  Sickle cell disease (SCD) is a severe genetic hemoglobin disorder that
results in premature destruction of red blood cells. Assessment of the severity
of the disease is a challenging task in clinical routine since the causes of
broad variance in SCD manifestation despite the common genetic cause remain
unclear. Identification of the biomarkers that would predict the severity grade
is of importance for prognosis and assessment of responsiveness of patients to
therapy. Detection of the changes in red blood cell (RBC) density through
separation of Percoll density gradient could be such marker as it allows to
resolve intercellular differences and follow the most damaged dense cells prone
to destruction and vaso-occlusion. Quantification of the images obtained from
the distribution of RBCs in Percoll gradient and interpretation of the obtained
is an important prerequisite for establishment of this approach. Here, we
propose a novel approach combining a graph convolutional network, a
convolutional neural network, fast Fourier transform, and recursive feature
elimination to predict the severity of SCD directly from a Percoll image. Two
important but expensive laboratory blood test parameters measurements are used
for training the graph convolutional network. To make the model independent
from such tests during prediction, the two parameters are estimated by a neural
network from the Percoll image directly. On a cohort of 216 subjects, we
achieve a prediction performance that is only slightly below an approach where
the groundtruth laboratory measurements are used. Our proposed method is the
first computational approach for the difficult task of SCD severity prediction.
The two-step approach relies solely on inexpensive and simple blood analysis
tools and can have a significant impact on the patients' survival in
underdeveloped countries where access to medical instruments and doctors is
limited


  We propose an effective deep learning model for signal reconstruction, which
requires no signal prior, no noise model calibration, and no clean samples.
This model only assumes that the noise is independent of the measurement and
that the true signals share the same structured information. We demonstrate its
performance on a variety of real-world applications, from sub-\r{A}ngstr\"{o}m
resolution atomic images to sub-arcsecond resolution astronomy images.


  Gamma ray bursts can potentially be used as distance indicators, providing
the possibility of extending the Hubble diagram to redshifts ~7. Here we follow
the analysis of Schaefer (2007), with the aim of distinguishing the timescape
cosmological model from the \LambdaCDM model by means of the additional
leverage provided by GRBs in the range 2 < z < 7. We find that the timescape
model fits the GRB sample slightly better than the \LambdaCDM model, but that
the systematic uncertainties are still too little understood to distinguish the
models.


  We present time-domain THz spectroscopy of thin films of the heavy-fermion
superconductor CeCoIn$_5$. The complex optical conductivity is analyzed through
a Drude model and extended Drude model analysis. Below the $\approx$ 40 K Kondo
coherence temperature, a narrow Drude-like peak forms, as the result of the $f$
orbital - conduction electron hybridization and the formation of the
heavy-fermion state. Via an extended Drude model analysis, we measure the
frequency-dependent scattering rate ($1/ \tau $) and effective mass
($m^*/m_b$). This scattering rate shows a linear dependence on temperature,
which matches the dependence of the resistivity as expected. Nonetheless, the
width of the low-frequency Drude peak (characterized by a {\it renormalized}
quasiparticle scattering rate ($1 / \tau^* = m_b/ m^* \tau $) does show a $T^2$
dependence giving evidence for a hidden Fermi state.


  We prove some recent experimental observations of D. Reznik concerning
periodic billiard orbits in ellipses. For example, the sum of cosines of the
angles of a periodic billiard polygon remains constant in the one-parameter
family of such polygons (that exist due to the Poncelet porism). In our proofs,
we use geometric and complex analytic methods.


  The search for exotic Higgs decays are an essential probe of new physics. In
particular, the small width of the Higgs boson makes its decay uniquely
sensitive to the existence of light hidden sectors. Here we assess the
potential of an exotic Higgs decay search for h -> 2X -> bbmumu to constrain
theories with light CP-even (X = s) and CP-odd (X = a) singlet scalars in the
mass range of 15 to 60 GeV. This decay channel arises naturally in many
scenarios, such as the Standard Model augmented with a singlet, the
two-Higgs-doublet model with a singlet (2HDM+S) -- which includes the
Next-to-Minimal Supersymmetric Standard Model (NMSSM) -- and in hidden valley
models. The 2b2mu channel may represent the best discovery avenue for many
models. It has competitive reach, and is less reliant on low-pT b- and
tau-reconstruction compared to other channels like 4b, 4tau, and 2tau2mu. We
analyze the sensitivity of a 2b2mu search for the 8 and 14 TeV LHC, including
the HL-LHC. We consider three types of analyses, employing conventional
resolved b-jets with a clustering radius of R ~ 0.4, thin b-jets with R=0.2,
and jet substructure techniques, respectively. The latter two analyses improve
the reach for m_X ~ 15 GeV, for which the two b-jets are boosted and often
merged. We find that Br(h -> 2X -> 2b2mu) can be constrained at the few x 10^-5
level across the entire considered mass range of X at the HL-LHC. This
corresponds to a 1 - 10% reach in Br(h->2X) in 2HDM+S models, including the
NMSSM, depending on the type of Higgs Yukawa couplings.


  Long baseline laser interferometers used for gravitational wave detection
have proven to be very complicated to control. In order to have sufficient
sensitivity to astrophysical gravitational waves, a set of multiple coupled
optical cavities comprising the interferometer must be brought into resonance
with the laser field. A set of multi-input, multi-output servos then lock these
cavities into place via feedback control. This procedure, known as lock
acquisition, has proven to be a vexing problem and has reduced greatly the
reliability and duty factor of the past generation of laser interferometers. In
this article, we describe a technique for bringing the interferometer from an
uncontrolled state into resonance by using harmonically related external fields
to provide a deterministic hierarchical control. This technique reduces the
effect of the external seismic disturbances by four orders of magnitude and
promises to greatly enhance the stability and reliability of the current
generation of gravitational wave detector. The possibility for using
multi-color techniques to overcome current quantum and thermal noise limits is
also discussed.


