  We consider the spatial central force problem with a real analytic potential.
We prove that for all analytic potentials, but the Keplerian and the Harmonic
ones, the Hamiltonian fulfills a nondegeneracy property needed for the
applicability of Nekhoroshev's theorem. We deduce stability of the actions over
exponentially long times when the system is subject to arbitrary analytic
perturbation. The case where the central system is put in interaction with a
slow system is also studied and stability over exponentially long time is
proved.


  In this work we propose a new neural network architecture that efficiently
implements and learns general purpose set-equivariant functions. Such a
function f maps a set of entities x = {x1, . . . , xn} from one domain to a set
of same cardinality y = f (x) = {y1, . . . , yn} in another domain regardless
of the ordering of the entities. The architecture is based on a gated recurrent
network which is iteratively applied to all entities individually and at the
same time syncs with the progression of the whole population. In reminiscence
to this pattern, which can be frequently observed in nature, we call our
approach SWARM mapping. Set-equivariant and generally permutation invariant
functions are important building blocks for many state of the art machine
learning approaches. Even in applications where the permutation invariance is
not of primary interest, as to be seen in the recent success of attention based
transformer models (Vaswani et. al. 2017). Accordingly, we demonstrate the
power and usefulness of SWARM mappings in different applications. We compare
the performance of our approach with another recently proposed set-equivariant
function, the Set Transformer (Lee et.al. 2018) and we demonstrate that models
solely based on SWARM layers gives state of the art results.


  We demonstrate that very massive (>10^8\msun), cosmologically nearby (z<1)
black hole binaries (MBHBs), which are primary targets for ongoing and upcoming
pulsar timing arrays (PTAs), are particularly appealing multimessenger
carriers. According to current models for massive black hole formation and
evolution, the planned Square Kilometer Array (SKA) will collect gravitational
wave signals from thousands of such massive systems, being able to individually
resolve and locate in the sky several of them (maybe up to a hundred). By
employing a standard model for the evolution of MBHBs in circumbinary discs,
with the aid of dedicated numerical simulations, we characterize the gas-binary
interplay, identifying possible electromagnetic signatures of the PTA sources.
We concentrate our investigation on two particularly promising scenarios in the
high energy domain, namely, the detection of X-ray periodic variability and of
double broad K\alpha iron lines. Up to several hundreds of periodic X-ray
sources with a flux >10^-13 erg s^-1 cm^-2 will be in the reach of upcoming
X-ray observatories. Double relativistic K\alpha lines may be observable in a
handful of low redshift (z<0.3) sources by proposed deep X-ray probes, such as
Athena. (Abridged)


  We investigate in this work a recently proposed diagrammatic quantum Monte
Carlo method --- the inchworm Monte Carlo method --- for open quantum systems.
We establish its validity rigorously based on resummation of Dyson series.
Moreover, we introduce an integro-differential equation formulation for open
quantum systems, which illuminates the mathematical structure of the inchworm
algorithm. This new formulation leads to an improvement of the inchworm
algorithm by introducing classical deterministic time-integration schemes. The
numerical method is validated by applications to the spin-boson model.


  It is shown that $c=-29/16$ is the unique rational number of smallest
denominator, and the unique rational number of smallest numerator, for which
the map $f_c(x) = x^2+c$ has a rational periodic point of period $3$. Several
arithmetic conditions on the set of all such rational numbers $c$ and the
rational orbits of $f_c(x)$ are proved. A graph on the numerators of the
rational $3$-periodic points of maps $f_c$ is considered which reflects
connections between solutions of norm equations from the cubic field of
discriminant $-23$.


  In this paper, we propose two approaches to apply boundary conditions for
bond-based peridynamic models. There has been in recent years a renewed
interest in the class of so-called non-local models, which include peridynamic
models, for the simulation of structural mechanics problems as an alternative
approach to classical local continuum models. However, a major issue, which is
often disregarded when dealing with this class of models, is concerned with the
manner by which boundary conditions should be prescribed. Our point of view
here is that classical boundary conditions, since applied on surfaces of solid
bodies, are naturally associated with local models. The paper describes two
methods to incorporate classical Dirichlet and Neumann boundary conditions into
bond-based peridynamics. The first method consists in artificially extending
the domain with a thin boundary layer over which the displacement field is
required to behave as an odd function with respect to the boundary points. The
second method resorts to the idea that peridynamic models and local models
should be compatible in the limit that the so-called horizon vanishes. The
approach consists then in decreasing the horizon from a constant value in the
interior of the domain to zero at the boundary so that one can directly apply
the classical boundary conditions. We present the continuous and discrete
formulations of the two methods and assess their performance on several
numerical experiments dealing with the simulation of a one-dimensional bar.


  We have obtained single-epoch optical photometry for 201 quasars, taken from
the FIRST Bright Quasar Survey, which span a wide range in radio loudness.
Comparison with the magnitudes of these objects on the POSS-I plates provides
by far the largest sample of long-term variability amplitudes for
radio-selected quasars yet produced. We find the quasars to be more variable in
the blue than in the red band, consistent with work on optically selected
samples. The previously noted trend of decreasing variability with increasing
optical luminosity applies only to radio-quiet objects. Furthermore, we do not
confirm a rise in variability amplitude with redshift, nor do we see any
dependence on radio flux or luminosity. The variability over a radio-optical
flux ratio range spanning a factor of 60,000 from radio-quiet to extreme
radio-loud objects is largely constant, although there is a suggestion of
greater variability in the extreme radio-loud objects. We demonstrate the
importance of Malmquist bias in variability studies, and develop a procedure to
correct for the bias in order to reveal the underlying variability properties
of the sample.


  One of the open problems posed in [3] is: what is the minimal number k such
that an open, flexible k-chain can interlock with a flexible 2-chain? In this
paper, we establish the assumption behind this problem, that there is indeed
some k that achieves interlocking. We prove that a flexible 2-chain can
interlock with a flexible, open 16-chain.


  The value of a locally frozen magnetic field in a region with a diameter of
0.5 mm in a 0.5-mm-thick YBa2Cu3O7-x plate was investigated as a function of
the excitation field (to 2x10^4 A/m), plate cooling mode (in the absence or
presence of a field; i.e., zero-field cooling (ZFC) or field coupling (FC)),
and local demagnetizing field. Analysis of the measurement results in the noted
range of excitation fields showed the following: (i) the dependence on the
excitation field for the ZFC mode is explained by the local inhomogeneity of
critical currents of weak links in the ceramic Josephson medium and is limited
by their maximum value at the temperature of the experiment (77 K); (ii) the
dependence on the excitation field for the FC mode contains a portion of the
magnetic phase transition from the frozen current structure, typical of the
initial portion of the dependence, to the current structure characteristic of
the ZFC freezing mode, and is limited by this transition; and (iii) the
dependence on the demagnetizing field for the ZFC mode can be explained by the
stable coexistence (without annihilation) of microscopic current loops with
opposite current directions in the ceramics.


  Recommender systems in academia are not widely available. This may be in part
due to the difficulty and cost of developing and maintaining recommender
systems. Many operators of academic products such as digital libraries and
reference managers avoid this effort, although a recommender system could
provide significant benefits to their users. In this paper, we introduce Mr.
DLib's "Recommendations as-a-Service" (RaaS) API that allows operators of
academic products to easily integrate a scientific recommender system into
their products. Mr. DLib generates recommendations for research articles but in
the future, recommendations may include call for papers, grants, etc. Operators
of academic products can request recommendations from Mr. DLib and display
these recommendations to their users. Mr. DLib can be integrated in just a few
hours or days; creating an equivalent recommender system from scratch would
require several months for an academic operator. Mr. DLib has been used by
GESIS Sowiport and by the reference manager JabRef. Mr. DLib is open source and
its goal is to facilitate the application of, and research on, scientific
recommender systems. In this paper, we present the motivation for Mr. DLib, the
architecture and details about the effectiveness. Mr. DLib has delivered 94m
recommendations over a span of two years with an average click-through rate of
0.12%.


  We provide a short and elementary proof for the recently proved result by G.
da Prato and H. Frankowska that -- under minimal assumptions -- a closed set is
invariant with respect to a stochastic control system if and only if it is
invariant with respect to the (associated) deterministic control system.


  We study single-hadron inclusive electroproduction in ep scattering at DESY
HERA at next-to-leading order in the parton model of quantum chromodynamics
endowed with non-perturbative fragmentation functions. Specifically, we
consider charged-hadron production, with unspecified transverse momentum p_T,
in the Breit frame and D^{*+-} production as a function of p_T, and perform
comparisons with recent data from the H1 Collaboration.


  We consider the hydrodynamic regime of theories with quantum anomalies for
global currents. We show that a hitherto discarded term in the conserve current
is not only allowed by symmetries, but is in fact required by triangle
anomalies and the second law of thermodynamics. This term leads to a number of
new effects, one of which is chiral separation in a rotating fluid at nonzero
chemical potential. The new kinetic coefficients can be expressed, in a unique
fashion, through the anomalies coefficients and the equation of state. We
briefly discuss the relevance of this new hydrodynamic term for physical
situations, including heavy ion collisions.


  In this work, we present the Densely Connected Temporal Convolutional Network
(DC-TCN) for lip-reading of isolated words. Although Temporal Convolutional
Networks (TCN) have recently demonstrated great potential in many vision tasks,
its receptive fields are not dense enough to model the complex temporal
dynamics in lip-reading scenarios. To address this problem, we introduce dense
connections into the network to capture more robust temporal features.
Moreover, our approach utilises the Squeeze-and-Excitation block, a
light-weight attention mechanism, to further enhance the model's classification
power. Without bells and whistles, our DC-TCN method has achieved 88.36%
accuracy on the Lip Reading in the Wild (LRW) dataset and 43.65% on the
LRW-1000 dataset, which has surpassed all the baseline methods and is the new
state-of-the-art on both datasets.


  Arrival times of requests to print in a student laboratory were analyzed.
Inter-arrival times between subsequent requests follow a universal scaling law
relating time intervals and the size of the request, indicating a scale
invariant dynamics with respect to the size. The cumulative distribution of
file sizes is well-described by a modified power law often seen in
non-equilibrium critical systems. For each user, waiting times between their
individual requests show long range dependence and are broadly distributed from
seconds to weeks. All results are incompatible with Poisson models, and may
provide evidence of critical dynamics associated with voluntary thought
processes in the brain.


