improve model
performance during inference. This paper presents a straightforward but
effective technique to improve abstractive summaries by fine-tuning pre-trained
language models, and training them with the BRIO paradigm. We build a text
summarization dataset for Vietnamese, called VieSum. We perform experiments
with abstractive summarization models trained with the BRIO paradigm on the
CNNDM and the VieSum datasets. The results show that the models, trained on
basic hardware, outperform all existing abstractive summarization models,
especially for Vietnamese.


  A Fixed-Parameter Tractable (\FPT) $\rho$-approximation algorithm for a
minimization (resp. maximization) parameterized problem $P$ is an FPT algorithm
that, given an instance $(x, k)\in P$ computes a solution of cost at most $k
\cdot \rho(k)$ (resp. $k/\rho(k)$) if a solution of cost at most (resp. at
least) $k$ exists; otherwise the output can be arbitrary. For well-known
intractable problems such as the W[1]-hard {Clique} and W[2]-hard {Set Cover}
problems, the natural question is whether we can get any \FPT-approximation. It
is widely believed that both {Clique} and {Set-Cover} admit no FPT
$\rho$-approximation algorithm, for any increasing function $\rho$. Assuming
standard conjectures such as the Exponential Time Hypothesis (ETH)
\cite{eth-paturi} and the Projection Games Conjecture (PGC) \cite{r3}, we make
the first progress towards proving this conjecture by showing that
  1. Under the ETH and PGC, there exist constants $F_1, F_2 >0$ such that the
{Set Cover} problem does not admit an FPT approximation algorithm with ratio
$k^{F_1}$ in $2^{k^{F_2}}\cdot \text{poly}(N,M)$ time, where $N$ is the size of
the universe and $M$ is the number of sets.
  2. Unless $\NP\subseteq \SUBEXP$, for every $1> \delta > 0$ there exists a
constant $F(\delta)>0$ such that {Clique} has no FPT cost approximation with
ratio $k^{1-\delta}$ in $2^{k^{F}}\cdot \text{poly}(n)$ time, where $n$ is the
number of vertices in the graph.
  In the second part of the paper we consider various W[1]-hard problems such
as {\dst}, {\dsf}, Directed Steiner Network and {\mec}. For all these problem
we give polynomial time $f(\text{OPT})$-approximation algorithms for some small
function $f$ (the largest approximation ratio we give is $\text{OPT}^2$).


  Open access WiFi hotspots are widely deployed in many public places,
including restaurants, parks, coffee shops, shopping malls, trains, airports,
hotels, and libraries. While these hotspots provide an attractive option to
stay connected, they may also track user activities and share user/device
information with third-parties, through the use of trackers in their captive
portal and landing websites. In this paper, we present a comprehensive privacy
analysis of 67 unique public WiFi hotspots located in Montreal, Canada, and
shed some light on the web tracking and data collection behaviors of these
hotspots. Our study reveals the collection of a significant amount of
privacy-sensitive personal data through the use of social login (e.g., Facebook
and Google) and registration forms, and many instances of tracking activities,
sometimes even before the user accepts the hotspot's privacy and terms of
service policies. Most hotspots use persistent third-party tracking cookies
within their captive portal site; these cookies can be used to follow the
user's browsing behavior long after the user leaves the hotspots, e.g., up to
20 years. Additionally, several hotspots explicitly share (sometimes via HTTP)
the collected personal and unique device information with many third-party
tracking domains.


  Traditional executable delivery models pose challenges for IoT devices with
limited storage, necessitating the download of complete executables and
dependencies. Network solutions like NFS, designed for data files, encounter
high IO overhead for irregular access patterns. This paper introduces SYSFLOW,
a lightweight network-based executable delivery system for IoT. SYSFLOW
delivers on-demand, redirecting local disk IO to the server through optimized
network IO. To optimize cache hit rates, SYSFLOW employs server-side
action-based prefetching, reducing latency by 45.1% to 75.8% compared to native
Linux filesystems on SD cards. In wired environments, SYSFLOW's latency is up
to 67.7% lower than NFS. In wireless scenarios, SYSFLOW performs 22.9% worse
than Linux, comparable with Linux and outperforming NFS by up to 60.7%. While
SYSFLOW's power consumption may be 6.7% higher than NFS, it offers energy
savings due to lower processing time.


  Directional detection is a promising dark matter search strategy. Indeed,
weakly interacting massive particle (WIMP)-induced recoils would present a
direction dependence toward the Cygnus constellation, while background-induced
recoils exhibit an isotropic distribution in the Galactic rest frame. Taking
advantage of these characteristic features and even in the presence of a
sizeable background, it has recently been shown that data from forthcoming
directional detectors could lead either to a competitive exclusion or to a
conclusive discovery, depending on the value of the WIMP-nucleon cross section.
However, it is possible to further exploit these upcoming data by using the
strong dependence of the WIMP signal with : the WIMP mass and the local WIMP
velocity distribution. Using a Markov chain Monte Carlo analysis of recoil
events, we show for the first time the possibility to constrain the unknown
WIMP parameters, both from particle physics (mass and cross section) and
Galactic halo (velocity dispersion along the three axis), leading to an
identification of non-baryonic dark matter.


  We apply here methods of inhomogeneous random graphs to a class of random
distance graphs. This provides an example outside of the rank 1 models which is
still solvable as long as the largest connected component is concerned. In
particular, we show that some random distance graphs behave exactly as the
classical Erd\H{o}s-R\'enyi model not only in the supercritical phase (as was
already known) but in the subcritical case as well.


  Deep neural networks (DNNs) and more precisely recurrent neural networks
(RNNs) are at the core of modern automatic speech recognition systems, due to
their efficiency to process input sequences. Recently, it has been shown that
different input representations, based on multidimensional algebras, such as
complex and quaternion numbers, are able to bring to neural networks a more
natural, compressive and powerful representation of the input signal by
outperforming common real-valued NNs. Indeed, quaternion-valued neural networks
(QNNs) better learn both internal dependencies, such as the relation between
the Mel-filter-bank value of a specific time frame and its time derivatives,
and global dependencies, describing the relations that exist between time
frames. Nonetheless, QNNs are limited to quaternion-valued input signals, and
it is difficult to benefit from this powerful representation with real-valued
input data. This paper proposes to tackle this weakness by introducing a
real-to-quaternion encoder that allows QNNs to process any one dimensional
input features, such as traditional Mel-filter-banks for automatic speech
recognition.


  HTTP-based video streaming technologies allow for flexible rate selection
strategies that account for time-varying network conditions. Such rate changes
may adversely affect the user's Quality of Experience; hence online prediction
of the time varying subjective quality can lead to perceptually optimised
bitrate allocation policies. Recent studies have proposed to use dynamic
network approaches for continuous-time prediction; yet they do not consider
multiple video quality models as inputs nor consider forecasting ensembles.
Here we address the problem of predicting continuous-time subjective quality
using multiple inputs fed to a non-linear autoregressive network. By
considering multiple network configurations and by applying simple averaging
forecasting techniques, we are able to considerably improve prediction
performance and decrease forecasting errors.


  We study a relativistic spin-1/2 fermion subjected to a Dirac oscillator
coupling and a constant magnetic field. An interplay between opposed chirality
interactions culminates in the appearance of a relativistic quantum phase
transition, which can be fully characterized. We obtain analytical expressions
for the energy gap, order parameter, and canonical quantum fluctuations across
the critical point. Moreover, we also discuss the effect of this phase
transition on the statistics of the chiral bosonic ensemble, where its super-
or sub-Poissonian nature can be controled by means of external parameters.
Finally, we study the entanglement properties between the degrees of freedom in
the relativistic ground state, where an interesting transition between a
bi-separable and a genuinely tripartite entangled state occurs.


  For a group $ G $ we consider its tensor square $G \otimes G$ and exterior
square $G \wedge G$. We prove that for a circularly orderable group $G$, under
some assumptions on $H_1(G)$ and $H_2(G)$, its exterior square and tensor
square are left-orderable. This yields an obstruction for a circularly
orderable group $G$ to have torsion. We apply these results to study circular
orderability of tabulated virtual knot groups.


  It is shown that in systems with time-dependent and/or spatially nonuniform
temperature $T(t,x)$, (i) most of the transport processes is weakly
non-ergodic, and (ii) the diffusion (Brownian motion, BM) is anomalous. A few
examples of simple arrangements, easy for experimental realization, are
discussed in detail. Proposed measurements will enable also the observation of
transitions from ergodic to weakly non-ergodic and from normal to anomalous
diffusion. New effects are predicted: (i) zero-mean oscillations of $T(t)$
accelerate BM (pumping effect), (ii) the combination of temporal and spatial
variations of temperature may lead to superballistic BM, (iii) linear gradients
of $T(x)$ result in an exponential acceleration of BM. One can expect similar
effects in inflationary systems with time-dependent metrics.


