  In this paper, we investigate the use of diffusion models which are
pre-trained on large-scale image-caption pairs for open-vocabulary 3D semantic
understanding. We propose a novel method, namely Diff2Scene, which leverages
frozen representations from text-image generative models, along with
salient-aware and geometric-aware masks, for open-vocabulary 3D semantic
segmentation and visual grounding tasks. Diff2Scene gets rid of any labeled 3D
data and effectively identifies objects, appearances, materials, locations and
their compositions in 3D scenes. We show that it outperforms competitive
baselines and achieves significant improvements over state-of-the-art methods.
In particular, Diff2Scene improves the state-of-the-art method on ScanNet200 by
12%.


  Sampling the parameters of high-dimensional Continuous Time Markov Chains
(CTMC) is a challenging problem with important applications in many fields of
applied statistics. In this work a recently proposed type of non-reversible
rejection-free Markov Chain Monte Carlo (MCMC) sampler, the Bouncy Particle
Sampler (BPS), is brought to bear to this problem. BPS has demonstrated its
favorable computational efficiency compared with state-of-the-art MCMC
algorithms, however to date applications to real-data scenario were scarce. An
important aspect of the practical implementation of BPS is the simulation of
event times. Default implementations use conservative thinning bounds. Such
bounds can slow down the algorithm and limit the computational performance. Our
paper develops an algorithm with an exact analytical solution to the random
event times in the context of CTMCs. Our local version of BPS algorithm takes
advantage of the sparse structure in the target factor graph and we also
provide a framework for assessing the computational complexity of local BPS
algorithms.


  In this work, we study the higher order Kirchhoff type Choquard equation
$(KC)$ involving a critical exponential non-linearity and singular weights. We
prove the existence of solution to $(KC)$ using Mountain pass Lemma in light of
Moser-Trudinger and singular Adams-Moser inequalities. In the second part of
the paper, using the Nehari manifold technique and minimization over its
suitable subsets, we prove the existence of at least two solutions to the
Kirchhoff type Choquard equation $(\mathcal{P_{\la,M}})$ involving
convex-concave type non-linearity.


  Boards of large corporations sharing some of their directors are connected in
complex networks. Boards are responsible for corporations' long-term strategy
and are often involved in decisions about a common topic related to the belief
in economical growth or recession.
  We are interested in understanding under which conditions a large majority of
boards making a same decision can emerge in the network. We present a model
where board directors are engaged in a decision making dynamics based on "herd
behavior". Boards influence each other through shared directors.
  We find that imitation of colleagues and opinion bias due to the interlock do
not trigger an avalanche of identical decisions over the board network, whereas
the information about interlocked boards' decisions does. There is no need to
invoke global public information, nor external driving forces.
  This model provides a simple endogenous mechanism to explain the fact that
boards of the largest corporations of a country can, in the span of a few
months, take the same decisions about general topics.


  Many examples are given of irreducible Harish-Chandra modules for type C
having reducible leading term cycles. Examples are also given of irreducibles
in category O having reducible associated varieties in type C.


  One of the biggest hurdles robotics faces is the facet of sophisticated and
hard-to-engineer behaviors. Reinforcement learning offers a set of tools, and a
framework to address this problem. In parallel, the misgivings of robotics
offer a solid testing ground and evaluation metric for advancements in
reinforcement learning. The two disciplines go hand-in-hand, much like the
fields of Mathematics and Physics. By means of this survey paper, we aim to
invigorate links between the research communities of the two disciplines by
focusing on the work done in reinforcement learning for locomotive and control
aspects of robotics. Additionally, we aim to highlight not only the notable
successes but also the key challenges of the application of Reinforcement
Learning in Robotics. This paper aims to serve as a reference guide for
researchers in reinforcement learning applied to the field of robotics. The
literature survey is at a fairly introductory level, aimed at aspiring
researchers. Appropriately, we have covered the most essential concepts
required for research in the field of reinforcement learning, with robotics in
mind. Through a thorough analysis of this problem, we are able to manifest how
reinforcement learning could be applied profitably, and also focus on
open-ended questions, as well as the potential for future research.


  We provide more sample-efficient versions of some basic routines in quantum
data analysis, along with simpler proofs. Particularly, we give a quantum
"Threshold Search" algorithm that requires only $O((\log^2 m)/\epsilon^2)$
samples of a $d$-dimensional state $\rho$. That is, given observables $0 \le
A_1, A_2, ..., A_m \le 1$ such that $\mathrm{tr}(\rho A_i) \ge 1/2$ for at
least one $i$, the algorithm finds $j$ with $\mathrm{tr}(\rho A_j) \ge
1/2-\epsilon$. As a consequence, we obtain a Shadow Tomography algorithm
requiring only $\tilde{O}((\log^2 m)(\log d)/\epsilon^4)$ samples, which
simultaneously achieves the best known dependence on each parameter $m$, $d$,
$\epsilon$. This yields the same sample complexity for quantum Hypothesis
Selection among $m$ states; we also give an alternative Hypothesis Selection
method using $\tilde{O}((\log^3 m)/\epsilon^2)$ samples.


  In suitably initialized wide networks, small learning rates transform deep
neural networks (DNNs) into neural tangent kernel (NTK) machines, whose
training dynamics is well-approximated by a linear weight expansion of the
network at initialization. Standard training, however, diverges from its
linearization in ways that are poorly understood. We study the relationship
between the training dynamics of nonlinear deep networks, the geometry of the
loss landscape, and the time evolution of a data-dependent NTK. We do so
through a large-scale phenomenological analysis of training, synthesizing
diverse measures characterizing loss landscape geometry and NTK dynamics. In
multiple neural architectures and datasets, we find these diverse measures
evolve in a highly correlated manner, revealing a universal picture of the deep
learning process. In this picture, deep network training exhibits a highly
chaotic rapid initial transient that within 2 to 3 epochs determines the final
linearly connected basin of low loss containing the end point of training.
During this chaotic transient, the NTK changes rapidly, learning useful
features from the training data that enables it to outperform the standard
initial NTK by a factor of 3 in less than 3 to 4 epochs. After this rapid
chaotic transient, the NTK changes at constant velocity, and its performance
matches that of full network training in 15% to 45% of training time. Overall,
our analysis reveals a striking correlation between a diverse set of metrics
over training time, governed by a rapid chaotic to stable transition in the
first few epochs, that together poses challenges and opportunities for the
development of more accurate theories of deep learning.


  We analyze the limit behavior as $s\to 1^-$ of the solution to the fractional
Poisson equation $(-\Delta)^s u_s=f_s$, $x\in\Omega$ with homogeneous Dirichlet
boundary conditions $u_s\equiv 0$, $x\in\Omega^c$. We show that $\lim_{s\to
1^-} u_s =u$, with $-\Delta u =f$, $x\in\Omega$ and $u=0$,
$x\in\partial\Omega$. Our results are complemented by a discussion on the rate
of convergence and on extensions to the parabolic setting.


  The famous result of Lindemann and Weierstrass says that if
$a_{1},a_{2},\ldots,a_{n}$ are distinct algebraic numbers, then
$e^{a_{1}},e^{a_{2}},\ldots,e^{a_{n}}$ are linearly independent complex numbers
over the field $\overline{\mathbb{Q}}$ of all algebraic numbers.
  Starting from some basic ideas of Hermite, Lindemann, Hilbert, Hurwitz and
Baker, in this note we provide an easy to understand and self-contained proof
for the Lindemann-Weierstrass Theorem. In an introductory section we have
gathered all the algebraic number theory tools that are necessary to prove the
main theorem. All these auxiliary results are fully proved in a simple and
elementary way, so that the paper can be read even by an undergraduate student.


  Let $C$ be an algebraic curve of genus $g\ge2$. A coherent system on $C$
consists of a pair $(E,V)$, where $E$ is an algebraic vector bundle over $C$ of
rank $n$ and degree $d$ and $V$ is a subspace of dimension $k$ of the space of
sections of $E$. The stability of the coherent system depends on a parameter
$\alpha$. We study the geometry of the moduli space of coherent systems for
$0<d\le2n$. We show that these spaces are irreducible whenever they are
non-empty and obtain necessary and sufficient conditions for non-emptiness.


  Shortly after the discovery of PSR J1906+0746, some hints of profile
variations were already interpreted as first signs of relativistic
spin-precession occuring. Using observations from the Nan\c{c}ay, Arecibo and
Green Bank Radio Observatories, we report here the measurement of pulse profile
and polarimetric variations. Using the Rotating Vector Model, we show that PSR
J1906+0746 is likely to be an orthogonal rotator ($\alpha \simeq 80^\circ$).
Fitting our polarimetric data to a precession model, we determined the geometry
of the pulsar and found a wide misalignment angle ($\delta = 89_{-44}^{+85}$
deg, 95% C.L.), although the uncertainty is large. Assuming this geometry, we
constructed the beam maps of both magnetic poles.


  We establish a relative Bertini type theorem for multiplier ideal sheaves.
Then we prove a relative version of the Koll\'ar--Nadel type vanishing theorem
as an application.


  Asteroseismology has been shown to be, together with stellar modelling, an
invaluable tool in constraining properties of novel physics. In this work, we
study for the first time the influence of axionic production in the evolution
of a late main-sequence star, comparing computational models with observational
data in order to constrain the axion-photon $g_{a\gamma}$ coupling parameter.
We first perform a high-precision calibration of a stellar model to our target
star, in order to obtain a benchmark for our other diagnostics. We then apply a
two-stage test, first using global quantities and then resorting to precision
seismic ratios. We find that seismology allows us to place an independent upper
bound of $g_{a\gamma} \leq 0.98\times 10^{-10} $ GeV$^{-1}$ at a $68\%$
confidence level (CL), in the same order of magnitude as both the most recent
constraints from the observation of globular clusters and previous bounds
obtained through stellar modelling, but more stringent than most current direct
axion detections. We also suggest a more conservative limit of $g_{a\gamma}
\leq 1.38\times 10^{-10} $ GeV$^{-1}$ at a $95\%$ CL. Moreover, this new
diagnostic method can be applied to stellar data that will be obtained in
future asteroseismic projects.


