  Artificial Intelligence applications show great potential for
language-related tasks that rely on next-word prediction. The current
generation of large language models have been linked to claims about human-like
linguistic performance and their applications are hailed both as a key step
towards Artificial General Intelligence and as major advance in understanding
the cognitive, and even neural basis of human language. We analyze the
contribution of large language models as theoretically informative
representations of a target system vs. atheoretical powerful mechanistic tools,
and we identify the key abilities that are still missing from the current state
of development and exploitation of these models.


  Proton-antiproton annihilation into two pions and three pions is studied in a
baryon-exchange model. Annihilation diagrams involving nucleon as well as
Delta-resonance exchanges are included consistently in the two- and three-pion
channels. Effects from the initial-state interaction are fully taken into
account. A comparison of the influence of the Delta exchange on the considered
annihilation channels is made and reveals that its importance for three-pion
annihilation is strongly reduced as compared to two-pion annihilation. It is
found that annihilation into three uncorrelated pions can yield up to 10 % of
the total experimentally observed three-pion annihilation cross section.


  The role of strong absorption of particles in intermidiate and final states
has been considered. The range of applicability of phenomenological model of
absorption has been studied. This model is nonuniversal. Its applicability
depends on the type of interaction Hamiltonian and matrix element used. We also
demonstrate that the violation of the unitarity condition can produce a
qualitative error in the results. The absorption (decay) in the final state
does not tend to suppress the total process probability as well as the
probability of the channel corresponding to absorption. This is true for the
reactions, decays and $n\bar{n}$ conversion in the medium.


  We present an empirical study aimed at analysing the use of viewpoints in an
industrial Concurrent Engineering context. Our focus is on the viewpoints
expressed in the argumentative process taking place in evaluation meetings. Our
results show that arguments enabling a viewpoint or proposal to be defended are
often characterized by the use of constraints. Firstly, we show that, even if
some constraints are apparently identically used by the different specialists
involved in meetings, various meanings and weightings are associated with these
constraints by these different specialists. Secondly, we show that the implicit
or explicit nature of constraints depends on several interlocutive factors.
Thirdly, we show that an argument often covers not only one constraint but a
network of constraints. The type of combination reflects viewpoints which have
specific status in the meeting. Then, we will propose a first model of the
dynamics of viewpoints confrontation/integration.


  We study the correlation between CP violation in neutrino oscillations and
leptogenesis in the framework with two heavy Majorana neutrinos and three light
neutrinos. Among three unremovable CP phases, a heavy Majorana phase
contributes to leptogenesis. We show how the heavy Majorana phase contributes
to Jarlskog determinant $J$ as well as neutrinoless double $\beta$ decay by
identifying a low energy CP violating phase which signals the CP violating
phase for leptogenesis. For some specific cases of the Dirac mass term of
neutrinos, a direct relation between lepton number asymmetry and $J$ is
obtained. For the most general case of the framework, we study the effect on $
J $ coming from the phases which are not related to leptogenesis, and also show
how the correlation can be lost in the presence of those phases.


  We consider the nonleptonic and semileptonic decays of $D_s$-mesons into
$\phi$ and $f_0(980)$ mesons. QCD sum rules are used to calculate the form
factors associated with these decays, and the correspondig decay rates. On the
basis of data on $D_s^+\to\pi^+\pi^+\pi^-$, which goes dominantly via the
transition $D_s^+\to \pi^+f_0(980)$, we conclude that there is space for a
sizeable light quark component on $f_0(980)$.


  The non-modal self-heating mechanism driven by the velocity shear in
kinematically complex magnetohydrodynamic (MHD) plasma flows is considered. The
study is based on the full set of MHD equations including dissipative terms.
The equations are linearized and unstable modes in the flow are looked for. Two
different cases are specified and studied: (a) the instability related to an
exponential evolution of the wave vector; and (b) the parametric instability,
which takes place when the components of the wave vector evolve in time
periodically. By examining the dissipative terms, it is shown that the
self-heating rate provided by viscous damping is of the same order of magnitude
as that due to the magnetic resistivity. It is found that the heating
efficiency of the exponential instability is higher than that of the parametric
instability.


  Despite the recent excitement over the r-mode instability of rotating stars,
these modes are not yet well-understood for stellar models appropriate to
neutron stars - perfect fluid models in which both the equilibrium and
perturbed configurations obey the same one-parameter equation of state.
  In spherical stars of this kind, r-modes and g-modes form a degenerate
zero-frequency subspace. Rotation splits the degeneracy to zeroth order in the
star's angular velocity $\Omega$, and the resulting modes are generically
hybrids, whose limit as $\Omega\to 0$ is a stationary current with axial and
polar parts. Lindblom and Ipser have recently found these hybrid rotational
modes in an analytic study of the Maclaurin spheroids. This dissertation
studies them in Newtonian stars (both uniform density models and $n=1$
polytropes) and reports a first computation of the hybrid rotational modes of
relativistic stars.


  The state of a continuously monitored qubit evolves stochastically,
exhibiting competition between coherent Hamiltonian dynamics and diffusive
partial collapse dynamics that follow the measurement record. We couple these
distinct types of dynamics together by linearly feeding the collected record
for dispersive energy measurements directly back into a coherent Rabi drive
amplitude. Such feedback turns the competition cooperative, and effectively
stabilizes the qubit state near a target state. We derive the conditions for
obtaining such dispersive state stabilization and verify the stabilization
conditions numerically. We include common experimental nonidealities, such as
energy decay, environmental dephasing, detector efficiency, and feedback delay,
and show that the feedback delay has the most significant negative effect on
the feedback protocol. Setting the measurement collapse timescale to be long
compared to the feedback delay yields the best stabilization.


  Recent work has focused on the problem of nonparametric estimation of
information divergence functionals. Many existing approaches are restrictive in
their assumptions on the density support set or require difficult calculations
at the support boundary which must be known a priori. The MSE convergence rate
of a leave-one-out kernel density plug-in divergence functional estimator for
general bounded density support sets is derived where knowledge of the support
boundary is not required. The theory of optimally weighted ensemble estimation
is generalized to derive a divergence estimator that achieves the parametric
rate when the densities are sufficiently smooth. The asymptotic distribution of
this estimator and some guidelines for tuning parameter selection are provided.
Based on the theory, an empirical estimator of R\'{e}nyi-$\alpha$ divergence is
proposed that outperforms the standard kernel density plug-in estimator,
especially in high dimension. The estimator is shown to be robust to the choice
of tuning parameters. As an illustration, we use the estimator to estimate
bounds on the Bayes error rate of a classification problem.


  Let S be a smooth projective surface, and consider the following two
subvarieties of the Hilbert scheme parameterizing closed subschemes of S of
length n:
  A = {subschemes with support in a fixed point of S}
  B = {subschemes with support in one (variable) point of S}
  A and B have complementary dimensions in the Hilbert scheme. We prove that
the intersection number [A].[B] = n(-1)^(n-1), answering a question by H.
Nakajima.


  Protein-protein interactions (protein functionalities) are mediated by water,
which compacts individual proteins and promotes close and temporarily stable
large-area protein-protein interfaces. In their classic paper Kyte and
Doolittle (KD) concluded that the "simplicity and graphic nature of
hydrophobicity scales make them very useful tools for the evaluation of protein
structures". In practice, however, attempts to develop hydrophobicity scales
(for example, compatible with classical force fields (CFF) in calculating the
energetics of protein folding) have encountered many difficulties. Here we
suggest an entirely different approach, based on the idea that proteins are
self-organized networks, subject to finite-scale criticality (like some network
glasses). We test this proposal against two small proteins that are delicately
balanced between alpha and alpha/beta structures, with different functions
encoded with only 12% of their amino acids. This example explains why protein
structure prediction is so challenging, and it provides a severe test for the
accuracy and content of hydrophobicity scales. The new method confirms KD's
evaluation, and at the same time suggests that protein structure, dynamics and
function can be best discussed without using CFF.


  Monolayer MnO$_2$ is one of the few predicted two-dimensional (2D)
ferromagnets that has been experimentally synthesized and is commercially
available. The Mermin-Wagner theorem states that magnetic order in a 2D
material cannot persist unless magnetic anisotropy (MA) is present and
perpendicular to the plane, which permits a finite critical temperature.
Previous computational studies have predicted the magnetic ordering and Curie
temperature of 2D MnO$_2$ with DFT+U (Density Funtional Theory + Hubbard U
correction), with the results having a strong dependence on the Hubbard U
parameter. Diffusion Monte Carlo (DMC) is a correlated electronic structure
method that has had demonstrated success for the electronic and magnetic
properties of a variety of 2D and bulk systems since it has a weaker dependence
on the starting Hubbard parameter and density functional. In this study, we
used DMC and DFT+U to calculate the magnetic properties of monolayer MnO$_2$.
We found that the ferromagnetic ordering is more favorable than
antiferromagnetic and determined a statistical bound on the magnetic exchange
parameter ($J$). In addition, we performed spin-orbit MA energy calculations
using DFT+U and using our DMC and DFT+U parameters along with the analytical
model of Torelli and Olsen, we estimated an upper bound of 28.8 K for the
critical temperature of MnO$_2$. These QMC results intend to serve as an
accurate theoretical benchmark, necessary for the realization and development
of future 2D magnetic devices. These results also demonstrate the need for
accurate methodologies to predict magnetic properties of correlated 2D
materials.


  We propose a geometric correspondence between (a) linearly degenerate systems
of conservation laws with rectilinear rarefaction curves and (b) congruences of
lines in projective space whose developable surfaces are planar pencils of
lines. We prove that in projective 4-space such congruences are necessarily
linear. Based on the results of Castelnuovo, the classification of
three-component systems is obtained, revealing a close relationship of the
problem with projective geometry of the Veronese variety and the theory of
associativity equations of two-dimensional topological field theory.


  We study the chemodynamical properties of the Carina dwarf spheroidal by
combining an intermediate spectroscopic resolution dataset of more than 900 red
giant and red clump stars, with high-precision photometry to derive the
atmospheric parameters, metallicities and age estimates for our targets. Within
the red giant branch population, we find evidence for the presence of three
distinct stellar sub-populations with different metallicities, spatial
distributions, kinematics and ages. As in the Fornax and Sculptor dwarf
spheroidals, the subpopulation with the lowest average metallicity is more
extended and kinematically hotter than all other populations. However, we
identify an inversion in the parallel ordering of metallicity, kinematics and
characteristic length scale in the two most metal rich subpopulations, which
therefore do not contribute to a global negative chemical gradient. Contrary to
common trends in the chemical properties with radius, the metal richest
population is more extended and mildly kinematically hotter than the main
component of intermediate metallicity. More investigations are required to
ascertain the nature of this inversion, but we comment on the mechanisms that
might have caused it.


