  We analyze the dispersions of distributed lossless source coding (the
Slepian-Wolf problem), the multiple-access channel and the asymmetric broadcast
channel. For the two-encoder Slepian-Wolf problem, we introduce a quantity
known as the entropy dispersion matrix, which is analogous to the scalar
dispersions that have gained interest recently. We prove a global dispersion
result that can be expressed in terms of this entropy dispersion matrix and
provides intuition on the approximate rate losses at a given blocklength and
error probability. To gain better intuition about the rate at which the
non-asymptotic rate region converges to the Slepian-Wolf boundary, we define
and characterize two operational dispersions: the local dispersion and the
weighted sum-rate dispersion. The former represents the rate of convergence to
a point on the Slepian-Wolf boundary while the latter represents the fastest
rate for which a weighted sum of the two rates converges to its asymptotic
fundamental limit. Interestingly, when we approach either of the two corner
points, the local dispersion is characterized not by a univariate Gaussian but
a bivariate one as well as a subset of off-diagonal elements of the
aforementioned entropy dispersion matrix. Finally, we demonstrate the
versatility of our achievability proof technique by providing inner bounds for
the multiple-access channel and the asymmetric broadcast channel in terms of
dispersion matrices. All our proofs are unified a so-called vector rate
redundancy theorem which is proved using the multidimensional Berry-Esseen
theorem.


  In this article, we derive a unique procedure for quantum state estimation
from a simple, self-evident principle: an experimentalist's estimate of the
quantum state generated by an apparatus should be constrained by honesty. A
skeptical observer should subject the estimate to a test that guarantees that a
self-interested experimentalist will report the true state as accurately as
possible. We also find a non-asymptotic, operational interpretation of the
quantum relative entropy function.


  The formalism of nonequilibrium perturbation theory was constructed by
Schwinger and Keldysh and then was developed with the diagrammatical technique
by Lifshitz and Pitaevskii. Until now there has been widespread application to
various researches in physics, condensed matter, plasmas, atoms and molecules,
nuclear matter etc.. In spite of this, the formalism has not been established
as perturbation theory. For example, there is no perturbative method to derive
arbitrary self-energy properly. In addition, the connection with other
formalism, e.g., the Matsubara imaginary-time perturbative formalism is
uncertain. Although there must be the relationship between self-energies in the
perturbative formalism, such basic problems remain to be solved. The solution
is given by the present work. The real-time perturbative expansion is performed
on the basis of the adiabatic theorem. As the results, the requirements of
self-energies as functions in time are demonstrated and the formulated
self-energies meet the known relations. Besides, it gives exact agreement with
functions derived by perturbative expansion in imaginary-time and analytical
continuity. As a consequence, it implies that the present formalism can be
generalized.


  We investigate the temporal accuracy of two generalized-$\alpha$ schemes for
the incompressible Navier-Stokes equations. The conventional approach treats
the pressure with the backward Euler method while discretizing the remainder of
the Navier-Stokes equations with the generalized-$\alpha$ method. We developed
a suite of numerical codes using inf-sup stable higher-order non-uniform
rational B-spline (NURBS) elements for spatial discretization. In doing so, we
are able to achieve very high spatial accuracy and, furthermore, to perform
temporal refinement without consideration of the stabilization terms, which can
degenerate for small time steps. Numerical experiments suggest that only
first-order accuracy is achieved, at least for the pressure, in this aforesaid
approach. Evaluating the pressure at the intermediate time step recovers
second-order accuracy, and the numerical implementation, in fact, becomes
simpler. Therefore, although the pressure can be viewed as a Lagrange
multiplier enforcing the incompressibility constraint, its temporal
discretization is not independent and should be subject to the
generalized-$\alpha$ method in order to maintain second-order accuracy of the
overall algorithm.


  It is an important feature of our existing physical theories that observables
generate one-parameter groups of transformations. In classical Hamiltonian
mechanics and quantum mechanics, this is due to the fact that the observables
form a Lie algebra, and it manifests itself in Noether's theorem. In this
paper, we introduce Lie quandles as the minimal mathematical structure needed
to express the idea that observables generate transformations. This is based on
the notion of a quandle used most famously in knot theory, whose main defining
property is the self-distributivity equation $x \triangleright (y
\triangleright z) = (x \triangleright y) \triangleright (x \triangleright z)$.
We argue that Lie quandles can be thought of as nonlinear generalizations of
Lie algebras.
  We also observe that taking convex combinations of points in vector spaces,
which physically corresponds to mixing states, satisfies the same form of
self-distributivity.


  Recently, powerful Large Language Models (LLMs) have become easily accessible
to hundreds of millions of users worldwide. However, their strong capabilities
and vast world knowledge do not come without associated privacy risks. In this
work, we focus on the emerging privacy threat LLMs pose - the ability to
accurately infer personal information from online texts. Despite the growing
importance of LLM-based author profiling, research in this area has been
hampered by a lack of suitable public datasets, largely due to ethical and
privacy concerns associated with real personal data. In this work, we take two
steps to address this problem: (i) we construct a simulation framework for the
popular social media platform Reddit using LLM agents seeded with synthetic
personal profiles; (ii) using this framework, we generate SynthPAI, a diverse
synthetic dataset of over 7800 comments manually labeled for personal
attributes. We validate our dataset with a human study showing that humans
barely outperform random guessing on the task of distinguishing our synthetic
comments from real ones. Further, we verify that our dataset enables meaningful
personal attribute inference research by showing across 18 state-of-the-art
LLMs that our synthetic comments allow us to draw the same conclusions as
real-world data. Together, this indicates that our dataset and pipeline provide
a strong and privacy-preserving basis for future research toward understanding
and mitigating the inference-based privacy threats LLMs pose.


  This work focuses on the identification of reliable and repeatable spatial
(three-dimensional) trajectories that link the Earth and the Moon. For this
purpose, this paper aims to extend the 2:1 resonant prograde family and 2:1
resonant retrograde family to three dimensions and to introduce spatial orbits
that are not currently present in the literature. These orbits, named the 2:1
resonant spatial family, bifurcate from the two-dimensional families and
smoothly transition between them in phase space. The stability properties of
this new family of resonant orbits are discussed, and, interestingly, this
family includes marginally stable members. Furthermore, this new family of
orbits is applied to several engineering problems in the Earth-Moon system.
First, this paper selects an appropriate member of the 2:1 resonant spatial
family on the basis of its stability properties and relationships with other
multibody orbits in the regime. Next, this work combines this trajectory with
momentum exchange tethers to transit payloads throughout the system in a
reliable and repeatable fashion. Finally, this paper studies the process of
aborting a momentum exchange tether catch and related recovery opportunities.


  We have estimated the age of the young moving group TW Hydrae Association, a
cohort of a few dozen stars and brown dwarfs located near the Sun which share
the same kinematic properties and, presumably, the same origin and age. The
chronology has been determined by analyzing different properties (magnitudes,
colors, activity, lithium) of its members and comparing them with several
well-known star forming regions and open clusters, as well as theoretical
models. In addition, by using medium-resolution optical spectra of two M8
members of the association (2M1139 and 2M1207 -an accreting brown dwarf with a
planetary mass companion), we have derived spectral types and measured H(alpha)
and lithium equivalent widths. We have also estimated their effective
temperature and gravity, which were used to produce an independent age
estimation for these two brown dwarfs. We have also collected spectra of
2M1315, a candidate member with a L5 spectral type and measured its H(alpha)
equivalent width. Our age estimate for the association, 10 Myr (in the range
3-20 Myr), agrees with previous values cited in the literature. In the case of
the two brown dwarfs, we have derived an age of 15 Myr (in the range 5-30 Myr),
which also agree with our estimate for the whole group. We compared our results
with recent articles published on the same subject using other techniques, and
discuss the limits of the age-dating techniques.


  If the primordial fluctuations are non-Gaussian, then this non-Gaussianity
will be apparent in the cosmic microwave background (CMB) sky. With their
sensitive all-sky observation, MAP and Planck satellites should be able to
detect weak non-Gaussianity in the CMB sky. On large angular scale, there is a
simple relationship between the CMB temperature and the primordial curvature
perturbation. On smaller scales; however, the radiation transfer function
becomes more complex. In this paper, we present the angular bispectrum of the
primary CMB anisotropy that uses the full transfer function. We find that the
bispectrum has a series of acoustic peaks that change a sign, and a period of
acoustic oscillations is twice as long as that of the angular power spectrum.
Using a single non-linear coupling parameter to characterize the amplitude of
the bispectrum, we estimate the expected signal-to-noise ratio for COBE, MAP,
and Planck experiments. We find that the detection of the primary bispectrum by
any kind of experiments should be problematic for the simple slow-roll
inflationary scenarios. We compare the sensitivity of the primary bispectrum to
the primary skewness and conclude that when we can compute the predicted form
of the bispectrum, it becomes a ``matched filter'' for detecting the
non-Gaussianity in the data, and much more powerful tool than the skewness. We
also show that MAP and Planck can separate the primary bispectrum from various
secondary bispectra on the basis of the shape difference. The primary CMB
bispectrum is a test of the inflationary scenario, and also a probe of the
non-linear physics in the very early universe.


  We compute the Hamiltonian surface charges of gravity for a family of
conservative boundary conditions, that include Dirichlet, Neumann, and York's
mixed boundary conditions defined by holding fixed the conformal induced metric
and the trace of the extrinsic curvature. We show that for all boundary
conditions considered, canonical methods give the same answer as covariant
phase space methods improved by a boundary Lagrangian, a prescription recently
developed in the literature and thus supported by our results. The procedure
also suggests a new integrable charge for the Einstein-Hilbert Lagrangian,
different from the Komar charge for non-Killing and non-tangential
diffeomorphisms. We study how the energy depends on the choice of boundary
conditions, showing that both the quasi-local and the asymptotic expressions
are affected. Finally, we generalize the analysis to non-orthogonal corners,
confirm the matching between the covariant and canonical results without any
change in the prescription, and discuss the subtleties associated with this
case.