  By using projection superoperators, we present a new derivation of the
quantum master equation first obtained by the Authors in Phys. Rev. E {\bf 68},
066112 (2003). We show that this equation describes the dynamics of a subsystem
weakly interacting with an environment of finite heat capacity and initially
described by a microcanonical distribution. After applying the rotating wave
approximation to the equation, we show that the subsystem dynamics preserves
the energy of the total system (subsystem plus environment) and tends towards
an equilibrium state which corresponds to equipartition inside the energy shell
of the total system. For infinite heat capacity environments, this equation
reduces to the Redfield master equation for a subsystem interacting with a
thermostat. These results should be of particular interest to describe
relaxation and decoherence in nanosystems where the environment can have a
finite number of degrees of freedom and the equivalence between the
microcanonical and the canonical ensembles is thus not always guaranteed.


  Conversations aimed at determining good recommendations are iterative in
nature. People often express their preferences in terms of a critique of the
current recommendation (e.g., "It doesn't look good for a date"), requiring
some degree of common sense for a preference to be inferred. In this work, we
present a method for transforming a user critique into a positive preference
(e.g., "I prefer more romantic") in order to retrieve reviews pertaining to
potentially better recommendations (e.g., "Perfect for a romantic dinner"). We
leverage a large neural language model (LM) in a few-shot setting to perform
critique-to-preference transformation, and we test two methods for retrieving
recommendations: one that matches embeddings, and another that fine-tunes an LM
for the task. We instantiate this approach in the restaurant domain and
evaluate it using a new dataset of restaurant critiques. In an ablation study,
we show that utilizing critique-to-preference transformation improves
recommendations, and that there are at least three general cases that explain
this improved performance.


  Over the past few years, several research groups have introduced innovative
hardware designs for Trusted Execution Environments (TEEs), aiming to secure
applications against potentially compromised privileged software, including the
kernel. Since 2017, Tang et al. introduced a new class of software-enabled
hardware attacks, which leverages energy management mechanisms. These attacks
aim at bypassing TEE security guarantees and exposing sensitive information
like cryptographic keys. They have increased in prevalence over the past few
years. Despite that, current RISC-V TEE architectures have yet to incorporate
them into their threat models. Proprietary implementations, such as Arm
TrustZone and Intel SGX, embed countermeasures. However, these countermeasures
are not viable in the long term and hinder the capabilities of energy
management mechanisms. This article presents the first comprehensive knowledge
survey of these attacks, along with an evaluation of literature
countermeasures. Our analysis highlights a substantial security gap between
assumed threat models and the actual ones, presenting considerable threats in
modern systems-on-chip that can undermine even the security guarantees provided
by TEEs. We advocate for the enhancement of the next generation of RISC-V TEEs
to address these attacks within their threat models, and we believe this study
will spur further community efforts in this direction.


  In time-series forecasting, future target values may be affected by both
intrinsic and extrinsic effects. When forecasting blood glucose, for example,
intrinsic effects can be inferred from the history of the target signal alone
(\textit{i.e.} blood glucose), but accurately modeling the impact of extrinsic
effects requires auxiliary signals, like the amount of carbohydrates ingested.
Standard forecasting techniques often assume that extrinsic and intrinsic
effects vary at similar rates. However, when auxiliary signals are generated at
a much lower frequency than the target variable (e.g., blood glucose
measurements are made every 5 minutes, while meals occur once every few hours),
even well-known extrinsic effects (e.g., carbohydrates increase blood glucose)
may prove difficult to learn. To better utilize these \textit{sparse but
informative variables} (SIVs), we introduce a novel encoder/decoder forecasting
approach that accurately learns the per-timepoint effect of the SIV, by (i)
isolating it from intrinsic effects and (ii) restricting its learned effect
based on domain knowledge. On a simulated dataset pertaining to the task of
blood glucose forecasting, when the SIV is accurately recorded our approach
outperforms baseline approaches in terms of rMSE (13.07 [95% CI: 11.77,14.16]
vs. 14.14 [12.69,15.27]). In the presence of a corrupted SIV, the proposed
approach can still result in lower error compared to the baseline but the
advantage is reduced as noise increases. By isolating their effects and
incorporating domain knowledge, our approach makes it possible to better
utilize SIVs in forecasting.


  Muon neutrino and antineutrino disappearance probabilities are identical in
the standard three-flavor neutrino oscillation framework, but CPT violation and
non-standard interactions can violate this symmetry. In this work we report the
measurements of $\sin^{2} \theta_{23}$ and $\Delta m_{32}^2$ independently for
neutrinos and antineutrinos. The aforementioned symmetry violation would
manifest as an inconsistency in the neutrino and antineutrino oscillation
parameters. The analysis discussed here uses a total of 1.97$\times$10$^{21}$
and 1.63$\times$10$^{21}$ protons on target taken with a neutrino and
antineutrino beam respectively, and benefits from improved flux and
cross-section models, new near detector samples and more than double the data
reducing the overall uncertainty of the result. No significant deviation is
observed, consistent with the standard neutrino oscillation picture.


  The calculation of nuclear electromagnetic sum rules by directly
diagonalizing the nuclear Hamiltonian in a large basis is numerically
challenging and has not been performed for $A>2$ nuclei. With the significant
progress of high performance computing, we show that calculating sum rules
using numerous discretized continuum states obtained by directly diagonalizing
the ab initio no-core shell model Hamiltonian is achievable numerically.
Specifically, we calculate the $^{4}$He electric dipole ($E1$) polarizability,
that is an inverse energy weighted sum rule, employing the Daejeon16 $NN$
interaction. We demonstrate that the calculations are numerically tractable as
the dimension of the basis increases and are convergent. Our results for the
$^{4}$He electric dipole polarizability are consistent with the most recent
experimental data and are compared with those of other theoretical studies
employing different techniques and various interactions.


  We conjecture a new lower bound on the algebraic connectivity of a graph that
involves the number of vertices of high eccentricity in a graph. We prove that
this lower bound implies a strengthening of the Laplacian Spread Conjecture. We
discuss further conjectures, also strengthening the Laplacian Spread
Conjecture, that include a conjecture for simple graphs and a conjecture for
weighted graphs.


  We derive quantum electrodynamics corrections to the fine structure in
three-electron atomic systems at $m \alpha^6$ and $m \alpha^7 \ln \alpha$
orders and present their numerical evaluations for the Li atom and Be$^+$ ion.


  Artificial intelligence (AI) has emerged as a transformative and versatile
tool, breaking new frontiers across scientific domains. Among its most
promising applications, AI research is blossoming in concrete science and
engineering, where it has offered new insights towards mixture design
optimization and service life prediction of cementitious systems. This chapter
aims to uncover the main research interests and knowledge structure of the
existing literature on AI for concrete materials. To begin with, a total of 389
journal articles published from 1990 to 2020 were retrieved from the Web of
Science. Scientometric tools such as keyword co-occurrence analysis and
documentation co-citation analysis were adopted to quantify features and
characteristics of the research field. The findings bring to light pressing
questions in data-driven concrete research and suggest future opportunities for
the concrete community to fully utilize the capabilities of AI techniques.


  We investigate orthogonal and symplectic bundles with parabolic structure,
over a curve.


  Recent measurements of the temperature of the sky in the radio band, combined
with literature data, have convincingly shown the existence of a cosmic radio
background with an amplitude of $\sim 1$ K at 1 GHz and a spectral energy
distribution that is well described by a power law with index $\alpha \simeq
-0.6$. The origin of this signal remains elusive, and it has been speculated
that it could be dominated by the contribution of star-forming galaxies at high
redshift \change{if the far infrared-radio correlation $q(z)$ evolved} in time.
\change{We fit observational data from several different experiments by the
relation $q(z) \simeq q_0 - \beta \log(1+z)$ with $q_0 = 2.783 \pm 0.024$ and
$\beta = 0.705 \pm 0.081$ and estimate the total radio emission of the whole
galaxy population at any given redshift from the cosmic star formation rate
density at that redshift. It is found that} star-forming galaxies can only
account for $\sim$13 percent of the observed intensity of the cosmic radio
background.


  This manuscript investigates the existence and uniqueness of solutions to the
first order fractional anti-periodic boundary value problem involving
Caputo-Katugampola (CK) derivative. A variety of tools for analysis this paper
through the integral equivalent equation of the given problem, fixed point
theorems of Leray--Schauder, Krasnoselskii's, and Banach are used. Examples of
the obtained results are also presented.


  The estimation of the polarization $P$ of extragalactic compact sources in
Cosmic Microwave Background images is a very important task in order to clean
these images for cosmological purposes -- as, for example, to constrain the
tensor-to-scalar ratio of primordial fluctuations during inflation -- and also
to obtain relevant astrophysical information about the compact sources
themselves in a frequency range, $\nu \sim 10$--$200$ GHz, where observations
have only very recently started to be available. In this paper we propose a
Bayesian maximum a posteriori (MAP) approach estimation scheme which
incorporates prior information about the distribution of the polarization
fraction of extragalactic compact sources between 1 and 100 GHz. We apply this
Bayesian scheme to white noise simulations and to more realistic simulations
that include CMB intensity, Galactic foregrounds and instrumental noise with
the characteristics of the QUIJOTE experiment Wide Survey at 11 GHz. Using
these simulations, we also compare our Bayesian method with the frequentist
Filtered Fusion method that has been already used in WMAP data and in the
\emph{Planck} mission. We find that the Bayesian method allows us to decrease
the threshold for a feasible estimation of $P$ to levels below $\sim 100$ mJy
(as compared to $\sim 500$ mJy that was the equivalent threshold for the
frequentist Filtered Fusion). We compare the bias introduced by the Bayesian
method and find it to be small in absolute terms. Finally, we test the
robustness of the Bayesian estimator against uncertainties in the prior and in
the flux density of the sources. We find that the Bayesian estimator is robust
against moderate changes in the parameters of the prior and almost insensitive
to realistic errors in the estimated photometry of the sources.


  $k$-core decomposition is widely used to identify the center of a large
network, it is a pruning process in which the nodes with degrees less than $k$
are recursively removed. Although the simplicity and effectiveness of this
method facilitate its implementation on broad applications across many
scientific fields, it produces few analytical results. We here simplify the
existing theoretical framework to a simple iterative relationship and obtain
the exact analytical solutions of the $k$-core pruning process on large
uncorrelated networks. From these solutions we obtain such statistical
properties as the degree distribution and the size of the remaining subgraph in
each of the pruning steps. Our theoretical results resolve the long-lasting
puzzle of the $k$-core pruning dynamics and provide an intuitive description of
the dynamic process.


