  We consider a scheme of quantum teleportation where a receiver has multiple
(N) output ports and obtains the teleported state by merely selecting one of
the N ports according to the outcome of the sender's measurement. We
demonstrate that such teleportation is possible by showing an explicit protocol
where N pairs of maximally entangled qubits are employed. The optimal
measurement performed by a sender is the square-root measurement, and a perfect
teleportation fidelity is asymptotically achieved for a large N limit. Such
asymptotic teleportation can be utilized as a universal programmable processor.


  Recent results on diffractive dijet and vector boson production and exclusive
dijet production from the Collider Detector at Fermilab (CDF) experiment are
presented.


  Program semantics learning is the core and fundamental for various code
intelligent tasks e.g., vulnerability detection, clone detection. A
considerable amount of existing works propose diverse approaches to learn the
program semantics for different tasks and these works have achieved
state-of-the-art performance. However, currently, a comprehensive and
systematic study on evaluating different program representation techniques
across diverse tasks is still missed.
  From this starting point, in this paper, we conduct an empirical study to
evaluate different program representation techniques. Specifically, we
categorize current mainstream code representation techniques into four
categories i.e., Feature-based, Sequence-based, Tree-based, and Graph-based
program representation technique and evaluate its performance on three diverse
and popular code intelligent tasks i.e., {Code Classification}, Vulnerability
Detection, and Clone Detection on the public released benchmark. We further
design three {research questions (RQs)} and conduct a comprehensive analysis to
investigate the performance. By the extensive experimental results, we conclude
that (1) The graph-based representation is superior to the other selected
techniques across these tasks. (2) Compared with the node type information used
in tree-based and graph-based representations, the node textual information is
more critical to learning the program semantics. (3) Different tasks require
the task-specific semantics to achieve their highest performance, however
combining various program semantics from different dimensions such as control
dependency, data dependency can still produce promising results.


  We study the coherent dynamics of two interacting particles in a quantum
double-well and show that the Shannon entropy can be a definitive signature of
entanglement as an alternative to concur- rence, a connection not reported
previously. This physical model, involving tunneling and contact interaction,
is akin to the Hubbard model which explains the physics of interacting
particles in peri- odic potentials. We show that an interplay between tunneling
and contact interaction produces Bell like eigenstates ensuring thereby that
the concurrence and the Shannon entropy, two quantities with vastly different
physical interpretations, develop the same time dependence. Our result, applied
to three experimentally realized physical models works for both spatial and
spin degrees of freedom and is significant as it provides a novel measure of
entanglement applicable to many systems currently being explored for scalable
quantum computation.


  Proportional-integral-derivative (PID) controllers have been widely used in
the process industry. However, the satisfactory control performance of a PID
controller depends strongly on the tuning parameters. Conventional PID tuning
methods require extensive knowledge of the system model, which is not always
known especially in the case of complex dynamical systems. In contrast,
reinforcement learning-based PID tuning has gained popularity since it can
treat PID tuning as a black-box problem and deliver the optimal PID parameters
without requiring explicit process models. In this paper, we present a novel
entropy-maximizing twin-delayed deep deterministic policy gradient (EMTD3)
method for automating the PID tuning. In the proposed method, an
entropy-maximizing stochastic actor is employed at the beginning to encourage
the exploration of the action space. Then a deterministic actor is deployed to
focus on local exploitation and discover the optimal solution. The
incorporation of the entropy-maximizing term can significantly improve the
sample efficiency and assist in fast convergence to the global solution. Our
proposed method is applied to the PID tuning of a second-order system to verify
its effectiveness in improving the sample efficiency and discovering the
optimal PID parameters compared to traditional TD3.


  We study the problems of counting the homomorphisms, counting the copies, and
counting the induced copies of a $k$-vertex graph $H$ in a $d$-degenerate
$n$-vertex graph $G$. Our main result establishes exhaustive and explicit
complexity classifications for counting subgraphs and induced subgraphs. We
show that the (not necessarily induced) copies of $H$ in $G$ can be counted in
time $f(k,d)\cdot n^{\max(\mathsf{imn}(H),1)}\cdot \log n$, where $f$ is some
computable function and $\mathsf{imn}(H)$ is the size of the largest induced
matching of $H$. Whenever the class of allowed patterns has unbounded induced
matching number, this algorithm is essentially optimal: Unless the Exponential
Time Hypothesis (ETH) fails, there is no algorithm running in time $f(k,d)\cdot
n^{o(\mathsf{imn}(H)/\log \mathsf{imn}(H))}$ for any function $f$. In case of
counting induced subgraphs, we obtain a similar classification along the
independence number $\alpha$: we can count the induced copies of $H$ in $G$ in
time $f(k,d)\cdot n^{\alpha(H)}\cdot \log n$, and if the class of allowed
patterns has unbounded independence number, an algorithm running in time
$f(k,d)\cdot n^{o(\alpha(H)/\log \alpha(H))}$ is impossible, unless ETH fails.
In the language of parameterized complexity, our results yield dichotomies in
fixed-parameter tractable and $\#\mathsf{W}[1]$-hard cases if we parameterize
by the size of the pattern and the degeneracy of the host graph. Our results
imply that several patterns cannot be counted in time $f(k,d)\cdot n^{o(k/\log
k)}$, including $k$-matchings, $k$-independent sets, (induced) $k$-paths,
(induced) $k$-cycles, and induced $(k,k)$-bicliques, unless ETH fails. Those
lower bounds for exact counting are complemented with new algorithms for
approximate counting of subgraphs and induced subgraphs in degenerate graphs.


  We revisit the MIC-harmonic oscillator in flat space with monopole
interaction and derive the polynomial algebra satisfied by the integrals of
motion and its energy spectrum using the ad hoc recurrence approach. We
introduce a superintegrable monopole system in generalized Taub-NUT space. The
Schr\"{o}dinger equation of this model is solved in spherical coordinates in
the framework of St\"{a}ckel transformation. It is shown that wave functions of
the quantum system can be expressed in terms of the product of Laguerre and
Jacobi polynomials. We construct ladder and shift operators based on the
corresponding wave functions and obtain the recurrence formulas. By applying
these recurrence relations, we construct higher order algebraically independent
integrals of motion. We show the integrals form a polynomial algebra. We
construct the structure functions of the polynomial algebra and obtain the
degenerate energy spectra of the model.


  This paper introduces a probabilistic framework to quantify community
vulnerability towards power losses due to extreme weather events. To analyze
the impact of weather events on the power grid, the wind fields of historical
hurricanes from 2000 to 2018 on the Texas coast are modeled using their
available parameters, and probabilistic storm surge scenarios are constructed
utilizing the hurricane characteristics. The vulnerability of hurricanes and
storm surges is evaluated on a 2000 bus synthetic power grid model on the
geographical footprint of Texas. The load losses, obtained via branch and
substation outages, are then geographically represented at the county level and
integrated with the publicly available Social Vulnerability Index to evaluate
the Integrated Community Vulnerability Index (ICVI), which reflects the impacts
of these extreme weather events on the socioeconomic and community power
systems. The analysis concludes that the compounded impact of power outages due
to extreme weather events can amplify the vulnerability of affected
communities. Such analysis can help the system planners and operators make an
informed decision.


  The transmission grating spectrometers on AXAF have sufficient sensitivity
and energy resolution to detect resonance X-ray absorption lines in the spectra
of distant quasars. Such lines would be produced in an ionized, and somewhat
chemically enriched component of the inter-galactic medium. The bulk of the
baryonic content of the universe at redshifts <=2-3 could be in this form and
would thus be revealed by AXAF.


  The multilayer multiconfiguration time-dependent Hartree (ML-MCTDH) method
and the density matrix renormalization group (DMRG) are powerful workhorses
applied mostly in different scientific fields. Although both methods are based
on tensor network states, very different mathematical languages are used for
describing them. This severely limits knowledge transfer and sometimes leads to
re-inventions of ideas well known in the other field. Here, we review ML-MCTDH
and DMRG theory using both MCTDH expressions and tensor network diagrams. We
derive the ML-MCTDH equations of motions using diagrams and compare them with
time-dependent and time-independent DMRG algorithms. We further review two
selected recent advancements. The first advancement is related to optimizing
unoccupied single-particle functions in MCTDH, which corresponds to subspace
enrichment in the DMRG. The second advancement is related to finding optimal
tree structures and on highlighting similarities and differences of tensor
networks used in MCTDH and DMRG theories. We hope that this contribution will
foster more fruitful cross-fertilization of ideas between ML-MCTDH and DMRG.


  Water photolysis and hydrogen loss from the upper atmospheres of terrestrial
planets is of fundamental importance to climate evolution but remains poorly
understood in general. Here we present a range of calculations we performed to
study the dependence of water loss rates from terrestrial planets on a range of
atmospheric and external parameters. We show that CO2 can only cause
significant water loss by increasing surface temperatures over a narrow range
of conditions, with cooling of the middle and upper atmosphere acting as a
bottleneck on escape in other circumstances. Around G-stars, efficient loss
only occurs on planets with intermediate CO2 atmospheric partial pressures (0.1
to 1 bar) that receive a net flux close to the critical runaway greenhouse
limit. Because G-star total luminosity increases with time but XUV/UV
luminosity decreases, this places strong limits on water loss for planets like
Earth. In contrast, for a CO2-rich early Venus, diffusion limits on water loss
are only important if clouds caused strong cooling, implying that scenarios
where the planet never had surface liquid water are indeed plausible. Around
M-stars, water loss is primarily a function of orbital distance, with planets
that absorb less flux than ~270 W/m^2 (global mean) unlikely to lose more than
one Earth ocean of H2O over their lifetimes unless they lose all their
atmospheric N2/CO2 early on. Because of the variability of H2O delivery during
accretion, our results suggest that many 'Earth-like' exoplanets in the
habitable zone may have ocean-covered surfaces, stable CO2/H2O-rich
atmospheres, and high mean surface temperatures.