  Local search is a powerful heuristic in optimization and computer science,
the complexity of which was studied in the white box and black box models. In
the black box model, we are given a graph $G = (V,E)$ and oracle access to a
function $f : V \to \mathbb{R}$. The local search problem is to find a vertex
$v$ that is a local minimum, i.e. with $f(v) \leq f(u)$ for all $(u,v) \in E$,
using as few queries as possible. The query complexity is well understood on
the grid and the hypercube, but much less is known beyond.
  We show the query complexity of local search on $d$-regular expanders with
constant degree is $\Omega\left(\frac{\sqrt{n}}{\log{n}}\right)$, where $n$ is
the number of vertices. This matches within a logarithmic factor the upper
bound of $O(\sqrt{n})$ for constant degree graphs from Aldous (1983), implying
that steepest descent with a warm start is an essentially optimal algorithm for
expanders. The best lower bound known from prior work was
$\Omega\left(\frac{\sqrt[8]{n}}{\log{n}}\right)$, shown by Santha and Szegedy
(2004) for quantum and randomized algorithms.
  We obtain this result by considering a broader framework of graph features
such as vertex congestion and separation number. We show that for each graph,
the randomized query complexity of local search is
$\Omega\left(\frac{n^{1.5}}{g}\right)$, where $g$ is the vertex congestion of
the graph; and $\Omega\left(\sqrt[4]{\frac{s}{\Delta}}\right)$, where $s$ is
the separation number and $\Delta$ is the maximum degree. For separation number
the previous bound was $\Omega\left(\sqrt[8]{\frac{s}{\Delta}}
/\log{n}\right)$, given by Santha and Szegedy for quantum and randomized
algorithms.
  We also show a variant of the relational adversary method from Aaronson
(2006), which is asymptotically at least as strong as the version in Aaronson
(2006) for all randomized algorithms and strictly stronger for some problems.


  Necessary and sufficient conditions for the internal stability of formations
whose dynamics are obtained is determined by linear differential equations.


  We present the status of an ongoing project aimed at the inclusion of
isospin-breaking corrections arising from the non-degeneracy of the light quark
masses and electromagnetic interactions in calculations of the low-lying octet
and decuplet baryon masses. Our ultimate goal is to perform a precision
determination of the lattice scale including isospin-breaking effects. We apply
the perturbative formalism to isospin-symmetric $N_f=2+1$ CLS ensembles. We
show results on baryon masses up to leading order in isospin-breaking
corrections on two ensembles with $m_\pi\approx290\,\text{MeV}$ and
$m_\pi\approx215\,\text{MeV}$ at a lattice spacing of
$a\approx0.076\,\text{fm}$.


  This study investigates the prospect of using reduced graphene oxide
encapsulated between thin wood membranes as a composite filter for water
filtration. Graphene oxide (GO) with a volume range of 0.2 to 0.5 mL was
deposited on a 95 mm2 area of thin sheets of Balsa wood with a thickness range
of 2.20 - 3.50 mm. The dried GO deposits were laser treated in reduced oxygen
environment to prepare a reduced graphene oxide-wood composite (rGO-wood). The
rGO substrate of the composite was sandwiched between 2 wood sheets which were
glued using polydimethylsiloxane (PDMS) to give a stable membrane. Fabricated
membranes improved the Allura red rejection from 8.29 $\pm$ 0.69% in the
controls (sandwiched wood sheets without rGO) to 93.14 $\pm$ 1.31% in the 1 mL
total rGO composites. The ratio of percentage permeance drop of the treatments
over the controls ranged from 62.8 $\pm$ 4.3 to 85.4 $\pm$ 5.2% from the 0.4 to
1 mL rGO composites respectively. The correlation coefficient (r) between the
GO volume and rejection was 0.94 suggesting that the GO treatments provided
some control over the transport of molecules through the membrane. The
correlation coefficient between wood thickness, and Allura red rejection shows
that the wood sheets did not influence the rejection. The permeance was weakly
affected by the membrane thickness (r = -0.46). An 93% improvement in rejection
performance of the composite membranes over the controls was observed. The
overall data suggests that the wood-rGO composite has potential for water
filtration applications.


  This paper gives a leisurely introduction to Calabi-Yau manifolds and special
Lagrangian submanifolds from the differential geometric point of view, followed
by a survey of recent results on singularities of special Lagrangian
submanifolds, and their application to the SYZ Conjecture.
  It is aimed at graduate students in Geometry, String Theorists, and others
wishing to learn the subject, and is designed to be fairly self-contained. It
is based on lecture courses given at Nordfjordeid, Norway and MSRI, Berkeley in
June and July 2001.
  We introduce Calabi-Yau m-folds via holonomy groups, Kahler geometry and the
Calabi Conjecture, and special Lagrangian m-folds via calibrated geometry.
`Almost Calabi-Yau m-folds' (a generalization of Calabi-Yau m-folds useful in
special Lagrangian geometry) are explained and the deformation theory and
moduli spaces of compact special Lagrangian submanifolds in (almost) Calabi-Yau
m-folds is described.
  In the final part we consider isolated singularities of special Lagrangian
m-folds, focussing mainly on singularities locally modelled on cones, and the
expected behaviour of singularities of compact special Lagrangian m-folds in
generic (almost) Calabi-Yau m-folds. String Theory, Mirror Symmetry and the SYZ
Conjecture are briefly discussed, and some results of the author on
singularities of special Lagrangian fibrations of Calabi-Yau 3-folds are
described.


  We calculate the magnetic quadrupole moment (MQM) of the deuteron at leading
order in the systematic expansion provided by chiral effective field theory. We
take into account parity and time-reversal violation which, at the quark-gluon
level, results from the QCD vacuum angle and dimension-six operators that
originate from physics beyond the Standard Model. We show that the deuteron MQM
can be expressed in terms of five low-energy constants that appear in the
parity- and time-reversal-violating nuclear potential and electromagnetic
current, four of which also contribute to the electric dipole moments of light
nuclei. We conclude that the deuteron MQM has an enhanced sensitivity to the
QCD vacuum angle and that its measurement would be complementary to the
proposed measurements of light-nuclear EDMs.


  ULTRASAT (ULtraviolet TRansient Astronomy SATellite) is a wide-angle space
telescope that will perform deep time-resolved surveys in the near-ultraviolet
spectrum. ULTRASAT is a space mission led by the Weizmann Institute of Science
and the Israel Space Agency and is planned for launch in 2025. The camera
implements backside-illuminated, stitched pixel sensors. The pixel has a
dual-conversion-gain 4T architecture, with a pitch of $9.5$ $\mu m$ and is
produced in a $180$ $nm$ process by Tower Semiconductor. Before the final
sensor was available for testing, test sensors provided by Tower were used to
gain first insights into the pixel's radiation tolerance. One of the main
contributions to sensor degradation due to radiation for the ULTRASAT mission
is Total Ionizing Dose (TID). TID measurements on the test sensors have been
performed with a Co-60 gamma source at Helmholz Zentrum Berlin and CC-60
facility at CERN and preliminary results are presented.


  We investigate the prompt photon production in deep inelastic scattering at
HERA in the framework of kt-factorization QCD approach. Our study is based on
the off-shell partonic amplitude e q* -> e gamma q, where the photon radiation
from the leptons as well as from the quarks is taken into account. The
unintegrated quark densities in a proton are determined using the
Kimber-Martin-Ryskin prescription. The conservative error analisys is
performed. We investigate both inclusive and jet associated prompt photon
production rates. Our predictions are compared with the recent experimental
data taken by the H1 and ZEUS collaborations. We demonstrate that in the
kt-factorization approach the contribution from the quark radiation subprocess
(QQ mechanism) is enhanced as compared to the leading-order collinear
approximation.


  We describe in some details an idea of M. Kontsevich how one can try to find
a counterexample to the Hodge conjecture using tropical geometry.


  In this summary report of the 2001 Snowmass Electroweak Symmetry Breaking
Working Group, the main candidates for theories of electroweak symmetry
breaking are surveyed, and the criteria for distinguishing among the different
approaches are discussed. The potential for observing electroweak symmetry
breaking phenomena at the upgraded Tevatron and the LHC is described. We
emphasize the importance of a high-luminosity $e^+e^-$ linear collider for
precision measurements to clarify the underlying electroweak symmetry breaking
dynamics. Finally, we note the possible roles of the $\mu^+\mu^-$ collider and
VLHC for further elucidating the physics of electroweak symmetry breaking.


  We investigate the nature of the broad-band emission associated with the
low-power radio hotspots 3C105 South and 3C445 South. Both hotspot regions are
resolved in multiple radio/optical components. High-sensitivity radio VLA,
NIR/optical VLT and HST, and X-ray Chandra data have been used to construct the
multi-band spectra of individual hotspot components. The radio-to-optical
spectra of both hotspot regions are well fitted by a synchrotron model with
steep spectral indices ~0.8 and break frequencies 10^12-10^14 Hz. 3C105 South
is resolved in two optical components: a primary one, aligned with the jet
direction and possibly marking the first jet impact with the surrounding
medium, and a secondary, further out from the jet and extended in a direction
perpendicular to it. This secondary region is interpreted as a splatter-spot
formed by the deflection of relativistic plasma from the primary hotspot. Radio
and optical images of 3C445 South show a spectacular 10-kpc arc-shape structure
characterized by two main components, and perpendicular to the jet direction.
HST images in I and B bands further resolve the brightest components into thin
elongated features. In both 3C105 South and 3C445 South the main hotspot
components are enshrouded by diffuse optical emission on scale of several kpcs,
indicating that very high energy particles, possibly injected at strong shocks,
are continuously re-accelerated in situ by additional acceleration mechanisms.
We suggest that stochastic processes, linked to turbulence and instabilities,
could provide the required additional re-acceleration.


