  We consider effects of magnetic field on the thermopower and
thermoconductance of a single-electron transistor based on a quantum dot
strongly coupled to one of the leads by a single-mode quantum point contact. We
show appearance of two new energy scales: T_{min} ~ |r|^2 E_C(B/B_C)^2
depending on a ratio of magnetic field B and the field B_C corresponding to a
full polarization of point contact and T_{max} ~ |r|^2 E_C depending on a
reflection amplitude r and charging energy E_C. We predict that the behavior of
thermoelectric coefficients is consistent with the Fermi-liquid theory at
temperatures T << T_{min}, while crossover from Non-Fermi-liquid regime
associated with a two-channel Kondo effect to Fermi-liquid single-channel Kondo
behavior can be seen at T_{min}<T<T_{max}.


  The equivalence of correct Hamiltonian and naive Lagrangian (Faddeev--Popov)
path integral quantization (Matthews's theorem) is proven for gauge theories
with arbitrary effective interaction terms. Effective gauge-boson
self-interactions and effective interactions with scalar and fermion fields are
considered. This result becomes extended to effective gauge theories with
higher derivatives of the fields.


  Text analytics based on supervised machine learning classifiers has shown
great promise in a multitude of domains, but has yet to be applied to
Seismology. We test various standard models (Naive Bayes, k-Nearest Neighbors,
Support Vector Machines, and Random Forests) on a seismological corpus of 100
articles related to the topic of precursory accelerating seismicity, spanning
from 1988 to 2010. This corpus was labelled in Mignan (2011) with the precursor
whether explained by critical processes (i.e., cascade triggering) or by other
processes (such as signature of main fault loading). We investigate rather the
classification process can be automatized to help analyze larger corpora in
order to better understand trends in earthquake predictability research. We
find that the Naive Bayes model performs best, in agreement with the machine
learning literature for the case of small datasets, with cross-validation
accuracies of 86% for binary classification. For a refined multiclass
classification ('non-critical process' < 'agnostic' < 'critical process
assumed' < 'critical process demonstrated'), we obtain up to 78% accuracy.
Prediction on a dozen of articles published since 2011 shows however a weak
generalization with a F1-score of 60%, only slightly better than a random
classifier, which can be explained by a change of authorship and use of
different terminologies. Yet, the model shows F1-scores greater than 80% for
the two multiclass extremes ('non-critical process' versus 'critical process
demonstrated') while it falls to random classifier results (around 25%) for
papers labelled 'agnostic' or 'critical process assumed'. Those results are
encouraging in view of the small size of the corpus and of the high degree of
abstraction of the labelling. Domain knowledge engineering remains essential
but can be made transparent by an investigation of Naive Bayes keyword
posterior probabilities.


  The core of evidence-based medicine is to read and analyze numerous papers in
the medical literature on a specific clinical problem and summarize the
authoritative answers to that problem. Currently, to formulate a clear and
focused clinical problem, the popular PICO framework is usually adopted, in
which each clinical problem is considered to consist of four parts:
patient/problem (P), intervention (I), comparison (C) and outcome (O). In this
study, we compared several classification models that are commonly used in
traditional machine learning. Next, we developed a multitask classification
model based on a soft-margin SVM with a specialized feature engineering method
that combines 1-2gram analysis with TF-IDF analysis. Finally, we trained and
tested several generic models on an open-source data set from BioNLP 2018. The
results show that the proposed multitask SVM classification model based on
1-2gram TF-IDF features exhibits the best performance among the tested models.


  The rapid development of medical sensors has increased the interest in
Wireless Body Area Network (WBAN) applications where physiological data from
the human body and its environment is gathered, monitored, and analyzed to take
the proper measures. In WBANs, it is essential to design MAC protocols that
ensure adequate Quality of Service (QoS) such as low delay and high
scalability. This paper investigates Medium Access Control (MAC) protocols used
in WBAN, and compares their performance in a high traffic environment. Such
scenario can be induced in case of emergency for example, where physiological
data collected from all sensors on human body should be sent simultaneously to
take appropriate action. This study can also be extended to cover collaborative
WBAN systems where information from different bodies is sent simultaneously
leading to high traffic. OPNET simulations are performed to compare the delay
and scalability performance of the different MAC protocols under the same
experimental conditions and to draw conclusions about the best protocol to be
used in a high traffic environment.


  We establish universal behavior in temperature dependencies of some
observables in $(s+id)$-wave BCS superconductivity in the presence of a weak
$s$ wave. There also could appear a second second-order phase transition. As
temperature is lowered past the usual critical temperature $T_c$, a less
ordered superconducting phase is created in $d$ wave, which changes to a more
ordered phase in $(s+id)$ wave at $T_{c1}$ ($< T_c$). The presence of two phase
transitions manifest in two jumps in specific heat at $T_c$ and $T_{c1}$. The
temperature dependencies of susceptibility, penetration depth, and thermal
conductivity also confirm the new phase transition.


  In this dissertation we use the D3/D7 model in gauge/gravity duality to
investigate the effects of finite temperature and particle density on matter in
strongly coupled super-Yang-Mills gauge theories, which we interpret as models
for the quark-gluon plasma (QGP).
  We start with an introduction to the AdS/CFT correspondence and the setup we
use subsequently. We then investigate the effects of finite temperature and
particle density on fundamental matter from three different viewpoints.
  (i) We study meson spectra. Our observations at finite temperature and
particle density are in qualitative agreement with phenomenological models and
experimental observations. They agree with previous publications in the
according limits.
  (ii) We study the temperature and density dependence of transport properties
of fundamental matter in the QGP. In particular, we obtain diffusion
coefficients. Furthermore, in a kinetic model we estimate the effects of the
coupling strength on meson diffusion and therewith equilibration processes in
the QGP.
  (iii) We observe the effects of finite temperature and density on the phase
structure of fundamental matter in the holographic QGP. We trace out the phase
transition lines of different phases in the phase diagram.


  One route to numerically propagating quantum systems is time-dependent
density functional theory (TDDFT). The application of TDDFT to a particular
system's time evolution is predicated on $V$-representability which we have
analyzed in a previous publication. Here we describe a newly developed solver
for the scalar time-dependent Kohn-Sham potential. We present and interpret the
force-balance equation central to our numerical method, describe details of its
implementation, and present illustrative numerical results for one- and
two-electron systems. A new characterization of $V$-representability for
one-electron systems is also included along with possible improvements and
future directions.


  The density matrix renormalization group method is generalized to one
dimensional random systems. Using this method, the energy gap distribution of
the spin-1/2 random antiferromagnetic Heisenberg chain is calculated. The
results are consistent with the predictions of the renormalization group theory
demonstrating the effectiveness of the present method in random systems. The
possible application of the present method to other random systems is
discussed.


  We prove an equivariant analogue of Grothendieck's theorem for vector bundles
on the one dimensional projective space over complex numbers.


  We present observations of the H-alpha, H-beta, [SII] 6716, 6731 and [NII]
6583 emission lines in the galactic HII region Sh2-235 with the Mapper of
Narrow Galaxy Lines (MaNGaL), a tunable filter at the 1-m telescope of Special
Astrophysical Observatory of the Russian Academy of Sciences. We show that the
HII region is obscured by neutral material with AV = 2-4 mag. The area with the
highest AV is situated to the south-west from the ionizing star and coincides
with a maximum detected electron density of >=300 cm(-3). The combination of
these results with archive AKARI far-infrared data allows us to estimate the
contribution of the front and rear walls to the total column density of neutral
material in S235, and explain the three-dimensional structure of the region.
The HII region consist of a denser, more compact portion deeply embedded in the
neutral medium and the less dense and obscured gas. The front and rear walls of
the HII region are inhomogeneous, with the material in the rear wall having a
higher column density. We find a two-sided photodissociation region in the
dense clump S235 East 1, illuminated by a UV field with G0=50-70 and 200 Habing
units in the western and eastern parts, respectively.


  Hardy (1993) and Unruh (2018) constructed a family of non-maximally entangled
states of pairs of particles giving rise to correlations that cannot be
accounted for with a local hidden-variable theory. Rather than pointing to
violations of some Bell inequality, however, they pointed to clashes with the
basic rules of logic. Specifically, they constructed these states and the
associated measurement settings in such a way that the outcomes will satisfy a
set of two or three conditionals, which we call Hardy-Unruh chains, but not a
conditional entailed by this set. Quantum mechanics avoids such broken 'if ...
then ...' arrows because it cannot simultaneously assign truth values to all
conditionals involved. Measurements to determine the truth value of some
preclude measurements to determine the truth value of others. Hardy-Unruh
chains thus nicely illustrate quantum contextuality: which variables do and do
not get definite values depends on what measurements we decide to perform. We
use the framework inspired by Bub (2016) and Pitowsky (1989) and developed in
Janas, Cuffaro and Janssen (2022} to construct and analyze Hardy-Unruh chains
in terms of fictitious bananas mimicking the behavior of spin-1/2 particles.


  Plate tectonics is a geophysical process currently unique to Earth, has an
important role in regulating the Earth's climate, and may be better understood
by identifying rocky planets outside our solar system with tectonic activity.
The key criterion for whether or not plate tectonics may occur on a terrestrial
planet is if the stress on a planet's lithosphere from mantle convection may
overcome the lithosphere's yield stress. Although many rocky exoplanets closely
orbiting their host stars have been detected, all studies to date of plate
tectonics on exoplanets have neglected tidal stresses in the planet's
lithosphere. Modeling a rocky exoplanet as a constant density, homogeneous,
incompressible sphere, we show the tidal stress from the host star acting on
close-in planets may become comparable to the stress on the lithosphere from
mantle convection. We also show that tidal stresses from planet-planet
interactions are unlikely to be significant for plate tectonics, but may be
strong enough to trigger Earthquakes. Our work may imply planets orbiting close
to their host stars are more likely to experience plate tectonics, with
implications for exoplanetary geophysics and habitability. We produce a list of
detected rocky exoplanets under the most intense stresses. Atmospheric and
topographic observations may confirm our predictions in the near future.
Investigations of planets with significant tidal stress can not only lead to
observable parameters linked to the presence of active plate tectonics, but may
also be used as a tool to test theories on the main driving force behind
tectonic activity.