  We provide APASS photometry in the Landolt BV and Sloan g'r'i' bands for all
the 425,743 stars included in the latest 4th RAVE Data Release. The internal
accuracy of the APASS photometry of RAVE stars, expressed as error of the mean
of data obtained and separately calibrated over a median of 4 distinct
observing epochs and distributed between 2009 and 2013, is 0.013, 0.012, 0.012,
0.014 and 0.021 mag for B, V, g', r' and i' band, respectively. The equally
high external accuracy of APASS photometry has been verified on secondary
Landolt and Sloan photometric standard stars not involved in the APASS
calibration process, and on a large body of literature data on field and
cluster stars, confirming the absence of offsets and trends. Compared with the
Carlsberg Meridian Catalog (CMC-15), APASS astrometry of RAVE stars is accurate
to a median value of 0.098 arcsec. Brightness distribution functions for the
RAVE stars have been derived in all bands. APASS photometry of RAVE stars,
augmented by 2MASS JHK infrared data, has been chi2 fitted to a densely
populated synthetic photometric library designed to widely explore in
temperature, surface gravity, metallicity and reddening. Resulting Teff and
E(B-V), computed over a range of options, are provided and discussed, and will
be kept updated in response to future APASS and RAVE data releases. In the
process it is found that the reddening caused by an homogeneous slab of dust,
extending for 140 pc on either side of the Galactic plane and responsible for
E(B-V,poles)=0.036 +/- 0.002 at the galactic poles, is a suitable approximation
of the actual reddening encountered at Galactic latitudes |b|>=25 deg.


  In this paper the authors describe a theoretical simple statistical modelling
of relaxation process in metal-oxide semiconductor devices that governs its
degradation. Basically, starting from an initial state where a given number of
traps are occupied, the dynamics of the relaxation process is measured
calculating the density of occupied traps and its fluctuations (second moment)
as function of time. Our theoretical results show a universal logarithmic law
for the density of occupied traps $\bar{<n(t)>} \sim \phi (T,E_{F}) (A+B \ln
t)$, i.e., the degradation is logarithmic and its amplitude depends on the
temperature and Fermi Level of device. Our approach reduces the work to the
averages determined by simple binomial sums that are corroborated by our Monte
Carlo simulations and by experimental results from literature, which bear in
mind enlightening elucidations about the physics of degradation of
semiconductor devices of our modern life.


  The 2.12 micron v=1-0 S(1) line of molecular hydrogen has been imaged in the
Hourglass region of M8. The line is emitted from a roughly bipolar region,
centred around the O7 star Herschel 36. The peak H2 1-0 S(1) line intensity is
8.2 x 10E-15 erg s-1 cm-2 arcsec-2. The line centre emission velocity varies
from -25 kms in the SE lobe to +45 kms in the NW lobe. The distribution is
similar to that of the CO J=3-2 line. The H2 line appears to be shock-excited
when a bipolar outflow from Herschel 36 interacts with the ambient molecular
cloud. The total luminosity of all H2 lines is estimated to be ~ 16 Lsun and
the mass of the hot molecular gas ~9 x 10E-4 Msun (without any correction for
extinction).


  The SAE AutoDrive Challenge is a three-year collegiate competition to develop
a self-driving car by 2020. The second year of the competition was held in June
2019 at MCity, a mock town built for self-driving car testing at the University
of Michigan. Teams were required to autonomously navigate a series of
intersections while handling pedestrians, traffic lights, and traffic signs.
Zeus is aUToronto's winning entry in the AutoDrive Challenge. This article
describes the system design and development of Zeus as well as many of the
lessons learned along the way. This includes details on the team's
organizational structure, sensor suite, software components, and performance at
the Year 2 competition. With a team of mostly undergraduates and minimal
resources, aUToronto has made progress towards a functioning self-driving
vehicle, in just two years. This article may prove valuable to researchers
looking to develop their own self-driving platform.


  This paper describes the efficient method of fogging in Tensai Gothalo.
Tensai Gothalo is a novel dynamic router device developed in Gautam-Asami
Laboratory of Wakkanai Hokusei Gakuen University which has sensing, actuating,
monitoring and movable capability. Similarly, fogging is a new concept of cloud
computing at which the data plane is defined in user device. In this paper we
would like to present the stepwise explanation about how to fog in Tensai
Gothalo. Furthermore, we will elaborate a technique to decentralize data with
improvement in QoS and reducing latency without affecting the legacy services
of clouds that can still work together while needed.


  By focusing on the asymmetric shape of cross section, we analyze the pairing
effect on the partial wave components of cross section for neutron elastic
scattering off stable and unstable nuclei within the Hartree-Fock-Bogoliubov
(HFB) framework. Explicit expressions for Fano parameters $q_{lj}$ and
  $\epsilon_{lj}$ have been derived and the pairing effects have been analyzed
in term of these parameters, and the Fano effect was found on the neutron
elastic scattering off the stable nucleus in terms of the pairing correlation.
Fano effect was appeared as the asymmetric line-shape of the cross section
caused by the small absolute value of $q_{lj}$ due to the small pairing effect
on the deep-lying hole state of the stable nucleus. In the case of the unstable
nuclei, the large $q_{lj}$ value is expected because of the small absolute
value of the Fermi energy. The quasiparticle resonance with the large $q_{lj}$
forms the Breit-Wigner type shape in the elastic scattering cross section.


  For a normal F-finite variety $X$ and a boundary divisor $\Delta$ we give a
uniform description of an ideal which in characteristic zero yields the
multiplier ideal, and in positive characteristic the test ideal of the pair
$(X,\Delta)$. Our description is in terms of regular alterations over $X$, and
one consequence of it is a common characterization of rational singularities
(in characteristic zero) and F-rational singularities (in characteristic $p$)
by the surjectivity of the trace map $\pi_* \omega_Y \to \omega_X$ for every
such alteration $\pi \: Y \to X$.
  Furthermore, building on work of B. Bhatt, we establish up-to-finite-map
versions of Grauert-Riemenscheneider and Nadel/Kawamata-Viehweg vanishing
theorems in the characteristic $p$ setting without assuming $W2$ lifting, and
show that these are strong enough in some applications to extend sections.


  We introduce the Mori-Zwanzig (MZ) Modal Decomposition (MZMD), a novel
technique for performing modal analysis of large scale spatio-temporal
structures in complex dynamical systems, and show that it represents an
efficient generalization of Dynamic Mode Decomposition (DMD). The MZ formalism
provides a mathematical framework for constructing non-Markovian reduced-order
models of resolved variables from high-dimensional dynamical systems,
incorporating the effects of unresolved dynamics through the memory kernel and
orthogonal dynamics. We present a formulation and analysis of the modes and
spectrum from MZMD and compare it to DMD when applied to a complex flow: a
Direct Numerical Simulation (DNS) data-set of laminar-turbulent boundary-layer
transition flow over a flared cone at Mach 6. We show that the addition of
memory terms by MZMD improves the resolution of spatio-temporal structures
within the transitional/turbulent regime, which contains features that arise
due to nonlinear mechanisms, such as the generation of the so-called "hot"
streaks on the surface of the flared cone. As a result, compared to DMD, MZMD
improves future state prediction accuracy, while requiring nearly the same
computational cost.


  Complex determinants, Gramians and blades (simple or decomposable
multivectors) are related to volumes, but not in the same way as real ones. We
present their complete geometric interpretations, including complex
orientations. We also link real and complex concepts via a reality index, which
measures the lack of holomorphy of real subspaces of $\mathbb{C}^n$. These
results lead to volumetric Pythagorean theorems, a new complex Hodge star, and
interpretations for Grassmannian metrics, among other uses.


  Transient detection and flux measurement via image subtraction stand at the
base of time domain astronomy. Due to the varying seeing conditions, the image
subtraction process is non-trivial, and existing solutions suffer from a
variety of problems. Starting from basic statistical principles, we develop the
optimal statistic for transient detection, flux measurement and any
image-difference hypothesis testing. We derive a closed-form statistic that:
(i) Is mathematically proven to be the optimal transient detection statistic in
the limit of background-dominated noise; (ii) Is numerically stable; (iii) For
accurately registered, adequately sampled images, does not leave subtraction or
deconvolution artifacts; (iv) Allows automatic transient detection to the
theoretical sensitivity limit by providing credible detection significance; (v)
Has uncorrelated white noise; (vi) Is a sufficient statistic for any further
statistical test on the difference image, and in particular, allows to
distinguish particle hits and other image artifacts from real transients; (vii)
Is symmetric to the exchange of the new and reference images; (viii) Is at
least an order of magnitude faster to compute than some popular methods; and
(ix) Is straightforward to implement. Furthermore, we present extensions of
this method that make it resilient to registration errors, color-refraction
errors, and any noise source that can be modelled. In addition, we show that
the optimal way to prepare a reference image is the proper image coaddition
presented in Zackay \& Ofek (2015b). We demonstrate this method on simulated
data and real observations from the Palomar Transient Factory data release 2.
We provide an implementation of this algorithm in MATLAB and Python.


  Standard optical pump-probe methods analyze a system's temporal response to a
laser pulse within sub-femtoseconds to several nanoseconds, constrained by the
optical delay line's length. While resistance is a sensitive detector in
various fields, its measurements are typically slow (>microseconds) due to
stabilization requirements. We suggest here a time-resolved pump-probe
technique which combines an optical pump pulse and a rectangular electrical
probe pulse through the sample, measuring transmission in a 50 Ohm matched
circuit with a digital oscilloscope. This allows electrically-driven delays
from nanoseconds to seconds. Demonstrations include studying heat-induced
changes in a thin amorphous VO$_x$ film and carrier relaxation in a CdS
photoresistor, showcasing potential applications in heat transfer, biochemical
reactions, and gradual electronic transformations.


