  The quantum adversary method is one of the most successful techniques for
proving lower bounds on quantum query complexity. It gives optimal lower bounds
for many problems, has application to classical complexity in formula size
lower bounds, and is versatile with equivalent formulations in terms of weight
schemes, eigenvalues, and Kolmogorov complexity. All these formulations rely on
the principle that if an algorithm successfully computes a function then, in
particular, it is able to distinguish between inputs which map to different
values.
  We present a stronger version of the adversary method which goes beyond this
principle to make explicit use of the stronger condition that the algorithm
actually computes the function. This new method, which we call ADV+-, has all
the advantages of the old: it is a lower bound on bounded-error quantum query
complexity, its square is a lower bound on formula size, and it behaves well
with respect to function composition. Moreover ADV+- is always at least as
large as the adversary method ADV, and we show an example of a monotone
function for which ADV+-(f)=Omega(ADV(f)^1.098). We also give examples showing
that ADV+- does not face limitations of ADV like the certificate complexity
barrier and the property testing barrier.


  Scattering and transition amplitudes with three-hadron final states play an
important role in nuclear and particle physics. However, predicting such
quantities using numerical Lattice QCD is very difficult, in part because of
the effects of Euclidean time and finite volume. In this review we highlight
recent formal developments that work towards overcoming these issues. We
organize the presentation into three parts: large volume expansions,
non-relativistic nonperturbative analyses, and nonperturbative studies based in
relativistic field theory.
  In the first part we discuss results for ground state energies and matrix
elements given by expanding in inverse box length, $1/L$. We describe
complications that arise at $\mathcal O(1/L^6)$ and include a table summarizing
the results of different calculations.
  In the second part we summarize three recent non-relativistic
non-perturbative studies and highlight the main conclusions of these works.
This includes demonstrating that the three-particle finite-volume spectrum is
determined, up to exponentially suppressed effects, by on-shell amplitudes, as
well as recovering a finite-volume quantization condition for scattering a
stable particle off a two-particle bound state. In this part we also highlight
recent work concerning a three-particle bound state in a finite volume.
  In the third and final part, we review recent work based in non-perturbative
relativistic field theory. Here the finite-volume spectrum has been related to
an intermediate infinite-volume quantity which itself is related via a known
integral equation to the relativistic, model-independent three-particle
scattering amplitude. We motivate the appearance of the intermediate quantity,
explain how it is related to the standard amplitude, and discuss prospects for
using the result to constrain three-particle observables.


  We show that the dynamics of (vertical) Franck-Condon excitations in the
regime where Holstein-coupled vibrational modes mix strongly with electronic
degrees of freedom sharply contrasts with the known self-localized behavior of
vibrationally relaxed excitations. Instead, the strongly-coupled modes are
found to periodically induce resonances between interacting electronic sites,
during which effective excitation transfer occurs, allowing Franck-Condon
excitations to attain substantial mean square displacements under conditions
where relaxed excitations are essentially trapped to a single site. In
demonstrating this behavior, we employ a multi-set matrix product state
formalism. We find this numerically exact technique to be a remarkably
efficient approach to the notoriously difficult problem posed by the Holstein
model in the regime where the electronic coupling, the vibrational quantum, and
the vibrational reorganization energy are comparable in magnitude.


  Here we deal in a pedagogical way with an approach to construct an algebraic
structure for the Quantum Mechanical measurement processes from the concept of
\emph{Measurement Symbol}. Such concept was conceived by Julian S. Schwinger
and constitutes a fundamental piece in his variational formalism and its
several applications.


  We generalize classical theorems due to Lichnerowicz and Hitchin on the
existence of Riemannian metrics of positive scalar curvature on spin manifolds
to the case of foliated spin manifolds. As a consequence, we show that there is
no foliation of positive leafwise scalar curvature on any torus, which
generalizes the famous theorem of Schoen-Yau and Gromov-Lawson on the
non-existence of metrics of positive scalar curvature on torus to the case of
foliations. Moreover, our method, which is partly inspired by the analytic
localization techniques of Bismut-Lebeau, also applies to give a new proof of
the celebrated Connes vanishing theorem without using noncommutative geometry.


  Functional data analysis offers a diverse toolkit of statistical methods
tailored for analyzing samples of real-valued random functions. Recently,
samples of time-varying random objects, such as time-varying networks, have
been increasingly encountered in modern data analysis. These data structures
represent elements within general metric spaces that lack local or global
linear structures, rendering traditional functional data analysis methods
inapplicable. Moreover, the existing methodology for time-varying random
objects does not work well in the presence of outlying objects. In this paper,
we propose a robust method for analysing time-varying random objects. Our
method employs pointwise Fr\'{e}chet medians and then constructs pointwise
distance trajectories between the individual time courses and the sample
Fr\'{e}chet medians. This representation effectively transforms time-varying
objects into functional data. A novel robust approach to functional principal
component analysis based on a Winsorized U-statistic estimator of the
covariance structure is introduced. The proposed robust analysis of these
distance trajectories is able to identify key features of time-varying objects
and is useful for downstream analysis. To illustrate the efficacy of our
approach, numerical studies focusing on dynamic networks are conducted. The
results indicate that the proposed method exhibits good all-round performance
and surpasses the existing approach in terms of robustness, showcasing its
superior performance in handling time-varying objects data.


  A complex scalar k is said to be an extended eigenvalue of a bounded linear
operator A on a complex Hilbert space if there is a nonzero operator X such
that AX=kXA. There are some solutions to the problem of computing the extended
eigenvalues for composition operators induced on the Fock space and Bergman
space by linear fractional transformationsof the complex plane in this paper.


  Every isometry $\sigma$ of a positive-definite even lattice $Q$ can be lifted
to an automorphism of the lattice vertex algebra $V_Q$. An important problem in
vertex algebra theory and conformal field theory is to classify the
representations of the $\sigma$-invariant subalgebra $V_Q^\sigma$ of $V_Q$,
known as an orbifold. In the case when $\sigma$ is an isometry of $Q$ of order
two, we classify the irreducible modules of the orbifold vertex algebra
$V_Q^\sigma$ and identify them as submodules of twisted or untwisted
$V_Q$-modules. The examples where $Q$ is a root lattice and $\sigma$ is a
Dynkin diagram automorphism are presented in detail.


  A model of an Er$^{3+}$/Yb$^{3+}$ co-doped glass gain medium is considered.
The system of rate equations taking into account a minimum set of processes
required to describe experimental data is built. As a result, a moderate number
of fitting parameters is used, thereby increasing the model reliability. For
the populations of the energy levels of Er$^{3+}$ and Yb$^{3+}$ ions, analytic
expressions are obtained, as well as for the gain factor of the medium. The
model was validated by a series of measurements of gain in Er$^{3+}$/Yb$^{3+}$
fiber amplifiers of different lengths at variable pump powers. The parameters
of the model are found to fit the experimental data, taking into account the
spatial inhomogeneity of the pump power along the fiber. The maximum population
inversion of erbium at high pumping powers is analytically expressed, which
describes the "bottleneck" effect. The power required to pump the fiber along
the entire length is found.


  The transition from formulations with extra dimensions to Kaluza-Klein
theories, aimed at extending the Standard Model, bears the ingredients of
hidden symmetries and the Kaluza-Klein mechanism for mass generation. We
explore these essential aspects in detail, and find that much can be said about
them with no reference to the specific geometry of compact extra dimensions:
the low-energy theory is determined, included dynamic variables and symmetries;
mass terms arise; eigenfunctions that define Kaluza-Klein fields are fixed by
the appropriate choice a Casimir invariant; there is a set of Kaluza-Klein
pseudo-Goldstone bosons. Throughout our presentation, similarities and
differences among spontaneous symmetry breaking, commonly present in
conventional Standard Model extensions, and what happens in Kaluza-Klein
theories are signaled and discussed.


  Query Expansion using Pseudo Relevance Feedback is a useful and a popular
technique for reformulating the query. In our proposed query expansion method,
we assume that relevant information can be found within a document near the
central idea. The document is normally divided into sections, paragraphs and
lines. The proposed method tries to extract keywords that are closer to the
central theme of the document. The expansion terms are obtained by
equi-frequency partition of the documents obtained from pseudo relevance
feedback and by using tf-idf scores. The idf factor is calculated for number of
partitions in documents. The group of words for query expansion is selected
using the following approaches: the highest score, average score and a group of
words that has maximum number of keywords. As each query behaved differently
for different methods, the effect of these methods in selecting the words for
query expansion is investigated. From this initial study, we extend the
experiment to develop a rule-based statistical model that automatically selects
the best group of words incorporating the tf-idf scoring and the 3 approaches
explained here, in the future. The experiments were performed on FIRE 2011
Adhoc Hindi and English test collections on 50 queries each, using Terrier as
retrieval engine.


  We investigate theoretically the ground-state configurations of
two-dimensional charged-particle systems with an elliptical hard-wall boundary
and their vibrational eigenmodes. The systems exhibit a series of structural
transitions, finally changing from a zigzag structure to a one-dimensional
Coulomb chain, as the eccentricity of the elliptical hard-wall boundary is
increased.


  We analyse Gaia EDR3 and re-calibrated HST proper motion data from the
core-collapsed and non core-collapsed globular clusters NGC 6397 and NGC 3201,
respectively, with the Bayesian mass-orbit modelling code MAMPOSSt-PM. We use
Bayesian evidence and realistic mock data sets constructed with AGAMA to select
between different mass models. In both clusters, the velocities are consistent
with isotropy within the extent of our data. We robustly detect a dark central
mass (DCM) of roughly 1000 solar masses in both clusters. Our MAMPOSSt-PM fits
strongly prefer an extended DCM in NGC 6397, while only presenting a mild
preference for it in NGC 3201, with respective sizes of a roughly one and a few
per cent of the cluster effective radius. We explore the astrophysics behind
our results with the CMC Monte Carlo N-body code, whose snapshots best matching
the phase space observations lead to similar values for the mass and size of
the DCM. The internal kinematics are thus consistent with a population of
hundreds of massive white dwarfs in NGC 6397, and roughly 100 segregated
stellar-mass black holes in NGC 3201, as previously found with CMC. Such
analyses confirm the accuracy of both mass-orbit modelling and Monte Carlo
N-body techniques, which together provide more robust predictions on the DCM of
globular clusters (core-collapsed or not). This opens possibilities to
understand a vast range of interesting astrophysical phenomena in clusters,
such as fast radio bursts, compact object mergers, and gravitational waves.