  We study the complete evolution of a flat and homogeneous universe dominated
by tachyonic matter. We demonstrate the attractor behaviour of the tachyonic
inflation using the Hamilton-Jacobi formalism. We else obtain analytical
approximations to the trajectories of the tachyon field in different regions.
The numerical calculation shows that an initial non-vanishing momentum does not
prevent the onset of inflation. The slow-rolling solution is an attractor.


  We propose an effective subspace selection scheme as a post-processing step
to improve results obtained by sparse subspace clustering (SSC). Our method
starts by the computation of stable subspaces using a novel random sampling
scheme. Thus constructed preliminary subspaces are used to identify the
initially incorrectly clustered data points and then to reassign them to more
suitable clusters based on their goodness-of-fit to the preliminary model. To
improve the robustness of the algorithm, we use a dominant nearest subspace
classification scheme that controls the level of sensitivity against
reassignment. We demonstrate that our algorithm is convergent and superior to
the direct application of a generic alternative such as principal component
analysis. On several popular datasets for motion segmentation and face
clustering pervasively used in the sparse subspace clustering literature the
proposed method is shown to reduce greatly the incidence of clustering errors
while introducing negligible disturbance to the data points already correctly
clustered.


  In this paper, we construct degenerate soliton solutions (which preserve
$\cal{PT}$-symmetry/break $\cal{PT}$-symmetry) to the nonlocal Manakov system
through a nonstandard bilinear procedure. Here by degenerate we mean the
solitons that are present in both the modes which propagate with same velocity.
The degenerate nonlocal soliton solution is constructed after briefly
indicating the form of nondegenerate one-soliton solution. To derive these
soliton solutions, we simultaneously solve the nonlocal Manakov equation and a
pair of coupled equations that arise from the zero curvature condition. The
later consideration yields general soliton solution which agrees with the
solutions that are already reported in the literature under certain specific
parametric choice. We also discuss the salient features associated with the
obtained degenerate soliton solutions.


  We propose a post-processing algorithm for fair classification that mitigates
model bias under a unified family of group fairness criteria covering
statistical parity, equal opportunity, and equalized odds, applicable to
multi-class problems and both attribute-aware and attribute-blind settings. It
achieves fairness by re-calibrating the output score of the given base model
with a "fairness cost" -- a linear combination of the (predicted) group
memberships. Our algorithm is based on a representation result showing that the
optimal fair classifier can be expressed as a linear post-processing of the
loss function and the group predictor, derived via using these as sufficient
statistics to reformulate the fair classification problem as a linear program.
The parameters of the post-processor are estimated by solving the empirical LP.
Experiments on benchmark datasets show the efficiency and effectiveness of our
algorithm at reducing disparity compared to existing algorithms, including
in-processing, especially on larger problems.


  The theory of optical effects in hydrogenated graphene (graphane) in
terahertz and infrared range is developed, including the analysis of complex
conductivity, reflection coefficient for graphane on a substrate and dispersion
of surface plasmon-polaritons. The calculations are based on quite simple
analytical approximation of graphane band structure in the vicinity of
Gamma-point and on the modified model of quantum coherence relaxation.
Comparison of the obtained theoretical results with corresponding experimental
data can be used both for the determination of graphane characteristics (Fermi
level, relaxation rate etc.) and for the investigation of potential
applications of this material in the design of new optical elements.


  Document translation poses a challenge for Neural Machine Translation (NMT)
systems. Most document-level NMT systems rely on meticulously curated
sentence-level parallel data, assuming flawless extraction of text from
documents along with their precise reading order. These systems also tend to
disregard additional visual cues such as the document layout, deeming it
irrelevant. However, real-world documents often possess intricate text layouts
that defy these assumptions. Extracting information from Optical Character
Recognition (OCR) or heuristic rules can result in errors, and the layout
(e.g., paragraphs, headers) may convey relationships between distant sections
of text. This complexity is particularly evident in widely used PDF documents,
which represent information visually. This paper addresses this gap by
introducing M3T, a novel benchmark dataset tailored to evaluate NMT systems on
the comprehensive task of translating semi-structured documents. This dataset
aims to bridge the evaluation gap in document-level NMT systems, acknowledging
the challenges posed by rich text layouts in real-world applications.


  The rapid development of virtual network architecture makes it possible for
wireless network to be widely used. With the popularity of artificial
intelligence (AI) industry in daily life, efficient resource allocation of
wireless network has become a problem. Especially when network users request
wireless network resources from different management domains, they still face
many practical problems. From the perspective of virtual network embedding
(VNE), this paper designs and implements a multi-objective optimization VNE
algorithm for wireless network resource allocation. Resource allocation in
virtual network is essentially a problem of allocating underlying resources for
virtual network requests (VNRs). According to the proposed objective formula,
we consider the optimization mapping cost, network delay and VNR acceptance
rate. VNE is completed by node mapping and link mapping. In the experiment and
simulation stage, it is compared with other VNE algorithms, the cross domain
VNE algorithm proposed in this paper is optimal in the above three indicators.
This shows the effectiveness of the algorithm in wireless network resource
allocation.


  With asymptotic method developed from weak turbulent theory, the kinetic
equations for QGP are expanded in fluctuation field potential $A^T_\mu $.
Considering the second-order and third-order currents, we derive the nonlinear
permeability tensor function from Yang-Mills field equation, and find that the
third-order current is more important in turbulent theory. The nonlinear
permeability formulae for longitudinal color oscillations show that the
non-Abelian effects are more important than the Abelian-like effects. To
compare with other works, we give the numerical result of the damping rate for
the modes with zero wave vector.


  Using "soft" targets to improve model performance has been shown to be
effective in classification settings, but the usage of soft targets for
regression is a much less studied topic in machine learning. The existing
literature on the usage of soft targets for regression fails to properly assess
the method's limitations, and empirical evaluation is quite limited. In this
work, we assess the strengths and drawbacks of existing methods when applied to
molecular property regression tasks. Our assessment outlines key biases present
in existing methods and proposes methods to address them, evaluated through
careful ablation studies. We leverage these insights to propose Distributional
Mixture of Experts (DMoE): A model-independent, and data-independent method for
regression which trains a model to predict probability distributions of its
targets. Our proposed loss function combines the cross entropy between
predicted and target distributions and the L1 distance between their expected
values to produce a loss function that is robust to the outlined biases. We
evaluate the performance of DMoE on different molecular property prediction
datasets -- Open Catalyst (OC20), MD17, and QM9 -- across different backbone
model architectures -- SchNet, GemNet, and Graphormer. Our results demonstrate
that the proposed method is a promising alternative to classical regression for
molecular property prediction tasks, showing improvements over baselines on all
datasets and architectures.


  Coherent electron displacement is a conventional strategy for processing
quantum information, as it enables to interconnect distinct sites in a network
of atoms. The efficiency of the processing relies on the precise control of the
mechanism, which has yet to be established. Here, we theoretically demonstrate
a new route to drive the electron displacement on a timescale faster than that
of the dynamical distortion of the electron wavepacket by utilizing attosecond
single-cycle pulses. The characteristic feature of these pulses relies on a
vast momentum transfer to an electron, leading to its displacement following a
unidirectional path. The scenario is illustrated by revealing the
spatiotemporal nature of the displaced wavepacket encoding a quantum
superposition state. We map out the associated phase information and retrieve
it over long distances from the origin. Moreover, we show that a sequence of
such pulses applied to a chain of ions enables attosecond control of the
directionality of the coherent motion of the electron wavepacket back and forth
between the neighbouring sites. An extension to a two-electron spin state
demonstrates the versatility of the use of these pulses. Our findings establish
a promising route for advanced control of quantum states using attosecond
single-cycle pulses, which pave the way towards ultrafast processing of quantum
information as well as imaging.


  We prove that for any prime $p>2$, $q=p^\nu$ a power of $p$, $n\ge p$ and
$G=S_n$ or $G=A_n$ (symmetric or alternating group) there exists a Galois
extension $K/\mathbb F_q(T)$ ramified only over $\infty$ with
$\mathrm{Gal}(K/\mathbb F_q(T))=G$. This confirms a conjecture of Abhyankar for
the case of symmetric and alternating groups over finite fields of odd
characteristic.


  Let $\big(M,g^{TM}\big)$ be a noncompact complete spin Riemannian manifold of
even dimension $n$, with $k^{TM}$ denote the associated scalar curvature. Let
$f\colon M\rightarrow S^{n}(1)$ be a smooth area decreasing map, which is
locally constant near infinity and of nonzero degree. We show that if
$k^{TM}\geq n(n-1)$ on the support of ${\rm d}f$, then $ \inf \big(k^{TM}\big)<
0$. This answers a question of Gromov. We use a simple deformation of the Dirac
operator to prove the result. The odd dimensional analogue is also presented.


  V476 Cyg (Nova Cyg 1920) is a bright, fast nova reaching a photographic
magnitude of 2.0. Using the Zwicky Transient Facility (ZTF) public database, I
found that this nova is currently a dwarf nova with a cycle length of ~24 d.
Compared to other classical novae currently in dwarf nova-type states,
outbursts of V476 Cyg are rapidly rising and short with durations of a few
days. Based on the AAVSO observations, this nova was probably already in the
dwarf nova-type phase in 2016, 96 years after the nova eruption. I found a
possible orbital period of 0.1018002(6) d using the ZTF data, which would place
the object in the period gap. This supposed short orbital period appears to
explain the features and faint absolute magnitudes of the observed dwarf nova
outbursts. If this period is confirmed, V476 Cyg is a classical nova with the
shortest orbital period with distinct dwarf nova outbursts and in which a nova
eruption was recorded in the modern era. I also compared with the outburst
properties with V446 Her (Nova Her 1960), which currently shows SS Cyg-type
outbursts. The transition to the dwarf nova-phase in V476 Cyg occurred much
earlier (~100 yr) than what has been supposed (~1000 yr) for classical novae
below the period gap. V476 Cyg would not only provide an ideal laboratory of
the behavior of an irradiated accretion disk in which tidal instability is
expected to work, but also an ideal laboratory of the effect of a massive white
dwarf on dwarf nova outbursts.