  The accurate determination of the galaxy cluster mass-observable relations is
one of the major challenge of modern astrophysics and cosmology. We present a
new statistical methodology to constrain the evolution of the mass-observable
relations. Instead of measuring individual mass of galaxy clusters, we only
consider large scale homogeneity of the Universe. In this case, we expect the
present galaxy cluster mass function to be the same everywhere in the Universe.
Using relative abundance matching, we contraint the relation between the
richness, $\lambda(z)$, and the expected present mass, $M(t_0)$, of galaxy
clusters. We apply this approach to the redMaPPer galaxy cluster catalogue in
10 redshift bins from $z=0.1$ to $0.6$. We found that the $\lambda(z)$-$M(t_0)$
relation is not evolving from $z=0.1$ to $0.4$, whereas it starts to
significantly evolve at higher redshift. This results implies that the
redMaPPer richness appears to be a better proxy for the expected present-day
galaxy cluster mass than for the mass at the observational redshift. Assuming
cosmology and galaxy cluster mass accretion history, it is possible to convert
$M(t_0)$ to the mass at the galaxy cluster redshift $M(t_z)$. We found a
significant evolution of the $\lambda(z)$-$M(t_z)$ over all the covered
redshift range. Consequently, we provide a new redshift-dependent richness-mass
relation for the redMaPPer galaxy cluster catalogue. This results demonstrates
the efficiency of this new methodology to probe the evolution of scaling
relations compared to individual galaxy cluster mass estimation.


  The influence of carbon concentration on the superconductivity (SC) in
MgC$_{x}$Ni$_3$ has been investigated by measuring the low temperature specific
heat combined with first principles electronic structure calculation. It is
found that the specific heat coefficient $\gamma_n=C_{en}/T$ of the
superconducting sample ($x\approx1$) in normal state is twice that of the
non-superconducting one ($x\approx 0.85$). The comparison of measured
$\gamma_n$ and the calculated electronic density of states (DOS) shows that the
effective mass renormalization changes remarkably as the carbon concentration
changes. The large mass renormalization for the superconducting sample and the
low $T_{c}$(7K) indicate that more than one kind of boson mediated
electron-electron interactions exist in MgC$_{x}$Ni$_3$.


  In the relative trace formula approach to the arithmetic Gan-Gross-Prasad
conjecture, we formulate a local conjecture (arithmetic transfer) in the case
of an exotic smooth formal moduli space of p-divisible groups, associated to a
unitary group relative to a ramified quadratic extension of a p-adic field. We
prove our conjecture in the case of a unitary group in three variables.


  The $l_2$ flattening lemma of Johnson and Lindenstrauss [JL84] is a powerful
tool for dimension reduction. It has been conjectured that the target dimension
bounds can be refined and bounded in terms of the intrinsic dimensionality of
the data set (for example, the doubling dimension). One such problem was
proposed by Lang and Plaut [LP01] (see also
[GKL03,MatousekProblems07,ABN08,CGT10]), and is still open. We prove another
result in this line of work:
  The snowflake metric $d^{1/2}$ of a doubling set $S \subset l_2$ embeds with
constant distortion into $l_2^D$, for dimension $D$ that depends solely on the
doubling constant of the metric. In fact, the distortion can be made
arbitrarily close to 1, and the target dimension is polylogarithmic in the
doubling constant. Our techniques are robust and extend to the more difficult
spaces $l_1$ and $l_\infty$, although the dimension bounds here are
quantitatively inferior than those for $l_2$.


  Let $M$ be a smooth, orientable, closed, connected $4$-manifold and suppose
that $H_1(M;\mathbb{Z})$ is finitely generated and has no $2$-torsion. We give
a homotopy decomposition of the suspension of $M$ in terms of spheres, Moore
spaces and $\Sigma\mathbb{C}P^{2}$. This is used to calculate any reduced
generalized cohomology theory of $M$ as a group and to determine the homotopy
types of certain current groups and gauge groups.


  I extend the definitions of schemes relative to monoids with zero - and
therefore, toric geometry - to the world of formal schemes. This expands the
usual framework to include, for instance, models for Mumford's degenerating
Abelian varieties.
  Following the usual toric paradigm, normal formal monoid schemes can be
classified in terms of certain cone complexes, and their properties understood
in combinatorial terms. I use this to give a simple algebraisation criterion.
  I also reformulate the traditional notions of separated and proper morphism
in a manner amenable to the context of relative formal geometry, and give
characterisations in terms of the topology of cone complexes.


  We study a class of second order variational inequalities with bilateral
constraints. Under certain conditions we show the existence of a unique
viscosity solution of these variational inequalities and give a stochastic
representation to this solution. As an application, we study a stochastic game
with stopping times and show the existence of a saddle point equilibrium.


  Sparsely-gated mixture-of-experts (MoE) has been widely adopted to scale deep
learning models to trillion-plus parameters with fixed computational cost. The
algorithmic performance of MoE relies on its token routing mechanism that
forwards each input token to the right sub-models or experts. While token
routing dynamically determines the amount of expert workload at runtime,
existing systems suffer inefficient computation due to their static execution,
namely static parallelism and pipelining, which does not adapt to the dynamic
workload. We present Flex, a highly scalable stack design and implementation
for MoE with dynamically adaptive parallelism and pipelining. Flex designs an
identical layout for distributing MoE model parameters and input data, which
can be leveraged by all possible parallelism or pipelining methods without any
mathematical inequivalence or tensor migration overhead. This enables adaptive
parallelism/pipelining optimization at zero cost during runtime. Based on this
key design, Flex also implements various MoE acceleration techniques.
Aggregating all techniques, Flex finally delivers huge speedup at any scale --
4.96x and 5.75x speedup of a single MoE layer over 16 and 2,048 A100 GPUs,
respectively, over the previous state-of-the-art. Our evaluation shows that
Flex efficiently and effectively runs a real-world MoE-based model named
SwinV2-MoE, built upon Swin Transformer V2, a state-of-the-art computer vision
architecture. On efficiency, Flex accelerates SwinV2-MoE, achieving up to 1.55x
and 2.11x speedup in training and inference over Fairseq, respectively. On
effectiveness, the SwinV2-MoE model achieves superior accuracy in both
pre-training and down-stream computer vision tasks such as COCO object
detection than the counterpart dense model, indicating the readiness of Flex
for end-to-end real-world model training and inference.


  In this article, we study the index of log Calabi--Yau pairs $(X,B)$ of
coregularity 0. We show that $2\lambda(K_X+B)\sim 0$, where $\lambda$ is the
Weil index of $(X,B)$. This is in contrast to the case of klt Calabi--Yau
varieties, where the index can grow doubly exponentially with the dimension.
Our sharp bound on the index extends to the context of generalized log
Calabi--Yau pairs, semi-log canonical pairs, and isolated log canonical
singularities of coregularity 0. As a consequence, we show that the index of a
variety appearing in the Gross--Siebert program or in the Kontsevich--Soibelman
program is at most $2$. Finally, we discuss applications to Calabi--Yau
varieties endowed with a finite group action, including holomorphic symplectic
varieties endowed with a purely non-symplectic automorphism.


  We introduce the matroid-minor coalgebra C, which has labeled matroids as
distinguished basis, and coproduct given by splitting a matroid into a
submatroid and complementary contraction all possible ways. We introduce two
new bases for C; the first of these is is related to the distinguished basis by
Mobius inversion over the rank-preserving weak order on matroids, the second by
Mobius inversion over the suborder excluding matroids that are irreducible with
respect to the free product operation. We show that the subset of each of these
bases corresponding to the set of irreducible matroids is a basis for the
subspace of primitive elements of C. Projecting C onto the matroid-minor Hopf
algebra H, we obtain bases for the subspace of primitive elements of H.


  The largest gap in our understanding of nature at the fundamental level is
perhaps a unified description of gravity and quantum theory. Although there are
currently a variety of theoretical approaches to this question, experimental
research in this field is inhibited by the expected Planck-scale suppression of
quantum-gravity effects. However, the breakdown of spacetime symmetries has
recently been identified as a promising signal in this context: a number of
models for underlying physics can accommodate minuscule Lorentz and CPT
violation, and such effects are amenable to ultrahigh-precision tests. This
presentation will give an overview of the subject. Topics such as motivations,
the SME test framework, mechanisms for relativity breakdown, and experimental
tests will be reviewed. Emphasis is given to observations involving antimatter.


  We used two-dimensional Finte-Difference-Time-Domain (FDTD) software to study
the transition behavior of nano-particles from scatterers to an optically
uniform medium. We measured the transmission efficiency of the dipole source,
which is located in the high refractive index medium(index=2.00) and
encapsulated by low index resin(index=1.41). In an effort to compose
index-matched resin and to reduce internal reflection, high-index
nano-particles are added to low-index resin in simulations of various sizes and
densities. As the size of the nano-particles and the average spacing between
particles are reduced to 0.02 lambda and 0.07 lambda respectively, the
transmission efficiency improves two-fold compared to that without
nanoparticles. The numerical results can be used to understand the optical
behavior of nano-particles and to improve the extraction efficiency of high
brightness light-emitting-diodes(LEDs), through the use of nano-composite
encapsulant.


  Tight-binding model on a finite chain is studied with four-fold alternated
hopping parameters $t_{1,2,3,4}$. Imposing the open boundary conditions, the
corresponding recursion is solved analytically with special attention paid to
the occurrence of edge states. Corresponding results are strongly corroborated
by numeric calculations. It is shown that in the system there exist four
different edge phases if the number of sites is odd, and eight edges phases if
the chain comprises even number of sites. Phases are labelled by
$\sigma_1\equiv{\rm sgn}(t_1t_3-t_2t_4)$, $\sigma_2\equiv{\rm
sgn}(t_1t_4-t_2t_3)$ and $\sigma_3\equiv{\rm sgn}(t_1t_2-t_3t_4)$. It is shown
that these quantities represent gauge invariant topological indices emerging in
the corresponding infinite chains.


  A new approach is given to property $(P_q)$ defined by Catlin for $q=1$ in a
global and by Sibony in a local context, subsequently extended by Fu-Straube
for $q>1$. This property is known to imply compactness and global regularity in
the $\bar\partial$-Neumann problem by a result of Kohn-Nirenberg, as well as
condition $R$ by a result of Bell-Ligocka. In particular, we provide a
self-contained proof of property $(P_q)$ for pseudoconvex hypersurfaces of
finite D'Angelo $q$-type, the case originally studied by Catlin. Moreover, our
proof covers more general classes of hypersurfaces inspired by a recent work of
Huang-Yin. Proofs are broken down into isolated steps, some of which do not
require pseudoconvexity.
  Our tools include: a new multitype invariant based on distinguished nested
sequences of $(1,0)$ subbundles, defined in terms of derivatives of the Levi
form; real and complex formal orbits; $k$-jets of functions relative to pairs
of formal submanifolds; relative contact orders generalizing the usual contact
orders; a new notion of supertangent vector fields having higher than expected
relative contact orders; and a formal variant of a result by Diederich-Forn\ae
ss arising as a key step in their proof of Kohn's ideal termination in the
real-analytic case.


