  Technological advancements focus on developing comfortable and acceptable
driving characteristics in autonomous vehicles. Present driving functions
predominantly possess predefined parameters, and there is no universally
accepted driving style for autonomous vehicles. While driving may be
technically safe and the likelihood of road accidents is reduced, passengers
may still feel insecure due to a mismatch in driving styles between the human
and the autonomous system. Incorporating driving style preferences into
automated vehicles enhances acceptance, reduces uncertainty, and poses the
opportunity to expedite their adoption. Despite the increased research focus on
driving styles, there remains a need for comprehensive studies investigating
how variations in the driving context impact the assessment of automated
driving functions. Therefore, this work evaluates lateral driving style
preferences for autonomous vehicles on rural roads, considering different
weather and traffic situations. A controlled study was conducted with a variety
of German participants utilizing a high-fidelity driving simulator. The
subjects experienced four different driving styles, including mimicking of
their own driving behavior under two weather conditions. A notable preference
for a more passive driving style became evident based on statistical analyses
of participants' responses during and after the drives. This study could not
confirm the hypothesis that subjects prefer to be driven by mimicking their own
driving behavior. Furthermore, the study illustrated that weather conditions
and oncoming traffic substantially influence the perceived comfort during
autonomous rides. The gathered dataset is openly accessible at
https://www.kaggle.com/datasets/jhaselberger/idcld-subject-study-on-driving-style-preferences.


  Offline learning has become widely used due to its ability to derive
effective policies from offline datasets gathered by expert demonstrators
without interacting with the environment directly. Recent research has explored
various ways to enhance offline learning efficiency by considering the
characteristics (e.g., expertise level or multiple demonstrators) of the
dataset. However, a different approach is necessary in the context of zero-sum
games, where outcomes vary significantly based on the strategy of the opponent.
In this study, we introduce a novel approach that uses unsupervised learning
techniques to estimate the exploited level of each trajectory from the offline
dataset of zero-sum games made by diverse demonstrators. Subsequently, we
incorporate the estimated exploited level into the offline learning to maximize
the influence of the dominant strategy. Our method enables interpretable
exploited level estimation in multiple zero-sum games and effectively
identifies dominant strategy data. Also, our exploited level augmented offline
learning significantly enhances the original offline learning algorithms
including imitation learning and offline reinforcement learning for zero-sum
games.


  It has already been observed that audio-visual embedding is more robust than
uni-modality embedding for person verification. Here, we proposed a novel
audio-visual strategy that considers aggregators from a fusion perspective.
First, we introduced weight-enhanced attentive statistics pooling for the first
time in face verification. We find that a strong correlation exists between
modalities during pooling, so joint attentive pooling is proposed which
contains cycle consistency to learn the implicit inter-frame weight. Finally,
each modality is fused with a gated attention mechanism to gain robust
audio-visual embedding. All the proposed models are trained on the VoxCeleb2
dev dataset and the best system obtains 0.18%, 0.27%, and 0.49% EER on three
official trial lists of VoxCeleb1 respectively, which is to our knowledge the
best-published results for person verification.


  We present an abstract framework for asymptotic analysis of convergence based
on the notions of eventual families of sets that we define. A family of subsets
of a given set is called here an "eventual family" if it is upper hereditary
with respect to inclusion. We define accumulation points of eventual families
in a Hausdorff Topological space and define the "image family" of an eventual
family. Focusing on eventual families in the set of the integers enables us to
talk about sequences of points. We expand our work to the notion of a
"multiset" which is a modification of the concept of a set that allows for
multiple instances of its elements and enable the development of
"multifamilies" which are either "increasing" or "decreasing". The abstract
structure created here is motivated by, and feeds back to, our look at the
convergence analysis of an iterative process for asymptotically finding a
common fixed point of a family of operators.


  Predicate encryption is a new type of public key encryption that enables
searches on encrypted data. By using predicate encryption, we can search
keywords or attributes on encrypted data without decrypting ciphertexts. Hidden
vector encryption (HVE) is a special kind of predicate encryption. HVE supports
the evaluation of conjunctive equality, comparison, and subset operations
between attributes in ciphertexts and attributes in tokens. In this paper, we
construct efficient HVE schemes in prime order bilinear groups derived from
previous HVE schemes in composite order bilinear groups, and prove their
selective security under simple assumptions. To achieve this result, we present
a conversion method that transforms HVE schemes from composite order bilinear
groups into prime order bilinear groups. Our method supports any types of prime
order bilinear groups and uses simple assumptions.


  These notes discuss various aspect of the ``representation theory'' of
Poisson manifolds, with focus on Morita equivalence and Picard groups. We give
a brief introduction to Poisson geometry (including Dirac and twisted Poisson
structures) and algebraic Morita theory before presenting the geometric Morita
theory of Poisson manifolds. We also point out the connections with the theory
of symplectic groupoids and hamiltonian actions.


  The Canarias InfraRed Camera Experiment (CIRCE) is a near-infrared (1-2.5
micron) imager, polarimeter and low-resolution spectrograph operating as a
visitor instrument for the Gran Telescopio Canarias 10.4-meter telescope. It
was designed and built largely by graduate students and postdocs, with help
from the UF astronomy engineering group, and is funded by the University of
Florida and the U.S. National Science Foundation. CIRCE is intended to help
fill the gap in near-infrared capabilities prior to the arrival of EMIR to the
GTC, and will also provide the following scientific capabilities to compliment
EMIR after its arrival: high-resolution imaging, narrowband imaging,
high-time-resolution photometry, imaging polarimetry, low resolution
spectroscopy. In this paper, we review the design, fabrication, integration,
lab testing, and on-sky performance results for CIRCE. These include a novel
approach to the opto-mechanical design, fabrication, and alignment.


  A common configuration for a stand-alone PV power system may consist of three
converters: a buck converter for the PV panelto charge the battery, a boost
converter for the battery to discharge to the load and one for the load voltage
regulation. Such a systemrequires a coordinated control scheme for three
converters which can be complicated. A simple structure for a stand-alone PV
plantconsists of a PV array, a battery unit, and its associated bidirectional
converter which is a combination of a buck and boost converter.When controlled
properly the system can provide uninterrupted power to the load, despite the
intermittent availability of sunlight. Inthis paper complete design of the
converter is carried out and the simulation has been performed using Psim. From
the simulation,the graphs are presented to show the converter working in buck
mode and boost mode. Controller is designed to take care of modetransition,
buck to boost and boost to buck mode automatically based on source voltage.
Hardware implementation has been doneusing microcontroller (8051).


  We show how to approximate Dirac dynamics for electronic initial states by
semi- and non-relativistic dynamics. To leading order, these are generated by
the semi- and non-relativistic Pauli hamiltonian where the kinetic energy is
related to $\sqrt{m^2 + \xi^2}$ and $\xi^2 / 2m$, respectively. Higher-order
corrections can in principle be computed to any order in the small parameter
v/c which is the ratio of typical speeds to the speed of light. Our results
imply the dynamics for electronic and positronic states decouple to any order
in v/c << 1.
  To decide whether to get semi- or non-relativistic effective dynamics, one
needs to choose a scaling for the kinetic momentum operator. Then the effective
dynamics are derived using space-adiabatic perturbation theory by Panati et. al
with the novel input of a magnetic pseudodifferential calculus adapted to
either the semi- or non-relativistic scaling.


  An $XY$ model, generalized by inclusion of up to an infinite number of
higher-order pairwise interactions with an exponentially decreasing strength,
is studied by spin-wave theory and Monte Carlo simulations. At low temperatures
the model displays a quasi-long-range order phase characterized by an
algebraically decaying correlation function with the exponent $\eta = T/[2 \pi
J(p,\alpha)]$, nonlinearly dependent on the parameters $p$ and $\alpha$ that
control the number of the higher-order terms and and the decay rate of their
intensity, respectively. At higher temperatures the system shows a crossover
from the continuous Berezinskii-Kosterlitz-Thouless to the first-order
transition for the parameter values corresponding to a highly nonlinear shape
of the potential well. The role of of topological excitations (vortices) in
changing the nature of the transition is discussed.


  Rare decays are flavour changing neutral current processes that are
loop-suppressed in the Standard Model (SM). New particles in SM extensions can
therefore give significant contributions, modifying branching fractions and
angular distributions. Consequently, rare decays are particularly sensitive
probes for New Physics (NP). These proceedings summarize the latest results
from the LHCb experiment on rare decays. While most results are in good
agreement with SM predictions, some tensions that recently appeared in rare
semileptonic $b\to s\ell^+\ell^-$ decays are also discussed.


  We study the reflectional symmetry of a surface in the Euclidean
3-dimensional space with a cross-cap singularity with respect to planes. This
symmetry is picked up by the singularities of folding maps on the cross-cap. We
give a list of the generic singularities that appear in the members of the
family of folding maps on a cross-cap and characterise them geometrically.


  Recent data on the $D^*$ masses confirm the QCD prediction based on the
\REF{\ICHJL}{Isaac Cohen and Harry J. Lipkin, \plb{84}{323}{1979}} very general
assumption $\({\ICHJL}\)$ that the spin-dependence of the quark couplings to
photons and gluons have the same structure and differ only in the value of the
coupling constants, This prediction with no free parameters and using the $\Xi$
and $\Xi^*$ masses as input follows from noting that the charmed antiquark
$\bar c$ and the strange diquark (ss) have exactly the same couplings to both
the photon (Q=-2/3) and to the gluon (both are color antitriplets). New
predictions are obtained with this approach. Corrections to the naive model are
investigated and shown to be small.


  In Gribov-Zwanziger scenario of color confinement, Zwanziger proposed two
types of horizon function in lattice Landau gauge, the first one and the second
one of which foundation was discussed in detail. The second type horizon
function is focussed on in the present study. Its derivation and the horizon
condition are briefly reviewed along the line of Zwanziger, and it is also
reviewed that this horizon condition and Kugo-Ojima color confinement criterion
coincide in the continuum limit. In case of free boundary condition in contrast
to periodic boundary condition, it was shown that the horizon condition holds
for each gauge fixed configuration in Landau gauge. It is clarified that this
fact can be derived from some identity relation which holds on an arbitrary
network of links in Landau gauge. Thus the origin of the pointwise validity of
horizon condition is highlighted.