  As known all physical properties of solids are described well by the system
of quantum linear harmonic oscillators. It is shown in the present paper that
the system consisting of classical linear harmonic oscillators having
temperature dependent masses or (and) frequencies has the same partition
function as the system consisting of quantum linear harmonic oscillators having
temperature independent masses and frequencies while the means of the square
displacements of the positions of the oscillators from their mean positions for
the system consisting of classical linear harmonic oscillators having: the
temperature dependent masses; temperature dependent frequencies; and
temperature dependent masses and frequencies differ from each other and from
that of the system consisting of quantum linear harmonic oscillators, and
hence, the system consisting of classical linear harmonic oscillators describes
well the thermodynamic properties of the system consisting of quantum linear
harmonic oscillators and solids.


  The study of infall motion helps us to understand the initial stages of star
formation. In this paper, we use the IRAM 30-m telescope to make mapping
observations of 24 infall sources confirmed in previous work. The lines we use
to track gas infall motions are HCO+ (1-0) and H13CO+ (1-0). All 24 sources
show HCO+ emissions, while 18 sources show H13CO+ emissions. The HCO+
integrated intensity maps of 17 sources show clear clumpy structures; for the
H13CO+ line, 15 sources show clumpy structures. We estimated the column density
of HCO+ and H13CO+ using the RADEX radiation transfer code, and the obtained
[HCO+]/[H2] and [H13CO+]/[HCO+] of these sources are about 10^-11 ~ 10^-7 and
10^-3~1, respectively. Based on the asymmetry of the line profile of the HCO+,
we distinguish these sources: 19 sources show blue asymmetric profiles, and the
other sources show red profiles or symmetric peak profiles. For eight sources
that have double-peaked blue line profiles and signal-to-noise ratios greater
than 10, the RATRAN model is used to fit their HCO^+ (1-0) lines, and to
estimate their infall parameters. The mean Vin of these sources are 0.3 ~ 1.3
km/s, and the Min are about 10^-3 ~ 10^-4 Msun/yr , which are consistent with
the results of intermediate or massive star formation in previous studies. The
Vin estimated from the Myers model are 0.1 ~ 1.6 km/s, and the Min are within
10^-3 ~ 10^-5 Msun/yr. In addition, some identified infall sources show other
star-forming activities, such as outflows and maser emissions. Especially for
those sources with a double-peaked blue asymmetric profile, most of them have
both infall and outflow evidence.


  Resistive Random-Access Memory (RRAM) is well-suited to accelerate neural
network (NN) workloads as RRAM-based Processing-in-Memory (PIM) architectures
natively support highly-parallel multiply-accumulate (MAC) operations that form
the backbone of most NN workloads. Unfortunately, NN workloads such as
transformers require support for non-MAC operations (e.g., softmax) that RRAM
cannot provide natively. Consequently, state-of-the-art works either integrate
additional digital logic circuits to support the non-MAC operations or offload
the non-MAC operations to CPU/GPU, resulting in significant performance and
energy efficiency overheads due to data movement.
  In this work, we propose NEON, a novel compiler optimization to enable the
end-to-end execution of the NN workload in RRAM. The key idea of NEON is to
transform each non-MAC operation into a lightweight yet highly-accurate neural
network. Utilizing neural networks to approximate the non-MAC operations
provides two advantages: 1) We can exploit the key strength of RRAM, i.e.,
highly-parallel MAC operation, to flexibly and efficiently execute non-MAC
operations in memory. 2) We can simplify RRAM's microarchitecture by
eliminating the additional digital logic circuits while reducing the data
movement overheads. Acceleration of the non-MAC operations in memory enables
NEON to achieve a 2.28x speedup compared to an idealized digital logic-based
RRAM. We analyze the trade-offs associated with the transformation and
demonstrate feasible use cases for NEON across different substrates.


  We report on the measurements of strange hadron ($K^0_S$, $\Lambda$, $\Xi$)
production in the most central (0-20%) d+Au collisions at \sqrt{s_{NN}} = 200
GeV in STAR. Significant strangeness enhancement is observed in the most
central d+Au collisions, especially for multi-strange hyperons ($\Xi$). The
nuclear modification factor ($R_{dAu}$) of identified particles shows possible
particle type -- baryon (protons, $\Lambda$), meson ($\pi$,$K^0_S$, $\phi$) --
dependence at intermediate $p_T$ (2 - 3.5 GeV/c), indicating that the final
state may have an impact on the Cronin effect.


  We study the phase transition of the escape rate of exchange-coupled dimer of
single-molecule magnets which are coupled either ferromagnetic ally or
antiferromagnetically in a staggered magnetic field and an easy $z$-axis
anisotropy. The Hamiltonian for this system has been used to study molecular
dimer nanomagnets [Mn$_4$]$_2$. We generalize the method of mapping a
single-molecule magnetic spin problem onto a quantum-mechanical particle to
dimeric molecular nanomagnets. The problem is mapped to a single particle
quantum-mechanical Hamiltonian in terms of the relative coordinate and a
coordinate dependent reduced mass. It is shown that the presence of the
external staggered magnetic field creates a phase boundary separating the
first- from the second-order transition. With the set of parameters used by R.
Tiron, $\textit{et al}$, \prl {\bf 91}, 227203 (2003), and S. Hill, $\textit{et
al}$ science {\bf 302}, 1015 (2003) to fit experimental data for [Mn$_{4}$]$_2$
dimer we find that the critical temperature at the phase boundary is $T^{(c)}_0
=0.29K$. Therefore, thermally activated transitions should occur for
temperatures greater than $T^{(c)}_0$.


  The calculation of contributions from the chromomagnetic and
four-top-quark-operators within Standard Model Effective Field Theory (SMEFT)
to Higgs boson pair production in gluon fusion is presented, in combination
with NLO QCD corrections. Here we focus on the $\gamma_5$-scheme dependence
introduced by the four-top-quark-operators and the interplay with other
operators contributing to this process in SMEFT.


  A summary of some lines of ideas leading to model-independent frameworks of
relativistic quantum field theory is given. It is followed by a discussion of
the Reeh-Schlieder theorem and geometric modular action of Tomita-Takesaki
modular objects associated with the quantum field vacuum state and certain
algebras of observables. The distillability concept, which is significant in
specifying useful entanglement in quantum information theory, is discussed
within the setting of general relativistic quantum field theory.


  In this work we aim at understanding the effect of n-type and p-type
substitutional doping in the case of matrix-embedded and freestanding Si
nanocrystals. By means of ab-initio calculations we identify the preferential
positioning of the dopants and its effect on the structural properties with
respect to the undoped case. Subsequently, we consider the case of phosphorus
and boron co-doped nanocrystals showing that, against the single-doping
situation, the energetics strongly favors the binding of the impurities at the
NC surface. Indeed we demonstrate that the polar B-P bond forms a stable
permanent electric dipole that radially points inwards the nanocrystal. Such
noteworthy characteristic and its physical consequences are discussed alongside
new potential applications.


  We study the composition of the Galactic interstellar medium (ISM) toward the
Galactic center region (5 < |l| < 20 degree) by utilizing X-ray absorption
features of three bright low-mass X-ray binaries (LMXBs), GX 13+1, GX 5-1, and
GX 340+0, observed with the Chandra HETGS. We detect X-ray absorption fine
structure (XAFS) of the Si K-edge, characterized by a narrow and a broad
absorption feature at 1846 and ~1865 eV, respectively. Comparison with ground
experimental data indicates that most of the ISM Si exists in the form of
silicates, although a composition of "pure" forsterite is ruled out. The XAFS
spectra of the sulfur K-edge indicate that a significant fraction of S exists
in the gas phase. From each source, we derive the column densities of Mg, S,
Si, and Fe from the K-edge depth and that of O (or H) from the absorption of
the continuum. The elemental abundance ratios are found to be consistent
between the three targets: the mean values of O/Si, Mg/Si, S/Si, and Fe/Si are
determined to be 0.55+-0.17, 1.14+-0.13, 1.03+-0.12, and 0.97+-0.31 solar,
respectively (90% error in the mean value). We discuss the origins of the
overabundances of the heavy metals relative to O in the Galactic ISM by
comparison with the abundance pattern of the intracluster medium in clusters of
galaxies. Assuming that most of the Mg and Si atoms are depleted into silicates
of either the proxine or olivine family, we estimate that the number ratio of
Mg to Fe in olivine is >~1.2 and that 17%-43% of the total O atoms in the ISM
must be contained in silicate grains.


  Quantum walks are versatile simulators of topological phases and phase
transitions as observed in condensed matter physics. Here, we utilize a step
dependent coin in quantum walks and investigate what topological phases we can
simulate with it, their topological invariants, bound states and possibility of
phase transitions. These quantum walks simulate non-trivial phases
characterized by topological invariants (winding number) $\pm 1$ which are
similar to the ones observed in topological insulators and polyacetylene. We
confirm that the number of phases and their corresponding bound states increase
step dependently. In contrast, the size of topological phase and distance
between two bound states are decreasing functions of steps resulting into
formation of multiple phases as quantum walks proceed (multiphase
configuration). We show that, in the bound states, the winding number and group
velocity are ill-defined, and the second moment of the probability density
distribution in position space undergoes an abrupt change. Therefore, there are
phase transitions taking place over the bound states and between two
topological phases with different winding numbers.


  We consider an extension of the Next-to-Minimal Supersymmetric Standard Model
by three right-handed neutrinos and a pair of neutrinophilic Higgs superfields.
The small neutrino masses arise naturally from a small vacuum expectation value
of the additional Higgs fields (hence without lepton number violation), while
the lightest right-handed sneutrinos can constitute asymmetric Dark Matter. The
right-handed sneutrino and baryon asymmetries are connected through equilibrium
processes in the early universe, explaining the coincidence of the DM and
baryon abundances. We show that particle physics and astrophysical constraints
are satisfied.


  Although Faster R-CNN and its variants have shown promising performance in
object detection, they only exploit simple first-order representation of object
proposals for final classification and regression. Recent classification
methods demonstrate that the integration of high-order statistics into deep
convolutional neural networks can achieve impressive improvement, but their
goal is to model whole images by discarding location information so that they
cannot be directly adopted to object detection. In this paper, we make an
attempt to exploit high-order statistics in object detection, aiming at
generating more discriminative representations for proposals to enhance the
performance of detectors. To this end, we propose a novel Multi-scale
Location-aware Kernel Representation (MLKP) to capture high-order statistics of
deep features in proposals. Our MLKP can be efficiently computed on a modified
multi-scale feature map using a low-dimensional polynomial kernel
approximation.Moreover, different from existing orderless global
representations based on high-order statistics, our proposed MLKP is location
retentive and sensitive so that it can be flexibly adopted to object detection.
Through integrating into Faster R-CNN schema, the proposed MLKP achieves very
competitive performance with state-of-the-art methods, and improves Faster
R-CNN by 4.9% (mAP), 4.7% (mAP) and 5.0% (AP at IOU=[0.5:0.05:0.95]) on PASCAL
VOC 2007, VOC 2012 and MS COCO benchmarks, respectively. Code is available at:
https://github.com/Hwang64/MLKP.