  One-dimensional valence bond solid (VBS) states represent the simplest
symmetry protected topological phases. We show that their ground state
entanglement spectrum contains both topological and non-topological structures.
For the $SO(3)$ symmetric VBS states with odd-integer spins, the two-fold
topological degeneracy is associated with an underlying $Z_{2}\times Z_{2}$
symmetry that protects the corresponding topological phase. In general, for the
$SO(2S+1)$ symmetric VBS states with integer spins $S$, the corresponding
protecting symmetry is identified as the $(Z_{2}\times Z_{2})^{S}$ symmetry,
yielding the $2^{S}$-fold topological degeneracy. The topological degeneracy
and associated protecting symmetry can be identified by a non-local unitary
transformation, which changes the topological order of the VBS states into
conventional ferromagnetic order.


  MOdified Newtonian Dynamics (MOND) is an interesting alternative to dark
matter in extragalactic systems. We here examine the possibility that mild or
even strong MOND behavior may become evident well inside the solar system, in
particular near saddle points of the total gravitational potential. Whereas in
Newtonian theory tidal stresses are finite at saddle points, they are expected
to diverge in MOND, and to remain distinctly large inside a sizeable oblate
ellipsoid around the saddle point. We work out the MOND effects using the
nonrelativistic limit of the T$e$V$e$S theory, both in the perturbative nearly
Newtonian regime and in the deep MOND regime. While strong MOND behavior would
be a spectacular ``backyard'' vindication of the theory, pinpointing the
MOND-bubbles in the setting of the realistic solar system may be difficult.
Space missions, such as the LISA Pathfinder, equipped with sensitive
accelerometers, may be able to explore the larger perturbative region.


  This paper explores the use of a Bayesian non-parametric topic modeling
technique for the purpose of anomaly detection in video data. We present
results from two experiments. The first experiment shows that the proposed
technique is automatically able characterize the underlying terrain, and detect
anomalous flora in image data collected by an underwater robot. The second
experiment shows that the same technique can be used on images from a static
camera in a dynamic unstructured environment. In the second dataset, consisting
of video data from a static seafloor camera capturing images of a busy coral
reef, the proposed technique was able to detect all three instances of an
underwater vehicle passing in front of the camera, amongst many other
observations of fishes, debris, lighting changes due to surface waves, and
benthic flora.


  The magneto-optical properties of titanium dioxide systems are related to the
presence of impurity states in the band gap due to oxygen vacancies. To
understand about the interplay between localized electrons and structural
distortions at the vacancy sites and the magneto-optical properties, we employ
a self-interaction corrected density functional theory method to calculate bulk
and small nanoparticles of rutile, anatase, and brookite titania. Our
computations reveal bipolaron configurations associated to an oxygen vacancy
with optical transition levels in the band gap. The ground state for these
bipolarons is a spin-triplet state in bulk rutile TiO2 and also in the
nanoparticles independently of the crystal phase, a result which may support
the idea of oxygen vacancies as a source of magnetism in this material. The
ground state for bipolarons in bulk anatase TiO2 is however a spin-singlet
state, different from the spin-triplet configuration reported in a previous
work based on hybrid functionals.


  The capacity region of the $K$-user Gaussian interference channel (GIC) is a
long-standing open problem and even capacity outer bounds are little known in
general. A significant progress on degrees-of-freedom (DoF) analysis, a
first-order capacity approximation, for the $K$-user GIC has provided new
important insights into the problem of interest in the high signal-to-noise
ratio (SNR) limit. However, such capacity approximation has been observed to
have some limitations in predicting the capacity at \emph{finite} SNR. In this
work, we develop a new upper-bounding technique that utilizes a new type of
genie signal and applies \emph{time sharing} to genie signals at $K$ receivers.
Based on this technique, we derive new upper bounds on the sum capacity of the
three-user GIC with constant, complex channel coefficients and then generalize
to the $K$-user case to better understand sum-rate behavior at finite SNR. We
also provide closed-form expressions of our upper bounds on the capacity of the
$K$-user symmetric GIC easily computable for \emph{any} $K$. From the
perspectives of our results, some sum-rate behavior at finite SNR is in line
with the insights given by the known DoF results, while some others are not. In
particular, the well-known $K/2$ DoF achievable for almost all constant real
channel coefficients turns out to be not embodied as a substantial performance
gain over a certain range of the cross-channel coefficient in the $K$-user
symmetric real case especially for \emph{large} $K$. We further investigate the
impact of phase offset between the direct-channel coefficient and the
cross-channel coefficients on the sum-rate upper bound for the three-user
\emph{complex} GIC. As a consequence, we aim to provide new findings that could
not be predicted by the prior works on DoF of GICs.


  Recently proposed stabilization mechanism of the Randall-Sundrum metric gives
rise to a scalar radion, which couples universally to matter with a weak
interaction ($\simeq 1$ TeV) scale. Demanding that gauge boson scattering as
described by the effective low enerrgy theory be unitary upto a given scale
leads to significant constraints on the mass of such a radion.


  Arcus is a proposed soft x-ray grating spectrometer Explorer. It aims to
explore cosmic feedback by mapping hot gases within and between galaxies and
galaxy clusters and characterizing jets and winds from supermassive black
holes, and to investigate the dynamics of protoplanetary discs and stellar
accretion. Arcus features 12 m-focal-length grazing-incidence silicon pore
optics (SPO) developed for the Athena mission. Critical-angle transmission
(CAT) gratings efficiently disperse high diffraction orders onto CCDs. We
report new and improved x-ray performance results for Arcus-like CAT gratings,
including record resolving power for two co-aligned CAT gratings. Multiple
Arcus prototype grating facets were illuminated by an SPO at the PANTER
facility. The facets consist of $32\times32.5$ mm$^2$ patterned silicon
membranes, bonded to metal frames. The bonding angle is adjusted according to
the measured average tilt angle of the grating bars in the membrane. Two
simultaneously illuminated facets show minor broadening of the Al-K$_{\alpha}$
doublet in 18$^{\rm th}$ and 21$^{\rm st}$ orders with a best fit record
effective resolving power of $R_G \approx 1.3^{+\infty}_{-0.5}\times10^4$
($3\sigma$), about 3-4 times the Arcus requirement. We measured the diffraction
efficiency of quasi-fully illuminated gratings at O-K wavelengths in orders 4-7
in an Arcus-like configuration and compare results with synchrotron spot
measurements. After corrections for geometrical effects and bremsstrahlung
continuum we find agreement between full and spot illumination at the two
different facilities, as well as with the models used for Arcus effective area
predictions. We find that these flight-like gratings meet diffraction
efficiency and greatly exceed resolving power Arcus requirements.


  A key ingredient of the Kochen-Specker theorem is the so-called functional
composition principle, which asserts that hidden states must ascribe values to
observables in a way that is consistent with all functional relations between
them. This principle is motivated by the assumption that, like functions of
observables in classical mechanics, a function $g(A)$ of an observable A in
quantum theory is simply a logically possible observable derived from $A$, and
that measuring $g(A)$ consists in measuring $A$ and post-processing the
resulting value via $g$. In this paper we show that, under suitable conditions,
this reasonable assumption is in conflict with the collapse postulate. We then
discuss possible solutions to this conflict, and we argue that the most
justifiable and less radical one consists in adapting the collapse postulate by
taking measurement contexts into account. This dependence on contexts arises
for precisely the same reason why noncontextual hidden-variable models are
ruled out by the Kochen-Specker theorem, namely that an observable A can
typically be written as a function $A=g(B)=h(C)$ of noncommuting observables
$B,C$.


  Objective and interpretable metrics to evaluate current artificial
intelligent systems are of great importance, not only to analyze the current
state of such systems but also to objectively measure progress in the future.
In this work, we focus on the evaluation of image generation tasks. We propose
a novel approach, called Fuzzy Topology Impact (FTI), that determines both the
quality and diversity of an image set using topology representations combined
with fuzzy logic. When compared to current evaluation methods, FTI shows better
and more stable performance on multiple experiments evaluating the sensitivity
to noise, mode dropping and mode inventing.


  The flow of a viscous fluid is perturbed by its internal friction which
generates heat and leads to a small temperature change. This does not occur for
an ideal fluid. We would like to resolve this picture as a function of the
dynamical macroscopic scales of both problems. In order to do this we will
study the evolution of the Navier-Stokes Hamiltonian with the classical
similarity renormalization group in the region of small viscosity. The
connection between the Euler and Navier-Stokes fluids will be pursued, but also
the viscous structures that arise will be studied in their own right to
determine the low-order velocity correlators of realistic fluids such as
single-component air and water. The canonical coordinate of the Navier-Stokes
Hamiltonian is a vector field that stores the initial position of all the fluid
particles. Thus these appear to be natural coordinates for studying arbitrary
separations of fluid particles over time. This connection will be pursued and
the region where the classic 1926 Richardson 4/3 scaling law holds will be
determined. The evolution of the Euler Hamiltonian will also be studied and we
will attempt to map its singular structures to those of the small-viscosity
Navier-Stokes fluid.


  We study matrix identities involving multiplication and unary operations such
as transposition or Moore-Penrose inversion. We prove that in many cases such
identities admit no finite basis.


