  Some of the Galactic outer halo globular clusters are excellent tools to
probe gravitational theories in the regime of weak accelerations (Baumgardt et
al. 2005). The measurement of the line-of-sight velocity dispersion among stars
in these clusters will differentiate between the validity of Newtonian dynamics
(low velocity dispersion) and the possiblity of modified Newtonian dynamics
(MOND) or dark matter dominated globular clusters (high velocity dispersion).
In this paper, the properties of probable member stars of the three best-case
gravitational theory-testing clusters AM 1, Pal 3 and Pal 14 are presented. The
member selection is based on VLT photometry in Johnson BV. The positions of the
stars were determined with an accuracy of the order <=0.2 arcsec, allowing
their direct use for follow-up spectroscopy. The distance, reddening, age, and
metallicities of the clusters were estimated from isochrone fitting.
Furthermore, improved structural parameters, like central coordinates,
ellipticit


  The external field control of antiferromagnetism is a significant subject
both for basic science and technological applications. As a useful macroscopic
response to detect magnetic states, the anomalous Hall effect (AHE) is known
for ferromagnets, but it has never been observed in antiferromagnets until the
recent discovery in Mn3Sn. Here we report another example of the AHE in a
related antiferromagnet, namely, in the hexagonal chiral antiferromagnet Mn3Ge.
Our single-crystal study reveals that Mn3Ge exhibits a giant anomalous Hall
conductivity $|\sigma_{xz}|$ ~ 60 per ohm per cm at room temperature and
approximately 380 per ohm per cm at 5 K in zero field, reaching nearly half of
the value expected for the quantum Hall effect per atomic layer with Chern
number of unity. Our detailed analyses on the anisotropic Hall conductivity
indicate that in comparison with the in-plane-field components $|\sigma_{xz}|$
and $|\sigma_{zy}|$, which are very large and nearly comparable in size, we
find $|\sigma_{yx}|$ obtained in the field along the c axis is found to be much
smaller. The anomalous Hall effect shows a sign reversal with the rotation of a
small magnetic field less than 0.1 T. The soft response of the AHE to magnetic
field should be useful for applications, for example, to develop switching and
memory devices based on antiferromagnets.


  Two dynamical systems with same symmetry should have features in common, and
as far as their shared symmetry is concerned, one may represent the other. The
three light quark constituents of the hadrons, a) have an approximate flavor
SU(3) symmetry, b) have an exact color SU(3) symmetry, and c) as spin 1/2
particles, have a Lorentz SO(3,1) symmetry. So does a 3D harmonic oscillator.
a) Its Hamiltonian has the SU(3) symmetry, breakable if the 3 fundamental modes
of oscillation are not identical. b) The 3 directions of oscillation have the
permutation symmetry. This enables one to create three copies of unbreakable
SU(3) symmetry for each mode of the oscillation, and mimic the color of the
elementary particles. And c) The Lagrangian of the 3D oscillator has the
SO(3,1) symmetry. This can be employed to accommodate the spin of the
particles. In this paper we draw up a one-to-one correspondence between the
eigen modes of the Poisson bracket operator of the 3D oscillator and the flavor
multiplets of the particles, and between the permuted modes of the oscillator
and the color and anticolor multiplets of the particles. Gluons are represented
by the generators of the color SU(3) symmetry of the oscillator. Harmonic
oscillators are common place objects and, wherever encountered, are
analytically solvable. Elementary particles, on the other hand, are abstract
entities far from one's reach. Understanding of one may help a better
appreciation of the other.


  We demonstrate that the high isotropy of the Cosmic Microwave Background
(CMB), combined with the Copernican principle, is not sufficient to prove
homogeneity of the universe -- in contrast to previous results on this subject.
The crucial additional factor not included in earlier work is the acceleration
of the fundamental observers. We find the complete class of irrotational
perfect fluid spacetimes admitting an exactly isotropic radiation field for
every fundamental observer and show that are FLRW if and only if the
acceleration is zero. While inhomogeneous in general, these spacetimes all
possess three-dimensional symmetry groups, from which it follows that they also
admit a thermodynamic interpretation. In addition to perfect fluids models we
also consider multi-component fluids containing non-interacting radiation, dust
and a quintessential scalar field or cosmological constant in which the
radiation is isotropic for the geodesic (dust) observers. It is shown that the
non-acceleration of the fundamental observers forces these spacetimes to be
FLRW. While it is plausible that fundamental observers (galaxies) in the real
universe follow geodesics, it is strictly necessary to determine this from
local observations for the cosmological principle to be more than an
assumption. We discuss how observations may be used to test this.


  We present a simple thought experiment in which a beam of light of a finite
length (a light-pulse) enters a detection device that consists of a wave
detector and a light bulb. We examine the experiment from the reference frame
where the device is stationary and from the reference frame where it moves at a
constant velocity along the axis of the beam. We use basic special relativity
to derive the formulas that show how the length of the light beam is
transformed between the reference frames. We further show that the uncommon
formulas for length transformation of a light beam do not contradict the usual
relativistic result that a uniformly moving non-light object will be Lorentz
contracted along the direction of its motion.


  Community detection, which focuses on clustering nodes or detecting
communities in (mostly) a single network, is a problem of considerable
practical interest and has received a great deal of attention in the research
community. While being able to cluster within a network is important, there are
emerging needs to be able to cluster multiple networks. This is largely
motivated by the routine collection of network data that are generated from
potentially different populations. These networks may or may not have node
correspondence. When node correspondence is present, we cluster networks by
summarizing a network by its graphon estimate, whereas when node correspondence
is not present, we propose a novel solution for clustering such networks by
associating a computationally feasible feature vector to each network based on
trace of powers of the adjacency matrix. We illustrate our methods using both
simulated and real data sets, and theoretical justifications are provided in
terms of consistency.


  Resting-state functional magnetic resonance imaging (rs-fMRI) is a
noninvasive technique pivotal for understanding human neural mechanisms of
intricate cognitive processes. Most rs-fMRI studies compute a single static
functional connectivity matrix across brain regions of interest, or dynamic
functional connectivity matrices with a sliding window approach. These
approaches are at risk of oversimplifying brain dynamics and lack proper
consideration of the goal at hand. While deep learning has gained substantial
popularity for modeling complex relational data, its application to uncovering
the spatiotemporal dynamics of the brain is still limited. We propose a novel
interpretable deep learning framework that learns goal-specific functional
connectivity matrix directly from time series and employs a specialized graph
neural network for the final classification. Our model, DSAM, leverages
temporal causal convolutional networks to capture the temporal dynamics in both
low- and high-level feature representations, a temporal attention unit to
identify important time points, a self-attention unit to construct the
goal-specific connectivity matrix, and a novel variant of graph neural network
to capture the spatial dynamics for downstream classification. To validate our
approach, we conducted experiments on the Human Connectome Project dataset with
1075 samples to build and interpret the model for the classification of sex
group, and the Adolescent Brain Cognitive Development Dataset with 8520 samples
for independent testing. Compared our proposed framework with other
state-of-art models, results suggested this novel approach goes beyond the
assumption of a fixed connectivity matrix and provides evidence of
goal-specific brain connectivity patterns, which opens up the potential to gain
deeper insights into how the human brain adapts its functional connectivity
specific to the task at hand.


  A series of five unusual slow glitches of the radio pulsar B1822-09 (PSR
J1825-0935) were observed over the 1995-2005 interval. This phenomenon is
understood in a solid quark star model, where the reasonable parameters for
slow glitches are presented in the paper. It is proposed that, because of
increasing shear stress as a pulsar spins down, a slow glitch may occur,
beginning with a collapse of a superficial layer of the quark star. This layer
of material turns equivalently to viscous fluid at first, the viscosity of
which helps deplete the energy released from both the accumulated elastic
energy and the gravitation potential. This performs then a process of slow
glitch. Numerical calculations show that the observed slow glitches could be
reproduced if the effective coefficient of viscosity is ~10^2 cm^{2}/s and the
initial velocity of the superficial layer is order of 10^{-10} cm/s in the
coordinate rotating frame of the star.


  The classical dynamics of collisionless cold dark matter, commonly described
by fluid variables or a phase-space distribution, can be captured in a single
semiclassical wavefunction. We illustrate how classical multi-streaming creates
wave interference in a toy model corresponding to the dynamics of the
Zel'dovich approximation and link it to diffraction optics. Wave interference
dresses the classical skeleton of cold dark matter with universal features akin
to the physical imprints of wavelike (or fuzzy) dark matter. We untangle this
wave interference to obtain single-stream wavefunctions corresponding to the
classical fluid streams, by writing the wavefunction in an integral form. Our
wave decomposition captures the full phase-space information and isolates the
multi-stream phenomena related to vorticity and velocity dispersion. We link
the wave interference features of our system to the standard forms of
diffraction catastrophe integrals, which produce bright caustics in optical
fields analogous to the cold dark matter density field. Our two complementary
descriptions of dark matter wave-fields present rich universal features that
can unlock new ways of modelling and probing wavelike dark matter on the scales
of the cosmic web.


  Common explanations for shortcut learning assume that the shortcut improves
prediction under the training distribution but not in the test distribution.
Thus, models trained via the typical gradient-based optimization of
cross-entropy, which we call default-ERM, utilize the shortcut. However, even
when the stable feature determines the label in the training distribution and
the shortcut does not provide any additional information, like in perception
tasks, default-ERM still exhibits shortcut learning. Why are such solutions
preferred when the loss for default-ERM can be driven to zero using the stable
feature alone? By studying a linear perception task, we show that default-ERM's
preference for maximizing the margin leads to models that depend more on the
shortcut than the stable feature, even without overparameterization. This
insight suggests that default-ERM's implicit inductive bias towards max-margin
is unsuitable for perception tasks. Instead, we develop an inductive bias
toward uniform margins and show that this bias guarantees dependence only on
the perfect stable feature in the linear perception task. We develop loss
functions that encourage uniform-margin solutions, called margin control
(MARG-CTRL). MARG-CTRL mitigates shortcut learning on a variety of vision and
language tasks, showing that better inductive biases can remove the need for
expensive two-stage shortcut-mitigating methods in perception tasks.


