  The $\eta$-meson photoproduction cross sections have been measured on the C
and Cu targets for the photon energies between 600 and 1100 MeV to investigate
the behavior of the $S_{11}$(1535) resonance in a nucleus. The excitation
functions of the cross section as well as the angular and momentum
distributions of $\eta$-mesons are in quantitative agreement with the Quantum
Molecular Dynamics (QMD) model calculations in which the $\eta$-meson emission
processes other than the $S_{11}$(1535) resonance are also incorporated as
proposed in the $\eta$-MAID model. It is shown that the excitation of the
$D_{15}$(1675) resonance might play an important role for $E_{\gamma}>900$ MeV.


  With the dual aims of enlarging the list of extremely metal-poor stars
identified in the Galaxy, and boosting the numbers of moderately
metal-deficient stars in directions that sample the rotational properties of
the thick disk, we have used the 2.5m Isaac Newton Telescope and the
Intermediate Dispersion Spectrograph to carry out a survey of brighter
(primarily northern hemisphere) metal-poor candidates selected from the HK
objective-prism/interference-filter survey of Beers and collaborators. Over the
course of only three observing runs (15 nights) we have obtained
medium-resolution (resolving power ~ 2000) spectra for 1203 objects (V ~
11-15). Spectral absorption-line indices and radial velocities have been
measured for all of the candidates. Metallicities, quantified by [Fe/H], and
intrinsic (B-V)o colors have been estimated for 731 stars with effective
temperatures cooler than roughly 6500 K, making use of artificial neural
networks (ANNs), trained with spectral indices. We show that this method
performs as well as a previously explored Ca II K calibration technique, yet it
presents some practical advantages. Among the candidates in our sample, we
identify 195 stars with [Fe/H] <= -1.0, 67 stars with [Fe/H] <= -2.0, and 12
new stars with [Fe/H] <= -3.0. Although the EFECTIVE YIELD of metal-poor stars
in our sample is not as large as previous HK survey follow-up programs, the
rate of discovery per unit of telescope time is quite high.


  Quantum computing (QC) has the potential to revolutionize fields like machine
learning, security, and healthcare. Quantum machine learning (QML) has emerged
as a promising area, enhancing learning algorithms using quantum computers.
However, QML models are lucrative targets due to their high training costs and
extensive training times. The scarcity of quantum resources and long wait times
further exacerbate the challenge. Additionally, QML providers may rely on a
third-party quantum cloud for hosting the model, exposing the models and
training data. As QML-as-a-Service (QMLaaS) becomes more prevalent, reliance on
third party quantum clouds can pose a significant threat. This paper shows that
adversaries in quantum clouds can use white-box access of the QML model during
training to extract the state preparation circuit (containing training data)
along with the labels. The extracted training data can be reused for training a
clone model or sold for profit. We propose a suite of techniques to prune and
fix the incorrect labels. Results show that $\approx$90\% labels can be
extracted correctly. The same model trained on the adversarially extracted data
achieves approximately $\approx$90\% accuracy, closely matching the accuracy
achieved when trained on the original data. To mitigate this threat, we propose
masking labels/classes and modifying the cost function for label obfuscation,
reducing adversarial label prediction accuracy by $\approx$70\%.


  In this work we present results for pairing gaps in $\beta$--stable neutron
star matter with electrons and muons using a relativistic
Dirac--Brueckner--Hartree--Fock approach, starting with modern meson--exchange
models for the nucleon--nucleon interaction. Results are given for
superconducting $^1S_0$ protons and $^3P_2$ and $^1D_2$ neutron superfluids. A
comparison is made with recent non--relativistic calculations and the
implications for neutron star cooling are discussed.


  Permutation codes under different metrics have been extensively studied due
to their potentials in various applications. Generalized Cayley metric is
introduced to correct generalized transposition errors, including previously
studied metrics such as Kendall's $\tau$-metric, Ulam metric and Cayley metric
as special cases. Since the generalized Cayley distance between two
permutations is not easily computable, Yang et al. introduced a related metric
of the same order, named the block permutation metric. Given positive integers
$n$ and $d$, let $\mathcal{C}_{B}(n,d)$ denote the maximum size of a
permutation code in $S_n$ with minimum block permutation distance $d$. In this
paper, we focus on the theoretical bounds of $\mathcal{C}_{B}(n,d)$ and the
constructions of permutation codes under block permutation metric. Using a
graph theoretic approach, we improve the Gilbert-Varshamov type bound by a
factor of $\Omega(\log{n})$, when $d$ is fixed and $n$ goes into infinity. We
also propose a new encoding scheme based on binary constant weight codes.
Moreover, an upper bound beating the sphere-packing type bound is given when
$d$ is relatively close to $n$.


  Pipelined Krylov subspace methods typically offer improved strong scaling on
parallel HPC hardware compared to standard Krylov subspace methods for large
and sparse linear systems. In pipelined methods the traditional synchronization
bottleneck is mitigated by overlapping time-consuming global communications
with useful computations. However, to achieve this communication hiding
strategy, pipelined methods introduce additional recurrence relations for a
number of auxiliary variables that are required to update the approximate
solution. This paper aims at studying the influence of local rounding errors
that are introduced by the additional recurrences in the pipelined Conjugate
Gradient method. Specifically, we analyze the impact of local round-off effects
on the attainable accuracy of the pipelined CG algorithm and compare to the
traditional CG method. Furthermore, we estimate the gap between the true
residual and the recursively computed residual used in the algorithm. Based on
this estimate we suggest an automated residual replacement strategy to reduce
the loss of attainable accuracy on the final iterative solution. The resulting
pipelined CG method with residual replacement improves the maximal attainable
accuracy of pipelined CG, while maintaining the efficient parallel performance
of the pipelined method. This conclusion is substantiated by numerical results
for a variety of benchmark problems.


  The performance of a two-phase Ar avalanche detector in a single electron
counting mode was studied, with regard to potential application in coherent
neutrino-nucleus scattering and dark matter search experiments. The detector
comprised of a 1 cm thick liquid Ar layer and a triple-GEM multiplier operated
in the saturated vapour above the liquid phase. Successful operation of the
detector in single electron counting mode, in the gain range from 6000 to
40000, has for the first time been demonstrated.


  We present a study of the effect of High Column Density (HCD) systems on the
Lyman \alpha forest correlation function on large scales. We study the effect
both numerically, by inserting HCD systems on mock spectra for a specific
model, and analytically, in the context of two-point correlations and linear
theory. We show that the presence of HCDs substantially contributes to the
noise of the correlation function measurement, and systematically alters the
measured redshift-space correlation function of the Lyman \alpha forest,
increasing the value of the density bias factor and decreasing the redshift
distortion parameter $\beta_\alpha$ of the Lyman \alpha forest. We provide
simple formulae for corrections on these derived parameters, as a function of
the mean effective optical depth and bias factor of the host halos of the HCDs,
and discuss the conditions under which these expressions should be valid. In
practice, precise corrections to the measured parameters of the Lyman \alpha
forest correlation for the HCD effects are more complex than the simple
analytical approximations we present, owing to non-linear effects of the damped
wings of the HCD systems and the presence of three-point terms. However, we
conclude that an accurate correction for these HCD effects can be obtained
numerically and calibrated with observations of the HCD-Lyman \alpha
cross-correlation. We also discuss an analogous formalism to treat and correct
for the contaminating effect of metal lines overlapping the Lyman \alpha forest
spectra.


  We report a novel process variation-aware compact model of strip waveguides
that is suitable for circuit-level simulation of waveguide-based process design
kit (PDK) elements. The model is shown to describe both loss and -- using a
novel expression for the thermo-optic effect in high index contrast materials
-- the thermo-optic behavior of strip waveguides. A novel group extraction
method enables modeling the effective index's ($n_{\mathrm{eff}}$) sensitivity
to local process variations without the presumption of variation source. Use of
Euler-bend Mach-Zehnder interferometers (MZIs) fabricated in a 300~mm wafer run
allow model parameter extraction at widths up to 2.5~$\mu$m (highly multi-mode)
with strong suppression of higher-order mode excitation. Experimental results
prove the reported model can self-consistently describe waveguide phase, loss,
and thermo-optic behavior across all measured devices over an unprecedented
range of optical bandwidth, waveguide widths, and temperatures.


  We study the driven-dissipative dynamics of a network of spin-1/2 systems
coupled to one or more chiral 1D bosonic waveguides within the framework of a
Markovian master equation. We determine how the interplay between a coherent
drive and collective decay processes can lead to the formation of pure
multipartite entangled steady states. The key ingredient for the emergence of
these many-body dark states is an asymmetric coupling of the spins to left and
right propagating guided modes. Such systems are motived by experimental
possibilities with internal states of atoms coupled to optical fibers, or
motional states of trapped atoms coupled to a spin-orbit coupled Bose-Einstein
condensate. We discuss the characterization of the emerging multipartite
entanglement in this system in terms of the Fisher information.


  We present simulations of particle beam channeling in carbon nanotubes and
evaluate the possibilities for experimental observation of channeling effect in
straight and bent nanotubes at IHEP and LNF. Different particle species are
considered: protons of 1.3 and 70 GeV, and positrons of 0.5 GeV. Predictions
are made for the experiments, with analysis of requirements on the quality of
nanosamples and resolution of the experimental set-up. Based on Monte Carlo
simulations, the capabilities of nanotube channeling technique for particle
beam steering are discussed.


  We attack generalized Thomson problems with a continuum formalism which
exploits a universal long range interaction between defects depending on the
Young modulus of the underlying lattice. Our predictions for the ground state
energy agree with simulations of long range power law interactions of the form
1/r^{gamma} (0 < gamma < 2) to four significant digits. The regime of grain
boundaries is studied in the context of tilted crystalline order and the
generality of our approach is illustrated with new results for square tilings
on the sphere.


  We introduce a gradient-based approach for the problem of Bayesian optimal
experimental design to learn causal models in a batch setting -- a critical
component for causal discovery from finite data where interventions can be
costly or risky. Existing methods rely on greedy approximations to construct a
batch of experiments while using black-box methods to optimize over a single
target-state pair to intervene with. In this work, we completely dispose of the
black-box optimization techniques and greedy heuristics and instead propose a
conceptually simple end-to-end gradient-based optimization procedure to acquire
a set of optimal intervention target-state pairs. Such a procedure enables
parameterization of the design space to efficiently optimize over a batch of
multi-target-state interventions, a setting which has hitherto not been
explored due to its complexity. We demonstrate that our proposed method
outperforms baselines and existing acquisition strategies in both single-target
and multi-target settings across a number of synthetic datasets.


