  An overview on recent developments in thermal interfaces is given with a
focus on a novel thermal interface technology that allows the formation of 2-3
times thinner bondlines with strongly improved thermal properties at lower
assembly pressures. This is achieved using nested hierarchical surface channels
to control the particle stacking with highly particle-filled materials.
Reliability testing with thermal cycling has also demonstrated a decrease in
thermal resistance after extended times with longer overall lifetime compared
to a flat interface.


  We study the flow from the theory of D2-branes in a G2 holonomy background to
M2-branes in a Spin(7) holonomy background. We consider in detail the UV and IR
regimes, and the effect of topology change of the background on the field
theory. We conjecture a non-Abelian N=1 mirror symmetry.


  If a small compact object orbits a black hole, it is known that it can excite
the black hole's quasinormal modes (QNMs), leading to high-frequency
oscillations ("wiggles") in the radiated field at $\mathcal{J}^+$, and in the
radiation-reaction self-force acting on the object after its orbit passes
through periapsis. Here we survey the phenomenology of these wiggles across a
range of black hole spins and equatorial orbits. In both the scalar-field and
gravitational cases we find that wiggles are a generic feature across a wide
range of parameter space, and that they are observable in field perturbations
at fixed spatial positions, in the self-force, and in radiated fields at
$\mathcal{J}^+$. For a given charge or mass of the small body, the QNM
excitations have the highest amplitudes for systems with a highly spinning
central black hole, a prograde orbit with high eccentricity, and an orbital
periapsis close to the light ring. The QNM amplitudes depend smoothly on the
orbital parameters, with only very small amplitude changes when the orbit's
(discrete) frequency spectrum is tuned to match QNM frequencies. The
association of wiggles with QNM excitations suggest that they represent a
situation where the \emph{nonlocal} nature of the self-force is particularly
apparent, with the wiggles arising as result of QNM excitation by the compact
object near periapsis, and then encountered later in the orbit.
Astrophysically, the effects of wiggles at $\mathcal{J}^+$ might allow direct
observation of Kerr QNMs in extreme-mass-ratio inspiral (EMRI) binary black
hole systems, potentially enabling new tests of general relativity.


  A retraction is a homomorphism from a graph $G$ to an induced subgraph $H$ of
$G$ that is the identity on $H$. In a long line of research, retractions have
been studied under various algorithmic settings. Recently, the problem of
approximately counting retractions was considered. We give a complete
trichotomy for the complexity of approximately counting retractions to all
square-free graphs (graphs that do not contain a cycle of length $4$). It turns
out there is a rich and interesting class of graphs for which this problem is
complete in the class $\#\mathrm{BIS}$. As retractions generalise
homomorphisms, our easiness results extend to the important problem of
approximately counting homomorphisms. By giving new $\#\mathrm{BIS}$-easiness
results we now settle the complexity of approximately counting homomorphisms
for a whole class of non-trivial graphs which were previously unresolved.


  For a compact set $K\subset \mathbb C,$ a finite positive Borel measure $\mu$
on $K,$ and $1 \le t < \i,$ let $\text{Rat}(K)$ be the set of rational
functions with poles off $K$ and let $R^t(K, \mu)$ be the closure of
$\text{Rat}(K)$ in $L^t(\mu).$ For a bounded Borel subset $\mathcal D\subset
\mathbb C,$ let $\area_{\mathcal D}$ denote the area (Lebesgue) measure
restricted to $\mathcal D$ and let $H^\i (\mathcal D)$ be the weak-star closed
sub-algebra of $L^\i(\area_{\mathcal D})$ spanned by $f,$ bounded and analytic
on $\mathbb C\setminus E_f$ for some compact subset $E_f \subset \mathbb
C\setminus \mathcal D.$ We show that if $R^t(K, \mu)$ contains no non-trivial
direct $L^t$ summands, then there exists a Borel subset $\mathcal R \subset K$
whose closure contains the support of $\mu$ and there exists an isometric
isomorphism and a weak-star homeomorphism $\rho$ from $R^t(K, \mu) \cap
L^\infty(\mu)$ onto $H^\infty(\mathcal R)$ such that $\rho(r) = r$ for all
$r\in\text{Rat}(K).$ Consequently, we obtain some structural decomposition
theorems for $\rtkmu$.


  A combination of first principle molecular dynamics (MD) simulations with a
rate equation model (MD-RE approach) is presented to study the trapping and the
scattering of rare gas atoms from metal surfaces. The temporal evolution of the
atom fractions that are either adsorbed or scattered into the continuum is
investigated in detail. We demonstrate that for this description one has to
consider trapped, quasi-trapped and scattering states, and present an energetic
definition of these states. The rate equations contain the transition
probabilities between the states. We demonstrate how these rate equations can
be derived from kinetic theory. Moreover, we present a rigorous way to
determine the transition probabilities from a microscopic analysis of the
particle trajectories generated by MD simulations. Once the system reaches
quasi-equilibrium, the rates converge to stationary values, and the subsequent
thermal adsorption/desorption dynamics is completely described by the rate
equations without the need to perform further time-consuming MD simulations.
  As a proof of concept of our approach, MD simulations for argon atoms
interacting with a platinum (111) surface are presented. A detailed
deterministic trajectory analysis is performed, and the transition rates are
constructed. The dependence of the rates on the incidence conditions and the
lattice temperature is analyzed. Based on this example, we analyze the time
scale of the gas-surface system to approach the quasi-stationary state. The
MD-rate equation model has great relevance for the plasma-surface modeling as
it makes an extension of accurate simulations to long, experimentally relevant
time scales possible. Its application to the computation of atomic sticking
probabilities is given in the second part (paper II).


  Maintaining stability in an uncertain environment is essential for proper
functioning of living systems. Robust perfect adaptation (RPA) is a property of
a system that generates an output at a fixed level even after fluctuations in
input stimulus without fine-tuning parameters, and it is important to
understand how this feature is implemented through biochemical networks. The
existing literature has mainly focused on RPA of the concentration of a chosen
chemical species, and no generic analysis has been made on RPA of reaction
fluxes, that play an equally important role. Here, we identify structural
conditions on reaction networks under which all the reaction fluxes exhibit RPA
against the perturbation of the parameters inside a subnetwork. Based on this
understanding, we give a recipe for obtaining a simpler reaction network, from
which we can fully recover the steady-state reaction fluxes of the original
system. This helps us identify key parameters that determine the fluxes and
study the properties of complex reaction networks using a smaller one without
losing any information about steady-state reaction fluxes.


  We have designed a polarization-splitting grating coupler (PSGC) in
silicon-oninsulator (SOI) that has 1.2 dB peak loss in numerical simulations,
which is the best simulated performance of PSGCs without a bottom reflector to
the best of our knowledge. Adjoint method-based shape optimization enables us
to explore complex geometries that are intractable with conventional design
approaches. Physics-based process-independent knowledge of PSGCs is extracted
from the adjoint optimization and can be transferred to other platforms with a
minimum of effort.


  The wide popularity of short videos on social media poses new opportunities
and challenges to optimize recommender systems on the video-sharing platforms.
Users sequentially interact with the system and provide complex and
multi-faceted responses, including watch time and various types of interactions
with multiple videos. One the one hand, the platforms aims at optimizing the
users' cumulative watch time (main goal) in long term, which can be effectively
optimized by Reinforcement Learning. On the other hand, the platforms also
needs to satisfy the constraint of accommodating the responses of multiple user
interactions (auxiliary goals) such like, follow, share etc. In this paper, we
formulate the problem of short video recommendation as a Constrained Markov
Decision Process (CMDP). We find that traditional constrained reinforcement
learning algorithms can not work well in this setting. We propose a novel
two-stage constrained actor-critic method: At stage one, we learn individual
policies to optimize each auxiliary signal. At stage two, we learn a policy to
(i) optimize the main signal and (ii) stay close to policies learned at the
first stage, which effectively guarantees the performance of this main policy
on the auxiliaries. Through extensive offline evaluations, we demonstrate
effectiveness of our method over alternatives in both optimizing the main goal
as well as balancing the others. We further show the advantage of our method in
live experiments of short video recommendations, where it significantly
outperforms other baselines in terms of both watch time and interactions. Our
approach has been fully launched in the production system to optimize user
experiences on the platform.


  A superconductor can interact with magnets through the magnetic field which
is induced by the supercurrent. However, this interaction is rather weak and
requires relatively large magnets to reach a sizable hybridization of microwave
electromagnetic modes with magnons. The size of the device can be drastically
reduced by means of the large interfacial exchange interaction between spins in
a magnet and a superconductor. In the spin-orbit coupled superconductor this
interaction can be transmitted to the s-wave condensate through a mixing of
triplet and singlet Cooper pairs. In this paper we theoretically consider thin
antiferromagnetic films, which make a spin compensated contact to a
superconducting wire, which forms a cavity for Mooij-Sch\"{o}n plasma waves.
The effective Lagrangian is derived where the interaction of these waves with
magnons is taken into account. Frequencies of hybrid magnon-plasmon modes are
calculated. A system of two micromagnets placed far apart are also considered
and their mutual coupling through the cavity mode is calculated.


  Scenes play a crucial role in breaking the storyline of movies and TV
episodes into semantically cohesive parts. However, given their complex
temporal structure, finding scene boundaries can be a challenging task
requiring large amounts of labeled training data. To address this challenge, we
present a self-supervised shot contrastive learning approach (ShotCoL) to learn
a shot representation that maximizes the similarity between nearby shots
compared to randomly selected shots. We show how to apply our learned shot
representation for the task of scene boundary detection to offer
state-of-the-art performance on the MovieNet dataset while requiring only ~25%
of the training labels, using 9x fewer model parameters and offering 7x faster
runtime. To assess the effectiveness of ShotCoL on novel applications of scene
boundary detection, we take on the problem of finding timestamps in movies and
TV episodes where video-ads can be inserted while offering a minimally
disruptive viewing experience. To this end, we collected a new dataset called
AdCuepoints with 3,975 movies and TV episodes, 2.2 million shots and 19,119
minimally disruptive ad cue-point labels. We present a thorough empirical
analysis on this dataset demonstrating the effectiveness of ShotCoL for ad
cue-points detection.


