  Jets are collimated sprays of particles resulting from fragmentation of hard
scattered partons. They are measured in different types of collisions at
different energies to test perturbative Quantum Chromodynamic calculations and
are used to study the hard scattering, fragmentation and hadronisation of
partons. These phenomena, measured in simple systems such as proton--proton
collisions, serve as a baseline to investigate their modifications by hot and
dense nuclear matter created in high energy heavy-ion collisions.
  We have analysed data from minimum bias proton--proton collisions at centre
of mass energy of 2.76 and 7 TeV collected using the ALICE detector system at
the LHC and reconstructed the inclusive jet cross section from charged tracks
at midrapidity. We present jet spectra reconstructed using the infrared and
colinear safe anti-kT algorithm with underlying event subtraction, corrected
for detector effects via unfolding for both collision energies. Furthermore,
results from analyses of fragmentation distributions and jet shape observables
are shown. All results are compared with measurements of other LHC experiments
and with Monte Carlo generators.


  We consider mesoscopic thermal transport between two bulk dielectrics joined
by a narrow wire or weak mechanical link. In the ``tunneling'' regime where the
phonon transmission probability through the link is small and the thermal
conductance is much less than the value for a single channel, the thermal
current is determined by a product of the local vibrational spectral densities
of the two bodies. We derive an expression for the thermal current that is a
thermal analog of the well-known formula for the electrical current through a
tunneling barrier.


  We investigate heat transport through a one-dimensional open coupled scalar
field theory, depicted as a network of harmonic oscillators connected to
thermal baths at the boundaries. The non-Hermitian dynamical matrix of the
network undergoes a stability-to-instability transition at the exceptional
points as the coupling strength between the scalar fields increases. The open
network in the unstable regime, marked by the emergence of inverted oscillator
modes, does not acquire a steady state, and the heat conduction is then
unbounded for general bath couplings. In this work, we engineer a unique bath
coupling where a single bath is connected to two fields at each edge with the
same strength. This configuration leads to a finite steady-state heat
conduction in the network, even in the unstable regime. We also study general
bath couplings, e.g., connecting two fields to two separate baths at each
boundary, which shows an exciting signature of approaching the unstable regime
for massive fields. We derive analytical expressions for high-temperature
classical heat current through the network for different bath couplings at the
edges and compare them. Furthermore, we determine the temperature dependence of
low-temperature quantum heat current in different cases. Our study will help to
probe topological phases and phase transitions in various quadratic Hermitian
bosonic models whose dynamical matrices resemble non-Hermitian Hamiltonians,
hosting exciting topological phases.


  I survey recent successes in the application of relativistic MHD and
force-free electrodynamics to the modeling of the pulsars' rotational energy
loss mechanism as well as to the structure and emission characteristics of
Pulsar Wind Nebulae. I suggest that unsteady reconnection in the current sheet
separating the closed from the open zones of the magnetosphere is responsible
for the torque fluctuations observed in some pulsars, as well as for departures
of the braking index from the canonical value of 3. I emphasize the
significance of the boundary layer between the closed and open zones as the
active site in the outer magnetopshere. I elaborate on the conflict between the
models currently in use to interpret the gamma ray and X-ray pulses from these
systems with the electric current flows found in the spin down models. Because
the polar cap ``gap'' is the essential component in the supply of plasma to
pulsar magnetospheres and to pulsar wind nebulae, I emphasize the importance of
high sensitivity gamma ray observations of pulsars with core components of
radio emission and high magnetospheric voltage, since these observations will
look directly into the polar plasma production region. I also discuss the shock
conversion of flow energy into the spectra of the synchrotron emitting
particles in the Nebulae. I comment on the prospects for future developments
and improvements in all these areas.


  We prove that all connected n-point Green functions of four-dimensional
topological Yang-Mills theories quantized in the (anti-)self-dual Landau gauges
are tree-level exact, i.e. there are no radiative corrections in this gauge
choice.


  We present the first sample of spectroscopically confirmed heavily reddened
broad-line quasars selected using the new near infra-red VISTA Hemisphere
Survey and WISE All-Sky Survey. Observations of four candidates with (J-K)>2.5
and K<=16.5 over ~180 sq deg, leads to confirmation that two are highly
dust-reddened broad-line Type 1 quasars at z~2. The typical dust extinctions
are AV ~ 2-2.5 mags. We measure black-hole masses of ~10^9 M0 and extinction
corrected bolometric luminosities of ~10^47 erg/s, making these among the
brightest Type 1 quasars currently known. Despite this, these quasars lie well
below the detection limits of wide-field optical surveys like the SDSS with iAB
> 22. We also present WISE photometry at 3--22um, for our full sample of
spectroscopically confirmed reddened quasars including those selected from the
UKIDSS Large Area Survey (Banerji et al. 2012a). We demonstrate that the
rest-frame infrared SEDs of these reddened quasars are similar to UV-luminous
Type 1 quasars with significant hot dust emission and starburst quasar hosts
like Mrk231. The average 12um flux density of our reddened quasars is similar
to that of the recently discovered HyLIRG WISE1814+3412 (z=2.452) at similar
redshifts, with two of our reddened quasars also having comparable 22um flux
densities to this extreme HyLIRG. These optically faint, heavily reddened
broad-line quasars are therefore among the most mid infrared luminous galaxies
at z~2, now being discovered using WISE.


  We calculate the tunnelling density of states (TDoS) for a quantum dot in the
Coulomb blockade regime, using a functional integral representation with
allowing correctly for the charge quantisation. We show that in addition to the
well-known gap in the TDoS in the Coulomb-blockade valleys, there is a
suppression of the TDoS at the peaks. We show that such a suppression is
necessary in order to get the correct result for the peak of the differential
conductance through an almost close quantum dot.


  The multifractal behavior for tick data of prices is investigated in Korean
financial market. Using the rescaled range analysis(R/S analysis), we show the
multifractal nature of returns for the won-dollar exchange rate and the KOSPI.
We also estimate the Hurst exponent and the generalized $q$th-order Hurst
exponent in the unversal multifractal framework. Particularly, our financial
market is a persistent process with long-run memory effects, and the
statistical value of the Hurst exponents occurs the crossovers at charateristic
time scales. It is found that the probability distribution of returns is well
consistent with a Lorentz distribution, significantly different from fat-tailed
properties.


  We present model calculations of ionization fractions for elements in the
warm (log T = 4), low-density photoionized interstellar medium (WIM) of the
Milky Way. We model the WIM as a combination of overlapping low-excitation H II
regions having n(HII)/n(H) > 0.8. Our standard model incorporates an intrinsic
elemental abundance pattern similar to that found for warm neutral clouds in
the Galaxy and includes the effects of interstellar dust grains. The radiation
field is characterized by an ionizing spectrum of a star with T ~ 35,000 K and
an ionization parameter log(q) ~ -4.0. The emergent emission line strengths are
in agreement with the observed ratios of [SII]/Halpha, [NII]/Halpha,
[SII]/[NII], [OI]/Halpha, [OIII]/Halpha, and He I/Halpha in the Galactic WIM.
Although the forbidden emission-line intensities depend strongly upon the input
model parameters, the ionization fractions of the 20 elements studied in this
work are robust over a wide range of physical conditions considered in the
models. These ionization fractions have direct relevance to absorption-line
determinations of the elemental abundances in the warm neutral and ionized
gases in the Milky Way and other late-type galaxies. We demonstrate a method
for estimating the WIM contributions to the observed column densities of singly
and doubly ionized atoms used to derive abundances in the warm neutral gas. We
apply this approach to study the gas-phase abundances of the warm interstellar
clouds toward the halo star HD 93521.


  Variational auto-encoders (VAEs) have proven to be a well suited tool for
performing dimensionality reduction by extracting latent variables lying in a
potentially much smaller dimensional space than the data. Their ability to
capture meaningful information from the data can be easily apprehended when
considering their capability to generate new realistic samples or perform
potentially meaningful interpolations in a much smaller space. However, such
generative models may perform poorly when trained on small data sets which are
abundant in many real-life fields such as medicine. This may, among others,
come from the lack of structure of the latent space, the geometry of which is
often under-considered. We thus propose in this paper to see the latent space
as a Riemannian manifold endowed with a parametrized metric learned at the same
time as the encoder and decoder networks. This metric is then used in what we
called the Riemannian Hamiltonian VAE which extends the Hamiltonian VAE
introduced by arXiv:1805.11328 to better exploit the underlying geometry of the
latent space. We argue that such latent space modelling provides useful
information about its underlying structure leading to far more meaningful
interpolations, more realistic data-generation and more reliable clustering.


  A formulation of quantum mechanics, which begins by postulating assertions
for individual physical systems, is given. The statistical predictions of
quantum mechanics for infinite ensembles are then derived from its assertions
for individual systems. A discussion of the meaning of the "state" of an
individual quantum mechanical system is given, and an application is made to
the clarification of some of the paradoxical features of the theory.


  This work focuses on the context of software explainability, which is the
production of software capable of explaining to users the dynamics that govern
its internal functioning. User models that include information about their
requirements and their perceptions of explainability are fundamental when
building software with such capability. This study investigates the process of
creating personas that include information about users' explainability
perceptions and needs. The proposed approach is based on data collection with
questionnaires, modeling of empathy maps, grouping the maps, generating
personas from them and evaluation employing the Persona Perception Scale
method. In an empirical study, personas are created from 61 users' response
data to a questionnaire. The generated personas are evaluated by 60 users and
38 designers considering attributes of the Persona Perception Scale method. The
results include a set of 5 distinct personas that users rate as representative
of them at an average level of 3.7 out of 5, and designers rate as having
quality 3.5 out of 5. The median rate is 4 out of 5 in the majority of criteria
judged by users and designers. Both the personas and their creation and
evaluation approach are contributions of this study to the design of software
that satisfies the explainability requirement.


  We investigate spatial adiabatic passage of a Bose-Einstein condensate in a
triple well potential within the three-mode approximation. By rewriting the
dynamics in the so-called time-dependent dark/dressed basis, we analytically
derive the optimal conditions for the non-linear parameter and the on-site
energies of each well to achieve a highly efficient condensate transport. We
show that the non-linearity yields a high-efficiency plateau for the condensate
transport as a function of the on-site energy difference between the outermost
wells, favoring the robustness of the transport. We also analyze the case of
different non-linearities in each well, which, for certain parameter values,
leads to an increase of the width of this plateau.


