  DFT calculations within the generalized Bloch theorem approach show that
interfacial Dzyaloshinskii-Moriya interactions (DMI) at both interfaces of
Graphene/Co$_n$/Pt(111) multilayer heterostructures are decoupled for $n \geq
3$. Unlike the property of magnetocrystalline anisotropy for this system, DMI
is not affected by stacking defects in the Co layer. The effect of Graphene
(Gr) is to invert the chirality of the vaccum/Co interfacial DMI, overall
reducing the DMI of the heterostructure, which is nevertheless dominated by the
strong spin-orbit coupling (SOC) of Pt. A spectral analysis in the reciprocal
space shows that DMI at both the Gr/Co and Co/Pt interfaces have the same
nature, namely SOC-split hybrid bands of $d$-orbital character. This proves
that a DMI model based on a single band, such the Rashba DMI model, is
insuficient to describe the behaviour of this family of Gr-capped $3d/5d$ metal
heterostructures.


  We examine the inverse procedure of the Radford-Majid bosonization for Hopf
superalgebras and give a handy method for enumerating Hopf superalgebras whose
bosonization is isomorphic to a given Hopf algebra. As an application, we
classify Hopf superalgebras of dimension up to 5 and give examples of higher
dimensions.


  We present the \textbf{D}ecoupled \textbf{VI}deo \textbf{S}egmentation (DVIS)
framework, a novel approach for the challenging task of universal video
segmentation, including video instance segmentation (VIS), video semantic
segmentation (VSS), and video panoptic segmentation (VPS). Unlike previous
methods that model video segmentation in an end-to-end manner, our approach
decouples video segmentation into three cascaded sub-tasks: segmentation,
tracking, and refinement. This decoupling design allows for simpler and more
effective modeling of the spatio-temporal representations of objects,
especially in complex scenes and long videos. Accordingly, we introduce two
novel components: the referring tracker and the temporal refiner. These
components track objects frame by frame and model spatio-temporal
representations based on pre-aligned features. To improve the tracking
capability of DVIS, we propose a denoising training strategy and introduce
contrastive learning, resulting in a more robust framework named DVIS++.
Furthermore, we evaluate DVIS++ in various settings, including open vocabulary
and using a frozen pre-trained backbone. By integrating CLIP with DVIS++, we
present OV-DVIS++, the first open-vocabulary universal video segmentation
framework. We conduct extensive experiments on six mainstream benchmarks,
including the VIS, VSS, and VPS datasets. Using a unified architecture, DVIS++
significantly outperforms state-of-the-art specialized methods on these
benchmarks in both close- and open-vocabulary settings.
Code:~\url{https://github.com/zhang-tao-whu/DVIS_Plus}.


  We explore the star formation history of the Upper Scorpius OB association,
the youngest part of Sco OB2. A wide field (160 square-degree) survey for
low-mass pre-main sequence (PMS) stars enabled us to increase the number of
known low-mass members of Upper Scorpius to nearly 100 stars. In a detailed
analysis of the locations of these stars in the HR diagram, taking proper
account of the uncertainties and the effects of unresolved binaries, we find a
mean stellar age of about 5 Myr and no evidence for a significant age
dispersion among these stars. This implies that the star formation history of
the Upper Scorpius association was dominated by a short star-burst, which
started about 5 Myr ago and ended probably not more than one or two Myr later.
Interestingly, the structure and kinematics of the HI shells surrounding the
Sco OB2 association show that the shock wave of a supernova explosion in the
nearby Upper Centaurus-Lupus association, the oldest part of Sco OB2, crossed
Upper Scorpius just about 5 Myr ago. This strongly suggests that this supernova
shock wave triggered the star-burst in Upper Scorpius.


  We describe generalized running key ciphers and apply them for analysis of
two Shannon's methods. In particular, we suggest some estimation of the cipher
equivocation and the probability of correct deciphering without key.


  The largest solar flare have been recorded in gamma flash and hard spectra up
to tens GeV energy. The present building and upgrade of Hyper- Kamiokande (HK)
in Japan and Korea, (as well as Deep Core, PINGU) Megatons neutrino detectors
do offer a novel way to detectable trace of solar flares: their sudden
anti-neutrino (or neutrino) imprint made by proton scattering and pion decays
via Delta resonance production on solar corona foot-point. These signals might
be observable at largest flare by HK via soft spectra up to tens-hundred MeV
energy and by IceCube-PINGU at higher, GeVs energies. We show the expected rate
of signals for the most powerful solar flare occurred in recent decades
extrapolated for future Megaton detectors. The neutrino solar flare detection
with its prompt alarm system may alert astronauts on space journey allowing
them to hide themselves into inner rocket container surrounded by fuel or water
supply. These container walls are able to defend astronauts from the main
lethal (the dominant soft component) radiation wind due to such largest solar
flares.


  Identifying the relationship between healthcare attributes, lifestyles, and
personality is vital for understanding and improving physical and mental
conditions. Machine learning approaches are promising for modeling their
relationships and offering actionable suggestions. In this paper, we propose
Virtual Human Generative Model (VHGM), a machine learning model for estimating
attributes about healthcare, lifestyles, and personalities. VHGM is a deep
generative model trained with masked modeling to learn the joint distribution
of attributes conditioned on known ones. Using heterogeneous tabular datasets,
VHGM learns more than 1,800 attributes efficiently. We numerically evaluate the
performance of VHGM and its training techniques. As a proof-of-concept of VHGM,
we present several applications demonstrating user scenarios, such as virtual
measurements of healthcare attributes and hypothesis verifications of
lifestyles.


  Two leading dark matter candidates from supersymmetry and other theories of
physics beyond the standard model are WIMPs and weak scale gravitinos. If the
lightest stable particle is a gravitino, then a WIMP will decay into it with a
natural lifetime of order a month ~ M_{pl}^2/M_{weak}^3. We show that if the
bulk of dark matter today came from decays of neutral particles with lifetimes
of order a year or smaller, then it could lead to a reduction in the amount of
small scale substructure, less concentrated halos and constant density cores in
the smallest mass halos. Such beneficial effects may therefore be realized
naturally, as discussed by Cembranos, Feng, Rajaraman, and Takayama, in the
case of supersymmetry.


  A potential candidate technology for the development of future 6G networks
has been recognized as Reconfigurable Intelligent Surface (RIS). However, due
to the variation in radio link quality, traditional passive RISs only
accomplish a minimal signal gain in situations with strong direct links between
user equipment (UE) and base station (BS). In order to get over this
fundamental restriction of smaller gain, the idea of active RISs might be a
suitable solution. In contrast to current passive RIS, which simply reflects
and directs signals without any additional amplification, active RISs have the
ability to enhance reflected signals by the incorporation of amplifiers inside
its elements. However, with additional amplifiers, apart from the relatively
complex attributes of RIS-assisted arrangements, the additional energy
consumption of such technologies is often disregarded. So, there might be a
tradeoff between the additional energy consumption for the RIS technologies and
the overall gain acquired by deploying this potential advancement. The
objective of this work is to provide a primary idea of a three-layer hybrid
RIS-assisted configuration that is responsive to both active and passive RIS,
as well as an additional dormant or inactive state. The single RIS structure
should be capable of adjusting its overall configuration in response to
fluctuations in transmit power and radio link quality. Furthermore, our
fabricated passive RIS-assisted structure verifies a portion of the proposed
idea, with simulations highlighting its advantages over standalone passive or
active RIS-assisted technologies.


  We study the design of auctions with dynamic scoring, which allocate a single
item according to a given scoring rule. We are motivated by online advertising
auctions when users interact with a platform over the course of a session. The
platform ranks ads based on a combination of bids and quality scores, and
updates the quality scores throughout the session based on the user's online
activity. The platform must decide when to show an ad during the session. By
delaying the auction, the auctioneer acquires information about an ad's
quality, improving her chances of selecting a high quality ad. However
information is costly, because delay reduces market thickness and in turn
revenue. When should the auctioneer allocate the impression to balance these
forces?
  We develop a theoretical model to study the effect of market design on the
trade-off between market thickness and information. In particular, we focus on
first- and second-price auctions. The auctioneer can commit to the auction
format, but not to its timing: her decision can thus be cast as a real options
problem. We show that under optimal stopping the first-price auction allocates
efficiently but with delay. Instead, the second-price auction generates more
revenue by avoiding delay. The auctioneer benefits from introducing reserve
prices, more so in a first-price auction.


  We retrospect three abstract models for heat engines which include a classic
abstract model in textbook of thermal physics, a primary abstract model for
finite-time heat engines, and a refined abstract model for finite-time heat
engines. The detailed models of heat engines in literature of finite-time
thermodynamics may be mapped into the refined abstract model. The future
developments based on the refined abstract model are also surveyed.


  Elastic proton scattering on ${}^{3}{H}$ nucleus is studied inbetween p-%
${}^{3}{H}$ and n-${}^{3}{He}$ thresholds, in the energy region where
$\alpha$-particles first excited state is inbeded in the continuum. For this
aim Faddeev-Yakubovski equations are solved in configuration space, fully
considering effects due to the isospin breaking as well as rigorously treating
Coulomb interaction. Different realistic nuclear Hamiltonians are tested,
elucidating open problems in nuclear interaction description.


  In the SM gauge symmetries and fermion content of neutrinos, charged leptons
and quarks, we study the effective four-fermion operators of Einstein-Cartan
type and their contributions to the Schwinger-Dyson equations of fermion
self-energy functions. The study is motivated by the speculation that these
four-fermion operators are probably originated due to the quantum gravity that
provides the natural regularization for chiral-symmetric gauge field theories.
In the chiral-gauge symmetry breaking phase, as to achieve the energetically
favorable ground state, only the top-quark mass is generated via the
spontaneous symmetry breaking, and other fermion masses are generated via the
explicit symmetry breaking induced by the top-quark mass, four-fermion
interactions and fermion-flavor mixing matrices. A phase transition from the
symmetry breaking phase to the chiral-gauge symmetric phase at TeV scale occurs
and the drastically fine-tuning problem can be resolved. In the infrared
fixed-point domain of the four-fermion coupling for the SM at low energies, we
qualitatively obtain the hierarchy patterns of the SM fermion Dirac masses,
Yukawa couplings and family-flavor mixing matrices with three additional
right-handed neutrinos $\nu^f_R$. Large Majorana masses and lepton-symmetry
breaking are originated by the four-fermion interactions among $\nu^f_R$ and
their left-handed conjugated fields $\nu^{fc}_R$. Light masses of gauged
Majorana neutrinos in the normal hierarchy ($10^{-5}-10^{-2}$ eV) are obtained
consistently with neutrino oscillations. We present some discussions on the
composite Higgs phenomenology and forward-backward asymmetry of $t\bar
t$-production, as well as remarks on the candidates of light and heavy dark
matter particles (fermions, scalar and pseudoscalar bosons).