  From just a short glance at a video, we can often tell whether a person's
action is intentional or not. Can we train a model to recognize this? We
introduce a dataset of in-the-wild videos of unintentional action, as well as a
suite of tasks for recognizing, localizing, and anticipating its onset. We
train a supervised neural network as a baseline and analyze its performance
compared to human consistency on the tasks. We also investigate self-supervised
representations that leverage natural signals in our dataset, and show the
effectiveness of an approach that uses the intrinsic speed of video to perform
competitively with highly-supervised pretraining. However, a significant gap
between machine and human performance remains. The project website is available
at https://oops.cs.columbia.edu


  (Abridged) The thermal state of the intracluster medium results from a
competition between gas cooling and heating. The heating comes from two
distinct sources: gravitational heating from the collapse of the dark matter
halo and thermal input from galaxy/black hole formation. However, a long
standing problem has been that cosmological simulations based on smoothed
particle hydrodynamics (SPH) and Eulerian mesh codes predict different results
even when cooling and galaxy/black hole heating are switched off. Clusters
formed in SPH simulations show near powerlaw entropy profiles, while those
formed in mesh simulations develop a core and do not allow gas to reach such
low entropies. Since the cooling rate is closely connected to the minimum
entropy of the gas, the differences are of potentially key importance.
  In this paper, we investigate the origin of this discrepancy. By comparing
simulations run using the GADGET-2 SPH code and the FLASH adaptive Eulerian
mesh code, we show that the discrepancy arises during the idealised merger of
two clusters. The difference is not sensitive to the resolution of our
simulations, nor is it is due differences in the gravity solvers, Galilean
non-invariance of the mesh code, or an effect of unsuitable artificial
viscosity in the SPH code. Instead, we find that the difference is inherent to
the treatment of eddies and fluid instabilities. These are suppressed in the
SPH simulations, while the cluster mergers generate strong vortices in the mesh
simulations that efficiently mix the fluid and erase the low entropy gas.
Consequently, particles in the SPH simulations retain a close connection to
their initial entropy, while this connection is much weaker in the mesh
simulations. We discuss the potentially profound implications of these results.


  We present an overview of a theory of complex dimensions of self-similar
fractal strings, and compare this theory to the theory of varieties over a
finite field from the geometric and the dynamical point of view. Then we
combine the several strands to discuss a possible approach to establishing a
cohomological interpretation of the complex dimensions.


  Polymeric materials that couple deformation and electrostatics have the
potential for use in soft sensors and actuators with potential applications
ranging from robotic, biomedical, energy, aerospace and automotive
technologies. In contrast to the mechanics of polymers that has been studied
using statistical mechanics approaches for decades, the coupled response under
deformation and electrical field has largely been modeled only
phenomenologically at the continuum scale. In this work, we examine the physics
of the coupled deformation and electrical response of an
electrically-responsive polymer chain using statistical mechanics. We begin
with a simple anisotropic model for the electrostatic dipole response to
electric field of a single monomer, and use a separation of energy scales
between the electrostatic field energy and the induced dipole field energy to
reduce the nonlocal and infinite-dimensional statistical averaging to a simpler
local finite-dimensional averaging. In this simplified setting, we derive the
equations of the most likely monomer orientation density using the maximum term
approximation, and a chain free energy is derived using this approximation.
These equations are investigated numerically and the results provide insight
into the physics of electro-mechanically coupled elastomer chains. Closed-form
approximations are also developed in the limit of small electrical energy with
respect to thermal energy; in the limit of small mechanical tension force
acting on the chain; and using asymptotic matching for general chain
conditions.


  The objective of this paper is to prove the convergence of a linear implicit
multi-step numerical method for ordinary differential equations. The algorithm
is obtained via Taylor approximations. The convergence is proved following the
Dahlquist theory. As an additional topic, the time stability is established
too. Comparative tests between some of the most known numerical methods and
this method are presented.


  We study symplectic structures on nilpotent Lie algebras. Since the
classification of nilpotent Lie algebras in any dimension seems to be a crazy
dream, we approach this study in case of 2-step nilpotent Lie algebras (in this
sub-case also, the classification fo the dimension greater than 8 seems very
difficult), using not a classification but a description of subfamilies
associated with the characteristic sequence. We begin with the dimension $8$,
first step where the classification becomes difficult.


  In this paper we consider a general problem set-up for a wide class of convex
and robust distributed optimization problems in peer-to-peer networks. In this
set-up convex constraint sets are distributed to the network processors who
have to compute the optimizer of a linear cost function subject to the
constraints. We propose a novel fully distributed algorithm, named
cutting-plane consensus, to solve the problem, based on an outer polyhedral
approximation of the constraint sets. Processors running the algorithm compute
and exchange linear approximations of their locally feasible sets.
Independently of the number of processors in the network, each processor stores
only a small number of linear constraints, making the algorithm scalable to
large networks. The cutting-plane consensus algorithm is presented and analyzed
for the general framework. Specifically, we prove that all processors running
the algorithm agree on an optimizer of the global problem, and that the
algorithm is tolerant to node and link failures as long as network connectivity
is preserved. Then, the cutting plane consensus algorithm is specified to three
different classes of distributed optimization problems, namely (i) inequality
constrained problems, (ii) robust optimization problems, and (iii) almost
separable optimization problems with separable objective functions and coupling
constraints. For each one of these problem classes we solve a concrete problem
that can be expressed in that framework and present computational results. That
is, we show how to solve: position estimation in wireless sensor networks, a
distributed robust linear program and, a distributed microgrid control problem.


  Binary Decision Diagram (BDD) based set bounds propagation is a powerful
approach to solving set-constraint satisfaction problems. However, prior BDD
based techniques in- cur the significant overhead of constructing and
manipulating graphs during search. We present a set-constraint solver which
combines BDD-based set-bounds propagators with the learning abilities of a
modern SAT solver. Together with a number of improvements beyond the basic
algorithm, this solver is highly competitive with existing propagation based
set constraint solvers.


  High-resolution parameter estimation algorithms designed to exploit the prior
knowledge about incident signals from strictly second-order (SO) non-circular
(NC) sources allow for a lower estimation error and can resolve twice as many
sources. In this paper, we derive the R-D NC Standard ESPRIT and the R-D NC
Unitary ESPRIT algorithms that provide a significantly better performance
compared to their original versions for arbitrary source signals. They are
applicable to shift-invariant R-D antenna arrays and do not require a
centrosymmetric array structure. Moreover, we present a first-order asymptotic
performance analysis of the proposed algorithms, which is based on the error in
the signal subspace estimate arising from the noise perturbation. The derived
expressions for the resulting parameter estimation error are explicit in the
noise realizations and asymptotic in the effective signal-to-noise ratio (SNR),
i.e., the results become exact for either high SNRs or a large sample size. We
also provide mean squared error (MSE) expressions, where only the assumptions
of a zero mean and finite SO moments of the noise are required, but no
assumptions about its statistics are necessary. As a main result, we
analytically prove that the asymptotic performance of both R-D NC ESPRIT-type
algorithms is identical in the high effective SNR regime. Finally, a case study
shows that no improvement from strictly non-circular sources can be achieved in
the special case of a single source.


  The presence or absence of variations in the mass-independent abundances of
Si isotopes in bulk meteorites provides important clues concerning the
evolution of the early solar system. No Si isotopic anomalies have been found
within the level of analytical precision of 15 ppm in 29Si/28Si across a wide
range of inner solar system materials, including terrestrial basalts,
chondrites, and achondrites. A possible exception is the angrites, which may
exhibit small excesses of 29Si. However, the general absence of anomalies
suggests that primitive meteorites and differentiated planetesimals formed in a
reservoir that was isotopically homogenous with respect to Si. Furthermore, the
lack of resolvable anomalies in the Calcium-Aluminum-rich Inclusion measured
here suggests that any nucleosynthetic anomalies in Si isotopes were erased
through mixing in the solar nebula prior to the formation of refractory solids.
The homogeneity exhibited by Si isotopes may have implications for the
distribution of Mg isotopes in the solar nebula. Based on supernova
nucleosynthetic yield calculations, the expected magnitude of heavy-isotope
overabundance is larger for Si than for Mg, suggesting that any potential Mg
heterogeneity, if present, exists below the 15 ppm level.


  We study consequences of gauge invariance and charge conservation of an
electron gas in a strong random potential perturbed by a weak electromagnetic
field. We use quantum equations of motion and Ward identities for one- and
two-particle averaged Green functions to establish exact relations between
density and current response functions. In particular we find precise
conditions under which we can extract the current-current correlation function
from the density-density correlation function and vice versa. We use these
results in two different ways to extend validity of a formula associating the
density response function with the electrical conductivity from semiclassical
equilibrium to quantum nonequilibrium systems. Finally we introduce quantum
diffusion via a response relating the current with the negative gradient of the
charge density. With the aid of this response function we derive a quantum
version of the Einstein relation and prove the existence of the diffusion pole
in the zero-temperature electron-hole correlation function with the the
long-range spatial fluctuations controlled by the static diffusion constant.


  We propose the novel idea of interoperator fixed-mobile network sharing,
which can be software-defined and readily-deployed. We study the benefits which
the sharing brings in terms of resiliency, and show that, with the appropriate
placement of a few active nodes, the mean service downtime can be reduced more
than threefold by providing interoperator communication to as little as one
optical network unit in one hundred. The implementation of the proposed idea
can be carried out in stages when needed (the pay-as-you-grow deployment), and
in those parts of the network where high service availability is needed most,
e.g., in a business district. While the performance should expectedly increase,
we show the resiliency is brought almost out of thin air by using redundant
resources of different operators. We evaluated the service availability for
87400 networks with the relative standard error of the sample mean below 1%.


  With the rapid development of short-pulse intense laser sources, studies of
matter under extreme irradiation conditions enter further unexplored regimes.
In addition, an application of X-ray Free- Electron Lasers (XFELs), delivering
intense femtosecond X-ray pulses allows to investigate sample evolution in IR
pump - X-ray probe experiments with an unprecedented time resolution. Here we
present the detailed study of periodic plasma created from the colloidal
crystal. Both experimental data and theory modeling show that the periodicity
in the sample survives to a large extent the extreme excitation and shock wave
propagation inside the colloidal crystal. This feature enables probing the
excited crystal, using the powerful Bragg peak analysis, in contrast to the
conventional studies of dense plasma created from bulk samples for which
probing with Bragg diffraction technique is not possible. X-ray diffraction
measurements of excited colloidal crystals may then lead towards a better
understanding of matter phase transitions under extreme irradiation conditions.