  In the path integral expression for a Feynman propagator of a spinless
particle of mass $m$, the path integral amplitude for a path of proper length
${\cal R}(x,x'| g_{\mu\nu})$ connecting events $x$ and $x'$ in a spacetime
described by the metric tensor $g_{\mu\nu}$ is $\exp-[m {\cal R}(x,x'|
g_{\mu\nu})]$. In a recent paper, assuming the path integral amplitude to be
invariant under the duality transformation ${\cal R} \to (L_P^2/{\cal R})$,
Padmanabhan has evaluated the modified Feynman propagator in an arbitrary
curved spacetime. He finds that the essential feature of this `principle of
path integral duality' is that the Euclidean proper distance $(\Delta x)^2$
between two infinitesimally separated spacetime events is replaced by $[(\Delta
x)^2 + 4L_P^2 ]$. In other words, under the duality principle the spacetime
behaves as though it has a `zero-point length' $L_P$, a feature that is
expected to arise in a quantum theory of gravity. In the Schwinger's proper
time description of the Feynman propagator, the weightage factor for a path
with a proper time $s$ is $\exp-(m^2s)$. Invoking Padmanabhan's `principle of
path integral duality' corresponds to modifying the weightage factor
$\exp-(m^2s)$ to $\exp-[m^2s + (L_P^2/s)]$. In this paper, we use this modified
weightage factor in Schwinger's proper time formalism to evaluate the quantum
gravitational corrections to some of the standard quantum field theoretic
results in flat and curved spacetimes. We find that the extra factor
$\exp-(L_P^2/s)$ acts as a regulator at the Planck scale thereby `removing' the
divergences that otherwise appear in the theory. Finally, we discuss the wider
implications of our analysis.


  We report a time-resolved study of the dynamics associated with the slow
compaction of a granular column submitted to thermal cycles. The column height
displays a complex behavior: for a large amplitude of the temperature cycles,
the granular column settles continuously, experiencing a small settling at each
cycle; By contrast, for small-enough amplitude, the column exhibits a
discontinuous and intermittent activity: successive collapses are separated by
quiescent periods whose duration is exponentially distributed. We then discuss
potential mechanisms which would account for both the compaction and the
transition at finite amplitude.


  In this paper, we consider the problem of real-time transmission scheduling
over time-varying channels. We first formulate the transmission scheduling
problem as a Markov decision process (MDP) and systematically unravel the
structural properties (e.g. concavity in the state-value function and
monotonicity in the optimal scheduling policy) exhibited by the optimal
solutions. We then propose an online learning algorithm which preserves these
structural properties and achieves -optimal solutions for an arbitrarily small
. The advantages of the proposed online method are that: (i) it does not
require a priori knowledge of the traffic arrival and channel statistics and
(ii) it adaptively approximates the state-value functions using piece-wise
linear functions and has low storage and computation complexity. We also extend
the proposed low-complexity online learning solution to the prioritized data
transmission. The simulation results demonstrate that the proposed method
achieves significantly better utility (or delay)-energy trade-offs when
comparing to existing state-of-art online optimization methods.


  This paper presents a novel path integral formalism for Einstein's theory of
gravitation from the viewpoint of optimal control theory. Despite its close
relation to the well-known variational principles of physicists, optimal
control turns out to be more general. Within this context, a Lagrangian
different from the Einstein-Hilbert Lagrangian is defined. Einstein field
equations are recovered exactly with variations of the new action functional.
The quantum theory is obtained using Ashtekar variables and the loop scalar
product. By means of example, the tunneling process of a black hole into
another black hole or into a white hole is investigated with a toy model.


  A pseudopotential of $C_{60}^-$ has been constructed from ab-initio
quantum-mechanical calculations. Since the obtained pseudopotential can be
easily fitted by rather simple analytical approximation it can be effectively
used both in classical and quantum molecular dynamics of fullerene-based
compounds.


  Let $\cal A$ be a maximal (or more generally a hereditary) order in a central
simple algebra over a global field $F$ of positive characteristic. We study the
reduction of the modular scheme of $\cal A$-elliptic sheaves at all places of
$F$. We show that some of these varieties - for different $\cal A$ - are twists
of each other. This result can be viewed as a global analogue of the
Cherednik-Drinfeld theorem for these varieties.


  The arc-routing problems are known to be notoriously hard. We study here a
natural arc-routing problem on trees and more generally on bounded tree-width
graphs and surprisingly show that it can be solved in a polynomial time. This
implies a sub-exponential algorithm for the planar graphs and small number of
maintaining cars, which is of practical relevance.


  This paper presents a two-dimensional infinite dipole array system with a
mushroom-like high-impedance surface (HIS) ground plane with wide-angle
scanning capability in the E-plane. The unit cell of the proposed antenna array
consists of a dipole antenna and a four-by-four HIS ground. The simulation
results show that the proposed antenna array can achieve a wide scanning angle
of up to 65$^{\circ}$ in the E-plane with an excellent impedance match and a
small $S11$. Floquet mode analysis is utilized to analyze the active impedance
and the reflection coefficient. Good agreement is obtained between the
theoretical results and the simulations. Using numerical and theoretical
analyses, we reveal the mechanism of such excellent wide scanning properties.
For the range of small scanning angles, these excellent properties result
mainly from the special reflection phase of the HIS ground, which can cause the
mutual coupling between the elements of the real array to be compensated by the
mutual coupling effect between the real array and the mirror array. For the
range of large scanning angles, since the surface wave (SW) mode could be
resonantly excited by a high-order Floquet mode $\textrm{TM}_{-1,0}$ from the
array and since the SW mode could be converted into a leaky wave (LW) mode by
the scattering of the array, the radiation field from the LW mode is nearly in
phase with the direct radiating field from the array. Therefore, with help from
the special reflection phase of the HIS and the designed LW mode of the HIS
ground, the antenna array with an HIS ground can achieve wide-angle scanning
performance.


  We study topological phase transitions in discrete gauge theories in two
spatial dimensions induced by the formation of a Bose condensate. We analyse a
general class of euclidean lattice actions for these theories which contain one
coupling constant for each conjugacy class of the gauge group. To probe the
phase structure we use a complete set of open and closed anyonic string
operators. The open strings allow one to determine the particle content of the
condensate, whereas the closed strings enable us to determine the matrix
elements of the modular $S$-matrix, also in the broken phase. From the measured
broken $S$-matrix we may read off the sectors that split or get identified in
the broken phase, as well as the sectors that are confined. In this sense the
modular $S$-matrix can be employed as a matrix valued non-local order parameter
from which the low-energy effective theories that occur in different regions of
parameter space can be fully determined.
  To verify our predictions we studied a non-abelian anyon model based on the
quaternion group $H=\bar{D_2}$ of order eight by Monte Carlo simulation. We
probe part of the phase diagram for the pure gauge theory and find a variety of
phases with magnetic condensates leading to various forms of (partial)
confinement in complete agreement with the algebraic breaking analysis. Also
the order of various transitions is established.


  In general, recommender systems are designed to provide personalized items to
a user. But in few cases, items are recommended for a group, and the challenge
is to aggregate the individual user preferences to infer the recommendation to
a group. It is also important to consider the similarity of characteristics
among the members of a group to generate a better recommendation. Members of an
automatically identified group will have similar characteristics, and reaching
a consensus with a decision-making process is preferable in this case. It
requires users-items and their rating interactions over a utility matrix to
auto-detect the groups in group recommendations. We may not overlook other
intrinsic information to form a group. The textual information also plays a
pivotal role in user clustering. In this paper, we auto-detect the groups based
on the textual similarity of the metadata (review texts). We consider the order
in user preferences in our models. We have conducted extensive experiments over
two real-world datasets to check the efficacy of the proposed models. We have
also conducted a competitive comparison with a baseline model to show the
improvements in the quality of recommendations.


  Quantum mechanics---the theory describing the fundamental workings of
nature---is famously counterintuitive: it predicts that a particle can be in
two places at the same time, and that two remote particles can be inextricably
and instantaneously linked. These predictions have been the topic of intense
metaphysical debate ever since the theory's inception early last century.
However, supreme predictive power combined with direct experimental observation
of some of these unusual phenomena leave little doubt as to its fundamental
correctness. In fact, without quantum mechanics we could not explain the
workings of a laser, nor indeed how a fridge magnet operates. Over the last
several decades quantum information science has emerged to seek answers to the
question: can we gain some advantage by storing, transmitting and processing
information encoded in systems that exhibit these unique quantum properties?
Today it is understood that the answer is yes. Many research groups around the
world are working towards one of the most ambitious goals humankind has ever
embarked upon: a quantum computer that promises to exponentially improve
computational power for particular tasks. A number of physical systems,
spanning much of modern physics, are being developed for this task---ranging
from single particles of light to superconducting circuits---and it is not yet
clear which, if any, will ultimately prove successful. Here we describe the
latest developments for each of the leading approaches and explain what the
major challenges are for the future.


  Image metrics predict the perceived per-pixel difference between a reference
image and its degraded (e. g., re-rendered) version. In several important
applications, the reference image is not available and image metrics cannot be
applied. We devise a neural network architecture and training procedure that
allows predicting the MSE, SSIM or VGG16 image difference from the distorted
image alone while the reference is not observed. This is enabled by two
insights: The first is to inject sufficiently many un-distorted natural image
patches, which can be found in arbitrary amounts and are known to have no
perceivable difference to themselves. This avoids false positives. The second
is to balance the learning, where it is carefully made sure that all image
errors are equally likely, avoiding false negatives. Surprisingly, we observe,
that the resulting no-reference metric, subjectively, can even perform better
than the reference-based one, as it had to become robust against
mis-alignments. We evaluate the effectiveness of our approach in an image-based
rendering context, both quantitatively and qualitatively. Finally, we
demonstrate two applications which reduce light field capture time and provide
guidance for interactive depth adjustment.


