  We introduce the graph parameter boolean-width, related to the number of
different unions of neighborhoods across a cut of a graph. Boolean-width is
similar to rank-width, which is related to the number of $GF[2]$-sums (1+1=0)
of neighborhoods instead of the boolean-sums (1+1=1) used for boolean-width. We
give algorithms for a large class of NP-hard vertex subset and vertex
partitioning problems that are FPT when parameterized by either boolean-width,
rank-width or clique-width, with runtime single exponential in either parameter
if given the pertinent optimal decomposition. To compare boolean-width versus
rank-width or clique-width, we first show that for any graph, the square root
of its boolean-width is never more than its rank-width. Next, we exhibit a
class of graphs, the Hsu-grids, for which we can solve NP-hard problems in
polynomial time, if we use the right parameter. An $n \times \frac{n}{10}$
Hsu-grid on ${1/10}n^2$ vertices has boolean-width $\Theta(\log n)$ and
rank-width $\Theta(n)$. Moreover, any optimal rank-decomposition of such a
graph will have boolean-width $\Theta(n)$, i.e. exponential in the optimal
boolean-width. A main open problem is to approximate the boolean-width better
than what is given by the algorithm for rank-width [Hlin\v{e}n\'y and Oum,
2008]


  We have found that Proxima Centauri, the star closest to our Sun, will pass
close to a pair of faint background stars in the next few years. Using Hubble
Space Telescope (HST) images obtained in 2012 October, we determine that the
passage close to a mag 20 star will occur in 2014 October (impact parameter
1.6"), and to a mag 19.5 star in 2016 February (impact parameter 0.5"). As
Proxima passes in front of these stars, the relativistic deflection of light
will cause shifts in the positions of the background stars by ~0.5 and 1.5 mas,
respectively, readily detectable by HST imaging, and possibly by Gaia and
ground-based facilities such as VLT. Measurement of these astrometric shifts
offers a unique and direct method to measure the mass of Proxima. Moreover, if
Proxima has a planetary system, the planets may be detectable through their
additional microlensing signals, although the probability of such detections is
small. With astrometric accuracies of 0.03 mas (achievable with HST spatial
scanning), centroid shifts caused by Jovian planets are detectable at
separations of up to 2.0" (corresponding to 2.6 AU at the distance of Proxima),
and centroid shifts by Earth-mass planets are detectable within a small band of
8 mas (corresponding to 0.01 AU) around the source trajectories. Jovian planets
within a band of about 28 mas (corresponding to 0.036 AU) around the source
trajectories would produce a brightening of the source by >0.01 mag and could
hence be detectable. Estimated timescales of the astrometric and photometric
microlensing events due to a planet range from a few hours to a few days, and
both methods would provide direct measurements of the planetary mass.


  Every robotic network cloud system can be seen as a graph with nodes as
hardware with independent computational processing powers and edges as data
transmissions between nodes. When assigning a task to a node we may change
several values corresponding to the node such as distance to other nodes, the
time to complete all of its tasks, the energy level of the node, energy
consumed while performing all of its tasks, geometrical position, communication
with other nodes, and so on. These values can be seen as fingerprints for the
current state of the node which can be evaluated as a subspace of a hyperspace.
We proposed a theoretical model describing how assigning tasks to a node will
change the subspace of the hyperspace, and from that, we show how to obtain the
optimal task allocation. We described the communication instability between
nodes and the capability of nodes as subspaces of a hyperspace. We translate
task scheduling to nodes as finding the maximum volume of the hyperspace.


  We present an analysis of the neutral hydrogen (HI) content and distribution
of galaxies in groups as a function of their parent dark matter halo mass. The
Arecibo Legacy Fast ALFA survey alpha.40 data release allows us, for the first
time, to study the HI properties of over 740 galaxy groups in the volume of sky
common to the SDSS and ALFALFA surveys. We assigned ALFALFA HI detections a
group membership based on an existing magnitude/volume-limited SDSS DR7
group/cluster catalog. Additionally, we assigned group "proximity" membership
to HI detected objects whose optical counterpart falls below the limiting
optical magnitude--thereby not contributing substantially to the estimate of
the group stellar mass, but significantly to the total group HI mass. We find
that only 25% of the HI detected galaxies reside in groups or clusters, in
contrast to approximately half of all optically detected galaxies. Further, we
plot the relative positions of optical and HI detections in groups as a
function of parent dark matter halo mass to reveal strong evidence that HI is
being processed in galaxies as a result of the group environment: as optical
membership increases, groups become increasingly deficient of HI rich galaxies
at their center and the HI distribution of galaxies in the most massive groups
starts to resemble the distribution observed in comparatively more extreme
cluster environments. We find that the lowest HI mass objects lose their gas
first as they are processed in the group environment, and it is evident that
the infall of gas rich objects is important to the continuing growth of large
scale structure at the present epoch, replenishing the neutral gas supply of
groups. Finally, we compare our results to those of cosmological simulations
and find that current models cannot simultaneously predict the HI selected halo
occupation distribution for both low and high mass halos.


  In this work we analyze how the spectrum of primordial scalar perturbations
is modified, within the emergent universe scenario, when a particular version
of the Continuous Spontaneous Localization (CSL) model is incorporated as the
generating mechanism of initial perturbations, providing also an explanation to
the quantum-to-classical transition of such perturbations. On the other hand, a
phase of super-inflation, prior to slow-roll inflation, is a characteristic
feature of the emergent universe hypothesis. In recent works, it was shown that
the super-inflation phase could generically induce a suppression of the
temperature anisotropies of the CMB at large angular scales. We study here
under what conditions the CSL maintains or modifies these characteristics of
the emergent universe and their compatibility with the CMB observations.


  In order to characterize nuclear matter under extreme conditions, we
calculate all diffusion transport coefficients related to baryon, electric and
strangeness charge for both a hadron resonance gas and a simplified kinetic
model of the quark-gluon plasma. We demonstrate that the diffusion currents do
not depend only on gradients of their corresponding charge density. Instead, we
show that there exists coupling between the different charge currents, in such
a way that it is possible for density gradients of a given charge to generate
dissipative currents of another charge. Within this scheme, the charge
diffusion coefficient is best viewed as a matrix, in which the diagonal terms
correspond to the usual charge diffusion coefficients, while the off-diagonal
terms describe the coupling between the different currents. In this letter, we
calculate for the first time the complete diffusion matrix including the three
charges listed above. We find that the baryon diffusion current is strongly
affected by baryon charge gradients, but also by its coupling to gradients in
strangeness. The electric charge diffusion current is found to be strongly
affected by electric and strangeness gradients, whereas strangeness currents
depend mostly on strange and baryon gradients.


  Recently, it was argued that new-physics (NP) effects in B decays can be
approximately parametrized in terms of a few quantities. As a result, CP
violation in the $B$ system allows one not only to detect the presence of new
physics (NP), but also to measure its parameters. This will allow a partial
identification of the NP, before its production at high-energy colliders. In
this paper, we examine three methods for measuring NP parameters. The first
uses a technique involving both $\btos$ and $\btod$ penguin B decays. Depending
on which pair of decays is used, the theoretical error is in the range 5--15%.
The second involves a comparison of $B\to \pi K$ and $B\to\pi\pi$ decays.
Although the theoretical error is large ($\gsim 25%$), the method can be
performed now, with presently-available data. The third is via a time-dependent
angular analysis of $\bvv$ decays. In this case, there is no theoretical error,
but the technique is experimentally challenging, and the method applies only to
those NP models whose weak phase is universal to all NP operators. A reliable
identification of the NP will involve the measurement of the NP parameters in
many different ways, and with as many B decay modes as possible, so that it
will be important to use all of these methods.


  We report on terahertz emission from FeMnPt/Ru/FeMnPt and
Pt/CoFeB/Ru/CoFeB/Pt synthetic antiferromagnet (SAF) structures upon
irradiation by a femtosecond laser; the former is via the anomalous Hall
effect, whereas the latter is through the inverse spin Hall effect. The
antiparallel alignment of the two ferromagnetic layers leads to a terahertz
emission peak amplitude that is almost double that for a corresponding
single-layer or bilayer emitter with the same equivalent thickness. In
addition, we demonstrate by both simulation and experiment that terahertz
emission provides a powerful tool to probe the magnetization reversal processes
of individual ferromagnetic layers in a SAF structure, as the terahertz signal
is proportional to the vector difference of the magnetizations of the two
ferromagnetic layers.


  Both caching and interference alignment (IA) are promising techniques for
future wireless networks. Nevertheless, most of existing works on cache-enabled
IA wireless networks assume that the channel is invariant, which is unrealistic
considering the time-varying nature of practical wireless environments. In this
paper, we consider realistic time-varying channels. Specifically, the channel
is formulated as a finite-state Markov channel (FSMC). The complexity of the
system is very high when we consider realistic FSMC models. Therefore, we
propose a novel big data reinforcement learning approach in this paper. Deep
reinforcement learning is an advanced reinforcement learning algorithm that
uses deep $Q$ network to approximate the $Q$ value-action function. Deep
reinforcement learning is used in this paper to obtain the optimal IA user
selection policy in cache-enabled opportunistic IA wireless networks.
Simulation results are presented to show the effectiveness of the proposed
scheme.


  This paper summarizes observed and physical properties of all types of core
collapse supernovae. Despite the great diversity displayed by these objects,
several regularities emerge which suggest that 1) there is a continuum in the
properties of these objects, 2) the mass of the envelope is one of the driving
parameters of the explosion, or it is correlated with some other property of
the core, with the latter determining the outcome of the explosion, and 3) the
physics of the core and explosion mechanism of all core collapse supernovae are
not be fundamentally different, regardless of the external appearance of the
supernova. Far above in energy scale and 56Ni production lies SN 1998bw, the
only supernova firmly associated with a gamma-ray burst.


  We analyze the performance of the best-response dynamic across all
normal-form games using a random games approach. The playing sequence -- the
order in which players update their actions -- is essentially irrelevant in
determining whether the dynamic converges to a Nash equilibrium in certain
classes of games (e.g. in potential games) but, when evaluated across all
possible games, convergence to equilibrium depends on the playing sequence in
an extreme way. Our main asymptotic result shows that the best-response dynamic
converges to a pure Nash equilibrium in a vanishingly small fraction of all
(large) games when players take turns according to a fixed cyclic order. By
contrast, when the playing sequence is random, the dynamic converges to a pure
Nash equilibrium if one exists in almost all (large) games.