  The p+Pb and Pb+Pb runs at the LHC have opened a possibility to investigate
the validity of collinear factorization in a clearly higher center-of-mass
energy scale than earlier in nuclear collisions. Indeed, some processes that
have been measured routinely in p+p($\overline {\rm p}$) collisions and
utilized for years in free proton PDF fits, can now finally be reached also in
the nuclear case. Such new data are expected to provide conclusive answers
concerning the universality of the nuclear PDFs. In this talk, I will contrast
some of the first p+Pb and Pb+Pb measurements to the predictions based on the
nuclear PDFs.


  The product of the gluon dressing function and the square of the ghost
dressing function in the Landau gauge can be regarded to represent, apart from
the inverse power corrections 1/Q^{2n}, a nonperturbative generalization A(Q^2)
of the perturbative QCD running coupling a(Q^2)=alpha_s(Q^2)/pi. Recent large
volume lattice calculations for these dressing functions indicate that the
coupling defined in such a way goes to zero as A(Q^2)~Q^2 when the squared
momenta Q^2 go to zero (Q^2<<1 GeV^2). In this work we construct such a QCD
coupling A(Q^2) which fulfills also various other physically motivated
conditions. At high momenta it becomes the underlying perturbative coupling
a(Q^2) to a very high precision. And at intermediate low squared momenta Q^2~1
GeV^2 it gives results consistent with the data of the semihadronic tau lepton
decays as measured by OPAL and ALEPH. The coupling is constructed in a
dispersive way, resulting as a byproduct in the holomorphic behavior of A(Q^2)
in the complex Q^2-plane which reflects the holomorphic behavior of the
spacelike QCD observables. Application of the Borel sum rules to tau-decay V+A
spectral functions allows us to obtain values for the gluon (dimension-4)
condensate and the dimension-6 condensate, which reproduce the measured OPAL
and ALEPH data to a significantly better precision than the perturbative MSbar
coupling approach.


  Medical clowns help hospitalized children in reducing pain and anxiety
symptoms and increase the level of satisfaction in children's wards.
Unfortunately, there is a shortage of medical clowns around the world.
Furthermore, isolated children can not enjoy this service. This study explored
the concept of a Robotic Medical Clown (RMC) and its role. We used mixed
methods of elicitation to create a design space model for future robotic
medical clowns. We investigated the needs, perceptions, and preferences of
children and teenagers using four methods: interviewing medical clowns to learn
how they perceive their role and the potential role of an RMC, conducting focus
groups with teenagers, a one-on-one experience of children with a robot, and an
online questionnaire. The concept of RMCs was acceptable to children,
teenagers, and medical clowns. We found that the RMC's appearance affects the
perception of its characters and role. Future work should investigate the
interaction in hospitals.


  The open clusters in the Kepler and CoRoT fields potentially provide tight
constraints for tests of stellar models and observational methods because they
allow a combination of complementary methods. We are in the process of identi-
fying and measuring parameters for detached eclipsing binaries (dEBs) in the
open clusters in the Kepler and CoRoT fields. We make use of measurements of
dEBs in the clusters to test the accuracy of asteroseismic scaling relations
for mass. We are able to provide strong indications that the asteroseismic
scaling relations over- estimate the stellar mass, but we are not yet able to
distinguish between different proposed corrections from the literature. We
argue how our ongoing measurements of more dEBs in more clusters, complemented
by dEBs in the field, should be able to break the degeneracy. We also briefly
describe how we can identify cluster stars that have evolved through
non-standard evolution by making use of ensemble asteroseismology.


  In the first part of this paper, we consider a partially overdetermined mixed
boundary value problem in space forms and generalize the main result in
\cite{GX} into the case of general domains with partial umbilical boundary in
space forms. Precisely, we prove that a partially overdetermined problem in a
domain with partial umbilical boundary admits a solution if and only if the
rest part of the boundary is also part of an umbilical hypersurface. In the
second part of this paper, we prove a Heintze-Karcher-Ros type inequality for
embedded hypersurfaces with free boundary lying on a horosphere or an
equidistant hypersurface in the hyperbolic space. As an application, we show
Alexandrov type theorem for constant mean curvature hypersurfaces with free
boundary in these settings.


  We present non-LTE time-dependent radiative-transfer simulations of supernova
(SN) IIb/Ib/Ic spectra and light curves, based on ~1B-energy piston-driven
ejecta, with and without 56Ni, produced from single and binary Wolf-Rayet (W-R)
stars evolved at solar and sub-solar metallicities. Our bolometric light curves
show a 10-day long post-breakout plateau with a luminosity of 1-5x10^7Lsun. In
our 56Ni-rich models, with ~3Msun ejecta masses, this plateau precedes a
20-30-day long re-brightening phase initiated by the outward-diffusing heat
wave powered by radioactive decay at depth. In low ejecta-mass models with
moderate mixing, Gamma-ray leakage starts as early as ~50d after explosion and
causes the nebular luminosity to steeply decline by ~0.02mag/d. Such
signatures, which are observed in standard SNe IIb/Ib/Ic, are consistent with
low-mass progenitors derived from a binary-star population. We propose that the
majority of stars with an initial mass ~<20Msun yield SNe II-P if 'effectively"
single, SNe IIb/Ib/Ic if part of a close binary system, and SN-less black holes
if more massive. Our ejecta, with outer hydrogen mass fractions as low as
~>0.01 and a total hydrogen mass of ~>0.001Msun, yield the characteristic SN
IIb spectral morphology at early times. However, by ~15d after the explosion,
only Halpha may remain as a weak absorption feature. Our binary models,
characterised by helium surface mass fractions of ~>0.85, systematically show
HeI lines during the post-breakout plateau, irrespective of the 56Ni abundance.
Synthetic spectra show a strong sensitivity to metallicity, which offers the
possibility to constrain it directly from SN spectroscopic modelling.


  Entanglement concentration is a critical technique to prevent degraded
fidelity and security in long-distance quantum communication. We propose novel
practical entanglement concentration protocols (ECPs) for less-entangled Bell
and Greenberger-Horne-Zeilinger states with unknown parameters by solely using
simple linear optics. We avoid the need for the post-selection principles or
photon-number-resolving detector to identify the parity-check measurement
completely by orchestrating auxiliary time degree of freedom, and the success
of ECPs is exactly heralded by the detection signatures without destroying the
incident qubits. Additionally, the outting incident photons kept are in the
maximally entangled or the less-entangled state, and the success probability
can be increased by recycling the latter. The heralded and the basic linear
optical elements make our practical ECPs are accessible to experimental
investigation with current technology.


  We focus on the analysis of local minimizers of the Mahler volume, that is to
say the local solutions to the problem $$\min\{
M(K):=|K||K^\circ|\;/\;K\subset\R^d\textrm{ open and convex}, K=-K\}, $$ where
$K^\circ:=\{\xi\in\R^d ; \forall x\in K, x\cdot\xi<1\}$ is the polar body of
$K$, and $|\cdot|$ denotes the volume in $\R^d$. According to a famous
conjecture of Mahler the cube is expected to be a global minimizer for this
problem. We express the Mahler volume in terms of the support functional of the
convex body, which allows us to compute first and second derivatives, and leads
to a concavity property of the functional. As a consequence, we prove first
that any local minimizer has a Gauss curvature that vanishes at any point where
it is defined. Going more deeply into the analysis in the two-dimensional case,
we also prove that any local minimizer must be a parallelogram. We thereby
retrieve and improve an original result of Mahler, who showed that
parallelograms are global minimizers in dimension 2, and also the case of
equality of Reisner, who proved that they are the only global minimizers.


  Mined bitexts can contain imperfect translations that yield unreliable
training signals for Neural Machine Translation (NMT). While filtering such
pairs out is known to improve final model quality, we argue that it is
suboptimal in low-resource conditions where even mined data can be limited. In
our work, we propose instead, to refine the mined bitexts via automatic
editing: given a sentence in a language xf, and a possibly imperfect
translation of it xe, our model generates a revised version xf' or xe' that
yields a more equivalent translation pair (i.e., <xf, xe'> or <xf', xe>). We
use a simple editing strategy by (1) mining potentially imperfect translations
for each sentence in a given bitext, (2) learning a model to reconstruct the
original translations and translate, in a multi-task fashion. Experiments
demonstrate that our approach successfully improves the quality of CCMatrix
mined bitext for 5 low-resource language-pairs and 10 translation directions by
up to ~ 8 BLEU points, in most cases improving upon a competitive
back-translation baseline.


  Modern silicon tracking detectors based on hybrid or fully integrated CMOS
technology are continuing to push to thinner sensors. The ionization energy
loss fluctuation in very thin silicon sensors significantly deviates from the
Landau distribution. Therefore, we have developed a charge deposition setup
that implements the Bichsel straggling function, which accounts for
shell-effects. This enhanced simulation is important for comparing with
testbeam or collision data with thin sensors as demonstrated by reproducing
more realistically the degraded position resolution compared with na\"{i}ve
ionization models based on simple Landau-like fluctuation. Our implementation
of the Bichsel model and the multipurpose photo absorption ionization (PAI)
model in Geant4 produce similar results above a few microns thickness. Below a
few microns, the PAI model does not fully capture the complete shell effects
that are in the Bichsel model. The code is made publicly available as part of
the Allpix software package in order to facilitate predictions for new detector
designs and comparisons with testbeam data.