  This technical report presents a solution for the 2020 Traffic4Cast
Challenge. We consider the traffic forecasting problem as a future frame
prediction task with relatively weak temporal dependencies (might be due to
stochastic urban traffic dynamics) and strong prior knowledge, \textit{i.e.},
the roadmaps of the cities. For these reasons, we use the U-Net as the backbone
model, and we propose a roadmap generation method to make the predicted traffic
flows more rational. Meanwhile, we use a fine-tuning strategy based on the
validation set to prevent overfitting, which effectively improves the
prediction results. At the end of this report, we further discuss several
approaches that we have considered or could be explored in future work: (1)
harnessing inherent data patterns, such as seasonality; (2) distilling and
transferring common knowledge between different cities. We also analyze the
validity of the evaluation metric.


  Enhancement of network lifetime is a key design criterion for most of the
energy constrained networks as nodes are battery operated. In multi-hop
wireless network, proper utilization of battery power is very much necessary to
maintain network connectivity. If the battery power of a node drains quickly
then its connectivity in its neighborhood will be lost. So the study of network
lifetime is very much crucial as compared to other network parameters.
Considering this importance we made an attempt to study the behaviour of three
most common routing protocols in ad hoc network. Extensive simulations are done
on AODV, DSR and ZRP to determine the network lifetime at different node
mobility and at different network load. Simulation results suggest that AODV is
the most energy efficient protocol as compared to other


  Recent developments in the 3D scanning technologies have made the generation
of highly accurate 3D point clouds relatively easy but the segmentation of
these point clouds remains a challenging area. A number of techniques have set
precedent of either planar or primitive based segmentation in literature. In
this work, we present a novel and an effective primitive based point cloud
segmentation algorithm. The primary focus, i.e. the main technical contribution
of our method is a hierarchical tree which iteratively divides the point cloud
into segments. This tree uses an exclusive energy function and a 3D
convolutional neural network, HollowNets to classify the segments. We test the
efficacy of our proposed approach using both real and synthetic data obtaining
an accuracy greater than 90% for domes and minarets.


  Explaining why an answer is (or is not) returned by a query is important for
many applications including auditing, debugging data and queries, and answering
hypothetical questions about data. In this work, we present the first practical
approach for answering such questions for queries with negation (first- order
queries). Specifically, we introduce a graph-based provenance model that, while
syntactic in nature, supports reverse reasoning and is proven to encode a wide
range of provenance models from the literature. The implementation of this
model in our PUG (Provenance Unification through Graphs) system takes a
provenance question and Datalog query as an input and generates a Datalog
program that computes an explanation, i.e., the part of the provenance that is
relevant to answer the question. Furthermore, we demonstrate how a desirable
factorization of provenance can be achieved by rewriting an input query. We
experimentally evaluate our approach demonstrating its efficiency.


  We study the Symmetry Topological Field Theory in holography associated with
4d $\mathcal{N}=1$ Super Yang-Mills theory with gauge algebra
$\mathfrak{su}(M)$. From this, all the bulk symmetry operators are computed and
matched to various D-brane configurations. The fusion algebra of the operators
emerges from brane dynamics. In particular, we show that the symmetry operators
are purely determined from the center-of-mass modes of the branes. We identify
the TQFT fusion coefficients with the relative motion of the branes. We also
establish the origin of condensation defects, arising from fusion of
non-invertible operators, as the consequence of tachyon condensation in
brane-anti-brane pairs.


  The main goal of this paper is to introduce the notion of a primitive form
for a generic family of Hurwitz covers of $\mathbb{P}^1$ with a fixed
ramification profile over infinity. We prove that primitive forms are in
one-to-one correspondence with semi-simple Frobenius structures on the base of
the family. Furthermore, we introduce the notion of a polynomial primitive form
and show that the corresponding class of Frobenius manifolds contains the
Hurwitz Frobenius manifolds of Dubrovin. Finally, we apply our theory to
investigate the relation between the Eynard--Orantin recursion and Frobenius
manifolds.


  Let k, r > 1 be two integers. We prove an asymptotic formula for the number
of k-free values of the r variables polynomial t_1...t_r - 1 over the integral
points of [1, x]^r.


  We consider the class of noisy multi-layered sigmoid recurrent neural
networks with $w$ (unbounded) weights for classification of sequences of length
$T$, where independent noise distributed according to $\mathcal{N}(0,\sigma^2)$
is added to the output of each neuron in the network. Our main result shows
that the sample complexity of PAC learning this class can be bounded by $O
(w\log(T/\sigma))$. For the non-noisy version of the same class (i.e.,
$\sigma=0$), we prove a lower bound of $\Omega (wT)$ for the sample complexity.
Our results indicate an exponential gap in the dependence of sample complexity
on $T$ for noisy versus non-noisy networks. Moreover, given the mild
logarithmic dependence of the upper bound on $1/\sigma$, this gap still holds
even for numerically negligible values of $\sigma$.


  We find low energy equivalences between $N=2$ supersymmetric gauge theories
with different simple gauge groups with and without matter. We give a
construction of equivalences based on subgroups and find all examples with
maximal simple subgroups. This is used to solve some theories with exceptional
gauge groups $G_2$ and $F_4$. We are also able to solve an $E_6$ theory on a
codimension one submanifold of its moduli space.


  We present an effective field theory for a unified description of transport
in normal and superconducting metals in the presence of generic spin-orbit
coupling (SOC). The structure of the quantum kinetic theory in the diffusive
regime is determined by a set of fundamental constraints -- charge conjugation
symmetry, the causality principle, and the crystal symmetry of a material.
These symmetries uniquely fix the action of the Keldysh non-linear $\sigma$
model (NLSM), which at the saddle point yields the quantum kinetic Usadel-type
equation. Our phenomenological approach is reminiscent of the Ginzburg-Landau
theory, but is valid for superconductors in the whole temperature range,
describes the diffusive transport in the normal state, and naturally captures
the effects of superconducting fluctuations. As an application, we derive the
NLSM and quantum transport equations which include all effects of spin-orbit
coupling, allowed by the crystal symmetry, for example, the spin Hall, spin
current swapping or spin-galvanic effects. Our approach can be extended to
systems with broken time reversal symmetry, as well as to the description of
hybrid interfaces, where the spin-charge interconversion can be enhanced due to
strong interfacial SOC.


  A number of direct detection experiments are searching for electron
excitations created by scattering of sub-GeV dark matter. We present an
alternate formulation of dark matter-electron scattering in terms of the
dielectric response of a material. For dark matter which couples to electrons,
this approach automatically accounts for in-medium screening effects, which
were not included in previous rate calculations for semiconductor targets. We
show that the screening effects appear for both scalar and vector mediators.
The result is a non-negligible reduction of reach for direct detection
experiments which use dielectric materials as targets. We also explore
different determinations of the dielectric response, including first-principles
density functional theory (DFT) calculations and a data-driven analytic
approximation using a Mermin oscillator model.


  Millimeter-sized single crystals of LaFeAsO were grown from NaAs flux and the
in-plane optical properties were studied over a wide frequency range. A sizable
electronic correlation effect was indicated from the analysis of the
free-carrier spectral weight. With decreasing temperature from 300 K, we
observed a continuous suppression of the spectral weight near 0.6 eV. But a
spin-density-wave gap formation at lower energy scale was seen only in the
broken-symmetry state. We elaborate that both the itinerancy and local spin
interactions of Fe\emph{3d} electrons are present for the FeAs-based systems;
however, the establishment of the long-range magnetic order at low temperature
has a dominantly itinerant origin.


  We investigate the non-perturbative features of $\phi^4_2$ theory in two
dimensions, using Monte Carlo lattice methods. In particular we determine the
ratio $f_0 \equiv g/\mu^2$, where g is the unrenormalised coupling, in the
infinite volume and continuum limit. Our final result is $f_0$ = 11.055(14).


  Given a tensor category C, one constructs its Drinfeld center Z(C) which is a
braided tensor category, having as objects pairs (X, lambda), where X in Obj(C)
and lambda is a half-braiding. For a premodular category C, we construct a new
category Zel(C) which we call the Elliptic Drinfeld Center, which has objects
(X, lambda1, lambda2), where the lambda i's are half-braidings that satisfy
some compatibility conditions. We discuss an SL2(Z)-action on Zel(C) that is
related to the anomaly appearing in Reshetikhin-Turaev theory. This
construction is motivated from the study of the extended Crane-Yetter TQFT, in
particular the category associated to the once punctured torus.


  Direct-detection experiments sensitive to low-energy electron recoils from
sub-GeV dark matter (DM) interactions will also be sensitive to solar neutrinos
via coherent neutrino-nucleus scattering (CNS), since the recoiling nucleus can
produce a small ionization signal. Solar neutrinos constitute both an
interesting signal in their own right and a potential background to a DM search
that cannot be controlled or reduced by improved shielding, material
purification and handling, or improved detector design. We explore these two
possibilities in detail for semiconductor (Si and Ge) and Xe targets,
considering several possibilities for the unmeasured ionization efficiency at
low energies. For DM-electron-scattering searches, neutrinos start being an
important background for exposures larger than ~1-10 kg-years in Si and Ge, and
for exposures larger than ~0.1-1 kg-year in Xe. For the absorption of bosonic
DM (dark photons and axion-like particles) by electrons, neutrinos are most
relevant for masses below ~1 keV and again slightly more important in Xe.
Treating the neutrinos as a signal, we find that the CNS of B-8 neutrinos can
be observed with ~2 sigma significance with exposures of ~2, 7, and 20 kg-years
in Xe, Ge, and Si, respectively, assuming there are no other backgrounds. We
give an example for how this would constrain non-standard neutrino
interactions. Neutrino components at lower energy can only be detected if the
ionization efficiency is sufficiently large. In this case, observing pep
neutrinos via CNS requires exposures ~10-100 kg-years in Si or Ge (~1000
kg-years in Xe), and observing CNO neutrinos would require an order of
magnitude more exposure. Only Si could potentially detect Be-7 neutrinos. These
measurements would allow for a direct measurement of the electron-neutrino
survival probability over a wide energy range.