  A set of exact closed-form Bloch-state solutions to the stationary
Gross-Pitaevskii equation are obtained for a Bose-Einstein condensate in a
one-dimensional periodic array of quantum wells, i.e. a square-well periodic
potential. We use these exact solutions to comprehensively study the Bloch
band, the compressibility, effective mass and the speed of sound as functions
of both the potential depth and interatomic interaction. According to our
study, a periodic array of quantum wells is more analytically tractable than
the sinusoidal potential and allows an easier experimental realization than the
Kr\"onig-Penney potential, therefore providing a useful theoretical model for
understanding Bose-Einstein condensates in a periodic potential.


  We regularize the 3-D quantum electrodynamics by a parity invariant
Pauli-Villars regularization method. We find that in the perturbation theory
the Chern-Simons term is not induced in the massless fermion case and induced
in the massive fermion case.


  The Born cross sections are measured for the first time for the processes
$e^+e^-\to D_s^{*+}D_{s0}^*(2317)^- +c.c.$ and $e^+e^-\to
D_s^{*+}D_{s1}(2460)^- +c.c.$ at the center-of-mass energy $\sqrt{s}=$
4.600~GeV, 4.612~GeV, 4.626~GeV, 4.640~GeV, 4.660~GeV, 4.68~GeV, and 4.700~GeV,
and for $e^+e^-\to D_s^{*+}D_{s1}(2536)^- +c.c.$ at $\sqrt{s}=$ 4.660~GeV,
4.680~GeV, and 4.700~GeV, using data samples collected with the BESIII detector
at the BEPCII collider. No structures are observed in cross-section
distributions for any of the processes.


  On the basis of first-principles G0W0 calculations we study systematically
how the electronic levels of a benzene molecule are renormalized by substrate
polarization when physisorbed on different metallic and semiconducting
surfaces. The polarization-induced reduction of the energy gap between occupied
and unoccupied molecular levels is found to scale with the substrate density of
states at the Fermi level (for metals) and substrate band gap (for
semiconductors). These conclusions are further supported by GW calculations on
simple lattice models. By expressing the electron self-energy in terms of the
substrate's joint density of states we relate the level shift to the surface
electronic structure thus providing a microscopic explanation of the trends in
the G0W0 calculations. While image charge effects are not captured by
semi-local and hybrid exchange-correlation functionals, we find that error
cancellations lead to remarkably good agreement between the G0W0 and Kohn-Sham
energies for the occupied orbitals of the adsorbed molecule.


  For a time-dependent $\tau$-periodic harmonic oscillator of two linearly
independent homogeneous solutions of classical equation of motion which are
bounded all over the time (stable), it is shown, there is a representation of
states cyclic up to multiplicative constants under $\tau$-evolution or
$2\tau$-evolution depending on the model. The set of the wave functions is
complete. Berry's phase which could depend on the choice of representation can
be defined under the $\tau$- or $2\tau$-evolution in this representation. If a
homogeneous solution diverges as the time goes to infinity, it is shown that,
Berry's phase can not be defined in any representation considered. Berry's
phase for the driven harmonic oscillator is also considered. For the cases
where Berry's phase can be defined, the phase is given in terms of solutions of
the classical equation of motion.


  Physical Unclonable Functions (PUFs) are emerging as promising security
primitives for IoT devices, providing device fingerprints based on physical
characteristics. Despite their strengths, PUFs are vulnerable to machine
learning (ML) attacks, including conventional and reliability-based attacks.
Conventional ML attacks have been effective in revealing vulnerabilities of
many PUFs, and reliability-based ML attacks are more powerful tools that have
detected vulnerabilities of some PUFs that are resistant to conventional ML
attacks. Since reliability-based ML attacks leverage information of PUFs'
unreliability, we were tempted to examine the feasibility of building defense
using reliability enhancing techniques, and have discovered that majority
voting with reasonably high repeats provides effective defense against existing
reliability-based ML attack methods. It is known that majority voting reduces
but does not eliminate unreliability, we are motivated to investigate if new
attack methods exist that can capture the low unreliability of highly but
not-perfectly reliable PUFs, which led to the development of a new reliability
representation and the new representation-enabled attack method that has
experimentally cracked PUFs enhanced with majority voting of high repetitions.


  We study how vortices in dense superfluid hadronic matter can connect to
vortices in superfluid quark matter, as in rotating neutron stars, focusing on
the extent to which quark-hadron continuity can be maintained. As we show, a
singly quantized vortex in three-flavor symmetric hadronic matter can connect
smoothly to a singly quantized non-Abelian vortex in three-flavor symmetric
quark matter in the color-flavor locked (CFL) phase, without the necessity for
boojums appearing at the transition.


  We present a phenomenological fluid dynamics model for a dilatant fluid, i.e.
a severe shear thickening fluid, by introducing a state variable. The
Navier-Stokes equation is coupled with the state variable field, which evolves
in response to the local shear stress as the fluid is sheared. The viscosity is
assumed to depend upon the state variable and to diverge at a certain value due
to jamming. We demonstrate that the coupling of the fluid dynamics with the
shear thickening leads to an oscillatory instability in the shear flow. The
model also shows a peculiar response of the fluid to a strong external impact.


  We have performed cascade genetic algorithm and ab initio atomistic
thermodynamics under the framework of first-principles density functional
theory to study the (meta-)stability of a wide range of LixNy clusters. We
found that hybrid xc-functional is essential to address this problem as a
local/semi-local functional simply fails even to predict a qualitative
prediction. Most importantly, we find that though in bulk Lithium Nitride, Li
rich phase, i.e. Li3N, is the stable stoichiometry, in small LixNy clusters
N-rich phases are more stable at thermodynamic equilibrium. We further show a
that these N-rich clusters are promising hydrogen storage material because of
their easy adsorption and desorption ability at respectively low (< 300K) and
moderately high temperature (> 600K).


  The electromagnetic effects of the finite size of the nucleon are implemented
self-consistently on top of the Skyrme Hartree-Fock calculation, where the
electric form factors of both protons and neutrons are considered. Furthermore,
the vacuum polarization and the electromagnetic spin-orbit interaction are
taken into account. The self-consistent finite-size effects give a different
Coulomb potential from the conventional one and affect the neutrons as well.
The contribution of the finite-size effects to the total energy reaches $ 7 \,
\mathrm{MeV} $ in $ {}^{208} \mathrm{Pb} $. The vacuum polarization and the
electromagnetic spin-orbit interaction are also non-negligible, especially in
the heavy nuclei. These effects provide a comparable contribution of the total
energy to that of the isospin symmetry-breaking terms of the nuclear
interaction. The mirror nuclei mass difference in $ {}^{48} \mathrm{Ca} $--$
{}^{48} \mathrm{Ni} $ is also studied, and its value is improved by
approximately one order of magnitude.


  This article reviews Michel Henon's contributions to a diverse set of
problems in astrophysical dynamics, including violent relaxation, Saturn's
rings, roundoff error in orbit integrations, and planet formation.


  Liquid scintillator (LS) will be adopted as the detector material in JUNO
(Jiangmen Underground Neutrino Observatory). The energy resolution requirement
of JUNO is 3%, which has never previously been reached. To achieve this energy
resolution, the light yield of liquid scintillator is an important factor. PPO
(the fluor) and bis-MSB (the wavelength shifter) are the two main materials
dissolved in LAB. To study the influence of these two materials on the
transmission of scintillation photons in LS, 25 and 12 cm-long quartz vessels
were used in a light yield experiment. LS samples with different concentration
of PPO and bis-MSB were tested. At these lengths, the light yield growth is not
obvious when the concentration of PPO is higher than 4 g/L. The influence from
bis-MSB becomes insignificant when its concentration is higher than 8 mg/L.
This result could provide some useful suggestions for the JUNO LS.


  A mechanism of ultra-high energy cosmic ray acceleration in extragalactic
radio sources, at the interface between the relativistic jet and the
surrounding medium, is discussed as a supplement to the shock acceleration in
`hot spots'. Due to crossing the tangential discontinuity of the velocity the
particle can gain an amount of energy comparable to the energy gain at the
shock crossing. However, the spectrum of particles accelerated at the jet side
boundary is expected to be much flatter than the one formed at the shock. Due
to this fact, particles accelerated at the boundary can dominate the overall
spectrum at highest energies. In conditions characteristic to extragalactic
jets' terminal shocks, the mechanism naturally provides the particles with E ~
10^20 eV and complies with the efficiency requirements. The spectrum formation
near the cut-off energy due to action of both the shock acceleration and the
tangential discontinuity acceleration is modelled with the Monte Carlo particle
simulations. It confirms that the upper energy limit can surpass the shock
acceleration estimate.


  State-of-the-art AI systems can be significantly improved without expensive
retraining via "post-training enhancements"-techniques applied after initial
training like fine-tuning the system to use a web browser. We review recent
post-training enhancements, categorizing them into five types: tool-use,
prompting methods, scaffolding, solution selection, and data generation.
Different enhancements improve performance on different tasks, making it hard
to compare their significance. So we translate improvements from different
enhancements into a common currency, the compute-equivalent gain: how much
additional training compute would be needed to improve performance by the same
amount as the enhancement. Our non-experimental work shows that post-training
enhancements have significant benefits: most surveyed enhancements improve
benchmark performance by more than a 5x increase in training compute, some by
more than 20x. Post-training enhancements are relatively cheap to develop:
fine-tuning costs are typically <1% of the original training cost. Governing
the development of capable post-training enhancements may be challenging
because frontier models could be enhanced by a wide range of actors.