  Recently, crowd counting using supervised learning achieves a remarkable
improvement. Nevertheless, most counters rely on a large amount of manually
labeled data. With the release of synthetic crowd data, a potential alternative
is transferring knowledge from them to real data without any manual label.
However, there is no method to effectively suppress domain gaps and output
elaborate density maps during the transferring. To remedy the above problems,
this paper proposes a Domain-Adaptive Crowd Counting (DACC) framework, which
consists of a high-quality image translation and density map reconstruction. To
be specific, the former focuses on translating synthetic data to realistic
images, which prompts the translation quality by segregating
domain-shared/independent features and designing content-aware consistency
loss. The latter aims at generating pseudo labels on real scenes to improve the
prediction quality. Next, we retrain a final counter using these pseudo labels.
Adaptation experiments on six real-world datasets demonstrate that the proposed
method outperforms the state-of-the-art methods.


  The Heat Kernel Method is applied to the constituent quark model. We
calculate the effect of thermal quark fluctuations on the meson action and the
resulting quark condensate and pi pi-scattering amplitude at finite
temperature. The quarks produce a chiral phase transition only by their effect
on the mesonic coupling constants. The s-wave isospin zero pi pi-scattering
amplitude diverges near the phase transition showing the necessity for a more
sophisticated treatment of meson fluctuations.


  We explore the complex design space of behaviour planning for autonomous
driving. Design choices that successfully address one aspect of behaviour
planning can critically constrain others. To aid the design process, in this
work we decompose the design space with respect to important choices arising
from the current state of the art approaches, and describe the resulting
trade-offs. In doing this, we also identify interesting directions of future
work.


  We adopt an effective-lagrangian approach to compute the new-physics
contributions to T-violating triple-product correlations in charmless Lambda_b
decays. We use factorization and work to leading order in the heavy-quark
expansion. We find that the standard-model (SM) predictions for such
correlations can be significantly modified. For example, triple products which
are expected to vanish in the SM can be enormous (~50%) in the presence of new
physics. By measuring triple products in a variety of Lambda_b decays, one can
diagnose which new-physics operators are or are not present. Our general
results can be applied to any specific model of new physics by simply
calculating which operators appear in that model.


  We present results from ab initio No-Core Full Configuration simulations of
the exotic proton-rich nucleus $^{14}$F whose first experimental observation is
expected soon. The calculations with JISP16 NN interaction are performed up to
the $N_{\max}=8$ basis space. The binding energy is evaluated using an
extrapolation technique. This technique is generalized to excitation energies,
verified in calculations of $^6$Li and applied to $^{14}$B, the $^{14}$F
mirror, for which some data are available.


  We consider two number-theoretic problems arising from Fuglede's spectral set
conjecture: characterizing finite sets that tile integers, and finding
polynomials with (0,1) coefficients whose roots have a certain multiplicative
structure. We verify several special cases of the relevant conjectures; in
particular, we find necessary and sufficient conditions for a set A to tile the
integers if A is a direct sum of cyclic subsets.


  We study the response of F-actin bundles to driving forces through a simple
analytical model. We consider two filaments connected by reversibly bound
crosslinks and driven by an external force. Two failure modes under load can be
defined. \textit{Brittle failure} is observed when crosslinks suddenly and
collectively unbind, leading to catastrophic loss of bundle integrity. During
\textit{ductile failure}, on the other hand, bundle integrity is maintained,
however at the cost of crosslink reorganization and defect formation. We
present phase diagrams for the onset of failure, highlighting the importance of
the crosslink stiffness for these processes. Crossing the phase boundaries,
force-deflection curves display (frequency-dependent) hysteresis loops,
reflecting the first-order character of the failure processes. We evidence how
the introduction of defects can lead to complex elasto-plastic relaxation
processes, once the force is switched off. Depending on, both, the time-scale
for defect motion as well as the crosslink stiffness, bundles can remain in a
quasi-permanent plastically deformed state for a very long time.


  The KM3NeT Collaboration has already produced more than one thousand
acquisition boards, used for building two deep-sea neutrino detectors at the
bottom of the Mediterranean Sea, with the aim of instrumenting a volume of
several cubic kilometers with light sensors to detect the Cherenkov radiation
produced in neutrino interactions. The the so-called Digital Optical Modules,
house the PMTs and the acquisition and control electronics of the module, the
Central Logic Board, which includes a Xilinx FPGA and embedded soft processor.
The present work presents the architecture and functionalities of the software
embedded in the soft processor of the Central Logic Board.


  We show the flaw in a theorem of Konno, Namiki, Soshi, and Sudbury in [3] and
provide the necessary correction in the case of the Finite Hadamard walk and
use it to show that a conjecture of Ambainis, Bach, Nayak, Vishwanath, and
Watrous in [1] is false.


  We present 74 MHz radio continuum observations of the Galactic center region.
These measurements show nonthermal radio emission arising from molecular clouds
that is unaffected by free-free absorption along the line of sight. We focus on
one cloud, G0.13--0.13, representative of the population of molecular clouds
that are spatially correlated with steep spectrum
(alpha^{74MHz}_{327MHz}=1.3\pm0.3) nonthermal emission from the Galactic center
region. This cloud lies adjacent to the nonthermal radio filaments of the Arc
near l~0.2^0 and is a strong source of 74 MHz continuum, SiO (2-1) and FeI
Kalpha 6.4 keV line emission. This three-way correlation provides the most
compelling evidence yet that relativistic electrons, here traced by 74 MHz
emission, are physically associated with the G0.13--0.13 molecular cloud and
that low energy cosmic ray electrons are responsible for the FeI Kalpha line
emission. The high cosmic ray ionization rate ~10-13 s-1 H-1 is responsible for
heating the molecular gas to high temperatures and allows the disturbed gas to
maintain a high velocity dispersion. LVG modeling of multi-transition SiO
observations of this cloud implies H2 densities ~104-5 cm-3 and high
temperatures. The lower limit to the temperature of G0.13-0.13 is ~100K,
whereas the upper limit is as high as 1000K. Lastly, we used a time-dependent
chemical model in which cosmic rays drive the chemistry of the gas to
investigate for molecular line diagnostics of cosmic ray heating. When the
cloud reaches chemical equilibrium, the abundance ratios of HCN/HNC and
N2H+/HCO+ are consistent with measured values. In addition, significant
abundance of SiO is predicted in the cosmic ray dominated region of the
Galactic center. We discuss different possibilities to account for the origin
of widespread SiO emission detected from Galactic center molecular clouds.


  Scientists have long speculated about the potential habitability of Venus,
not at the 700K surface, but in the cloud layers located at 48-60 km altitudes,
where temperatures match those found on Earth's surface. However, the
prevailing belief has been that Venus' clouds cannot support life due to the
cloud chemical composition of concentrated sulfuric acid-a highly aggressive
solvent. In this work, we study 20 biogenic amino acids at the range of Venus'
cloud sulfuric acid concentrations (81% and 98% w/w, the rest water) and
temperatures. We find 19 of the biogenic amino acids we tested are either
unreactive (13 in 98% w/w and 12 in 81% w/w) or chemically modified in the side
chain only, after four weeks. Our major finding, therefore, is that the amino
acid backbone remains intact in concentrated sulfuric acid. These findings
significantly broaden the range of biologically relevant molecules that could
be components of a biochemistry based on a concentrated sulfuric acid solvent.


  We use transductive regression techniques to learn mappings between source
and target features of given parallel corpora and use these mappings to
generate machine translation outputs. We show the effectiveness of $L_1$
regularized regression (\textit{lasso}) to learn the mappings between sparsely
observed feature sets versus $L_2$ regularized regression. Proper selection of
training instances plays an important role to learn correct feature mappings
within limited computational resources and at expected accuracy levels. We
introduce \textit{dice} instance selection method for proper selection of
training instances, which plays an important role to learn correct feature
mappings for improving the source and target coverage of the training set. We
show that $L_1$ regularized regression performs better than $L_2$ regularized
regression both in regression measurements and in the translation experiments
using graph decoding. We present encouraging results when translating from
German to English and Spanish to English. We also demonstrate results when the
phrase table of a phrase-based decoder is replaced with the mappings we find
with the regression model.


  How predictable is the next move of an animal? Specifically, which factors
govern the short- and long-term motion patterns and the overall dynamics of
landbound, plant-eating animals and ruminants in particular? To answer this
question, we here study the movement dynamics of springbok antelopes Antidorcas
marsupialis. We propose complementary statistical analysis techniques combined
with machine learning approaches to analyze, across multiple time scales, the
springbok motion recorded in long-term GPS-tracking of collared springboks at a
private wildlife reserve in Namibia. As a new result, we are able to predict
the springbok movement within the next hour with a certainty of about 20\%. The
remaining 80\% are stochastic in nature and are induced by unaccounted factors
in the modeling algorithm and by individual behavioral features of springboks.
We find that directedness of motion contributes approximately 17\% to this
predicted fraction. We find that the measure for directedeness is strongly
dependent on the daily cycle. The previously known daily affinity of springboks
to their water points, as predicted from our machine learning algorithm,
overall accounts for only 3\% of this predicted deterministic component of
springbok motion. Moreover, the resting points are found to affect the motion
of springboks at least as much as the formally studied effects of water points.
The generality of these statements for the motion patterns and their underlying
behavioral reasons for other ruminants can be examined on the basis of our
statistical analysis tools.


  Double (quasi-)Poisson algebras were introduced by Van den Bergh as
non-commutative analogues of algebras endowed with a (quasi-)Poisson bracket.
In this work, we provide a study of morphisms of double (quasi-)Poisson
algebras, which we relate to the $H_0$-Poisson structures of Crawley-Boevey. We
prove in particular that the double (quasi-)Poisson algebra structure defined
by Van den Bergh for an arbitrary quiver only depends upon the quiver seen as
an undirected graph, up to isomorphism. We derive from our results a
representation theoretic description of action-angle duality for several
classical integrable systems.


  New precise data on the low energy electron-positron annihilation into
hadrons from Novosibirsk are used to obtain bounds on the elasticity parameter
and the difference between the phase of the pion form factor and that of pion
pion scattering


  Given a polynomial $p$ of degree $d$ and a bound $\kappa$ on a condition
number of $p$, we present the first root-finding algorithms that return all its
real and complex roots with a number of bit operations quasi-linear in $d
\log^2(\kappa)$. More precisely, several condition numbers can be defined
depending on the norm chosen on the coefficients of the polynomial. Let $p(x) =
\sum\_{k=0}^d a\_k x^k = \sum\_{k=0}^d \sqrt{\binom d k} b\_k x^k$. We call the
condition number associated with a perturbation of the $a\_k$ the hyperbolic
condition number $\kappa\_h$, and the one associated with a perturbation of the
$b\_k$ the elliptic condition number $\kappa\_e$. For each of these condition
numbers, we present algorithms that find the real and the complex roots of $p$
in $O\left(d\log^2(d\kappa)\ \text{polylog}(\log(d\kappa))\right)$ bit
operations.Our algorithms are well suited for random polynomials since
$\kappa\_h$ (resp. $\kappa\_e$) is bounded by a polynomial in $d$ with high
probability if the $a\_k$ (resp. the $b\_k$) are independent, centered Gaussian
variables of variance $1$.


