  The ChaLearn large-scale gesture recognition challenge has been run twice in
two workshops in conjunction with the International Conference on Pattern
Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV)
2017, attracting more than $200$ teams round the world. This challenge has two
tracks, focusing on isolated and continuous gesture recognition, respectively.
This paper describes the creation of both benchmark datasets and analyzes the
advances in large-scale gesture recognition based on these two datasets. We
discuss the challenges of collecting large-scale ground-truth annotations of
gesture recognition, and provide a detailed analysis of the current
state-of-the-art methods for large-scale isolated and continuous gesture
recognition based on RGB-D video sequences. In addition to recognition rate and
mean jaccard index (MJI) as evaluation metrics used in our previous challenges,
we also introduce the corrected segmentation rate (CSR) metric to evaluate the
performance of temporal segmentation for continuous gesture recognition.
Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM)
baseline method, determining the video division points based on the skeleton
points extracted by convolutional pose machine (CPM). Experiments demonstrate
that the proposed Bi-LSTM outperforms the state-of-the-art methods with an
absolute improvement of $8.1\%$ (from $0.8917$ to $0.9639$) of CSR.


  The conversion of gas into stars is a fundamental process in astrophysics and
cosmology. Stars are known to form from the gravitational collapse of dense
clumps in interstellar molecular clouds, and it has been proposed that the
resulting star formation rate is proportional to either the amount of mass
above a threshold gas surface density, or the gas volume density. These
star-formation prescriptions appear to hold in nearby molecular clouds in our
Milky Way Galaxy's disk as well as in distant galaxies where the star formation
rates are often much larger. The inner 500 pc of our Galaxy, the Central
Molecular Zone (CMZ), contains the largest concentration of dense, high-surface
density molecular gas in the Milky Way, providing an environment where the
validity of star-formation prescriptions can be tested. Here we show that by
several measures, the current star formation rate in the CMZ is an
order-of-magnitude lower than the rates predicted by the currently accepted
prescriptions. In particular, the region 1 deg < l < 3.5 deg, |b| < 0.5 deg
contains ~10^7 Msun of dense molecular gas -- enough to form 1000 Orion-like
clusters -- but the present-day star formation rate within this gas is only
equivalent to that in Orion. In addition to density, another property of
molecular clouds, such as the amplitude of turbulent motions, must be included
in the star-formation prescription to predict the star formation rate in a
given mass of molecular gas.


  The present politically correct consensus is that increased exchange of
scientific insight, knowledge, practitioners and skills at the global level
brings significant benefits to all. The quantifiable scientometric changes
during the last decade, however, suggest that many areas of knowledge are
evolving in the opposite direction. Despite an increase during the last decade
of the numbers of journals and academic articles published, increases in the
number of citations the published articles receive, and increases in the number
of countries participating; important parts of the academic activity are
becoming more nationalistic. In addition, international collaboration is
decreasing in several subject areas, and in several geographic regions. For
example, countries in Asia are becoming scientifically more isolated; and
academics working in the humanities in all the regions of the world are very
nationalistic and are becoming more so. The precise consequences of this
dynamics are difficult to predict, but it certainly will have reverberations
beyond academia. The tendency of the humanities to become more provincial will
certainly not help in reducing international conflicts arising from poor
understanding of cultural differences and of diverging sociopolitical world
views. More and better data on these trends should give us a better
understanding for eventually improving academic policies worldwide.
  Keywords: International Collaboration, National, Science, Humanities, Policy


  Converging diverging micro nozzle is fundamentally intended for flow
acceleration through the generation of kinetic energy for the advanced
micro-propulsion systems. Such supersonic micro nozzles have significant
applications in the launching, propulsion and rapid directional control of the
micro-satellites for a better maneuver. Micro scale analysis of such flow
devices is extended to the rarefied flow regime. Present study has addressed
thrust vectoring in a planar converging-diverging supersonic micro nozzle by
the bypass mass injection technique. Direct Simulation Monte Carlo (DSMC)
method has been used for numerical modelling. Primary focus is given on the
thrust vectoring control of the micro nozzle with a throat height of 20 micro
meter and an expansion ratio of 1.7. For the secondary injection, a rectangular
channel of 5 different bypass widths (2 - 12 micro meter) is considered for two
different outlet pressures (Pout = 10 kPa and 40 kPa) while keeping the inlet
pressure (Pin) and temperature (Tin) fixed at 1 atm and 300 k respectively. The
physical behavior of the micro nozzle is acknowledged through the analysis of
Mach, pressure, temperature and density contours. Numerical results reveal that
the secondary flow injection is adapted into the primary flow through the
formation of a pressure bump in the diverging section. Moreover, the total mass
flow rate, secondary flow percentage, thrust force, the thrust coefficient and
specific impulse increase with the bypass channel width. A change in thrust
direction is obtained which in turn produces a considerable vectoring effect in
the supersonic micro nozzle. The vectoring angle for Pout of 40 kPa peaks at 6
micro meter bypass channel whereas a gradual increase of the vectoring angle
with the bypass channel width is observed for Pout = 10 kPa.


  In this work, we investigate production of Drell-Yan (DY) pairs in
proton-nucleus collisions in kinematic regions where the corresponding
coherence length does not exceed the nuclear radius, $R_A$, and the quantum
coherence effects should be treated with a special care. The results for the
nucleus-to-nucleon production ratio available in the literature so far are
usually based on the assumption of a very long coherence length (LCL) $l_c\gg
R_A$. Since the onset of coherence effects is controlled by the coherence
length $l_c$, we estimated its magnitude in various kinematic regions of the DY
process and found that the LCL approximation should not be used at small and
medium c.m. collision energies ($\sqrt{s} \lesssim 200$ GeV) as well as at
large dilepton invariant masses. In order to obtain realistic predictions, we
computed for the first time the DY cross section using the generalised color
dipole approach based on the rigorous Green function formalism, which naturally
incorporates the color transparency and quantum coherence effects and hence
allows to estimate the nuclear shadowing with no restrictions on the CL. In
addition to the shadowing effect, we studied a complementary effect of initial
state interactions (ISI) that causes an additional suppression at large values
of the Feynman variable. Numerical results for the nuclear modification factor
accounting for the ISI effect and the finite $l_c$ are compared to the data
available from the fixed-target FNAL measurements and a good agreement has been
found. Besides, we present new predictions for the nuclear suppression as a
function of dilepton rapidity and invariant mass in the kinematic regions that
can be probed by the RHIC collider as well as by the planned AFTER@LHC and LHCb
fixed-target experiments.


  As robots enter everyday spaces like offices, the sounds they create affect
how they are perceived. We present Music Mode, a novel mapping between a
robot's joint motions and sounds, programmed by artists and engineers to make
the robot generate music as it moves. Two experiments were designed to
characterize the effect of this musical augmentation on human users. In the
first experiment, a robot performed three tasks while playing three different
sound mappings. Results showed that participants observing the robot perceived
it as more safe, animate, intelligent, anthropomorphic, and likable when
playing the Music Mode Orchestra software. To test whether the results of the
first experiment were due to the Music Mode algorithm, rather than music alone,
we conducted a second experiment. Here the robot performed the same three
tasks, while a participant observed via video, but the Orchestra music was
either linked to its movement or random. Participants rated the robots as more
intelligent when the music was linked to the movement. Robots using Music Mode
logged approximately two hundred hours of operation while navigating, wiping
tables, and sorting trash, and bystander comments made during this operating
time served as an embedded case study. This paper has both designerly
contributions and engineering contributions. The contributions are: (1) an
interdisciplinary choreographic, musical, and coding design process to develop
a real-world robot sound feature, (2) a technical implementation for
movement-based sound generation, and (3) two experiments and an embedded case
study of robots running this feature during daily work activities that resulted
in increased likeability and perceived intelligence of the robot.


  HR\,8799 is a $\lambda$ Bootis, $\gamma$ Doradus star hosting a planetary
system and a debris disk with two rings. This makes this system a very
interesting target for asteroseismic studies. In particular, this work is
devoted to the determination of the internal metallicity of this star, linked
with its $\lambda$ Bootis nature, and its age, taking the advantage of its
$\gamma$ Doradus-type pulsations. To do so we have used the equilibrium code
CESAM and the non-adiabatic pulsational code GraCo. We have applied the
Frequency Ratio Method and the Time Dependent Convection theory to estimate the
mode identification, the Brunt-Va\"is\"al\"a frequency integral and the mode
instability, making a selection of the possible models fulfilling all
observational constraints. Using the position of the star in the HR diagram,
the solar metallicity models is discarded. This result contradicts one of the
main assumptions of the most accepted hypothesis explaining the $\lambda$
Bootis nature, the accretion/diffusion of gas from a star with solar
metallicity. Therefore, in sight of these new results, a revision of this
hypothesis is needed. The inclusion of accurate internal chemical mixing is
necessary. The use of the asteroseismological constraints provides a very
accurate determination of the physical characteristics of HR\,8799: an age in
the ranges [1123, 1625] and [26, 430] Myr, and a mass in the ranges [1.32,
1.33] and [1.44, 1.45] $M_{\odot}$, respectively, depending on the visual angle
$i$. The determination of this angle and more accurate multicolor photometric
observations can definitively fix the mass, metallicity and age of this star.
In particular, an accurate age estimation is needed for a correct understanding
of the planetary system. In our study we have found that the age widely used
for modelling the system is unlike.


  Robots equipped with rich sensing modalities (e.g., RGB-D cameras) performing
long-horizon tasks motivate the need for policies that are highly
memory-efficient. State-of-the-art approaches for controlling robots often use
memory representations that are excessively rich for the task or rely on
hand-crafted tricks for memory efficiency. Instead, this work provides a
general approach for jointly synthesizing memory representations and policies;
the resulting policies actively seek to reduce memory requirements.
Specifically, we present a reinforcement learning framework that leverages an
implementation of the group LASSO regularization to synthesize policies that
employ low-dimensional and task-centric memory representations. We demonstrate
the efficacy of our approach with simulated examples including navigation in
discrete and continuous spaces as well as vision-based indoor navigation set in
a photo-realistic simulator. The results on these examples indicate that our
method is capable of finding policies that rely only on low-dimensional memory
representations, improving generalization, and actively reducing memory
requirements.