  We report quasi-two-dimensional Dirac fermions and quantum magnetoresistance
in LaAgBi$_2$. The band structure shows several narrow bands with nearly linear
energy dispersion and Dirac-cone-like points at the Fermi level. The quantum
oscillation experiments revealed one quasi-two-dimensional Fermi pocket and
another complex pocket with small cyclotron resonant mass. The in-plane
transverse magnetoresistance exhibits a crossover at a critical field $B^*$
from semiclassical weak-field $B^2$ dependence to the high-field unsaturated
linear magnetoresistance which is attributed to the quantum limit of the Dirac
fermions. Our results suggest the existence of quasi 2D Dirac fermions in
rare-earth based layered compounds with two-dimensional double-sized Bi square
nets, similar to (Ca,Sr)MnBi$_{2}$, irrespective of magnetic order.


  For the simplest inflation models, the recent detection of a large primordial
B-mode polarization signal by the BICEP2 experiment indicates a slight tension
with the upper limit on the tensor-to-scalar ratio, $r$, from the Planck
satellite. Here, we discuss spatially varying $r$ as a possible explanation for
this discrepancy. This idea seems attractive since it may also explain part of
the hemispherical temperature power asymmetry seen by WMAP and Planck at large
angular scales. If these two aspects are indeed connected, the model suggests
that in the Northern hemisphere $r$ should be much smaller, a hypothesis that
could be confirmed with future B-mode experiments, providing a test for the
stationarity of primordial tensor contributions across the sky. The BICEP2
measurement furthermore rules out that a simple dipolar modulation of $r$ alone
can be responsible for the full hemispherical power asymmetry.


  The increased deployment of intermittent renewable energy generators opens up
opportunities for grid-connected energy storage. Batteries offer significant
flexibility but are relatively expensive at present. Battery lifetime is a key
factor in the business case, and it depends on usage, but most techno-economic
analyses do not account for this. For the first time, this paper quantifies the
annual benefits of grid-connected batteries including realistic physical
dynamics and nonlinear electrochemical degradation. Three lithium-ion battery
models of increasing realism are formulated, and the predicted degradation of
each is compared with a large-scale experimental degradation data set
(Mat4Bat). A respective improvement in RMS capacity prediction error from 11\%
to 5\% is found by increasing the model accuracy. The three models are then
used within an optimal control algorithm to perform price arbitrage over one
year, including degradation. Results show that the revenue can be increased
substantially while degradation can be reduced by using more realistic models.
The estimated best case profit using a sophisticated model is a 175%
improvement compared with the simplest model. This illustrates that using a
simplistic battery model in a techno-economic assessment of grid-connected
batteries might substantially underestimate the business case and lead to
erroneous conclusions.


  In this expository article, we describe the classification of the subalgebras
of the rank 2 semisimple Lie algebras. Their semisimple subalgebras are
well-known, and in a recent series of papers, we completed the classification
of the subalgebras of the classical rank 2 semisimple Lie algebras. Finally,
Mayanskiy finished the classification of the subalgebras of the remaining rank
2 semisimple Lie algebra, the exceptional Lie algebra $G_2$. We identify
subalgebras of the classification in terms of a uniform classification scheme
of Lie algebras of low dimension. The classification is up to inner
automorphism, and the ground field is the complex numbers.


  The high cosmological precision offered by the next generation of galaxy
surveys hinges on improved corrections for Galactic dust extinction. We explore
the possibility of estimating both the dust extinction and large-scale
structure from a single photometric galaxy survey, making use of the
predictable manner in which Milky Way dust affects the measured brightness and
colors of galaxies in a given sky location in several redshift bins. To test
our method, we use a synthetic catalog from a cosmological simulation designed
to model the Vera C. Rubin Observatory Legacy Survey of Space and Time. At high
Galactic latitude ($|b|\gtrsim20^\circ$) and a resolution of $1^\circ$ ($7'$),
we predict the uncertainty in the measurement of dust extinction, $E(B-V)$, to
be $0.005\ \mathrm{mag}$ ($0.015\ \mathrm{mag}$). This is similar to the
uncertainty of existing dust maps, illustrating the feasibility of our method.
Simultaneous estimation of large-scale structure is predicted to recover the
galaxy overdensity $\delta$ with a precision of $\sim0.01$ ($\sim0.05$) at
$1^\circ$ ($7'$) resolution. We also introduce a Bayesian formalism that
combines prior information from existing dust maps with the likelihood of
Galactic dust extinction determined from the excursion of observed galaxy
properties.


  Cross-domain analogical reasoning is a core creative ability that can be
challenging for humans. Recent work has shown some proofs-of concept of Large
language Models' (LLMs) ability to generate cross-domain analogies. However,
the reliability and potential usefulness of this capacity for augmenting human
creative work has received little systematic exploration. In this paper, we
systematically explore LLMs capacity to augment cross-domain analogical
reasoning. Across three studies, we found: 1) LLM-generated cross-domain
analogies were frequently judged as helpful in the context of a problem
reformulation task (median 4 out of 5 helpfulness rating), and frequently (~80%
of cases) led to observable changes in problem formulations, and 2) there was
an upper bound of 25% of outputs bring rated as potentially harmful, with a
majority due to potentially upsetting content, rather than biased or toxic
content. These results demonstrate the potential utility -- and risks -- of
LLMs for augmenting cross-domain analogical creativity.


  A mid-Infrared frequency comb is produced via an optical parametric
oscillator (OPO) pumped by an amplified 100 MHz Yb:fiber mode-locked laser. We
use this source to make measurements of the concentration of the
atmospherically-relevant species of CH4 and H2O over a bandwidth of 100 nm
centered at 3.25 um. Multiple absorption lines for each species are detected
with millisecond acquisition time using a virtually-imaged phased array (VIPA)
spectrometer. The measured wavelength-dependent absorption profile is compared
to and fitted by a model, yielding quantitative values of the atmospheric
concentration of both CH4 and H2O in a controlled indoor environment, as well
as over a 26 m open air outdoor path.


  Phosphorene, the single- or few-layer form of black phosphorus, was recently
rediscovered as a twodimensional layered material holding great promise for
applications in electronics and optoelectronics. Research into its fundamental
properties and device applications has since seen exponential growth. In this
Perspective, we review recent progress in phosphorene research, touching upon
topics on fabrication, properties, and applications; we also discuss challenges
and future research directions. We highlight the intrinsically anisotropic
electronic, transport, optoelectronic, thermoelectric, and mechanical
properties of phosphorene resulting from its puckered structure in contrast to
those of graphene and transition-metal dichalcogenides. The facile fabrication
and novel properties of phosphorene have inspired design and demonstration of
new nanodevices; however, further progress hinges on resolutions to technical
obstructions like surface degradation effects and non-scalable fabrication
techniques. We also briefly describe the latest developments of more
sophisticated design concepts and implementation schemes that address some of
the challenges in phosphorene research. It is expected that this fascinating
material will continue to offer tremendous opportunities for research and
development for the foreseeable future.


  Rare gas or metal clusters are known to absorb laser energy very efficiently.
Upon cluster expansion the Mie plasma frequency may become equal to the laser
frequency. This linear resonance has been well studied both experimentally and
theoretically employing pump probe schemes. In this work we focus on the
few-cycle regime or the early stage of the cluster dynamics where linear
resonance is not met but nevertheless efficient absorption of laser energy
persists. By retrieving time-dependent oscillator frequencies from
particle-in-cell simulation results, we show that nonlinear resonance is the
dominant mechanism behind outer ionization and energy absorption in near
infrared laser-driven clusters.


  We study R\'enyi entropy of locally excited states with considering the
thermal and boundary effects respectively in two dimensional conformal field
theories (CFTs). Firstly we consider locally excited states obtained by acting
primary operators on a thermal state in low temperature limit. The R\'enyi
entropy is summation of contribution from thermal effect and local excitation.
Secondly, we mainly study the R\'enyi entropy of locally excited states in 2D
CFT with a boundary. We show that the evolution of R\'enyi entropy does not
depend on the choice of boundary conditions and boundary will change the time
evolution of R\'enyi entropy. Moreover, in 2D rational CFTs with a boundary, we
show that the R\'enyi entropy always coincides with the log of quantum
dimension of the primary operator during some periods of the evolution. We make
use of a quasi-particle picture to understand this phenomenon. In terms of
quasi-particle interpretation, the boundary behaves as an infinite potential
barrier which reflects any energy moving towards the boundary.


  Online reviews in the form of user-generated content (UGC) significantly
impact consumer decision-making. However, the pervasive issue of not only human
fake content but also machine-generated content challenges UGC's reliability.
Recent advances in Large Language Models (LLMs) may pave the way to fabricate
indistinguishable fake generated content at a much lower cost. Leveraging
OpenAI's GPT-4-Turbo and DALL-E-2 models, we craft AiGen-FoodReview, a
multi-modal dataset of 20,144 restaurant review-image pairs divided into
authentic and machine-generated. We explore unimodal and multimodal detection
models, achieving 99.80% multimodal accuracy with FLAVA. We use attributes from
readability and photographic theories to score reviews and images,
respectively, demonstrating their utility as hand-crafted features in scalable
and interpretable detection models, with comparable performance. The paper
contributes by open-sourcing the dataset and releasing fake review detectors,
recommending its use in unimodal and multimodal fake review detection tasks,
and evaluating linguistic and visual features in synthetic versus authentic
data.


  Quantum mechanics has led not only to new physical theories, but also a new
understanding of information and computation. Quantum information began by
yielding new methods for achieving classical tasks such as factoring and key
distribution but also suggests a completely new set of quantum problems, such
as sending quantum information over quantum channels or efficiently performing
particular basis changes on a quantum computer. This thesis contributes two
new, purely quantum, tools to quantum information theory--coherent classical
communication in the first half and an efficient quantum circuit for the Schur
transform in the second half.