  We investigate quantum nonlocality of a single-photon entangled state under
feasible measurement techniques consisting of on-off and homodyne detections
along with unitary operations of displacement and squeezing. We test for a
potential violation of the Clauser-Horne-Shimony-Holt (CHSH) inequality, in
which each of the bipartite party has a freedom to choose between 2 measurement
settings, each measurement yielding a binary outcome. We find that
single-photon quantum nonlocality can be detected when two or less of the 4
total measurements are carried out by homodyne detection. The largest violation
of the CHSH inequality is obtained when all four measurements are
squeezed-and-displaced on-off detections. We test robustness of violations
against imperfections in on-off detectors and single-photon sources, finding
that the squeezed-and-displaced measurement schemes perform better than the
displacement-only measurement schemes.


  We prove that deep neural networks are capable of approximating solutions of
semilinear Kolmogorov PDE in the case of gradient-independent,
Lipschitz-continuous nonlinearities, while the required number of parameters in
the networks grow at most polynomially in both dimension $d \in \mathbb{N}$ and
prescribed reciprocal accuracy $\varepsilon$. Previously, this has only been
proven in the case of semilinear heat equations.


  A high-performance parallel algorithm is proposed for modeling the
propagation of acoustic and elastic waves in inhomogeneous media. An initial
boundary-value problem is replaced by a series of boundary-value problems for a
constant elliptic operator and different right-hand sides via the integral
Laguerre transform. It is proposed to solve difference equations by the
conjugate gradient method for acoustic equations and by the GMRES$(k)$ method
for modeling elastic waves. A preconditioning operator was the Laplace operator
that is inverted using the variable separation method. The novelty of the
proposed algorithm is using the Dichotomy Algorithm (Terekhov, 2010), which was
designed for solving a series of tridiagonal systems of linear equations, in
the context of the preconditioning operator inversion. Via considering
analytical solutions, it is shown that modeling wave processes for long
instants of time requires high-resolution meshes. The proposed parallel
fine-mesh algorithm enabled to solve real application seismic problems in
acceptable time and with high accuracy. By solving model problems, it is
demonstrated that the considered parallel algorithm possesses high performance
and efficiency over a wide range of the number of processors (from 2 to 8192).


  Digital news platforms use news recommenders as the main instrument to cater
to the individual information needs of readers. Despite an increasingly
language-diverse online community, in which many Internet users consume news in
multiple languages, the majority of news recommendation focuses on major,
resource-rich languages, and English in particular. Moreover, nearly all news
recommendation efforts assume monolingual news consumption, whereas more and
more users tend to consume information in at least two languages. Accordingly,
the existing body of work on news recommendation suffers from a lack of
publicly available multilingual benchmarks that would catalyze development of
news recommenders effective in multilingual settings and for low-resource
languages. Aiming to fill this gap, we introduce xMIND, an open, multilingual
news recommendation dataset derived from the English MIND dataset using machine
translation, covering a set of 14 linguistically and geographically diverse
languages, with digital footprints of varying sizes. Using xMIND, we
systematically benchmark several state-of-the-art content-based neural news
recommenders (NNRs) in both zero-shot (ZS-XLT) and few-shot (FS-XLT)
cross-lingual transfer scenarios, considering both monolingual and bilingual
news consumption patterns. Our findings reveal that (i) current NNRs, even when
based on a multilingual language model, suffer from substantial performance
losses under ZS-XLT and that (ii) inclusion of target-language data in FS-XLT
training has limited benefits, particularly when combined with a bilingual news
consumption. Our findings thus warrant a broader research effort in
multilingual and cross-lingual news recommendation. The xMIND dataset is
available at https://github.com/andreeaiana/xMIND.


  We prove a stochastic maximum principle for a control problem where the state
equation is delayed both in the state and in the control, and also the final
cost functional may depend on the past trajectories. The adjoint equations turn
out to be a new form of linear anticipated backward stochastic differential
equations (ABSDEs in the following), and we prove a direct formula to solve
these equations.


  We have re-examined the nature of the cluster galaxy luminosity function
using the data from the Edinburgh-Durham Southern Galaxy Catalogue and the
Edinburgh-Milano Redshift Survey. We derive a best fit luminosity function over
the range M(bj)=-18 to -21, for a composite sample of 22 of the richer clusters
that has M*=-20.16+/-0.02 and alpha=-1.22+/-0.04. The dominant error in these
values results from the choice of background subtraction method. From extensive
simulations we can show that when the LF is fitted over this narrow range, it
is difficult to discriminate against bright values of M*in the single cluster
fits, but that faint values provide a strong test of the universality of the
luminosity function. We find that all the individual cluster data are well fit
by a Schechter function with alpha fixed at -1.25, and that <=10% of these have
fitted values of M* that disagree from the average at the 99% confidence level.
We further show that fitting only a single parameter Schechter function to
composite subsets of the data can give erroneous results for the derived M*. By
considering two parameter fits, the results of Monte-Carlo simulations and
direct two-sample chi-squared tests we conclude that there is only weak
evidence for differences between the data when broken down into subsets based
on physical properties (Bautz-Morgan class, richness, velocity dispersion):
from our simulations, only the evidence for a difference between subsets based
on velocity dispersion may in fact be significant. We find no evidence at all
that a Schechter function is not a good model for the intrinsic cluster
luminosity function over this absolute magnitude range. Models that invoke
strong evolution of galaxy luminosity of all galaxies within clusters are
inconsistent with our results.


  With the advent of prosumers, the traditional centralized operation may
become impracticable due to computational burden, privacy concerns, and
conflicting interests. In this paper, an energy sharing mechanism is proposed
to accommodate prosumers' strategic decision-making on their self-production
and demand in the presence of capacity constraints. Under this setting,
prosumers play a generalized Nash game. We prove main properties of the game:
an equilibrium exists and is partially unique; no prosumer is worse off by
energy sharing and the price-of-anarchy is 1-O(1/I) where I is the number of
prosumers. In particular, the PoA tends to 1 with a growing number of
prosumers, meaning that the resulting total cost under the proposed energy
sharing approaches social optimum. We prove that the corresponding prosumers'
strategies converge to the social optimal solution as well. Finally we propose
a bidding process and prove that it converges to the energy sharing equilibrium
under mild conditions. Illustrative examples are provided to validate the
results.