  The interaction of ultrasonic waves with fluid-embedded viscoelastic plates,
pipes, and shells, have been subject to extensive theoretical and experimental
studies over several decades. In normal-incidence through-transmission
measurements of a water-embedded solid plate using ultrasonic piezoelectric
transducer sound fields, significant deviations from plane wave theory have
recently been observed. To quantitatively describe such measured phenomena,
finite element modeling (FEM), also combined with an angular spectrum method
(ASM), have been used for three-dimensional (3D) simulation of the
voltage-to-sound-pressure signal propagation through the electro-acoustic
measurement system consisting of the piezoelectric transducer, the
water-embedded steel plate, and the fluid regions at both sides of the plate.
The observed phenomena of frequency downshift of the plate resonance, increased
sound pressure level through the plate, and beam narrowing / widening, are
ascribed to the finite angular spectrum of the beam, that excites a region of
negative and zero group velocity for the leaky Lamb mode in question.


  In this paper we study two functions $F(x)$ and $J(x)$, originally found by
Herglotz in 1923 and later rediscovered and used by one of the authors in
connection with the Kronecker limit formula for real quadratic fields. We
discuss many interesting properties of these functions, including special
values at rational or quadratic irrational arguments as rational linear
combinations of dilogarithms and products of logarithms, functional equations
coming from Hecke operators, and connections with Stark's conjecture. We also
discuss connections with 1-cocycles for the modular group
$\mathrm{PSL}(2,\mathbb{Z})$.


  Knowledge-based visual question answering (VQA) involves questions that
require world knowledge beyond the image to yield the correct answer. Large
language models (LMs) like GPT-3 are particularly helpful for this task because
of their strong knowledge retrieval and reasoning capabilities. To enable LM to
understand images, prior work uses a captioning model to convert images into
text. However, when summarizing an image in a single caption sentence, which
visual entities to describe are often underspecified. Generic image captions
often miss visual details essential for the LM to answer visual questions
correctly. To address this challenge, we propose PromptCap (Prompt-guided image
Captioning), a captioning model designed to serve as a better connector between
images and black-box LMs. Different from generic captions, PromptCap takes a
natural-language prompt to control the visual entities to describe in the
generated caption. The prompt contains a question that the caption should aid
in answering. To avoid extra annotation, PromptCap is trained by examples
synthesized with GPT-3 and existing datasets. We demonstrate PromptCap's
effectiveness on an existing pipeline in which GPT-3 is prompted with image
captions to carry out VQA. PromptCap outperforms generic captions by a large
margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks
(60.4% on OK-VQA and 59.6% on A-OKVQA). Zero-shot results on WebQA show that
PromptCap generalizes well to unseen domains.


  In this Part 2 we study further experimental properties of two-layer exchange
flows in a stratified inclined duct (SID), which are turbulent,
strongly-stratified, shear-driven, and continuously-forced. We analyse the same
state-of-the-art data sets using the same core shear layer methodology as in
Part 1, but we focus here on turbulent energetics and mixing statistics. The
detailed analysis of kinetic and scalar energy budgets reveals the specificity
and scalings of SID turbulence, while energy spectra provide insight into the
current strengths and limitations of our experimental data. The anisotropy of
the flow at different scales characterises the turbulent kinetic energy
production and dissipation mechanisms of Holmboe waves and turbulence. We then
assess standard mixing parameterisations models relying on uniform eddy
diffusivities, mixing lengths, flux parameters, buoyancy Reynolds numbers or
turbulent Froude numbers, and we compare representative values with the
stratified mixing literature. The dependence of these measures of mixing on
controllable flow parameters is also elucidated, providing asymptotic estimates
that may be extrapolated to more strongly turbulent flows, quantified by the
product of the tilt angle of the duct and the Reynolds number. These insights
may serve as benchmark for the future generation of experimental data with
superior spatio-temporal resolution required to probe increasingly vigorous
turbulence.


  In this paper, we consider the low-energy scale inverse seesaw mechanism in
which the observed neutrino mass and lepton mixing are explained by introducing
right handed neutrinos and the gauge-singlet fermions with experimentally
testable energy scale. Moreover, the presence of such new fermions leads to
unitarity violation in lepton mixing due to significantly large mixing between
active neutrinos and the heavy fermions. In addition to this, such large lepton
mixing also gives rise to potentially large lepton flavor violation, which
allows to constrain the non-unitarity parameters via lepton flavor violating
decays ($l_i \to l_j \gamma$). We make use of these constraints on
non-unitarity parameters and investigate their effects on the determination of
current unknown oscillation parameters at long-baseline experiments. We find
that non-unitarity parameters are sensitive to NO$\nu$A experiment. However, it
is observed that NO$\nu$A experiment is not expected to improve the current
knowledge of non-unitarity parameter $\eta_{21}$. We also find that the
sensitivities to current unknowns are deteriorated significantly in presence of
non-unitary lepton mixing and these sensitivities crucially depend upon the new
CP-violating phase in the non-unitary mixing. Further, we find that the
degeneracy resolution capability of NO$\nu$A experiment is reduced in the
presence of non-unitarity parameters. However, the synergy between the
currently running experiments T2K and NO$\nu$A can improve the parameter
degeneracy resolution and hence there is enhancement in the sensitivities of
unknowns.


  There has been an increasing research focus on quantum algorithms for
condensed matter systems recently, particularly on calculating energy band
structures. Here, we propose a quantum algorithm, the powered full quantum
eigensolver(P-FQE), by using the exponentiation of operators of the full
quantum eigensolver(FQE). This leads to an exponential increase in the success
probability of measuring the target state in certain circumstances where the
number of generating elements involved in the exponentiation of operators
exhibit a log polynomial dependence on the number of orbitals. Furthermore, we
conduct numerical calculations for band structure determination of the twisted
double-layer graphene. We experimentally demonstrate the feasibility and
robustness of the P-FQE algorithm using superconducting quantum computers for
graphene and Weyl semimetal. One significant advantage of our algorithm is its
ability to reduce the requirements of extremely high-performance hardware,
making it more suitable for energy spectra determination on noisy
intermediate-scale quantum (NISQ) devices.


  Electrocardiogram (ECG) is an authoritative source to diagnose and counter
critical cardiovascular syndromes such as arrhythmia and myocardial infarction
(MI). Current machine learning techniques either depend on manually extracted
features or large and complex deep learning networks which merely utilize the
1D ECG signal directly. Since intelligent multimodal fusion can perform at the
stateof-the-art level with an efficient deep network, therefore, in this paper,
we propose two computationally efficient multimodal fusion frameworks for ECG
heart beat classification called Multimodal Image Fusion (MIF) and Multimodal
Feature Fusion (MFF). At the input of these frameworks, we convert the raw ECG
data into three different images using Gramian Angular Field (GAF), Recurrence
Plot (RP) and Markov Transition Field (MTF). In MIF, we first perform image
fusion by combining three imaging modalities to create a single image modality
which serves as input to the Convolutional Neural Network (CNN). In MFF, we
extracted features from penultimate layer of CNNs and fused them to get unique
and interdependent information necessary for better performance of classifier.
These informational features are finally used to train a Support Vector Machine
(SVM) classifier for ECG heart-beat classification. We demonstrate the
superiority of the proposed fusion models by performing experiments on
PhysioNets MIT-BIH dataset for five distinct conditions of arrhythmias which
are consistent with the AAMI EC57 protocols and on PTB diagnostics dataset for
Myocardial Infarction (MI) classification. We achieved classification accuracy
of 99.7% and 99.2% on arrhythmia and MI classification, respectively.


  Continual learning algorithms are typically exposed to untrusted sources that
contain training data inserted by adversaries and bad actors. An adversary can
insert a small number of poisoned samples, such as mislabeled samples from
previously learned tasks, or intentional adversarial perturbed samples, into
the training datasets, which can drastically reduce the model's performance. In
this work, we demonstrate that continual learning systems can be manipulated by
malicious misinformation and present a new category of data poisoning attacks
specific for continual learners, which we refer to as {\em Poisoning Attacks
Against Continual Learners} (PACOL). The effectiveness of labeling flipping
attacks inspires PACOL; however, PACOL produces attack samples that do not
change the sample's label and produce an attack that causes catastrophic
forgetting. A comprehensive set of experiments shows the vulnerability of
commonly used generative replay and regularization-based continual learning
approaches against attack methods. We evaluate the ability of label-flipping
and a new adversarial poison attack, namely PACOL proposed in this work, to
force the continual learning system to forget the knowledge of a learned
task(s). More specifically, we compared the performance degradation of
continual learning systems trained on benchmark data streams with and without
poisoning attacks. Moreover, we discuss the stealthiness of the attacks in
which we test the success rate of data sanitization defense and other outlier
detection-based defenses for filtering out adversarial samples.


  We show by a detailed study of the mean-field approximation, the Gaussian
approximation, the perturbation expansion, and the field-theoretic
renormalization-group analysis of a $\varphi^{3}$ theory that its instability
fixed points with their associated instability exponents are quite probably
relevant to the scaling and universality behavior exhibited by the first-order
phase transitions in a field-driven scalar $\phi^4$ model below its critical
temperature near their instability points. Finite-time scaling and leading
corrections to scaling are considered. We also show that the instability
exponents of the first-order phase transitions equal those of the Yang--Lee
edge singularity and employ the latter to improve our estimates of the former.
The outcomes agree well with existent numerical results.


  This paper has been withdrawn as it has been superseded by postings
arXiv:quant-ph/0702147 and arXiv:0712.2935


  We study the simply connected inextendable Lorentzian surfaces admitting a
Killing vector field. We construct a natural family of such surfaces, that we
call "universal extensions". They are characterized by a condition of symmetry,
the "reflexivity", and a by a rather weak completeness assumption, the absence
of "saddles at infinity". Considering these surfaces as model spaces, we study
their minimal quotients, divisible open sets and conjugate points. We show
uniformisation results (by an open subset of one of these universal extensions,
which is uniquely determined) in the following cases: compact surfaces and
analytical surfaces. It allows us to give a classification of Lorentzian tori
and Klein bottles with a Killing vector field.