  High-dimensional distributions, especially those with heavy tails, are
notoriously difficult for off-the-shelf MCMC samplers: the combination of
unbounded state spaces, diminishing gradient information, and local moves
results in empirically observed ``stickiness'' and poor theoretical mixing
properties -- lack of geometric ergodicity. In this paper, we introduce a new
class of MCMC samplers that map the original high-dimensional problem in
Euclidean space onto a sphere and remedy these notorious mixing problems. In
particular, we develop random-walk Metropolis type algorithms as well as
versions of the Bouncy Particle Sampler that are uniformly ergodic for a large
class of light and heavy-tailed distributions and also empirically exhibit
rapid convergence in high dimensions. In the best scenario, the proposed
samplers can enjoy the ``blessings of dimensionality'' that the convergence is
faster in higher dimensions.


  Measurement noise is an integral part while collecting data of a physical
process. Thus, noise removal is a necessary step to draw conclusions from these
data, and it often becomes quite essential to construct dynamical models using
these data. We discuss a methodology to learn differential equation(s) using
noisy and sparsely sampled measurements. In our methodology, the main
innovation can be seen in of integration of deep neural networks with a
classical numerical integration method. Precisely, we aim at learning a neural
network that implicitly represents the data and an additional neural network
that models the vector fields of the dependent variables. We combine these two
networks by enforcing the constraint that the data at the next time-steps can
be given by following a numerical integration scheme such as the fourth-order
Runge-Kutta scheme. The proposed framework to learn a model predicting the
vector field is highly effective under noisy measurements. The approach can
handle scenarios where dependent variables are not available at the same
temporal grid. We demonstrate the effectiveness of the proposed method to
learning models using data obtained from various differential equations. The
proposed approach provides a promising methodology to learn dynamic models,
where the first-principle understanding remains opaque.


  Semantic, instance, and panoptic segmentations have been addressed using
different and specialized frameworks despite their underlying connections. This
paper presents a unified, simple, and effective framework for these essentially
similar tasks. The framework, named K-Net, segments both instances and semantic
categories consistently by a group of learnable kernels, where each kernel is
responsible for generating a mask for either a potential instance or a stuff
class. To remedy the difficulties of distinguishing various instances, we
propose a kernel update strategy that enables each kernel dynamic and
conditional on its meaningful group in the input image. K-Net can be trained in
an end-to-end manner with bipartite matching, and its training and inference
are naturally NMS-free and box-free. Without bells and whistles, K-Net
surpasses all previous published state-of-the-art single-model results of
panoptic segmentation on MS COCO test-dev split and semantic segmentation on
ADE20K val split with 55.2% PQ and 54.3% mIoU, respectively. Its instance
segmentation performance is also on par with Cascade Mask R-CNN on MS COCO with
60%-90% faster inference speeds. Code and models will be released at
https://github.com/ZwwWayne/K-Net/.


  We prove a surprising symmetry between the law of the size $G_n$ of the
greedy independent set on a uniform Cayley tree $ \mathcal{T}_n$ of size $n$
and that of its complement. We show that $G_n$ has the same law as the number
of vertices at even height in $ \mathcal{T}_n$ rooted at a uniform vertex. This
enables us to compute the exact law of the $G_n$. We also give a Markovian
construction of the greedy independent set, which highlights the symmetry of
$G_n$ and whose proof uses a new Markovian exploration of rooted Cayley trees
which is of independent interest.


  For an abelian totally real number field $F$ and an odd prime number $p$
which splits totally in $F$, we present a functorial approach to special
"$p$-units" previously built by D. Solomon using "wild" Euler systems. This
allows us to prove a conjecture of Solomon on the annihilation of the $p$-class
group of $F$ (in the particular context here), as well as related annihilation
results and index formulae.


  In superfluid $^3$He turbulence is carried predominantly by the superfluid
component. To explore the statistical properties of this quantum turbulence and
its differences from the classical counterpart we adopt the time-honored
approach of shell models. Using this approach we provide numerical simulations
of a Sabra-shell model that allows us to uncover the nature of the energy
spectrum in the relevant hydrodynamic regimes. These results are in qualitative
agreement with analytical expressions for the superfluid turbulent energy
spectra that were found using a differential approximation for the energy flux.


  The rate constants for recombination and exchange processes are studied in
terms of two different flux correlation approaches: one is the Yamamoto
approach, which is based on the linear response theory, and the other is the
Miller one. Using those approaches we consider two exactly solvable cases,
i.e., the free particle and the parabolic potential models. Since the rate
constants for recombination and exchange processes are calculated by Laplace
transforms of the flux correlation functions, the two approaches give different
results. In the present calculation, we find that the rate constant in the
Yamamoto approach is larger than that in the Miller approach by about 40% at
low temperature ($\sim 100$ K) and high pressure ($\sim 1$ GPa). The difference
is considerable in the region where quantum effects dominate.


  Background: Using the chiral (Kyushu) $g$-matrix folding model with the
densities calculated with Gogny-HFB (GHFB) with the angular momentum projection
(AMP), we determined the central values of matter radius and neutron skin from
the central values of reaction cross sections $\sigma_{\rm R}({\rm EXP})$ of
p+$^{40,48}$Ca and p+$^{208}$Pb scattering. As for p+$^{58}$Ni scattering,
$\sigma_{\rm R}({\rm EXP})$ are available as a function of incident energy
$E_{\rm in}$. Aim: Our aim is to determine matter radius $r_{m}$ and skin
$r_{\rm skin}$ for $^{58}$Ni from the $\sigma_{\rm R}({\rm EXP})$ of
p+$^{58}$Ni scattering by using the Kyushu $g$-matrix folding model with the
GHFB+AMP densities. Results: For p+$^{58}$Ni scattering, the Kyushu $g$-matrix
folding model with the GHFB+AMP densities reproduces $\sigma_{\rm R}({\rm
EXP})$ in $8.8 \leq E_{\rm in} \leq 81$MeV. For $E_{\rm in}=81$MeV, we define
the factor $F$ as $F=\sigma_{\rm R}({\rm EXP})/\sigma_{\rm R}({\rm
AMP})=0.9775$. The $F\sigma_{\rm R}({\rm AMP})$ be much the same as the center
values of $\sigma_{\rm R}({\rm EXP})$ in $8.8 \leq E_{\rm in} \leq 81$MeV. We
then determine $r_{\rm m}({\rm EXP})$ from the center values of $\sigma_{\rm
R}({\rm EXP})$, using $\sigma_{\rm R}({\rm EXP})=C r_{m}^{2}({\rm EXP})$ with
$C=r_{m}^{2}({\rm AMP})/ (F\sigma_{\rm R}({\rm AMP}))$. The $r_{m}({\rm EXP})$
thus obtained are averaged over $E_{\rm in}$. The averaged value is $r_{m}({\rm
EXP})=3.697$fm. Eventually, we obtain $r_{\rm skin}({\rm EXP})=0.023$fm from
$r_{\rm m}=3.697$fm and $r_p({\rm EXP})=3.685$fm of electron scattering.


  We revisit the Next-to-Leading Order (two-loop) contributions to the
Anomalous Dimensions of $\Delta F = 1$ four-quark operators in QCD. We devise a
test for anomalous dimensions, that we regard as of general interest, and by
means of which we detect a problem in the results available in the literature.
Deconstructing the steps leading to the available result, we identify the
source of the problem, which is related to the operator known as $Q_{11}$. We
show how to fix the problem and provide the corrected anomalous dimensions.
With the insight of our findings, we propose an alternative approach to the one
used in the literature which does not suffer from the identified disease, and
which confirms our corrected results. We assess the numerical impact of our
corrections, which happens to be in the ballpark of $5\%$ in certain entries of
the evolution matrix. Our results are important for the correct resummation of
Next-to-Leading Logarithms in analyses of physics beyond the Standard Model in
$\Delta F = 1$ processes, such as the decays of Kaons and $B$-mesons.


  The transverse momentum spectra of charged particles have been measured in pp
and PbPb collisions at sqrt(sNN) = 2.76 TeV by the CMS experiment at the LHC.
In the transverse momentum range pt = 5-10 GeV/c, the charged particle yield in
the most central PbPb collisions is suppressed by up to a factor of 5 compared
to the pp yield scaled by the number of incoherent nucleon-nucleon collisions.
At higher pt, this suppression is significantly reduced, approaching roughly a
factor of 2 for particles with pt in the range pt=40-100 GeV/c.


  We re-examine attempts to study the many-body localization transition using
measures that are physically natural on the ergodic/quantum chaotic regime of
the phase diagram. Using simple scaling arguments and an analysis of various
models for which rigorous results are available, we find that these measures
can be particularly adversely affected by the strong finite-size effects
observed in nearly all numerical studies of many-body localization. This
severely impacts their utility in probing the transition and the localized
phase. In light of this analysis, we argue that a recent study [\v{S}untajs et
al., arXiv:1905.06345] of the behavior of the Thouless energy and level
repulsion in disordered spin chains likely reaches misleading conclusions, in
particular as to the absence of MBL as a true phase of matter.


  The clustering of local extrema will be exploited to examine Gaussianity,
asymmetry, and the footprint of the cosmic-string network on the CMB observed
by Planck. The number density of local extrema ($n_{\rm pk}$ for peak and
$n_{\rm tr}$ for trough) and sharp clipping ($n_{\rm pix}$) statistics support
the Gaussianity hypothesis for all component separations. However, the pixel at
the threshold reveals a more consistent treatment with respect to end-to-end
simulations. A very tiny deviation from associated simulations in the context
of trough density, in the threshold range $\theta\in [-2-0]$ for NILC and CR
component separations, are detected. The unweighted two-point correlation
function, of the local extrema, illustrates good consistency between different
component separations and corresponding Gaussian simulations for almost all
available thresholds. However, for high thresholds, a small deficit in the
clustering of peaks is observed with respect to the Planck fiducial
$\Lambda$CDM model. To put a significant constraint on the amplitude of the
mass function based on the value of $\Psi$ around the Doppler peak
($\theta\approx 70-75$ arcmin), we should consider $\vartheta\lesssim 0.0$. The
scale-independent bias factors for the peak above a threshold for large
separation angle and high threshold level are in agreement with the value
expected for a pure Gaussian CMB. Applying the $n_{\rm pk}$, $n_{\rm tr}$,
$\Psi_{\rm pk-pk}$ and $\Psi_{\rm tr-tr}$ measures on the tessellated CMB map
with patches of $7.5^2$ deg$^2$ size prove statistical isotropy in the Planck
maps. The peak clustering analysis puts the upper bound on the cosmic-string
tension, $G\mu^{(\rm up)} \lesssim 5.59\times 10^{-7}$, in SMICA.