  The fact that a proper HVAC control strategy can reduce the energy
consumption of a building by up to 45% has driven significant research in
demand-based HVAC control. This paper presents a novel framework for modeling
and analysis of thermal dynamics in smart buildings that incorporates
building's thermal properties, a stochastic occupancy model and heating
strategies. Each zone of a building is modeled with the help of discrete time
Markov rewards formalism where the states represent the occupancy of that zone
(either occupied or empty), and the state rewards incorporate the thermal
dynamics and heating strategy. To demonstrate the applicability of our proposed
framework, we evaluate and compare six different heating strategies for the two
zone scenario of a university building. The obtained quantitative results from
the PRISM probabilistic model checker show that one of the evaluated control
strategies (viz. selective strategy) satisfies our requirement in terms of
maintaining the occupants' comfort while being up to 13.5 times more cost
effective when compared to the other evaluated strategies. Such evaluations
demonstrate the framework's ability to assist in selecting the control strategy
tailored around the occupancy pattern and building's thermal property.


  This paper introduces a novel anomaly detection (AD) problem that focuses on
identifying `odd-looking' objects relative to the other instances within a
scene. Unlike the traditional AD benchmarks, in our setting, anomalies in this
context are scene-specific, defined by the regular instances that make up the
majority. Since object instances are often partly visible from a single
viewpoint, our setting provides multiple views of each scene as input. To
provide a testbed for future research in this task, we introduce two
benchmarks, ToysAD-8K and PartsAD-15K. We propose a novel method that generates
3D object-centric representations for each instance and detects the anomalous
ones through a cross-examination between the instances. We rigorously analyze
our method quantitatively and qualitatively in the presented benchmarks.


  Dependency parsing is a tool widely used in the field of Natural language
processing and computational linguistics. However, there is hardly any work
that connects dependency parsing to monotonicity, which is an essential part of
logic and linguistic semantics. In this paper, we present a system that
automatically annotates monotonicity information based on Universal Dependency
parse trees. Our system utilizes surface-level monotonicity facts about
quantifiers, lexical items, and token-level polarity information. We compared
our system's performance with existing systems in the literature, including
NatLog and ccg2mono, on a small evaluation dataset. Results show that our
system outperforms NatLog and ccg2mono.


  In the recent years, many low-threshold dark matter (DM) direct detection
experiments have reported the observation of unexplained excesses of events at
low energies. Exemplary for these, the experiment CRESST has detected
unidentified events below an energy of about 200 eV - a result hampering the
detector performance in the search for GeV-scale DM. In this work, we test the
impact of nuclear recoil timing information on the potential for DM signal
discovery and model selection on a low-threshold experiment limited by the
presence of an unidentified background resembling this population of low-energy
events. Among the different targets explored by the CRESST collaboration, here
we focus on Al2O3, as a sapphire detector was shown to reach an energy
threshold as low as 19.7 eV [1]. We test the ability of a low-threshold
experiment to discover a signal above a given background, or to reject the
spin-independent (SI) interaction in favour of a magnetic dipole (MD) coupling
in terms of p-values. We perform our p-value calculations: 1) taking timing
information into account; and 2) assuming that the latter is not available. By
comparing the two approaches, we find that under our assumptions timing
information has a marginal impact on the potential for DM signal discovery,
while provides more significant results for the selection between the two
models considered. For the model parameters explored here, we find that the
p-value for rejecting SI-interactions in favour of a MD-coupling is about 0.11
when the experimental exposure is 460 g x year and smaller (about 0.06) if
timing information is available. The conclusion on the role of timing
information remains qualitatively unchanged for exposures as large as 1 kg x
year. At the same time, our results show that a 90% C.L. rejection of
SI-interactions in favour of a MD-coupling is within reach of an upgrade of the
CRESST experiment [2].


  This work proposes a method for depth completion of sparse LiDAR data using a
convolutional neural network which can be used to generate semi-dense depth
maps and "almost" full 3D point-clouds with significantly lower root mean
squared error (RMSE) over state-of-the-art methods. We add an "Error
Prediction" unit to our network and present a novel and simple end-to-end
method that learns to predict an error-map of depth regression task. An
"almost" dense high-confidence/low-variance point-cloud is more valuable for
safety-critical applications specifically real-world autonomous driving than a
full point-cloud with high error rate and high error variance. Using our
predicted error-map, we demonstrate that by up-filling a LiDAR point cloud from
18,000 points to 285,000 points, versus 300,000 points for full depth, we can
reduce the RMSE error from 1004 to 399. This error is approximately 60% less
than the state-of-the-art and 50% less than the state-of-the-art with RGB
guidance (we did not use RGB guidance in our algorithm). In addition to
analyzing our results on Kitti depth completion dataset, we also demonstrate
the ability of our proposed method to extend to new tasks by deploying our
"Error Prediction" unit to improve upon the state-of-the-art for monocular
depth estimation. Codes and demo videos are available at
http://github.com/hekmak/Conf-net.


  Optical mode crossing is not a plausible explanation for the new broad
Brillouin doublet nor for the strong acoustic anomalies observed at low
temperatures in SrTiO3. Data presented to support that explanation are also
inconclusive.


  To be the fittest is central to proliferation in evolutionary games.
Individuals thus adopt the strategies of better performing players in the hope
of successful reproduction. In structured populations the array of those that
are eligible to act as strategy sources is bounded to the immediate neighbors
of each individual. But which one of these strategy sources should potentially
be copied? Previous research dealt with this question either by selecting the
fittest or by selecting one player uniformly at random. Here we introduce a
parameter $u$ that interpolates between these two extreme options. Setting $u$
equal to zero returns the random selection of the opponent, while positive $u$
favor the fitter players. In addition, we divide the population into two
groups. Players from group $A$ select their opponents as dictated by the
parameter $u$, while players from group $B$ do so randomly irrespective of $u$.
We denote the fraction of players contained in groups $A$ and $B$ by $v$ and
$1-v$, respectively. The two parameters $u$ and $v$ allow us to analyze in
detail how aspirations in the context of the prisoner's dilemma game influence
the evolution of cooperation. We find that for sufficiently positive values of
$u$ there exist a robust intermediate $v \approx 0.5$ for which cooperation
thrives best. The robustness of this observation is tested against different
levels of uncertainty in the strategy adoption process $K$ and for different
interaction networks. We also provide complete phase diagrams depicting the
dependence of the impact of $u$ and $v$ for different values of $K$, and
contrast the validity of our conclusions by means of an alternative model where
individual aspiration levels are subject to evolution as well. Our study
indicates that heterogeneity in aspirations may be key for the sustainability
of cooperation in structured populations.


  In this thesis we provide the results obtained through lattice Monte Carlo
simulations of the bosonic BMN and the bosonic BFSS matrix models. The
simulations are performed using Hybrid Monte Carlo (HMC) algorithm. The BMN
matrix model is expected to have a Hagedorn/deconfinement type phase transition
as the temperature is varied in the system. The Polyakov loop is used as an
order parameter for detecting the phase transition. Besides the Polyakov loop,
other prime observables such as the internal energy and the extent of space
were also computed. We also check the validity of numerical simulation
algorithms by exploring the behavior of various relevant toy models. As the
main result of this thesis, we present a parametrized phase diagram of the
bosonic BMN matrix model constructed using two dimensionless parameters: a
dimensionless coupling constant and a dimensionless temperature.


  In this paper we study the algebraic geometry of any two-point code on the
Hermitian curve and reveal the purely geometric nature of their dual minimum
distance. We describe the minimum-weight codewords of many of their dual codes
through an explicit geometric characterization of their supports. In
particular, we show that they appear as sets of collinear points in many cases.


  We consider the coupled nonlinear Dirac equations (NLDE's) in 1+1 dimensions
with scalar-scalar self interactions $\frac{ g_1^2}{2} ( {\bpsi} \psi)^2 +
\frac{ g_2^2}{2} ( {\bphi} \phi)^2 + g_3^2 ({\bpsi} \psi) ( {\bphi} \phi)$ as
well as vector-vector interactions of the form $\frac{g_1^2 }{2} (\bpsi
\gamma_{\mu} \psi)(\bpsi \gamma^{\mu} \psi)+ \frac{g_2^2 }{2} (\bphi
\gamma_{\mu} \phi)(\bphi \gamma^{\mu} \phi) + g_3^2 (\bpsi \gamma_{\mu}
\psi)(\bphi \gamma^{\mu} \phi ). $ Writing the two components of the assumed
solitary wave solution of these equations in the form $\psi = e^{-i \omega_1 t}
\{R_1 \cos \theta, R_1 \sin \theta \}$, $\phi = e^{-i \omega_2 t} \{R_2 \cos
\eta, R_2\sin \eta \}$, and assuming that $ \theta(x),\eta(x)$ have the {\it
same} functional form they had when $g_3$=0, which is an approximation
consistent with the conservation laws, we then find approximate analytic
solutions for $R_i(x)$ which are valid for small values of $g_3^2/ g_2^2 $ and
$g_3^2/ g_1^2$. In the nonrelativistic limit we show that both of these coupled
models go over to the same coupled nonlinear Schr\"odinger equation for which
we obtain two exact pulse solutions vanishing at $x \rightarrow \pm \infty$.