  Beryllium is a simple alkali earth metal, but has been the target of
intensive studies for decades because of its unusual electron behaviors at
surfaces. Puzzling aspects include (i) severe deviations from the description
of the nearly free electron picture, (ii) anomalously large electron-phonon
coupling effect, and (iii) giant Friedal oscillations. The underlying origins
for such anomalous surface electron behaviors have been under active debate,
but with no consensus. Here, by means of first-principle calculations, we
discover that this pure metal system, surprisingly, harbors the Dirac node line
(DNL) that in turn helps to rationalize many of the existing puzzles. The DNL
is featured by a closed line consisting of linear band crossings and its
induced topological surface band agrees well with previous photoemission
spectroscopy observation on Be (0001) surface. We further reveal that each of
the elemental alakali earth metals of Mg, Ca, and Sr also harbors the DNL, and
speculate that the fascinating topological property of DNL might naturally
exist in other elemental metals as well.


  A set A of positive integers is called a h-bais of [0,n] if each integer in
[0,n]is a sum of no more than h members of A. In this paper, we will give a new
construction for h-basis.


  This paper proposes a novel approach to estimate the steady-state angle
stability limit (SSASL) by using the nonlinear power system dynamic model in
the modal space. Through two linear changes of coordinates and a simplification
introduced by the steady-state condition, the nonlinear power system dynamic
model is transformed into a number of single-machine-like power systems whose
power-angle curves can be derived and used for estimating the SSASL. The
proposed approach estimates the SSASL of angles at all machines and all buses
without the need for manually specifying the scenario, i.e. setting sink and
source areas, and also without the need for solving multiple nonlinear power
flows. Case studies on 9-bus and 39-bus power systems demonstrate that the
proposed approach is always able to capture the aperiodic instability in an
online environment, showing promising performance in the online monitoring of
the steady-state angle stability over the traditional power flow-based
analysis.


  Superconductivity in FeSe was investigated under high pressure through the
measurements of DC magnetization by using a diamond anvil cell. We successfully
observed that the disappearance of the superconductivity as a results of the
appearance of the non-superconducting ortho I\hspace{-1pt}I phase above $\sim$7
GPa ($\sim$5 GPa), when Ar (glycerin) is used as the pressure transmitting
media. Contrary to this, it has been found that the superconductivity even
survives under pressure above 7 GPa, when the thickness ($t$) of a
platelet-single crystal specimen is reduced. The survival of the
superconductivity above 7 GPa is consistent with a previous observation under
hydrostatic pressure by using a cubic anvil apparatus, suggesting that the
hydrostaticity of the pressure is improved by reducing $t$. It is also inferred
that the appearance of the ortho I\hspace{-1pt}I phase is due to uniaxial
stress along the [001] direction.


  We study theoretically the renormalization of the spin-orbit coupling
constant of two-dimensional electrons by electron-electron interactions. We
demonstrate that, similarly to the $g$ factor, the renormalization corresponds
to the enhancement, although the magnitude of the enhancement is weaker than
that for the $g$ factor. For high electron concentrations (small interaction
parameter $r_s$) the enhancement factor is evaluated analytically within the
static random phase approximation. For large $r_s\sim 10$ we use an approximate
expression for effective electron-electron interaction, which takes into
account the local field factor, and calculate the enhancement numerically. We
also study the interplay between the interaction-enhanced Zeeman splitting and
interaction-enhanced spin-orbit coupling.


  Identifying singers is an important task with many applications. However, the
task remains challenging due to many issues. One major issue is related to the
confounding factors from the background instrumental music that is mixed with
the vocals in music production. A singer identification model may learn to
extract non-vocal related features from the instrumental part of the songs, if
a singer only sings in certain musical contexts (e.g., genres). The model
cannot therefore generalize well when the singer sings in unseen contexts. In
this paper, we attempt to address this issue. Specifically, we employ
open-unmix, an open source tool with state-of-the-art performance in source
separation, to separate the vocal and instrumental tracks of music. We then
investigate two means to train a singer identification model: by learning from
the separated vocal only, or from an augmented set of data where we
"shuffle-and-remix" the separated vocal tracks and instrumental tracks of
different songs to artificially make the singers sing in different contexts. We
also incorporate melodic features learned from the vocal melody contour for
better performance. Evaluation results on a benchmark dataset called the
artist20 shows that this data augmentation method greatly improves the accuracy
of singer identification.


  We provide a compact 200 line MATLAB code demonstrating how topology
optimization (TopOpt) as an inverse design tool may be used in photonics,
targeting the design of two-dimensional dielectric metalenses and a metallic
reflector as examples. The physics model is solved using the finite element
method, and the code utilizes MATLAB's fmincon algorithm to solve the
optimization problem. In addition to presenting the code itself, we briefly
discuss a number of extensions and provide the code required to implement some
of these. Finally, we demonstrate the superiority of using a gradient-based
method compared to a genetic-algorithm-based method (using MATLAB's ga
algorithm) for solving inverse design problems in photonics. The MATLAB
software is freely available in the paper and may be downloaded from
https://www.topopt.mek.dtu.dk.


  After the initiation of the explosion of core-collapse supernovae, neutrinos
emitted from the nascent neutron star drive a supersonic baryonic outflow. This
neutrino-driven wind interacts with the more slowly moving, earlier supernova
ejecta forming a wind termination shock (or reverse shock), which changes the
local wind conditions and their evolution. Important nucleosynthesis processes
(alpha-process, charged-particle reactions, r-process, and vp-process) occur or
might occur in this environment. The nucleosynthesis depends on the long-time
evolution of density, temperature, and expansion velocity. Here we present
two-dimensional hydrodynamical simulations with an approximate description of
neutrino-transport effects, which for the first time follow the post-bounce
accretion, onset of the explosion, wind formation, and the wind expansion
through the collision with the preceding supernova ejecta. Our results
demonstrate that the anisotropic ejecta distribution has a great impact on the
position of the reverse shock, the wind profile, and the long-time evolution.
This suggests that hydrodynamic instabilities after core bounce and the
consequential asymmetries may have important effects on the
nucleosynthesis-relevant conditions in the neutrino-heated baryonic mass flow
from proto-neutron stars.


  Compressed beamforming algorithm is used in the current Wi-Fi standard to
reduce the beamforming feedback overhead (BFO). However, with each new
amendment of the standard the number of supported antennas in Wi-Fi devices
increases, leading to increased BFO and hampering the throughput despite using
compressed beamforming. In this paper, a novel index-based method is presented
to reduce the BFO in Wi-Fi links. In particular, a k-means clustering-based
approach is presented to generate candidate beamforming feedback matrices,
thereby reducing the BFO to only the index of the said candidate matrices. With
extensive simulation results, we compare the newly proposed method with the
IEEE 802.11be baseline and our previously published index-based method. We show
approximately 54% gain in throughput at high signal-to-noise (SNR) against the
IEEE 802.11be baseline. Our comparison also shows approximately 4 dB gain
compared to our previously published method at the packet-error-rate (PER) of
0.01 using MCS index 11. Additionally, we also discuss the impact of the
distance metric chosen for clustering as well as candidate selection on the
link performance.


  The Double Asteroid Redirection Test (DART) spacecraft will impact into the
asteroid Dimorphos on September 26, 2022 as a test of the kinetic impactor
technique for planetary defense. The efficiency of the deflection following a
kinetic impactor can be represented using the momentum enhancement factor,
Beta, which is dependent on factors such as impact geometry and the specific
target material properties. Currently, very little is known about Dimorphos and
its material properties that introduces uncertainty in the results of the
deflection efficiency observables, including crater formation, ejecta
distribution, and Beta. The DART Impact Modeling Working Group (IWG) is
responsible for using impact simulations to better understand the results of
the DART impact. Pre-impact simulation studies also provide considerable
insight into how different properties and impact scenarios affect momentum
enhancement following a kinetic impact. This insight provides a basis for
predicting the effects of the DART impact and the first understanding of how to
interpret results following the encounter. Following the DART impact, the
knowledge gained from these studies will inform the initial simulations that
will recreate the impact conditions, including providing estimates for
potential material properties of Dimorphos and Beta resulting from DARTs
impact. This paper summarizes, at a high level, what has been learned from the
IWG simulations and experiments in preparation for the DART impact. While
unknown, estimates for reasonable potential material properties of Dimorphos
provide predictions for Beta of 1-5, depending on end-member cases in the
strength regime.


  We prove `polynomial in $k$' bounds on the size of the Bergman kernel for the
space of holomorphic Siegel cusp forms of degree $n$ and weight $k$. When
$n=1,2$ our bounds agree with the conjectural bounds on the aforementioned
size, while the lower bounds match for all $n \ge 1$. For an $L^2$-normalised
Siegel cusp form $F$ of degree $2$, our bound for its sup-norm is $O_\epsilon
(k^{9/4+\epsilon})$. Further, we show that in any compact set $\Omega$ (which
does not depend on $k$) contained in the Siegel fundamental domain of
$\mathrm{Sp}(2, \mathbb Z)$ on the Siegel upper half space, the sup-norm of $F$
is $O_\Omega(k^{3/2 - \eta})$ for some $\eta>0$, going beyond the `generic'
bound in this setting.


  In this paper, we present a novel algorithm for classifying ex vivo tissue
that comprises multi-channel bioimpedance analysis and a hardware neural
network. When implemented in a mixed-signal 180 nm CMOS process, the classifier
has an estimated power budget of 39 mW and an area of 30 mm2. This means that
the classifier can be integrated into the tip of a surgical margin assessment
probe, for in vivo use during radical prostatectomy. We tested our classifier
on digital phantoms of prostate tissue and also on an animal model of ex vivo
bovine tissue. The classifier achieved an accuracy of 90% on the prostate
tissue phantoms, and an accuracy of 84% on the animal model.


  We report the measurement of a Bell inequality violation with a single atom
and a single photon prepared in a probabilistic entangled state. This is the
first demonstration of such a violation with particles of different species.
The entanglement characterization of this hybrid system may also be useful in
quantum information applications.