  This paper is concerned with the weak solvability of fully nonlinear
parabolic variational inequalities with time dependent convex constraints. As
possible approaches to such problems, there are for instance the
time-discretization method and the fixed point method of Schauder type with
appropriate compactness theorems. In this paper, our attention is paid to the
latter approach. However, there has not been prepared any appropriate
compactness theorem up to date that enables us the direct application of fixed
point method to variational inequalities of parabolic type. In order to
establish it we have to start on the set up of a new compactness theorem for a
wide class of parabolic variational inequalities.


  We present progress made by the Hadron Spectrum Collaboration (HSC) in
determining the tower of excited nucleon states using 2+1-flavor anisotropic
clover lattices. The HSC has been investigating interpolating operators
projected into irreducible representations of the cubic group in order to
better calculate two-point correlators for nucleon spectroscopy; results are
published for quenched and 2-flavor anisotropic Wilson lattices. In this work,
we present the latest results using a new technique, distillation, which allows
us to reach higher statistics than before. Future directions will be outlined
at the end.


  Superconducting quantum circuit is a promising system for building quantum
computer. With this system we demonstrate the universal quantum computations,
including the preparing of initial states, the single-qubit operations, the
two-qubit universal gate operations between arbitrary qubits, the multiple
pairs of two-qubit gate operations in parallel, the coupling operations on a
group of qubits in parallel, the coupling operations on multiple groups of
qubits in parallel, the coupling operations on multiple pairs and multiple
groups of qubits in parallel. Within available technology, a universal quantum
computer consists of more than 50 qubits allowing operations is achievable with
the design.


  The floating-zone method with different growth ambiances has been used to
selectively obtain hexagonal or orthorhombic DyMnO$_{3}$ single crystals. The
crystals were characterized by X-ray powder diffraction of ground specimen and
a structure refinement as well as electron diffraction. We report magnetic
susceptibility, magnetisation, and specific heat studies of this multiferroic
compound in both the hexagonal and the orthorhombic structure. The hexagonal
DyMnO$_{3}$ shows magnetic ordering of Mn$^{3+}$ (S =2) spins on a triangular
Mn lattice at $T^{\mathrm {Mn}}_{N}$ = 57 K characterized by a cusp in the
specific heat. This transition is not apparent in the magnetic susceptibility
due to the frustration on the Mn triangular lattice and the dominating
paramagnetic susceptibility of the Dy$^{3+}$ (S=9/2) spins. At $T^{\mathrm
{Dy}}_{N}$ = 3 K, a partial antiferromagnetic order of Dy moments has been
observed. In comparison, the magnetic data for orthorhombic DyMnO$_{3}$ display
three transitions. The data broadly agree with results from earlier neutron
diffraction experiments, which allows for the following assignment: a
transition from an incommensurate antiferromagnetic ordering of Mn$^{3+}$ spins
at $T^{\mathrm {Mn}}_N$ = 39 K, a {\textit{lock--in}} transition at $T_{\mathrm
{lock-in}}$ = 16 K and a second antiferromagnetic transition at $T^{\mathrm
{Dy}}_N$ = 5 K due to the ordering of Dy moments. Both the hexagonal and the
orthorhombic crystals show magnetic anisotropy and complex magnetic properties
due to 4$f-4f$ and 4$f-3d$ couplings.


  A phenomenological mean-field theory is used to investigate the properties of
solvent-diluted di-block copolymers (BCP), in which the two BCP components (A
and B) form a variety of phases that are diluted by a solvent (S). Using this
approach, we model mixtures of di-block copolymers and a solvent and obtained
the corresponding critical behavior. In the low solvent limit, we find how the
critical point depends on the solvent density. Due to the non-linear nature of
the coupling between the A/B and BCP/solvent concentrations, the A/B modulation
induces modulations in the polymer-solvent relative concentration with a double
wavenumber. The free boundary separating the polymer-rich phase from the
solvent-rich one is studied in two situations. First, we show how the presence
of a chemically patterned substrate leads to deformations of the BCP
film/solvent interface, creation of terraces in lamellar BCP film and even
formation of multi-domain droplets as induced by the patterned substrate. Our
results are in agreement with previous self-consistent field theory
calculations. Second, we compare the surface tension between parallel lamellae
coexisting with a solvent phase with that of a perpendicular one, and show that
the surface tension has a non-monotonic dependence on temperature. The
anisotropic surface tension can lead to deformation of spherical BCP droplets
into lens-shaped ones, together with re-orientation of the lamellae inside the
droplet during the polymer/solvent phase separation process in agreement with
experiment.


  The near-field Lienard-Wiechert potential solution of a longitudinally
oscillating electrical field produced by an oscillating charge is presented,
and the results are compared to the R. P. Feynman multipole far-field solution.
The results indicate that the phase speed of a longitudinally oscillating
electrical field is much faster than the speed of light in the near field. A
similar analysis is presented for a longitudinally oscillating gravitational
field produced by a vibrating mass. The result also indicates that the phase
speed of a longitudinally oscillating gravitational field is also much faster
than the speed of light in the near-field. The possibility of measuring the
group speed of a longitudinally oscillating electrical field and a
longitudinally oscillating gravitational field, which is commonly thought to be
equal to the speed of light, is now being considered. The basic idea is to
amplitude-modulate the longitudinal vibration of a charge or a mass and to
measure the resultant longitudinal vibration of a nearby charge or a mass due
to electrical or gravitational interaction. The modulation signal can then be
extracted using a diode detector and the group speed can then be determined
from the oscillation frequency, the separation distance between the masses, and
the measurement of the phase shift of the modulation signal. If the group speed
is equal to the speed of light, then phase shifts on the order of 1 microdegree
could be generated with a typical experimental set-up. An analysis using the
classical definition of group velocity for a longitudinally oscillating
electrical field is presented, and the results indicate that the group speed is
also much faster that the speed of light in the near field, which should not be
possible due to causality violation.


  Teaching machines to read natural language documents remains an elusive
challenge. Machine reading systems can be tested on their ability to answer
questions posed on the contents of documents that they have seen, but until now
large scale training and test datasets have been missing for this type of
evaluation. In this work we define a new methodology that resolves this
bottleneck and provides large scale supervised reading comprehension data. This
allows us to develop a class of attention based deep neural networks that learn
to read real documents and answer complex questions with minimal prior
knowledge of language structure.


  The article gives a ring theoretic perspective on cluster algebras.
Gei{\ss}-Leclerc-Schr\"oer prove that all cluster variables in a cluster
algebra are irreducible elements. Furthermore, they provide two necessary
conditions for a cluster algebra to be a unique factorization domain, namely
the irreducibility and the coprimality of the initial exchange polynomials.
  We present a sufficient condition for a cluster algebra to be a unique
factorization domain in terms of primary decompositions of certain ideals
generated by initial cluster variables and initial exchange polynomials. As an
application, the criterion enables us to decide which coefficient-free cluster
algebras of Dynkin type A, D or E are unique factorization domains. Moreover,
it yields a normal form for irreducible elements in cluster algebras that
satisfy the condition. Proof techniques include methods from commutative
algebra.
  In addition, we state a conjecture about the range of application of the
criterion.


  Semi-analytic expressions for the static limit of the T-matrix for
electromagnetic scattering are derived for a circular torus, expressed in bases
of both toroidal and spherical harmonics. The scattering problem for an
arbitrary static excitation is solved using toroidal harmonics and the extended
boundary condition method to obtain analytic expressions for auxiliary Q and
P-matrices, from which the T-matrix is given by their division. By applying the
basis transformations between toroidal and spherical harmonics, the
quasi-static limit of the T-matrix block for electric multipole coupling is
obtained. For the toroidal geometry there are two similar T-matrices on a
spherical basis, for computing the scattered field both near the origin and in
the far field. Static limits of the optical cross-sections are computed, and
analytic expressions for the limit of a thin ring are derived.


  We give a brief summary of results and ongoing research in the application of
linearized theory to the study of black hole collisions in the limit in which
the holes start close to each other. This approximation can be a valuable tool
for comparison and code-checking of full numerical relativity computations. The
approximation works quite well for the head-on case and this is motivation to
pursue its use in other more interesting contexts. We summarize current efforts
towards establishing the domain of validity of the approximation and its use in
generation and evolution of initial data for more interesting physical cases.


  Recent advances in semi-supervised object detection (SSOD) are largely driven
by consistency-based pseudo-labeling methods for image classification tasks,
producing pseudo labels as supervisory signals. However, when using pseudo
labels, there is a lack of consideration in localization precision and
amplified class imbalance, both of which are critical for detection tasks. In
this paper, we introduce certainty-aware pseudo labels tailored for object
detection, which can effectively estimate the classification and localization
quality of derived pseudo labels. This is achieved by converting conventional
localization as a classification task followed by refinement. Conditioned on
classification and localization quality scores, we dynamically adjust the
thresholds used to generate pseudo labels and reweight loss functions for each
category to alleviate the class imbalance problem. Extensive experiments
demonstrate that our method improves state-of-the-art SSOD performance by 1-2%
AP on COCO and PASCAL VOC while being orthogonal and complementary to most
existing methods. In the limited-annotation regime, our approach improves
supervised baselines by up to 10% AP using only 1-10% labeled data from COCO.


  The microwave power, dc magnetic field, frequency and temperature dependence
of the surface resistance of MgB2 films and powder samples were studied. Sample
quality is relatively easy to identify by a number of characteristics, the most
clear being the breakdown in the omega squared law for poor quality samples.
Analysis of the experimental data suggests the most attractive procedure for
high quality film growth for technical applications.


  We model the thermal effect of young stars on their surrounding environment
in order to understand clustered star formation. We take radiative heating of
dust, dust-gas collisional heating, cosmic-ray heating, and molecular cooling
into account. Using Dusty, a spherical continuum radiative transfer code, we
model the dust temperature distribution around young stellar objects with
various luminosities and surrounding gas and dust density distributions. We
have created a grid of dust temperature models, based on our modeling with
Dusty, which we can use to calculate the dust temperature in a field of stars
with various parameters. We then determine the gas temperature assuming energy
balance. Our models can be used to make large-scale simulations of clustered
star formation more realistic.