  The phase diagrams of the spin-$3/2$ Blume-Capel model with competing short
and long-range interactions were studied through the free energy density
obtained by analytical methods. The competition emerges when positive
short-range interactions of strength $K$ arranged in a linear chain tend to
establish an anti-parallel spin order, whereas negative long-range interactions
$-J$ tend to align them in parallel. Thus, no ferromagnetic order exists for
$K/J > 0.25$. So, the phase-diagrams were scanned by varying the values of $K$
in this interval. As in other similar study done for the spin-1 case, the
second-order frontier separating the ferromagnetic and the paramagnetic phases
is transformed gradually into a first-order line, when $K/J$ is greater than a
certain critical value. Accordingly, there is a subinterval of $K$, for which
two tricritical points appear restricting the length of the second-order
frontier. Nevertheless, for greater values of $K/J$, the
ferromagnetic-paramagnetic frontier becomes wholly of first order. Also, the
tipical coexistence line, which divides two different ferromagnetic phases of
magnetization $m=3/2$ and $m=1/2$, becomes more complex by giving rise to
another line of coexistence with a reentrant behavior that encloses a third
ordered phase. In this case, the competition is such that there is a region in
the phase diagram, where for each spin $i$ with $S_{i}=3/2$ ($S_{i}=-3/2$),
there is another one spin $j$ with $S_{j}=-1/2$ ($S_{j}=1/2$), so the absolute
value of the magnetization per spin is one.


  A recently proposed method for computer simulations in the
isothermal-isobaric (NPT) ensemble, based on Langevin-type equations of motion
for the particle coordinates and the ``piston'' degree of freedom, is
re-derived by straightforward application of the standard Kramers-Moyal
formalism. An integration scheme is developed which reduces to a
time-reversible symplectic integrator in the limit of vanishing friction. This
algorithm is hence expected to be quite stable for small friction, allowing for
a large time step. We discuss the optimal choice of parameters, and present
some numerical test results.


  Calculations that demonstrate the influence of three key nuclear reaction
rates on the evolution of Asymptotic Giant Branch stars have been carried out.
We study the case of a star with an initial mass of 2Msun and a metallicity of
Z=0.01, somewhat less than the solar metallicity. The dredge-up of nuclear
processed material from the interior of the star, and the yield predictions for
carbon, are sensitive to the rate of the N14(p,gamma)O15 and triple-alpha
reactions. These reactions dominate the H- and He-burning shells of stars in
this late evolutionary phase. Published uncertainty estimates for each of these
two rates propagated through stellar evolution calculations cause uncertainties
in carbon enrichment and yield predictions of about a factor of two. The other
important He-burning reaction C12(alpha,gamma)O16, although associated with the
largest uncertainty in our study, does not have a significant influence on the
abundance evolution compared to other modelling uncertainties. This finding
remains valid when the entire evolution from the main-sequence to the tip of
the AGB is considered. We discuss the experimental sources of the rate
uncertainties addressed here, and give some outlook for future work.


  Accurate forecasting of space weather requires knowledge of the source
regions where solar energetic particles (SEP) and eruptive events originate.
Recent work has linked several major SEP events in 2014, January, to specific
features in the host active region (AR 11944). In particular, plasma
composition measurements in and around the footpoints of hot, coronal loops in
the core of the active region were able to explain the values later measured
in-situ by the Wind spacecraft. Due to important differences in elemental
composition between SEPs and the solar wind, the magnitude of the Si/S
elemental abundance ratio emerged as a key diagnostic of SEP seed population
and solar wind source locations. We seek to understand if the results are
typical of other active regions, even if they are not solar wind sources or SEP
productive. In this paper, we use a novel composition analysis technique,
together with an evolutionary magnetic field model, in a new approach to
investigate a typical solar active region (AR 11150), and identify the
locations of highly fractionated (high Si/S abundance ratio) plasma. Material
confined near the footpoints of coronal loops, as in AR 11944, that in this
case have expanded to the AR periphery, show the signature, and can be released
from magnetic field opened by reconnection at the AR boundary. Since the
fundamental characteristics of closed field loops being opened at the AR
boundary is typical of active regions, this process is likely to be general.


  Conventional federated learning (FL) frameworks follow a server-driven model
where the server determines session initiation and client participation, which
faces challenges in accommodating clients' asynchronous needs for model
updates. We introduce Client-Driven Federated Learning (CDFL), a novel FL
framework that puts clients at the driving role. In CDFL, each client
independently and asynchronously updates its model by uploading the locally
trained model to the server and receiving a customized model tailored to its
local task. The server maintains a repository of cluster models, iteratively
refining them using received client models. Our framework accommodates complex
dynamics in clients' data distributions, characterized by time-varying mixtures
of cluster distributions, enabling rapid adaptation to new tasks with superior
performance. In contrast to traditional clustered FL protocols that send
multiple cluster models to a client to perform distribution estimation, we
propose a paradigm that offloads the estimation task to the server and only
sends a single model to a client, and novel strategies to improve estimation
accuracy. We provide a theoretical analysis of CDFL's convergence. Extensive
experiments across various datasets and system settings highlight CDFL's
substantial advantages in model performance and computation efficiency over
baselines.


  Performance variability management is an active research area in
high-performance computing (HPC). We focus on input/output (I/O) variability.
To study the performance variability, computer scientists often use grid-based
designs (GBDs) to collect I/O variability data, and use mathematical
approximation methods to build a prediction model. Mathematical approximation
models could be biased particularly if extrapolations are needed. Space-filling
designs (SFDs) and surrogate models such as Gaussian process (GP) are popular
for data collection and building predictive models. The applicability of SFDs
and surrogates in the HPC variability needs investigation. We investigate their
applicability in the HPC setting in terms of design efficiency, prediction
accuracy, and scalability. We first customize the existing SFDs so that they
can be applied in the HPC setting. We conduct a comprehensive investigation of
design strategies and the prediction ability of approximation methods. We use
both synthetic data simulated from three test functions and the real data from
the HPC setting. We then compare different methods in terms of design
efficiency, prediction accuracy, and scalability. In synthetic and real data
analysis, GP with SFDs outperforms in most scenarios. With respect to
approximation models, GP is recommended if the data are collected by SFDs. If
data are collected using GBDs, both GP and Delaunay can be considered. With the
best choice of approximation method, the performance of SFDs and GBD depends on
the property of the underlying surface. For the cases in which SFDs perform
better, the number of design points needed for SFDs is about half of or less
than that of the GBD to achieve the same prediction accuracy. SFDs that can be
tailored to high dimension and non-smooth surface are recommended especially
when large numbers of input factors need to be considered in the model.


  We investigate the origin and fate of the recently discovered gas cloud G2
close to the Galactic Center. Our hydrodynamical simulations focussing on the
dynamical evolution of the cloud in combination with currently available
observations favor two scenarios: a Compact Cloud which started around the year
1995 and a Spherical Shell of gas, with an apocenter distance within the
disk(s) of young stars and a radius of a few times the size of the Compact
Cloud. The former is able to explain the detected signal of G2 in the
position-velocity diagram of the Br gamma emission of the year 2008.5 and
2011.5 data. The latter can account for both, G2's signal as well as the
fainter extended tail-like structure G2t seen at larger distances from the
black hole and smaller velocities. In contrast, gas stripped from a compact
cloud by hydrodynamical interactions is not able to explain the location of the
detected G2t emission in the observed position-velocity diagrams. This favors
the Spherical Shell Scenario and might be a severe problem for the Compact
Cloud as well as the so-called Compact Source Scenario. From these first
idealized simulations we expect a roughly constant feeding of the supermassive
black hole through a nozzle-like structure over a long period, starting shortly
after the closest approach in 2013.51 for the Compact Cloud. If the matter
accretes in the hot accretion mode, we do not expect a significant boost of the
current activity of Sgr A* for the Compact Cloud model, but a boost of the
average infrared and X-ray luminosity by roughly a factor of 80 for the
Spherical Shell scenario with order of magnitude variations on a timescale of a
few months. The near-future evolution of the cloud will be a sensitive probe of
the conditions of the gas distribution in the milli-parsec environment of the
massive black hole in the Galactic Center.


  We discuss boundedness and distortion in transformation groups. We show that
the groups $\mathrm{Diff}^r_0(\mathbb{R}^n)$ and
$\mathrm{Diff}^r(\mathbb{R}^n)$ have the strong distortion property, whenever
$0 \leq r \leq \infty, r \neq n+1$. This implies in particular that every
abstract length function on these groups is bounded. With related techniques we
show that, for $M$ a closed manifold or homeomorphic to the interior of a
compact manifold with boundary, the groups $\mathrm{Diff}_0^r(M)$ satisfy a
relative Higman embedding type property, introduced by Schreier. This answers a
problem asked by Schreier in the famous Scottish Book.


  Point source atom interferometry is a promising approach for implementing
robust, high-sensitivity, rotation sensors using cold atoms. However, its scale
factor, i.e., the ratio between the interferometer signal and the actual
rotation rate, depends on the initial conditions of the atomic cloud, which may
drift in time and result in bias instability, particularly in compact devices
with short interrogation times. We present two methods to stabilize the scale
factor, one relying on a model-based correction which exploits correlations
between multiple features of the interferometer output and works on a
single-shot basis, and the other a self-calibrating method where a known bias
rotation is applied to every other measurement, requiring no prior knowledge of
the underlying model but reducing the sensor bandwidth by a factor of two. We
demonstrate both schemes experimentally with complete suppression of scale
factor drifts, maintaining the original rotation sensitivity and allowing for
bias-free operation over several hours.


  Determining whether a configurable software system has a performance bug or
it was misconfigured is often challenging. While there are numerous debugging
techniques that can support developers in this task, there is limited empirical
evidence of how useful the techniques are to address the actual needs that
developers have when debugging the performance of configurable software
systems; most techniques are often evaluated in terms of technical accuracy
instead of their usability. In this paper, we take a human-centered approach to
identify, design, implement, and evaluate a solution to support developers in
the process of debugging the performance of configurable software systems. We
first conduct an exploratory study with 19 developers to identify the
information needs that developers have during this process. Subsequently, we
design and implement a tailored tool, adapting techniques from prior work, to
support those needs. Two user studies, with a total of 20 developers, validate
and confirm that the information that we provide helps developers debug the
performance of configurable software systems.


  We perform a systematic search of quasi-periodic variabilities in
$\gamma$-ray active galactic nuclei (AGNs) in the third \emph{Fermi} Large Area
Telescope source catalog (3FGL). We employe two techniques, Lomb-Scargle
Periodogram (LSP) and Weighted Wavelet Z-transform (WWZ), to obtain power
spectra of $\gamma$-ray light curves covering from 2008 August to 2016
December. The results show that besides several objects that have been reported
in previous works, an additional source, the FSRQ PKS 0601-70 has a possible
quasi-periodic variability of 450 days in its $\gamma$-ray light curves with
the significance of $>3\sigma$. The physical implications of our findings are
discussed.