  The remarkable progress in the field of laser spectroscopy induced by the
invention of the frequency-comb laser has enabled many new high-precision tests
of fundamental theory and searches for new physics. Extending frequency-comb
based spectroscopy techniques to the vacuum (VUV) and extreme ultraviolet (XUV)
spectral range would enable measurements in e.g. heavier hydrogen-like systems
and open up new possibilities for tests of quantum electrodynamics and
measurements of fundamental constants. The main approaches rely on
high-harmonic generation (HHG), which is known to induce spurious phase shifts
from plasma formation. After our initial report (Physical Review Letters 123,
143001 (2019)), we give a detailed account of how the Ramsey-comb technique is
used to probe the plasma dynamics with high precision, and enables accurate
spectroscopy in the VUV. A series of Ramsey fringes is recorded to track the
phase evolution of a superposition state in xenon atoms, excited by two
up-converted frequency-comb pulses. Phase shifts of up to 1 rad induced by HHG
were observed at ns timescales and with mrad-level accuracy at $110$ nm. Such
phase shifts could be reduced to a negligible level, enabling us to measure the
$5p^6 \rightarrow 5p^5 8s~^2[3/2]_1$ transition frequency in $^{132}Xe$ at 110
nm (seventh harmonic) with sub-MHz accuracy. The obtained value is $10^4$ times
more precise than the previous determination and the fractional accuracy of
$2.3 \times 10^{-10}$ is $3.6$ times better than the previous best
spectroscopic measurement using HHG. The isotope shifts between $^{132}Xe$ and
two other isotopes were determined with an accuracy of $420$ kHz. The method
can be readily extended to achieve kHz-level accuracy, e.g. to measure the
$1S-2S$ transition in $He^+$. Therefore, the Ramsey-comb method shows great
promise for high-precision spectroscopy of targets requiring VUV and XUV
wavelengths.


  The security of private information is becoming the bedrock of an
increasingly digitized society. While the users are flooded with passwords and
PINs, these gold-standard explicit authentications are becoming less popular
and valuable. Recent biometric-based authentication methods, such as facial or
finger recognition, are getting popular due to their higher accuracy. However,
these hard-biometric-based systems require dedicated devices with powerful
sensors and authentication models, which are often limited to most of the
market wearables. Still, market wearables are collecting various private
information of a user and are becoming an integral part of life: accessing
cars, bank accounts, etc. Therefore, time demands a burden-free implicit
authentication mechanism for wearables using the less-informative
soft-biometric data that are easily obtainable from modern market wearables. In
this work, we present a context-dependent soft-biometric-based authentication
system for wearables devices using heart rate, gait, and breathing audio
signals. From our detailed analysis using the "leave-one-out" validation, we
find that a lighter $k$-Nearest Neighbor ($k$-NN) model with $k = 2$ can obtain
an average accuracy of $0.93 \pm 0.06$, $F_1$ score $0.93 \pm 0.03$, and {\em
false positive rate} (FPR) below $0.08$ at 50\% level of confidence, which
shows the promise of this work.


  Decision systems deal with a large volume of data stored in new databases
called data warehouses. Data warehouses are typically modeled by a star schema
that conventionally presents a central fact table and a set of dimension
tables. The corresponding queries for this type of model are therefore very
complex. In order to reduce the cost of executing complex queries, which
contain very expensive joins, the solution envisaged would be to guarantee a
good physical design of the data warehouses. Binary join indexes are very
suitable to reduce the cost of executing these joins. In this work, we proposed
a binary join index selection approach based on the notion of minimal
transversal. The final configuration obtained is composed of several indexes,
which make it possible to optimize the execution cost of the query set.


  Aerial surveillance from Unmanned Aerial Vehicles (UAVs), i.e. with moving
cameras, is of growing interest for police as well as disaster area monitoring.
For more detailed ground images the camera resolutions are steadily increasing.
Simultaneously the amount of video data to transmit is increasing
significantly, too. To reduce the amount of data, Region of Interest (ROI)
coding systems were introduced which mainly encode some regions in higher
quality at the cost of the remaining image regions. We employ an existing ROI
coding system relying on global motion compensation to retain full image
resolution over the entire image. Different ROI detectors are used to
automatically classify a video image on board of the UAV in ROI and non-ROI. We
propose to replace the modified Advanced Video Coding (AVC) video encoder by a
modified High Efficiency Video Coding (HEVC) encoder. Without any change of the
detection system itself, but by replacing the video coding back-end we are able
to improve the coding efficiency by 32% on average although regular HEVC
provides coding gains of 12-30% only for the same test sequences and similar
PSNR compared to regular AVC coding. Since the employed ROI coding mainly
relies on intra mode coding of new emerging image areas, gains of HEVC-ROI
coding over AVC-ROI coding compared to regular coding of the entire frames
including predictive modes (inter) depend on sequence characteristics. We
present a detailed analysis of bit distribution within the frames to explain
the gains. In total we can provide coding data rates of 0.7-1.0 Mbit/s for full
HDTV video sequences at 30 fps at reasonable quality of more than 37 dB.


  Binary Cu-Zr system is a representative bulk glassformer demonstrating high
glass forming ability due to pronounced icosahedral local ordering. From the
first glance, Ni-Zr system is the most natural object to expect the same
behavior because nickel and copper are neighbours in the periodic table and
have similar physicochemical properties. However, doing molecular dynamics
simulations of $\rm Ni_{\alpha}Zr_{1-\alpha}$ alloys described by embedded atom
model potential, we observe different behaviour. We conclude that the Ni-Zr
system has the same glass-forming ability as an additive binary Lennard-Jones
mixture without any chemical interaction. The structural analysis reveals that
icosahedral ordering in Ni-Zr alloys is much less pronounced than that in the
Cu-Zr ones. We suggest that lack of icosahedral ordering due to peculiarities
of interatomic interactions is the reason of relatively poor glass-forming
ability of Ni-Zr system.


  As the classical $(p,q)$-Poincar\'e inequality is known to fail for $0 < p <
1$, we introduce the notion of weighted multilinear Poincar\'e inequality as a
natural alternative when $m$-fold products and $1/m < p$ are considered. We
prove such weighted multilinear Poincar\'e inequalities in the subelliptic
context associated to vector fields of H\"ormader type. We do so by
establishing multilinear representation formulas and weighted estimates for
multilinear potential operators in spaces of homogeneous type.


  In a recent paper, a genuine four-partite entangled state is proposed [Y. Yeo
and W. K. Chua, Phys. Rev. Lett. 96, 060502 (2006)], which has been found to
have many interesting entanglement properties. We show this state is locally
equivalent to some graph states.


  Artificial Neural Networks were recently shown to be an efficient
representation of highly-entangled many-body quantum states. In practical
applications, neural-network states inherit numerical schemes used in
Variational Monte Carlo, most notably the use of Markov-Chain Monte-Carlo
(MCMC) sampling to estimate quantum expectations. The local stochastic sampling
in MCMC caps the potential advantages of neural networks in two ways: (i) Its
intrinsic computational cost sets stringent practical limits on the width and
depth of the networks, and therefore limits their expressive capacity; (ii) Its
difficulty in generating precise and uncorrelated samples can result in
estimations of observables that are very far from their true value. Inspired by
the state-of-the-art generative models used in machine learning, we propose a
specialized Neural Network architecture that supports efficient and exact
sampling, completely circumventing the need for Markov Chain sampling. We
demonstrate our approach for two-dimensional interacting spin models,
showcasing the ability to obtain accurate results on larger system sizes than
those currently accessible to neural-network quantum states.


  The first step towards investigating the effectiveness of a treatment via a
randomized trial is to split the population into control and treatment groups
then compare the average response of the treatment group receiving the
treatment to the control group receiving the placebo.
  In order to ensure that the difference between the two groups is caused only
by the treatment, it is crucial that the control and the treatment groups have
similar statistics. Indeed, the validity and reliability of a trial are
determined by the similarity of two groups' statistics. Covariate balancing
methods increase the similarity between the distributions of the two groups'
covariates. However, often in practice, there are not enough samples to
accurately estimate the groups' covariate distributions. In this paper, we
empirically show that covariate balancing with the Standardized Means
Difference (SMD) covariate balancing measure, as well as Pocock's sequential
treatment assignment method, are susceptible to worst-case treatment
assignments. Worst-case treatment assignments are those admitted by the
covariate balance measure, but result in highest possible ATE estimation
errors. We developed an adversarial attack to find adversarial treatment
assignment for any given trial. Then, we provide an index to measure how close
the given trial is to the worst-case. To this end, we provide an
optimization-based algorithm, namely Adversarial Treatment ASsignment in
TREatment Effect Trials (ATASTREET), to find the adversarial treatment
assignments.


  An extension of the Asakura-Oosawa-Vrij model of hard sphere colloids and
non-adsorbing polymers, that takes polymer non-ideality into account through a
repulsive stepfunction pair potential between polymers, is studied with grand
canonical Monte Carlo simulations and density functional theory. Simulation
results validate previous theoretical findings for the shift of the bulk fluid
demixing binodal upon increasing strength of polymer-polymer repulsion,
promoting the tendency to mix. For increasing strength of the polymer-polymer
repulsion, simulation and theory consistently predict the interfacial tension
of the free colloidal liquid-gas interface to decrease significantly for fixed
colloid density difference in the coexisting phases, and to increase for fixed
polymer reservoir packing fraction.


  We examine the relation of BSS-reducibility on subsets of the real numbers.
The question was asked recently (and anonymously) whether it is possible for
the halting problem H in BSS-computation to be BSS-reducible to a countable
set. Intuitively, it seems that a countable set ought not to contain enough
information to decide membership in a reasonably complex (uncountable) set such
as H. We confirm this intuition, and prove a more general theorem linking the
cardinality of the oracle set to the cardinality, in a local sense, of the set
which it computes. We also mention other recent results on BSS-computation and
algebraic real numbers.