  Time is a parameter playing a central role in our most fundamental modeling
of natural laws. Relativity theory shows that the comparison of times measured
by different clocks depends on their relative motions and on the strength of
the gravitational field in which they are embedded. In standard cosmology, the
time parameter is the one measured by fundamental clocks, i.e. clocks at rest
with respect to the expanding space. This proper time is assumed to flow at a
constant rate throughout the whole history of the Universe. We make the
alternative hypothesis that the rate at which cosmological time flows depends
on the dynamical state of the Universe. In thermodynamics, the arrow of time is
strongly related to the second law, which states that the entropy of an
isolated system will always increase with time or, at best, stay constant.
Hence, we assume that time measured by fundamental clocks is proportional to
the entropy of the region of the Universe that is causally connected to them.
Under that simple assumption, we build a cosmological model that explains the
Type Ia Supernovae data (the best cosmological standard candles) without the
need for exotic dark matter nor dark energy.


  It is widely accepted that conservation laws, especially energy-momentum
conservation, have fundamental importance for both classical and quantum
systems in physics. A widely used method to derive the conservation laws is
based on Noether's theorem. However, for classical relativistic particle-field
systems, this process is still impeded. Different from the quantum situation,
the obstruction emerged when we regard the particle's field as a classical
world line. The difficulties come from two aspects. One is the mass-shell
constraint and the other comes from the heterogeneous-manifolds that particles
and fields reside on. This study develops a general geometric (manifestly
covariant) field theory for classical relativistic particle-field systems. In
considering the mass-shell constraint, the Euler-Lagrange-Barut (ELB) equation
as a geometric version of the Euler-Lagrange (EL) equation is applied to
determine the world lines of the relativistic particles. As a differential
equation in the standard field theory, the infinitesimal criterion of the
symmetry condition is converted into an integro-differential equation. To
overcome the second difficulty, we develop a weak ELB equation on the 4D
space-time. The weak version of the ELB equation will play an essential role in
establishing the connections between symmetries and local conservation laws.
Using field theory together with the weak ELB equation developed here, the
conservation laws can be systematically derived from the symmetries that the
systems admit.


  Automatic histopathology image segmentation is crucial to disease analysis.
Limited available labeled data hinders the generalizability of trained models
under the fully supervised setting. Semi-supervised learning (SSL) based on
generative methods has been proven to be effective in utilizing diverse image
characteristics. However, it has not been well explored what kinds of generated
images would be more useful for model training and how to use such images. In
this paper, we propose a new data guided generative method for histopathology
image segmentation by leveraging the unlabeled data distributions. First, we
design an image generation module. Image content and style are disentangled and
embedded in a clustering-friendly space to utilize their distributions. New
images are synthesized by sampling and cross-combining contents and styles.
Second, we devise an effective data selection policy for judiciously sampling
the generated images: (1) to make the generated training set better cover the
dataset, the clusters that are underrepresented in the original training set
are covered more; (2) to make the training process more effective, we identify
and oversample the images of "hard cases" in the data for which annotated
training data may be scarce. Our method is evaluated on glands and nuclei
datasets. We show that under both the inductive and transductive settings, our
SSL method consistently boosts the performance of common segmentation models
and attains state-of-the-art results.


  We prove that wild ramification of a constructible sheaf on a surface is
determined by that of the restrictions to all curves. We deduce from this
result that the Euler-Poincar\'e characteristic of a constructible sheaf on a
variety of arbitrary dimension over an algebraically closed field is determined
by wild ramification of the restrictions to all curves. We similarly deduce
from it that so is the alternating sum of the Swan conductors of the cohomology
groups, for a constructible sheaf on a variety over a local field.


  The "Switchboard benchmark" is a very well-known test set in automatic speech
recognition (ASR) research, establishing record-setting performance for systems
that claim human-level transcription accuracy. This work highlights
lesser-known practical considerations of this evaluation, demonstrating major
improvements in word error rate (WER) by correcting the reference
transcriptions and deviating from the official scoring methodology. In this
more detailed and reproducible scheme, even commercial ASR systems can score
below 5% WER and the established record for a research system is lowered to
2.3%. An alternative metric of transcript precision is proposed, which does not
penalize deletions and appears to be more discriminating for human vs. machine
performance. While commercial ASR systems are still below this threshold, a
research system is shown to clearly surpass the accuracy of commercial human
speech recognition. This work also explores using standardized scoring tools to
compute oracle WER by selecting the best among a list of alternatives. A phrase
alternatives representation is compared to utterance-level N-best lists and
word-level data structures; using dense lattices and adding out-of-vocabulary
words, this achieves an oracle WER of 0.18%.


  For whole-body (WB) kinetic modeling based on a typical PET scanner, a
multi-pass multi-bed scanning protocol is necessary given the limited axial
field-of-view. Such a protocol introduces loss of early-dynamics in
time-activity curves (TACs) and sparsity in TAC measurements, inducing
uncertainty in parameter estimation when using least-squares estimation (LSE)
(i.e., common standard) especially for kinetic microparameters. We present a
method to reliably estimate microparameters, enabling accurate parametric
imaging, on regular-axial field-of-view PET scanners. Our method, denoted
parameter combination-driven estimation (PCDE), relies on generation of
reference truth TAC database, and subsequently selecting, the best parameter
combination as the one arriving at TAC with highest total similarity score
(TSS), focusing on general image quality, overall visibility, and tumor
detectability metrics. Our technique has two distinctive characteristics: 1)
improved probability of having one-on-one mapping between early and late
dynamics in TACs (the former missing from typical protocols), and 2) use of
multiple aspects of TACs in selection of best fits. To evaluate our method
against conventional LSE, we plotted tradeoff curves for noise and bias. In
addition, the overall SNR and spatial noise were calculated and compared.
Furthermore, CNR and TBR were also calculated. We also tested our proposed
method on patient data (18F-DCFPyL PET scans) to further verify clinical
applicability. Significantly improved general image quality performance was
verified in microparametric images (e.g. noise-bias performance). The overall
visibility and tumor detectability were also improved. Finally, for our patient
studies, improved overall visibility and tumor detectability were demonstrated
in micoparametric images, compared to use of conventional parameter estimation.


  Study of stability of nuclei, flow and multifragmentation in heavy-ion
collisions.


  This paper presents the analysis of the impact of a floating-point number
precision reduction on the quality of text classification. The precision
reduction of the vectors representing the data (e.g. TF-IDF representation in
our case) allows for a decrease of computing time and memory footprint on
dedicated hardware platforms. The impact of precision reduction on the
classification quality was performed on 5 corpora, using 4 different
classifiers. Also, dimensionality reduction was taken into account. Results
indicate that the precision reduction improves classification accuracy for most
cases (up to 25% of error reduction). In general, the reduction from 64 to 4
bits gives the best scores and ensures that the results will not be worse than
with the full floating-point representation.


  In this paper a stationary action principle is proven to hold for capillary
fluids, i.e. fluids for which the deformation energy has the form suggested,
starting from molecular arguments, for instance by Cahn and Hilliard. Remark
that these fluids are sometimes also called Korteweg-de Vries or Cahn-Allen. In
general continua whose deformation energy depend on the second gradient of
placement are called second gradient (or Piola-Toupin or Mindlin or
Green-Rivlin or Germain or second gradient) continua. In the present paper, a
material description for second gradient continua is formulated. A Lagrangian
action is introduced in both material and spatial description and the
corresponding Euler-Lagrange bulk and boundary conditions are found. These
conditions are formulated in terms of an objective deformation energy volume
density in two cases: when this energy is assumed to depend on either C and
grad C or on C^-1 and grad C^-1 ; where C is the Cauchy-Green deformation
tensor. When particularized to energies which characterize fluid materials, the
capillary fluid evolution conditions (see e.g. Casal or Seppecher for an
alternative deduction based on thermodynamic arguments) are recovered. A
version of Bernoulli law valid for capillary fluids is found and, in the
Appendix B, useful kinematic formulas for the present variational formulation
are proposed. Historical comments about Gabrio Piola's contribution to
continuum analytical mechanics are also presented. In this context the reader
is also referred to Capecchi and Ruta.


  The paper addresses the problem of providing suitable reference trajectories
in motion planning problems for autonomous vehicles. Among the various
approaches to compute a reference trajectory, our aim is to find those
trajectories which optimize a given performance criterion, for instance fuel
consumption, comfort, safety, time, and obey constraints, e.g. collision
avoidance, safety regions, control bounds. This task can be approached by
geometric shortest path problems or by optimal control problems, which need to
be solved efficiently. To this end we use direct discretization schemes and
model-predictive control in combination with sensitivity updates to predict
optimal solutions in the presence of perturbations. Applications arising in
autonomous driving are presented. In particular, a distributed control
algorithm for traffic scenarios with several autonomous vehicles that use
car-to-car communication is introduced.


  Given the rapid evolution of threats to cyber systems, new management
approaches are needed that address risk across all interdependent domains
(i.e., physical, information, cognitive, and social) of cyber systems. Further,
the traditional approach of hardening of cyber systems against identified
threats has proven to be impossible. Therefore, in the same way that biological
systems develop immunity as a way to respond to infections and other attacks,
so too must cyber systems adapt to ever-changing threats that continue to
attack vital system functions, and to bounce back from the effects of the
attacks. Here, we explain the basic concepts of resilience in the context of
systems, discuss related properties, and make business case of cyber
resilience. We also offer a brief summary of ways to assess cyber resilience of
a system, and approaches to improving cyber resilience.


  Originally published as a Supplemental Appendix to Adjoint Equations in
Stability Analysis, Annu. Rev. Fluid Mech. 46:493-517 (2014)


  We study the effect of electron-phonon interactions on the electrical
conductance of a helical edge state of a two-dimensional topological insulator.
We show that the edge deformation caused by bulk acoustic phonons modifies the
spin texture of the edge state, and that the resulting spin-phonon coupling
leads to inelastic backscattering which makes the transport diffusive. Using a
semiclassical Boltzmann equation we compute the electrical conductivity and
show that it exhibits a metallic Bloch-Gr\"uneisen law. At temperatures on the
order of the Debye temperature of the host material, spin-phonon scattering
thus lowers the conductivity of the edge state drastically. Transport remains
ballistic only for short enough edges, and in this case the correction to the
quantized conductance vanishes as $\delta G \propto T^5$ at low temperatures.
Relying only on parallel transport of the helical spin texture along the
deformed edge, the coupling strength is determined by the host material's
density and sound velocity. Our results impose fundamental limits for the
finite-temperature conductivity of a helical edge channel.