  It has been considered that the effective bending rigidity of fluid membranes
should be reduced by thermal undulations. However, recent thorough
investigation by Pinnow and Helfrich revealed significance of measure factors
for the partition sum. Accepting the local curvature as a statistical measure,
they found that fluid membranes are stiffened macroscopically. In order to
examine this remarkable idea, we performed extensive ab initio simulations for
a fluid membrane. We set up transfer matrix which is diagonalized by means of
the density-matrix renormalization group. Our method has an advantage in that
it allows us to survey various statistical measures. As a consequence, we found
that the effective bending rigidity flows toward strong coupling under the
choice of local curvature as a statistical measure. On the contrary, for other
measures such as normal displacement and tilt angle, we found clear tendency
toward softening.


  A convective dynamo operating during the crystallization of white dwarfs is
one of the promising channels to produce their observed strong magnetic fields.
Although the magnitude of the fields generated by crystallization dynamos is
uncertain, their timing may serve as an orthogonal test of this channel's
contribution. The carbon-oxygen cores of $M\approx 0.5-1.0\,{\rm M}_\odot$
white dwarfs begin to crystallize at an age $t_{\rm cryst}\propto M^{-5/3}$,
but the magnetic field is initially trapped in the convection zone - deep
inside the CO core. Only once a mass of $m_{\rm cryst}$ has crystallized, the
convection zone approaches the white dwarf's helium layer, such that the
magnetic diffusion time through the envelope shortens sufficiently for the
field to break out to the surface, where it can be observed. This breakout time
is longer than $t_{\rm cryst}$ by a few Gyr, scaling as $t_{\rm break}\propto
t_{\rm cryst}f^{-1/2}$, where $f\equiv 1-m_{\rm cryst}/M$ depends on the white
dwarf's initial C/O profile before crystallization. The first appearance of
strong magnetic fields $B\gtrsim 1\textrm{ MG}$ in volume-limited samples
approximately coincides with our numerically computed $t_{\rm break}(M)$ -
potentially signalling crystallization dynamos as a dominant magnetization
channel. However, some observed magnetic white dwarfs are slightly younger,
challenging this scenario. The dependence of the breakout process on the white
dwarf's C/O profile implies that magnetism may probe the CO phase diagram, as
well as uncertainties during the core helium burning phase in the white dwarf's
progenitor, such as the $^{12}{\rm C}(\alpha,\gamma)^{16}{\rm O}$ nuclear
reaction.


  This work aims to introduce a heuristic timestep-adaptive algorithm for
Computational Fluid Dynamics (CFD) and Fluid-Structure Interaction (FSI)
problems where the flow is dominated by the pressure. In such scenarios, many
time-adaptive algorithms based on the interplay of implicit and explicit time
schemes fail to capture the fast transient dynamics of pressure fields. We
present an algorithm that relies on a temporal error estimator using Backward
Differentiation Formulae (BDF$k$) of order $k=2,3$. Specifically, we
demonstrate that the implicit BDF$3$ solution can be well approximated by
applying a single Newton-type nonlinear solver correction to the implicit
BDF$2$ solution. The difference between these solutions determines our adaptive
temporal error estimator. The effectiveness of our approach is confirmed by
numerical experiments conducted on a backward-facing step flow CFD test case
with Reynolds number $300$ and on a two-dimensional haemodynamics FSI
benchmark.


  In the present note, we study a new method of constructing efficient
coverings for Kronecker powers of matrices, recently proposed by J. Alman, Y.
Guan, A. Padaki [arXiv, 2022]. We provide an alternative proof for the case of
symmetric matrices in a stronger form. As a consequence, the previously known
upper bound on the depth-2 additive complexity of the boolean $N\times N$
Kneser-Sierpinski matrices is improved to $O(N^{1.251})$.


  The nontrivial topology of {\it p}-wave superfluids make these systems
attractive candidate in information technology. In this work we report on the
topological state of a {\it p}-wave nonequilibrium excitonic insulator (NEQ-EI)
and show how to steer a nontopological band-insulator with bright $p$-excitons
toward this state by a suitable laser pulse, thus achieving a dynamical
topological phase transition. The underlying mechanism behind the transition is
the broken gauge-symmetry of the NEQ-EI which causes self-sustained persistent
oscillations of the excitonic condensate and hence a Floquet topological state
for high enough exciton densities. We show the formation of Floquet Majorana
modes at the boundaries of the open system and discuss topological spectral
signatures for ARPES experiments. We emphasize that the topological properties
of a {\it p}-wave NEQ-EI arise exclusively from the electron-hole Coulomb
interaction as the system is not driven by external fields.


  The comprehensive low-temperature specific heat C(T) data identify both an
isotropic s-wave and an extended s-wave order parameters coexisting in a
superconducting single crystal FeSe with Tc=8.11 K. The isotropic gap
{\Delta}0=1.33 meV on the hole Fermi sheets and the extended s-wave gap
{\Delta}={\Delta}e(1+\alpha*cos2_theta) with {\Delta}e=1.13 meV and \alpha=0.78
on the electron Fermi sheets. The extended s-wave is rather anisotropic but the
low energy quasiparticle excitations demonstrate no sign of the accidental
nodes. The coefficient \gamma(H) manifesting the quasiparticle contribution to
C is a non-linear function of the applied magnetic field H in the mixed state
in accord with the anisotropic multi-order parameters.


  In this paper, we will show that one can use certain types of symplectic
homology as an invariant of 3-dimensional Besse manifolds, which are contact
manifolds admitting a periodic Reeb flow and hence allow Seifert fibration
structure. For simplicity, we will assume our contact structures to be trivial
plane bundles. We will also compute Robbin-Salamon indices of periodic Reeb
orbits in Besse manifolds and obtain more precise information about the
symplectic homology. In the computations, invariants of the Seifert fibration
such as the Euler number and the orbifold Euler characteristic play an
important role.


  Giant radio galaxy (GRG) lobes are excellent laboratories to study the
evolution of the particle and B-field energetics. However, these results are
based on assumptions of the shape and extension of the GRG lobe electron
spectrum. We re-examine the energetics of GRG lobes as derived by inverse
Compton scattering of CMB photons (ICS-CMB) by relativistic electrons in RG
lobes to assess the physical conditions of RG lobes, their energetics and their
radiation regime. We consider the GRG DA 240 recently observed by Suzaku as a
reference case and we also discuss other RG lobes observed with Chandra and
XMM. We model the spectral energy distribution of the DA 240 East lobe to get
constraint on the shape and the extension of the electron spectrum in the lobe
by using multi-frequency information from radio to gamma-rays. We use radio and
X-ray data to constrain the shape and normalization of the electron spectrum
and we then calculate the SZ effect expected in GRG lobes that is sensitive to
the total electron energy density. We show that the electron energy density U_e
derived form X-ray observations yields only a rough lower limit to its actual
value and that most of the estimates of U_e based on X-ray measurements have to
be increased even by a large factor by considering realistic estimates of the
lower electron momentum p_1. This brings RG lobes away from the equipartition
condition towards a particle-dominated and Compton power dominance regime. We
use the distribution of RG lobes in the U_e/U_B vs. U_e/U_CMB plane as a
further divide between different physical regimes of particle and field
dominance, and radiation mechanism dominance in RG lobes. We conclude that the
SZ effect produced by ICS-CMB mechanism observable in RG lobes provides
reliable estimate of p_1 and U_e and is the best tool to determine the total
energy density of RG lobes and to assess their physical regime.


  The Erd\"{o}s-Straus conjecture states that the equation
$\frac{4}{n}=\frac{1}{x}+\frac{1}{y}+\frac{1}{z}$ has positive integer
solutions $x, y, z$ for every postive integers $n\ge 2$. We generalize the
Erd\"{o}s-Straus equation, state several methods for obtaining well-defined
equations, and some features of well-defined equations. Using well-defined
equations, we explain the inefficiency of the solutions that did not lead to a
complete proof of the problem. Finally, we prove that this conjecture cannot be
completely proven by the methods expressed in various articles. For this
reason, we express conjectures equivalent to the Erd\"{o}s-Straus conjecture
and make the concept of conjecture clearer. The well-defined equations of
Erd\"{o}s-Straus have a lot to do with the concept of the least common multiple
and the greatest common divisor, and in the last section, we will independently
express the functions and features based on the subject of the least common
multiple.


  Dark energy in the universe is assumed to be vacuum energy. The
energy-momentum of vacuum is described by a scale-dependent cosmological
constant. The equations of motion imply for the density of matter (dust) the
sum of the usual matter density (luminous matter) and an additional matter
density (dark matter) similar to the dark energy. The scale-dependent
cosmological constant is given up to an exponent which is approximated by the
experimentally decided density parameters of dark matter and dark energy. This
yields that dark matter is one third of dark energy for all times implying an
explanation of the coincidence of dark matter and dark energy. In the final
state, the universe becomes dark, consisting of dark matter and dark energy.


  We analyze a stimulated revival (echo) effect for the breathing modes of the
atomic oscillations in optical lattices. The effect arises from the dephasing
due to the weak anharmonicity being partly reversed in time by means of
additional parametric excitation of the optical lattice. The shape of the echo
response is obtained by numerically simulating the equation of motion for the
atoms with subsequent averaging over the thermal initial conditions. A
qualitative analysis of the phenomenon shows that the suggested echo mechanism
combines the features of both spin and phonon echoes.


  Many processes in nature (e.g., physical and biogeochemical processes in
hyporheic zones, and arterial mass transport) occur near the interface of
free-porous media. A firm understanding of these processes needs an accurate
prescription of flow dynamics near the interface which (in turn) hinges on an
appropriate description of interface conditions along the interface of
free-porous media. Although the conditions for the flow dynamics at the
interface of free-porous media have received considerable attention, many of
these studies were empirical and lacked a firm theoretical underpinning. In
this paper, we derive a complete and self-consistent set of conditions for flow
dynamics at the interface of free-porous media. We first propose a principle of
virtual power by incorporating the virtual power expended at the interface of
free-porous media. Then by appealing to the calculus of variations, we obtain a
complete set of interface conditions for flows in coupled free-porous media. A
noteworthy feature of our approach is that the derived interface conditions
apply to a wide variety of porous media models. We also show that the two most
popular interface conditions -- the Beavers-Joseph condition and the
Beavers-Joseph-Saffman condition -- are special cases of the approach presented
in this paper. The proposed principle of virtual power also provides a minimum
power theorem for a class of flows in coupled free-porous media, which has a
similar mathematical structure as the ones enjoyed by flows in uncoupled free
and porous media.