  Single-shot ultrafast absorbance spectroscopy based on the frequency encoding
of the kinetics is analyzed theoretically and implemented experimentally. The
kinetics are sampled in the frequency domain using linearly chirped, amplified
33 fs FWHM pulses derived from a Ti:sapphire laser. A variable length grating
pair compressor is used to achieve the time resolution of 500-1000 channels per
a 2-to-160 ps window with sensitivity > 5x10-4. In terms of the acquisition
time, FDSS has an advantage over the pump-probe spectroscopy in a situation
when the "noise" is dominated by amplitude variations of the signal, due to the
pump and flow instabilities. The possibilities of FDSS are illustrated with the
kinetics obtained in multiphoton ionization of water and aqueous iodide and
one-photon excitation of polycrystalline ZnSe and thin-film amorphous Si:H.
Unlike other "single-shot" techniques, FDSS can be implemented for fluid
samples flowing in a high-speed jet and for thin solid samples that exhibit
interference fringes; no a priori knowledge of the excitation profile of the
pump across the beam is needed. Another advantage is that due to the
interference of quasimonochromatic components of the chirped probe pulse, an
oscillation pattern near the origin of the FDSS kinetics emerges. This pattern
is unique and can be used to determine the complex dielectric function of the
photogenerated species.


  I review recent observational progress on Anomalous X-ray Pulsars, with an
emphasis on timing, variability, and spectra. Highlighted results include the
recent timing and flux stabilization of the notoriously unstable AXP 1E
1048.1-5937, the remarkable glitches seen in two AXPs, the newly recognized
variety of AXP variability types, including outbursts, bursts, flares, and
pulse profile changes, as well as recent discoveries regarding AXP spectra,
including their surprising hard X-ray and far-infrared emission, as well as the
pulsed radio emission seen in one source. Much has been learned about these
enigmatic objects over the past few years, with the pace of discoveries
remaining steady. However additional work on both observational and theoretical
fronts is needed before we have a comprehensive understanding of AXPs and their
place in the zoo of manifestations of young neutron stars.


  We propose a nonmonotonic Description Logic of typicality able to account for
the phenomenon of concept combination of prototypical concepts. The proposed
logic relies on the logic of typicality ALC TR, whose semantics is based on the
notion of rational closure, as well as on the distributed semantics of
probabilistic Description Logics, and is equipped with a cognitive heuristic
used by humans for concept composition. We first extend the logic of typicality
ALC TR by typicality inclusions whose intuitive meaning is that "there is
probability p about the fact that typical Cs are Ds". As in the distributed
semantics, we define different scenarios containing only some typicality
inclusions, each one having a suitable probability. We then focus on those
scenarios whose probabilities belong to a given and fixed range, and we exploit
such scenarios in order to ascribe typical properties to a concept C obtained
as the combination of two prototypical concepts. We also show that reasoning in
the proposed Description Logic is EXPTIME-complete as for the underlying ALC.


  We report the fabrication and photoresponse of 5 nm thick
La$_{1.85}$Sr$_{0.15}$CuO$_{4}$ nanostrip with a width of 100nm. The I-V
characteristics of the nanostrip show a hysteresis and a sharp voltage jump at
$I_{c}$. The $J_{c}$(3K) of the nanostrip is 2.3 x 10$^{7}$ A/cm$^{2}$. The
nanostrip exhibits photoresponse signals when illuminated by a pulse laser at
1560 nm wavelength with a bias current just below $I_{c}$. The height of the
signal reduces as the optical intensity decreases and disappears below -10 dBm.
The signal also decreases as the temperature increases, but it exists up to 30
K. These results suggest the possibility of La$_{1.85}$Sr$_{0.15}$CuO$_{4}$
nanostrip as single-photon detector (SSPD, SNSPD) working at high temperature
by further reducing the cross-section of the strip.


  This chapter surveys progress on three related topics in perturbations of
Markov chains: the motivating question of when and how "perturbed" MCMC chains
are developed, the theoretical problem of how perturbation theory can be used
to analyze such chains, and finally the question of how the theoretical
analyses can lead to practical advice.


  Empirical relations between major UV and extreme UV spectral lines are one of
the inputs for models of chromospheric and coronal spectral radiances and
irradiances. They are also needed for the interpretation of some of the
observations of the Solar Orbiter mission. We aim to determine an empirical
relation between the intensities of the HI 121.6 nm and HeII 30.4 nm Lyman
$\alpha$ lines. Images at 121.6 nm from the Chromospheric Lyman Alpha Spectro
Polarimeter (CLASP) and Multiple XUV Imager (MXUVI) sounding rockets were
co-registered with simultaneous images at 30.4 nm from the EIT and AIA orbital
telescopes in order to derive a spatially resolved relationship between the
intensities. We have obtained a relationship between the HI 121.6 nm and HeII
30.4 nm intensities that is valid for a wide range of solar features,
intensities, and activity levels. Additional SUMER data have allowed the
derivation of another relation between the HI 102.5 nm (Ly$\beta$) and HeII
30.4 nm lines for quiet-Sun regions. We combined these two relationships to
obtain a Ly$\alpha$/Ly$\beta$ intensity ratio that is comparable to the few
previously published results. The relationship between the HI 121.6~nm and HeII
30.4 nm lines is consistent with the one previously obtained using irradiance
data. We have also observed that this relation is stable in time but that its
accuracy depends on the spatial resolution of the observations. The derived
Ly$\alpha$/Ly$\beta$ intensity ratio is also compatible with previous results.


  The piezoelectricity of group IV monochalcogenides (MXs, with M = Ge, Sn and
X = S, Se) has attracted much attention due to their substantially higher
piezoelectric coefficients compared to other 2D materials. However, with
increasing layer number, their piezoelectricity rapidly disappears due to the
antiferroelectric stacking order, severely limiting their practical
applications. Using first-principles calculations, we investigated the
piezoelectricity of MXs with the ferroelectric AA stacking configuration, which
has recently been stabilized in experiments. We found that AA-stacked MXs have
a ferroelectric ground state with the smallest lattice constant among other
stacking configurations, resulting in a giant piezoelectric coefficient, which
is the first demonstration of a strategy where the piezoelectric coefficients
can increase with the number of layers. This can be attributed to a strong
negative correlation between the lattice constant along the armchair direction
and the piezoelectric coefficient, and spontaneous compressive strain
stabilized in ferroelectric AA stacking configuration.


  Online multi-object tracking is a fundamental problem in time-critical video
analysis applications. A major challenge in the popular tracking-by-detection
framework is how to associate unreliable detection results with existing
tracks. In this paper, we propose to handle unreliable detection by collecting
candidates from outputs of both detection and tracking. The intuition behind
generating redundant candidates is that detection and tracks can complement
each other in different scenarios. Detection results of high confidence prevent
tracking drifts in the long term, and predictions of tracks can handle noisy
detection caused by occlusion. In order to apply optimal selection from a
considerable amount of candidates in real-time, we present a novel scoring
function based on a fully convolutional neural network, that shares most
computations on the entire image. Moreover, we adopt a deeply learned
appearance representation, which is trained on large-scale person
re-identification datasets, to improve the identification ability of our
tracker. Extensive experiments show that our tracker achieves real-time and
state-of-the-art performance on a widely used people tracking benchmark.


  Excitable media are ubiquitous in nature, and in such systems the local
excitation tends to self-organize in traveling waves, or in rotating
spiral-shaped patterns in two or three spatial dimensions. Examples include
waves during a pandemic or electrical scroll waves in the heart. Here we show
that such phenomena can be extended to a space of four or more dimensions and
propose that connections of excitable elements in a network setting can be
regarded as additional spatial dimensions. Numerical simulations are performed
in four dimensions using the FitzHugh-Nagumo model, showing that the vortices
rotate around a two-dimensional surface which we define as the superfilament.
Evolution equations are derived for general superfilaments of co-dimension two
in an N -dimensional space and their equilibrium configurations are proven to
be minimal surfaces. We suggest that biological excitable systems, such as the
heart or brain which have non-local connections can be regarded, at least
partially, as multidimensional excitable media and discuss further possible
studies in this direction.


  Knowledge tracing (KT) is a crucial technique to predict students' future
performance by observing their historical learning processes. Due to the
powerful representation ability of deep neural networks, remarkable progress
has been made by using deep learning techniques to solve the KT problem. The
majority of existing approaches rely on the \emph{homogeneous question}
assumption that questions have equivalent contributions if they share the same
set of knowledge components. Unfortunately, this assumption is inaccurate in
real-world educational scenarios. Furthermore, it is very challenging to
interpret the prediction results from the existing deep learning based KT
models. Therefore, in this paper, we present QIKT, a question-centric
interpretable KT model to address the above challenges. The proposed QIKT
approach explicitly models students' knowledge state variations at a
fine-grained level with question-sensitive cognitive representations that are
jointly learned from a question-centric knowledge acquisition module and a
question-centric problem solving module. Meanwhile, the QIKT utilizes an item
response theory based prediction layer to generate interpretable prediction
results. The proposed QIKT model is evaluated on three public real-world
educational datasets. The results demonstrate that our approach is superior on
the KT prediction task, and it outperforms a wide range of deep learning based
KT models in terms of prediction accuracy with better model interpretability.
To encourage reproducible results, we have provided all the datasets and code
at \url{https://pykt.org/}.


  A new family of distributions on the circle is introduced which are a
generalization of the Cardioid distributions. The elementary properties such as
mean, variance and the characteristic function are computed. The distribution
is either unimodal or bimodal. The modes are computed. The symmetry of the
distribution is characterized. The parameters are shown to be canonic (i.e.
uniquely determined by the distribution). We also show that this new family is
a subset of distributions whose Fourier series has degree at most 2 and study
the implications of this property.


  The Standard Model extension with additional Lorentz violating terms allows
for redefining the equation of motion of a propagating left-handed fermionic
particle. The obtained Dirac-type equation can be embedded in a generalized
Lorentz-invariance preserving-algebra through the definition of Lorentz
algebra-like generators with a light-like preferred axis. The resulting
modification to the fermionic equation of motion introduces some novel
ingredients to the phenomenological analysis of the cross section of the
tritium $\beta$-decay. Assuming lepton number conservation, our formalism
provides a natural explanation for the tritium $\beta$-decay end-point via an
effective neutrino mass term without the need of a sterile right-handed state.