  In this article, we define functions analogous to Ramanujan's function $f(n)$
defined in his famous paper "Modular equations and approximations to $\pi$". We
then use these new functions to study Ramanujan's series for $1/\pi$ associated
with the classical, cubic and quartic bases.


  The FCAL collaboration is preparing large-scale prototypes of special
calorimeters to be used in the very forward region at future electron-positron
colliders for a precise measurement of integrated luminosity and for instant
luminosity measurement and assisting beam-tuning. LumiCal is designed as a
silicon-tungsten sandwich calorimeter with very thin sensor planes to keep the
Moli\`ere radius small, facilitating such the measurement of electron showers
in the presence of background. Dedicated front-end electronics has been
developed to match the timing and dynamic range requirements. A partially
instrumented prototype was investigated in a 1 to 5 GeV electron beam at the
DESY II synchrotron. In the recent beam tests, a multi-plane compact prototype
was equipped with thin detector planes fully assembled with readout electronics
and installed in 1 mm gaps between tungsten plates of one radiation length
thickness. High statistics data were used to perform sensor alignment, and to
measure the longitudinal and transversal shower development in the sandwich.
This talk covers the latest status of the calorimeter prototype development and
selected performance results, obtained in test beam measurements, the prospects
for the upcoming DESY test beam, as well as the expected simulation
performance.


  Tomography of single-particle-resolved detectors is of primary importance for
characterizing particle correlations with applications in quantum metrology,
quantum simulation and quantum computing. However, it is a non-trivial task in
practice due to the unavoidable presence of noise that affects the measurement
but does not originate from the detector. In this work, we address this problem
for a three-dimensional single-atom-resolved detector where shot-to-shot atom
number fluctuations are a central issue to perform a quantum detector
tomography. We overcome this difficulty by exploiting the parallel measurement
of counting statistics in sub-volumes of the detector, from which we evaluate
the effect of shot-to-shot fluctuations and perform a local tomography of the
detector. In addition, we illustrate the validity of our method from applying
it to Gaussian quantum states with different number statistics. Finally, we
show that the response of Micro-Channel Plate detectors is well-described from
using a binomial distribution with the detection efficiency as a single
parameter.


  We introduce the notion of anti-dendriform algebras as a new approach of
splitting the associativity. They are characterized as the algebras with two
operations whose sum is associative and the negative left and right
multiplication operators compose the bimodules of the sum associative algebras,
justifying the notion due to the comparison with the corresponding
characterization of dendriform algebras. The notions of anti-$\mathcal
O$-operators and anti-Rota-Baxter operators on associative algebras are
introduced to interpret anti-dendriform algebras. In particular, there are
compatible anti-dendriform algebra structures on associative algebras with
nondegenerate commutative Connes cocycles. There is an important observation
that there are correspondences between certain subclasses of dendriform and
anti-dendriform algebras in terms of $q$-algebras. As a direct consequence, we
give the notion of Novikov-type dendriform algebras as an analogue of Novikov
algebras for dendriform algebras, whose relationship with Novikov algebras is
consistent with the one between dendriform and pre-Lie algebras. Finally we
extend to provide a general framework of introducing the notions of analogues
of anti-dendriform algebras, which interprets a new splitting of operations.


  Using redshift samples, we calculate the cross-correlation functions of LSB
galaxies with normal galaxies in complete samples (i.e. CfA and IRAS), which
enables us to compare directly the amplitudes and shapes of the correlation
functions. For pair separations $r\gs 2\mpc$, we find that the shape is in
agreement with that of the correlation functions for other galaxies. The
amplitudes ($A$) of $\xiab (r)$ are lower than those of the autocorrelation
functions for the CfA and IRAS samples, with $A_{\rm LSB-CfA}:A_{\rm
CfA-CfA}\approx 0.4$ and $A_{\rm LSB-IRAS}:A_{\rm IRAS-IRAS}\approx 0.6$. These
results suggest that LSB galaxies are imbedded in the same large scale
structure as other galaxies, but are less strongly clustered. This offers the
hope that LSB galaxies may be unbiased tracers of the mass density on large
scales. For $r\ls 2\mpc$, the cross-correlation functions are significantly
lower than that expected from the extrapolation of $\xiab$ on larger scales,
showing that the formation and survival of LSB galaxies may be inhibited by
interaction with neighboring galaxies.
  The model which matches these observations suggests that strong luminosity
segregation in galaxy clustering is not a necessary consequence of biased
galaxy formation, unless the effect of surface brightness is taken in to
account. It is also implies a significant mass density in LSB galaxies.


  The standard and renormalized coupled cluster methods with singles, doubles,
and noniterative triples and their generalizations to excited states, based on
the equation of motion coupled cluster approach, are applied to the He-4 and
O-16 nuclei. A comparison of coupled cluster results with the results of the
exact diagonalization of the Hamiltonian in the same model space shows that the
quantum chemistry inspired coupled cluster approximations provide an excellent
description of ground and excited states of nuclei. The bulk of the correlation
effects is obtained at the coupled cluster singles and doubles level. Triples,
treated noniteratively, provide the virtually exact description.


  In this work, we present a 2x2 near-field multi-input multiple-output (MIMO)
prototype for bit-error-rate (BER) and error vector magnitude (EVM)
measurements in a metal enclosure. The near-field MIMO prototype is developed
using software-defined-radios (SDRs) for over-the-air transmission of QPSK
modulated baseband waveforms. We check the near-field MIMO BER and EVM
measurements in three different scenarios in a highly reflecting metal
enclosure environment. In the first scenario, the line-of-sight (LOS)
communication link is investigated when the mode-stirrer is stationary. In
stationary channel conditions near-field MIMO BER and EVM measurements are
performed. In the second scenario, BER and EVM measurements are performed in
dynamic channel conditions when the mode-stirrer is set to move continuously.
In the third scenario, LOS communication near-field MIMO BER and EVM
measurements are performed in stationary channel conditions but now in the
presence of MIMO interference. In three different scenarios, near-field MIMO
BER and EVM measurements are investigated at different Tx USRP gain values and
in the presence of varying levels of MIMO interference.


  We show that the distribution of the conductance in quasi-one-dimensional
systems with surface disorder is correctly described by the
Dorokhov-Mello-Pereyra-Kumar equation if one includes direct processes in the
scattering matrix S through Poisson's kernel. Although our formulation is valid
for any arbitrary number of channels, we present explicit calculations in the
one channel case. Ours result is compared with solutions of the Schroedinger
equation for waveguides with surface disorder calculated numerically using the
R-matrix method.


  The precessing jet-nozzle scenario previously proposed was applied to
model-fit the kinematics of five superluminal components (C19,C20,C21,B5 and
B7) of jet-B in blazar 3C345. Based on a specific pattern for the the
precessing common trajectory of jet-B, the kinematic properties (including
trajectory,coordinates, core separation and apparent velocity) were
model-fitted and their flux evolution could be studied. Through
model-simulation of their kinematic behavior, the bulk Lorentz factor, viewing
angle and Doppler factor were derived as continuous functions of time and the
association of their flux evolution with their Doppler-boosting effect was
investigated. The 43GHz light-curves of the five superluminal components can be
well interpreted in terms of their Doppler effect. The close association of
their flux evolution with the Doppler-boosting effect firmly validates our
precessing nozzle scenario and supports the traditional point-view that
superluminal components are physical entities (traveling shocks or plasmoids)
participating relativistic motion toward us at small angles. The
model-simulation of kinematic behavior of superluminal components by using our
precessing nozzle scenario with specific patterns (helical or ballistic)
assumed for the precessing common trajectories yields the model-derived bulk
Lorentz factor, apparent velocity, viewing angle and Doppler factor as
continuous functions of time, which are most applicable to study the connection
of flux evolution with Doppler boosting effect for the superluminal components.


  Since the experimental realization of synthetic gauge fields for neutral
atoms, the simulation of topologically non-trivial phases of matter with
ultracold atoms has become a major focus of cold atom experiments. However,
several obvious differences exist between cold atom and solid state systems,
for instance the finite size of the atomic cloud and the smooth confining
potential. In this article we show that sharp boundaries are not required to
realize quantum Hall or quantum spin Hall physics in optical lattices and, on
the contrary, that edge states which belong to a smooth confinement exhibit
additional interesting properties, such as spatially resolved splitting and
merging of bulk bands and the emergence of robust auxiliary states in bulk gaps
to preserve the topological quantum numbers. In addition, we numerically
validate that these states are robust against disorder. Finally, we analyze
possible detection methods, with a focus on Bragg spectroscopy, to demonstrate
that the edge states can be detected and that Bragg spectroscopy can reveal how
topological edge states are connected to the different bulk bands.


  Transition absorption of electromagnetic field energy by an electron passing
through a boundary between two media with different dielectric permittivities
is considered both classically and quantum mechanically. It is shown that
transition absorption can make a substantial contribution to the process of
electron photoemission from metals due to the surface photoelectric effect.


  Force-free extrapolations are widely used to study the magnetic field in the
solar corona based on surface measurements. The extrapolations assume that the
ratio of internal energy of the plasma to magnetic energy, the plasma-beta is
negligible. Despite the widespread use of this assumption observations, models,
and theoretical considerations show that beta is of the order of a few percent
to more than 10%, and thus not small. We investigate what consequences this has
for the reliability of extrapolation results. We use basic concepts starting
with the force and the energy balance to infer relations between plasma-beta
and free magnetic energy, to study the direction of currents in the corona with
respect to the magnetic field, and to estimate the errors in the free magnetic
energy by neglecting effects of the plasma (beta<<1). A comparison with a 3D
MHD model supports our basic considerations. If plasma-beta is of the order of
the relative free energy (the ratio of the free magnetic energy to the total
magnetic energy) then the pressure gradient can balance the Lorentz force. This
is the case in the solar corona, and therefore the currents are not properly
described. In particular the error in terms of magnetic energy by neglecting
the plasma is of the order of the free magnetic energy, so that the latter can
not be reliably determined by an extrapolation. While a force-free
extrapolation might capture the magnetic structure and connectivity of the
coronal magnetic field, the derived currents and free magnetic energy are not
reliable. Thus quantitative results of extrapolations on the location and
amount of heating in the corona (through current dissipation) and on the energy
storage of the magnetic field (e.g. for eruptive events) are limited.