  We present the first calculation of fermion spectral function at finite
temperature in quark-meson model in the framework of the functional
renormalization group (FRG). We compare the results in two truncations, after
first evolving flow equation of effective potential, we investigate the
spectral function either by taking the IR values as input to calculate one-loop
self-energy or by taking the scale-dependent values as input to evolve the flow
equation of the fermion two-point function. The latter one is a self-consistent
procedure in the framework of FRG. In both truncations, we find a multi-peak
structure in the spectral function, indicating quark collective excitations
realized in terms of the Landau damping. However, in contrast to fermion
zero-mode in the one-loop truncation, we find a fermion soft-mode in the
self-consistent truncation, which approaches the zero-mode as temperature
increases.


  Motivated by issues dealing with delivery of emergency medical products
during humanitarian disasters, this paper addresses the general problem of
delivering perishable items to remote demands accessible only by helicopters or
drones. Each drone operates out of platforms that may be move when not in use
and each drone has a limited delivery range to service a demand points.
Associated with each demand point is a disutility function, or a cost function,
with respect to time that reflects preferred delivery clock time for the
demanded item, as well as the item's perishability characteristic that models
nonincreasing quality with time. The paper first addresses the problem of
locating the platforms as well concurrently determining which platform serves
which demand points and in what order to minimize total disutility for product
delivery. The second scenario addresses the two-period problem where the
platforms can be relocated, using useable road networks, after the first
period. It can be easily proven that continuous time versions of these problems
are NP-Hard. However, a practical "time-slot" version of the problem, where
time is discretized into slots, can be solved by standard optimization
software. Extensive computational experiments, using different drone delivery
ranges as well as different drone fleet sizes, provide valuable insights on the
performance of such drone delivery systems.


  In this study, we present initial results of the coupling between the Inner
Magnetospheric Particle Transport and Acceleration Model (IMPTAM) and the
Versatile Electron Radiation Belt (VERB-3D) code. IMPTAM traces electrons of 10
- 100 keV energies from the plasma sheet (L = 9 Re) to inner L-shell regions.
The flux evolution modeled by IMPTAM is used at the low energy and outer L*
computational boundaries of the VERB code (assuming a dipole approximation) to
perform radiation belt simulations of energetic electrons. The model was tested
on the March 17th, 2013 storm, for a six-day period. Four different simulations
were performed and their results compared to satellites observations from Van
Allen probes and GOES. The coupled IMPTAM-VERB model reproduces evolution and
storm-time features of electron fluxes throughout the studied storm in
agreement with the satellite data (within ~0.5 orders of magnitude). Including
dynamics of the low energy population at L* = 6.6 increases fluxes closer to
the heart of the belt and has a strong impact in the VERB simulations at all
energies. However, inclusion of magnetopause losses leads to drastic flux
decreases even below L* = 3. The dynamics of low energy electrons (max. 10s of
keV) do not affect electron fluxes at energies >= 900 keV. Since the
IMPTAM-VERB coupled model is only driven by solar wind parameters and the Dst
and Kp indexes, it is suitable as a forecasting tool. In this study, we
demonstrate that the estimation of electron dynamics with
satellite-data-independent models is possible and very accurate.


  Porous carbon is well known as a good candidate for the development of
electrochemical double-layer capacitors. Predominantly, many conventional
carbons are microporous and often well described by the assumption of slit pore
geometry. However, there is a class of carbons that is significantly different
from the others, namely templated mesoporous carbons. In this work, we study
electrosorption-induced deformation in CMK-3-like mesopores having nonconvex
geometry applying a mean-field approach. The model is based on the modified
Poisson-Boltzmann equation taking into account the excluded volume of the ions
within the hard-sphere model in the Percus-Yevick approximation. We assume that
the deformation is caused by two effects: ion osmotic pressure and
electrostatic interactions of the electric double layers on charged rods. The
latter were calculated via the Maxwell stress tensor agreeing with the modified
Poisson-Boltzmann equation. We estimated the pore-load modulus of the
CMK-3-like material and found an agreement with the previously obtained values
by small-angle neutron scattering (SANS). Additionally, we studied the
differential capacitance in the non-convex pore geometry and found that the
behavior of the differential capacitance profiles was similar to that of the
profiles obtained for flat electric double layers. Namely, we obtained the
crowding regime at rather high voltages and more pronounced profile asymmetry
with increasing differences in the ionic sizes.


  Hybrid systems are characterized by having an interaction between continuous
dynamics and discrete events. The contribution of this paper is to provide
hybrid systems with a novel geometric formulation so that controls can be
added. Using this framework we describe some new global controllability tests
for hybrid control systems exploiting the geometry and the topology of the set
of jump points, where the instantaneous change of dynamics take place.
Controllability is understood as the existence of a feasible trajectory for the
system joining any two given points. As a result we describe examples where
none of the continuous control systems are controllable, but the associated
hybrid system is controllable because of the characteristics of the jump set.


  By numerical simulations, we investigate the onset of synchronization of
networked phase oscillators under two different weighting schemes. In scheme-I,
the link weights are correlated to the product of the degrees of the connected
nodes, so this kind of networks is named as the weight-degree correlated (WDC)
network. In scheme-II, the link weights are randomly assigned to each link
regardless of the node degrees, so this kind of networks is named as the
weight-degree uncorrelated (WDU) network. Interestingly, it is found that by
increasing a parameter that governs the weight distribution, the onset of
synchronization in WDC network is monotonically enhanced, while in WDU network
there is a reverse in the synchronization performance. We investigate this
phenomenon from the viewpoint of gradient network, and explain the contrary
roles of coupling gradient on network synchronization: gradient promotes
synchronization in WDC network, while deteriorates synchronization in WDU
network. The findings highlight the fact that, besides the link weight, the
correlation between the weight and node degree is also important to the network
dynamics.


  We investigate the phenomenology of the dark $Z$ boson, $Z_d$, which is
associated with a new Abelian gauge symmetry and couples to the standard model
particles via kinetic mixing $\varepsilon$ and mass mixing $\varepsilon_Z^{}$.
We examine two cases: (i) $Z_d$ is lighter than the $Z$ boson, and (ii) $Z_d$
is heavier than that. In the first case, it is known that $Z_d$ causes a
deviation in the weak mixing angle at low energies from the standard model
prediction. We study the prediction in the model and compare it with the latest
experimental data. In the second case, the $Z$-$Z_d$ mixing enhances the $W$
boson mass. We investigate the effect of $Z_d$ on various electroweak
observables including the $W$ boson mass using the $S$, $T$, and $U$
parameters. We point out an interesting feature: in the limit $\varepsilon \to
0$, the equation $S = - U$ holds independently of the mass of $Z_d$ and the
size of $\varepsilon_Z^{}$, while $|S|\gg |U|$ in many new physics models. We
find that the dark $Z$ boson with a mass of $O(100)~\mathrm{GeV}$ with a
relatively large mass mixing can reproduce the CDF result within $2\sigma$
while avoiding all other experimental constraints. Such dark $Z$ bosons are
expected to be tested at future high-energy colliders.


  AC-specific heat measurements remain as the foremost thermodynamic
experimental method to underpin phase transitions in tiny samples. However, its
performance under combined extreme conditions of high-pressure, very low
temperature and intense magnetic fields needs to be broadly extended for
investigation of quantum phase transition in strongly correlated electron
systems. In this communication, we discuss the determination of specific heat
on the quantum paramagnetic$-$insulator SrCu$_{2}$(BO$_{3}$)$_{2}$ by applying
the AC-specific heat technique under extreme conditions. In order to apply this
technique to insulating samples we sputtered a metallic thin film-heater and
attached thermometer onto sample. Besides that, we performed full frequency
scans with the aim to get quantitative specific heat data. Our results show
that we can determine the sample heat capacity within 5$\%$ of accuracy respect
to an adiabatic technique. This allows to uncover low energy scales that
characterize the ground state of quantum spin entanglement in
SrCu$_{2}$(BO$_{3}$)$_{2}$.


  Recently, deep learning has been advancing the state of the art in artificial
intelligence to a new level, and humans rely on artificial intelligence
techniques more than ever. However, even with such unprecedented advancements,
the lack of explanation regarding the decisions made by deep learning models
and absence of control over their internal processes act as major drawbacks in
critical decision-making processes, such as precision medicine and law
enforcement. In response, efforts are being made to make deep learning
interpretable and controllable by humans. In this paper, we review visual
analytics, information visualization, and machine learning perspectives
relevant to this aim, and discuss potential challenges and future research
directions.


  The field containing the candidate High Mass X-ray Binary IGR J01363+6610 was
observed by XMM-Newton on 2009 July 31 for 28 ks. A Be star was previously
suggested as the possible counterpart of the INTEGRAL source, and although
Chandra, during a 2007 observation, did not detect an X-ray source at the
position of the Be star, we find a variable source (XMMU J013549.5+661243) with
an average X-ray flux of 2e-13 ergs/cm2/s (0.2-12 keV, unabsorbed) at this
position with XMM-Newton. The spectrum of this source is consistent with a hard
power-law with a photon index of 1.4 +/- 0.3 and a column density of 1.5e22
cm^-2 (90% confidence errors). These results, along with our optical
investigation of other X-ray sources in the field, makes the association with
the Be star very likely, and the 2 kpc distance estimate for the Be star
indicates an X-ray luminosity of 9.1e31 ergs/s. This is lower than typical for
a Be X-ray binary, and the upper limit on the luminosity was even lower
(<1.4e31 ergs/s assuming the same spectral model) during the Chandra
observation. We discuss possible implications of the very low quiescent
luminosity for the physical properties of IGR J01363+6610.


