  We considered the phase coherence dynamics in a Two-Frequency and
Two-Coupling (TFTC) model of coupled oscillators, where coupling strength and
natural oscillator frequencies for individual oscillators may assume one of two
values (positive/negative). The bimodal distributions for the coupling
strengths and frequencies are either correlated or uncorrelated. To study how
correlation affects phase coherence, we analyzed the TFTC model by means of
numerical simulations and exact dimensional reduction methods allowing to study
the collective dynamics in terms of local order parameters. The competition
resulting from distributed coupling strengths and natural frequencies produces
nontrivial dynamic states. For correlated disorder in frequencies and coupling
strengths, we found that the entire oscillator population splits into two
subpopulations, both phase-locked (Lock-Lock) or one phase-locked, and the
other drifting (Lock-Drift), where the mean-fields of the sub-populations
maintain a constant non-zero phase difference. For uncorrelated disorder, we
found that the oscillator population may split into four phase-locked
subpopulations, forming phase-locked pairs which are either mutually
frequency-locked (Stable Lock-Lock-Lock-Lock) or drifting (Breathing
Lock-Lock-Lock-Lock), thus resulting in a periodic motion of the global
synchronization level. Finally, we found for both types of disorder that a
state of Incoherence exists; however, for correlated coupling strengths and
frequencies, incoherence is always unstable, whereas it is only neutrally
stable for the uncorrelated case. Numerical simulations performed on the model
show good agreement with the analytic predictions. The simplicity of the model
promises that real-world systems can be found which display the dynamics
induced by correlated/uncorrelated disorder.


  The string-based Bern-Kosower rules provide an efficient way for obtaining
parameter integral representations of the one-loop N - photon/gluon amplitudes
involving a scalar, spinor or gluon loop, starting from a master formula and
using a certain integration-by-parts (`IBP') procedure. Strassler observed that
this algorithm also relates to gauge invariance, since it leads to the
absorption of polarization vectors into field strength tensors. Here we present
a systematic IBP algorithm that works for arbitrary N and leads to an integrand
that is not only suitable for the application of the Bern-Kosower rules but
also optimized with respect to gauge invariance. In the photon case this means
manifest transversality at the integrand level, in the gluon case that a form
factor decomposition of the amplitude into transversal and longitudinal parts
is generated naturally by the IBP, without the necessity to consider the
nonabelian Ward identities. Our algorithm is valid off-shell, and provides an
extremely efficient way of calculating the one-loop one-particle-irreducible
off-shell Green's functions (`vertices') in QCD. It can also be applied
essentially unchanged to the one-loop gauge boson amplitudes in open string
theory. In the abelian case, we study the systematics of the IBP also for the
practically important case of the one-loop N - photon amplitudes in a constant
field.


  A semi-classical analysis of magneto-thermopower behaviour, namely, the
\textit{Seebeck} and \textit{Nernst} effect (NE) in quantum wells of IV-VI lead
salts with significant extrinsic Rashba spin-orbit coupling (RSOC) is performed
in this report. In addition to the spin-dependent Seebeck effect that has been
observed before, we also theoretically predict a similar spin-delineated
behaviour for its thermal analog, the spin-dependent NE. The choice of lead
salts follows from a two-fold advantage they offer, in part, to their superior
thermoelectric properties, especially PbTe, while their low band gaps and high
spin-orbit coupling make them ideal candidates to study \textit{RSOC} in
nanostructures. The calculations show a larger longitudinal magneto-thermopower
for the spin-up electrons while the transverse components are nearly identical.
In contrast, for a magnetic field free case, the related power factor
calculations reveal a significantly higher contribution from the spin-down
ensemble and suffer a reduction with an increase in the electron density. We
also discuss qualitatively the limitations of the semi-classical approach for
the extreme case of a high magnetic field and allude to the observed
thermopower behaviour when the quantum Hall regime is operational. Finally,
techniques to modulate the thermopower are briefly outlined.


  We prove a recent conjecture of Hadjicostas concerning a double integral
formula involving the zeta and the gamma functions.


  Understanding better the dynamics and steady states of systems strongly
coupled to thermal baths is a great theoretical challenge with promising
applications in several fields of quantum technologies. Among several
strategies to gain access to the steady state, one consists in obtaining
approximate expressions of the mean force Gibbs state, the reduced state of the
global system-bath thermal state, largely credited to be the steady state.
Here, we present analytical expressions of corrective terms to the ultrastrong
coupling limit of the mean force Gibbs state, which has been recently derived.
We find that the first order term precisely coincides with the first order
correction obtained from a dynamical approach -- master equation in the
strong-decoherence regime. This strengthens the identification of the reduced
steady state with the mean force Gibbs state. Additionally, we also compare our
expressions with another recent result obtained from a high temperature
expansion of the mean force Gibbs state. We observe numerically a good
agreement for ultra strong coupling as well as for high temperatures. This
confirms the validity of all these results. In particular, we show that, in
term of coherences, all three results allow one to sketch the transition from
ultrastrong coupling to weak coupling.


  In this paper, we realize automatic visual recognition and direction
estimation of pointing. We introduce the first neural pointing understanding
method based on two key contributions. The first is the introduction of a
first-of-its-kind large-scale dataset for pointing recognition and direction
estimation, which we refer to as the DP Dataset. DP Dataset consists of more
than 2 million frames of 33 people pointing in various styles annotated for
each frame with pointing timings and 3D directions. The second is DeePoint, a
novel deep network model for joint recognition and 3D direction estimation of
pointing. DeePoint is a Transformer-based network which fully leverages the
spatio-temporal coordination of the body parts, not just the hands. Through
extensive experiments, we demonstrate the accuracy and efficiency of DeePoint.
We believe DP Dataset and DeePoint will serve as a sound foundation for visual
human intention understanding.


  Manipulation is a key capability in domestic service robots, as can be seen
in the rulebooks of last Robocup@Home editions. Currently, object recognition
is performed based mostly on visual information. Some robots use also 3D
information such as point clouds or laser scans but, to the knowledge of
authors, robots don't use physical properties to improve object recognition.
Estimation of an object's weight during a manipulation task is something new in
the @Home league and such ability can improve performance of domestic service
robots. In this work we propose to estimate the weight of the grasped object
using Sliding Mode Observers. If we consider the manipulator without load as
the nominal system and object's weight as a fault signal, we can estimate such
weight by an appropriate filtering of the output error injection term of the
sliding mode observer. To implement our proposal we used MATLAB and Simulink
Robotics System Toolbox, ROS Toolbox and Simscape. To improve computation time
we exported all algorithms to standalone ROS nodes from Simulink models. Tests
were performed using two platforms: Justina's left manipulator (a robot
developed at Biorobotics Laboratory, UNAM) and Neuronics Katana manipulators.
We present results in simulation and discuss the performance of the proposed
system and the possible sources of error. Finally we present our conclusions
and state the future work.


  In this work, we present a modeling framework for minimax mixed 0-1 fuzzy
linear problems. It is based on extending the usual rewriting of crisp minimax
problems via auxiliary variables to model the maximum of a finite set of fuzzy
linear functions. We establish that the considered problem can be equivalently
formulated as a multiple objective mixed integer programming problem. The
framework is applied to a fully fuzzy version of the capacitated center
facility location problem.


  Training a small student network with the guidance of a larger teacher
network is an effective way to promote the performance of the student. Despite
the different types, the guided knowledge used to distill is always kept
unchanged for different teacher and student pairs in previous knowledge
distillation methods. However, we find that teacher and student models with
different networks or trained from different initialization could have distinct
feature representations among different channels. (e.g. the high activated
channel for different categories). We name this incongruous representation of
channels as teacher-student knowledge discrepancy in the distillation process.
Ignoring the knowledge discrepancy problem of teacher and student models will
make the learning of student from teacher more difficult. To solve this
problem, in this paper, we propose a novel student-dependent distillation
method, knowledge consistent distillation, which makes teacher's knowledge more
consistent with the student and provides the best suitable knowledge to
different student networks for distillation. Extensive experiments on different
datasets (CIFAR100, ImageNet, COCO) and tasks (image classification, object
detection) reveal the widely existing knowledge discrepancy problem between
teachers and students and demonstrate the effectiveness of our proposed method.
Our method is very flexible that can be easily combined with other
state-of-the-art approaches.


  We develop a model-independent approach to lagrangian perturbation theory for
the large scale structure of the universe. We focus on the displacement field
for dark matter particles, and derive its most general structure without
assuming a specific form for the equations of motion, but implementing a set of
general requirements based on symmetry principles and consistency with the
perturbative approach. We present explicit results up to sixth order, and
provide an algorithmic procedure for arbitrarily higher orders. The resulting
displacement field is expressed as an expansion in operators built up from the
linear density field, with time-dependent coefficients that can be obtained, in
a specific model, by solving ordinary differential equations. The derived
structure is general enough to cover a wide spectrum of models beyond
$\Lambda$CDM, including modified gravity scenarios of the Hordenski type and
models with multiple dark matter species. This work is a first step towards a
complete model-independent lagrangian forward model, to be employed in
cosmological analyses with power spectrum and bispectrum, other summary
statistics, and field-level inference.


  We study the ground state of the 1D Kitaev-Heisenberg (KH) model using the
density-matrix renormalization group and Lanczos exact diagonalization methods.
We obtain a rich ground-state phase diagram as a function of the ratio between
Heisenberg ($J=\cos\phi)$ and Kitaev ($K=\sin\phi$) interactions. Depending on
the ratio, the system exhibits four long-range ordered states:
ferromagnetic-$z$ , ferromagnetic-$xy$, staggered-$xy$, N\'eel-$z$, and two
liquid states: Tomonaga-Luttinger liquid and spiral-$xy$. The two Kitaev points
$\phi=\frac{\pi}{2}$ and $\phi=\frac{3\pi}{2}$ are singular. The
$\phi$-dependent phase diagram is similar to that for the 2D honeycomb-lattice
KH model. Remarkably, all the ordered states of the honeycomb-lattice KH model
can be interpreted in terms of the coupled KH chains. We also discuss the
magnetic structure of the K-intercalated RuCl$_3$, a potential Kitaev material,
in the framework of the 1D KH model. Furthermore, we demonstrate that the
low-lying excitations of the 1D KH Hamiltonian can be explained within the
combination of the known six-vertex model and spin-wave theory.