  We implement quantum corrections for a magnetic monopole in a classically
conformally invariant theory containing gravity. This yields the trace
(conformal) anomaly and introduces a length scale in a natural fashion via the
process of renormalization. We evaluate the one-loop effective potential and
extract the vacuum expectation value (VEV) from it; spontaneous symmetry
breaking is radiatively induced. The VEV is set at the renormalization scale
$M$ and we exchange the dimensionless scalar coupling constant for the
dimensionful VEV via dimensional transmutation. The asymptotic (background)
spacetime is anti-de Sitter (AdS) and its Ricci scalar is determined entirely
by the VEV. We obtain analytical asymptotic solutions to the coupled set of
equations governing gravitational, gauge and scalar fields that yield the
magnetic monopole in an AdS spacetime.


  We investigate cofree coalgebras, and limits and colimits of coalgebras in
some abelian monoidal categories of interest, such as bimodules over a ring,
and modules and comodules over a bialgebra or Hopf algebra. We find concrete
generators for the categories of coalgebras in these monoidal categories, and
explicitly construct cofree coalgebras, products and limits of coalgebras in
each case. This answers an open question of A. Agore on the existence of a
cofree coring, and constructs the cofree (co)module coalgebra on a
$B$-(co)module, for a bialgebra $B$.


  For $k\geq 2$, the $k$-generalized Fibonacci sequence $(F_n^{(k)})_{n}$ is
defined by the initial values $0,0,\ldots,0,1$ ($k$ terms) and such that each
term afterwards is the sum of the $k$ preceding terms. In this paper, we search
for powers of two of the form $F_n^{(k)}+F_m^{(k)}$. This work is related to a
recent result by Bravo and Luca concerning the case $k=2$.


  Wave-particle duality in quantum mechanics allows for a halo bound state
whose spatial extension far exceeds a range of the interaction potential. What
is even more striking is that such quantum halos can be arbitrarily large on
special occasions. The two examples known so far are the Efimov effect and the
super Efimov effect, which predict that spatial extensions of higher excited
states grow exponentially and double exponentially, respectively. Here, we
establish yet another new class of arbitrarily large quantum halos formed by
spinless bosons with short-range interactions in two dimensions. When the
two-body interaction is absent but the three-body interaction is resonant, four
bosons exhibit an infinite tower of bound states whose spatial extensions scale
as $R_n\sim e^{(\pi n)^2/27}$ for a large $n$. The emergent scaling law is
universal and is termed a semisuper Efimov effect, which together with the
Efimov and super Efimov effects constitutes a trio of few-body universality
classes allowing for arbitrarily large quantum halos.


  Many countries have introduced social distancing rules during the COVID-19
pandemic in order to reduce spreading of the virus in public spaces. This has
inspired research on pedestrian dynamics in more ways than just one. Many
studies have been performed, not only to determine the effectiveness of such
measures, but also in order to understand its effects on crowd dynamics in
general. In this Perspective article, we reflect on the insights derived from
these investigations and their relevance for future advancements in the field
of pedestrian dynamics. This includes impacts on safety regulations and the
potential for new theoretical and experimental approaches which may hold
relevance even beyond applied restrictions.


  Motivated by lunar exploration, we consider deploying a network of mobile
robots to explore an unknown environment while acting as a cooperative
positioning system. Robots measure and communicate position-related data in
order to perform localization in the absence of infrastructure-based solutions
(e.g. stationary beacons or GPS). We present Trilateration for Exploration and
Mapping (TEAM), a novel algorithm for low-complexity localization and mapping
with robotic networks. TEAM is designed to leverage the capability of
commercially-available ultra-wideband (UWB) radios on board the robots to
provide range estimates with centimeter accuracy and perform anchorless
localization in a shared, stationary frame. It is well-suited for
feature-deprived environments, where feature-based localization approaches
suffer. We provide experimental results in varied Gazebo simulation
environments as well as on a testbed of Turtlebot3 Burgers with Pozyx UWB
radios. We compare TEAM to the popular Rao-Blackwellized Particle Filter for
Simultaneous Localization and Mapping (SLAM). We demonstrate that TEAM requires
an order of magnitude less computational complexity and reduces the necessary
sample rate of LiDAR measurements by an order of magnitude. These advantages do
not require sacrificing performance, as TEAM reduces the maximum localization
error by 50% and achieves up to a 28% increase in map accuracy in
feature-deprived environments and comparable map accuracy in other settings.


  Colorization involves the synthesis of colors on a target image while
preserving structural content as well as the semantics of the target image.
This is a well-explored problem in 2D with many state-of-the-art solutions. We
propose a novel deep learning-based approach for the colorization of 3D medical
volumes. Our system is capable of directly mapping the colors of a 2D
photograph to a 3D MRI volume in real-time, producing a high-fidelity color
volume suitable for photo-realistic visualization. Since this work is first of
its kind, we discuss the full pipeline in detail and the challenges that it
brings for 3D medical data. The colorization of medical MRI volume also entails
modality conversion that highlights the robustness of our approach in handling
multi-modal data.


  The effective approach to the foundation of the nonequilibrium statistical
mechanics on the basis of dynamics was formulated by Bogoliubov in his seminal
works. His ideas of reduced description were proved as very powerful and found
a broad applicability to quite general time-dependent problems of physics and
mechanics. In this paper we analyzed thoroughly the time evolution of open
systems in context of the nonequilibrium statistical operator method (NSO).
This method extends the statistical method of Gibbs to irreversible processes
and incorporates the ideas of reduced description. The purpose of the present
study was to elucidate the basic aspects of the NSO method and some few
selected approaches to the nonequilibrium statistical mechanics. The suitable
procedure of averaging (smoothing) and the notion of irreversibility were
discussed in this context. We were focused on the physical consistency of the
method as well as on its operational ability to emphasize and address a few
important reasons for such a workability.


  This paper studies the problem of detecting and segmenting acute intracranial
hemorrhage on head computed tomography (CT) scans. We propose to solve both
tasks as a semantic segmentation problem using a patch-based fully
convolutional network (PatchFCN). This formulation allows us to accurately
localize hemorrhages while bypassing the complexity of object detection. Our
system demonstrates competitive performance with a human expert and the
state-of-the-art on classification tasks (0.976, 0.966 AUC of ROC on
retrospective and prospective test sets) and on segmentation tasks (0.785 pixel
AP, 0.766 Dice score), while using much less data and a simpler system. In
addition, we conduct a series of controlled experiments to understand "why"
PatchFCN outperforms standard FCN. Our studies show that PatchFCN finds a good
trade-off between batch diversity and the amount of context during training.
These findings may also apply to other medical segmentation tasks.


  We propose a holographic duality for a four dimensional WZW model with target
manifold $\mathrm{SO}(8)$, coupled to scalar-flat K\"ahler gravity on an
asymptotically flat, four dimensional background known as the Burns metric. The
holographic dual is a two dimensional chiral algebra built out of gauged
beta-gamma systems with $\mathrm{SO}(8)$ flavor. We test the duality by
matching two-point correlators of soft gluon currents with two-point gluon
amplitudes, and their leading OPE coefficients with collinear limits of
three-point gluon amplitudes.


  We propose a new, controlled approximation scheme that explicitly includes
the effects of non-local correlations on the $D=\infty$ solution. In contrast
to usual $D=\infty$, the selfenergy is selfconsistently coupled to two-particle
correlation functions. The formalism is general, and is applied to the
two-dimensional Falicov-Kimball model.
  Our approach possesses all the strengths of the large-D solution, and allows
one to undertake a systematic study of the effects of inclusion of k-dependent
effects on the $D=\infty$ picture. Results for the density of states
$\rho(\omega)$, and the single particle spectral density for the 2D
Falicov-Kimball model always yield positive definite $\rho(\omega)$, and the
spectral function shows striking new features inaccessible in $D=\infty$. Our
results are in good agreement with the exact results known on the 2D
Falikov-Kimball model.


  Naturally occurring background radiation is a source of correlated
decoherence events in superconducting qubits that will challenge
error-correction schemes. To characterize the radiation environment in an
unshielded laboratory, we performed broadband, spectroscopic measurements of
background events in silicon substrates located inside a millikelvin
refrigerator, an environment representative of superconducting qubit systems.
We measured the background spectra in silicon substrates of two thicknesses,
0.5 mm and 1.5 mm, and obtained the average event rate and the integrated power
deposition. In a 25 mm^2 area and the thinner substrate, these values are 0.023
events per second and 4.9 keV/s, counting events that deposit at least 40 keV.
We find the background spectrum to be nearly featureless. Its intensity
decreases by a factor of 40,000 between 100 keV and 3 MeV for silicon
substrates 0.5 mm thick. We find the cryogenic measurements to be in good
agreement with predictions based on measurements of the terrestrial gamma-ray
flux, published models of cosmic-ray fluxes, a crude model of the cryostat, and
radiation-transport simulations. No free parameters are required to predict the
background spectra in the silicon substrates. The good agreement between
measurements and predictions allow assessment of the relative contributions of
terrestrial and cosmic background sources and their dependence on substrate
thickness. Our spectroscopic measurements are performed with superconducting
microresonators that transduce deposited energy to a readily detectable
electrical signal. We find that gamma-ray emissions from radioisotopes are
responsible for the majority of events depositing E<1.5 MeV, while nucleons
among the cosmic-ray secondary particles cause most events that deposit more
energy. These results suggest several paths to reducing the impact of
background radiation on quantum circuits.


  The paper studies the modular representation theory of the cyclotomic Hecke
algebras of type $G(r,p,n)$ with $(\eps,q)$-separated parameters. We show that
the decomposition numbers of these algebras are completely determined by the
decomposition matrices of related cyclotomic Hecke algebras of type $G(s,1,m)$,
where $1\le s\le r$ and $1\le m\le n$. Furthermore, the proof gives an explicit
algorithm for computing these decomposition numbers. Consequently, in
principle, the decomposition matrices of these algebras are now known in
characteristic zero. In proving these results, we develop a Specht module
theory for these algebras, explicitly construct their simple modules and
introduce and study analogues of the cyclotomic Schur algebras of type
$G(r,p,n)$ when the parameters are $(\eps,q)$-separated. The main results of
the paper rest upon two Morita equivalences: the first reduces the calculation
of all decomposition numbers to the case of the \textit{$l$-splittable
decomposition numbers} and the second Morita equivalence allows us to compute
these decomposition numbers using an analogue of the cyclotomic Schur algebras
for the Hecke algebras of type $G(r,p,n)$.