  We use the plethystic exponential and the Molien-Weyl formula to compute the
Hilbert series (generating funtions), which count gauge invariant operators in
N=1 supersymmetric SU(N_c), Sp(N_c), SO(N_c) and G_2 gauge theories with 1
adjoint chiral superfield, fundamental chiral superfields, and zero classical
superpotential. The structure of the chiral ring through the generators and
relations between them is examined using the plethystic logarithm and the
character expansion technique. The palindromic numerator in the Hilbert series
implies that the classical moduli space of adjoint SQCD is an affine Calabi-Yau
cone over a weighted projective variety.


  Using the SDSS spectroscopic sample, we estimate the dark matter halo spin
parameter lambda for ~53,000 disk galaxies for which MOPED star formation
histories are available. We investigate the relationship between spin and total
stellar mass, star formation history, and environment. First, we find a clear
anti-correlation between stellar mass and spin, with low mass galaxies
generally having high dark matter spins. Second, galaxies which have formed
more than ~5% of their stars in the last 0.2 Gyr have more broadly distributed
and typically higher spins (including a significant fraction with lambda > 0.1)
than galaxies which formed a large fraction of their stars more than 10 Gyr
ago. Finally, we find little or no correlation between the value of spin of the
dark halo and environment as determined both by proximity to a new cluster
catalog and a marked correlation study. This agrees well with the predictions
from linear hierarchical torquing theory and numerical simulations.


  Multi-label network classification is a well-known task that is being used in
a wide variety of web-based and non-web-based domains. It can be formalized as
a multi-relational learning task for predicting nodes labels based on their
relations within the network. In sparse networks, this prediction task can be
very challenging when only implicit feedback information is available such as
in predicting user interests in social networks. Current approaches rely on
learning per-node latent representations by utilizing the network structure,
however, implicit feedback relations are naturally sparse and contain only
positive observed feedbacks which mean that these approaches will treat all
observed relations as equally important. This is not necessarily the case in
real-world scenarios as implicit relations might have semantic weights which
reflect the strength of those relations. If those weights can be approximated,
the models can be trained to differentiate between strong and weak relations.
In this paper, we propose a weighted personalized two-stage multi-relational
matrix factorization model with Bayesian personalized ranking loss for network
classification that utilizes basic transitive node similarity function for
weighting implicit feedback relations. Experiments show that the proposed model
significantly outperforms the state-of-art models on three different real-world
web-based datasets and a biology-based dataset.


  We present the discovery of the Optical Transient (OT) of the long-duration
gamma-ray burst GRB000926. The optical transient was detected independently
with the Nordic Optical Telescope and at Calar Alto 22.2 hours after the burst.
At this time the magnitude of the transient was R = 19.36. The transient faded
with a decay slope of about 1.7 during the first two days after which the slope
increased abruptly (within a few hours) to about 2.4. The light-curve started
to flatten off after about a week indicating the presence of an underlying
extended object. This object was detected in a deep image obtained one month
after the GRB at R=23.87+-0.15 and consists of several compact knots within
about 5 arcsec. One of the knots is spatially coincident with the position of
the OT and hence most likely belongs to the host galaxy. Higher resolution
imaging is needed to resolve whether all the compact knots belong to the host
galaxy or to several independent objects. In a separate paper we present a
discussion of the optical spectrum of the OT, and its inferred redshift (Moller
et al. in prep.).


  The dielectric constant of interfacial water has been predicted to be smaller
than that of bulk water (= 80) because the rotational freedom of water dipoles
is expected to decrease near surfaces, yet experimental evidence is lacking. We
report local capacitance measurements for water confined between two
atomically-flat walls separated by various distances down to 1 nm. Our
experiments reveal the presence of an interfacial layer with vanishingly small
polarization such that its out-of-plane dielectric constant is only
approximately 2. The electrically dead layer is found to be two to three
molecules thick. These results provide much needed feedback for theories
describing water-mediated surface interactions and behavior of interfacial
water, and show a way to investigate the dielectric properties of other fluids
and solids under extreme confinement.


  The competition between unitary quantum dynamics and dissipative stochastic
effects, as emerging from continuous-monitoring processes, can culminate in
measurement-induced phase transitions. Here, a many-body system abruptly
passes, when exceeding a critical measurement rate, from a highly entangled
phase to a low-entanglement one. We consider a different perspective on
entanglement phase transitions and explore whether these can emerge when the
measurement process itself is modified, while keeping the measurement rate
fixed. To illustrate this idea, we consider a noninteracting fermionic system
and focus on diffusive detection processes. Through extensive numerical
simulations, we show that, upon varying a suitable \textit{unraveling
parameter} -- interpolating between measurements of different quadrature
operators -- the system displays a transition from a phase with area-law
entanglement to one where entanglement scales logarithmically with the system
size. Our findings may be relevant for tailoring quantum correlations in noisy
quantum devices and for conceiving optimal classical simulation strategies.


  We establish an explicit correspondence between ergodicity breaking in a
system described by power-law tail distributions and the divergence of the
moments of these distributions.


  We study the effect of atom-molecule internal tunneling on the ground state
of atom-molecule Bose-Einstein condensates in a double-well potential. In the
absence of internal tunneling between atomic and molecular states, the ground
state is symmetric, which has equal-particle populations in two wells. From the
linear stability analysis, we show that the symmetric stationary state becomes
dynamically unstable at a certain value of the atom-molecule internal tunneling
strength. Above the critical value of the internal tunneling strength, the
ground state bifurcates to the particle-localized ground states. The origin of
this transition can be attributed to the effective attractive inter-atomic
interaction induced by the atom-molecule internal tunneling. This effective
interaction is similar to that familiar in the context of BCS-BEC crossover in
a Fermi gas with Feshbach resonance. Furthermore, we point out the possibility
of reentrant transition in the case of the large detuning between the atomic
and molecular states.


  Qualitons, topological excitations with the quantum numbers of quarks, may
provide an accurate description of what is meant by constituent quarks in QCD.
Their existence hinges crucially on an effective Lagrangian description of QCD
in which a pseudoscalar colour-octet of fields enters as a new variable. We
show here how such new fields may be extracted from the fundamental QCD
Lagrangian using the gauge-symmetric collective field technique.


  We show that for complex analytic K3 surfaces any torsion class in
H^2(X,O_X^*) comes from an Azumaya algebra. In other words, the Brauer group
equals the cohomological Brauer group. For algebraic surfaces, such results go
back to Grothendieck. In our situation, we use twistor spaces to deform a given
analytic K3 surface to suitable projective K3 surfaces, and then stable bundles
and hyperholomorphy conditions to pass back and forth between the members of
the twistor family.


  An overview of the recent results obtained by the ALICE Collaboration from
the analysis of the pp, p-Pb and Pb-Pb data samples collected during LHC run I
and the first half of run II is presented.


  Optical fields with rich and well-developed spatio-temporal structure,
including ultra-short structured light pulses, are essentially
non-monochromatic and contain a continuous spectrum of monochromatic
constituents. However, some substantial features of such fields and physical
mechanisms determining their behavior can be understood based on simplified
models including only two monochromatic paraxial components. We consider
examples of such model beams, their specific spatial and temporal properties as
well as their descriptive abilities for the meaningful characterization of
realistic spatio-temporal light fields. In particular, the proposed model
enables an explicit consistent analysis of the photon-probability distributions
in non-monochromatic fields, which confirms a high degree of coincidence
between the "energy center" and "probability center" of the field.
Simultaneously, particular features of the two-component bi-chromatic paraxial
fields (periodic and rotational character of the longitudinal and temporal
evolution, specific deformations of the propagating-beam transverse intensity
profile, etc.) are inspected using numerical examples.


  Microscopic traffic simulation provides a controllable, repeatable, and
efficient testing environment for autonomous vehicles (AVs). To evaluate AVs'
safety performance unbiasedly, the probability distributions of environment
statistics in the simulated naturalistic driving environment (NDE) need to be
consistent with those from the real-world driving environment. However,
although human driving behaviors have been extensively investigated in the
transportation engineering field, most existing models were developed for
traffic flow analysis without considering the distributional consistency of
driving behaviors, which could cause significant evaluation biasedness for AV
testing. To fill this research gap, a distributional consistent NDE modeling
framework is proposed in this paper. Using large-scale naturalistic driving
data, empirical distributions are obtained to construct the stochastic human
driving behavior models under different conditions. To address the error
accumulation problem during the simulation, an optimization-based method is
further designed to refine the empirical behavior models. Specifically, the
vehicle state evolution is modeled as a Markov chain and its stationary
distribution is twisted to match the distribution from the real-world driving
environment. The framework is evaluated in the case study of a multi-lane
highway driving simulation, where the distributional accuracy of the generated
NDE is validated and the safety performance of an AV model is effectively
evaluated.


  We introduce a class of variational states to study ground state properties
and real-time dynamics in (2+1)-dimensional compact QED. These are based on
complex Gaussian states which are made periodic in order to account for the
compact nature of the $U(1)$ gauge field. Since the evaluation of expectation
values involves infinite sums, we present an approximation scheme for the whole
variational manifold. We calculate the ground state energy density for lattice
sizes up to $20 \times 20$ and extrapolate to the thermodynamic limit for the
whole coupling region. Additionally, we study the string tension both by
fitting the potential between two static charges and by fitting the exponential
decay of spatial Wilson loops. As the ansatz does not require a truncation in
the local Hilbert spaces, we analyze truncation effects which are present in
other approaches. The variational states are benchmarked against exact
solutions known for the one plaquette case and exact diagonalization results
for a $\mathbb{Z}_3$ lattice gauge theory. Using the time-dependent variational
principle, we study real-time dynamics after various global quenches, e.g. the
time evolution of a strongly confined electric field between two charges after
a quench to the weak-coupling regime. Up to the points where finite size
effects start to play a role, we observe equilibrating behavior.