  Recently, we proposed short-time Fourier transform (STFT)-based loss
functions for training a neural speech waveform model. In this paper, we
generalize the above framework and propose a training scheme for such models
based on spectral amplitude and phase losses obtained by either STFT or
continuous wavelet transform (CWT), or both of them. Since CWT is capable of
having time and frequency resolutions different from those of STFT and is cable
of considering those closer to human auditory scales, the proposed loss
functions could provide complementary information on speech signals.
Experimental results showed that it is possible to train a high-quality model
by using the proposed CWT spectral loss and is as good as one using STFT-based
loss.


  We study the implicit bias of gradient flow (i.e., gradient descent with
infinitesimal step size) on linear neural network training. We propose a tensor
formulation of neural networks that includes fully-connected, diagonal, and
convolutional networks as special cases, and investigate the linear version of
the formulation called linear tensor networks. With this formulation, we can
characterize the convergence direction of the network parameters as singular
vectors of a tensor defined by the network. For $L$-layer linear tensor
networks that are orthogonally decomposable, we show that gradient flow on
separable classification finds a stationary point of the $\ell_{2/L}$
max-margin problem in a "transformed" input space defined by the network. For
underdetermined regression, we prove that gradient flow finds a global minimum
which minimizes a norm-like function that interpolates between weighted
$\ell_1$ and $\ell_2$ norms in the transformed input space. Our theorems
subsume existing results in the literature while removing standard convergence
assumptions. We also provide experiments that corroborate our analysis.


  We calculate the one-loop effective potential at finite temperature for the
Horava-Lifshitz-like QED and Yukawa-like theories for arbitrary values of the
critical exponent and the space-time dimension. Additional remarks on the zero
temperature situation are also presented.


  Pockets of viscous fluid coalescing beneath an elastic plate are encountered
in a wide range of natural phenomena and engineering processes, spanning across
scales. As the pockets merge, a bridge is formed with a height increasing as
the plate relaxes. We study the spatiotemporal dynamics of such an
elasto-hydrodynamic coalescence process by combining experiments, lubrication
theory and numerical simulations. The bridge height exhibits an exponential
growth with time, which corresponds to a self-similar solution of the
bending-driven thin-film equation. We address this unique self-similarity and
the self-similar shape of the bridge, both of which are corroborated in
numerical simulations and experiments.


  The statistical-physics-based Kirchhoff-law-Johnson-noise (KLJN) key exchange
offers a new and simple unclonable system for credit/debit card chip
authentication and payment. The key exchange, the authentication and the
communication are unconditionally secure so that neither mathematics- nor
statistics-based attacks are able to crack the scheme. The ohmic connection and
the short wiring lengths between the chips in the card and the terminal
constitute an ideal setting for the KLJN protocol, and even its simplest
versions offer unprecedented security and privacy for credit/debit card chips
and applications of physical unclonable functions.


  Neural architecture search (NAS) has recently been addressed from various
directions, including discrete, sampling-based methods and efficient
differentiable approaches. While the former are notoriously expensive, the
latter suffer from imposing strong constraints on the search space.
Architecture optimization from a learned embedding space for example through
graph neural network based variational autoencoders builds a middle ground and
leverages advantages from both sides. Such approaches have recently shown good
performance on several benchmarks. Yet, their stability and predictive power
heavily depends on their capacity to reconstruct networks from the embedding
space. In this paper, we propose a two-sided variational graph autoencoder,
which allows to smoothly encode and accurately reconstruct neural architectures
from various search spaces. We evaluate the proposed approach on neural
architectures defined by the ENAS approach, the NAS-Bench-101 and the
NAS-Bench-201 search space and show that our smooth embedding space allows to
directly extrapolate the performance prediction to architectures outside the
seen domain (e.g. with more operations). Thus, it facilitates to predict good
network architectures even without expensive Bayesian optimization or
reinforcement learning.


  We focus on the study of the stability properties of ground-states for the
system of $M$ coupled semilinear Schr\"odinger equations with power-type
nonlinearities and couplings. Our results are generalizations of the theory for
the single equation and the technique for this kind of results is simplified.
Depending on the power of the nonlinearity, we may observe stability,
instability and weak instability. We also obtain results for three distinct
classes of bound-states, which is a special feature of the $M\ge2$ case.


  To help provide insight into the remarkable catalytic behavior of the
oxygen/silver system for heterogeneous oxidation reactions, purely sub-surface
oxygen, and structures involving both on-surface and sub-surface oxygen, as
well as oxide-like structures at the Ag(111) surface have been studied for a
wide range of coverages and adsorption sites using density-functional theory.
Adsorption on the surface in fcc sites is energetically favorable for low
coverages, while for higher coverage a thin surface-oxide structure is
energetically favorable. This structure has been proposed to correspond to the
experimentally observed (4x4) phase. With increasing O concentrations, thicker
oxide-like structures resembling compressed Ag2O(111) surfaces are
energetically favored. Due to the relatively low thermal stability of these
structures, and the very low sticking probability of O2 at Ag(111), their
formation and observation may require the use of atomic oxygen (or ozone, O3)
and low temperatures. We also investigate diffusion of O into the sub-surface
region at low coverage (0.11 ML), and the effect of surface Ag vacancies in the
adsorption of atomic oxygen and ozone-like species. The present studies,
together with our earlier investigations of on-surface and
surface-substitutional adsorption, provide a comprehensive picture of the
behavior and chemical nature of the interaction of oxygen and Ag(111), as well
as of the initial stages of oxide formation.


  We present a theoretical analysis of the relaxation cascade of a photoexcited
electron in graphene in the presence of RPA screened electron-electron
interaction. We calculate the relaxation rate of high energy electrons and the
jump-size distribution of the random walk constituting the cascade which
exhibits fat tails. We find that the statistics of the entire cascade are
described by L\'evy flights with constant drift instead of standard
drift-diffusion in energy space. The L\'evy flight manifests nontrivial scaling
relations of the fluctuations in the cascade time, which is related to the
problem of the first passage time of L\'evy processes. Furthermore we determine
the transient differential transmission of graphene after an excitation by a
laser pulse taking into account the fractional kinetics of the relaxation
dynamics.


  Taking as starting point the planar model arising from the dimensional
reduction of the Abelian-Higgs Carroll-Field-Jackiw model, we write down and
study the extended Maxwell equations and the corresponding wave equations for
the potentials. The solutions for these equations correspond to the usual ones
for the MCS-Proca system, supplemented with background-dependent correction
terms. In the case of a purely timelike background, exact algebraic solutions
are presented which possess a similar behavior to the MCS-Proca counterparts
near and far from the origin. On the other hand, for a purely spacelike
background, only approximate solutions are feasible. They consist of
non-trivial analytic expressions with a manifest evidence of spatial
anisotropy, which is consistent with the existence of a privileged direction in
space. These solutions also behave similarly to the MCS-Proca ones near and far
from the origin.


  We present a new predictive approach based on SUSY $SO(10)$ theory. The
inter-family hierarchy is first generated in the sector of hypothetical
superheavy fermions and then transfered inversely to ordinary quarks and
leptons by means of the universal seesaw mechanism. The obtained mass matrices
are simply parametrized by two small complex coefficients $\eps_u$ and
$\eps_d$, which can be given by the ratio of the GUT scale $M_G\simeq 10^{16}$
GeV and some higher scale $M\simeq 10^{17}-10^{18}$ GeV (presumably superstring
scale). The model provides a possibility for doublet-triplet splitting without
fine tuning and the Higgsino mediated $d=5$ operators for the proton decay are
naturally suppressed.
  Our ansatz provides the correct {\em qualitative} picture of fermion mass
hierarchy and mixing pattern, provided that $\eps_d/\eps_u\sim 10$. The running
masses of the first family fermions: electron, u-quark and d-quark obey an
approximate $SO(10)$ symmetry limit. At GUT scale we have: $u\sim d\simeq 3e$,
$(\frac{\eps_u}{\eps_d})c\sim s\simeq \frac{1}{3}\mu$ and
$(\frac{\eps_u}{\eps_d})^2t\sim b\simeq \tau$. The Cabibbo angle is large:
$s_{12}\simeq \sqrt{m_d/m_s}$ while other mixing angles have their natural
size: $s_{23}\sim m_s/m_b$ and $s_{13}\sim m_d/m_b$. We have many strong {\em
quantitative} predictions though no special `zero' texture is utilized (in
contrast to the known predictive frameworks). Namely, taking as input the
lepton, c-quark and b-quark masses, $m_s/m_d$ mass ratio and Cabibbo angle, we
can obtain the light (u,d,s) quark masses, top mass and $\tan\beta$. The top
quark is naturally in the 100 GeV range, but not too heavy: $m_t<165$ GeV. The
lower bound $M_t>150$ GeV (160 GeV)


  Recent years have witnessed increasing attention in cartoon media, powered by
the strong demands of industrial applications. As the first step to understand
this media, cartoon face recognition is a crucial but less-explored task with
few datasets proposed. In this work, we first present a new challenging
benchmark dataset, consisting of 389,678 images of 5,013 cartoon characters
annotated with identity, bounding box, pose, and other auxiliary attributes.
The dataset, named iCartoonFace, is currently the largest-scale, high-quality,
richannotated, and spanning multiple occurrences in the field of image
recognition, including near-duplications, occlusions, and appearance changes.
In addition, we provide two types of annotations for cartoon media, i.e., face
recognition, and face detection, with the help of a semi-automatic labeling
algorithm. To further investigate this challenging dataset, we propose a
multi-task domain adaptation approach that jointly utilizes the human and
cartoon domain knowledge with three discriminative regularizations. We hence
perform a benchmark analysis of the proposed dataset and verify the superiority
of the proposed approach in the cartoon face recognition task. We believe this
public availability will attract more research attention in broad practical
application scenarios.


  Mobile ad hoc networks are expected to support voice traffic. The requirement
for small delay and jitter of voice traffic poses a significant challenge for
medium access control (MAC) in such networks. User mobility makes it more
complex due to the associated dynamic path attenuation. In this paper, a MAC
scheme for mobile ad hoc networks supporting voice traffic is proposed. With
the aid of a low-power probe prior to DATA transmissions, resource reservation
is achieved in a distributed manner, thus leading to small delay and jitter.
The proposed scheme can automatically adapt to dynamic path attenuation in a
mobile environment. Simulation results demonstrate the effectiveness of the
proposed scheme.