  The observation of the nature and world represents the main source of human
knowledge on the basis of our reason. At the present it is also the use of
precise measurement approaches, which may contribute significantly to the
knowledge of the world but cannot substitute fully the knowledge of the whole
reality obtained also with the help of our senses. It is not possible to omit
the ontological nature of matter world. However, any metaphysical consideration
was abandoned when mainly under the influence of positivistic philosophy
phenomenological models started to be strongly preferred and any intuitive
approach based on human senses has been refused. Their success in application
region has seemed to provide decisive support for such preference. However, it
is limited practically to the cases when only interpolation between measured
data is involved. When the extrapolation is required the ontological models are
much more reliable and practically indispensable in realistic approach.


  Quantum measurements can produce randomness arising from the uncertainty
principle. When measuring a state with von Neumann measurements, the intrinsic
randomness can be quantified by the quantum coherence of the state on the
measurement basis. Unlike projection measurements, there are additional and
possibly hidden degrees of freedom in apparatus for generic measurements. We
propose an adversary scenario for general measurements with arbitrary input
states, based on which, we characterize the intrinsic randomness.
Interestingly, we discover that under certain measurements, such as the
symmetric and information-complete measurement, all states have nonzero
randomness, inspiring a new design of source-independent random number
generators without state characterization. Furthermore, our results show that
intrinsic randomness can quantify coherence under general measurements, which
generalizes the result in the standard resource theory of state coherence.


  We extend some results by Gol'dshtein, Kuz'minov, and Shvedov about the
$L_p$-cohomology of warped cylinders to $L_{p,q}$-cohomology for different $p$
and $q$. As an application, we establish some sufficient conditions for the
nontriviality of the $L_{p,q}$-torsion of a surface of revolution in terms of
some Hardy constants.


  We prove that in both the free and the wired uniform spanning forest (FUSF
and WUSF) of any unimodular random rooted network (in particular, of any Cayley
graph), it is impossible to distinguish the connected components of the forest
from each other by invariantly defined graph properties almost surely. This
confirms a conjecture of Benjamini, Lyons, Peres and Schramm.
  We use this to answer positively two additional questions of Benjamini,
Lyons, Peres and Schramm under the assumption of unimodularity. We prove that
on any unimodular random rooted network, the FUSF is either connected or has
infinitely many connected components almost surely, and, if the FUSF and WUSF
are distinct, then every component of the FUSF is transient and
infinitely-ended almost surely. All of these results are new even for Cayley
graphs.


  We review our current knowledge about a particular case of decoupled gas
kinematics -- inner ionized-gas polar disks. Though more difficult to be
noticed, they seem to be more numerous than their large-scale counterparts; our
recent estimates imply about 10 per cent of early-type disk galaxies to be
hosts of inner polar disks. Since in the most cases the kinematics of the inner
polar gaseous disks is decoupled from the kinematics of the outer large-scale
gaseous disks and since they nested around very old stellar nuclei, we
speculate that the inner polar disks may be relics of very early events of
external gas accretion several Gyr ago. Such view is in agreement with our new
paradigm of the disk galaxies evolution.


  Inertial particles advected in chaotic flows often accumulate in strange
attractors. While moving in these fractal sets they usually approach each other
and collide. Here we consider inertial particles aggregating upon collision.
The new particles formed in this process are larger and follow the equation of
motion with a new parameter. These particles can in turn fragment when they
reach a certain size or shear forces become sufficiently large. The resulting
system consists of a large set of coexisting dynamical systems with a varying
number of particles. We find that the combination of aggregation and
fragmentation leads to an asymptotic steady state. The asymptotic particle size
distribution depends on the mechanism of fragmentation. The size distributions
resulting from this model are consistent with those found in rain drop
statistics and in stirring tank experiments.


  Fibre optic technology connects the world through the Internet, enables
remote sensing, and connects disparate functional optical devices. Highly
confined silicon photonics promises extreme scale and functional integration.
However, the optical modes of silicon nanowire waveguides and optical fibres
are very different, making efficient fibre-chip coupling a challenge. Vertical
grating couplers, the dominant coupling method today, have limited optical
bandwidth and are naturally out-of-plane. Here we demonstrate a new method that
is low-loss, broadband, easily manufacturable, and naturally planar. We
adiabatically couple a tapering silicon nanowire waveguide to a conic
nanotapered optical fibre, measuring transmission between 2.0 and 2.2 micron
wavelength. The silicon chip is fabricated at a commercial foundry and then
post-processed to release the tapering nanowires. We estimate an optimal
per-coupler transmission of -0.48 dB (maximum; 95% confidence interval [+0.46,
-1.68] dB) and a 1-dB bandwidth of 295 nm . With automated measurements, we
quantify the device tolerance to lateral misalignment, measuring a flat
response within +/- 0.968 micron. This design can enable low-loss modular
systems of integrated photonics irrespective of material and waveband.


  In this paper we evaluate the relevance of the model size for speaker
identification. We show that it is possible to improve the identification rates
if a different model size is used for each speaker. We also present some
criteria for selecting the model size, and a new algorithm that outperforms the
classical system with a fixed model size.


  Efficient and effective real-world image super-resolution (Real-ISR) is a
challenging task due to the unknown complex degradation of real-world images
and the limited computation resources in practical applications. Recent
research on Real-ISR has achieved significant progress by modeling the image
degradation space; however, these methods largely rely on heavy backbone
networks and they are inflexible to handle images of different degradation
levels. In this paper, we propose an efficient and effective
degradation-adaptive super-resolution (DASR) network, whose parameters are
adaptively specified by estimating the degradation of each input image.
Specifically, a tiny regression network is employed to predict the degradation
parameters of the input image, while several convolutional experts with the
same topology are jointly optimized to specify the network parameters via a
non-linear mixture of experts. The joint optimization of multiple experts and
the degradation-adaptive pipeline significantly extend the model capacity to
handle degradations of various levels, while the inference remains efficient
since only one adaptively specified network is used for super-resolving the
input image. Our extensive experiments demonstrate that the proposed DASR is
not only much more effective than existing methods on handling real-world
images with different degradation levels but also efficient for easy
deployment. Codes, models and datasets are available at
https://github.com/csjliang/DASR.


  This paper conveys the importance of using suitable data visualizations for
electrical energy consumption and the effect it carries on reducing said
consumption. Data visualization tools construct an important pillar in energy
micro-moments, i.e., the concept of providing the right information at the
right time in the right way for a specific power consumer. Such behavioral
change can be triggered with the help of good recommendations and suitable
visualizations to convey the right message. A questionnaire is built as a
mobile application to evaluate different groups of conventional and novel
visualizations. Conventional charts are restricted to bar, line and stacked
area charts, while novel visualizations contain heatmap, spiral and
appliance-level stacked bar charts. Significant findings gathered from
participants' responses indicate that they are slightly inclined towards
conventional charts. However, their understanding of the novel charts is better
by 8% when the analysis questions are investigated. Finally, a question is
answered on whether a group of visualizations should be discarded completely,
or some modifications can be applied.


  This review describes the evidence for small-scale structure, `clumping', in
the radiation line-driven winds of hot, massive stars. In particular, we focus
on examining to what extent simulations of the strong instability inherent to
line-driving can explain the multitude of observational evidence for wind
clumping, as well as on how to properly account for extensive structures in
density and velocity when interpreting the various wind diagnostics used to
derive mass-loss rates.


  Large Language Models (LLMs), with their abilities in knowledge acquisition
and reasoning, can potentially enhance the various aspects of Self-adaptive
Systems (SAS). Yet, the potential of LLMs in SAS remains largely unexplored and
ambiguous, due to the lack of literature from flagship conferences or journals
in the field, such as SEAMS and TAAS. The interdisciplinary nature of SAS
suggests that drawing and integrating ideas from related fields, such as
software engineering and autonomous agents, could unveil innovative research
directions for LLMs within SAS. To this end, this paper reports the results of
a literature review of studies in relevant fields, summarizes and classifies
the studies relevant to SAS, and outlines their potential to specific aspects
of SAS.


  During November 26-29, 2003 XMM-Newton observed soft (0.2-2 keV) X-ray
emission from Jupiter for 69 hours. The low-latitude X-ray disk emission of
Jupiter is observed to be almost uniform in intensity with brightness that is
consistent with a solar-photon driven process. The simultaneous lightcurves of
Jovian equatorial X-rays and solar X-rays (measured by the TIMED/SEE and GOES
satellites) show similar day-to-day variability. A large solar X-ray flare
occurring on the Jupiter-facing side of the Sun is found to have a
corresponding feature in the Jovian X-rays. These results support the
hypothesis that X-ray emission from Jovian low-latitudes are solar X-rays
scattered from the planet's upper atmosphere, and suggest that the Sun directly
controls the non-auroral X-rays from Jupiter's disk. Our study also suggests
that Jovian equatorial X-rays can be used to monitor the solar X-ray flare
activity on the hemisphere of the Sun that is invisible to space weather
satellites.


  We discuss the recent NA50 J/Psi production data in Pb--Pb collisions, in
particular the second drop at high transverse energies which correspond to the
most central collisions. Using a model which relates the J/Psi suppression to
the local energy density, we show that the data can be explained by taking into
account transverse energy fluctuations at a given impact parameter. Predictions
of this model for RHIC are briefly discussed.


  We rederive relativistic hydrodynamics as a Lagrangian effective theory using
the doubled coordinates technique, allowing us to include dissipative terms. We
include Navier-Stokes shear and bulk terms, as well as Israel-Stewart
relaxation time terms, within this formalism. We show how the inclusion of
shear viscosity, and the requirement of a bounded energy-momentum "vacuum",
forces the inclusion of the Israel-Stewart term into the theory, thereby
providing a justification for the origin and uniqueness of these terms.


  Elementary integration methods for waveguides are compared. One using
non-local loading rules based on Heaviside step functions, one using input side
integration and one using integration of the output of traveling velocity
waves. We show that most approaches can be made consistent with the wave
equation in principle, under proper circumstances. Of all methods studied the
Heaviside method is the only method shown to not suffer from undesirable
numerical difficulties and amendable to standard waveguide loop filtering
practices, yet it gives incorrect results for Neumann boundary conditions. We
also discuss localized velocity excitations, time-limited input-side
excitations and the relation of loop filters to wave variable type.