  In this work we generalize standard Decision Theory by assuming that two
outcomes can also be incomparable. Two motivating scenarios show how
incomparability may be helpful to represent those situations where, due to lack
of information, the decision maker would like to maintain different options
alive and defer the final decision. In particular, a new axiomatization is
given which turns out to be a weakening of the classical set of axioms used in
Decision Theory. Preliminary results show how preferences involving complex
distributions are related to judgments on single alternatives.


  There is a growing interest in learning data representations that work well
for many different types of problems and data. In this paper, we look in
particular at the task of learning a single visual representation that can be
successfully utilized in the analysis of very different types of images, from
dog breeds to stop signs and digits. Inspired by recent work on learning
networks that predict the parameters of another, we develop a tunable deep
network architecture that, by means of adapter residual modules, can be steered
on the fly to diverse visual domains. Our method achieves a high degree of
parameter sharing while maintaining or even improving the accuracy of
domain-specific representations. We also introduce the Visual Decathlon
Challenge, a benchmark that evaluates the ability of representations to capture
simultaneously ten very different visual domains and measures their ability to
recognize well uniformly.


  We present an analytical theory of topologically protected photonic states
for the two-dimensional Maxwell equations for a class of continuous periodic
dielectric structures, modulated by a domain wall. We further numerically
confirm the applicability of this theory for three-dimensional structures.


  Two of the most important aspects of electric vehicles are their efficiency
or achievable range. In order to achieve high efficiency and thus a long range,
it is essential to avoid over-dimensioning the drive train. Therefore, the
drive train has to be kept as lightweight as possible while at the same time
being utilized to the best possible extent. This can only be achieved if the
dynamic behavior of the drive train is accurately known by the controller. The
task of the controller is to achieve a desired torque at the wheels of the car
by controlling the currents of the electric motor. With machine learning
modeling techniques, accurate models describing the behavior can be extracted
from measurement data and then used by the controller. For the comparison of
the different modeling approaches, a data set consisting of about 40 million
data points was recorded at a test bench for electric drive trains. The data
set is published on Kaggle, an online community of data scientists.


  Hard exclusive processes, such as Deeply Virtual Compton Scattering (DVCS),
allow to access generalized parton distributions (GPDs). By means of an Impulse
Approximation (IA) calculation, it is shown here how, in the low momentum
transfer region, the sum of the GPDs H and E is dominated by the neutron
contribution. Thanks to this property, 3He could open a new way to access the
neutron structure information. In this work, a simple and efficient extraction
procedure of the neutron GPDs, able to take into account the nuclear effects
included in IA analysis, is proposed.


  Flexoelectricity is a universal electromechanical coupling effect whereby all
dielectric materials polarize in response to strain gradients. In particular,
nanoscale flexoelectricity promises exotic phenomena and functions, but
reliable characterization methods are required to unlock its potential. Here,
we report anomalous mechanical control of quantum tunnelling that allows for
characterizing nanoscale flexoelectricity. By applying strain gradients with an
atomic force microscope tip, we systematically polarize an ultrathin film of
otherwise nonpolar SrTiO$_{3}$, and simultaneously measure tunnel current
across it. The measured tunnel current exhibits critical behaviour as a
function of strain gradients, which manifests large modification of tunnel
barrier profiles via flexoelectricity. Further analysis of this critical
behaviour reveals significantly enhanced flexocoupling strength in ultrathin
SrTiO$_{3}$, compared to that in bulk, rendering flexoelectricity more potent
at the nanoscale. Our study not only suggests possible applications exploiting
dynamic mechanical control of quantum effect, but also paves the way to
characterize nanoscale flexoelectricity.


  Bernoulli's equation, which relates the pressure of an ideal fluid in motion
with its velocity and height under certain conditions, is a central topic in
General Physics courses for Science and Engineering students. This equation,
frequently used both textbooks as in science outreach activities or museums, is
often extrapolated to explain situations in which it is no longer valid. A
common example is to assume that, in any situation, higher speed means lower
pressure, a conclusion that is only acceptable under certain conditions. In
this paper we report the results of an investigation with university students
on some misconceptions present in fluid dynamics. We found that after
completing the General Physics courses, many students have not developed a
correct model about the interaction of a fluid element with its environment and
extrapolate the idea that higher speed implies lower pressure in situations
where it is no longer valid. We also show that an approach to fluid dynamics
based on Newton's laws is more natural to address these misconceptions.


  This paper presents Video-P2P, a novel framework for real-world video editing
with cross-attention control. While attention control has proven effective for
image editing with pre-trained image generation models, there are currently no
large-scale video generation models publicly available. Video-P2P addresses
this limitation by adapting an image generation diffusion model to complete
various video editing tasks. Specifically, we propose to first tune a
Text-to-Set (T2S) model to complete an approximate inversion and then optimize
a shared unconditional embedding to achieve accurate video inversion with a
small memory cost. For attention control, we introduce a novel
decoupled-guidance strategy, which uses different guidance strategies for the
source and target prompts. The optimized unconditional embedding for the source
prompt improves reconstruction ability, while an initialized unconditional
embedding for the target prompt enhances editability. Incorporating the
attention maps of these two branches enables detailed editing. These technical
designs enable various text-driven editing applications, including word swap,
prompt refinement, and attention re-weighting. Video-P2P works well on
real-world videos for generating new characters while optimally preserving
their original poses and scenes. It significantly outperforms previous
approaches.