  We work with completed adelic structures on an arithmetic surface and justify
that the construction under consideration is compatible with Arakelov geometry.
The ring of completed adeles is algebraically and topologically self-dual and
fundamental adelic subspaces are self orthogonal with respect to a natural
differential pairing. We show that the Arakelov intersection pairing can be
lifted to an idelic intersection pairing.


  We report on the measurement of detailed balance violation in a coupled,
noise-driven linear electronic circuit consisting of two nominally identical RC
elements that are coupled via a variable capacitance. The state variables are
the time-dependent voltages across each of the two primary capacitors, and the
system is driven by independent noise sources in series with each of the
resistances. From the recorded time histories of these two voltages, we
quantify violations of detailed balance by three methods: 1) explicit
construction of the probability current density, 2) by constructing the
time-dependent stochastic area, and 3) by constructing statistical fluctuation
loops. In comparing the three methods, we find that the stochastic area is
relatively simple to implement, computationally inexpensive, and provides a
highly sensitive means for detecting violations of detailed balance.


  This is a short review, aimed at a general audience, of several current
subjects of research in cosmology. The topics discussed include the cosmic
microwave background (CMB), with particular emphasis on its relevance for
testing inflation; dark matter, with a brief review of astrophysical evidence
and more emphasis on particle candidates; and cosmic acceleration and some of
the ideas that have been put forward to explain it. A glossary of technical
terms and acronyms is provided.


  We perform aperture photometry and profile fitting on 419 globular cluster
(GC) candidates with mV \leq 23 mag identified in Hubble Space Telescope
Advanced Camera for Surveys BVI imaging, and estimate the effective radii of
the clusters. We identify 85 previously known spectroscopically-confirmed
clusters, and newly identify 136 objects as good cluster candidates within the
3{\sigma} color and size ranges defined by the spectroscopically confirmed
clusters, yielding a total of 221 probable GCs. The luminosity function peak
for the 221 probable GCs with estimated total dereddening applied is V ~(20.26
\pm 0.13) mag, corresponding to a distance of ~3.7\pm0.3 Mpc. The blue and red
GC candidates, and the metal-rich (MR) and metal-poor (MP) spectroscopically
confirmed clusters, are similar in half-light radius, respectively. Red
confirmed clusters are about 6% larger in median half-light radius than blue
confirmed clusters, and red and blue good GC candidates are nearly identical in
half-light radius. The total population of confirmed and "good" candidates
shows an increase in half-light radius as a function of galactocentric
distance.


  We study the properties of a family of distances between functions of a
single variable. These distances are examples of integral probability metrics,
and have been used previously for comparing probability measures on the line;
special cases include the Earth Mover's Distance and the Kolmogorov Metric. We
examine their properties for general signals, proving that they are robust to a
broad class of deformations. We also establish corresponding robustness results
for the induced sliced distances between multivariate functions. Finally, we
establish error bounds for approximating the univariate metrics from finite
samples, and prove that these approximations are robust to additive Gaussian
noise. The results are illustrated in numerical experiments, which include
comparisons with Wasserstein distances.


  We consider corrections to vanishing U_{e3} and maximal atmospheric neutrino
mixing originating from the relation U = U_ell^dagger U_nu, where U is the PMNS
mixing matrix and U_ell (U_nu) is associated with the diagonalization of the
charged lepton (neutrino) mass matrix. We assume that in the limit of U_ell or
U_nu being the unit matrix, one has U_{e3} = 0 and theta_{23} = pi/4, while the
solar neutrino mixing angle is a free parameter. Well-known special cases of
the indicated scenario are the bimaximal and tri-bimaximal mixing schemes. If
U_{e3} \neq 0 and theta_{23} \neq pi/4 due to corrections from the charged
leptons, |U_{e3}| can be sizable (close to the existing upper limit) and we
find that the value of the solar neutrino mixing angle is linked to the
magnitude of CP violation in neutrino oscillations. In the alternative case of
the neutrino sector correcting U_{e3} = 0 and theta_{23} = pi/4, we obtain a
generically smaller |U_{e3}| than in the first case. Now the magnitude of CP
violation in neutrino oscillations is connected to the value of the atmospheric
neutrino mixing angle theta_{23}. We find that both cases are in agreement with
present observations. We also introduce parametrization independent "sum-rules"
for the oscillation parameters.


  Understanding the mobility patterns of commuter train passengers is crucial
for developing efficient and sustainable transportation systems in urban areas.
Traditional technologies, such as Automated Passenger Counters (APC) can
measure the aggregated numbers of passengers entering and exiting trains,
however, they do not provide detailed information nor passenger movements
beyond the train itself. To overcome this limitation we investigate the
potential combination of traditional APC with an emerging source capable of
collecting detailed mobility demand data. This new data source derives from the
pilot project TravelSense, led by the Helsinki Regional Transport Authority
(HSL), which utilizes Bluetooth beacons and HSL's mobile phone ticket
application to track anonymous passenger multimodal trajectories from origin to
destination. By combining TravelSense data with APC we are able to better
understand the structure of train users' journeys by identifying the origin and
destination locations, modes of transport used to access commuter train
stations, and boarding and alighting numbers at each station. These insights
can assist public transport planning decisions and ultimately help to
contribute to the goal of sustainable cities and communities by promoting the
use of seamless and environmentally friendly transportation options.


  Population size estimation requires access to unit-level data in order to
correctly apply capture-recapture methods. Unfortunately, for reasons of
confidentiality access to such data may be limited. To overcome this issue we
apply and extend the hierarchical Poisson-Gamma model proposed by Zhang (2008),
which initially was used to estimate the number of irregular foreigners in
Norway.
  The model is an alternative to the current capture-recapture approach as it
does not require linking multiple sources and is solely based on aggregated
administrative data that include (1) the number of apprehended irregular
foreigners, (2) the number of foreigners who faced criminal charges and (3) the
number of foreigners registered in the central population register. The model
explicitly assumes a relationship between the unauthorized and registered
population, which is motivated by the interconnection between these two groups.
This makes the estimation conditionally dependent on the size of regular
population, provides interpretation with analogy to registered population and
makes the estimated parameter more stable over time.
  In this paper, we modify the original idea to allow for covariates and
flexible count distributions in order to estimate the number of irregular
foreigners in Poland in 2019. We also propose a parametric bootstrap for
estimating standard errors of estimates. Based on the extended model we
conclude that in as of 31.03.2019 and 30.09.2019 around 15,000 and 20,000
foreigners and were residing in Poland without valid permits. This means that
those apprehended by the Polish Border Guard account for around 15-20% of the
total.


  Atom segmentation and localization, noise reduction and deblurring of
atomic-resolution scanning transmission electron microscopy (STEM) images with
high precision and robustness is a challenging task. Although several
conventional algorithms, such has thresholding, edge detection and clustering,
can achieve reasonable performance in some predefined sceneries, they tend to
fail when interferences from the background are strong and unpredictable.
Particularly, for atomic-resolution STEM images, so far there is no
well-established algorithm that is robust enough to segment or detect all
atomic columns when there is large thickness variation in a recorded image.
Herein, we report the development of a training library and a deep learning
method that can perform robust and precise atom segmentation, localization,
denoising, and super-resolution processing of experimental images. Despite
using simulated images as training datasets, the deep-learning model can
self-adapt to experimental STEM images and shows outstanding performance in
atom detection and localization in challenging contrast conditions and the
precision consistently outperforms the state-of-the-art two-dimensional
Gaussian fit method. Taking a step further, we have deployed our deep-learning
models to a desktop app with a graphical user interface and the app is free and
open-source. We have also built a TEM ImageNet project website for easy
browsing and downloading of the training data.


  Gravitational microlensing is the only method capable of exploring the entire
population of free-floating planets down to Mars-mass objects, because the
microlensing signal does not depend on the brightness of the lensing object. A
characteristic timescale of microlensing events depends on the mass of the
lens: the less massive the lens, the shorter the microlensing event. A previous
analysis of 474 microlensing events found an excess of very short events (1-2
days) - more than known stellar populations would suggest - indicating the
existence of a large population of unbound or wide-orbit Jupiter-mass planets
(reported to be almost twice as common as main-sequence stars). These results,
however, do not match predictions of planet formation theories and are in
conflict with surveys of young clusters. Here we report the analysis of a six
times larger sample of microlensing events discovered during the years
2010-2015. Although our survey has very high sensitivity (detection efficiency)
to short-timescale (1--2 days) microlensing events, we found no excess of
events with timescales in this range, with a 95% upper limit on the frequency
of Jupiter-mass free-floating or wide-orbit planets of 0.25 planet per
main-sequence star. We detected a few possible ultrashort-timescale events
(with timescales of less than 0.5 day), which may indicate the existence of
Earth- and super-Earth-mass free-floating planets, as predicted by
planet-formation theories. [abridged]


  We study the countable set of rates of growth of a hyperbolic group with
respect to all its finite generating sets. We prove that the set is
well-ordered, and that every real number can be the rate of growth of at most
finitely many generating sets up to automorphism of the group. We prove that
the ordinal of the set of rates of growth is at least $\omega^\omega$, and in
case the group is a limit group (e.g., free and surface groups), it is
$\omega^\omega$.
  We further study the rates of growth of all the finitely generated subgroups
of a hyperbolic group with respect to all their finite generating sets. This
set is proved to be well-ordered as well, and every real number can be the rate
of growth of at most finitely many isomorphism classes of finite generating
sets of subgroups of a given hyperbolic group. Finally, we strengthen our
results to include rates of growth of all the finite generating sets of all the
subsemigroups of a hyperbolic group.