  In recent years, Artificial Intelligence (AI) systems have surpassed human
intelligence in a variety of computational tasks. However, AI systems, like
humans, make mistakes, have blind spots, hallucinate, and struggle to
generalize to new situations. This work explores whether AI can benefit from
creative decision-making mechanisms when pushed to the limits of its
computational rationality. In particular, we investigate whether a team of
diverse AI systems can outperform a single AI in challenging tasks by
generating more ideas as a group and then selecting the best ones. We study
this question in the game of chess, the so-called drosophila of AI. We build on
AlphaZero (AZ) and extend it to represent a league of agents via a
latent-conditioned architecture, which we call AZ_db. We train AZ_db to
generate a wider range of ideas using behavioral diversity techniques and
select the most promising ones with sub-additive planning. Our experiments
suggest that AZ_db plays chess in diverse ways, solves more puzzles as a group
and outperforms a more homogeneous team. Notably, AZ_db solves twice as many
challenging puzzles as AZ, including the challenging Penrose positions. When
playing chess from different openings, we notice that players in AZ_db
specialize in different openings, and that selecting a player for each opening
using sub-additive planning results in a 50 Elo improvement over AZ. Our
findings suggest that diversity bonuses emerge in teams of AI agents, just as
they do in teams of humans and that diversity is a valuable asset in solving
computationally hard problems.


  For avoiding the exposure of plaintexts in cloud environments, some
homomorphic hashing algorithms have been proposed to generate the hash value of
each plaintext, and cloud environments only store the hash values and calculate
the hash values for future needs. However, longer hash value generation time
and longer hash value summary time may be required by these homomorphic hashing
algorithms with higher security strengths. Therefore, this study proposes a
homomorphic hashing based on elliptic curve cryptography (ECC) to provide a
homomorphic hashing function in accordance with the characteristics of ECC.
Furthermore, mathematical models and practical cases have been given to prove
the proposed method. In experiments, the results show that the proposed method
have higher efficiency with different security strengths.


  Lidar sensors are widely used in various applications, ranging from
scientific fields over industrial use to integration in consumer products. With
an ever growing number of different driver assistance systems, they have been
introduced to automotive series production in recent years and are considered
an important building block for the practical realisation of autonomous
driving. However, due to the potentially large amount of Lidar points per scan,
tailored algorithms are required to identify objects (e.g. pedestrians or
vehicles) with high precision in a very short time. In this work, we propose an
algorithmic approach for real-time instance segmentation of Lidar sensor data.
We show how our method leverages the properties of the Euclidean distance to
retain three-dimensional measurement information, while being narrowed down to
a two-dimensional representation for fast computation. We further introduce
what we call "skip connections", to make our approach robust against
over-segmentation and improve assignment in cases of partial occlusion. Through
detailed evaluation on public data and comparison with established methods, we
show how these aspects enable state-of-the-art performance and runtime on a
single CPU core.


  We revisit the results of Zamolodchikov and others on the deformation of
two-dimensional quantum field theory by the determinant $\det T$ of the stress
tensor, commonly referred to as $T\overline T$. Infinitesimally this is
equivalent to a random coordinate transformation, with a local action which is,
however, a total derivative and therefore gives a contribution only from
boundaries or nontrivial topology. We discuss in detail the examples of a
torus, a finite cylinder, a disk and a more general simply connected domain. In
all cases the partition function evolves according to a linear diffusion-type
equation, and the deformation may be viewed as a kind of random walk in moduli
space. We also discuss possible generalizations to higher dimensions.


  We develop importance sampling based efficient simulation techniques for
three commonly encountered rare event probabilities associated with random
walks having i.i.d. regularly varying increments; namely, 1) the large
deviation probabilities, 2) the level crossing probabilities, and 3) the level
crossing probabilities within a regenerative cycle. Exponential twisting based
state-independent methods, which are effective in efficiently estimating these
probabilities for light-tailed increments are not applicable when the
increments are heavy-tailed. To address the latter case, more complex and
elegant state-dependent efficient simulation algorithms have been developed in
the literature over the last few years. We propose that by suitably decomposing
these rare event probabilities into a dominant and further residual components,
simpler state-independent importance sampling algorithms can be devised for
each component resulting in composite unbiased estimators with desirable
efficiency properties. When the increments have infinite variance, there is an
added complexity in estimating the level crossing probabilities as even the
well known zero-variance measures have an infinite expected termination time.
We adapt our algorithms so that this expectation is finite while the estimators
remain strongly efficient. Numerically, the proposed estimators perform at
least as well, and sometimes substantially better than the existing
state-dependent estimators in the literature.


  The boundary OSP(1|2) WZNW model possesses two types of branes, which are
localized on supersymmetric Euclidean AdS$_2$ and on two-dimensional
superspheres. We compute the coupling of closed strings to these branes with
two different methods. The first one uses factorization constraints and the
other one a correspondence to boundary N=1 super-Liouville field theory, which
we proof with path integral techniques. We check that the results obey the
Cardy condition and reproduce the semi-classical computations. For the check we
also compute the spectral density of open strings that are attached to the
non-compact branes.


  We give a new method for solving a problem originally solved about 20 years
ago by Sinnott and Kubert, namely that of computing the cohomology of the
universal ordinary distribution with respect to the action of the two-element
group generated by complex conjugation. We develop the method in sufficient
generality so as to be able to calculate analogous cohomology groups in the
function field setting which have not previously been calculated. In
particular, we are able to confirm a conjecture of L.~S.~Yin conditional on
which Yin was able to obtain results on unit indices generalizing those of
Sinnott in the classical cyclotomic case and Galovich-Rosen in the Carlitz
cyclotomic case. The Farrell-Tate cohomology theory for groups of finite
virtual cohomological dimension plays a key role in our proof of Yin's
conjecture. The methods developed in the paper have recently been used by
P.~Das to illuminate the structure of the Galois group of the algebraic
extension of the rational number field generated by the roots of unity and the
algebraic $\Gamma$-monomials.
  This paper has appeared as Contemp. Math. 224 (1999) 1-27.


  Higher homotopies are nowadays playing a prominent role in mathematics as
well as in certain branches of theoretical physics. We recall some of the
connections between the past and the present developments. Higher homotopies
were isolated within algebraic topology at least as far back as the 1940's.
Prompted by the failure of the Alexander-Whitney multiplication of cocycles to
be commutative, Steenrod developed certain operations which measure this
failure in a coherent manner. Dold and Lashof extended Milnor's classifying
space construction to associative H-spaces, and a careful examination of this
extension led Stasheff to the discovery of An-spaces and Ainfty-spaces as
notions which control the failure of associativity in a coherent way so that
the classifying space construction can still be pushed through. Algebraic
versions of higher homotopies have, as we all know, led Kontsevich eventually
to the proof of the formality conjecture. Homological perturbation theory
(HPT), in a simple form first isolated by Eilenberg and Mac Lane in the early
1950's, has nowadays become a standard tool to handle algebraic incarnations of
higher homotopies. A basic observation is that higher homotopy structures
behave much better relative to homotopy than strict structures, and HPT enables
one to exploit this observation in various concrete situations which, in
particular, leads to the effective calculation of various invariants which are
otherwise intractable. Higher homotopies abound but they are rarely recognized
explicitly and their significance is hardly understood; at times, their
appearance might at first glance even come as a surprise, for example in the
Kodaira-Spencer approach to deformations of complex manifolds or in the theory
of foliations.


  In broad applications, it is routinely of interest to assess whether there is
evidence in the data to refute the assumption of conditional independence of
$Y$ and $X$ conditionally on $Z$. Such tests are well developed in parametric
models but are not straightforward in the nonparametric case. We propose a
general Bayesian approach, which relies on an encompassing nonparametric Bayes
model for the joint distribution of $Y$, $X$ and $Z$. The framework allows $Y$,
$X$ and $Z$ to be random variables on arbitrary spaces, and can accommodate
different dimensional vectors having a mixture of discrete and continuous
measurement scales. Using conditional mutual information as a scalar summary of
the strength of the conditional dependence relationship, we construct null and
alternative hypotheses. We provide conditions under which the correct
hypothesis will be consistently selected. Computational methods are developed,
which can be incorporated within MCMC algorithms for the encompassing model.
The methods are applied to variable selection and assessed through simulations
and criminology applications.


  We are developing photon-counting cameras employing cryogenic arrays of
energy-resolving TES (Transition Edge Sensor) pixels. These are being tested in
ground-based instruments, but will have their greatest impact when employed on
space platforms, where they can cover the 10micrometer-100nm range with high
time- and moderate energy- resolution. Here we summarize briefly existing
device performance, current directions in array camera development and
anticipated capabilities.


  We study the extensions of two left modules $W_1, W_2$ for a meromorphic
open-string vertex algebra $V$. We show that the extensions satisfying some
technical but natural convergence conditions are in bijective correspondence to
the first cohomology classes associated to the $V$-bimodule $\mathcal{H}_N(W_1,
W_2)$ constructed in \cite{HQ-Red}. When $V$ is grading-restricted and contains
a nice vertex subalgebra $V_0$, those convergence conditions hold
automatically. In addition, we show that the dimension of $\text{Ext}^1(W_1,
W_2)$ is bounded above by the fusion rule $N\binom{W_2}{VW_1}$ in the category
of $V_0$-modules. In particular, if the fusion rule is finite, then
$\text{Ext}^1(W_1, W_2)$ is finite-dimensional. We also give an example of an
abelian category consisting of certain modules of the Virasoro VOA that does
not contain any nice subalgebras, while the convergence conditions hold for
every object.


  Iteration is a programming operation that traditionally refers to visiting
the elements of a data structure in sequence. However, modern programming
systems such as Rust, Java, and C# generalise iteration far beyond the
traditional use case. They allow iterators to be parameterised with
(potentially side-effectful) closures and support the composition of iterators
to form iterator chains, where each iterator in the chain consumes values from
its predecessor and produces values for its successor. Such generalisations
pose three major challenges for modular specification and verification of
iterators and the client code using them: (1) How can parameterised iterators
be specified modularly and their (accumulated) side effects reasoned about? (2)
How can the behaviour of an iterator chain be derived from the specifications
of its component iterators? (3) How can proofs about such iterators be
automated?
  We present the first methodology for the modular specification and
verification of advanced iteration idioms with side-effectful computations. It
addresses the three challenges above using a combination of inductive two-state
invariants, higher-order closure contracts, and separation logic-like
ownership. We implement and our methodology in a state-of-the-art SMT-based
Rust verifier. Our evaluation shows that our methodology is sufficiently
expressive to handle advanced and idiomatic iteration idioms and requires
modest annotation overhead.


  We establish global existence for the energy-critical nonlinear Schr\"odinger
equation on $\mathbb{S}^3$. This follows similar lines to the work on
$\mathbb{T}^3$ but requires new extinction results for linear solutions and
bounds on the first nonlinear iterate at a Euclidean profile that are adapted
to the new geometry.