  There is a growing interest in searching for topology in fractal dimensions
with the aim of finding different properties and advantages compared to the
integer dimensional case. It has previously been shown that the Laughlin state
can be adapted to fractal lattices. A key element in doing so is to replace the
uniform background charge by a background charge that resides only on the
lattice sites. This motivates the study of Hofstadter type models on fractal
lattices, in which the magnetic field is present only at the lattice sites.
Here, we study such models for hardcore bosons on finite lattices derived from
the Sierpinski carpet and on square lattices with open boundary conditions. We
find that the system sizes that we can investigate with exact diagonalization
are generally too small to judge whether these local models are topological or
not. Studying the particle densities on the lattices derived from the
Sierpinski carpet, we find that the densities tend to accumulate in the regions
that are locally similar to a square lattice. Such accumulation seems to be
incompatible with the uniform densities in fractional quantum Hall systems,
which might suggest that the models are not topological. Our computations
provide guidance for future searches for topology in finite systems. We also
propose a scheme to implement both fractal lattices and our proposed local
Hamiltonian with ultracold atoms in optical lattices, which could allow for
quantum simulators to go beyond the numerical results presented here.


  Let $\F$ be a field with a non-trivial involution $c: \alpha \to \alpha^c$.
An element $g \in {\rm GL}_n(\F)$ is called $c$-real if it is conjugate to
$(g^c)^{-1}$. We prove that for $n \geq 2$, $g \in {\rm GL}_n(\F)$ is $c$-real
if and only if it has a representation in some unitary group of degree $n$ over
$\F$.


  Image processing and recognition are an important part of the modern society,
with applications in fields such as advanced artificial intelligence, smart
assistants, and security surveillance. The essential first step involved in
almost all the visual tasks is background subtraction with a static camera.
Ensuring that this critical step is performed in the most efficient manner
would therefore improve all aspects related to objects recognition and
tracking, behavior comprehension, etc.. Although background subtraction method
has been applied for many years, its application suffers from real-time
requirement. In this letter, we present a novel approach in implementing the
background subtraction. The proposed method uses compressed, low-resolution
grayscale image for the background subtraction. These low-resolution grayscale
images were found to preserve the salient information very well. To verify the
feasibility of our methodology, two prevalent methods, ViBe and GMM, are used
in the experiment. The results of the proposed methodology confirm the
effectiveness of our approach.


  Context. The radiative energy balance in the solar chromosphere is dominated
by strong spectral lines that are formed out of LTE. It is computationally
prohibitive to solve the full equations of radiative transfer and statistical
equilibrium in 3D time dependent MHD simulations.
  Aims. To find simple recipes to compute the radiative energy balance in the
dominant lines under solar chromospheric conditions.
  Methods. We use detailed calculations in time-dependent and 2D MHD snapshots
to derive empirical formulae for the radiative cooling and heating.
  Results. The radiative cooling in neutral hydrogen lines and the Lyman
continuum, the H and K and intrared triplet lines of singly ionized calcium and
the h and k lines of singly ionized magnesium can be written as a product of an
optically thin emission (dependent on temperature), an escape probability
(dependent on column mass) and an ionization fraction (dependent on
temperature). In the cool pockets of the chromosphere the same transitions
contribute to the heating of the gas and similar formulae can be derived for
these processes. We finally derive a simple recipe for the radiative heating of
the chromosphere from incoming coronal radiation. We compare our recipes with
the detailed results and comment on the accuracy and applicability of the
recipes.


  Consider the semialgebraic structure over the real field. More generally, let
an ominimal structure be over a real closed field. We show that a definable
metric space X with a definable metric d is embedded into a Euclidean space so
that its closure is compact and the metric on the image induced by d is
extended to a definable metric on the closure if and only if the limit of
d(r(t);r(t)) is 0 as t converges to 0 for any definable continuous curve r from
(0, 1] to X (Theorem 1). We also find two compact semialgebraic metric spaces
over the real field which are isometric but not semialgebraically isometric
(Theorem 2). A version of blow up is the key to the proof of Theorem 1. Using
it in the same way, we prove a resolution of singularities of definable sets
(Theorem 3). We prove the theorems by a constructive procedure.


  Recent measurements are suggesting that we live in a flat Universe and that
its present accelerating stage is driven by a dark energy component whose
equation of state may evolve in time. Assuming two different parameterizations
for the function $\omega(z)$, we constrain their free parameters from a joint
analysis involving measurements from X-Ray luminosity of galaxy clusters and
SNe type Ia data.


  Recently, neural networks have been extensively employed to solve partial
differential equations (PDEs) in physical system modeling. While major studies
focus on learning system evolution on predefined static mesh discretizations,
some methods utilize reinforcement learning or supervised learning techniques
to create adaptive and dynamic meshes, due to the dynamic nature of these
systems. However, these approaches face two primary challenges: (1) the need
for expensive optimal mesh data, and (2) the change of the solution space's
degree of freedom and topology during mesh refinement. To address these
challenges, this paper proposes a neural PDE solver with a neural mesh adapter.
To begin with, we introduce a novel data-free neural mesh adaptor, called
Data-free Mesh Mover (DMM), with two main innovations. Firstly, it is an
operator that maps the solution to adaptive meshes and is trained using the
Monge-Amp\`ere equation without optimal mesh data. Secondly, it dynamically
changes the mesh by moving existing nodes rather than adding or deleting nodes
and edges. Theoretical analysis shows that meshes generated by DMM have the
lowest interpolation error bound. Based on DMM, to efficiently and accurately
model dynamic systems, we develop a moving mesh based neural PDE solver
(MM-PDE) that embeds the moving mesh with a two-branch architecture and a
learnable interpolation framework to preserve information within the data.
Empirical experiments demonstrate that our method generates suitable meshes and
considerably enhances accuracy when modeling widely considered PDE systems. The
code can be found at: https://github.com/Peiyannn/MM-PDE.git.


  As an important and challenging problem in computer vision, learning based
optical flow estimation aims to discover the intrinsic correspondence structure
between two adjacent video frames through statistical learning. Therefore, a
key issue to solve in this area is how to effectively model the multi-scale
correspondence structure properties in an adaptive end-to-end learning fashion.
Motivated by this observation, we propose an end-to-end multi-scale
correspondence structure learning (MSCSL) approach for optical flow estimation.
In principle, the proposed MSCSL approach is capable of effectively capturing
the multi-scale inter-image-correlation correspondence structures within a
multi-level feature space from deep learning. Moreover, the proposed MSCSL
approach builds a spatial Conv-GRU neural network model to adaptively model the
intrinsic dependency relationships among these multi-scale correspondence
structures. Finally, the above procedures for correspondence structure learning
and multi-scale dependency modeling are implemented in a unified end-to-end
deep learning framework. Experimental results on several benchmark datasets
demonstrate the effectiveness of the proposed approach.


  Purpose: To propose a deep learning-based reconstruction framework for
ultrafast and robust diffusion tensor imaging and fiber tractography. Methods:
We propose SuperDTI to learn the nonlinear relationship between
diffusion-weighted images (DWIs) and the corresponding tensor-derived
quantitative maps as well as the fiber tractography. Super DTI bypasses the
tensor fitting procedure, which is well known to be highly susceptible to noise
and motion in DWIs. The network is trained and tested using datasets from Human
Connectome Project and patients with ischemic stroke. SuperDTI is compared
against the state-of-the-art methods for diffusion map reconstruction and fiber
tracking. Results: Using training and testing data both from the same protocol
and scanner, SuperDTI is shown to generate fractional anisotropy and mean
diffusivity maps, as well as fiber tractography, from as few as six raw DWIs.
The method achieves a quantification error of less than 5% in all regions of
interest in white matter and gray matter structures. We also demonstrate that
the trained neural network is robust to noise and motion in the testing data,
and the network trained using healthy volunteer data can be directly applied to
stroke patient data without compromising the lesion detectability. Conclusion:
This paper demonstrates the feasibility of superfast diffusion tensor imaging
and fiber tractography using deep learning with as few as six DWIs directly,
bypassing tensor fitting. Such a significant reduction in scan time may allow
the inclusion of DTI into the clinical routine for many potential applications.


  These notes are based on a course for a general audience given at the Centro
de Modeliamento Matem\'atico of the University of Chile, in December 2004.
  We study the mean convergence of multiple ergodic averages, that is, averages
of a product of functions taken at different times. We also describe the
relations between this area of ergodic theory and some classical and some
recent results in additive number theory.


  This work presents a study on parallel and distributional deep reinforcement
learning applied to the mapless navigation of UAVs. For this, we developed an
approach based on the Soft Actor-Critic method, producing a distributed and
distributional variant named PDSAC, and compared it with a second one based on
the traditional SAC algorithm. In addition, we also embodied a prioritized
memory system into them. The UAV used in the study is based on the Hydrone
vehicle, a hybrid quadrotor operating solely in the air. The inputs for the
system are 23 range findings from a Lidar sensor and the distance and angles
towards a desired goal, while the outputs consist of the linear, angular, and,
altitude velocities. The methods were trained in environments of varying
complexity, from obstacle-free environments to environments with multiple
obstacles in three dimensions. The results obtained, demonstrate a concise
improvement in the navigation capabilities by the proposed approach when
compared to the agent based on the SAC for the same amount of training steps.
In summary, this work presented a study on deep reinforcement learning applied
to mapless navigation of drones in three dimensions, with promising results and
potential applications in various contexts related to robotics and autonomous
air navigation with distributed and distributional variants.