  When \lambda_{T} << d_{T}, where \lambda_{T} is the de Broglie wavelength and
d_{T}, the distance of closest approach of thermal electrons, a classical
analysis of the energy of a plasma can be made. In all the classical analysis
made until now, it was assumed that the frequency of the fluctuations \omega <<
T (k_{B}=\hbar=1). Using the fluctuation-dissipation theorem, we evaluate the
energy of a plasma, allowing the frequency of the fluctuations to be arbitrary.
We find that the energy density is appreciably larger than previously thought
for many interesting plasmas, such as the plasma of the Universe before the
recombination era.


  The shapes of Stokes profiles contain much information about the atmospheric
conditions that produced them. However, a variety of different atmospheric
structures can produce very similar profiles. Thus, it is important for proper
interpretation of observations to have a good understanding of how the shapes
of Stokes profiles depend on the underlying atmosphere. An excellent tool in
this regard is forward modeling, i.e. computing and studying synthetic spectra
from realistic simulations of the solar atmosphere. Modern simulations
routinely produce several hundred thousand spectral profiles per snapshot. With
such numbers, it becomes necessary to use automated procedures in order to
organize the profiles according to their shape. Here we illustrate the use of
two complementary methods, k-means and k-Shape, to cluster similarly shaped
profiles, and demonstrate how the resulting clusters can be combined with
knowledge of the simulation's atmosphere to interpret spectral shapes. We
generate synthetic Stokes profiles for the Ca II 854.2 nm line using the
Multi3D code from a Bifrost simulation snapshot. We then apply the k-means and
k-Shape clustering techniques to group the profiles together according to their
shape. We show and compare the classes of profile shapes we retrieve from
applying both k-means and k-Shape to our synthetic intensity spectra. We then
show the structure of the underlying atmosphere for two particular classes of
profile shapes retrieved by the clustering, and demonstrate how this leads to
an interpretation for the formation of those profile shapes. Furthermore, we
apply both methods to the subset of our profiles containing the strongest
Stokes V signals, and demonstrate how k-Shape can be qualitatively better than
k-means at retrieving complex profile shapes when using a small number of
clusters.


  We develop the first theoretical model for the analytical description of ion
concentration polarization (ICP)-based electrokinetic molecular concentration,
which had not been possible due to the extraordinary complexity of the system.
We define the two separate limits for the enrichment factor achievable in a
given system and derive the scaling laws for critical parameters, which are
validated by numerical simulations and experiments. This work provides clear
theoretical explanations on the diverse experimental behaviors previously
observed yet unexplainable, while setting solid foundation for the engineering
of ICP-based concentrators and other fluid-coupled electrokinetic systems.


  In the last decades, digital manufacturing has constituted the headline of
what is starting to be known as the 'fourth industrial revolution', where the
fabrication processes comprise a hybrid of technologies that blur the lines
between fundamental sciences, engineering, and even medicine as never seen
before. One of the reasons why this mixture is inevitable has to do with the
fact that we live in an era that incorporates technology in every single aspect
of our daily lives. In the industry, this has translated into fabrication
versatility, as follows: design changes on a final product are just one click
away, fabrication chains have evolved towards continuous roll-to roll
processes, and, most importantly, the overall costs and fabrication speeds are
matching and overcoming most of the traditional fabrication methods.
Laser-induced forward transfer (LIFT) stands out as a versatile set of
fabrication techniques, being the closest approach to an all-in-one additive
manufacturing method compatible with virtually any material. In this technique,
laser radiation is used to propel the material of interest and deposit it at
user-defined locations with high spatial resolution. By selecting the proper
laser parameters and considering the interaction of the laser light with the
material, it is possible to transfer this technique from robust inorganic
materials to fragile biological samples. In this work, we first present a brief
introduction on the current developments of the LIFT technique by surveying
recent scientific review publications. Then, we provide a general research
overview by making an account of the publication and citation numbers of
scientific papers on the LIFT technique considering the last three decades. At
the same time, we highlight the geographical distribution and main research
institutions that contribute to this scientific output.


  Neural networks are susceptible to adversarial examples-small input
perturbations that cause models to fail. Adversarial training is one of the
solutions that stops adversarial examples; models are exposed to attacks during
training and learn to be resilient to them. Yet, such a procedure is currently
expensive-it takes a long time to produce and train models with adversarial
samples, and, what is worse, it occasionally fails. In this paper we
demonstrate data pruning-a method for increasing adversarial training
efficiency through data sub-sampling.We empirically show that data pruning
leads to improvements in convergence and reliability of adversarial training,
albeit with different levels of utility degradation. For example, we observe
that using random sub-sampling of CIFAR10 to drop 40% of data, we lose 8%
adversarial accuracy against the strongest attackers, while by using only 20%
of data we lose 14% adversarial accuracy and reduce runtime by a factor of 3.
Interestingly, we discover that in some settings data pruning brings benefits
from both worlds-it both improves adversarial accuracy and training time.


  Context. The [Mg/Fe] abundance ratios are a fundamental fossil signature to
trace the chemical evolution of the disc. Despite of the huge observational and
theoretical effort, discrepancies between models and data are still present and
several explanations have been put forward to explain the [$\alpha$/Fe]
bimodality. Aims. In this work, we take advantage of a new AMBRE:HARPS dataset,
which provides new and more precise [Mg/Fe] estimations, as well as reliable
stellar ages for a subsample of stars, to study the evolution of the solar
neighbourhood. Methods. The above data are compared with detailed chemical
evolution models for the Milky Way, exploring the most used prescriptions for
stellar yields and different formation scenarios for the Galactic disc, i.e.
the delayed two-infall and the parallel model, also including prescriptions for
stellar radial migration. Results. We see that most of the stellar yields
struggle to reproduce the observed trend of the data and that semi-empirical
yields are still the best to describe the [Mg/Fe] evolution in the thick and
thin discs. In particular, most of the yields still predict a steeper decrease
of the [Mg/Fe] ratio at high metallicity than what is shown by the data. The
bulk of the data are well reproduced by the parallel and two-infall scenarios,
but both scenarios have problems in explaining the most metal-rich and
metal-poor tails of the low-$\alpha$ data. These tails can be explained in
light of radial migration from inner and outer disc regions, respectively.
Conclusions. Despite of the evidence of stellar migration, it is difficult to
estimate the actual contribution of stars from other parts of the disc to the
solar vicinity. However, the comparison between data and models suggests that
peculiar histories of star formation, such as that of the two-infall model, are
still needed to reproduce the observed distribution of stars.


  The paper proposes a method for the correct by design coordination of
autonomous driving systems (ADS). It builds on previous results on collision
avoidance policies and the modeling of ADS by combining descriptions of their
static environment in the form of maps, and the dynamic behavior of their
vehicles. An ADS is modeled as a dynamic system involving a set of vehicles
coordinated by a Runtime that based on vehicle positions on a map and their
kinetic attributes, computes free spaces for each vehicle. Vehicles are bounded
to move within the corresponding allocated free spaces. We provide a correct by
design safe control policy for an ADS if its vehicles and the Runtime respect
corresponding assume-guarantee contracts. The result is established by showing
that the composition of assume-guarantee contracts is an inductive invariant
that entails ADS safety. We show that it is practically possible to define
speed control policies for vehicles that comply with their contracts.
Furthermore, we show that traffic rules can be specified in a linear-time
temporal logic, as a class of formulas that constrain vehicle speeds. The main
result is that, given a set of traffic rules, it is possible to derive free
space policies of the Runtime such that the resulting system behavior is safe
by design with respect to the rules.


  A large fraction of the recently discovered Galactic Very High Energy (VHE)
source population remains unidentified to date. VHE gamma-ray emission traces
high energy particles in these sources, but for example in case of hadronic
processes also the gas density at the emission site. Moreover, the particles
have sufficiently long lifetimes to be able to escape from their acceleration
sites. Therefore, the gamma-ray sources or at least the areas of maximum
surface brightness are in many cases spatially offset from the actual
accelerators. A promising way to identify the objects in which the particles
are accelerated seems to be to search for emission signatures of the
acceleration process (like emission from shock-heated plasma). Also the
particles themselves (through primary or secondary synchrotron emission) can be
traced in lower wavebands. Those signatures are best visible in the X-ray band,
and current X-ray observatories are well suited to conduct such follow-up
observations. Some aspects of the current status of these investigations are
reviewed.


  Based on the generalized non-Markovian equations obtained earlier for a
nonequilibrium one-particle distribution function and potential part of the
averaged enthalpy density [Markiv B.B., Omelyan I.P, Tokarchuk M.V., Condens.
Matter Phys., 2010, 13, 23005] a spectrum of collective excitations is
investigated, where the potential of interaction between particles is presented
as a sum of the potential of hard spheres and a certain long-range potential.


  We perform magnetohydrodynamic simulations of accreting, equal-mass binary
black holes in full general relativity focusing on the impact of black hole
spin on the dynamical formation and evolution of minidisks. We find that during
the late inspiral the sizes of minidisks are primarily determined by the
interplay between the tidal field and the effective innermost stable orbit
around each black hole. Our calculations support that a minidisk forms when the
Hill sphere around each black hole is significantly larger than the black
hole's effective innermost stable orbit. As the binary inspirals, the radius of
the Hill sphere decreases, and minidisk sconsequently shrink in size. As a
result, electromagnetic signatures associated with minidisks may be expected to
gradually disappear prior to merger when there are no more stable orbits within
the Hill sphere. In particular, a gradual disappearance of a hard
electromagnetic component in the spectrum of such systems could provide a
characteristic signature of merging black hole binaries. For a binary of given
total mass, the timescale to minidisk "evaporation" should therefore depend on
the black hole spins and the mass ratio. We also demonstrate that accreting
binary black holes with spin have a higher efficiency for converting accretion
power to jet luminosity. These results could provide new ways to estimate black
hole spins in the future.