  Topological feedback entropy (TFE) was introduced in 2004 to measure the
intrinsic rate at which a continuous, fully observed, deterministic control
system generates information for controlled set-invariance. In this paper, we
generalise this notion in two directions, first to continuous, partially
observed systems and then to discontinuous, fully observed systems. In each
case, we show that the corresponding generalised TFE coincides with the the
smallest feedback bit rate that allows a form of controlled invariance to be
achieved.


  The $p$-rank of a Steiner triple system $B$ is the dimension of the linear
span of the set of characteristic vectors of blocks of $B$, over GF$(p)$. We
derive a formula for the number of different Steiner triple systems of order
$v$ and given $2$-rank $r_2$, $r_2<v$, and a formula for the number of Steiner
triple systems of order $v$ and given $3$-rank $r_3$, $r_3<v-1$. Also, we prove
that there are no Steiner triple systems of $2$-rank smaller than $v$ and, at
the same time, $3$-rank smaller than $v-1$. Our results extend previous work on
enumerating Steiner triple systems according to the rank of their codes, mainly
by Tonchev, V.A.Zinoviev and D.V.Zinoviev for the binary case and by Jungnickel
and Tonchev for the ternary case.


  In CAGD the design of a surface that interpolates an arbitrary quadrilateral
mesh is definitely a challenging task. The basic requirement is to satisfy both
criteria concerning the regularity of the surface and aesthetic concepts. With
regard to the aesthetic quality, it is well known that interpolatory methods
often produce shape artifacts when the data points are unevenly spaced. In the
univariate setting, this problem can be overcome, or at least mitigated, by
exploiting a proper non-uniform parametrization, that accounts for the geometry
of the data. Moreover, recently, the same principle has been generalized and
proven to be effective in the context of bivariate interpolatory subdivision
schemes. In this paper, we propose a construction for parametric surfaces of
good aesthetic quality and high smoothness that interpolate quadrilateral
meshes of arbitrary topology. In the classical tensor product setting the same
parameter interval must be shared by an entire row or column of mesh edges.
Conversely, in this paper, we assign a different parameter interval to each
edge of the mesh. This particular structure, which we call an augmented
parametrization, allows us to interpolate each section polyline of the mesh at
parameters values that prevent wiggling of the resulting curve or other
interpolation artifacts. This yields high quality interpolatory surfaces. The
proposed surfaces are a generalization of the local univariate spline
interpolants introduced in Beccari et al.(2013) and Antonelli et al.(2014),
that can have arbitrary continuity and arbitrary order of polynomial
reproduction. In particular, these surfaces retain the same smoothness of the
underlying class of univariate splines in the regular regions of the mesh.
Moreover, in mesh regions containing vertices of valence other than 4, we
suitably define G1- or G2-continuous surface patches that join the neighboring
regular ones.


  Line intensity mapping (LIM) is a promising probe to study star formation,
the large-scale structure of the Universe, and the epoch of reionization (EoR).
Since carbon monoxide (CO) is the second most abundant molecule in the Universe
except for molecular hydrogen ${\rm H}_2$, it is suitable as a tracer for LIM
surveys. However, just like other LIM surveys, CO intensity mapping also
suffers strong foreground contamination that needs to be eliminated for
extracting valuable astrophysical and cosmological information. In this work,
we take $^{12}$CO($\it J$=1-0) emission line as an example to investigate
whether deep learning method can effectively recover the signal by removing the
foregrounds. The CO(1-0) intensity maps are generated by N-body simulations
considering CO luminosity and halo mass relation, and we discuss two cases with
median and low CO signals by comparing different relations. We add foregrounds
generated from real observations, including thermal dust, spinning dust,
free-free, synchrotron emission and CMB anisotropy. The beam with sidelobe
effect is also considered. Our deep learning model is built upon ResUNet, which
combines image generation algorithm UNet with the state-of-the-art architecture
of deep learning, ResNet. The principal component analysis (PCA) method is
employed to preprocess data before feeding it to the ResUNet. We find that, in
the case of low instrumental noise, our UNet can efficiently reconstruct the CO
signal map with correct line power spectrum by removing the foregrounds and
recovering PCA signal loss and beam effects. Our method also can be applied to
other intensity mappings like neutral hydrogen 21cm surveys.


  Exotic states of topological materials are challenging or impossible to
create under ambient conditions.1-4 Moreover, it is unclear whether topological
superconductivity, as a critical element for topological quantum computing,
exists in any naturally occurring materials.5-7 Although these problems can be
overcome through the combination of materials in heterostructures, there are
still many requisites, such as low temperatures and specific magnetic
fields.8,9 Herein, an intrinsic topological superconductor that does not depend
on particular external conditions is demonstrated. It is accomplished utilizing
the unique properties of polyaromatic hydrocarbons (PAHs), which have been
proposed to have persistent ring current.10-12 According to the
Su-Schrieffer-Heeger(SSH)13 and Kitaev14 models, PAHs can have a non-trivial
edge mode, so that perpendicularly stacked PAHs are expected to have Majorana
hinge and corner modes.15 Intrinsic persistent ring current of HYLION-12 is
demonstrated by MPMS.16 Coherent Quantum Phase Slip(CQPS), the Constant
Conductance Plateau (CCP) and the zero bias conductance peak(ZBP) which is
signatures of hinge modes are confirmed through the Josephson junction device
of pelletized orthorhombic phase organic crystals of HYLION-12 by transport
spectroscopy.17,18 They are signatures of Majorana hinge and corner modes. In
addition, the braidinglike operation by transport spectroscopy shows the
emergence of the most important and critical elements of quantum computers that
can be realized without an external magnetic field at room temperature.


  OPERA (Oscillation Project with Emulsion tRacking Apparatus)is an
international collaboration between Europe and Asia, aiming to give the first
direct proof of tau neutrino appearance in a pure muon neutrino beam, in order
to validate the hypothesis for atmospheric neutrino oscillations. The first
european long baseline neutrino beam called CNGS is produced at CERN and sent
in the direction of the Gran Sasso underground laboratory 730 km away, where
the OPERA detector is located. Since 2006 the electronic detector part is fully
commissioned and running. Cosmic ray events have been recorded on a regular
basis and the first neutrino beam events have been observed in the target
elements made of very precise emulsion films and lead sheets during the last
run in autumn 2007. This paper reviews the status of the detector, the beam
performances, the first results from the neutrino event analysis and the
prospects.


  This paper is devoted to show the results obtained by using the
magnitude-squared coherence for determining order-chaos transition in a system
described by the logistic equation dynamics. For determining the power spectral
density of a chaotic finite-duration discrete-time sequence the Welch average
periodogram method was used. This method has the advantage that can be applied
to any stationary signal by using the discrete Fourier transform (DFT)
representation of a discrete-time series which allows an effective computation
via fast Fourier transform (FFT) algorithm, and that can be applied to a
discrete-time series shorter than that required by nonlinear dynamical analysis
methods. The estimate of the Inverse Average Magnitude-Squared Coherence Index
(IAMSCI) for each discrete-time series in the set obtained from the logistic
mapping was calculated. The control parameter, {\bf r}, ranges in the interval
[2.8,4] for producing the discrete-time series set. When the condition $r \geq
3.57$ is satisfied, each discrete-time series exhibited a positive value for
IAMSCI estimate, indicating a high level of coherence loss of the signal and
corresponding to a chaotic behavior of the dynamical system. Its effectiveness
was demonstrated by comparing the results with those obtained by calculating
the largest Lyapounov exponent of the time series set obtained from the
logistic equation.


  The formation of new shell gaps in intermediate mass neutron-rich nuclei is
investigated within the relativistic Hartree-Fock-Bogoliubov theory, and the
role of the Lorentz pseudo-vector and tensor interactions is analyzed. Based on
the Foldy-Wouthuysen transformation, we discuss in detail the role played by
the different terms of the Lorentz pseudo-vector and tensor interactions in the
appearing of the $N=16$, 32 and 34 shell gaps. The nuclei $^{24}$O, $^{48}$Si
and $^{52,54}$Ca are predicted with a large shell gap and zero ($^{24}$O,
$^{52}$Ca) or almost zero ($^{48}$Si, $^{54}$Ca) pairing gap, making them
candidates for new magic numbers in exotic nuclei. We find from our analysis
that the Lorentz pseudo-vector and tensor interactions induce very specific
evolutions of single-particle energies, which could clearly sign their presence
and reveal the need for relativistic approaches with exchange interactions.


  In this paper, we introduce the commutativity degree of a finite-dimensional
Lie algebra over a finite field and determine upper and lower bounds for it.
Moreover, we study some relations between the notion of commutativity degree
and known concepts in Lie algebras.


  In this paper we describe a condition under which a given function that
commute with an idempotent function on an infinite set is a Cayley function
using its functional digraph.


  We provide a simple non-perturbative formulation for non-commutative
four-dimensional N = 2 supersymmetric Yang-Mills theories. The formulation is
constructed by a combination of deconstruction (orbifold projection), momentum
cut-off and matrix model techniques. We also propose a moduli fixing term that
preserves lattice supersymmetry on the deconstruction formulation. Although the
analogous formulation for four-dimensional N = 2 supersymmetric Yang-Mills
theories is proposed also in Nucl.Phys.B857(2012), our action is simpler and
better suited for computer simulations. Moreover, not only for the
non-commutative theories, our formulation has a potential to be a
non-perturbative tool also for the commutative four-dimensional N = 2
supersymmetric Yang-Mills theories.


  Recent research on finding important intermediate nodes in a network
suspected to contain criminal activity is highly dependent on network
centrality values. Betweenness centrality, for example, is widely used to rank
the nodes that act as brokers in the shortest paths connecting all source and
all the end nodes in a network. However both the shortest path node betweenness
and the linearly scaled betweenness can only show rankings for all the nodes in
a network. In this paper we explore the mathematical concept of pair-dependency
on intermediate nodes, adapting the concept to criminal relationships and
introducing a new source-intermediate reliance measure. To illustrate our
measure, we apply it to rank the nodes in the Enron email dataset and the
Noordin Top Terrorist networks. We compare the reliance ranking with Google
PageRank, Markov centrality as well as betweenness centrality and show that a
criminal investigation using the reliance measure, will lead to a different
prioritisation in terms of possible people to investigate. While the ranking
for the Noordin Top terrorist network nodes yields more extreme differences
than for the Enron email transaction network, in the latter the reliance values
for the set of finance managers immediately identified another employee
convicted of money laundering.