  The main purpose of this paper is to study both the underdamped and the
overdamped dynamics of the nonlinear Helmholtz oscillator with a fractional
order damping. For that purpose, we use the Grunwald-Letnikov fractional
derivative algorithm in order to get the numerical simulations. Here, we
investigate the effect of taking the fractional derivative in the dissipative
term in function of the parameter a. Our main findings show that the
trajectories can remain inside the well or can escape from it depending on a
which plays the role of a control parameter. Besides, the parameter a is also
relevant for the creation or destruction of chaotic motions. On the other hand,
the study of the escape times of the particles from the well, as a result of
variations of the initial conditions and the undergoing force F, is reported by
the use of visualization techniques such as basins of attraction and
bifurcation diagrams, showing a good agreement with previous results. Finally,
the study of the escape times versus the fractional parameter a shows an
exponential decay which goes to zero when a is larger than one. All the results
have been carried out for weak damping where chaotic motions can take place in
the non-fractional case and also for a stronger damping (overdamped case),
where the influence of the fractional term plays a crucial role to enhance
chaotic motions. We expect that these results can be of interest in the field
of fractional calculus and its applications.


  In a recent paper the first coauthor presented a new parabolic extension
(PADM) of the standard 3+1 Arnowitt, Deser, Misner formulation of the equations
of general relativity. By parabolizing first-order ADM in a certain way, the
PADM formulation turns it into a mixed hyperbolic - second-order parabolic,
well-posed system. The surface of constraints of PADM becomes a local attractor
for all solutions and all possible well-posed gauge conditions. This paper
describes a numerical implementation of PADM and studies its accuracy and
stability in a series of standard numerical tests. Numerical properties of PADM
are compared with those of standard ADM and its hyperbolic Kidder, Scheel,
Teukolsky (KST) extension. The PADM scheme is numerically stable, convergent
and second-order accurate. The new formulation has better control of the
constraint-violating modes than ADM and KST.


  We investigate stringy excitations in Randall-Sundrum effective theories for
electroweak symmetry breaking arising from embedding in string theory. RS is
dual to a confining gauge theory, which we expect to have "QCD strings," or
color flux tubes. Stringy constructions of RS-like theories allow us to
investigate the mass of these string states, which typically grows with a small
fractional power of the number of colors N of the dual gauge theory. There are
two known strong constraints on N for RS-like theories. The first arises from
demanding that the Standard Model gauge couplings do not have a Landau pole at
low scales. The second arises from demanding that the first-order confining
phase transition in the early universe is able to proceed without leaving an
empty universe, i.e. that the rate of bubble nucleation is not too small. We
find that these constraints on N imply that string states are generically at
most a factor of a few heavier than the lightest KK states, and we cannot
self-consistently remain in the limit N, lambda >> 1. We examine various string
constructions of AdS or RS-like backgrounds, including orbifolds, theories on
M5-branes, theories on D4-branes, and the recent F-theory construction of
Polchinski and Silverstein. In every case we find that there are strong bounds
on the mass of new stringy states. We briefly discuss important
phenomenological implications due to the presence of such light stringy
excitations, such as precision electroweak and flavor observables, as well as
collider signals.


  We theoretically investigate the Kondo effect of a T-shaped
triple-quantum-dot structure, by means of the numerical renormalization group
method. It is found that at the point of electron-hole symmetry, the system's
entropy has opportunities to exhibit three kinds of transition processes for
different interdot couplings, with the decrease of temperature. This leads to
the different pictures of the Kondo physics, including the three-stage Kondo
effect. Next when the electron-hole symmetry is broken or the structural
parameters are changed, the Kondo resonance can also be observed in the
conductance spectrum. However, it shows alternative dependence on the relevant
quantities, i.e., the Coulomb interaction and interdot couplings. All these
phenomena exhibit the abundant and interesting Kondo physics in this system. We
believe that this work can be helpful for further understanding the Kondo
effect in the triple-quantum-dot structures.


  In this work, we have designed obtained a novel multiferroic nanocomposite
using the high-pressure torsion (HPT) method. The crystal structure, phase
composition, morphology, ferromagnetic (FM) and ferroelectric (FE) properties
of the initial powders and "multiferroic-ferromagnetic" nanocomposites have
been studied comprehensively. The initial powders and their composites show the
perovskite and spinel crystalline phases for the FE and FM fractions,
respectively. After HPT, the particle sizes of the initial powders are
decreased significantly. It is shown that the novel nanocomposite consists of
exchange-interacting FE and FM phases and demonstrates improved magnetic and
electrical properties in low fields at room temperature. A colossal increase in
residual polarization with an increase in external high-pressure is found in
new composite. The obtained results make it possible to consider the novel
nanocomposite as a new functional material for its use both in electronic
devices for monitoring ultra-high-pressure and in integrated circuits of
high-speed computing nanosystems with low switching energy. The HPT method is a
promising method for obtaining new heterophase multiferroic nanosystems.


  The stability of binary orbits can significantly shape the gravity wave
signal which future Earth-based interferometers hope to detect. The inner most
stable circular orbit has been of interest as it marks the transition from the
late inspiral to final plunge. We consider purely relativistic orbits beyond
the circular assumption. Homoclinic orbits are of particular importance to the
question of stability as they lie on the boundary between dynamical stability
and instability. We identify these, estimate their rate of energy loss to
gravity waves, and compute their gravitational waveforms.


  Evaluating anomaly detection algorithms in time series data is critical as
inaccuracies can lead to flawed decision-making in various domains where
real-time analytics and data-driven strategies are essential. Traditional
performance metrics assume iid data and fail to capture the complex temporal
dynamics and specific characteristics of time series anomalies, such as early
and delayed detections. We introduce Proximity-Aware Time series anomaly
Evaluation (PATE), a novel evaluation metric that incorporates the temporal
relationship between prediction and anomaly intervals. PATE uses
proximity-based weighting considering buffer zones around anomaly intervals,
enabling a more detailed and informed assessment of a detection. Using these
weights, PATE computes a weighted version of the area under the Precision and
Recall curve. Our experiments with synthetic and real-world datasets show the
superiority of PATE in providing more sensible and accurate evaluations than
other evaluation metrics. We also tested several state-of-the-art anomaly
detectors across various benchmark datasets using the PATE evaluation scheme.
The results show that a common metric like Point-Adjusted F1 Score fails to
characterize the detection performances well, and that PATE is able to provide
a more fair model comparison. By introducing PATE, we redefine the
understanding of model efficacy that steers future studies toward developing
more effective and accurate detection models.


  The Standard Model predicts a long-range force, proportional to $G_F^2/r^5$,
between fermions due to the exchange of a pair of neutrinos. This quantum force
is feeble and has not been observed yet. In this paper, we compute this force
in the presence of neutrino backgrounds, both for isotropic and directional
background neutrinos. We find that for the case of directional background the
force can have a $1/r$ dependence and it can be significantly enhanced compared
to the vacuum case. In particular, background effects caused by reactor, solar,
and supernova neutrinos enhance the force by many orders of magnitude. The
enhancement, however, occurs only in the direction parallel to the direction of
the background neutrinos. We discuss the experimental prospects of detecting
the neutrino force in neutrino backgrounds and find that the effect is close to
the available sensitivity of the current fifth force experiments. Yet, the
angular spread of the neutrino flux and that of the test masses reduce the
strength of this force. The results are encouraging and a detailed experimental
study is called for to check if the effect can be probed.


  Accurately detecting student behavior in classroom videos can aid in
analyzing their classroom performance and improving teaching effectiveness.
However, the current accuracy rate in behavior detection is low. To address
this challenge, we propose the Student Classroom Behavior Detection method,
based on improved YOLOv7. First, we created the Student Classroom Behavior
dataset (SCB-Dataset), which includes 18.4k labels and 4.2k images, covering
three behaviors: hand raising, reading, and writing. To improve detection
accuracy in crowded scenes, we integrated the biformer attention module and
Wise-IoU into the YOLOv7 network. Finally, experiments were conducted on the
SCB-Dataset, and the model achieved an<EMAIL_ADDRESS>of 79%, resulting in a 1.8%
improvement over previous results. The SCB-Dataset and code are available for
download at: https://github.com/Whiffe/SCB-dataset.


  Constraints on the binary fraction of young massive stellar objects (mYSOs)
are important for binary and massive star formation theory. Here, we present
speckle imaging of 34 mYSOs located in the Large (1/2 $Z_{\odot}$) and Small
Magellanic Clouds ($\sim$1/5 $Z_{\odot}$), probing projected separations
between the 2000-20000 au (at angular scales of 0.02-0.2") range, for stars
above 8 $M_{\odot}$. We find two wide binaries in the Large Magellanic Cloud
(from a sample of 23 targets), but none in a sample of 11 in the Small
Magellanic Cloud, leading us to adopt a wide binary fraction of 9$\pm$5%, and
$<$5%, respectively. We rule out a wide binary fraction greater than 35% in the
Large, and 38% in the Small Magellanic Cloud at the 99% confidence level. This
is in contrast to the wide binary fraction of mYSOs in the Milky Way (presumed
$Z_{\odot}$), which within the physical parameter space probed by this study is
$\sim$15-60% from the literature. We argue that while selection effects could
be responsible for the lower binary fraction observed; it is more likely that
there are underlying physical mechanisms responsible for the observed
properties. This indicates that metallicity and environmental effects may
influence the formation of wide binaries among massive stars. Future larger,
statistically more significant samples of high-mass systems in low-metallicity
environments, and for comparison in the Milky Way, are essential to confirm or
repudiate our claim.


  The exact analytical lattice results for the partition function of the free
neutral scalar field in one spatial dimension in both the configuration and the
momentum space were obtained in the framework of the path integral method. The
symmetric square matrices of the bilinear forms on the vector space of fields
in both configuration space and momentum space were found explicitly. The exact
lattice results for the partition function were generalized to the
three-dimensional spatial momentum space and the main thermodynamic quantities
were derived both on the lattice and in the continuum limit. The thermodynamic
properties and the finite volume corrections to the thermodynamic quantities of
the free real scalar field were studied. We found that on the finite lattice
the exact lattice results for the free massive neutral scalar field agree with
the continuum limit only in the region of small values of temperature and
volume. However, at these temperatures and volumes the continuum physical
quantities for both massive and massless scalar field deviate essentially from
their thermodynamic limit values and recover them only at high temperatures
or/and large volumes in the thermodynamic limit.