  Treatment switching in a randomized controlled trial is said to occur when a
patient randomized to one treatment arm switches to another treatment arm
during follow-up. This can occur at the point of disease progression, whereby
patients in the control arm may be offered the experimental treatment. It is
widely known that failure to account for treatment switching can seriously
dilute the estimated effect of treatment on overall survival. In this paper, we
aim to account for the potential impact of treatment switching in a re-analysis
evaluating the treatment effect of NucleosideReverse Transcriptase Inhibitors
(NRTIs) on a safety outcome (time to first severe or worse sign or symptom) in
participants receiving a new antiretroviral regimen that either included or
omitted NRTIs in the Optimized Treatment That Includes or OmitsNRTIs (OPTIONS)
trial. We propose an estimator of a treatment causal effect under a structural
cumulative survival model (SCSM) that leverages randomization as an
instrumental variable to account for selective treatment switching. Unlike
Robins' accelerated failure time model often used to address treatment
switching, the proposed approach avoids the need for artificial censoring for
estimation. We establish that the proposed estimator is uniformly consistent
and asymptotically Gaussian under standard regularity conditions. A consistent
variance estimator is also given and a simple resampling approach provides
uniform confidence bands for the causal difference comparing treatment groups
overtime on the cumulative intensity scale. We develop an R package named
"ivsacim" implementing all proposed methods, freely available to download from
R CRAN. We examine the finite performance of the estimator via extensive
simulations.


  We present a full investigation of scalar perturbations in a rather generic
model for a regular bouncing universe, where the bounce is triggered by an
effective perfect fluid with negative energy density. Long before and after the
bounce the universe is dominated by a source with positive energy density,
which may be a perfect fluid, a scalar field, or any other source with an
intrinsic isocurvature perturbation. Within this framework, we present an
analytical method to accurately estimate the spectrum of large-scale scalar
perturbations until their reentry, long after the bounce. We also propose a
simple way to identify non-singular gauge-invariant variables through the
bounce and present the results of extensive numerical tests in several possible
realizations of the scenario. In no case do we find that the spectrum of the
pre-bounce growing mode of the Bardeen potential can be transferred to a
post-bounce constant mode.


  Transport properties of permalloy doped with V, Co, Pt, and Au are explored
via ab-initio calculations. The Kubo-Bastin formula is evaluated within the
fully relativistic Korringa-Kohn-Rostoker Green function formalism. Finite
temperature effects are treated by means of the alloy analogy model. It is
shown that the fact that the host is disordered and not crystalline has a
profound effect on how the conductivities characterizing the anomalous Hall
effect and the spin Hall effect depend on the dopant concentration. Several
relationships between quantities characterizing charge and spin transport are
highlighted. The decrease of the longitudinal charge conductivity with
increasing doping depends on the dopant type, following the sequence
Co-Au-Pt-V. The dependence of the anomalous Hall and spin Hall conductivities
on the dopant concentration is found to be non-monotonic. Introducing a finite
temperature changes the overall trends significantly. The theoretical results
are compared with available experimental data.


  We demonstrate the control of electron-electron correlation in frustrated
double ionization (FDI) of the two-electron triatomic molecule D$_{3}^{+}$ when
driven by two orthogonally polarized two-color laser fields. We employ a
three-dimensional semi-classical model that fully accounts for the electron and
nuclear motion in strong fields. We analyze the FDI probability and the
distribution of the momentum of the escaping electron along the polarization
direction of the longer wavelength and more intense laser field. These
observables when considered in conjunction bear clear signatures of the
prevalence or absence of electron-electron correlation in FDI, depending on the
time-delay between the two laser pulses. We find that D$_{3}^{+}$ is a better
candidate compared to H$_{2}$ for demonstrating also experimentally that
electron-electron correlation indeed underlies FDI.


  Stars of ~8-100 solar masses end their lives as core-collapse supernovae
(SNe). In the process they emit a powerful burst of neutrinos, produce a
variety of elements, and leave behind either a neutron star or a black hole.
The wide mass range for SN progenitors results in diverse neutrino signals,
explosion energies, and nucleosynthesis products. A major mechanism to produce
nuclei heavier than iron is rapid neutron capture, or the r process. This
process may be connected to SNe in several ways. A brief review is presented on
current understanding of neutrino emission, explosion, and nucleosynthesis of
SNe.


  A bipartite graph extensively models relationships between real-world
entities of two different types, such as user-product data in e-commerce. Such
graph data are inherently becoming more and more streaming, entailing
continuous insertions and deletions of edges. A butterfly (i.e., 2x2 bi-clique)
is the smallest non-trivial cohesive structure that plays a crucial role.
Counting such butterfly patterns in streaming bipartite graphs is a core
problem in applications such as dense subgraph discovery and anomaly detection.
Yet, existing approximate solutions consider insert-only streams and, thus,
achieve very low accuracy in fully dynamic bipartite graph streams that involve
both insertions and deletions of edges. Adapting them to consider deletions is
not trivial either, because different sampling schemes and new accuracy
analyses are required. In this paper, we propose Abacus, a novel approximate
algorithm that counts butterflies in the presence of both insertions and
deletions by utilizing sampling. We prove that Abacus always delivers unbiased
estimates of low variance. Furthermore, we extend Abacus and devise a parallel
mini-batch variant, namely, Parabacus, which counts butterflies in parallel.
Parabacus counts butterflies in a load-balanced manner using versioned samples,
which results in significant speedup and is thus ideal for critical
applications in the streaming environment. We evaluate Abacus/Parabacus using a
diverse set of real bipartite graphs and assess its performance in terms of
accuracy, throughput, and speedup. The results indicate that our proposal is
the first capable of efficiently providing accurate butterfly counts in the
most generic setting, i.e., a fully dynamic graph streaming environment that
entails both insertions and deletions. It does so without sacrificing
throughput and even improving it with the parallel version.


  In this work we consider the numerical solution of incompressible flows on
two-dimensional manifolds. Whereas the compatibility demands of the velocity
and the pressure spaces are known from the flat case one further has to deal
with the approximation of a velocity field that lies only in the tangential
space of the given geometry. Abandoning $H^1$-conformity allows us to construct
finite elements which are -- due to an application of the Piola transformation
-- exactly tangential. To reintroduce continuity (in a weak sense) we make use
of (hybrid) discontinuous Galerkin techniques. To further improve this
approach, $H(\operatorname{div}_{\Gamma})$-conforming finite elements can be
used to obtain exactly divergence-free velocity solutions. We present several
new finite element discretizations. On a number of numerical examples we
examine and compare their qualitative properties and accuracy.


  We analyze a self-consistent theory of crack growth controlled by a
cumulative damage variable d(t) dependent on stress history. As a function of
the damage exponent $m$, which controls the rate of damage dd/dt \propto
sigma^m as a function of local stress $\sigma$, we find two regimes. For 0 < m
< 2, the model predicts a finite-time singularity. This retrieves previous
results by Zobnin for m=1 and by Bradley and Wu for 0 < m < 2. To improve on
this self-consistent theory which neglects the dependence of stress on damage,
we apply the functional renormalization method of Yukalov and Gluzman and find
that divergences are replaced by singularities with exponents in agreement with
those found in acoustic emission experiments. For m =2 and m > 2, the rupture
dynamics is not defined without the introduction of a regularizing scheme. We
investigate three regularization schemes involving respectively a saturation of
damage, a minimum distance of approach to the crack tip and a fixed stress
maximum. In the first and third schemes, the finite-time singularity is
replaced by a crack dynamics defined for all times but which is controlled by
either the existence of a microscopic scale at which the stress is regularized
or by the maximum sustainable stress. In the second scheme, a finite-time
singularity is again found. In the first two schemes within this regime m > 2,
the theory has no continuous limit.


  In this article we propose a novel Device-to-Device (D2D) Crowd framework for
5G mobile edge computing, where a massive crowd of devices at the network edge
leverage the network-assisted D2D collaboration for computation and
communication resource sharing among each other. A key objective of this
framework is to achieve energy-efficient collaborative task executions at
network-edge for mobile users. Specifically, we first introduce the D2D Crowd
system model in details, and then formulate the energy-efficient D2D Crowd task
assignment problem by taking into account the necessary constraints. We next
propose a graph matching based optimal task assignment policy, and further
evaluate its performance through extensive numerical study, which shows a
superior performance of more than 50% energy consumption reduction over the
case of local task executions. Finally, we also discuss the directions of
extending the D2D Crowd framework by taking into variety of application
factors.


  We consider periodic single-photon sources with combined multiplexing in
which the outputs of several time-multiplexed sources are spatially
multiplexed. We give a full statistical description of such systems in order to
optimize them with respect to maximal single-photon probability. We carry out
the optimization for a particular scenario which can be realized in bulk optics
and its expected performance is potentially the best at the present state of
the art. We find that combined multiplexing outperforms purely spatially or
time multiplexed sources for certain parameters only, and we characterize these
cases. Combined multiplexing can have the advantages of possibly using less
nonlinear sources, achieving higher repetition rates, and the potential
applicability for continuous pumping. We estimate an achievable single-photon
probability between 85% and 89%.


  We discuss the emerging advances and opportunities at the intersection of
machine learning (ML) and climate physics, highlighting the use of ML
techniques, including supervised, unsupervised, and equation discovery, to
accelerate climate knowledge discoveries and simulations. We delineate two
distinct yet complementary aspects: (1) ML for climate physics and (2) ML for
climate simulations. While physics-free ML-based models, such as ML-based
weather forecasting, have demonstrated success when data is abundant and
stationary, the physics knowledge and interpretability of ML models become
crucial in the small-data/non-stationary regime to ensure generalizability.
Given the absence of observations, the long-term future climate falls into the
small-data regime. Therefore, ML for climate physics holds a critical role in
addressing the challenges of ML for climate simulations. We emphasize the need
for collaboration among climate physics, ML theory, and numerical analysis to
achieve reliable ML-based models for climate applications.