  We observe the continuous emission of photons into a waveguide from a
superconducting qubit without the application of an external drive. To explain
this observation, we build a two-bath model where the qubit couples
simultaneously to a cold bath (the waveguide) and a hot bath (a secondary
environment). Our results show that the thermal-photon occupation of the hot
bath is up to 0.14 photons, 35 times larger than the cold waveguide, leading to
nonequilibrium heat transport with a power of up to 132 zW, as estimated from
the qubit emission spectrum. By adding more isolation between the sample output
and the first cold amplifier in the output line, the heat transport is strongly
suppressed. Our interpretation is that the hot bath may arise from active
two-level systems being excited by noise from the output line. We also apply a
coherent drive, and use the waveguide to measure thermodynamic work and heat,
suggesting waveguide spectroscopy is a useful means to study quantum heat
engines and refrigerators. Finally, based on the theoretical model, we propose
how a similar setup can be used as a noise spectrometer which provides a new
solution for calibrating the background noise of hybrid quantum systems.


  Automated slicing aims to identify subsets of evaluation data where a trained
model performs anomalously. This is an important problem for machine learning
pipelines in production since it plays a key role in model debugging and
comparison, as well as the diagnosis of fairness issues. Scalability has become
a critical requirement for any automated slicing system due to the large search
space of possible slices and the growing scale of data. We present Autoslicer,
a scalable system that searches for problematic slices through distributed
metric computation and hypothesis testing. We develop an efficient strategy
that reduces the search space through pruning and prioritization. In the
experiments, we show that our search strategy finds most of the anomalous
slices by inspecting a small portion of the search space.


  The need for accurate and fast scale-resolving simulations of fluid flows,
where turbulent dispersion is a crucial physical feature, is evident.
Large-eddy simulations (LES) are computationally more affordable than direct
numerical simulations, but their accuracy depends on sub-grid scale models and
the quality of the computational mesh. In order to compensate related errors, a
data assimilation approach for LES is devised in this work.
  The presented method is based on variational assimilation of sparse
time-averaged velocity reference data through a stationary corrective forcing
term in the LES momentum equation working with the time-averaged LES momentum
equation allows to employ a stationary discrete adjoint method. Therefore, the
stationary forcing in the unsteady LES momentum equation is iteratively updated
within the gradient-based optimization framework in conjunction with the
adjoint gradient. The implementation is based on a three-dimensional LES solver
in OpenFOAM, which is extended to allow the computation of adjoint quantities
and, ultimately, the adjoint gradient.
  Efficacy of the proposed framework is demonstrated for turbulent flow over
periodic hills and around a square cylinder. Coarse meshes are leveraged to
further enhance the speed of the optimization procedure. Time- and
spanwise-averaged velocity reference data from high-fidelity simulations is
taken from the literature.
  Our results demonstrate that adjoint-based assimilation of averaged velocity
enables the optimization of the mean flow, vortex shedding frequency (i.e.,
Strouhal number), and turbulent kinetic energy. This highlights the superiority
of scale-resolving simulations such as LES over simulations based on the
(unsteady) Reynolds-averaged equations.


  In this paper we consider an optimal control problem governed by a
time-dependent variational inequality arising in quasistatic plasticity with
linear kinematic hardening. We address certain continuity properties of the
forward operator, which imply the existence of an optimal control. Moreover, a
discretization in time is derived and we show that every local minimizer of the
continuous problem can be approximated by minimizers of modified, time-discrete
problems.


  We investigate the equilibrium state and the collective modes of an excitonic
insulator (EI) in a Fabry-P\'erot cavity. In an EI, two bands of a
semiconductor or semimetal spontaneously hybridize due to the Coulomb
interaction between electrons and holes, leading to the opening of a gap. The
coupling to the electromagnetic field reduces the symmetry of the system with
respect to phase rotations of the excitonic order parameter from $U(1)$ to
$Z_2$. While the reduction to a discrete symmetry would in general lead to a
gapped phase mode and enhance the stability of the ordered phase, the coupling
to the cavity leaves the mean-field ground state unaffected. Its energy remains
invariant under $U(1)$ phase rotations, in spite of the lower $Z_2$ symmetry
imposed by the cavity. In dipolar gauge, this can be traced back to the
balancing of the linear light-matter coupling and the dipolar self-interaction
at zero frequency. At nonzero frequency, however, the collective excitations do
reflect the lower $Z_2$ symmetry, which shows that fluctuations beyond
mean-field could play a crucial role in finding the true phase at finite
temperature.


  We present a study of the evolution of magnetism from the quantum critical
system YbRh2Si2 to the stable trivalent Yb system YbCo2Si2. Single crystals of
Yb(Rh_(1-x)Co_x)2Si2 were grown for 0 < x < 1 and studied by means of magnetic
susceptibility, electrical resistivity, and specific heat measurements, as well
as photoemission spectroscopy. The results evidence a complex magnetic phase
diagram, with a non-monotonic evolution of T_N and two successive transitions
for some compositions resulting in two tricritical points. The strong
similarity with the phase diagram of YbRh2Si2 under pressure indicates that Co
substitution basically corresponds to the application of positive chemical
pressure. Analysis of the data proves a strong reduction of the Kondo
temperature T_K with increasing Co content, T_K becoming smaller than T_N for x
~ 0.5, implying a strong localization of the 4f electrons. Furthermore,
low-temperature susceptibility data confirm a competition between ferromagnetic
and antiferromagnetic exchange. The series Yb(Rh_(1-x)Co_x)2Si2 provides an
excellent experimental opportunity to gain a deeper understanding of the
magnetism at the quantum critical point in the vicinity of YbRh2Si2 where the
antiferromagnetic phase disappears (T_N=>0).


  Until recently, cyber-physical systems, especially those with safety-critical
properties that manage critical infrastructure (e.g. power generation plants,
water treatment facilities, etc.) were considered to be invulnerable against
software security breaches. The recently discovered 'W32.Stuxnet' worm has
drastically changed this perception by demonstrating that such systems are
susceptible to external attacks. Here we present an architecture that enhances
the security of safety-critical cyber-physical systems despite the presence of
such malware. Our architecture uses the property that control systems have
deterministic execution behavior, to detect an intrusion within 0.6 {\mu}s
while still guaranteeing the safety of the plant. We also show that even if an
attack is successful, the overall state of the physical system will still
remain safe. Even if the operating system's administrative privileges have been
compromised, our architecture will still be able to protect the physical system
from coming to harm.


  The nature of dark matter is one of the greatest mysteries in modern physics
and astronomy. A wide variety of experiments have been carried out worldwide to
search for the evidence of particle dark matter. Chinese physicists started
experimental search for dark matter about ten years ago, and have produced
results with high scientific impact. In this paper, we present an overview of
the dark matter program in China, and discuss recent results and future
directions.


  MR-guided microwave ablation (MWA) has proven effective in treating
hepatocellular carcinoma (HCC) with small-sized tumors, but the
state-of-the-art technique suffers from sub-optimal workflow due to speed and
accuracy of needle placement. This paper presents a compact body-mounted
MR-conditional robot that can operate in closed-bore MR scanners for accurate
needle guidance. The robotic platform consists of two stacked Cartesian XY
stages, each with two degrees of freedom, that facilitate needle guidance. The
robot is actuated using 3D-printed pneumatic turbines with MR-conditional bevel
gear transmission systems. Pneumatic valves and control mechatronics are
located inside the MRI control room and are connected to the robot with
pneumatic transmission lines and optical fibers. Free space experiments
indicated robot-assisted needle insertion error of 2.6$\pm$1.3 mm at an
insertion depth of 80 mm. The MR-guided phantom studies were conducted to
verify the MR-conditionality and targeting performance of the robot. Future
work will focus on the system optimization and validations in animal trials.


  Tensor-based morphometry (TBM) aims at showing local differences in brain
volumes with respect to a common template. TBM images are smooth but they
exhibit (especially in diseased groups) higher values in some brain regions
called lateral ventricles. More specifically, our voxelwise analysis shows both
a mean-variance relationship in these areas and evidence of spatially dependent
skewness. We propose a model for 3-dimensional functional data where mean,
variance, and skewness functions vary smoothly across brain locations. We model
the voxelwise distributions as skew-normal. The smooth effects of age and sex
are estimated on a reference population of cognitively normal subjects from the
Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and mapped across
the whole brain. The three parameter functions allow to transform each TBM
image (in the reference population as well as in a test set) into a Gaussian
process. These subject-specific normative maps are used to derive indices of
deviation from a healthy condition to assess the individual risk of
pathological degeneration.


  We study the real time dynamics of a quantum system with potential barrier
coupled to a heat-bath environment. Employing the path integral approach an
evolution equation for the time dependent density matrix is derived. The time
evolution is evaluated explicitly near the barrier top in the temperature
region where quantum effects become important. It is shown that there exists a
quasi-stationary state with a constant flux across the potential barrier. This
state generalizes the Kramers flux solution of the classical Fokker-Planck
equation to the quantum regime. In the temperature range explored the quantum
flux state depends only on the parabolic approximation of the anharmonic
barrier potential near the top. The parameter range within which the solution
is valid is investigated in detail. In particular, by matching the flux state
onto the equilibrium state on one side of the barrier we gain a condition on
the minimal damping strength. For very high temperatures this condition reduces
to a known result from classical rate theory. Within the specified parameter
range the decay rate out of a metastable state is calculated from the flux
solution. The rate is shown to coincide with the result of purely thermodynamic
methods. The real time approach presented can be extended to lower temperatures
and smaller damping.


  Personal recollections on theoretical particle physics in the years when the
Standard Theory was formed. In the background, the remarkable development of
Italian theoretical physics in the second part of the last century, with great
personalities like Bruno Touschek, Raoul Gatto, Nicola Cabibbo and their
schools.


  Android's graphical password unlock remains one of the most widely used
schemes for phone unlock authentication, and it is has been studied extensively
in the last decade since its launch. We have learned that users' choice of
patterns mimics the poor password choices in other systems, such as PIN or
text-based passwords. A wide variety of analysis and data collections methods
was used to reach these conclusions, but what is missing from the literature is
a systemized comparison of the related work in this space that compares both
the methodology and the results. In this paper, we take a detailed accounting
of the different methods applied to data collection and analysis for Android
unlock patterns. We do so in two dimensions. First we systemize prior work into
a detailed taxonomy of collection methods, and in the second dimension, we
perform a detailed analysis of 9 different data sets collected using different
methods. While this study focuses singularly on the collection methods and
comparisons of the Android pattern unlock scheme, we believe that many of the
findings generalize to other graphical password schemes, unlock authentication
technology, and other knowledge-based authentication schemes.