  Dynamics of coupled semiconductor lasers is investigated by numerical
simulations. A realistic laser simulation engine is used to study the
synchronization and dynamical regime in two mutually coupled Fabry-Perot and/or
DFB lasers. Both, single- and multi-mode operation regimes are studied with
emphasis on the role of the multiple laser-cavity modes. Our findings indicate
that the two laser synchronize within each laser-cavity mode, while the
synchronization across different cavity modes is significantly weaker.


  We summarize the results of 7 mm VLBA imaging of Sgr A* and discuss some of
the difficulties of accurately constraining the size of Sgr A* with VLBI
observations. Our imaging results are fully consistent with the hypothesis that
the VLBA image of Sgr A* is a resolved elliptical Gaussian caused by the
scattering of an intervening thermal plasma. We show that determination of the
minor axis size at 7 mm with the VLBA is very unreliable.
  We also present new polarimetric observations from the VLA and from the BIMA
array of Sgr A*. At 4.8 GHz, we find an upper limit to the polarization of
0.1%. At 86 GHz, we report a marginal detection of $1 \pm 1$% linear
polarization. We discuss the effects of interstellar propagation on the linear
polarization and consider the significance of very low intrinsic linear
polarization in Sgr A*.


  We prove Milnor-Wood inequalities for local products of manifolds. As a
consequence, we establish the generalized Chern Conjecture for products
$M\times \Sigma^k$ for any product of a manifold $M$ with a product of $k$
copies of a surface $\Sigma$ for $k$ sufficiently large.


  We present an early study designed to analyze how city planning and the
health of senior citizens can benefit from the use of augmented reality (AR)
using Microsoft's HoloLens. We also explore whether AR and VR can be used to
help city planners receive real-time feedback from citizens, such as the
elderly, on virtual plans, allowing for informed decisions to be made before
any construction begins.


  A recent Google study [Phys. Rev. X, 6:031015 (2016)] compared a D-Wave 2X
quantum processing unit (QPU) to two classical Monte Carlo algorithms:
simulated annealing (SA) and quantum Monte Carlo (QMC). The study showed the
D-Wave 2X to be up to 100 million times faster than the classical algorithms.
The Google inputs are designed to demonstrate the value of collective
multiqubit tunneling, a resource available to D-Wave QPUs but not to simulated
annealing. But the computational hardness in these inputs is highly localized
in gadgets, with only a small amount of complexity coming from global
interactions, meaning that the relevance to real-world problems is limited.
  In this study we provide a new synthetic problem class that addresses the
limitations of the Google inputs while retaining their strengths. We use simple
clusters instead of more complex gadgets and more emphasis is placed on
creating computational hardness through frustrated global interactions like
those seen in interesting real-world inputs. The logical problems used to
generate these inputs can be solved in polynomial time [J. Phys. A, 15:10
(1982)]. However, for general heuristic algorithms that are unaware of the
planted problem class, the frustration creates meaningful difficulty in a
controlled environment ideal for study.
  We use these inputs to evaluate the new 2000-qubit D-Wave QPU. We include the
HFS algorithm---the best performer in a broader analysis of Google inputs---and
we include state-of-the-art GPU implementations of SA and QMC. The D-Wave QPU
solidly outperforms the software solvers: when we consider pure annealing time
(computation time), the D-Wave QPU reaches ground states up to 2600 times
faster than the competition. In the task of zero-temperature Boltzmann sampling
from challenging multimodal inputs, the D-Wave QPU holds a similar advantage as
quantum sampling bias does not seem significant.


  Let $f:Y\to X$ be a finite morphism between Fano manifolds $Y$ and $X$ such
that the Fano index of $X$ is greater than 1. On the one hand, when both $X$
and $Y$ are fourfolds of Picard number 1, we show that the degree of $f$ is
bounded in terms of $X$ and $Y$ unless $X\cong\mathbb{P}^4$; hence, such $X$
does not admit any non-isomorphic surjective endomorphism. On the other hand,
when $X=Y$ is either a fourfold or a del Pezzo manifold, we prove that, if $f$
is an int-amplified endomorphism, then $X$ is toric. Moreover, we classify all
the singular quadrics admitting non-isomorphic endomorphisms.


  Electrocardiogram (ECG) artifact contamination often occurs in surface
electromyography (sEMG) applications when the measured muscles are in proximity
to the heart. Previous studies have developed and proposed various methods,
such as high-pass filtering, template subtraction and so forth. However, these
methods remain limited by the requirement of reference signals and distortion
of original sEMG. This study proposed a novel denoising method to eliminate ECG
artifacts from the single-channel sEMG signals using fully convolutional
networks (FCN). The proposed method adopts a denoise autoencoder structure and
powerful nonlinear mapping capability of neural networks for sEMG denoising. We
compared the proposed approach with conventional approaches, including
high-pass filters and template subtraction, on open datasets called the
Non-Invasive Adaptive Prosthetics database and MIT-BIH normal sinus rhythm
database. The experimental results demonstrate that the FCN outperforms
conventional methods in sEMG reconstruction quality under a wide range of
signal-to-noise ratio inputs.


  [Abridged] Superluminous Supernovae (SN2006gy, SN2005gj, SN2005ap, SN2008fz,
SN2003ma) have been a challenge to explain by standard models. We present an
alternative scenario involving a quark-nova (QN), an explosive transition of
the newly born neutron star to a quark star in which a second explosion
(delayed) occurs inside the already expanding ejecta of a normal SN. The
reheated SN ejecta can radiate at higher levels for longer periods of time
primarily due to reduced adiabatic expansion losses, unlike the standard SN
case. Our model is successfully applied to SN2006gy, SN2005gj, SN2005ap,
SN2008fz, SN2003ma with encouraging fits to the lightcurves. There are four
predictions in our model: (i) superluminous SNe optical lightcurves should show
a double-hump with the SN hump at weaker magnitudes occurring days to weeks
before the QN; (ii) Two shock breakouts should be observed vis-a-vis one for a
normal SN. Depending on the time delay, this would manifest as two distinct
spikes in the X-ray region or a broadening of the first spike for extremely
short delays; (iii) The QN deposits heavy elements of mass number A> 130 at the
base of the preceeding SN ejecta. These QN r-processed elements should be
visible in the late spectrum (few days-weeks in case of strong ejecta mixing)
of the superluminous SN; (iv) The QN yield will also contain lighter elements
(Hydrogen and Helium). We expect the late spectra to include H_alpha emission
lines that should be distinct in their velocity signature from standard H_alpha
emission.