  We study asymptotic expansions of the smeared L2-traces Fexp(-t P^2) and
FPexp(-tP^2), where P is an operator of Dirac type and F is an auxiliary smooth
endomorphism. We impose chiral bag boundary conditions depending on an angle
theta. Studying the theta-dependence of the above trace invariants,
theta-independent pieces are identified. The associated stability theorems
allow one to show the regularity of the eta function for the problem and to
determine the most important heat kernel coefficient on a four dimensional
manifold.


  Many problems in evolutionary theory are cast in dyadic terms, such as the
polar oppositions of organism and environment. We argue that a triadic
conceptual structure offers an alternative perspective under which the
information generating role of evolution as a physical process can be analyzed,
and propose a new diagrammatic approach. Peirce's natural philosophy was deeply
influenced by his reception of both Darwin's theory and thermodynamics. Thus,
we elaborate on a new synthesis which puts together his theory of signs and
modern Maximum Entropy approaches to evolution. Following recent contributions
to the naturalization of Peircean semiosis, we show that triadic structures
involve the conjunction of three different kinds of causality, efficient,
formal and final. We apply this on Ulanowicz's analysis of autocatalytic cycles
as primordial patterns of life. This paves the way for a semiotic view of
thermodynamics which is built on the idea that Peircean interpretants are
systems of physical inference devices evolving under natural selection. In this
view, the principles of Maximum Entropy, Maximum Power, and Maximum Entropy
Production work together to drive the emergence of information carrying
structures, which at the same time maximize information capacity as well as the
gradients of energy flows, such that ultimately, contrary to Schroedinger's
seminal contribution, the evolutionary process is seen to be a physical
expression of the Second Law.


  With AKARI, we obtain the spatially-resolved near-infrared (2.5 - 5.0 um)
spectra for the nearby starburst galaxy M82. These spectra clearly show the
absorption features due to interstellar ices. Based on the spectra, we created
the column density maps of H_2O and CO_2 ices. As a result, we find that the
spatial distribution of H_2O ice is significantly different from that of CO_2
ice; H_2O ice is widely distributed, while CO_2 ice is concentrated near the
galactic center. Our result for the first time reveals variations in CO_2/H_2O
ice abundance ratio on a galactic scale, suggesting that the ice-forming
interstellar environment changes within a galaxy. We discuss the cause of the
spatial variations in the ice abundance ratio, utilizing spectral information
on the hydrogen recombination Br{\alpha} and Br{\beta} lines and the polycyclic
aromatic hydrocarbon 3.3 um emission appearing in the AKARI near-infrared
spectra.


  We perform a comprehensive analysis of the spectrum of proper oscillations
(quasinormal modes), transmission/reflection coefficients and Hawking radiation
for a massive charged scalar field in the background of the Kerr-Newman black
hole immersed in an asymptotically homogeneous magnetic field. There are two
main effects: the Zeeman shift of the particle energy in the magnetic field and
the difference of values of an electromagnetic potential between the horizon
and infinity, i.e. the Faraday induction. We have shown that "turning on" the
magnetic field induces a stronger energy-emission rate and leads to
"recharging" of the black hole. Thus, a black hole immersed in a magnetic field
evaporates much quicker, achieving thereby an extremal state in a shorter
period of time. Quasinormal modes are moderately affected by the presence of a
magnetic field which is assumed to be relatively small compared to the
gravitational field of the black hole.


  Recent multimodal models such as DALL-E and CM3 have achieved remarkable
progress in text-to-image and image-to-text generation. However, these models
store all learned knowledge (e.g., the appearance of the Eiffel Tower) in the
model parameters, requiring increasingly larger models and training data to
capture more knowledge. To integrate knowledge in a more scalable and modular
way, we propose a retrieval-augmented multimodal model, which enables a base
multimodal model (generator) to refer to relevant text and images fetched by a
retriever from external memory (e.g., documents on the web). Specifically, for
the retriever, we use a pretrained CLIP, and for the generator, we train a CM3
Transformer on the LAION dataset. Our resulting model, named
Retrieval-Augmented CM3 (RA-CM3), is the first multimodal model that can
retrieve and generate both text and images. We show that RA-CM3 significantly
outperforms baseline multimodal models such as DALL-E and CM3 on both image and
caption generation tasks (12 FID and 17 CIDEr improvements on MS-COCO), while
requiring much less compute for training (<30% of DALL-E). Moreover, we show
that RA-CM3 exhibits novel capabilities, such as faithful image generation and
multimodal in-context learning (e.g., image generation from demonstrations).


  Nongradient SDEs with small white noise often arise when modeling biological
and ecological time-irreversible processes. If the governing SDE were gradient,
the maximum likelihood transition paths, transition rates, expected exit times,
and the invariant probability distribution would be given in terms of its
potential function. The quasipotential plays a similar role for nongradient
SDEs. Unfortunately, the quasipotential is the solution of a functional
minimization problem that can be obtained analytically only in some special
cases. We propose a Dijkstra-like solver for computing the quasipotential on
regular rectangular meshes in 3D. This solver results from a promotion and an
upgrade of the previously introduced ordered line integral method with the
midpoint quadrature rule for 2D SDEs. The key innovations that have allowed us
to keep the CPU times reasonable while maintaining good accuracy are $(i)$ a
new hierarchical update strategy, $(ii)$ the use of Karush-Kuhn-Tucker theory
for rejecting unnecessary simplex updates, and $(iii)$ pruning the number of
admissible simplexes and a fast search for them. An extensive numerical study
is conducted on a series of linear and nonlinear examples where the
quasipotential is analytically available or can be found at transition states
by other methods. In particular, the proposed solver is applied to Tao's
examples where the transition states are hyperbolic periodic orbits, and to a
genetic switch model by Lv et al. (2014). The C source code implementing the
proposed algorithm is available at M. Cameron's web page.


  The mainstream workflow of image recognition applications is first training
one global model on the cloud for a wide range of classes and then serving
numerous clients, each with heterogeneous images from a small subset of classes
to be recognized. From the cloud-client discrepancies on the range of image
classes, the recognition model is desired to have strong adaptiveness,
intuitively by concentrating the focus on each individual client's local
dynamic class subset, while incurring negligible overhead. In this work, we
propose to plug a new intra-client and inter-image attention (ICIIA) module
into existing backbone recognition models, requiring only one-time cloud-based
training to be client-adaptive. In particular, given a target image from a
certain client, ICIIA introduces multi-head self-attention to retrieve relevant
images from the client's historical unlabeled images, thereby calibrating the
focus and the recognition result. Further considering that ICIIA's overhead is
dominated by linear projection, we propose partitioned linear projection with
feature shuffling for replacement and allow increasing the number of partitions
to dramatically improve efficiency without scarifying too much accuracy. We
finally evaluate ICIIA using 3 different recognition tasks with 9 backbone
models over 5 representative datasets. Extensive evaluation results demonstrate
the effectiveness and efficiency of ICIIA. Specifically, for ImageNet-1K with
the backbone models of MobileNetV3-L and Swin-B, ICIIA can improve the testing
accuracy to 83.37% (+8.11%) and 88.86% (+5.28%), while adding only 1.62% and
0.02% of FLOPs, respectively.


  In previous work, the Hamilton-Jacobi equation has been associated with the
metrics of general relativity and shown to be a generalized Dirac equation for
quantum mechanics. This lends itself to a natural definition of wave-particle
duality. This theory is now further developed to show that a free spinless
quantum particle moving with velocity v obeys the standard wave equation of
electro-magnetism. We also discuss the implications for the zitterbewegung
problem and its relationship to isotropy. Moreover, it is shown that for the
theory to be consistent, the momentum defined by the Hamilton-Jacobi function
presupposes the existence of a universal parameter internal to the system
defined by dt/m(t) for particles with mass, where t has the units of time and m
= m(t) has the units of mass.


  We characterize a new mid-infrared frequency comb generator based on
difference frequency generation around 3.2 microns. High power per comb mode
(>10-7 W/mode) is obtained over a broad spectral span (>700 nm). The source is
used for direct absorption spectroscopy with a Michelson-based Fourier
transform interferometer.


  The identification of string relics, or of other very massive states, at
forthcoming ultra high energy cosmic rays experiments, requires a good
reconstruction of the main properties of the extensive air showers produced by
the collision of the primary protons with the atmosphere. In particular, the
current hadronization models used in the simulations need to incorporate
possible new interactions. We briefly discuss these aspects and then proceed by
describing some of the observables which characterize the atmospheric shower.
The linear growth of the multiplicities - as a function of the energy - for all
the main particles in the shower can play an interesting role in an attempt to
identify channels of missing energy due to a plausible dark matter component.


  In this paper, we unify the theory of SSD spaces, part of the theory of
strongly representable multifunctions, and the theory of the equivalence of
various classes of maximally monotone multifunctions.


  In this article, we prove a novel $\mathrm{L}^2$-maximum principle for
harmonic functions on the disk with respect to circular arcs. More precisely,
we prove that for any harmonic function $u$ on a disk $\Omega$ with
non-tangential maximal function in $\mathrm{L}^2(\partial \Omega)$, the
supremum of $\lVert u \rVert_{\mathrm{L}^2 (\Gamma)}$ over circular arcs
$\Gamma \subset \overline{\Omega}$ is attained at the boundary $\Gamma =
\partial \Omega$. We achieve this through a sharp geometry-dependent estimate
on the norm $\lVert u \rVert_{\mathrm{L}^2(\Gamma)}$ in the special case where
$\Gamma$ is a circular arc intersecting the boundary of $\Omega$ in exactly two
points and the boundary data $u\rvert_{\partial \Omega}$ is supported along one
of the connected components of $\partial \Omega\setminus \overline{\Gamma}$. As
a corollary of this result, we also deduce new $\mathrm{L}^p$ maximum
principles with $p \in [2,\infty)$ for circular arcs on the disk. These results
have applications in the convergence analysis of Schwarz domain decomposition
methods on the union of overlapping disks.
  We have discovered a critical error in the proof of Lemma 3.9 (highlighted in
red in the paper), and therefore, the proof of Theorem 1.2 presented here is
only valid under the restriction $\pi/2 \leq \theta+\sigma\leq 3\pi/2$, where
$\theta,\sigma$ are the angles described in Section 2. In particular, the
proofs of Corollaries 1.3--1.5 are incomplete.