  This paper proposes a supervised machine learning approach for predicting the
root cause of a given bug report. Knowing the root cause of a bug can help
developers in the debugging process - either directly or indirectly by choosing
proper tool support for the debugging task. We mined 54755 closed bug reports
from the issue trackers of 103 GitHub projects and applied a set of heuristics
to create a benchmark consisting of 10459 reports. A subset was manually
classified into three groups (semantic, memory, and concurrency) based on the
bugs' root causes. Since the types of root cause are not equally distributed, a
combination of keyword search and random selection was applied. Our data set
for the machine learning approach consists of 369 bug reports (122 concurrency,
121 memory, and 126 semantic bugs). The bug reports are used as input to a
natural language processing algorithm. We evaluated the performance of several
classifiers for predicting the root causes for the given bug reports. Linear
Support Vector machines achieved the highest mean precision (0.74) and recall
(0.72) scores. The created bug data set and classification are publicly
available.


  We have studied a sample of more than 25 000 young stars with proper motions
and trigonometric parallaxes from the Gaia DR2 catalogue. The relative errors
of their parallaxes do not exceed 10\%. The selection of stars belonging to
active star-forming regions was made by Marton et al. based on data from the
Gaia DR2 catalogue by invoking infrared measurements from the WISE and Planck
catalogues. Low-mass T Tauri stars constitute the majority of sample stars. The
following parameters of the angular velocity of Galactic rotation have been
found from them: $\Omega_0 =28.40\pm0.11$ km s$^{-1}$ kpc$^{-1}$,
$\Omega^{'}_0=-3.933\pm0.033$ km s$^{-1}$ kpc$^{-2}$ and
$\Omega^{''}_0=0.804\pm0.040$ km s$^{-1}$ kpc$^{-3}$. The Oort constants are
$A=15.73\pm0.32$ km s$^{-1}$ kpc$^{-1}$ and $B=-12.67\pm0.34$ km s$^{-1}$
kpc$^{-1}$, while the circular rotation velocity of the solar neighborhood
around the Galactic center is $V_0=227\pm4$ km s$^{-1}$ for the adopted
Galactocentric distance of the Sun $R_0=8.0\pm0.15$ kpc.


  Modern deep convolutional neural networks (CNNs) for image classification and
object detection are often trained offline on large static datasets. Some
applications, however, will require training in real-time on live video streams
with a human-in-the-loop. We refer to this class of problem as time-ordered
online training (ToOT). These problems will require a consideration of not only
the quantity of incoming training data, but the human effort required to
annotate and use it. We demonstrate and evaluate a system tailored to training
an object detector on a live video stream with minimal input from a human
operator. We show that we can obtain bounding box annotation from
weakly-supervised single-point clicks through interactive segmentation.
Furthermore, by exploiting the time-ordered nature of the video stream through
object tracking, we can increase the average training benefit of human
interactions by 3-4 times.


  Neural networks (NNs) have been shown to be competitive against
state-of-the-art feature engineering and random forest (RF) classification of
periodic variable stars. Although previous work utilising NNs has made use of
periodicity by period folding multiple-cycle time-series into a single cycle --
from time-space to phase-space -- no approach to date has taken advantage of
the fact that network predictions should be invariant to the initial phase of
the period-folded sequence. Initial phase is exogenous to the physical origin
of the variability and should thus be factored out. Here, we present
cyclic-permutation invariant networks, a novel class of NNs for which
invariance to phase shifts is guaranteed through polar coordinate convolutions,
which we implement by means of "Symmetry Padding." Across three different
datasets of variable star light curves, we show that two implementations of the
cyclic-permutation invariant network: the iTCN and the iResNet, consistently
outperform non-invariant baselines and reduce overall error rates by between 4%
to 22%. Over a 10-class OGLE-III sample, the iTCN/iResNet achieves an average
per-class accuracy of 93.4%/93.3%, compared to RNN/RF accuracies of 70.5%/89.5%
in a recent study using the same data. Finding improvement on a non-astronomy
benchmark, we suggest that the methodology introduced here should also be
applicable to a wide range of science domains where periodic data abounds due
to physical symmetries.


  We study the dynamics of a damped harmonic oscillator in the presence of a
retarded potential with state-dependent time-delayed feedback. In the limit of
small time-delays, we show that the oscillator is equivalent to a Li\'enard
system. This allows us to analytically predict the value of the first Hopf
bifurcation, unleashing a self-oscillatory motion. We compute bifurcation
diagrams for several model parameter values and analyse multistable domains in
detail. Using the Lyapunov energy function, two well-resolved energy levels
represented by two coexisting stable limit cycles are discerned. Further
exploration of the parameter space reveals the existence of a superposition
limit cycle, encompassing two degenerate coexisting limit cycles at the
fundamental energy level. When the system is driven very far from equilibrium,
a multiscale strange attractor displaying intrinsic and robust intermittency is
uncovered.


  We study fault-tolerant error correction in a quantum memory constructed as a
two-dimensional model of Fibonacci anyons on a torus, in the presence of
thermal noise represented by pair-creation processes and measurement errors.
The correction procedure is based on the cellular automaton decoders
originating in the works of G\'acs and Harrington. Through numerical
simulations, we observe that this code behaves fault-tolerantly and that
threshold behavior is likely present. Hence, we provide strong evidence for the
existence of a fault-tolerant universal non-Abelian topological quantum
computer.


  Using density-functional theory in combination with a thermodynamic formalism
we calculate the relative stability of various structural models of the polar
O-terminated (000-1)-O surface of ZnO. Model surfaces with different
concentrations of oxygen vacancies and hydrogen adatoms are considered.
Assuming that the surfaces are in thermodynamic equilibrium with an O2 and H2
gas phase we determine a phase diagram of the lowest-energy surface structures.
  For a wide range of temperatures and pressures we find that hydrogen will be
adsorbed at the surface, preferentially with a coverage of 1/2 monolayer. At
high temperatures and low pressures the hydrogen can be removed and a structure
with 1/4 of the surface oxygen atoms missing becomes the most stable one. The
clean, defect-free surface can only exist in an oxygen-rich environment with a
very low hydrogen partial pressure. However, since we find that the
dissociative adsorption of molecular hydrogen and water (if also the
Zn-terminated surface is present) is energetically very preferable, it is very
unlikely that a clean, defect-free (000-1)-O surface can be observed in
experiment.


  In this Letter, we use a model fluid mechanics experiment to elucidate the
impact of curvature heterogeneities on two-dimensional fields deriving from
harmonic potential functions. This result is directly relevant to explain the
smooth stationary structures in physical systems as diverse as curved liquid
crystal and magnetic films, heat and Ohmic transport in wrinkled
two-dimensional materials and flows in confined channels. Combining
microfluidic experiments and theory, we explain how curvature heterogeneities
shape confined viscous flows. We show that isotropic bumps induce local
distortions to Darcy's flows whereas anisotropic curvature heterogeneities
disturb them algebraically over system-spanning scales. Thanks to an
electrostatic analogy, we gain insight into this singular geometric
perturbation, and quantitatively explain it using both conformal mapping and
numerical simulations. Altogether our findings establish the robustness of our
experimental observations and their broad relevance to all Laplacian problems
beyond the specifics of our fluid mechanics experiment.


  A goal of low-level neural processes is to build an efficient code extracting
the relevant information from the sensory input. It is believed that this is
implemented in cortical areas by elementary inferential computations
dynamically extracting the most likely parameters corresponding to the sensory
signal. We explore here a neuro-mimetic feed-forward model of the primary
visual area (VI) solving this problem in the case where the signal may be
described by a robust linear generative model. This model uses an over-complete
dictionary of primitives which provides a distributed probabilistic
representation of input features. Relying on an efficiency criterion, we derive
an algorithm as an approximate solution which uses incremental greedy inference
processes. This algorithm is similar to 'Matching Pursuit' and mimics the
parallel architecture of neural computations. We propose here a simple
implementation using a network of spiking integrate-and-fire neurons which
communicate using lateral interactions. Numerical simulations show that this
Sparse Spike Coding strategy provides an efficient model for representing
visual data from a set of natural images. Even though it is simplistic, this
transformation of spatial data into a spatio-temporal pattern of binary events
provides an accurate description of some complex neural patterns observed in
the spiking activity of biological neural networks.


  We show that the bipartite logarithmic entanglement negativity (EN) of
quantum spin models obeys an area law at all nonzero temperatures. We develop
numerical linked cluster (NLC) expansions for the `area-law' logarithmic
entanglement negativity as a function of temperature and other parameters. For
one-dimensional models the results of NLC are compared with exact
diagonalization on finite systems and are found to agree very well. The NLC
results are also obtained for two dimensional XXZ and transverse-field Ising
models. In all cases, we find a sudden onset (or sudden death) of negativity at
a finite temperature above which the negativity is zero. We use perturbation
theory to develop a physical picture for this sudden onset (or sudden death).
The onset of EN or its magnitude are insensitive to classical
finite-temperature phase transitions, supporting the argument for absence of
any role of quantum mechanics at such transitions. On approach to a quantum
critical point at $T=0$, negativity shows critical scaling in size and
temperature.


  Precision weak gravitational lensing experiments require measurements of
galaxy shapes accurate to <1 part in 1000. We investigate measurement biases,
noted by Voigt and Bridle (2009) and Melchior et al. (2009), that are common to
shape measurement methodologies that rely upon fitting elliptical-isophote
galaxy models to observed data. The first bias arises when the true galaxy
shapes do not match the models being fit. We show that this "underfitting bias"
is due, at root, to these methods' attempts to use information at high spatial
frequencies that has been destroyed by the convolution with the point-spread
function (PSF) and/or by sampling. We propose a new shape-measurement technique
that is explicitly confined to observable regions of k-space. A second bias
arises for galaxies whose ellipticity varies with radius. For most
shape-measurement methods, such galaxies are subject to "ellipticity gradient
bias". We show how to reduce such biases by factors of 20--100 within the new
shape-measurement method. The resulting shear estimator has multiplicative
errors <1 part in 1000 for high-S/N images, even for highly asymmetric
galaxies. Without any training or recalibration, the new method obtains Q=3000
in the GREAT08 Challenge of blind shear reconstruction on low-noise galaxies,
several times better than any previous method.