  We have investigated the lepton flavor violation in the supersymmetric
framework assuming the large mixing angle MSW solution with the
quasi-degenerate and the inverse-hierarchical neutrino masses. In the case of
the quasi-degenerate neutrinos, the predicted branching ratio BR$(\mu \to e
\gamma)$ strongly depends on $m_\nu$ and $U_{e3}$. For $U_{e3}\simeq 0.05$ with
$m_\nu \simeq 0.3 \eV$, the prediction is close to the present experimental
upper bound if the right-handed Majorana neutrino masses are degenerate. On the
other hand, the prediction is larger than the experimental upper bound for
$U_{e3}\geq 0.05$ in the case of the inverse-hierarchical neutrino masses.


  Automated segmentation of medical imaging is of broad interest to clinicians
and machine learning researchers alike. The goal of segmentation is to increase
efficiency and simplicity of visualization and quantification of regions of
interest within a medical image. Image segmentation is a difficult task because
of multiparametric heterogeneity within the images, an obstacle that has proven
especially challenging in efforts to automate the segmentation of brain lesions
from non-contrast head computed tomography (CT). In this research, we have
experimented with multiple available deep learning architectures to segment
different phenotypes of hemorrhagic lesions found after moderate to severe
traumatic brain injury (TBI). These include: intraparenchymal hemorrhage (IPH),
subdural hematoma (SDH), epidural hematoma (EDH), and traumatic contusions. We
were able to achieve an optimal Dice Coefficient1 score of 0.94 using UNet++ 2D
Architecture with Focal Tversky Loss Function, an increase from 0.85 using UNet
2D with Binary Cross-Entropy Loss Function in intraparenchymal hemorrhage (IPH)
cases. Furthermore, using the same setting, we were able to achieve the Dice
Coefficient score of 0.90 and 0.86 in cases of Extra-Axial bleeds and Traumatic
contusions, respectively.


  We show canonicity and normalization for dependent type theory with a
cumulative sequence of universes and a type of Boolean. The argument follows
the usual notion of reducibility, going back to Godel's Dialectica
interpretation and the work of Tait. A key feature of our approach is the use
of a proof relevant notion of reducibility.


  A theorem of Scott gives an upper bound for the normalized volume of lattice
polygons with exactly $i>0$ interior lattice points. We will show that the same
bound is true for the normalized volume of lattice polytopes of degree 2 even
in higher dimensions. In particular, there is only a finite number of quadratic
polynomials with fixed leading coefficient being the $h^*$-polynomial of a
lattice polytope.


  The paper studies a Milne type problem for a linearized quantum Boltzmann
equation. Existence and uniqueness of the solution, together with asymptotic
properties are proven for a given energy flow. The energy flow is proportional
to the asymptotic limit of the mass flow, and the collision frequency is
aymptoticlaly cubic in velocity. The setting differs from the one for the
classical Boltzmann and related equations, where the fluid-dynamic mass flow
along half-line is constant. Here it is no more constant. Instead the study is
based on the energy flow which is no more fluid-dynamic, and on the entropy
flow which differs from the classical case.


  We analyze four-dimensional Friedmann-Lemaitre-Robertson-Walker cosmologies
in type IIB, arising from a M-theory dual, and find that the null energy
condition (NEC) has to be obeyed by them (except for the negatively curved
case) in order for the M-theory action to have a Wilsonian effective
description. However, this does not imply that the M-theory metric has to obey
the 11d NEC. Thus, we propose a new swampland conjecture -- the 4d NEC is a
consistency condition for any theory to have a completion within M-theory --
with an explicit derivation of it for cosmological backgrounds from a top-down
perspective. We briefly discuss the cosmological consequences of such a
condition derived from M-theory.


  *** To appear in Theory and Practice of Logic Programming (TPLP) ***
  Within the context of constraint solving, a portfolio approach allows one to
exploit the synergy between different solvers in order to create a globally
better solver. In this paper we present SUNNY: a simple and flexible algorithm
that takes advantage of a portfolio of constraint solvers in order to compute
--- without learning an explicit model --- a schedule of them for solving a
given Constraint Satisfaction Problem (CSP). Motivated by the performance
reached by SUNNY vs. different simulations of other state of the art
approaches, we developed sunny-csp, an effective portfolio solver that exploits
the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests
conducted on exhaustive benchmarks of MiniZinc models show that the actual
performance of SUNNY conforms to the predictions. This is encouraging both for
improving the power of CSP portfolio solvers and for trying to export them to
fields such as Answer Set Programming and Constraint Logic Programming.


  With molecular simulation for water and a tunable hydrophobic substrate, we
apply the instantaneous interface construction [A. P. Willard and D. Chandler,
J. Phys. Chem. B, 114, 1954 (2010)] to examine the similarity between a
water-vapor interface and a water-hydrophobic surface interface. The intrinsic
interface refers to molecular structure in terms of distances from the
instantaneous interface. We show that attractive interactions between a
hydrophobic surface and water affect capillary wave fluctuations of the
instantaneous liquid interface, but these attractive interactions have
essentially no effect on the intrinsic interface. Further, the intrinsic
interface of liquid water and a hydrophobic substrate differs little from that
of water and its vapor.The same is not true, we show, for an interface between
water and a hydrophilic substrate. In that case, strong directional
substrate-water interactions disrupt the liquid-vapor-like interfacial hydrogen
bonding network.


  We prove, with a purely analytic technique, a one-side Liouville theorem for
a class of Ornstein--Uhlenbeck operators ${\mathcal L_0}$ in $\mathbb{R}^N$, as
a consequence of a
  Liouville theorem at "$t=- \infty$" for the corresponding Kolmogorov
operators ${\mathcal L_0} - \partial_t$ in $\mathbb{R}^{N+1}$. In turn, this
last result is proved as a corollary of a global Harnack inequality for
non-negative solutions to $({\mathcal L_0} - \partial_t) u = 0$ which seems to
have an independent interest in its own right.
  We stress that our Liouville theorem for ${\mathcal L_0}$ cannot be obtained
by a probabilistic approach based on recurrence if $N>2$.
  We provide a self-contained proof of a Liouville theorem involving recurrent
Ornstein--Uhlenbeck stochastic processes in the Appendix.


  We study graviton-graviton scattering in partial-wave amplitudes after
unitarizing their Born terms. In order to apply $S$-matrix techniques, based on
unitarity and analyticity, we introduce an $S$-matrix associated to this
resummation that is free of infrared divergences. This is achieved by removing
the diverging phase factor calculated by Weinberg that multiplies the $S$
matrix, and that stems from the virtual infrared gravitons. A scalar
graviton-graviton resonance with vacuum quantum numbers is obtained as a pole
in the nonperturbative $S$-wave amplitude, which is called the graviball. Its
resonant effects along the physical real $s$-axis may peak at values
substantially lower than the UV cutoff squared of the theory, similarly to the
$\sigma$ resonance in QCD. These techniques are also applied to study
nonrelativistic Coulomb scattering up to next-to-leading order in the
unitarization program. A comparison with the exact known solution is very
illuminating.


  The new perturbation theory for the problem of nonstationary anharmonic
oscillator with polynomial nonstationary perturbation is proposed. As a zero
order approximation the exact wave function of harmonic oscillator with
variable frequency in external field is used. Based on some intrinsic
properties of unperturbed wave function the variational-iterational method is
proposed, that make it possible to correct both the amplitude and the phase of
wave function. As an application the first order correction are proposed both
for wave function and S-matrix elements for asymmetric perturbation potential
of type $V(x,\tau)=\alpha (\tau)x^3+\beta (\tau)x^4.$ The transition amplitude
''ground state - ground state'' $W_{00}(\lambda ;\rho)$ is analyzed in detail
depending on perturbation parameter $\lambda $ (including strong coupling
region $% \lambda $ $\sim 1$) and one-dimensional refraction coefficient $\rho
$.


  Message passing graph neural networks (GNNs) are known to have their
expressiveness upper-bounded by 1-dimensional Weisfeiler-Leman (1-WL)
algorithm. To achieve more powerful GNNs, existing attempts either require ad
hoc features, or involve operations that incur high time and space
complexities. In this work, we propose a general and provably powerful GNN
framework that preserves the scalability of the message passing scheme. In
particular, we first propose to empower 1-WL for graph isomorphism test by
considering edges among neighbors, giving rise to NC-1-WL. The expressiveness
of NC-1-WL is shown to be strictly above 1-WL and below 3-WL theoretically.
Further, we propose the NC-GNN framework as a differentiable neural version of
NC-1-WL. Our simple implementation of NC-GNN is provably as powerful as
NC-1-WL. Experiments demonstrate that our NC-GNN performs effectively and
efficiently on various benchmarks.


  Let $A$ be a right linear operator on a two-sided quaternionic Banach space
$X$. The paper studies the Drazin inverse for right linear operators on a
quaternionic Banach space. It is shown that if $A$ is Drazin invertible then
the Drazin inverse of $A$ is given by $f(A)$ where $f$ is $0$ in an axially
symmetric neighborhood of $0$ and $f(q) = q^{-1}$ in an axially symmetric
neighborhood of the nonzero spherical spectrum of $A$. Some results analogous
to the ones concerning the Drazin inverse of operators on complex Banach spaces
are proved in the quaternionic context.


  Domain shift significantly influences the performance of deep learning
algorithms, particularly for object detection within volumetric 3D images.
Annotated training data is essential for deep learning-based object detection.
However, annotating densely packed objects is time-consuming and costly.
Instead, we suggest training models on individually scanned objects, causing a
domain shift between training and detection data. To address this challenge, we
introduce the BugNIST dataset, comprising 9154 micro-CT volumes of 12 bug types
and 388 volumes of tightly packed bug mixtures. This dataset is characterized
by having objects with the same appearance in the source and target domains,
which is uncommon for other benchmark datasets for domain shift. During
training, individual bug volumes labeled by class are utilized, while testing
employs mixtures with center point annotations and bug type labels. Together
with the dataset, we provide a baseline detection analysis, with the aim of
advancing the field of 3D object detection methods.


  IIn recent years, there has been a growing interest in applying data
assimilation (DA) methods, originally designed for state estimation, to the
model selection problem. In this setting, Carrassi et al. (2017) introduced the
contextual formulation of model evidence (CME) and showed that CME can be
efficiently computed using a hierarchy of ensemble-based DA procedures.
Although Carrassi et al. (2017) analyzed the DA methods most commonly used for
operational atmospheric and oceanic prediction worldwide, they did not study
these methods in conjunction with localization to a specific domain. Yet any
application of ensemble DA methods to realistic geophysical models requires the
implementation of some form of localization. The present study extends the
theory for estimating CME to ensemble DA methods with domain localization. The
domain-localized CME (DL-CME) developed herein is tested for model selection
with two models: (i) the Lorenz 40-variable mid-latitude atmospheric dynamics
model (L95); and (ii) the simplified global atmospheric SPEEDY model. The CME
is compared to the root-mean-square-error (RMSE) as a metric for model
selection. The experiments show that CME improves systematically over the RMSE,
and that this skill improvement is further enhanced by applying localization in
the estimate of the CME, using the DL-CME. The potential use and range of
applications of the CME and DL-CME as a model selection metric are also
discussed.