  We present an experimentally realizable stabilized charge pumping scheme in a
linear array of Cooper-pair boxes. The system design intrinsically protects the
pumping mechanism from severe errors, especially current reversal and
spontaneous charge excitation. The quantum Zeno effect is implemented to
further diminish pumping errors. The characteristics of this scheme are
considered from the perspective of improving the current standard. Such an
improvement bears relevence to the closure of the so-called measurement
triangle (see D. Averin [Nature 434, 285 (2005)]).


  This paper extends work done to date on quantum computation by associating
potentials with different types of computation steps. Quantum Turing machine
Hamiltonians, generalized to include potentials, correspond to sums over tight
binding Hamiltonians each with a different potential distribution. Which
distribution applies is determined by the initial state. An example, which
enumerates the integers in succession as binary strings, is analyzed. It is
seen that for some initial states the potential distributions have
quasicrystalline properties and are similar to a substitution sequence.


  We theoretically propose a circuit QED system implemented with triple-leg
stripline resonator (TSR). Unlikely from linear stripline resonator, the
fundamental intra-cavity microwave modes of the TSR are two-fold degenerate.
When a superconducting qubit is placed near one of the TSR legs, one
fundamental mode is directly coupled to the qubit, while the other one remains
uncoupled. Our system closely resembles an optical cavity QED system, where an
atom in a cavity couples only to the incident photon with a specific
polarization by placing a polarization beamsplitter in front of the optical
cavity.
  Using our circuit QED system, we have theoretically studied a two-qubit
quantum gate operation in a hybrid qubit composed of flying microwave qubit and
superconducting qubit. We have demonstrated that for the hybrid qubit, the
quantum controlled phase flip (CPF) gate can be reliably implemented for the
experimentally available set of parameters.


  We construct the geometric Satake equivalence for quasi-split reductive
groups over nonarchimedean local fields, using \'etale Artin-Tate motives with
$\mathbb{Z}[\frac{1}{p}]$-coefficients. We consider local fields of both equal
and mixed characteristic. Along the way, we extend the work of
Gaussent--Littelmann on the connection between LS galleries and MV cycles to
the case of residually split reductive groups. As an application, we generalize
Zhu's integral Satake isomorphism for spherical Hecke algebras to ramified
groups. Moreover, for residually split groups, we define generic spherical
Hecke algebras, and construct generic Satake and Bernstein isomorphisms.


  Surrogate modeling and uncertainty quantification tasks for PDE systems are
most often considered as supervised learning problems where input and output
data pairs are used for training. The construction of such emulators is by
definition a small data problem which poses challenges to deep learning
approaches that have been developed to operate in the big data regime. Even in
cases where such models have been shown to have good predictive capability in
high dimensions, they fail to address constraints in the data implied by the
PDE model. This paper provides a methodology that incorporates the governing
equations of the physical model in the loss/likelihood functions. The resulting
physics-constrained, deep learning models are trained without any labeled data
(e.g. employing only input data) and provide comparable predictive responses
with data-driven models while obeying the constraints of the problem at hand.
This work employs a convolutional encoder-decoder neural network approach as
well as a conditional flow-based generative model for the solution of PDEs,
surrogate model construction, and uncertainty quantification tasks. The
methodology is posed as a minimization problem of the reverse Kullback-Leibler
(KL) divergence between the model predictive density and the reference
conditional density, where the later is defined as the Boltzmann-Gibbs
distribution at a given inverse temperature with the underlying potential
relating to the PDE system of interest. The generalization capability of these
models to out-of-distribution input is considered. Quantification and
interpretation of the predictive uncertainty is provided for a number of
problems.


  We study the low-energy dynamics of an acoustic black hole near the sonic
horizon. For the experimental test of black hole evaporation in the laboratory,
the decay rate (greybody factor) of the acoustic black hole (sonic hole) can be
calculated by the usual low-energy perturbation method. As a consequence, we
obtain the decay rate of the sonic horizon from the absorption and the
reflection coefficients. Moreover, we show that the thermal emission from the
sonic horizon is only proportional to a control parameter which describes the
velocity of the fluid.


  We consider a stochastic optimal control problem in a market model with
temporary and permanent price impact, which is related to an expected utility
maximization problem under finite fuel constraint. We establish the initial
condition fulfilled by the corresponding value function and show its first
regularity property. Moreover, we can prove the existence and uniqueness of
optimal strategies under rather mild model assumptions. On the one hand, this
result is of independent interest. On the other hand, it will then allow us to
derive further regularity properties of the corresponding value function, in
particular its continuity and partial differentiability. As a consequence of
the continuity of the value function, we will prove the dynamic programming
principle without appealing to the classical measurable selection arguments.


  We show that (local) confluence of terminating locally constrained rewrite
systems is undecidable, even when the underlying theory is decidable. Several
confluence criteria for logically constrained rewrite systems are known. These
were obtained by replaying existing proofs for plain term rewrite systems in a
constrained setting, involving a non-trivial effort. We present a simple
transformation from logically constrained rewrite systems to term rewrite
systems such that critical pairs of the latter correspond to constrained
critical pairs of the former. The usefulness of the transformation is
illustrated by lifting the advanced confluence results based on (almost)
development closed critical pairs as well as on parallel critical pairs to the
constrained setting.


  The recent reports of superconductivity in Nd1-xSrxNiO2/SrTiO3
heterostructures have reinvigorated interest in potential superconductivity of
low-valence nickelates. Synthesis of Ni1+-containing compounds is notoriously
difficult. In the current work, a combined sol-gel combustion and high-pressure
annealing technique was employed to prepare polycrystalline perovskite
Nd1-xSrxNiO3 (x = 0, 0.1 and 0.2). Metal nitrates and metal acetates were used
as starting materials, and the latter were found to be superior to the former
in terms of safety and reactivity. The Nd1-xSrxNiO3 compounds were subsequently
reduced to Nd1 xSrxNiO2 using calcium hydride in a sealed, evacuated quartz
tube. To understand the synthesis pathway, the evolution from NdNiO3 to NdNiO2
was monitored using in-situ synchrotron X ray diffraction during the reduction
process. Electrical transport properties were consistent with an
insulator-metal transition occurring between x = 0 and 0.1 for Nd1-xSrxNiO3.
Superconductivity was not observed in our bulk samples of Nd1-xSrxNiO2. Neutron
diffraction experiments at 3 K and 300 K were performed on Nd0.9Sr0.1NiO2, in
which no magnetic Bragg reflections were observed, and the results of
structural Rietveld refinement are provided.


  We comment on some apparently weak points in the novel strategies recently
developed by various authors aiming at a proof of the Riemann hypothesis. After
noting the existence of relevant previous papers where similar tools have been
used, we refine some of these strategies. It is not clear at the moment if the
problems we point out here can be resolved rigorously, and thus a proof of the
RH be obtained, along the lines proposed. However, a specific suggestion of a
procedure to overcome the encountered difficulties is made, what constitutes a
step towards this goal.


  The homology groups of many natural sequences of groups
$\{G_n\}_{n=1}^{\infty}$ (e.g. general linear groups, mapping class groups,
etc.) stabilize as $n \rightarrow \infty$. Indeed, there is a well-known
machine for proving such results that goes back to early work of Quillen.
Church and Farb discovered that many sequences of groups whose homology groups
do not stabilize in the classical sense actually stabilize in some sense as
representations. They called this phenomena representation stability. We prove
that the homology groups of congruence subgroups of $GL_n(R)$ (for almost any
reasonable ring $R$) satisfy a strong version of representation stability that
we call central stability. The definition of central stability is very
different from Church-Farb's definition of representation stability (it is
defined via a universal property), but we prove that it implies representation
stability. Our main tool is a new machine for proving central stability that is
analogous to the classical homological stability machine.


  We calculate, in a relatively explicit way, the Hamiltonian systems which
arise from the Evens-Lu construction of homogeneous Poisson structures on both
compact and noncompact type symmetric spaces. A corollary is that the
Hamiltonian system arising in the noncompact case is isomorphic to the generic
Hamiltonian system arising in the compact case. In the group case these systems
are also isomorphic to those arising from the Bruhat Poisson structure on the
flag space, and hence, by results of Lu, can be completely factored.


  Current theories about mechanisms promoting species co-existence in diverse
communities assume that species only interact ecologically. Species are treated
as discrete evolutionary entities, even though abundant empirical evidence
indicates that patterns of gene flow such as selfing and hybridization
frequently occur in plant and animal groups. Here, we allow mating behavior to
respond to local species composition and abundance in a data-driven
meta-community model of species co-existence. While individuals primarily
out-cross, they also maintain some small capacity for selfing and
hybridization. Mating choice is treated as a 'fuzzy' behavior, determined by an
interaction between intrinsic properties affecting mate choice and extrinsic
properties of the local community, primarily the density and availability of
sympatric inter-fertile species. When mate choice is strongly limited, even low
survivorship of selfed offspring (<10%) can prevent extinction of rare species.
With increasing mate choice, low hybridization success rates (~20%) maintain
community level diversity for substantially longer periods of time. Given the
low species densities and high diversity of tropical tree communities, the
evolutionary costs of competition among sympatric congeneric species are
negligible because direct competition is infrequent. In diverse communities,
many species are chronically rare and thus vulnerable to stochastic extinction,
which occurs rapidly if species are completely reproductively isolated. By
incorporating fuzzy mating behavior into models of species co-existence, a more
realistic understanding of the extinction process can be developed. Fuzzy
mating strategies, potentially an important mechanism for rare species to
escape extinction and gain local adaptations, should be incorporated into
forest management strategies.


  This paper describes results characterizing the range of the time-t heat
operator on various manifolds, including Euclidean spaces, spheres, and
hyperbolic spaces. The guiding principle behind these results is this: The
functions in the range of the heat operator should be, roughly, those functions
having an analytic continuation to the appropriate complexified manifold with
growth in the imaginary directions at most like that of the time-t heat heat
kernel.