  An invited research highlight article on Charged lepton flavor violation
searches in the charmonium system.


  Molecule oxygen resembles 3d and 4f metals in exhibiting long-range spin
ordering and electron strong correlated behaviors in compounds. The
ferromagnetic spin ordering and half-metallicity, however, are quite elusive
and have not been well acknowl-edged. In this article, we address this issue to
study how spins will interact each other if the oxygen dimers are arranged in a
dif-ferent way from that in the known super- and per-oxides by first principles
calculations. Based on the results of structure search, thermodynamic study and
lattice dynamics, we show that tetragonal {\alpha}-BaNaO4 is a stable
half-metal with a Curie temperature at 120 K, a first example in this class of
compounds. Like 3d and 4f metals, the O2 dimer carries a local magnetic moment
0.5 {\mu}B due to the unpaired electrons in its {\pi}* orbitals. This compound
can be regarded as forming from the O2 dimer layers stacking in a head to head
way. Different from that in AO2 (A=K, Rb, Cs), the spins are both
ferromagnetically coupled within and between the layers. Spin polarization
occurs in {\pi}* orbitals with spin-up electrons fully occupying the valence
band and spin-down electrons partially the conduction band, forming the
semiconducting and metallic channels, respectively. Our results highlight the
im-portance of geometric arrangement of O2 dimers in inducing ferromagnetism
and other novel properties in O2 dimer containing compounds.


  The capacity to resist attacks from the environment is crucial to the
survival of all organisms. We quantitatively analyze the susceptibility of
protein interaction networks of numerous organisms to random and malicious
attacks. We find for all organisms studied that random rewiring improves
protein network robustness, so that actual networks are more fragile than
rewired surrogates. This unexpected fragility contrasts with the behavior of
networks such as the Internet, whose robustness decreases with random rewiring.
We trace this surprising effect to the modular structure of protein networks.


  We study, in general terms, the process by which a pure state can
``self-thermalize'' and {\em appear} to be described by a microcanonical
density matrix. This requires a quantum mechanical version of the Gibbsian
coarse graining that conceptually underlies classical statistical mechanics. We
introduce some extra degrees of freedom that are necessary for this.
Interaction between these degrees and the system can be understood as a process
of resonant absorption and emission of ``soft quanta''. This intuitive picture
allows one to state a criterion for when self thermalization occurs. This
paradigm also provides a method for calculating the thermalization rate using
the usual formalism of atomic physics for calculating decay rates. We contrast
our prescription for coarse graining, which is somewhat dynamical, with the
earlier approaches that are intrinsically kinematical. An important motivation
for this study is the black hole information paradox.


  Coronal loops act as resonant cavities for low frequency fluctuations that
are transmitted from the deeper layers of the solar atmosphere and are
amplified in the corona, triggering nonlinear interactions. However trapping is
not perfect, some energy leaks down to the chromosphere, thus limiting the
turbulence development and the associated heating. We consider the combined
effects of turbulence and leakage in determining the energy level and
associated heating rate in models of coronal loops which include the
chromosphere and transition region. We use a piece-wise constant model for the
Alfven speed and a Reduced MHD - Shell model to describe the interplay between
turbulent dynamics in the direction perpendicular to the mean field and
propagation along the field. Turbulence is sustained by incoming fluctuations
which are equivalent, in the line-tied case, to forcing by the photospheric
shear flows. While varying the turbulence strength, we compare systematically
the average coronal energy level (E) and dissipation rate (D) in three models
with increasing complexity: the classical closed model, the semi-open corona
model, and the corona-chromosphere (or 3-layer) model, the latter two models
allowing energy leakage. We find that:
  (i) Leakage always plays a role (even for strong turbulence), E and D are
systematically lower than in the line-tied model. (ii) E is close to the
resonant prediction, i.e., assuming effective turbulent correlation time longer
than the Alfven coronal crossing time (Ta). (iii) D is close to the value given
by the ratio of photospheric energy divided by Ta (iv) The coronal spectra
exibits an inertial range with 5/3 spectral slope, and a large scale peak of
trapped resonant modes that inhibit nonlinear couplings. (v) In the realistic
3-layer model, the two-component spectrum leads to a damping time equal to the
Kolmogorov time reduced by a factor u_rms/Va_corona


  We study by means of the recursive Green's function technique the local
density-of-states of (finite and semi-infinite) multi-band spin-orbit coupled
semiconducting nanowires in proximity to an s-wave superconductor and attached
to normal-metal electrodes. When the nanowire is coupled to a normal electrode,
the zero-energy peak, corresponding to the Majorana state in the topological
phase, broadens with increasing transmission between the wire and the leads,
eventually disappearing for ideal interfaces. Interestingly, for a finite
transmission a peak is present also in the normal electrode, even though it has
a smaller amplitude and broadens more rapidly with the strength of the
coupling. Unpaired Majorana states can survive close to a topological phase
transition even when the number of open channels (defined in the absence of
superconductivity) is even. We finally study the Andreev-bound-state spectrum
in superconductor-normal metal-superconductor junctions and find that in
multi-band nanowires the distinction between topologically trivial and
non-trivial systems based on the number of zero-energy crossings is preserved.


  We obtain uniform asymptotics for polynomials orthogonal on a fixed and
varying arc of the unit circle with a positive analytic weight function. We
also complete the proof of the large $s$ asymptotic expansion for the Fredholm
determinant with the kernel $\sin z/(\pi z)$ on the interval $[0,s]$, verifying
a conjecture of Dyson for the constant term in the expansion. In the Gaussian
Unitary Ensemble of random matrices, this determinant describes the probability
for an interval of length $s$ in the bulk scaling limit to be free from the
eigenvalues.


  We present a new analysis of the diffuse soft $\gamma$-ray emission towards
the inner Galaxy as measured by the SPectrometer aboard the INTEGRAL satellite
(SPI) with 16 years of data taking. The analysis implements a spatial template
fit of SPI data and an improved instrumental background model. We characterize
the contribution of Primordial Black Holes (PBH) as dark matter (DM) candidates
evaporating into $\mathcal{O}$(1) MeV photons by including, for the first time,
the spatial distribution of their signal into the fitting procedure. No PBH
signal is detected, and we set the strongest limit on PBH DM for masses up to
$4 \times 10^{17}$ g, significantly closing in into the so-called asteroid mass
range.


  We investigate the role of polarization effects in orbital angular momentum
modes of an optical fiber. Specifically, we revisit the removal of the
degeneracy of the $HE$ and the $EH$ vector modes that exists within the weakly
guiding approximation (WGA); within WGA, these two vector modes coalesce into a
single spatial (scalar) orbital angular momentum (OAM) mode, defined by a
common topological charge and associated with left or right circular
polarization. While the polarization term of the vector wave equation acting as
a Hermitian perturbation operator accounts for the splitting of the $HE$ and
the $EH$ vector modes, its extension to explain the accompanying field
correction, called the polarization-induced field, encounters hurdles; this
field is characterized by a topological charge differing by two in magnitude.
The problems are critically examined with a possible remedy and its
implications.


  $^{51}$V NMR studies on CaV2O4 single crystals and $^{17}$O NMR studies on
$^{17}$O-enriched powder samples are reported. The temperature dependences of
the $^{17}$O NMR line width and nuclear spin-lattice relaxation rate give
strong evidence for a long-range antiferromagnetic transition at Tn = 78 K in
the powder. Magnetic susceptibility measurements show that Tn = 69 K in the
crystals. A zero-field $^{51}$V NMR signal was observed at low temperatures (f
$\approx$ 237 MHz at 4.2 K) in the crystals. The field swept spectra with the
field in different directions suggest the presence of two antiferromagnetic
substructures. Each substructure is collinear, with the easy axes of the two
substructures separated by an angle of 19(1) degree, and with their average
direction pointing approximately along the b-axis of the crystal structure. The
two spin substructures contain equal number of spins. The temperature
dependence of the ordered moment, measured up to 45 K, shows the presence of an
energy gap Eg in the antiferromagnetic spin wave excitation spectrum.
Antiferromagnetic spin wave theory suggests that Eg lies between 64 and 98 K.


  As a formation route for objects such as giant planets and low-mass stars in
protostellar discs (as well as stars in AGN discs), theories of
self-gravitating disc fragmentation need to be able to predict the initial
masses of fragments. We describe a means by which the local Jeans mass inside
the spiral structure of a self-gravitating disc can be estimated. If such a
self-gravitating disc satisfies the criteria for disc fragmentation, this
estimate provides a lower limit for the initial mass of any fragments formed.
We apply this approach to a series of self-gravitating protostellar disc
models, to map out the typical masses of fragments produced by this formation
mode. We find a minimum fragment mass of around 3 Jupiter masses, which is
insensitive to the stellar mass, and that - within the parameter space surveyed
- fragments with masses between 10 and 20 Jupiter masses are the most common.
We also describe how the Jeans mass allows us to derive a more general
criterion for disc fragmentation, which accounts for the processes of viscous
heating, radiative cooling, accretion and the disc's thermal history. We
demonstrate how such a criterion can be determined, and show that in limiting
cases it recovers several fragmentation criteria that have been posited in the
past, including the minimum cooling time/maximum stress criterion.


  As deep learning continues to dominate all state-of-the-art computer vision
tasks, it is increasingly becoming an essential building block for robotic
perception. This raises important questions concerning the safety and
reliability of learning-based perception systems. There is an established field
that studies safety certification and convergence guarantees of complex
software systems at design-time. However, the unknown future deployment
environments of an autonomous system and the complexity of learning-based
perception make the generalization of design-time verification to run-time
problematic. In the face of this challenge, more attention is starting to focus
on run-time monitoring of performance and reliability of perception systems
with several trends emerging in the literature. This paper attempts to identify
these trends and summarise the various approaches to the topic.


  This paper is concerned with objective value performance of the scenario
approach for robust convex optimization. A novel method is proposed to derive
probabilistic bounds for the objective value from scenario programs with a
finite number of samples. This method relies on a max-min reformulation and the
concept of complexity of robust optimization problems. With additional
continuity and regularity conditions, via sensitivity analysis, we also provide
explicit bounds which outperform an existing result in the literature. To
illustrate the improvements of our results, we also provide a numerical
example.