  The use of deep unfolding networks in compressive sensing (CS) has seen wide
success as they provide both simplicity and interpretability. However, since
most deep unfolding networks are iterative, this incurs significant
redundancies in the network. In this work, we propose a novel recursion-based
framework to enhance the efficiency of deep unfolding models. First, recursions
are used to effectively eliminate the redundancies in deep unfolding networks.
Secondly, we randomize the number of recursions during training to decrease the
overall training time. Finally, to effectively utilize the power of recursions,
we introduce a learnable unit to modulate the features of the model based on
both the total number of iterations and the current iteration index. To
evaluate the proposed framework, we apply it to both ISTA-Net+ and COAST.
Extensive testing shows that our proposed framework allows the network to cut
down as much as 75% of its learnable parameters while mostly maintaining its
performance, and at the same time, it cuts around 21% and 42% from the training
time for ISTA-Net+ and COAST respectively. Moreover, when presented with a
limited training dataset, the recursive models match or even outperform their
respective non-recursive baseline. Codes and pretrained models are available at
https://github.com/Rawwad-Alhejaili/Recursions-Are-All-You-Need .


  In this note, we show nonlinear stability in $L^\infty$ for Lipschitz
solutions to genuinely nonlinear, multi-dimensional scalar conservation laws.
As an application, we are able to compute explicit algebraic decay rates of the
$L^\infty$ norm of perturbations of global-in-time Lipschitz solutions,
including perturbations of planar rarefaction waves. Our analysis uses the De
Giorgi method applied to the kinetic formulation and is an extension of the
method introduced recently by Silvestre in [Comm. Pure Appl. Math.,
72(6):1321-1348, 2019].


  The spectral anisotropy of turbulent structures has been measured in the
solar wind since 1990, relying on the assumption of axisymmetry about the mean
magnetic field, B0. However, several works indicate that this hypothesis might
be partially wrong, thus raising two questions: (i) is it correct to interpret
measurements at 1 AU (the so-called Maltese cross) in term of a sum of slab and
2D turbulence? (ii) what information is really contained in the Maltese cross?
  We solve direct numerical simulations of the MHD equations including the
transverse stretching exerted by the solar wind flow and study the genuine 3D
anisotropy of turbulence as well as that one resulting from the assumption of
axisymmetry about B0. We show that the evolution of the turbulent spectrum from
0.2 to 1 AU depends strongly on its initial anisotropy. An axisymmetric
spectrum with respect to B0 keeps its axisymmetry, i.e., resists stretching
perpendicular to radial, while an isotropic spectrum becomes essentially
axisymmetric with respect to the radial direction.
  We conclude that close to the Sun, slow-wind turbulence has a spectrum that
is axisymmetric around B0 and the measured 2D component at 1 AU describes the
real shape of turbulent structures. On the contrary, fast-wind turbulence has a
more isotropic spectrum at the source and becomes radially symmetric at 1 AU.
Such structure is hidden by the symmetrization applied to the data that instead
returns a slab geometry.


  The magnetic properties and structural stability of Nd$_{2}$Fe$_{14}X$ ($X$ =
B, C, N, O, F) are theoretically studied by first-principles calculations
focusing on the role of $X$. We find that B reduces the magnetic moment (per
formula unit) and magnetization (per volume) in Nd$_{2}$Fe$_{14}$B. The
crystal-field parameter $A_2^0 \langle r^2 \rangle$ of Nd is not enhanced
either, suggesting that B has minor roles in the uniaxial magnetocrystalline
anisotropy of Nd. These findings are in contrast to the long-held belief that B
works positively for the magnetic properties of Nd$_{2}$Fe$_{14}$B. As $X$
changes from B to C, N, O and F, both the magnetic properties and stability
vary significantly. The formation energies of Nd$_{2}$Fe$_{14}X$ and
$\alpha$-Fe relative to that of Nd$_{2}$Fe$_{17}X$ are negative for $X$ = B and
C, whereas they are positive when $X$ = N, O and F. This indicates that B plays
an important role in stabilizing the Nd$_{2}$Fe$_{14}$B phase.


  Imitation Learning (IL) is a promising paradigm for teaching robots to
perform novel tasks using demonstrations. Most existing approaches for IL
utilize neural networks (NN), however, these methods suffer from several
well-known limitations: they 1) require large amounts of training data, 2) are
hard to interpret, and 3) are hard to repair and adapt. There is an emerging
interest in programmatic imitation learning (PIL), which offers significant
promise in addressing the above limitations. In PIL, the learned policy is
represented in a programming language, making it amenable to interpretation and
repair. However, state-of-the-art PIL algorithms assume access to action labels
and struggle to learn from noisy real-world demonstrations. In this paper, we
propose PLUNDER, a novel PIL algorithm that integrates a probabilistic program
synthesizer in an iterative Expectation-Maximization (EM) framework to address
these shortcomings. Unlike existing PIL approaches, PLUNDER synthesizes
probabilistic programmatic policies that are particularly well-suited for
modeling the uncertainties inherent in real-world demonstrations. Our approach
leverages an EM loop to simultaneously infer the missing action labels and the
most likely probabilistic policy. We benchmark PLUNDER against several
established IL techniques, and demonstrate its superiority across five
challenging imitation learning tasks under noise. PLUNDER policies achieve 95%
accuracy in matching the given demonstrations, outperforming the next best
baseline by 19%. Additionally, policies generated by PLUNDER successfully
complete the tasks 17% more frequently than the nearest baseline.


  Obscured AGN are fundamental to understand the history of Super Massive Black
Hole growth and their influence on galaxy formation. However, the Compton-thick
AGN (NH>1e24 cm^-2) population is basically unconstrained, with less than few
dozen confirmed Compton-thick AGN found and studied so far. A way to select
heavily obscured AGN is to compare the X-ray emission below 10 keV (which is
strongly depressed) with the emission from other bands less affected by the
absorption, i.e. the IR band. To this end, we have cross-correlated the 2XMM
catalogue with the IRAS Point Source catalogue and, by using the X-ray to
infrared flux ratio and X-ray colors, we selected a well defined sample of
Compton-thick AGN candidates at z<0.1. The aim of this work is to confirm the
nature and to study one of these local Compton-thick AGN candidates, the nearby
(z=0.029) Seyfert 2 galaxy IRAS 04507+0358, by constraining the amount of
intrinsic absorption (NH) and thus the intrinsic luminosity. To this end we
obtained deep (100 ks) Suzaku observations (AO4 call) and performed a joint fit
with SWIFT-BAT data. We analyzed XMM-Newton, Suzaku and SWIFT-BAT data and we
present here the results of this broad-band (0.4-100 keV) spectral analysis. We
found that the broad-band X-ray emission of IRAS 04507+0358 requires a large
amount of absorption (larger than 1e24 cm^-2) to be well reproduced, thus
confirming the Compton-thick nature of this source. In particular, the most
probable scenario is that of a mildly (NH (1.3-1.5)x1e24 cm^-2, L(2-10 keV)
(5-7)x1e43 erg s^-1) Compton-thick AGN.


  We consider the BTW model in random link lattices with finite range
interaction (RLFRI). The degree distribution for nodes is considered to be
uniform in the interval $(0,n_0)$. We numerically calculate the exponents of
the distribution functions in terms of $(n_0,R)$ in which $R$ is the range of
interactions. Dijkstra radius is utilized to calculate the fractal dimension of
the avalanches. Our analysis shows that there is, at least one length scale
($r_0(n_0,R)$) in which the fractal dimension is changed. We find that for the
scales smaller than $r_0(n_0,R)$, which is typically one decade, the fractal
dimension is nearly independent of $n_0$ and $R$ and is equal to $1.4$, i.e.
close to that of the BTW in the regular lattice ($1.25$). Using this fact and
other analysis, we conclude that the BTW-type behaviors are dominant for small
values of $n_0$ and $R$, whereas for large values of these parameters a new
regime is seen in which the exponent of distribution function of avalanche
masses is nearly $1.4$. We also numerically calculate the explicit form of the
\textit{number of unstable nodes} (NUN) as a time dependent process and show
that for regular lattice it is (up to a normalization) proportional to a one
dimensional Weiner process and for RLFRI it acquires a drift term. Using this
dynamical variable it is numerically shown that we can not continuously
approach the regular lattice limit by decreasing $R$.


  According to Zipf's meaning-frequency law, words that are more frequent tend
to have more meanings. Here it is shown that a linear dependency between the
frequency of a form and its number of meanings is found in a family of models
of Zipf's law for word frequencies. This is evidence for a weak version of the
meaning-frequency law. Interestingly, that weak law (a) is not an inevitable of
property of the assumptions of the family and (b) is found at least in the
narrow regime where those models exhibit Zipf's law for word frequencies.


  The vanishing of the divergence of the total stress tensor (magnetic plus
kinetic) in a neighborhood of an equilibrium plasma containing a toroidal
surface of discontinuity gives boundary and jump conditions that strongly
constrain allowable continuations of the magnetic field across the surface. The
boundary conditions allow the magnetic fields on either side of the
discontinuity surface to be described by surface magnetic potentials, reducing
the continuation problem to that of solving a Hamilton--Jacobi equation. The
characteristics of this equation obey Hamiltonian equations of motion, and a
necessary condition for the existence of a continued field across a general
toroidal surface is that there exist invariant tori in the phase space of this
Hamiltonian system. It is argued from the Birkhoff theorem that existence of
such an invariant torus is also, in general, sufficient for continuation to be
possible. An important corollary is that the rotational transform of the
continued field on a surface of discontinuity must, generically, be irrational.


  Cold spray technology, distinguished by its unique ability to deposit
particles in a solid state, offers unparalleled potential for applications such
as coating, repair, and additive manufacturing. In this research, we employed a
peridynamic modeling approach to systematically analyze the effects of key
powder features on deposit properties. Notably, our model incorporates the
explicit modeling of nanometer-sized oxide layers, providing a more accurate
representation of real-world conditions. We assessed the influence of
parameters such as particle size, oxidation conditions, and material type on
crucial deposition indicators like critical velocity and compression ratio. Our
results demonstrated excellent agreement with reported experimental data,
affirming the effectiveness of the proposed numerical framework. Specifically,
our findings highlighted that, for a given impact velocity, smaller particles,
thicker oxide layers, and harder materials contribute to limited oxide removal
and a decreased likelihood of successful adhesion. Additionally, we present a
predictive framework that establishes a correlation between particle size and
critical velocity, providing valuable insights for optimizing cold spray
deposition parameters. This innovative approach not only enhances our
understanding of the intricate interplay between powder features and deposit
properties but also paves the way for a more efficient and cost-effective
optimization process in cold spray applications.