  We continue our study of the correlators of a recently discovered family of
BPS Wilson loops in N=4 supersymmetric U(N) Yang-Mills theory. We perform
explicit computations at weak coupling by means of analytical and numerical
methods finding agreement with the exact formula derived from localization. In
particular we check the localization prediction at order g^6 for different BPS
"latitude" configurations, the N=4 perturbative expansion reproducing the
expected results within a relative error of 10^(-4). On the strong coupling
side we present a supergravity evaluation of the 1/8 BPS correlator in the
limit of large separation, taking into account the exchange of all relevant
modes between the string world-sheets. While reproducing the correct
geometrical dependence, we find that the associated coefficient does not match
the localization result.


  This chapter reviews the theoretical description of interacting fermions in
one dimension. The Luttinger liquid concept is elucidated using the
Tomonaga-Luttinger model as well as integrable lattice models. Weakly coupled
chains and attempts to experimentally verify the theoretical predictions are
discussed.


  Randomization testing is a fundamental method in statistics, enabling
inferential tasks such as testing for (conditional) independence of random
variables, constructing confidence intervals in semiparametric location models,
and constructing (by inverting a permutation test) model-free prediction
intervals via conformal inference. Randomization tests are exactly valid for
any sample size, but their use is generally confined to exchangeable data. Yet
in many applications, data is routinely collected adaptively via, e.g.,
(contextual) bandit and reinforcement learning algorithms or adaptive
experimental designs. In this paper we present a general framework for
randomization testing on adaptively collected data (despite its
non-exchangeability) that uses a novel weighted randomization test, for which
we also present novel computationally tractable resampling algorithms for
various popular adaptive assignment algorithms, data-generating environments,
and types of inferential tasks. Finally, we demonstrate via a range of
simulations the efficacy of our framework for both testing and
confidence/prediction interval construction.


  We study the gluon fusion into pairs of off-shell Z bosons and their
subsequent decay into charged lepton pairs at hadron colliders. We do not
restrict the intermediate state Z bosons to the narrow width approximation but
allow for arbitrary invariant masses. We compare the strength of this process
with the known leading order results for q q_bar -> Z Z -> 4 l and for g g -> H
-> Z Z -> 4 l .


  Recently, learning based video compression methods attract increasing
attention. However, the previous works suffer from error propagation due to the
accumulation of reconstructed error in inter predictive coding. Meanwhile, the
previous learning based video codecs are also not adaptive to different video
contents. To address these two problems, we propose a content adaptive and
error propagation aware video compression system. Specifically, our method
employs a joint training strategy by considering the compression performance of
multiple consecutive frames instead of a single frame. Based on the learned
long-term temporal information, our approach effectively alleviates error
propagation in reconstructed frames. More importantly, instead of using the
hand-crafted coding modes in the traditional compression systems, we design an
online encoder updating scheme in our system. The proposed approach updates the
parameters for encoder according to the rate-distortion criterion but keeps the
decoder unchanged in the inference stage. Therefore, the encoder is adaptive to
different video contents and achieves better compression performance by
reducing the domain gap between the training and testing datasets. Our method
is simple yet effective and outperforms the state-of-the-art learning based
video codecs on benchmark datasets without increasing the model size or
decreasing the decoding speed.


  It is commonplace in pulsar and fast radio burst (FRB) literature to estimate
sky temperature by frequency-scaling of the Haslam et al. (1982) 408 MHz map. I
suggest that this practice should stop, in favor of using readily-available
global sky models of diffuse foregrounds. This practical change will improve
accuracy of pulse flux estimates.


  We study the causality violation in the non-local phi ^{4}-theory (as
formulated by Kleppe and Woodard) containing a finite mass scale Lambda .
Starting from the Bogoliubov-Shirkov criterion for causality, we construct and
study combinations of S-matrix elements that signal the violation of causality
in the one loop approximation. We find that the causality violation in the
exclusive process \phi +\phi --> \phi +\phi grows with energy, but the growth
with energy, (for low to moderate energies) is suppressed to all orders
compared to what one would expect purely from dimensional considerations. We
however find that the causality violation in other processes such as \phi
+\phi--> \phi +\phi +\phi +\phi grows with energy as expected from dimensional
considerations at low to moderate energies. For high enough energies comparable
to the mass scale Lambda, however, we find a rapid (exponential-like) growth in
the degree of causality violation. We suggest a scenario, based on an earlier
work, that will enable one to evade a large theoretical causality violation at
high energies, should it be unobserved experimentally.


  We study the Large Magellanic Cloud Papillon Nebula (N159-5), a conspicuous
High Excitation Blob (HEB) lying in the star forming complex N159. Using JHK
near-infrared photometry obtained at the ESO VLT with the ISAAC camera, we
examine the stellar populations associated with the Papillon, tracing their
history using stellar evolution models. Two populations are revealed: one
composed of young, massive stars with an age ~ 3 Myr, and a second consisting
of older lower mass stars of age spreading between 1 and 10 Gyr. We analyze the
properties of those populations and discuss their significance in the context
of N159. We also estimate that if the star at the center of the Papillon is
single its initial mass is ~ 50 solar masses and it is affected by an
extinction Av ~ 7 mag.