  We study the many-body ground states of two-component hardcore bosons in
topological triangular lattice models. Utilizing exact diagonalization and
density-matrix renormalization group calculations, we demonstrate that at
commensurate two-thirds filling per lattice site, two-component bosonic integer
quantum hall (BIQH) effect emerges with the associated
$\mathbf{K}=\begin{pmatrix} 0 & 1\\ 1 & 0\\ \end{pmatrix}$ matrix under strong
intercomponent Hubbard repulsion. The topological nature is further elucidated
by (i) a unique ground state degeneracy with a robust spectrum gap, (ii) a
quantized topological Chern number matrix $\mathbf{C}=\mathbf{K}^{-1}$, and
(iii) two counterpropagating edge branches. Moreover, with increasing
nearest-neighbor repulsions, the ground state undergoes a first-order
transition from a BIQH liquid to a commensurate solid order.


  What is the minimal size quantum circuit required to exactly implement a
specified n-qubit unitary operation, U, without the use of ancilla qubits? We
show that a lower bound on the minimal size is provided by the length of the
minimal geodesic between U and the identity, I, where length is defined by a
suitable Finsler metric on SU(2^n). The geodesic curves of such a metric have
the striking property that once an initial position and velocity are set, the
remainder of the geodesic is completely determined by a second order
differential equation known as the geodesic equation. This is in contrast with
the usual case in circuit design, either classical or quantum, where being
given part of an optimal circuit does not obviously assist in the design of the
rest of the circuit. Geodesic analysis thus offers a potentially powerful
approach to the problem of proving quantum circuit lower bounds. In this paper
we construct several Finsler metrics whose minimal length geodesics provide
lower bounds on quantum circuit size, and give a procedure to compute the
corresponding geodesic equation. We also construct a large class of solutions
to the geodesic equation, which we call Pauli geodesics, since they arise from
isometries generated by the Pauli group. For any unitary U diagonal in the
computational basis, we show that: (a) provided the minimal length geodesic is
unique, it must be a Pauli geodesic; (b) finding the length of the minimal
Pauli geodesic passing from I to U is equivalent to solving an exponential size
instance of the closest vector in a lattice problem (CVP); and (c) all but a
doubly exponentially small fraction of such unitaries have minimal Pauli
geodesics of exponential length.


  We aim to explore the bubble nature of the exotic nucleus $^{20}$N within the
microscopic antisymmetrized molecular dynamics (AMD) approach. Constraining its
structural parameters, we analyse its static properties. Subsequently, we use
the AMD infused finite-range distorted-wave Born approximation theory to
calculate the Coulomb breakup of $^{20}$N as an indirect approach to estimate
the $^{19}$N$(n,\gamma)^{20}$N radiative capture rate.


  We present the results of a search for the production of new particles
decaying into two jets in pbarp collisions at sqrt{s} = 1.8 TeV, using the
DZero 1992-1995 data set corresponding to 109 pb^-1. We exclude at the 95%
confidence level the production of excited quarks (q*) with masses below 775
GeV/c^2, the most restictive limit to date. We also exclude standard-model-like
W' (Z') bosons with masses between 300 and 800 GeV/c^2 (400 and 640 GeV/c^2). A
W' boson with mass $<$ 300 GeV/c^2 has been excluded by previous measurements,
and our lower limit is therefore the most stringent to date.


  We used VLT/XShooter to target a sample of nearby analogs of Lyman Break
Galaxies (LBGs). These Lyman Break Analogs (LBAs) are similar to the LBGs in
many of their physical properties. We determine electron temperatures using the
weak [O III]4363 emission line, and determine the oxygen abundance (O/H) using
the direct and strong line methods. We show that the direct and strong line
abundances are consistent with established relations within ~0.2 dex. The
analogs have nitrogen-to-oxygen ratios (N/O) and ionization parameters (q) that
are, on average, offset with respect to typical local galaxies but similar to
galaxies at z ~ 2 and other analogs. The N/O and q excesses correlate with the
offsets observed in the strong line ratios, again similar to z ~ 2. The star
formation rate surface densities are consistent with the high electron density
and ionization, indicating that the interstellar medium (ISM) pressure is set
by feedback from the starbursts. For a given O/H, the apparent N/O excess
arises due to the offset in O/H with respect to the local mass-metallicity
relation. This can be explained by recent inflow of relatively metal-poor gas
which lowers O/H while leaving N/O unchanged. The difficulties in determining
even basic ISM parameters in these nearby analogs illustrates some of the
challenges we face at much higher redshifts, where similar rest-frame optical
diagnostics for large samples of galaxies can be accessed with JWST.


  Numerical homogenization tries to approximate the solutions of elliptic
partial differential equations with strongly oscillating coefficients by
functions from modified finite element spaces. We present in this paper a class
of such methods that are very closely related to the method of M{\aa}lqvist and
Peterseim [Math. Comp. 83, 2014]. Like the method of M{\aa}lqvist and
Peterseim, these methods do not make explicit or implicit use of a scale
separation. Their compared to that in the work of M{\aa}lqvist and Peterseim
strongly simplified analysis is based on a reformulation of their method in
terms of variational multiscale methods and on the theory of iterative methods,
more precisely, of additive Schwarz or subspace decomposition methods.


  RNA molecules follow a succession of enzyme-mediated processing steps from
transcription until maturation. The participating enzymes, for example the
spliceosome for mRNAs and Drosha and Dicer for microRNAs, are also produced in
the cell and their copy-numbers fluctuate over time. Enzyme copy-number changes
affect the processing rate of the substrate molecules; high enzyme numbers
increase the processing probability, low enzyme numbers decrease it. We study
different RNA processing cascades where enzyme copy-numbers are either fixed or
fluctuate. We find that for fixed enzyme-copy numbers the substrates at
steady-state are Poisson-distributed, and the whole RNA cascade dynamics can be
understood as a single birth-death process of the mature RNA product. In this
case, solely fluctuations in the timing of RNA processing lead to variation in
the number of RNA molecules. However, we show analytically and numerically that
when enzyme copy-numbers fluctuate, the strength of RNA fluctuations increases
linearly with the RNA transcription rate. This linear effect becomes stronger
as the speed of enzyme dynamics decreases relative to the speed of RNA
dynamics. Interestingly, we find that under certain conditions, the RNA cascade
can reduce the strength of fluctuations in the expression level of the mature
RNA product. Finally, by investigating the effects of processing polymorphisms
we show that it is possible for the effects of transcriptional polymorphisms to
be enhanced, reduced, or even reversed. Our results provide a framework to
understand the dynamics of RNA processing.


  Microstructure emission, involving short time scale, often quasi-periodic,
intensity fluctuations in subpulse emission, is well known in normal period
pulsars. In this letter, we present the first detections of quasi-periodic
microstructure emission from millisecond pulsars (MSPs), from Giant Metrewave
Radio Telescope (GMRT) observations of two MSPs at 325 and 610 MHz. Similar to
the characteristics of microstructure observed in normal period pulsars, we
find that these features are often highly polarized, and exhibit quasi-periodic
behavior on top of broader subpulse emission, with periods of the order of a
few $\mu$s. By measuring their widths and periodicities from single pulse
intensity profiles and their autocorrelation functions, we extend the
microstructure timescale - rotation period relationship by more than an order
of magnitude down to rotation periods $\sim$ 5 ms, and find it to be consistent
with the relationship derived earlier for normal pulsars. The similarity of
behavior is remarkable, given the significantly different physical properties
of MSPs and normal period pulsars, and rules out several previous speculations
about the possible different characteristics of microstructure in MSP radio
emission. We discuss the possible reasons for the non-detection of these
features in previous high time resolution MSP studies along with the physical
implications of our results, both in terms of a geometric beam sweeping model
and temporal modulation model for micropulse production.


  Within the area of speech enhancement, there is an ongoing interest in the
creation of neural systems which explicitly aim to improve the perceptual
quality of the processed audio. In concert with this is the topic of
non-intrusive (i.e. without clean reference) speech quality prediction, for
which neural networks are trained to predict human-assigned quality labels
directly from distorted audio. When combined, these areas allow for the
creation of powerful new speech enhancement systems which can leverage large
real-world datasets of distorted audio, by taking inference of a pre-trained
speech quality predictor as the sole loss function of the speech enhancement
system. This paper aims to identify a potential pitfall with this approach,
namely hallucinations which are introduced by the enhancement system `tricking'
the speech quality predictor.


  In this paper, we answer a question of Dwyer, Greenlees, and Iyengar by
proving a local ring $R$ is a complete intersection if and only if every
complex of $R$-modules with finitely generated homology is proxy small.
Moreover, we establish that a commutative noetherian ring $R$ is locally a
complete intersection if and only if every complex of $R$-modules with finitely
generated homology is virtually small.


  Distributed computing is critically important for modern statistical
analysis. Herein, we develop a distributed quasi-Newton (DQN) framework with
excellent statistical, computation, and communication efficiency. In the DQN
method, no Hessian matrix inversion or communication is needed. This
considerably reduces the computation and communication complexity of the
proposed method. Notably, related existing methods only analyze numerical
convergence and require a diverging number of iterations to converge. However,
we investigate the statistical properties of the DQN method and theoretically
demonstrate that the resulting estimator is statistically efficient over a
small number of iterations under mild conditions. Extensive numerical analyses
demonstrate the finite sample performance.


  An increase in the quality factor of superconducting radiofrequency cavities
is achieved by minimizing the surface resistance during processing steps. The
surface resistance is the sum of temperature independent residual resistance
and temperature/material dependent Bardeen-Cooper-Schrieffer (BCS) resistance.
High temperature heat treatment usually reduces the impurities concentration
from the bulk niobium, lowering the residual resistance. The BCS part can be
reduced by selectively doping non-magnetic impurities. The increase in quality
factor, termed as Q-rise, was observed in cavities when titanium or nitrogen
thermally diffused in the inner cavity surface.