  This paper revisits the modeling of multicomponent diffusion within the
framework of thermodynamics of irreversible processes. We briefly review the
two well-known main approaches, leading to the generalized Fick-Onsager
multicomponent diffusion fluxes or to the generalized Maxwell-Stefan equations.
The latter approach has the advantage that the resulting fluxes are consistent
with non-negativity of the partial mass densities for non-singular and
non-degenerate Maxwell-Stefan diffusivities. On the other hand, this approach
requires computationally expensive matrix inversions since the fluxes are only
implicitly given. We propose and discuss a novel and more direct closure which
avoids the inversion of the Maxwell-Stefan equations. It is shown that all
three closures are actually equivalent under the natural requirement of
positivity for the concentrations, thus revealing the general structure of
continuum thermodynamical diffusion fluxes. As a special case, the new closure
also gives rise to a core-diagonal diffusion model in which only those
cross-effects are present that are necessary to guarantee consistency with
total mass conservation, plus a compositional dependence of the diffusivity.
This core-diagonal closure turns out to provide a rigorous fundament for recent
extensions of the Darken equation from binary mixtures to the general
multicomponent case. As an outcome of our investigation, we also address
different questions related to the sign of multicomponent thermodynamic or
Fickian diffusion coefficients. We show rigorously that in general the second
law requires positivity properties for tensors and operators rather than for
scalar diffusivities.


  Let X=Sl(3,Z)\Sl(3,R)/SO(3,R). Let N(lambda) denote the dimension of the
space of cusp forms with Laplace eigenvalue less than lambda. We prove that
N(lambda)=C lambda^(5/2)+O(lambda^2) where C is the appropriate constant
establishing Weyl's law with a good error term for the noncompact space X. The
proof uses the Selberg trace formula in a form that is modified from the work
of Wallace and also draws on results of Stade and Wallace and techniques of
Huntley and Tepper. We also, in the course of the proof, give an upper bound on
the number of cusp forms that can violate the Ramanujan conjecture.


  We consider high dimensional random optimization problems where the dynamical
variables are subjected to non-convex excluded volume constraints. We focus on
the case in which the cost function is a simple quadratic cost and the excluded
volume constraints are modeled by a perceptron constraint satisfaction problem.
We show that depending on the density of constraints, one can have different
situations. If the number of constraints is small, one typically has a phase
where the ground state of the cost function is unique and sits on the boundary
of the island of configurations allowed by the constraints. In this case, there
is a hypostatic number of marginally satisfied constraints. If the number of
constraints is increased one enters a glassy phase where the cost function has
many local minima sitting again on the boundary of the regions of allowed
configurations. At the phase transition point, the total number of marginally
satisfied constraints becomes equal to the number of degrees of freedom in the
problem and therefore we say that these minima are isostatic. We conjecture
that by increasing further the constraints the system stays isostatic up to the
point where the volume of available phase space shrinks to zero. We derive our
results using the replica method and we also analyze a dynamical algorithm, the
Karush-Kuhn-Tucker algorithm, through dynamical mean-field theory and we show
how to recover the results of the replica approach in the replica symmetric
phase.


  We examine the non-ergodic properties of scaled Brownian motion, a
non-stationary stochastic process with a time dependent diffusivity of the form
$D(t)\simeq t^{\alpha-1}$. We compute the ergodicity breaking parameter EB in
the entire range of scaling exponents $\alpha$, both analytically and via
extensive computer simulations of the stochastic Langevin equation. We
demonstrate that in the limit of long trajectory lengths $T$ and short lag
times $\Delta$ the EB parameter as function of the scaling exponent $\alpha$
has no divergence at $\alpha=1/2$ and present the asymptotes for EB in
different limits. We generalise the analytical and simulations results for the
time averaged and ergodic properties of scaled Brownian motion in the presence
of ageing, that is, when the observation of the system starts only a finite
time span after its initiation. The approach developed here for the calculation
of the higher time averaged moments of the particle displacement can be applied
to derive the ergodic properties of other stochastic processes such as
fractional Brownian motion.


  COVID-19 has forced quarantine measures in several countries across the
world. These measures have proven to be effective in significantly reducing the
prevalence of the virus. To date, no effective treatment or vaccine is
available. In the effort of preserving both public health as well as the
economical and social textures, France and Italy governments have partially
released lockdown measures. Here we extrapolate the long-term behavior of the
epidemics in both countries using a Susceptible-Exposed-Infected-Recovered
(SEIR) model where parameters are stochastically perturbed to handle the
uncertainty in the estimates of COVID-19 prevalence. Our results suggest that
uncertainties in both parameters and initial conditions rapidly propagate in
the model and can result in different outcomes of the epidemics leading or not
to a second wave of infections. Using actual knowledge, asymptotic estimates of
COVID-19 prevalence can fluctuate of order of ten millions units in both
countries.


  Detecting and analyzing dense groups or communities from social and
information networks has attracted immense attention over last one decade due
to its enormous applicability in different domains. Community detection is an
ill-defined problem, as the nature of the communities is not known in advance.
The problem has turned out to be even complicated due to the fact that
communities emerge in the network in various forms - disjoint, overlapping,
hierarchical etc. Various heuristics have been proposed depending upon the
application in hand. All these heuristics have been materialized in the form of
new metrics, which in most cases are used as optimization functions for
detecting the community structure, or provide an indication of the goodness of
detected communities during evaluation. There arises a need for an organized
and detailed survey of the metrics proposed with respect to community detection
and evaluation. In this survey, we present a comprehensive and structured
overview of the start-of-the-art metrics used for the detection and the
evaluation of community structure. We also conduct experiments on synthetic and
real-world networks to present a comparative analysis of these metrics in
measuring the goodness of the underlying community structure.


  Direct linkages between regular or irregular isometric embeddings of surfaces
and steady compressible or incompressible fluid dynamics are investigated in
this paper. For a surface $(M,g)$ isometrically embedded in $\mathbb{R}^3$, we
construct a mapping which sends the second fundamental form of the embedding to
the density, velocity, and pressure of steady fluid flows on $(M,g)$. From the
PDE perspectives, this mapping sends solutions to the Gauss--Codazzi equations
to the steady Euler equations. Several families of special solutions of
physical or geometrical significance are studied in detail, including the
Chaplygin gas on standard and flat tori, as well as the irregular isometric
embeddings of the flat torus. We also discuss tentative extensions to
multi-dimensions.


  In 2009 Chmutov introduced the idea of partial duality for embeddings of
graphs in surfaces. We discuss some alternative descriptions of partial
duality, which demonstrate the symmetry between vertices and faces. One is in
terms of band decompositions, and the other is in terms of the gem
(graph-encoded map) representation of an embedding. We then use these to
investigate when a partial dual is a closed 2-cell embedding, in which every
face is bounded by a cycle in the graph. We obtain a necessary and sufficient
condition for a partial dual to be closed 2-cell, and also a sufficient
condition for no partial dual to be closed 2-cell.


  We present a long BeppoSAX observation of Abell 754 that reports a nonthermal
excess with respect to the thermal emission at energies greater than ~45 keV. A
VLA radio observation at 1.4 GHz definitely confirms the existence of diffuse
radio emission in the central region of the cluster, previously suggested by
images at 74 and 330 MHz (Kassim et al 2001), and reports additional features.
Besides, our observation determines a steeper radio halo spectrum in the
330-1400 MHz frequency range with respect to the spectrum detected at lower
frequencies, indicating the presence of a spectral cutoff. The presence of a
radio halo in A754, considered the prototype of a merging cluster, reinforces
the link between formation of Mpc-scale radio regions and very recent or
current merger processes. The radio results combined with the hard X-ray excess
detected by BeppoSAX give information on the origin of the electron population
responsible for nonthermal phenomena in galaxy clusters. We discuss also the
possibility that 26W20, a tailed radio galaxy with BL Lac characteristics
located in the field of view of the PDS, could be responsible for the observed
nonthermal hard X-ray emission.


  A group $G$ is called subgroup conjugacy separable (abbreviated as SCS), if
any two finitely generated and non-conjugate subgroups of $G$ remain
non-conjugate in some finite quotient of $G$. We prove that free groups and the
fundamental groups of orientable closed compact surfaces are SCS.


  The recently discovered twist-bend nematic phase, $\rm N_{tb}$, is a
non-uniform equilibrium nematic phase that presents a spontaneous bend with a
precession of the nematic director, $\textbf{n}$, on a conical helix with a
tilt angle $\theta$ and helical pitch $P$. The stability of the $\rm N_{tb}$
phase has been recently demonstrated from the elastic point of view by
extending the Frank elastic energy density of the nematic phase to include the
symmetry element of helical axis, $\bf t$. In the present communication, we
investigate the influence of an external bulk field (magnetic or electric) on
the pitch of the $\rm N_{tb}$ phase. For a material with positive magnetic
susceptibility anisotropy, when the magnetic field $\bf{H}$ is parallel to the
helical axis, $\bf{t}$, the field modifies the coupling parameter between the
director and the helical axis, thus shifting the interval of values for which
this coupling results in the $\rm N_{tb}$ phase. If $\bf{H}$ is perpendicular
to $\bf{t}$, it is shown that the pitch of the nematic director $\bf n$
precession increases with $\bf{H}$, and at a critical value of $H_c$ it becomes
infinite as in a cholesteric liquid crystal, that is, one gets a uniform
orientation of n. Our results include the behaviour of a cholesteric under
field as a limiting case.


  Biofilms are communities of bacteria that exhibit a multitude of multiscale
biomechanical behaviors. Recent experimental advances have lead to
characterizations of these behaviors in terms of measurements of the
viscoelastic moduli of biofilms grown in bioreactors and the fracture and
fragmentation properties of biofilms. These properties are macroscale features
of biofilms; however, a previous work by our group has shown that heterogeneous
microscale features are critical in predicting biofilm rheology. In this paper
we use tools from statistical physics to develop a generative statistical model
of the positions of bacteria in biofilms. We show through simulation that the
macroscopic mechanical properties of biofilms depend on the choice of
microscale spatial model. Our key finding is that a biologically inspired model
of the locations of bacteria in a biofilm is critical to the simulation of
biofilms with realistic in silico mechanical properties and statistical
characteristics.