  We investigate various aspects of the Lanczos coefficients in a family of
free Lifshitz scalar theories, characterized by their integer dynamical
exponent, at finite temperature. In this non-relativistic setup, we examine the
effects of mass, finite ultraviolet cutoff, and finite lattice spacing on the
behavior of the Lanczos coefficients. We also investigate the effect of the
dynamical exponent on the asymptotic behavior of the Lanczos coefficients,
which show a universal scaling behavior. We carefully examine how these results
can affect different measures in Krylov space, including Krylov complexity and
entropy. Remarkably, we find that our results are similar to those previously
observed in the literature for relativistic theories.


  We give a new proof of the Semistable Reduction Theorem for curves. The main
idea is to present a curve $Y$ over a local field $K$ as a finite cover of the
projective line $X=\PP^1_K$. By successive blowups (and after replacing $K$ by
a suitable finite extension) we construct a semistable model of $X$ whose
normalization with respect to the cover is a semistable model of $Y$.


  The recent advancements in computing systems and wireless communications have
made healthcare systems more efficient than before. Modern healthcare devices
can monitor and manage different health conditions of the patients
automatically without any manual intervention from medical professionals.
Additionally, the use of implantable medical devices (IMDs), body area networks
(BANs), and Internet of Things (IoT) technologies in healthcare systems improve
the overall patient monitoring and treatment process. However, these systems
are complex in software and hardware, and optimizing between security, privacy,
and treatment is crucial for healthcare systems as any security or privacy
violation can lead to severe effects on patients' treatments and overall health
conditions. Indeed, the healthcare domain is increasingly facing security
challenges and threats due to numerous design flaws and the lack of proper
security measures in healthcare devices and applications. In this paper, we
explore various security and privacy threats to healthcare systems and discuss
the consequences of these threats. We present a detailed survey of different
potential attacks and discuss their impacts. Furthermore, we review the
existing security measures proposed for healthcare systems and discuss their
limitations. Finally, we conclude the paper with future research directions
toward securing healthcare systems against common vulnerabilities.


  High-Performance Computing (HPC) clusters are made up of a variety of node
types (usually compute, I/O, service, and GPGPU nodes) and applications don't
use nodes of a different type the same way. Resulting communication patterns
reflect organization of groups of nodes, and current optimal routing algorithms
for all-to-all patterns will not always maximize performance for group-specific
communications. Since application communication patterns are rarely available
beforehand, we choose to rely on node types as a good guess for node usage. We
provide a description of node type heterogeneity and analyse performance
degradation caused by unlucky repartition of nodes of the same type. We provide
an extension to routing algorithms for Parallel Generalized Fat-Tree topologies
(PGFTs) which balances load amongst groups of nodes of the same type. We show
how it removes these performance issues by comparing results in a variety of
situations against corresponding classical algorithms.


  The core-level electronic structures of the exfoliated graphene sheets on a
Au-coated SiOx substrate have been studied by synchrotron radiation
photoelectron spectroscopy (SR-PES) on a micron-scale. The graphene was firstly
demonstrated its visibility on the Au-coated SiOx substrate by micro-optical
characterization, and then conducted into SR-PES study. Because of the
elimination of charging effect, precise C 1s core-level characterization
clearly shows graphitic and contaminated carbon states of graphene. Different
levels of Au-coating-induced p-type doping on single- and double-layer graphene
sheets were also examined in the C 1s core-level shift. The Au-coated SiOx
substrate can be treated as a simple but high-throughput platform for in situ
studying graphene under further hybridization by PES.


  Quantum computers are analog devices; thus they are highly susceptible to
accumulative errors arising from classical control electronics. Fast
operation--as necessitated by decoherence--makes gating errors very likely. In
most current designs for scalable quantum computers it is not possible to
satisfy both the requirements of low decoherence errors and low gating errors.
Here we introduce a hardware-based technique for pseudo-digital gate operation.
We perform self-consistent simulations of semiconductor quantum dots, finding
that pseudo-digital techniques reduce operational error rates by more than two
orders of magnitude, thus facilitating fast operation.


  We propose a model of nonequilibrium quantum transport of particles and
energy in a system connected to mesoscopic Fermi reservoirs (meso-reservoir).
The meso-reservoirs are in turn thermalized to prescribed temperatures and
chemical potentials by a simple dissipative mechanism described by the Lindblad
equation. As an example, we study transport in monoatomic and diatomic chains
of non-interacting spinless fermions. We show numerically the breakdown of the
Onsager reciprocity relation due to the dissipative terms of the model.


  This paper presents the identification of galaxy clusters from the
photometric redshift catalog based on three imaging surveys of SCUSS, SDSS, and
unWISE. By applying a fast clustering algorithm, we obtain a total of 19,610
clusters in the redshift range of $0.05 < z < 0.65$ over a sky area of about
3,700 deg$^2$ in the south Galactic gap. Monte Carlo simulations show that the
false detection rate is about 8.9\%. The redshift uncertainty is estimated to
be about 0.013. The mass and richness of detected clusters are derived through
the calibration based on the measurements of X-ray emission and
Sunyaev--Zel'dovich effect.


  CO2 chemisorption on SrTiO3(001) surfaces is studied using ab initio
calculations in order to establish new chemical sensing mechanisms. We find
that CO2 adsorption opens the material band gap, however, while the adsorption
on the TiO2-terminated surface neutralizes surface states at the valence band
(VB) maximum, CO2 on the SrO-terminated surfaces suppresses the conduction band
(CB) minimum. For the TiO2-terminated surface, the effect is explained by the
passivation of dangling bonds, whereas for the SrO-terminated surface, the
suppression is caused by the surface relaxation. Modulation of the VB states
implies a more direct change in charge distribution, and thus the induced
change in band gap is more prominent at the TiO2 termination. Further, we show
that both CO2 adsorption energy and surface band gap are strongly dependent on
CO2 coverage, suggesting that the observed effect can be utilized for sensing
application in a wide range of CO2 concentrations.


  Embedding molecular symmetries into machine-learning models is key for
efficient learning of chemico-physical scalar properties, but little evidence
on how to extend the same strategy to tensorial quantities exists. Here we
formulate a scalable equivariant machine-learning model based on local atomic
environment descriptors. We apply it to a series of molecules and show that
accurate predictions can be achieved for a comprehensive list of dielectric and
magnetic tensorial properties of different ranks. These results show that
equivariant models are a promising platform to extend the scope of machine
learning in materials modelling.


  We consider how to assign labels to any undirected graph with n nodes such
that, given the labels of two nodes and no other information regarding the
graph, it is possible to determine the distance between the two nodes. The
challenge in such a distance labeling scheme is primarily to minimize the
maximum label lenght and secondarily to minimize the time needed to answer
distance queries (decoding). Previous schemes have offered different trade-offs
between label lengths and query time. This paper presents a simple algorithm
with shorter labels and shorter query time than any previous solution, thereby
improving the state-of-the-art with respect to both label length and query time
in one single algorithm. Our solution addresses several open problems
concerning label length and decoding time and is the first improvement of label
length for more than three decades.
  More specifically, we present a distance labeling scheme with label size (log
3)/2 + o(n) (logarithms are in base 2) and O(1) decoding time. This outperforms
all existing results with respect to both size and decoding time, including
Winkler's (Combinatorica 1983) decade-old result, which uses labels of size
(log 3)n and O(n/log n) decoding time, and Gavoille et al. (SODA'01), which
uses labels of size 11n + o(n) and O(loglog n) decoding time. In addition, our
algorithm is simpler than the previous ones. In the case of integral edge
weights of size at most W, we present almost matching upper and lower bounds
for label sizes. For r-additive approximation schemes, where distances can be
off by an additive constant r, we give both upper and lower bounds. In
particular, we present an upper bound for 1-additive approximation schemes
which, in the unweighted case, has the same size (ignoring second order terms)
as an adjacency scheme: n/2. We also give results for bipartite graphs and for
exact and 1-additive distance oracles.


  A non-equilibrium Green's function formalism is used to study in detail the
ballistic conductance of asymmetrically biased side-gated quantum point
contacts (QPCs) in the presence of lateral spin-orbit coupling and
electron-electron interaction for a wide range of QPC dimensions and gate bias
voltage. Various conductance anomalies are predicted below the first quantized
conductance plateau (G0=2e2/h) which occur due to spontaneous spin polarization
in the narrowest portion of the QPC. The number of observed conductance
anomalies increases with increasing aspect ratio (length/width) of the QPC
constriction. These anomalies are fingerprints of spin textures in the narrow
portion of the QPC.


  Reference star differential imaging (RDI) is a powerful strategy for high
contrast imaging. Using example observations taken with the vortex coronagraph
mode of Keck/NIRC2 in $L^\prime$ band, we demonstrate that RDI provides
improved sensitivity to point sources at small angular separations compared to
angular differential imaging (ADI). Applying RDI to images of the low-mass
stellar companions HIP 79124 C (192 mas separation, $\Delta L^\prime$=4.01) and
HIP 78233 B (141 mas separation, $\Delta L^\prime$=4.78), the latter a first
imaging detection, increases the significance of their detections by up to a
factor of 5 with respect to ADI. We compare methods for reference frames
selection and find that pre-selection of frames improves detection significance
of point sources by up to a factor of 3. In addition, we use observations of
the circumstellar disks around MWC 758 and 2MASS J16042165-2130284 to show that
RDI allows for accurate mapping of scattered light distributions without
self-subtraction artifacts.


  We trap and cool a gas composed of 40K and 87Rb, using a two-species
magneto-optical trap (MOT). This trap represents the first step towards cooling
the Bose-Fermi mixture to quantum degeneracy. Laser light for the MOT is
derived from laser diodes and amplified with a single high power semiconductor
amplifier chip. The four-color laser system is described, and the
single-species and two-species MOTs are characterized. Atom numbers of 1x10^7
40K and 2x10^9 87Rb are trapped in the two-species MOT. Observation of trap
loss due to collisions between species is presented and future prospects for
the experiment are discussed.