  We present the first part of our DARTTS-S (Disks ARound TTauri Stars with
SPHERE) survey: Observations of 8 TTauri stars which were selected based on
their strong (sub-)mm excesses using SPHERE / IRDIS polarimetric differential
imaging (PDI) in the J and H bands. All observations successfully detect the
disks, which appear vastly different in size, from $\approx$80 au in scattered
light to $>$400 au, and display total polarized disk fluxes between 0.06% and
0.89% of the stellar flux. For five of these disks, we are able to determine
the three-dimensional structure and the flaring of the disk surface, which
appears to be relatively consistent across the different disks, with flaring
exponents $\alpha$ between $\approx$1.1 and $\approx$1.6. We also confirm
literature results w.r.t. the inclination and position angle of several of our
disk, and are able to determine which side is the near side of the disk in most
cases. While there is a clear trend of disk mass with stellar ages ($\approx$1
Myr to $>$10 Myr), no correlations of disk structures with age were found.
There are also no correlations with either stellar mass or sub-mm flux. We do
not detect significant differences between the J and H bands. However, we note
that while a high fraction (7/8) of the disks in our sample show ring-shaped
sub-structures, none of them display spirals, in contrast to the disks around
more massive Herbig Ae/Be stars, where spiral features are common.


  Neural operators (NOs) have emerged as effective tools for modeling complex
physical systems in scientific machine learning. In NOs, a central
characteristic is to learn the governing physical laws directly from data. In
contrast to other machine learning applications, partial knowledge is often
known a priori about the physical system at hand whereby quantities such as
mass, energy and momentum are exactly conserved. Currently, NOs have to learn
these conservation laws from data and can only approximately satisfy them due
to finite training data and random noise. In this work, we introduce
conservation law-encoded neural operators (clawNOs), a suite of NOs that endow
inference with automatic satisfaction of such conservation laws. ClawNOs are
built with a divergence-free prediction of the solution field, with which the
continuity equation is automatically guaranteed. As a consequence, clawNOs are
compliant with the most fundamental and ubiquitous conservation laws essential
for correct physical consistency. As demonstrations, we consider a wide variety
of scientific applications ranging from constitutive modeling of material
deformation, incompressible fluid dynamics, to atmospheric simulation. ClawNOs
significantly outperform the state-of-the-art NOs in learning efficacy,
especially in small-data regimes.


  In this paper we extend PALPS, a process calculus proposed for the
spatially-explicit individual-based modeling of ecological systems, with the
notion of a policy. A policy is an entity for specifying orderings between the
different activities within a system. It is defined externally to a PALPS model
as a partial order which prescribes the precedence order between the activities
of the individu- als of which the model is comprised. The motivation for
introducing policies is twofold: one the one hand, policies can help to reduce
the state-space of a model, on the other hand, they are useful for exploring
the behavior of an ecosystem under different assumptions on the ordering of
events within the system. To take account of policies, we refine the semantics
of PALPS via a transition relation which prunes away executions that do not
respect the defined policy. Furthermore, we propose a translation of PALPS into
the probabilistic model checker PRISM . We illustrate our framework by applying
PRISM on PALPS models with policies for conducting simulation and reachability
analysis.


  We study a spin-phononic system where diamond defect centers are interfaced
with a quasi-one-dimensional phononic crystal. We show that, a single defect
center, coupled to the phonon modes of a phononic crystal waveguide near the
band gap, can seed its own phononic cavity mode with an exponentially decaying
envelope around the defect center's position. The spin-induced phononic cavity,
with a greatly reduced and tunable mode volume, allows coherent phonon-mediated
interactions between distant spins with a highly tunable range, enabling access
to a variety of long-range interacting spin models. This work opens prospects
for exploring quantum many-body physics and quantum information processing with
defect centers and periodic phononic nanostructures.


  This paper concerns the $L^2$ essential spectrum of the Laplacian $\Delta$
and the drift Laplacian $\Delta_f$ on complete Riemannian manifolds endowed
with a weighted measure $e^{-f}d\;vol_g$. We prove that the essential spectrum
of the drift Laplacian $\Delta_f$ is $[0,+\infty) $ provided the Bakry-\'Emery
curvature tensor $Ric_f$ is nonnegative and $f$ has sublinear growth . When
$Ric_f \geq 1/2 g$ and $|\nabla f|^2 \leq f$, we show that the essential
spectrum of the Laplacian is also $[0,+\infty)$. During the proofs of these
results, the $f$-volume growth estimate plays an important role and may be of
independent interest.


  A new three-dimensional model of the solar photosphere is presented in this
paper and made publicly available to the community. This model has the
peculiarity that it has been obtained by inverting spectro-polarimetric
observations, rather than from numerical radiation hydrodynamical simulations.
The data used here are from the spectro-polarimeter onboard the Hinode
satellite, which routinely delivers Stokes I, Q, U and V profiles in the 6302 A
spectral region with excellent quality, stability and spatial resolution
(approximately 0.3"). With such spatial resolution, the major granular
components are well resolved, which implies that the derived model needs no
micro- or macro-turbulence to properly fit the widths of the observed spectral
lines. Not only this model fits the observed data used for its construction,
but it can also fit previous solar atlas observations satisfactorily.


  In the present paper we extend the Schwinger model of the nucleon to the
inclusion of loop corrections. Starting from a chiral model of nucleon and
delta for the mesons $\pi,\rho$ and $A_1$ we extend our nonperturbative
sefconsistent regularisation scheme to the calculation of the nucleon form
factors. Chiral symmetry requires loops according to the interactions ${\cal
L}_{\pi\pi\rho}, {\cal L}_{\rho\rho\rho}, {\cal L}_{\rho\pi A_1}, {\cal
L}_{\rho A_1 A_1}$.The coupling constants are strictly introduced by chiral
symmetry. The use of low energy theorems and SU(3) relations allow an otherwise
parameterfree calculation of the form factors of the nucleon.


  Loss functions engineering and the assessment of forecasting performances are
two crucial and intertwined aspects of supervised machine learning. This paper
focuses on binary classification to introduce a class of loss functions that
are defined on probabilistic confusion matrices and that allow an automatic and
a priori maximization of the skill scores. The performances of these loss
functions are validated during the training phase of two experimental
forecasting problems, thus showing that the probability distribution function
associated with the confusion matrices significantly impacts the outcome of the
score maximization process.


  The Hilbert spaces for stable scattering states and particles are determined
by the representations of the characterizing Euclidean and Poincar\'e group and
given, respectively, by the square integrable functions on the momentum
2-spheres for a fixed absolute value of momentum and on the energy-momentum
3-hyperboloids for a particle mass. The Hilbert spaces for the corresponding
unstable states and particles are not characterized by square integrable
functions Their scalar products are defined by positive type functions for the
cyclic representations of the time, space and spacetime translations involved.
Those cyclic, but reducible translation representations are irreducible as
representations of the corresponding affine operation groups which involve also
the time, space and spacetime reflection group, characteristic for unstable
structures.


  The steering effect on the growth of islands is investigated by combining
molecular dynamics (MD) and kinetic Monte Carlo (KMC) simulations. Dynamics of
depositing atoms and kinetics of atoms on a substrate are realized by MD and
KMC, respectively. The reported experimental results on the asymmetric island
growth [van Dijken {\it et al.}, Phys. Rev. Lett. {\bf 82}, 4038 (1999).] is
well reproduced. A salient phenomenon, the reversal of the asymmetry, is found
as the island size increases, and attributed to the asymmetric flux on the
lower terrace of island.


  Magnitude is a numerical invariant of finite metric spaces, recently
introduced by T. Leinster, which is analogous in precise senses to the
cardinality of finite sets or the Euler characteristic of topological spaces.
It has been extended to infinite metric spaces in several a priori distinct
ways. This paper develops the theory of a class of metric spaces, positive
definite metric spaces, for which magnitude is more tractable than in general.
Positive definiteness is a generalization of the classical property of negative
type for a metric space, which is known to hold for many interesting classes of
spaces. It is proved that all the proposed definitions of magnitude coincide
for compact positive definite metric spaces and further results are proved
about the behavior of magnitude as a function of such spaces. Finally, some
facts about the magnitude of compact subsets of l_p^n for p \le 2 are proved,
generalizing results of Leinster for p=1,2, using properties of these spaces
which are somewhat stronger than positive definiteness.


  Unlabeled data is a key component of modern machine learning. In general, the
role of unlabeled data is to impose a form of smoothness, usually from the
similarity information encoded in a base kernel, such as the
$\epsilon$-neighbor kernel or the adjacency matrix of a graph. This work
revisits the classical idea of spectrally transformed kernel regression (STKR),
and provides a new class of general and scalable STKR estimators able to
leverage unlabeled data. Intuitively, via spectral transformation, STKR
exploits the data distribution for which unlabeled data can provide additional
information. First, we show that STKR is a principled and general approach, by
characterizing a universal type of "target smoothness", and proving that any
sufficiently smooth function can be learned by STKR. Second, we provide
scalable STKR implementations for the inductive setting and a general
transformation function, while prior work is mostly limited to the transductive
setting. Third, we derive statistical guarantees for two scenarios: STKR with a
known polynomial transformation, and STKR with kernel PCA when the
transformation is unknown. Overall, we believe that this work helps deepen our
understanding of how to work with unlabeled data, and its generality makes it
easier to inspire new methods.


  If dark matter is composed of weakly interacting particles with mass in the
GeV-TeV range, their annihilation or decay may produce gamma rays that could be
detected by gamma-ray telescopes. Observations of dwarf spheroidal satellite
galaxies of the Milky Way (dSphs) benefit from the relatively accurate
predictions of dSph dark matter content to produce robust constraints to the
dark matter properties. The sensitivity of these observations for the search
for dark matter signals can be optimized thanks to the use of advanced
statistical techniques able to exploit the spectral and morphological
peculiarities of the expected signal. In this paper, I review the status of the
dark matter searches from observations of dSphs with the current generation of
gamma-ray telescopes: Fermi-LAT, H.E.S.S, MAGIC, VERITAS and HAWC. I will
describe in detail the general statistical analysis framework used by these
instruments, putting in context the most recent experimental results and
pointing out the most relevant differences among the different particular
implementations. This~will facilitate the comparison of the current and future
results, as well as their eventual integration in a multi-instrument and
multi-target dark matter search.