  We present results and conjectures on the van der Waerden numbers w(2;3,t)
and on the new palindromic van der Waerden numbers pdw(2;3,t). We have computed
the new number w(2;3,19) = 349, and we provide lower bounds for 20 <= t <= 39,
where for t <= 30 we conjecture these lower bounds to be exact. The lower
bounds for 24 <= t <= 30 refute the conjecture that w(2;3,t) <= t^2, and we
present an improved conjecture. We also investigate regularities in the good
partitions (certificates) to better understand the lower bounds.
  Motivated by such reglarities, we introduce *palindromic van der Waerden
numbers* pdw(k; t_0,...,t_{k-1}), defined as ordinary van der Waerden numbers
w(k; t_0,...,t_{k-1}), however only allowing palindromic solutions (good
partitions), defined as reading the same from both ends. Different from the
situation for ordinary van der Waerden numbers, these "numbers" need actually
to be pairs of numbers. We compute pdw(2;3,t) for 3 <= t <= 27, and we provide
lower bounds, which we conjecture to be exact, for t <= 35.
  All computations are based on SAT solving, and we discuss the various
relations between SAT solving and Ramsey theory. Especially we introduce a
novel (open-source) SAT solver, the tawSolver, which performs best on the SAT
instances studied here, and which is actually the original DLL-solver, but with
an efficient implementation and a modern heuristic typical for look-ahead
solvers (applying the theory developed in the SAT handbook article of the
second author).


  In this paper, we give quantum algorithms for two fundamental computation
problems: solving polynomial systems over finite fields and optimization where
the arguments of the objective function and constraints take values from a
finite field or a bounded interval of integers. The quantum algorithms can
solve these problems with any given success probability and have polynomial
runtime complexities in the size of the input, the degree of the inequality
constraints, and the condition number of certain matrices derived from the
problem. So, we achieved exponential speedup for these problems when their
condition numbers are small. As applications, quantum algorithms are given to
three basic computational problems in cryptography: the polynomial system with
noise problem, the short integer solution problem, the shortest vector problem,
as well as the cryptanalysis for the lattice based NTRU cryptosystem. It is
shown that these problems and NTRU can against quantum computer attacks only if
their condition numbers are large, so the condition number could be used as a
new criterion for the lattice based post-quantum cryptosystems.


  We show that the representation of an image in a deep neural network (DNN)
can be manipulated to mimic those of other natural images, with only minor,
imperceptible perturbations to the original image. Previous methods for
generating adversarial images focused on image perturbations designed to
produce erroneous class labels, while we concentrate on the internal layers of
DNN representations. In this way our new class of adversarial images differs
qualitatively from others. While the adversary is perceptually similar to one
image, its internal representation appears remarkably similar to a different
image, one from a different class, bearing little if any apparent similarity to
the input; they appear generic and consistent with the space of natural images.
This phenomenon raises questions about DNN representations, as well as the
properties of natural images themselves.


  According to two estimated relations between the initial period and the
dynamo-generated magnetic dipole field of pulsars, we calculate the statistical
distributions of pulsar initial periods. It is found that proto-pulsars are
very likely to have rotation periods between 20 and 30 ms, and that most of the
pulsars rotate initially at a period < 60 ms.


  We study mono-W signals of dark matter (DM) production at the LHC, in the
context of gauge invariant renormalizable models. We analyze two simplified
models, one involving an s-channel Z' mediator and the other a t-channel
colored scalar mediator, and consider examples in which the DM-quark couplings
are either isospin conserving or isospin violating after electroweak symmetry
breaking. While previous work on mono-W signals have focused on isospin
violating EFTs, obtaining very strong limits, we find that isospin violating
effects are small once such physics is embedded into a gauge invariant
simplified model. We thus find that the 8 TeV mono-W results are much less
constraining than those arising from mono-jet searches. Considering both the
leptonic (mono-lepton) and hadronic (mono fat jet) decays of the W, we
determine the 14 TeV LHC reach of the mono-W searches with 3000 fb$^{-1}$ of
data. While a mono-W signal would provide an important complement to a mono-jet
discovery channel, existing constraints on these models imply it will be a
challenging signal to observe at the 14 TeV LHC.


  We have observed the micro-quasar V4641 Sgr (SAX J1819.3-2525) at the time of
flare during May 2002, using the Giant Metrewave Radio Telescope at low radio
frequencies (244 and 610 MHz). This is the lowest frequency radio detection of
the source. The source showed clear signature of spectral evolution from
optically thick to thin state. We model the spectral evolution of the source to
obtain some of the source parameters. Assuming the value of magnetic field
strength to be in the range of 0.1 to 1 Gauss, we obtain the initial velocity
of expansion of the jet to be in the range $\sim$ 0.45c to 0.25c. This is
consistent with the known expansion velocities of the jet with other
microquasars.


  Pretraining reinforcement learning (RL) models on offline datasets is a
promising way to improve their training efficiency in online tasks, but
challenging due to the inherent mismatch in dynamics and behaviors across
various tasks. We present a model-based RL method that learns to transfer
potentially useful dynamics and action demonstrations from offline data to a
novel task. The main idea is to use the world models not only as simulators for
behavior learning but also as tools to measure the task relevance for both
dynamics representation transfer and policy transfer. We build a time-varying,
domain-selective distillation loss to generate a set of offline-to-online
similarity weights. These weights serve two purposes: (i) adaptively
transferring the task-agnostic knowledge of physical dynamics to facilitate
world model training, and (ii) learning to replay relevant source actions to
guide the target policy. We demonstrate the advantages of our approach compared
with the state-of-the-art methods in Meta-World and DeepMind Control Suite.


  This paper describes the FellWalker algorithm, a watershed algorithm that
segments a 1-, 2- or 3-dimensional array of data values into a set of disjoint
clumps of emission, each containing a single significant peak. Pixels below a
nominated constant data level are assumed to be background pixels and are not
assigned to any clump. FellWalker is thus equivalent in purpose to the
CLUMPFIND algorithm. However, unlike CLUMPFIND, which segments the array on the
basis of a set of evenly-spaced contours and thus uses only a small fraction of
the available data values, the FellWalker algorithm is based on a
gradient-tracing scheme which uses all available data values. Comparisons of
CLUMPFIND and FellWalker using a crowded field of artificial Gaussian clumps,
all of equal peak value and width, suggest that the results produced by
FellWalker are less dependent on specific parameter settings than are those of
CLUMPFIND.


  Most multi-class classifiers make their prediction for a test sample by
scoring the classes and selecting the one with the highest score. Analyzing
these prediction scores is useful to understand the classifier behavior and to
assess its reliability. We present an interactive visualization that
facilitates per-class analysis of these scores. Our system, called Classilist,
enables relating these scores to the classification correctness and to the
underlying samples and their features. We illustrate how such analysis reveals
varying behavior of different classifiers. Classilist is available for use
online, along with source code, video tutorials, and plugins for R, RapidMiner,
and KNIME at https://katehara.github.io/classilist-site/.


  Maintaining or even improving gate performance with growing numbers of
parallel controlled qubits is a vital requirement for fault-tolerant quantum
computing. For superconducting quantum processors, though isolated one- or
two-qubit gates have been demonstrated with high-fidelity, implementing these
gates in parallel commonly shows worse performance. Generally, this degradation
is attributed to various crosstalks between qubits, such as quantum crosstalk
due to residual inter-qubit coupling. An understanding of the exact nature of
these crosstalks is critical to figuring out respective mitigation schemes and
improved qubit architecture designs with low crosstalk. Here we give a
theoretical analysis of quantum crosstalk impact on simultaneous gate
operations in a qubit architecture, where fixed-frequency transmon qubits are
coupled via a tunable bus, and sub-100-ns controlled-Z (CZ) gates can be
realized by applying a baseband flux pulse on the bus. Our analysis shows that
for microwave-driven single-qubit gates, the dressing from the qubit-qubit
coupling can cause non-negligible cross-driving errors when qubits operate near
frequency collision regions. During CZ gate operations, although unwanted
near-neighbor interactions are nominally turned off, sub-MHz parasitic
next-near-neighbor interactions involving spectator qubits can still exist,
causing considerable leakage or control error when one operates qubit systems
around these parasitic resonance points. To ensure high-fidelity simultaneous
operations, there could raise a request to figure out a better way to balance
the gate error from target qubit systems themselves and the error from
non-participating spectator qubits. Overall, our analysis suggests that towards
useful quantum processors, the qubit architecture should be examined carefully
in the context of high-fidelity simultaneous gate operations in a scalable
qubit lattice.


  Partial operators can have void or unbounded spectra. Contrarily to what is
written in Dunford-Schwarz, the reason is not in the fact they are unounded
operators.


  People with vocal and hearing disabilities use sign language to express
themselves using visual gestures and signs. Although sign language is a
solution for communication difficulties faced by deaf people, there are still
problems as most of the general population cannot understand this language,
creating a communication barrier, especially in places such as banks, airports,
supermarkets, etc. [1]. A sign language recognition(SLR) system is a must to
solve this problem. The main focus of this model is to develop a real-time
word-level sign language recognition system that would translate sign language
to text. Much research has been done on ASL(American sign language). Thus, we
have worked on ISL(Indian sign language) to cater to the needs of the deaf and
hard-of-hearing community of India[2]. In this research, we provide an Indian
Sign Language-based Sign Language recognition system. For this analysis, the
user must be able to take pictures of hand movements using a web camera, and
the system must anticipate and display the name of the taken picture. The
acquired image goes through several processing phases, some of which use
computer vision techniques, including grayscale conversion, dilatation, and
masking. Our model is trained using a convolutional neural network (CNN), which
is then utilized to recognize the images. Our best model has a 99% accuracy
rate[3].


  A model for quantized gravitation based on the simplicial lattice
discretization is studied in detail using a comprehensive finite size scaling
analysis combined with renormalization group methods. The results are
consistent with a value for the universal critical exponent for gravitation
$\nu=1/3$, and suggest a simple relationship between Newton's constant, the
gravitational correlation length and the observable average space-time
curvature. Some perhaps testable phenomenological implications of these results
are discussed. To achieve a high numerical accuracy in the evaluation of the
lattice path integral a dedicated parallel machine was assembled.


  This paper introduces a new type of unsupervised learning algorithm, based on
the alignment of sentences and Harris's (1951) notion of interchangeability.
The algorithm is applied to an untagged, unstructured corpus of natural
language sentences, resulting in a labelled, bracketed version of the corpus.
Firstly, the algorithm aligns all sentences in the corpus in pairs, resulting
in a partition of the sentences consisting of parts of the sentences that are
similar in both sentences and parts that are dissimilar. This information is
used to find (possibly overlapping) constituents. Next, the algorithm selects
(non-overlapping) constituents. Several instances of the algorithm are applied
to the ATIS corpus (Marcus et al., 1993) and the OVIS (Openbaar Vervoer
Informatie Systeem (OVIS) stands for Public Transport Information System.)
corpus (Bonnema et al., 1997). Apart from the promising numerical results, the
most striking result is that even the simplest algorithm based on alignment
learns recursion.