  We present measurements of the power spectra of cosmic infrared background
(CIB) and cosmic microwave background (CMB) fluctuations in six frequency
bands. Maps at the lower three frequency bands, 95, 150, and 220 GHz (3330,
2000, 1360 $\mu$m) are from the South Pole Telescope, while the upper three
frequency bands, 600, 857, and 1200 GHz (500, 350, 250 $\mu$m) are observed
with Herschel/SPIRE. From these data, we produce 21 angular power spectra (six
auto- and fifteen cross-frequency) spanning the multipole range $600 \le \ell
\le 11,000$. Our measurements are the first to cross-correlate measurements
near the peak of the CIB spectrum with maps at 95 GHz, complementing and
extending the measurements from Planck Collaboration et al. (2014) at 218, 550,
and 857 GHz. The observed fluctuations originate largely from clustered,
infrared-emitting, dusty star-forming galaxies, the CMB, and to a lesser extent
radio galaxies, active galactic nuclei, and the Sunyaev-Zel'dovich effect.


  If dark matter is an axion-like-particle a significant fraction of the
present day relic abundance could be concentrated in compact gravitationally
bound miniclusters. We study the minicluster masses compatible with the dark
matter relic density constraint. If they form from fluctuations produced by PQ
symmetry breaking, minicluster masses up to hundreds of solar masses are
possible, although over most of the parameter space they are much lighter. The
size of these objects is typically within a few orders of magnitude of an
astronomical unit. We also show that miniclusters can form if an axion gets
mass from a hidden sector with a first order phase transition that takes a
relatively long time to complete. Therefore they can appear in models where PQ
symmetry is broken before inflation, compatible with large axion decay
constants and string theory UV completions.


  An experimental study of the dispersion cancellation occurring in
frequency-entangled photon pairs is presented. The approach uses time-resolved
up conversion of the pairs, which has temporal resolution at the fs level, and
group-delay dispersion sensitivity of $\approx \ 20 \, \mathrm{fs}^2$ under
experimental conditions. The cancellation is demonstrated with dispersion
stronger than $\pm 10^3 \, \mathrm{fs}^2$ in the signal $(-)$ and idler $(+)$
modes. The observations represent the generation, compression, and
characterization of ultrashort biphotons with correlation width as small as 6.8
times the degenerate optical period.


  We review and investigate the general theory of thermodynamics of
computation, and derive the fundamental inequalities that set the lower bounds
of the work requirement and the heat emission during a computation. These
inequalities constitute the generalized Landauer principle, where the
information contents are involved in the second law of thermodynamics. We
discuss in detail the relationship between the thermodynamic and logical
reversibilities; the former is related to the entropy production in the total
system including a heat bath, while the latter is related to the entropy change
only in the logical states of the memory. In particular, we clarify that any
logically irreversible computation can be performed in a thermodynamically
reversible manner in the quasi-static limit, which does not contradict the
conventional Landauer principle. Our arguments would serve as the theoretical
foundation of thermodynamics of computation in terms of modern statistical
physics.


  Machine learning has had a great deal of success in image processing.
However, the focus of this work has largely been on realistic images, ignoring
more niche art styles such as pixel art. Additionally, many traditional machine
learning models that focus on groups of pixels do not work well with pixel art,
where individual pixels are important. We propose the Pixel VQ-VAE, a
specialized VQ-VAE model that learns representations of pixel art. We show that
it outperforms other models in both the quality of embeddings as well as
performance on downstream tasks.


  Previously, we derived exact relationships between the properties of a linear
time-invariant control system and properties of an anomaly detector that
quantified the impact an attacker can have on the system if that attacker aims
to remain stealthy to the detector. A necessary first step in this process is
to be able to precisely tune the detector to a desired level of performance
(false alarm rate) under normal operation, typically through the selection of a
threshold parameter. To-date efforts have only considered Gaussian noises. Here
we generalize the approach to tune a chi-squared anomaly detector for noises
with non-Gaussian distributions. Our method leverages a Gaussian Mixture Model
to represent the arbitrary noise distributions, which preserves analytic
tractability and provides an informative interpretation in terms of a
collection of chi-squared detectors and multiple Gaussian disturbances.


  The National Center for Advancing Translational Sciences(NCATS) Biomedical
Data Translator (Translator) aims to attenuate problems faced by translational
scientists. Translator is a multi-agent architecture consisting of six
autonomous relay agents (ARAs) and eight knowledge providers (KPs). In this
paper, we present the design of the Explanatory Agent (xARA), a case-based ARA
that answers biomedical queries by accessing multiple KPs, ranking results, and
explaining the ranking of results. The Explanatory Agent is designed with five
knowledge containers that include the four original knowledge containers and
one additional container for explanation - the Explanation Container. The
Explanation Container is case-based and designed with its own knowledge
containers.


  We propose the new exactly solvable model for bosons corresponding to the
attractive pairing interaction. Using the electrostatic analogy, the solution
of this model in thermodynamic limit is found. The transition from the
superfluid phase with the Bose condensate and the Bogoliubov - type spectrum of
excitations in the weak coupling regime to the incompressible phase with the
gap in the excitation spectrum in the strong coupling regime is observed.


  Autonomous vehicle (AV) stacks are typically built in a modular fashion, with
explicit components performing detection, tracking, prediction, planning,
control, etc. While modularity improves reusability, interpretability, and
generalizability, it also suffers from compounding errors, information
bottlenecks, and integration challenges. To overcome these challenges, a
prominent approach is to convert the AV stack into an end-to-end neural network
and train it with data. While such approaches have achieved impressive results,
they typically lack interpretability and reusability, and they eschew
principled analytical components, such as planning and control, in favor of
deep neural networks. To enable the joint optimization of AV stacks while
retaining modularity, we present DiffStack, a differentiable and modular stack
for prediction, planning, and control. Crucially, our model-based planning and
control algorithms leverage recent advancements in differentiable optimization
to produce gradients, enabling optimization of upstream components, such as
prediction, via backpropagation through planning and control. Our results on
the nuScenes dataset indicate that end-to-end training with DiffStack yields
substantial improvements in open-loop and closed-loop planning metrics by,
e.g., learning to make fewer prediction errors that would affect planning.
Beyond these immediate benefits, DiffStack opens up new opportunities for fully
data-driven yet modular and interpretable AV architectures. Project website:
https://sites.google.com/view/diffstack