  This volume holds the proceedings of the Collective Intelligence 2012
conference in Cambridge, Massachusetts. It contains the full papers, poster
papers, and plenary abstracts.
  Collective intelligence has existed at least as long as humans have, because
families, armies, countries, and companies have all - at least sometimes -
acted collectively in ways that seem intelligent. But in the last decade or so
a new kind of collective intelligence has emerged: groups of people and
computers, connected by the Internet, collectively doing intelligent things.
For example, Google technology harvests knowledge generated by millions of
people creating and linking web pages and then uses this knowledge to answer
queries in ways that often seem amazingly intelligent. Or in Wikipedia,
thousands of people around the world have collectively created a very large and
high quality intellectual product with almost no centralized control, and
almost all as volunteers! These early examples of Internet-enabled collective
intelligence are not the end of the story but just the beginning. And in order
to understand the possibilities and constraints of these new kinds of
intelligence, we need a new interdisciplinary field.


  Loss Data Analytics is an interactive, online, freely available text. The
idea behind the name Loss Data Analytics is to integrate classical loss data
models from applied probability with modern analytic tools. In particular, we
seek to recognize that big data (including social media and usage based
insurance) are here and high speed computation is readily available.
  The online version contains many interactive objects (quizzes, computer
demonstrations, interactive graphs, video, and the like) to promote deeper
learning. A subset of the book is available for offline reading in pdf and EPUB
formats. The online text will be available in multiple languages to promote
access to a worldwide audience.


  A connected digraph in which the in-degree of any vertex equals its
out-degree is Eulerian; this baseline result is used as the basis of existence
proofs for universal cycles (also known as ucycles or generalized deBruijn
cycles or U-cycles) of several combinatorial objects. The existence of ucycles
is often dependent on the specific representation that we use for the
combinatorial objects. For example, should we represent the subset $\{2,5\}$ of
$\{1,2,3,4,5\}$ as "25" in a linear string? Is the representation "52"
acceptable? Or it it tactically advantageous (and acceptable) to go with
$\{0,1,0,0,1\}$? In this paper, we represent combinatorial objects as graphs,
as in \cite{bks}, and exhibit the flexibility and power of this representation
to produce {\it graph universal cycles}, or {\it Gucycles}, for $k$-subsets of
an $n$-set; permutations (and classes of permutations) of
$[n]=\{1,2,\ldots,n\}$, and partitions of an $n$-set, thus revisiting the
classes first studied in \cite{cdg}. Under this graphical scheme, we will
represent $\{2,5\}$ as the subgraph $A$ of $C_5$ with edge set consisting of
$\{2,3\}$ and $\{5,1\}$, namely the "second" and "fifth" edges in $C_5$.
Permutations are represented via their permutation graphs, and set partitions
through disjoint unions of complete graphs.


  Recently it has been shown that without any access to the contextual passage,
multiple choice reading comprehension (MCRC) systems are able to answer
questions significantly better than random on average. These systems use their
accumulated "world knowledge" to directly answer questions, rather than using
information from the passage. This paper examines the possibility of exploiting
this observation as a tool for test designers to ensure that the use of "world
knowledge" is acceptable for a particular set of questions. We propose
information-theory based metrics that enable the level of "world knowledge"
exploited by systems to be assessed. Two metrics are described: the expected
number of options, which measures whether a passage-free system can identify
the answer a question using world knowledge; and the contextual mutual
information, which measures the importance of context for a given question. We
demonstrate that questions with low expected number of options, and hence
answerable by the shortcut system, are often similarly answerable by humans
without context. This highlights that the general knowledge 'shortcuts' could
be equally used by exam candidates, and that our proposed metrics may be
helpful for future test designers to monitor the quality of questions.


  For an ergodic Markov chain $\{X(t)\}$ on $\Bbb N$, with a stationary
distribution $\pi$, let $T_n>0$ denote a hitting time for $[n]^c$, and let
$X_n=X(T_n)$. Around 2005 Guy Louchard popularized a conjecture that, for $n\to
\infty$, $T_n$ is almost Geometric($p$), $p=\pi([n]^c)$, $X_n$ is almost
stationarily distributed on $[n]^c$, and that $X_n$ and $T_n$ are almost
independent, if $p(n):=\sup_ip(i,[n]^c)\to 0$ exponentially fast. For the
chains with $p(n) \to 0$ however slowly, and with
$\sup_{i,j}\,\|p(i,\cdot)-p(j,\cdot)\|_{TV}<1$, we show that Louchard's
conjecture is indeed true even for the hits of an arbitrary $S_n\subset\Bbb N$
with $\pi(S_n)\to 0$. More precisely, a sequence of $k$ consecutive hit
locations paired with the time elapsed since a previous hit (for the first hit,
since the starting moment) is approximated, within a total variation distance
of order $k\,\sup_ip(i,S_n)$, by a $k$-long sequence of independent copies of
$(\ell_n,t_n)$, where $\ell_n= \text{Geometric}\,(\pi(S_n))$, $t_n$ is
distributed stationarily on $S_n$, and $\ell_n$ is independent of $t_n$. The
two conditions are easily met by the Markov chains that arose in Louchard's
studies as likely sharp approximations of two random compositions of a large
integer $\nu$, a column-convex animal (cca) composition and a Carlitz (C)
composition. We show that this approximation is indeed very sharp for most of
the parts of the random compositions. Combining the two approximations in a
tandem, we are able to determine the limiting distributions of $\mu=o(\ln\nu)$
and $\mu=o(\nu^{1/2})$ largest parts of the random cca composition and the
random C-composition, respectively. (Submitted to Annals of Probability in
August, 2009.)


  Van-der-Waals heterostructures show many intriguing phenomena including
ultrafast charge separation following strong excitonic absorption in the
visible spectral range. However, despite the enormous potential for future
applications in the field of optoelectronics, the underlying microscopic
mechanism remains controversial. Here we use time- and angle-resolved
photoemission spectroscopy combined with microscopic many-particle theory to
reveal the relevant microscopic charge transfer channels in epitaxial
WS$_2$/graphene heterostructures. We find that the timescale for efficient
ultrafast charge separation in the material is determined by direct tunneling
at those points in the Brillouin zone where WS$_2$ and graphene bands cross,
while the lifetime of the charge separated transient state is set by
defect-assisted tunneling through localized sulphur vacanices. The subtle
interplay of intrinsic and defect-related charge transfer channels revealed in
the present work can be exploited for the design of highly efficient light
harvesting and detecting devices.


  Norm estimates are developed between the Bochner integral of a vector-valued
function in Banach spaces having the Radon-Nikodym property and the convex
combination of function values taken on a division of the interval [a,b].


  Simultaneous Localization and Mapping, commonly known as SLAM, has been an
active research area in the field of Robotics over the past three decades. For
solving the SLAM problem, every robot is equipped with either a single sensor
or a combination of similar/different sensors. This paper attempts to review,
discuss, evaluate and compare these sensors. Keeping an eye on future, this
paper also assesses the characteristics of these sensors against factors
critical to the long-term autonomy challenge.


  By combining ab-initio electron theory and statistical mechanics, the
physical properties of the ternary intermetallic system Ni-Fe-Al in the ground
state and at finite temperatures were investigated. The Ni-Fe-Al system is not
only of high technological interest, but exhibits also rich physics, e.g., a
delicate interplay between structure and magnetism over a wide composition
range and substantial electronic correlations which is challenging for modern
electronic structure methods. The new Stuttgart ab-initio mixed-basis
pseudopotential code in the generalized gradient approximation (GGA) was used
to determine the energetics in the ground state. Therewith, in combination with
the cluster expansion (CE) method a representation of the energy landscape at
$T$=0 over the whole Gibbs triangle was elaborated. At finite temperatures, the
cluster variation method (CVM) in tetrahedron approximation was employed in
order to calculate the ab-initio ternary phase diagram on the bcc and fcc
lattice. Thereby, a miscibility gap in the ternary B2 phase was theoretically
verified.


  We present a data-driven method for separating complex, multiscale systems
into their constituent time-scale components using a recursive implementation
of dynamic mode decomposition (DMD). Local linear models are built from
windowed subsets of the data, and dominant time scales are discovered using
spectral clustering on their eigenvalues. This approach produces time series
data for each identified component, which sum to a faithful reconstruction of
the input signal. It differs from most other methods in the field of
multiresolution analysis (MRA) in that it 1) accounts for spatial and temporal
coherencies simultaneously, making it more robust to scale overlap between
components, and 2) yields a closed-form expression for local dynamics at each
scale, which can be used for short-term prediction of any or all components.
Our technique is an extension of multi-resolution dynamic mode decomposition
(mrDMD), generalized to treat a broader variety of multiscale systems and more
faithfully reconstruct their isolated components. In this paper we present an
overview of our algorithm and its results on two example physical systems, and
briefly discuss some advantages and potential forecasting applications for the
technique.


  We report the discovery of a barium blue straggler star (BSS) in M67,
exhibiting enhancements in slow neutron-capture ($s$-) process elements.
Spectroscopic analysis of two BSSs (WOCS\,9005 \& WOCS\,1020) and 4 stars
located near the main-sequence turn-off using GALAH spectra, showed that
WOCS\,9005 has a significantly high abundance of the s-process elements
([Ba/Fe] = 0.75$\pm$0.08, [Y/Fe] = 1.09$\pm$0.07, [La/Fe] = 0.65$\pm$0.06). The
BSS (WOCS\,9005) is a spectroscopic binary with a known period, eccentricity
and a suspected white dwarf (WD) companion with a kinematic mass of 0.5
M$_\odot$. The first `sighting' of the WD in this barium BSS is achieved
through multi-wavelength spectral energy distribution (SED) with the crucial
far-UV data from the UVIT/\textit{AstroSat}. The parameters of the hot and cool
companions are derived using binary fits of the SED using two combinations of
models, yielding a WD with T$_{eff}$ in the range 9750--15250 K. Considering
the kinematic mass limit, the cooling age of the WD is estimated as $\sim$ 60
Myr. The observed enhancements are attributed to a mass transfer (MT) from a
companion asymptotic giant branch star, now a WD. We estimate the accreted mass
to be 0.15 M$_{\odot}$, through wind accretion, which increased the envelope
mass from 0.45 M$_{\odot}$. The detection of chemical enhancement, as well as
the sighting of WD in this system have been possible due to the recent MT in
this binary, as suggested by the young WD.


  A graph G is perfect if for every induced subgraph H, the chromatic number of
H equals the size of the largest complete subgraph of H, and G is Berge if no
induced subgraph of G is an odd cycle of length at least 5 or the complement of
one. The "strong perfect graph conjecture" (Berge, 1961) asserts that a graph
is perfect if and only if it is Berge.
  A stronger conjecture was made recently by Conforti, Cornuejols and Vuskovic
-- that every Berge graph either falls into one of a few basic classes, or it
has a kind of separation that cannot occur in a minimal imperfect graph.
  In this paper we prove both these conjectures.