  Blogs are a source of grey literature which are widely adopted by software
practitioners for disseminating opinion and experience. Analysing such articles
can provide useful insights into the state-of-practice for software engineering
research. However, there are challenges in identifying higher quality content
from the large quantity of articles available. Credibility assessment can help
in identifying quality content, though there is a lack of existing corpora.
Credibility is typically measured through a series of conceptual criteria, with
'argumentation' and 'evidence' being two important criteria.
  We create a corpus labelled for argumentation and evidence that can aid the
credibility community. The corpus consists of articles from the blog of a
single software practitioner and is publicly available.
  Three annotators label the corpus with a series of conceptual credibility
criteria, reaching an agreement of 0.82 (Fleiss' Kappa). We present preliminary
analysis of the corpus by using it to investigate the identification of claim
sentences (one of our ten labels).
  We train four systems (Bert, KNN, Decision Tree and SVM) using three feature
sets (Bag of Words, Topic Modelling and InferSent), achieving an F1 score of
0.64 using InferSent and a Linear SVM.
  Our preliminary results are promising, indicating that the corpus can help
future studies in detecting the credibility of grey literature. Future research
will investigate the degree to which the sentence level annotations can infer
the credibility of the overall document.


  The assembly of molecular networks into structures such as random tilings and
glasses has recently been demonstrated for a number of two-dimensional systems.
These structures are dynamically-arrested on experimental timescales so the
critical regime in their formation is that of initial growth. Here we identify
a transition from energetic to entropic stabilisation in the nucleation and
growth of a molecular rhombus tiling. Calculations based on a lattice gas model
show that clustering of topological defects and the formation of faceted
boundaries followed by a slow relaxation to equilibrium occurs under conditions
of energetic stabilisation. We also identify an entropically-stabilised regime
in which the system grows directly into an equilibrium configuration without
the need for further relaxation. Our results provide a methodology for
identifying equilibrium and non-equilibrium randomness in the growth of
molecular tilings, and we demonstrate that equilibrium spatial statistics are
compatible with exponentially slow dynamical behaviour.


  Within food webs, species can be partitioned into groups according to various
criteria. Two notions have received particular attention: trophic groups, which
have been used for decades in the ecological literature, and more recently,
modules. The relationship between these two group definitions remains unknown
in empirical food webs because they have so far been studied separately. While
recent developments in network theory have led to efficient methods for
detecting modules in food webs, the determination of trophic groups (sets of
species that are functionally similar) is based on subjective expert knowledge.
Here, we develop a novel algorithm for trophic group detection. We apply this
method to several well-resolved empirical food webs, and show that aggregation
into trophic groups allows the simplification of food webs while preserving
their information content. Furthermore, we reveal a 2-level hierarchical
structure where modules partition food webs into large bottom-top trophic
pathways whereas trophic groups further partition these pathways into sets of
species with similar trophic connections. Bringing together trophic groups and
modules provides new perspectives to the study of dynamical and functional
consequences of food-web structure, bridging topological analysis and dynamical
systems. Trophic groups have a clear ecological meaning in terms of trophic
similarity, and are found to provide a trade-off between network complexity and
information loss.


  In this paper, we introduce a new sequence-to-sequence learning framework for
RGB-based and multi-modal object tracking. First, we present SeqTrack for
RGB-based tracking. It casts visual tracking as a sequence generation task,
forecasting object bounding boxes in an autoregressive manner. This differs
from previous trackers, which depend on the design of intricate head networks,
such as classification and regression heads. SeqTrack employs a basic
encoder-decoder transformer architecture. The encoder utilizes a bidirectional
transformer for feature extraction, while the decoder generates bounding box
sequences autoregressively using a causal transformer. The loss function is a
plain cross-entropy. Second, we introduce SeqTrackv2, a unified
sequence-to-sequence framework for multi-modal tracking tasks. Expanding upon
SeqTrack, SeqTrackv2 integrates a unified interface for auxiliary modalities
and a set of task-prompt tokens to specify the task. This enables it to manage
multi-modal tracking tasks using a unified model and parameter set. This
sequence learning paradigm not only simplifies the tracking framework, but also
showcases superior performance across 14 challenging benchmarks spanning five
single- and multi-modal tracking tasks. The code and models are available at
https://github.com/chenxin-dlut/SeqTrackv2.


  We give a simple proof that it is impossible to guarantee the classicality of
inputs into any mistrustful quantum cryptographic protocol. The argument
illuminates the impossibility of unconditionally secure quantum implementations
of essentially classical tasks such as bit commitment with a certified
classical committed bit, classical oblivious transfer, and secure classical
multi-party computations of secret classical data. It applies to both
non-relativistic and relativistic protocols.


  Attention mechanism has demonstrated great potential in fine-grained visual
recognition tasks. In this paper, we present a counterfactual attention
learning method to learn more effective attention based on causal inference.
Unlike most existing methods that learn visual attention based on conventional
likelihood, we propose to learn the attention with counterfactual causality,
which provides a tool to measure the attention quality and a powerful
supervisory signal to guide the learning process. Specifically, we analyze the
effect of the learned visual attention on network prediction through
counterfactual intervention and maximize the effect to encourage the network to
learn more useful attention for fine-grained image recognition. Empirically, we
evaluate our method on a wide range of fine-grained recognition tasks where
attention plays a crucial role, including fine-grained image categorization,
person re-identification, and vehicle re-identification. The consistent
improvement on all benchmarks demonstrates the effectiveness of our method.
Code is available at https://github.com/raoyongming/CAL


  The sequence of a protein is not only constrained by its physical and
biochemical properties under current selection, but also by features of its
past evolutionary history. Understanding the extent and the form that these
evolutionary constraints may take is important to interpret the information in
protein sequences. To study this problem, we introduce a simple but physical
model of protein evolution where selection targets allostery, the functional
coupling of distal sites on protein surfaces. This model shows how the
geometrical organization of couplings between amino acids within a protein
structure can depend crucially on its evolutionary history. In particular, two
scenarios are found to generate a spatial concentration of functional
constraints: high mutation rates and fluctuating selective pressures. This
second scenario offers a plausible explanation for the high tolerance of
natural proteins to mutations and for the spatial organization of their least
tolerant amino acids, as revealed by sequence analyses and mutagenesis
experiments. It also implies a faculty to adapt to new selective pressures that
is consistent with observations. Besides, the model illustrates how several
independent functional modules may emerge within a same protein structure,
depending on the nature of past environmental fluctuations. Our model thus
relates the evolutionary history and evolutionary potential of proteins to the
geometry of their functional constraints, with implications for decoding and
engineering protein sequences.


  LCDM is remarkably successful in predicting the cosmic microwave background
and large-scale structure, and LCDM parameters have been determined with only
mild tensions between different types of observations. Hydrodynamical
simulations starting from cosmological initial conditions are increasingly able
to capture the complex interactions between dark matter and baryonic matter in
galaxy formation. Simulations with relatively low resolution now succeed in
describing the overall galaxy population. For example, the EAGLE simulation in
volumes up to 100 cubic Mpc reproduces the observed local galaxy mass function
nearly as well as semi-analytic models. It once seemed that galaxies are pretty
smooth, that they generally grow in size as they evolve, and that they are a
combination of disks and spheroids. But recent HST observations combined with
high-resolution hydrodynamic simulations are showing that most star-forming
galaxies are very clumpy; that galaxies often undergo compaction which reduces
their radius and increases their central density; and that most lower-mass
star-forming galaxies are not spheroids or disks but are instead elongated when
their centers are dominated by dark matter. We also review LCDM challenges on
smaller scales: cusp-core, "too big to fail," and substructure issues. Although
starbursts can rapidly drive gas out of galaxy centers and thereby reduce the
dark matter density, it remains to be seen whether this or other baryonic
physics can explain the observed rotation curves of the entire population of
dwarf and low surface brightness galaxies. If not, perhaps more complicated
physics such as self-interacting dark matter may be needed. But standard LCDM
appears to be successful in predicting the dark matter halo substructure that
is now observed via gravitational lensing and breaks in cold stellar streams,
and any alternative theory must do at least as well.


  The Urysohn space is a complete separable metric space, universal among
separable metric spaces for extending finite partial isometries into it. We
present an alternative construction of the Urysohn space which enables us to
show that extending isometries can be done in a canonical and continuous way,
and allows us to equip the Urysohn space with algebraic structure. This is
achieved in a constructive setting without assuming any choice principles.


  A nonnegative real function f is bell-shaped if it converges to zero at plus
and minus infinity and the nth derivative of f changes sign n times for every n
= 0, 1, 2, ... Similarly, a two-sided nonnegative sequence a(k) is bell-shaped
if it converges to zero at plus and minus infinity and the nth iterated
difference of a(k) changes sign n times for every n = 0, 1, 2, ... A
characterisation of bell-shaped functions was given by Thomas Simon and the
first named author, and recently a similar result for one-sided bell-shaped
sequences was found by the authors. In the present article we give a complete
description of two-sided bell-shaped sequences. Our main result proves that
bell-shaped sequences are convolutions of P\'olya frequency sequences and what
we call absolutely monotone-then-completely monotone sequences, and it provides
an equivalent, and relatively easy to verify, condition in terms of holomorphic
extensions of the generating function.


  In this paper we establish the orbital stability of periodic traveling waves
for a general class of dispersive equations. We use the Implicit Function
Theorem to guarantee the existence of smooth solutions depending of the
corresponding wave speed. Essentially, our method establishes that if the
linearized operator has only one negative eigenvalue which is simple and zero
is a simple eigenvalue the orbital stability is determined provided that a
convenient condition about the average of the wave is satisfied. We use our
approach to prove the orbital stability of periodic dnoidal waves associated
with the Kawahara equation.


  The thermal stability of $\alpha$-, $\beta$-, 6,6,12-graphyne and graphdiyne
was studied by a statistic model, which was seriously tested by classical
molecular dynamics simulations. By first-principles calculations of related
potential energy curves, the model predicts that all the lifetime of
free-standing single layer graphynes considered is more than 10$^{44}$ years at
room temperature. When the temperature gets up to 1000 K, they are still very
stable, but quickly turn into graphene if the temperature is about 2000 K


  Quantum computing allows for the potential of significant advancements in
both the speed and the capacity of widely used machine learning techniques.
Here we employ quantum algorithms for the Hopfield network, which can be used
for pattern recognition, reconstruction, and optimization as a realization of a
content-addressable memory system. We show that an exponentially large network
can be stored in a polynomial number of quantum bits by encoding the network
into the amplitudes of quantum states. By introducing a classical technique for
operating the Hopfield network, we can leverage quantum algorithms to obtain a
quantum computational complexity that is logarithmic in the dimension of the
data. We also present an application of our method as a genetic sequence
recognizer.