  The universal power law for the spectrum of one-dimensional breaking Riemann
waves is justified for the simple wave equation. The spectrum of spatial
amplitudes at the breaking time $t = t_b$ has an asymptotic decay of
$k^{-4/3}$, with corresponding energy spectrum decaying as $k^{-8/3}$. This
spectrum is formed by the singularity of the form $(x-x_b)^{1/3}$ in the wave
shape at the breaking time. This result remains valid for arbitrary nonlinear
wave speed. In addition, we demonstrate numerically that the universal power
law is observed for long time in the range of small wave numbers if small
dissipation or dispersion is accounted in the viscous Burgers or Korteweg-de
Vries equations.


  We study a discrete time multitype branching random walk on a finite space
with finite set of types. Particles follow a Markov chain on the spatial space
whereas offspring distributions are given by a random field that is fixed
throughout the evolution of the particles. Our main interest lies in the
averaged (annealed) expectation of the population size, and its long-time
asymptotics. We first derive, for fixed time, a formula for the expected
population size with fixed offspring distributions, which is reminiscent of a
Feynman-Kac formula. We choose Weibull-type distributions with parameter
$1/\rho_{ij}$ for the upper tail of the mean number of $j$ type particles
produced by an $i$ type particle. We derive the first two terms of the
long-time asymptotics, which are written as two coupled variational formulas,
and interpret them in terms of the typical behavior of the system.


  Aspect sentiment triplet extraction (ASTE) aims to extract aspect term,
sentiment and opinion term triplets from sentences. Since the initial datasets
used to evaluate models on ASTE had flaws, several studies later corrected the
initial datasets and released new versions of the datasets independently. As a
result, different studies select different versions of datasets to evaluate
their methods, which makes ASTE-related works hard to follow. In this paper, we
analyze the relation between different versions of datasets and suggest that
the entire-space version should be used for ASTE. Besides the sentences
containing triplets and the triplets in the sentences, the entire-space version
additionally includes the sentences without triplets and the aspect terms which
do not belong to any triplets. Hence, the entire-space version is consistent
with real-world scenarios and evaluating models on the entire-space version can
better reflect the models' performance in real-world scenarios. In addition,
experimental results show that evaluating models on non-entire-space datasets
inflates the performance of existing models and models trained on the
entire-space version can obtain better performance.


  In this paper, we consider stochastic monotone Nash games where each player's
strategy set is characterized by possibly a large number of explicit convex
constraint inequalities. Notably, the functional constraints of each player may
depend on the strategies of other players, allowing for capturing a subclass of
generalized Nash equilibrium problems (GNEP). While there is limited work that
provide guarantees for this class of stochastic GNEPs, even when the functional
constraints of the players are independent of each other, the majority of the
existing methods rely on employing projected stochastic approximation (SA)
methods. However, the projected SA methods perform poorly when the constraint
set is afflicted by the presence of a large number of possibly nonlinear
functional inequalities. Motivated by the absence of performance guarantees for
computing the Nash equilibrium in constrained stochastic monotone Nash games,
we develop a single timescale randomized Lagrangian multiplier stochastic
approximation method where in the primal space, we employ an SA scheme, and in
the dual space, we employ a randomized block-coordinate scheme where only a
randomly selected Lagrangian multiplier is updated. We show that our method
achieves a convergence rate of
$\mathcal{O}\left(\frac{\log(k)}{\sqrt{k}}\right)$ for suitably defined
suboptimality and infeasibility metrics in a mean sense.


  In the paper we describe the derivations of complex $n$-dimensional naturally
graded filiform Leibniz algebras $NGF_1, NGF_2\ \text{and} \ \ NGF_3.$ We show
that the dimension of the derivation algebras of $NGF_1$ and $NGF_2$ equals
$n+1$ and $n+2,$ respectively, while the dimension of the derivation algebra of
$NGF_3$ is equal to $2n-1.$ The second part of the paper deals with the
description of the derivations of complex $n$-dimensional filiform non Lie
Leibniz algebras, obtained from naturally graded non Lie filiform Leibniz
algebras. It is well known that this class is split into two classes denoted by
$FLb_n$ and $SLb_n.$ Here we found that for $L\in FLb_n$ we have $n-1 \leq dim\
Der(L)\leq n+1$ and for algebras $L$ from $SLb_n$ the inequality $n-1\leq dim\
Der(L) \leq n+2$ holds true.


  The sine-Gordon field theory emerges as the low-energy description in a
wealth of quantum many-body systems. Recent efforts have been directed towards
realizing quantum simulators of the model, by interfering two weakly coupled
one-dimensional cold atomic gases. The weak interactions within the atomic
clouds provide a sine-Gordon realization in the semiclassical regime.
Furthermore, the complex microscopic dynamics prevents a quantitative
understanding of the effective sine-Gordon validity realm. In this work, we
focus on a spin ladder realization and observe the emergent sine-Gordon
dynamics deep in the quantum regime. We use matrix-product state techniques to
numerically characterize the low-energy sector of the system and compare it
with the exact field theory predictions. From this comparison, we obtain
quantitative boundaries for the validity of the sine-Gordon description. We
provide encompassing evidence for the emergent field theory by probing its rich
spectrum and by observing the signatures of integrable dynamics in scattering
events.


  Atomic cluster-based networks represent a promising architecture for the
realization of neuromorphic computing systems, which may overcome some of the
limitations of the current computing paradigm. The formation and breakage of
synapses between the clusters are of utmost importance for the functioning of
these computing systems. This paper reports the results of molecular dynamics
simulations of synapse (bridge) formation at elevated temperatures and thermal
breaking processes between 2.8 nanometer-sized Au$_{1415}$ clusters deposited
on a carbon substrate, a model system. Crucially, we find that the bridge
formation process is driven by the diffusion of gold atoms along the substrate,
however small the gap between the clusters themselves. The complementary
simulations of the bridge-breaking process reveal the existence of a threshold
bias voltage to activate bridge rupture via Joule heating. These results
provide an atomistic-level understanding of the fundamental dynamical processes
occurring in neuromorphic cluster arrays.


  Presence of noise in the labels of large scale facial expression datasets has
been a key challenge towards Facial Expression Recognition (FER) in the wild.
During early learning stage, deep networks fit on clean data. Then, eventually,
they start overfitting on noisy labels due to their memorization ability, which
limits FER performance. This work proposes an effective training strategy in
the presence of noisy labels, called as Consensual Collaborative Training (CCT)
framework. CCT co-trains three networks jointly using a convex combination of
supervision loss and consistency loss, without making any assumption about the
noise distribution. A dynamic transition mechanism is used to move from
supervision loss in early learning to consistency loss for consensus of
predictions among networks in the later stage. Inference is done using a single
network based on a simple knowledge distillation scheme. Effectiveness of the
proposed framework is demonstrated on synthetic as well as real noisy FER
datasets. In addition, a large test subset of around 5K images is annotated
from the FEC dataset using crowd wisdom of 16 different annotators and reliable
labels are inferred. CCT is also validated on it. State-of-the-art performance
is reported on the benchmark FER datasets RAFDB (90.84%) FERPlus (89.99%) and
AffectNet (66%). Our codes are available at https://github.com/1980x/CCT.


  Zero-shot translation, translating between language pairs on which a Neural
Machine Translation (NMT) system has never been trained, is an emergent
property when training the system in multilingual settings. However, naive
training for zero-shot NMT easily fails, and is sensitive to hyper-parameter
setting. The performance typically lags far behind the more conventional
pivot-based approach which translates twice using a third language as a pivot.
In this work, we address the degeneracy problem due to capturing spurious
correlations by quantitatively analyzing the mutual information between
language IDs of the source and decoded sentences. Inspired by this analysis, we
propose to use two simple but effective approaches: (1) decoder pre-training;
(2) back-translation. These methods show significant improvement (4~22 BLEU
points) over the vanilla zero-shot translation on three challenging
multilingual datasets, and achieve similar or better results than the
pivot-based approach.


  In a recent manuscript, D.Vogan conjectures that four canonical
globalizations of Harish-Chandra modules commute with certain n-cohomology
groups. In this article we prove that Vogan's conjecture holds for one of the
globalizations if and only if it holds for the dual. Using a previously
published result of one of the authors, which establishes the conjecture for
the minimal globalization, we can therefore deduce Vogan's conjecture for the
maximal globalization.


  User feedback has grown in importance for organizations to improve software
products. Prior studies focused primarily on feedback collection and reported a
high-level overview of the processes, often overlooking how practitioners
reason about, and act upon this feedback through a structured set of
activities. In this work, we conducted an exploratory interview study with 40
practitioners from 32 organizations of various sizes and in several domains
such as e-commerce, analytics, and gaming. Our findings indicate that
organizations leverage many different user feedback sources. Social media
emerged as a key category of feedback that is increasingly critical for many
organizations. We found that organizations actively engage in a number of
non-trivial activities to curate and act on user feedback, depending on its
source. We synthesize these activities into a life cycle of managing user
feedback. We also report on the best practices for managing user feedback that
we distilled from responses of practitioners who felt that their organization
effectively understood and addressed their users' feedback. We present
actionable empirical results that organizations can leverage to increase their
understanding of user perception and behavior for better products thus reducing
user attrition.


  This paper explores the proof theory necessary for recommending an expressive
but decidable first-order system, named MAV1, featuring a de Morgan dual pair
of nominal quantifiers. These nominal quantifiers called `new' and `wen' are
distinct from the self-dual Gabbay-Pitts and Miller-Tiu nominal quantifiers.
The novelty of these nominal quantifiers is they are polarised in the sense
that `new' distributes over positive operators while `wen' distributes over
negative operators. This greater control of bookkeeping enables private names
to be modelled in processes embedded as formulae in MAV1. The technical
challenge is to establish a cut elimination result, from which essential
properties including the transitivity of implication follow. Since the system
is defined using the calculus of structures, a generalisation of the sequent
calculus, novel techniques are employed. The proof relies on an intricately
designed multiset-based measure of the size of a proof, which is used to guide
a normalisation technique called splitting. The presence of equivariance, which
swaps successive quantifiers, induces complex inter-dependencies between
nominal quantifiers, additive conjunction and multiplicative operators in the
proof of splitting. Every rule is justified by an example demonstrating why the
rule is necessary for soundly embedding processes and ensuring that cut
elimination holds.