  Elaborating on ideas of Elkies, we show how recursive equations for towers of
Drinfeld modular curves $(X_0(P^n))_{n\ge 0}$ for $P\in \mathbb F_q[T]$ can be
read of directly from the modular polynomial $\Phi_P(X,Y)$ and how this
naturally leads to recursions of depth two. Although the modular polynomial
$\Phi_T(X,Y)$ is not known in general, using generators and relations given by
Schweizer, we find unreduced recursive equations over $\mathbb F_q(T)$ for the
tower $(X_0(T^n))_{n\ge 2}$ and of a small variation of it (its partial Galois
closure). Reducing at various primes, one obtains towers over finite fields,
which are optimal, i.e., reach the Drinfeld--Vladut bound, over a quadratic
extension of the finite field. We give a proof of the optimality of these
towers, which is elementary and does not rely on their modular interpretation
except at one point. We employ the modular interpretation to determine the
splitting field of certain polynomials, which are analogues of the Deuring
polynomial. For these towers, the particular case of reduction at the prime
$T-1$ corresponds to towers introduced by Elkies and Garcia--Stichtenoth.


  Controlling audible sound requires inherently broadband and subwavelength
acoustic solutions, which are to date, crucially missing. This includes current
noise absorption methods, such as porous materials or acoustic resonators,
which are typically inefficient below 1 kHz, or fundamentally narrowband. Here,
we solve this vexing issue by introducing the concept of plasmacoustic
metalayers. We demonstrate that the dynamics of small layers of air plasma can
be controlled to interact with sound in an ultrabroadband way and over
deep-subwavelength distances. Exploiting the unique physics of plasmacoustic
metalayers, we experimentally demonstrate perfect sound absorption and tunable
acoustic reflection over more than two frequency decades, from several Hz to
the kHz range, with transparent plasma layers of thicknesses down to
$\lambda/1000$. Such unprecedented bandwidth and compactness opens new doors in
a variety of applications, including noise control, audio-engineering, room
acoustics, imaging and metamaterial design.


  Sr$_2$RuO$_4$, an unconventional superconductor, is known to possess an
incommensurate spin density wave instability driven by Fermi surface nesting.
Here we report a static spin density wave ordering with a commensurate
propagation vector $q_c$ = (0.25 0.25 0) in Fe-doped Sr$_2$RuO$_4$, despite the
magnetic fluctuations persisting at the incommensurate wave vectors $q_{ic}$ =
(0.3 0.3 L) as in the parent compound. The latter feature is corroborated by
the first principles calculations, which show that Fe substitution barely
changes the nesting vector of the Fermi surface. These results suggest that in
addition to the known incommensurate magnetic instability, Sr$_2$RuO$_4$ is
also in proximity to a commensurate magnetic tendency that can be stabilized
via Fe doping.


  We have used the sub-millimeter array to image the molecular envelope around
IRC+10420. Our observations reveal a large and clumpy expanding envelope around
the star. The molecular envelope shows a clear asymmetry in $^{12}$CO J=2--1
emission in the South-West direction. The elongation of the envelope is found
even more pronounced in the emission of $^{13}$CO J=2--1 and SO J$_{\rm
K}$=6$_5$--5$_4$. A small positional velocity gradient across velocity channels
is seen in these lines, suggesting the presence of a weak bipolar outflow in
the envelope of IRC+10420. In the higher resolution $^{12}$CO J=2--1 map, we
find that the envelope has two components: (1) an inner shell (shell I) located
between radius of about 1"-2"; (2) an outer shell (shell II) located between 3"
to 6" in radius. These shells represent two previous mass-loss episodes from
IRC+10420. We attempt to derive in self-consistent manner the physical
conditions inside the envelope by modelling the dust properties, and the
heating and cooling of molecular gas. We estimate a mass loss rate of $\sim$9
10$^{-4}$ M$_\odot$ yr$^{-1}$ for shell I and 7 10$^{-4}$ M$_\odot$ yr$^{-1}$
for shell II. The gas temperature is found to be unusually high in IRC+10420 in
comparison with other oxygen-rich envelopes. The elevated gas temperature is
mainly due to higher heating rate, which results from the large luminosity of
the central s tar. We also derive an isotopic ratio $^{12}$C/$^{13}$C = 6.


  Pairing correlations on generalized t-U-J two-leg ladders are reported. We
find that the pairing correlations on the usual t-U Hubbard ladder are
significantly enhanced by the addition of a nearest-neighbor exchange
interaction J. Likewise, these correlations are also enhanced for the t-J model
when the onsite Coulomb interaction is reduced from infinity. Moreover, the
pairing correlations are larger on a t-U-J ladder than on a t-Jeff ladder in
which Jeff has been adjusted so that the two models have the same spin gap at
half-filling. This enhancement of the pairing correlations is associated with
an increase in the pair-binding energy and the pair mobility in the t-U-J model
and point to the importance of the charge transfer nature of the cuprate
systems.


  OSIRIS-REx is the third spacecraft in the NASA New Frontiers Program and is
planned for launch in 2016. OSIRIS-REx will orbit the near-Earth asteroid
(101955) Bennu, characterize it, and return a sample of the asteroid's regolith
back to Earth. The Regolith X-ray Imaging Spectrometer (REXIS) is an instrument
on OSIRIS-REx designed and built by students at MIT and Harvard. The purpose of
REXIS is to collect and image sun-induced fluorescent X-rays emitted by Bennu,
thereby providing spectroscopic information related to the elemental makeup of
the asteroid regolith and the distribution of features over its surface.
Telescopic reflectance spectra suggest a CI or CM chondrite analog meteorite
class for Bennu, where this primitive nature strongly motivates its study. A
number of factors, however, will influence the generation, measurement, and
interpretation of the X-ray spectra measured by REXIS. These include: the
compositional nature and heterogeneity of Bennu, the time-variable Solar state,
X-ray detector characteristics, and geometric parameters for the observations.
In this paper, we will explore how these variables influence the precision to
which REXIS can measure Bennu's surface composition. By modeling the
aforementioned factors, we place bounds on the expected performance of REXIS
and its ability to ultimately place Bennu in an analog meteorite class.