  Consider the minimum spanning tree (MST) of the complete graph with n
vertices, when edges are assigned independent random weights. Endow this tree
with the graph distance renormalized by n^{1/3} and with the uniform measure on
its vertices. We show that the resulting space converges in distribution, as n
tends to infinity, to a random measured metric space in the
Gromov-Hausdorff-Prokhorov topology. We additionally show that the limit is a
random binary R-tree and has Minkowski dimension 3 almost surely. In
particular, its law is mutually singular with that of the Brownian continuum
random tree or any rescaled version thereof. Our approach relies on a coupling
between the MST problem and the Erd\"os-R\'enyi random graph. We exploit the
explicit description of the scaling limit of the Erd\"os-R\'enyi random graph
in the so-called critical window, established by the first three authors in an
earlier paper, and provide a similar description of the scaling limit for a
"critical minimum spanning forest" contained within the MST.


  We introduce FEDQ-Trust, an innovative data-driven trust prediction approach
designed for mobile edge-based Internet of Things (IoT) environments. The
decentralized nature of mobile edge environments introduces challenges due to
variations in data distribution, impacting the accuracy and training efficiency
of existing distributed data-driven trust prediction models. FEDQ-Trust
effectively tackles the statistical heterogeneity challenges by integrating
Federated Expectation-Maximization with Deep Q Networks. Federated
Expectation-Maximization's robust handling of statistical heterogeneity
significantly enhances trust prediction accuracy. Meanwhile, Deep Q Networks
streamlines the model training process, efficiently reducing the number of
training clients while maintaining model performance. We conducted a suite of
experiments within simulated MEC-based IoT settings by leveraging two
real-world IoT datasets. The experimental results demonstrate that our model
achieved a significant convergence time reduction of 97% to 99% while ensuring
a notable improvement of 8% to 14% in accuracy compared to state-of-the-art
models.


  In the paper we consider self-adjoint partial integral operators of Fredholm
type $T$ with a degenerate kernel on the space $L_2([a,b]\times[c,d]).$
Essential and discrete spectra of $T$ are described.


  We have shown that belief modelling for dialogue can be simplified if the
assumption is made that the participants are cooperating, i.e., they are not
committed to any goals requiring deception. In such domains, there is no need
to maintain individual representations of deeply nested beliefs; instead, three
specific types of belief can be used to summarize all the states of nested
belief that can exist about a domain entity.
  Here, we set out to design a ``compiler'' for belief models. This system will
accept as input a description of agents' interactions with a task domain
expressed in a fully-expressive belief logic with non-monotonic and temporal
extensions. It generates an operational belief model for use in that domain,
sufficient for the requirements of cooperative dialogue, including the
negotiation of complex domain plans. The compiled model incorporates the belief
simplification mentioned above, and also uses a simplified temporal logic of
belief based on the restricted circumstances under which beliefs can change.
  We shall review the motivation for creating such a system, and introduce a
general procedure for taking a logical specification for a domain and procesing
it into an operational model. We shall then discuss the specific changes that
are made during this procedure for limiting the level of abstraction at which
the concepts of belief nesting, default reasoning and time are expressed.
Finally we shall go through a worked example relating to the Map Task, a simple
cooperative problem-solving exercise.


  In addition to the narrow spin-one resonances $\rho_T$, $\omega_T$ and $a_T$
occurring in low-scale technicolor, there will be relatively narrow scalars in
the mass range 200 to 600--700 GeV. We study the lightest isoscalar state,
$\sigma_T$. In several important respects it is like a heavy Higgs boson with a
small vev. It may be discovered with high luminosity at the LHC where it is
produced via weak boson fusion and likely has substantial $W^+ W^-$ and
$Z^0Z^0$ decay modes.


  The psychological state of flow has been linked to optimizing human
performance. A key condition of flow emergence is a match between the human
abilities and complexity of the task. We propose a simple computational model
of flow for Artificial Intelligence (AI) agents. The model factors the standard
agent-environment state into a self-reflective set of the agent's abilities and
a socially learned set of the environmental complexity. Maximizing the flow
serves as a meta control for the agent. We show how to apply the meta-control
policy to a broad class of AI control policies and illustrate our approach with
a specific implementation. Results in a synthetic testbed are promising and
open interesting directions for future work.


  Pulsars are potentially the most remarkable physical laboratories we will
ever use. Although in many senses they are extremely clean systems there are a
large number of instabilities and variabilities seen in the emission and
rotation of pulsars. These need to be recognised in order to both fully
understand the nature of pulsars, and to enable their use as precision tools
for astrophysical investigations. Here I describe these effects, discuss the
wide range of timescales involved, and consider the implications for precision
pulsar timing.


  The Kaczmarz algorithm is an iterative method that solves linear systems of
equations. It stands out among iterative algorithms when dealing with large
systems for two reasons. First, at each iteration, the Kaczmarz algorithm uses
a single equation, resulting in minimal computational work per iteration.
Second, solving the entire system may only require the use of a small subset of
the equations. These characteristics have attracted significant attention to
the Kaczmarz algorithm. Researchers have observed that randomly choosing
equations can improve the convergence rate of the algorithm. This insight led
to the development of the Randomized Kaczmarz algorithm and, subsequently,
several other variations emerged. In this paper, we extensively analyze the
native Kaczmarz algorithm and many of its variations using large-scale dense
random systems as benchmarks. Through our investigation, we have verified that,
for consistent systems, various row sampling schemes can outperform both the
original and Randomized Kaczmarz method. Specifically, sampling without
replacement and using quasirandom numbers are the fastest techniques. However,
for inconsistent systems, the Conjugate Gradient method for Least-Squares
problems overcomes all variations of the Kaczmarz method for these types of
systems.


  130 years ago, Omori formulated the first law of earthquake physics. The
essence of the law is that the frequency of aftershocks decreases
hyperbolically over time. 100 years ago, Hirano doubted the universality of
Omori law and proposed a power law for the evolution of aftershocks. Our paper
is devoted to these two outstanding events, which played a significant role in
the development of seismology. The paper also contains a brief summary of the
modern approach to the construction of a phenomenological theory of
aftershocks. Within the framework of the phenomenological approach, the epoch
of harmonic evolution of the source, cooling down after the main shock, the
bifurcation of the source, the cumulative effect of a round-the-world echo, the
modulation of global seismicity by spheroidal oscillations of the Earth, mirror
triads, migration of aftershocks, and a number of other previously unknown
geophysical phenomena were discovered. It is emphasized that the
phenomenological theory has been formed in recent years on the basis of a
thorough analysis of the fundamental research of the pioneers who laid the
foundations of modern seismology. Keywords: earthquake, aftershock, Omori law,
Hirano-Utsu law, source deactivation, proper time, evolution equation.


  Large language models (LMs) are currently trained to predict tokens given
document prefixes, enabling them to directly perform long-form generation and
prompting-style tasks which can be reduced to document completion. Existing
pretraining pipelines train LMs by concatenating random sets of short documents
to create input contexts but the prior documents provide no signal for
predicting the next document. We instead present In-Context Pretraining, a new
approach where language models are pretrained on a sequence of related
documents, thereby explicitly encouraging them to read and reason across
document boundaries. We can do In-Context Pretraining by simply changing the
document ordering so that each context contains related documents, and directly
applying existing pretraining pipelines. However, this document sorting problem
is challenging. There are billions of documents and we would like the sort to
maximize contextual similarity for every document without repeating any data.
To do this, we introduce approximate algorithms for finding related documents
with efficient nearest neighbor search and constructing coherent input contexts
with a graph traversal algorithm. Our experiments show In-Context Pretraining
offers a simple and scalable approach to significantly enhance LMs'performance:
we see notable improvements in tasks that require more complex contextual
reasoning, including in-context learning (+8%), reading comprehension (+15%),
faithfulness to previous contexts (+16%), long-context reasoning (+5%), and
retrieval augmentation (+9%).


  Let C be an algebraic geometric code of dimension k and length n constructed
on a curve X over $F_q$. Let s(C) be the state complexity of C and set
w(C):=min{k,n-k}, the Wolf upper bound on s(C). We introduce a numerical
function R that depends on the gonality sequence of X and show that s(C)\geq
w(C)-R(2g-2), where g is the genus of X. As a matter of fact, R(2g-2)\leq
g-(\gamma_2-2) with \gamma_2 being the gonality over F_q of X, and thus in
particular we have that s(C)\geq w(C)-g+\gamma_2-2.


  Photon loss represents a major challenge for the implementation of quantum
communication protocols with untrusted devices, e.g. in the device-independent
(DI) or semi-DI approaches. Determining critical loss thresholds is usually
done in case-by-case studies. In the present work, we develop a general
framework for characterizing the admissible levels of loss and noise in a wide
range of scenarios and protocols with untrusted measurement devices. In
particular, we present general bounds that apply to prepare-and-measure
protocols for the semi-DI approach, as well as to Bell tests for DI protocols.
A key step in our work is to establish a general connection between quantum
protocols with untrusted measurement devices and the fundamental notions of
channel extendibility and joint-measurability, which capture essential aspects
of the communication and measurement of quantum information. In particular,
this leads us to introduce the notion of partial joint-measurability, which
naturally arises within quantum cryptography.


  We compare the notions "Decisiveness" and "Success" for certain weighted
voting systems and various underlying voting measures. In particular, we
compute the success rate for the Shapley-Shubik meassure and, more generally,
for Common Belief Models.


  The training phases of Deep neural network~(DNN) consumes enormous processing
time and energy. Compression techniques utilizing the sparsity of DNNs can
effectively accelerate the inference phase of DNNs. However, it can be hardly
used in the training phase because the training phase involves dense
matrix-multiplication using General Purpose Computation on Graphics Processors
(GPGPU), which endorse regular and structural data layout. In this paper, we
propose the Approximate Random Dropout that replaces the conventional random
dropout of neurons and synapses with a regular and predefined patterns to
eliminate the unnecessary computation and data access. To compensate the
potential performance loss we develop a SGD-based Search Algorithm to produce
the distribution of dropout patterns. We prove our approach is statistically
equivalent to the previous dropout method. Experiments results on MLP and LSTM
using well-known benchmarks show that the proposed Approximate Random Dropout
can reduce the training time by $20\%$-$77\%$ ($19\%$-$60\%$) when dropout rate
is $0.3$-$0.7$ on MLP (LSTM) with marginal accuracy drop.