  We provide a detailed discussion on the electromagnetic modeling and
classification of polarization converting bianisotropic metasurfaces. To do so,
we first present a general approach to compute the scattering response of such
metasurfaces, which relies on a generalized sheet transition conditions based
susceptibility model. Then, we review how the fundamental properties of
reciprocity, energy conservation, rotation invariance and matching may be
expressed in terms of metasurface susceptibilities and scattering parameters,
and show how these properties may affect and limit the polarization effects of
metasurfaces. Finally, we connect together the metasurface susceptibility model
to the structural symmetries of scattering particles and their associated
polarization effects. This work thus provides a detailed understanding of the
polarization conversion properties of metasurfaces and may prove to be of
particular interest for their practical implementation.


  We identify a new mechanism of internal dissipation of rotational kinetic
energy in spinning dust grains, arising from the reorientation of nuclear
angular momentum, e.g., spins of protons. Grain rotation induces magnetization
of the nuclear spin system, with net alignment of nuclear spins parallel to the
grain angular velocity. When the grain does not rotate around a principal axis,
the nuclear magnetization vector precesses in grain body coordinates, resulting
in dissipation of energy. The analogous process involving electron spins was
discovered by Purcell and termed ``Barnett relaxation''. We revisit the physics
of the ``Barnett relaxation'' process and correct the estimate for the Barnett
relaxation rate. We show that nuclear relaxation can be orders of magnitude
more important than Barnett relaxation. This finding implies that the processes
of ``thermal flipping'' and ``thermal trapping'' are important for a broad
range of grain sizes.


  A weakly-supervised semantic segmentation framework with a tied
deconvolutional neural network is presented. Each deconvolution layer in the
framework consists of unpooling and deconvolution operations. 'Unpooling'
upsamples the input feature map based on unpooling switches defined by
corresponding convolution layer's pooling operation. 'Deconvolution' convolves
the input unpooled features by using convolutional weights tied with the
corresponding convolution layer's convolution operation. The
unpooling-deconvolution combination helps to eliminate less discriminative
features in a feature extraction stage, since output features of the
deconvolution layer are reconstructed from the most discriminative unpooled
features instead of the raw one. This results in reduction of false positives
in a pixel-level inference stage. All the feature maps restored from the entire
deconvolution layers can constitute a rich discriminative feature set according
to different abstraction levels. Those features are stacked to be selectively
used for generating class-specific activation maps. Under the weak supervision
(image-level labels), the proposed framework shows promising results on lesion
segmentation in medical images (chest X-rays) and achieves state-of-the-art
performance on the PASCAL VOC segmentation dataset in the same experimental
condition.


  We present an improved measurement of the Hubble constant (H_0) using the
'inverse distance ladder' method, which adds the information from 207 Type Ia
supernovae (SNe Ia) from the Dark Energy Survey (DES) at redshift 0.018 < z <
0.85 to existing distance measurements of 122 low redshift (z < 0.07) SNe Ia
(Low-z) and measurements of Baryon Acoustic Oscillations (BAOs). Whereas
traditional measurements of H_0 with SNe Ia use a distance ladder of parallax
and Cepheid variable stars, the inverse distance ladder relies on absolute
distance measurements from the BAOs to calibrate the intrinsic magnitude of the
SNe Ia. We find H_0 = 67.8 +/- 1.3 km s-1 Mpc-1 (statistical and systematic
uncertainties, 68% confidence). Our measurement makes minimal assumptions about
the underlying cosmological model, and our analysis was blinded to reduce
confirmation bias. We examine possible systematic uncertainties and all are
below the statistical uncertainties. Our H_0 value is consistent with estimates
derived from the Cosmic Microwave Background assuming a LCDM universe (Planck
Collaboration et al. 2018).


  We show that different rates should be used for borrowing and discount rates,
and that the risk-free rate should be used for discounting when assessing and
comparing the cost of energy accross diffferent producers and technologies, on
the example of photovoltaics. Recent quantitative models using the same rate
for borrowing and discounting lead to an underestimation of the cost for risky
borrowers and to distorted sensitivities of the cost to financial and
non-financial factors. Specifically, it is shown that they may lead to gross
underestimation of the importance of solar-to-electricity conversion
efficiency. The importance of device efficiency is re-established under the
treatment of the discount rate proposed here. The effects on the cost of energy
of the installation efficiency and degradation rate, on the discount rate and
risk premium as well as on the project lifetime are estimated.


  We study tensor norms over Banach spaces and their relations to quantum
information theory, in particular their connection with two-prover games. We
consider a version of the Hilbertian tensor norm $\gamma_2$ and its dual
$\gamma_2^*$ that allow us to consider games with arbitrary output alphabet
sizes. We establish direct-product theorems and prove a generalized
Grothendieck inequality for these tensor norms. Furthermore, we investigate the
connection between the Hilbertian tensor norm and the set of quantum
probability distributions, and show two applications to quantum information
theory: firstly, we give an alternative proof of the perfect parallel
repetition theorem for entangled XOR games; and secondly, we prove a new upper
bound on the ratio between the entangled and the classical value of two-prover
games.


  A ubiquitous building block of signaling pathways is a cycle of covalent
modification (e.g., phosphorylation and dephosphorylation in MAPK cascades).
Our paper explores the kind of information processing and filtering that can be
accomplished by this simple biochemical circuit.
  Signaling cycles are particularly known for exhibiting a highly sigmoidal
(ultrasensitive) input-output characteristic in a certain steady-state regime.
Here we systematically study the cycle's steady-state behavior and its response
to time-varying stimuli. We demonstrate that the cycle can actually operate in
four different regimes, each with its specific input-output characteristics.
These results are obtained using the total quasi-steady-state approximation,
which is more generally valid than the typically used Michaelis-Menten
approximation for enzymatic reactions. We invoke experimental data that
suggests the possibility of signaling cycles operating in one of the new
regimes.
  We then consider the cycle's dynamic behavior, which has so far been
relatively neglected. We demonstrate that the intrinsic architecture of the
cycles makes them act - in all four regimes - as tunable low-pass filters,
filtering out high-frequency fluctuations or noise in signals and environmental
cues. Moreover, the cutoff frequency can be adjusted by the cell. Numerical
simulations show that our analytical results hold well even for noise of large
amplitude. We suggest that noise filtering and tunability make signaling cycles
versatile components of more elaborate cell signaling pathways.


  This paper reviews recent advances in the field of optimization under
uncertainty via a modern data lens, highlights key research challenges and
promise of data-driven optimization that organically integrates machine
learning and mathematical programming for decision-making under uncertainty,
and identifies potential research opportunities. A brief review of classical
mathematical programming techniques for hedging against uncertainty is first
presented, along with their wide spectrum of applications in Process Systems
Engineering. A comprehensive review and classification of the relevant
publications on data-driven distributionally robust optimization, data-driven
chance constrained program, data-driven robust optimization, and data-driven
scenario-based optimization is then presented. This paper also identifies
fertile avenues for future research that focuses on a closed-loop data-driven
optimization framework, which allows the feedback from mathematical programming
to machine learning, as well as scenario-based optimization leveraging the
power of deep learning techniques. Perspectives on online learning-based
data-driven multistage optimization with a learning-while-optimizing scheme is
presented.


  We study inverse $\pi$-extensions of the semigroup $\mathbb{Z}_+$. It is
shown that $\pi$-extension of the semigroup $\mathbb{Z}_+$ is inverse, iff its
$\pi$-extension coincides with $\pi(\mathbb{Z}_+)$. The existence of a
non-inverse $\pi$-extension for semigroup $\mathbb{Z}_+$ is proved.


  The beta asymmetry in neutron beta decay is used to determine the ratio of
axial-vector coupling to vector coupling most precisely. In electron
spectroscopy, backscattering of electrons from detectors can be a major source
of systematic error. We present the determination of the correction for
undetected backscattering for electron detection with the instrument PERKEO
III. For the electron asymmetry, undetected backscattering leads to a
fractional correction of $5\times 10^{-4}$, i.e. a change by 40% of the total
systematic uncertainty.


  The magnetic hyperfine structure constants have been calculated for low-lying
levels in neutral gold atom and gold-like ion of mercury taking into account
Bohr--Weisskopf (BW) effect. BW effect is represented as a product of atomic
and nuclear ($d_\mathrm{nuc}$) factors. We have calculated the atomic factors,
which enable one to extract BW-correction values for far from stability gold
nuclei from the experimental data. The possible uncertainty of our atomic
calculations have been estimated by the comparison with the available
experimental data. It has been shown that the standard single-particle approach
in $d_\mathrm{nuc}$ calculation reasonably well describes experimental data for
$11/2^-$ gold isomers and $3/2^+$ ground state of $\rm ^{199}Au$. At the same
time, it fails to describe the hyperfine constant in $^{197}\mathrm{Au}$. This
indicates the more pronounced configuration mixing in $\rm ^{197}Au$ than in
$\rm ^{199}Au$.


  The paper gives an overview of strangeness-production experiments at the
Cooler Synchrotron COSY. Results on kaon-pair and $\phi$ meson production in
$pp$, $pd$ and $dd$ collisions, hyperon-production experiments and $\Lambda p$
final-state interaction studies are presented as well as a search for a
strangeness $S=-1$ resonance in the $\Lambda p$ system.


  We study the singularities of varieties obtained as infinitesimal quotients
by $1$-foliations in positive characteristic. (1) We show that quotients by
(log) canonical $1$-foliations preserve the (log) singularities of the MMP. (2)
We prove that quotients by multiplicative derivations preserve many properties,
amongst which most $F$-singularities. (3) We formulate a notion of families of
$1$-foliations, and investigate the corresponding families of quotients.


  In this paper, we target the practical implementation issues of quantum
multicast networks. First, we design a recursive lossless compression that
allows us to control the trade-off between the circuit complexity and the
dimension of the compressed quantum state. We give a formula that describes the
trade-off, and further analyze how the formula is affected by the controlling
parameter of the recursive procedure. Our recursive lossless compression can be
applied in a quantum multicast network where the source outputs homogeneous
quantum states (many copies of a quantum state) to a set of destinations
through a bottleneck. Such a recursive lossless compression is extremely useful
in the current situation where the technology of producing large-scale quantum
circuits is limited. Second, we develop two lossless compression schemes that
work for heterogeneous quantum states (many copies of a set of quantum states)
when the set of quantum states satisfies a certain structure. The heterogeneous
compression schemes provide extra compressing power over the homogeneous
compression scheme. Finally, we realize our heterogeneous compression schemes
in several quantum multicast networks, including the single-source
multi-terminal model, the multi-source multi-terminal model, and the ring
networks. We then analyze the bandwidth requirements for these network models.