  Recent studies have integrated convolution into transformers to introduce
inductive bias and improve generalization performance. However, the static
nature of conventional convolution prevents it from dynamically adapting to
input variations, resulting in a representation discrepancy between convolution
and self-attention as self-attention calculates attention matrices dynamically.
Furthermore, when stacking token mixers that consist of convolution and
self-attention to form a deep network, the static nature of convolution hinders
the fusion of features previously generated by self-attention into convolution
kernels. These two limitations result in a sub-optimal representation capacity
of the constructed networks. To find a solution, we propose a lightweight Dual
Dynamic Token Mixer (D-Mixer) that aggregates global information and local
details in an input-dependent way. D-Mixer works by applying an efficient
global attention module and an input-dependent depthwise convolution separately
on evenly split feature segments, endowing the network with strong inductive
bias and an enlarged effective receptive field. We use D-Mixer as the basic
building block to design TransXNet, a novel hybrid CNN-Transformer vision
backbone network that delivers compelling performance. In the ImageNet-1K image
classification task, TransXNet-T surpasses Swin-T by 0.3% in top-1 accuracy
while requiring less than half of the computational cost. Furthermore,
TransXNet-S and TransXNet-B exhibit excellent model scalability, achieving
top-1 accuracy of 83.8% and 84.6% respectively, with reasonable computational
costs. Additionally, our proposed network architecture demonstrates strong
generalization capabilities in various dense prediction tasks, outperforming
other state-of-the-art networks while having lower computational costs. Code is
available at https://github.com/LMMMEng/TransXNet.


  By coupling a probe transition to a Rydberg state using electro-magnetically
induced transparency (EIT) we map the strong dipole-dipole interactions onto an
optical field. We characterize the resulting cooperative optical non-linearity
as a function of probe strength and density. We show that the effect of dipole
blockade cannot be described using a mean-field but requires an $N$-atom
cooperative model. Good quantitative agreement is obtained for three atoms per
blockade with the $n=60$ Rydberg state. We place an upper-limit on the
dephasing rate of the blockade spheres of $<110$ kHz.


  Controlling photon emission by single quantum emitters with nanostructures is
crucial for scalable on-chip quantum information processing. Nowadays
nanoresonators can affect the lifetime of emitters and ultimately induce strong
coupling between the emitters and the light field, while nanoantennas can
control the directionality of the emission. Expanding this control to the
manipulation of the emission of orbital angular momentum-changing transitions
would enable coupling between long-lived solid-state qubits and flying qubits.
As these transitions are associated with circular rather than linear dipoles,
such control requires detailed knowledge of the spatially dependent interaction
of a complex dipole with highly structured optical eigenstates containing local
helicity. Using a classical analogue, we experimentally map the coupling of
circular dipoles to photonic modes in a model structure, a photonic crystal
waveguide. We show that depending on the local helicity the dipoles can be made
to couple to modes either propagating to the left or to the right. The maps are
in excellent agreement with calculations. Our measurements, therefore,
demonstrate the coupling of spin to photonic pathway with near-unity (0.8 $\pm$
0.1) efficiency.


  The commitment to single-precision floating-point arithmetic is widespread in
the deep learning community. To evaluate whether this commitment is justified,
the influence of computing precision (single and double precision) on the
optimization performance of the Conjugate Gradient (CG) method (a second-order
optimization algorithm) and RMSprop (a first-order algorithm) has been
investigated. Tests of neural networks with one to five fully connected hidden
layers and moderate or strong nonlinearity with up to 4 million network
parameters have been optimized for Mean Square Error (MSE). The training tasks
have been set up so that their MSE minimum was known to be zero. Computing
experiments have disclosed that single-precision can keep up (with superlinear
convergence) with double-precision as long as line search finds an improvement.
First-order methods such as RMSprop do not benefit from double precision.
However, for moderately nonlinear tasks, CG is clearly superior. For strongly
nonlinear tasks, both algorithm classes find only solutions fairly poor in
terms of mean square error as related to the output variance. CG with double
floating-point precision is superior whenever the solutions have the potential
to be useful for the application goal.


  In this paper, we address the challenge of reconstructing general articulated
3D objects from a single video. Existing works employing dynamic neural
radiance fields have advanced the modeling of articulated objects like humans
and animals from videos, but face challenges with piece-wise rigid general
articulated objects due to limitations in their deformation models. To tackle
this, we propose Quasi-Rigid Blend Skinning, a novel deformation model that
enhances the rigidity of each part while maintaining flexible deformation of
the joints. Our primary insight combines three distinct approaches: 1) an
enhanced bone rigging system for improved component modeling, 2) the use of
quasi-sparse skinning weights to boost part rigidity and reconstruction
fidelity, and 3) the application of geodesic point assignment for precise
motion and seamless deformation. Our method outperforms previous works in
producing higher-fidelity 3D reconstructions of general articulated objects, as
demonstrated on both real and synthetic datasets. Project page:
https://chaoyuesong.github.io/REACTO.


  Neural Scene Flow Prior (NSFP) is of significant interest to the vision
community due to its inherent robustness to out-of-distribution (OOD) effects
and its ability to deal with dense lidar points. The approach utilizes a
coordinate neural network to estimate scene flow at runtime, without any
training. However, it is up to 100 times slower than current state-of-the-art
learning methods. In other applications such as image, video, and radiance
function reconstruction innovations in speeding up the runtime performance of
coordinate networks have centered upon architectural changes. In this paper, we
demonstrate that scene flow is different -- with the dominant computational
bottleneck stemming from the loss function itself (i.e., Chamfer distance).
Further, we rediscover the distance transform (DT) as an efficient,
correspondence-free loss function that dramatically speeds up the runtime
optimization. Our fast neural scene flow (FNSF) approach reports for the first
time real-time performance comparable to learning methods, without any training
or OOD bias on two of the largest open autonomous driving (AV) lidar datasets
Waymo Open and Argoverse.


  In this paper we study the problem of object detection for RGB-D images using
semantically rich image and depth features. We propose a new geocentric
embedding for depth images that encodes height above ground and angle with
gravity for each pixel in addition to the horizontal disparity. We demonstrate
that this geocentric embedding works better than using raw depth images for
learning feature representations with convolutional neural networks. Our final
object detection system achieves an average precision of 37.3%, which is a 56%
relative improvement over existing methods. We then focus on the task of
instance segmentation where we label pixels belonging to object instances found
by our detector. For this task, we propose a decision forest approach that
classifies pixels in the detection window as foreground or background using a
family of unary and binary tests that query shape and geocentric pose features.
Finally, we use the output from our object detectors in an existing superpixel
classification framework for semantic scene segmentation and achieve a 24%
relative improvement over current state-of-the-art for the object categories
that we study. We believe advances such as those represented in this paper will
facilitate the use of perception in fields like robotics.


  In an improved model of protostar mass functions (PMFs), protostars gain mass
from isothermal cores in turbulent clumps. Their mass accretion rate is similar
to Shu accretion at low mass, and to reduced Bondi accretion at high mass.
Accretion durations follow a simple expression in which higher-mass protostars
accrete for longer times. These times are set by ejections, stellar feedback,
and gravitational competition, which terminate accretion and reduce its
efficiency. The mass scale is the mass of a critically stable isothermal core.
In steady state, the PMF approaches a power law at high mass due to competition
between clump accretion and accretion stopping. The power law exponent is the
ratio of the time scales of accretion and accretion stopping. The luminosity
function (PLF) peaks near 1 L_Sun, due to inefficient accretion of core gas.
Models fit observed PLFs in four large embedded clusters. These indicate that
their underlying PMFs may be top-heavy compared to the IMF, depending on the
model of protostar radius.


  A new single-index model that reflects the time-dynamic effects of the single
index is proposed for longitudinal and functional response data, possibly
measured with errors, for both longitudinal and time-invariant covariates. With
appropriate initial estimates of the parametric index, the proposed estimator
is shown to be $\sqrt{n}$-consistent and asymptotically normally distributed.
We also address the nonparametric estimation of regression functions and
provide estimates with optimal convergence rates. One advantage of the new
approach is that the same bandwidth is used to estimate both the nonparametric
mean function and the parameter in the index. The finite-sample performance for
the proposed procedure is studied numerically.


  The partial-wave expansion used to treat the distortion of scattered
electrons by the nuclear Coulomb field is simpler and considerably less
time-consuming when applied to the production of muons and electrons by low and
intermediate-energy neutrinos. For angle-integrated cross sections, however, a
modification of the "effective-momentum-transfer" approximation seems to work
so well that for muons the full distorted-wave treatment is usually
unnecessary, even at kinetic energies as low as an MeV and in nuclei as heavy
as lead. The method does not work as well for electron production at low
energies, but there a Fermi function usually proves adequate. Scattering of
electron-neutrinos from muon decay on iodine and of atmospheric neutrinos on
iron are discussed in light of these results.


  Anomalous dynamics characterized by non-Gaussian probability distributions
(PDFs) and/or temporal long-range correlations can cause subtle modifications
of conventional fluctuation relations. As prototypes we study three variants of
a generic time-fractional Fokker-Planck equation with constant force. Type A
generates superdiffusion, type B subdiffusion and type C both super- and
subdiffusion depending on parameter variation. Furthermore type C obeys a
fluctuation-dissipation relation whereas A and B do not. We calculate
analytically the position PDFs for all three cases and explore numerically
their strongly non-Gaussian shapes. While for type C we obtain the conventional
transient work fluctuation relation, type A and type B both yield deviations by
featuring a coefficient that depends on time and by a nonlinear dependence on
the work. We discuss possible applications of these types of dynamics and
fluctuation relations to experiments.


  We present an overview and first results from a $M$-band spectroscopic survey
of planet-forming disks performed with iSHELL on IRTF, using two slits that
provide resolving power R $\approx$ 60,000-92,000 (5-3.3 km/s). iSHELL provides
a nearly complete coverage at 4.52-5.24 $\mu$m in one shot, covering $>50$
lines from the R and P branches of $^{12}$CO and $^{13}$CO for each of multiple
vibrational levels, and providing unprecedented information on the excitation
of multiple emission and absorption components. Some of the most notable new
findings of this survey are: 1) the detection of two CO Keplerian rings at $<2$
au (in HD 259431), 2) the detection of H${_2}$O ro-vibrational lines at 5
$\mu$m (in AS 205 N), and 3) the common kinematic variability of CO lines over
timescales of 1-14 years. By homogeneously analyzing this survey together with
a previous VLT-CRIRES survey of cooler stars, we discuss a unified view of CO
spectra where emission and absorption components scan the disk surface across
radii from a dust-free region within dust sublimation out to $\approx10$ au. We
classify two fundamental types of CO line shapes interpreted as emission from
Keplerian rings (double-peak lines) and a disk surface plus a low-velocity part
of a wind (triangular lines), where CO excitation reflects different emitting
regions (and their gas-to-dust ratio) rather than just the irradiation
spectrum. A disk+wind interpretation for the triangular lines naturally
explains several properties observed in CO spectra, including the line
blue-shifts, line shapes that turn into narrow absorption at high inclinations,
and the frequency of disk winds as a function of stellar type.