  Clinical trials are crucial for drug development but are time consuming,
expensive, and often burdensome on patients. More importantly, clinical trials
face uncertain outcomes due to issues with efficacy, safety, or problems with
patient recruitment. If we were better at predicting the results of clinical
trials, we could avoid having to run trials that will inevitably fail more
resources could be devoted to trials that are likely to succeed. In this paper,
we propose Hierarchical INteraction Network (HINT) for more general, clinical
trial outcome predictions for all diseases based on a comprehensive and diverse
set of web data including molecule information of the drugs, target disease
information, trial protocol and biomedical knowledge. HINT first encode these
multi-modal data into latent embeddings, where an imputation module is designed
to handle missing data. Next, these embeddings will be fed into the knowledge
embedding module to generate knowledge embeddings that are pretrained using
external knowledge on pharmaco-kinetic properties and trial risk from the web.
Then the interaction graph module will connect all the embedding via domain
knowledge to fully capture various trial components and their complex relations
as well as their influences on trial outcomes. Finally, HINT learns a dynamic
attentive graph neural network to predict trial outcome. Comprehensive
experimental results show that HINT achieves strong predictive performance,
obtaining 0.772, 0.607, 0.623, 0.703 on PR-AUC for Phase I, II, III, and
indication outcome prediction, respectively. It also consistently outperforms
the best baseline method by up to 12.4\% on PR-AUC.


  We have studied properties of quantum charge fluids interacting with random
impurities or heavy polarons using a microscopic Hamiltonian with the full
many-body Coulomb interaction with the use of variational many-body formalism.
At zero temperature and high enough density the bosonic fluid is
superconducting, but when density decreases the Coulomb interaction will be
strongly over-screened and impurities or polarons begin to trap charge carriers
forming bound quasiparticle like clusters, which we call Coulomb bubbles or
clumps. These bubbles are embedded inside the superconductor and form nuclei of
a new insulating state- the local Charge Density Wave(CDW). The growth of a
bubble is terminated by the Coulomb force. The fluid contains two groups of
charge carriers associated with free and localized states. The insulating state
arises via a percolation of the insulating islands of bubbles, which cluster
and prevent the flow of the electrical supercurrent through the system.
  The picture is consistent with the Gorkov and Teitelbaum (GT) analysis of the
transport, Hall effect data and the ARPES spectra as well as with nanoscale
superstructures observed in Scanning Tunneling Microscope(STM) experiments. The
scenario of the clump formation may be also applicable to pnictides, where two
types of clumps may arise even at very high temperatures. The paper has been
published in the book "Condensed Matter Theories" volume 24.


  Combining several independent measurements of the same physical quantity is
one of the most important tasks in metrology. Small samples, biased input
estimates, not always adequate reported uncertainties, and unknown error
distribution make a rigorous solution very difficult, if not impossible. For
this reason, many methods to compute a common mean and its uncertainty were
proposed, each with own advantages and shortcomings. Most of them are variants
of the weighted average (WA) approach with different strategies to compute WA
and its standard deviation. Median estimate became also increasingly popular
during recent years. In this paper, these two methods in most widely used
modifications are compared using simulated and real data. To overcome some
problems of known approaches to compute the WA uncertainty, a new combined
estimate has been proposed. It has been shown that the proposed method can help
to obtain more robust and realistic estimate suitable for both consistent and
discrepant measurements.


  Determination of the accurate astrophysical S factor of
{$^{12}$C($\alpha,\gamma$)$^{16}$O} reaction has been regarded as a holy grail
of nuclear astrophysics for decades. In current stellar models, a knowledge of
that value to better than 10\% is desirable. Due to the practical issues,
tremendous experimental and theoretical efforts over nearly 50 years are not
able to reach this goal, and the published values contradicted with each other
strongly and their uncertainties are 2 times larger than the required
precision. To this end we have developed a Reduced R-matrix Theory, based on
the classical R-matrix theory of Lane and Thomas, which treats primary
transitions to ground state and four bound states as the independent reaction
channels in the channel spin representation. With the coordination of
covariance statistics and error propagation theory, a global fitting for almost
all available experimental data of $^{16}$O system has been multi-iteratively
analyzed by our powerful code. A reliable, accurate and self-consistent
astrophysical S factor of {$^{12}$C($\alpha,\gamma$)$^{16}$O} was obtained with
a recommended value $S_{tot}$ (300) = 162.7 $\pm$ 7.3 keV b (4.5\%) which could
meet the required precision.


  The transition between the two lowest-luminosity spectral classes of brown
dwarfs--the L dwarfs and T dwarfs--is traversed by nearly all brown dwarfs as
they cool over time. Yet distinct features of this transition, such as the
"J-band bump" and an unusually high rate of multiplicity, remain outstanding
problems, although evidence points to condensate cloud evolution as a critical
component. Using a Monte Carlo population simulation that incorporates the
empirical spectral properties of unresolved brown dwarfs in magnitude-limited
samples, I demonstrate that the J-band bump and enhanced multiplicity naturally
emerge from a short timescale of photospheric cloud dissipation. This timescale
may help constrain future evolutionary models exploring the cloud dissipation
process.


  Quantum electrodynamic equations for magnetic resonance- and optical
spectroscopic transitions have been for the first time obtained. New phenomena
- stochastic electrical and magnetic spin wave resonances are predicted to be
the effects of EM-field quantization.


  The dynamical behaviour of near by trajectories is being estimated by
Lyapunov Characteristic Exponents(LCEs) in the Generalized Photogravitational
Chermnykh-Like problem. It is found that the trajectories of the Lagrangian
point $L_4$ move along the epicycloid path, and spirally depart from the
vicinity of the point. The LCEs remain positive for all the cases and depend on
the initial deviation vector as well as renormalization time step. It is
noticed that the trajectories are chaotic in nature and the $L_4$ is
asymptotically stable. The effects of radiation pressure, oblateness and mass
of the belt are also examined in the present model.


  We compare and contrast the stellar structures of isolated Local Group dwarf
galaxies, as traced by their oldest stellar populations, with the satellite
dwarf galaxies of the Milky Way and M31. All Local Group dwarfs with Mv < -6
and surface brightness < 26.5 mags. per square arcsec. are considered, taking
advantage of measurements from surveys that use similar observations and
analysis techniques. For the isolated dwarfs, we use the results from Solitary
Local (Solo) Dwarf Galaxy Survey. We begin by confirming that the structural
and dynamical properties of the two satellite populations are not obviously
statistically different from each other, but we note that there many more
satellites around M31 than around the Milky Way down to equivalent magnitude
and surface brightness limits. We find that dwarfs in close proximity to a
massive galaxy generally show more scatter in their Kormendy relations than
those in isolation. Specifically, isolated Local Group dwarf galaxies show a
tighter trend of half-light radius versus magnitude than the satellite
populations, and similar effects are also seen for related parameters. There
appears to be a transition in the structural and dynamical properties of the
dwarf galaxy population around ~400 kpc from the Milky Way and M31, such that
the smallest, faintest, most circular dwarf galaxies are found closer than this
separation. We discuss the impact of selection effects on our analysis, and we
argue that our results point to the significance of tidal interactions on the
population of systems within approximately 400 kpc from the MW and M31.


  In this paper we study the map associating to a linear differential operator
with rational coefficients its monodromy data. The operator has one regular and
one irregular singularity of Poincare' rank 1. We compute the Poisson structure
of the corresponding Monodromy Preserving Deformation Equations on the space of
the monodromy data.


  For quadratic spaces which represent 1 there is a characterization of
hermitian compositions in the language of algebras-with-involutions using the
even Clifford algebra. We extend this notion to define a generalized
composition based on quadratic pairs and determine the degrees of minimal
compositions for any given quadratic pair.


  We present a new approach to tracking of radiation sources moving on smooth
trajectories which can be approximated with piece-wise linear joins or
piece-wise linear parabolas. We employ the use of cheap binary proximity
sensors which only indicate when a radiation source enters and leaves its
sensing range. We present two separate cases. The first is considering that the
trajectory can be approximated with piece-wise linear joins. We develop a novel
scalable approach in terms of the number of sensors required. Robustness
analysis is done with respect to uncertainties in the timing recordings by the
radiation sensors. We show that in the noise free case, a minimum of three
sensors will suffice to recover one piece of the linear join with probability
one, even in the absence of knowledge of the speed and statistics of the
radiation source. Second, we tackle a more realistic approximation of
trajectories of radiation sources -- piece-wise parabolic joins -- and show
that no more than six sensors are required in the noise free case to track one
piece of the parabola with probability one. Next we present an upper bound on
the achievable error variance in the estimation of the constant speed and the
angle of elevation of linear trajectories. Finally, a comprehensive set of
simulations are presented to illustrate the robustness of our approach in the
presence of uncertainties.


  Consider a multi-cell mobile edge computing network, in which each user
wishes to compute the product of a user-generated data matrix with a
network-stored matrix. This is done through task offloading by means of input
uploading, distributed computing at edge nodes (ENs), and output downloading.
Task offloading may suffer long delay since servers at some ENs may be
straggling due to random computation time, and wireless channels may experience
severe fading and interference. This paper aims to investigate the interplay
among upload, computation, and download latencies during the offloading process
in the high signal-to-noise ratio regime from an information-theoretic
perspective. A policy based on cascaded coded computing and on coordinated and
cooperative interference management in uplink and downlink is proposed and
proved to be approximately optimal for a sufficiently large upload time. By
investing more time in uplink transmission, the policy creates data redundancy
at the ENs, which can reduce the computation time, by enabling the use of coded
computing, as well as the download time via transmitter cooperation. Moreover,
the policy allows computation time to be traded for download time. Numerical
examples demonstrate that the proposed policy can improve over existing schemes
by significantly reducing the end-to-end execution time.


  In this paper we prove classification results for solutions to subcritical
and critical semilinear elliptic equations with a nonnegative potential on
noncompact manifolds with nonnegative Ricci curvature. We show in the
subcritical case that all nonnegative solutions vanish identically. Moreover,
under some natural assumptions, in the critical case we prove a strong rigidity
result, namely we classify all nontrivial solutions showing that they exist
only if the potential is constant and the manifold is isometric to the
Euclidean space.