  Recent advancements in the Neural Radiance Field (NeRF) have bolstered its
capabilities for novel view synthesis, yet its reliance on dense multi-view
training images poses a practical challenge. Addressing this, we propose
HourglassNeRF, an effective regularization-based approach with a novel
hourglass casting strategy. Our proposed hourglass is conceptualized as a
bundle of additional rays within the area between the original input ray and
its corresponding reflection ray, by featurizing the conical frustum via
Integrated Positional Encoding (IPE). This design expands the coverage of
unseen views and enables an adaptive high-frequency regularization based on
target pixel photo-consistency. Furthermore, we propose luminance consistency
regularization based on the Lambertian assumption, which is known to be
effective for training a set of augmented rays under the few-shot setting.
Leveraging the inherent property of a Lambertian surface, which retains
consistent luminance irrespective of the viewing angle, we assume our proposed
hourglass as a collection of flipped diffuse reflection rays and enhance the
luminance consistency between the original input ray and its corresponding
hourglass, resulting in more physically grounded training framework and
performance improvement. Our HourglassNeRF outperforms its baseline and
achieves competitive results on multiple benchmarks with sharply rendered fine
details. The code will be available.


  In this paper, we prove the asymptotic stability of the family of solitons of
the Benjamin-Ono equation in the energy space. The proof is based on a
Liouville property for solutions close to the solitons for this equation, in
the spirit of Martel and Merle (arXiv:0706.1174v2). As a corollary of the
proofs, we obtain the asymptotic stability of exact multi-solitons.


  Many Machine Learning algorithms are formulated as regularized optimization
problems, but their performance hinges on a regularization parameter that needs
to be calibrated to each application at hand. In this paper, we propose a
general calibration scheme for regularized optimization problems and apply it
to the graphical lasso, which is a method for Gaussian graphical modeling. The
scheme is equipped with theoretical guarantees and motivates a thresholding
pipeline that can improve graph recovery. Moreover, requiring at most one line
search over the regularization path, the calibration scheme is computationally
more efficient than competing schemes that are based on resampling. Finally, we
show in simulations that our approach can improve on the graph recovery of
other approaches considerably.


  We present a synthesis framework to map logic networks into quantum circuits
for quantum computing. The synthesis framework is based on LUT networks
(lookup-table networks), which play a key role in conventional logic synthesis.
Establishing a connection between LUTs in a LUT network and reversible
single-target gates in a reversible network allows us to bridge conventional
logic synthesis with logic synthesis for quantum computing, despite several
fundamental differences. We call our synthesis framework LUT-based Hierarchical
Reversible Logic Synthesis (LHRS). Input to LHRS is a classical logic network;
output is a quantum network (realized in terms of Clifford+$T$ gates). The
framework offers to trade-off the number of qubits for the number of quantum
gates. In a first step, an initial network is derived that only consists of
single-target gates and already completely determines the number of qubits in
the final quantum network. Different methods are then used to map each
single-target gate into Clifford+$T$ gates, while aiming at optimally using
available resources. We demonstrate the effectiveness of our method in
automatically synthesizing IEEE compliant floating point networks up to double
precision. As many quantum algorithms target scientific simulation
applications, they can make rich use of floating point arithmetic components.
But due to the lack of quantum circuit descriptions for those components, it
can be difficult to find a realistic cost estimation for the algorithms. Our
synthesized benchmarks provide cost estimates that allow quantum algorithm
designers to provide the first complete cost estimates for a host of quantum
algorithms. Thus, the benchmarks and, more generally, the LHRS framework are an
essential step towards the goal of understanding which quantum algorithms will
be practical in the first generations of quantum computers.


  If one takes naturalness seriously and also assumes a weakly coupled
extension of the Standard Model (SM) then there are predictions for
phenomenology that can be inferred in a model independent framework. The first
such prediction is that there must be some colored particle with mass O(TeV)
that cancels the top loop contribution to the quadratic divergence of the Higgs
mass. In this paper we begin a model independent analysis of the phenomenology
of this "top partner," t'. We make one additional assumption that it is odd
under a parity which is responsible for the stability of a WIMP dark matter
candidate, N. We focus on three questions to be explored at the LHC: discovery
opportunities, mass determination, and spin determination of this top partner.
We find that within a certain region of masses for the t' and N, t'\bar{t'} is
easily discovered in the t\bar{t}+2N decay with the tops decaying fully
hadronically. We show that without having to rely on other channels for new
physics that for a a given t' spin the masses of t' and N can be measured using
kinematic information (e.g. average MET or H_T) and total cross section. A
degeneracy due to the spin remains, but with several hundred inverse fb of
luminosity we demonstrate potentially useful new methods for determining the t'
spin over a wide range of masses. Our methods could be useful for
distinguishing supersymmetric and non-supersymmetric models.


  The organizations and researchers producing research software face a common
problem of making their software sustainable beyond funding provided by a
single research project. This is addressed by research software engineers
through building communities around their software, providing appropriate
licensing, creating reliable and reproducible research software, making it
sustainable and impactful, promoting, and ensuring that the research software
is easy to adopt in research workflows, etc. As a result, numerous practices
and guidelines exist to enhance research software quality, reusability, and
sustainability. However, there is a lack of a unified framework to
systematically integrate these practices and help organizations and research
software developers refine their development and management processes. Our
paper aims at bridging this gap by introducing a novel framework: RSMM. It is
designed through systematic literature review and insights from interviews with
research software project experts. In short, RSMM offers a structured pathway
for evaluating and refining research software project management by
categorizing 79 best practices into 17 capabilities across 4 focus areas. From
assessing code quality and security to measuring impact, sustainability, and
reproducibility, the model provides a complete evaluation of a research
software project maturity. With RSMM, individuals as well as organizations
involved in research software development gain a systematic approach to
tackling various research software engineering challenges. By utilizing RSMM as
a comprehensive checklist, organizations can systematically evaluate and refine
their project management practices and organizational structure.


  Recent frequency-modulated atomic force microscopy (FM-AFM) can measure
three-dimensional force distribution between a probe and a sample surface in
liquid. The force distribution is, in the present circumstances, assumed to be
solvation structure on the sample surface, because the force distribution and
solvation structure have somewhat similar shape. However, the force
distribution is exactly not the solvation structure. If we would like to obtain
the solvation structure by using the liquid AFM, a method for transforming the
force distribution into the solvation structure is necessary. Therefore, in
this letter, we present the transforming method in a brief style. We call this
method as a solution for an inverse problem, because the solvation structure is
obtained at first and the force distribution is obtained next in general
calculation processes. The method is formulated (mainly) by statistical
mechanics of liquid.


  The in-medium modification to the neutrino-nucleon scattering rate is
calculated in the relativistic random phase approximation in the framework of a
hadronic meson exchange model, in view of applications to neutrino transport in
supernovae and protoneutron stars


  We have studied Shubnikov-de Haas oscillations in a two-dimensional electron
system in silicon at low electron densities. Near the metal-insulator
transition, only "spin" minima of the resistance at Landau-level filling
factors 2, 6, 10, and 14 are seen, while the "cyclotron" minima at filling
factors 4, 8, and 12 disappear. A simple explanation of the observed behavior
requires a giant enhancement of the spin splitting near the metal-insulator
transition.


  We propose a model for neutrino mass generation in wich no physics beyond a
TeV is required. We extend the standard model by adding two charged singlet
fields with lepton number two. Dirac neutrino masses $m_{\nu_D} \leq MeV$ are
generated at the one loop level. Small left handed majorana neutrino masses can
be generated via the seesaw mechanism with right handed neutrino masses $M_R$
are of order TeV scale.


  The Atacama Large Millimeter/submillimeter Array (ALMA) serendipitously
detected H$_2$O $J_{Ka, Kc} = 10_{2,9} - 9_{3,6}$ emission at 321 GHz in NGC
1052. This is the first submillimeter maser detection in a radio galaxy and the
most luminous 321-GHz H$_2$O maser known to date with the isotropic luminosity
of 1090 $L_{\odot}$. The line profile consists of a broad velocity component
with FWHM $= 208 \pm 12$ km s$^{-1}$ straddling the systemic velocity and a
narrow component with FWHM $= 44 \pm 3$ km s$^{-1}$ blueshifted by 160 km
s$^{-1}$. The profile is significantly different from the known 22-GHz $6_{1,6}
- 5_{2,3}$ maser which shows a broad profile redshifted by 193 km s$^{-1}$. The
submillimeter maser is spatially unresolved with a synthesized beam of
$0^{\prime \prime}.68 \times 0^{\prime \prime}.56$ and coincides with the
continuum core position within 12 pc. These results indicate amplification of
the continuum emission through high-temperature ($>1000$ K) and dense ($n({\rm
H}_2{\rm O}) > 10^4$ cm$^{-3}$) molecular gas in front of the core


  We use angle-resolved photoemission spectroscopy (ARPES) to investigate the
electronic properties of the newly discovered oxypnictide superconductor,
NdFeAsO_{1-x}F_x. We find a well-defined Fermi surface that consists of a large
hole pocket at the Brillouin zone center and a smaller electron pocket in each
corner of the Brillouin zone. The overall location and shape of the Fermi
surface agrees reasonably well with calculations. The band dispersion is quite
complicated with many flat bands located just below the chemical potential. We
observe a superconducting gap of 20 meV, which indicates that this system is in
the strong coupling regime. The emergence of a coherent peak below the critical
temperature Tc and diminished spectral weight at the chemical potential above
Tc closely resembles the spectral characteristics of the cuprates.


  We describe recent work connecting combinatorics and tropical/non-Archimedean
geometry to Diophantine geometry, particularly the uniformity conjectures for
rational points on curves and for torsion packets of curves. The method of
Chabauty--Coleman lies at the heart of this connection, and we emphasize the
clarification that tropical geometry affords throughout the theory of $p$-adic
integration, especially to the comparison of analytic continuations of $p$-adic
integrals and to the analysis of zeros of integrals on domains admitting
monodromy.