  Brain function results from communication between neurons connected by
complex synaptic networks. Synapses are themselves highly complex and diverse
signaling machines, containing protein products of hundreds of different genes,
some in hundreds of copies, arranged in precise lattice at each individual
synapse. Synapses are fundamental not only to synaptic network function but
also to network development, adaptation, and memory. In addition, abnormalities
of synapse numbers or molecular components are implicated in most mental and
neurological disorders. Despite their obvious importance, mammalian synapse
populations have so far resisted detailed quantitative study. In human brains
and most animal nervous systems, synapses are very small and very densely
packed: there are approximately 1 billion synapses per cubic millimeter of
human cortex. This volumetric density poses very substantial challenges to
proteometric analysis at the critical level of the individual synapse. The
present work describes new probabilistic image analysis methods for
single-synapse analysis of synapse populations in both animal and human brains.


  We consider a heat conduction problem $S$ with mixed boundary conditions in a
$n$-dimensional domain $\Omega$ with regular boundary and a family of problems
$S_{\alpha}$ with also mixed boundary conditions in $\Omega$, where $\alpha>0$
is the heat transfer coefficient on the portion of the boundary $\Gamma_{1}$.
In relation to these state systems, we formulate Neumann boundary optimal
control problems on the heat flux $q$ which is definite on the complementary
portion $\Gamma_{2}$ of the boundary of $\Omega$. We obtain existence and
uniqueness of the optimal controls, the first order optimality conditions in
terms of the adjoint state and the convergence of the optimal controls, the
system state and the adjoint state when the heat transfer coefficient $\alpha$
goes to infinity. Furthermore, we formulate particular boundary optimal control
problems on a real parameter $\lambda$, in relation to the parabolic problems
$S$ and $S_{\alpha}$ and to mixed elliptic problems $P$ and $P_{\alpha}$. We
find a explicit form for the optimal controls, we prove monotony properties and
we obtain convergence results when the parameter time goes to infinity.


  The present knowledge about hadrons produced in relativistic heavy ion
collisions is compatible with chemical freeze-out happening when the energy
density divided by the particle density reaches the value of 1 GeV. This
observation is used to determine the energy dependence of the chemical
freeze-out parameters T_{ch} and mu_B^{ch} for beam energies varying between 1
and 200 A.GeV. The consequences of this energy dependence are studied for
various particle ratios. Predictions for particle ratios at beam energy 40
A.GeV are presented. The conditions for thermal freeze-out are also determined.
These correspond either to an energy density of 45 MeV/fm**3 or to a particle
density of 0.05/fm**3.


  A resolving set of a graph is a set of vertices with the property that the
list of distances from any vertex to those in the set uniquely identifies that
vertex. In this paper, we construct a resolving set of Johnson graphs, doubled
Odd graphs, doubled Grassmann graphs and twisted Grassmann graphs,
respectively, and obtain the upper bounds on the metric dimension of these
graphs.


  We present preliminary polarisation VLBI maps of the gravitational lens
system B0218+357, made at 3 different frequencies.


  A central theme in the field of survey statistics is estimating
population-level quantities through data coming from potentially
non-representative samples of the population. Multilevel Regression and
Poststratification (MRP), a model-based approach, is gaining traction against
the traditional weighted approach for survey estimates. MRP estimates are
susceptible to bias if there is an underlying structure that the methodology
does not capture. This work aims to provide a new framework for specifying
structured prior distributions that lead to bias reduction in MRP estimates. We
use simulation studies to explore the benefit of these prior distributions and
demonstrate their efficacy on non-representative US survey data. We show that
structured prior distributions offer absolute bias reduction and variance
reduction for posterior MRP estimates in a large variety of data regimes.


  Entanglement of spin and orbital degrees of freedom drives the formation of
novel quantum and topological physical states. Discovering new spin-orbit
entangled ground states and emergent phases of matter requires both
experimentally probing the relevant energy scales and applying suitable
theoretical models. Here we report resonant inelastic x-ray scattering
measurements of the transition metal oxides Ca$_3$LiOsO$_6$ and Ba$_2$YOsO$_6$.
We invoke an intermediate coupling approach that incorporates both spin-orbit
coupling and electron-electron interactions on an even footing and reveal the
ground state of $5d^3$ based compounds, which has remained elusive in
previously applied models, is a novel spin-orbit entangled J=3/2 electronic
ground state. This work reveals the hidden diversity of spin-orbit controlled
ground states in 5d systems and introduces a new arena in the search for
spin-orbit controlled phases of matter.


  The reversibility of the transfer of energy from the magnetic field to the
surrounding plasma during magnetic reconnection is examined. Trajectories of
test particles in an analytic model of the fields demonstrate that
irreversibility is associated with separatrix crossings and regions of weaker
magnetic field. Inclusion of a guide field increases the degree of
reversibility. Full kinetic simulations with a particle-in-cell code support
these results and demonstrate that while time-reversed simulations at first
"un-reconnect", they eventually evolve into a reconnecting state.


  We have determined fluorine abundances from the F II 4789 and F IV 4060
nebular emission lines for a sample of planetary nebulae (PNe). Our results
show that fluorine is generally overabundant in PNe, thus providing new
evidence for the synthesis of fluorine in asymptotic giant branch (AGB) stars.
[F/O] is found to be positively correlated with the C/O abundance ratio, in
agreement with the predictions of theoretical models of fluorine production in
thermally pulsing AGB stars. A large enhancement of fluorine is observed in the
Wolf-Rayet PN NGC 40, suggesting that high mass-loss rates probably favor the
survival of fluorine.


  We develop a general strategy for constructing the explicit Local Langlands
Correspondences for $p$-adic reductive groups via reduction to LLC for
supercuspidal representations of proper Levi subgroups, using Hecke algebra
techniques.
  As an example of our general strategy, we construct the explicit Local
Langlands Correspondence for the exceptional group $G_2$ over a nonarchimedean
local field, with explicit $L$-packets and explicit matching between the group
and Galois sides. We also give a list of characterizing properties for our LLC.
In \cite{G2-stability}, we complete unique characterization using stability
property of our $L$-packets. For intermediate series, we build on our previous
results on Hecke algebras. For principal series, we improve previous works of
Muic etc. and obtain more explicit descriptions on both group and Galois sides.
  Moreover, we show the existence of non-unipotent \textit{singular}
supercuspidal representations of $G_2$, and exhibit them in \textit{mixed}
$L$-packets mixing supercuspidal representations with non-supercuspidal ones.
Furthermore, our LLC satisfies a list of expected properties, including the
compatibility with cuspidal support.


  FeSe has a unique ground state in which superconductivity coexists with a
nematic order without long-range magnetic ordering at ambient pressure. Here,
to study how the pairing interaction evolves with nematicity, we measured the
thermal conductivity and specific heat of FeSe$_{1-x}$S$_x$, where the
nematicity is suppressed by isoelectronic sulfur substitution. We find that in
the whole nematic ($0\leq x \leq 0.17$) and tetragonal ($x=0.20$) regimes, the
application of small magnetic field causes a steep increase of both quantities.
This indicates the existence of deep minima or line nodes in the
superconducting gap function, implying that the pairing interaction is
significantly anisotropic in both the nematic and the tetragonal regimes.
Moreover, the present results indicate that the position of gap minima/nodes in
the tetragonal regime appears to be essentially different from that in the
nematic regime. These results place an important constraint on current
theories.


  The paper presents a description of some point of view of different authors
related to the failures and exceptions that appear in workflows, as a direct
consequence of unavailability of resources involved in the workflow. Each of
these interpretations is typical for a certain situation, depending on the
authors' interpretation of failures and exceptions in workflows modeling real
dynamical systems.


  The vision of building computational hardware for problem optimization has
spurred large efforts in the physics community. In particular, networks of Kerr
parametric oscillators (KPOs) are envisioned as simulators for finding the
ground states of Ising Hamiltonians. It was shown, however, that KPO networks
can feature large numbers of unexpected solutions that are difficult to sample
with the existing deterministic (i.e., adiabatic) protocols. In this work, we
experimentally realize a system of two classical coupled KPOs, and we find good
agreement with the predicted mapping to Ising states. We then introduce a
protocol based on stochastic sampling of the system, and we show how the
resulting probability distribution can be used to identify the ground state of
the corresponding Ising Hamiltonian. This method is akin to a Monte Carlo
sampling of multiple out-of-equilibrium stationary states and is less prone to
become trapped in local minima than deterministic protocols.


  With the advent of spatially resolved fluorescence imaging in quantum gas
microscopes, it is now possible to directly image glassy phases and probe the
local effects of disorder in a highly controllable setup. Here we present
numerical calculations using a spatially-resolved local mean-field theory, show
that it captures the essential physics of the disordered system and use it to
simulate the density distributions seen in single-shot fluorescence microscopy.
From these simulated images we extract local properties of the phases which are
measurable by a quantum gas microscope and show that unambiguous detection of
the Bose glass is possible. In particular, we show that experimental
determination of the Edwards-Anderson order parameter is possible in a strongly
correlated quantum system using existing experiments. We also suggest
modifications to the experiments which will allow further properties of the
Bose glass to be measured.


  In this article we define a special class of weak expectations for a
representation of a separable unital C*-algebra, called decomposable weak
expectation. We give necessary and sufficient conditions for such kind of weak
expectations to exist for a given representation. Then we define decomposable
measures on the state space of a C*-algebra and show that the GNS
representation of a state admits a decomposable weak expectation if and only if
there is a decomposable measure on the state space. Further we give an example
of a decomposable weak expectation.


  Most of the synthesis literature has focused on studying how to synthesize a
strategy to fulfill a task. This task is a duty for the agent. In this paper,
we argue that intelligent agents should also be equipped with rights, that is,
tasks that the agent itself can choose to fulfill (e.g., the right of
recharging the battery). The agent should be able to maintain these rights
while acting for its duties. We study this issue in the context of LTLf
synthesis: we give duties and rights in terms of LTLf specifications, and
synthesize a suitable strategy to achieve the duties that can be modified
on-the-fly to achieve also the rights, if the agent chooses to do so. We show
that handling rights does not make synthesis substantially more difficult,
although it requires a more sophisticated solution concept than standard LTLf
synthesis. We also extend our results to the case in which further duties and
rights are given to the agent while already executing.