  Let $C$ be a smooth complex projective curve and $G$ a connected complex
reductive group. We prove that if the center $Z(G)$ of $G$ is disconnected,
then the Kirwan map $H^*\big(\operatorname{Bun}(G,C),\mathbb{Q}\big)\rightarrow
H^*\big(\mathcal{M}_{\operatorname{Higgs}}^{\operatorname{ss}},\mathbb{Q}\big)$
from the cohomology of the moduli stack of $G$-bundles to the moduli stack of
semistable $G$-Higgs bundles, fails to be surjective: more precisely, the
"variant cohomology" (and variant intersection cohomology) of the stack
$\mathcal{M}_{\operatorname{Higgs}}^{\operatorname{ss}}$ of semistable
$G$-Higgs bundles, is always nontrivial. We also show that the image of the
pullback map
$H^*\big(M_{\operatorname{Higgs}}^{\operatorname{ss}},\mathbb{Q}\big)\rightarrow
H^*\big(\mathcal{M}_{\operatorname{Higgs}}^{\operatorname{ss}},\mathbb{Q}\big)$,
from the cohomology of the moduli space of semistable $G$-Higgs bundles to the
stack of semistable $G$-Higgs bundles, cannot be contained in the image of the
Kirwan map. The proof uses a Borel-Quillen--style localization result for
equivariant cohomology of stacks to reduce to an explicit construction and
calculation.


  A discretisation method with the $H_{\rm div}$ inner product for the electric
field integral equation~(EFIE) is proposed. The EFIE with the conventional
Galerkin discretisation shows bad accuracy for problems with a small frequency,
a problem known as the low-frequency breakdown. The discretisation method
proposed in this paper utilises the $H_{\rm div}$ scalar product with a scalar
coefficient for the Galerkin discretisation and overcomes the low-frequency
problem with an appropriately chosen coefficient. As regards the
preconditioning, we find that a naive use of the widely-used Calderon
preconditioning is not efficient for reducing the computational time with the
new discretisation. We therefore propose a new preconditioning which can
accelerate the computation successfully. The efficiency of the proposed
discretisation and preconditioning is verified through some numerical examples.


  We study the stability of extreme Reissner-Nordstr\"om-AdS black hole under
massless scalar perturbations. We show that the perturbation on the horizon of
the extreme Reissner-Nordstr\"om-AdS black hole experiences a power-law decay,
instead of an exponential decay as observed in the nonextreme AdS black hole.
On the horizon of the extreme Reissner-Nordstr\"om-AdS black hole, the blow up
happens at lower order derivative of the scalar field compared with that of the
extreme Reissner-Nordstr\"om black hole, which shows that extreme AdS black
holes tend to instability in comparison to black holes in asymptotic flat
space-times.


  It is possible for the SM fields to propagate into one or more large extra
compact dimensions. Such fields have associated KK excitations that produce
additional contributions to the SM processes. We calculate the effects that
these KK excitations have on cross sections for $e^{+}e^{-}$ collider processes
in a model in which the SM gauge bosons, and perhaps the Higgs, can propagate
into one TeV$^{-1}$-size extra compact dimension. Specifically, we consider
muon pair production, Bhabha scattering, dijet production, Higgs production,
and single-photon production. At prospective high energy $e^{+}e^{-}$
colliders, we find significant deviations from the SM cross sections: For
example, a 600 GeV collider produces a 20% reduction in the cross section for
muon pair production as compared to the SM for a compactification scale of 3.5
TeV. The effect is much smaller at LEP2 energies, where the compactification
scale must be lower than 2 TeV in order to produce a 6% effect.


  We study the duality between the two dimensional black hole and the
sine-Liouville conformal field theories via exact operator quantization of a
classical scattering problem. The ideas are first illustrated in Liouville
theory, which is dual to itself under the interchange of the Liouville
parameter b by 1/b. In both cases, a classical scattering problem does not
determine uniquely the quantum reflection coefficient. The latter is only fixed
by assuming that the dual scattering problem has the same reflection
coefficient. We also discuss the relation of this approach to the method that
exploits the parafermionic symmetry of the model to compute the reflection
coefficient.


  The rotational spectra of DCO+ and its two isotopomers D{13}^CO+ and
DC{18}^O+, produced in a negative glow discharge cell, have been recorded in
the 137-792 GHz region, which includes lines from J=2-1 up to J=11-10. The
determined rotational and centrifugal distortion constants allow to predict the
DCO+ rotational spectrum up to 1000 GHz with an accuracy of 1 part in 10^8 or
better. This is important for kinematic studies of dense molecular cloud cores
and for future far-infrared observations. We also report on the first detection
of the hyperfine structure of the DCO+(1-0) line made at the IRAM 30m antenna,
toward the quiescent starless cloud core L1512, in the Taurus Molecular Cloud.
We point out that this is the first observation of the hyperfine splitting due
to the deuteron. This allowed us to quantify the effects of the hyperfine
splitting on the line width determination; if the hyperfine structure is not
taken into account in the line fit, the DCO+(1-0) line width is overestimated
by a significant factor (> 2).


  In projective algebraic geometry, there are classical and fundamental results
that describe the structure of geometry and syzygies, and many of them
characterize varieties of minimal degree and del Pezzo varieties. In this
paper, we consider analogous objects in the category of higher secant
varieties. Our main theorems say that there is a matryoshka structure among
those basic objects including a generalized $K_{p,1}$ theorem, syzygetic and
geometric characterizations of higher secant varieties of minimal degree and
del Pezzo higher secant varieties, defined in this paper. For our purpose, we
prove a weak form of the generalized Bronowski's conjecture raised by C.
Ciliberto and F. Russo that relates the identifiability for higher secant
varieties to the geometry of tangential projections.


  The purpose of this foundational paper is to introduce various notions and
constructions in order to develop the homotopy theory for differential graded
operads over any ring. The main new idea is to consider the action of the
symmetric groups as part of the defining structure of an operad and not as the
underlying category. We introduce a new dual category of higher cooperads, a
new higher bar-cobar adjunction with the category of operads, and a new higher
notion of homotopy operads, for which we establish the relevant homotopy
properties. For instance, the higher bar-cobar construction provides us with a
cofibrant replacement functor for operads over any ring. All these
constructions are produced conceptually by applying the curved Koszul duality
for colored operads. This paper is a first step toward a new Koszul duality
theory for operads, where the action of the symmetric groups is properly taken
into account.


  Extending BLMSSM with exotic Higgs superfields $(\Phi_{NL},\varphi_{NL})$ and
superfields ($Y,Y^\prime$), one obtains the new model called as EBLMSSM, where
exotic leptons are heavy and have tree level couplings with SM lepton. In this
model, some new parameters with CP-violating phases are considered, so there
are new contributions to lepton anomalous magnetic dipole moments (MDMs) and
electric dipole moments (EDMs). Therefore, we study the one-loop, two-loop
Barr-Zee and two-loop Rainbow type corrections to lepton MDMs and EDMs in the
EBLMSSM. Considering the constraints from the lightest CP-even Higgs mass and
decays, we calculate the corresponding numerical results. In our used parameter
space, the new physics contributions to lepton MDMs are large, which can remedy
the deviation between the SM prediction and experimental result well. New
introduced CP-violating phases also affect the lepton EDMs in a certain degree.


  Neural radiance fields (NeRF) shows powerful performance in novel view
synthesis and 3D geometry reconstruction, but it suffers from critical
performance degradation when the number of known viewpoints is drastically
reduced. Existing works attempt to overcome this problem by employing external
priors, but their success is limited to certain types of scenes or datasets.
Employing monocular depth estimation (MDE) networks, pretrained on large-scale
RGB-D datasets, with powerful generalization capability would be a key to
solving this problem: however, using MDE in conjunction with NeRF comes with a
new set of challenges due to various ambiguity problems exhibited by monocular
depths. In this light, we propose a novel framework, dubbed D\"aRF, that
achieves robust NeRF reconstruction with a handful of real-world images by
combining the strengths of NeRF and monocular depth estimation through online
complementary training. Our framework imposes the MDE network's powerful
geometry prior to NeRF representation at both seen and unseen viewpoints to
enhance its robustness and coherence. In addition, we overcome the ambiguity
problems of monocular depths through patch-wise scale-shift fitting and
geometry distillation, which adapts the MDE network to produce depths aligned
accurately with NeRF geometry. Experiments show our framework achieves
state-of-the-art results both quantitatively and qualitatively, demonstrating
consistent and reliable performance in both indoor and outdoor real-world
datasets. Project page is available at https://ku-cvlab.github.io/DaRF/.


  We apply Kahan's discretisation method to three classes of 2-dimensional
quadratic vector fields with quadratic, resp cubic, resp quartic Hamiltonians.
We show that the maps obtained in this way can be geometrically understood as
the composition of two involutions, one of which is a (linear) symmetry switch,
and the other is a generalised Manin involution.


  A popular model of decoherence based on the linear coupling to harmonic
oscillator heat baths is analized and shown to be inappropriate in the regime
where decoherence dominates over energy dissipation, called pure decoherence
regime. The similar mechanism essentially related to the energy conservation
implies that, on the contrary to the recent conjectures, chaotic environments
can be less efficient decoherers than regular ones. Finally, the elastic
scattering mechanism is advocated as the simplest source of pure decoherence.