  In view of the trend towards extensive application of digital controllers in
the PV inverter of large-scale centralized photovoltaic (LSCPV) plant and the
increasing number of grid-connected LSCPV plants, this paper investigates in
detail the influence of the digital time delay of the inverter digital
controller on the stable grid-hosting capacity of LSCPV plant. The studies are
based on the Norton equivalent model of the grid-connected LSCPV system
considering the digital time delay when modelling the digital control system of
the PV inverter. Taking into account the actual situation in LSCPV plant, the
stable grid-hosting capacity of LSCPV is discussed for the cases in which the
PV inverters in the LSCPV plant have the same and different digital time delay
values by using the root-locus method. Simulation results of the digitally
controlled grid-connected LSCPV system model validates the theoretical
analysis.


  In this paper we propose a novel geometric method, based on singular
perturbations, to approximate isochrones of relaxation oscillators and predict
the qualitative shape of their (finite) phase response curve. This approach
complements the infinitesimal phase response curve approach to relaxation
oscillators and overcomes its limitations near the singular limit. We
illustrate the power of the methodology by deriving semi-analytic formula for
the (finite) phase response curve of generic planar relaxation oscillators to
impulses and square pulses of finite duration and verify its goodness
numerically on the FitzHugh-Nagumo model.


  Using the Cryogenic, High-Accuracy Refraction Measuring System (CHARMS) at
NASA's Goddard Space Flight Center, we have measured the absolute refractive
index of five specimens taken from a very large boule of Corning 7980 fused
silica from temperatures ranging from 30 to 310 K at wavelengths from 0.4 to
2.6 microns with an absolute uncertainty of +/-1 x 10^-5. Statistical
variations in derived values of the thermo-optic coefficient (dn/dT) are at the
+/-2 x 10^-8/K level. Graphical and tabulated data for absolute refractive
index, dispersion, and thermo-optic coefficient are presented for selected
wavelengths and temperatures along with estimates of uncertainty in index.
Coefficients for temperature-dependent Sellmeier fits of measured refractive
index are also presented to allow accurate interpolation of index to other
wavelengths and temperatures. We compare our results to those from an
independent investigation (which used an interferometric technique for
measuring index changes as a function of temperature) whose samples were
prepared from the same slugs of material from which our prisms were prepared in
support of the Kepler mission. We also compare our results with sparse
cryogenic index data from measurements of this material from the literature.


  Continuous long-term monitoring of motor health is crucial for the early
detection of abnormalities such as bearing faults (up to 51% of motor failures
are attributed to bearing faults). Despite numerous methodologies proposed for
bearing fault detection, most of them require normal (healthy) and abnormal
(faulty) data for training. Even with the recent deep learning (DL)
methodologies trained on the labeled data from the same machine, the
classification accuracy significantly deteriorates when one or few conditions
are altered. Furthermore, their performance suffers significantly or may
entirely fail when they are tested on another machine with entirely different
healthy and faulty signal patterns. To address this need, in this pilot study,
we propose a zero-shot bearing fault detection method that can detect any fault
on a new (target) machine regardless of the working conditions, sensor
parameters, or fault characteristics. To accomplish this objective, a 1D
Operational Generative Adversarial Network (Op-GAN) first characterizes the
transition between normal and fault vibration signals of (a) source machine(s)
under various conditions, sensor parameters, and fault types. Then for a target
machine, the potential faulty signals can be generated, and over its actual
healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN
fault detector can then be trained to detect the real faulty condition in real
time whenever it occurs. To validate the proposed approach, a new benchmark
dataset is created using two different motors working under different
conditions and sensor locations. Experimental results demonstrate that this
novel approach can accurately detect any bearing fault achieving an average
recall rate of around 89% and 95% on two target machines regardless of its
type, severity, and location.


  The power consumption of a microprocessor is a huge channel for information
leakage. While the most popular exploitation of this channel is to recover
cryptographic keys from embedded devices, other applications such as mobile app
fingerprinting, reverse engineering of firmware, and password recovery are
growing threats. Countermeasures proposed so far are tuned to specific
applications, such as crypto-implementations. They are not scalable to the
large number and variety of applications that typically run on a general
purpose microprocessor.
  In this paper, we investigate the design of a microprocessor, called PARAM
with increased resistance to power based side-channel attacks. To design PARAM,
we start with identifying the most leaking modules in an open-source RISC V
processor. We evaluate the leakage in these modules and then add suitable
countermeasures. The countermeasures depend on the cause of leakage in each
module and can vary from simple modifications of the HDL code ensuring secure
translation by the EDA tools, to obfuscating data and address lines thus
breaking correlation with the processor's power consumption. The resultant
processor is instantiated on the SASEBO-GIII FPGA board and found to resist
Differential Power Analysis even after one million power traces. Compared to
contemporary countermeasures for power side-channel attacks, overheads in area
and frequency are minimal.


  The properties of current-carrying steady states of strongly correlated
systems away from the linear-response regime are of topical interest. In this
article, we review the renormalized perturbation theory, or renormalized SPT of
reference [1] for the Anderson model. We present an extension to higher orders
and compare the higher-order results with NRG calculations. Finally, we
elucidate the role of Ward identities in calculating out-of-equilibrium
properties and address claims made in the literature.


  Broadband quantum light is a vital resource for quantum metrology and
spectroscopy applications such as quantum optical coherence tomography or
entangled two photon absorption. For entangled two photon absorption in
particular, very high photon flux combined with high time-frequency
entanglement is crucial for observing a signal. So far these conditions could
be met by using high power lasers driving degenerate, type 0 bulk-crystal
spontaneous parametric down conversion (SPDC) sources. This naturally limits
the available wavelength ranges and precludes deterministic splitting of the
generated output photons. In this work we demonstrate an integrated two-colour
SPDC source utilising a group-velocity matched lithium niobate waveguide,
reaching both exceptional brightness
$1.52\cdot10^6\frac{\mathrm{pairs}}{\mathrm{s\,mW\,GHz}}$ and large bandwidth
($7.8\,$THz FWHM) while pumped with a few mW of continuous wave (CW) laser
light. By converting a narrow band pump to broadband pulses the created photon
pairs show correlation times of $\Delta \tau \approx 120\,\text{fs}$ while
maintaining the narrow bandwidth $\Delta \omega_p \ll 1\,\text{MHz}$ of the CW
pump light, yielding strong time-frequency entanglement. Furthermore our
process can be adapted to a wide range of central wavelengths.


  We propose to synthesize feasible caging grasps for a target object through
computing Caging Loops, a closed curve defined in the shape embedding space of
the object. Different from the traditional methods, our approach decouples
caging loops from the surface geometry of target objects through working in the
embedding space. This enables us to synthesize caging loops encompassing
multiple topological holes, instead of always tied with one specific handle
which could be too small to be graspable by the robot gripper. Our method
extracts caging loops through a topological analysis of the distance field
defined for the target surface in the embedding space, based on a rigorous
theoretical study on the relation between caging loops and the field topology.
Due to the decoupling, our method can tolerate incomplete and noisy surface
geometry of an unknown target object captured on-the-fly. We implemented our
method with a robotic gripper and demonstrate through extensive experiments
that our method can synthesize reliable grasps for objects with complex surface
geometry and topology and in various scales.


  In this work we propose a novel model-based deep convolutional autoencoder
that addresses the highly challenging problem of reconstructing a 3D human face
from a single in-the-wild color image. To this end, we combine a convolutional
encoder network with an expert-designed generative model that serves as
decoder. The core innovation is our new differentiable parametric decoder that
encapsulates image formation analytically based on a generative model. Our
decoder takes as input a code vector with exactly defined semantic meaning that
encodes detailed face pose, shape, expression, skin reflectance and scene
illumination. Due to this new way of combining CNN-based with model-based face
reconstruction, the CNN-based encoder learns to extract semantically meaningful
parameters from a single monocular input image. For the first time, a CNN
encoder and an expert-designed generative model can be trained end-to-end in an
unsupervised manner, which renders training on very large (unlabeled) real
world data feasible. The obtained reconstructions compare favorably to current
state-of-the-art approaches in terms of quality and richness of representation.


  In this white paper for the Snowmass process, we discuss the prospects of
probing new physics explanations of the persistent rare $B$ decay anomalies
with a muon collider. If the anomalies are indirect signs of heavy new physics,
non-standard rates for $\mu^+ \mu^- \to b s$ production should be observed with
high significance at a muon collider with center of mass energy of $\sqrt{s} =
10$ TeV. The forward-backward asymmetry of the $b$-jet provides diagnostics of
the chirality structure of the new physics couplings. In the absence of a
signal, $\mu^+ \mu^- \to b s$ can indirectly probe new physics scales as large
as $86$ TeV. Beam polarization would have an important impact on the new
physics sensitivity.


  Assessment of mental workload in real-world conditions is key to ensure the
performance of workers executing tasks that demand sustained attention.
Previous literature has employed electroencephalography (EEG) to this end
despite having observed that EEG correlates of mental workload vary across
subjects and physical strain, thus making it difficult to devise models capable
of simultaneously presenting reliable performance across users. Domain
adaptation consists of a set of strategies that aim at allowing for improving
machine learning systems performance on unseen data at training time. Such
methods, however, might rely on assumptions over the considered data
distributions, which typically do not hold for applications of EEG data.
Motivated by this observation, in this work we propose a strategy to estimate
two types of discrepancies between multiple data distributions, namely marginal
and conditional shifts, observed on data collected from different subjects.
Besides shedding light on the assumptions that hold for a particular dataset,
the estimates of statistical shifts obtained with the proposed approach can be
used for investigating other aspects of a machine learning pipeline, such as
quantitatively assessing the effectiveness of domain adaptation strategies. In
particular, we consider EEG data collected from individuals performing mental
tasks while running on a treadmill and pedaling on a stationary bike and
explore the effects of different normalization strategies commonly used to
mitigate cross-subject variability. We show the effects that different
normalization schemes have on statistical shifts and their relationship with
the accuracy of mental workload prediction as assessed on unseen participants
at training time.