  Precoding design for the downlink of multiuser multiple-input multiple-output
(MU-MIMO) systems is a fundamental problem. In this paper, we aim to maximize
the weighted sum rate (WSR) while considering both quality-of-service (QoS)
constraints of each user and per-antenna power constraints (PAPCs) in the
downlink MU-MIMO system. To solve the problem, we reformulate the original
problem to an equivalent problem by using the well-known weighted minimal mean
square error (WMMSE) framework, which can be tackled by iteratively solving
three subproblems. Since the precoding matrices are coupled among the QoS
constraints and PAPCs, we adopt alternating direction method of multipliers
(ADMM) to obtain a distributed solution. Simulation results validate the
effectiveness of the proposed algorithm.


  Manual annotation of ICD-9 codes is a time consuming and error-prone process.
Deep learning based systems tackling the problem of automated ICD-9 coding have
achieved competitive performance. Given the increased proliferation of
electronic medical records, such automated systems are expected to eventually
replace human coders. In this work, we investigate how a simple typo-based
adversarial attack strategy can impact the performance of state-of-the-art
models for the task of predicting the top 50 most frequent ICD-9 codes from
discharge summaries. Preliminary results indicate that a malicious adversary,
using gradient information, can craft specific perturbations, that appear as
regular human typos, for less than 3% of words in the discharge summary to
significantly affect the performance of the baseline model.


  Single-cell RNA sequencing (scRNA-seq) has revolutionized biological
discovery, providing an unbiased picture of cellular heterogeneity in tissues.
While scRNA-seq has been used extensively to provide insight into both healthy
systems and diseases, it has not been used for disease prediction or
diagnostics. Graph Attention Networks (GAT) have proven to be versatile for a
wide range of tasks by learning from both original features and graph
structures. Here we present a graph attention model for predicting disease
state from single-cell data on a large dataset of Multiple Sclerosis (MS)
patients. MS is a disease of the central nervous system that can be difficult
to diagnose. We train our model on single-cell data obtained from blood and
cerebrospinal fluid (CSF) for a cohort of seven MS patients and six healthy
adults (HA), resulting in 66,667 individual cells. We achieve 92 % accuracy in
predicting MS, outperforming other state-of-the-art methods such as a graph
convolutional network and a random forest classifier. Further, we use the
learned graph attention model to get insight into the features (cell types and
genes) that are important for this prediction. The graph attention model also
allow us to infer a new feature space for the cells that emphasizes the
differences between the two conditions. Finally we use the attention weights to
learn a new low-dimensional embedding that can be visualized. To the best of
our knowledge, this is the first effort to use graph attention, and deep
learning in general, to predict disease state from single-cell data. We
envision applying this method to single-cell data for other diseases.


  Ground state preparation is a key area where quantum computers are expected
to prove advantageous. Double-bracket quantum algorithms (DBQAs) have been
recently proposed to diagonalize Hamiltonians and in this work we show how to
use them to prepare ground states. We propose to improve an initial state
preparation by adding a few steps of DBQAs. The interfaced method
systematically achieves a better fidelity while significantly reducing the
computational cost of the procedure. For a Heisenberg model, we compile our
algorithm using CZ and single-qubit gates into circuits that match capabilities
of near-term quantum devices. Moreover, we show that DBQAs can benefit from the
experimental availability of increasing circuit depths. Whenever an approximate
ground state can be prepared without exhausting the available circuit depth,
then DBQAs can be enlisted to algorithmically seek a higher fidelity
preparation.


  In a magnetic field, spin-ladders undergo two zero-temperature phase
transitions at the critical fields Hc1 and Hc2. An experimental review of
static and dynamical properties of spin-ladders close to these critical points
is presented. The scaling functions, universal to all quantum critical points
in one-dimension, are extracted from (a) the thermodynamic quantities
(magnetization) and (b) the dynamical functions (NMR relaxation). A simple
mapping of strongly coupled spin ladders in a magnetic field on the exactly
solvable XXZ model enables to make detailed fits and gives an overall
understanding of a broad class of quantum magnets in their gapless phase
(between Hc1 and Hc2). In this phase, the low temperature divergence of the NMR
relaxation demonstrates its Luttinger liquid nature as well as the novel
quantum critical regime at higher temperature. The general behaviour close
these quantum critical points can be tied to known models of quantum magnetism.


  For three natural classes of dynamic decision problems; 1. additively
separable problems, 2. discounted problems, and 3. discounted problems for a
fixed discount factor; we provide necessary and sufficient conditions for one
sequential experiment to dominate another in the sense that the dominant
experiment is preferred to the other for any decision problem in the specified
class. We use these results to study the timing of information arrival in
additively separable problems.


  We report on the analysis of high-resolution optical spectra for 77 subdwarf
B (sdB) stars from the ESO Supernova Ia Progenitor Survey. Effective
temperature, surface gravity, and photospheric helium abundance are determined
simultaneously by spectral line profile fitting of hydrogen and helium lines,
and are found to be in agreement with previous studies of sdB stars. 24 objects
show spectral signs of a cool companion, being either companion absorption
lines or a flux contribution at Halpha. Five stars with relatively high
luminosity show peculiar Halpha profiles, possibly indicating stellar winds.
Our results are compared to recent theoretical simulations by Han et al. (2003)
for the distribution in effective temperature and surface gravity, and are
found to agree very well with these calculations. Finally we present a binary
system consisting of two helium-rich hot subdwarfs.


  We investigate several measures of peripherality for vertices and edges in
networks. We improve asymptotic bounds on the maximum value achieved by edge
peripherality, edge sum peripherality, and the Trinajsti\'c index over $n$
vertex graphs. We also prove similar results on the maxima over $n$-vertex
bipartite graphs, trees, and graphs with a fixed diameter. Finally, we refute
two conjectures of Furtula, the first on necessary conditions for minimizing
the Trinajsti\'c index and the second about maximizing the Trinajsti\'c index.


  Random effect models are popular statistical models for detecting and
correcting spurious sample correlations due to hidden confounders in
genome-wide gene expression data. In applications where some confounding
factors are known, estimating simultaneously the contribution of known and
latent variance components in random effect models is a challenge that has so
far relied on numerical gradient-based optimizers to maximize the likelihood
function. This is unsatisfactory because the resulting solution is poorly
characterized and the efficiency of the method may be suboptimal. Here we prove
analytically that maximum-likelihood latent variables can always be chosen
orthogonal to the known confounding factors, in other words, that
maximum-likelihood latent variables explain sample covariances not already
explained by known factors. Based on this result we propose a restricted
maximum-likelihood method which estimates the latent variables by maximizing
the likelihood on the restricted subspace orthogonal to the known confounding
factors, and show that this reduces to probabilistic PCA on that subspace. The
method then estimates the variance-covariance parameters by maximizing the
remaining terms in the likelihood function given the latent variables, using a
newly derived analytic solution for this problem. Compared to gradient-based
optimizers, our method attains greater or equal likelihood values, can be
computed using standard matrix operations, results in latent factors that don't
overlap with any known factors, and has a runtime reduced by several orders of
magnitude. Hence the restricted maximum-likelihood method facilitates the
application of random effect modelling strategies for learning latent variance
components to much larger gene expression datasets than possible with current
methods.


  One of the most important tasks for ensuring safe autonomous driving systems
is accurately detecting road traffic lights and accurately determining how they
impact the driver's actions. In various real-world driving situations, a scene
may have numerous traffic lights with varying levels of relevance to the
driver, and thus, distinguishing and detecting the lights that are relevant to
the driver and influence the driver's actions is a critical safety task. This
paper proposes a traffic light detection model which focuses on this task by
first defining salient lights as the lights that affect the driver's future
decisions. We then use this salience property to construct the LAVA Salient
Lights Dataset, the first US traffic light dataset with an annotated salience
property. Subsequently, we train a Deformable DETR object detection transformer
model using Salience-Sensitive Focal Loss to emphasize stronger performance on
salient traffic lights, showing that a model trained with this loss function
has stronger recall than one trained without.


  In this lecture we give an elementary introduction to the natural realization
of non-perturbative N=2 quantum field theories as a low energy limit of
classical string theory. We review a systematic construction of six, five, and
four dimensional gauge theories using geometrical data, which provides the
exact, non-perturbative solution via mirror symmetry. This construction has
lead to the exact solution of a large class of gravity free gauge theories,
including Super Yang Mills (SYM) theories as well as non-conventional quantum
field theories without a known Lagrangian description.


  Gamma-ray bursts (GRBs) are fascinating events due to their panchromatic
nature. We study optical plateaus in GRB afterglows via an extended search into
archival data. We comprehensively analyze all published GRBs with known
redshifts and optical plateaus observed by many ground-based telescopes (e.g.,
Subaru Telescope, RATIR) around the world and several space-based observatories
such as the Neil Gehrels Swift Observatory. We fit 500 optical light curves
(LCs), showing the existence of the plateau in 179 cases. This sample is 75%
larger than the previous one (arXiv:2105.10717), and it is the largest
compilation so far of optical plateaus. We discover the 3D fundamental plane
relation at optical wavelengths using this sample. This correlation is between
the rest-frame time at the end of the plateau emission, $T^{*}_{\rm opt}$, its
optical luminosity, $L_{\rm opt}$, and the peak in the optical prompt emission,
$L_{\rm peak, opt}$, thus resembling the three-dimensional (3D) X-ray
fundamental plane relation (arXiv:1604.06840). We correct our sample for
redshift evolution and selection effects, discovering that this correlation is
indeed intrinsic to GRB physics. We investigate the rest-frame end time
distributions in X-rays and optical ($T^{*}_{\rm opt}$, $T^{*}_{\rm X}$), and
conclude that the plateau is achromatic only when selection biases are not
considered. We also investigate if the 3D optical correlation may be a new
discriminant between optical GRB classes and find that there is no significant
separation between the classes compared to the Gold sample plane after
correcting for evolution.