  We present a new initial data formulation to solve the full set of Einstein
equations for spacetimes that contain a black hole under general conditions.
The method can be used to construct complete initial data for spacetimes (the
full metric) that contain a black hole. Contrary to most current studies the
formulation requires minimal assumptions. For example, rather than imposing the
form of the spatial conformal metric we impose 3 gauge conditions adapted to
the coordinates describing the system under consideration. For stationary,
axisymmetric spacetimes our method yields Kerr-Schild black holes in vacuum and
rotating equilibrium neutron stars. We demonstrate the power of our new method
by solving for the first time the whole system of Einstein equations for a
nonaxisymmetric, self-gravitating torus in the presence of a black hole. The
black hole has dimensionless spin $J_{\rm bh}/M_{\rm bh}^2=0.9918$, a rotation
axis tilted at a $30^\circ$ angle with respect to the angular momentum of the
disk, and a mass of $\sim 1/5$ of the disk.


  The theory of Schwarzschild geodesics is revisited. Basing on a result by
Weierstrass and Biermann, we derive a formula describing all non radial,
timelike and null trajectories in terms of Weierstrass elliptic functions.
Quite remarkably, a single formula works for an entire geodesic trajectory,
even if it passes through turning points. Using this formula, we derive
expressions for the proper and coordinate time along the geodesic.


  We use the singular manifold method to obtain the Lax pair, Darboux
transformations and soliton solutions for a (2+1) dimensional integrable
equation.


  We consider D1-branes in a RR flux background and show that there is a low
energy - large flux double scaling limit where the D1-branes action is
dominated by a Chern-Simons-Myers coupling term. As a classical solution to the
matrix model, we find a novel quantized geometry characterized by a quantum
Nambu 3-bracket. Infinite dimensional representations of the quantum Nambu
geometry are constructed which demonstrate that the quantum Nambu geometry is
intrinsically different from the ordinary Lie algebra type noncommutative
geometry. Matrix models for the IIB string, IIA string and M-theory in the
corresponding backgrounds are constructed. A classical solution of a quantum
Nambu geometry in the IIA Matrix string theory gives rise to an expansion of
the fundamental strings into a system of multiple D4-branes and the fluctuation
is found to describe an action for a non-abelian 3-form field strength which is
a natural non-abelian generalization of the PST action for a single D4-brane.
In view of the recent proposals of the M5-branes theory in terms of the
D4-branes, we suggest a natural way to include all the KK modes and propose an
action for the the multiple M5-branes in a constant C-field. The worldvolume of
the M5-branes in a C-field is found to be described by a quantum Nambu geometry
with self-dual parameters. It is intriguing that our action is naturally
formulated in terms of a 1-form gauge field living on a six dimensional quantum
Nambu geometry.


  This paper focuses on the problem of \emph{constrained} \emph{stochastic}
optimization. A zeroth order Frank-Wolfe algorithm is proposed, which in
addition to the projection-free nature of the vanilla Frank-Wolfe algorithm
makes it gradient free. Under convexity and smoothness assumption, we show that
the proposed algorithm converges to the optimal objective function at a rate
$O\left(1/T^{1/3}\right)$, where $T$ denotes the iteration count. In
particular, the primal sub-optimality gap is shown to have a dimension
dependence of $O\left(d^{1/3}\right)$, which is the best known dimension
dependence among all zeroth order optimization algorithms with one directional
derivative per iteration. For non-convex functions, we obtain the
\emph{Frank-Wolfe} gap to be $O\left(d^{1/3}T^{-1/4}\right)$. Experiments on
black-box optimization setups demonstrate the efficacy of the proposed
algorithm.


  Building on works of Boulanger and Goto, we show that Goto's scalar curvature
is the moment map for an action of generalized Hamiltonian automorphisms of the
associated Courant algebroid, constrained by the choice of an adapted volume
form. We derive an explicit formula for Goto's scalar curvature, and show that
it is constant for generalized K\"ahler-Ricci solitons. Restricting to the
generically symplectic type case, we realize the generalized K\"ahler class as
the complexified orbit of the Hamiltonian action above. This leads to a natural
extension of Mabuchi's metric and $K$-energy, implying a conditional uniqueness
result. Finally, in this setting we derive a Calabi-Matsushima-Lichnerowicz
obstruction and a Futaki invariant.


  This paper studies least-squares ReLU neural network method for solving the
linear advection-reaction problem with discontinuous solution. The method is a
discretization of an equivalent least-squares formulation in the set of neural
network functions with the ReLU activation function. The method is capable of
approximating the discontinuous interface of the underlying problem
automatically through the free hyper-planes of the ReLU neural network and,
hence, outperforms mesh-based numerical methods in terms of the number of
degrees of freedom. Numerical results of some benchmark test problems show that
the method can not only approximate the solution with the least number of
parameters, but also avoid the common Gibbs phenomena along the discontinuous
interface. Moreover, a three-layer ReLU neural network is necessary and
sufficient in order to well approximate a discontinuous solution with an
interface in $\mathbb{R}^2$ that is not a straight line.


  We present a systematic study of particle transport by diffusion in young
pulsar wind nebulae (PWNe). We selected nine bright sources that are well
resolved with the Chandra X-ray Observatory. We analyzed archival data to
obtain their radial profiles of photon index ($\Gamma$) and surface brightness
($\Sigma$) in a consistent way. These profiles were then fit with a pure
diffusion model that was tested on Crab, 3C 58 and G21.5$-$0.9 before. In
addition to the spectral softening due to the diffusion, we calculated the
synchrotron power and built up the theoretical surface brightness profile. For
each source, we performed separate fits to the $\Gamma$ and the $\Sigma$
profiles. We found these two profiles of most PWNe are similar, except for Crab
and Vela. Both profiles can be well described by our model, suggesting that
diffusion dominates the particle transport in most sampled PWNe. The
discrepancy of parameters between the $\Gamma$ profile and $\Sigma$ profiles is
relatively large for 3C 58 and G54.1+0.3. This difference could be attributed
to the elongated shape, reflecting boundary, and the non-uniform magnetic
field. Finally, we found no significant correlations between diffusion
parameters and physical parameters of PWN and pulsar.


  In loop quantum cosmology the quantum dynamics is well understood. We
approximate the full quantum dynamics in the infinite dimensional Hilbert space
by projecting it on a finite dimensional submanifold thereof, spanned by
suitably chosen semiclassical states. This submanifold is isomorphic with the
classical phase space and the projected dynamical flow provides effective
equations incorporating the leading quantum corrections to the classical
equations of motion. Numerical work has been done using quantum states which
are semiclassical at late times. These states follow the classical trajectory
until the density is on the order of 1% of the Planck density then deviate
strongly from the classical trajectory. The effective equations we obtain
reproduce this behavior to surprising accuracy.


  Early X-ray surveys of the Magellanic Clouds (MCs) were performed with the
imaging instruments of the Einstein, ASCA and ROSAT satellites revealing
discrete X-ray sources and large-scale diffuse emission. Large samples of
supernova remnants, high and low mass X-ray binaries and super-soft X-ray
sources could be studied in detail. Today, the major X-ray observatories
XMM-Newton and Chandra with their advanced angular and spectral resolution and
extended energy coverage are ideally suited for detailed population studies of
the X-ray sources in these galaxies and to draw conclusions on our own Galaxy.
We summarize our knowledge about the X-ray source populations in the MCs from
past missions and present first results from systematic studies of the Small
Magellanic Cloud (SMC) using the growing number of archival XMM-Newton
observations.


  In this paper we review the definitions of homogeneous and alternative links.
We also give two new characterizations of an alternative link diagram, one
within the context of the enhanced checkerboard graph and another from the
labeled Seifert graph.


  We report the first continuous-variable quantum key distribution experiment
that enables the generation of secure key over a 60 km fiber channel with
locally generated local oscillator. This is achieved by controlling the excess
noise using machine learning for phase noise compensation while operating the
system at a low modulation variance


  A $D$-dimensional Markovian open quantum system will undergo stochastic
evolution which preserves pure states, if one monitors without loss of
information the bath to which it is coupled. If a finite ensemble of pure
states satisfies a particular set of constraint equations then it is possible
to perform the monitoring in such a way that the (discontinuous) trajectory of
the conditioned system state is, at all long times, restricted to those pure
states. Finding these physically realizable ensembles (PREs) is typically very
difficult, even numerically, when the system dimension is larger than 2. In
this paper, we develop symmetry-based techniques that potentially greatly
reduce the difficulty of finding a subset of all possible PREs. The two
dynamical symmetries considered are an invariant subspace and a Wigner
symmetry. An analysis of previously known PREs using the developed techniques
provides us with new insights and lays the foundation for future studies of
higher dimensional systems.


  Automotive software development requires engineers to test their systems to
detect violations of both functional and drivability requirements. Functional
requirements define the functionality of the automotive software. Drivability
requirements refer to the driver's perception of the interactions with the
vehicle; for example, they typically require limiting the acceleration and jerk
perceived by the driver within given thresholds. While functional requirements
are extensively considered by the research literature, drivability requirements
garner less attention. This industrial paper describes our experience assessing
the usefulness of an automated search-based software testing (SBST) framework
in generating failure-revealing test cases for functional and drivability
requirements. Our experience concerns the VI-CarRealTime simulator, an
industrial virtual modeling and simulation environment widely used in the
automotive domain. We designed a Cruise Control system in Simulink for a
four-wheel vehicle, in an iterative fashion, by producing 21 model versions. We
used the SBST framework for each version of the model to search for
failure-revealing test cases revealing requirement violations. Our results show
that the SBST framework successfully identified a failure-revealing test case
for 66.7% of our model versions, requiring, on average, 245.9s and 3.8
iterations. We present lessons learned, reflect on the generality of our
results, and discuss how our results improve the state of practice.