  We theoretically investigate the transport properties of cold bosonic atoms
in a quasi one-dimensional triple-well potential that consists of two large
outer wells, which act as microscopic source and drain reservoirs, and a small
inner well, which represents a quantum-dot-like scattering region. Bias and
gate "voltages" introduce a time-dependent tilt of the triple-well
configuration, and are used to shift the energetic level of the inner well with
respect to the outer ones. By means of exact diagonalization considering a
total number of six atoms in the triple-well potential, we find diamond-like
structures for the occurrence of single-atom transport in the parameter space
spanned by the bias and gate voltages. We discuss the analogy with Coulomb
blockade in electronic quantum dots, and point out how one can infer the
interaction energy in the central well from the distance between the diamonds.


  Stellar occultations have been used to search for Kuiper Belt and Oort Cloud
objects. We propose a search for interstellar objects based on the
characteristic durations ($\sim 0.1 \mathrm{s}$) of their stellar occultation
signals and high inclination relative to the ecliptic plane. An all-sky
monitoring program of all $\sim 7 \times 10^6$ stars with $R \lesssim 12.5$
using 1-m telescopes with $0.1 \; \mathrm{s}$ cadences is predicted to discover
$\sim 1$ interstellar object per year.


  Federated learning (FL) promotes predictive model training at the Internet of
things (IoT) devices by evading data collection cost in terms of energy, time,
and privacy. We model the learning gain achieved by an IoT device against its
participation cost as its utility. Due to the device-heterogeneity, the local
model learning cost and its quality, which can be time-varying, differs from
device to device. We show that this variation results in utility unfairness
because the same global model is shared among the devices. By default, the
master is unaware of the local model computation and transmission costs of the
devices, thus it is unable to address the utility unfairness problem. Also, a
device may exploit this lack of knowledge at the master to intentionally reduce
its expenditure and thereby enhance its utility. We propose to control the
quality of the global model shared with the devices, in each round, based on
their contribution and expenditure. This is achieved by employing differential
privacy to curtail global model divulgence based on the learning contribution.
In addition, we devise adaptive computation and transmission policies for each
device to control its expenditure in order to mitigate utility unfairness. Our
results show that the proposed scheme reduces the standard deviation of the
energy cost of devices by 99% in comparison to the benchmark scheme, while the
standard deviation of the training loss of devices varies around 0.103.


  This paper analyzes a two-level algorithm for the weak Galerkin (WG) finite
element methods based on local Raviart-Thomas (RT) and Brezzi-Douglas-Marini
(BDM) mixed elements for two- and three-dimensional diffusion problems with
Dirichlet condition. We first show the condition numbers of the stiffness
matrices arising from the WG methods are of $O(h^{-2})$. We use an extended
version of the Xu-Zikatanov (XZ) identity to derive the convergence of the
algorithm without any regularity assumption. Finally we provide some numerical
results.


  Possibilities of observing a signal of an $\eta ^{\prime} n$ bound state are
investigated by considering photoproductions of the $\eta$ and $\eta ^{\prime}$
mesons on a deuteron target with forward proton emission. For this purpose, we
take the $\eta ^{\prime} n$ interaction from the linear sigma model with a
coupling to $\eta n$, in which an $s$-wave $\eta ^{\prime} n$ bound state can
be dynamically generated, and we fix the $\gamma p \to \eta p$ and $\eta
^{\prime} p$ scattering amplitudes so as to reproduce the experimental cross
sections with forward proton emission. By using these $\gamma p \to \eta ^{(
\prime )} p$ and $\eta ^{( \prime )} n \to \eta ^{( \prime )} n$ amplitudes, we
calculate cross sections of the $\gamma d \to \eta n p$ and $\eta ^{\prime} n
p$ reactions with forward proton emission in single and $\eta ^{(\prime
)}$-exchange double scattering processes. As a result, we find that the signal
of the $\eta ^{\prime} n$ bound state can be seen below the $\eta ^{\prime} n$
threshold in the $\eta n$ invariant mass spectrum of the $\gamma d \to \eta n
p$ reaction and is comparable with the contribution from the quasifree $\eta
^{\prime}$ production above the $\eta ^{\prime} n$ threshold. We also discuss
the behavior of the signal of the $\eta ^{\prime} n$ bound state in several
experimental conditions and model parameters.


  Inspired by an $L_p$ Steiner formula for the $L_p$ affine surface area proved
by Tatarko and Werner, we define, in analogy to the classical Steiner formula,
$L_p$-Steiner quermassintegrals. Special cases include the classical mixed
volumes, the dual mixed volumes, the $L_p$ affine surface areas and the mixed
$L_p$ affine surface areas. We investigate the properties of the $L_p$-Steiner
quermassintegrals in a special class of convex bodies. In particular, we show
that they are rotation and reflection invariant valuations in this class of
convex bodies with a certain degree of homogeneity. Such valuations seem new
and have not been observed before.


  Numerical, two-dimensional, time-dependent hydrodynamical models of
geometrically thick accretion discs around black holes are presented. Accretion
flows with non-effective radiation cooling (ADAFs) can be both convectively
stable or unstable depending on the value of the viscosity parameter \alpha.
The high viscosity flows (\alpha~1) are stable and have a strong equatorial
inflow and bipolar outflows. The low viscosity flows (\alpha<0.1) are
convectively unstable and this induces quasi-periodic variability.


  A numerical method to obtain the spectrum of smooth normal-superconductor
junctions in nanowires, able to host Majorana zero modes, is presented.
Softness in the potential and superconductor interfaces yields opposite effects
on the protection of Majorana modes. While a soft potential is a hindrance for
protection, a soft superconductor gap transition greatly favors it. Our method
also points out the possibility of extended Majorana states when propagating
modes are active far from the junction, although this requires equal incident
fluxes in all open channels.


  As the fields of optical microscopy, semiconductor technology and fundamental
science increasingly aim for precision at or below the nanoscale, there is a
burgeoning demand for sub-nanometric displacement and position sensing. We show
that the speckle patterns produced by multiple reflections of light inside an
integrating sphere provide an exceptionally sensitive probe of displacement. We
use an integrating sphere split into two equal and independent hemispheres, one
of which is free to move in any given direction. The relative motion of the two
hemispheres produces a change in the speckle pattern from which we can
analytically infer the amplitude of the displacement. The method allows
displacement measurement with uncertainty as small as 40 pm ($\lambda/20,000$)
in a facile implementation. We show how, under realistic experimental
parameters, the uncertainty in displacement could be improved to tens of
femtometres, or $\lambda/10^{7}$.


  The thermodynamic mixing properties of alkali feldspar solid solutions
between the Na and K end members were computed through atomistic simulations
using a neural network potential. We performed combined molecular dynamics and
Monte Carlo simulations in the semi-grand canonical ensemble at 800 {\deg}C and
considered three quenched disorder states in the Al-Si-O framework ranging from
fully ordered to fully disordered. The excess Gibbs energy of mixing, excess
enthalpy of mixing and excess entropy of mixing are in good agreement with
literature data. In particular, the notion that increasing disorder in the
Al-Si-O framework correlates with increasing ideality of Na-K mixing is
successfully predicted. Finally, a recently proposed short range ordering of Na
and K in the alkali sublattice is observed, which may be considered as a
precursor to exsolution lamellae, a characteristic phenomenon in alkali
feldspar of intermediate composition leading to perthite formation during
cooling.


  This work introduces a novel probabilistic deep learning technique called
deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification
of both epistemic and aleatoric uncertainty. By assuming the data generating
process follows that of a Gaussian mixture, DGMEs are capable of approximating
complex probability distributions, such as heavy-tailed or multimodal
distributions. Our contributions include the derivation of an
expectation-maximization (EM) algorithm used for learning the model parameters,
which results in an upper-bound on the log-likelihood of training data over
that of standard deep ensembles. Additionally, the proposed EM training
procedure allows for learning of mixture weights, which is not commonly done in
ensembles. Our experimental results demonstrate that DGMEs outperform
state-of-the-art uncertainty quantifying deep learning models in handling
complex predictive densities.


  Most methods of orbit determination are often difficult for numerical
implementations since they are developed before the computer era. The recently
developed mathematical technique of semi-definite programming (SDP) has been
implemented for many problems in scientific fields including astrometry. This
is a good opportunity to resolve orbits of binary systems located in the
southern hemisphere since more than seventy years Bosscha Observatory had been
continuously conducting observations of binary systems. Here we describe
prospects of application of SDP for deriving orbital parameters of binary
systems using data supplied by Bosscha Observatory that has been published in
the Centre de Donnees astronomiques de Strasbourg. This study will support
observers at Bosscha Observatory to appropriately select target stars belong to
binary systems for their ongoing researches. Since SDP is a powerful scheme,
free trial-and-error and human-independent judgment, we suggest that SDP may
become a standard method for determining orbital parameters of binary systems.


  Loosely inspired by the somewhat fanciful notion of detecting an arbitrarily
advanced alien civilization, we consider a general-relativistic thin-shell
Dyson mega-sphere completely enclosing a central star-like object, and perform
a full general-relativistic analysis using the Israel--Lanczos--Sen junction
conditions. We focus attention on the surface mass density, the surface stress,
the classical energy conditions, and the forces between hemispheres. We find
that in the physically acceptable region the NEC, WEC, and SEC are always
satisfied, while the DEC can be violated if the Dyson mega-sphere is
sufficiently close to forming a black hole. We also demonstrate that the
original quasi-local version of the maximum force conjecture, F <= {1/4}
F_{Stoney} = {1/4} F_{Planck}, can easily be violated if the Dyson mega-sphere
is sufficiently compact, that is, sufficiently close to forming a black hole.
Interestingly there is a finite region of parameter space where one can violate
the original quasi-local version of the maximum force conjecture without
violating the DEC. Finally, we very briefly discuss the possibility of nested
thin-shell mega-spheres (Matrioshka configurations) and thick-shell Dyson
mega-spheres.


  We conjecture a closed-form expression for the Schur limit of the
superconformal index of two infinite series of Argyres-Douglas (AD)
superconformal field theories (SCFTs): the (A_1,A_{2n-3}) and the (A_1,D_{2n})
theories. While these SCFTs can be realized at special points on the Coulomb
branch of certain N=2 gauge theories, their superconformal R symmetries are
emergent, and hence their indices cannot be evaluated by localization. Instead,
we construct the (A_1, A_{2n-3}) and (A_1, D_{2n}) indices by using a relation
to two-dimensional q-deformed Yang-Mills theory and data from the class S
construction. Our results generalize the indices derived from the torus
partition functions of the two-dimensional chiral algebras associated with the
(A_1, A_3) and (A_1, D_4) SCFTs. As checks of our conjectures, we study the
consistency of our results with an S-duality recently discussed by us in
collaboration with Giacomelli and Papageorgakis, we reproduce known Higgs
branch relations, we check consistency with a series of renormalization group
flows, and we verify that the small S^1 limits of our indices reproduce
expected Cardy-like behavior. We will discuss the S^1 reduction of our indices
in a separate paper.