  Due to the high energies and long distances involved, astrophysical
observations provide a unique opportunity to test possible signatures of
Lorentz Invariance Violation (LIV). Superluminal LIV enables the decay of
photons at high energy over relatively short distances, giving astrophysical
spectra which have a hard cutoff above this energy. The High Altitude Water
Cherenkov (HAWC) observatory is the most sensitive currently-operating
gamma-ray observatory in the world above 10 TeV. Together with the recent
development of an energy-reconstruction algorithm for HAWC using an artificial
neural network, HAWC can make detailed measurements of gamma-ray energies above
100 TeV. With these observations, HAWC can limit the LIV energy scale greater
than $10^{31}$ eV, over 800 times the Planck energy scale. This limit on LIV is
over 60 times more constraining than the best previous value for $\rm
E_{LIV}^{(1)}$.


  In Natural Language Generation (NLG), contemporary Large Language Models
(LLMs) face several challenges, such as generating fluent yet inaccurate
outputs and reliance on fluency-centric metrics. This often leads to neural
networks exhibiting "hallucinations". The SHROOM challenge focuses on
automatically identifying these hallucinations in the generated text. To tackle
these issues, we introduce two key components, a data augmentation pipeline
incorporating LLM-assisted pseudo-labelling and sentence rephrasing, and a
voting ensemble from three models pre-trained on Natural Language Inference
(NLI) tasks and fine-tuned on diverse datasets.


  Attacks to networks are becoming more complex and sophisticated every day.
Beyond the so-called script-kiddies and hacking newbies, there is a myriad of
professional attackers seeking to make serious profits infiltrating in
corporate networks. Either hostile governments, big corporations or mafias are
constantly increasing their resources and skills in cybercrime in order to spy,
steal or cause damage more effectively. traditional approaches to Network
Security seem to start hitting their limits and it is being recognized the need
for a smarter approach to threat detections. This paper provides an
introduction on the need for evolution of Cyber Security techniques and how
Artificial Intelligence could be of application to help solving some of the
problems. It provides also, a high-level overview of some state of the art AI
Network Security techniques, to finish analysing what is the foreseeable future
of the application of AI to Network Security.


  A vision-based keystroke inference attack is a side-channel attack in which
an attacker uses an optical device to record users on their mobile devices and
infer their keystrokes. The threat space for these attacks has been studied in
the past, but we argue that the defining characteristics for this threat space,
namely the strength of the attacker, are outdated. Previous works do not study
adversaries with vision systems that have been trained with deep neural
networks because these models require large amounts of training data and
curating such a dataset is expensive. To address this, we create a large-scale
synthetic dataset to simulate the attack scenario for a keystroke inference
attack. We show that first pre-training on synthetic data, followed by adopting
transfer learning techniques on real-life data, increases the performance of
our deep learning models. This indicates that these models are able to learn
rich, meaningful representations from our synthetic data and that training on
the synthetic data can help overcome the issue of having small, real-life
datasets for vision-based key stroke inference attacks. For this work, we focus
on single keypress classification where the input is a frame of a keypress and
the output is a predicted key. We are able to get an accuracy of 95.6% after
pre-training a CNN on our synthetic data and training on a small set of
real-life data in an adversarial domain adaptation framework. Source Code for
Simulator:
https://github.com/jlim13/keystroke-inference-attack-synthetic-dataset-generator-


  We establish the C^{1,1} regularity of quasi-psh envelopes in a Kahler class,
confirming a conjecture of Berman.


  With a growing demand for quasi-instantaneous communication services such as
real-time video streaming, cloud gaming, and industry 4.0 applications,
multi-constraint Traffic Engineering (TE) becomes increasingly important. While
legacy TE management planes have proven laborious to deploy, Segment Routing
(SR) drastically eases the deployment of TE paths and is thus increasingly
adopted by Internet Service Providers (ISP). There is a clear need in computing
and deploying Delay-Constrained Least-Cost paths (DCLC) with SR for real-time
interactive services. However, most current DCLC solutions are not tailored for
SR. They also often lack efficiency or guarantees. Similarly to approximation
schemes, we argue that the challenge is to design an algorithm providing both
performances and guarantees. However, conversely to most of these schemes, we
also consider operational constraints to provide a practical, high-performance
implementation.
  We leverage the inherent limitations of delay measurements and account for
the operational constraint added by SR to design a new algorithm, best2cop,
providing guarantees and performance in all cases. Best2cop outperforms a
state-of-the-art algorithm on both random and real networks of up to 1000
nodes. Relying on commodity hardware with a single thread, our algorithm
retrieves all non-superfluous 3-dimensional routes in only 250ms and 100ms
respectively. This execution time is further reduced using multiple threads, as
the design of best2cop enables a speedup almost linear in the number of cores.
Finally, we extend best2cop to deal with massive scale ISP by leveraging the
multi-area partitioning of these deployments. Thanks to our new topology
generator specifically designed to model the realistic patterns of such massive
IP networks, we show that best2cop solves DCLC-SR in approximately 1 second
even for ISP having more than 100000 routers.


  We study quadrupole and monopole core polarization using harmonic oscillator
wave functions but with different length parameters for the valence particle as
compared to the core. We use perturbation theory with a delta interaction. The
results also hold for a density dependent delta interaction [1]. We study how
the amount of core polarization varies with the distance of the valence
particle from the core


  Let $d \geq 2$ and let $N(y)$ be the fundamental solution of the Laplace
equation in $R^d$ We consider the aggregation equation $$ \frac{\partial
\rho}{\partial t} + \operatorname{div}(\rho v) =0, v = -\nabla N * \rho$$ with
initial data $\rho(x,0) = \chi_{D_0}$, where $\chi_{D_0}$ is the indicator
function of a bounded domain $D_0 \subset R^d.$ We now fix $0 < \gamma < 1$ and
take $D_0$ to be a bounded $C^{1+\gamma}$ domain (a domain with smooth boundary
of class $C^{1+\gamma}$). Then we have Theorem: If $D_0$ is a $C^{1 + \gamma}$
domain, then the initial value problem above has a solution given by
$$\rho(x,t) = \frac{1}{1 -t} \chi_{D_t}(x), \quad x \in R^d, \quad 0 \le t <
1$$ where $D_t$ is a $C^{1 + \gamma}$ domain for all $0 \leq t < 1$.


  With the rapid development of robot and other intelligent and autonomous
agents, how a human could be influenced by a robot's expressed mood when making
decisions becomes a crucial question in human-robot interaction. In this pilot
study, we investigate (1) in what way a robot can express a certain mood to
influence a human's decision making behavioral model; (2) how and to what
extent the human will be influenced in a game theoretic setting. More
specifically, we create an NLP model to generate sentences that adhere to a
specific affective expression profile. We use these sentences for a humanoid
robot as it plays a Stackelberg security game against a human. We investigate
the behavioral model of the human player.


  Energy efficiency is a big concern in industrial sectors. Finding the root
cause of anomaly state of energy efficiency can help to improve energy
efficiency of industrial systems and therefore save energy cost. In this
research, we propose to use transfer entropy (TE) for root cause analysis on
energy efficiency of industrial systems. A method, called TE flow, is proposed
in that a TE flow from physical measurements of each subsystem to the energy
efficiency indicator along timeline is considered as causal strength for
diagnosing root cause of anomaly states of energy efficiency of a system. The
copula entropy-based nonparametric TE estimator is used in the proposed method.
We conducted experiments on real data collected from a compressing air system
to verify the proposed method. Experimental results show that the TE flow
method successfully identified the root cause of the energy (in)efficiency of
the system.


  The key elements of the Unified Model are reviewed and checked against modern
experimental data. For medium-mass or heavy nuclei it is found that separation
between collective and intrinsic degrees freedom becomes invalid for after
exciting five to ten collective quanta along the yrast line and two quanta
above it. The microscopic derivation of the Bohr Hamiltonian by means of
adiabatic time-dependent mean field theory is presented and results compared
with experiment. The description of the strong coupling between the rotational
and intrinsic degrees of freedom in framework of the rotating mean field is
described from a conceptual point of view. The classification of rotational
bands as configurations of rotating quasiparticles is introduced. Using the
concept of spontaneous symmetry breaking the microscopic underpinning of the
rotational degrees is refined. Resulting phenomena, as tilted-axis rotation,
transverse wobbling, chirality, magnetic rotation, and band termination are
discussed.


  We study the independence structure of finitely exchangeable distributions
over random vectors and random networks. In particular, we provide necessary
and sufficient conditions for an exchangeable vector so that its elements are
completely independent or completely dependent. We also provide a sufficient
condition for an exchangeable vector so that its elements are marginally
independent. We then generalize these results and conditions for exchangeable
random networks. In this case, it is demonstrated that the situation is more
complex. We show that the independence structure of exchangeable random
networks lies in one of six regimes that are two-fold dual to one another,
represented by undirected and bidirected independence graphs in graphical model
sense with graphs that are complement of each other. In addition, under certain
additional assumptions, we provide necessary and sufficient conditions for the
exchangeable network distributions to be faithful to each of these graphs.


  Observing Earth-like exoplanets orbiting within the habitable zone of
Sun-like stars and studying their atmospheres in reflected starlight requires
contrasts of $\sim1\mathrm{e}{-10}$ in the visible. At such high contrast,
starlight reflected by exozodiacal dust is expected to be a significant source
of contamination. Here, we present high-fidelity simulations of coronagraphic
observations of a synthetic Solar System located at a distance of 10 pc and
observed with a 12 m and an 8 m circumscribed aperture diameter space telescope
operating at 500 nm wavelength. We explore different techniques to subtract the
exozodi and stellar speckles from the simulated images in the face-on, the 30
deg inclined, and the 60 deg inclined case and quantify the remaining
systematic noise as a function of the exozodiacal dust level of the system. We
find that in the face-on case, the exozodi can be subtracted down to the photon
noise limit for exozodi levels up to $\sim1000$ zodi using a simple toy model
for the exozodiacal disk, whereas in the 60 deg inclined case this only works
up to $\sim50$ zodi. We also investigate the impact of larger wavefront errors
and larger system distance, finding that while the former have no significant
impact, the latter has a strong (negative) impact. Ultimately, we derive a
penalty factor as a function of the exozodi level and system inclination that
should be considered in exoplanet yield studies as a realistic estimate for the
excess systematic noise from the exozodi.