  Many-body localization (MBL) lends remarkable robustness to nonequilibrium
phases of matter. Such phases can show topological and symmetry breaking order
in their ground and excited states, but they may also belong to an fanomalous
localized topological phase (ALT phase). All eigenstates in an ALT phase are
trivial, in that they can be deformed to product states, but the entire
Hamiltonian cannot be deformed to a trivial localized model without going
through a delocalization transition. Using a correspondence between MBL phases
with short-ranged entanglement and locality preserving unitaries -- called
quantum cellular automata (QCA) -- we reduce the classification of ALT phases
to that of QCA. This method extends to periodically (Floquet) and
quasiperiodically driven ALT phases, and captures anomalous Floquet phases
within the same framework as static phases. The QCA framework further
generalizes to include symmetry-enriched ALT phases (SALT phases), and provides
a large class of soluble models suitable for realization in quantum simulators.
In systematizing the study of ALT phases, we both greatly extend the
classification of interacting nonequilibrium systems and clarify a confusion in
the literature which implicitly equates nontrivial Hamiltonians with nontrivial
ground states.


  This paper aims to identify in a practical manner unknown physical
parameters, such as mechanical models of actuated robot links, which are
critical in dynamical robotic tasks. Key features include the use of an
off-the-shelf physics engine and the Bayesian optimization framework. The task
being considered is locomotion with a high-dimensional, compliant Tensegrity
robot. A key insight, in this case, is the need to project the model
identification challenge into an appropriate lower dimensional space for
efficiency. Comparisons with alternatives indicate that the proposed method can
identify the parameters more accurately within the given time budget, which
also results in more precise locomotion control.


  We present a three-dimensional simulation of the corona of an FK Com-type
rapidly rotating G giant using a magnetohydrodynamic model that was originally
developed for the solar corona in order to capture the more realistic,
non-potential coronal structure. We drive the simulation with surface maps for
the radial magnetic field obtained from a stellar dynamo model of the FK Com
system. This enables us to obtain the coronal structure for different field
topologies representing different periods of time. We find that the corona of
such an FK Com-like star, including the large scale coronal loops, is dominated
by a strong toroidal component of the magnetic field. This is a result of part
of the field being dragged by the radial outflow, while the other part remains
attached to the rapidly rotating stellar surface. This tangling of the magnetic
field,in addition to a reduction in the radial flow component, leads to a
flattening of the gas density profile with distance in the inner part of the
corona. The three-dimensional simulation provides a global view of the coronal
structure. Some aspects of the results, such as the toroidal wrapping of the
magnetic field, should also be applicable to coronae on fast rotators in
general, which our study shows can be considerably different from the
well-studied and well-observed solar corona. Studying the global structure of
such coronae should also lead to a better understanding of their related
stellar processes, such as flares and coronal mass ejections, and in
particular, should lead to an improved understanding of mass and angular
momentum loss from such systems.


  In this paper, we prove the local uniqueness of an inverse problem arising in
the nonstationary flow of a nonhomogeneous incompressible asymmetric fluid in a
bounded domain with smooth boundary. The direct problem is an initial-boundary
value problem for a system for the velocity field, the angular velocity of
rotation of the fluid particles, the mass density and the pressure
distribution. The inverse problem consists in the external force recover
assuming an integral measurements on the boundary. We characterize the inverse
problem solutions using an operator equation of second kind, which is deduced
form the application of the Helmholtz decomposition. We introduce several
estimates which implies the hypothesis of the Tikhonov fixed point theorem.


  We report manipulation of a Kondo resonance originated from the spin-electron
interactions between a two-dimensional molecular assembly of TBrPP-Co molecules
and a Cu(111) surface at 4.6 K using a low temperature scanning tunneling
microscope. By manipulating nearest-neighbor molecules with a scanning
tunneling microscope tip we are able to tune the spin-electron coupling of the
center molecule inside a small hexagonal molecular assembly in a controlled
step-by-step manner. The Kondo temperature increases from 105 to 170 K with a
decreasing the number of nearest neighbor molecules from six to zero. This
Kondo temperature variation is originated from the scattering of surface
electrons by the molecules located at the edges of the molecular layer, which
reduces spin-electron coupling strength for the molecules inside the layer.
Investigations on different molecular arrangements indicate that the observed
Kondo resonance is independent on the molecular lattice.


  Carbon and nitrogen austenites, modeled by Fe8N and Fe8C superstructures are
studied by full-potential LAPW method. Structure parameters, electronic and
magnetic properties as well as hyperfine interaction parameters are obtained.
Calculations prove that Fe-C austenite can be successfully modeled by ordered
Fe8C superstructure. The results show that chemical Fe-C bond in Fe8C has
higher covalent part than in Fe8N. Detailed analysis of electric field gradient
formation for both systems is performed. The calculation of electric field
gradient allow us to carry out a good interpretation of Moessbauer spectra for
Fe-C and Fe-N systems.


  The separated radial part of a sourceless massive complex scalar field
equation on the Kerr-Newman black hole background is shown to be a generalized
spin-weighted spheroidal wave equation of imaginary number order. While the
separated angular part is an ordinary spheroidal wave equation. General exact
solutions in integral forms and in power series expansion as well as several
special solutions with physical interest are given for the radial equation in
the non-extreme case. In the extreme case, power series solution to the radial
equation is briefly studied. Recurrence relations between coefficients in power
series expansion of general solutions and connection between the radial
equation are discussed in both cases.


  A numerical renormalization-group study of the conductance through a quantum
wire side-coupled to a quantum dot is reported. The temperature and the
dot-energy dependence of the conductance are examined in the light of a
recently derived linear mapping between the Kondo-regime temperature-dependent
conductance and the universal function describing the conductance for the
symmetric Anderson model of a quantum wire with an embedded quantum dot. Two
conduction paths, one traversing the wire, the other a bypass through the
quantum dot, are identified. A gate potential applied to the quantum wire is
shown to control the flow through the bypass. When the potential favors
transport through the wire, the conductance in the Kondo regime rises from
nearly zero at low temperatures to nearly ballistic at high temperatures. When
it favors the dot, the pattern is reversed: the conductance decays from nearly
ballistic to nearly zero. When the fluxes through the two paths are comparable,
the conductance is nearly temperature-independent in the Kondo regime, and a
Fano antiresonance in the fixed-temperature plot of the conductance as a
function of the dot energy signals interference. Throughout the Kondo regime
and, at low temperatures, even in the mixed-valence regime, the numerical data
are in excellent agreement with the universal mapping.


  The Fermi-LAT has detected more than 3000 sources in the GeV $\gamma$-ray
regime. The majority are extra-galactic and these sources are dominated by
blazars. However, $\sim28$ per cent of the sources in Fermi 3LAC are listed as
blazar candidates of uncertain type (BCU). Increasing the number of classified
Fermi-LAT sources is important for improving our understanding of
extra-galactic $\gamma$-ray sources and can be used to search for new very high
energy sources. We report on the optical spectroscopy of seven selected
unclassified BCU sources during 2014 and 2015 undertaken using the SAAO 1.9-m
and Southern African Large Telescope (SALT). Based on the identified spectral
lines we have classified three of the sources as FSRQs and the remaining four
as BL Lac objects, determining the redshift for four sources.


  The first-order binomial autoregressive (BAR(1)) model is the most frequently
used tool to analyze the bounded count time series. The BAR(1) model is
stationary and assumes process parameters to remain constant throughout the
time period, which may be incompatible with the non-stationary real data, which
indicates piecewise stationary characteristic. To better analyze the
non-stationary bounded count time series, this article introduces the BAR(1)
process with multiple change-points, which contains the BAR(1) model as a
special case. Our primary goals are not only to detect the change-points, but
also to give a solution to estimate the number and locations of the
change-points. For this, the cumulative sum (CUSUM) test and minimum
description length (MDL) principle are employed to deal with the testing and
estimation problems. The proposed approaches are also applied to analysis of
the Harmonised Index of Consumer Prices of the European Union.


  We study the Higgs pair production process at a muon collider using b-pair
decays of the Higgs bosons. Efficient identification and good measurement
resolution for the b-jet pair invariant mass are crucial for unearthing the
di-Higgs signal. However, the beam-induced background has potential to
drastically degrade the performance. We report on the full simulation studies
of the degradation of the reconstructed b-jet pair invariant mass in di-Higgs
events, considering only the beam-induced background in the calorimeter.
Mitigation strategies for the suppression of the beam-induced background are
underway. We also report prospects for the measurement of the Standard Model
Higgs pair production at the Muon Colliders at various benchmarks of the
collider center of mass energy and integrated luminosity using a fast
simulation program.