  We show that phase transitions in Ising systems with planar defects, i.e.,
disorder perfectly correlated in two dimensions are destroyed by smearing. This
is caused by effects similar to but stronger than the Griffiths phenomena:
Exponentially rare spatial regions can develop true static long-range order
even when the bulk system is still in its disordered phase. Close to the
smeared transition, the order parameter is very inhomogeneous in space, with
the thermodynamic (average) order parameter depending exponentially on
temperature. We determine the behavior using extremal statistics, and we
illustrate the results by computer simulations.


  We construct a Brody hyperbolic Horikawa surface that is a double cover of
$\mathbb{P}^2$ branched along a smooth curve of degree $10$. We also construct
Brody hyperbolic double covers of Hirzebruch surfaces with branch loci of the
lowest possible bidegree.


  In this paper, we put forth a long short-term memory (LSTM) nudging framework
for the enhancement of reduced order models (ROMs) of fluid flows utilizing
noisy measurements. We build on the fact that in a realistic application, there
are uncertainties in initial conditions, boundary conditions, model parameters,
and/or field measurements. Moreover, conventional nonlinear ROMs based on
Galerkin projection (GROMs) suffer from imperfection and solution instabilities
due to the modal truncation, especially for advection-dominated flows with slow
decay in the Kolmogorov width. In the presented LSTM-Nudge approach, we fuse
forecasts from a combination of imperfect GROM and uncertain state estimates,
with sparse Eulerian sensor measurements to provide more reliable predictions
in a dynamical data assimilation framework. We illustrate the idea with the
viscous Burgers problem, as a benchmark test bed with quadratic nonlinearity
and Laplacian dissipation. We investigate the effects of measurements noise and
state estimate uncertainty on the performance of the LSTM-Nudge behavior. We
also demonstrate that it can sufficiently handle different levels of temporal
and spatial measurement sparsity. This first step in our assessment of the
proposed model shows that the LSTM nudging could represent a viable realtime
predictive tool in emerging digital twin systems.


  Recently, cosmology has seen a surge in alternative models that purport to
solve the discrepancy between the values of the Hubble constant $H_0$ as
measured by cosmological microwave background anisotropies and local
supernovae, respectively. In particular, many of the most successful approaches
have involved varying fundamental constants, such as an alternative value of
the fine structure constant and time-varying values of the electron mass, the
latter of which showed particular promise as the strongest candidate in several
earlier studies. Inspired by these approaches, in this paper, we investigate a
cosmological model where the value of the geometric constant $\pi$ is taken to
be a free model parameter. Using the latest CMB data from Planck as well as
baryon-acoustic oscillation data, we constrain the parameters of the model and
find a strong correlation between $\pi$ and $H_0$, with the final constraint
$H_0 = 71.3 \pm 1.1 \ \mathrm{ km/s/Mpc}$, equivalent to a mere $1.5\sigma$
discrepancy with the value measured by the SH0ES collaboration. Furthermore,
our results show that $\pi = 3.206 \pm 0.038$ at $95 \%$ C.L., which is in good
agreement with several external measurements discussed in the paper. Hence, we
conclude that the $\pi \Lambda$CDM model presented in this paper, which has
only a single extra parameter, currently stands as the perhaps strongest
solution to the Hubble tension.


  We present the detailed analysis of a new hybrid (p- and g-mode) sdB
pulsator, TIC441725813 (TYC 4427-1021-1), discovered and monitored by TESS over
670 days. The TESS light curves available for this star were analysed using
prewhitening techniques to extract mode frequencies accurately. The pulsation
spectrum is then interpreted through methods that include asymptotic period
spacing relationships and rotational multiplets identification. We also
exploited a high signal-to-noise ratio (S/N), low-resolution spectrum of
TIC441725813 using grids of non-local thermodynamic equilibrium (NLTE) model
atmospheres to derive its atmospheric parameters. Interestingly, several
frequency multiplets interpreted as rotational splittings of deep-probing
g-modes indicate a slow rotation period of at least $85.3 \pm 3.6$ day, while
splittings of mostly envelope-probing p-modes suggest a significantly shorter
rotation period of $17.9 \pm 0.7$ day, which implies the core (mainly the
helium mantle with possibly the deeper partially-mixed helium-burning core that
it surrounds) rotates at least ~4.7 times slower than the envelope. The radial
velocity curves indicate that TIC441725813 is in a close binary system with a
low-luminosity companion, possibly a white dwarf. While elusive in the
available TESS photometry, a low-frequency signal that would correspond to a
period of $\sim 6.7$ h is found, albeit at low S/N. TIC441725813 is a
particularly interesting sdB star whose envelope rotates faster than the core.
We hypothesise that this might be caused by the effects of tidal interaction
with a companion, although in the present case, the presence of such a
companion will have to be further investigated. This analysis paves the way
toward a more detailed seismic probing of TIC441725813 using optimisation
techniques, which will be presented in a second paper.


  This paper responds to claims that causal closure of the underlying
microphysics determines brain outcomes as a matter of principle, even if we
cannot hope to ever carry out the needed calculations in practice. Following
two papers of mine where I claim firstly that downward causation enables
genuine causal powers to occur at higher emergent levels in biology (and hence
in the brain) [arXiv:2004.13591], and that secondly causal closure is in
reality an interlevel affair involving even social levels [arXiv:2006.00972],
Carlo Rovelli has engaged with me in a dialogue where he forcefully restates
the reductionist position that microphysics alone determines all, specifically
the functioning of the brain. Here I respond to that claim in depth, claiming
that if one firstly takes into account the difference between synchronic and
diachronic emergence, and secondly takes seriously the well established nature
of biology in general and neuroscience in particular, my position is indeed
correct.


  We present an efficient parallel derandomization method for randomized
algorithms that rely on concentrations such as the Chernoff bound. This settles
a classic problem in parallel derandomization, which dates back to the 1980s.
Consider the \textit{set balancing} problem where $m$ sets of size at most $s$
are given in a ground set of size $n$, and we should partition the ground set
into two parts such that each set is split evenly up to a small additive
(discrepancy) bound. A random partition achieves a discrepancy of $O(\sqrt{s
\log m})$ in each set, by Chernoff bound. We give a deterministic parallel
algorithm that matches this bound, using near-linear work and polylogarithmic
depth. The previous results were weaker in discrepancy and/or work bounds:
Motwani, Naor, and Naor [FOCS'89] and Berger and Rompel [FOCS'89] achieve
discrepancy $s^{\varepsilon} \cdot O(\sqrt{s \log m})$ with work
$\tilde{O}(m+n+\sum_{i=1}^{m} |S_i|) \cdot m^{\Theta(1/\varepsilon)}$ and
polylogarithmic depth; the discrepancy was optimized to $O(\sqrt{s \log m})$ in
later work, e.g. by Harris [Algorithmica'19], but the work bound remained high
at $\tilde{O}(m^4n^3)$. Ghaffari, Grunau, and Rozhon [FOCS'23] achieve
discrepancy $s/poly(\log(nm)) + O(\sqrt{s \log m})$ with near-linear work and
polylogarithmic-depth. Notice that this discrepancy is barely sublinear with
respect to the trivial bound of $s$. Our method relies on a novel bootstrapping
idea that uses crude partitioning algorithms as a subroutine. In particular, we
solve the problem recursively, by using the crude partition in each iteration
to split the variables into many smaller parts, and then we find a constraint
for the variables in each part such that we reduce the overall number of
variables in the problem. The scheme relies on an interesting application of
the multiplicative weights update method to control the variance losses in each
iteration.


  We present a new evolutionary synthesis (ES) tool specifically developed for
the analysis of multi-color data of young star clusters (YSCs) in interacting
galaxies. Our ESO/ST-ECF ASTROVIRTEL project provides an unprecedented database
of UV--optical--NIR observations from the HST/WFPC2, HST/NICMOS and VLT data
archives. Comparison of these data with ES models for clusters of various
metallicities which include gaseous emission as well as various degrees of dust
extinction, allows one to independently determine metallicities, ages and
extinction for individual clusters. These, in turn, are the basis to derive the
mass functions of the YSC systems and to predict the future evolution of their
luminosity functions and color distributions. Comparing YSC systems of various
ages will, we anticipate, allow us to ``see cluster disruption processes at
work''.


  With the growth of the internet, it is becoming hard to manage, configure and
monitor networks. Recent trends to control and operate them is artificial
intelligence based automation to minimize human intervention. Albeit this
concept has been introduced since a decade with several different names, but
the underlying goal remains the same, which is to make network intelligent
enough to assemble, reassemble if configuration changes, and detect a problem
on its own and fix it. As a result, in addition to Data Plane, Control Plane
and Management Plane, a new plane called Artificial Intelligence (AI) Plane is
introduced. Our main objective is to analyze all major AI plane techniques,
frameworks and algorithms proposed in various types of networks. We propose a
comprehensive and network independent framework to cover all aspects of AI
plane, in particular we provide a systematically means of comparison. In
conjunction to make AI plane understand simpler, this framework highlights
relevant challenges and design considerations for future research. To the best
of our knowledge this is the first survey report which represents a complete
comparison of AI planes with their investigation issues in several types of
networks.


  This note is intended to give a new proof on Marten's theorem stating that
$\dim(\wrd) \leq d-2r$ for any smooth curve with equality occurring exactly in
the case when C is hyper-elliptic. The proof follows the general lines of the
proof given in 'The Geometry of Algebraic Curves'. What is different is that it
uses Hopf's theorem to simplify the proof and strengthen the conclusions of the
theorem.


  Neuromorphic computing has come to refer to a variety of brain-inspired
computers, devices, and models that contrast the pervasive von Neumann computer
architecture. This biologically inspired approach has created highly connected
synthetic neurons and synapses that can be used to model neuroscience theories
as well as solve challenging machine learning problems. The promise of the
technology is to create a brain-like ability to learn and adapt, but the
technical challenges are significant, starting with an accurate neuroscience
model of how the brain works, to finding materials and engineering
breakthroughs to build devices to support these models, to creating a
programming framework so the systems can learn, to creating applications with
brain-like capabilities. In this work, we provide a comprehensive survey of the
research and motivations for neuromorphic computing over its history. We begin
with a 35-year review of the motivations and drivers of neuromorphic computing,
then look at the major research areas of the field, which we define as
neuro-inspired models, algorithms and learning approaches, hardware and
devices, supporting systems, and finally applications. We conclude with a broad
discussion on the major research topics that need to be addressed in the coming
years to see the promise of neuromorphic computing fulfilled. The goals of this
work are to provide an exhaustive review of the research conducted in
neuromorphic computing since the inception of the term, and to motivate further
work by illuminating gaps in the field where new research is needed.