  Deep convolutional neural networks are known to specialize in distilling
compact and robust prior from a large amount of data. We are interested in
applying deep networks in the absence of training dataset. In this paper, we
introduce deep audio prior (DAP) which leverages the structure of a network and
the temporal information in a single audio file. Specifically, we demonstrate
that a randomly-initialized neural network can be used with carefully designed
audio prior to tackle challenging audio problems such as universal blind source
separation, interactive audio editing, audio texture synthesis, and audio
co-separation. To understand the robustness of the deep audio prior, we
construct a benchmark dataset \emph{Universal-150} for universal sound source
separation with a diverse set of sources. We show superior audio results than
previous work on both qualitative and quantitative evaluations. We also perform
thorough ablation study to validate our design choices.


  A degree-corrected distribution-free model is proposed for weighted social
networks with latent structural information. The model extends the previous
distribution-free models by considering variation in node degree to fit
real-world weighted networks, and it also extends the classical
degree-corrected stochastic block model from un-weighted network to weighted
network. We design an algorithm based on the idea of spectral clustering to fit
the model. Theoretical framework on consistent estimation for the algorithm is
developed under the model. Theoretical results when edge weights are generated
from different distributions are analyzed. We also propose a general modularity
as an extension of Newman's modularity from un-weighted network to weighted
network. Using experiments with simulated and real-world networks, we show that
our method significantly outperforms the uncorrected one, and the general
modularity is effective.


  Monocular depth estimation is an important task that can be applied to many
robotic applications. Existing methods focus on improving depth estimation
accuracy via training increasingly deeper and wider networks, however these
suffer from large computational complexity. Recent studies found that edge
information are important cues for convolutional neural networks (CNNs) to
estimate depth. Inspired by the above observations, we present a novel
lightweight Edge Guided Depth Estimation Network (EGD-Net) in this study. In
particular, we start out with a lightweight encoder-decoder architecture and
embed an edge guidance branch which takes as input image gradients and
multi-scale feature maps from the backbone to learn the edge attention
features. In order to aggregate the context information and edge attention
features, we design a transformer-based feature aggregation module (TRFA). TRFA
captures the long-range dependencies between the context information and edge
attention features through cross-attention mechanism. We perform extensive
experiments on the NYU depth v2 dataset. Experimental results show that the
proposed method runs about 96 fps on a Nvidia GTX 1080 GPU whilst achieving the
state-of-the-art performance in terms of accuracy.


  Personalization customizes information access. The PIPE ("Personalization is
Partial Evaluation") modeling methodology represents interaction with an
information space as a program. The program is then specialized to a user's
known interests or information seeking activity by the technique of partial
evaluation. In this paper, we elaborate PIPE by considering requirements
analysis in the personalization lifecycle. We investigate the use of scenarios
as a means of identifying and analyzing personalization requirements. As our
first result, we show how designing a PIPE representation can be cast as a
search within a space of PIPE models, organized along a partial order. This
allows us to view the design of a personalization system, itself, as
specialized interpretation of an information space. We then exploit the
underlying equivalence of explanation-based generalization (EBG) and partial
evaluation to realize high-level goals and needs identified in scenarios; in
particular, we specialize (personalize) an information space based on the
explanation of a user scenario in that information space, just as EBG
specializes a theory based on the explanation of an example in that theory. In
this approach, personalization becomes the transformation of information spaces
to support the explanation of usage scenarios. An example application is
described.


  Recent work has proposed a method for imposing T-duality on the metric,
$B$-field, and dilaton of the classical effective action of string theory
without using Kaluza-Klein reduction. Specifically, the $D$-dimensional
effective action should be invariant under global $O(D,D)$ transformations,
provided that the partial derivatives along the $\beta$-parameters of the
non-geometrical elements of the $O(D,D)$ group become zero. In this paper, we
speculate that the global $\beta$-symmetry can be utilized to identify both
bulk and boundary couplings.
  We demonstrate that the Gibbons-Hawking term at the leading order of
$\alpha'$ is reproduced by this symmetry. Additionally, for closed spacetime
manifolds at order $\alpha'$, we show that the 20 independent geometrical
couplings at this order are fixed by this symmetry up to field redefinitions.
Specifically, we show that the invariance of the most general covariant and
gauge invariant bulk action at order $\alpha'$ under the most general covariant
deformed $\beta$-transformation at order $\alpha'$ fixes the action up to 13
parameters. These parameters reflect the field redefinitions freedom for the
closed spacetime manifolds. For particular values of these parameters, we
recover the effective action in the Metsaev-Tseytlin and in the Meissner
schemes.


  Identifying protein functional sites (PFSs) and protein-ligand interactions
(PLIs) are critically important in understanding the protein function and the
involved biochemical reactions. As large amount of unknown proteins are quickly
accumulated in this post-genome era, an urgent task arises to predict PFSs and
PLIs at residual level. Nowadays many knowledge-based methods have been well
developed for prediction of PFSs, however, accurate methods for PLI prediction
are still lacking. In this study, we have presented a new method for prediction
of PLIs and PFSs based on sequence of the inquiry protein. The key of the
method hinges on a function- and interaction-annotated protein domain profile
database, called fiDPD, which was built from the Structural Classification of
Proteins (SCOP) database, using a hidden Markov model program. The method was
applied to 13 target proteins from the recent Critical Assessment of Structure
Prediction (CASP10/11). Our calculations gave a Matthews correlation
coefficient (MCC) value of 0.66 for prediction of PFSs, and an 80% recall in
prediction of PLIs. Our method reveals that PLIs are conserved during the
evolution of proteins, and they can be reliably predicted from fiDPD. fiDPD can
be used as a complement to existent bioinformatics tools for protein function
annotation.


  The Prototype Imaging Spectrograph for Coronagraphic Exoplanet Studies
(PISCES) is a high contrast integral field spectrograph (IFS) whose design was
driven by WFIRST coronagraph instrument requirements. We present commissioning
and operational results using PISCES as a camera on the High Contrast Imaging
Testbed at JPL. PISCES has demonstrated ability to achieve high contrast
spectral retrieval with flight-like data reduction and analysis techniques.


  In safety-critical applications that rely on the solution of an optimization
problem, the certification of the optimization algorithm is of vital
importance. Certification and suboptimality results are available for a wide
range of optimization algorithms. However, a typical underlying assumption is
that the operations performed by the algorithm are exact, i.e., that there is
no numerical error during the mathematical operations, which is hardly a valid
assumption in a real hardware implementation. This is particularly true in the
case of fixed-point hardware, where computational inaccuracies are not
uncommon. This article presents a certification procedure for the proximal
gradient method for box-constrained QP problems implemented in fixed-point
arithmetic. The procedure provides a method to select the minimal fractional
precision required to obtain a certain suboptimality bound, indicating the
maximum number of iterations of the optimization method required to obtain it.
The procedure makes use of formal verification methods to provide arbitrarily
tight bounds on the suboptimality guarantee. We apply the proposed
certification procedure on the implementation of a non-trivial model predictive
controller on 32-bit fixed-point hardware.


  Recent theoretical work has shown that higher-order moments of the Berry
curvature, e.g., Berry curvature quadrupole or hexapole moments can produce the
leading order nonlinear anomalous Hall response (NLAH) in systems with special
magnetic point group symmetry. Recent experimental work has reported the
observation of the Berry curvature quadrupole-induced third-order NLAH (i.e.,
Hall voltage proportional to the third power of the external electric field)
from cryogenic conditions to room temperature in an epitaxially grown material
platform with broken time-reversal symmetry. In this paper, using semiclassical
Boltzmann formalism in the relaxation time approximation, we compute the Berry
curvature quadrupole-induced nonlinear anomalous thermal Hall and Nernst
coefficients in time-reversal broken systems. In systems where Berry curvature
monopole and dipole moments vanish by symmetry, our results predict the
behavior of the leading order transverse anomalous thermoelectric coefficients
proportional to the third power of the applied longitudinal temperature
gradient, and are guaranteed to exist in systems that have already exhibited
the third-order nonlinear anomalous Hall effect in recent experiments.


  Mapping the unresolved intensity of the 21-cm emission of neutral hydrogen
(HI) is now regarded as one the most promising tools for cosmological
investigation in the coming decades. Here, we investigate, for the first time,
extensions of the standard cosmological model, such as modified gravity and
primordial non-Gaussianity, taking self-consistently into account the present
constraints on the astrophysics of HI clustering in the treatment of the
brightness temperature fluctuations. To understand the boundaries within which
results thus obtained can be considered reliable, we examine the robustness of
cosmological parameter estimation performed via studies of 21-cm intensity
mapping, against our knowledge of the astrophysical processes leading to HI
clustering. Modelling of astrophysical effects affects cosmological observables
through the relation linking the overall HI mass in a bound object, to the mass
of the underlying dark matter halo that hosts it. We quantify the biases in
estimates of standard cosmological parameters and those describing modified
gravity and primordial non-Gaussianity, that are obtained if one misconceives
the slope of the relation between HI mass and halo mass, or the lower virial
velocity cut-off for a dark matter halo to be able to host HI. Remarkably, we
find that astrophysical uncertainties will not affect searches for primordial
non-Gaussianity - one of the strongest science cases for HI intensity mapping -
despite the signal being deeply linked to the HI bias.


  We introduce the new concept of an EBV to assess the sensitivity of model
outputs to changes in initial conditions for weather forecasting. The new
algorithm, which we call the "Ensemble Bred Vector" or EBV, is based on
collective dynamics in essential ways. By construction, the EBV algorithm
produces one or more dominant vectors.
  We investigate the performance of EBV, comparing it to the BV algorithm as
well as the finite-time Lyapunov Vectors. We give a theoretical justification
to the observed fact that the vectors produced by BV, EBV, and the finite-time
Lyapunov vectors are similar for small amplitudes.
  Numerical comparisons of BV and EBV for the 3-equation Lorenz model and for a
forced, dissipative partial differential equation of Cahn-Hilliard type that
arises in modeling the thermohaline circulation, demonstrate that the EBV
yields a size-ordered description of the perturbation field, and is more robust
than the BV in the higher nonlinear regime. The EBV yields insight into the
fractal structure of the Lorenz attractor, and of the inertial manifold for the
Cahn-Hilliard-type partial differential equation.