  The goal of an auction is to determine commodity prices such that all
participants are perfectly happy. Such a solution is called a competitive
equilibrium and does not exist in general. For this reason we are interested in
solutions which are similar to a competitive equilibrium. The article
introduces two relaxations of a competitive equilibrium for general auctions.
Both relaxations determine one price per commodity by solving a difficult
non-convex optimization problem. The first model is a mathematical program with
equilibrium constraints (MPEC), which ensures that each participant is either
perfectly happy or his bid is rejected. An exact algorithm and a heuristic are
provided for this model. The second model is a relaxation of the first one and
only ensures that no participant incurs a loss. In an optimal solution to the
second model, no participant can be made better off without making another one
worse off.


  Thermal higgsino dark matter (DM), with a mass near 1.1 TeV, is one of the
most well-motivated and untested DM candidates. Leveraging recent hydrodynamic
cosmological simulations that give DM density profiles in Milky Way analogue
galaxies we show that the line-like gamma-ray signal predicted from higgsino
annihilation in the Galactic Center could be detected at high significance with
the upcoming Cherenkov Telescope Array (CTA) and Southern Wide-field Gamma-ray
Observatory (SWGO) for all but the most pessimistic DM profiles. We perform the
most sensitive search to-date for the line-like signal using 15 years of data
from the Fermi Large Area Telescope, coming within an order one factor of the
necessary sensitivity to detect the higgsino for some Milky Way analogue DM
density profiles. We show that H.E.S.S. has sub-leading sensitivity relative to
Fermi for the higgsino at present. In contrast, we analyze H.E.S.S. inner
Galaxy data for the thermal wino model with a mass near 2.8 TeV; we find no
evidence for a DM signal and exclude the wino by over a factor of two in
cross-section for all DM profiles considered. In the process, we identify and
attempt to correct what appears to be an inconsistency in previous H.E.S.S.
inner Galaxy analyses for DM annihilation related to the analysis effective
area, which may weaken the DM cross-section sensitivity claimed in those works
by around an order of magnitude.


  Designing and building automated systems with which people can interact
naturally is one of the emerging objective of Mechatronics. In this perspective
multimodality and adaptivity represent focal issues, enabling users to
communicate more freely and naturally with automated systems. One of the basic
problem of multimodal interaction is the fusion process. Current approaches to
fusion are mainly two: the former implements the multimodal fusion at dialogue
management level, whereas the latter at grammar level. In this paper, we
propose a multimodal attribute grammar, that provides constructions both for
representing input symbols from different modalities and for modeling semantic
and temporal features of multimodal input symbols, enabling the specification
of multimodal languages. Moreover, an application of the proposed approach in
the context of a multimodal language specification to control a driver
assistance system, as robots using different integrated interaction modalities,
is given.


  Major observational efforts in the coming decade are designed to probe the
equation of state of dark energy. Measuring a deviation of the
equation-of-state parameter w from -1 would indicate a dark energy that cannot
be represented solely by a cosmological constant. While it is commonly assumed
that any implied modification to the LambdaCDM model amounts to the addition of
new dynamical fields, we propose here a framework for investigating whether or
not such new fields are required when cosmological observations are combined
with a set of minimal assumptions about the nature of gravitational physics. In
our approach, we treat the additional degrees of freedom as perturbatively
constrained and calculate a number of observable quantities, such as the Hubble
expansion rate and the cosmic acceleration, for a homogeneous Universe. We show
that current observations place our Universe within the perturbative validity
of our framework and allow for the presence of non-dynamical gravitational
degrees of freedom at cosmological scales.


  Let $f$ and $g$ be spectrally normalized holomorphic newforms of even weight
$k \geq2$ on $\Gamma_0(q)$. If $f\neq g$, then assume that $q$ is squarefree.
For a nice test function $\psi$ supported on $\Gamma_0(1)\backslash\mathbb{H}$,
we establish the best known bounds (uniform in $k$, $q$, and $\psi$) for \[
\int_{\Gamma_0(q)\backslash\mathbb{H}}\psi(z)f(z)\overline{g(z)}y^{k}\frac{dxdy}{y^2}-\mathbf{1}_{f
= g}\frac{3}{\pi}\int_{\Gamma_0(1)\backslash\mathbb{H}}\psi(z)\frac{dx
dy}{y^2}.\] When $f=g$, our results yield an effective holomorphic variant of
quantum unique ergodicity, refining work of Holowinsky-Soundararajan and
Nelson-Pitale-Saha. When $f \neq g$, our results extend and improve the
effective decorrelation result of Huang for $q=1$. To prove our results, we
refine Soundararajan's weak subconvexity bound for Rankin-Selberg
$L$-functions.


  We identify the compactness threshold for optimizing sequences of the Airy--
Strichartz inequality as an explicit multiple of the sharp constant in the
Strichartz inequality. In particular, if the sharp constant in the
Airy--Strichartz inequality is strictly smaller than this multiple of the sharp
constant in the Strichartz inequality, then there is an optimizer for the
former inequality. Our result is valid for the full range of Airy--Strichartz
inequalities (except the endpoints) both in the diagonal and off-diagonal
cases.


  Numerical integration of stochastic differential equations is commonly used
in many branches of science. In this paper we present how to accelerate this
kind of numerical calculations with popular NVIDIA Graphics Processing Units
using the CUDA programming environment. We address general aspects of numerical
programming on stream processors and illustrate them by two examples: the noisy
phase dynamics in a Josephson junction and the noisy Kuramoto model. In
presented cases the measured speedup can be as high as 675x compared to a
typical CPU, which corresponds to several billion integration steps per second.
This means that calculations which took weeks can now be completed in less than
one hour. This brings stochastic simulation to a completely new level, opening
for research a whole new range of problems which can now be solved
interactively.


  Realistic models of magnetic reconnection in the solar chromosphere must take
into account that the plasma is partially ionized and that plasma conditions
within any two magnetic flux bundles undergoing reconnection may not be the
same. Asymmetric reconnection in the chromosphere may occur when newly emerged
flux interacts with pre-existing, overlying flux. We present 2.5D simulations
of asymmetric reconnection in weakly ionized, reacting plasmas where the
magnetic field strengths, ion and neutral densities, and temperatures are
different in each upstream region. The plasma and neutral components are
evolved separately to allow non-equilibrium ionization. As in previous
simulations of chromospheric reconnection, the current sheet thins to the scale
of the neutral-ion mean free path and the ion and neutral outflows are strongly
coupled. However, the ion and neutral inflows are asymmetrically decoupled. In
cases with magnetic asymmetry, a net flow of neutrals through the current sheet
from the weak field (high density) upstream region into the strong field
upstream region results from a neutral pressure gradient. Consequently,
neutrals dragged along with the outflow are more likely to originate from the
weak field region. The Hall effect leads to the development of a characteristic
quadrupole magnetic field modified by asymmetry, but the X-point geometry
expected during Hall reconnection does not occur. All simulations show the
development of plasmoids after an initial laminar phase.


  This paper presents a deep neural-network-based hierarchical graphical model
for individual and group activity recognition in surveillance scenes. Deep
networks are used to recognize the actions of individual people in a scene.
Next, a neural-network-based hierarchical graphical model refines the predicted
labels for each class by considering dependencies between the classes. This
refinement step mimics a message-passing step similar to inference in a
probabilistic graphical model. We show that this approach can be effective in
group activity recognition, with the deep graphical model improving recognition
rates over baseline methods.


  Several unexpected features have been observed in the microwave sky at large
angular scales, both by WMAP an by Planck. Among those features is a lack of
both variance and correlation on the largest angular scales, alignment of the
lowest multipole moments with one another and with the motion and geometry of
the Solar System, a hemispherical power asymmetry or dipolar power modulation,
a preference for odd parity modes and an unexpectedly large cold spot in the
Southern hemisphere. The individual p-values of the significance of these
features are in the per mille to per cent level, when compared to the
expectations of the best-fit inflationary $\Lambda$CDM model. Some pairs of
those features are demonstrably uncorrelated, increasing their combined
statistical significance and indicating a significant detection of CMB features
at angular scales larger than a few degrees on top of the standard model.
Despite numerous detailed investigations, we still lack a clear understanding
of these large-scale features, which seem to imply a violation of statistical
isotropy and scale invariance of inflationary perturbations. In this
contribution we present a critical analysis of our current understanding and
discuss several ideas of how to make further progress.


  SICANE is a neutron scattering multidetector facility for the determination
of the quenching factor (ratio of the response to nuclear recoils and to
electrons) of cryogenic detectors used in direct WIMP searches. Well collimated
monoenergetic neutron beams are obtained with inverse (p,n) reactions. The
facility is described, and results obtained for the quenching factors of
scintillation in NaI(Tl) and of heat and ionization in Ge are presented.


  In this paper, we deduce some properties of $f$-sets of connected graphs.
Also, we introduce the concept of fixing share of each vertex of a fixing set
$D$ to see the participation of each vertex when fixing a connected graph $G$.
We define a parameter, called the fixing percentage, by using the concept of
fixing share, which is helpful in determining the measure of the amount of
fixing done by the elements of $D$ in $G$.


  The problem of aggregation is considerable importance in many disciplines. In
this paper, a new type of operator called visibility graph averaging (VGA)
aggregation operator is proposed. This proposed operator is based on the
visibility graph which can convert a time series into a graph. The weights are
obtained according to the importance of the data in the visibility graph.
Finally, the VGA operator is used in the analysis of the TAIEX database to
illustrate that it is practical and compared with the classic aggregation
operators, it shows its advantage that it not only implements the aggregation
of the data purely, but also conserves the time information, and meanwhile, the
determination of the weights is more reasonable.