  The k-sat problem is a prototypical constraint satisfaction problem. There
are many algorithms to study k-sat problem, BP algorithm is famous one of them.
But BP algorithm does not converge when $\alpha$(constraint density)is bigger
than some threshold value. In this paper we use CCCP (Concave Convex Procedure)
algorithm to study 3-sat problem and we get better results than BP algorithm
that CCCP algorithm still converges when BP algorithm does not converge. Our
work almost builds on recent results by Yuille \cite{Yuille2002} who apply the
CCCP algorithm to Bethe and Kikuchi free energies and obtained two algorithms
on 2D and 3D spin glasses. Our implementation of CCCP algorithm on 3-sat
problem is some different from his implementation and we have some different
views about CCCP algorithm's some properties. Some difference of these maybe
because of CCCP algorithm have different properties and implementation process
on different problem and some others of these are related to the CCCP algorithm
itself. Our work indicates that CCCP algorithm has more learning and inference
applications.


  Fix a rational basepoint b and a rational number c. For the quadratic
dynamical system f_c(x) = x^2+c, it has been shown that the number of rational
points in the backward orbit of b is bounded independent of the choice of
rational parameter c. In this short note we investigate the dependence of the
bound on the basepoint b, assuming a strong form of the Mordell Conjecture.


  Two complementary techniques are developed for obtaining the asymptotic form
of gravitational-wave data at large radii from numerical simulations, in the
form of easily implemented algorithms. It is shown that, without extrapolation,
near-field effects produce errors in extracted waveforms that can significantly
affect LIGO data analysis. The extrapolation techniques are discussed in the
context of Newman--Penrose data applied to extrapolation of waveforms from an
equal-mass, nonspinning black-hole binary simulation. The results of the two
methods are shown to agree within error estimates. The various benefits and
deficiencies of the methods are discussed.


  The Kronecker coefficient g_{\lambda \mu \nu} is the multiplicity of the
GL(V)\times GL(W)-irreducible V_\lambda \otimes W_\mu in the restriction of the
GL(X)-irreducible X_\nu via the natural map GL(V)\times GL(W) \to GL(V \otimes
W), where V, W are \mathbb{C}-vector spaces and X = V \otimes W. A fundamental
open problem in algebraic combinatorics is to find a positive combinatorial
formula for these coefficients.
  We construct two quantum objects for this problem, which we call the
nonstandard quantum group and nonstandard Hecke algebra. We show that the
nonstandard quantum group has a compact real form and its representations are
completely reducible, that the nonstandard Hecke algebra is semisimple, and
that they satisfy an analog of quantum Schur-Weyl duality.
  Using these nonstandard objects as a guide, we follow the approach of Adsul,
Sohoni, and Subrahmanyam to construct, in the case dim(V) = dim(W) =2, a
representation \check{X}_\nu of the nonstandard quantum group that specializes
to Res_{GL(V) \times GL(W)} X_\nu at q=1. We then define a global crystal basis
+HNSTC(\nu) of \check{X}_\nu that solves the two-row Kronecker problem: the
number of highest weight elements of +HNSTC(\nu) of weight (\lambda,\mu) is the
Kronecker coefficient g_{\lambda \mu \nu}. We go on to develop the beginnings
of a graphical calculus for this basis, along the lines of the U_q(\sl_2)
graphical calculus, and use this to organize the crystal components of
+HNSTC(\nu) into eight families. This yields a fairly simple, explicit and
positive formula for two-row Kronecker coefficients, generalizing a formula of
Brown, van Willigenburg, and Zabrocki. As a byproduct of the approach, we also
obtain a rule for the decomposition of Res_{GL_2 \times GL_2 \rtimes \S_2}
X_\nu into irreducibles.


  We point out that when a quadratic type Li\'enard equation is suitably
interpreted shows branching due to the double valuedness of the governing
Hamiltonian. Under certain approximation of the guiding coupling constant we
derive its quantum counterpart that is guided by a momentum-dependent mass
function.


  A general approach is proposed to solve the coupled Gross-Pitaevskii
equations (CGP) for K-species Bose-Einstein condensates (BEC). Analytical
solutions have been obtained under the Thomas-Fermi approximation. We aim at
finding out the common features of the K-species BEC. In particular, two types
of phase-transitions, full-state-transition and partial-state transition, are
found. In the former all species are involved in the transition, while in the
latter only a few specified species are essentially involved. This leads to the
criticality and the hidden criticality (previously found in multi-band
superconductivity). We further found that the former originates from the
singularity of the whole matrix of the CGP, while the latter originates from
the singularity of a specified sub-matrix (which is contributed by only a few
specified species). It is emphasized that the singularity is not a by-product
of the TFA, but is inherent in the CGP.


  A Steiner quadruple system of order v is a 3-(v,4,1) design, and will be
denoted SQS(v). Using the classification of finite 2-transitive permutation
groups all SQS(v) with a flag-transitive automorphism group are completely
classified, thus solving the "still open and longstanding problem of
classifying all flag-transitive 3-(v,k,1) designs" for the smallest value of k.
Moreover, a generalization of a result of H. Lueneburg (1965, Math. Z. 89,
82-90) is achieved.


  A powerful technique to calculate gravitational radiation from binary systems
involves a perturbative expansion: if the masses of the two bodies are very
different, the "small" body is treated as a point particle of mass $m_p$ moving
in the gravitational field generated by the large mass $M$, and one keeps only
linear terms in the small mass ratio $m_p/M$. This technique usually yields
finite answers, which are often in good agreement with fully nonlinear
numerical relativity results, even when extrapolated to nearly comparable mass
ratios. Here we study two situations in which the point-particle approximation
yields a divergent result: the instantaneous flux emitted by a small body as it
orbits the light ring of a black hole, and the total energy absorbed by the
horizon when a small body plunges into a black hole. By integrating the
Teukolsky (or Zerilli/Regge-Wheeler) equations in the frequency and time
domains we show that both of these quantities diverge. We find that these
divergences are an artifact of the point-particle idealization, and are able to
interpret and regularize this behavior by introducing a finite size for the
point particle. These divergences do not play a role in black-hole imaging,
e.g. by the Event Horizon Telescope.


  In this paper we investigate the entropy of gravitational Chern-Simons terms
for the horizon with non-vanishing extrinsic curvatures, or the holographic
entanglement entropy for arbitrary entangling surface. In 3D we find no anomaly
of entropy appears. But the squashed cone method can not be used directly to
get the correct result. For higher dimensions the anomaly of entropy would
appear, still, we can not use the squashed cone method directly. That is
becasuse the Chern-Simons action is not gauge invariant. To get a reasonable
result we suggest two methods. One is by adding a boundary term to recover the
gauge invariance. This boundary term can be derived from the variation of the
Chern-Simons action. The other one is by using the Chern-Simons relation
$d\bm{\Omega_{4n-1}}=tr(\bm{R}^{2n})$. We notice that the entropy of
$tr(\bm{R}^{2n})$ is a total derivative locally, i.e. $S=d s_{CS}$. We propose
to identify $s_{CS}$ with the entropy of gravitational Chern-Simons terms
$\Omega_{4n-1}$. In the first method we could get the correct result for Wald
entropy in arbitrary dimension. In the second approach, in addition to Wald
entropy, we can also obtain the anomaly of entropy with non-zero extrinsic
curvatures. Our results imply that the entropy of a topological invariant, such
as the Pontryagin term $tr(\bm{R}^{2n})$ and the Euler density, is a
topological invariant on the entangling surface.


  For a commutative ring R we investigate the property that the sets of minimal
primes of finitely generated ideals of R is always finite. We prove this
property passes to polynomial ring extensions (in an arbitrary number of
variables) over R as well as to R-algebras which are finitely presented as
R-modules.


  We prove a theorem about the derivation algebra of the tensor product of two
algebras. As an application, we determine the derivation algebra of the fixed
point algebra of the tensor product of two algebras, with respect to the tensor
product of two finite order automorphisms of the involved algebras. These
results generalize some well-know theorems in the literature.


  We stress that specific texture zeros of lepton mass matrices, which might
dynamically arise from a new kind of flavor symmetry, can help us to establish
simple and testable relations between the lepton flavor mixing parameters and
lepton mass ratios. We present a brief review of one-zero, two-zero and
three-zero textures of the neutrino mass matrix. Their phenomenological
consequences on neutrino mixing and CP violation are also discussed.


  The ability to predict the intentions of people based solely on their visual
actions is a skill only performed by humans and animals. The intelligence of
current computer algorithms has not reached this level of complexity, but there
are several research efforts that are working towards it. With the number of
classification algorithms available, it is hard to determine which algorithm
works best for a particular situation. In classification of visual human intent
data, Hidden Markov Models (HMM), and their variants, are leading candidates.
  The inability of HMMs to provide a probability in the observation to
observation linkages is a big downfall in this classification technique. If a
person is visually identifying an action of another person, they monitor
patterns in the observations. By estimating the next observation, people have
the ability to summarize the actions, and thus determine, with pretty good
accuracy, the intention of the person performing the action. These visual cues
and linkages are important in creating intelligent algorithms for determining
human actions based on visual observations.
  The Evidence Feed Forward Hidden Markov Model is a newly developed algorithm
which provides observation to observation linkages. The following research
addresses the theory behind Evidence Feed Forward HMMs, provides mathematical
proofs of their learning of these parameters to optimize the likelihood of
observations with a Evidence Feed Forwards HMM, which is important in all
computational intelligence algorithm, and gives comparative examples with
standard HMMs in classification of both visual action data and measurement
data; thus providing a strong base for Evidence Feed Forward HMMs in
classification of many types of problems.


  The relaxation dynamics of superexcited superfluid He nanodroplets is
thoroughly investigated by means of extreme-ultraviolet (XUV) femtosecond
electron and ion spectroscopy complemented by time-dependent density functional
theory (TDDFT). Three main paths leading to the emission of electrons and ions
are identified: Droplet autoionization, pump-probe photoionization, and
autoionization induced by re-excitation of droplets relaxing into levels below
the droplet ionization threshold. The most abundant product of both droplet
autoionization and photoionization is He$_2^+$, whereas the delayed appearance
of He$^+$ is indicative of the ejection of excited He atoms from the droplets.
The state-resolved time-dependent photoelectron spectra reveal that
intermediate excited states of the droplets are populated in the course of the
relaxation, terminating in the lowest-lying metastable singlet and triplet He
atomic states. The slightly faster relaxation of the triplet state compared to
the singlet state is in agreement with the simulation showing faster formation
of a bubble around a He atom in the triplet state.