  We study the interaction of relativistic jets with their environment, using
3-dimensional relativistic particle-in-cell simulations for two cases of jet
composition: (i) electron-proton ($e^{-}-p^{+}$) and (ii) electron-positron
($e^{\pm}$) plasmas containing helical magnetic fields. We have performed
simulations of "global" jets containing helical magnetic fields in order to
examine how helical magnetic fields affect kinetic instabilities such as the
Weibel instability, the kinetic Kelvin-Helmholtz instability and the Mushroom
instability. We have found that these kinetic instabilities are suppressed and
new types of instabilities can grow. For the $e^{-}-p^{+}$ jet, a
recollimation-like instability occurs and jet electrons are strongly perturbed,
whereas for the $e^{\pm}$ jet, a recollimation-like instability occurs at early
times followed by kinetic instability and the general structure is similar to a
simulation without a helical magnetic field. We plan to perform further
simulations using much larger systems to confirm these new findings.


  Multiplex networks are a common modeling framework for interconnected systems
and multimodal data, yet we still lack fundamental insights for how
multiplexity affects stochastic processes. We introduce a novel ``Markov chains
of Markov chains'' model called multiplex Markov chains (MMCs) such that with
probably $(1-\omega)\in [0,1]$ random walkers remain in the same layer and
follow (layer-specific) intralayer Markov chains, whereas with probability
$\omega$ they move to different layers following (node-specific) interlayer
Markov chains. One main finding is the identification of multiplex convection,
whereby a stationary distribution exhibits circulating flows that involve
multiple layers. Convection cycles are well understood in fluids, but are
insufficiently explored on networks. Our experiments reveal that one mechanism
for convection is the existence of imbalances for the (intralayer) degrees of
nodes in different layers. To gain further insight, we employ spectral
perturbation theory to characterize the stationary distribution for the limits
of small and large $\omega$, and we show that MMCs inherently exhibit
optimality for intermediate $\omega$ in terms of their convergence rate and the
extent of convection. As an application, we conduct an MMC-based analysis of
brain-activity data, finding MMCs to differ between healthy persons and those
with Alzheimer's disease. Overall, our work suggests MMCs and convection as two
important new directions for network-related research.


  Optical flow estimation is a fundamental task in computer vision. Recent
direct-regression methods using deep neural networks achieve remarkable
performance improvement. However, they do not explicitly capture long-term
motion correspondences and thus cannot handle large motions effectively. In
this paper, inspired by the traditional matching-optimization methods where
matching is introduced to handle large displacements before energy-based
optimizations, we introduce a simple but effective global matching step before
the direct regression and develop a learning-based matching-optimization
framework, namely GMFlowNet. In GMFlowNet, global matching is efficiently
calculated by applying argmax on 4D cost volumes. Additionally, to improve the
matching quality, we propose patch-based overlapping attention to extract large
context features. Extensive experiments demonstrate that GMFlowNet outperforms
RAFT, the most popular optimization-only method, by a large margin and achieves
state-of-the-art performance on standard benchmarks. Thanks to the matching and
overlapping attention, GMFlowNet obtains major improvements on the predictions
for textureless regions and large motions. Our code is made publicly available
at https://github.com/xiaofeng94/GMFlowNet


  Motivated by observations of turbulence in the strongly stratified ocean
thermocline, we use direct numerical simulations to investigate the interaction
of a sinusoidal shear flow and a large-amplitude internal gravity wave. Despite
strong nonlinearities in the flow and a lack of scale separation, we find that
linear ray tracing theory is qualitatively useful in describing the early
development of the flow as the wave is refracted by the shear. Consistent with
the linear theory, the energy of the wave accumulates in regions of negative
mean shear where we observe evidence of convective and shear instabilities.
Streamwise-aligned convective rolls emerge the fastest, but their contribution
to irreversible mixing is dwarfed by shear-driven billow structures that
develop later. Although the wave strongly distorts the buoyancy field on which
these billows develop, the mixing efficiency of the subsequent turbulence is
similar to that arising from Kelvin-Helmholtz instability in a stratified shear
layer. We run simulations at Reynolds numbers of 5000 and 8000, and vary the
initial amplitude of the internal gravity wave. For high values of initial wave
amplitude, the results are qualitatively independent of $Re$. Smaller initial
wave amplitudes delay the onset of the instabilities, and allow for significant
laminar diffusion of the internal wave, leading to reduced turbulent activity.
We discuss the complex interaction between the mean flow, internal gravity wave
and turbulence, and its implications for internal wave-driven mixing in the
ocean.


  In this paper, we investigate the ideal structure of uniform Roe algebras for
general metric spaces beyond the scope of Yu's property A. Inspired by the
ideal of ghost operators coming from expander graphs and in contrast to the
notion of geometric ideal, we introduce a notion of ghostly ideal in a uniform
Roe algebra, whose elements are locally invisible in certain directions at
infinity. We show that the geometric ideal and the ghostly ideal are
respectively the smallest and the largest element in the lattice of ideals with
a common invariant open subset of the unit space of the coarse groupoid by
Skandalis-Tu-Yu, and hence the study of ideal structure can be reduced to
classifying ideals between the geometric and the ghostly ones. As an
application, we provide a concrete description for the maximal ideals in a
uniform Roe algebra in terms of the minimal points in the Stone-\v{C}ech
boundary of the space. We also provide a criterion to ensure that the geometric
and the ghostly ideals have the same $K$-theory, which helps to recover
counterexamples to the Baum-Connes type conjectures. Moreover, we introduce a
notion of partial Property A for a metric space to characterise the situation
in which the geometric ideal coincides with the ghostly ideal.


  Motivated by the novel phenomena observed in the layered material $\rm
SrCu_2(BO_3)_2$, the Shastry-Sutherland model (SSM) has been extensively
studied as the minimal model for $\rm SrCu_2(BO_3)_2$. However, the nature of
its quantum phase transition from the plaquette valence-bond solid (PVBS) to
antiferromagnetic (AFM) phase is under fierce debate, posing a challenge to
understand the underlying quantum criticality. Via the state-of-the-art tensor
network simulations, we study the ground state of the SSM on large-scale size
up to $20 \times 20$ sites. We identify the continuous transition nature
accompanied by an emergent O(4) symmetry between the PVBS and AFM phase, which
strongly suggests a deconfined quantum critical point (DQCP). Furthermore, we
map out the phase diagram of an extended SSM that can be continuously tuned to
the SSM, which demonstrates the same DQCP phenomena along a whole critical
line. Our results indicate a compelling scenario for understanding the origin
of the proposed proximate DQCP in recent experiments of $\rm SrCu_2(BO_3)_2$.


  Public blockchains are decentralized networks where each participating node
executes the same decision-making process. This form of decentralization does
not scale well because the same data are stored on each network node, and
because all nodes must validate each transaction prior to their confirmation.
One solution approach decomposes the nodes of a blockchain network into subsets
called "shards", each shard processing and storing disjoint sets of
transactions in parallel. To fully benefit from the parallelism of sharded
blockchains, the processing load of shards must be evenly distributed. However,
the problem of computing balanced workloads is theoretically hard and further
complicated in practice as transaction processing times are unknown prior to be
assigned to shards. In this paper we introduce a dynamic workload-balancing
algorithm where the allocation strategy of transactions to shards is
periodically adapted based on the recent workload history of shards. Our
algorithm is an adaptation to sharded blockchains of a consensus-based
load-balancing algorithm. It is a fully distributed algorithm inline with
network based applications such as blockchains. Some preliminary results are
reported based on simulations that shard transactions of three well-known
blockchain platforms.


  Interactions between organisms are mediated by an intricate network of
physico-chemical substances and other organisms. Understanding the dynamics of
mediators and how they shape the population spatial distribution is key to
predict ecological outcomes and how they would be transformed by changes in
environmental constraints. However, due to the inherent complexity involved,
this task is often unfeasible, from the empirical and theoretical perspectives.
In this paper, we make progress in addressing this central issue, creating a
bridge that provides a two-way connection between the features of the ensemble
of underlying mediators and the wrinkles in the population density induced by a
landscape defect (or spatial perturbation). The bridge is constructed by
applying the Feynman-Vernon decomposition, which disentangles the influences
among the focal population and the mediators in a compact way. This is achieved
though an interaction kernel, which effectively incorporates the mediators'
degrees of freedom, explaining the emergence of nonlocal influence between
individuals, an ad hoc assumption in modeling population dynamics. Concrete
examples are worked out and reveal the complexity behind a possible top-down
inference procedure.


  We show that a random walk on the mapping class group of an orientable
surface of finite type makes linear progress in the relative metric, which is
quasi-isometric to the complex of curves.


  We investigate the problem of multiplex graph embedding, that is, graphs in
which nodes interact through multiple types of relations (dimensions). In
recent years, several methods have been developed to address this problem.
However, the need for more effective and specialized approaches grows with the
production of graph data with diverse characteristics. In particular,
real-world multiplex graphs may exhibit a high number of dimensions, making it
difficult to construct a single consensus representation. Furthermore,
important information can be hidden in complex latent structures scattered in
multiple dimensions. To address these issues, we propose HMGE, a novel
embedding method based on hierarchical aggregation for high-dimensional
multiplex graphs. Hierarchical aggregation consists of learning a hierarchical
combination of the graph dimensions and refining the embeddings at each
hierarchy level. Non-linear combinations are computed from previous ones, thus
uncovering complex information and latent structures hidden in the multiplex
graph dimensions. Moreover, we leverage mutual information maximization between
local patches and global summaries to train the model without supervision. This
allows to capture of globally relevant information present in diverse locations
of the graph. Detailed experiments on synthetic and real-world data illustrate
the suitability of our approach to downstream supervised tasks, including link
prediction and node classification.