  The coexistence of multiple quasi-degenerate orders is the hallmark of the
strongly correlated materials. Experiments often reveal several spatially
modulated orders in the underdoped cuprates. This has come to the forefront
with the possible detection of the pair density wave states. However,
microscopic calculations often struggle to stabilize such spatially modulating
orders as the ground state in the strong correlation limit. This work uses the
$t-t^\prime-J$-model with an additional nearest-neighbor repulsion to stabilize
spatially oscillating charge, bond, and pairing orders in the underdoped
regime. We employ the standard Gutzwiller approach while treating the
inhomogeneity for the spatial orders using the self-consistent
Hartree-Fock-Bogoliubov methodology. Our calculations reveal that
unidirectional bond density states coexisting with charge and pairing
modulations can have lower energy than the uniform superconducting state over
an extensive doping range. These modulating states vanish monotonically as the
modulation wavevector becomes shorter with increased dopings. The finite
momentum orders melt upon increasing doping to a vestigial nematic state which
breaks the rotational symmetry of the system. The spatial features of the
ground state at each doping reveal multiple wavevectors, which potentially
drives the incommensuration of charge orders. Interestingly, the spatially
modulating states are absent when the strong correlations criteria are relaxed,
suggesting that the removal of double occupancy aids the stabilization of
density wave orders.


  Deep convolutional neural networks continue to advance the state-of-the-art
in many domains as they grow bigger and more complex. It has been observed that
many of the parameters of a large network are redundant, allowing for the
possibility of learning a smaller network that mimics the outputs of the large
network through a process called Knowledge Distillation. We show, however, that
standard Knowledge Distillation is not effective for learning small models for
the task of pedestrian detection. To improve this process, we introduce a
higher-dimensional hint layer to increase information flow. We also estimate
the variance in the outputs of the large network and propose a loss function to
incorporate this uncertainty. Finally, we attempt to boost the complexity of
the small network without increasing its size by using as input hand-designed
features that have been demonstrated to be effective for pedestrian detection.
We succeed in training a model that contains $400\times$ fewer parameters than
the large network while outperforming AlexNet on the Caltech Pedestrian
Dataset.


  Using high resolution angle-resolved photoemission data in conjunction with
that from neutron and other probes, we show that electron-phonon (el-ph)
coupling is strong in cuprates superconductors and it plays an important role
in pairing. In addition to the strong electron correlation, the inclusion of
phonons provides a theoretical framework explaining many important phenomena
that cannot be understood by a strongly correlated electronic model alone.
Especially it is indispensable to explain the difference among materials. The
phonons with the wave number around the (0,qx) and (qx,0) axes create the
d-wave pairing while that near (pi,pi) are pair breaking. Therefore the
half-breathing mode of the oxygen motions helps d-wave superconductivity.


  Foundation models are subject to an ongoing heated debate, leaving open the
question of progress towards AGI and dividing the community into two camps: the
ones who see the arguably impressive results as evidence to the scaling
hypothesis, and the others who are worried about the lack of interpretability
and reasoning capabilities. By investigating to which extent causal
representations might be captured by these large scale language models, we make
a humble efforts towards resolving the ongoing philosophical conflicts.


  The isomonodromy deformation equation for a 2x2 matrix linear ODE with a
large parameter can be locally reduced to a (hyper)elliptic equation. To
globalize this result, we apply the isomonodromy deformation method and obtain
the modulation equations for the asymptotic algebraic curve. The method is
applied to the degenerate solution of the Painleve first equation.


  We establish two types of characterizations for high order anisotropic
Sobolev spaces. In particular, we prove high order anisotropic versions of
Bourgain-Brezis- Mironescu's formula and Nguyen's formula.


  We present {\em smart drill-down}, an operator for interactively exploring a
relational table to discover and summarize "interesting" groups of tuples. Each
group of tuples is described by a {\em rule}. For instance, the rule $(a, b,
\star, 1000)$ tells us that there are a thousand tuples with value $a$ in the
first column and $b$ in the second column (and any value in the third column).
Smart drill-down presents an analyst with a list of rules that together
describe interesting aspects of the table. The analyst can tailor the
definition of interesting, and can interactively apply smart drill-down on an
existing rule to explore that part of the table. We demonstrate that the
underlying optimization problems are {\sc NP-Hard}, and describe an algorithm
for finding the approximately optimal list of rules to display when the user
uses a smart drill-down, and a dynamic sampling scheme for efficiently
interacting with large tables. Finally, we perform experiments on real datasets
on our experimental prototype to demonstrate the usefulness of smart drill-down
and study the performance of our algorithms.


  Armed with a decade of social media data, I explore the impact of investor
emotions on earnings announcements. In particular, I test whether the emotional
content of firm-specific messages posted on social media just prior to a firm's
earnings announcement predicts its earnings and announcement returns. I find
that investors are typically excited about firms that end up exceeding
expectations, yet their enthusiasm results in lower announcement returns.
Specifically, a standard deviation increase in excitement is associated with an
7.8 basis points lower announcement return, which translates into an
approximately -5.8% annualized loss. My findings confirm that emotions and
market dynamics are closely related and highlight the importance of considering
investor emotions when assessing a firm's short-term value.


  The human microbiome has an important role in determining health. Mediation
analyses quantify the contribution of the microbiome in the causal path between
exposure and disease; however, current mediation models cannot fully capture
the high dimensional, correlated, and compositional nature of microbiome data
and do not typically accommodate dichotomous outcomes. We propose a novel
approach that uses inverse odds weighting to test for the mediating effect of
the microbiome. We use simulation to demonstrate that our approach gains power
for high dimensional mediators, and it is agnostic to the effect of
interactions between the exposure and mediators. Our application to infant gut
microbiome data from the New Hampshire Birth Cohort Study revealed a mediating
effect of 6-week infant gut microbiome on the relationship between maternal
prenatal antibiotic use during pregnancy and incidence of childhood allergy by
5 years of age.


  This paper presents Saharaline, an intervention designed to provide
collective social support for teachers in low-income schools. Implemented as a
WhatsApp-based helpline, Saharaline enables teachers to reach out for
personalized, long-term assistance with a wide range of problems and stressors,
including pedagogical, emotional, and technological challenges. Depending on
the support needed, teachers' requests are routed to appropriate domain experts
-- staff employed by educational non-profit organizations who understand
teachers' on-the-ground realities -- who offer localized and contextualized
assistance. Via a three-month exploratory deployment with 28 teachers in India,
we show how Saharaline's design enabled a collective of diverse education
experts to craft and deliver localized solutions that teachers could
incorporate into their practice. We conclude by reflecting on the efficacy of
our intervention in low-resource work contexts and provide recommendations to
enhance collective social support interventions similar to Saharaline.


  Recently, the use of circle representation has emerged as a method to improve
the identification of spherical objects (such as glomeruli, cells, and nuclei)
in medical imaging studies. In traditional bounding box-based object detection,
combining results from multiple models improves accuracy, especially when
real-time processing isn't crucial. Unfortunately, this widely adopted strategy
is not readily available for combining circle representations. In this paper,
we propose Weighted Circle Fusion (WCF), a simple approach for merging
predictions from various circle detection models. Our method leverages
confidence scores associated with each proposed bounding circle to generate
averaged circles. Our method undergoes thorough evaluation on a proprietary
dataset for glomerular detection in object detection within whole slide imaging
(WSI). The findings reveal a performance gain of 5 %, respectively, compared to
existing ensemble methods. Furthermore, the Weighted Circle Fusion technique
not only improves the precision of object detection in medical images but also
notably decreases false detections, presenting a promising direction for future
research and application in pathological image analysis.


  Existing deep learning models applied to reaction prediction in organic
chemistry can reach high levels of accuracy (> 90% for Natural Language
Processing-based ones). With no chemical knowledge embedded than the
information learnt from reaction data, the quality of the data sets plays a
crucial role in the performance of the prediction models. While human curation
is prohibitively expensive, the need for unaided approaches to remove
chemically incorrect entries from existing data sets is essential to improve
artificial intelligence models' performance in synthetic chemistry tasks. Here
we propose a machine learning-based, unassisted approach to remove chemically
wrong entries from chemical reaction collections. We applied this method to the
collection of chemical reactions Pistachio and to an open data set, both
extracted from USPTO (United States Patent Office) patents. Our results show an
improved prediction quality for models trained on the cleaned and balanced data
sets. For the retrosynthetic models, the round-trip accuracy metric grows by 13
percentage points and the value of the cumulative Jensen Shannon divergence
decreases by 30% compared to its original record. The coverage remains high
with 97%, and the value of the class-diversity is not affected by the cleaning.
The proposed strategy is the first unassisted rule-free technique to address
automatic noise reduction in chemical data sets.


  In the presence of conservation laws, superpositions of eigenstates of the
corresponding conserved quantities cannot be generated by quantum dynamics.
Thus, any such coherence represents a potentially valuable resource of
asymmetry, which can be used, for example, to enhance the precision of quantum
metrology or to enable state transitions in quantum thermodynamics. Here we ask
if such superpositions, already present in a reference system, can be broadcast
to other systems, thereby distributing asymmetry indefinitely at the expense of
creating correlations. We prove a no-go theorem showing that this is forbidden
by quantum mechanics in every finite-dimensional system. In doing so we also
answer some open questions in the quantum information literature concerning the
sharing of timing information of a clock and the possibility of catalysis in
quantum thermodynamics. We also prove that even weaker forms of broadcasting,
of which Aberg's `catalytic coherence' is a particular example, can only occur
in the presence of infinite-dimensional reference systems. Our results set
fundamental limits to the creation and manipulation of quantum coherence and
shed light on the possibilities and limitations of quantum reference frames to
act catalytically without being degraded.


  We theoretically study the magnetoelectric coupling in a quantum anomalous
Hall insulator state induced by interfacing a dynamic magnetization texture to
a topological insulator. In particular, we propose that the quantum anomalous
Hall insulator with a magnetic configuration of a domain wall, when contacted
by electrical reservoirs, acts as a magnetoelectric piston. A moving domain
wall pumps charge current between electrical leads in a closed circuit, while
applying an electrical bias induces reciprocal domain-wall motion. This
piston-like action is enabled by a finite reflection of charge carriers via
chiral modes imprinted by the domain wall. Moreover, we find that, when
compared with the recently discovered spin-orbit torque-induced domain-wall
motion in heavy metals, the reflection coefficient plays the role of an
effective spin-Hall angle governing the efficiency of the proposed electrical
control of domain walls. Quantitatively, this effective spin-Hall angle is
found to approach a universal value of $2$, providing an efficient scheme to
reconfigure the domain-wall chiral interconnects for possible memory and logic
applications.