  A magnetic field dependent Feshbach resonance has been observed in the
elastic scattering collision rate between atoms in the F = 2, M = -2 state of
85 Rb. Changing the magnetic field by several Gauss caused the collision rate
to vary by a factor of 10,000, and the sign of the scattering length could be
reserved. The resonance peak is at 155.2(4) G and its width is 11.6(5) G. From
these results we extract much improved values for the three quantities that
characterize the interaction porential: the van der Waals coefficient C6, the
singlet scattering length, and the triplet scattering length.


  The application of postselection to a weak quantum measurement leads to the
phenomenon of weak values. Expressed in units of the measurement strength, the
displacement of a quantum coherent measuring device is ordinarily bounded by
the eigenspectrum of the measured observable. Postselection can enable an
interference effect that moves the average displacement far outside this range,
bringing practical benefits in certain situations. Employing the Fisher
information metric, we argue that the amplified displacement offers no
fundamental metrological advantage, due to the necessarily reduced probability
of success. Our understanding of metrological advantage is the possibility of a
lower uncertainty in the estimate of an unknown parameter with a large number
of trials. We analyze a situation in which the detector is pixelated with a
finite resolution, and in which the detector is afflicted by random
displacements: imperfections which degrade the fundamental limits of parameter
estimation. Surprisingly, weak-value amplification is no more robust to them
than a technique making no use of the amplification effect brought about by a
final, postselected measurement.


  The interaction of heavy partons, charm and beauty, with the matter created
in heavy ion collisions has been of great interest in recent years. Heavy
partons were predicted to interact less strongly with the matter than light
partons. In apparent contrast to these predictions, unexpectedly strong
suppression of non-photonic electrons from heavy flavor decays has been seen.
However, significant experimental uncertainties remain, both in the
measurements themselves and in the separation of the contribution from charm
and beauty, which have complicated the interpretation of these results. The
current experimental situation is critically reviewed and prospects for making
these measurements more easily interpretable discussed.


  Quantum spin liquid involves fractionalized quasipariticles such as spinons
and visons. They are expressed as itinerant Majorana fermions and $Z_2$ fluxes
in the Kitaev model with bond-dependent exchange interactions on a honeycomb
spin lattice. The observation has recently attracted attention for a candidate
material $\alpha$-RuCl$_3$, showing spin liquid behaviour induced by a magnetic
field. Since the observable spin excitation is inherently composed of the two
quasiparticles, which further admix each other by setting in the magnetic field
as well as non-Kitaev interactions, their individual identification remains
challenging. Here we report an emergent low-lying spin excitation through
nuclear magnetic and quadrupole resonance measurements down to $\sim 0.4$ K
corresponding to $1/500$ of the exchange energy under the finely tuned magnetic
field across the quantum critical point. We determined the critical behaviour
of low-lying excitations and found evolution of two kinds of the spin gap at
high fields. The two excitations exhibit repulsive magnetic field dependence,
suggesting anti-crossing due to the hybridization between fractionalized
quasiparticles.


  We construct and study global solutions for the 3-dimensional incompressible
MHD systems with arbitrary small viscosity. In particular, we provide a
rigorous justification for the following dynamical phenomenon observed in many
contexts: the solution initially behaves like non-dispersive waves and the
shape of the solution persists for a very long time (proportional to the
Reynolds number), thereafter, the solution will be damped due to the long-time
accumulation of the diffusive effects, eventually, the total energy of the
system becomes extremely small compared to the viscosity so that the diffusion
takes over and the solution afterwards decays fast in time.
  We do not assume any condition on the symmetry or on the vorticity. The size
of data and the a priori estimates do not depend on viscosity. The proof is
builded upon a novel use of the basic energy identity and a geometric study of
the characteristic hypersurfaces. The approach is partly inspired by
Christodoulou-Klainerman's proof of the nonlinear stability of Minkowski space
in general relativity.


  Ocean-atmosphere chemistry on Earth has undergone dramatic evolutionary
changes through its long history, with potentially significant ramifications
for the emergence and long-term stability of atmospheric biosignatures. Though
a great deal of work has centered on refining our understanding of false
positives for remote life detection, much less attention has been paid to the
possibility of false negatives, that is, cryptical biospheres that are
widespread and active on a planet's surface but are ultimately undetectable or
difficult to detect in the composition of a planet's atmosphere. Here, we
summarize recent developments from geochemical proxy records and Earth system
models that provide insight into the long-term evolution of the most readily
detectable potential biosignature gases on Earth - oxygen (O2), ozone (O3), and
methane (CH4). We suggest that the canonical O2-CH4 disequilibrium biosignature
would perhaps have been challenging to detect remotely during Earth's ~4.5
billion year history and that in general atmospheric O2/O3 levels have been a
poor proxy for the presence of Earth's biosphere for all but the last ~500
million years. We further suggest that detecting atmospheric CH4 would have
been problematic for most of the last ~2.5 billion years of Earth's history.
More broadly, we stress that internal oceanic recycling of biosignature gases
will often render surface biospheres on ocean-bearing silicate worlds cryptic,
with the implication that the planets most conducive to the development and
maintenance of a pervasive biosphere will often be challenging to characterize
via conventional atmospheric biosignatures.


  When used by autonomous vehicles for trajectory planning or obstacle
avoidance, depth estimation methods need to be reliable. Therefore, estimating
the quality of the depth outputs is critical. In this paper, we show how
M4Depth, a state-of-the-art depth estimation method designed for unmanned
aerial vehicle (UAV) applications, can be enhanced to perform joint depth and
uncertainty estimation. For that, we present a solution to convert the
uncertainty estimates related to parallax generated by M4Depth into uncertainty
estimates related to depth, and show that it outperforms the standard
probabilistic approach. Our experiments on various public datasets demonstrate
that our method performs consistently, even in zero-shot transfer. Besides, our
method offers a compelling value when compared to existing multi-view depth
estimation methods as it performs similarly on a multi-view depth estimation
benchmark despite being 2.5 times faster and causal, as opposed to other
methods. The code of our method is publicly available at
https://github.com/michael-fonder/M4DepthU .


  Deep XMM and Chandra observations of ClJ1226.9+3332 at z=0.89 have enabled
the most detailed X-ray mass analysis of any such high-redshift galaxy cluster.
The XMM temperature profile of the system shows no sign of central cooling,
with a hot core and a radially declining profile. A temperature map shows
asymmetry with a hot region that appears to be associated with a subclump of
galaxies at the cluster redshift, but is not visible in the X-ray surface
brightness. This is likely to be result of a merger event in the cluster, but
does not appear to significantly affect the overall temperature profile. The
XMM temperature profile, and combined Chandra and XMM emissivity profile
allowed precise measurements of the global properties of ClJ1226.9+3332; we
find kT=10.4+/-0.6keV, Z=0.16+/-0.05\Zsol, and M=5.2^{+1.0}_{-0.8}x10^{14}Msol.
We obtain profiles of the metallicity, entropy, cooling time and gas fraction,
and find a high concentration parameter for the total density profile of the
system. The global properties are compared with the local LT and MT relations,
and we are able to make the first observational test of the predicted evolution
of the YM relation. We find that departures from these scaling relations are
most likely caused by an underestimate of the total mass by ~30% in the X-ray
hydrostatic mass analysis due to the apparent recent or ongoing merger
activity.


  The application of mobile computing is currently altering patterns of our
behavior to a greater degree than perhaps any other invention. In combination
with the introduction of BLE (Bluetooth Low Energy) and similar technologies
enabling context-awareness, designers are today finding themselves empowered to
build experiences and facilitate interactions with our physical surroundings in
ways not possible before. The aim of this thesis is to present a research
project, currently underway at the University of Cambridge, which is dealing
with implementation of a BLE system into a museum environment. By assessing the
technology, describing the design decisions as well as presenting a qualitative
evaluation, this paper seeks to provide insight into some of the challenges and
possible solutions connected to the process of developing ubiquitous BLE
computing systems for public spaces. The project outcome revealed the potential
use of BLE to engage whole new groups of audiences as well as made me argue in
favor of a more seamful approach to the design of these systems.


  We give a brief review of the main results of the theory of elliptic
hypergeometric functions -- a new class of special functions of mathematical
physics. We prove the most general univariate exact integration formula
generalizing Euler's beta integral, which is called the elliptic beta integral.
An elliptic analogue of the Gauss hypergeometric function is constructed
together with the elliptic hypergeometric equation for it. Biorthogonality
relations for this function and its particular subcases are described. We list
known elliptic beta integrals on root systems and consider symmetry
transformations for the corresponding elliptic hypergeometric functions of the
higher order.


  We consider the incompressible Navier-stokes equations (NS) in
$\mathbb{R}^{n}$ for $n\geq2$. Global well-posedness is proved in critical
Besov-weak-Herz spaces (BWH-spaces) that consist in Besov spaces based on
weak-Herz spaces. These spaces are larger than some critical spaces considered
in previous works for (NS). For our purposes, we need to develop a basic theory
for BWH-spaces containing properties and estimates such as heat semigroup
estimates, embedding theorems, interpolation properties, among others. In
particular, it is proved a characterization of Besov-weak-Herz spaces as
interpolation of Sobolev-weak-Herz ones, which is key in our arguments.
Self-similarity and asymptotic behavior of solutions are also discussed. Our
class of spaces and its properties developed here could also be employed to
study other PDEs of elliptic, parabolic and conservation-law type.


  Motivated by debates about California's net migration loss, we employ valued
exponential-family random graph models to analyze the inter-county migration
flow networks in the United States. We introduce a protocol that visualizes the
complex effects of potential underlying mechanisms, and perform in silico
knockout experiments to quantify their contribution to the California Exodus.
We find that racial dynamics contribute to the California Exodus, urbanization
ameliorates it, and political climate and housing costs have little impact.
Moreover, the severity of the California Exodus depends on how one measures it,
and California is not the state with the most substantial population loss. The
paper demonstrates how generative statistical models can provide mechanistic
insights beyond simple hypothesis-testing.