  Spatially dependent birth-death processes can be modelled by kinetic models
such as the BBGKY hierarchy. Diffusion in infinite dimensional systems can be
modelled with Brownian motion in Hilbert space. In this work Doi field
theoretic formalism is utilised to establish dualities between these classes of
processes. This enables path integral methods to calculate expectations of
duality functions. These are exemplified with models ranging from stochastic
cable signalling to jump-diffusion processes.


  We suggest that the dark side of the universe builds a dark medium modifying
the Standard Model particle dispersion relations. We introduce the generic form
for the modified dispersion relations and provide explicit models of the dark
medium. We use the derived neutrino dispersion relations to show that the size
of the corrections are smaller than the SN1987a bounds. We argue that before
invoking modifications of Einstein's theory of relativity one should
investigate the dark medium effects.


  Arts and Giesl proved that the termination of a first-order rewrite system
can be reduced to the study of its "dependency pairs". We extend these results
to rewrite systems on simply typed lambda-terms by using Tait's computability
technique.


  We investigate the asymptotic behaviour of the second-order correlation
function of the characteristic polynomial of a Hermitian Wigner matrix at the
edge of the spectrum. We show that the suitably rescaled second-order
correlation function is asymptotically given by the Airy kernel, thereby
generalizing the well-known result for the Gaussian Unitary Ensemble (GUE).
Moreover, we obtain similar results for real-symmetric Wigner matrices.


  In minimal trinification models light neutrino masses can be generated via a
radiative see-saw mechanism, where the masses of the right-handed neutrinos
originate from loops involving Higgs and fermion fields at the unification
scale. This mechanism is absent in models aiming at solving or ameliorating the
hierarchy problem, such as low-energy supersymmetry, since the large
seesaw-scale disappears. In this case, neutrino masses need to be generated via
a TeV-scale mechanism. In this paper, we investigate an inverse seesaw
mechanism and discuss some phenomenological consequences.


  We show how to effectively solve 5-term $S$-unit equations when the set of
primes $S$ has cardinality at most 3, and use this to provide an explicit
answer to an old question of D.J. Newman on representations of integers as sums
of $S$-units.


  The magnetic field dependence of the electronic properties of \textit{real}
single vertical quantum dots in circular and rectangular mesas is investigated
within a full three-dimensional multiscale self-consistent approach without any
{\it \'a priori} assumptions about the shape and strength of the confinement
potential. The calculated zero field electron addition energies are in good
agreement with available experimental data for both mesa geometries. Charging
diagrams in a magnetic field for number of electrons up to five are also
computed. Consistent with the experimental data, we found that the charging
curves for the rectangular mesa dot in a magnetic field are flatter and exhibit
less features than for a circular mesa dot. Evolution of the singlet-triplet
energy separation in the two electron system for both dot geometries in
magnetic field was also investigated. In the limit of large field, beyond the
singlet-triplet transition, the singlet-triplet energy difference continues to
become more negative in a circular mesa dot without any saturation within the
range of considered magnetic fields whilst it is predicted to asymptotically
approach zero for the rectangular mesa dot. This different behavior is
attributed to the symmetry "breaking" that occurs in the singlet wave-functions
in the rectangular mesa dot but not in the circular one.


  Even though the idea of partitioning provenance graphs for access control was
previously proposed, employing segments of the provenance DAG for fine-grained
access control to provenance data has not been thoroughly explored. Hence, we
take segments of a provenance graph, based on the extended OPM, and defined use
a variant of regular expressions, and utilize them in our fine-grained access
control language. It can not only return partial graphs to answer access
requests but also introduce segments as restrictions in order to screen
targeted data.


  Interactive data exploration (IDE) is an effective way of comprehending big
data, whose volume and complexity are beyond human abilities. The main goal of
IDE is to discover user interest regions from a database through multi-rounds
of user labelling. Existing IDEs adopt active-learning framework, where users
iteratively discriminate or label the interestingness of selected tuples. The
process of data exploration can be viewed as the process of training a
classifier, which determines whether a database tuple is interesting to a user.
An efficient exploration thus takes very few iterations of user labelling to
reach the data region of interest. In this work, we consider the data
exploration as the process of few-shot learning, where the classifier is
learned with only a few training examples, or exploration iterations. To this
end, we propose a learning-to-explore framework, based on meta-learning, which
learns how to learn a classifier with automatically generated meta-tasks, so
that the exploration process can be much shortened. Extensive experiments on
real datasets show that our proposal outperforms existing explore-by-example
solutions in terms of accuracy and efficiency.


  We report ab initio investigations of hexagon-shaped, [111]/[0001] oriented
III-V semiconductor nanowires with varying crystal structure, surface
passivation, surface orientation, and diameter. Their stability is dominated by
the free surface energies of the corresponding facets which differ only weakly
from those of free surfaces. We observe a phase transition between local
zinc-blende and wurtzite geometry versus preparation conditions of the
surfaces, which is accompanied by a change in the facet orientation. The
influence of the actual III-V compound remains small. The atomic relaxation of
nanowires gives rise to smaller bond lengths in comparison to the bulk
zinc-blende structures resulting also in somewhat reduced bilayer thicknesses
parallel to the growth direction.


  We examine the motion of charged particles in gravitational and
electro-magnetic background fields. We study in particular the deviation of
world lines, describing the relative acceleration between particles on
different space-time trajectories. Two special cases of background fields are
considered in detail: (a) pp-waves, a combination of gravitational and
electro-magnetic polarized plane waves travelling in the same direction; (b)
the Reissner-Nordstr{\o}m solution. We perform a non-trivial check by computing
the precession of the periastron for a charged particle in the
Reissner-Nordstr{\o}m geometry both directly by solving the geodesic equation,
and using the world-line deviation equation. The results agree to the order of
approximation considered.


  We report the discovery of a new open cluster (OC) in the Galaxy at
$\ell=167.0^\circ$ and $b=-1.0^\circ$. Its field includes the planetary nebula
(PN) PK 167-0.1. We study the possible associations of the PN/OC pairs NGC
2818/NGC 2818A, NGC 2438/M 46 (NGC 2437), PK 6+2.5/NGC 6469, as well as of the
PN PK 167-0.1 with New Cluster 1. The analyses are based on near-infrared
colour-magnitude diagrams (CMDs) and stellar radial density profiles (RDPs).
NGC 6469 is located in a heavily contaminated bulge field. The CMD morphology,
especially for the latter two cases, is defined with a field star
decontamination algorithm applied to the 2MASS \jj, \hh, and \ks photometry.
Field decontamination for the OCs NGC 2818A and M 46 produced better defined
CMDs and more accurate cluster parameters than in the literature. Those pieces
of evidence point to M 46 as physically associated with the PN NGC 2438. The
same occurs for the OC NGC 2818A and the PN NGC 2818, however previous radial
velocity arguments indicate that they are not associated. The OC NGC 6469 does
not appear to be associated with the PN PK 6+2.5, which probably belongs to the
bulge. Finally, the distance of the OC New Cluster 1 is consistent with a
physical association with the PN PK 167-0.1.


  We consider a class of general spin Hamiltonians of the form
$H_s(t)=H_0(t)+H'(t)$ where $H_0(t)$ and $H'(t)$ describe the dipole
interaction of the spins with an arbitrary time-dependent magnetic field and
the internal interaction of the spins, respectively. We show that if $H'(t)$ is
rotationally invariant, then $H_s(t)$ admits the same dynamical invariant as
$H_0(t)$. A direct application of this observation is a straightforward
rederivation of the results of Yan et al [Phys. Lett. A, Vol: 251 (1999) 289
and Vol: 259 (1999) 207] on the Heisenberg spin system in a changing magnetic
field.


  The possibility of velocity shear-induced linear transformations of different
magnetohydrodynamic waves in the solar wind is studied both analytically and
numerically. A quantitative analysis of the wave transformation processes for
all possible plasma-$\beta$ regimes is performed. By applying the obtained
criteria for effective wave coupling to the solar wind parameters, we show that
velocity shear-induced linear transformations of Alfv\'en waves into
magneto-acoustic waves could effectively take place for the relatively
low-frequency Alfv\'en waves in the energy containing interval. The obtained
results are in a good qualitative agreement with the observed features of
density perturbations in the solar wind.


  The trend in media consumption towards streaming and portability offers new
challenges and opportunities for signal processing in audio and acoustics. The
most significant embodiment of this trend is that most music consumption now
happens on-the-go which has recently led to an explosion in headphone sales and
small portable speakers. In particular, premium headphones offer a gateway for
a younger generation to experience high quality sound. Additionally, through
technologies incorporating head-related transfer functions headphones can also
offer unique new experiences in gaming, augmented reality, and surround sound
listening. Home audio has also seen a transition to smaller sound systems in
the form of sound bars. This speaker configuration offers many exciting
challenges for surround sound reproduction which has traditionally used five
speakers surrounding the listener. Furthermore, modern home entertainment
systems offer more than just content delivery; users now expect wireless and
connected smart devices with video conferencing, gaming, and other interactive
capabilities. With this comes challenges for voice interaction at a distance
and in demanding conditions, e.g., during content playback, and opportunities
for new smart interactive experiences based on awareness of environment and
user biometrics.


  In this article, we consider analytic perturbations of isometries of an
analytic Riemannian manifold M. We prove that, under some conditions, a
finitely presented group of such small enough perturbations is analytically
conjugate on M to the same group of isometry it is a perturbation of. Our
result relies on a "Diophantine-like" condition, relating the actions of the
isometry group and the eigenvalues of the Laplace-Beltrami operator. Our result
generalizes Arnold-Herman's theorem about diffemorphisms of the circle that are
small perturbations of rotations.


  Critically ill patients in regular wards are vulnerable to unanticipated
clinical dete- rioration which requires timely transfer to the intensive care
unit (ICU). To allow for risk scoring and patient monitoring in such a setting,
we develop a novel Semi- Markov Switching Linear Gaussian Model (SSLGM) for the
inpatients' physiol- ogy. The model captures the patients' latent clinical
states and their corresponding observable lab tests and vital signs. We present
an efficient unsupervised learn- ing algorithm that capitalizes on the
informatively censored data in the electronic health records (EHR) to learn the
parameters of the SSLGM; the learned model is then used to assess the new
inpatients' risk for clinical deterioration in an online fashion, allowing for
timely ICU admission. Experiments conducted on a het- erogeneous cohort of
6,094 patients admitted to a large academic medical center show that the
proposed model significantly outperforms the currently deployed risk scores
such as Rothman index, MEWS, SOFA and APACHE.