  We give a construction of a universal average of Lie algebra elements whose
exponentiation gives (when there is an associated Lie group) a totally
symmetric geometric mean of Lie group elements (sufficiently closed to the
identity) with the property that in an action of the group on a space $X$ for
which $n$ elements all take a particular point $a\in{}X$ to a common point
$b\in{}X$, also the mean will take $a$ to $b$. The construction holds without
the necessity for the existence of a Lie group and the universal average
$\mu_n(x_1,\ldots,x_n)$ is a totally symmetric universal expression in the free
Lie algebra generated by $x_1,\ldots,x_n$. Its expansion up to three brackets
is found explicitly and various properties of iterated averages are given.
There are applications to the construction of explicit symmetric differential
graded Lie algebra models. This work is based on the second author's minor
thesis.


  Following the techniques of [4], we formulate a Normal Form Lemma suited to
close to be integrable Hamiltonian systems where not all the coordinates are
action angles. The Lemma turns to be useful in the theory of KAM tori of
Sun-Earth-Asteroids systems.


  We emphasize that the stabilizing symmetry for dark matter (DM) particles
does not have to be the commonly used parity (Z_2) symmetry. We therefore
examine the potential of the colliders to distinguish models with parity
stabilized DM from models in which the DM is stabilized by other symmetries. We
often take the latter to be a Z_3 symmetry for illustration. We focus on
signatures where a single particle, charged under the DM stabilization symmetry
decays into the DM and Standard Model (SM) particles. Such a Z_3-charged
"mother" particle can decay into one or two DM particles along with the same SM
particles. This can be contrasted with the decay of a Z_2-charged mother
particle, where only one DM particle appears. Thus, if the intermediate
particles in these decay chains are off-shell, then the reconstructed invariant
mass of the SM particles exhibits two kinematic edges for the Z_3 case but only
one for the Z_2 case. For the case of on-shell intermediate particles,
distinguishing the two symmetries requires more than the kinematic edges. In
this case, we note that certain decay chain "topologies" of the mother particle
which are present for the Z_3 case (but absent for the Z_2 case) generate a
"cusp" in the invariant mass distribution of the SM particles. We demonstrate
that this cusp is generally invariant of the various spin configurations. We
further apply these techniques within the context of explicit models.


  In solving hard computational problems, semidefinite program (SDP)
relaxations often play an important role because they come with a guarantee of
optimality. Here, we focus on a popular semidefinite relaxation of K-means
clustering which yields the same solution as the non-convex original
formulation for well segregated datasets. We report an unexpected finding: when
data contains (greater than zero-dimensional) manifolds, the SDP solution
captures such geometrical structures. Unlike traditional manifold embedding
techniques, our approach does not rely on manually defining a kernel but rather
enforces locality via a nonnegativity constraint. We thus call our approach
NOnnegative MAnifold Disentangling, or NOMAD. To build an intuitive
understanding of its manifold learning capabilities, we develop a theoretical
analysis of NOMAD on idealized datasets. While NOMAD is convex and the globally
optimal solution can be found by generic SDP solvers with polynomial time
complexity, they are too slow for modern datasets. To address this problem, we
analyze a non-convex heuristic and present a new, convex and yet efficient,
algorithm, based on the conditional gradient method. Our results render NOMAD a
versatile, understandable, and powerful tool for manifold learning.


  We present the direct detection of molecular hydrogen at the highest redshift
known today (z_abs=4.224) in a Damped Lyman-alpha (DLA) system toward the
quasar PSS J1443+2724. This absorber is remarkable for having one of the
highest metallicities amongst DLA systems at z_abs>3, with a measured iron
abundance relative to Solar of -1.12+/-0.10. We provide for the first time in
this system accurate measurements of NI, MgII, SII and ArI column densities.
The sulfur and nitrogen abundances relative to Solar, -0.63+/-0.10 and
-1.38+/-0.10 respectively, correspond exactly to the primary nitrogen
production plateau. H2 absorption lines are detected in four different
rotational levels (J=0, 1, 2 and 3) of the vibrational ground-state in three
velocity components with total column densities of log N(H2)=17.67, 17.97,
17.48 and 17.26 respectively. The J=4 level is tentatively detected in the
strongest component with log N(H2)~14. The mean molecular fraction is log
f=-2.38+/-0.13, with f=2N(H2)/(2N(H2)+N(HI)). We also measure log
N(HD)/N(H2)<-4.2. The excitation temperatures T_{01} for the two main
components of the system are 96 and 136 K respectively. We argue that the
absorbing galaxy, whose star-formation activity must have started at least
2-5x10^8 yrs before z=4.224, is in a quiescent state at the time of
observation. The density of the gas is small, n_H<=50 cm^{-3}, and the
temperature is of the order of T~90-180 K. The high excitation of neutral
carbon in one of the components can be explained if the temperature of the
Cosmic Microwave Background Radiation has the value expected at the absorber
redshift, T=14.2 K.


  A bottleneck simulation of road traffic on a loop, using the deterministic
cellular automata (CA) Nagel-Schreckenberg model with zero dawdling
probability, reveals three types of stationary wave solutions. They consist of
i) two shock waves, one at each bottleneck boundary, ii) one shock wave at the
boundary and one on the "open" road, and iii) the trivial solution, i.e.
homogeneous, uniform flow. These solutions are selected dynamically from a
range of kinematicly permissible stationary shocks. This is similar in fashion
to the wave selection in a bottleneck simulation of the optimal-velocity (OV)
model, which is explained by a travelling wave phase-plane analysis of the
corresponding continuum model. It is yet another strong indication that CA and
OV models share certain underlying dynamics, although the former are discrete
in space and time while the latter are continuous.


  Preferences often change -- even in short time intervals -- due to either the
mere passage of time (present-biased preferences) or changes in environmental
conditions (state-dependent preferences). On the basis of the empirical
findings in the context of state-dependent preferences, we critically discuss
the Aristotelian view of unitary decision makers in economics and urge a more
Heraclitean perspective on human decision-making. We illustrate that the
conceptualization of preferences as present-biased or state-dependent has very
different normative implications under the Aristotelian view, although both
concepts are empirically hard to distinguish. This is highly problematic, as it
renders almost any paternalistic intervention justifiable.


  A classical theorem of Wielandt states that any two subnormal subgroups of a
finite group $G$ generate a subnormal subgroup of $G$. We prove versions of
this theorem for regular localities and for fusion systems. Along the way we
prove also a purely group-theoretical result which may be of independent
interest.


  We analyse so-called computable laws, i.e., laws that can be enforced by
automatic procedures. These laws should be logically perfect and unambiguous,
but sometimes they are not. We use a regulation on road transport to illustrate
this issue, and show what some fragments of this regulation would look like if
rewritten in the image of logic. We further propose desiderata to be fulfilled
by computable laws, and provide a critical platform from which to assess
existing laws and a guideline for composing future ones.


  Let $T$ be an infinite rooted tree with weights $w_e$ assigned to its edges.
Denote by $m_n(T)$ the minimum weight of a path from the root to a node of the
$n$th generation. We consider the possible behaviour of $m_n(T)$ with focus on
the two following cases: we say $T$ is explosive if \[ \lim_{n\to \infty}m_n(T)
< \infty, \] and say that $T$ exhibits linear growth if \[ \liminf_{n\to
\infty} \frac{m_n(T)}{n} > 0. \]
  We consider a class of infinite randomly weighted trees related to the
Poisson-weighted infinite tree, and determine precisely which trees in this
class have linear growth almost surely. We then apply this characterization to
obtain new results concerning the event of explosion in infinite randomly
weighted spherically-symmetric trees, answering a question of Pemantle and
Peres. As a further application, we consider the random real tree generated by
attaching sticks of deterministic decreasing lengths, and determine for which
sequences of lengths the tree has finite height almost surely.


  We study the (global) Bishop problem for small perturbations of
$\mathbf{S}^n$ --- the unit sphere of $\mathbb{C}\times\mathbb{R}^{n-1}$ --- in
$\mathbb{C}^n$. We show that if $S\subset\mathbb{C}^n$ is a sufficiently-small
perturbation of $\mathbf{S}^n$ (in the $\mathcal{C}^3$-norm), then $S$ bounds
an $(n+1)$-dimensional ball $M\subset\mathbb{C}^n$ that is foliated by analytic
disks attached to $S$. Furthermore, if $S$ is either smooth or real analytic,
then so is $M$ (upto its boundary). Finally, if $S$ is real analytic (and
satisfies a mild condition), then $M$ is both the envelope of holomorphy and
the polynomially convex hull of $S$. This generalizes the previously known case
of $n=2$ (CR singularities are isolated) to higher dimensions (CR singularities
are nonisolated).


  In this paper, we study the non-linear backward problems (with deterministic
or stochastic durations) of stochastic differential equations on the Sierpinski
gasket. We prove the existence and uniqueness of solutions of backward
stochastic differential equations driven by Brownian martingale (defined in
Section [sec:-1]) on the Sierpinski gasket constructed by S. Goldstein and S.
Kusuoka. The exponential integrability of quadratic processes for martingale
additive functionals is obtained, and as an application, a Feynman-Kac
representation formula for weak solutions of semi-linear parabolic PDEs on the
gasket is also established.


  The quasi-particle structure of the higher spin XXZ model is studied. We
obtained a new description of crystals associated with the level $k$ integrable
highest weight $U_q(\widehat{sl_2})$ modules in terms of the creation operators
at $q=0$ (the crystaline spinon basis). The fermionic character formulas and
the Yangian structure of those integrable modules naturally follow from this
description. We have also derived the conjectural formulas for the multi
quasi-particle states at $q=0$.


  The security level of the achievability scheme for Wyner's wiretap channel
model is examined from the perspective of the probability of correct decoding,
$P_c$, at the wiretap channel decoder. In particular, for finite-alphabet
memoryless channels, the exact random coding exponent of $P_c$ is derived as a
function of the total coding rate $R_1$ and the rate of each sub-code $R_2$.
Two different representations are given for this function and its basic
properties are provided. We also characterize the region of pairs of rates
$(R_1,R_2)$ of full security in the sense of the random coding exponent of
$P_c$, in other words, the region where the exponent of this achievability
scheme is the same as that of blind guessing at the eavesdropper side. Finally,
an analogous derivation of the correct-decoding exponent is outlined for the
case of the Gaussian channel.