  We deal with the gap function and the thermodynamical potential in the
BCS-Bogoliubov theory of superconductivity, where the gap function is a
function of the temperature $T$ only. We show that the squared gap function is
of class $C^2$ on the closed interval $[ 0, T_c ]$ and point out some more
properties of the gap function. Here, $T_c$ stands for the transition
temperature. On the basis of this study we then give, examining the
thermodynamical potential, a mathematical proof that the transition to a
superconducting state is a second-order phase transition. Furthermore, we
obtain a new and more precise form of the gap in the specific heat at constant
volume from a mathematical point of view.


  The consistently low activity level of the old solar analog 51 Peg not only
facilitated the discovery of the first hot Jupiter, but also led to the
suggestion that the star could be experiencing a magnetic grand minimum.
However, the 50 year time series showing minimal chromospheric variability
could also be associated with the onset of weakened magnetic braking (WMB),
where sufficiently slow rotation disrupts cycling activity and the production
of large-scale magnetic fields by the stellar dynamo, thereby shrinking the
Alfven radius and inhibiting the efficient loss of angular momentum to
magnetized stellar winds. In this Letter, we evaluate the magnetic evolutionary
state of 51 Peg by estimating its wind braking torque. We use new
spectropolarimetric measurements from the Large Binocular Telescope to
reconstruct the large-scale magnetic morphology, we reanalyze archival X-ray
measurements to estimate the mass-loss rate, and we detect solar-like
oscillations in photometry from the Transiting Exoplanet Survey Satellite,
yielding precise stellar properties from asteroseismology. Our estimate of the
wind braking torque for 51 Peg clearly places it in the WMB regime, driven by
changes in the mass-loss rate and the magnetic field strength and morphology
that substantially exceed theoretical expectations. Although our revised
stellar properties have minimal consequences for the characterization of the
exoplanet, they have interesting implications for the current space weather
environment of the system.


  An understanding of the high-temperature copper oxide (cuprate)
superconductors has eluded the physics community for over thirty years, and
represents one of the greatest unsolved problems in condensed matter physics.
Particularly enigmatic is the normal state from which superconductivity
emerges, so much so that this phase has been dubbed a "strange metal." In this
article, we will review recent research into this strange metallic state as
realized in the electron-doped cuprates with a focus on their transport
properties. The electron-doped compounds differ in several ways from their more
thoroughly studied hole-doped counterparts, and understanding these asymmetries
of the phase diagram may prove crucial to developing a final theory of the
cuprates. Most of the experimental results discussed in this review have yet to
be explained and remain an outstanding challenge for theory.


  To leverage the full potential of quantum error-correcting stabilizer codes
it is crucial to have an efficient and accurate decoder. Accurate, maximum
likelihood, decoders are computationally very expensive whereas decoders based
on more efficient algorithms give sub-optimal performance. In addition, the
accuracy will depend on the quality of models and estimates of error rates for
idling qubits, gates, measurements, and resets, and will typically assume
symmetric error channels. In this work, instead, we explore a model-free,
data-driven, approach to decoding, using a graph neural network (GNN). The
decoding problem is formulated as a graph classification task in which a set of
stabilizer measurements is mapped to an annotated detector graph for which the
neural network predicts the most likely logical error class. We show that the
GNN-based decoder can outperform a matching decoder for circuit level noise on
the surface code given only simulated experimental data, even if the matching
decoder is given full information of the underlying error model. Although
training is computationally demanding, inference is fast and scales
approximately linearly with the space-time volume of the code. We also find
that we can use large, but more limited, datasets of real experimental data
[Google Quantum AI, Nature {\bf 614}, 676 (2023)] for the repetition code,
giving decoding accuracies that are on par with minimum weight perfect
matching. The results show that a purely data-driven approach to decoding may
be a viable future option for practical quantum error correction, which is
competitive in terms of speed, accuracy, and versatility.


  In this work we revisit the notion of the (future) causal completion of a
globally hyperbolic spacetime and endow it with the structure of a Lorentzian
pre-length space. We further carry out this construction for a certain class of
generalized Robertson-Walker spacetimes.


  Convolutional neural networks (CNN) are widely used in resource-constrained
devices in IoT applications. In order to reduce the computational complexity
and memory footprint, the resource-constrained devices use fixed-point
representation. This representation consumes less area and energy in hardware
with similar classification accuracy compared to the floating-point ones.
However, to employ the low-precision fixed-point representation, various
considerations to gain high accuracy are required. Although many quantization
and re-training techniques are proposed to improve the inference accuracy,
these approaches are time-consuming and require access to the entire dataset.
This paper investigates the effect of different fixed-point hardware units on
CNN inference accuracy. To this end, we provide a framework called Fixflow to
evaluate the effect of fixed-point computations performed at hardware level on
CNN classification accuracy. We can employ different fixed-point considerations
at the hardware accelerators.This includes rounding methods and adjusting the
precision of the fixed-point operation's result. Fixflow can determine the
impact of employing different arithmetic units (such as truncated multipliers)
on CNN classification accuracy. Moreover, we evaluate the energy and area
consumption of these units in hardware accelerators. We perform experiments on
two common MNIST and CIFAR-10 datasets. Our results show that employing
different methods at the hardware level specially with low-precision, can
significantly change the classification accuracy.


  We explore the possibility of using a double-tip STM to probe the single
electron Green function of a sample surface, and describe a few important
applications: (1) Probing constant energy surfaces in $\k$-space by ballistic
transport; (2) Measuring scattering phase shifts of defects; (3) Observing the
transition from ballistic to diffusive transport to localization; and (4)
Measuring inelastic mean free paths.


  Determining the temperature distribution of coronal plasmas can provide
stringent constraints on coronal heating. Current observations with the Extreme
ultraviolet Imaging Spectrograph onboard Hinode and the Atmospheric Imaging
Assembly onboard the Solar Dynamics Observatory provide diagnostics of the
emission measure distribution (EMD) of the coronal plasma.
  Here we test the reliability of temperature diagnostics using 3D radiative
MHD simulations. We produce synthetic observables from the models, and apply
the Monte Carlo Markov chain EMD diagnostic. By comparing the derived EMDs with
the "true" distributions from the model we assess the limitations of the
diagnostics, as a function of the plasma parameters and of the signal-to-noise
of the data.
  We find that EMDs derived from EIS synthetic data reproduce some general
characteristics of the true distributions, but usually show differences from
the true EMDs that are much larger than the estimated uncertainties suggest,
especially when structures with significantly different density overlap along
the line-of-sight. When using AIA synthetic data the derived EMDs reproduce the
true EMDs much less accurately, especially for broad EMDs. The differences
between the two instruments are due to the: (1) smaller number of constraints
provided by AIA data, (2) broad temperature response function of the AIA
channels which provide looser constraints to the temperature distribution.
  Our results suggest that EMDs derived from current observatories may often
show significant discrepancies from the true EMDs, rendering their
interpretation fraught with uncertainty. These inherent limitations to the
method should be carefully considered when using these distributions to
constrain coronal heating.


  We are discussing the possibility to find a proper unique conditions for an
experimental study of the Schr\"odinger quantization problem in the neutron
stars physics. A simple toy model for physically different quantizations is
formulated and a possible physical consequences are derived.


  Action segmentation of behavioral videos is the process of labeling each
frame as belonging to one or more discrete classes, and is a crucial component
of many studies that investigate animal behavior. A wide range of algorithms
exist to automatically parse discrete animal behavior, encompassing supervised,
unsupervised, and semi-supervised learning paradigms. These algorithms -- which
include tree-based models, deep neural networks, and graphical models -- differ
widely in their structure and assumptions on the data. Using four datasets
spanning multiple species -- fly, mouse, and human -- we systematically study
how the outputs of these various algorithms align with manually annotated
behaviors of interest. Along the way, we introduce a semi-supervised action
segmentation model that bridges the gap between supervised deep neural networks
and unsupervised graphical models. We find that fully supervised temporal
convolutional networks with the addition of temporal information in the
observations perform the best on our supervised metrics across all datasets.


  Our new, deep, high resolution H-alpha and matching R-band UKST
multi-exposure stack of the central 25 sq. degrees of the LMC promises to
provide an unprecedented homogeneous sample of >1,000 new PNe. Our preliminary
2dF spectroscopy on the AAT has vindicated our selection process and confirmed
136 new PNe and 57 emission-line stars out of a sample of 263 candidate sources
within an initial 2.5 sq. deg. area. To date approximately one third of the
entire LMC has been scanned for candidates (~7.5 sq.deg.). More than 750 new
emission sources have been catalogued so far along with independent
re-identification of all known and possible PNe found from other surveys. Once
our image analysis is complete, we plan comprehensive spectroscopic follow-up
of the whole sample, not only to confirm our PN candidates but also to derive
nebula temperatures and densities which, with the aid of photoionization
modeling, will yield stellar parameters which are vital for constructing H-R
diagrams for these objects. A prime objective of the survey is to produce a
Luminosity Function which will be the most accurate and comprehensive ever
derived in terms of numbers, magnitude range and evolutionary state; offering
significant new insights into the LMC's evolutionary history. The observation
and measurement of our newly discovered AGB halos around 60% of these PN will
also assist in determining the initial- to final-mass ratios for this phase of
stellar evolution.


  This paper presents an online transfer learning framework for improving
temperature predictions in residential buildings. In transfer learning,
prediction models trained under a set of available data from a target domain
(e.g., house with limited data) can be improved through the use of data
generated from similar source domains (e.g., houses with rich data). Given also
the need for prediction models that can be trained online (e.g., as part of a
model-predictive-control implementation), this paper introduces the generalized
online transfer learning algorithm (GOTL). It employs a weighted combination of
the available predictors (i.e., the target and source predictors) and
guarantees convergence to the best weighted predictor. Furthermore, the use of
Transfer Component Analysis (TCA) allows for using more than a single source
domains, since it may facilitate the fit of a single model on more than one
source domains (houses). This allows GOTL to transfer knowledge from more than
one source domains. We further validate our results through experiments in
climate control for residential buildings and show that GOTL may lead to
non-negligible energy savings for given comfort levels.