  The main purpose of this paper is to derive a new perturbation theory (PT)
that has converging series. Such series arise in the nonlocal scalar quantum
field theory (QFT) with fractional power potential. We construct PT for the
generating functional (GF) of complete Green functions (including disconnected
parts of functions) $\mathcal{Z}\left[j\right]$ as well as for GF of connected
Green functions $\mathcal{G}\left[j\right]=\ln \mathcal{Z}\left[j\right]$ in
powers of coupling constant $g$. It has infrared (IR)-finite terms. We prove
that the obtained series, which has the form of a grand canonical partition
function (GCPF), is dominated by a convergent series, in other words, has
majorant, which allows to expand beyond the weak coupling $g$ limit. Vacuum
energy density in second order in $g$ is calculated and researched for
different types of Gaussian part $S_{0}[\phi]$ of the action $S[\phi]$. Further
in the paper, using the polynomial expansion, the general calculable series for
$\mathcal{G}\left[j\right]$ is derived. We provide, compare and research
simplifications in cases of second-degree polynomial and hard-sphere gas (HSG)
approximations. The developed formalism allows us to research the physical
properties of the considering system across the entire range of coupling
constant $g$, in particular, the vacuum energy density.


  Optimal prefix codes are studied for pairs of independent, integer-valued
symbols emitted by a source with a geometric probability distribution of
parameter $q$, $0{<}q{<}1$. By encoding pairs of symbols, it is possible to
reduce the redundancy penalty of symbol-by-symbol encoding, while preserving
the simplicity of the encoding and decoding procedures typical of Golomb codes
and their variants. It is shown that optimal codes for these so-called
two-dimensional geometric distributions are \emph{singular}, in the sense that
a prefix code that is optimal for one value of the parameter $q$ cannot be
optimal for any other value of $q$. This is in sharp contrast to the
one-dimensional case, where codes are optimal for positive-length intervals of
the parameter $q$. Thus, in the two-dimensional case, it is infeasible to give
a compact characterization of optimal codes for all values of the parameter
$q$, as was done in the one-dimensional case. Instead, optimal codes are
characterized for a discrete sequence of values of $q$ that provide good
coverage of the unit interval. Specifically, optimal prefix codes are described
for $q=2^{-1/k}$ ($k\ge 1$), covering the range $q\ge 1/2$, and $q=2^{-k}$
($k>1$), covering the range $q<1/2$. The described codes produce the expected
reduction in redundancy with respect to the one-dimensional case, while
maintaining low complexity coding operations.


  We classify kinematical Lie algebras in dimension $D \geq 4$. This is
approached via the classification of deformations of the relevant static
kinematical Lie algebra. We also classify the deformations of the universal
central extension of the static kinematical Lie algebra in dimension $D\geq 4$.
In addition we determine which of these Lie algebras admit an invariant inner
product.


  We report measurements of London penetration depth $\lambda(T)$ for the
noncentrosymmetric superconductor BiPd by using a tunnel diode oscillator.
Pronounced anisotropic behavior is observed in the low-temperature penetration
depth; the in-plane penetration depth $\lambda_{ac}(T)$ follows an exponential
decrease, but the interplane penetration depth $\lambda_b(T)$ shows
power-law-type behavior. The superfluid density $\rho_s(T)$, converted from the
penetration depth $\lambda(T)$, is best fitted by an anisotropic two-band BCS
model. We argue that such a complex order parameter is attributed to the
admixture of spin-singlet and spin-triplet pairing states as a result of
antisymmetric spin-orbit coupling in BiPd.


  The structural behavior of UO2 under high pressure up to 300GPa has been
studied by first-principles calculations with LSDA+U approximation. The results
show that a pressure-induced structural transition to the cotunnite-type
(orthorhombic Pnma) phase occurs at 38GPa. It agrees well with the
experimentally observed ~42 GPa. An isostructural transition following that is
also predicted to take place from 80 to 130GPa, which has not yet been observed
in experiments. Further high compression beyond 226GPa will result in a
metallic and paramagnetic transition. It corresponds to a volume of 90A^3 per
cell, in good agreement with a previous theoretical analysis in the reduction
of volume required to delocalize 5f states.


  We study the statistics of the gravitational (Newtonian) force in a
particular kind of weakly correlated distribution of point-like and unitary
mass particles generated by the so-called Gauss-Poisson point process. In
particular we extend to these distributions the analysis a' la Chandrasekhar
introduced for purely Poisson processes. In this way we can find the asymptotic
behavior of the probability density function of the force for large values of
the field as a generalization of the Holtzmark statistics. The validity of the
introduced approximations is positively tested through a direct comparison with
the analysis of the statistics of the gravitational force in numerical
simulations of Gauss-Poisson processes. Moreover the statistics of the force
felt by a particle due to only its first nearest neighbor is analytically a
numerical studied, resulting to be the dominant contribution to the total
force.


  The newly discovered exotic $\Theta$ baryon of mass 1540 MeV (and very small
width) truly has a very low mass, if it is a pentaquark system of even parity.
A schematic model in which the coherent interaction of $u\bar s$ and $d\bar s$
pairs leads to a very large residual (non-confining) attractive interaction is
introduced. This collective vibrational model accounts for the mass and small
decay width to the $KN$ channel, but yields a significant coupling to the
virtual $K^*N$ channel. The schematic model predicts an attractive
$\Theta$-nucleon interaction strong enough to bind a $\Theta$ particle to a
nucleus in a state that is stable against decay via strong interactions. The
discovery of $\Theta$-nuclei could be a definitive proof that the $\Theta$
parity is even.


  We study diffusive shock acceleration (DSA) of electrons in non-relativistic
quasi-perpendicular shocks using self-consistent one-dimensional
particle-in-cell (PIC) simulations. By exploring the parameter space of sonic
and Alfv\'{e}nic Mach numbers we find that high Mach number quasi-perpendicular
shocks can efficiently accelerate electrons to power-law downstream spectra
with slopes consistent with DSA prediction. Electrons are reflected by magnetic
mirroring at the shock and drive non-resonant waves in the upstream. Reflected
electrons are trapped between the shock front and upstream waves and undergo
multiple cycles of shock drift acceleration before the injection into DSA.
Strong current-driven waves also temporarily change the shock obliquity and
cause mild proton pre-acceleration even in quasi-perpendicular shocks, which
otherwise do not accelerate protons. These results can be used to understand
nonthermal emission in supernova remnants and intracluster medium in galaxy
clusters.


  The rapid population aging has stimulated the development of assistive
devices that provide personalized medical support to the needies suffering from
various etiologies. One prominent clinical application is a computer-assisted
speech training system which enables personalized speech therapy to patients
impaired by communicative disorders in the patient's home environment. Such a
system relies on the robust automatic speech recognition (ASR) technology to be
able to provide accurate articulation feedback. With the long-term aim of
developing off-the-shelf ASR systems that can be incorporated in clinical
context without prior speaker information, we compare the ASR performance of
speaker-independent bottleneck and articulatory features on dysarthric speech
used in conjunction with dedicated neural network-based acoustic models that
have been shown to be robust against spectrotemporal deviations. We report ASR
performance of these systems on two dysarthric speech datasets of different
characteristics to quantify the achieved performance gains. Despite the
remaining performance gap between the dysarthric and normal speech, significant
improvements have been reported on both datasets using speaker-independent ASR
architectures.


  Hadron Molecules are particles made out of hadrons that are held together by
self interactions. In this report we discuss seven such molecules and their
self interactions. The $f_0(980)$, $a_0(980)$, $f_1(1400)$, $\Delta N(2150)$
and $\pi_1(1400)$ molecular structure is given. We predict that two more states
the $K\overline{K}K(1500)$ and $a_1(1400)$ should be found.


  It has recently been argued that non-trivial Brans-Dicke black hole solutions
different from the usual Schwarzschild solution could exist. We attemt here to
``censor'' these non-trivial Brans-Dicke black hole solutions by examining
their thermodynamic properties. Quantities like Hawking temperature and entropy
of the black holes are computed. Analysis of the behaviors of these
thermodynamic quantities appears to show that even in Brans-Dicke gravity, the
usual Schwarzschild spacetime turns out to be the only physically relevant
uncharged static black hole solution.


  For deep ordinal classification, learning a well-structured feature space
specific to ordinal classification is helpful to properly capture the ordinal
nature among classes. Intuitively, when Euclidean distance metric is used, an
ideal ordinal layout in feature space would be that the sample clusters are
arranged in class order along a straight line in space. However, enforcing
samples to conform to a specific layout in the feature space is a challenging
problem. To address this problem, in this paper, we propose a novel Constrained
Proxies Learning (CPL) method, which can learn a proxy for each ordinal class
and then adjusts the global layout of classes by constraining these proxies.
Specifically, we propose two kinds of strategies: hard layout constraint and
soft layout constraint. The hard layout constraint is realized by directly
controlling the generation of proxies to force them to be placed in a strict
linear layout or semicircular layout (i.e., two instantiations of strict
ordinal layout). The soft layout constraint is realized by constraining that
the proxy layout should always produce unimodal proxy-to-proxies similarity
distribution for each proxy (i.e., to be a relaxed ordinal layout). Experiments
show that the proposed CPL method outperforms previous deep ordinal
classification methods under the same setting of feature extractor.


  In this paper we link the physics of topological nonlinear {\sigma} models
with that of Chern-Simons insulators. We show that corresponding to every
2n-dimensional Chern-Simons insulator there is a (n-1)-dimensional topological
nonlinear {\sigma} model with the Wess-Zumino-Witten term. Breaking internal
symmetry in these nonlinear {\sigma} models leads to nonlinear {\sigma} models
with the {\theta} term. [This is analogous to the dimension reduction leading
from 2n-dimensional Chern-Simons insulators to (2n-1) and (2n-2)-dimensional
topological insulators protected by discrete symmetries.] The correspondence
described in this paper allows one to derive the topological term in a theory
involving fermions and order parameters (we shall referred to them as
"fermion-{\sigma} models") when the conventional gradient-expansion method
fails. We also discuss the quantum number of solitons in topological nonlinear
{\sigma} model and the electromagnetic action of the (2n-1)-dimensional
topological insulators. Throughout the paper we use a simple model to
illustrate how things work.