  For any real a>0 we determine the supremum of the real \sigma\ such that
\zeta(\sigma+it) = a for some real t. For 0 < a < 1, a = 1, and a > 1 the
results turn out to be quite different.}
  We also determine the supremum E of the real parts of the `turning points',
that is points \sigma+it where a curve Im \zeta(\sigma+it) = 0 has a vertical
tangent. This supremum E (also considered by Titchmarsh) coincides with the
supremum of the real \sigma\ such that \zeta'(\sigma+it) = 0 for some real t.
  We find a surprising connection between the three indicated problems:
\zeta(s) = 1, \zeta'(s) = 0 and turning points of \zeta(s). The almost extremal
values for these three problems appear to be located at approximately the same
height.


  We show that for those lattices of Voronoi's first kind with known obtuse
superbasis, a closest lattice point can be computed in $O(n^4)$ operations
where $n$ is the dimension of the lattice. To achieve this a series of relevant
lattice vectors that converges to a closest lattice point is found. We show
that the series converges after at most $n$ terms. Each vector in the series
can be efficiently computed in $O(n^3)$ operations using an algorithm to
compute a minimum cut in an undirected flow network.


  How to effectively represent molecules is a long-standing challenge for
molecular property prediction and drug discovery. This paper studies this
problem and proposes to incorporate chemical domain knowledge, specifically
related to chemical reactions, for learning effective molecular
representations. However, the inherent cross-modality property between chemical
reactions and molecules presents a significant challenge to address. To this
end, we introduce a novel method, namely MolKD, which Distills cross-modal
Knowledge in chemical reactions to assist Molecular property prediction.
Specifically, the reaction-to-molecule distillation model within MolKD
transfers cross-modal knowledge from a pre-trained teacher network learning
with one modality (i.e., reactions) into a student network learning with
another modality (i.e., molecules). Moreover, MolKD learns effective molecular
representations by incorporating reaction yields to measure transformation
efficiency of the reactant-product pair when pre-training on reactions.
Extensive experiments demonstrate that MolKD significantly outperforms various
competitive baseline models, e.g., 2.1% absolute AUC-ROC gain on Tox21. Further
investigations demonstrate that pre-trained molecular representations in MolKD
can distinguish chemically reasonable molecular similarities, which enables
molecular property prediction with high robustness and interpretability.


  The impact of QCD instantons on scalar glueball properties is studied in the
framework of an instanton-improved operator product expansion (IOPE) for the
0^{++} glueball correlation function. Direct instanton contributions are found
to strongly dominate over those from perturbative fluctuations and soft vacuum
fields. All IOPE sum rules, including the one involving a subtraction constant,
show a high degree of stability and are, in contrast to previous glueball sum
rules, consistent with the low-energy theorem for the zero-momentum correlator.
The predicted glueball mass m_{G}=1.53 \pm 0.2 GeV is less sensitive to the
instanton contributions then the glueball coupling (residue) f_{G}=1.01\pm 0.25
GeV, which increases by about half an order of magnitude. Both glueball
properties are shown to obey scaling relations as a function of the average
instanton size and density.


  Vacuum polarization is a part of the initial-state radiative correction for
the cross-section of $e^+e^-$ annihilation processes. In the energy region in
the vicinity of narrow resonances $J/\psi$ and $\psi(3686)$, the vacuum
polarization contribution from the resonant component has a significant effect
on the line-shape of the lepton pair production cross-section. This paper
discusses some basic concepts and describes an analytical calculation of the
cross-section of $e^+e^-\to\gamma^\ast/\psi\to\mu^+\mu^-$ considering the
single and double vacuum polarization effect of the virtual photon propagator.
Moreover, it presents some numerical comparisons with the traditional
treatments.


  Bootstrap is a principled and powerful frequentist statistical tool for
uncertainty quantification. Unfortunately, standard bootstrap methods are
computationally intensive due to the need of drawing a large i.i.d. bootstrap
sample to approximate the ideal bootstrap distribution; this largely hinders
their application in large-scale machine learning, especially deep learning
problems. In this work, we propose an efficient method to explicitly
\emph{optimize} a small set of high quality ``centroid'' points to better
approximate the ideal bootstrap distribution. We achieve this by minimizing a
simple objective function that is asymptotically equivalent to the Wasserstein
distance to the ideal bootstrap distribution. This allows us to provide an
accurate estimation of uncertainty with a small number of bootstrap centroids,
outperforming the naive i.i.d. sampling approach. Empirically, we show that our
method can boost the performance of bootstrap in a variety of applications.


  We report the experimental observation of mutual synchronization of magnetic
nanooscillators driven by pure spin current generated by nonlocal spin
injection. We show that the oscillators efficiently synchronize due to the
direct spatial overlap of the dynamical modes excited by spin current, which is
facilitated by the large size of the auto-oscillation area inherent to these
devices. The synchronization occurs within an interval of the driving current
determined by the competition between the dynamic nonlinearity that facilitates
synchronization, and the short-wavelength magnetic fluctuations enhanced by the
spin current that suppress synchronization. The demonstrated synchronization
effects can be utilized to control the spatial and spectral characteristics of
the dynamical states induced by spin currents.


  The emergence of passive galaxies in the early Universe results from the
interplay among the processes responsible for their rapid assembly and for the
abrupt shut-down of their SF. Investigating the individual properties and
demographics of early passive galaxies will improve our understanding of these
mechanisms. In this work we present a follow-up analysis of the z>3 passive
galaxy candidates selected by Merlin et al. (2019) in the CANDELS fields. We
begin by first confirming the accuracy of their passive classification by
exploiting their sub-mm emission to demonstrate the lack of ongoing SF. Using
archival ALMA observations we are able to confirm at least 61% of the observed
candidates as passive. While the remainder lack sufficiently deep data for
confirmation, we are able to validate the entire sample in a statistical sense.
We then estimate the Stellar Mass Function (SMF) of all 101 passive candidates
in three redshift bins from z=5 to z=3. We adopt a stepwise approach that has
the advantage of taking into account photometric errors, observational
incompleteness, and the Eddington bias without any a-posteriori correction. We
observe a pronounced evolution in the SMF around z~4, indicating that we are
witnessing the emergence of the passive population at this epoch. Massive
(M>10^11Msun) passive galaxies, only accounting for a small (<10%) fraction of
galaxies at z>4, become dominant at later epochs. Thanks to a combination of
photometric quality, sample selection and methodology, we overall find a higher
density of passive galaxies than previous works. The comparison with
theoretical predictions, despite a qualitative agreement, denotes a still
incomplete understanding of the physical processes responsible for the
formation of these galaxies. Finally, we extrapolate our results to predict the
number of early passive galaxies expected in surveys carried out with future
facilities.


  It is shown how nonlinear versions of quantum mechanics can be refolmulated
in terms of a (linear) C*-algebraic theory. Then also their symmetries are
described as automorphisms of the correspondong C*-algebra. The requirement of
"conservation of transition probabilities" is discussed.


  Hardcore bosons on honeycomb lattice ribbons with zigzag edges are studied
using exact numerical simulations. We map out the phase diagrams of ribbons
with different widths, which contain superfluid and insulator phases at various
fillings. We show that charge domain walls are energetically favorable, in
sharp contrast to the more typical occupation of a set of sites on a single
sublattice of the bipartite geometry at $\rho=\frac{1}{2}$ filling. This
`self-organized domain wall' separates two charge-density-wave (CDW) regions
with opposite Berry curvatures. Associated with the change of topological
properties, superfluid transport occurs down the domain wall. Our results
provide a concrete context to observe bosonic topological phenomena and can be
simulated experimentally using bosonic cold atoms trapped in designed optical
lattices.


  If $(X, \le_X)$ is a partially ordered set satisfying certain necessary
conditions for $X$ to be order-isomorphic to the spectrum of a Noetherian
domain of dimension two, we describe a new poset $(\text{str } X,
\le_{\text{str } X})$ that completely determines $X$ up to isomorphism. The
order relation $\le_{\text{str } X}$ imposed on $\text{str } X$ is modeled
after R. Wiegand's well-known "P5" condition that can be used to determine when
a given partially ordered set $(U, \le_U)$ of a certain type is
order-isomorphic to $(\text{Spec } \mathbb Z[x], \subseteq).$


  We use Krylov complexity to study operator growth in the $q$-body dissipative
SYK model, where the dissipation is modeled by linear and random $p$-body
Lindblad operators. In the large $q$ limit, we analytically establish the
linear growth of two sets of coefficients for any generic jump operators. We
numerically verify this by implementing the bi-Lanczos algorithm, which
transforms the Lindbladian into a pure tridiagonal form. We find that the
Krylov complexity saturates inversely with the dissipation strength, while the
dissipative timescale grows logarithmically. This is akin to the behavior of
other $\mathfrak{q}$-complexity measures, namely out-of-time-order correlator
(OTOC) and operator size, which we also demonstrate. We connect these
observations to continuous quantum measurement processes. We further
investigate the pole structure of a generic auto-correlation and the
high-frequency behavior of the spectral function in the presence of
dissipation, thereby revealing a general principle for operator growth in
dissipative quantum chaotic systems.


  Each year, wildfires destroy larger areas of Spain, threatening numerous
ecosystems. Humans cause 90% of them (negligence or provoked) and the behaviour
of individuals is unpredictable. However, atmospheric and environmental
variables affect the spread of wildfires, and they can be analysed by using
deep learning. In order to mitigate the damage of these events we proposed the
novel Wildfire Assessment Model (WAM). Our aim is to anticipate the economic
and ecological impact of a wildfire, assisting managers resource allocation and
decision making for dangerous regions in Spain, Castilla y Le\'on and
Andaluc\'ia. The WAM uses a residual-style convolutional network architecture
to perform regression over atmospheric variables and the greenness index,
computing necessary resources, the control and extinction time, and the
expected burnt surface area. It is first pre-trained with self-supervision over
100,000 examples of unlabelled data with a masked patch prediction objective
and fine-tuned using 311 samples of wildfires. The pretraining allows the model
to understand situations, outclassing baselines with a 1,4%, 3,7% and 9%
improvement estimating human, heavy and aerial resources; 21% and 10,2% in
expected extinction and control time; and 18,8% in expected burnt area. Using
the WAM we provide an example assessment map of Castilla y Le\'on, visualizing
the expected resources over an entire region.