  Modelling spatio-temporal processes on road networks is a task of growing
importance. While significant progress has been made on developing
spatio-temporal graph neural networks (Gnns), existing works are built upon
three assumptions that are not practical on real-world road networks. First,
they assume sensing on every node of a road network. In reality, due to
budget-constraints or sensor failures, all locations (nodes) may not be
equipped with sensors. Second, they assume that sensing history is available at
all installed sensors. This is unrealistic as well due to sensor failures, loss
of packets during communication, etc. Finally, there is an assumption of static
road networks. Connectivity within networks change due to road closures,
constructions of new roads, etc. In this work, we develop FRIGATE to address
all these shortcomings. FRIGATE is powered by a spatio-temporal Gnn that
integrates positional, topological, and temporal information into rich
inductive node representations. The joint fusion of this diverse information is
made feasible through a novel combination of gated Lipschitz embeddings with
Lstms. We prove that the proposed Gnn architecture is provably more expressive
than message-passing Gnns used in state-of-the-art algorithms. The higher
expressivity of FRIGATE naturally translates to superior empirical performance
conducted on real-world network-constrained traffic data. In addition, FRIGATE
is robust to frugal sensor deployment, changes in road network connectivity,
and temporal irregularity in sensing.


  In the last two decades, considerable advances have been made in the
investigation of nano-photonics in photonic crystals. Previous theoretical
investigations of photon dynamics were carried out at zero temperature. Here,
we investigate micro/nano cavity photonics in photonic crystals at finite
temperature. Due to photonic-band-gap-induced non-Markovian dynamics, we
discover that cavity photons in photonic crystals do not obey the standard
Bose-Einstein statistical distribution. Within the photonic band gap and in the
vicinity of the band edge, cavity photons combine nontrivial quantum
dissipation with thermal fluctuations to form photon states that can memorize
the initial cavity state information. As a result, Bose-Einstein distribution
is completely broken down in these regimes, even if the thermal energy is
larger than the photonic band gap.


  We propose a stochastic order parameter equation for describing phase
coexistence in steady heat conduction near equilibrium. By analyzing the
stochastic dynamics with a non-equilibrium adiabatic boundary condition, where
total energy is conserved over time, we derive a variational principle that
determines thermodynamic properties in non-equilibrium steady states. The
resulting variational principle indicates that the temperature of the interface
between the ordered region and the disordered region becomes greater (less)
than the equilibrium transition temperature in the linear response regime when
the thermal conductivity in the ordered region is less (greater) than that in
the disordered region. This means that a super-heated ordered (super-cooled
disordered) state appears near the interface, which was predicted by an
extended framework of thermodynamics proposed in [N. Nakagawa and S.-i. Sasa,
Liquid-gas transitions in steady heat conduction, Phys. Rev. Lett. {\bf 119},
260602, (2017).]


  Seemingly different interpretations or descriptions of Einstein's model for
the heat capacity of a solid can be found in textbooks and the literature. The
purpose of this note is to clarify the equivalence of the different
descriptions, which all lead to the same Einstein expression for the heat
capacity of a solid of N atoms.


  We build interacting Fock spaces from association schemes and set up quantum
walks on the resulting regular graphs (distance-regular and
distance-transitive). The construction is valid for growing graphs and the
interacting Fock space is well defined asymptotically for the growing graph. To
realize the quantum walks defined on the graphs in terms of anyons we switch to
the dual view of the association schemes and identify the corresponding modular
tensor categories from the Bose-Mesner algebra. Informally, the fusion ring
induced by the association scheme and a topological twist can be the basis for
developing a modular tensor category and thus a system of anyons. Finally, we
demonstrate the framework in the case of Grover quantum walk on
distance-regular graph in terms of anyon systems for the graphs considered. In
the dual perspective interacting Fock spaces gather a new meaning in terms of
any


  We calculate the dispersion relations for electromagnetic waves propagating
at the interface between two dissimilar Drude metals in an external magnetic
field B parallel to the interface. The propagating modes are bound to the
inteface and travel perpendicular to B. In certain frequency ranges, the waves
can propagate in one direction only. The frequency range for these one-way
modes increases with increasing B. One group of modes occurs at moderate
frequencies, between the lower and upper plasma frequencies of the two metals.
The other occurs at much lower frequencies, between their lower and upper
cyclotron frequencies. We discuss possible ways to realize such modes in real
materials, including dissimilar superconductors.


  Using computer simulations, we identify the mechanisms causing aggregation
and structural arrest of colloidal suspensions interacting with a short-ranged
attraction at moderate and high densities. Two different non-ergodicity
transitions are observed. As the density is increased, a glass transition takes
place, driven by excluded volume effects. In contrast, at moderate densities,
gelation is approached as the strength of the attraction increases. At high
density and interaction strength, both transitions merge, and a logarithmic
decay in the correlation function is observed. All of these features are
correctly predicted by mode coupling theory.


  Let $R$ be an exchange ring. We prove that the relative elementary subgroups
$E_n(R,I)$ are normal in the general linear group $GL_n(R)$ if $n\geq 1$ and
that the standard commutator formula
$E_n(R,I)=[E_n(R),E_n(R,I)]=[E_n(R),C_n(R,I)]$ holds if $n\geq 3$. Moreover, we
classify the subgroups of $GL_n(R)$ that are normalised by the elementary
subgroup $E_n(R)$ in the case $n\geq 3$.


  We tackle the problem of minimum-time flight for a quadrotor through a
sequence of waypoints in the presence of obstacles while exploiting the full
quadrotor dynamics. Early works relied on simplified dynamics or polynomial
trajectory representations that did not exploit the full actuator potential of
the quadrotor, and, thus, resulted in suboptimal solutions. Recent works can
plan minimum-time trajectories; yet, the trajectories are executed with control
methods that do not account for obstacles. Thus, a successful execution of such
trajectories is prone to errors due to model mismatch and in-flight
disturbances. To this end, we leverage deep reinforcement learning and
classical topological path planning to train robust neural-network controllers
for minimum-time quadrotor flight in cluttered environments. The resulting
neural network controller demonstrates substantially better performance of up
to 19\% over state-of-the-art methods. More importantly, the learned policy
solves the planning and control problem simultaneously online to account for
disturbances, thus achieving much higher robustness. As such, the presented
method achieves 100% success rate of flying minimum-time policies without
collision, while traditional planning and control approaches achieve only 40%.
The proposed method is validated in both simulation and the real world, with
quadrotor speeds of up to 42km/h and accelerations of 3.6g.


  This paper describes the application of a high-level language and method in
developing simpler specifications of more complex variants of the Paxos
algorithm for distributed consensus. The specifications are for Multi-Paxos
with preemption, replicated state machine, and reconfiguration and optimized
with state reduction and failure detection. The language is DistAlgo. The key
is to express complex control flows and synchronization conditions precisely at
a high level, using nondeterministic waits and message-history queries. We
obtain complete executable specifications that are almost completely
declarative---updating only a number for the protocol round besides the sets of
messages sent and received.
  We show the following results: 1.English and pseudocode descriptions of
distributed algorithms can be captured completely and precisely at a high
level, without adding, removing, or reformulating algorithm details to fit
lower-level, more abstract, or less direct languages. 2.We created higher-level
control flows and synchronization conditions than all previous specifications,
and obtained specifications that are much simpler and smaller, even matching or
smaller than abstract specifications that omit many algorithm details. 3.The
simpler specifications led us to easily discover useless replies, unnecessary
delays, and liveness violations (if messages can be lost) in previous published
specifications, by just following the simplified algorithm flows. 4.The
resulting specifications can be executed directly, and we can express
optimizations cleanly, yielding drastic performance improvement over naive
execution and facilitating a general method for merging processes. 5.We
systematically translated the resulting specifications into TLA+ and developed
machine-checked safety proofs, which also allowed us to detect and fix a subtle
safety violation in an earlier unpublished specification.


  Excesses of Galactic gamma rays in the 1-100 GeV region observed by EGRET and
by WMAP have been interpreted as signals of dark matter (LSP) annihilation. We
argue that the excess of TeV gamma rays from the Galactic center observed by
H.E.S.S. signals a heavier dark matter component with a very small relic
density.


  It is shown that, with some reasonable assumptions, the theory of general
relativity can be made compatible with quantum mechanics by using the field
equations of general relativity to construct a Robertson-Walker metric for a
quantum particle so that the line element of the particle can be transformed
entirely to that of the Minkowski spacetime, which is assumed by a quantum
observer, and the spacetime dynamics of the particle described by a Minkowski
observer takes the form of quantum mechanics. Spacetime structure of a quantum
particle may have either positive or negative curvature. However, in order to
be describable using the familiar framework of quantum mechanics, the spacetime
structure of a quantum particle must be "quantised" by an introduction of the
imaginary number $i$. If a particle has a positive curvature then the
quantisation is equivalent to turning the pseudo-Riemannian spacetime of the
particle into a Riemannian spacetime. This means that it is assumed the
particle is capable of measuring its temporal distance like its spatial
distances. On the other hand, when a particle has a negative curvature and a
negative energy density then quantising the spacetime structure of the particle
is equivalent to viewing the particle as if it had a positive curvature and a
positive energy density.


  Straight-forward derivation of planetary parameters can only be achieved in
transiting planetary systems. However, planetary attributes such as radius and
mass strongly depend on stellar host parameters. Discovering a transit host
star to be multiple leads to a necessary revision of the derived stellar and
planetary parameters. Based on our observations of 14 transiting exoplanet
hosts, we derive parameters of the individual components of three transit host
stars (WASP-2, TrES-2, and TrES-4) which we detected to be binaries. Two of
these have not been known to be multiple before. Parameters of the
corresponding exoplanets are revised. High-resolution "Lucky Imaging" with
AstraLux at the 2.2m Calar Alto telescope provided near diffraction limited
images in i' and z' passbands. These results have been combined with existing
planetary data in order to recalibrate planetary attributes. Despite the
faintness (delta mag ~ 4) of the discovered stellar companions to TrES-2,
TrES-4, and WASP-2, light-curve deduced parameters change by up to more than
1sigma. We discuss a possible relation between binary separation and planetary
properties, which - if confirmed - could hint at the influence of binarity on
the planet formation process.