  We study the phenomenon of turbulence from the point of view of statistical
physics. We discuss what makes the turbulent states different from the
thermodynamic equilibrium and give the turbulent analog of the partition
function. Then, using the soluble theory of turbulence of waves as an example,
we construct the turbulent action and show how one can compute the turbulent
correlation functions perturbatively thus developing the turbulent Feynman
diagrams. And at last, we discuss which part of what we learnt from the
turbulence of waves can be used in other types of turbulence, in particular,
the hydrodynamic turbulence of fluids. This paper is based on the talk
delivered at SMQFT (1993) conference at the University of Southern California.


  The rapid growth in data on the internet requires a data mining process to
reach a decision to support insight. The Persian language has strong potential
for deep research in any aspect of natural language processing, especially
sentimental analysis approach. Thousands of websites and blogs updates and
modifies by Persian users around the world that contains millions of Persian
context. This range of application requires a comprehensive structured
framework to extract beneficial information for helping enterprises to enhance
their business and initiate a customer-centric management process by producing
effective recommender systems. Sentimental analysis is an intelligent approach
for extracting useful information from huge amounts of data to help an
enterprise for smart management process. In this road, machine learning and
deep learning techniques will become very helpful but there is the number of
challenges which are face to them. This paper tried to present and assert the
most important challenges of sentimental analysis in the Persian language. This
language is an Indo-European language which spoken by over 110 million people
around the world and is an official language in Iran, Tajikistan, and
Afghanistan. Its also widely used in Uzbekistan, Pakistan and Turkish by order.


  The restrictions that nature places on the distribution of correlations in a
multipartite quantum system play fundamental roles in the evolution of such
systems, and yield vital insights into the design of protocols for the quantum
control of ensembles with potential applications in the field of quantum
computing. We show how this entanglement sharing behavior may be studied in
increasingly complex systems of both theoretical and experimental significance
and demonstrate that entanglement sharing, as well as other unique features of
entanglement, e.g. the fact that maximal information about a multipartite
quantum system does not necessarily entail maximal information about its
component subsystems, may be understood as specific consequences of the
phenomenon of complementarity extended to composite quantum systems. We also
present a local hidden-variable model supplemented by an efficient amount of
classical communication that reproduces the quantum-mechanical predictions for
the entire class of Gottesman-Knill circuits. The success of our simulation
provides strong evidence that the power of quantum computation arises not
directly from entanglement, but rather from the nonexistence of an efficient,
local realistic description of the computation, even when augmented by an
efficient amount of nonlocal, but classical communication. Finally, we note
that the findings presented in this thesis support the conjecture that Hilbert
space dimension is an objective property of a quantum system since it
constrains the number of valid conceptual divisions of the system into
subsystems.


  We devise an L-BFGS method for optimization problems in which the objective
is the sum of two functions, where the Hessian of the first function is
computationally unavailable while the Hessian of the second function has a
computationally available approximation that allows for cheap matrix-vector
products. This is a prototypical setting for many inverse problems. The
proposed L-BFGS method exploits the structure of the objective to construct a
more accurate Hessian approximation than in standard L-BFGS. In contrast to
existing works on structured L-BFGS, we choose the first part of the seed
matrix, which approximates the Hessian of the first function, as a diagonal
matrix rather than a multiple of the identity. We derive two suitable formulas
for the coefficients of the diagonal matrix and show that this boosts
performance on real-life image registration problems, which are highly
non-convex inverse problems. The new method converges globally and linearly on
non-convex problems under mild assumptions in a general Hilbert space setting,
making it applicable to a broad class of inverse problems. An implementation of
the method is freely available.


  We prove that a stochastic flow of reflected Brownian motions in a smooth
multidimensional domain is differentiable with respect to its initial position.
The derivative is a linear map represented by a multiplicative functional for
reflected Brownian motion. The method of proof is based on excursion theory and
analysis of the deterministic Skorokhod equation.


  In this brief review we explicitly calculate the radiative corrections to the
Chern-Simons-like term in the cases of zero and finite temperature, and in the
gravity theory. Our results are obtained under the general guidance of
dimensional regularization.


  Today it is crucial for organizations to pay even greater attention on
quality management as the importance of this function in achieving ultimate
business objectives is increasingly becoming clearer. Importance of the Quality
Management Function in achieving basic need by ensuring compliance with
Capability Maturity Model Integrated or International Organization for
Standardization is a basic demand from business nowadays. However, Quality
Management Function and its processes need to be made much more mature to
prevent delivery outages and to achieve business excellence through their
review and auditing capability. Many organizations now face challenges in
determining the maturity of the Quality Management group along with the service
offered by them and the right way to elevate the maturity of the same. The
objective of this whitepaper is to propose a new model, the Audit Maturity
Model which will provide organizations with a measure of their maturity in
quality management in the perspective of auditing, along with recommendations
for preventing delivery outage, and identifying risk to achieve business
excellence. This will enable organizations to assess Quality Management
maturity higher than basic hygiene and will also help them to identify gaps and
to take corrective actions for achieving higher maturity levels. Hence the
objective is to envisage a new auditing model as a part of organisation quality
management function which can be a guide for them to achieve higher level of
maturity and ultimately help to achieve delivery and business excellence.


  Using the theory of group action, we first introduce the concept of the
automorphism group of an exponential family or a graphical model, thus
formalizing the general notion of symmetry of a probabilistic model. This
automorphism group provides a precise mathematical framework for lifted
inference in the general exponential family. Its group action partitions the
set of random variables and feature functions into equivalent classes (called
orbits) having identical marginals and expectations. Then the inference problem
is effectively reduced to that of computing marginals or expectations for each
class, thus avoiding the need to deal with each individual variable or feature.
We demonstrate the usefulness of this general framework in lifting two classes
of variational approximation for MAP inference: local LP relaxation and local
LP relaxation with cycle constraints; the latter yields the first lifted
inference that operate on a bound tighter than local constraints. Initial
experimental results demonstrate that lifted MAP inference with cycle
constraints achieved the state of the art performance, obtaining much better
objective function values than local approximation while remaining relatively
efficient.


  The general abstract arbitrary order (N) Cauchy problem was solved in a
closed form as a sum of exponential propagator functions. The infinite sparse
exponential series was solved with the aid of a homogeneous differential
equation. It generated a linear combination of exponential functions. The
Cauchy problem solution was formed with N linear combinations of N exponential
propagators.


  On the basis of physical considerations we propose a one-dimensional discrete
lattice model for the density relaxation of granular materials under tapping.
Solving the difference equation numerically, we find a logarithmic
time-dependence of the density relaxation. This is in agreement with
experimental results of Knight et al. [Phys. Rev. E 51, 3957(1995)]. The origin
of this anomalous relaxation is elucidated analytically by solving the equation
of its continuum version asymptotically in time.


  We study the bulk and boundary properties of fragile topological insulators
(TIs) protected by inversion symmetry, mostly focusing on the class A of the
Altland-Zirnbauer classification. First, we propose an efficient method for
diagnosing fragile band topology by using the symmetry data in momentum space.
Using this method, we show that among all the possible parity configurations of
inversion-symmetric insulators, at least 17 percent of them have fragile
topology in 2D while fragile TIs are less than 3 percent in 3D. Second, we
study the bulk-boundary correspondence of fragile TIs protected by inversion
symmetry. In particular, we generalize the notion of $d$-dimensional ($d$D)
$k$th-order TIs, which is normally defined for $0<k\le d$, to the cases with
$k>d$, and show that they all have fragile topology. In terms of the Dirac
Hamiltonian, a $d$D $k$th-order TI has $(k-1)$ boundary mass terms. We show
that a minimal fragile TI with the filling anomaly can be considered as the
$d$D $(d+1)$th-order TI, and all the other $d$D $k$th-order TIs with $k>(d+1)$
can be constructed by stacking $d$D $(d+1)$th-order TIs. Although $d$D
$(d+1)$th-order TIs have no in-gap states, the boundary mass terms carry an odd
winding number along the boundary, which induces localized charges on the
boundary at the positions where the boundary mass terms change abruptly. In the
cases with $k>(d+1)$, we show that the net parity of the system with boundaries
can distinguish topological insulators and trivial insulators. Also, by
studying the (nested) Wilson loop spectra, we determine the minimal number of
bands to resolve the Wannier obstruction, which is consistent with the
prediction from our diagnosis method of fragile topology. Finally, we show that
a $(d+1)$D $(k-1)$th-order TI can be obtained by an adiabatic pumping of $d$D
$k$th-order TI, which generalizes the previous study of the 2D 3rd-order TI.


  The auditory attention decoding (AAD) approach was proposed to determine the
identity of the attended talker in a multi-talker scenario by analyzing
electroencephalography (EEG) data. Although the linear model-based method has
been widely used in AAD, the linear assumption was considered oversimplified
and the decoding accuracy remained lower for shorter decoding windows.
Recently, nonlinear models based on deep neural networks (DNN) have been
proposed to solve this problem. However, these models did not fully utilize
both the spatial and temporal features of EEG, and the interpretability of DNN
models was rarely investigated. In this paper, we proposed novel convolutional
recurrent neural network (CRNN) based regression model and classification
model, and compared them with both the linear model and the state-of-the-art
DNN models. Results showed that, our proposed CRNN-based classification model
outperformed others for shorter decoding windows (around 90% for 2 s and 5 s).
Although worse than classification models, the decoding accuracy of the
proposed CRNN-based regression model was about 5% greater than other regression
models. The interpretability of DNN models was also investigated by visualizing
layers' weight.