  We present the discovery of TOI-1994b, a low-mass brown dwarf transiting a
hot subgiant star on a moderately eccentric orbit. TOI-1994 has an effective
temperature of $7700^{+720}_{-410}$ K, V magnitude of 10.51 mag and log(g) of
$3.982^{+0.067}_{-0.065}$. The brown dwarf has a mass of $22.1^{+2.6}_{-2.5}$
$M_J$, a period of 4.034 days, an eccentricity of $0.341^{+0.054}_{-0.059}$,
and a radius of $1.220^{+0.082}_{-0.071}$ $R_J$. TOI-1994b is more eccentric
than other transiting brown dwarfs with similar masses and periods. The
population of low mass brown dwarfs may have properties similar to planetary
systems if they were formed in the same way, but the short orbital period and
high eccentricity of TOI-1994b may contrast this theory. An evolved host
provides a valuable opportunity to understand the influence stellar evolution
has on the substellar companion's fundamental properties. With precise age,
mass, and radius, the global analysis and characterization of TOI-1994b
augments the small number of transiting brown dwarfs and allows the testing of
substellar evolution models.


  Electronic eigen-states of a square graphene quantum dot(GQD) terminated by
both zigzag and armchair edges are derived in the theoretical framework of
Dirac equation. We find that the Dirac equation can determine the eigen-energy
spectrum of a GQD with high accuracy even if its size is reduced to a few
nanometers. More importantly, from the Dirac equation description we can
readily work out the number and energy gap of the conjugate surface states,
which are intimately associated with the magnetic properties of the GQD. By
using the Hartree-Fock mean field approach, we study the size dependence of the
magnetic ordering formation in this square GQD. We find that there exists a
critical size of the width between the two zigzag edges to indicate the onset
of the stable magnetic ordering. On the other hand, when such a width increases
further, the magnetic ground state energy of a charge neutral GQD tends to a
saturated value. These results coincide with the previous results obtained from
the first principle calculation. Then, based on the Dirac equation solution
about the surface state, we establish a simple two-state model which can
quantitatively explain the size dependence of the magnetic ordering in the
square GQD.


  The regression of a functional response on a set of scalar predictors can be
a challenging task, especially if there is a large number of predictors, or the
relationship between those predictors and the response is nonlinear. In this
work, we propose a solution to this problem: a feed-forward neural network (NN)
designed to predict a functional response using scalar inputs. First, we
transform the functional response to a finite-dimensional representation and
construct an NN that outputs this representation. Then, we propose to modify
the output of an NN via the objective function and introduce different
objective functions for network training. The proposed models are suited for
both regularly and irregularly spaced data, and a roughness penalty can be
further applied to control the smoothness of the predicted curve. The
difficulty in implementing both those features lies in the definition of
objective functions that can be back-propagated. In our experiments, we
demonstrate that our model outperforms the conventional function-on-scalar
regression model in multiple scenarios while computationally scaling better
with the dimension of the predictors.


  The combinatorial optimization problem is one of the important applications
in neural network computation. The solutions of linearly constrained continuous
optimization problems are difficult with an exact algorithm, but the algorithm
for the solution of such problems is derived by using logarithm barrier
function. In this paper we have made an attempt to solve the linear constrained
optimization problem by using general logarithm barrier function to get an
approximate solution. In this case the barrier parameters behave as temperature
decreasing to zero from sufficiently large positive number satisfying convexity
of the barrier function. We have developed an algorithm to generate decreasing
sequence of solution converging to zero limit.


  Query Optimization remains an open problem for Big Data Management Systems.
Traditional optimizers are cost-based and use statistical estimates of
intermediate result cardinalities to assign costs and pick the best plan.
However, such estimates tend to become less accurate because of filtering
conditions caused either from undetected correlations between multiple
predicates local to a single dataset, predicates with query parameters, or
predicates involving user-defined functions (UDFs). Consequently, traditional
query optimizers tend to ignore or miscalculate those settings, thus leading to
suboptimal execution plans. Given the volume of today's data, a suboptimal plan
can quickly become very inefficient.
  In this work, we revisit the old idea of runtime dynamic optimization and
adapt it to a shared-nothing distributed database system, AsterixDB. The
optimization runs in stages (re-optimization points), starting by first
executing all predicates local to a single dataset. The intermediate result
created from each stage is used to re-optimize the remaining query. This
re-optimization approach avoids inaccurate intermediate result cardinality
estimations, thus leading to much better execution plans. While it introduces
the overhead for materializing these intermediate results, our experiments show
that this overhead is relatively small and it is an acceptable price to pay
given the optimization benefits. In fact, our experimental evaluation shows
that runtime dynamic optimization leads to much better execution plans as
compared to the current default AsterixDB plans as well as to plans produced by
static cost-based optimization (i.e. based on the initial dataset statistics)
and other state-of-the-art approaches.


  In this paper, using the localization technique we analyze the large N limit
of the mass deformed Aharony-Bergman-Jafferis-Maldacena (ABJM) theory on the
three sphere with a finite mass parameter and finite Chern-Simons levels. We
find two different solutions of the saddle point equations in the large N
limit. With these solutions we compute the free energy and find that there is a
first order phase transition. Our results may predict a phase transition in the
dual gravity theory.


  The origin of the similary-sized binary asteroid (90) Antiope remains an
unsolved puzzle. To constrain the origin of this unique double system, we
recorded individual spectra of the components using SPIFFI, a near-infrared
integral field spectrograph fed by SINFONI, an adaptive optics module available
on VLT-UT4. Using our previously published orbital model, we requested
telescope time when the separation of the components of (90) Antiope was larger
than 0.087", to minimize the contamination between components, during the
February 2009 opposition. Several multi-spectral data-cubes in J band (SNR=40)
and H+K band (SNR=100) were recorded in three epochs and revealed the two
components of (90) Antiope. After developing a specific photometric extraction
method and running an error analysis by Monte-Carlo simulations, we
successfully extracted reliable spectra of both components from 1.1 to 2.4 um
taken on the night of February 21, 2009. These spectra do not display any
significant absorption features due to mafic mineral, ices, or organics, and
their slopes are in agreement with both components being C- or Cb- type
asteroids. Their constant flux ratio indicates that both components' surface
reflectances are quite similar, with a 1-sigma variation of 7%. By comparison
with 2MASS J, H, K color distribution of observed Themis family members, we
conclude that both bodies were most likely formed at the same time and from the
same material. The similarly-sized system could indeed be the result of the
breakup of a rubble-pile proto-Antiope into two equal-sized bodies, but other
scenarios of formation implying a common origin should also be considered.


  We test the effectiveness of the multicanonical algorithm for the tertiary
structure prediction of peptides and proteins. As a simple example we study
Met-enkephalin. The lowest-energy conformation obtained agrees with that
determined by other methods such as Monte Carlo simulated annealing. But unlike
to simulated annealing the relationship to the canonical ensemble remains
exactly controlled. Thermodynamic quantities at various temperature can be
calculated from one run.


  Static lookup tables as often used in the position calculation electronics in
position sensitive detectors suffer from the well known problem that the
propagation of digitization errors in the division leads to unequal
efficiencies for the different output channels. In this paper we present a
solution to the construction of such a lookup table which is optimal in the
sense that the least possible efficiency errors are committed in the class of
monotone lookup tables. The obtained resolution is comparable to what is
obtained using the standard implementation of the fixed point division. After a
theoretical explanation, we present some experimental results confirming our
claim.


  We consider the problem of sparsity testing in the high-dimensional linear
regression model. The problem is to test whether the number of non-zero
components (aka the sparsity) of the regression parameter $\theta^*$ is less
than or equal to $k_0$. We pinpoint the minimax separation distances for this
problem, which amounts to quantifying how far a $k_1$-sparse vector $\theta^*$
has to be from the set of $k_0$-sparse vectors so that a test is able to reject
the null hypothesis with high probability. Two scenarios are considered. In the
independent scenario, the covariates are i.i.d. normally distributed and the
noise level is known. In the general scenario, both the covariance matrix of
the covariates and the noise level are unknown. Although the minimax separation
distances differ in these two scenarios, both of them actually depend on $k_0$
and $k_1$ illustrating that for this composite-composite testing problem both
the size of the null and of the alternative hypotheses play a key role.


  Let $V=\oC^n$ and let $T:=T(V)\otimes T(V^*)$ be the mixed tensor algebra
over $V$. We characterize those subsets $A$ of $T$ for which there is a
subgroup $G$ of the unitary group $\UU(n)$ such that $A=T^G$. They are
precisely the nondegenerate contraction-closed graded $*$-subalgebras of $T$.
While the proof makes use of the First Fundamental Theorem for $\GL(n,\oC)$ (in
the sense of Weyl), the characterization has as direct consequences First
Fundamental Theorems for several subgroups of $\GL(n,\oC)$. Moreover, a Galois
connection between linear algebraic $*$-subgroups of $\GL(n,\oC)$ and
nondegenerate contraction-closed $*$-subalgebras of $T$ is derived.


  Signals of heavy particle production during inflation are encoded as
non-analytic momentum scaling in primordial non-Gaussianity. These non-analytic
signatures can be sourced by Standard Model particles with a modified Higgs
scale uplifted by the slow-roll dynamics of inflation. We show that such a
lifting mechanism becomes more efficient with the presence of a strong
Higgs-inflaton mixing, where the Higgs mass scale is further increased by a
small speed of sound in the effective theory of inflation. As a primary step
towards detecting new particles in the cosmological collider program,
non-Gaussianity due to heavy Higgs production in the strong-mixing regime can
act as important background signals to be tested by future cosmological
surveys.


  In this paper we show that the price of stability of Shapley network design
games on undirected graphs with k players is at most (k^3(k+1)/2-k^2) /
(1+k^3(k+1)/2-k^2) H_k = (1 - \Theta(1/k^4)) H_k, where H_k denotes the k-th
harmonic number. This improves on the known upper bound of H_k, which is also
valid for directed graphs but for these, in contrast, is tight. Hence, we give
the first non-trivial upper bound on the price of stability for undirected
Shapley network design games that is valid for an arbitrary number of players.
Our bound is proved by analyzing the price of stability restricted to Nash
equilibria that minimize the potential function of the game. We also present a
game with k=3 players in which such a restricted price of stability is 1.634.
This shows that the analysis of Bil\`o and Bove (Journal of Interconnection
Networks, Volume 12, 2011) is tight. In addition, we give an example for three
players that improves the lower bound on the (unrestricted) price of stability
to 1.571.