  We show that if a homogeneous polynomial $f$ in $n$ variables has Waring rank
$n+1$, then the corresponding projective hypersurface $f=0$ has at most
isolated singularities, and the type of these singularities is completely
determined by the combinatorics of a hyperplane arrangement naturally
associated with the Waring decomposition of $f$. We also discuss the relation
between the Waring rank and the type of singularities on a plane curve, when
this curve is defined by the suspension of a binary form, or when the Waring
rank is 5.


  We present a dynamic Monte Carlo study of the Kosterlitz-Thouless phase
transition for the spin-1/2 quantum XY model in two dimensions. The short-time
dynamic scaling behaviour is found and the dynamical exponent $\theta$, $z$ and
the static exponent $\eta$ are determined at the transition temperature.


  Given a probability space $(\Omega,{\mathcal A},P)$, random variables
$L,M\colon\Omega\to\mathbb R$ and $g\in L^1(\mathbb R)$ we obtain two
characterizations of these $f\in L^1(\mathbb R)$ which are solutions of the
inhomogeneous refinement equation with a random affine map of the form
$f(x)=\int_\Omega |L(\omega)|f(L(\omega)x-M(\omega))P(d\omega)+g(x)$.


  We study the integral transform over a general family of broken rays in
$\mathbb{R}^2$. It is natural for broken rays to have conjugate points, for
example, when they are reflected from a curved boundary. If there are conjugate
points, we show that the singularities cannot be recovered from local data and
therefore artifacts arise in the reconstruction. We apply these conclusions to
two examples, the V-line Radon transform and the parallel ray transform. In
each example, a detailed discussion of the local and global recovery of
singularities is given and we perform numerical experiments to illustrate the
results.


  Suppose that red and blue points occur as independent homogeneous Poisson
processes in R^d. We investigate translation-invariant schemes for perfectly
matching the red points to the blue points. For any such scheme in dimensions
d=1,2, the matching distance X from a typical point to its partner must have
infinite d/2-th moment, while in dimensions d>=3 there exist schemes where X
has finite exponential moments. The Gale-Shapley stable marriage is one natural
matching scheme, obtained by iteratively matching mutually closest pairs. A
principal result of this paper is a power law upper bound on the matching
distance X for this scheme. A power law lower bound holds also. In particular,
stable marriage is close to optimal (in tail behavior) in d=1, but far from
optimal in d>=3. For the problem of matching Poisson points of a single color
to each other, in d=1 there exist schemes where X has finite exponential
moments, but if we insist that the matching is a deterministic factor of the
point process then X must have infinite mean.


  Although the security of quantum cryptography is provable based on the
principles of quantum mechanics, it can be compromised by the flaws in the
design of quantum protocols and the noise in their physical implementations.
So, it is indispensable to develop techniques of verifying and debugging
quantum cryptographic systems. Model-checking has proved to be effective in the
verification of classical cryptographic protocols, but an essential difficulty
arises when it is applied to quantum systems: the state space of a quantum
system is always a continuum even when its dimension is finite. To overcome
this difficulty, we introduce a novel notion of quantum Markov chain, specially
suited to model quantum cryptographic protocols, in which quantum effects are
entirely encoded into super-operators labelling transitions, leaving the
location information (nodes) being classical. Then we define a quantum
extension of probabilistic computation tree logic (PCTL) and develop a
model-checking algorithm for quantum Markov chains.


  Mono- and multifilamentary MgB2/Fe tapes and wires with high transport
critical current densities have been prepared using the powder-in-tube (PIT)
process. The fabrication details are described. The effect of powder grain
sizes and recrystallization temperature on jc has been investigated. At 25K and
1 T, jc values close to 105 A/cm2 were measured, while jc of 106 A/cm2 were
extrapolated for 4.2K/0T in our monofilamentary tape. MgB2/Fe tapes exhibit
high exponential n factors for the resistive transition: n ~ 80 and 40 were
found at 5 T and 7 T, respectively. The highest transport jc values obtained so
far in MgB2/Fe wires with 7 filaments were 1.1 * 105 A/cm2 at 4.2 K and in a
field of 2 T, which is still lower than for monofilamentary tapes. Improved
deformation and recovering processing is expected to lead to higher jc values.


  Efficient phase cycling schemes remain a challenge for NMR techniques if the
pulse sequences involve a large number of rf-pulses. Especially complex is the
Carr Purcell Meiboom Gill (CPMG) pulse sequence where the number of rf-pulses
can range from hundreds to several thousands. Our recent implementation of
Magnetic Resonance Pore Imaging (MRPI) is based on a CPMG rf-pulse sequence in
order to refocus the effect of internal gradients inherent in porous media.
While the spin dynamics for spin-$1/2$ systems in CPMG like experiments are
well understood it is still not straight forward to separate the desired
pathway from the spectrum of unwanted coherence pathways. In this contribution
we apply Phase Incremented Echo Train Acquisition (PIETA) to MRPI. We show how
PIETA offers a convenient way to implement a working phase cycling scheme and
how it allows one to gain deeper insights into the amplitudes of undesired
pathways.


  Understanding how "black-box" models arrive at their predictions has sparked
significant interest from both within and outside the AI community. Our work
focuses on doing this by generating local explanations about individual
predictions for tree-based ensembles, specifically Gradient Boosting Decision
Trees (GBDTs). Given a correctly predicted instance in the training set, we
wish to generate a counterfactual explanation for this instance, that is, the
minimal perturbation of this instance such that the prediction flips to the
opposite class. Most existing methods for counterfactual explanations are (1)
model-agnostic, so they do not take into account the structure of the original
model, and/or (2) involve building a surrogate model on top of the original
model, which is not guaranteed to represent the original model accurately.
There exists a method specifically for random forests; we wish to extend this
method for GBDTs. This involves accounting for (1) the sequential dependency
between trees and (2) training on the negative gradients instead of the
original labels.


  The main purpose of this paper is to investigate the strong approximation of
the $p$-fold integrated empirical process, $p$ being a fixed positive integer.
More precisely, we obtain the exact rate of the approximations by a sequence of
weighted Brownian bridges and a weighted Kiefer process. Our arguments are
based in part on results of Koml\'os, Major and Tusn\'ady (1975). We also
obtain an exponential bound for the tail probability of the weighted
approximation to the $p$-fold integrated empirical process. Applications
include the two-sample testing procedures together with the change-point
problems. We also consider the strong approximation of integrated empirical
processes when the parameters are estimated. We study the behavior of the
self-intersection local time of the partial sum process representation of
integrated empirical processes. Finally, simulation results are provided to
illustrate the finite sample performance of the proposed statistical tests
based on the integrated empirical processes.


  Automatic 3D neuron reconstruction is critical for analysing the morphology
and functionality of neurons in brain circuit activities. However, the
performance of existing tracing algorithms is hinged by the low image quality.
Recently, a series of deep learning based segmentation methods have been
proposed to improve the quality of raw 3D optical image stacks by removing
noises and restoring neuronal structures from low-contrast background. Due to
the variety of neuron morphology and the lack of large neuron datasets, most of
current neuron segmentation models rely on introducing complex and
specially-designed submodules to a base architecture with the aim of encoding
better feature representations. Though successful, extra burden would be put on
computation during inference. Therefore, rather than modifying the base
network, we shift our focus to the dataset itself. The encoder-decoder backbone
used in most neuron segmentation models attends only intra-volume voxel points
to learn structural features of neurons but neglect the shared intrinsic
semantic features of voxels belonging to the same category among different
volumes, which is also important for expressive representation learning. Hence,
to better utilise the scarce dataset, we propose to explicitly exploit such
intrinsic features of voxels through a novel voxel-level cross-volume
representation learning paradigm on the basis of an encoder-decoder
segmentation model. Our method introduces no extra cost during inference.
Evaluated on 42 3D neuron images from BigNeuron project, our proposed method is
demonstrated to improve the learning ability of the original segmentation model
and further enhancing the reconstruction performance.


  In order to provide robust, reliable, and accurate position and velocity
control of motor drives, friction compensation has emerged as a key difficulty.
Non-characterised friction could give rise to large position errors and
vibrations which could be intensified by stick-slip motion and limit cycles.
This paper presents an application of two data-driven nonlinear model
identification techniques to discover the governing equations of motor dynamics
that also characterise friction. Namely, the extraction of low-power data from
time-delayed coordinates of motor velocity and sparse regression on nonlinear
terms was applied to data acquired from a Brushless DC (BLDC) motor, to
identify the underlying dynamics. The latter can be considered an extension of
the conventional linear motor model commonly used in many model-based
controllers. The identified nonlinear model was then contrasted with a
nonlinear model that included the LuGre friction model and a linear model
without friction. A nonlinear grey box model estimation method was used to
calculate the optimum friction parameters for the LuGre model. The resulting
nonlinear motor model with friction characteristics was then validated using a
feedback friction compensation algorithm. The novel model showed more than 90%
accuracy in predicting the motor states in all considered input excitation
signals. In addition, the model-based friction compensation scheme showed a
relative increase in performance when compared with a system without friction
compensation.


  We investigate Relational Graph Attention Networks, a class of models that
extends non-relational graph attention mechanisms to incorporate relational
information, opening up these methods to a wider variety of problems. A
thorough evaluation of these models is performed, and comparisons are made
against established benchmarks. To provide a meaningful comparison, we retrain
Relational Graph Convolutional Networks, the spectral counterpart of Relational
Graph Attention Networks, and evaluate them under the same conditions. We find
that Relational Graph Attention Networks perform worse than anticipated,
although some configurations are marginally beneficial for modelling molecular
properties. We provide insights as to why this may be, and suggest both
modifications to evaluation strategies, as well as directions to investigate
for future work.


  Since the first surprising results on the spin structure of the proton by the
EMC experiment in the late 1980s, much progress has been made in understanding
the origin of the proton spin. However, the sea quark contribution to the
proton spin, for example, the helicity distributions of the strange quark
(anti-quark), $s(\bar{s})$, is still not well constrained by experimental data.
Since the $s(\bar{s})$ is expected to carry a substantial fraction of the spin
of the $\Lambda(\bar{\Lambda})$ hyperon, measurements of the longitudinal spin
transfer, $D_{LL}$, of the $\Lambda(\bar{\Lambda}$) hyperon can thus shed light
on the helicity distribution of the $s(\bar{s})$ and the longitudinally
polarized fragmentation functions. In these proceedings, we will present the
status of the $\Lambda(\bar{\Lambda}$) $D_{LL}$ analysis using data collected
at RHIC-STAR in 2015, for the pseudo-rapidity $|\eta| < 1.2$ and transverse
momenta up to $8.0$ GeV$/c$. The $D_{LL}$ as a function of the longitudinal
momentum fraction of the $\Lambda(\bar{\Lambda})$ hyperon in the jet is also
investigated. This data set is about twice as large as the 2009 data used for
the previously published $D_{LL}$ results.