  We study the regularity of the extremal solution of the semilinear biharmonic
equation $\bi u=\f{\lambda}{(1-u)^2}$, which models a simple
Micro-Electromechanical System (MEMS) device on a ball $B\subset\IR^N$, under
Dirichlet boundary conditions $u=\partial_\nu u=0$ on $\partial B$. We complete
here the results of F.H. Lin and Y.S. Yang \cite{LY} regarding the
identification of a "pull-in voltage" $\la^*>0$ such that a stable classical
solution $u_\la$ with $0<u_\la<1$ exists for $\la\in (0,\la^*)$, while there is
none of any kind when $\la>\la^*$. Our main result asserts that the extremal
solution $u_{\lambda^*}$ is regular $(\sup_B u_{\lambda^*} <1)$ provided $ N
\le 8$ while $u_{\lambda^*} $ is singular ($\sup_B u_{\lambda^*} =1$) for $N
\ge 17$, in which case $1-C_0|x|^{4/3}\leq u_{\lambda^*} (x) \leq 1-|x|^{4/3}$
on the unit ball, where $ C_0:= <(\frac{\lambda^*}{\bar{\lambda}}>)^{1/3}$ and
$ \bar{\lambda}:= \frac{8 (N-{2/3}) (N- {8/3})}{9}$. The singular character of
the extremal solution for the remaining cases (i.e., when $9\leq N\leq 16$)
requires a computer assisted proof and will not be addressed in this paper.


  The radio observation of 21\,cm-line signal from the Epoch of Reionization
(EoR) enables us to explore the evolution of galaxies and intergalactic medium
in the early universe. However, the detection and imaging of the 21\,cm-line
signal are tough due to the foreground and instrumental systematics. In order
to overcome these obstacles, as a new approach, we propose to take a cross
correlation between observed 21\,cm-line data and 21\,cm-line images generated
from the distribution of the Lyman-$\alpha$ emitters (LAEs) through machine
learning. In order to create 21\,cm-line maps from LAE distribution, we apply
conditional Generative Adversarial Network (cGAN) trained with the results of
our numerical simulations. We find that the 21\,cm-line brightness temperature
maps and the neutral fraction maps can be reproduced with correlation function
of 0.5 at large scales $k<0.1~{\rm Mpc}^{-1}$. Furthermore, we study the
detectability of the the cross correlation assuming the the LAE deep survey of
the Subaru Hyper Suprime Cam, the 21\,cm observation of the MWA Phase II and
the presence of the foreground residuals. We show that the signal is detectable
at $k < 0.1~{\rm Mpc}^{-1}$ with 1000 hours of MWA observation even if the
foreground residuals are 5 times larger than the 21\,cm-line power spectrum.
Our new approach of cross correlation with image construction using the cGAN
can not only boost the detectability of EoR 21\,cm-line signal but also allow
us to estimate the 21\,cm-line auto-power spectrum.


  The expected information gain is an important quality criterion of Bayesian
experimental designs, which measures how much the information entropy about
uncertain quantity of interest $\theta$ is reduced on average by collecting
relevant data $Y$. However, estimating the expected information gain has been
considered computationally challenging since it is defined as a nested
expectation with an outer expectation with respect to $Y$ and an inner
expectation with respect to $\theta$. In fact, the standard, nested Monte Carlo
method requires a total computational cost of $O(\varepsilon^{-3})$ to achieve
a root-mean-square accuracy of $\varepsilon$. In this paper we develop an
efficient algorithm to estimate the expected information gain by applying a
multilevel Monte Carlo (MLMC) method. To be precise, we introduce an antithetic
MLMC estimator for the expected information gain and provide a sufficient
condition on the data model under which the antithetic property of the MLMC
estimator is well exploited such that optimal complexity of
$O(\varepsilon^{-2})$ is achieved. Furthermore, we discuss how to incorporate
importance sampling techniques within the MLMC estimator to avoid arithmetic
underflow. Numerical experiments show the considerable computational cost
savings compared to the nested Monte Carlo method for a simple test case and a
more realistic pharmacokinetic model.


  We study the probability that chordal $\text{SLE}_{8/3}$ in the unit disk
from $\exp(ix)$ to 1 avoids the disk of radius $q$ centered at zero. We find
the initial/boundary-value problem satisfied by this probability as a function
of $x$ and $a=\ln q$, and show that asymptotically as $q$ tends to one this
probability decays like $\exp(-cx/(1-q))$ with $c=5\pi/8$ for $x\in[0,\pi]$. We
also give a representation of this probability as a functional of a Legendre
process.


  Compton scattering is one of the promising probe to quantitate of the Li
under in-operando condition, since high-energy X-rays which have high
penetration power into the materials are used as incident beam and Compton
scattered energy spectrum have specific line-shape by the elements. We develop
in-operando quantitation method of Li composition in the electrodes by using
line-shape (Sparameter) analysis of Compton scattered energy spectrum. In this
study, we apply S-parameter analysis to commercial coin cell Li-ion
rechargeable battery and obtain the variation of S-parameters during
charge/discharge cycle at positive and negative electrodes. By using
calibration curves for Li composition in the electrodes, we determine the
change of Li composition of positive and negative electrodes through
S-parameters, simultaneously.


  Predicting query execution time is a fundamental issue underlying many
database management tasks. Existing predictors rely on information such as
cardinality estimates and system performance constants that are difficult to
know exactly. As a result, accurate prediction still remains elusive for many
queries. However, existing predictors provide a single, point estimate of the
true execution time, but fail to characterize the uncertainty in the
prediction. In this paper, we take a first step towards providing uncertainty
information along with query execution time predictions. We use the query
optimizer's cost model to represent the query execution time as a function of
the selectivities of operators in the query plan as well as the constants that
describe the cost of CPU and I/O operations in the system. By treating these
quantities as random variables rather than constants, we show that with low
overhead we can infer the distribution of likely prediction errors. We further
show that the estimated prediction errors by our proposed techniques are
strongly correlated with the actual prediction errors.


  We consider holographic thermalization in the presence of a Weyl correction
in five dimensional AdS space. We first obtain the Weyl corrected black brane
solution perturbatively, up to first order in the coupling. The corresponding
AdS-Vaidya like solution is then constructed. This is then used to numerically
analyze the time dependence of the two point correlation functions and the
expectation values of rectangular Wilson loops in the boundary field theory,
and we discuss how the Weyl correction can modify the thermalization time
scales in the dual field theory. In this context, the subtle interplay between
the Weyl coupling constant and the chemical potential is studied in detail.


  In this article, we present the challenge of software citation as a method to
ensure credit for and identification, discovery, and reuse of software in
scientific and engineering research. We discuss related work and key
challenges/research directions, including suggestions for metadata necessary
for software citation.


  The challenges faced in the digital world are many, which could be resolved
with some biometric recognition methods. These biometric recognition methods
are encompassed within watermarking technology, steganography, cryptography,
and many other schemes of security. These methods assist in securing digital
images with the authentication of their owner. This paper briefly
contextualizes the digital watermarking technique, which is referred to as
Natural Preserving Transform (NPT) and Hartley Transform, which is endeavored
in the face recognition process. The non-blind extraction and quasi-blind
extraction techniques are used for extracting the watermark from the image in
the proposed system. This paper articulates the application of this
watermarking technique employed in face recognition through watermarking of
various face images to multiple backgrounds encompassing gray-scale images.
Natural Preserve Transform is employed as a part of the fuzzy logic
watermarking. In the proposed system, NPT is employed for encoding a logo of
grayscale watermarking text or logo image to a host image located anywhere. The
robustness and performance of the proposed system are experimentally tested
with the help of image processing operations like image compression, noise
degradation, cropping. Due to its unique feature of uniform distribution of
face images, this technique is selected among other methods in digital
watermarking. The system is tested for its efficiency with experimental
analyses, which could be confirmed with the results of the simulation. The
above system is proposed for copyright protection, authentication, and security
requirements.


  We report the discovery of a rare compact early-type galaxy, SDSS
J085431.18+173730.5 (hereafter cE\_AGN). It has an half light radius of R$_{e}$
= 490 pc and a brightness of M$_{r}$ = $-$18.08 mag. Optical spectroscopy
available from the Sloan Digital Sky Survey (SDSS) reveals the presence of
prominent broad-line emissions with the H$\alpha$ broad component width of
FWHM=2400 km/s. The black hole (BH) mass, as estimated from the luminosity and
width of the broad H$\alpha$ emission, is 2.1$\times$10$^{6}$ M$_{\sun}$. With
the help of surface photometry, we perform a detailed analysis of the
structural properties. The observed light distribution is best modeled with a
double S\'ersic function. Fixing the outer component as an exponential disk, we
find that the inner component has a S\'ersic index of n = 1.4. Considering the
inner component as bulge/spheroidal we find that cE\_AGN remains consistent in
both BH mass -- bulge mass relation and BH mass -- bulge S\'ersic index
relation. Given these observational properties, we discuss its possible origin
investigating the surrounding environment where it is located.