  Typically, voice conversion is regarded as an engineering problem with
limited training data. The reliance on massive amounts of data hinders the
practical applicability of deep learning approaches, which have been
extensively researched in recent years. On the other hand, statistical methods
are effective with limited data but have difficulties in modelling complex
mapping functions. This paper proposes a voice conversion method that works
with limited data and is based on stochastic variational deep kernel learning
(SVDKL). At the same time, SVDKL enables the use of deep neural networks'
expressive capability as well as the high flexibility of the Gaussian process
as a Bayesian and non-parametric method. When the conventional kernel is
combined with the deep neural network, it is possible to estimate non-smooth
and more complex functions. Furthermore, the model's sparse variational
Gaussian process solves the scalability problem and, unlike the exact Gaussian
process, allows for the learning of a global mapping function for the entire
acoustic space. One of the most important aspects of the proposed scheme is
that the model parameters are trained using marginal likelihood optimization,
which considers both data fitting and model complexity. Considering the
complexity of the model reduces the amount of training data by increasing the
resistance to overfitting. To evaluate the proposed scheme, we examined the
model's performance with approximately 80 seconds of training data. The results
indicated that our method obtained a higher mean opinion score, smaller
spectral distortion, and better preference tests than the compared methods.


  A one-shot algorithm called iterationless DANSE (iDANSE) is introduced to
perform distributed adaptive node-specific signal estimation (DANSE) in a fully
connected wireless acoustic sensor network (WASN) deployed in an environment
with non-overlapping latent signal subspaces. The iDANSE algorithm matches the
performance of a centralized algorithm in a single processing cycle while
devices exchange fused versions of their multichannel local microphone signals.
Key advantages of iDANSE over currently available solutions are its
iterationless nature, which favors deployment in real-time applications, and
the fact that devices can exchange fewer fused signals than the number of
latent sources in the environment. The proposed method is validated in
numerical simulations including a speech enhancement scenario.


  We consider the free energy of a mean-field quantum spin glass described by a
$ p $-spin interaction and a transversal magnetic field. Recent rigorous
results for the case $ p= \infty $, i.e. the quantum random energy model
(QREM), are reviewed. We show that the free energy of the $ p $-spin model
converges in a joint thermodynamic and $ p \to \infty $ limit to the free
energy of the QREM.


  Random projections are able to perform dimension reduction efficiently for
datasets with nonlinear low-dimensional structures. One well-known example is
that random matrices embed sparse vectors into a low-dimensional subspace
nearly isometrically, known as the restricted isometric property in compressed
sensing. In this paper, we explore some applications of random projections in
deep neural networks. We provide the expressive power of fully connected neural
networks when the input data are sparse vectors or form a low-dimensional
smooth manifold. We prove that the number of neurons required for approximating
a Lipschitz function with a prescribed precision depends on the sparsity or the
dimension of the manifold and weakly on the dimension of the input vector. The
key in our proof is that random projections embed stably the set of sparse
vectors or a low-dimensional smooth manifold into a low-dimensional subspace.
Based on this fact, we also propose some new neural network models, where at
each layer the input is first projected onto a low-dimensional subspace by a
random projection and then the standard linear connection and non-linear
activation are applied. In this way, the number of parameters in neural
networks is significantly reduced, and therefore the training of neural
networks can be accelerated without too much performance loss.


  We present AvatarReX, a new method for learning NeRF-based full-body avatars
from video data. The learnt avatar not only provides expressive control of the
body, hands and the face together, but also supports real-time animation and
rendering. To this end, we propose a compositional avatar representation, where
the body, hands and the face are separately modeled in a way that the
structural prior from parametric mesh templates is properly utilized without
compromising representation flexibility. Furthermore, we disentangle the
geometry and appearance for each part. With these technical designs, we propose
a dedicated deferred rendering pipeline, which can be executed in real-time
framerate to synthesize high-quality free-view images. The disentanglement of
geometry and appearance also allows us to design a two-pass training strategy
that combines volume rendering and surface rendering for network training. In
this way, patch-level supervision can be applied to force the network to learn
sharp appearance details on the basis of geometry estimation. Overall, our
method enables automatic construction of expressive full-body avatars with
real-time rendering capability, and can generate photo-realistic images with
dynamic details for novel body motions and facial expressions.


  We report on three numerical experiments on the implementation of Time-Delay
Interferometry (TDI) for LISA, performed with Synthetic LISA, a C++/Python
package that we developed to simulate the LISA science process at the level of
scientific and technical requirements. Specifically, we study the laser-noise
residuals left by first-generation TDI when the LISA armlengths have a
realistic time dependence; we characterize the armlength-measurements
accuracies that are needed to have effective laser-noise cancellation in both
first- and second-generation TDI; and we estimate the quantization and
telemetry bitdepth needed for the phase measurements. Synthetic LISA generates
synthetic time series of the LISA fundamental noises, as filtered through all
the TDI observables; it also provides a streamlined module to compute the TDI
responses to gravitational waves according to a full model of TDI, including
the motion of the LISA array and the temporal and directional dependence of the
armlengths. We discuss the theoretical model that underlies the simulation, its
implementation, and its use in future investigations on system characterization
and data-analysis prototyping for LISA.


  We describe methods for proving upper and lower bounds on infinite-time
averages in deterministic dynamical systems and on stationary expectations in
stochastic systems. The dynamics and the quantities to be bounded are assumed
to be polynomial functions of the state variables. The methods are
computer-assisted, using sum-of-squares polynomials to formulate sufficient
conditions that can be checked by semidefinite programming. In the
deterministic case, we seek tight bounds that apply to particular local
attractors. An obstacle to proving such bounds is that they do not hold
globally; they are generally violated by trajectories starting outside the
local basin of attraction. We describe two closely related ways past this
obstacle: one that requires knowing a subset of the basin of attraction, and
another that considers the zero-noise limit of the corresponding stochastic
system. The bounding methods are illustrated using the van der Pol oscillator.
We bound deterministic averages on the attracting limit cycle above and below
to within 1%, which requires a lower bound that does not hold for the unstable
fixed point at the origin. We obtain similarly tight upper and lower bounds on
stochastic expectations for a range of noise amplitudes. Limitations of our
methods for certain types of deterministic systems are discussed, along with
prospects for improvement.


  This paper proposes a multitask learning framework for probabilistic model
updating by jointly using strain and acceleration measurements. This framework
can enhance the structural damage assessment and response prediction of
existing steel frame structures with quantified uncertainty. Multitask learning
may be used to address multiple similar inference tasks simultaneously to
achieve a more robust prediction performance by transferring useful knowledge
from one task to another, even in situations of data scarcity. In the proposed
model-updating procedure, a spatial frame is decomposed into multiple planar
frames that are viewed as multiple tasks and jointly analyzed based on the
hierarchical Bayesian model, leading to robust estimation results. The
procedure uses a displacement-stress relationship in the modal space because it
directly reflects the elemental stiffness and requires no prior knowledge
concerning the mass, unlike most existing model-updating techniques. Validation
of the proposed framework by using a full-scale vibration test on a one-story,
one-bay by one-bay moment resisting steel frame, wherein structural damage to
the column bases is simulated by loosening the anchor bolts, is presented. The
experimental results suggest that the displacement-stress relationship has
sufficient sensitivity toward localized damage, and the Bayesian multitask
learning approach may result in the efficient use of measurements such that the
uncertainty involved in model parameter estimation is reduced. The proposed
framework facilitates more robust and informative model updating.


  We have obtained high resolution images of the central regions of 14
reverberation-mapped active galactic nuclei (AGN) using the Hubble Space
Telescope Advanced Camera for Surveys High Resolution Camera to account for
host-galaxy starlight contamination of measured AGN luminosities. We measure
the host-galaxy starlight contribution to the continuum luminosity at 5100 A
through the typical ground-based slit position and geometry used in the
reverberation-mapping campaigns. We find that removing the starlight
contribution results in a significant correction to the luminosity of each AGN,
both for lower luminosity sources, as expected, but also for the higher
luminosity sources such as the PG quasars. After accounting for the host galaxy
starlight, we revisit the well-known broad-line region radius--luminosity
relationship for nearby AGN. We find the power-law slope of the relationship
for the H beta line to be 0.518 +/- 0.039, shallower than previously reported
and consistent with the slope of 0.5 expected from the naive theoretical
assumption that all AGN have, on average, the same ionizing spectrum and the
same ionization parameter and gas density in the H beta line-emitting region.


  Many approaches for verifying input-output properties of neural networks have
been proposed recently. However, existing algorithms do not scale well to large
networks. Recent work in the field of model compression studied binarized
neural networks (BNNs), whose parameters and activations are binary. BNNs tend
to exhibit a slight decrease in performance compared to their full-precision
counterparts, but they can be easier to verify. This paper proposes a simple
mixed integer programming formulation for BNN verification that leverages
network structure. We demonstrate our approach by verifying properties of BNNs
trained on the MNIST dataset and an aircraft collision avoidance controller. We
compare the runtime of our approach against state-of-the-art verification
algorithms for full-precision neural networks. The results suggest that the
difficulty of training BNNs might be worth the reduction in runtime achieved by
our verification algorithm.


  In this study, the wake flow around a circular nano-cylinder is numerically
investigated with molecular dynamics simulation to reveal the micro/nano size
effect on the wake flow. The cavitation occurring when Reynolds number (Re) >
101 can effectively influence the wake flow. The Strouhal number (St) of the
wake flow increases with the Re at low Re, but steadily decreases with the Re
after the cavitation appears. The dominant frequency of the lift force
fluctuation can be higher than that of the velocity fluctuation, and be drowned
in the chaotic fluctuating background of the Brownian forces when Re {\geq}
127. Also because of the strong influence of the Brownian forces, no dominant
frequency of the drag force fluctuation can be observed. The Jz number, which
is defined as the ratio between the mean free path {\lambda} of the fluid
molecules and the equilibrium distance of potential energy {\sigma}, is newly
introduced in order to consider the internal size effect of fluid. The St of
the wake flow increases with the Jz until it falls to zero sharply when Jz
{\approx} 1.7. It denotes the discontinuity of the fluid can eventually
eliminate the vortex generation and shedding. Meanwhile, the St decreases with
the Kn because of the intensification of the cavitation.


  The Schur-Horn theorem is a classical result in matrix analysis which
characterizes the existence of positive semidefinite matrices with a given
diagonal and spectrum. In recent years, this theorem has been used to
characterize the existence of finite frames whose elements have given lengths
and whose frame operator has a given spectrum. We provide a new generalization
of the Schur-Horn theorem which characterizes the spectra of all possible
finite frame completions. That is, we characterize the spectra of the frame
operators of the finite frames obtained by adding new vectors of given lengths
to an existing frame. We then exploit this characterization to give a new and
simple algorithm for computing the optimal such completion.