  Working in the linear sigma model with quarks, we compute the
finite-temperature effective potential in the presence of a weak magnetic
field, including the contribution of the pion ring diagrams and considering the
sigma as a classical field. In the approximation where the pion self-energy is
computed perturbatively, we show that there is a region of the parameter space
where the effect of the ring diagrams is to preclude the phase transition from
happening. Inclusion of the magnetic field has small effects that however
become more important as the system evolves to the lowest temperatures allowed
in the analysis.


  Using strong-coupling quantum field theory we calculate highly accurate
critical exponents nu, eta from new seven-loop expansions in three dimensions.
Our theoretical value for the critical exponent alpha of the specific heat near
the lambda-point of superfluid helium is alpha =-0.01294+-0.00060, in excellent
agreement with the space shuttle experimental value alpha =-0.01285+-0.00038.


  This paper focuses on the mathematical approaches to the analysis of
stability that is a crucial step in the design of dynamical systems. Three
methods are presented, namely, absolutely integrable impulse response, Fourier
integral, and Laplace transform. The superiority of Laplace transform over the
other methods becomes clear for several reasons that include the following: 1)
It allows for the analysis of the stable, as well as, the unstable systems. 2)
It not only determines absolute stability (a yes/no answer), but also shines
light on the relative stability (how stable/unstable the system is), allowing
for a design with a good degree of stability. 3) Its algebraic and convolution
properties significantly simplify the mathematical manipulations involved in
the analysis, especially when tackling a complex system composed of several
simpler ones. A brief relevant introduction to the subject of systems is
presented for the unfamiliar reader. Additionally, appropriate physical
concepts and examples are presented for better clarity.


  Membership Inference Attacks (MIAs) determine whether a specific data point
was included in the training set of a target model. In this paper, we introduce
the Semantic Membership Inference Attack (SMIA), a novel approach that enhances
MIA performance by leveraging the semantic content of inputs and their
perturbations. SMIA trains a neural network to analyze the target model's
behavior on perturbed inputs, effectively capturing variations in output
probability distributions between members and non-members. We conduct
comprehensive evaluations on the Pythia and GPT-Neo model families using the
Wikipedia dataset. Our results show that SMIA significantly outperforms
existing MIAs; for instance, SMIA achieves an AUC-ROC of 67.39% on Pythia-12B,
compared to 58.90% by the second-best attack.


  A study of the high angular momentum particles 'atmosphere' near the
Schwarzschild black hole horizon suggested that strong gravitational
interactions occur at invariant distance of the order of $\sqrt[3]{M}$ (A.
Casher et. al). We present a generalization of this result to the Kerr-Newman
black hole case. It is shown that the larger charge and angular momentum black
hole bears, the larger invariant distance at which strong gravitational
interactions occur becomes. This invariant distance is of order
$\sqrt[3]{r_+^2/\hh}.$ This implies, that the Planckian structure of the
Hawking radiation of extreme black holes is completely broken.


  We investigate the orientation correlation of giant elliptical galaxies by
measuring the intrinsic ellipticity correlation function of 83,773 luminous red
galaxies (LRGs) at redshifts 0.16 -- 0.47 from the Sloan Digital Sky Survey. We
have accurately determined the correlation up to 30 $h^{-1}$Mpc. Luminosity
dependence of the ellipticity correlation is also detected although the error
bars are large, while no evidence is found for its redshift evolution between
$z=0.2$ and $z=0.4$. Then we use a cosmological $N$-body simulation to examine
misalignment between the central LRGs and their parent dark matter halos.
Central and satellite galaxies are assigned to simulated halos by employing a
halo occupation distribution model for the LRGs. The ellipticity correlation is
predicted to have the same shape as but an amplitude about 4 times higher than
our observation if the central LRGs are perfectly aligned with their host
halos. This indicates that the central LRG galaxies are preferentially but not
perfectly aligned with their host halos. With the assumption that there is a
misalignment angle between a central LRG and its host halo which follows a
Gaussian distribution with a zero mean and a width $\sigma_\theta$, we obtain a
tight constraint on the misalignment parameter,
$\sigma_\theta={35.4}^{+4.0}_{-3.3}$ deg. This type of intrinsic ellipticity
correlation, if not corrected, can lead to contamination at 5% level to the
shear power spectrum in weak lensing surveys of limiting magnitude
$R_{AB}=24.5$ if the source central galaxies follow the same misalignment
distribution as the LRGs.


  The slow solar wind is generally believed to result from the interaction of
open and closed coronal magnetic flux at streamers and pseudostreamers. We use
3-dimensional magnetohydrodynamic simulations to determine the detailed
structure and dynamics of open-closed interactions that are driven by
photospheric convective flows. The photospheric magnetic field model includes a
global dipole giving rise to a streamer together with a large parasitic
polarity region giving rise to a pseudostreamer that separates a satellite
coronal hole from the main polar hole. Our numerical domain extends out to 30
solar radii and includes an isothermal solar wind, so that the coupling between
the corona and heliosphere can be calculated rigorously. This system is driven
by imposing a large set of quasi-random surface flows that capture the driving
of coronal flux in the vicinity of streamer and pseudostreamer boundaries by
the supergranular motions. We describe the resulting structures and dynamics.
Interchange reconnection dominates the evolution at both streamer and
pseudostreamer boundaries, but the details of the resulting structures are
clearly different from one another. Additionally, we calculate in situ
signatures of the reconnection and determine the dynamic mapping from the inner
heliosphere back to the Sun for a test spacecraft orbit. We discuss the
implications of our results for interpreting observations from inner
heliospheric missions, such as Parker Solar Probe and Solar Orbiter, and for
space weather modeling of the slow solar wind.


  We present a result on the number of decoupled molecules for systems binding
two different types of ligands. In the case of $n$ and $2$ binding sites
respectively, we show that, generically, there are $2(n!)^{2}$ decoupled
molecules with the same binding polynomial. For molecules with more binding
sites for the second ligand, we provide computational results.


  A class of stochastic processes strongly related to random sums plays an
important role in network and in finance. In this paper we study this kind of
stochastic process discuss an overtime unchanged parameter and reveal its
asymptotic behavior.


  We study dynamics and bifurcations of 2-dimensional reversible maps having a
symmetric saddle fixed point with an asymmetric pair of nontransversal
homoclinic orbits (a symmetric nontransversal homoclinic figure-8). We consider
one-parameter families of reversible maps unfolding the initial homoclinic
tangency and prove the existence of infinitely many sequences (cascades) of
bifurcations related to the birth of asymptotically stable, unstable and
elliptic periodic orbits.


  We describe the geometry of the R x SO(3) x SO(6) x U(1) invariant half-BPS
M-theory configurations considered in LLM in terms of their electrostatic
variables. We discuss both regular configurations, such as AdS_4 x S^7 and
AdS_7 x S^4 vacua or simple excited solutions, and singular ones such as the
superstar geometries. This allows us to identify the appropriate boundary
conditions describing the most general smooth and superstar-like singular
configurations. We also compute their masses, matching the expected result from
their microscopic interpretation, but now at finite radius of curvature.


  The adoption of machine learning in materials science has rapidly transformed
materials property prediction. Hurdles limiting full capitalization of recent
advancements in machine learning include the limited development of methods to
learn the underlying interactions of multiple elements, as well as the
relationships among multiple properties, to facilitate property prediction in
new composition spaces. To address these issues, we introduce the Hierarchical
Correlation Learning for Multi-property Prediction (H-CLMP) framework that
seamlessly integrates (i) prediction using only a material's composition, (ii)
learning and exploitation of correlations among target properties in
multi-target regression, and (iii) leveraging training data from tangential
domains via generative transfer learning. The model is demonstrated for
prediction of spectral optical absorption of complex metal oxides spanning 69
3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear
composition-property relationships in composition spaces for which no training
data is available, which broadens the purview of machine learning to the
discovery of materials with exceptional properties. This achievement results
from the principled integration of latent embedding learning, property
correlation learning, generative transfer learning, and attention models. The
best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T))
wherein a generative adversarial network is trained on computational density of
states data and deployed in the target domain to augment prediction of optical
absorption from composition. H-CLMP(T) aggregates multiple knowledge sources
with a framework that is well-suited for multi-target regression across the
physical sciences.


  We investigate the dynamical role of inhibitory and highly connected nodes
(hub) in synchronization and input processing of leaky-integrate-and-fire
neural networks with short term synaptic plasticity. We take advantage of a
heterogeneous mean-field approximation to encode the role of network structure
and we tune the fraction of inhibitory neurons $f_I$ and their connectivity
level to investigate the cooperation between hub features and inhibition. We
show that, depending on $f_I$, highly connected inhibitory nodes strongly drive
the synchronization properties of the overall network through dynamical
transitions from synchronous to asynchronous regimes. Furthermore, a metastable
regime with long memory of external inputs emerges for a specific fraction of
hub inhibitory neurons, underlining the role of inhibition and connectivity
also for input processing in neural networks.


  In 2006, during a meeting of a working group of scientists in La Jolla,
California at The Neurosciences Institute (NSI), Gerald Edelman described a
roadmap towards the creation of a Conscious Artifact. As far as I know, this
roadmap was not published. However, it did shape my thinking and that of many
others in the years since that meeting. This short paper, which is based on my
notes taken during the meeting, describes the key steps in this roadmap. I
believe it is as groundbreaking today as it was more than 15 years ago.


  Ken Wilson's ideas on the renormalization group were shaped by his attempts
to build a theory of the strong interactions based on the concepts of quantum
field theory. I describe the development of his ideas by reviewing four of
Wilson's most important papers. [contribution to the Journal of Statistical
Physics Special Issue in Memory of K. G. Wilson]


  This paper presents a novel method for transferring motion planning and
control policies between a teacher and a learner robot. With this work, we
propose to reduce the sim-to-real gap, transfer knowledge designed for a
specific system into a different robot, and compensate for system aging and
failures. To solve this problem we introduce a Schwarz-Christoffel
mapping-based method to geometrically stretch and fit the control inputs from
the teacher into the learner command space. We also propose a method based on
primitive motion generation to create motion plans and control inputs
compatible with the learner's capabilities. Our approach is validated with
simulations and experiments with different robotic systems navigating occluding
environments.