  The non-abelian flat directions in the tachyon potential of stable non-BPS
branes recently found are shown to persist to all orders in alpha' at tree
level in the string coupling. We also obtain the non-abelian Born-Infeld action
including the tachyon potential for a stack of stable non-BPS branes on a
critical orbifold. Finally we discuss stable soliton states on the non-BPS
brane.


  Recent results of the Heidelberg-Moscow double beta decay experiment are
presented. After 8.6 kg$\cdot$a of measuring time no signal is seen for the
neutrinoless decay mode. A half-life limit of $T_{1/2}^{0\nu\beta\beta} >
5.1\cdot 10^{24}$~a is deduced which converts into a neutrino mass limit of
$\langle m_{\nu_e}\rangle < 0.68 $ eV (90\% CL). The experiment thus is the
first one penetrating into the sub-eV range for the neutrino mass. For the
$2\nu$ mode a half life of $T_{1/2}^{2\nu\beta\beta} = (1.53\pm0.04
_{stat}\pm0.13_{sys})\cdot 10^{24}$ a is derived. More than 10000 $2\nu$ double
beta events are observed. This is the first high statistics observation of this
nuclear decay mode. Limits on more exotic decay modes are also presented.
Concerning dark matter the experiment now gives the sharpest limits for the
observation of WIMPs.


  Protein phosphorylation is a reversible post-translational modification
commonly used by cell signaling networks to transmit information about the
extracellular environment into intracellular organelles for the regulation of
the activity and sorting of proteins within the cell. For this study we
reconstructed a literature-based mammalian kinase-substrate network from
several online resources. The interactions within this directed graph network
connect kinases to their substrates, through specific phosphosites including
kinase-kinase regulatory interactions. However, the "signs" of links,
activation or inhibition of the substrate upon phosphorylation, within this
network are mostly unknown. Here we show how we can infer the "signs"
indirectly using data from quantitative phosphoproteomics experiments applied
to mammalian cells combined with the literature-based kinase-substrate network.
Our inference method was able to predict the sign for 321 links and 153
phosphosites on 120 kinases, resulting in signed and directed subnetwork of
mammalian kinase-kinase interactions. Such an approach can rapidly advance the
reconstruction of cell signaling pathways and networks regulating mammalian
cells.


  Here we demonstrate a sieve for analysing primes and their composites, using
equivalence classes based on the modulo 6 return value as applied to the
Natural numbers. Five features of this 'Hexile' sieve are reviewed. The first
aspect, is that it narrows the search for primes to one-third of the Natural
numbers. The second feature is that we can obtain from the equivalence class
formulae, a property of its diophantine equations to distinguish between primes
and composites resulting from multiplication of these primes. Thirdly we can
from these diophantine formulations ascribe a non-random occurence to not only
the composites in the two equivalence classes but by default and as a
consequence : non-randomness of occurence to the resident primes. Fourthly we
develop a theoretical basis for sieving primes. Of final mention is that the
diophantine equations allows another route to a prime counting function using
combinatorics or numerical analysis.


  Manifold calculus is a form of functor calculus concerned with functors from
some category of manifolds to spaces. A weakness in the original formulation is
that it is not continuous in the sense that it does not handle well the natural
enrichments. In this paper, we correct this by defining an enriched version of
manifold calculus which essentially extends the discrete setting. Along the
way, we recast the Taylor tower as a tower of homotopy sheafifications. As a
spin-off we obtain a natural connection to operads: the limit of the Taylor
tower is a certain (derived) space of right module maps over the framed little
discs operad.


  We derive the $k_T$ resummation for doubly heavy-flavored $B_c$ meson decays
by including the charm quark mass effect into the known formula for a
heavy-light system. The resultant Sudakov factor is employed in the
perutrbative QCD study of the "golden channel" $B_c^+ \to J/\psi \pi^+$. With a
reasonable model for the $B_c$ meson distribution amplitude, which maintains
approximate on-shell conditions of both the partonic bottom and charm quarks,
it is observed that the imaginary piece of the $B_c \to J/\psi$ transition form
factor appears to be power suppressed, and the $B_c^+ \to J/\psi \pi^+$
branching ratio is not lower than $10^{-3}$. The above improved perutrbative
QCD formalism is applicable to $B_c$ meson decays to other charmonia and
charmed mesons.


  This note presents a regularity result with proof for an initial-boundary
value problem of a linear parabolic system involving curl of the unknown vector
field, subjected to the boundary condition of prescribing the tangential
component of the solution.


  Code Large Language Models (Code LLMs) have emerged as powerful tools,
revolutionizing the software development landscape by automating the coding
process and reducing time and effort required to build applications. This paper
focuses on training Code LLMs to specialize in the field of quantum computing.
We begin by discussing the unique needs of quantum computing programming, which
differ significantly from classical programming approaches or languages. A Code
LLM specializing in quantum computing requires a foundational understanding of
quantum computing and quantum information theory. However, the scarcity of
available quantum code examples and the rapidly evolving field, which
necessitates continuous dataset updates, present significant challenges.
Moreover, we discuss our work on training Code LLMs to produce high-quality
quantum code using the Qiskit library. This work includes an examination of the
various aspects of the LLMs used for training and the specific training
conditions, as well as the results obtained with our current models. To
evaluate our models, we have developed a custom benchmark, similar to
HumanEval, which includes a set of tests specifically designed for the field of
quantum computing programming using Qiskit. Our findings indicate that our
model outperforms existing state-of-the-art models in quantum computing tasks.
We also provide examples of code suggestions, comparing our model to other
relevant code LLMs. Finally, we introduce a discussion on the potential
benefits of Code LLMs for quantum computing computational scientists,
researchers, and practitioners. We also explore various features and future
work that could be relevant in this context.


  We summarize a recently proposed concrete programme for investigating the
(semi)classical limit of canonical, Lorentzian, continuum quantum general
relativity in four spacetime dimensions. The analysis is based on a novel set
of coherent states labelled by graphs. These fit neatly together with an
Infinite Tensor Product (ITP) extension of the currently used Hilbert space.
The ITP construction enables us to give rigorous meaning to the infinite volume
(thermodynamic) limit of the theory which has been out of reach so far.


  The presence of non-thermal electrons and large scale magnetic fields in the
intra-cluster medium (ICM) is known through the detection of mega-parsec (Mpc)
scale diffuse radio synchrotron emission. Although a significant amount of
progress in finding new diffuse radio sources has happened in the last decade,
most of the investigation has been constrained towards massive low-redshift
clusters. In this work, we explore clusters with redshift $z>0.3$ in search of
diffuse radio emission, at 325 MHz with the Giant Metrewave Radio Telescope
(GMRT). This campaign has resulted in the discovery of 2 new radio halos
(SPT-CL J0013-4906 and SPT-CL J0304-4401) along with 2 other detections (SPT-CL
J2031-4037 and SPT-CL J2248-4431), previously reported (at 325 MHz) in the
literature. In addition, we detect a halo candidate in 1 cluster in our sample,
and upper limits for halos are placed in 8 clusters where no diffuse emission
is detected. In the $P_{1.4} - L_\mathrm{X}$ plane, the detected halos follow
the observed correlation, whereas the upper limits lie above the correlation
line, indicating the possibility of future detection with sensitive
observations.


  We propose a hierarchical learning strategy aimed at generating sparse
representations and associated models for large noisy datasets. The hierarchy
follows from approximation spaces identified at successively finer scales. For
promoting model generalization at each scale, we also introduce a novel,
projection based penalty operator across multiple dimension, using permutation
operators for incorporating proximity and ordering information. The paper
presents a detailed analysis of approximation properties in the reconstruction
Reproducing Kernel Hilbert Spaces (RKHS) with emphasis on optimality and
consistency of predictions and behavior of error functionals associated with
the produced sparse representations. Results show the performance of the
approach as a data reduction and modeling strategy on both synthetic
(univariate and multivariate) and real datasets (time series). The sparse model
for the test datasets, generated by the presented approach, is also shown to
efficiently reconstruct the underlying process and preserve generalizability.


  An ordered-logit model is developed to study the effects of Automated
Vehicles (AVs) in the traffic mix on the average stress level of a pedestrian
when crossing an urban street at mid-block. Information collected from a
galvanic skin resistance sensor and virtual reality experiments are transformed
into a dataset with interpretable average stress levels (low, medium, and high)
and geometric, traffic, and environmental conditions. Modelling results
indicate a decrease in average stress level with the increase in the percentage
of AVs in the traffic mix.


  In the case of underground experiments for neutrino physics or rare event
searches, the background caused by cosmic muons contributes significantly and
therefore must be identified and rejected. We proposed and optimized a new
detector using liquid scintillator with wavelenghth-shifting fibers which can
be employed as a veto detector for cosmic muons background rejection. From the
prototype study, it has been found that the detector has good performances and
is capable of discriminating between muons induced signals and environmental
radiation background. Its muons detection efficiency is greater than 98$\%$,
and on average, 58 photo-electrons (p.e.) are collected when a muon passes
through the detector. To optimize the design and enhance the collection of
light, the reflectivity of the coating materials has been studied in detail. A
Monte Carlo simulation of the detector has been developed and compared to the
performed measurements showing a good agreement between data and simulation
results.


  We report direct experimental evidence of room temperature spin filtering in
magnetic tunnel junctions (MTJs) containing CoFe2O4 tunnel barriers via
tunneling magnetoresistance (TMR) measurements.
Pt(111)/CoFe2O4(111)/gamma-Al2O3(111)/Co(0001) fully epitaxial MTJs were grown
in order to obtain a high quality system, capable of functioning at room
temperature. Spin polarized transport measurements reveal significant TMR
values of -18% at 2 K and -3% at 290 K. In addition, the TMR ratio follows a
unique bias voltage dependence that has been theoretically predicted to be the
signature of spin filtering in MTJs containing magnetic barriers. CoFe2O4
tunnel barriers therefore provide a model system to investigate spin filtering
in a wide range of temperatures.