  We present DeepSAT, a novel end-to-end learning framework for the Boolean
satisfiability (SAT) problem. Unlike existing solutions trained on random SAT
instances with relatively weak supervision, we propose applying the knowledge
of the well-developed electronic design automation (EDA) field for SAT solving.
Specifically, we first resort to logic synthesis algorithms to pre-process SAT
instances into optimized and-inverter graphs (AIGs). By doing so, the
distribution diversity among various SAT instances can be dramatically reduced,
which facilitates improving the generalization capability of the learned model.
Next, we regard the distribution of SAT solutions being a product of
conditional Bernoulli distributions. Based on this observation, we approximate
the SAT solving procedure with a conditional generative model, leveraging a
novel directed acyclic graph neural network (DAGNN) with two polarity
prototypes for conditional SAT modeling. To effectively train the generative
model, with the help of logic simulation tools, we obtain the probabilities of
nodes in the AIG being logic `1' as rich supervision. We conduct comprehensive
experiments on various SAT problems. Our results show that, DeepSAT achieves
significant accuracy improvements over state-of-the-art learning-based SAT
solutions, especially when generalized to SAT instances that are relatively
large or with diverse distributions.


  The beating pattern of Shubnikov-de Haas oscillations is reproduced in both
the crossed and tilted magnetic field configuration and in presence of
zero-field valley splitting in Si-MOSFET system. The consequences of IQHE in
extremely dilute 2DEG are discussed.


  We investigated the scenario of time-dependent diffusive interaction between
dark matter and dark energy and showed that such a model can be accommodated
within the observations of luminosity distance - redshift data in Supernova Ia
(SNe Ia) observations. We obtain constrains on different relevant parameters of
this model from the observational data. We consider a homogeneous scalar field
$\phi(t)$ driven by a $k-$essence Lagrangian of the from $L = V(\phi)F(X)$ with
constant potential $V(\phi) = V$, to describe the dynamics of dark energy in
this model. Using the temporal behaviour of the FRW scale factor, the equation
of state and total energy density of the dark fluid, extracted from the
analysis of SNe Ia (JLA) data, we have obtained the time-dependence of the
$k-$essence scalar field and also reconstructed form of the function $F(X)$ in
the $k-$essence Lagrangian.


  Excited-state spectroscopy from the first Facility for Rare Isotope Beams
(FRIB) experiment is reported. A 24(2)-$\mu$s isomer was observed with the FRIB
Decay Station initiator (FDSi) through a cascade of 224- and 401-keV $\gamma$
rays in coincidence with $^{32}\textrm{Na}$ nuclei. This is the only known
microsecond isomer ($1{\text{ }\mu\text{s}}\leq T_{1/2} < 1\text{ ms}$) in the
region. This nucleus is at the heart of the $N=20$ island of shape inversion
and is at the crossroads of spherical shell-model, deformed shell-model, and ab
initio theories. It can be represented as the coupling of a proton hole and
neutron particle to $^{32}\textrm{Mg}$, $^{32}\textrm{Mg}+\pi^{-1} + \nu^{+1}$.
This odd-odd coupling and isomer formation provides a sensitive measure of the
underlying shape degrees of freedom of $^{32}\textrm{Mg}$, where the onset of
spherical-to-deformed shape inversion begins with a low-lying deformed $2^+$
state at 885 keV and a low-lying shape-coexisting $0_2^+$ state at 1058 keV. We
suggest two possible explanations for the 625-keV isomer in $^{32}$Na: a $6^-$
spherical shape isomer that decays by $E2$ or a $0^+$ deformed spin isomer that
decays by $M2$. The present results and calculations are most consistent with
the latter, indicating that the low-lying states are dominated by deformation.


  Using the formalism of relativistic acoustic geometry we study the expanding
chiral fluid in the regime of broken chiral symmetry near the QCD chiral phase
transition temperature T_c. The dynamics of pions below T_c is described by the
equation of motion for a massless scalar field propagating in curved spacetime
similar to an open FRW universe. The metric tensor depends locally on the soft
pion dispersion relation and the four-velocity of the fluid. In the
neighborhood of the critical point an analogue trapped region forms with the
analogue trapped horizon as its boundary. We show that the associated surface
gravity diverges near the critical point as 1/(T_c-T). Hence, if the horizon
forms close to the critical temperature the analogue Hawking temperature may be
comparable with or even larger than the background fluid temperature.


  We investigate the charged Schwarzschild-Anti-deSitter (SAdS) BH
thermodynamics in 5d Einstein-Gauss-Bonnet gravity with electromagnetic field.
The Hawking-Page phase transitions between SAdS BH and pure AdS space are
studied. The corresponding phase diagrams (with critical line defined by GB
term coefficient and electric charge) are drawn. The possibility to account for
higher derivative Maxwell terms is mentioned.
  In frames of proposed dS/CFT correspondence it is demonstrated that brane
gravity maybe localized similarly to AdS/CFT.
  SdS BH thermodynamics in 5d Einstein and Einstein-Gauss-Bonnet gravity is
considered. The corresponding (complicated) surface counterterms are found and
used to get the conserved BH mass, free energy and entropy. The interesting
feature of of higher derivative gravity is the possibility for negative (or
zero) SdS (or SAdS) BH entropy which depends on the parameters of higher
derivative terms. We speculate that the appearence of negative entropy may
indicate a new type instability where a transition between SdS (SAdS) BH with
negative entropy to SAdS (SdS) BH with positive entropy would occur.


  Wolf-Rayet (WR) stars are the evolved descendants of massive O-type stars and
are considered to be progenitor candidates for Type Ib/c core-collapse
supernovae (SNe). Recent results of our HST/WFC3 survey of Wolf-Rayet stars in
M101 are summarised based on the detection efficiency of narrow-band optical
imaging compared to broad-band methods. Weshow that on average of 42% WR stars,
increasing to ~85% in central regions, are only detected in the narrow-band
imaging. Hence, the non-detection of a WR star at the location of ~10 Type Ib/c
SNe in broad-band imaging is no longer strong evidence for a non-WR progenitor
channel.


  Hand-wearable robots, specifically exoskeletons, are designed to aid hands in
daily activities, playing a crucial role in post-stroke rehabilitation and
assisting the elderly. Our contribution to this field is a textile robotic
glove with integrated actuators. These actuators, powered by pneumatic
pressure, guide the user's hand to a desired position. Crafted from textile
materials, our soft robotic glove prioritizes safety, lightweight construction,
and user comfort. Utilizing the ruffles technique, integrated actuators
guarantee high performance in blocking force and bending effectiveness. Here,
we present a participant study confirming the effectiveness of our robotic
device on a healthy participant group, exploiting EMG sensing.


  Just Noticeable Difference (JND) model developed based on Human Vision System
(HVS) through subjective studies is valuable for many multimedia use cases. In
the streaming industries, it is commonly applied to reach a good balance
between compression efficiency and perceptual quality when selecting video
encoding recipes. Nevertheless, recent state-of-the-art deep learning based JND
prediction model relies on large-scale JND ground truth that is expensive and
time consuming to collect. Most of the existing JND datasets contain limited
number of contents and are limited to a certain codec (e.g., H264). As a
result, JND prediction models that were trained on such datasets are normally
not agnostic to the codecs. To this end, in order to decouple encoding recipes
and JND estimation, we propose a novel framework to map the difference of
objective Video Quality Assessment (VQA) scores, i.e., VMAF, between two given
videos encoded with different encoding recipes from the same content to the
probability of having just noticeable difference between them. The proposed
probability mapping model learns from DCR test data, which is significantly
cheaper compared to standard JND subjective test. As we utilize objective VQA
metric (e.g., VMAF that trained with contents encoded with different codecs) as
proxy to estimate JND, our model is agnostic to codecs and computationally
efficient. Throughout extensive experiments, it is demonstrated that the
proposed model is able to estimate JND values efficiently.


  We find exact, analytic solutions of the holographic AC conductivity at
arbitrary frequency $\omega$ and temperature $T$, in contrast to previous works
where the AC conductivity was analytically obtained usually at small $\omega$
and $T$. These solutions enable us to study the analyticity properties of the
current-current correlator $G(\omega)$ in detail. The first system we study is
the AdS$_5$ planar black hole with momentum dissipation, whose extremal limit
has an AdS$_2$ factor. Then we study AdS$_4$ and AdS$_5$ Einstein-dilaton
systems whose special cases are maximal gauged supergravities. The solutions
show how the poles move and how branch cuts emerge as the temperature varies.
As a byproduct, we obtain an R-current correlator in $\mathcal{N}=4$
super-Yang-Mills theory on a sphere at finite temperature in the large $N$ and
strong coupling limit.


  Safety concerns have risen as robots become more integrated into our daily
lives and continue to interact closely with humans. One of the most crucial
safety priorities is preventing collisions between robots and people walking
nearby. Despite developing various algorithms to address this issue, evaluating
their effectiveness on a cost-effective test bench remains a significant
challenge. In this work, we propose a solution by introducing a simple yet
functional platform that enables researchers and developers to assess how
humans interact with mobile robots. This platform is designed to provide a
quick yet accurate evaluation of the performance of safe interaction algorithms
and make informed decisions for future development. The platform's features and
structure are detailed, along with the initial testing results using two
preliminary algorithms. The results obtained from the evaluation were
consistent with theoretical calculations, demonstrating its effectiveness in
assessing human-robot interaction. Our solution provides a preliminary yet
reliable approach to ensure the safety of both robots and humans in their daily
interactions.


  In a time-harmonic setting, we show for heterogeneous acoustic and
homogeneous electromagnetic wavesguides stability estimates with the stability
constant depending linearly on the length $L$ of the waveguide. These stability
estimates are used for the analysis of the (ideal) ultraweak (UW) variant of
the Discontinuous Petrov Galerkin (DPG) method. For this UW DPG, we show that
the stability deterioration with $L$ can be countered by suitably scaling the
test norm of the method. We present the ``full envelope approximation'', a UW
DPG method based on non-polynomial ansatz functions that allows for treating
long waveguides.