  Closed oriented 4-manifolds with the same geometrically 2-dimensional
fundamental group (satisfying certain properties) are classified up to
$s$-cobordism by their $w_2$-type, equivariant intersection form and the
Kirby-Siebenmann invariant. As an application, we obtain a complete
homeomorphism classification of closed oriented 4-manifolds with solvable
Baumslag-Solitar fundamental groups, including a precise realization result.


  The aim of this paper is to give an example of a non-commutative discrete
hypergroup associated with $q$-disk polynomials. These are polynomials
$R_{l,m}^{(\a)}$ in two non-commuting variables which are expressed through
little $q$-Jacobi polynomials and that appear, for the value $\a=n-2$, as zonal
spherical functions on a quantum analogue of the homogeneous space
$U(n)/U(n-1)$. This fact was first proved in [NYM] (see also [Fl]). In a
previous paper [Fl] we proved an addition formula for these $q$-disk
polynomials. It is this addition formula that will allow us to prove positivity
of linearization coefficients in a manner similar to [Koo1], and to construct
from it a DJS-hypergroup following [Koo4].


  Intermediate frequency range (511 - 514 cm-1) Si phonons in Si-SiO2
nanocomposites are shown to have contribution from both core1 and
surface/interface1 Si phonons, where, ratio of contribution of the two depends
on the size of a Si nanocrystal. Further, laser heating experiment shows that
contribution of the core phonon increases due to increase in size of a
nanocrystal. Wavelength dependent Raman mapping reveals that interface phonons
are observable due to Resonance Raman scattering. This can well be corroborated
with the absorption spectra. This understanding can be gainfully used to
manipulate and characterize Si-SiO2 nanocomposite, simultaneously for
photovoltaic device applications.


  Recent studies have highlighted that deep neural networks (DNNs) are
vulnerable to adversarial attacks, even in a black-box scenario. However, most
of the existing black-box attack algorithms need to make a huge amount of
queries to perform attacks, which is not practical in the real world. We note
one of the main reasons for the massive queries is that the adversarial example
is required to be visually similar to the original image, but in many cases,
how adversarial examples look like does not matter much. It inspires us to
introduce a new attack called \emph{input-free} attack, under which an
adversary can choose an arbitrary image to start with and is allowed to add
perceptible perturbations on it. Following this approach, we propose two
techniques to significantly reduce the query complexity. First, we initialize
an adversarial example with a gray color image on which every pixel has roughly
the same importance for the target model. Then we shrink the dimension of the
attack space by perturbing a small region and tiling it to cover the input
image. To make our algorithm more effective, we stabilize a projected gradient
ascent algorithm with momentum, and also propose a heuristic approach for
region size selection. Through extensive experiments, we show that with only
1,701 queries on average, we can perturb a gray image to any target class of
ImageNet with a 100\% success rate on InceptionV3. Besides, our algorithm has
successfully defeated two real-world systems, the Clarifai food detection API
and the Baidu Animal Identification API.


  We present the necessary and sufficient condition for the violation of a new
series of multipartite Bell's inequalities with many measurement settings.


  It is shown that the metric of a massless particle obtained from boosting the
Schwarzschild metric to the velocity of light, has four Killing vectors
corresponding to an $E(2)\times \RR$ symmetry-group. This is in agreement with
the expectations based on flat-space kinematics but is in contrast to previous
statements in the literature \cite{Schueck}. Moreover, it also goes beyond the
general Jordan-Ehlers-Kundt-(JEK)-classification of gravitational pp-waves as
given in \cite{JEK}.


  We present the analysis of infrared (IR) observations of the planetary nebula
NGC 40 together with spectral analysis of its [WC]-type central star HD 826.
Spitzer IRS observations were used to produce spectral maps centred at
polycyclic aromatic hydrocarbons (PAH) bands and ionic transitions to compare
their spatial distribution. The ionic lines show a clumpy distribution of
material around the main cavity of NGC 40, with the emission from [Ar II] being
the most extended, whilst the PAHs show a rather smooth spatial distribution.
Analysis of ratio maps shows the presence of a toroidal structure mainly seen
in PAH emission, but also detected in a Herschel PACS 70 mic image. We argue
that the toroidal structure absorbs the UV flux from HD 826, preventing the
nebula to exhibit lines of high-excitation levels as suggested by previous
authors. We discuss the origin of this structure and the results from the
spectral analysis of HD 826 under the scenario of a late thermal pulse.


  The effects of spin-orbit (SOC) and electron-phonon coupling on the
collective excitation of doped monolayer Sb$_2$ are investigated using density
functional and many-body perturbation theories. The spin-orbit coupling is
exclusively important for the monolayer Sb$_2$ and it leads to the
reconstruction of the electronic band structure. In particular, plasmon modes
of monolayer Sb$_2$ are quite sensitive to the SOC and are characterized by
very low damping rates owing to small electron-phonon scatterings. Our results
show plasmons in antimonene are significantly less damped compared to monolayer
graphene when plasmon energies are $\hbar \omega> 0.2$ eV due to smaller
plasmon-phonon coupling in the former material.


  We discuss the algebraic classification of the Weyl tensor in higher
dimensional Lorentzian manifolds. This is done by characterizing algebraically
special Weyl tensors by means of the existence of aligned null vectors of
various orders of alignment. Further classification is obtained by specifying
the alignment type and utilizing the notion of reducibility. For a complete
classification it is then necessary to count aligned directions, the dimension
of the alignment variety, and the multiplicity of principal directions. The
present classification reduces to the classical Petrov classification in four
dimensions. Some applications are briefly discussed.


  Faddeev calculations of low-energy $\Lambda$-deuteron elastic scattering are
performed up to $E_{cm}=20$ MeV across the deuteron threshold. Phase shifts of
the $s$-wave with $J=1/2$ and $J=3/2$ are calculated using strangeness $S=-1$
hyperon-nucleon interactions in chiral effective field theory NLO13 and NLO19
parametrized by the J{\"u}lich-Bonn group. Effective range parameters, such as
a scattering length and an effective range, are determined through the
calculated phase shifts. $\Lambda$-deuteron momentum correlation functions are
evaluated using the $\Lambda$-deuteron relative wave function constructed from
half-off-shell $t$-matrices. They are compared with those evaluated using an
approximate formula.


  We study homogeneous Lorentzian manifolds $M = G/L$ of a connected reductive
Lie group $G$ modulo a connected reductive subgroup $L$, under the assumption
that $M$ is (almost) $G$-effective and the isotropy representation is totally
reducible. We show that the description of such manifolds reduces to the case
of semisimple Lie groups $G$. Moreover, we prove that such a homogeneous space
is reductive. We describe all totally reducible subgroups of the Lorentz group
and divide them into three types. The subgroups of Type I are compact, while
the subgroups of Type II and Type III are non-compact. The explicit description
of the corresponding homogeneous Lorentzian spaces of Type II and III (under
some mild assumption) is given. We also show that the description of Lorentz
homogeneous manifolds $M = G/L$ of Type I, reduces to the description of
subgroups $L$ such that $M=G/L$ is an admissible manifold, i.e., an effective
homogeneous manifold that admits an invariant Lorentzian metric. Whenever the
subgroup $L$ is a maximal subgroup with these properties, we call such a
manifold minimal admissible. We classify all minimal admissible homogeneous
manifolds $G/L$ of a compact semisimple Lie group $G$ and describe all
invariant Lorentzian metrics on them.


  The periodic-orbit theory based on the improved stationary-phase method
within the phase-space path integral approach is presented for the
semiclassical description of the nuclear shell structure, concerning the main
topics of the fruitful activity of V. G. Solovjov. We apply this theory to
study bifurcations and symmetry breaking phenomena in a radial power-law
potential which is close to the realistic Woods-Saxon one up to about the Fermi
energy. Using the realistic parametrization of nuclear shapes we explain the
origin of the double-humped fission barrier and the asymmetry in the fission
isomer shapes by the bifurcations of periodic orbits. The semiclassical origin
of the oblate-prolate shape asymmetry and tetrahedral shapes is also suggested
within the improved periodic-orbit approach. The enhancement of shell
structures at some surface diffuseness and deformation parameters of such
shapes are explained by existence of the simple local bifurcations and new
non-local bridge-orbit bifurcations in integrable and partially integrable
Fermi systems. We obtained good agreement between the semiclassical and quantum
shell-structure components of the level density and energy for several surface
diffuseness and deformation parameters of the potentials, including their
symmetry breaking and bifurcation values.


  In geometrothermodynamics (GTD), to study the geometric properties of the
equilibrium space three thermodynamic metrics have been proposed so far. These
metrics are obtained by using the condition of Legendre invariance and can be
computed explicitly once a thermodynamic potential is specified as the
fundamental equation. We use the remaining diffeomorphism invariance in the
phase and equilibrium spaces to show that the components of the GTD-metrics can
be interpreted as the second moment of the fluctuation of a new thermodynamic
potential. This result establishes a direct connection between GTD and
fluctuation theory. In this way, the diffeomorphism invariance of GTD allows us
to introduce new thermodynamic coordinates and new thermodynamic potentials,
which are not related by means of Legendre transformations to the fundamental
thermodynamic potentials.


  Recently, a method was described for deriving Holographic RG equation in
$AdS_{D+1}$ space starting from an Exact RG equation of a $D$-dimensional
boundary CFT (Sathiapalan, Sonoda, 2017). The evolution operator corresponding
to the Exact RG equation was rewritten as a functional integral of a $D+1$
dimensional field theory in $AdS_{D+1}$ space. This method has since been
applied to elementary scalars and composite scalars in the $O(N)$ model
(Sathiapalan, 2020). In this paper, we apply this technique to the conserved
vector current and the energy momentum tensor of a boundary CFT, the $O(N)$
model at a fixed point. These composite spin one and spin two operators are
represented by auxiliary fields and extend into the bulk as gauge fields and
metric perturbations. We obtain, at the free level, the (gauge fixed) Maxwell
and Einstein actions. While the steps involved are motivated by the AdS/CFT
correspondence, none of the steps logically require the AdS/CFT conjecture for
their justification.