  Overlap-free words are words over the binary alphabet $A=\{a, b\}$ that do
not contain factors of the form $xvxvx$, where $x \in A$ and $v \in A^*$. We
analyze the asymptotic growth of the number $u_n$ of overlap-free words of
length $n$ as $ n \to \infty$. We obtain explicit formulas for the minimal and
maximal rates of growth of $u_n$ in terms of spectral characteristics (the
lower spectral radius and the joint spectral radius) of certain sets of
matrices of dimension $20 \times 20$. Using these descriptions we provide new
estimates of the rates of growth that are within 0.4% and $0.03 %$ of their
exact values. The best previously known bounds were within 11% and 3%
respectively. We then prove that the value of $u_n$ actually has the same rate
of growth for ``almost all'' natural numbers $n$. This ``average'' growth is
distinct from the maximal and minimal rates and can also be expressed in terms
of a spectral quantity (the Lyapunov exponent). We use this expression to
estimate it. In order to obtain our estimates, we introduce new algorithms to
compute spectral characteristics of sets of matrices. These algorithms can be
used in other contexts and are of independent interest.


  The growing use of deep learning in safety-critical applications, such as
medical imaging, has raised concerns about limited labeled data, where this
demand is amplified as model complexity increases, posing hurdles for domain
experts to annotate data. In response to this, active learning (AL) is used to
efficiently train models with limited annotation costs. In the context of deep
neural networks (DNNs), AL often uses confidence or probability outputs as a
score for selecting the most informative samples. However, modern DNNs exhibit
unreliable confidence outputs, making calibration essential. We propose an AL
framework that self-calibrates the confidence used for sample selection during
the training process, referred to as Confident Active Learning with Integrated
CalibratiOn (CALICO). CALICO incorporates the joint training of a classifier
and an energy-based model, instead of the standard softmax-based classifier.
This approach allows for simultaneous estimation of the input data distribution
and the class probabilities during training, improving calibration without
needing an additional labeled dataset. Experimental results showcase improved
classification performance compared to a softmax-based classifier with fewer
labeled samples. Furthermore, the calibration stability of the model is
observed to depend on the prior class distribution of the data.


  Let $G$ be a graph and $g,f:V(G)\to2^N$ be two set functions such that
$g(v)\le f(v)$ and $g(v)\equiv f(v)\pmod 2$ for every $v\in V(G)$. An
orientation $O$ of $G$ is called a $(g,f)$-parity orientation if $g(v)\le
d^+_O(v)\le f(v)$ and $g(v)\equiv d^+_O(v)\pmod 2$ for every $v\in V(G)$. In
this paper, we give a Tutte-type characterization for a graph to have a
$(g,f)$-parity orientation.


  We propose different ways of lifting the notion of Dyck language from words
to 2-dimensional (2D) pictures, by means of new definitions of increasing
comprehensiveness. Two of the proposals are based on alternative definitions of
a Dyck language, which are equivalent over words but not on pictures. First,
the property that any two pairs of matching parentheses are well-nested or
disjoint is rephrased for rectangular boxes and leads to the well-nested Dyck,
$DW_k$. This is a generalization of the known Chinese box language, but, unlike
the Chinese boxes, $DW_k$ is not recognizable by a tiling system. Second, the
Dyck cancellation rule is rephrased as a neutralization rule, mapping a
quadruple of symbols representing the corners of a subpicture onto neutral
symbols.The neutralizable Dyck language $DN_k$ is obtained by iterating
neutralizations, starting from 2-by-2 subpictures, until the picture is wholly
neutralized. Third, we define the Dyck crossword $DC_k$ as the row-column
combination of Dyck word languages, which prescribes that each column and row
is a Dyck word. The relation between matching parentheses is represented in
$DC_k$ by an edge of a graph situated on the picture grid. Such edges form a
circuit, of path length multiple of four, of alternating row and column
matches. Length-four circuits have rectangular shape, while longer ones exhibit
a large variety of forms. A proper subset of $DC_k$, called quaternate, is also
introduced by excluding all circuits of length greater than 4. We prove that
$DN_k$ properly includes $DW_k$, and that it coincides with the quaternate
$DC_k$ such that the neutralizability relation between subpictures induces a
partial order. The 2D languages well-nested, neutralizable, quaternate and Dyck
crossword are ordered by strict inclusions. This work can be also seen as a
first step towards the definition of context-free picture languages.


  Deep pre-trained contextualized encoders like BERT (Delvin et al., 2019)
demonstrate remarkable performance on a range of downstream tasks. A recent
line of research in probing investigates the linguistic knowledge implicitly
learned by these models during pre-training. While most work in probing
operates on the task level, linguistic tasks are rarely uniform and can be
represented in a variety of formalisms. Any linguistics-based probing study
thereby inevitably commits to the formalism used to annotate the underlying
data. Can the choice of formalism affect probing results? To investigate, we
conduct an in-depth cross-formalism layer probing study in role semantics. We
find linguistically meaningful differences in the encoding of semantic role-
and proto-role information by BERT depending on the formalism and demonstrate
that layer probing can detect subtle differences between the implementations of
the same linguistic formalism. Our results suggest that linguistic formalism is
an important dimension in probing studies, along with the commonly used
cross-task and cross-lingual experimental settings.


  An enhanced concentration of 60Fe was found in a deep ocean's crust in 2004
in a layer corresponding to an age of ~2 Myr. The confirmation of this signal
in terrestrial archives as supernova-induced and detection of other
supernova-produced radionuclides is of great interest. We have identified two
suitable marine sediment cores from the South Australian Basin and estimated
the intensity of a possible signal of the supernova-produced radionuclides
26Al, 53Mn, 60Fe and the pure r-process element 244Pu in these cores. A finding
of these radionuclides in a sediment core might allow to improve the time
resolution of the signal and thus to link the signal to a supernova event in
the solar vicinity ~2 Myr ago. Furthermore, it gives an insight on
nucleosynthesis scenarios in massive stars, the condensation into dust grains
and transport mechanisms from the supernova shell into the solar system.


  This note provides an interim summary of the current recommendations of the
PDF4LHC working group for the use of parton distribution functions (PDFs) and
of PDF uncertainties at the LHC, for cross section and cross section
uncertainty calculations. It also contains a succinct user guide to the
computation of PDF uncertainties and correlations using available PDF sets.
  A companion note (the PDF4LHC Working Group Interim Report) summarizes
predictions for benchmark cross sections at the LHC (7 TeV) at NLO using modern
PDFs currently available from 6 PDF fitting groups.


  Electric vehicles (EVs) provide a cleaner alternative that not only reduces
greenhouse gas emissions but also improves air quality and reduces noise
pollution. The consumer market for electrical vehicles is growing very rapidly.
Designing a network with adequate capacity and types of public charging
stations is a challenge that needs to be addressed to support the current trend
in the EV market. In this research, we propose a choice modeling approach
embedded in a two-stage stochastic programming model to determine the optimal
layout and types of EV supply equipment for a community while considering
randomness in demand and drivers' behaviors. Some of the key random data
parameters considered in this study are: EV's dwell time at parking {\sv
location}, battery's state of charge, distance from home, willingness to walk,
drivers' arrival patterns, and traffic on weekdays and weekends. The two-stage
model uses the sample average approximation method, which asymptotically
converges to an optimal solution. To address the computational challenges for
large-scale instances, we propose an outer approximation decomposition
algorithm. We conduct extensive computational experiments to quantify the
efficacy of the proposed approach. In addition, we present the results and a
sensitivity analysis for a case study based on publicly available data sources.


  Software vulnerabilities enable exploitation by malicious hackers,
compromising systems and data security. This paper examines bug bounty programs
(BBPs) that incentivize ethical hackers to discover and responsibly disclose
vulnerabilities to software vendors. Using game-theoretic models, we capture
the strategic interactions between software vendors, ethical hackers, and
malicious hackers. First, our analysis shows that software vendors can increase
expected profits by participating in BBPs, explaining their growing adoption
and the success of BBP platforms. Second, we find that vendors with BBPs will
release software earlier, albeit with more potential vulnerabilities, as BBPs
enable coordinated vulnerability disclosure and mitigation. Third, the optimal
number of ethical hackers to invite to a BBP depends solely on the expected
number of malicious hackers seeking exploitation. This optimal number of
ethical hackers is lower than but increases with the expected malicious hacker
count. Finally, higher bounties incentivize ethical hackers to exert more
effort, thereby increasing the probability that they will discover severe
vulnerabilities first while reducing the success probability of malicious
hackers. These findings highlight BBPs' potential benefits for vendors beyond
profitability. Earlier software releases are enabled by managing risks through
coordinated disclosure. As cybersecurity threats evolve, BBP adoption will
likely gain momentum, providing vendors with a valuable tool for enhancing
security posture and stakeholder trust. Moreover, BBPs envelop vulnerability
identification and disclosure into new market relationships and transactions,
impacting software vendors' incentives regarding product security choices like
release timing.


  Grant-free access is considered as a key enabler to massive machine-type
communications (mMTC) as it promotes energy-efficiency and small signalling
overhead. Due to the sporadic user activity in mMTC, joint user identification
and channel estimation (JUICE) is a main challenge. This paper addresses the
JUICE in single-cell mMTC with single-antenna users and a multi-antenna base
station (BS) under spatially correlated fading channels. In particular, by
leveraging the sporadic user activity, we solve the JUICE in a multi
measurement vector compressed sensing (CS) framework under two different cases,
with and without the knowledge of prior channel distribution information (CDI)
at the BS. First, for the case without prior information, we formulate the
JUICE as an iterative reweighted $\ell_{2,1}$-norm minimization problem.
Second, when the CDI is known to the BS, we exploit the available information
and formulate the JUICE from a Bayesian estimation perspective as a maximum
\emph{a posteriori} probability (MAP) estimation problem. For both JUICE
formulations, we derive efficient iterative solutions based on the alternating
direction method of multipliers (ADMM). The numerical experiments show that the
proposed solutions achieve higher channel estimation quality and activity
detection accuracy with shorter pilot sequences compared to existing
algorithms.


  SU(2) gauge theory in the nonlinear gauge of the Curci-Ferrari type is
studied in low-energy region. We give a classical solution that connects color
electric charges. Its dual solution, which has a configuration of monopole, is
also presented. Due to the gluon condensation subsequent to the ghost
condensation, these classical fields become massive. The massive Lagrangian
with the classical solution and that with the dual solution are derived. We
show that these Lagrangians produce a linear potential between a quark and an
antiquark. This is the mechanism of quark confinement that is different from
the magnetic monopole condensation.