  A prevailing paradigm in neural text generation is one-shot generation, where
text is produced in a single step. The one-shot setting is inadequate, however,
when the constraints the user wishes to impose on the generated text are
dynamic, especially when authoring longer documents. We address this limitation
with an interactive text generation setting in which the user interacts with
the system by issuing commands to edit existing text. To this end, we propose a
novel text editing task, and introduce WikiDocEdits, a dataset of
single-sentence edits crawled from Wikipedia. We show that our Interactive
Editor, a transformer-based model trained on this dataset, outperforms
baselines and obtains positive results in both automatic and human evaluations.
We present empirical and qualitative analyses of this model's performance.


  Successful control of a rigid-body rotating in three dimensional space
requires accurate estimation of its attitude. The attitude dynamics are highly
nonlinear and are posed on the Special Orthogonal Group $SO(3)$. In addition,
measurements supplied by low-cost sensing units pose a challenge for the
estimation process. This paper proposes a novel stochastic nonlinear
neural-adaptive-based filter on $SO(3)$ for the attitude estimation problem.
The proposed filter produces good results given measurements extracted from
low-cost sensing units (e.g., IMU or MARG sensor modules). The filter is
guaranteed to be almost semi-globally uniformly ultimately bounded in the mean
square. In addition to Lie Group formulation, quaternion representation of the
proposed filter is provided. The effectiveness of the proposed neural-adaptive
filter is tested and evaluated in its discrete form under the conditions of
large initialization error and high measurement uncertainties. keywords /
index-terms: Neuro-adaptive, stochastic differential equations (SDEs), Brownian
motion process, attitude estimator, Special Orthogonal Group, Unit-quaternion,
SO(3), IMU, MARG.


  After reviewing recent work on the dynamics of young supernova remnants
(YSNRs) and discussing how YSNRs can be used as physics laboratories, I discuss
several challenges: Where are the very young SNRs in the Galaxy? Can very young
SNRs produce gamma ray bursts? The Connections Challenge: Can one infer the
nature of the supernova and its progenitor star from observations of the YSNR?


  This thesis focuses on the derivation of holographic backgrounds from the
field theory side, without using any supergravity equations of motion. Instead,
we rely on the addition of probe D-branes to the stack of D-branes generating
the background. From the field theory description of the probe branes, one can
compute an effective action for the probes (in a suitable
low-energy/near-horizon limit) by integrating out the background branes.
Comparing this action with the generic probe D-brane action then allows to
determine the holographic background dual to the considered field theory
vacuum.
  In the first part, the required pre-requisites of field and string theory are
recalled and this strategy to derive holographic backgrounds is explained in
more detail on the basic case of D3-branes in flat space probed by a small
number of D-instantons.
  The second part contains our original results, which have already appeared in
arXiv:1301.3738, arXiv:1301.7062 and arXiv:1312.0621. We first derive the duals
to three continuous deformations (Coulomb branch, $\beta$ and non-commutative
deformations) of N=4 super-Yang-Mills. We then derive the enhan\c{c}on
mechanism in a simple N=2 quiver gauge theory setup by using a fractional
D-instanton as a probe and exploiting recent exact results on the Coulomb
branch of N=2 quivers. Finally, we obtain the near-horizon D4-brane geometry by
probing the D4-branes with a small number of D0-branes.


  Larger and deeper networks generalise well despite their increased capacity
to overfit. Understanding why this happens is theoretically and practically
important. One recent approach looks at the infinitely wide limits of such
networks and their corresponding kernels. However, these theoretical tools
cannot fully explain finite networks as the empirical kernel changes
significantly during gradient-descent-based training in contrast to infinite
networks. In this work, we derive an iterative linearised training method as a
novel empirical tool to further investigate this distinction, allowing us to
control for sparse (i.e. infrequent) feature updates and quantify the frequency
of feature learning needed to achieve comparable performance. We justify
iterative linearisation as an interpolation between a finite analog of the
infinite width regime, which does not learn features, and standard gradient
descent training, which does. Informally, we also show that it is analogous to
a damped version of the Gauss-Newton algorithm -- a second-order method. We
show that in a variety of cases, iterative linearised training surprisingly
performs on par with standard training, noting in particular how much less
frequent feature learning is required to achieve comparable performance. We
also show that feature learning is essential for good performance. Since such
feature learning inevitably causes changes in the NTK kernel, we provide direct
negative evidence for the NTK theory, which states the NTK kernel remains
constant during training.


  The ability of to explain neural network decisions goes hand in hand with
their safe deployment. Several methods have been proposed to highlight features
important for a given network decision. However, there is no consensus on how
to measure effectiveness of these methods. We propose a new procedure for
evaluating explanations. We use it to investigate visual explanations extracted
from a range of possible sources in a neural network. We quantify the benefit
of combining these sources and challenge a recent appeal for taking bias
parameters into account. We support our conclusions with a general assessment
of the impact of bias parameters in ImageNet classifiers


  SU(4) dynamical symmetry is shown to imply a no-double-occupancy constraint
on the minimal symmetry description of antiferromagnetism and d-wave
superconductivity. This implies a maximum doping fraction of 1/4 for cuprates
and provides a microscopic critique of the projected SO(5) model. We propose
that SU(4) superconductors are representative of a class of compounds that we
term non-abelian superconductors. We further suggest that non-abelian
superconductors may exist having SU(4) symmetry and therefore cuprate-like
dynamics, but without d-wave hybridization.


  In two previous papers the author developed a second-order price adjustment
(t\^atonnement) process. This paper extends the approach to include both
quantity and price adjustments. We demonstrate three results: a analogue to
physical energy, called "activity" arises naturally in the model, and is not
conserved in general; price and quantity trajectories must either end at a
local minimum of a scalar potential or circulate endlessly; and disturbances
into a subspace of substitutable commodities decay over time. From this we
argue, although we do not prove, that the model features global stability,
combined with local instability, a characteristic of many real markets.
Following these observations and a brief survey of empirical results for
price-setting and consumption behavior in markets for "real" goods (as opposed
to financial markets), we conjecture that Stigler and Becker's well-known
theory of consumer preference opens the possibility of substantial degeneracy
in commodity space, and therefore that price and quantity trajectories could
lie on a relatively low-dimensional subspace within the full commodity space.


  Cloud computing provides a computing platform for the users to meet their
demands in an efficient, cost-effective way. Virtualization technologies are
used in the clouds to aid the efficient usage of hardware. Virtual machines
(VMs) are utilized to satisfy the user needs and are placed on physical
machines (PMs) of the cloud for effective usage of hardware resources and
electricity in the cloud. Optimizing the number of PMs used helps in cutting
down the power consumption by a substantial amount.
  In this paper, we present an optimal technique to map virtual machines to
physical machines (nodes) such that the number of required nodes is minimized.
We provide two approaches based on linear programming and quadratic programming
techniques that significantly improve over the existing theoretical bounds and
efficiently solve the problem of virtual machine (VM) placement in data
centers.


  We establish an experimental method for a detailed investigation of inelastic
collisional properties between ytterbium (Yb) in the metastable
${}^3\mathrm{P}_2$ state and ground state lithium (Li). By combining an optical
lattice and a direct excitation to the ${}^3\mathrm{P}_2$ state we achieve high
selectivity on the collisional partners. Using this method we determine
inelastic loss coefficients in collisions between
$^{174}$Yb(${}^3\mathrm{P}_2$) with magnetic sublevels of $m_J=0$ and $-2$ and
ground state $^6$Li to be $(4.4\pm0.3)\times10^{-11}~\mathrm{cm}^3/\mathrm{s}$
and $(4.7\pm0.8)\times10^{-11}~\mathrm{cm}^3/\mathrm{s}$, respectively. Absence
of spin changing processes in Yb(${}^3\mathrm{P}_2$)-Li inelastic collisions at
low magnetic fields is confirmed by inelastic loss measurements on the $m_J=0$
state. We also demonstrate that our method allows us to look into loss
processes in few-body systems separately.


  We present Tarski, a tool for specifying configurable trace semantics to
facilitate automated reasoning about traces. Software development projects
require that various types of traces be modeled between and within development
artifacts. For any given artifact (e.g., requirements, architecture models and
source code), Tarski allows the user to specify new trace types and their
configurable semantics, while, using the semantics, it automatically infers new
traces based on existing traces provided by the user, and checks the
consistency of traces. It has been evaluated on three industrial case studies
in the automotive domain (https://modelwriter.github.io/Tarski/).


  Recently, it has been proposed that the natural connectivity can be used to
efficiently characterise the robustness of complex networks. Natural
connectivity quantifies the redundancy of alternative routes in a network by
evaluating the weighted number of closed walks of all lengths and can be
regarded as the average eigenvalue obtained from the graph spectrum. In this
article, we explore the natural connectivity of random graphs both analytically
and numerically and show that it increases linearly with the average degree. By
comparing with regular ring lattices and random regular graphs, we show that
random graphs are more robust than random regular graphs; however, the
relationship between random graphs and regular ring lattices depends on the
average degree and graph size. We derive the critical graph size as a function
of the average degree, which can be predicted by our analytical results. When
the graph size is less than the critical value, random graphs are more robust
than regular ring lattices, whereas regular ring lattices are more robust than
random graphs when the graph size is greater than the critical value.


  Machine reading comprehension tasks require a machine reader to answer
questions relevant to the given document. In this paper, we present the first
free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3),
containing 13,369 documents (dialogues or more formally written mixed-genre
texts) and their associated 19,577 multiple-choice free-form questions
collected from Chinese-as-a-second-language examinations.
  We present a comprehensive analysis of the prior knowledge (i.e., linguistic,
domain-specific, and general world knowledge) needed for these real-world
problems. We implement rule-based and popular neural methods and find that
there is still a significant performance gap between the best performing model
(68.5%) and human readers (96.0%), especially on problems that require prior
knowledge. We further study the effects of distractor plausibility and data
augmentation based on translated relevant datasets for English on model
performance. We expect C^3 to present great challenges to existing systems as
answering 86.8% of questions requires both knowledge within and beyond the
accompanying document, and we hope that C^3 can serve as a platform to study
how to leverage various kinds of prior knowledge to better understand a given
written or orally oriented text. C^3 is available at https://dataset.org/c3/.