  We describe the emergence of a Convolution Bottleneck (CBN) structure in
CNNs, where the network uses its first few layers to transform the input
representation into a representation that is supported only along a few
frequencies and channels, before using the last few layers to map back to the
outputs. We define the CBN rank, which describes the number and type of
frequencies that are kept inside the bottleneck, and partially prove that the
parameter norm required to represent a function $f$ scales as depth times the
CBN rank $f$. We also show that the parameter norm depends at next order on the
regularity of $f$. We show that any network with almost optimal parameter norm
will exhibit a CBN structure in both the weights and - under the assumption
that the network is stable under large learning rate - the activations, which
motivates the common practice of down-sampling; and we verify that the CBN
results still hold with down-sampling. Finally we use the CBN structure to
interpret the functions learned by CNNs on a number of tasks.


  The combination of large size, high stellar density, high metallicity, and
Sersic surface brightness profile of the spheroidal component of the Andromeda
galaxy (M31) within R_proj ~ 20 kpc suggest that it is unlike any subcomponent
of the Milky Way. In this work we capitalize on our proximity to and external
view of M31 to probe the kinematical properties of this "inner spheroid." We
employ a Markov chain Monte Carlo (MCMC) analysis of resolved stellar
kinematics from Keck/DEIMOS spectra of 5651 red giant branch stars to
disentangle M31's inner spheroid from its stellar disk. We measure the mean
velocity and dispersion of the spheroid in each of five spatial bins after
accounting for a locally cold stellar disk as well as the Giant Southern Stream
and associated tidal debris. For the first time, we detect significant spheroid
rotation (v_rot ~ 50 km/s) beyond R_proj ~ 5 kpc. The velocity dispersion
decreases from about 140 km/s at R_proj = 7 kpc to 120 km/s at R_proj = 14 kpc,
consistent to 2 sigma with existing measurements and models. We calculate the
probability that a given star is a member of the spheroid and find that the
spheroid has a significant presence throughout the spatial extent of our
sample. Lastly, we show that the flattening of the spheroid is due to velocity
anisotropy in addition to rotation. Though this suggests that the inner
spheroid of M31 more closely resembles an elliptical galaxy than a typical
spiral galaxy bulge, it should be cautioned that our measurements are much
farther out (2 - 14 r_eff) than for the comparison samples.


  This article discusses the main achievements and future perspectives of the
MAGNEX spectrometer at the INFN-LNS laboratory in Catania (Italy). MAGNEX is a
large acceptance magnetic spectrometer for the detection of the ions emitted in
nuclear collisions below Fermi energy. In the first part of the paper an
overview of the MAGNEX features is presented. The successful application to the
precise reconstruction of the momentum vector, to the identification of the ion
masses and to the determination of the transport efficiency is demonstrated by
in-beam tests. In the second part, an overview of the most relevant scientific
achievements is given. Results from nuclear elastic and inelastic scattering as
well as from transfer and charge exchange reactions in a wide range of masses
of the colliding systems and incident energies are shown. The role of MAGNEX in
solving old and new puzzles in nuclear structure and direct reaction mechanisms
is emphasized. One example is the recently observed signature of the long
searched Giant Pairing Vibration. Finally, the new challenging opportunities to
use MAGNEX for future experiments are briefly reported. In particular, the use
of double charge exchange reactions toward the determination of the nuclear
matrix elements entering in the expression of the half-life of neutrinoless
double beta decay is discussed. The new NUMEN project of INFN, aiming at these
investigations, is introduced. The challenges connected to the major technical
upgrade required by the project in order to investigate rare processes under
high fluxes of detected heavy ions are outlined.


  We investigate the transverse momentum dependence of pion HBT radii on the
basis of a hydrodynamical model. Recent experimental data show that
$R_{\text{out}}/R_{\text{side}} < 1$, which suggests a strong opaqueness of the
source. In addition to the opaqueness naturally caused by transverse flow, we
introduce an extrinsic opacity by imposing restrictions on the pion emission
angle. Comparing the HBT radii obtained from the normal Cooper-Frye
prescription and the opaque emission prescription, we find that
$R_{\text{out}}/R_{\text{side}}$ is less than unity only for small values of
the transverse momentum with an opaque source. However, HBT radii for large
values of the transverse momentum are dominated by the transverse flow effect
and are affected less by the source opaqueness.


  Tunneling two-level systems are ubiquitous in amorphous solids, and form a
major source of noise in systems such as nano-mechanical oscillators, single
electron transistors, and superconducting qubits. Occurance of defect tunneling
despite their coupling to phonons is viewed as a hallmark of weak defect-phonon
coupling. This is since strong coupling to phonons results in significant
phonon dressing and suppresses tunneling in two-level tunneling defects
effectively. Here we determine the dynamics of a crystalline tunnelling defect
strongly coupled to phonons incorporating the full 3D geometry in our
description. We find that inversion symmetric tunnelling is not dressed by
phonons whereas other tunnelling pathways are dressed by phonons and, thus, are
suppressed by strong defect-phonon coupling. We provide the linear acoustic and
dielectric response functions for a crystalline tunnelling defect for strong
defect-phonon coupling. This allows direct experimental determination of the
defect-phonon coupling. The singling out of inversion-symmetric tunneling
states in single tunneling defects is complementary to their dominance of the
low energy excitations in strongly disordered solids as a result of
inter-defect interactions for large defect concentrations. This suggests that
inversion symmetric two-level systems play a unique role in the low energy
properties of disordered solids.


  We use the martingale method to discuss the relationship between
mean-variance (MV) and monotone mean-variance (MMV) portfolio selections. We
propose a unified framework to discuss the relationship in general financial
markets without any specific setting or completeness requirement. We apply this
framework to a semimartingale market and find that MV and MMV are consistent if
and only if the variance-optimal signed martingale measure keeps non-negative.
Further, we provide an example to show the application of our result.


  A Direct Sum Theorem holds in a model of computation, when solving some k
input instances together is k times as expensive as solving one. We show that
Direct Sum Theorems hold in the models of deterministic and randomized decision
trees for all relations. We also note that a near optimal Direct Sum Theorem
holds for quantum decision trees for boolean functions.


  The main aim of this paper is to show that the nonlinear Choquet integral can
be used to construct nonlinear approximation operators, exactly as by the use
in probability of the Lebesgue-type integral, linear and positive approximation
operators are constructed. The so-called Feller constructive scheme is
generalized, by introducing discrete and non-discrete nonlinear approximation
operators in terms of the nonlinear Choquet integral with respect to a monotone
and subadditive set function. As particular cases, Bernstein-Choquet and
Picard-Choquet operators are introduced, for which qualitative and quantitative
approximation properties are obtained. In some subclasses of functions, they
have better approximation properties than the classical Berstein and Picard
operators.


  Boolean functional synthesis is a fundamental problem in computer science
with wide-ranging applications and has witnessed a surge of interest resulting
in progressively improved techniques over the past decade. Despite intense
algorithmic development, a large number of problems remain beyond the reach of
the state of the art techniques. Motivated by the progress in machine learning,
we propose Manthan, a novel data-driven approach to Boolean functional
synthesis. Manthan views functional synthesis as a classification problem,
relying on advances in constrained sampling for data generation, and advances
in automated reasoning for a novel proof-guided refinement and provable
verification. On an extensive and rigorous evaluation over 609 benchmarks, we
demonstrate that Manthan significantly improves upon the current state of the
art, solving 356 benchmarks in comparison to 280, which is the most solved by a
state of the art technique; thereby, we demonstrate an increase of 76
benchmarks over the current state of the art. Furthermore, Manthan solves 60
benchmarks that none of the current state of the art techniques could solve.
The significant performance improvements, along with our detailed analysis,
highlights several interesting avenues of future work at the intersection of
machine learning, constrained sampling, and automated reasoning.


  We study the light matter interaction in WS$_{2}$ nanotube-graphene hybrid
devices. Using scanning photocurrent microscopy we find that by engineering
graphene electrodes for WS$_{2}$ nanotubes we can improve the collection of
photogenerated carriers. We observe inhomogeneous spatial photocurrent response
with an external quantum efficiency of $\sim 1\%$ at 0 V bias. We show that
defects play an important role and can be utilized to enhance and tune
photocarrier generation.


  In the region between two closely located stiff inclusions, the stress, which
is the gradient of a solution to the Lam\'{e} system with partially infinite
coefficients, may become arbitrarily large as the distance between interfacial
boundaries of inclusions tends to zero. The primary aim of this paper is to
give a sharp description in terms of the asymptotic behavior of the stress
concentration, as the distance between interfacial boundaries of inclusions
goes to zero. For that purpose we capture all the blow-up factor matrices,
whose elements comprise of some certain integrals of the solutions to the case
when two inclusions are touching. Then we are able to establish the asymptotic
formulas of the stress concentration in the presence of two close-to-touching
$m$-convex inclusions in all dimensions. Furthermore, an example of curvilinear
squares with rounded-off angles is also presented for future application in
numerical computations and simulations.