  We construct an extension of the Poincare group which involves a mixture of
internal and space-time supersymmetries. The resulting group is an extension of
the superPoincare group with infinitely many generators which carry internal
and space-time indices. It is a closed algebra since all Jacobi identities are
satisfied and it has therefore explicit matrix representations. We investigate
the massless case and construct the irreducible representations of the extended
symmetry. They are divided into two sets, longitudinal and transversal
representations. The transversal representations involve an infinite series of
integer and half-integer helicities. Finally we suggest an extension of the
conformal group along the same line.


  Multi-label classification is becoming increasingly ubiquitous, but not much
attention has been paid to interpretability. In this paper, we develop a
multi-label classifier that can be represented as a concise set of simple
"if-then" rules, and thus, it offers better interpretability compared to
black-box models. Notably, our method is able to find a small set of relevant
patterns that lead to accurate multi-label classification, while existing
rule-based classifiers are myopic and wasteful in searching rules,requiring a
large number of rules to achieve high accuracy. In particular, we formulate the
problem of choosing multi-label rules to maximize a target function, which
considers not only discrimination ability with respect to labels, but also
diversity. Accounting for diversity helps to avoid redundancy, and thus, to
control the number of rules in the solution set. To tackle the said
maximization problem we propose a 2-approximation algorithm, which relies on a
novel technique to sample high-quality rules. In addition to our theoretical
analysis, we provide a thorough experimental evaluation, which indicates that
our approach offers a trade-off between predictive performance and
interpretability that is unmatched in previous work.


  The geometric formulation of Hamilton--Jacobi theory for systems with
nonholonomic constraints is developed, following the ideas of the authors in
previous papers. The relation between the solutions of the Hamilton--Jacobi
problem with the symplectic structure defined from the Lagrangian function and
the constraints is studied. The concept of complete solutions and their
relationship with constants of motion, are also studied in detail. Local
expressions using quasivelocities are provided. As an example, the nonholonomic
free particle is considered.


  Graphene based transistors relying on a conventional structure cannot switch
properly because of the absence of an energy gap in graphene. To overcome this
limitation, a barristor device was proposed, whose operation is based on the
modulation of the graphene-semiconductor (GS) Schottky barrier by means of a
top gate, and demonstrating an ON-OFF current ratio up to $10^5$. Such a large
number is likely due to the realization of an ultra clean interface with
virtually no interface trapped charge. However, it is indeed technologically
relevant to know the impact that the interface trapped charges might have on
the barristor's electrical properties. We have developed a physics based model
of the gate tunable GS heterostructure where non-idealities such as Fermi Level
Pinning (FLP) and a "bias dependent barrier lowering effect" has been
considered. Using the model we have made a comprehensive study of the
barristor's expected digital performance.


  Temperature dependence of the neutron-nucleus interaction is known as the
Doppler broadening of the cross-sections. This is a well-known effect due to
the thermal motion of the target nuclei that occurs in the neutron-nucleus
interaction. The fast computation of such effects is crucial for any nuclear
application. Mechanisms have been developed that allow determining the Doppler
effects in the cross-section, most of them based on the numerical resolution of
the equation known as Solbrig's kernel, which is a cross-section Doppler
broadening formalism derived from a free gas atoms distribution hypothesis.
This paper explores a novel non-linear approach based on deep learning
techniques. Deep neural networks are trained on synthetic and experimental
data, serving as an alternative to the cross-section Doppler Broadening (DB).
This paper explores the possibility of using physically informed neural
networks, where the network is physically regularized to be the solution of a
partial derivative equation, inferred from Solbrig's kernel. The learning
process is demonstrated by using the fission, capture, and scattering cross
sections for $^{235}U$ in the energy range from thermal to 2250 eV.


  We derive the analytical solutions of the bound timelike geodesic orbits in
Kerr spacetime. The analytical solutions are expressed in terms of the elliptic
integrals using Mino time $\lambda$ as the independent variable. Mino time
decouples the radial and polar motion of a particle and hence leads to forms
more useful to estimate three fundamental frequencies, radial, polar and
azimuthal motion, for the bound timelike geodesics in Kerr spacetime. This
paper gives the first derivation of the analytical expressions of the
fundamental frequencies. This paper also gives the first derivation of the
analytical expressions of all coordinates for the bound timelike geodesics
using Mino time. These analytical expressions should be useful not only to
investigate physical properties of Kerr geodesics but more importantly to
applications related to the estimation of gravitational waves from the extreme
mass ratio inspirals.


  The first variable star catalogue in a giant elliptical galaxy NGC 5128
(Centaurus A) is presented. Using multi-epoch observations with ISAAC at the
VLT we have detected 1504 red variables in two halo fields covering 10.46
arcmin square. For the variables with at least 10 good measurements, periods
and amplitudes were determined using Fourier analysis and non-linear sine-curve
fitting algorithms. The final catalogue contains 1146 long period variables
with well established light curve parameters. Periods, amplitudes, JsHKs
photometry as well as individual Ks-band magnitudes are provided for all the
variables. The distribution of amplitudes ranges from 0.3 to a few magnitudes
in the Ks-band, with a median value around 0.7 mag. The amplitudes, mean
magnitudes and periods indicate that the majority of variables belong to the
class of long period variables with semiregular and Mira variables. Exhaustive
simulations were performed in order to assess the completeness of our catalogue
and the accuracy of the derived periods.


  Mean-field games (MFGs) have shown strong modeling capabilities for large
systems in various fields, driving growth in computational methods for
mean-field game problems. However, high order methods have not been thoroughly
investigated. In this work, we explore applying general high-order numerical
schemes with finite element methods in the space-time domain for computing the
optimal transport (OT), mean-field planning (MFP), and MFG problems. We conduct
several experiments to validate the convergence rate of the high order method
numerically. Those numerical experiments also demonstrate the efficiency and
effectiveness of our approach.


  Multi-scale design has been considered in recent image super-resolution (SR)
works to explore the hierarchical feature information. Existing multi-scale
networks aim to build elaborate blocks or progressive architecture for
restoration. In general, larger scale features concentrate more on structural
and high-level information, while smaller scale features contain plentiful
details and textured information. In this point of view, information from
larger scale features can be derived from smaller ones. Based on the
observation, in this paper, we build a sequential hierarchical learning
super-resolution network (SHSR) for effective image SR. Specially, we consider
the inter-scale correlations of features, and devise a sequential multi-scale
block (SMB) to progressively explore the hierarchical information. SMB is
designed in a recursive way based on the linearity of convolution with
restricted parameters. Besides the sequential hierarchical learning, we also
investigate the correlations among the feature maps and devise a distribution
transformation block (DTB). Different from attention-based methods, DTB regards
the transformation in a normalization manner, and jointly considers the spatial
and channel-wise correlations with scaling and bias factors. Experiment results
show SHSR achieves superior quantitative performance and visual quality to
state-of-the-art methods with near 34\% parameters and 50\% MACs off when
scaling factor is $\times4$. To boost the performance without further training,
the extension model SHSR$^+$ with self-ensemble achieves competitive
performance than larger networks with near 92\% parameters and 42\% MACs off
with scaling factor $\times4$.


  We consider a Bayesian approach for the recovery of the thermal diffusivity
for the heat equation when the initial temperature map of the system is
unknown. This is a semiparametric inverse problem as the diffusivity is a
one-dimensional parameter while the initial condition is infinite-dimensional.
We assume that we have access to noisy observations of the system at the
initial time 0 and a final time T. We put a Gaussian process prior on the
initial condition, and prove a Bernstein-von Mises (BvM) theorem for the
marginal posterior of the thermal diffusivity provided that the prior
regularity satisfies a restriction determined by the smoothness of the initial
condition. We also investigate the behaviour of the marginal posterior for
different prior regularities numerically, indicating that the BvM result is not
valid if the prior regularity does not satisfy the restriction.


  Picard groups of tensor categories play an important role in rational
conformal field theory. The Picard group of the representation category C of a
rational vertex algebra can be used to construct examples of (symmetric
special) Frobenius algebras in C. Such an algebra A encodes all data needed to
ensure the existence of correlators of a local conformal field theory.
  The Picard group of the category of A-bimodules has a physical
interpretation, too: it describes internal symmetries of the conformal field
theory, and allows one to identify generalized Kramers-Wannier dualities of the
theory.
  When applying these general results to concrete models based on affine Lie
algebras, a detailed knowledge of certain representations of the modular group
is needed. We discuss a conjecture that relates these representations to those
furnished by twining characters of affine Lie algebras.


  It is quite possible that the Technicolor problems are related to the poorly
known self-energy expression, or the way chiral symmetry breaking (CSB) is
realized in non-abelian gauge theories. Actually, the only known laboratory to
test the CSB mechanism is QCD. The TC dynamics may be quite different from the
QCD , this fact has led to the walking TC proposal making the new strong
interaction almost conformal and changing appreciably its dynamical behavior.
There are different ways to obtain of extreme walking (or quasi-conformal)
technicolor theories, in this paper we propose an scheme to obtain this
behavior based on an extension of the electroweak sector of the standard model,
in the context of so called 331-TC model.


  Understanding stickiness and power-law behavior of Poincar\'e recurrence
statistics is an open problem for higher-dimensional systems, in contrast to
the well-understood case of systems with two degrees-of-freedom. We study such
intermittent behavior of chaotic orbits in three-dimensional volume-preserving
systems using the example of the Arnold-Beltrami-Childress map. The map has a
mixed phase space with a cylindrical regular region surrounded by a chaotic sea
for the considered parameters. We observe a characteristic overall power-law
decay of the cumulative Poincar\'e recurrence statistics with significant
oscillations superimposed. This slow decay is caused by orbits which spend long
times close to the surface of the regular region. Representing such
long-trapped orbits in frequency space shows clear signatures of partial
barriers and reveals that coupled resonances play an essential role. Using a
small number of the most relevant resonances allows for classifying
long-trapped orbits. From this the Poincar\'e recurrence statistics can be
divided into different exponentially decaying contributions which very
accurately explains the overall power-law behavior including the oscillations.


  We characterize the de Branges-Rovnyak spaces with complete Nevanlinna-Pick
property. Our method relies on the general theory of reproducing kernel Hilbert
spaces.


  I study the effects of disorder on the superconductivity of a three-band
model with repulsive interband pairing. Such a model can support several
possible superconducting order parameters, including a complex time-reversal
symmetry breaking (TRSB) state. Impurity scattering suppresses the critical
temperature of all these states, but the complex state survives, and remains a
part of the phase diagram of the model even in the presence of moderate amount
of disorder. This means that the TRSB states could be experimentally accessible
in multiband materials like iron pnictides and chalcogenides.