  We present Veech's proof of Sarnak's theorem on the M\"{o}bius flow which say
that there is a unique admissible measure on the M\"{o}bius flow. As a
consequence, we obtain that Sarnak's conjecture is equivalent to Chowla
conjecture with the help of Tao's logarithmic Theorem which assert that the
logarithmic Sarnak conjecture is equivalent to logaritmic Chowla conjecture,
furthermore, if the even logarithmic Sarnak's conjecture is true then there is
a subsequence with logarithmic density one along which Chowla conjecture holds,
that is, the M\"{o}bius function is quasi-generic.


  In this work, we study the performance of Reed--Solomon codes against
adversarial insertion-deletion (insdel) errors.
  We prove that over fields of size $n^{O(k)}$ there are $[n,k]$ Reed-Solomon
codes that can decode from $n-2k+1$ insdel errors and hence attain the
half-Singleton bound. We also give a deterministic construction of such codes
over much larger fields (of size $n^{k^{O(k)}}$). Nevertheless, for $k=O(\log n
/\log\log n)$ our construction runs in polynomial time. For the special case
$k=2$, which received a lot of attention in the literature, we construct an
$[n,2]$ Reed-Solomon code over a field of size $O(n^4)$ that can decode from
$n-3$ insdel errors. Earlier constructions required an exponential field size.
Lastly, we prove that any such construction requires a field of size
$\Omega(n^3)$.


  Many countries are rolling out smart electricity meters. These measure a
home's total power demand. However, research into consumer behaviour suggests
that consumers are best able to improve their energy efficiency when provided
with itemised, appliance-by-appliance consumption information. Energy
disaggregation is a computational technique for estimating
appliance-by-appliance energy consumption from a whole-house meter signal.
  To conduct research on disaggregation algorithms, researchers require data
describing not just the aggregate demand per building but also the `ground
truth' demand of individual appliances. In this context, we present UK-DALE: an
open-access dataset from the UK recording Domestic Appliance-Level Electricity
at a sample rate of 16 kHz for the whole-house and at 1/6 Hz for individual
appliances. This is the first open access UK dataset at this temporal
resolution. We recorded from five houses, one of which was recorded for 655
days, the longest duration we are aware of for any energy dataset at this
sample rate. We also describe the low-cost, open-source, wireless system we
built for collecting our dataset.


  The possibility of measuring the proton electromagnetic form factors in the
time-like region at FAIR with the \PANDA detector is discussed. Detailed
simulations on signal efficiency for the annihilation of $\bar p +p $ into a
lepton pair as well as for the most important background channels have been
performed. It is shown that precision measurements of the differential cross
section of the reaction $\bar p +p \to e^++ e^-$ can be obtained in a wide
angular and kinematical range. The individual determination of the moduli of
the electric and magnetic proton form factors will be possible up to a value of
momentum transfer squared of $q^2\simeq 14$ (GeV/c)$^2$. The total $\bar p
+p\to e^++e^-$ cross section will be measured up to $q^2\simeq 28$ (GeV/c)$^2$.
The results obtained from simulated events are compared to the existing data.
Sensitivity to the two photons exchange mechanism is also investigated.


  Quantum key distribution (QKD) can share an unconditional secure key between
two remote parties, but the deviation between theory and practice will break
the security of the generated key. In this paper, we evaluate the security of
QKD with weak basis-choice flaws, in which the random bits used by Alice and
Bob are weakly controlled by Eve. Based on the definition of Li \textit{et al.}
[Sci. Rep. 5, 16200 (2015)] and GLLP's analysis, we obtain a tight and
analytical bound to estimate the phase error and key rate for both the single
photon source and the weak coherent source. Our approach largely increases the
key rate from that of the original approach. Finally, we investigate and
confirm the security of BB84-QKD with a practical commercial devices.


  Experimental demonstrations of quantum annealing with native implementation
of Boolean logic Hamiltonians are reported. As a superconducting integrated
circuit, a problem Hamiltonian whose set of ground states is consistent with a
given truth table is implemented for quantum annealing with no redundant
qubits. As examples of the truth table, NAND and NOR are successfully
fabricated as an identical circuit. Similarly, a native implementation of a
multiplier comprising six superconducting flux qubits is also demonstrated.
These native implementations of Hamiltonians consistent with Boolean logic
provide an efficient and scalable way of applying annealing computation to
so-called circuit satisfiability problems that aim to find a set of inputs
consistent with a given output over any Boolean logic functions, especially
those like factorization through a multiplier Hamiltonian. A proof-of-concept
demonstration of a hybrid computing architecture for domain-specific quantum
computing is described.


  Social media, self-driving cars, and traffic cameras produce video streams at
large scales and cheap cost. However, storing and querying video at such scales
is prohibitively expensive. We propose to treat large-scale video analytics as
a data warehousing problem: Video is a format that is easy to produce but needs
to be transformed into an application-specific format that is easy to query.
Analogously, we define the problem of Video Extract-Transform-Load (V-ETL).
V-ETL systems need to reduce the cost of running a user-defined V-ETL job while
also giving throughput guarantees to keep up with the rate at which data is
produced. We find that no current system sufficiently fulfills both needs and
therefore propose Skyscraper, a system tailored to V-ETL. Skyscraper can
execute arbitrary video ingestion pipelines and adaptively tunes them to reduce
cost at minimal or no quality degradation, e.g., by adjusting sampling rates
and resolutions to the ingested content. Skyscraper can hereby be provisioned
with cheap on-premises compute and uses a combination of buffering and cloud
bursting to deal with peaks in workload caused by expensive processing
configurations. In our experiments, we find that Skyscraper significantly
reduces the cost of V-ETL ingestion compared to adaptions of current SOTA
systems, while at the same time giving robustness guarantees that these systems
are lacking.


  We employ mean value estimates of Weyl sums in order to obtain discrete
second moments of the Riemann Zeta-function with respect to polynomials near
the vertical line $1+i\mathbb{R}$.


  Unsupervised Domain Adaptation (UDA) methods for person Re-Identification
(Re-ID) rely on target domain samples to model the marginal distribution of the
data. To deal with the lack of target domain labels, UDA methods leverage
information from labeled source samples and unlabeled target samples. A
promising approach relies on the use of unsupervised learning as part of the
pipeline, such as clustering methods. The quality of the clusters clearly plays
a major role in methods performance, but this point has been overlooked. In
this work, we propose a multi-step pseudo-label refinement method to select the
best possible clusters and keep improving them so that these clusters become
closer to the class divisions without knowledge of the class labels. Our
refinement method includes a cluster selection strategy and a camera-based
normalization method which reduces the within-domain variations caused by the
use of multiple cameras in person Re-ID. This allows our method to reach
state-of-the-art UDA results on DukeMTMC-Market1501 (source-target). We surpass
state-of-the-art for UDA Re-ID by 3.4% on Market1501-DukeMTMC datasets, which
is a more challenging adaptation setup because the target domain (DukeMTMC) has
eight distinct cameras. Furthermore, the camera-based normalization method
causes a significant reduction in the number of iterations required for
training convergence.


  We give a criterion for a projectivized toric vector bundle to be a Mori
dream space and describe its Cox ring using generators and relations. Both of
these results are in terms of the matroids of all symmetric powers of the
bundle. We also give a criterion for a toric vector bundle to be big and
describe several interesting examples of toric vector bundles which highlights
how positivity properties for toric vector bundles are more complicated than
for toric line bundles.


  The X-ray integral field unit for the Athena mission consists of a
microcalorimeter transition edge sensor pixel array. Incoming photons generate
pulses which are analyzed in terms of energy, in order to assemble the X-ray
spectrum. Usually this is done by means of optimal filtering in either time or
frequency domain.
  In this paper we investigate an alternative method by means of principle
component analysis. This method attempts to find the main components of an
orthogonal set of functions to describe the data.
  We show, based on simulations, what the influence of various instrumental
effects is on this type of analysis. We compare analyses both in time and
frequency domain. Finally we apply these analyses on real data, obtained via
frequency domain multiplexing readout.


  We consider ensembles of Wigner matrices, whose entries are (up to the
symmetry constraints) independent and identically distributed random variables.
We show the convergence of the Stieltjes transform towards the Stieltjes
transform of the semicircle law on optimal scales and with the optimal rate.
Our bounds improve previous results, in particular from [22,10], by removing
the logarithmic corrections. As applications, we establish the convergence of
the eigenvalue counting functions with the rate $(\log N)/N$ and the rigidity
of the eigenvalues of Wigner matrices on the same scale. These bounds improve
the results of [22,10,23].


  The discovery of a population of young galaxies at an epoch when the universe
was about one tenth of its current age has shed new light on the question of
when and how galaxies formed. Within the context of popular models this is the
population of primeval galaxies that built themselves up to the size of
present--day galaxies through the process of repeated mergers. But the recent
detection of a large concentration of these primeval galaxies (Steidel et al.
1997) appears to be incompatible with hierarchical clustering models, which
generally predict that clusters of this size are fully formed later in time.
Here we use a combination of two powerful theoretical techniques
--semi-analytic modelling and N-body simulations-- to show that such large
concentrations should be quite common in a universe dominated by cold dark
matter, and that they are the progenitors of the rich galaxy clusters seen
today. We predict the clustering properties of primeval galaxies which should,
when compared with data that will be collected in the near future, test our
understanding of galaxy formation within the framework of a universe dominated
by cold dark matter.


  Vizing showed that it suffices to color the edges of a simple graph using
$\Delta + 1$ colors, where $\Delta$ is the maximum degree of the graph.
However, up to this date, no efficient distributed edge-coloring algorithms are
known for obtaining such a coloring, even for constant degree graphs. The
current algorithms that get closest to this number of colors are the randomized
$(\Delta + \tilde{\Theta}(\sqrt{\Delta}))$-edge-coloring algorithm that runs in
$\text{polylog}(n)$ rounds by Chang et al. (SODA '18) and the deterministic
$(\Delta + \text{polylog}(n))$-edge-coloring algorithm that runs in
$\text{poly}(\Delta, \log n)$ rounds by Ghaffari et al. (STOC '18).
  We present two distributed edge-coloring algorithms that run in
$\text{poly}(\Delta,\log n)$ rounds. The first algorithm, with randomization,
uses only $\Delta+2$ colors. The second algorithm is a deterministic algorithm
that uses $\Delta+ O(\log n/ \log \log n)$ colors. Our approach is to reduce
the distributed edge-coloring problem into an online, restricted version of
balls-into-bins problem. If $\ell$ is the maximum load of the bins, our
algorithm uses $\Delta + 2\ell - 1$ colors. We show how to achieve $\ell = 1$
with randomization and $\ell = O(\log n / \log \log n)$ without randomization.