  Vortex beams carrying orbital angular momentum (OAM) have been widely applied
in various electromagnetic, optical, and quantum systems. A tailored OAM
spectrum composed of several specific modes as expected holds a promise for
expanding the degrees of freedom of the systems. However, such a broadband
high-purity tailored spectrum is difficult to be achieved by the present
devices, where the broadband amplitude manipulation has not been explored yet.
In this work, inspired by the envelope-modulation theory, an elegant and
universal way to manipulate the OAM spectrum in wide bandwidth is proposed by
using a shape-tailored metasurface. Firstly, the rotating meta-atoms on a
triangular lattice are proved to have smaller coupling distortion than that on
a square lattice, and this behavior is critical for high-purity vortex spectrum
generation by the Pancharatnam-Berry-based metasurfaces. Secondly, a universal
modulation relation is established between the spatial arrangement of
metasurfaces and the generated vortex beams. Finally, the broadband modulated
OAM spectra and the comb-like OAM spectra are theoretically and experimentally
demonstrated by the shape-tailored metasurfaces. The proposed
amplitude-modulation scheme offers a novel concept and engineering route to
manipulate the OAM spectrum in wide bandwidth, which could promote the
development of OAM-based applications.


  In autonomous driving, goal-based multi-trajectory prediction methods are
proved to be effective recently, where they first score goal candidates, then
select a final set of goals, and finally complete trajectories based on the
selected goals. However, these methods usually involve goal predictions based
on sparse predefined anchors. In this work, we propose an anchor-free model,
named DenseTNT, which performs dense goal probability estimation for trajectory
prediction. Our model achieves state-of-the-art performance, and ranks 1st on
the Waymo Open Dataset Motion Prediction Challenge. Project page is at
https://github.com/Tsinghua-MARS-Lab/DenseTNT.


  In-vivo sensing, diagnosis and treatment of diseases is having a great
attention lately. With advanced computational systems, the processing of the
biological data as well as the prediction of diagnosis is becoming more
promising. However, the implementation of these systems inside the human body
has a major challenge; modeling the communication channel. To overcome this
problem, researchers are investigating the main factors that define the
characteristics of the communication channel between nano-devices. In this
work, we summarize the elements that contribute to the path loss encountered by
an EM wave traveling in water, skin or epidermis. Then, the impact of
nano-antenna directivity on the EM propagating wave is studied along with the
frequency and the communication distance. The simulation results show that the
nano-antenna directivity seems to have minor contributions 5 to 7 dB on the
total path loss inside the human body with respect to the distance 2 to 30 dB
and frequency 10 to 15 dB.


  The behavior of solitons in integrable theories is strongly constrained by
the integrability of the theory, that is by the existence of an infinite number
of conserved quantities that these theories are known to possess. As a result
the soliton scattering of such theories are expected to be trivial (with no
change of direction, velocity or shape). In this paper we present an extended
review on soliton scattering of two spatial dimensional integrable systems
which have been derived as dimensional reductions of the self-dual
Yang-Mills-Higgs equations and whose scattering properties are highly
non-trivial.


  We construct an example of a Peano continuum $X$ such that: (i) $X$ is a
one-point compactification of a polyhedron; (ii) $X$ is weakly homotopy
equivalent to a point (i.e. $\pi_n(X)$ is trivial for all $n \geq 0$); (iii)
$X$ is noncontractible; and (iv) $X$ is homologically and cohomologically
locally connected (i.e. $X$ is a $HLC$ and $clc$ space). We also prove that all
classical homology groups (singular, \v{C}ech, and Borel-Moore), all classical
cohomology groups (singular and \v{C}ech), and all finite-dimensional Hawaiian
groups of $X$ are trivial.


  Recent research in deep learning methodology has led to a variety of complex
modelling techniques in computer vision (CV) that reach or even outperform
human performance. Although these black-box deep learning models have obtained
astounding results, they are limited in their interpretability and transparency
which are critical to take learning machines to the next step to include them
in sensitive decision-support systems involving human supervision. Hence, the
development of explainable techniques for computer vision (XCV) has recently
attracted increasing attention. In the realm of XCV, Class Activation Maps
(CAMs) have become widely recognized and utilized for enhancing
interpretability and insights into the decision-making process of deep learning
models. This work presents a comprehensive overview of the evolution of Class
Activation Map methods over time. It also explores the metrics used for
evaluating CAMs and introduces auxiliary techniques to improve the saliency of
these methods. The overview concludes by proposing potential avenues for future
research in this evolving field.


  We present two metalanguages for developing $\textit{synthetic cost-aware
denotational semantics}$ of programming languages. Extending the recent work of
Niu et al. [2022] on $\textbf{calf}$, a dependent type theory for both cost and
behavioral verification, we define two metalanguages, $\textbf{calf}^\star$ and
$\textbf{calf}^\omega$, for studying cost-aware metatheory.
$\textbf{calf}^\star$ is an extension of $\textbf{calf}$ with universes and
inductive types, and $\textbf{calf}^\omega$ is a an extension of
$\textbf{calf}^\star$ with unbounded iteration.
  We construct denotational models of the simply-typed lambda calculus and
  Modernized Algol, a language with first-order store and while loops, and show
that they satisfy a $\textit{cost-aware}$ generalization of the classic
Plotkin-type computational adequacy theorem.
  Moreover, by developing our proofs in a synthetic language of
  $\textit{phase-separated}$ constructions of intension and extension, our
results easily $\textit{restrict}$ to the corresponding extensional theorems.
  Our work provides a positive answer to the conjecture raised in Niu et al.
[2022] and in light of $\textit{op. cit.}$'s work on algorithm analysis,
contributes a metalanguage for doing both cost-aware programming and
verification and cost-aware metatheory of programming languages.


  Customers are rapidly turning to social media for customer support. While
brand agents on these platforms are motivated and well-intentioned to help and
engage with customers, their efforts are often ignored if their initial
response to the customer does not match a specific tone, style, or topic the
customer is aiming to receive. The length of a conversation can reflect the
effort and quality of the initial response made by a brand toward collaborating
and helping consumers, even when the overall sentiment of the conversation
might not be very positive. Thus, through this study, we aim to bridge this
critical gap in the existing literature by analyzing language's content and
stylistic aspects such as expressed empathy, psycho-linguistic features,
dialogue tags, and metrics for quantifying personalization of the utterances
that can influence the engagement of an interaction. This paper demonstrates
that we can predict engagement using initial customer and brand posts.


  We review the studies on the emergent phases of superconductvity and
magnetism in the $f$-electron derived heavy-fermion (HF) systems by means of
the nuclear-quadrupole-resonance (NQR) under pressure. These studies have
unraveled a rich variety of the phenomena in the ground state of HF systems. In
this article, we highlight the novel phase diagrams of magnetism and
unconventional superconductivity (SC) in CeCu$_2$Si$_2$, HF antiferromagnets
CeRhIn$_5$, and CeIn$_3$. A new light is shed on the difference and common
features on the interplay between magnetism and SC on the magnetic criticality.


  We present a unified theorem for the convergence analysis of stochastic
gradient algorithms for minimizing a smooth and convex loss plus a convex
regularizer. We do this by extending the unified analysis of Gorbunov, Hanzely
\& Richt\'arik (2020) and dropping the requirement that the loss function be
strongly convex. Instead, we only rely on convexity of the loss function. Our
unified analysis applies to a host of existing algorithms such as proximal SGD,
variance reduced methods, quantization and some coordinate descent type
methods. For the variance reduced methods, we recover the best known
convergence rates as special cases. For proximal SGD, the quantization and
coordinate type methods, we uncover new state-of-the-art convergence rates. Our
analysis also includes any form of sampling and minibatching. As such, we are
able to determine the minibatch size that optimizes the total complexity of
variance reduced methods. We showcase this by obtaining a simple formula for
the optimal minibatch size of two variance reduced methods (\textit{L-SVRG} and
\textit{SAGA}). This optimal minibatch size not only improves the theoretical
total complexity of the methods but also improves their convergence in
practice, as we show in several experiments.


  Spectro-polarimetric measurements of the D3-HeI 5876 A line profile in 35
prominences have been performed in 2003 with the Gregory-Coud\'e Telescope in
Locarno. Two different experimental techniques (ZIMPOL and Beam-exchange
method) have been successfully employed to determine all four Stokes
components. Both give compatible results. The preliminary results as well as
the measurement techniques are reported.


  We introduce and parameterize a chemomechanical model of microtubule dynamics
on the dimer level, which is based on the allosteric tubulin model and includes
attachment, detachment and hydrolysis of tubulin dimers as well as stretching
of lateral bonds, bending at longitudinal junctions, and the possibility of
lateral bond rupture and formation. The model is computationally efficient such
that we reach sufficiently long simulation times to observe repeated
catastrophe and rescue events at realistic tubulin concentrations and
hydrolysis rates, which allows us to deduce catastrophe and rescue rates. The
chemomechanical model also allows us to gain insight into microscopic features
of the GTP-tubulin cap structure and microscopic structural features triggering
microtubule catastrophes and rescues. Dilution simulations show qualitative
agreement with experiments. We also explore the consequences of a possible
feedback of mechanical forces onto the hydrolysis process and the GTP-tubulin
cap structure.


  Pedestrians and vehicles often share the road in complex inner city traffic.
This leads to interactions between the vehicle and pedestrians, with each
affecting the other's motion. In order to create robust methods to reason about
pedestrian behavior and to design interfaces of communication between
self-driving cars and pedestrians we need to better understand such
interactions. In this paper, we present a data-driven approach to implicitly
model pedestrians' interactions with vehicles, to better predict pedestrian
behavior. We propose a LSTM model that takes as input the past trajectories of
the pedestrian and ego-vehicle, and pedestrian head orientation, and predicts
the future positions of the pedestrian. Our experiments based on a real-world,
inner city dataset captured with vehicle mounted cameras, show that the usage
of such cues improve pedestrian prediction when compared to a baseline that
purely uses the past trajectory of the pedestrian.


  Let $X$ be a hypersurface in $\mathbb{P}^N$ with $N\geq 3$ defined over a
finite field. The main result of this note is the classification, up to
projective equivalence, of hypersurfaces $X$ as above without a linear
component when the number of their rational points achieves the Homma-Kim
bound.


  We construct a list of minimal scale-invariant models at the TeV scale that
generate one-loop neutrino mass and give viable dark matter candidates. The
models generically contain a singlet scalar and a $Z_2$-odd sector comprised of
singlet, doublet and/or triplet SU(2) multiplets. The dark matter may reside in
a single multiplet or arise as an admixture of several multiplets. We find
fifteen independent models, for which the dark matter is a viable candidate and
neutrino mass results from a diagram with just one of the irreducible
scale-invariant one-loop topologies. Further "non-pure" cases give hybrid
one-/two-loop masses. All models predict new TeV scale physics, including a
singlet scalar that generically mixes with the Higgs boson.