  We consider the conformal decomposition of Einstein's constraint equations
introduced by Lichnerowicz and York, on a closed manifold. We establish
existence of non-CMC weak solutions using a combination of a priori estimates
for the individual Hamiltonian and momentum constraints, barrier constructions
for the Hamiltonian constraint, and topological fixed-point arguments. An
important new feature of these results is the absense of the near-CMC
assumption when the rescaled background metric is in the positive Yamabe class,
if the freely specifiable part of the data given by the matter fields (if
present) and the traceless-transverse part of the rescaled extrinsic curvature
are taken to be sufficiently small. In this case, the mean extrinsic curvature
can be taken to be an arbitrary smooth function without restrictions on the
size of its spatial derivatives, giving what are apparently the first non-CMC
existence results without the near-CMC assumption. Standard bootstrapping
arguments to increase the regularity of the conformal factor are blocked by the
use of a weak background metric. In the CMC case, we recover Maxwell's rough
solution results as a special case. Our results extend the 1996 non-CMC result
of Isenberg and Moncrief in three ways: (1) the near-CMC assumption is removed
in the case of the positive Yamabe class; (2) regularity is extended down to
the maximum allowed by the background metric and the matter; and (3) the result
holds for all three Yamabe classes. This last extension was also accomplished
recently by Allen, Clausen and Isenberg, although their result is restricted to
the near-CMC case and to smoother background metrics and data.


  We present a new evolutionary model that describes the population properties
of radio sources at frequencies <5 GHz, thus complementing the De Zotti et al.
(2005) model, holding at higher frequencies. We find that simple analytic
luminosity evolution is still sufficient to fit the wealth of available data on
local luminosity functions, multi-frequency source counts, and redshift
distributions. However, the fit requires a luminosity-dependent decline of
source luminosities at high redshifts, at least for steep-spectrum sources,
thus confirming earlier indications of a "downsizing" also for radio sources.
The upturn of source counts at sub-mJy levels is accounted for by a
straightforward extrapolation, using the empirical far-IR/radio correlation, of
evolutionary models matching the far-IR counts and redshift distributions of
star-forming galaxies. We also discuss the implications of the new model for
the interpretation of data on large-scale clustering of radio sources and on
the Integrated Sachs-Wolfe (ISW) effect, and for the investigation of the
contribution of discrete sources to the extragalactic background. As for the
ISW effect, a new analysis exploiting a very clean CMB map, yields at a
substantially higher significance than reported before.


  We present first results of near-infrared integral field spectroscopy of the
central 8''$\times$8'' of the Wolf-Rayet nucleus of the barred spiral galaxy
NGC 6764. In addition to stellar CO and Na I absorption lines as well as
recombination lines of H and He I the K-band spectrum shows strong emission
from molecular hydrogen. Analysis of the stellar nuclear light using population
synthesis in conjunction with NIR spectral synthesis suggests either the
presence of two starbursts with ages of $\sim$ 5 Myrs and $\sim$ 20 Myrs or
continuous star formation with an star formation rate of $\sim$ 0.4 \solm
yr^{-1} over the past 1 Gyr.


  Contemporary quantum technologies face major difficulties in fault tolerant
quantum computing with error correction, and focus instead on various shades of
quantum simulation (Noisy Intermediate Scale Quantum, NISQ) devices, analogue
and digital quantum simulators and quantum annealers. There is a clear need and
quest for such systems that, without necessarily simulating quantum dynamics of
some physical systems, can generate massive, controllable, robust, entangled,
and superposition states. This will, in particular, allow the control of
decoherence, enabling the use of these states for quantum communications (e.g.
to achieve efficient transfer of information in a safer and quicker way),
quantum metrology, sensing and diagnostics (e.g. to precisely measure phase
shifts of light fields, or to diagnose quantum materials). In this Chapter we
present a vision of the golden future of quantum simulators in the decades to
come.


  Professional players in social media (e.g., big companies, politician,
athletes, celebrities, etc) are intensively using Online Social Networks (OSNs)
in order to interact with a huge amount of regular OSN users with different
purposes (marketing campaigns, customer feedback, public reputation
improvement, etc). Hence, due to the large catalog of existing OSNs,
professional players usually count with OSN accounts in different systems. In
this context an interesting question is whether professional users publish the
same information across their OSN accounts, or actually they use different OSNs
in a different manner. We define as cross-posting activity the action of
publishing the same information in two or more OSNs. This paper aims at
characterizing the cross-posting activity of professional users across three
major OSNs, Facebook, Twitter and Google+. To this end, we perform a
large-scale measurement-based analysis across more than 2M posts collected from
616 professional users with active accounts in the three referred OSNs. Then we
characterize the phenomenon of cross posting and analyze the behavioral
patterns based on the identified characteristics.


  In this paper, we propose an alternative method to estimate room layouts of
cluttered indoor scenes. This method enjoys the benefits of two novel
techniques. The first one is semantic transfer (ST), which is: (1) a
formulation to integrate the relationship between scene clutter and room layout
into convolutional neural networks; (2) an architecture that can be end-to-end
trained; (3) a practical strategy to initialize weights for very deep networks
under unbalanced training data distribution. ST allows us to extract highly
robust features under various circumstances, and in order to address the
computation redundance hidden in these features we develop a principled and
efficient inference scheme named physics inspired optimization (PIO). PIO's
basic idea is to formulate some phenomena observed in ST features into
mechanics concepts. Evaluations on public datasets LSUN and Hedau show that the
proposed method is more accurate than state-of-the-art methods.


  We present observations of supernova (SN) 2008ge, which is spectroscopically
similar to the peculiar SN 2002cx, and its pre-explosion site that indicate
that its progenitor was probably a white dwarf. NGC 1527, the host galaxy of SN
2008ge, is an S0 galaxy with no evidence of star formation or massive stars.
Astrometrically matching late-time imaging of SN 2008ge to pre-explosion HST
imaging, we constrain the luminosity of the progenitor star. Since SN 2008ge
has no indication of hydrogen or helium in its spectrum, its progenitor must
have lost its outer layers before exploding, requiring that it be a white
dwarf, a Wolf-Rayet star, or a lower-mass star in a binary system. Observations
of the host galaxy show no signs of individual massive stars, star clusters, or
H II regions at the SN position or anywhere else, making a Wolf-Rayet
progenitor unlikely. Late-time spectroscopy of SN 2008ge show strong [Fe II]
lines with large velocity widths compared to other members of this class at
similar epochs. These previously unseen features indicate that a significant
amount of the SN ejecta is Fe (presumably the result of radioactive decay of
56Ni generated in the SN), further supporting a thermonuclear explosion.
Placing the observations of SN 2008ge in the context of observations of other
objects in the class of SN, we suggest that the progenitor was most likely a
white dwarf.


  The gauge invariance of the Aharonov-Bohm (AB) effect with a quantum
treatment for the electromagnetic field is demonstrated. We provide an exact
solution for the electromagnetic ground energy due to the interaction of the
quantum electromagnetic field with the classical charges and currents that act
as sources of the potentials in a classical description, in the Lorenz gauge.
Then, we use first-order perturbation theory to compute an extra change on the
electromagnetic ground energy due to the presence of a quantum charged particle
with known wave function in the system. This energy in general depends on the
quantum particle path in an interferometer, what results in an AB phase
difference between the paths. The gauge invariance of this AB phase difference
is then shown for the magnetic, electric, and the recently proposed
electrodynamic versions of the AB effect. However, the AB phase difference
could depend on the gauge for nonclosed paths, what reinforces the view that it
only can be measured in closed paths.


  We propose a novel Hermite-Taylor correction function method to handle
embedded boundary and interface conditions for Maxwell's equations. The
Hermite-Taylor method evolves the electromagnetic fields and their derivatives
through order $m$ in each Cartesian coordinate. This makes the development of a
systematic approach to enforce boundary and interface conditions difficult.
Here we use the correction function method to update the numerical solution
where the Hermite-Taylor method cannot be applied directly. The proposed
high-order method offers a flexible systematic approach to handle embedded
boundary and interface problems, including problems with discontinuous
solutions at the interface. This method is also easily adaptable to other first
order hyperbolic systems.


  We offer a new method for proving that the maximal eigenvalue of the
normalized graph Laplacian of a graph with $n$ vertices is at least
$\frac{n+1}{n-1}$ provided the graph is not complete and that equality is
attained if and only if the complement graph is a single edge or a complete
bipartite graph with both parts of size $\frac{n-1}2$. With the same method, we
also prove a new lower bound to the largest eigenvalue in terms of the minimum
vertex degree, provided this is at most $\frac{n-1}{2}$.


  Through chaos decomposition we improve the Varadhan estimate for the rate of
convergence of the centered approximate self-intersection local time of planar
Brownian motion.


  We perform an extensive analysis of optical counterparts of Planck PSZ2
clusters, considering matches with three recent catalogs built from SDSS data:
AMF DR9, redMaPPer (v6.3) and Wen et al (WHL). We significantly extend the
number of optical counterparts of detected Planck clusters, and characterize
the optical properties when multiple identifications in different catalogs
exist. For Planck clusters which already possess an external validation, we
analyze the redshift assignment for both optical and X--ray determinations. We
then analyze the Planck Cosmology sample and comment on redshift determination
and potential mass mis-determinations due to alignment issues. Finally, we
inspect the reconstructed $y$ map from Planck and reason on the detectability
of optical clusters. Overall, AMF DR9 main (extended) finds 485 (511) optical
matches, with 45 (55) previously unmatched PSZ2 clusters, to be compared with
the 374 optical matches already present in PSZ2. 29 of the 55 previously
unmatched clusters do not yet have a followup in the literature. 18 of these
are found in more than one SDSS catalog with consistent redshifts. We provide
redshift and mass estimates for the newly matched clusters, and discuss the
comparison with the follow-ups, when present. We find good agreement between
the redMaPPer and AMF DR9 redshift determinations. From the Planck Cosmology
sample, we find 14 clusters which merit further investigation, and discuss
possible alignment issues for 9 of these clusters. After inspecting the $y$
map, we provide a list of 229 optical clusters not included in the Planck PSZ2
catalog but showing a prominent $y$ signal. We have further investigated the 86
clusters with Planck S/N $>4.5$ using the MMF technique (applied to the Planck
HFI maps), and were able to detect 20 new cluster candidates that are not
included in the PSZ2 catalog.