  We measure the LyC escape fraction in 54 faint Lyman Alpha Emitters (LAEs) at
$z\simeq3.1$ in the GOODS-South field. With the average magnitude of $R=26.7$
AB ($M_{UV}=-18.8$, $L\simeq0.1L^*$), these galaxies represent a population of
compact young dwarf galaxies. Their properties are likely to resemble those in
the galaxies responsible for reionising the Universe at $z>6$. We do not detect
LyC emission in any individual LAEs in the deep {\em HST} F336W images, which
covers the rest-frame 820\r{A}. We do not detect the LyC emission of these LAEs
in the stacked F336W images, either. The $3\sigma$ upper limit of LyC escape
fractions is $f_{\rm esc}<14-32\%$. However, the high Ly$\alpha$ rest-frame
equivalent width, low stellar mass and UV luminosity of these LAEs suggest that
they should have $f_{\rm esc}>50\%$. The low LyC escape fraction from this work
and other stacking analysis suggest that the LyC leaking galaxies with $f_{\rm
esc} > 50\%$ at $z=2-3$ do not follow the relation between the $f_{\rm esc}$
and UV luminosity and Ly$\alpha$ equivalent width (EW) derived from typical
galaxies at similar redshift. Therefore, the UV luminosity and Ly$\alpha$
equivalent width (EW) are not the best indicators for the LyC escape fraction.


  We propose a new framework for designing estimators for off-policy evaluation
in contextual bandits. Our approach is based on the asymptotically optimal
doubly robust estimator, but we shrink the importance weights to minimize a
bound on the mean squared error, which results in a better bias-variance
tradeoff in finite samples. We use this optimization-based framework to obtain
three estimators: (a) a weight-clipping estimator, (b) a new weight-shrinkage
estimator, and (c) the first shrinkage-based estimator for combinatorial action
sets. Extensive experiments in both standard and combinatorial bandit benchmark
problems show that our estimators are highly adaptive and typically outperform
state-of-the-art methods.


  We present a formulation of soft-collinear effective theory (SCET) in the
two-jet sector as a theory of decoupled sectors of QCD coupled to Wilson lines.
The formulation is manifestly boost-invariant, does not require the
introduction of ultrasoft modes at the hard matching scale Q, and has manifest
power counting in inverse powers of Q. The spurious infrared divergences which
arise in SCET when ultrasoft modes are not included in loops disappear when the
overlap between the sectors is correctly subtracted, in a manner similar to the
familiar zero-bin subtraction of SCET. We illustrate this approach by analyzing
deep inelastic scattering in the endpoint region in SCET and comment on other
applications.


  We present improved estimates of the couplings, masses and mass ratios of the
$X_Q, Z_Q$ and $T_{QQ\bar q\bar q'}$ states using (inverse) QCD Laplace sum
rules (LSR), their ratios ${\cal R}$ and double ratios (DRSR), within stability
criteria. We conclude that the observed $X_c(3872)$ and $Z_c(3900)$ are
tetramoles states (superposition of quasi-degenerated molecule and tetraquark
states having similar couplings to the currents) with the predicted masses:
$M_{{\cal T}_{X_c}}=3876(44)$ MeV and $M_{{\cal T}_{Z_c}}=3900(42)$ MeV. We
also do an extensive analysis of the four-quark nature of different $T_{QQ\bar
q\bar q'}$ axial-vector states. Then, combining ${\cal R}$ and DRSR, we
reanalyze the observed state $X_c(3872)$ and we obtain a precise prediction of
$M_{T_{cc}^{1^+}}$=3886(6) MeV. Extending to the beauty sector, we find the
results: $M_{{\cal T}_{Z_b}}=10579(99)$ MeV and $M_{X_b}=10545(131)$ MeV.
Finally, we confront our combined LSR $\oplus$ DRSR results with the ones from
some other approaches (lattices and quark models).


  Software Defined Networks have opened the door to statistical and AI-based
techniques to improve efficiency of networking. Especially to ensure a certain
Quality of Service (QoS) for specific applications by routing packets with
awareness on content nature (VoIP, video, files, etc.) and its needs (latency,
bandwidth, etc.) to use efficiently resources of a network. Predicting various
Key Performance Indicators (KPIs) at any level may handle such problems while
preserving network bandwidth. The question addressed in this work is the design
of efficient and low-cost algorithms for KPI prediction, implementable at the
local level. We focus on end-to-end latency prediction, for which we illustrate
our approaches and results on a public dataset from the recent international
challenge on GNN [1]. We propose several low complexity, locally implementable
approaches, achieving significantly lower wall time both for training and
inference, with marginally worse prediction accuracy compared to
state-of-the-art global GNN solutions.


  We study the dynamics of merging compact binaries near a rotating
supermassive black hole (SMBH) in a hierarchical triple configuration. We
include various general relativistic effects that couple the inner orbit, the
outer orbit and the spin of the SMBH. During the orbital decay due to
gravitational radiation, the inner binary can encounter an "apsidal precession
resonance" and experience eccentricity excitation. This resonance occurs when
the apsidal precession rate of the inner binary matches that of the outer
binary, with the precessions driven by both Newtonian interactions and various
post-Newtonian effects. The eccentricity excitation requires the outer orbit to
have a finite eccentricity, and is most effective for triples with small mutual
inclinations, in contrast to the well-studied Lidov-Kozai effect. The resonance
and the associated eccentricity growth may occur while the binary emits
gravitational waves in the low-frequency band, and may be detectable by future
space-based gravitational wave detectors.


  An inclusive search for the non-Standard Model production of two isolated
leptons of the same electrical charge is presented. The search was performed
with 2010 collision data in the ATLAS detector at the Large Hadron Collider for
ee, e{\mu}, and {\mu}{\mu} channels. With 34 pb-1 of integrated luminosity, no
disagreement with the Standard Model prediction was found, and limits on the
fiducial cross section for generic same-sign production as well as four
model-dependent limits for models of new physics were set. An updated same sign
analysis with 1.6 fb-1 is underway and nearly complete for the {\mu}{\mu}
channel. With new event selection criteria, improved data-driven background
estimates, and significantly more collision data, the sensitivity is expected
to improve significantly beyond that of the analysis completed with data from
2010. Initial results from the updated analysis are presented.


  Bayesian inference is a widely used and powerful analytical technique in
fields such as astronomy and particle physics but has historically been
underutilized in some other disciplines including semiconductor devices. In
this work, we introduce Bayesim, a Python package that utilizes adaptive grid
sampling to efficiently generate a probability distribution over multiple input
parameters to a forward model using a collection of experimental measurements.
We discuss the implementation choices made in the code, showcase two examples
in photovoltaics, and discuss general prerequisites for the approach to apply
to other systems.


  We analyze the general relativistic oscillations of thin accretion disks
around compact astrophysical objects interacting with the surrounding medium
through non-gravitational forces. The interaction with the external medium (a
thermal bath) is modeled via a friction force, and a random force,
respectively. The general equations describing the stochastically perturbed
disks are derived by considering the perturbations of trajectories of the test
particles in equatorial orbits, assumed to move along the geodesic lines. By
taking into account the presence of a viscous dissipation and of a stochastic
force we show that the dynamics of the stochastically perturbed disks can be
formulated in terms of a general relativistic Langevin equation. The stochastic
energy transport equation is also obtained. The vertical oscillations of the
disks in the Schwarzschild and Kerr geometries are considered in detail, and
they are analyzed by numerically integrating the corresponding Langevin
equations. The vertical displacements, velocities and luminosities of the
stochastically perturbed disks are explicitly obtained for both the
Schwarzschild and the Kerr cases.


  We extract the pion fragmentation functions and their uncertainties from a
judicious choice of e+e- and semi-inclusive DIS data. These are used to study
the error propagation in the extraction of polarized parton densities from
semi-inclusive DIS asymmetries. We conclude that the uncertainties on polarized
PDs have been underestimated in the past.


  The recent discovery of a faint gamma-ray burst (GRB) coincident with the
gravitational wave (GW) event GW 170817 revealed the existence of a population
of low-luminosity short duration gamma-ray transients produced by neutron star
mergers in the nearby Universe. These events could be routinely detected by
existing gamma-ray monitors, yet previous observations failed to identify them
without the aid of GW triggers. Here we show that GRB150101B was an analogue of
GRB170817A located at a cosmological distance. GRB 150101B was a faint short
duration GRB characterized by a bright optical counterpart and a long-lived
X-ray afterglow. These properties are unusual for standard short GRBs and are
instead consistent with an explosion viewed off-axis: the optical light is
produced by a luminous kilonova component, while the observed X-rays trace the
GRB afterglow viewed at an angle of ~13 degrees. Our findings suggest that
these properties could be common among future electromagnetic counterparts of
GW sources.


  In this work, we analyze the kinetic stability of a solar wind electron
distribution composed of core and strahl subpopulations. The core is modeled by
a drifting Maxwellian distribution, while the strahl is modeled by an analytic
function recently derived in (Horaites et al. 2018) from the collisional
kinetic equation. We perform a numerical linear stability analysis using the
LEOPARD solver (Astfalk & Jenko 2017), which allows for arbitrary gyrotropic
distribution functions in a magnetized plasma. We find that for typical solar
wind conditions, the core-strahl distribution is unstable to the kinetic
Alfv\'en and magnetosonic modes. The maximum growth rates for these
instabilities occur at wavenumbers $k d_i \lesssim 1$, at moderately oblique
angles of propagation, thus providing a potential source of kinetic-scale
turbulence. In contrast with previous reports, we however do not find evidence
for a whistler instability directly associated with the electron strahl. This
may be related to the more realistic shape of the electron strahl distribution
function adopted in our work. We therefore suggest that the whistler modes
often invoked to explain anomalous scattering of strahl particles could appear
as a result of nonlinear mode coupling and turbulent cascade originating at
scales $k d_i \lesssim 1$.


  A key insight from statistical physics about spin systems on random graphs is
the central role played by Gibbs measures on trees. We determine the local weak
limit of the hardcore model on random regular graphs asymptotically until just
below its condensation threshold, showing that it converges in probability
locally in a strong sense to the free boundary condition Gibbs measure on the
tree. As a consequence we show that the reconstruction threshold on the random
graph, indicative of the onset of point to set spatial correlations, is equal
to the reconstruction threshold on the $d$-regular tree for which we determine
precise asymptotics. We expect that our methods will generalize to a wide range
of spin systems for which the second moment method holds.