  Using a holographic dual model of QCD, we compute the pion electromagnetic
form factor F_pi(Q^2) in the spacelike momentum transfer region, as well as
pion couplings to vector mesons g_rho^(n) pi pi. Spontaneous and explicit
chiral symmetry breaking are intrinsic features of this particular holographic
model. We consider variants with both ``hard-wall'' and ``soft-wall'' infrared
cutoffs, and find that the F_pi(Q^2) data tend to lie closer to the hard-wall
model predictions, although both are too shallow for large Q^2. By allowing the
parameters of the soft-wall model (originally fixed by observables such as
m_rho) to vary, one finds fits that tend to agree better with F_pi(Q^2). We
also compute the pion charge radius <r_pi^2> for a variety of parameter
choices, and use the values of f^(n)_rho, g_{rho^(n) pi pi} and m^(n)_rho to
observe the saturation of F_pi(0) by rho poles.


  Let K^0_lambda be the class of structures < lambda,<,A>, where A subseteq
lambda is disjoint from a club, and let K^1_lambda be the class of structures <
lambda,<,A>, where A subseteq lambda contains a club. We prove that if lambda =
lambda^{< kappa} is regular, then no sentence of L_{lambda^+ kappa} separates
K^0_lambda and K^1_lambda. On the other hand, we prove that if lambda = mu^+,
mu = mu^{< mu}, and a forcing axiom holds (and aleph_1^L= aleph_1 if mu =
aleph_0), then there is a sentence of L_{lambda lambda} which separates
K^0_lambda and K^1_lambda .


  Parity violation in a chiral, four-atom molecule is discussed. Given the
geometrical positions of the four atoms, we calculate the anapole moment of it.
This problem was first discussed by Khriplovich and Pospelov. We give a
detailed derivation for it so that it can be more accessible to wider range of
scientists. We correct errors in their results and generalize their initial
state to |s_{1/2}\rangle and |p_{1/2}\rangle states. We also discuss realistic
candidates of the chiral molecules to which this approach can be applied.


  We formulate a method to study two-body correlations in a condensate of N
identical bosons. We use the adiabatic hyperspheric approach and assume a
Faddeev like decomposition of the wave function. We derive for a fixed
hyperradius an integro-differential equation for the angular eigenvalue and
wave function. We discuss properties of the solutions and illustrate with
numerical results. The interaction energy is for N~20 five times smaller than
that of the Gross-Pitaevskii equation.


  Modern Algorithmic Trading ("Algo") allows institutional investors and
traders to liquidate or establish big security positions in a fully automated
or low-touch manner. Most existing academic or industrial Algos focus on how to
"slice" a big parent order into smaller child orders over a given time horizon.
Few models rigorously tackle the actual placement of these child orders.
Instead, placement is mostly done with a combination of empirical signals and
heuristic decision processes. A self-contained, realistic, and fully functional
Child Order Placement (COP) model may never exist due to all the inherent
complexities, e.g., fragmentation due to multiple venues, dynamics of limit
order books, lit vs. dark liquidity, different trading sessions and rules. In
this paper, we propose a reductionism COP model that focuses exclusively on the
interplay between placing passive limit orders and sniping using aggressive
takeout orders. The dynamic programming model assumes the form of a stochastic
linear-quadratic regulator (LQR) and allows closed-form solutions under the
backward Bellman equations. Explored in detail are model assumptions and
general settings, the choice of state and control variables and the cost
functions, and the derivation of the closed-form solutions.


  The nuclear physics landscape has been redesigned as a sequence of effective
field theories (EFTs) connected to the Standard Model through symmetries and
lattice simulations of Quantum Chromodynamics (QCD). EFTs in this sequence are
expansions around different low-energy limits of QCD, each with its own
characteristics, scales, and ranges of applicability regarding energy and
number of nucleons. We review each of the three main nuclear EFTs -- Chiral,
Pionless, Halo/Cluster -- highlighting their similarities, differences, and
connections. In doing so, we survey the structural properties and reactions of
nuclei that have been derived from the ab initio solution of the few- and
many-body problem built upon EFT input.


  Language-Image Pre-training has demonstrated promising results on zero-shot
and few-shot downstream tasks by prompting visual models with natural language
prompts. However, most recent studies only use a single prompt for tuning,
neglecting the inherent step-to-step cognitive reasoning process that humans
conduct in complex task settings, for example, when processing images from
unfamiliar domains. Chain of Thought is a simple and effective approximation to
human reasoning process and has been proven useful for natural language
processing (NLP) tasks. Based on this cognitive intuition, we believe that
conducting effective reasoning is also an important problem in visual tasks,
and a chain of thought could be a solution to this problem. In this work, we
propose a novel chain of thought prompt tuning for vision-language modeling.
Extensive experiments show that our method not only generalizes better in image
classification tasks, has greater transferability beyond a single dataset, and
has stronger domain generalization performance, but also performs much better
in imagetext retrieval and visual question answering, which require more
reasoning capabilities. We are the first to successfully adapt chain-of-thought
prompting that combines visual and textual embeddings. We will release our
codes


  A graphical model provides a compact and efficient representation of the
association structure of a multivariate distribution by means of a graph.
Relevant features of the distribution are represented by vertices, edges and
other higher-order graphical structures, such as cliques or paths. Typically,
paths play a central role in these models because they determine the
independence relationships among variables. However, while a theory of path
coefficients is available in models for directed graphs, little has been
investigated about the strength of the association represented by a path in an
undirected graph. Essentially, it has been shown that the covariance between
two variables can be decomposed into a sum of weights associated with each of
the paths connecting the two variables in the corresponding concentration
graph. In this context, we consider concentration graph models and provide an
extensive analysis of the properties of path weights and their interpretation.
More specifically, we give an interpretation of covariance weights through
their factorisation into a partial covariance and an inflation factor. We then
extend the covariance decomposition over the paths of an undirected graph to
other measures of association, such as the marginal correlation coefficient and
a quantity that we call the inflated correlation. The usefulness of these
findings is illustrated with an application to the analysis of dietary intake
networks.


  Spark is an in-memory analytics platform that targets commodity server
environments today. It relies on the Hadoop Distributed File System (HDFS) to
persist intermediate checkpoint states and final processing results. In Spark,
immutable data are used for storing data updates in each iteration, making it
inefficient for long running, iterative workloads. A non-deterministic garbage
collector further worsens this problem. Sparkle is a library that optimizes
memory usage in Spark. It exploits large shared memory to achieve better data
shuffling and intermediate storage. Sparkle replaces the current TCP/IP-based
shuffle with a shared memory approach and proposes an off-heap memory store for
efficient updates. We performed a series of experiments on scale-out clusters
and scale-up machines. The optimized shuffle engine leveraging shared memory
provides 1.3x to 6x faster performance relative to Vanilla Spark. The off-heap
memory store along with the shared-memory shuffle engine provides more than 20x
performance increase on a probabilistic graph processing workload that uses a
large-scale real-world hyperlink graph. While Sparkle benefits at most from
running on large memory machines, it also achieves 1.6x to 5x performance
improvements over scale out cluster with equivalent hardware setting.


  How to evaluate Large Language Models (LLMs) in code generation is an open
question. Existing benchmarks demonstrate poor alignment with real-world code
repositories and are insufficient to evaluate the coding abilities of LLMs.
This paper proposes a new benchmark - EvoCodeBench to address the preceding
problems, which has three primary advances. (1) EvoCodeBench aligns with
real-world repositories in multiple dimensions, e.g., code distributions and
dependency distributions. (2) EvoCodeBench offers comprehensive annotations
(e.g., requirements, reference code, and reference dependencies), and robust
evaluation metrics (e.g., Pass@k and Recall@k). (3) EvoCodeBench is an evolving
benchmark to avoid data leakage. We build an automatic pipeline to update
EvoCodeBench from the latest repositories. We release the first version -
EvoCodeBench-2403, containing 275 samples from 25 real-world repositories.
Based on EvoCodeBench, we propose repository-level code generation and evaluate
10 popular LLMs (e.g., gpt-4, gpt-3.5, DeepSeek Coder, StarCoder 2, CodeLLaMa,
Gemma, and Qwen 1.5). Our experiments reveal the coding abilities of these LLMs
in real-world repositories. For example, the highest Pass@1 of gpt-4 only is
20.73% in our experiments. We also analyze failed cases and summarize the
shortcomings of existing LLMs in EvoCodeBench. We release EvoCodeBench, all
prompts, and LLMs' completions for further community analysis.


  We report a precise measurement of the hyperfine interval in the ${2P}_{1/2}$
state of $^{7}$Li. The transition from the ground state ($D_1$ line) is
accessed using a diode laser and the technique of saturated-absorption
spectroscopy in hot Li vapor. The interval is measured by locking an
acousto-optic modulator to the frequency difference between the two hyperfine
peaks. The measured interval of 92.040(6)~MHz is consistent with an earlier
measurement reported by us using an atomic-beam spectrometer [Das and
Natarajan, J.\ Phys.\ B {\bf 41}, 035001 (2008)]. The interval yields the
magnetic dipole constant in the $P_{1/2}$ state as $A=46.047(3)$, which is
discrepant from theoretical calculations by $>80$~kHz.


  Detecting and recovering a low-rank signal in a noisy data matrix is a
fundamental task in data analysis. Typically, this task is addressed by
inspecting and manipulating the spectrum of the observed data, e.g.,
thresholding the singular values of the data matrix at a certain critical
level. This approach is well-established in the case of homoskedastic noise,
where the noise variance is identical across the entries. However, in numerous
applications, the noise can be heteroskedastic, where the noise characteristics
may vary considerably across the rows and columns of the data. In this
scenario, the spectral behavior of the noise can differ significantly from the
homoskedastic case, posing various challenges for signal detection and
recovery. To address these challenges, we develop an adaptive normalization
procedure that equalizes the average noise variance across the rows and columns
of a given data matrix. Our proposed procedure is data-driven and fully
automatic, supporting a broad range of noise distributions, variance patterns,
and signal structures. Our approach relies on recent results in random matrix
theory, which describe the resolvent of the noise via the so-called Dyson
equation. By leveraging this relation, we can accurately infer the noise level
in each row and each column directly from the resolvent of the data. We
establish that in many cases, our normalization enforces the standard spectral
behavior of homoskedastic noise -- the Marchenko-Pastur (MP) law, allowing for
simple and reliable detection of signal components. Furthermore, we demonstrate
that our approach can substantially improve signal recovery in heteroskedastic
settings by manipulating the spectrum after normalization. Lastly, we apply our
method to single-cell RNA sequencing and spatial transcriptomics data,
showcasing accurate fits to the MP law after normalization.