  We highlight the role of weak lensing measurements from current and upcoming
stage-IV imaging surveys in the search for cosmic inflation, specifically in
measuring the scalar spectral index $n_s$. To do so, we combine the Dark Energy
Survey 3 years of observation weak lensing and clustering data with BICEP/Keck,
Planck and Sloan Digital Sky Survey data in $r\Lambda$CDM where $r$ is the
tensor-to-scalar ratio. While there is no significant improvement in
constraining power, we obtain a 1$\sigma$ shift on $n_s$. Additionally, we
forecast a weak lensing and clustering data vector from the 10-year Legacy
Survey of Space and Time by the Vera C. Rubin Observatory and show its
combination with current data would improve their $n_s$ constraints by 25$\%$
in $r\Lambda$CDM.


  We use the coupled 2d-spin-3d-fermion model proposed by Rosch {\sl et. al.}
(Phys. Rev. Lett. {\bf 79}, 159 (1997)) to study the thermoelectric behaviour
of a heavy fermion compound when it is close to an antiferromagnetic quantum
critical point. When the low energy spin fluctuations are quasi two
dimensional, as has been observed in ${\rm YbRh}_2{\rm Si}_2$ and $ {\rm
CeCu}_{6-x}{\rm Au}_x $, with a typical 2d ordering wavevector and 3d Fermi
surface, the ``hot'' regions on the Fermi surface have a finite area. Due to
enhanced scattering with the nearly critical spin fluctuations, the electrons
in the hot region are strongly renormalized. We argue that there is an
intermediate energy scale where the qualitative aspects of the renormalized hot
electrons are captured by a weak-coupling perturbative calculation. Our
examination of the electron self energy shows that the entropy carried by the
hot electrons is larger than usual. This accounts for the anomalous logarithmic
temperature dependence of specific heat observed in these materials. We show
that the same mechanism produces logarithmic temperature dependence in
thermopower. This has been observed in $ {\rm CeCu}_{6-x}{\rm Au}_x $. We
expect to see the same behaviour from future experiments on ${\rm YbRh}_2{\rm
Si}_2$.


  We study the ratio of fragmentation fractions, $f_{\Xi_b}/f_{\Lambda_b}$,
from the measurement of $\Xi_b^0\to \Xi_c^+\pi^-$ and $\Lambda_b^0\to
\Lambda_c^+\pi^-$ with $\Xi_{c}^{+}/\Lambda_{c}^{+}\to p K^-\pi^+$. With the
branching fraction $\mathcal{B}(\Xi_c^+\to pK^-\pi^+)=(2.2\pm0.8)\%$ obtained
under the U-spin symmetry, the fragmentation ratio is determined as
$f_{\Xi_b}/f_{\Lambda_b}$=$0.054\pm0.020$. To reduce the above uncertainties,
we suggest to measure the branching fractions of $\Xi_c^+\to p \overline
K^{*0}$ and $\Lambda_c^+\to \Sigma^+ K^{*0}$ at BESIII, Belle(II) and LHCb.


  Knowledge distillation (KD) is an effective technique to transfer knowledge
from one neural network (teacher) to another (student), thus improving the
performance of the student. To make the student better mimic the behavior of
the teacher, the existing work focuses on designing different criteria to align
their logits or representations. Different from these efforts, we address
knowledge distillation from a novel data perspective. We argue that
transferring knowledge at sparse training data points cannot enable the student
to well capture the local shape of the teacher function. To address this issue,
we propose locally linear region knowledge distillation ($\rm L^2$RKD) which
transfers the knowledge in local, linear regions from a teacher to a student.
This is achieved by enforcing the student to mimic the outputs of the teacher
function in local, linear regions. To the end, the student is able to better
capture the local shape of the teacher function and thus achieves a better
performance. Despite its simplicity, extensive experiments demonstrate that
$\rm L^2$RKD is superior to the original KD in many aspects as it outperforms
KD and the other state-of-the-art approaches by a large margin, shows
robustness and superiority under few-shot settings, and is more compatible with
the existing distillation approaches to further improve their performances
significantly.


  We report on photo ionization of ultracold magnetically trapped Rb atoms on
an atom chip. The atoms are trapped at 5 $\mu $K in a strongly anisotropic
trap. Through a hole in the chip with a diameter of 150 $\mu $m two laser beams
are focussed onto a fraction of the atomic cloud. A first laser beam with a
wavelength of 778 nm excites the atoms via a two photon transition to the 5D
level. With a fiber laser at 1080 nm the excited atoms are photo ionized.
Ionization leads to depletion of the atomic density distribution observed by
absorption imaging. The resonant ionization spectrum is reported. The setup
used in this experiment is not only suitable to investigate BEC ion mixtures
but also single atom detection on an atom chip.


  We study the connectivity of the excursion sets of additive Gaussian fields,
i.e.\ stationary centred Gaussian fields whose covariance function decomposes
into a sum of terms that depend separately on the coordinates. Our main result
is that, under mild smoothness and correlation decay assumptions, the excursion
sets $\{f \le \ell\}$ of additive planar Gaussian fields are bounded almost
surely at the critical level $\ell_c = 0$. Since we do not assume positive
correlations, this provides the first examples of continuous
non-positively-correlated stationary planar Gaussian fields for which the
boundedness of the nodal domains has been confirmed. By contrast, in dimension
$d \ge 3$ the excursion sets have unbounded components at all levels.


  This paper defines Lebesgue measure preserving Thompson's monoid, denoted by
$\mathbb{G}$, which is modeled on Thompson's group $\mathbb{F}$ except that the
elements of $\mathbb{G}$ are non-invertible. Moreover, it is required that the
elements of $\mathbb{G}$ preserve Lebesgue measure. Monoid $\mathbb{G}$
exhibits very different properties from Thompson's group $\mathbb{F}$. The
paper studies a number of algebraic (group-theoretic) and dynamical properties
of $\mathbb{G}$ including approximation, mixing, periodicity, entropy,
decomposition, generators, and topological conjugacy.


  We propose Asymptotic Expansion Conjectures of the relative
Reshetikhin-Turaev invariants, of the relative Turaev-Viro invariants and of
the discrete Fourier transforms of the quantum 6j-symbols, and prove them for
families of special cases. The significance of these expansions is that we do
not specify the way that the sequence of the colorings converges to the limit.
As a consequence, the terms in the expansion will have to depend on the index
r, but the dependence is in a way that the terms are purely geometric
invariants of the metrics on the underlying manifold and only the metrics vary
with r.


  Using the highest-resolution X-ray observation of the Trumpler 15 star
cluster taken by the Chandra X-ray Observatory, we estimate the total size of
its stellar population by comparing the X-ray luminosity function of the
detected sources to a calibrator cluster, and identify for the first time a
significant fraction (~14%) of its individual members. The highest-resolution
near-IR observation of Trumpler 15 (taken by the HAWK-I instrument on the VLT)
was found to detect most of our X-ray selected sample of cluster members, with
a K-excess disk frequency of 3.8+-0.7%. The near-IR data, X-ray luminosity
function, and published spectral types of the brightest members support a
cluster age estimate (5-10 Myr) that is older than those for the nearby
Trumpler 14 and Trumpler 16 clusters, and suggest that high-mass members may
have already exploded as supernovae. The morphology of the inner ~0.7 pc core
of the cluster is found to be spherical. However, the outer regions (beyond 2
pc) are elongated, forming an `envelope' of stars that, in projection, appears
to connect Trumpler 15 to Trumpler 14; this morphology supports the view that
these clusters are physically associated. Clear evidence of mass segregation is
seen. This study appears in a Special Issue of the ApJS devoted to the Chandra
Carina Complex Project (CCCP), a 1.42 square degree Chandra X-ray survey of the
Great Nebula in Carina.


  For all systolic groups we construct boundaries which are EZ--structures.
This implies the Novikov conjecture for torsion--free systolic groups. The
boundary is constructed via a system of distinguished geodesics in a systolic
complex, which we prove to have coarsely similar properties to geodesics in
CAT(0) spaces.


  We prove that for a wide family of non-uniformly hyperbolic maps and
hyperbolic potentials we have equilibrium stability, i.e. the equilibrium
states depend continuously on the dynamics and the potential. For this we
deduce that the topological pressure is continuous as a function of the
dynamics and the potential. We also prove the existence of finitely many
ergodic equilibrium states for non-uniformly hyperbolic skew products and
hyperbolic H\"older continuous potentials. Finally we show that these
equilibrium states vary continuously in the weak$^\ast$ topology within such
systems.


  Recently, voice conversion (VC) has been widely studied. Many VC systems use
disentangle-based learning techniques to separate the speaker and the
linguistic content information from a speech signal. Subsequently, they convert
the voice by changing the speaker information to that of the target speaker. To
prevent the speaker information from leaking into the content embeddings,
previous works either reduce the dimension or quantize the content embedding as
a strong information bottleneck. These mechanisms somehow hurt the synthesis
quality. In this work, we propose AGAIN-VC, an innovative VC system using
Activation Guidance and Adaptive Instance Normalization. AGAIN-VC is an
auto-encoder-based model, comprising of a single encoder and a decoder. With a
proper activation as an information bottleneck on content embeddings, the
trade-off between the synthesis quality and the speaker similarity of the
converted speech is improved drastically. This one-shot VC system obtains the
best performance regardless of the subjective or objective evaluations.