  The extended structural context has made scientific paper summarization a
challenging task. This paper proposes CHANGES, a contrastive hierarchical graph
neural network for extractive scientific paper summarization. CHANGES
represents a scientific paper with a hierarchical discourse graph and learns
effective sentence representations with dedicated designed hierarchical graph
information aggregation. We also propose a graph contrastive learning module to
learn global theme-aware sentence representations. Extensive experiments on the
PubMed and arXiv benchmark datasets prove the effectiveness of CHANGES and the
importance of capturing hierarchical structure information in modeling
scientific papers.


  We propose a scheme to implement an optical Kagome lattice for ultra-cold
atoms with controllable s-wave interactions between nearest neighbor sites and
a gauge potential. The atoms occupy three different internal atomic levels with
electromagnetically-induced coupling between the levels. We show that by
appropriately shifting the triangular lattice potentials, experienced by atoms
in different levels, the Kagome lattice can be realized using only two standing
waves, generating a highly frustrated quantum system for the atoms.


  In this paper we introduce a notion of viscosity solutions for Eikonal
equations defined on topological networks. Existence of a solution for the
Dirichlet problem is obtained via representation formulas involving a distance
function associated to the Hamiltonian. A comparison theorem based on Ishii's
classical argument yields the uniqueness of the solution.


  The divergence of the constraint quantities is a major problem in
computational gravity today. Apparently, there are two sources for constraint
violations. The use of boundary conditions which are not compatible with the
constraint equations inadvertently leads to 'constraint violating modes'
propagating into the computational domain from the boundary. The other source
for constraint violation is intrinsic. It is already present in the initial
value problem, i.e. even when no boundary conditions have to be specified. Its
origin is due to the instability of the constraint surface in the phase space
of initial conditions for the time evolution equations. In this paper, we
present a technique to study in detail how this instability depends on gauge
parameters. We demonstrate this for the influence of the choice of the time
foliation in context of the Weyl system. This system is the essential
hyperbolic part in various formulations of the Einstein equations.


  We perform a statistical analysis of strong gravitational lensing by quasar
hosts of background galaxies, in the two competing models of dark matter halos
of quasars, HOD and CS models. Utilizing the BolshoiP Simulation we demonstrate
that strong gravitational lensing provides a potentially very powerful test of
models of quasar hosting halos. For quasars at $z=0.5$, the lensing probability
by quasars of background galaxies in the HOD model is higher than that of the
CS model by two orders of magnitude or more for lensing image separations in
the range of $\theta\sim 1.2-12~$arcsec. To observationally test this, we show
that, as an example, at the depth of the CANDELS wide field survey and with a
quasar sample of $1000$ at $z=0.5$, the two models can be differentiated at
$3-4\sigma$ confidence level.


  We study interactions between localized scatterers on metallic carbon
nanotubes by a mapping onto a one-dimensional Casimir problem. Backscattering
of electrons between localized scattering potentials mediates long range forces
between them. We model spatially localized scatterers by local and non-local
potentials and treat simultaneously the effects of intravalley and intervalley
backscattering. We find that the long range forces between scatterers exhibit
the universal power law decay of the Casimir force in one dimension, with
prefactors that control the sign and strength of the interaction. These
prefactors are nonuniversal and depend on the symmetry and degree of
localization of the scattering potentials. We find that local potentials
inevitably lead to a coupled valley scattering problem, though by contrast
non-local potentials lead to two decoupled single-valley problems in a
physically realized regime. The Casimir effect due to two-valley scattering
potentials is characterized by the appearance of spatially periodic modulations
of the force.


  Computational chemistry is the leading application to demonstrate the
advantage of quantum computing in the near term. However, large-scale
simulation of chemical systems on quantum computers is currently hindered due
to a mismatch between the computational resource needs of the program and those
available in today's technology. In this paper we argue that significant new
optimizations can be discovered by co-designing the application, compiler, and
hardware. We show that multiple optimization objectives can be coordinated
through the key abstraction layer of Pauli strings, which are the basic
building blocks of computational chemistry programs. In particular, we leverage
Pauli strings to identify critical program components that can be used to
compress program size with minimal loss of accuracy. We also leverage the
structure of Pauli string simulation circuits to tailor a novel hardware
architecture and compiler, leading to significant execution overhead reduction
by up to 99%. While exploiting the high-level domain knowledge reveals
significant optimization opportunities, our hardware/software framework is not
tied to a particular program instance and can accommodate the full family of
computational chemistry problems with such structure. We believe the co-design
lessons of this study can be extended to other domains and hardware
technologies to hasten the onset of quantum advantage.


  In adversarial interactions, one is often required to make strategic
decisions over multiple periods of time, wherein decisions made earlier impact
a player's competitive standing as well as how choices are made in later
stages. In this paper, we study such scenarios in the context of General Lotto
games, which models the competitive allocation of resources over multiple
battlefields between two players. We propose a two-stage formulation where one
of the players has reserved resources that can be strategically pre-allocated
across the battlefields in the first stage. The pre-allocation then becomes
binding and is revealed to the other player. In the second stage, the players
engage by simultaneously allocating their real-time resources against each
other. The main contribution in this paper provides complete characterizations
of equilibrium payoffs in the two-stage game, revealing the interplay between
performance and the amount of resources expended in each stage of the game. We
find that real-time resources are at least twice as effective as pre-allocated
resources. We then determine the player's optimal investment when there are
linear costs associated with purchasing each type of resource before play
begins, and there is a limited monetary budget.


  A number of recent analyses of cosmological data have reported hints for the
presence of extra radiation beyond the standard model expectation. In order to
test the robustness of these claims under different methods of constructing
parameter constraints, we perform a Bayesian posterior-based and a likelihood
profile-based analysis of current data. We confirm the presence of a slight
discrepancy between posterior- and profile-based constraints, with the
marginalised posterior preferring higher values of the effective number of
neutrino species N_eff. This can be traced back to a volume effect occurring
during the marginalisation process, and we demonstrate that the effect is
related to the fact that cosmic microwave background (CMB) data constrain N_eff
only indirectly via the redshift of matter-radiation equality. Once present CMB
data are combined with external information about, e.g., the Hubble parameter,
the difference between the methods becomes small compared to the uncertainty of
N_eff. We conclude that the preference of precision cosmological data for
excess radiation is "real" and not an artifact of a specific choice of
credible/confidence interval construction.


  Cellular heterogeneity is important to biological processes, including cancer
and development. However, proteome heterogeneity is largely unexplored because
of the limitations of existing methods for quantifying protein levels in single
cells. To alleviate these limitations, we developed Single Cell ProtEomics by
Mass Spectrometry (SCoPE-MS), and validated its ability to identify distinct
human cancer cell types based on their proteomes. We used SCoPE-MS to quantify
over a thousand proteins in differentiating mouse embryonic stem (ES) cells.
The single-cell proteomes enabled us to deconstruct cell populations and infer
protein abundance relationships. Comparison between single-cell proteomes and
transcriptomes indicated coordinated mRNA and protein covariation. Yet many
genes exhibited functionally concerted and distinct regulatory patterns at the
mRNA and the protein levels, suggesting that post-transcriptional regulatory
mechanisms contribute to proteome remodeling during lineage specification,
especially for developmental genes. SCoPE-MS is broadly applicable to measuring
proteome configurations of single cells and linking them to functional
phenotypes, such as cell type and differentiation potentials.


  Mixed dispersal syndromes have historically been regarded as bet-hedging
mechanisms that enhance survival in unpredictable environments, ensuring that
some propagules stay in the maternal environment while others can potentially
colonize new sites. However, this entails paying the costs of both dispersal
and non-dispersal. Propagules that disperse are likely to encounter unfavorable
conditions for establishment, while non-dispersing propagules might form
populations of close relatives burdened with inbreeding. Here, we investigate
the conditions under which mixed dispersal syndromes emerge and are
evolutionarily stable, taking into account the risks of both environmental
unpredictability and inbreeding. Using mathematical and computational modeling
we show that high dispersal propensity is favored whenever temporal
environmental unpredictability is low and inbreeding depression high, whereas
mixed dispersal syndromes are adaptive under conditions of high environmental
unpredictability, but more particularly if also inbreeding depression is small.
Although pure dispersers can be selected for under some circumstances, mixed
dispersal provides the optimal strategy under most parameterizations of our
models, indicating that this strategy is likely to be favored under a wide
variety of conditions. Furthermore, populations exhibiting any single phenotype
go inevitably extinct when environmental and genetic costs are high, whilst
mixed strategies can maintain viable populations even under such conditions.
Our models support the hypothesis that the interplay between inbreeding
depression and environmental unpredictability shapes dispersal syndromes, often
resulting in mixed strategies. Moreover, mixed dispersal seems to facilitate
persistence whenever conditions are critical or nearly critical for survival.


  In this paper, we introduce a two-dimensional fractional topological
superconductor (FTSC) as a strongly correlated topological state which can be
achieved by inducing superconductivity into an Abelian fractional quantum Hall
state, through the proximity effect. When the proximity coupling is weak, the
FTSC has the same topological order as its parent state and is thus Abelian.
However, upon increasing the proximity coupling, the bulk gap of such an
Abelian FTSC closes and reopens resulting in a new topological order: a
non-Abelian FTSC. Using several arguments we will conjecture that the conformal
field theory (CFT) that describes the edge state of the non-Abelian FTSC is
$U(1)/Z_2$ orbifold theory and use this to write down the ground-state wave
function. Further, we predict FTSC based on the Laughlin state at $\nu=1/m$
filling to host fractionalized Majorana zero modes bound to superconducting
vortices. These zero modes are non-Abelian quasiparticles which is evident in
their quantum dimension of $d_m=\sqrt{2m}$. Using the multi-quasi-particle wave
function based on the edge CFT, we derive the projective braid matrix for the
zero modes. Finally, the connection between the non-Abelian FTSCs and the
$Z_{2m}$ rotor model with a similar topological order is illustrated.


  Euclidean dual billiard outside a regular $N$-gon or $2N$-gon, for $N>3$ odd,
has aperiodic points.