  We study the ring of polyfunctions over $\mathbb Z/n\mathbb Z$. The ring of
polyfunctions over a commutative ring $R$ with unit element is the ring of
functions $f:R\to R$ which admit a polynomial representative $p\in R[x]$ in the
sense that $f(x)= p(x)$ for all $x\in R$. This allows to define a ring
invariant $s$ which associates to a commutative ring $R$ with unit element a
value in $\mathbb N\cup\{\infty\}$. The function $s$ generalizes the number
theoretic Smarandache function. For the ring $R=\mathbb Z/n\mathbb Z$ we
provide a unique representation of polynomials which vanish as a function. This
yields a new formula for the number $\Psi(n)$ of polyfunctions over $\mathbb
Z/n\mathbb Z$. We also investigate algebraic properties of the ring of
polyfunctions over $\mathbb Z/n\mathbb Z$. In particular, we identify the
additive subgroup of the ring and the ring structure itself. Moreover we derive
formulas for the size of the ring of polyfunctions in several variables over
$\mathbb Z/n\mathbb Z$, and we compute the number of polyfunctions which are
units of the ring.


  We present new evolutionary synthesis models for Simple Stellar Populations
for a wide range of ages and metallicities. The models are based on the Padova
isochrones. The core of the spectral library is provided by the medium
resolution Lejeune et al. atmosphere models. These spectra are complemented by
NLTE atmosphere models for hot stars that have an important impact in the
stellar cluster's ionizing spectra: O, B and WR stellar spectra at the early
ages, and spectra of post-AGB stars and planetary nebulae, at intermediate and
old ages. At young ages, our models compare well with other existing models but
we find that, the inclusion of the nebular continuum, not considered in several
other models, reddens significantly the integrated colours of very young
stellar populations. This is consistent with the results of spectral synthesis
codes particularly devised for the study of starburst galaxies. At intermediate
and old ages, the agreement with literature model is good and, in particular,
we reproduce well the observed colours of star clusters in LMC. Given the
ability to produce good integrated spectra from the far-UV to the infrared at
any age, we consider that our models are particularly suited for the study of
high redshift galaxies. These models are available on the web site
{http://www.fractal-es.com/SEDmod.htm} and also through the Virtual Observatory
Tools on the PopStar server.


  Unlike the one-loop QG divergences in D=4, which can all be transformed away,
those in arbitrary higher (even) dimension cannot.


  Deep inelastic scattering off the strongly coupled N=4 supersymmetric
Yang-Mills plasma at finite temperature can be computed within the AdS/CFT
correspondence, with results which are suggestive of a parton picture for the
plasma. Via successive branchings, essentially all partons cascade down to very
small values of the longitudinal momentum fraction x and to transverse momenta
smaller than the saturation momentum Q_s\sim T/x. This scale Q_s controls the
plasma interactions with a hard probe, in particular, the jet energy loss and
its transverse momentum broadening.


  We simultaneously observed the Stokes parameters of the photospheric iron
line pair at 630.2 nm and the intensity profile of the chromospheric Ca II H
line at 396.8 nm in a quiet Sun region at a heliocentric angle of 53 deg. We
perform a statistical analysis of network and inter-network properties.The
H-index is the integrated emission in a 0.1 nm band around the Ca core. We
separate a non-magnetically, H_non, and a magnetically, H_mag, heated component
from a non-heated component, H_co in the H-index. The average network and
inter-network H-indices are equal to 12 and 10 pm, respectively. The emission
in the network is correlated with the magnetic flux density, approaching a
value of H 10 pm for vanishing flux. The inter-network magnetic field is
dominated by weak field strengths with values down to 200 G and its mean
absolute flux density is 11 Mx cm$^{-2}$. We find that a dominant fraction of
the calcium emission caused by the heated atmosphere in the magnetic network,
has non-magnetic origin (H_mag = 2pm, H_non = 3pm). Considering the effect of
straylight, the contribution from an atmosphere with no temperature rise to the
H-index (H_co = 6pm) is about half of the observed H-index in the
inter-network. The H-index in the inter-network is not correlated to any
property of the photospheric magnetic field, suggesting that magnetic flux
concentrations have a negligible role in the chromospheric heating in this
region. The height range of the thermal coupling between the photosphere and
low/mid chromosphere increases in presence of magnetic field. In addition, we
demonstrate that a poor signal-to-noise level in the Stokes profiles leads to a
significant over-estimation of the magnetic field strength.


  Multivariate density estimation and graphical models play important roles in
statistical learning. The estimated density can be used to construct a
graphical model that reveals conditional relationships whereas a graphical
structure can be used to build models for density estimation. Our goal is to
construct a consolidated framework that can perform both density and graph
estimation. Denote $\bm{Z}$ as the random vector of interest with density
function $f(\bz)$. Splitting $\bm{Z}$ into two parts,
$\bm{Z}=(\bm{X}^T,\bm{Y}^T)^T$ and writing $f(\bz)=f(\bx)f(\by|\bx)$ where
$f(\bx)$ is the density function of $\bm{X}$ and $f(\by|\bx)$ is the
conditional density of $\bm{Y}|\bm{X}=\bx$. We propose a semiparametric
framework that models $f(\bx)$ nonparametrically using a smoothing spline ANOVA
(SS ANOVA) model and $f(\by|\bx)$ parametrically using a conditional Gaussian
graphical model (cGGM). Combining flexibility of the SS ANOVA model with
succinctness of the cGGM, this framework allows us to deal with
high-dimensional data without assuming a joint Gaussian distribution. We
propose a backfitting estimation procedure for the cGGM with a computationally
efficient approach for selection of tuning parameters. We also develop a
geometric inference approach for edge selection. We establish asymptotic
convergence properties for both the parameter and density estimation. The
performance of the proposed method is evaluated through extensive simulation
studies and two real data applications.


  Combining items of clothing into an outfit is a major task in fashion retail.
Recommending sets of items that are compatible with a particular seed item is
useful for providing users with guidance and inspiration, but is currently a
manual process that requires expert stylists and is therefore not scalable or
easy to personalise. We use a multilayer neural network fed by visual and
textual features to learn embeddings of items in a latent style space such that
compatible items of different types are embedded close to one another. We train
our model using the ASOS outfits dataset, which consists of a large number of
outfits created by professional stylists and which we release to the research
community. Our model shows strong performance in an offline outfit
compatibility prediction task. We use our model to generate outfits and for the
first time in this field perform an AB test, comparing our generated outfits to
those produced by a baseline model which matches appropriate product types but
uses no information on style. Users approved of outfits generated by our model
21% and 34% more frequently than those generated by the baseline model for
womenswear and menswear respectively.


  The two-phase sampling design is a cost-efficient way of collecting expensive
covariate information on a judiciously selected subsample. It is natural to
apply such a strategy for collecting genetic data in a subsample enriched for
exposure to environmental factors for gene-environment interaction (G x E)
analysis. In this paper, we consider two-phase studies of G x E interaction
where phase I data are available on exposure, covariates and disease status.
Stratified sampling is done to prioritize individuals for genotyping at phase
II conditional on disease and exposure. We consider a Bayesian analysis based
on the joint retrospective likelihood of phases I and II data. We address
several important statistical issues: (i) we consider a model with multiple
genes, environmental factors and their pairwise interactions. We employ a
Bayesian variable selection algorithm to reduce the dimensionality of this
potentially high-dimensional model; (ii) we use the assumption of gene-gene and
gene-environment independence to trade off between bias and efficiency for
estimating the interaction parameters through use of hierarchical priors
reflecting this assumption; (iii) we posit a flexible model for the joint
distribution of the phase I categorical variables using the nonparametric Bayes
construction of Dunson and Xing [J. Amer. Statist. Assoc. 104 (2009)
1042-1051].


  High-transition-temperature (high-Tc) superconductivity is ubiquitous in the
cuprates containing CuO2 planes but each cuprate has its own character. The
study of the material dependence of the d-wave superconducting gap (SG) should
provide important insights into the mechanism of high-Tc. However, because of
the 'pseudogap' phenomenon, it is often unclear whether the energy gaps
observed by spectroscopic techniques really represent the SG. Here, we report
spectroscopic imaging scanning tunneling microscopy (SI-STM) studies of
nearly-optimally-doped Ca2-xNaxCuO2Cl2 (Na-CCOC) with Tc = 25 ~ 28 K. They
enable us to observe the quasi-particle interference (QPI) effect in this
material, through which unambiguous new information on the SG is obtained. The
analysis of QPI in Na-CCOC reveals that the SG dispersion near the gap node is
almost identical to that of Bi2Sr2CaCu2Oy (Bi2212) at the same doping level,
while Tc of Bi2212 is 3 times higher than that of Na-CCOC. We also find that SG
in Na-CCOC is confined in narrower energy and momentum ranges than Bi2212. This
explains at least in part the remarkable material dependence of Tc


  A Technical Reference for Autonomous Vehicles (AVs), with part 1 focusing on
basic behaviour guidelines (TR68-1) is published with the intent to be a
reference for evaluation of appropriated behaviour on Autonomous Vehicles for
Singapore. This is based on applicability from Basic Theory of Driving (BTD)
and Final Theory of Driving (FTD) which are the traffic code/rules for human
driving. This report contains a consolidation of current guidelines from
TR68-1, BTD and FTD. It will allow an initial identification of missing
guidelines for AV behaviour on roads; however, it is difficult to identify
conflicting rules or gaps in guidance without going into identified traffic
situations. Identified situations for analysis were chosen from Centre of
Excellence for Testing & Research of Autonomous Vehicle (CETRAN) assessment
experience for further investigation. The outcome of the report proposes
additional behaviour characteristics and guidelines to situations identified to
close the gap between assessors and developers on expected AV behaviour. These
recommendations could improve current guidelines for AV behavioural in
assessment and generally for the local AV ecosystem for urban tropical roads in
Singapore. These recommendations could also serve as inputs for future TR 68-1
revisions where a sample set of reference situations can help to define clearer
expectations or requirements for AV behaviour in those situations. This will
help Singapore push forward in better definition of the expected AV behaviour
for AV systems.