  We report on the first Interface Region Imaging Spectrograph (IRIS) study of
cool transition region loops. This class of loops has received little attention
in the literature. A cluster of such loops was observed on the solar disk in
active region NOAA11934, in the Si IV 1402.8 \AA\ spectral raster and 1400 \AA\
slit-jaw (SJ) images. We divide the loops into three groups and study their
dynamics and interaction. The first group comprises relatively stable loops,
with 382--626\,km cross-sections. Observed Doppler velocities are suggestive of
siphon flows, gradually changing from -10 km/s at one end to 20 km/s at the
other end of the loops. Nonthermal velocities from 15 to 25 km/s were
determined. These physical properties suggest that these loops are impulsively
heated by magnetic reconnection occurring at the blue-shifted footpoints where
magnetic cancellation with a rate of $10^{15}$ Mx/s is found. The released
magnetic energy is redistributed by the siphon flows. The second group
corresponds to two footpoints rooted in mixed-magnetic-polarity regions, where
magnetic cancellation occurred at a rate of $10^{15}$ Mx/s and line profiles
with enhanced wings of up to 200 km/s were observed. These are suggestive of
explosive-like events. The Doppler velocities combined with the SJ images
suggest possible anti-parallel flows in finer loop strands. In the third group,
interaction between two cool loop systems is observed. Evidence for magnetic
reconnection between the two loop systems is reflected in the line profiles of
explosive events, and a magnetic cancellation rate of $3\times10^{15}$ Mx/s
observed in the corresponding area. The IRIS observations have thus opened a
new window of opportunity for in-depth investigations of cool transition region
loops. Further numerical experiments are crucial for understanding their
physics and their role in the coronal heating processes.


  In this paper, authentication for mobile radio frequency identification
(RFID) systems with low-cost tags is studied. Firstly, an adaptive modulus (AM)
encryption algorithm is proposed. Subsequently, in order to enhance the
security without additional storage of new key matrices, a self-updating
encryption order (SUEO) algorithm is designed. Furthermore, a diagonal block
local transpose key matrix (DBLTKM) encryption algorithm is presented, which
effectively expands the feasible domain of the key space. Based on the above
three algorithms, a novel joint AM-SUEO-DBLTKM encryption algorithm is
constructed. Making full use of the advantages of the proposed joint algorithm,
a two-way RFID authentication protocol, named AM-SUEO-DBLTKM-RFID, is proposed
for mobile RFID systems. In addition, the Burrows-Abadi-Needham (BAN) logic and
security analysis indicate that the proposed AM-SUEO-DBLTKM-RFID protocol can
effectively combat various typical attacks. Numerical results demonstrate that
the proposed AM-SUEO-DBLTKM algorithm can save 99.59\% of tag storage over
traditional algorithms. Finally, the low computational complexity as well as
the low storage cost of the proposed AM-SUEO-DBLTKM-RFID protocol facilitates
deployment within low-cost RFID tags.


  A self-consistent particle-in-cell simulation study is performed to
investigate the effect of driving frequency on the electric field
non-linearity, electron heating mechanism and electron energy distribution
function (EEDF) in a low pressure symmetric capacitively coupled plasma (CCP)
discharge at a constant electron plasma frequency. The driving frequency is
varied from 27.12 MHz to 100 MHz for a discharge gap of 3.2 cm in argon at a
gas pressure of 1 Pa. The simulation results provide insight of higher harmonic
generations in a CCP system for a constant electron response time. The
spatio-temporal evolution and spatial time averaged electron heating is
presented for different driving frequencies. The simulation results predict
that the electric field non-linearity increases with a rise in driving
frequency along with a concurrent increase in higher harmonic contents. In
addition to the electron heating and cooling near to the sheath edge a positive
<J.E> is observed in to the bulk plasma at higher driving frequencies. The EEDF
illustrate enhancement in the population of mid-energy range electrons as
driving frequency increases thereby changing the shape of EEDF from
bi-Maxwellian to nearly Maxwellian. For the constant ion flux on the electrode
surface, a decrease in the ion energy by more than half is observed with an
increase in driving frequency.


  To search for circumstellar disks around evolved stars, we targeted roughly
100 DA white dwarfs from the Palomar Green survey with the Peters Automated
Infrared Imaging Telescope (PAIRITEL). Here we report the discovery of a debris
disk around one of these targets, the pulsating white dwarf PG 1541+651 (KX
Draconis, hereafter PG1541). We detect a significant flux excess around PG1541
in the K-band. Follow-up near-infrared spectroscopic observations obtained at
the NASA Infrared Telescope Facility (IRTF) and photometric observations with
the warm Spitzer Space Telescope confirm the presence of a warm debris disk
within 0.13-0.36 Rsun (11-32x the stellar radius) at an inclination angle of
60deg. At Teff = 11880 K, PG1541 is almost a twin of the DAV white dwarf
G29-38, which also hosts a debris disk. All previously known dusty white dwarfs
are of the DAZ/DBZ spectral type due to accretion of metals from the disk.
High-resolution optical spectroscopy is needed to search for metal absorption
lines in PG1541 and to constrain the accretion rate from the disk. PG1541 is
only 55 pc away from the Sun and the discovery of its disk in our survey
demonstrates that our knowledge of the nearby dusty white dwarf population is
far from complete.


  Stronger constraints on the pseudoscalar coupling constants of an axion to a
proton and a neutron are obtained from an indirect measurement of the effective
Casimir pressure between two Au-coated plates by means of micromechanical
torsional oscillator. For this purpose, the additional effective pressure due
to two-axion exchange is calculated. The role of boundary effects and the
validity region of the proximity force approximation in application to forces
of axion origin are determined. The obtained constraints are up to factors of
380 and 3.15 stronger than those found recently from other laboratory
experiments and are relevant to axion masses from $10^{-3}$eV to 15eV.


  This paper builds on an innovative Information Retrieval tool, Ariadne. The
tool has been developed as an interactive network visualization and browsing
tool for large-scale bibliographic databases. It basically allows to gain
insights into a topic by contextualizing a search query (Koopman et al., 2015).
In this paper, we apply the Ariadne tool to a far smaller dataset of 111,616
documents in astronomy and astrophysics. Labeled as the Berlin dataset, this
data have been used by several research teams to apply and later compare
different clustering algorithms. The quest for this team effort is how to
delineate topics. This paper contributes to this challenge in two different
ways. First, we produce one of the different cluster solution and second, we
use Ariadne (the method behind it, and the interface - called LittleAriadne) to
display cluster solutions of the different group members. By providing a tool
that allows the visual inspection of the similarity of article clusters
produced by different algorithms, we present a complementary approach to other
possible means of comparison. More particular, we discuss how we can - with
LittleAriadne - browse through the network of topical terms, authors, journals
and cluster solutions in the Berlin dataset and compare cluster solutions as
well as see their context.


  In this paper, we study the problem of legal domain adaptation problem from
an imbalanced source domain to a partial target domain. The task aims to
improve legal judgment predictions for non-professional fact descriptions. We
formulate this task as a partial-and-imbalanced domain adaptation problem.
Though deep domain adaptation has achieved cutting-edge performance in many
unsupervised domain adaptation tasks. However, due to the negative transfer of
samples in non-shared classes, it is hard for current domain adaptation model
to solve the partial-and-imbalanced transfer problem. In this work, we explore
large-scale non-shared but related classes data in the source domain with a
hierarchy weighting adaptation to tackle this limitation. We propose to embed a
novel pArtial Imbalanced Domain Adaptation technique (AIDA) in the deep
learning model, which can jointly borrow sibling knowledge from non-shared
classes to shared classes in the source domain and further transfer the shared
classes knowledge from the source domain to the target domain. Experimental
results show that our model outperforms the state-of-the-art algorithms.


  We present measurements of the color and luminosity dependence of galaxy
clustering at z~1 in the DEEP2 Galaxy Redshift Survey. Using volume-limited
subsamples in bins of both color and luminosity, we find that: 1) The
clustering dependence is much stronger with color than with luminosity and is
as strong with color at z~1 as is found locally. We find no dependence of the
clustering amplitude on color for galaxies on the red sequence, but a
significant dependence on color for galaxies within the blue cloud. 2) For
galaxies in the range L/L*~0.7-2, a stronger large-scale luminosity dependence
is seen for all galaxies than for red and blue galaxies separately. The
small-scale clustering amplitude depends significantly on luminosity for blue
galaxies, with brighter samples having a stronger rise on scales r_p<0.5 Mpc/h.
3) Redder galaxies exhibit stronger small-scale redshift-space distortions
("fingers of god"), and both red and blue populations show large-scale
distortions in xi(r_p,pi) due to coherent infall. 4) While the clustering
length, r_0, increases smoothly with galaxy color (in narrow bins), its
power-law exponent, gamma, exhibits a sharp jump from the blue cloud to the red
sequence. The intermediate color `green' galaxy population likely includes
transitional galaxies moving from the blue cloud to the red sequence; on large
scales green galaxies are as clustered as red galaxies but show infall
kinematics and a small-scale correlation slope akin to the blue galaxy
population. 5) We compare our results to a semi-analytic galaxy formation model
applied to the Millenium Run simulation. Differences between the data and the
model suggest that in the model star formation is shut down too efficiently in
satellite galaxies.


  Calcium imaging is one of the most important tools in neurophysiology as it
enables the observation of neuronal activity for hundreds of cells in parallel
and at single-cell resolution. In order to use the data gained with calcium
imaging, it is necessary to extract individual cells and their activity from
the recordings. We present DISCo, a novel approach for the cell segmentation in
calcium imaging videos. We use temporal information from the recordings in a
computationally efficient way by computing correlations between pixels and
combine it with shape-based information to identify active as well as
non-active cells. We first learn to predict whether two pixels belong to the
same cell; this information is summarized in an undirected, edge-weighted grid
graph which we then partition. In so doing, we approximately solve the NP-hard
correlation clustering problem with a recently proposed greedy algorithm.
Evaluating our method on the Neurofinder public benchmark shows that DISCo
outperforms all existing models trained on these datasets.