  Over the last decade there have been many advances in studies of quantum
walks (QWs) including a momentum-space QW recently realized in our spinor
Bose-Einstein condensate system. This QW possessed behaviors that generally
agreed with theoretical predictions; however, it also showed momentum
distributions that were not adequately explained by the theory. We present a
theoretical model which proves that the coherent dynamics of the spinor
condensate is sufficient to explain the experimental data without invoking the
presence of a thermal cloud of atoms as in the original theory. Our numerical
findings are supported by an analytical prediction for the momentum
distributions in the limit of zero-temperature condensates. This current model
provides more complete explanations to the momentum-space QWs that can be
applied to study quantum search algorithms and topological phases in
Floquet-driven systems.


  Colorization is a process that converts a grayscale image into a color one
that looks as natural as possible. Over the years this task has received a lot
of attention. Existing colorization methods rely on different color spaces:
RGB, YUV, Lab, etc. In this chapter, we aim to study their influence on the
results obtained by training a deep neural network, to answer the question: "Is
it crucial to correctly choose the right color space in deep-learning based
colorization?". First, we briefly summarize the literature and, in particular,
deep learning-based methods. We then compare the results obtained with the same
deep neural network architecture with RGB, YUV and Lab color spaces.
Qualitative and quantitative analysis do not conclude similarly on which color
space is better. We then show the importance of carefully designing the
architecture and evaluation protocols depending on the types of images that are
being processed and their specificities: strong/small contours, few/many
objects, recent/archive images.


  Let $\Omega$ be a multiply-connected domain in $\mathbb{R}^n$ ($n\geq 2$) of
the form $\Omega=\Omega_{\text{out}}\setminus \bar{\Omega_{\text{in}}}.$ Set
$\Omega_D$ to be either $\Omega_{\text{out}}$ or $\Omega_{\text{in}}$. For
$p\in (1,\infty),$ and $q\in [1,p],$ let $\tau_{1,q}(\Omega)$ be the first
eigenvalue of
  \begin{equation*} -\Delta_p u =\tau \left(\int_{\Omega}|u|^q \text{d}x
\right)^{\frac{p-q}{q}} |u|^{q-2}u\;\text{in} \;\Omega,\; u
=0\;\text{on}\;\partial\Omega_D, \frac{\partial u}{\partial
\eta}=0\;\text{on}\; \partial \Omega\setminus \partial \Omega_D.
\end{equation*}
  Under the assumption that $\Omega_D$ is convex, we establish the following
reverse Faber-Krahn inequality $$\tau_{1,q}(\Omega)\leq
\tau_{1,q}({\Omega}^\bigstar),$$ where ${\Omega}^\bigstar=B_R\setminus
\bar{B_r}$ is a concentric annular region in $\mathbb{R}^n$ having the same
Lebesgue measure as $\Omega$ and such that (i) (when
$\Omega_D=\Omega_{\text{out}}$) $W_1(\Omega_D)= \omega_n R^{n-1}$, and
$(\Omega^\bigstar)_D=B_R$, (ii) (when $\Omega_D=\Omega_{\text{in}}$)
$W_{n-1}(\Omega_D)=\omega_nr$, and $(\Omega^\bigstar)_D=B_r$.
  Here $W_{i}(\Omega_D)$ is the $i^{\text{th}}$ $quermassintegral$ of
$\Omega_D.$ We also establish Sz. Nagy's type inequalities for parallel sets of
a convex domain in $\mathbb{R}^n$ ($n\geq 3$) for our proof.


  This article proposes a reading of Book I of Euclid's Elements with an
emphasis of the experiences of infinity supplied by it, as a preparatory study
for a research on the embodied cognition of infinity and its material anchors.
  --
  Cet article propose une lecture du premier livre des \'El\'ements d'Euclide.
Il soutient qu'une telle lecture procure des exp\'eriences de l'infini. Il
s'agit selon son auteur d'une \'etude pr\'eliminaire \`a une recherche sur la
cognition incarn\'ee de l'infini et de ses ancrages mat\'eriels.


  In optimal control,sensitivity relations are usually understood as inclusions
that identify the pair formed by the dual arc and the Hamiltonian, evaluated
along the associated minimizing trajectory, as a suitable generalized gradient
of the value function. In this paper, sensitivity relations are obtained for
the Mayer problem associated with the differential inclusion $\dot x\in F(x)$
and applied to derive optimality conditions. Our first application concerns the
maximum principle and consists in showing that a dual arc can be constructed
for every element of the superdifferential of the final cost. As our second
application, with every nonzero limiting gradient of the value function at some
point $(t,x)$ we associate a family of optimal trajectories at $(t,x)$ with the
property that families corresponding to distinct limiting gradients have empty
intersection.


  This paper studies fundamental limitations of performance for distributed
decision-making in robotic networks. The class of decision-making problems we
consider encompasses a number of prototypical problems such as average-based
consensus as well as distributed optimization, leader election, majority
voting, MAX, MIN, and logical formulas. We first propose a formal model for
distributed computation on robotic networks that is based on the concept of I/O
automata and is inspired by the Computer Science literature on distributed
computing clusters. Then, we present a number of bounds on time, message, and
byte complexity, which we use to discuss the relative performance of a number
of approaches for distributed decision-making. From a methodological
standpoint, our work sheds light on the relation between the tools developed by
the Computer Science and Controls communities on the topic of distributed
algorithms.


  Studies of hadron production in proton-proton collisions, including charged
particle transverse momentum, pseudo-rapidity and event-by-event multiplicity
distributions at \sqrt{s} = 0.9, 2.36 and 7 TeV are shown. Measured spectra of
identified strange hadrons, reconstructed based on their decay topology, are
also presented. Comparisons to several QCD Monte Carlo models and tunes are
discussed. Results on two-particle angular correlations over a broad range of
pseudo-rapidity and azimuthal angle in p-p collisions are presented at both
\sqrt{s} = 0.9 and 7 TeV. In high multiplicity events, a pronounced structure
emerges in the two-dimensional correlation function for particle pairs with
intermediate transverse momentum of 1-3 GeV/c. Furthermore, Bose-Einstein
correlations between identical particles are measured in samples of
proton-proton collisions at \sqrt{s} =0.9 and 7 TeV. Finally, a measurement of
the underlying activity in scattering processes with a p_T scale in the several
GeV region is also presented.


  This paper presents a novel framework for automatic speech-driven gesture
generation, applicable to human-agent interaction including both virtual agents
and robots. Specifically, we extend recent deep-learning-based, data-driven
methods for speech-driven gesture generation by incorporating representation
learning. Our model takes speech as input and produces gestures as output, in
the form of a sequence of 3D coordinates. Our approach consists of two steps.
First, we learn a lower-dimensional representation of human motion using a
denoising autoencoder neural network, consisting of a motion encoder MotionE
and a motion decoder MotionD. The learned representation preserves the most
important aspects of the human pose variation while removing less relevant
variation. Second, we train a novel encoder network SpeechE to map from speech
to a corresponding motion representation with reduced dimensionality. At test
time, the speech encoder and the motion decoder networks are combined: SpeechE
predicts motion representations based on a given speech signal and MotionD then
decodes these representations to produce motion sequences. We evaluate
different representation sizes in order to find the most effective
dimensionality for the representation. We also evaluate the effects of using
different speech features as input to the model. We find that mel-frequency
cepstral coefficients (MFCCs), alone or combined with prosodic features,
perform the best. The results of a subsequent user study confirm the benefits
of the representation learning.


  The optical spectra in the family of multiferroic manganites RMnO3 is a great
puzzle. Current models can not explain the fact that two strong electromagnons
are present in the non-collinear spin cycloidal phase, with only one
electromagnon surviving the transition into the collinear spin sinusoidal
phase. We show that this is a signature of the presence of anomalous
magnetoelectric coupling that breaks rotational invariance in spin space and
generates oscillatory polarization in the ground state.


  The self-assembly of proteins into $\beta$-sheet-rich amyloid fibrils has
been observed to occur with sigmoidal kinetics, indicating that the system
initially is trapped in a metastable state. Here, we use a minimal
lattice-based model to explore the thermodynamic forces driving amyloid
formation in a finite canonical ($NVT$) system. By means of
generalized-ensemble Monte Carlo techniques and a semi-analytical method, the
thermodynamic properties of this model are investigated for different sets of
intersheet interaction parameters. When the interactions support lateral growth
into multi-layered fibrillar structures, an evaporation/condensation transition
is observed, between a supersaturated solution state and a thermodynamically
distinct state where small and large fibril-like species exist in equilibrium.
Intermediate-size aggregates are statistically suppressed. These properties do
not hold if aggregate growth is one-dimensional.


  For a detailed comparison of the appearance of cluster of galaxies in X-rays
and in the optical, we have compiled a comprehensive database of X-ray and
optical properties of a sample of clusters based on the largest available X-ray
and optical surveys: the ROSAT All Sky Survey (RASS) and the Sloan Digital Sky
Survey (SDSS). The RASS-SDSS sample comprises 114 X-ray selected clusters. For
each system we have uniformly determined the X-ray and optical properties . For
a subsample of 53 clusters we have also compiled the temperature and the iron
abundance from the literature. The total optical luminosity can be determined
with a typical uncertainty of 20% with a result independent of the choice of
local or global background subtraction. We searched for parameters which
provide the best correlation between the X-ray luminosity and the optical
properties and found that the z band luminosity determined within a cluster
aperture of 0.5 Mpc h_{70}^{-1} provides the best correlation with a scatter of
about 60%. The resulting correlation of L_X and L_{op} in the z and i bands
shows a logarithmic slope of 0.38, a value not consistent with the assumption
of a constant M/L. Consistency is found, however, for an increasing M/L with
luminosity. This is confirmed by our analysis of the cluster mass to light
ratio for a subsample of 53 clusters. We found a significant dependence of M/L
on the cluster mass with a logarithmic slope ranging from 0.27 in the i and r
bands to 0.22 in the z band.


  A group is called matricial field (MF) if it admits finite dimensional
approximate unitary representations which are approximately faithful and
approximately contained in the left regular representation. This paper provides
a new class of MF groups by showing that given two amenable groups with a
common normal subgroup, the amalgamated free product is MF.