  Computed tomography is widely used as an imaging tool to visualize
three-dimensional structures with expressive bone-soft tissue contrast.
However, CT resolution and radiation dose are tightly entangled, highlighting
the importance of low-dose CT combined with sophisticated denoising algorithms.
Most data-driven denoising techniques are based on deep neural networks and,
therefore, contain hundreds of thousands of trainable parameters, making them
incomprehensible and prone to prediction failures. Developing understandable
and robust denoising algorithms achieving state-of-the-art performance helps to
minimize radiation dose while maintaining data integrity. This work presents an
open-source CT denoising framework based on the idea of bilateral filtering. We
propose a bilateral filter that can be incorporated into a deep learning
pipeline and optimized in a purely data-driven way by calculating the gradient
flow toward its hyperparameters and its input. Denoising in pure image-to-image
pipelines and across different domains such as raw detector data and
reconstructed volume, using a differentiable backprojection layer, is
demonstrated. Although only using three spatial parameters and one range
parameter per filter layer, the proposed denoising pipelines can compete with
deep state-of-the-art denoising architectures with several hundred thousand
parameters. Competitive denoising performance is achieved on x-ray microscope
bone data (0.7053 and 33.10) and the 2016 Low Dose CT Grand Challenge dataset
(0.9674 and 43.07) in terms of SSIM and PSNR. Due to the extremely low number
of trainable parameters with well-defined effect, prediction reliance and data
integrity is guaranteed at any time in the proposed pipelines, in contrast to
most other deep learning-based denoising architectures.


  In order to investigate the interaction between single vacancies in a
graphene sheet, we have used spin-polarized density functional theory (DFT).
Two distinct configurations were considered, either with the two vacancies
located in the same sublattice or in different sublattices, and the effect of
changing the separation between the vacancies was also studied. Our results
show that the ground state of the system is indeed magnetic, but the presence
of the vacancies in the same sublattice or in different sublattices and the
possible topological configurations can lead to different contributions from
the $\pi$ and $\sigma$ orbitals to magnetism. On the other hand, our findings
reveal that the net magnetic moment of the system with the two vacancies in the
same sublattice move towards the value of the magnetic moment per isolated
vacancy with the increase of the distance between the vacancies, which is
ascribed to the different contributions due to $\pi$ electrons. Moreover, it is
also found that the local magnetic moments for vacancies in the same sublattice
are in parallel configuration, while they have different orientations when the
vacancies are created in different sublattices. So, our findings have clearly
evidenced how difficult it would be to observe experimentally the emergence of
magnetic order in graphene-based systems containing randomly created atomic
vacancies, since the energy difference between cases of antiferromagnetic and
ferromagnetic order decreases quickly with the increase in the distance
separating each vacancy pair.


  In current studies of mean-field quantum spin systems, much attention is
placed on the calculation of the ground-state energy and the excitation gap,
especially the latter which plays an important role in quantum annealing. In
pure systems, the finite gap can be obtained by various existing methods such
as the Holstein-Primakoff transform, while the tunneling splitting at
first-order phase transitions has also been studied in detail using instantons
in many previous works. In disordered systems, however, it remains challenging
to compute the gap of large-size systems with specific realization of disorder.
Hitherto, only quantum Monte Carlo techniques are practical for such studies.
Recently, Knysh [Nature Comm. \textbf{7}, 12370 (2016)] proposed a method where
the exponentially large dimensionality of such systems is condensed onto a
random potential of much lower dimension, enabling efficient study of such
systems. Here we propose a slightly different approach, building upon the
method of static approximation of the partition function widely used for
analyzing mean-field models. Quantum effects giving rise to the excitation gap
and non-extensive corrections to the free energy are accounted for by
incorporating dynamical paths into the path integral. The time-dependence of
the trace of the time-ordered exponential of the effective Hamiltonian is
calculated by solving a differential equation perturbatively, yielding a
finite-size series expansion of the path integral. Formulae for the first
excited-state energy are proposed to aid in computing the gap. We illustrate
our approach using the infinite-range ferromagnetic Ising model and the
Hopfield model, both in the presence of a transverse field.


  In this paper we develop instanton method introduced in [1], [2], [3] to
analyze quantitatively performance of Low-Density-Parity-Check (LDPC) codes
decoded iteratively in the so-called error-floor regime. We discuss statistical
properties of the numerical instanton-amoeba scheme focusing on detailed
analysis and comparison of two regular LDPC codes: Tanner's (155, 64, 20) and
Margulis' (672, 336, 16) codes. In the regime of moderate values of the
signal-to-noise ratio we critically compare results of the instanton-amoeba
evaluations against the standard Monte-Carlo calculations of the
Frame-Error-Rate.


  We present a Green's function based framework for modeling the scanning
tunneling spectrum from the normal as well as the superconducting state of
complex materials where the nature of the tunneling process$-$ i.e. the effect
of the tunneling 'matrix element', is properly taken into account. The
formalism is applied to the case of optimally doped
Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ (Bi2212) high-Tc superconductor using a
large tight-binding basis set of electron and hole orbitals. The results show
clearly that the spectrum is modified strongly by the effects of the tunneling
matrix element and that it is not a simple replica of the local density of
states (LDOS) of the Cu-$d_{x^2-y^2}$ orbitals with other orbitals playing a
key role in shaping the spectra. We show how the spectrum can be decomposed
usefully in terms of tunneling 'channels' or paths through which the current
flows from various orbitals in the system to the scanning tip. Such an analysis
reveals symmetry forbidden and symmetry enhanced paths between the tip and the
cuprate layers. Significant contributions arise from not only the CuO$_2$ layer
closest to the tip, but also from the second CuO$_2$ layer. The spectrum also
contains a longer range background reflecting the non-local nature of the
underlying Bloch states. In the superconducting state, coherence peaks are
found to be dominated by the anomalous components of Green's function.


  We study the modular invariance of $N=2$ superconformal $SU(1,1)$ models. By
decomposing the characters of Kazama-Suzuki model $SU(3)/(SU(2)\times U(1))$
into an infinite sum of the characters of $(SU(1,1)/U(1))\times U(1)$ we
construct modular invariant partition functions of $(SU(1,1)/U(1))\times U(1)$.


  We study the ballistic conductivity of graphene bilayer in the presence of
next-nearest neighbor hoppings between the layers. An undoped and unbiased
system was found in Ref. [1] to show a nonuniversal (length-dependent)
conductivity $\sigma(L)$, approaching the value of
$\sigma_\star=3/\pi\simeq{}0.95$ for large $L$. Here we demonstrate
one-parameter scaling and determine the scaling function
$\beta(\sigma)=d\ln{}\!\sigma/d\ln{}\!L$. The scaling flow has an attractive
fixed point [$\,\beta(\sigma_\star)=0$, $\beta'(\sigma_\star)<0\,$] reproducing
the scenario predicted for random impurity scattering of Dirac fermions with
Coulomb repulsion, albeit the system considered is perfectly ballistic and
interactions are not taken into account. The role of electrostatic bias between
the layers is also briefly discussed.


  In 2013, Strauch asked how various sequences of real numbers defined from
trigonometric functions such as $x_n=(\cos n)^n$ distributed themselves$\pmod
1$. Strauch's inquiry is motivated by several such distribution results. For
instance, Luca proved that the sequence $x_n=(\cos \alpha n)^n\pmod 1$ is dense
in $[0,1]$ for any fixed real number $\alpha$ such that $\alpha/\pi$ is
irrational. Here we generalise Luca's results to other sequences of the form
$x_n=f(n)^n\pmod 1$. We also examine the size of the set $|\{n\leq
N:r<|\cos(n\pi\alpha)|^n\}|$ where $0<r<1$ and $\alpha$ are fixed such that
$\alpha/\pi$ is irrational.


  Typically, electronic health record data are not collected towards a specific
research question. Instead, they comprise numerous observations recruited at
different ages, whose medical, environmental and oftentimes also genetic data
are being collected. Some phenotypes, such as disease-onset ages, may be
reported retrospectively if the event preceded recruitment, and such
observations are termed ``prevalent". The standard method to accommodate this
``delayed entry" conditions on the entire history up to recruitment, hence the
retrospective prevalent failure times are conditioned upon and cannot
participate in estimating the disease-onset age distribution. An alternative
approach conditions just on survival up to recruitment age, plus the
recruitment age itself. This approach allows incorporating the prevalent
information but brings about numerical and computational difficulties. In this
work we develop consistent estimators of the coefficients in a regression model
for the age-at-onset, while utilizing the prevalent data. Asymptotic results
are provided, and simulations are conducted to showcase the substantial
efficiency gain that may be obtained by the proposed approach. In particular,
the method is highly useful in leveraging large-scale repositories for
replicability analysis of genetic variants. Indeed, analysis of urinary bladder
cancer data reveals that the proposed approach yields about twice as many
replicated discoveries compared to the popular approach.


  In this paper we have shown that the novel mass relation among the vector
states, $\phi(1020)$ and $\phi(1680)$, with quantum numbers J$^{PC}=1^{--}$ and
axial-vector strangeonium state $h_1(s\bar{s})$ with quantum numbers $1^{+-}$
is also valid for nonzero current quark mass. This relation predicts the mass
of recently discovered $h_1(s\bar{s})$ state by the Collaboration BESIII within
experimental accuracy.


  Experiments on ferromagnetic thin films have measured the dependence of the
hysteresis loop area on the amplitude and frequency of the external field,
$A$=$A(H_{0},\omega)$, and approximate agreement with numerical simulations of
Ising models has been reported. Here we present numerical and theoretical
calculations of $A$ in the low-frequency regime for two values of $H_{0}$,
which bracket a temperature and system-size dependent crossover field. Our
previous Monte Carlo studies have shown that the hysteretic response of the
kinetic Ising model is qualitatively different for amplitudes above and below
this crossover field. Using droplet theory, we derive analytic expressions for
the low-frequency asymptotic behavior of the hysteresis loop area. In both
field regimes, the loop area exhibits an extremely slow approach to an
asymptotic, logarithmic frequency dependence of the form $A \propto - [\ln
(H_{0} \omega)]^{-1}$. Our results are relevant to the interpretation of data
from experiments and simulations, on the basis of which power-law exponents for
the hysteresis-loop area have been reported.