  Recent advancements in Multi-modality Large Language Models (MLLMs) have
demonstrated remarkable capabilities in complex high-level vision tasks.
However, the exploration of MLLM potential in visual quality assessment, a
vital aspect of low-level vision, remains limited. To address this gap, we
introduce Q-Boost, a novel strategy designed to enhance low-level MLLMs in
image quality assessment (IQA) and video quality assessment (VQA) tasks, which
is structured around two pivotal components: 1) Triadic-Tone Integration:
Ordinary prompt design simply oscillates between the binary extremes of
$positive$ and $negative$. Q-Boost innovates by incorporating a `middle ground'
approach through $neutral$ prompts, allowing for a more balanced and detailed
assessment. 2) Multi-Prompt Ensemble: Multiple quality-centric prompts are used
to mitigate bias and acquire more accurate evaluation. The experimental results
show that the low-level MLLMs exhibit outstanding zeros-shot performance on the
IQA/VQA tasks equipped with the Q-Boost strategy.


  We have conducted an LTE abundance analysis for SAO 40039 a warm post-AGB
star whose spectrum is known to show surprisingly strong He I lines for its
effective temperature and has been suspected of being H-deficient and He-rich.
High-resolution optical spectra are analyzed using a family of model
atmospheres with different He/H ratios. Atmospheric parameters are estimated
from the ionization equilibrium set by neutral and singly ionized species of Fe
and Mg, the excitation of Fe I and Fe II lines and the wings of the Paschen
lines. On the assumption that the He I lines are of photospheric and not
chromospheric in origin, a He/H ratio of approximately unity is found by
imposing the condition that the adopted He/H ratio of the model atmosphere must
equal the ratio derived from the observed He I triplet lines at 5876, 4471 and
4713 Angstrom, and singlet lines at 4922 and 5015 Angstrom. Using the model
with the best-fitting atmospheric parameters for this He/H ratio, SAO 40039 is
confirmed to exhibit mild dust-gas depletion, i.e., the star has an atmosphere
deficient in elements of high condensation temperature. The star appears to be
moderately metal-deficient with [Fe/H]=-0.4 dex. But the star's intrinsic
metallicty as estimated from Na, S and Zn, elements of a low condensation
temperature, is [Fe/H]_o {[Fe/H]_o refers to the star's intrinsic metallicity}
~ -0.2. The star is enriched in N and perhaps O too, changes reflecting the
star's AGB past and the event that led to He enrichment.


  In this thesis, we investigate hidden symmetries for the Maldacena-Wilson
loop in N=4 super Yang-Mills theory, mainly focusing on its strong-coupling
description as a minimal surface in $AdS_5$. In the discussion of the symmetry
structure of the underlying string model, we highlight the role of the master
symmetry which can be employed to construct all symmetries of the model. The
algebra of these symmetries is worked out. For the concrete case of minimal
surfaces in $AdS_5$, we discuss the deformation of the four-cusp solution,
which provides the dual description of the four-gluon scattering amplitude.
This marks the first step toward transferring the master symmetry to scattering
amplitudes. Moreover, we compute the master and Yangian symmetry variations of
generic, smooth boundary curves. The discussion clarifies why previous attempts
to transfer the deformations of minimal surfaces in $AdS_3$ to weak coupling
were unsuccessful. We discuss several attempts to transfer the Yangian symmetry
to weak or arbitrary coupling, but ultimately conclude that a Yangian symmetry
of the Maldacena-Wilson loop seems not to be present. This is different for the
natural supersymmetric generalizations of the Maldacena--Wilson loop, Wilson
loops in superspace. Their one-loop expectation value is known to be Yangian
invariant. We discuss the strong-coupling counterpart of this finding by
considering minimal surfaces in the superspace of type IIB superstring theory
in $AdS_5 \times S^5$. The comparison of the strong-coupling invariance derived
here with the weak-coupling generators shows that the local term must depend on
the coupling in a non-trivial way. Additionally, we show that the higher-level
recurrences of the hypercharge generator, the so-called bonus symmetries, are
present in all higher levels of the Yangian.


  Open-world Relation Extraction (OpenRE) has recently garnered significant
attention. However, existing approaches tend to oversimplify the problem by
assuming that all unlabeled texts belong to novel classes, thereby limiting the
practicality of these methods. We argue that the OpenRE setting should be more
aligned with the characteristics of real-world data. Specifically, we propose
two key improvements: (a) unlabeled data should encompass known and novel
classes, including hard-negative instances; and (b) the set of novel classes
should represent long-tail relation types. Furthermore, we observe that popular
relations such as titles and locations can often be implicitly inferred through
specific patterns, while long-tail relations tend to be explicitly expressed in
sentences. Motivated by these insights, we present a novel method called KNoRD
(Known and Novel Relation Discovery), which effectively classifies explicitly
and implicitly expressed relations from known and novel classes within
unlabeled data. Experimental evaluations on several Open-world RE benchmarks
demonstrate that KNoRD consistently outperforms other existing methods,
achieving significant performance gains.


  We describe a simple but effective method for cross-lingual syntactic
transfer of dependency parsers, in the scenario where a large amount of
translation data is not available. The method makes use of three steps: 1) a
method for deriving cross-lingual word clusters, which can then be used in a
multilingual parser; 2) a method for transferring lexical information from a
target language to source language treebanks; 3) a method for integrating these
steps with the density-driven annotation projection method of Rasooli and
Collins (2015). Experiments show improvements over the state-of-the-art in
several languages used in previous work, in a setting where the only source of
translation data is the Bible, a considerably smaller corpus than the Europarl
corpus used in previous work. Results using the Europarl corpus as a source of
translation data show additional improvements over the results of Rasooli and
Collins (2015). We conclude with results on 38 datasets from the Universal
Dependencies corpora.


  This paper is addressed to the proof of the important role of measuring
apparatus, i.e. the measuring process, in the formation of necessary and
sufficient conditions for the explanation of a time modulation of K-shell
electron capture (EC) decay rates of hydrogen-like (H-like) heavy ions (or the
GSI oscillations) as the interference of neutrino mass-eigenstates of the
electron neutrino constituents. For our analysis we use a toy-model, which has
been recently proposed by Peshkin arXiv: 1403.4292 [nucl-th] for a verification
of the mechanism of the GSI oscillations as the interference of neutrino
mass-eigenstates by Ivanov and Kienle Phys. Rev. Lett. 103, 062502 (2009).


  We study the birational geometry of the moduli spaces of hyperelliptic curves
with marked points. We show that these moduli spaces have non
$\mathbb{Q}$-factorial singularities. We complete the Kodaira classification by
proving that these spaces have Kodaira dimension $4g+3$ when the number of
markings is $4g+6$ and are of general type when the number of markings is
$n\geq 4g+7$. Similarly, we consider the natural finite cover given by ordering
the Weierstrass points. In this case, we provide a full Kodaira classification
showing that the Kodaira dimension is negative when $n\leq 3$, one when $n=4$,
and of general type when $n\geq 5$. For this, we carry out a singularity
analysis of ordered and unordered pointed Hurwitz spaces. We show that the
ordered space has canonical singularities and the unordered space has
non-canonical singularities. We describe all non-canonical points and show that
pluricanonical forms defined on the full regular locus extend to any
resolution. Further, we provide a full classification of the structure of the
pseudo-effective cone of Cartier divisors for the moduli space of hyperelliptic
curves with marked points. We show the cone is non-polyhedral when the number
of markings is at least two and polyhedral in the remaining cases.


  We explore the possibility of subsequent star formation after a first star
forms in a Pop III object, by focusing on the radiation hydrodynamic (RHD)
feedback brought by ionizing photons as well as H2 dissociating photons. For
the purpose, we perform three-dimensional RHD simulations, where the radiative
transfer of ionizing photons and H2 dissociating photons from a first star is
self-consistently coupled with hydrodynamics based on a smoothed particle
hydrodynamics method. As a result, it is shown that density peaks above a
threshold density can keep collapsing owing to the shielding of H2 dissociating
radiation by an H2 shell formed ahead of a D-type ionization front. But, below
the threshold density, an M-type ionization front accompanied by a shock
propagates, and density peaks are radiation hydrodynamically evaporated by the
shock. The threshold density is dependent on the distance from a source star,
which is $\approx 10^2 cm^{-3}$ for the source distance of 30pc. Taking into
consideration that the extent of a Pop III object is $\approx 100$pc and
density peaks within it have the density of $10^{2-4}$cm$^{-3}$, it is
concluded that the secondary star formation is allowed in the broad regions in
a Pop III object.


  Using single-crystal organic field-effect transistors with the conduction
channel exposed to environmental agents we have observed generation of
electronic defects at the organic surface in the high-vacuum environment. Rapid
decrease of the source-drain current of an operating device is observed upon
exposure of the channel to the species generated by high-vacuum gauges. We
attribute this effect to interaction of the organic surface with electrically
neutral free radicals produced in the process of hydrocarbon cracking on hot
filaments with a relatively low activation energy Ea ~ 2.5 eV (240 kJ/mol). The
reported results might be important for optimizing the high-vacuum processes of
fabrication and characterization of a wide range of organic and molecular
electronic devices.


  Federated recommender system (FRS), which enables many local devices to train
a shared model jointly without transmitting local raw data, has become a
prevalent recommendation paradigm with privacy-preserving advantages. However,
previous work on FRS performs similarity search via inner product in continuous
embedding space, which causes an efficiency bottleneck when the scale of items
is extremely large. We argue that such a scheme in federated settings ignores
the limited capacities in resource-constrained user devices (i.e., storage
space, computational overhead, and communication bandwidth), and makes it
harder to be deployed in large-scale recommender systems. Besides, it has been
shown that transmitting local gradients in real-valued form between server and
clients may leak users' private information. To this end, we propose a
lightweight federated recommendation framework with privacy-preserving matrix
factorization, LightFR, that is able to generate high-quality binary codes by
exploiting learning to hash technique under federated settings, and thus enjoys
both fast online inference and economic memory consumption. Moreover, we devise
an efficient federated discrete optimization algorithm to collaboratively train
model parameters between the server and clients, which can effectively prevent
real-valued gradient attacks from malicious parties. Through extensive
experiments on four real-world datasets, we show that our LightFR model
outperforms several state-of-the-art FRS methods in terms of recommendation
accuracy, inference efficiency and data privacy.