  CODALEMA is one of the pioneer experiments dedicated to the recent field of
cosmic ray radio detection. It is located at the radio observatory of Nancay
(France). The detector setup combined until recently a ground particle detector
and an array of active dipole antennas covering a total area of 0.25 km^2. The
experiment is now going through a major upgrade with the deployment around the
existing apparatus of a Standalone Antenna Array, which consists of 60
standalone new generation radio-detection stations and which will cover an area
of 1.5 km^2 (33 stations deployed over the spring of 2011 and 27 stations to be
deployed in late 2011). This new setup is intended to tackle the remaining
unknowns of extensive air shower radio detection so as to make this technique a
reliable and mature tool for Ultra High Energy Cosmic Ray (UHECR) physics. The
latest results from the original CODALEMA array are discussed together with the
first results of the Standalone Antenna Array.


  This paper presents the results of a research study related to software
system failures, with the goal of understanding how we might better evolve,
maintain and support software systems in production. We have qualitatively
analyzed thirty incidents: fifteen collected through in depth interviews with
engineers, and fifteen sampled from publicly published incident reports
(generally produced as part of postmortem reviews). Our analysis focused on
understanding and categorizing how failures occurred, and how they were
detected, investigated and mitigated. We also captured analytic insights
related to the current state of the practice and associated challenges in the
form of 11 key observations. For example, we observed that failures can cascade
through a system leading to major outages; and that often engineers do not
understand the scaling limits of systems they are supporting until those limits
are exceeded. We argue that the challenges we have identified can lead to
improvements to how systems are engineered and supported.


  Anomaly detection of multivariate time series is meaningful for system
behavior monitoring. This paper proposes an anomaly detection method based on
unsupervised Short- and Long-term Mask Representation learning (SLMR). The main
idea is to extract short-term local dependency patterns and long-term global
trend patterns of the multivariate time series by using multi-scale residual
dilated convolution and Gated Recurrent Unit(GRU) respectively. Furthermore,
our approach can comprehend temporal contexts and feature correlations by
combining spatial-temporal masked self-supervised representation learning and
sequence split. It considers the importance of features is different, and we
introduce the attention mechanism to adjust the contribution of each feature.
Finally, a forecasting-based model and a reconstruction-based model are
integrated to focus on single timestamp prediction and latent representation of
time series. Experiments show that the performance of our method outperforms
other state-of-the-art models on three real-world datasets. Further analysis
shows that our method is good at interpretability.


  We present CDF results on the branching fractions and time-integrated direct
CP asymmetries for Bd, Bs and Lb decay modes into pairs of charmless charged
hadrons (pions, kaons and protons). The data-set for these measurements amounts
to 1fb^{-1} of pbar-p collisions at a center of mass energy 1.96TeV. We report
on the first observation of the Bs->Kpi, Lb-ppi and Lb->pK decay modes and on
the measurement of their branching fractions and direct CP asymmetries.


  We have performed a detailed study of the tunneling spectra of bicrystal
grain boundary junctions (GBJs) fabricated from the HTS YBCO, BSCCO, LSCO, and
NCCO. In all experiments the tunneling direction was along the CuO planes. With
the exception of NCCO, for all materials a pronounced zero bias conductance
peak was observed which decreases with increasing temperature and disappears at
the critical temperature. These results can be explained by the presence of a
dominating d-wave symmetry of the order parameter resulting in the formation of
zero energy Andreev bound states at surfaces and interfaces of HTS. The absence
of a ZBCP for NCCO is consistent with a dominating s-wave symmetry of the pair
potential in this material. The observed nonlinear shift of spectral weight to
finite energies by applying a magnetic field is in qualitative agreement with
recent theoretical predictions.


  We propose that the amplified density fluctuations by the vanishing sound
velocity effect during the cosmological quark-hadron phase transition lead to
quark-gluon plasma lumps decoupled from the expansion of the universe, which
may evolve to quark nuggets (QNs). Assuming power-law spectrum of density
fluctuations, we investigate the parameter ranges for the QNs to play the role
of baryonic dark matter and give inhomogeneities which could affect big-bang
nucleosynthesis within the observational bounds of CMBR anisotropy. The QNs can
give the strongest constraint ever found on the spectral index.


  The formation of positive ions of antihydrogen $\bar{\rm{H}}^+$ via the three
body reaction (i) $\rm{e}^+ + \rm{e}^- + \bar{\rm{H}} \rightarrow \rm{e}^- +
\bar{\rm{H}}^+$ is considered. In reaction (i), free positrons $\rm{e}^+$ are
incident on antihydrogen $\bar{\rm{H}}$ embedded in a gas of low-energy ($\sim
$ meV) electrons and, due to the positron-electron interaction, a positron is
attached to $\bar{\rm{H}}$ whereas an electron carries away the energy excess.
We compare reaction (i) with two radiative attachment mechanisms. One of them
is (ii) spontaneous radiative attachment, in which the ion is formed due to
spontaneous emission of a photon by a positron incident on $\bar{\rm{H}}$. The
other is (iii) two-center dileptonic attachment which takes place in the
presence of a neighboring atom B and in which an incident positron is attached
to $\bar{\rm{H}}$ via resonant transfer of energy to B with its subsequent
relaxation through spontaneous radiative decay. It is shown that reaction (i)
can strongly dominate over mechanisms (ii) and (iii) for positron energies
below $0.1$ eV. It is also shown that at the energies considered reaction (i)
will not be influenced by annihilation and that the reaction $\rm{e}^+ +
\rm{e}^+ + \bar{\rm{H}} \rightarrow \rm{e}^+ + \bar{\rm{H}}^+$ has a
vanishingly small rate compared to reaction (i).


  TeraHertz (THz) communications are envisioned as a promising technology,
owing to its unprecedented multi-GHz bandwidth. One fundamental challenge when
moving to new spectrum is to understand the science of radio propagation and
develop an accurate channel model. In this paper, a wideband channel
measurement campaign between 130 GHz and 143 GHz is investigated in a typical
meeting room. Directional antennas are utilized and rotated for resolving the
multi-path components (MPCs) in the angular domain. With careful system
calibration that eliminates system errors and antenna effects, a realistic
power delay profile is developed. Furthermore, a combined MPC clustering and
matching procedure with ray-tracing techniques is proposed to investigate the
cluster behavior and wave propagation of THz signals. In light of the
measurement results, physical parameters and insights in the THz indoor channel
are comprehensively analyzed, including the line-of-sight path loss, power
distributions, temporal and spatial features, and correlations among THz
multi-path characteristics. Finally, a hybrid channel model that combines
ray-tracing and statistical methods is developed for THz indoor communications.
Numerical results demonstrate that the proposed hybrid channel model shows good
agreement with the measurement and outperforms the conventional statistical and
geometric-based stochastic channel model in terms of the temporal-spatial
characteristics.


  We have made a serendipitous discovery of a massive cD galaxy at z=1.096 in a
candidate rich cluster in the HUDF area of GOODS-South. This brightest cluster
galaxy is the most distant cD galaxy confirmed to date. Ultra-deep HST/WFC3
images reveal an extended envelope starting from ~10 kpc and reaching ~70 kpc
in radius along the semi-major axis. The spectral energy distributions indicate
that both its inner component and outer envelope are composed of an old,
passively-evolving stellar population. The cD galaxy lies on the same mass-size
relation as the bulk of quiescent galaxies at similar redshifts. The cD galaxy
has a higher stellar mass surface density but a similar velocity dispersion to
those of more-massive, nearby cDs. If the cD galaxy is one of the progenitors
of today's more massive cDs, its size and stellar mass have had to increase on
average by factors of $3.4\pm1.1$ and $3.3\pm1.3$ over the past ~8 Gyrs,
respectively. Such increases in size and stellar mass without being accompanied
by significant increases in velocity dispersion are consistent with
evolutionary scenarios driven by both major and minor dry mergers. If such cD
envelopes originate from dry mergers, our discovery of even one example proves
that some BCGs entered the dry merger phase at epochs earlier than z=1. Our
data match theoretical models which predict that the continuance of dry mergers
at z<1 can result in structures similar to those of massive cD galaxies seen
today. Moreover, our discovery is a surprise given that the extreme depth of
the HUDF is essential to reveal such an extended cD envelope at z>1 and, yet,
the HUDF covers only a minuscule region of sky. Adding that cDs are rare, Our
serendipitous discovery hints that such cDs may be more common than expected.
[Abridged]


  We made a link between Extra Cellular Matrix (ECM) plasticity and the
morphologi cal changes in synapses after synaptic excitation. A recent study by
Zhang et al \cite{zhang} showed tha t transmembrane voltage causes movement of
the cell membrane. Here we will study the relation between th e mechanical
properties of collagen which is the major component of the ECM and synaptic
morphological c hanges in relation with the theory of DO Hebb \cite{hebb}


  We discuss the ultimate precision for ALR, and therefore for the weak mixing
angle, at a high-luminosity Linear Collider. Drawing on our experience at the
SLC, and considering various machine parameter sets for the NLC and for TESLA,
it emerges that a compromise between peak luminosity and precision will be a
likely outcome. This arises due to the severe requirements on the uncertainty
in the luminosity weighted collision energy (Ecm). We consider the cases with
and without a polarized positron beam.


  This early trial has been withdrawn by the author. For a completed and
published version cf. arXiv:math/0608597v5.


  Henn and Komiya-Kuribayashi listed, independently, the groups $G$ for which
$\widetilde{M_3^{Pl}(G)}$ is non-empty. In this paper, we determine the loci
$\widetilde{M_6^{Pl}(G)}$, corresponding to non-singular degree $5$ projective
plane curves, which are non-empty. Also, we present the analogy of Henn's
results for quartic curves concerning non-singular plane model equations
associated to these loci.