  The Internet of Vehicles (IoV), whereby interconnected vehicles communicate
with each other and with road infrastructure on a common network, has promising
socio-economic benefits but also poses new cyber-physical threats. Data on
vehicular attackers can be realistically gathered through cyber threat
intelligence using systems like honeypots. Admittedly, configuring honeypots
introduces a trade-off between the level of honeypot-attacker interactions and
any incurred overheads and costs for implementing and monitoring these
honeypots. We argue that effective deception can be achieved through
strategically configuring the honeypots to represent components of the IoV and
engage attackers to collect cyber threat intelligence. In this paper, we
present HoneyCar, a novel decision support framework for honeypot deception in
IoV. HoneyCar builds upon a repository of known vulnerabilities of the
autonomous and connected vehicles found in the Common Vulnerabilities and
Exposure (CVE) data within the National Vulnerability Database (NVD) to compute
optimal honeypot configuration strategies. By taking a game-theoretic approach,
we model the adversarial interaction as a repeated imperfect-information
zero-sum game in which the IoV network administrator chooses a set of
vulnerabilities to offer in a honeypot and a strategic attacker chooses a
vulnerability of the IoV to exploit under uncertainty. Our investigation is
substantiated by examining two different versions of the game, with and without
the re-configuration cost to empower the network administrator to determine
optimal honeypot configurations. We evaluate HoneyCar in a realistic use case
to support decision makers with determining optimal honeypot configuration
strategies for strategic deployment in IoV.


  In a series of papers in the last ten years, various aspects of the
mathematical foundations of the quantum theory of atoms in molecules have been
considered by this author and his coworkers in some details. Although these
considerations answered part of the questions raised by some critics on the
mathematical foundations of the quantum theory of atoms in molecules, however,
new mathematical problems also emerged during these studies that were reviewed
elsewhere [Int. J. Quantum Chem. 111, 4497 (2011)]. Beyond mathematical
subtleties of the formalism that were the original motivation for initial
exchanges and disputes, the questions raised by critics had a constructive
effect and prompted the author to propose a novel extension of the theory, now
called the multi-component quantum theory of atoms in molecules [Theor. Chem.
Acc. 132, 1365 (2013)]. Taking this background into account, in this paper a
new set of open problems is put forward that the author believes proper answers
to these questions, may open new doors for future theoretical developments of
the quantum theory of atoms in molecules. Accordingly, rather than emphasizing
on the rigorous mathematical formulation, the practical motivations behind
proposing these questions are discussed in detail and the relevant literature
are reviewed while when possible, evidence and routes to answers are also
provided. The author hopes that proposing these open questions as a compact
package may motivate more mathematically oriented people to participate in
future developments of the quantum theory of atoms in molecules and its
multi-component version.


  We study the duration of "high-flow states" in freeway traffic, defined as
the time periods for which traffic flows exceed a given flow threshold. Our
empirical data are surprisingly well represented by a power law. Moreover, the
power law exponent is approximately 2, in dependently of the chosen flow
threshold. In order to explain this discovery, we investigate a simple
theoretical model of heterogeneous traffic with overtaking maneuvers, which is
able to reproduce both, the empirical power law and its exponent.


  In this work we present a non-standard model for microbial enhanced oil
recovery including the oil-water interfacial area. Including the interfacial
area in the model, we eliminate the hysteresis in the capillary pressure
relationship. One of the characteristics that a surfactant should have, it is
biological production at the oil-water interface. Therefore, we consider the
production rate of surfactants not only as a function of the nutrient
concentration, but also the interfacial area. To solve the model equations, we
use an efficient and robust linearization scheme that considers a linear
approximation of the capillary pressure gradient. A comprehensive, 1D
implementation based on two-point flux approximation of the model is achieved.
We consider different parameterizations for the interfacial tension and
residual oil saturation reduction. Illustrative numerical simulations are
presented, where we study the spatial distribution and evolution in time of the
average pressure, water saturation, interfacial area, capillary pressure,
residual oil saturation and bacterial, nutrient and surfactant concentrations.
Inclusion of the interfacial area in the model leads to different predictions
of oil recovery. The model can also be used to design new experiments
contributing to a better understanding and optimization of MEOR.


  Understanding searchers' queries is an essential component of semantic search
systems. In many cases, search queries involve specific attributes of an entity
in a knowledge base (KB), which can be further used to find query answers. In
this study, we aim to move forward the understanding of queries by identifying
their related entity attributes from a knowledge base. To this end, we
introduce the task of entity attribute identification and propose two methods
to address it: (i) a model based on Markov Random Field, and (ii) a learning to
rank model. We develop a human annotated test collection and show that our
proposed methods can bring significant improvements over the baseline methods.


  We consider the scattering of probe particles on an ultra-boosted beam of
charge, in the case that the fields of the beam are strong and must be treated
non-perturbatively. We show that the fields of the ultra-boosted beam act as
stochastic plane waves - scattering amplitudes (of elastic scattering,
nonlinear Compton and nonlinear Breit-Wheeler) are obtained without
approximation by averaging plane wave scattering amplitudes over all possible
plane wave parameters. The relevant plane waves are ultra-short and, as such,
scattering on ultra-boosted beams does not exhibit the conjectured strong-field
behaviour of QED based on the locally constant field approximation.


  We explore the possibility of baryogenesis in the framework of quintessential
inflation. We focus on the model independent features of the underlying
paradigm and demonstrate that the required baryon asymmetry can successfully be
generated in this scenario. To this effect, we use the effective field theory
framework with desired terms in the Lagrangian necessary to mimic baryon number
violation \textit{\`{a} la} spontaneous baryogenesis which can successfully
evade Sakharov's requirement allowing us to generate the observed baryon
asymmetry in the equilibrium process. Our estimates are independent of the
underlying physical process responsible for baryon number violation. The
underlying framework of quintessential inflation essentially includes the
presence of kinetic regime after inflation which gives rise to blue spectrum of
gravitational wave background at high frequencies. In addition to baryogenesis,
we discuss the prospects of detection of relic gravitational wave background,
in the future proposed missions, sticking to model independent treatment.


  While humans are aware of their body and capabilities, robots are not. To
address this, we present in this paper a neural network architecture that
enables a dual-arm robot to get a sense of itself in an environment. Our
approach is inspired by human self-awareness developmental levels and serves as
the underlying building block for a robot to achieve awareness of itself while
carrying out tasks in an environment. We assume that a robot has to know itself
before interacting with the environment in order to be able to support
different robotic tasks. Hence, we implemented a neural network architecture to
enable a robot to differentiate its limbs from the environment using visual and
proprioception sensory inputs. We demonstrate experimentally that a robot can
distinguish itself with an accuracy of 88.7% on average in cluttered
environmental settings and under confounding input signals.


  We investigate quantum dots in semiconductor PbTe nanowire devices. Due to
the accessibility of ambipolar transport in PbTe, quantum dots can be occupied
both with electrons and holes. Owing to a very large dielectric constant in
PbTe of order 1000, we do not observe Coulomb blockade which typically
obfuscates the orbital and spin spectra. We extract large and highly
anisotropic effective Lande g-factors, in the range 20-44. The absence of
Coulomb blockade allows direct readout, at zero source-drain bias, of
spin-orbit hybridization energies of up to 600 microelectronvolt. These spin
properties make PbTe nanowires, the recently synthesized members of group IV-VI
materials family, attractive as a materials platform for quantum technology,
such as spin and topological qubits.


  This paper is devoted to overview of the authors works for numerical solution
of singular integral equations (SIE), polysingular integral equations and
multi-dimensional singular integral equations of the second kind. The authors
investigated onsidered iterative - projective methods and parallel methods for
solution of singular integral equations, polysingular integral equations and
multi-dimensional singular integral equations. The paper is the second part of
overview of the authors works devoted to numerical methods for calculation
singular and hypersingular integrals \cite{Boy23} and to approximate methods
for solution of singular integral equations.


  We study the dynamical simulation of open quantum spin chain with nearest
neighboring coupling, where each spin in the chain is associated with a
harmonic bath. This is an extension of our previous work [G. Wang and Z. Cai,
J. Chem. Theory Comput., 19, 8523--8540, 2023] by generalizing the application
of the inchworm method and the technique of modular path integrals from
diagonally coupled cases to off-diagonally coupled cases. Additionally, to
reduce computational and memory cost in long time simulation, we apply
tensor-train representation to efficiently represent the reduced density matrix
of the spin chains, and employ the transfer tensor method (TTM) to avoid
exponential growth of computational cost with respect to time. Abundant
numerical experiments are performed to validate our method.


  In order to study the growth of wind waves in finite depth we extend Miles'
theory to the finite depth domain. A depth-dependent wave growth rate is
derived from the dispersion relation of the wind/water interface. A suitable
dimensionless finite depth wave age parameter allows us to plot a family of
wave growth curves, each family member characterized by the water depth. Two
major results are that for small wave age, the wave growth rates are comparable
to those of deep water and for large wave age, a finite-depth wave-age-limited
growth is reached, with wave growth rates going to zero. The corresponding
limiting wave length and limiting phase speed are explicitely calculated in the
shallow and in the deep water cases. A qualitative agreement with well-known
empirical results is established and shows the robust consistency of the linear
theoretical approach.


  We study the non-degenerate parametric amplifier for matter waves,
implemented by colliding two Bose-Einstein condensates. The coherence of the
amplified waves is shown by observing high contrast interference with a
reference wave and by reversing the amplification process. Since our
experiments also place limits on all known sources of decoherence, we infer
that relative number squeezing is most likely present between the amplified
modes. Finally, we suggest that reversal of the amplification process may be
used to detect relative number squeezing without requiring single-particle
detection.