  We construct holomorphically varying families of Fatou-Bieberbach domains
with given centres in the complement of any compact polynomially convex subset
$K$ of $\mathbb C^n$ for $n>1$. This provides a simple proof of the recent
result of Yuta Kusakabe to the effect that the complement $\mathbb C^n\setminus
K$ of any polynomially convex subset $K$ of $\mathbb C^n$ is an Oka manifold.
The analogous result is obtained with $\mathbb C^n$ replaced by any Stein
manifold with the density property.


  We prove rigidity results for compact Riemannian manifolds in the spirit of
Tachibana. For example, we observe that manifolds with divergence free Weyl
tensors and $\lfloor \frac{n-1}{2} \rfloor$-nonnegative curvature operators are
locally symmetric or conformally equivalent to a quotient of the sphere. The
main focus of the paper is to prove similar results for manifolds with special
holonomy. In particular, we consider K\"ahler manifolds with divergence free
Bochner tensor. For quaternion K\"ahler manifolds we obtain a partial result
towards the LeBrun-Salamon conjecture.


  Experimental data from axially symmetric compression test at constant mean
pressure p on Hostun sand from Flavigny experiments on loose sands are used to
study the validity of an "isotropic" modelling at different densities . It is
found that the material response is not isotropic even at small deviatoric
stress. As an "isotropic" behaviour is found for compression test at constant
volume on the same sand, this new result questions the unicity of the
trajectory in the classical phase space of soil mechanics (q,p,v), with q being
thed deviatoric stress, v the specific volume. This asks whether the space
shall be taken larger than 3d or not. Pacs # : 5.40 ; 45.70 ; 62.20 ; 83.70.Fn


  In the present work we study the semileptonic decays of $\bar B_s$ mesons in
the context of nonrelativistic constituent quark models. We estimate the
uncertainties of our calculation using different interquark potentials to
obtain the meson wave functions. We check the results from our model against
the predictions of Heavy Quark Symmetry, in the limit of infinite heavy quark
mass. We also study the nonleptonic decays of $\bar B_s$ mesons within the
factorization approximation.


  A number of large current experiments aim to detect the signatures of the
Cosmic Reionization at redshifts z>6. Their success depends crucially on
understanding the character of the reionization process and its observable
consequences and designing the best strategies to use. We use large-scale
simulations of cosmic reionization to evaluate the reionization signatures at
redshifted 21-cm and small-scale CMB anisotropies in the best current model for
the background universe, with fundamental cosmological parameters given by WMAP
3-year results (WMAP3). We find that the optimal frequency range for observing
the ``global step'' of the 21-cm emission is 120-150 MHz, while statistical
studies should aim at 140-160 MHz, observable by GMRT. Some
strongly-nongaussian brightness features should be detectable at frequencies up
to ~190 MHz. In terms of sensitivity-signal trade-off relatively low
resolutions, corresponding to beams of at least a few arcminutes, are
preferable. The CMB anisotropy signal from the kinetic Sunyaev-Zel'dovich
effect from reionized patches peaks at tens of muK at arcminute scales and has
an rms of ~1 muK, and should be observable by the Atacama Cosmology Telescope
and the South Pole Telescope. We discuss the various observational issues and
the uncertainties involved, mostly related to the poorly-known reionization
parameters and, to a lesser extend, to the uncertainties in the background
cosmology.


  An X-ray imaging technique is used to probe the stability of 3-dimensional
granular packs in a slowly rotating drum. Well before the surface reaches the
avalanche angle, we observe intermittent plastic events associated with
collective rearrangements of the grains located in the vicinity of the free
surface. The energy released by these discrete events grows as the system
approaches the avalanche threshold. By testing various preparation methods, we
show that the pre-avalanche dynamics is not solely controlled by the difference
between the free surface inclination and the avalanche angle. As a consequence,
the measure of the pre-avalanche dynamics is unlikely to serve as a tool for
predicting macroscopic avalanches.


  Probably the most dramatic historical challenge to scientific realism
concerns Arnold Sommerfeld's 1916 derivation of the fine structure energy
levels of hydrogen. Not only were his predictions good, he derived exactly the
same formula that would later drop out of Dirac's 1928 treatment, something not
possible using 1925 Schroedinger-Heisenberg quantum mechanics. And yet the most
central elements of Sommerfeld's theory were not even approximately true: his
derivation leans heavily on a classical approach to elliptical orbits,
including the necessary adjustments to these orbits demanded by relativity.
Even physicists call Sommerfeld's success a 'miracle', which rather makes a
joke of the so-called 'no miracles argument'. However, this can all be turned
around. Here I argue that the realist has a story to tell vis-a-vis the
discontinuities between the old and the new theory, leading to a realist
defence based on sufficient continuity of relevant structure.


  Optical tweezer arrays of laser-cooled and individual controlled particles
have revolutionized the atomic, molecular and optical physics, and they afford
exquisite capabilities for applications in quantum simulation of many-body
physics, quantum computation and quantum sensing. Underlying this development
is the technical maturity of generating scalable optical beams, enabled by
active components and high numerical aperture objective. However, such a
complex combination of bulk optics outside the vacuum chamber is very sensitive
to any vibration and drift. Here we demonstrate the generation of 3*3 static
tweezer array with a single chip-scale multifunctional metasurface element in
vacuum, replacing the meter-long free space optics. Fluorescence counts on the
camera validates the successfully trapping of the atomic ensemble array.
Further, we discuss the strategy to achieve low scattering and crosstalk, where
a metasurface design featuring dual-wavelength independent control is included.
Our results, together with other recent development in integrated photonics for
cold atoms, could pave the way for compact and portable quantum sensors and
simulators in platforms of neutral atom arrays.


  Multiplex networks have become increasingly more prevalent in many fields,
and have emerged as a powerful tool for modeling the complexity of real
networks. There is a critical need for developing inference models for
multiplex networks that can take into account potential dependencies across
different layers, particularly when the aim is community detection. We add to a
limited literature by proposing a novel and efficient Bayesian model for
community detection in multiplex networks. A key feature of our approach is the
ability to model varying communities at different network layers. In contrast,
many existing models assume the same communities for all layers. Moreover, our
model automatically picks up the necessary number of communities at each layer
(as validated by real data examples). This is appealing, since deciding the
number of communities is a challenging aspect of community detection, and
especially so in the multiplex setting, if one allows the communities to change
across layers. Borrowing ideas from hierarchical Bayesian modeling, we use a
hierarchical Dirichlet prior to model community labels across layers, allowing
dependency in their structure. Given the community labels, a stochastic block
model (SBM) is assumed for each layer. We develop an efficient slice sampler
for sampling the posterior distribution of the community labels as well as the
link probabilities between communities. In doing so, we address some unique
challenges posed by coupling the complex likelihood of SBM with the
hierarchical nature of the prior on the labels. An extensive empirical
validation is performed on simulated and real data, demonstrating the superior
performance of the model over single-layer alternatives, as well as the ability
to uncover interesting structures in real networks.


  We extend QCD sum rule analysis to moderate energy fixed angle Compton
scattering. In this kinematic region there is a strong similarity to the sum
rule treatment of electromagnetic form factors, although the four-point
amplitude requires a modification of the Borel transform. To illustrate our
method, we derive the sum rules for helicity amplitudes in pion Compton
scattering and estimate their large-$t$ behavior in the local duality
approximation.


  We study the spatial and orientational distribution of rodlike counterions
(such as mobile nanorods) as well as the effective interaction mediated by them
between two plane-parallel surfaces that carry fixed (quenched) heterogeneous
charge distributions. The rodlike counterions are assumed to have an internal
charge distribution, specified by a multivalent monopolar moment and a finite
quadrupolar moment, and the quenched surface charge is assumed to be randomly
distributed with equal mean and variance on the two surfaces. While equally
charged surfaces are known to repel within the traditional mean-field theories,
the presence of multivalent counterions has been shown to cause attractive
interactions between uniformly charged surfaces due to the prevalence of strong
electrostatic couplings that grow rapidly with the counterion valency. We show
that the combined effects due to electrostatic correlations (caused by the
coupling between the mean surface field and the multivalent, monopolar, charge
valency of counterions) as well as the disorder-induced interactions (caused by
the coupling between the surface disorder field and the quadrupolar moment of
counterions) lead to much stronger attractive interactions between two randomly
charged surfaces. The interaction profile turns out to be a nonmonotonic
function of the intersurface separation, displaying an attractive minimum at
relatively small separations, where the ensuing attraction can exceed the
maximum strong-coupling attraction (produced by multivalent monopolar
counterions between uniformly charged surfaces) by more than an order of
magnitude.


  We introduce the task of spatially localizing narrated interactions in
videos. Key to our approach is the ability to learn to spatially localize
interactions with self-supervision on a large corpus of videos with
accompanying transcribed narrations. To achieve this goal, we propose a
multilayer cross-modal attention network that enables effective optimization of
a contrastive loss during training. We introduce a divided strategy that
alternates between computing inter- and intra-modal attention across the visual
and natural language modalities, which allows effective training via directly
contrasting the two modalities' representations. We demonstrate the
effectiveness of our approach by self-training on the HowTo100M instructional
video dataset and evaluating on a newly collected dataset of localized
described interactions in the YouCook2 dataset. We show that our approach
outperforms alternative baselines, including shallow co-attention and full
cross-modal attention. We also apply our approach to grounding phrases in
images with weak supervision on Flickr30K and show that stacking multiple
attention layers is effective and, when combined with a word-to-region loss,
achieves state of the art on recall-at-one and pointing hand accuracies.


  In this paper we present the 13 B and V color-magnitude diagrams discussed by
Piotto et al. in the same Procedings. The data have been collected with the
WFPC2 camera on HST. We emphasize the discovery of blue horizontal branches in
the two metal rich globular clusters NGC 6388 and NGC 6441, and the four-mode
HB of NGC 2808. The significance of the gaps in the HB of NGC 2808 is also
assesed.


  We consider a system of trimers and monomers on the triangular lattice, which
describes the adsorption problem on (111) $1 \times 1$ surface of diamond
structure. We give a mapping to a 3-state vertex model on the square lattice.
We treat the problem by the transfer-matrix method combined with the
density-matrix algorithm, to obtain thermodynamic quantities.