  Registries provide a mechanism with which VO applications can discover and
select resources--e.g. data and services--that are relevant for a particular
scientific problem. This specification defines the interfaces that support
interactions between applications and registries as well as between the
registries themselves. It is based on a general, distributed model composed of
so-called searchable and publishing registries. The specification has two main
components: an interface for searching and an interface for harvesting. All
interfaces are defined by a standard Web Service Description Language (WSDL)
document; however, harvesting is also supported through the existing Open
Archives Initiative Protocol for Metadata Harvesting, defined as an HTTP REST
interface. Finally, this specification details the metadata used to describe
registries themselves as resources using an extension of the VOResource
metadata schema.


  Estimation of heterogeneous treatment effects (HTE) is of prime importance in
many disciplines, ranging from personalized medicine to economics among many
others. Random forests have been shown to be a flexible and powerful approach
to HTE estimation in both randomized trials and observational studies. In
particular "causal forests", introduced by Athey, Tibshirani and Wager (2019),
along with the R implementation in package grf were rapidly adopted. A related
approach, called "model-based forests", that is geared towards randomized
trials and simultaneously captures effects of both prognostic and predictive
variables, was introduced by Seibold, Zeileis and Hothorn (2018) along with a
modular implementation in the R package model4you.
  Here, we present a unifying view that goes beyond the theoretical motivations
and investigates which computational elements make causal forests so successful
and how these can be blended with the strengths of model-based forests. To do
so, we show that both methods can be understood in terms of the same parameters
and model assumptions for an additive model under L2 loss. This theoretical
insight allows us to implement several flavors of "model-based causal forests"
and dissect their different elements in silico.
  The original causal forests and model-based forests are compared with the new
blended versions in a benchmark study exploring both randomized trials and
observational settings. In the randomized setting, both approaches performed
akin. If confounding was present in the data generating process, we found local
centering of the treatment indicator with the corresponding propensities to be
the main driver for good performance. Local centering of the outcome was less
important, and might be replaced or enhanced by simultaneous split selection
with respect to both prognostic and predictive effects.


  We describe the quantum cohomology rings of a class of toric varieties. The
description includes, in addition to the (already known) ring presentations,
the (new) analogues for toric varieties of the sorts of quantum Giambelli
formulas which exist already for Grassmannian varieties, flag varieties, etc.


  Data-driven brain parcellations aim to provide a more accurate representation
of an individual's functional connectivity, since they are able to capture
individual variability that arises due to development or disease. This renders
comparisons between the emerging brain connectivity networks more challenging,
since correspondences between their elements are not preserved. Unveiling these
correspondences is of major importance to keep track of local functional
connectivity changes. We propose a novel method based on graph edit distance
for the comparison of brain graphs directly in their domain, that can
accurately reflect similarities between individual networks while providing the
network element correspondences. This method is validated on a dataset of 116
twin subjects provided by the Human Connectome Project.


  Quantum chromodynamics (QCD) at non-zero isospin chemical potential is
studied in a canonical approach by analyzing systems of fixed isospin number
density. To construct these systems, we develop a range of new algorithms for
performing the factorially large numbers of Wick contractions required in
multi-hadron systems. We then use these methods to study systems with the
quantum numbers of up to 72 $\pi^+$'s on three ensembles of gauge
configurations with spatial extents $L\sim$ 2.0, 2.5 and 3.0 fm, and light
quark masses corresponding to a pion mass of {390 MeV}. The ground state
energies of these systems are extracted and the volume dependence of these
energies is utilized to determine the two- and three- body interactions amongst
$\pi^+$'s. The systems studied correspond to isospin densities of up to
$\rho_I\sim 9\ {\rm fm}^{-3}$ and probe isospin chemical potentials, $\mu_I$,
in the range $m_\pi\ \lsim \mu_I\ \lsim 4.5\ m_\pi$, allowing us to investigate
aspects of the QCD phase diagram at low temperature and for varying isospin
chemical potential. By studying the energy density of the system, we provide
numerical evidence for the conjectured transition of the system to a
Bose-Einstein condensed phase at $\mu_I\ \gsim m_\pi$.


  We address the well-posedness theory for the magento-geostrophic equation,
namely an active scalar equation in which the divergence-free drift velocity is
one derivative more singular than the active scalar. In the presence of
supercritical fractional diffusion given by (-\Delta)^\gamma, where 0<\gamma<1,
we discover that for \gamma>1/2 the equations are locally well-posed, while for
\gamma<1/2 they are ill-posed, in the sense that there is no Lipschitz solution
map. The main reason for the striking loss of regularity when \gamma goes below
1/2 is that the constitutive law used to obtain the velocity from the active
scalar is given by an unbounded Fourier multiplier which is both even and
anisotropic. Lastly, we note that the anisotropy of the constitutive law for
the velocity may be explored in order to obtain an improvement in the
regularity of the solutions when the initial data and the force have thin
Fourier support, i.e. they are supported on a plane in frequency space. In
particular, for such well-prepared data one may prove the local existence and
uniqueness of solutions for all values of \gamma \in (0,1).


  Speech language models (SpeechLMs) process and generate acoustic data only,
without textual supervision. In this work, we propose TWIST, a method for
training SpeechLMs using a warm-start from a pretrained textual language
models. We show using both automatic and human evaluations that TWIST
outperforms a cold-start SpeechLM across the board. We empirically analyze the
effect of different model design choices such as the speech tokenizer, the
pretrained textual model, and the dataset size. We find that model and dataset
scale both play an important role in constructing better-performing SpeechLMs.
Based on our observations, we present the largest (to the best of our
knowledge) SpeechLM both in terms of number of parameters and training data. We
additionally introduce two spoken versions of the StoryCloze textual benchmark
to further improve model evaluation and advance future research in the field.
We make speech samples, code and models publicly available:
https://pages.cs.huji.ac.il/adiyoss-lab/twist/ .


  In this work we examine how volume exclusion caused by regions of high
chromatin density might influence the time required for proteins to find
specific DNA binding sites. The spatial variation of chromatin density within
mouse olfactory sensory neurons is determined from soft X-ray tomography
reconstructions of five nuclei. We show that there is a division of the nuclear
space into regions of low-density euchromatin and high-density heterochromatin.
Volume exclusion experienced by a diffusing protein caused by this varying
density of chromatin is modeled by a repulsive potential. The value of the
potential at a given point in space is chosen to be proportional to the density
of chromatin at that location. The constant of proportionality, called the
volume exclusivity, provides a model parameter that determines the strength of
volume exclusion. Numerical simulations demonstrate that the mean time for a
protein to locate a binding site localized in euchromatin is minimized for a
finite, non-zero volume exclusivity. For binding sites in heterochromatin, the
mean time is minimized when the volume exclusivity is zero (the protein
experiences no volume exclusion). An analytical theory is developed to explain
these results. The theory suggests that for binding sites in euchromatin there
is an optimal level of volume exclusivity that balances a reduction in the
volume searched in finding the binding site, with the height of effective
potential barriers the protein must cross during the search process.


  Properties of low-energy excitations in one-dimensional superconductors and
density-wave systems are examined by the bosonization technique. In addition to
the usual spin and charge quantum numbers, a new, independently measurable
attribute is introduced to describe elementary, low-energy excitations. It can
be defined as a number w which determines, in multiple of $\pi$, how many times
the phase of the order parameter winds as an excitation is transposed from far
left to far right. The winding number is zero for electrons and holes with
conventional quantum numbers, but it acquires a nontrivial value w=1 for
neutral spin-1/2 excitations and for spinless excitations with a unit electron
charge. It may even be irrational, if the charge is irrational. Thus, these
excitations are topological, and they can be viewed as composite particles made
of spin or charge degrees of freedom and dressed by kinks in the order
parameter.


  There exist a variety of star-galaxy classification techniques, each with
their own strengths and weaknesses. In this paper, we present a novel
meta-classification framework that combines and fully exploits different
techniques to produce a more robust star-galaxy classification. To demonstrate
this hybrid, ensemble approach, we combine a purely morphological classifier, a
supervised machine learning method based on random forest, an unsupervised
machine learning method based on self-organizing maps, and a hierarchical
Bayesian template fitting method. Using data from the CFHTLenS survey, we
consider different scenarios: when a high-quality training set is available
with spectroscopic labels from DEEP2, SDSS, VIPERS, and VVDS, and when the
demographics of sources in a low-quality training set do not match the
demographics of objects in the test data set. We demonstrate that our Bayesian
combination technique improves the overall performance over any individual
classification method in these scenarios. Thus, strategies that combine the
predictions of different classifiers may prove to be optimal in currently
ongoing and forthcoming photometric surveys, such as the Dark Energy Survey and
the Large Synoptic Survey Telescope.


  Flag domains are open orbits of real semisimple Lie groups in flag manifolds
of their complexification. A special class of flag domains constitute the
classifying spaces for variations of Hodge structure, namely period domains or
more generally Mumford-Tate domains. In this paper we classify all proper,
equivariant embeddings of a flag domain of Hodge type, required to satisfy a
certain transversality condition, in a period domain and show that any such
flag domain must be a Hermitian symmetric space of non-compact type of
classical type. In particular, the classification describes also the proper,
equivariant embeddings of a flag domain of Hodge type in a period domain
satisfying Griffiths transversality.


  Complex video object segmentation serves as a fundamental task for a wide
range of downstream applications such as video editing and automatic data
annotation. Here we present the 2nd place solution in the MOSE track of PVUW
2024. To mitigate problems caused by tiny objects, similar objects and fast
movements in MOSE. We use instance segmentation to generate extra pretraining
data from the valid and test set of MOSE. The segmented instances are combined
with objects extracted from COCO to augment the training data and enhance
semantic representation of the baseline model. Besides, motion blur is added
during training to increase robustness against image blur induced by motion.
Finally, we apply test time augmentation (TTA) and memory strategy to the
inference stage. Our method ranked 2nd in the MOSE track of PVUW 2024, with a
$\mathcal{J}$ of 0.8007, a $\mathcal{F}$ of 0.8683 and a
$\mathcal{J}$\&$\mathcal{F}$ of 0.8345.