  We develop the notion of Brakke flow with free-boundary in a barrier surface.
Unlike the classical free-boundary mean curvature flow, the free-boundary
Brakke flow must "pop" upon tangential contact with the barrier. We prove a
compactness theorem for free-boundary Brakke flows, define a Gaussian
monotonicity formula valid at all points, and use this to adapt the local
regularity theorem of White to the free-boundary setting. We use Ilmanen's
elliptic regularization procedure to prove existence of free-boundary Brakke
flows.


  As we transition towards the deployment of data-driven controllers for
black-box cyberphysical systems, complying with hard safety constraints becomes
a primary concern. Two key aspects should be addressed when input-output data
are corrupted by noise: how much uncertainty can one tolerate without
compromising safety, and to what extent is the control performance affected? By
focusing on finite-horizon constrained linear-quadratic problems, we provide an
answer to these questions in terms of the model mismatch incurred during a
preliminary identification phase. We propose a control design procedure based
on a quasiconvex relaxation of the original robust problem and we prove that,
if the uncertainty is sufficiently small, the synthesized controller is safe
and near-optimal, in the sense that the suboptimality gap increases linearly
with the model mismatch level. Since the proposed method is independent of the
specific identification procedure, our analysis holds in combination with
state-of-the-art behavioral estimators beyond standard least-squares. The main
theoretical results are validated by numerical experiments.


  An essential ingredient in many examples of the conflict between quantum
theory and noncontextual hidden variables (e.g., the proof of the
Kochen-Specker theorem and Hardy's proof of Bell's theorem) is a set of atomic
propositions about the outcomes of ideal measurements such that, when outcome
noncontextuality is assumed, if proposition $A$ is true, then, due to
exclusiveness and completeness, a nonexclusive proposition $B$ ($C$) must be
false (true). We call such a set a {\em true-implies-false set} (TIFS) [{\em
true-implies-true set} (TITS)]. Here we identify all the minimal TIFSs and
TITSs in every dimension $d \ge 3$, i.e., the sets of each type having the
smallest number of propositions. These sets are important because each of them
leads to a proof of impossibility of noncontextual hidden variables and
corresponds to a simple situation with quantum vs classical advantage.
Moreover, the methods developed to identify them may be helpful to solve some
open problems regarding minimal Kochen-Specker sets.


  CaFe$_{2}$O$_{4}$ is an anisotropic $S={5\over 2}$ antiferromagnet with two
competing $A$ ($\uparrow \uparrow \downarrow \downarrow$) and $B$ ($\uparrow
\downarrow \uparrow \downarrow$) magnetic order parameters separated by static
antiphase boundaries at low temperatures. Neutron diffraction and bulk
susceptibility measurements, show that the spins near these boundaries are
weakly correlated and a carry an uncompensated ferromagnetic moment that can be
tuned with a magnetic field. Spectroscopic measurements find these spins are
bound with excitation energies less than the bulk magnetic spin-waves and
resemble the spectra from isolated spin-clusters. Localized bound orphaned
spins separate the two competing magnetic order parameters in
CaFe$_{2}$O$_{4}$.


  Using the most recent observational data concerning the Extragalactic
Background Light and the Radio Background, for a source at a redshift z_s < 3
we compute the energy E_0 of an observed gamma-ray photon in the range 10 GeV <
E_0 < 10^13 GeV such that the resulting optical depth tau_gamma(E_0,z_s) takes
the values 1, 2, 3 and 4.6, corresponding to an observed flux dimming of e^-1 =
0.37, e^-2 = 0.14, e^-3 = 0.05 and e^-4.6 = 0.01, respectively. Below a source
distance D = 8 kpc we find that tau_gamma(E_0,DH_0/c) < 1 for any value of E_0.
In the limiting case of a local Universe (z_s = 0) we compare our result with
the one derived in 1997 by Coppi and Aharonian. The present achievement is of
paramount relevance for the planned ground-based detectors like CTA, HAWC and
HiSCORE.


  The general relativistic dynamics of a wide class of self-interacting,
self-gravitating homogeneous scalar fields models is analyzed. The class is
characterized by certain general conditions on the scalar field potential,
which include both asymptotically polynomial and exponential behaviors. Within
this class, we show that the generic evolution is always divergent in a finite
time, and then make use of this result to construct cosmological models as well
as radiating collapsing star models of the Vaidya type. It turns out that
blackholes are generically formed in such models.


  The observation of Type Ia supernovae (SNe Ia) plays an essential role in
probing the expansion history of the universe. But the possible presence of
cosmic opacity can degrade the quality of SNe Ia. The gravitational-wave (GW)
standard sirens, produced by the coalescence of double neutron stars and black
hole--neutron star binaries, provide an independent way to measure the
distances of GW sources, which are not affected by cosmic opacity. In this
paper, we first propose that combining the GW observations of third-generation
GW detectors with SN Ia data in similar redshift ranges offers a novel and
model-independent method to constrain cosmic opacity. Through Monte Carlo
simulations, we find that one can constrain the cosmic opacity parameter
$\kappa$ with an accuracy of $\sigma_{\kappa}\sim0.046$ by comparing the
distances from 100 simulated GW events and 1048 current Pantheon SNe Ia. The
uncertainty of $\kappa$ can be further reduced to $\sim0.026$ if 800 GW events
are considered. We also demonstrate that combining 2000 simulated SNe Ia and
1000 simulated GW events could result in much severer constraints on the
transparent universe, for which $\kappa=0.0000\pm0.0044$. Compared to previous
opacity constraints involving distances from other cosmic probes, our method
using GW standard sirens and SN Ia standard candles at least achieves
competitive results.


  This paper is concerned with exact real solving of well-constrained,
bivariate polynomial systems. The main problem is to isolate all common real
roots in rational rectangles, and to determine their intersection
multiplicities. We present three algorithms and analyze their asymptotic bit
complexity, obtaining a bound of $\sOB(N^{14})$ for the purely projection-based
method, and $\sOB(N^{12})$ for two subresultant-based methods: this notation
ignores polylogarithmic factors, where $N$ bounds the degree and the bitsize of
the polynomials. The previous record bound was $\sOB(N^{14})$.
  Our main tool is signed subresultant sequences. We exploit recent advances on
the complexity of univariate root isolation, and extend them to sign evaluation
of bivariate polynomials over two algebraic numbers, and real root counting for
polynomials over an extension field. Our algorithms apply to the problem of
simultaneous inequalities; they also compute the topology of real plane
algebraic curves in $\sOB(N^{12})$, whereas the previous bound was
$\sOB(N^{14})$.
  All algorithms have been implemented in MAPLE, in conjunction with numeric
filtering. We compare them against FGB/RS, system solvers from SYNAPS, and
MAPLE libraries INSULATE and TOP, which compute curve topology. Our software is
among the most robust, and its runtimes are comparable, or within a small
constant factor, with respect to the C/C++ libraries.
  Key words: real solving, polynomial systems, complexity, MAPLE software


  We report a strictly non-exponential spontaneous decay dynamics of an excited
two-level atom placed inside or at different distances outside a carbon
nanotube. This is the result of strong non-Markovian memory effects arising
from the rapid frequency variation of the photonic density of states near the
nanotube. The system exhibits vacuum-field Rabi oscillations when the atom is
close enough to the nanotube surface and the atomic transition frequency is in
the vicinity of the resonance of the photonic density of states.


  We calculate the energy dispersion of the surface Andreev states and their
contribution to tunneling conductance for the order parameters with horizontal
and vertical lines of nodes proposed for superconducting Sr2RuO4. For vertical
lines, we find double peaks in tunneling spectra reflecting the van Hove
singularities in the density of surface states originating from the turning
points in their energy dispersion. For horizontal lines, we find a single
cusp-like peak at zero bias, which agrees very well with the experimental data
on tunneling in Sr2RuO4.


  We study the relation between perturbative knot invariants and the free
energies defined by topological string theory on the character variety of the
knot. Such a correspondence between SL(2;C) Chern-Simons gauge theory and the
topological open string theory was proposed earlier on the basis of the volume
conjecture and AJ conjecture. In this paper we discuss this correspondence
beyond the subleading order in the perturbative expansion on both sides. In the
computation of the perturbative invariants for the hyperbolic 3-manifold, we
adopt the state integral model for the hyperbolic knots, and the factorized AJ
conjecture for the torus knots. On the other hand, we iteratively compute the
free energies on the character variety using the Eynard-Orantin topological
recursion relation. We check the correspondence for the figure eight knot
complement and the once punctured torus bundle over S^1 with the holonomy L^2R
up to the fourth order. For the torus knots, we find trivial the recursion
relations on both sides.


  We study the exterior derivative as a symmetric unbounded operator on square
integrable 1-forms on a 3D bounded domain $D$. We aim to identify boundary
conditions that render this operator self-adjoint. By the symplectic version of
the Glazman-Krein-Naimark theorem this amounts to identifying complete
Lagrangian subspaces of the trace space of H(curl) equipped with a symplectic
pairing arising from the $\wedge$-product of 1-forms on $\partial D$.
Substantially generalizing earlier results, we characterize Lagrangian
subspaces associated with closed and co-closed traces. In the case of
non-trivial topology of the domain, different contributions from co-homology
spaces also distinguish different self-adjoint extension. Finally, all
self-adjoint extensions discussed in the paper are shown to possess a discrete
point spectrum, and their relationship with curl curl-operators is discussed.


  With the increasing attention to the integrated satellite-terrestrial
networks (ISTNs), the satellite gateway placement problem becomes of paramount
importance. The resulting network performance may vary depending on the
different design strategies. In this paper, a joint satellite gateway placement
and routing strategy for the terrestrial network is proposed to minimize the
overall cost of gateway deployment and traffic routing, while adhering to the
average delay requirement for traffic demands. Although traffic routing and
gateway placement can be solved independently, the dependence between the
routing decisions for different demands makes it more realistic to solve an
aggregated model instead. We develop a mixed-integer linear program (MILP)
formulation for the problem. We relax the integrality constraints to achieve a
linear program (LP) which reduces time-complexity at the expense of a
sub-optimal solution. We further propose a variant of the proposed model to
balance the load between the selected gateways.