  We discuss the effect of electron-electron interactions on the static
polarization properties of graphene beyond RPA. Divergent self-energy
corrections are naturally absorbed into the renormalized coupling constant
$\alpha$. We find that the lowest order vertex correction, which is the first
non-trivial correlation contribution, is finite, and about 30% of the RPA
result at strong coupling $\alpha \sim 1$. The vertex correction leads to
further reduction of the effective charge. Finite contributions to dielectric
screening are expected in all orders of perturbation theory.


  This paper is devoted to the quantification and analysis of marginal risk
contribution of a given single financial institution i to the risk of a
financial system s. Our work expands on the CoVaR concept proposed by Adrian
and Brunnermeier as a tool for the measurement of marginal systemic risk
contribution. We first give a mathematical definition of
CoVaR_{\alpha}^{s|L^i=l}. Our definition improves the CoVaR concept by
expressing CoVaR_{\alpha}^{s|L^i=l} as a function of a state l and of a given
probability level \alpha relative to i and s respectively. Based on Copula
theory we connect CoVaR_{\alpha}^{s|L^i=l} to the partial derivatives of Copula
through their probabilistic interpretation and definitions (Conditional
Probability). Using this we provide a closed formula for the calculation of
CoVaR_{\alpha}^{s|L^i=l} for a large class of (marginal) distributions and
dependence structures (linear and non-linear). Our formula allows a better
analysis of systemic risk using CoVaR in the sense that it allows to define
CoVaR_{\alpha}^{s|L^i=l} depending on the marginal distributions of the losses
of i and s respectively and the copula between L^i and L^s. We discuss the
implications of this in the context of the quantification and analysis of
systemic risk contributions. %some mathematical This makes possible the For
example we will analyse the marginal effects of L^i, L^s and C of the risk
contribution of i.


  We establish an index theorem for Toeplitz operators on odd dimensional spin
manifolds with boundary. It may be thought of as an odd dimensional analogue of
the Atiyah-Patodi-Singer index theorem for Dirac operators on manifolds with
boundary. In particular, there occurs naturally an invariant of $\eta$ type
associated to $K^1$ representatives on even dimensional manifolds, which should
be of independent interests. For example, it gives an intrinsic interpretation
of the so called Wess-Zumino term in the WZW theory in physics.


  We put forward a fundamental relationship between the normalized spectral
density of excess current noise and the imaginary part of complex conductivity.


  A key strength of managed runtimes over hardware is the ability to gain
detailed insight into the dynamic execution of programs with instrumentation.
Analyses such as code coverage, execution frequency, tracing, and debugging,
are all made easier in a virtual setting. As a portable, low-level bytecode,
WebAssembly offers inexpensive in-process sandboxing with high performance. Yet
to date, Wasm engines have not offered much insight into executing programs,
supporting at best bytecode-level stepping and basic source maps, but no
instrumentation capabilities. In this paper, we show the first non-intrusive
dynamic instrumentation system for WebAssembly in the open-source Wizard
Research Engine. Our innovative design offers a flexible, complete hierarchy of
instrumentation primitives that support building high-level, complex analyses
in terms of low-level, programmable probes. In contrast to emulation or machine
code instrumentation, injecting probes at the bytecode level increases
expressiveness and vastly simplifies the implementation by reusing the engine's
JIT compiler, interpreter, and deoptimization mechanism rather than building
new ones. Wizard supports both dynamic instrumentation insertion and removal
while providing consistency guarantees, which is key to composing multiple
analyses without interference. We detail a fully-featured implementation in a
high-performance multi-tier Wasm engine, show novel optimizations specifically
designed to minimize instrumentation overhead, and evaluate performance
characteristics under load from various analyses. This design is well-suited
for production engine adoption as probes can be implemented to have no impact
on production performance when not in use.


  In this paper, we extend the proximal point algorithm for vector optimization
from the Euclidean space to the Riemannian context. Under suitable assumptions
on the objective function the well definition and full convergence of the
method to a weak efficient point is proved.


  We consider gravitational massive scalar-scalar scattering from unitarity and
demonstrate how intermediate soft graviton behavior and the concept of
extracting classical physics from localization of integrands on velocity cuts
devise an efficient extraction scheme for computing the classical
post-Minkowskian radial action perturbatively. We demonstrate the computational
efficiency by deriving the scattering amplitudes in the probe regime to the
fifth post-Minkowskian order in arbitrary dimensions.


  In this paper, the problem of federated learning (FL) through digital
communication between clients and a parameter server (PS) over a multiple
access channel (MAC), also subject to differential privacy (DP) constraints, is
studied. More precisely, we consider the setting in which clients in a
centralized network are prompted to train a machine learning model using their
local datasets. The information exchange between the clients and the PS takes
places over a MAC channel and must also preserve the DP of the local datasets.
Accordingly, the objective of the clients is to minimize the training loss
subject to (i) rate constraints for reliable communication over the MAC and
(ii) DP constraint over the local datasets. For this optimization scenario, we
proposed a novel consensus scheme in which digital distributed stochastic
gradient descent (D-DSGD) is performed by each client. To preserve DP, a
digital artificial noise is also added by the users to the locally quantized
gradients. The performance of the scheme is evaluated in terms of the
convergence rate and DP level for a given MAC capacity. The performance is
optimized over the choice of the quantization levels and the artificial noise
parameters. Numerical evaluations are presented to validate the performance of
the proposed scheme.


  We study solutions of the time dependent Schr\"odinger equation on Riemannian
manifolds with oscillatory initial conditions given by Lagrangian states.
Semiclassical approximations describe these solutions for small h (where h is
the semiclassical parameter), but their accuracy for large times is in general
only understood up to the Ehrenfest time T ~ ln(1/h), and the most difficult
case is the one where the underlying classical system is chaotic.
  We show that on surfaces of constant negative curvature semiclassical
approximations remain accurate for times at least up to T ~ h^(-1/2) in the
case that the Lagrangian state is associated with an unstable manifold of the
geodesic flow.


  Adaptive optimization algorithms, such as Adam and RMSprop, have shown better
optimization performance than stochastic gradient descent (SGD) in some
scenarios. However, recent studies show that they often lead to worse
generalization performance than SGD, especially for training deep neural
networks (DNNs). In this work, we identify the reasons that Adam generalizes
worse than SGD, and develop a variant of Adam to eliminate the generalization
gap. The proposed method, normalized direction-preserving Adam (ND-Adam),
enables more precise control of the direction and step size for updating weight
vectors, leading to significantly improved generalization performance.
Following a similar rationale, we further improve the generalization
performance in classification tasks by regularizing the softmax logits. By
bridging the gap between SGD and Adam, we also hope to shed light on why
certain optimization algorithms generalize better than others.


  We consider the problem of a scalar field, non-minimally coupled to gravity
through a $-\xi\phi^{2}R$ term, in the presence of a Brane. Exact solutions,
for a wide range of values of the coupling parameter $\xi$, for both
$\phi$-dependent and $\phi$-independent Brane tension, are derived and their
behaviour is studied. In the case of a Randall-Sundrum geometry, a class of the
resulting scalar field solutions exhibits a folded-kink profile. We go beyond
the Randall-Sundrum geometry studying general warp factor solutions in the
presence of a kink scalar. Analytic and numerical results are provided for the
case of a Brane or for smooth geometries, where the scalar field acts as a
thick Brane. It is shown that finite geometries with warp factors that
asymptotically decrease exponentially are realizable for a wide range of
parameter values. We also study graviton localization in our setup and find
that the localizing potential for gravitons with the characteristic
volcano-like profile develops a local maximum located at the origin for high
values of the coupling $\xi$.


  A low-background neutron detector array was developed to measure the cross
section of the $^{13}$C($\alpha$,n)$^{16}$O reaction, which is the neutron
source for the $s$-process in AGB stars, in the Gamow window ($E_{c.m.}$ = 190
$\pm$ 40 keV) at the China Jinping Underground Laboratory (CJPL). The detector
array consists of 24 $^{3}$He proportional counters embedded in a polyethylene
cube. Due to the deep underground location and a borated polyethylene shield
around the detector array, a low background of 4.5(2)/hour was achieved. The
$^{51}$V(p, n)$^{51}$Cr reaction was used to determine the neutron detection
efficiency of the array for neutrons with energy $E_n$ $<$ 1 MeV. Geant4
simulations, which were shown to well reproduce experimental results, were used
to extrapolate the detection efficiency to higher energies for neutrons emitted
in the $^{13}$C($\alpha$,n) $^{16}$O reaction. The theoretical angular
distributions of the $^{13}$C($\alpha$,n)$^{16}$O reaction were shown to be
important in estimating the uncertainties of the detection efficiency.


  By proving graph theoretical versions of Green-Stokes, Gauss-Bonnet and
Poincare-Hopf, core ideas of undergraduate mathematics can be illustrated in a
simple graph theoretical setting. In this pedagogical exposition we present the
main proofs on a single page and add illustrations. While discrete Stokes is is
old, the other two results for graphs were found only recently.


  The soliton structure of a gauge theory proposed to describe chiral
excitations in the multi-Layer Fractional Quantum Hall Effect is investigated.
A new type of derivative multi-component nonlinear Schr\"{o}dinger equation
emerges as effective description of the system that supports novel chiral
solitons. We discuss the classical properties of the solutions and study
relations to integrable systems.


  We predict the branching ratios of Z-> e^{\pm} \mu^{\pm}, Z-> e^{\pm}
\tau^{\pm} and Z-> \mu^{\pm} \tau^{\pm} decays in the framework of the 2HDM, in
the split fermion scenario. We observe that the branching ratios are not
sensitive to a single extra dimension, however, this sensitivity is
considerably large for two extra dimensions.


  New physics interactions can affect the strength and structure of the $tbW$
vertex. We investigate the magnitudes and phases of "anomalous" contributions
to this vertex in a two-Higgs doublet and the minimal supersymmetric extension
of the standard model, and in a top-color assisted technicolor (TC2) model.
While the magnitudes of the anomalous couplings remain below 1 percent in the
first two models, TC2 interactions can reduce the left-chiral coupling $f_L$ by
several percent.