  The stability of statistical analysis is an important indicator for
reproducibility, which is one main principle of scientific method. It entails
that similar statistical conclusions can be reached based on independent
samples from the same underlying population. In this paper, we introduce a
general measure of classification instability (CIS) to quantify the sampling
variability of the prediction made by a classification method. Interestingly,
the asymptotic CIS of any weighted nearest neighbor classifier turns out to be
proportional to the Euclidean norm of its weight vector. Based on this concise
form, we propose a stabilized nearest neighbor (SNN) classifier, which
distinguishes itself from other nearest neighbor classifiers, by taking the
stability into consideration. In theory, we prove that SNN attains the minimax
optimal convergence rate in risk, and a sharp convergence rate in CIS. The
latter rate result is established for general plug-in classifiers under a
low-noise condition. Extensive simulated and real examples demonstrate that SNN
achieves a considerable improvement in CIS over existing nearest neighbor
classifiers, with comparable classification accuracy. We implement the
algorithm in a publicly available R package snn.


  Whataboutism, a potent tool for disrupting narratives and sowing distrust,
remains under-explored in quantitative NLP research. Moreover, past work has
not distinguished its use as a strategy for misinformation and propaganda from
its use as a tool for pragmatic and semantic framing. We introduce new datasets
from Twitter and YouTube, revealing overlaps as well as distinctions between
whataboutism, propaganda, and the tu quoque fallacy. Furthermore, drawing on
recent work in linguistic semantics, we differentiate the `what about' lexical
construct from whataboutism. Our experiments bring to light unique challenges
in its accurate detection, prompting the introduction of a novel method using
attention weights for negative sample mining. We report significant
improvements of 4% and 10% over previous state-of-the-art methods in our
Twitter and YouTube collections, respectively.


  Deep learning algorithms have demonstrated remarkable efficacy in various
medical image analysis (MedIA) applications. However, recent research
highlights a performance disparity in these algorithms when applied to specific
subgroups, such as exhibiting poorer predictive performance in elderly females.
Addressing this fairness issue has become a collaborative effort involving AI
scientists and clinicians seeking to understand its origins and develop
solutions for mitigation within MedIA. In this survey, we thoroughly examine
the current advancements in addressing fairness issues in MedIA, focusing on
methodological approaches. We introduce the basics of group fairness and
subsequently categorize studies on fair MedIA into fairness evaluation and
unfairness mitigation. Detailed methods employed in these studies are presented
too. Our survey concludes with a discussion of existing challenges and
opportunities in establishing a fair MedIA and healthcare system. By offering
this comprehensive review, we aim to foster a shared understanding of fairness
among AI researchers and clinicians, enhance the development of unfairness
mitigation methods, and contribute to the creation of an equitable MedIA
society.


  Federated learning allows loads of edge computing devices to collaboratively
learn a global model without data sharing. The analysis with partial device
participation under non-IID and unbalanced data reflects more reality. In this
work, we propose federated learning versions of adaptive gradient methods -
Federated AGMs - which employ both the first-order and second-order momenta, to
alleviate generalization performance deterioration caused by dissimilarity of
data population among devices. To further improve the test performance, we
compare several schemes of calibration for the adaptive learning rate,
including the standard Adam calibrated by $\epsilon$, $p$-Adam, and one
calibrated by an activation function. Our analysis provides the first set of
theoretical results that the proposed (calibrated) Federated AGMs converge to a
first-order stationary point under non-IID and unbalanced data settings for
nonconvex optimization. We perform extensive experiments to compare these
federated learning methods with the state-of-the-art FedAvg, FedMomentum and
SCAFFOLD and to assess the different calibration schemes and the advantages of
AGMs over the current federated learning methods.


  The method of ``Total Least Squares'' is proposed as a more natural way (than
ordinary least squares) to approximate the data if both the matrix and and the
right-hand side are contaminated by ``errors''. In this tutorial note, we give
a elementary unified view of ordinary and total least squares problems and
their solution. As the geometry underlying the problem setting greatly
contributes to the understanding of the solution, we introduce least squares
problems and their generalization via interpretations in both column space and
(the dual) row space and we shall use both approaches to clarify the solution.
After a study of the least squares approximation for simple regression we
introduce the notion of approximation in the sense of ``Total Least Squares
(TLS)'' for this problem and deduce its solution in a natural way. Next we
consider ordinary and total least squares approximations for multiple
regression problems and we study the solution of a general overdetermined
system of equations in TLS-sense. In a final section we consider
generalizations with multiple right-hand sides and with ``frozen'' columns. We
remark that a TLS-approximation needs not exist in general; however, the line
(or hyperplane) of best approximation in TLS-sense for a regression problem
does exist always.


  Accurately measuring the properties of the Higgs boson is one of the core
physics objectives of the Circular Electron Positron Collider (CEPC). As a
Higgs factory, the CEPC is expected to operate at a centre-of-mass energy of
$240\,GeV$, deliver an integrated luminosity of $5.6\,ab^{-1}$, and produce one
million Higgs bosons according to the CEPC Conceptual Design Report (CDR).
Combining measurements of the $\ell^+\ell^-H$, $\nu\bar{\nu} H$, and
$q\bar{q}H$ channels, we conclude that the signal strength of $H\to
b\bar{b}/c\bar{c}/gg$ can be measured with a relative accuracy (statistic
uncertainty only) of 0.27\%/4.03\%/1.56\%. Extrapolating to the recently
released TDR operating parameters corresponding to the integrated luminosity of
$20\,ab^{-1}$, the relative accuracy of $H\to b\bar{b}/c\bar{c}/gg$ signal
strength is 0.14\%/2.13\%/0.82\%. We analyze the dependence of the expected
accuracies on the critical detector performances: Color Singlet Identification
(CSI) for the $q\bar{q}H$ channel and flavor tagging for both $\nu\bar{\nu} H$
and $q\bar{q}H$ channels. We observe that compared to the baseline CEPC
detector performance, ideal flavor tagging increases the $H\to
b\bar{b}/c\bar{c}/gg$ signal strength accuracy by 2\%/63\%/13\% in the
$\nu\bar{\nu} H$ channel and 35\%/122\%/181\% in the $q\bar{q}H$ channel. In
addition, better performance of CSI can significantly improve the anticipated
accuracy of signal strength. The relevant systematics are also discussed in
this paper.


  We present HI absorption spectra of the black hole candidate X-ray binary
(XRB) MAXI J1348-630 using the Australian Square Kilometre Array Pathfinder
(ASKAP) and MeerKAT. The ASKAP HI spectrum shows a maximum negative radial
velocity (with respect to the local standard of rest) of $-31\pm4$ km s$^{-1}$
for MAXI J1348-630, as compared to $-50\pm4$ km s$^{-1}$ for a stacked spectrum
of several nearby extragalactic sources. This implies a most probable distance
of $2.2^{+0.5}_{-0.6}$ kpc for MAXI J1348-630, and a strong upper limit of the
tangent point distance at $5.3\pm0.1$ kpc. Our preferred distance implies that
MAXI J1348-630 reached $17\pm10$ % of the Eddington luminosity at the peak of
its outburst, and that the source transited from the soft to the hard X-ray
spectral state at $2.5\pm1.5$ % of the Eddington luminosity. The MeerKAT HI
spectrum of MAXI J1348-630 (obtained from the older, low-resolution 4k mode) is
consistent with the re-binned ASKAP spectrum, highlighting the potential of the
eventual capabilities of MeerKAT for XRB spectral line studies.


  It has been known that Abell 2255 is not a relaxed cluster, but it is
undergoing a merger. Here, we report on the analysis of the XMM-Newton
observations of this cluster. The X-ray data give us the opportunity to reveal
the complexity of the cluster, especially its temperature distribution. The
integrated spectrum is well fitted by a single temperature thermal model,
indicating a mean temperature of ~7 keV. However, the cluster is not isothermal
at this temperature: its eastern regions are significantly cooler, at ~5.5 keV,
whilst towards the West the temperature reaches ~8.5 keV.
  These temperature asymmetries can be explained if Abell 2255 has been
assembled recently by the merging of smaller subunits. It is now in the phase
after the cores of these subunits have collided (the `core-crossing' phase)
some 0.1-0.2 Gyr ago. A comparison with numerical simulations suggests that it
will settle down into a single relaxed cluster in ~(2-3) Gyr.


  Domain-specific contextualized language models have demonstrated substantial
effectiveness gains for domain-specific downstream tasks, like similarity
matching, entity recognition or information retrieval. However successfully
applying such models in highly specific language domains requires domain
adaptation of the pre-trained models. In this paper we propose the empirically
motivated Linguistically Informed Masking (LIM) method to focus
domain-adaptative pre-training on the linguistic patterns of patents, which use
a highly technical sublanguage. We quantify the relevant differences between
patent, scientific and general-purpose language and demonstrate for two
different language models (BERT and SciBERT) that domain adaptation with LIM
leads to systematically improved representations by evaluating the performance
of the domain-adapted representations of patent language on two independent
downstream tasks, the IPC classification and similarity matching. We
demonstrate the impact of balancing the learning from different information
sources during domain adaptation for the patent domain. We make the source code
as well as the domain-adaptive pre-trained patent language models publicly
available at https://github.com/sophiaalthammer/patent-lim.


  Ferroelectric oxides have attracted much attention due to their wide range of
applications, especially in electronic devices such as nonvolatile memories and
tunnel junctions. As a result, the monolithic integration of these materials
into silicon technology and its nanostructuration to develop alternative
cost-effective processes are among the central points in current technology. In
this work, we used a chemical route to obtain nanowire thin films of a novel
Sr1+{\delta}Mn8O16 (SMO) hollandite-type manganese oxide on silicon. Scanning
transmission electron microscopy combined with crystallographic computing
reveals a crystal structure comprising hollandite and pyrolusite units sharing
the edges of their MnO6 octahedra, resulting in three types of tunnels arranged
along the c axis, where ordering of the Sr atoms produces a natural symmetry
breaking. The novel structure gives rise to a ferroelectricity and
piezoelectricity, as revealed by local Direct Piezoelectric Force Microscopy
measurements, which confirmed the ferroelectric nature of SMO nanowire thin
films at room temperature and showed a piezoelectric coefficient d33 value of
22,6 pC/N. Moreover, we proved that flexible vertical SMO nanowires can be
harvested and converted into electric output energy through the piezoelectric
effect, showing an excellent deformability and high interface recombination.
This work indicates the possibility of engineering the integration of 1D
manganese oxides on silicon, a step which precedes the production of
microelectronic devices.


  If $V$ is a smooth projective variety defined over a local field $K$ with
finite residue field, so that its \'etale cohomology over the algebraic closure
$\bar{K}$ is supported in codimension 1, then the mod $p$ reduction of a
projective regular model carries a rational point. As a consequence, if the
Chow group of 0-cycles of $V$ over a large algebraically closed field is
trivial, then the mod $p$ reduction of a projective regular model carries a
rational point.