  Recently, the Belle II collaboration announced the first measurement of
$\mathcal B(B^+\to K^+\nu\bar\nu)$, which is found to be about $2.7\sigma$
higher than the SM prediction. We decipher the data with two new physics
scenarios: the underlying $b\to s \nu\bar\nu$ transition is, besides the SM
contribution, further affected by heavy new mediators that are much heavier
than the EW scale, or amended by an additional decay channel with undetected
light final states like dark matter or axion-like particles. These two
scenarios can be most conveniently analyzed in the SMEFT and the dark SMEFT
(DSMEFT) framework, respectively. We consider the flavour structures of the
resulting effective operators to be either generic or satisfy the minimal
flavour violation (MFV) hypothesis, both for the quark and lepton sectors. In
the first scenario, once the MFV is assumed, only one SM-like low-energy
effective operator induced by the SMEFT dim-6 operators can account for the
Belle II excess, the parameter space of which is, however, excluded by the
Belle upper bound on $\mathcal B(B^0\to K^{*0}\nu\bar\nu)$. In the second
scenario, it is found that the Belle II excess can be accommodated by 22 of the
DSMEFT operators involving one or two scalar, fermionic, or vector dark matters
as well as ALPs. These operators also receive dominant constraints from the
$B^0\to K^{*0}+$inv and $B_s\to$inv decays. In the MFV hypothesis, the number
of viable operators is reduced to 14, and the $B^+\to\pi^+ +$inv and
$K^+\to\pi^++$inv decays start to put further constraints. Within the parameter
space allowed by all the current experimental data, the $q^2$ distributions
(and $F_L$) of the $B\to K^{(*)}+$inv decays are studied for each viable
operator. In addition, we, for the first time, calculate systematically the
longitudinal polarization fraction $F_L$ of $K^*$ in the $B\to K^*+$inv decays
within the DLEFT.


  This paper studies the Laplacian spectrum and the average effective
resistance of (large) graphs that are sampled from graphons. Broadly speaking,
our main finding is that the Laplacian eigenvalues of a large dense graph can
be effectively approximated by using the degree function of the corresponding
graphon. More specifically, we show how to approximate the distribution of the
Laplacian eigenvalues and the average effective resistance (Kirchhoff index) of
the graph. For all cases, we provide explicit bounds on the approximation
errors and derive the asymptotic rates at which the errors go to zero when the
number of nodes goes to infinity. Our main results are proved under the
conditions that the graphon is piecewise Lipschitz and bounded away from zero.


  We show that the three strings vertex coefficients in light--cone open string
field theory satisfy the Hirota equations for the dispersionless Toda lattice
hierarchy. We show that Hirota equations allow us to calculate the correlators
of an associated quantum system where the Neumann coefficients represent the
two--point functions. We consider next the three strings vertex coefficients of
the light--cone string field theory on a maximally supersymmetric pp--wave
background. Using the previous results we are able to show that these Neumann
coefficients satisfy the Hirota equations for the full Toda lattice hierarchy
at least up to second order in the 'string mass' $\mu$.


  We calculate the eigenfrequencies and eigenfunctions of the acoustic
oscillations of giant exoplanets and explore the dependence of the
characteristic frequency and the eigenfrequencies on several parameters: the
planet mass, the planet radius, the core mass, and the heavy element mass
fraction in the envelope. We provide the eigenvalues for degree $l$ up to 8 and
radial order n up to 12. For the selected values of l and n, we find that the
pulsation eigenfrequencies depend strongly on the planet mass and radius,
especially at high frequency. We quantify this dependence through the
calculation of the characteristic frequency which gives us an estimate of the
scale of the eigenvalue spectrum at high frequency. For the mass range 0.5 <
M_P < 15 M_J, and fixing the planet radius to the Jovian value, we find that
the characteristic frequency is ~164.0 * (M_P/M_J)^(0.48) microHz, where M_P is
the planet mass and M_J is Jupiter's mass. For the radius range from 0.9 to 2.0
R_J, and fixing the planet's mass to the Jovian value, we find that the
characteristic frequency is ~164.0 * (R_P/R_J)^(-2.09) microHz, where R_P is
the planet radius and R_J is Jupiter's radius. We explore the influence of the
presence of a dense core on the pulsation frequencies and on the characteristic
frequency of giant exoplanets. We find that the presence of heavy elements in
the envelope affects the eigenvalue distribution in ways similar to the
presence of a dense core. Additionally, we apply our formalism to Jupiter and
Saturn and find results consistent with both the observationnal data of Gaulme
et al. (2011) and previous theoretical work.


  We enlarge the class of Rapoport-Zink spaces of Hodge type by modifying the
centers of the associated $p$-adic reductive groups. These such-obtained
Rapoport-Zink spaces are called of abelian type. The class of Rapoport-Zink
spaces of abelian type is strictly larger than the class of Rapoport-Zink
spaces of Hodge type, but the two type spaces are closely related as having
isomorphic connected components. The rigid analytic generic fibers of formal
Rapoport-Zink spaces of abelian type can be viewed as moduli spaces of local
$G$-shtukas in mixed characteristic in the sense of Scholze.
  We prove that Shimura varieties of abelian type can be uniformized by the
associated Rapoport-Zink spaces of abelian type. We construct and study the
Ekedahl-Oort stratifications for the special fibers of Rapoport-Zink spaces of
abelian type. As an application, we deduce a Rapoport-Zink type uniformization
for the supersingular locus of the moduli space of polarized K3 surfaces in
mixed characteristic. Moreover, we show that the Artin invariants of
supersingular K3 surfaces are related to some purely local invariants.


  We present SODA, a lightweight and open-source visualization library for
biological sequence annotations that enables straightforward development of
flexible, dynamic, and interactive web graphics. SODA is implemented in
TypeScript and can be used as a library within TypeScript and JavaScript.


  Due to an excellent mass resolution and a large amount of available data, the
CDF experiment, located at the Tevatron proton-antiproton accelerator, allows
the precise measurement of spectroscopic properties, like mass and decay width,
of a variety of states. This was exploited to examine the first orbital
excitations of the Lambdac baryon, the resonances Lambdac(2595) and
Lambdac(2625), in the decay channel Lambdac+ pi+ pi-, as well as the Lambdac
spin excitations Sigmac(2455) and Sigmac(2520) in its decays to Lambdac+ pi-
and Lambdac+ pi+ final states in a data sample corresponding to an integrated
luminosity of 5.2 invfb. We present measurements of the mass differences with
respect to the Lambdac and the decay widths of these states, using
significantly higher statistics than previous experiments.


  Gravitational waves (GW) emanating from unstable quasi-normal modes in
Neutron Stars (NS) could be accessible with the improved sensitivity of the
current GW detectors or with the next-generation GW detectors and, therefore,
can be employed to study the NS interior. Assuming f-mode excitation in
isolated pulsars with typical energy of pulsar glitches and considering
potential f-mode GW candidates for A+ (upgraded LIGO detectors operating at 5th
observation run design sensitivity) and Einstein Telescope (ET), we demonstrate
the inverse problem of NS asteroseismology within a Bayesian formalism to
constrain the nuclear parameters and NS Equation of State (EOS). We describe
the NS interior within relativistic mean field formalism. Taking the example of
glitching pulsars, we find that for a single event in A+ and ET, among the
nuclear parameters, the nucleon effective mass ($m^*$) within 90\% credible
interval (CI) can be restricted within $10\%$ and $5\%$, respectively. At the
same time, the incompressibility ($K$) and the slope of the symmetry energy
($L$) are only loosely constrained. Considering multiple (10) events in A+ and
ET, all the nuclear parameters are well constrained, especially $m^*$, which
can be constrained to 3\% and 2\% in A+ and ET, respectively. Uncertainty in
the observables of a $1.4M_{\odot}$ NS such as radius ($R_{1.4M_{\odot}}$),
f-mode frequency ($f_{1.4M_{\odot}}$), damping time ($\tau_{1.4M_{\odot}}$) and
a few EOS properties including squared speed of sound ($c_s^2$) are also
estimated.


  The SNS Project intends to integrate Conventional Facility Controls with its
EPICS-based Accelerator and Target Control Systems. EPICS will therefore be
used to provide distributed high-level access to all subsystems of the SNS
conventional facilities, including cooling water towers, chilled water,
de-ionized water, HVAC, waste processing, and power monitoring. EPICS will
provide operator displays and high-level control for more than 1000 process
variables. EPICS support will be provided by four IOCs using PowerPC-based
VMEbus controllers . Low-level control will be provided by Allen-Bradley
ControlLogix PLCs that will communicate among themselves using ControlNet and
with EPICS using EtherNet/IP [2]. Both the PLC layer and the EPICS layer will
be implemented by an industrial supplier. File server support will be
Linux-based and Concurrent Versions System (CVS) will be used to manage version
control, both for EPICS and for PLC program and configuration files. All system
process variable names, hardware, software, and database configuration
properties will be maintained in a master Oracle database which will be used to
generate and maintain EPICS and PLC databases for the entire project.


  Counting homomorphisms from a graph $H$ into another graph $G$ is a
fundamental problem of (parameterized) counting complexity theory. In this
work, we study the case where \emph{both} graphs $H$ and $G$ stem from given
classes of graphs: $H\in \mathcal{H}$ and $G\in \mathcal{G}$. By this, we
combine the structurally restricted version of this problem, with the
language-restricted version.
  Our main result is a construction based on Kneser graphs that associates
every problem $\tt P$ in $\#\mathsf{W[1]}$ with two classes of graphs
$\mathcal{H}$ and $\mathcal{G}$ such that the problem $\tt P$ is
\emph{equivalent} to the problem $\#{\tt HOM}(\mathcal{H}\to \mathcal{G})$ of
counting homomorphisms from a graph in $\mathcal{H}$ to a graph in
$\mathcal{G}$. In view of Ladner's seminal work on the existence of
$\mathsf{NP}$-intermediate problems [J.ACM'75] and its adaptations to the
parameterized setting, a classification of the class $\#\mathsf{W[1]}$ in
fixed-parameter tractable and $\#\mathsf{W[1]}$-complete cases is unlikely.
Hence, obtaining a complete classification for the problem $\#{\tt
HOM}(\mathcal{H}\to \mathcal{G})$ seems unlikely. Further, our proofs easily
adapt to $\mathsf{W[1]}$.
  In search of complexity dichotomies, we hence turn to special graph classes.
Those classes include line graphs, claw-free graphs, perfect graphs, and
combinations thereof, and $F$-colorable graphs for fixed graphs $F$: If the
class $\mathcal{G}$ is one of those classes and the class $\mathcal{H}$ is
closed under taking minors, then we establish explicit criteria for the class
$\mathcal{H}$ that partition the family of problems $\#{\tt
HOM}(\mathcal{H}\to\mathcal{G})$ into polynomial-time solvable and
$\#\mathsf{W[1]}$-hard cases. In particular, we can drop the condition of
$\mathcal{H}$ being minor-closed for $F$-colorable graphs.