  We establish the precise relation between the integral kernel of the
scattering matrix and the resonance in the massless Spin-Boson model which
describes the interaction of a two-level quantum system with a second-quantized
scalar field. For this purpose, we derive an explicit formula for the two-body
scattering matrix. We impose an ultraviolet cut-off and assume a slightly less
singular behavior of the boson form factor of the relativistic scalar field but
no infrared cut-off. The purpose of this work is to bring together scattering
and resonance theory and arrive at a similar result as provided by Simon in
[38], where it was shown that the singularities of the meromorphic continuation
of the integral kernel of the scattering matrix are located precisely at the
resonance energies. The corresponding problem has been open in quantum field
theory ever since. To the best of our knowledge, the presented formula provides
the first rigorous connection between resonance and scattering theory in the
sense of [38] in a model of quantum field theory.


  A new international Pilot Project for the re-determination of the ICRF was
initiated by the International VLBI Service for Geodesy and Astrometry (IVS) in
January 2005. The purpose of this project is to compare the individual CRF
solutions and to analyze their systematic and random errors with focus on the
selection of the optimal strategy for the next ICRF realization. Eight radio
source catalogues provided by the IVS Analysis Centers GA, SHAO, DGFI,
GIUB-BKG, JPL, MAO NANU, GSFC, USNO were analyzed. In present study, four
analytical models were used to investigate the systematic differences between
solutions: solid rotation, rotation and deformation (IERS method), and
expansion in orthogonal functions: Legendre-Fourier polynomials and spherical
functions. It was found that expansions by orthogonal function describe the
differences between individual catalogues better than the two former models.
Finally, the combined CRF was generated. Using the radio source positions from
this combined catalogue for estimation of EOP has shown improvement of the
uncertainty of the celestial pole offset time series.


  Determining the radial positions of galaxies up to a high accuracy depends on
the correct identification of salient features in their spectra. Classical
techniques for spectroscopic redshift estimation make use of template matching
with cross-correlation. These templates are usually constructed from empirical
spectra or simulations based on the modeling of local galaxies. We propose two
new spectroscopic redshift estimation schemes based on new learning techniques
for galaxy spectra representation, using either a dictionary learning technique
for sparse representation or denoising autoencoders. We investigate how these
representations impact redshift estimation. These methods have been tested on
realistic simulated galaxy spectra, with photometry modelled after the Large
Synoptic Survey Telescope (LSST) and spectroscopy reproducing properties of the
Sloan Digital Sky Survey (SDSS). They were compared to Darth Fader, a robust
technique extracting line features and estimating redshift through
eigentemplates cross-correlations. We show that both dictionary learning and
denoising autoencoders provide improved accuracy and reliability across all
signal-to-noise regimes and galaxy types. The representation learning framework
for spectroscopic redshift analysis introduced in this work offers high
performance in feature extraction and redshift estimation, improving on a
classical eigentemplates approach. This is a necessity for next-generation
galaxy surveys, and we demonstrate a successful application in realistic
simulated survey data.


  Cubic silicon carbide (3C-SiC) is an emerging material for high power and new
generation devices, but the development of high quality 3C-SiC layer still
represents a scientific and technological challenge especially when grown on a
Si substrate. In the present lecture, we discuss the use of a buffer layer
between the epitaxial layer and the substrate in order to reduce the
defectiveness and improve the overall quality of the SiC epi-film. In
particular, we find that the morphology and the quality of the epi-film depends
on the carbonization temperature and the concentration of Ge in close proximity
of the Si1-xGex/SiC interface. Ge segregation at the interface influences the
film quality, and in particular a [Ge]>12% in close proximity to the interface
leads to the formation of poly-crystalls, while close to 10% induces a mirror
like morphology. Moreover, by finely tuning the Ge concentration and
carbonization temperature, crystal quality higher than that observed for SiC
grown on bare silicon is achieved.


  For an odd prime $p$ and a number field $F$ containing a $p$th root of unity,
we study generalised Tate kernels, $D_F^{[i,n]}$, for $i\in \mathbb{Z}$ and
$n\geq 1$, having the properties that if $i\geq 2$ and if either $p$ does not
divide $i$ or $\mu_{p^n}\subset F$ then there are natural isomorphisms
$D_F^{[i,n]}\cong K^{\mbox{\tiny \'et}}_{2i-1}(O_F^S)/p^n$, and that they are
periodic modulo a power of $p$ which depends on $F$ and $n$. Our main result is
that if the Gross-Jaulent conjecture holds for $(F,p)$ then there is a natural
isomorphism $D_F^{[i,n]}\cong\mathcal{E}_F/p^n$ where $\mathcal{E}_F$ is the
Gross kernel. We apply this result to compute lower bounds for capitulation
kernels in even \'etale $K$-theory.


  A deep learning model, EscherNet 101, is constructed to categorize images of
2D periodic patterns into their respective 17 wallpaper groups. Beyond
evaluating EscherNet 101 performance by classification rates, at a micro-level
we investigate the filters learned at different layers in the network, capable
of capturing second-order invariants beyond edge and curvature.


  By allowing models to predict without task-specific training, in-context
learning (ICL) with pretrained LLMs has enormous potential in NLP. However, a
number of problems persist in ICL. In particular, its performance is sensitive
to the choice and order of in-context examples. Given the same set of
in-context examples with different orderings, model performance may vary
between near random to near state-of-the-art. In this work, we formulate
in-context example ordering as an optimization problem. We examine three
problem settings that differ in the assumptions they make about what is known
about the task. Inspired by the idea of learning from label proportions, we
propose two principles for in-context example ordering guided by model's
probability predictions. We apply our proposed principles to thirteen text
classification datasets and nine different autoregressive LLMs with 700M to 13B
parameters. We demonstrate that our approach outperforms the baselines by
improving the classification accuracy, reducing model miscalibration, and also
by selecting better in-context examples.


  Let $\Gamma$ be a simple finite graph with vertex set $V(\Gamma)$ and edge
set $E(\Gamma)$. Let $\mathcal{R}$ be an equivalence relation on $V(\Gamma)$.
The $\mathcal{R}$-super $\Gamma$ graph $\Gamma^{\mathcal{R}}$ is a simple graph
with vertex set $V(\Gamma)$ and two distinct vertices are adjacent if either
they are in the same $\mathcal{R}$-equivalence class or there are elements in
their respective $\mathcal{R}$-equivalence classes that are adjacent in the
original graph $\Gamma$. We first show that $\Gamma^{\mathcal{R}}$ is a
generalized join of some complete graphs and using this we obtain the adjacency
and Laplacian spectrum of conjugacy and order super commuting graphs of
dihedral group $D_{2n}\; (n\geq 3)$, generalized quaternion group $Q_{4m}
\;(m\geq 2)$ and the nonabelian group $\mathbb Z_p \rtimes \mathbb Z_q$ of
order $pq$, where $p$ and $q$ are distinct primes with $q|p-1$.


  We present the first clear observation of the doubly Cabibbo suppressed decay
D+ --> K-K+K+ and the first observation of the singly Cabibbo suppressed decay
Ds+ --> K-K+K+. These signals have been obtained by analyzing the high
statistics sample of photoproduced charm particles of the FOCUS(E831)
experiment at Fermilab. We measure the following relative branching ratios:
Gamma(D+ --> K-K+K+)/Gamma(D+ --> K-pi+pi+) = (9.49 +/- 2.17(statistical) +/-
0.22(systematic))x10^-4 and Gamma(Ds+ --> K-K+K+)/Gamma(Ds+ --> K-K+pi+) =
(8.95 +/- 2.12(statistical) +2.24(syst.) -2.31(syst.))x10^-3.


  This paper discusses ongoing work in demonstrating research in mobile
autonomy in challenging driving scenarios. In our approach, we address
fundamental technical issues to overcome critical barriers to assurance and
regulation for large-scale deployments of autonomous systems. To this end, we
present how we build robots that (1) can robustly sense and interpret their
environment using traditional as well as unconventional sensors; (2) can assess
their own capabilities; and (3), vitally in the purpose of assurance and trust,
can provide causal explanations of their interpretations and assessments. As it
is essential that robots are safe and trusted, we design, develop, and
demonstrate fundamental technologies in real-world applications to overcome
critical barriers which impede the current deployment of robots in economically
and socially important areas. Finally, we describe ongoing work in the
collection of an unusual, rare, and highly valuable dataset.


  We show that any $q$-multiplicative sequence which is \emph{oscillating} of
order $1$, i.e.\ does not correlate with linear phase functions $e^{2\pi i
n\alpha}$ ($\alpha \in \mathbb{R})$, is Gowers uniform of all orders, and hence
in particular does not correlate with polynomial phase functions $e^{2\pi i
p(n)}$ ($p \in \mathbb{R}[x]$). Quantitatively, we show that any
$q$-multiplicative sequence which is of Gelfond type of order 1 is
automatically of Gelfond type of all orders. Consequently, any such
$q$-multiplicative sequence is a good weight for ergodic theorems. We also
obtain combinatorial corollaries concerning linear patterns in sets which are
described in terms of sums of digits.


  Indistinguishable single photons in the telecom-bandwidth of optical fibers
are indispensable for long-distance quantum communication. Solid-state single
photon emitters have achieved excellent performance in key benchmarks, however,
the demonstration of indistinguishability at room-temperature remains a major
challenge. Here, we report room-temperature photon indistinguishability at
telecom wavelengths from individual nanotube defects in a fiber-based
microcavity operated in the regime of incoherent good cavity-coupling. The
efficiency of the coupled system outperforms spectral or temporal filtering,
and the photon indistinguishability is increased by more than two orders of
magnitude compared to the free-space limit. Our results highlight a promising
strategy to attain optimized non-classical light sources.


  In 1978, Robert Kibler at the National Security Agency in Fort Meade,
Maryland published a description of all noncyclic difference sets with $k <
20$. Kibler's decision to stop his extensive computer search for difference
sets at block size 19 was motivated partly by the difficult barrier at $k=20$,
the difference sets with parameters $(96,20,4)$. In this paper, we announce the
completion of the search for all $(96,20,4)$ difference sets, relying on the
computer software GAP and the work of numerous authors over the last few
decades. The difference sets and the symmetric designs they create are
summarized and links are provided to webpages which explicitly list the
difference sets. In addition, we use these $(96,20,4)$ difference sets to
construct all $(96, 20, 4, 4)$ and $(96, 19, 2, 4)$ partial difference sets and
briefly look at the corresponding strongly regular graphs.