  We study an iterative low-rank approximation method for the solution of the
steady-state stochastic Navier--Stokes equations with uncertain viscosity. The
method is based on linearization schemes using Picard and Newton iterations and
stochastic finite element discretizations of the linearized problems. For
computing the low-rank approximate solution, we adapt the nonlinear iterations
to an inexact and low-rank variant, where the solution of the linear system at
each nonlinear step is approximated by a quantity of low rank. This is achieved
by using a tensor variant of the GMRES method as a solver for the linear
systems. We explore the inexact low-rank nonlinear iteration with a set of
benchmark problems, using a model of flow over an obstacle, under various
configurations characterizing the statistical features of the uncertain
viscosity, and we demonstrate its effectiveness by extensive numerical
experiments.


  We establish the existence of solutions of fully nonlinear parabolic
second-order equations like $\partial_{t}u+H(v,Dv,D^{2}v,t,x)=0$ in smooth
cylinders without requiring $H$ to be convex or concave with respect to the
second-order derivatives. Apart from ellipticity nothing is required of $H$ at
points at which $|D^{2}v|\leq K$, where $K$ is any fixed constant. For large
$|D^{2}v|$ some kind of relaxed convexity assumption with respect to $D^{2}v$
mixed with a VMO condition with respect to $t,x$ are still imposed. The
solutions are sought in Sobolev classes. We also establish the solvability
without almost any conditions on $H$, apart from ellipticity, but of a
"cut-off" version of the equation $\partial_{t}u+H(v,Dv,D^{2}v,t,x)=0$.


  Phase diagrams exhibiting extended solid-solution and lens-like melting are
often reproduced using ideal solutions, where ideal mixing considers a fully
random configurational entropy of mixing. In the field of irreversible
thermodynamics, experimental measurements of the composition variation of
high-temperature electronic transport and molten-state properties suggest
however a strong role for short-range atomic ordering in these systems. Herein,
measurements of the thermopower and resistivity are reported for Cu-Ni
solid-solutions as a function of temperature and composition. The electronic
transport properties were interpreted with an irreversible thermodynamic
framework, revealing a large electronic contribution to the entropy of mixing.
Through appeal to a cluster model for the configurational entropy that uses the
electronic contribution to inform the existence of ordered associates, we
rationalize such contribution of the electronic entropy with the notion of an
ideal entropy of mixing commonly used to model such systems. These results
suggest that the short range order (S.R.O.) of the atoms plays a significant
role in both the solid and molten states, even when there are no dominant
intermetallic compounds in these alloys.


  This article introduces a new approach to implement positivity for the 2-loop
n-particle MHV amplituhedron, circumventing the conventional triangulation with
respect to positive variables of each cell carved out by the sign flips. This
approach is universal for all linear positive conditions and hence free of
case-by-case triangulation, as an application of the trick of positive infinity
first introduced in 1910.14612 for the multi-loop 4-particle amplituhedron.
Moreover, the proof of 2-loop n-particle MHV amplituhedron in 1812.01822 is
revised, and we explain the nontriviality and difficulty of using conventional
triangulation while the results have a simple universal pattern. A further
example is presented to tentatively explore its generalization towards handling
multiple positive conditions at 3-loop and higher.


  We investigate how the self gravity changes the orbital stability of Twotinos
using N-body simulations in which the gravitational interaction between
planetesimals are fully calculated. We show the timescale in which the half of
Twotinos becomes unstable, t_{half}, obey the formula, t_{half} = 4 \times
106(Mtot0.1M\oplus)-1(mp7.6 \times 1023g)-1(hi2i1/20.002)(years), if we assume
the primordial planetesimals disk have the power low surface mass density,
\Sigma = \Sigma_0 \times r-2/3. Where Mtot, mp, hi2i1/2 are the total mass of
large bodies of Twotinos, the maximum mass of planetesimals, and the
inclination dispersion. With this formulae, we conclude the total mass of
Twotinos is reduced to the order of 0.01 M\oplus by the self gravity and
secular perturbation of the Planets even if there was huge mass such as several
order of the earth mass in 1:2 MMR of Neptune at the early age of the solar
system. These results will invoke reexamination to many previous works which
tried to explain the dynamical evolution of TNOs.


  Cloud Computing has caused a paradigm shift in the world of computing.
Several use case scenarios have been floating around the programming world in
relation to this. Applications such as Spreadsheets have the capability to use
the Cloud framework to create complex web based applications. In our effort to
do the same, we have proposed a Spreadsheet on the cloud as the framework for
building new web applications, which will be useful in various scenarios,
specifically a School administration system and governance scenarios, such as
Health and Administration. This paper is a manifestation of this work, and
contains some use cases and architectures which can be used to realize these
scenarios in the most efficient manner.


  Superconducting [(Li(1-x)Fex)OH](Fe(1-y)Liy)Se (x ~ 0.2, y ~ 0.08) was
synthesized by hydrothermal methods and structurally characterized by single
crystal X-ray diffraction. The crystal structure contains anti-PbO type
(Fe(1-y)Liy)Se layers separated by layers of (Li(1-x)Fex)OH. Electrical
resistivity and magnetic susceptibility measurements reveal superconductivity
at 43 K. An anomaly in the diamagnetic shielding indicates ferromagnetic
ordering near 10 K while superconductivity is retained. The ferromagnetism
emerges from the iron atoms in the (Li(1-x)Fex)OH layer. Isothermal
magnetization measurements confirm the superposition of ferromagnetic with
superconducting hysteresis. The internal ferromagnetic field is larger than the
lower, but smaller than the upper critical field of the superconductor, which
gives evidence for a spontaneous vortex phase where both orders coexist.
57Fe-M\"ossbauer spectra, 7Li-NMR spectra, and muSR experiments consistently
support this rare situation, especially in a bulk material where magnetism
emerges from a 3d-element.


  Following the approach of Julien Lesgourgues [astro-ph/0409426], we analyze
the mathematical structure of the time co-ordinate of present day cosmological
models, where these models include a cosmological constant term to account for
the observed acceleration of the universe: we find that in all cases, except
for a set of measure zero in the parameter space, the time is given by an
(abelian) integral on a torus; the imaginary period of this integral then gives
a natural periodicity in imaginary time for the universe; following Stephen
Hawking, this periodicity may be interpreted either as giving a fundamental
mass scale for the universe, or (using Planck's constant) a fundamental
temperature, or both. The precise structure that emerges suggests that the
structure of time can be regarded as an order parameter arising perhaps in a
phase transition in the early universe; one might hope that this structure
would be predictable in any fundamental theory.


  In plasma turbulence theory, due to the complexity of the system with many
non-linearly interacting waves, the dynamics of the phases is often disregarded
and the so-called random-phase approximation (RPA) is used assuming the
existence of a Chirikov-like criterion for the onset of wave stochasticity. The
dynamical amplitudes are represented as complex numbers, $\psi = \psi_r +
i\psi_i = ae^{i\theta}$, with the amplitudes slowly varying whereas the phases
are rapidly varying and, in particular, distributed uniformly over the interval
$[0;2\pi)$. However, one could expect that the phase dynamics can play a role
in the self-organisation and the formation of coherent structures. In the same
manner it is also expected that the RPA falls short to take coherent
interaction between phases into account. In this work therefore, we studied the
role of phase dynamics and the coupling of phases between different modes on
the characteristic time evolution of the turbulent. We assume a simple
turbulent system where the so-called stochastic oscillator model can be
employed. The idea of interpreting turbulence by stochastic oscillators. The
stochastic oscillator models can be derived from radical simplifications of the
nonlinear terms in the Navier-Stokes or Gyro-Kinetic equations. In this
particular case we adopt the basic equation for the stochastic oscillator model
with passive advection and random forcing from Ref.


  Using the monotonicity formulas of Colding and Minicozzi, we prove that on
any complete, non-parabolic Riemannian manifold $(M^3, g)$ with non-negative
Ricci curvature, the asymptotic weighted scaling invariant integral of scalar
curvature has an explicit bound in form of asymptotic volume ratio.


  First steps are taken in a project to construct a general class of conformal
and perhaps, eventually, non-conformal quantum field theories of
(n-1)-dimensional extended objects in a d=2n dimensional conformal space-time
manifold M. The fields live on the spaces E of relative integral (n-1)-cycles
in M -- the integral (n-1)-currents of given boundary. Each E is a complete
metric space geometrically analogous to a Riemann surface $\Sigma$. For
example, if $M=S^d$, $\Sigma = S^2$. The quantum fields on E are to be mapped
to observables in a 2d CFT on $\Sigma$. The correlation functions on E are to
be given by the 2d correlation functions on $\Sigma$. The goal is to construct
a CFT of extended objects in d=2n dimensions for every 2d CFT, and eventually a
non-conformal QFT of extended objects for every non-conformal 2d QFT, so that
all the technology of 2d QFT can be applied to the construction and analysis of
quantum field theories of extended objects. The project depends crucially on
settling some mathematical questions about analysis in the spaces E. The
project also depends on extending the observables of 2d CFT from the finite
sets of points in a Riemann surface to the integral 0-currents.


  Convolutional Low-Density-Parity-Check (LDPC) ensembles have excellent
performance. Their iterative threshold increases with their average degree, or
with the size of the coupling window in randomized constructions. In the later
case, as the window size grows, the Belief Propagation (BP) threshold attains
the maximum-a-posteriori (MAP) threshold of the underlying ensemble. In this
contribution we show that a similar phenomenon happens for the growth rate of
coupled ensembles. Loosely speaking, we observe that as the coupling strength
grows, the growth rate of the coupled ensemble comes close to the concave hull
of the underlying ensemble's growth rate. For ensembles randomly coupled across
a window the growth rate actually tends to the concave hull of the underlying
one as the window size increases. Our observations are supported by the
calculations of the combinatorial growth rate, and that of the growth rate
derived from the replica method. The observed concavity is a general feature of
coupled mean field graphical models and is already present at the level of
coupled Curie-Weiss models. There, the canonical free energy of the coupled
system tends to the concave hull of the underlying one. As we explain, the
behavior of the growth rate of coupled ensembles is exactly analogous.