  In this article, we discuss the identity and indistinguishability of quantum
systems and the consequent need to introduce an extra postulate in Quantum
Mechanics to correctly describe situations involving indistinguishable
particles. This is, for electrons, the Pauli Exclusion Principle, or in
general, the Symmetrization Postulate. Then, we introduce fermions and bosons
and the distributions respectively describing their statistical behaviour in
indistinguishable situations. Following that, we discuss the spin-statistics
connection, as well as alternative statistics and experimental evidence for all
these results, including the use of bunching and antibunching of particles
emerging from a beam splitter as a signature for some bosonic or fermionic
states.


  Observations at UV and optical wavelengths have revealed that galaxies at
z~1-4 host star-forming regions, dubbed "clumps", which are believed to form
due to the fragmentation of gravitationally unstable, gas-rich disks. However,
the detection of the parent molecular clouds that give birth to such clumps is
still possible only in a minority of galaxies, mostly at z~1. We investigated
the [CII] and dust morphology of a z~3.4 lensed galaxy hosting four clumps
detected in the UV continuum. We aimed to observe the [CII] emission of
individual clumps that, unlike the UV, is not affected by dust extinction, to
probe their nature and cold gas content. We conducted ALMA observations probing
scales down to ~300 pc and detected three [CII] clumps. One (dubbed "NE")
coincides with the brightest UV clump, while the other two ("SW" and "C") are
not detected in the UV continuum. We do not detect the dust continuum. We
converted the [CII] luminosity of individual clumps into molecular gas mass and
found Mmol~10^8 Msun. By complementing it with the star formation rate (SFR)
estimate from the UV continuum, we estimated the gas depletion time (tdep) of
clumps and investigated their location in the Schmidt-Kennicutt plane. While
the NE clump has a short tdep=0.16 Gyr, comparable with high-redshift
starbursts, the SW and C clumps instead have longer tdep>0.65 Gyr and are
likely probing the initial phases of star formation. The lack of dust continuum
detection is consistent with the blue UV continuum slope estimated for this
galaxy (beta~-2.5) and it indicates that dust inhomogeneities do not
significantly affect the detection of UV clumps in this target. We pushed the
observation of the cold gas content of individual clumps up to z~3.4 and showed
that the [C II] line emission is a promising tracer of molecular clouds at high
redshift, allowing the detection of clumps with a large range of depletion
times.


  An initial-boundary value problem for an unsteady two-dimensional heat
conduction equation in a bounded domain modeling the unsteady temperature
distribution of permafrost soil in the vicinity of a main gas pipeline, taking
into account climate warming, is investigated. The parameters of the
mathematical model are selected in accordance with experimental data on gas
transportation in permafrost areas. The problem is solved numerically by the
finite element method. Modeling of the temperature field has been carried out
for 30 years since the start of the gas pipeline operation. Calculations are
carried out until the periodic temperature regime of the soil around the gas
pipeline is practically established. Under the initial conditions adopted in
the work, a periodic temperature regime at the top and bottom of the pipe is
established in approximately 12 years, and a periodic temperature regime in
depth is established in approximately 22 years. Two scenarios of climate
warming are considered: moderate RCP2.6 and more negative RCP8.5. It is shown
that significant changes in the ground temperature regime occur in the vicinity
of the pipe under both warming scenarios. Nevertheless, the calculations
demonstrate the preservation of permafrost even in a negative scenario of
climate warming.


  Diffusion models have emerged as effective tools for generating diverse and
high-quality content. However, their capability in high-resolution image
generation, particularly for panoramic images, still faces challenges such as
visible seams and incoherent transitions. In this paper, we propose
TwinDiffusion, an optimized framework designed to address these challenges
through two key innovations: the Crop Fusion for quality enhancement and the
Cross Sampling for efficiency optimization. We introduce a training-free
optimizing stage to refine the similarity of adjacent image areas, as well as
an interleaving sampling strategy to yield dynamic patches during the cropping
process. A comprehensive evaluation is conducted to compare TwinDiffusion with
the prior works, considering factors including coherence, fidelity,
compatibility, and efficiency. The results demonstrate the superior performance
of our approach in generating seamless and coherent panoramas, setting a new
standard in quality and efficiency for panoramic image generation.


  In this paper, we introduce an actor-critic algorithm called Deep Value Model
Predictive Control (DMPC), which combines model-based trajectory optimization
with value function estimation. The DMPC actor is a Model Predictive Control
(MPC) optimizer with an objective function defined in terms of a value function
estimated by the critic. We show that our MPC actor is an importance sampler,
which minimizes an upper bound of the cross-entropy to the state distribution
of the optimal sampling policy. In our experiments with a Ballbot system, we
show that our algorithm can work with sparse and binary reward signals to
efficiently solve obstacle avoidance and target reaching tasks. Compared to
previous work, we show that including the value function in the running cost of
the trajectory optimizer speeds up the convergence. We also discuss the
necessary strategies to robustify the algorithm in practice.


  We consider a specific graph dynamical system inspired by the famous Conway's
Game of Life in this work. We study the properties of the dynamical system on
different graphs and introduce a new efficient heuristic for graph isomorphism
testing. We use the evolution of our system to extract features from a graph in
a deterministic way and observe that the extracted features are unique and the
distance induced by that features satisfy triangle inequality for all connected
graphs with up to ten vertices.


  We introduce a superstatistical model for the progression statistics of
malignant cancer cells. The metastatic cascade is modeled as a complex
nonequilibrium system with several macroscopic pathways and inverse-chi-square
distributed parameters of the underlying Poisson processes. The predictions of
the model are in excellent agreement with observed survival time probability
distributions of breast cancer patients.


  Rectangular real $N \times (N + \nu)$ matrices $W$ with a Gaussian
distribution appear very frequently in data analysis, condensed matter physics
and quantum field theory. A central question concerns the correlations encoded
in the spectral statistics of $WW^T$. The extreme eigenvalues of $W W^T$ are of
particular interest. We explicitly compute the distribution and the gap
probability of the smallest non-zero eigenvalue in this ensemble, both for
arbitrary fixed $N$ and $\nu$, and in the universal large $N$ limit with $\nu$
fixed. We uncover an integrable Pfaffian structure valid for all even values of
$\nu\geq 0$. This extends previous results for odd $\nu$ at infinite $N$ and
recursive results for finite $N$ and for all $\nu$. Our mathematical results
include the computation of expectation values of half integer powers of
characteristic polynomials.


  We compute the red and blue shifts for astrophysical and cosmological
sources. In particular, we consider low, intermediate and high gravitational
energy domains. Thereby, we handle the binary system Earth - Mars as low energy
landscape whereas white dwarfs and neutron stars as higher energy sources. To
this end, we take into account a spherical Schwarzschild - de Sitter spacetime
and an axially symmetric Zipoy - Voorhees metric to model all the
aforementioned systems. Feasible outcomes come from modelling neutron stars and
white dwarfs with the Zipoy - Voorhees metric, where quadrupole effects are
relevant, and framing solar system objects using a Schwarzschild - de Sitter
spacetime. In the first case, large $\delta$ parameters seem to be favorite,
leading to acceptable bounds mainly for neutron stars. In the second case, we
demonstrate incompatible red and blue shifts with respect to lunar and
satellite laser ranging expectations, once the cosmological constant is taken
to Planck satellite's best fit. To heal this issue, we suggest coarse-grained
experimental setups and propose Phobos for working out satellite laser ranging
in order to get more suitable red and blue shift intervals, possibly more
compatible than current experimental bounds. Implications to cosmological
tensions are also debated.


  In this paper, we do automatic correctness assessment for patches generated
by program repair systems. We consider the human-written patch as ground truth
oracle and randomly generate tests based on it, a technique proposed by
Shamshiri et al., called Random testing with Ground Truth (RGT) in this paper.
We build a curated dataset of 638 patches for Defects4J generated by 14
state-of-the-art repair systems, we evaluate automated patch assessment on this
dataset. The results of this study are novel and significant: First, we improve
the state of the art performance of automatic patch assessment with RGT by 190%
by improving the oracle; Second, we show that RGT is reliable enough to help
scientists to do overfitting analysis when they evaluate program repair
systems; Third, we improve the external validity of the program repair
knowledge with the largest study ever.


  Large language models (LLMs) have demonstrated unparalleled prowess in
mimicking human-like text generation and processing. Among the myriad of
applications that benefit from LLMs, automated code generation is increasingly
promising. The potential to transform natural language prompts into executable
code promises a major shift in software development practices and paves the way
for significant reductions in manual coding efforts and the likelihood of
human-induced errors. This paper reports the results of a study that evaluates
the performance of various LLMs, such as Bard, ChatGPT-3.5, ChatGPT-4, and
Claude-2, in generating Python for coding problems. We focus on how levels of
prompt specificity impact the accuracy, time efficiency, and space efficiency
of the generated code. A benchmark of 104 coding problems, each with four types
of prompts with varying degrees of tests and specificity, was employed to
examine these aspects comprehensively. Our results indicate significant
variations in performance across different LLMs and prompt types, and its key
contribution is to reveal the ideal prompting strategy for creating accurate
Python functions. This study lays the groundwork for further research in LLM
capabilities and suggests practical implications for utilizing LLMs in
automated code generation tasks and test-driven development.


  We present a web application for the procedural generation of transformations
of 3D models. We generate the transformations by algorithmically generating the
vertex shaders of the 3D models. The vertex shaders are created with an
interactive genetic algorithm, which displays to the user the visual effect
caused by each vertex shader, allows the user to select the visual effect the
user likes best, and produces a new generation of vertex shaders using the user
feedback as the fitness measure of the genetic algorithm. We use genetic
programming to represent each vertex shader as a computer program. This paper
presents details of requirements specification, software architecture, high and
low-level design, and prototype user interface. We discuss the project's
current status and development challenges.