  A nontopological soliton solution of dilaton-Maxwell theory describes a
domain wall-like solution which confines magnetic flux in its core [G.W.
Gibbons and C.G. Wells, Class. Quant. Grav. 11, 2499 (1994)]. Since the
solution is not stabilized by a nontrivial topology of the vacuum manifold, it
is interesting to see if the static solution is stable against small
fluctuations. We consider the stability of the solution in response to small
fluctuations in the scalar and magnetic fields. It is determined that the
ansatz solution does indeed exhibit stability.


  Dialogue systems are increasingly using knowledge bases (KBs) storing
real-world facts to help generate quality responses. However, as the KBs are
inherently incomplete and remain fixed during conversation, it limits dialogue
systems' ability to answer questions and to handle questions involving entities
or relations that are not in the KB. In this paper, we make an attempt to
propose an engine for Continuous and Interactive Learning of Knowledge (CILK)
for dialogue systems to give them the ability to continuously and interactively
learn and infer new knowledge during conversations. With more knowledge
accumulated over time, they will be able to learn better and answer more
questions. Our empirical evaluation shows that CILK is promising.


  We search for faces of the convex set consisting of all separable states,
which are affinely isomorphic to simplices, to get separable states with unique
decompositions. In the two-qutrit case, we found that six product vectors
spanning a five dimensional space give rise to a face isomorphic to the
5-dimensional simplex with six vertices, under suitable linear independence
assumption.
  If the partial conjugates of six product vectors also span a 5-dimensional
space, then this face is inscribed in the face for PPT states whose boundary
shares the fifteen 3-simplices on the boundary of the 5-simplex. The remaining
boundary points consist of PPT entangled edge states of rank four. We also show
that every edge state of rank four arises in this way. If the partial
conjugates of the above six product vectors span a 6-dimensional space then we
have a face isomorphic to 5-simplex, whose interior consists of separable
states with unique decompositions, but with non-symmetric ranks. We also
construct a face isomorphic to the 9-simplex. As applications, we give answers
to questions in the literature \cite{chen_dj_semialg,chen_dj_ext_PPT}, and
construct $3\ot 3$ PPT states of type (9,5). For the qubit-qudit cases with
$d\ge 3$, we also show that $(d+1)$-dimensional subspaces give rise to faces
isomorphic to the $d$-simplices, in most cases.


  Advances in 3D-aware generative models have pushed the boundary of image
synthesis with explicit camera control. To achieve high-resolution image
synthesis, several attempts have been made to design efficient generators, such
as hybrid architectures with both 3D and 2D components. However, such a design
compromises multiview consistency, and the design of a pure 3D generator with
high resolution is still an open problem. In this work, we present Generative
Volumetric Primitives (GVP), the first pure 3D generative model that can sample
and render 512-resolution images in real-time. GVP jointly models a number of
volumetric primitives and their spatial information, both of which can be
efficiently generated via a 2D convolutional network. The mixture of these
primitives naturally captures the sparsity and correspondence in the 3D volume.
The training of such a generator with a high degree of freedom is made possible
through a knowledge distillation technique. Experiments on several datasets
demonstrate superior efficiency and 3D consistency of GVP over the
state-of-the-art.


  Measuring cross-sections at the LHC requires the luminosity to be determined
accurately at each centre-of-mass energy $\sqrt{s}$. In this paper results are
reported from the luminosity calibrations carried out at the LHC interaction
point 8 with the LHCb detector for $\sqrt{s}$ = 2.76, 7 and 8 TeV
(proton-proton collisions) and for $\sqrt{s_{NN}}$ = 5 TeV (proton-lead
collisions). Both the "van der Meer scan" and "beam-gas imaging" luminosity
calibration methods were employed. It is observed that the beam density profile
cannot always be described by a function that is factorizable in the two
transverse coordinates. The introduction of a two-dimensional description of
the beams improves significantly the consistency of the results. For
proton-proton interactions at $\sqrt{s}$ = 8 TeV a relative precision of the
luminosity calibration of 1.47% is obtained using van der Meer scans and 1.43%
using beam-gas imaging, resulting in a combined precision of 1.12%. Applying
the calibration to the full data set determines the luminosity with a precision
of 1.16%. This represents the most precise luminosity measurement achieved so
far at a bunched-beam hadron collider.


  Spontaneously generated coherence and enhanced dispersion in a V-type,
three-level atomic system interacting with a single mode field can considerably
reduce the radiative and cavity decay rates. This may eliminate the use of high
finesse, miniaturized cavities in optical cavity quantum electrodynamics
experiments under strong atom-field coupling conditions.


  We study the equilibrium properties of a one-dimensional mixture of two
Tonks-Girardeau gases on a ring geometry in the limit of strongly-repulsive
inter-species interactions. We derive the exact many-body wavefunction and
compare it to the $SU(2)$ solution where intra- and inter-species interactions
are also diverging but equal. We focus on the role of the $SU(2)$-symmetry
breaking on the behaviour of the large- and short-distance correlations by
studying the zero-momentum occupation number and the Tan's contact from the
asymptotic behavior of the momentum distribution. Although the symmetry is only
weakly broken, it has important consequences on spin correlations in the system
as the reduction by a factor of two of the zero-momentum occupation number with
respect to the $SU(2)$ case in the thermodynamic limit and the decrease of the
Tan's contact.


  This paper describes the submission from the University of Helsinki to the
shared task on cross-lingual dependency parsing at VarDial 2017. We present
work on annotation projection and treebank translation that gave good results
for all three target languages in the test set. In particular, Slovak seems to
work well with information coming from the Czech treebank, which is in line
with related work. The attachment scores for cross-lingual models even surpass
the fully supervised models trained on the target language treebank. Croatian
is the most difficult language in the test set and the improvements over the
baseline are rather modest. Norwegian works best with information coming from
Swedish whereas Danish contributes surprisingly little.


  We study the ground-state phase diagram of spinless and spin-1 bosons in
optical superlattices using a Bose-Hubbard Hamiltonian that includes
spin-dependent interactions. We decouple the unit cells of the superlattice via
a mean-field approach and take into account the dynamics within the unit cell
exactly. The system supports Mott-insulating as well as superfluid phases. The
transitions between these phases are second-order for spinless bosons and
second- or first-order for spin-1 bosons. Anti-ferromagnetic interactions
energetically penalize high-spin configurations and elongate all Mott lobes,
especially the ones corresponding to an even atom number on each lattice site.
We find that the quadratic Zeeman effect lifts the degeneracy between different
polar superfluid phases leading to additional metastable phases and first-order
phase transitions. Finally, we show that an energy offset between the two sites
of the unit cell induces a staircase of single-atom tunneling resonances which
surprisingly survives well into the superfluid regime.


  In this paper we study the problem of deriving further Sobolev inequalities
from a given Sobolev inequality. We use several different methods, including
Bessel potentials and Riesz transforms. We apply the results to the Ricci flow
to extend the author's results on the $W^{1,2}$ Sobolev inequality along the
Ricci flow to $W^{1,p}$ and $W^{2,p}$ Sobolev inequalities for general p.


  IceCube is a cubic kilometer-scale neutrino telescope under construction at
the South Pole since the austral summer 2004/2005. At the moment it is taking
data with 22 deployed strings. The full detector is expected to be completed in
2011 with up to 80 strings each holding 60 digital optical modules. The
progenitor detector AMANDA has been operating at the same site since 1997 and
is still running as an integral part of IceCube. A summary of AMANDA science
for its 10 years of standalone operations is presented, as well as the status
and first physics results of the IceCube project.


  We study the final-state interaction (FSI) effects in charmless B_{u,d,s} to
PP decays. We consider a FSI approach with both short- and long-distance
contributions, where the former are from in-elastic channels and are contained
in factorization amplitudes, while the latter are from the residual
rescattering among PP states. Flavor SU(3) symmetry is used to constrain the
residual rescattering S-matrix. We fit to all available data on the CP-averaged
decay rates and CP asymmetries, and make predictions on unmeasured ones. Our
main results are as follows: (i) Results are in agreement with data in the
presence of FSI. (ii) For B decays, the pi^+pi^- and pi^0pi^0 rates are
suppressed and enhanced respectively by FSI. (iii) The FSI has a large impact
on direct CP asymmetries of many modes. (iv) The deviation (Delta A) between
A(B{bar}^0 to K^-pi^+) and A(B^-to K^-\pi^0) can be understood in the FSI
approach. (v) Sizable and complex color-suppressed tree amplitudes, which are
crucial for the large \pi^0\pi^0 rate and Delta A, are generated through
exchange rescattering. The correlation of the ratio B(pi^0pi^0)/B(pi^+pi^-) and
Delta A is studied. (vi) The B^- to pi^-pi^0 direct CP violation is very small
and is not affected by FSI. (vii) Several B_s decay rates are enhanced. In
particular, the eta'eta' branching ratio is enhanced to the level of
1.0X10^{-4}, which can be checked experimentally. (viii) Time-dependent CP
asymmetries S in B_{d,s} decays are studied. CP asymmetries in these modes will
be useful to test the SM.


  The problem of automatic identification of physical activities performed by
human subjects is referred to as Human Activity Recognition (HAR). There exist
several techniques to measure motion characteristics during these physical
activities, such as Inertial Measurement Units (IMUs). IMUs have a cornerstone
position in this context, and are characterized by usage flexibility, low cost,
and reduced privacy impact. With the use of inertial sensors, it is possible to
sample some measures such as acceleration and angular velocity of a body, and
use them to learn models that are capable of correctly classifying activities
to their corresponding classes. In this paper, we propose to use Convolutional
Neural Networks (CNNs) to classify human activities. Our models use raw data
obtained from a set of inertial sensors. We explore several combinations of
activities and sensors, showing how motion signals can be adapted to be fed
into CNNs by using different network architectures. We also compare the
performance of different groups of sensors, investigating the classification
potential of single, double and triple sensor systems. The experimental results
obtained on a dataset of 16 lower-limb activities, collected from a group of
participants with the use of five different sensors, are very promising.


  Tiered trees were introduced as a combinatorial object for counting
absolutely indecomposable representation of certain quivers and torus orbit of
certain homogeneous variety. In this paper, we define a bijection between the
set of parallelogram polyominoes and graphical parking functions. Moreover, we
defined the space $\mathcal{S}_{G}$ for complete tiered graphs and described
tiered graphs in terms of Whitney's operations.