  Let $S_k(m):=1^k+2^k+\cdots+(m-1)^k$ denote a power sum. In 2011 Bernd
Kellner formulated the conjecture that for $m\ge 4$ the ratio $S_k(m+1)/S_k(m)$
of two consecutive power sums is never an integer. We will develop some
techniques that allow one to exclude many integers $\rho$ as a ratio and
combine them to exclude the integers $3\le \rho\le 1501$ and, assuming a
conjecture on irregular primes to be true, a set of density $1$ of ratios
$\rho$. To exclude a ratio $\rho$ one has to show that the Erd\H{o}s-Moser type
equation $(\rho-1)S_k(m)=m^k$ has no non-trivial solutions.


  Clarke and Pringle (2004) derived a proper viscosity formula in a rotating
gas by applying mean free path theory. We study their argument in detail and
show that their result can be derived with a much simpler calculational
procedure and physically clearer picture.


  We use a 200 $h^{-1}Mpc$ a side N-body simulation to study the mass accretion
history (MAH) of dark matter halos to be accreted by larger halos, which we
call infall halos. We define a quantity $a_{\rm nf}\equiv (1+z_{\rm
f})/(1+z_{\rm peak})$ to characterize the MAH of infall halos, where $z_{\rm
peak}$ and $z_{\rm f}$ are the accretion and formation redshifts, respectively.
We find that, at given $z_{\rm peak}$, their MAH is bimodal. Infall halos are
dominated by a young population at high redshift and by an old population at
low redshift. For the young population, the $a_{\rm nf}$ distribution is narrow
and peaks at about $1.2$, independent of $z_{\rm peak}$, while for the old
population, the peak position and width of the $a_{\rm nf}$ distribution both
increases with decreasing $z_{\rm peak}$ and are both larger than those of the
young population. This bimodal distribution is found to be closely connected to
the two phases in the MAHs of halos. While members of the young population are
still in the fast accretion phase at $z_{\rm peak}$, those of the old
population have already entered the slow accretion phase at $z_{\rm peak}$.
This bimodal distribution is not found for the whole halo population, nor is it
seen in halo merger trees generated with the extended Press-Schechter
formalism. The infall halo population at $z_{\rm peak}$ are, on average,
younger than the whole halo population of similar masses identified at the same
redshift. We discuss the implications of our findings in connection to the
bimodal color distribution of observed galaxies and to the link between central
and satellite galaxies.


  We present a novel sensor-based learning navigation algorithm to compute a
collision-free trajectory for a robot in dense and dynamic environments with
moving obstacles or targets. Our approach uses deep reinforcement
learning-based expert policy that is trained using a sim2real paradigm. In
order to increase the reliability and handle the failure cases of the expert
policy, we combine with a policy extraction technique to transform the
resulting policy into a decision tree format. The resulting decision tree has
properties which we use to analyze and modify the policy and improve
performance on navigation metrics including smoothness, frequency of
oscillation, frequency of immobilization, and obstruction of target. We are
able to modify the policy to address these imperfections without retraining,
combining the learning power of deep learning with the control of
domain-specific algorithms. We highlight the benefits of our algorithm in
simulated environments and navigating a Clearpath Jackal robot among moving
pedestrians. (Videos at this url:
https://gamma.umd.edu/researchdirections/xrl/navviper)


  In order to search for new VLBI sources in the Galactic plane that can be
used as phase reference sources in differential VLBI, we have conducted 22 GHz
observations of radio sources in the Galactic plane using the Japanese VLBI
Network (J-Net). We have observed 267 VLBI source candidates selected from
existing radio surveys and have detected 93 sources at the signal-to-noise
ratio larger than 5. While 42 of the 93 detected sources had already been
detected with VLBI at relatively lower frequency (typically 2 to 8 GHz), the
remaining 51 are found to be new VLBI sources detected for the first time.
These VLBI sources are located within $|b|\le 5^\circ$, and have a large number
of Galactic maser sources around them. Thus, they are potential candidates for
phase reference sources for VLBI Exploration of Radio Astrometry (VERA), which
is the first VLBI array dedicated to the phase referencing VLBI astrometry
aiming at measuring the parallax and proper motion of maser sources in the
whole Galaxy.


  We study the effect of dark matter (DM) particles in the Sun, focusing in
particular on the possible reduction of the solar neutrinos flux due to the
energy carried away by DM particles from the innermost regions of the Sun, and
to the consequent reduction of the temperature of the solar core. We find that
in the very low-mass range between 4 and 10 GeV, recently advocated to explain
the findings of the DAMA and CoGent experiments, the effects on neutrino fluxes
are detectable only for DM models with very small, or vanishing,
self-annihilation cross section, such as the so-called asymmetric DM models,
and we study the combination of DM masses and Spin Dependent cross sections
which can be excluded with current solar neutrino data. Finally, we revisit the
recent claim that DM models with large self-interacting cross sections can lead
to a modification of the position of the convective zone, alleviating or
solving the solar composition problem. We show that when the `geometric' upper
limit on the capture rate is correctly taken into account, the effects of DM
are reduced by orders of magnitude, and the position of the convective zone
remains unchanged.


  Massive multiple-input multiple-output (MIMO) technology has significantly
enhanced spectral and power efficiency in cellular communications and is
expected to further evolve towards extra-large-scale MIMO. However, centralized
processing for massive MIMO faces practical obstacles, including excessive
computational complexity and a substantial volume of baseband data to be
exchanged. To address these challenges, decentralized baseband processing has
emerged as a promising solution. This approach involves partitioning the
antenna array into clusters with dedicated computing hardware for parallel
processing. In this paper, we investigate the gradient-based Markov chain Monte
Carlo (MCMC) method -- an advanced MIMO detection technique known for its
near-optimal performance in centralized implementation -- within the context of
a decentralized baseband processing architecture. This decentralized design
mitigates the computation burden at a single processing unit by utilizing
computational resources in a distributed and parallel manner. Additionally, we
integrate the mini-batch stochastic gradient descent method into the proposed
decentralized detector, achieving remarkable performance with high efficiency.
Simulation results demonstrate substantial performance gains of the proposed
method over existing decentralized detectors across various scenarios.
Moreover, complexity analysis reveals the advantages of the proposed
decentralized strategy in terms of computation delay and interconnection
bandwidth when compared to conventional centralized detectors.


  Instanton-induced deep-inelastic processes are considered. Unlike previous
results we calculate nonzero quark modes contribution.


  We study the problem of estimation of the value N_gamma(\theta) = sum(i=1)^d
|\theta_i|^gamma for 0 < gamma <= 1 based on the observations y_i = \theta_i +
\epsilon\xi_i, i = 1,...,d, where \theta = (\theta_1,...,\theta_d) are unknown
parameters, \epsilon>0 is known, and \xi_i are i.i.d. standard normal random
variables. We prove that the non-asymptotic minimax risk on the class B_0(s) of
s-sparse vectors and we propose estimators achieving the minimax rate.


  This paper presents a method to learn hand-object interaction prior for
reconstructing a 3D hand-object scene from a single RGB image. The inference as
well as training-data generation for 3D hand-object scene reconstruction is
challenging due to the depth ambiguity of a single image and occlusions by the
hand and object. We turn this challenge into an opportunity by utilizing the
hand shape to constrain the possible relative configuration of the hand and
object geometry. We design a generalizable implicit function, HandNeRF, that
explicitly encodes the correlation of the 3D hand shape features and 2D object
features to predict the hand and object scene geometry. With experiments on
real-world datasets, we show that HandNeRF is able to reconstruct hand-object
scenes of novel grasp configurations more accurately than comparable methods.
Moreover, we demonstrate that object reconstruction from HandNeRF ensures more
accurate execution of downstream tasks, such as grasping and motion planning
for robotic hand-over and manipulation. The code is released here:
https://github.com/SamsungLabs/HandNeRF


  Similarity plays a fundamental role in many areas, including data mining,
machine learning, statistics and various applied domains. Inspired by the
success of ensemble methods and the flexibility of trees, we propose to learn a
similarity kernel called rpf-kernel through random projection forests
(rpForests). Our theoretical analysis reveals a highly desirable property of
rpf-kernel: far-away (dissimilar) points have a low similarity value while
nearby (similar) points would have a high similarity}, and the similarities
have a native interpretation as the probability of points remaining in the same
leaf nodes during the growth of rpForests. The learned rpf-kernel leads to an
effective clustering algorithm--rpfCluster. On a wide variety of real and
benchmark datasets, rpfCluster compares favorably to K-means clustering,
spectral clustering and a state-of-the-art clustering ensemble
algorithm--Cluster Forests. Our approach is simple to implement and readily
adapt to the geometry of the underlying data. Given its desirable theoretical
property and competitive empirical performance when applied to clustering, we
expect rpf-kernel to be applicable to many problems of an unsupervised nature
or as a regularizer in some supervised or weakly supervised settings.


  By identifying the socio-technical conditions required for teams to work
effectively remotely, the Distance Matters framework has been influential in
CSCW since its introduction in 2000. Advances in collaboration technology and
practices have since brought teams increasingly closer to achieving these
conditions. This paper presents a ten-month ethnography in a remote
organization, where we observed that despite exhibiting excellent remote
collaboration, teams paradoxically struggled to collaborate across team
boundaries. We extend the Distance Matters framework to account for inter-team
collaboration, arguing that challenges analogous to those in the original
intra-team framework -- common ground, collaboration readiness, collaboration
technology readiness, and coupling of work -- persist but are actualized
differently at the inter-team scale. Finally, we identify a fundamental tension
between the intra- and inter-team layers: the collaboration technology and
practices that help individual teams thrive (e.g., adopting customized
collaboration software) can also prompt collaboration challenges in the
inter-team layer, and conversely the technology and practices that facilitate
inter-team collaboration (e.g., strong centralized IT organizations) can harm
practices at the intra-team layer. The addition of the inter-team layer to the
Distance Matters framework opens new opportunities for CSCW, where balancing
the tension between team and organizational collaboration needs will be a
critical technological, operational, and organizational challenge for remote
work in the coming decades.