  This paper investigates wireless communications based on a new antenna array
architecture, termed modular extremely large-scale array (XL-array), where an
extremely large number of antenna elements are regularly arranged on a common
platform in a modular manner. Each module consists of a flexible/moderate
number of antenna elements, and different modules are separated with an
inter-module spacing that is typically much larger than the inter-element
spacing/signal wavelength for ease of deployment. By properly modelling the
variations of signal phase, amplitude and projected aperture across different
array modules/elements, we develop the new channel model and analyze the
signal-to-noise ratio (SNR) performance of the modular XL-array based
communications. Under the practical non-uniform spherical wave (NUSW) model,
the closed-form expression of the maximum achievable SNR is derived in terms of
key geometric parameters, including the total planar array size, module
separation distances along each dimension, as well as the user's location in
the three-dimensional (3D) space. Besides, the asymptotic SNR scaling laws are
revealed as the number of modules along different dimensions goes to infinity.
Moreover, we show that our developed near-field modelling and performance
analysis include the existing ones for the collocated XL-array, the far-field
uniform plane wave (UPW) model, as well as the one-dimensional (1D) modular
extremely large-scale uniform linear array (XL-ULA) as special cases. Extensive
simulation results are provided to validate our obtained results.


  Let $\mathbb{F}$ be an infinite field with characteristic different from two.
For a graph $G=(V,E)$ with $V={1,...,n}$, let $S(G;\mathbb{F})$ be the set of
all symmetric $n\times n$ matrices $A=[a_{i,j}]$ over $\mathbb{F}$ with
$a_{i,j}\not=0$, $i\not=j$ if and only if $ij\in E$. We show that if $G$ is the
complement of a partial $k$-tree and $m\geq k+2$, then for all nonsingular
symmetric $m\times m$ matrices $K$ over $\mathbb{F}$, there exists an $m\times
n$ matrix $U$ such that $U^T K U\in S(G;\mathbb{F})$. As a corollary we obtain
that, if $k+2\leq m\leq n$ and $G$ is the complement of a partial $k$-tree,
then for any two nonnegative integers $p$ and $q$ with $p+q=m$, there exists a
matrix in $S(G;\reals)$ with $p$ positive and $q$ negative eigenvalues.


  This paper introduces a novel benchmark to study the impact and relationship
of built environment elements on pedestrian collision prediction, intending to
enhance environmental awareness in autonomous driving systems to prevent
pedestrian injuries actively. We introduce a built environment detection task
in large-scale panoramic images and a detection-based pedestrian collision
frequency prediction task. We propose a baseline method that incorporates a
collision prediction module into a state-of-the-art detection model to tackle
both tasks simultaneously. Our experiments demonstrate a significant
correlation between object detection of built environment elements and
pedestrian collision frequency prediction. Our results are a stepping stone
towards understanding the interdependencies between built environment
conditions and pedestrian safety.


  It is shown that the principle of locality and noncommutative geometry can be
connnected by a sheaf theoretical method. In this framework quantum spaces are
introduced and examples in mathematical physics are given. With the language of
quantum spaces noncommutative principal and vector bundles are defined and
their properties are studied. Important constructions in the classical theory
of principal fibre bundles like associated bundles and differential calculi are
carried over to the quantum case. At the end $q$-deformed instanton models are
introduced for every integral index.


  Existing alignment methods share a common topology of information flow, where
reward information is collected from humans, modeled with preference learning,
and used to tune language models. However, this shared topology has not been
systematically characterized, nor have its alternatives been thoroughly
explored, leaving the problems of low data efficiency and unreliable
generalization unaddressed. As a solution, we introduce a theoretical framework
for investigating reward generalization in reinforcement learning from human
feedback (RLHF), focusing on the topology of information flow at both macro and
micro levels. At the macro level, we portray the RLHF information flow as an
autoencoding process over behavior distributions, formalizing the RLHF
objective of distributional consistency between human preference and model
behavior. At the micro level, we present induced Bayesian networks as a theory
of reward generalization in RLHF, introducing fine-grained dataset topologies
into generalization bounds. Combining analysis on both levels, we propose
reward modeling from tree-structured preference information. It is shown to
reduce reward uncertainty by up to $\Theta(\log n/\log\log n)$ times compared
to baselines, where $n$ is the dataset size. Validation on three NLP tasks
shows that our tree-based reward model achieves an average win rate of 65%
against baseline methods, thus improving reward generalization for free via
topology design.


  In the game of Cops and Robber, a team of cops attempts to capture a robber
on a graph $G$. Initially, all cops occupy some vertices in $G$ and the robber
occupies another vertex. In each round, a cop can move to one of its neighbors
or stay idle, after which the robber does the same. The robber is caught by a
cop if the cop lands on the same vertex which is currently occupied by the
robber. The minimum number of cops needed to guarantee capture of a robber on
$G$ is called the {\em cop number} of $G$, denoted by $c(G)$. We say a family
$\cal F$ of graphs is {\em cop-bounded} if there is a constant $M$ so that
$c(G)\leq M$ for every graph $G\in \cal F$. Joret, Kamin\'nski, and Theis
[Contrib. Discrete Math. 2010] proved that the class of all graphs not
containing a graph $H$ as an induced subgraph is cop-bounded if and only if $H$
is a linear forest; morerover, $C(G)\leq k-2$ if if $G$ is induced-$P_k$-free
for $k\geq 3$. In this paper, we consider the cop number of a family of graphs
forbidding certain two graphs and generalized some previous results.


  Predicting future behavior of other traffic participants is an essential task
that needs to be solved by automated vehicles and human drivers alike to
achieve safe and situationaware driving. Modern approaches to vehicles
trajectory prediction typically rely on data-driven models like neural
networks, in particular LSTMs (Long Short-Term Memorys), achieving promising
results. However, the question of optimal composition of the underlying
training data has received less attention. In this paper, we expand on previous
work on vehicle trajectory prediction based on neural network models employing
distributed representations to encode automotive scenes in a semantic vector
substrate. We analyze the influence of variations in the training data on the
performance of our prediction models. Thereby, we show that the models
employing our semantic vector representation outperform the numerical model
when trained on an adequate data set and thereby, that the composition of
training data in vehicle trajectory prediction is crucial for successful
training. We conduct our analysis on challenging real-world driving data.


  Let $k$ be a differential field of characteristic zero and $E$ be a
liouvillian extension of $k$. For any differential subfield $K$ intermediate to
$E$ and $k$, we prove that there is an element in the set $K-k$ satisfying a
linear homogeneous differential equation over $k$. We apply our results to
study liouvillian solutions of first order non linear differential equations
and provide generalisations and new proofs for several results of M. Singer and
M. Rosenlicht on this topic.


  A comprehensive formalism for the description of neutrino oscillations in the
Earth in a general scheme with three massive neutrinos and the mass hierarchy
m_1<<m_2<<m_3 is presented. Using this formalism, which is valid both in vacuum
and in a medium, the matter effect on the oscillations of low-energy neutrinos
is discussed, pointing out the existence of very long oscillations which are
independent of the neutrino masses and the neutrino energy, and are very
sensitive to the matter density along the neutrino trajectory. As an example of
application of the formulation, a fit of the Kamiokande atmospheric neutrino
data with the matter effect taken into account for neutrinos propagating in the
Earth is presented. The results of the fit indicate that 4*10^{-3} eV^2 < m_3^2
< 4*10^{-2} eV^2 and the oscillation amplitudes in all channels (nu_mu<->nu_e,
nu_mu<->nu_tau, nu_e<->nu_tau) could be large. Hence, long-baseline experiments
with reactor (CHOOZ and Palo Verde) and accelerator (K2K, MINOS and ICARUS)
neutrinos could observe neutrino oscillations in all channels with a relatively
large statistics.


  These lecture notes recall the conceptual developments which led from the
discovery of the neutron to our present understanding of strong interaction
physics.


  We show that the free locally convex space $L(X)$ over a Tychonoff space $X$
is a Mackey group iff $L(X)$ is a Mackey space iff $X$ is discrete.


  We provide a method for obtaining simple models of supersymmetry breaking,
with all small mass scales generated dynamically, and illustrate it with
explicit examples. We start from models of perturbative supersymmetry breaking,
such as O'Raifeartaigh and Fayet models, that would respect an $R$ symmetry if
their small input parameters transformed as the superpotential does. By
coupling the system to a pure supersymmetric Yang-Mills theory (or a more
general supersymmetric gauge theory with dynamically small vacuum expectation
values), these parameters are replaced by powers of its dynamical scale in a
way that is naturally enforced by the symmetry. We show that supersymmetry
breaking in these models may be straightforwardly mediated to the
supersymmetric Standard Model, obtain complete models of direct gauge
mediation, and comment on related model building strategies that arise in this
simple framework.


  The possibility that the stellar initial mass function (IMF) arises mostly
from cloud structure is investigated with fractal Brownian motion (fBm) clouds
that have power-law power spectra. An fBm cloud with a realistic projected
power spectrum slope of $\beta=2.8$ is found to have a mass function for clumps
exceeding a threshold density that is a power-law with a slope of
$\alpha=2.35$, the same as in the Salpeter IMF. Any hierarchically structured
cloud has a clump mass function with about the same slope. This result implies
that turbulent interstellar clouds produce dense substructure with the observed
pre-stellar core mass function built in from the start. Details of the clump
formation processes are not critical. The conversion of clumps into stars
involves a second step. A one-to-one correspondence between clump mass and star
mass is not necessary to convert the clump mass spectrum into an IMF with the
same power-law slope. As long as clumps have an internal stellar IMF from
sub-fragmentation, protostellar accretion, coalescence and other processes, and
the characteristic mass for this internal IMF scales with the clump mass, then
the IMF slope above the minimum characteristic mass will equal the clump mass
slope. A detailed review of IMF models illustrates the prominence of cloud
structure as a major component in a wide class of theories. Tests are proposed
to determine the relative importance of cloud structure and competitive
accretion in the IMF.


  We consider online power control for an energy harvesting system with random
i.i.d. energy arrivals and a finite size battery. We propose a simple online
power control policy for this channel that requires minimal information
regarding the distribution of the energy arrivals and prove that it is
universally near-optimal for all parameter values. In particular, the policy
depends on the distribution of the energy arrival process only through its mean
and it achieves the optimal long-term average throughput of the channel within
both constant additive and multiplicative gaps. Existing heuristics for online
power control fail to achieve such universal performance. This result also
allows us to approximate the long-term average throughput of the system with a
simple formula, which sheds some light on the qualitative behavior of the
throughput, namely how it depends on the distribution of the energy arrivals
and the size of the battery.