  Nano-diamonds have been observed in only a handful of circumstellar regions
$10-100$\,\tiny{ A.U.} from moderately bright stars ($T_{\rm eff} \sim
8,000-10,000$\,K). They have also been extracted from primitive meteorites;
some of these are clearly pre-solar, that is to say that they formed far from
the solar system and therefore traversed the interstellar medium, where they
must exist but, because we see no evidence of them, must be extremely well
hidden. Our goal is to understand if it is possible to constrain the sizes and
shapes of nano-diamonds in circumstellar media using the observed ratio,
[CH]/[CH$_2$], of their surface CH$_2$ and CH infrared bands at $\simeq
3.43\,\mu$m and $\simeq 3.53\,\mu$m, respectively. We calculated the CH and
CH$_2$ abundances on nano-diamonds using two approaches. The first assumes
regular and semi-regular polyhedra (tetrahedra, octahedra, and cubes and their
truncated forms). The second uses a diamond bonding network to derive the
structures of tetrahedral and octahedral particles, and their truncated
variants, and also of spherical nano-diamonds. As a function of the particle
size and shape, and for the two different calculation methods, we derived the
relative abundance ratio [CH]/[CH$_2$], which can then be weighted by their
laboratory-measured infrared band intensities. The two methods give good
agreement and indicate that the spread in values, over the different particle
forms, is more that an order of magnitude for any size. We conclude that the
ratio [CH]/[CH$_2$], and their infrared band ratio, strongly depend upon
particle size and shape. For a given shape or size, the ratio can vary by more
than an order of magnitude. It may therefore be difficult to constrain
nano-diamond sizes using the observed $3-4\,\mu$m spectra alone. James Webb
Space Telescope (JWST) mid-infrared spectra may help, but only if bands are
size-specific.


  Tropical varieties capture combinatorial information about how coordinates of
points in a classical variety approach zero or infinity. We present algorithms
for computing the rays of a complex and real tropical curve defined by
polynomials with constant coefficients. These algorithms rely on homotopy
continuation, monodromy loops, and Cauchy integrals. Several examples are
presented which are computed using an implementation that builds on the
numerical algebraic geometry software Bertini.


  We methodically model the broad-band Suzaku spectra of a small sample of six
'bare' Seyfert galaxies: Ark 120, Fairall 9, MCG-02-14-009, Mrk 335, NGC 7469
and SWIFT J2127.4+5654. The analysis of bare Seyferts allows a consistent and
physical modelling of AGN due to a weak amount of any intrinsic warm
absorption, removing the degeneracy between the spectral curvature due to warm
absorption and the red-wing of the Fe K region. Through effective modelling of
the broad-band spectrum and investigating the presence of narrow neutral or
ionized emission lines and reflection from distant material, we obtain an
accurate and detailed description of the Fe K line region using models such as
laor, kerrdisk and kerrconv.
  Results suggest that ionized emission lines at 6.7 keV and 6.97 keV
(particularly Fe XXVI) are relatively common and the inclusion of these lines
can greatly affect the parameters obtained with relativistic models i.e. spin,
emissivity, inner radius of emission and inclination. Moderately broad
components are found in all objects, but typically the emission originates from
tens of Rg, rather than within <6Rg of the black hole. Results obtained with
kerrdisk line profiles suggest an average emissivity of q~2.3 at intermediate
spin values with all objects ruling out the presence of a maximally spinning
black hole at the 90% confidence level. We also present new spin constraints
for Mrk 335 and NGC 7469 with intermediate values of a=0.70(+0.12,-0.01) and
a=0.69(+0.09,-0.09) respectively.


  Controlling quasiparticle dynamics can improve the performance of
superconducting devices. For example, it has been demonstrated effective in
increasing lifetime and stability of superconducting qubits. Here we study how
to optimize the placement of normal-metal traps in transmon-type qubits. When
the trap size increases beyond a certain characteristic length, the details of
the geometry and trap position, and even the number of traps, become important.
We discuss for some experimentally relevant examples how to shorten the decay
time of the excess quasiparticle density. Moreover, we show that a trap in the
vicinity of a Josephson junction can reduce the steady-state quasiparticle
density near that junction, thus suppressing the quasiparticle-induced
relaxation rate of the qubit. Such a trap also reduces the impact of
fluctuations in the generation rate of quasiparticles, rendering the qubit more
stable.


  Increasingly better observations of resolved protoplanetary disks show a wide
range of conditions in which planets can be formed. Many transitional disks
show gaps in their radial density structure, which are usually interpreted as
signatures of planets. It has also been suggested that observed inhomogeneities
in transitional disks are indicative of dust traps which may help the process
of planet formation. However, it is yet to be seen if the configuration of
fully evolved exoplanetary systems can yield information about the later stages
of their primordial disks. We use synthetic exoplanet population data from
Monte Carlo simulations of systems forming under different density perturbation
conditions, which are based on current observations of transitional disks. The
simulations use a core instability, oligarchic growth, dust trap analytical
model that has been benchmarked against exoplanetary populations.


  Interactions of the type ep -> eXY are studied, where the component X of the
hadronic final state contains two jets and is well separated in rapidity from a
leading baryonic system Y. Analyses are performed of both resolved and direct
photoproduction and of deep-inelastic scattering with photon virtualities in
the range 7.5 < Q^2 < 80 GeV^2. Cross sections are presented where Y has mass
M_Y < 1.6 GeV, the squared four-momentum transferred at the proton vertex
satisfies |t| < 1 GeV^2 and the two jets each have transverse momentum p^jet_T
> 5 GeV relative to the photon direction in the rest frame of X. Models based
on a factorisable diffractive exchange with a gluon dominated structure,
evolved to a scale set by the transverse momentum p^hat_T of the outgoing
partons from the hard interaction, give good descriptions of the data.
Exclusive qqbar production, as calculated in perturbative QCD using the squared
proton gluon density, represents at most a small fraction of the measured cross
section. The compatibility of the data with a breaking of diffractive
factorisation due to spectator interactions in resolved photoproduction is
investigated.


  We prove a local energy identity for a class of distributional solutions, in
$L_{2,\infty} \cap W^{1,0}_2$, of parabolic equations with divergence-free
drift.


  The non-trivial topology of the three-dimensional (3D) topological insulator
(TI) dictates the appearance of gapless Dirac surface states. Intriguingly,
when a 3D TI is made into a nanowire, a gap opens at the Dirac point due to the
quantum confinement, leading to a peculiar Dirac sub-band structure. This gap
is useful for, e.g., future Majorana qubits based on TIs. Furthermore, these
Dirac sub-bands can be manipulated by a magnetic flux and are an ideal platform
for generating stable Majorana zero modes (MZMs), which play a key role in
topological quantum computing. However, direct evidence for the Dirac sub-bands
in TI nanowires has not been reported so far. Here we show that by growing very
thin ($\sim$40-nm diameter) nanowires of the bulk-insulating topological
insulator (Bi$_{1-x}$Sb$_x$)$_2$Te$_3$ and by tuning its chemical potential
across the Dirac point with gating, one can unambiguously identify the Dirac
sub-band structure. Specifically, the resistance measured on gate-tunable
four-terminal devices was found to present non-equidistant peaks as a function
of the gate voltage, which we theoretically show to be the unique signature of
the quantum-confined Dirac surface states. These TI nanowires open the way to
address the topological mesoscopic physics, and eventually the Majorana physics
when proximitised by an $s$-wave superconductor.


  A Relational Database Management System (RDBMS) is one of the fundamental
software that supports a wide range of applications, making it critical to
identify bugs within these systems. There has been active research on testing
RDBMS, most of which employ crash or use metamorphic relations as the oracle.
Although existing approaches can detect bugs in RDBMS, they are far from
comprehensively evaluating the RDBMS's correctness (i.e., with respect to the
semantics of SQL). In this work, we propose a method to test the semantic
conformance of RDBMS i.e., whether its behavior respects the intended semantics
of SQL. Specifically, we have formally defined the semantics of SQL and
implemented them in Prolog. Then, the Prolog implementation serves as the
reference RDBMS, enabling differential testing on existing RDBMS. We applied
our approach to four widely-used and thoroughly tested RDBMSs, i.e., MySQL,
TiDB, SQLite, and DuckDB. In total, our approach uncovered 19 bugs and 11
inconsistencies, which are all related to violating the SQL specification or
missing/unclear specification, thereby demonstrating the effectiveness and
applicability of our approach.


  DevOps is a quite effective approach for managing software development and
operation, as confirmed by plenty of success stories in real applications and
case studies. DevOps is now becoming the main-stream solution adopted by the
software industry in development, able to reduce the time to market and costs
while improving quality and ensuring evolvability and adaptability of the
resulting software architecture. Among the aspects to take into account in a
DevOps process, data is assuming strategic importance, since it allows to gain
insights from the operation directly into the development, the main objective
of a DevOps approach. Data can be therefore considered as the fuel of the
DevOps process, requiring proper solutions for its management. Based on the
amount of data generated, its variety, velocity, variability, value and other
relevant features, DevOps data management can be mainly framed into the BigData
category. This allows exploiting BigData solutions for the management of DevOps
data generated throughout the process, including artefacts, code,
documentation, logs and so on. This paper aims at investigating data management
in DevOps processes, identifying related issues, challenges and potential
solutions taken from the BigData world as well as from new trends adopting and
adapting DevOps approaches in data management, i.e. DataOps.


  Currently, much effort is being put into detecting unconventional p-wave
superconductivity in Josephson junctions based on topological insulators (TIs).
For that purpose we propose to use superconducting Klein tunneling, i.e. the
reflectionless passage of Cooper pairs through a potential barrier in a gated
ballistic junction. This phenomenon occurs due to the fact that the
supercurrent is carried by helical Andreev bound states (ABSs) characterized by
spin-momentum locking similar to the normal-state carriers. We derive the
spectrum of the helical ABSs and the corresponding Josephson current for a
junction made on the surface of a three-dimensional TI. The superconducting
Klein tunneling is predicted to yield a non-sinusoidal current-phase relation
and an anomalous critical current $I_c$ that does not vanish with increasing
barrier strength. We also analyze the dependence of the I_cR_n product (where
R_n is the normal-state junction resistance) on the microscopic parameters of
the superconductor/TI interface, which leads to lower I_cR_n values than
expected from previous models of the proximity-effect Josephson junctions.


  We describe a geometric-combinatorial algorithm that allows one, using solely
the system of weights and roots, to determine the Hesselink strata of the
null-cone of a linear representation of a reductive algebraic group and
calculate their dimensions. In particular, it provides a constructive approach
to calculating the dimension of the null-cone and determining all its
irreducible components of maximal dimension. In the case of the adjoint
representation (and, more generally, a $\theta$-representation), the algorithm
turns into the classification algorithm for the conjugacy classes of nilpotent
elements in a semisimple Lie algebra (respectively, homogeneous nilpotent
elements in a cyclically graded semisimple Lie algebra).


  Contemporary graph learning algorithms are not well-defined for large
molecules since they do not consider the hierarchical interactions among the
atoms, which are essential to determine the molecular properties of
macromolecules. In this work, we propose Multiresolution Graph Transformers
(MGT), the first graph transformer architecture that can learn to represent
large molecules at multiple scales. MGT can learn to produce representations
for the atoms and group them into meaningful functional groups or repeating
units. We also introduce Wavelet Positional Encoding (WavePE), a new positional
encoding method that can guarantee localization in both spectral and spatial
domains. Our proposed model achieves competitive results on two macromolecule
datasets consisting of polymers and peptides, and one drug-like molecule
dataset. Importantly, our model outperforms other state-of-the-art methods and
achieves chemical accuracy in estimating molecular properties (e.g., GAP, HOMO
and LUMO) calculated by Density Functional Theory (DFT) in the polymers
dataset. Furthermore, the visualizations, including clustering results on
macromolecules and low-dimensional spaces of their representations, demonstrate
the capability of our methodology in learning to represent long-range and
hierarchical structures. Our PyTorch implementation is publicly available at
https://github.com/HySonLab/Multires-Graph-Transformer


  Galactic gas outflows are driven by stellar feedback with dominant
contribution from supernovae (SN) explosions. The question of whether the
energy deposited by SNe initiates a large scale outflow or gas circulation on
smaller scales -- between discs and intermediate haloes, depends on SN rate and
their distribution in space and time. We consider here gas circulation by
disc-wide unclustered SNe with galactic star formation rate in the range from
{$\simeq 6\times 10^{-4}$ to $\simeq 6\times
10^{-2}~M_\odot$~yr$^{-1}$~kpc$^{-2}$, corresponding to mid-to-high star
formation observed in galaxies. We show that such disc-wide SN explosion regime
can form circulation of warm ($T\sim 10^4$ K) and cold ($T<10^3$ K) phases
within a few gas scale heights, and elevation of hot {($T>10^5$ K)} gas at
higher ($z>1$ kpc) heights. We found that the threshold energy input rate for
hot gas outflows with disc-wide supernovae explosions is estimated to be of the
order $\sim 4\times 10^{-4}$~erg~s$^{-1}$~cm$^{-2}$. We discuss the
observational manifestations of such phenomena in optical and X-ray bands. In
particular, we found that for face-on galaxies with SF ($\Sigma_{_{\rm
SF}}>0.02~M_\odot$~yr$^{-1}$~kpc$^{-2}$), the line profiles of ions typical for
warm gas show a double-peak shape, corresponding to out-of-plane outflows. In
the X-ray bands, galaxies with high SF rates ($\Sigma_{_{\rm
SF}}>0.006~M_\odot$~yr$^{-1}$~kpc$^{-2}$) can be bright, with a smooth surface
brightness in low-energy bands ($0.1\hbox{--}0.3$~keV) and patchy at higher
energies ($1.6\hbox{--}8.3$~keV).}


  Silicon-on-insulator (SOI) enables for capability improvement of modern
information processing systems by replacing some of their electrical
counterparts. With the miniaturization of SOI platform, backscattering
suppression is one of the central issues to avoid energy loss and signal
distortion in telecommunications. Valley, a new degree of freedom, provides an
intriguing way for topologically robust information transfer and unidirectional
flow of light, in particular for subwavelength strategy that still remains
challenge in topological nanophotonics. Here, we realize topological transport
in a SOI valley photonic crystal (VPC) slab. In such inversion asymmetry slab,
singular Berry curvature near Brillouin zone corners guarantees
valley-dependent topological edge states below light cone, maintaining a
balance between in-plane robustness and out-of-plane radiation. Topologically
robust transport at telecommunication wavelength is observed along two
sharp-bend VPC interfaces with a compact size (< 10 um), showing flat-top high
transmission of around 10% bandwidth. Furthermore, topological photonic routing
is achieved in a bearded-stack VPC interface, originating from broadband
unidirectional excitation of the valley-chirality-locked edge state by using a
microdisk as a phase vortex generator. Control of valley in SOI platform not
only shows a prototype of integrated photonic devices with promising
applications for delay line, routing, optical isolation and dense wavelength
division multiplexing for information processing based on topological
nanophotonics, but also opens a new door towards the observation of non-trivial
states even in non-Hermitain systems.


  To identify the causes of performance problems or to predict process
behavior, it is essential to have correct and complete event data. This is
particularly important for distributed systems with shared resources, e.g., one
case can block another case competing for the same machine, leading to
inter-case dependencies in performance. However, due to a variety of reasons,
real-life systems often record only a subset of all events taking place. To
understand and analyze the behavior and performance of processes with shared
resources, we aim to reconstruct bounds for timestamps of events in a case that
must have happened but were not recorded by inference over events in other
cases in the system. We formulate and solve the problem by systematically
introducing multi-entity concepts in event logs and process models. We
introduce a partial-order based model of a multi-entity event log and a
corresponding compositional model for multi-entity processes. We define
PQR-systems as a special class of multi-entity processes with shared resources
and queues. We then study the problem of inferring from an incomplete event log
unobserved events and their timestamps that are globally consistent with a
PQR-system. We solve the problem by reconstructing unobserved traces of
resources and queues according to the PQR-model and derive bounds for their
timestamps using a linear program. While the problem is illustrated for
material handling systems like baggage handling systems in airports, the
approach can be applied to other settings where recording is incomplete. The
ideas have been implemented in ProM and were evaluated using both synthetic and
real-life event logs.


  Symmetry breaking at the mean-field level leads to an appearance of a
symmetry restoring Nambu-Goldstone (NG) mode in the linear response theory.
These modes represent a special kind of collective motion of the system.
However, they can interfere with the calculated intrinsic physical excitations
and, hence, they are often called as spurious modes. I discuss translational
and rotational NG mode and the inertia parameter associated with these modes,
by using the finite amplitude method formalism. I will also discuss how to
remove spurious mode from the calculated transition strength function.


  We present the results of 850 $\mu$m polarization and C$^{18}$O (3-2) line
observations toward the western hub-filament structure (W-HFS) of the dark
Streamer in IC 5146 using the James Clerk Maxwell Telescope (JCMT)
SCUBA-2/POL-2 and HARP instruments. We aim to investigate how the relative
importance of the magnetic field, gravity, and turbulence affects core
formation in HFS by comparing the energy budget of this region. We identified
four 850 $\mu$m cores and estimated the magnetic field strengths ($B_{\rm
pos}$) of the cores and the hub and filament using the
Davis-Chandrasekhar-Fermi method. The estimated $B_{\rm pos}$ is $\sim$80 to
1200 $\mu$G. From Wang et al., $B_{\rm pos}$ of E-47, a core in the eastern hub
(E-hub), and E-hub were re-estimated to be 500 and 320 $\mu$G, respectively,
with the same method. We measured the gravitational ($E_{\rm G}$), kinematic
($E_{\rm K}$), and magnetic energies ($E_{\rm B}$) in the filament and hubs and
compared the relative importance among them. We found that an $E_{\rm
B}$-dominant filament has $aligned$ fragmentation type, while $E_{\rm
G}$-dominant hubs show $no$ and $clustered$ fragmentation types. In the $E_{\rm
G}$ dominant hubs, it seems that the portion of $E_{\rm K}$ determines whether
the hub becomes to have $clustered$ (the portion of $E_{\rm K}\sim20\%$) or
$no$ fragmentation type ($\sim10\%$). We propose an evolutionary scenario for
the E- and W-HFSs, where the HFS forms first by the collision of turbulent
flows, and then the hubs and filaments can go into various types of
fragmentation depending on their energy balance of gravity, turbulence, and
magnetic field.


  Shell model turbulence is a simplified mathematical framework that captures
essential features of incompressible fluid turbulence such as the energy
cascade, intermittency and anomalous scaling of the fluid observables. We
perform a precision analysis of shell model of a complex velocity field in the
steady state turbulent regime, including a calculation of the leading hundred
anomalous scaling exponents, the probability distribution function of the
magnitude and phase of the velocity and the correlations among them at
different shells. We analyze the tail of velocity distribution function and
find that the high moments exhibit a linear scaling that differs from
Kolomogorov's. We explain the origin of this asymptotic scaling that offers a
new insight to the structure of fluid turbulence.


  In this article, we explore the potential of using latent diffusion models, a
family of powerful generative models, for the task of reconstructing
naturalistic music from electroencephalogram (EEG) recordings. Unlike simpler
music with limited timbres, such as MIDI-generated tunes or monophonic pieces,
the focus here is on intricate music featuring a diverse array of instruments,
voices, and effects, rich in harmonics and timbre. This study represents an
initial foray into achieving general music reconstruction of high-quality using
non-invasive EEG data, employing an end-to-end training approach directly on
raw data without the need for manual pre-processing and channel selection. We
train our models on the public NMED-T dataset and perform quantitative
evaluation proposing neural embedding-based metrics. We additionally perform
song classification based on the generated tracks. Our work contributes to the
ongoing research in neural decoding and brain-computer interfaces, offering
insights into the feasibility of using EEG data for complex auditory
information reconstruction.


  The atomic and band structures of the (6,0) zigzag carbon nanotube at its
axial elongation are calculated by semiempirical molecular orbital and by
tight-binding methods. The ground state of the nanotube is found to have a
Kekule structure with four types of bonds and difference between lengths of
long and short bonds of about 0.005 nm. The structural phase transition is
revealed at ~9% elongation, resulting in a quinoid structure with two types of
bonds. This structural phase transition is followed by the transition from a
narrow gap to moderate gap semiconductor. Validity of the semiempirical PM3
method is discussed.


  We study the dynamical states that emerge in a small-world network of
recurrently coupled excitable neurons through both numerical and analytical
methods. These dynamics depend in large part on the fraction of long-range
connections or `short-cuts' and the delay in the neuronal interactions.
Persistent activity arises for a small fraction of `short-cuts', while a
transition to failure occurs at a critical value of the `short-cut' density.
The persistent activity consists of multi-stable periodic attractors, the
number of which is at least on the order of the number of neurons in the
network. For long enough delays, network activity at high `short-cut' densities
is shown to exhibit exceedingly long chaotic transients whose failure-times
averaged over many network configurations follow a stretched exponential. We
show how this functional form arises in the ensemble-averaged activity if each
network realization has a characteristic failure-time which is exponentially
distributed.