  We explore the main characteristics of big brain network data that offer
unique statistical challenges. The brain networks are biologically expected to
be both sparse and hierarchical. Such unique characterizations put specific
topological constraints onto statistical approaches and models we can use
effectively. We explore the limitations of the current models used in the field
and offer alternative approaches and explain new challenges.


  Hyperoctahedral homology for involutive algebras is the homology theory
associated to the hyperoctahedral crossed simplicial group. It is related to
equivariant stable homotopy theory via the homology of equivariant infinite
loop spaces. In this paper we show that there is an E-infinity algebra
structure on the simplicial module that computes hyperoctahedral homology. We
deduce that hyperoctahedral homology admits Dyer-Lashof homology operations.
Furthermore, there is a Pontryagin product which gives hyperoctahedral homology
the structure of an associative, graded-commutative algebra. We also give an
explicit description of hyperoctahedral homology in degree zero. Combining this
description and the Pontryagin product we show that hyperoctahedral homology
fails to preserve Morita equivalence.


  We investigate various notions of peaking behaviour for states on a
$\mathrm{C}^*$-algebra, where the peaking occurs within an operator system. We
pay particularly close attention to the existence of sequences of elements
forming an approximation of the characteristic function of a point in the state
space. We exploit such characteristic sequences to localize the
$\mathrm{C}^*$-algebra at a given state, and use this localization procedure to
verify a variation of Arveson's hyperrigidity conjecture for arbitrary operator
systems.


  In these proceedings we review the flavour phenomenology of 2HDMs with
generic Yukawa structures. We first consider the quark sector and find that
despite the stringent constraints from FCNC processes large effects in tauonic
B decays are still possible. We then consider lepton flavour observables, show
correlations between \mu -> e\gamma and \mu^- -> e^-e^+e^- in the 2HDM of type
III and give upper bounds on the lepton flavour violating B decay B_d -> \mu e.


  A resource selection function is a model of the likelihood that an available
spatial unit will be used by an animal, given its resource value. But how do we
appropriately define availability? Step-selection analysis deals with this
problem at the scale of the observed positional data, by matching each used
step (connecting two consecutive observed positions of the animal) with a set
of available steps randomly sampled from a distribution of observed steps or
their characteristics. Here we present a simple extension to this approach,
termed integrated step-selection analysis (iSSA), which relaxes the implicit
assumption that observed movement attributes (i.e. velocities and their
temporal autocorrelations) are independent of resource selection. Instead, iSSA
relies on simultaneously estimating movement and resource-selection parameters,
thus allowing simple likelihood-based inference of resource selection within a
mechanistic movement model. We provide theoretical underpinning of iSSA, as
well as practical guidelines to its implementation. Using computer simulations,
we evaluate the inferential and predictive capacity of iSSA compared to
currently used methods. Our work demonstrates the utility of iSSA as a general,
flexible and user-friendly approach for both evaluating a variety of ecological
hypotheses, and predicting future ecological patterns.


  Within the framework of a dinuclear system (DNS) model, the
evaporation-residue excitation functions and the quasi-fission mass yields in
the $^{48}$Ca induced fusion reactions are investigated systematically and
compared with available experimental data. Maximal production cross sections of
superheavy nuclei based on stable actinide targets are obtained. Isotopic
trends in the production of the superheavy elements Z=110, 112-118 based on the
actinide isotopic targets are analyzed systematically. Optimal evaporation
channels and combinations as well as the corresponding excitation energies are
proposed. The possible factors that influencing the isotopic dependence of the
production cross sections are analyzed. The formation of the superheavy nuclei
based on the isotopes U with different projectiles are also investigated and
calculated.


  Preoperative gestures include tactile sampling of the mechanical properties
of biological tissue for both histological and pathological considerations.
Tactile properties used in conjunction with visual cues can provide useful
feedback to the surgeon. Development of novel cost effective haptic-based
simulators and their introduction in the minimally invasive surgery learning
cycle can absorb the learning curve for your residents. Receiving pre-training
in a core set of surgical skills can reduce skill acquisition time and risks.
We present the integration of a real-time surface stiffness adjustment
algorithm and a novel paradigm -- force maps -- in a visuo-haptic simulator
module designed to train internal organs disease diagnostics through palpation.


  Recently virtual platforms and virtual prototyping techniques have been
widely applied for accelerating software development in electronics companies.
It has been proved that these techniques can greatly shorten time-to-market and
improve product quality. One challenge is how to test and validate a virtual
prototype. In this paper, we present how to conduct regression testing of
virtual prototypes in different versions using symbolic execution. Suppose we
have old and new versions of a virtual prototype, we first apply symbolic
execution to the new version and collect all path constraints. Then the
collected path constraints are used for guiding the symbolic execution of the
old version. For each path explored, we compare the device states between two
versions to check if they behave the same. We have applied this approach to a
widely-used virtual prototype and detected numerous differences. The
experimental results show that our approach is useful and efficient.


  We introduce a patch to the commonly used public codes CAMB and CosmoMC that
allows the user to implement a general modification of the equations describing
the growth of cosmological perturbations, while preserving the covariant
conservation of the energy-momentum. This patch replaces the previously
publicly released code MGCAMB, while also extending it in several ways. The new
version removes the limitation of late-time-only modifications to the perturbed
Einstein equations, and includes several parametrization introduced in the
literature. To demonstrate the use of the patch, we obtain joint constraints on
the neutrino mass and parameters of a scalar-tensor gravity model from CMB, SNe
and ISW data as measured from the correlation of CMB with large scale
structure.


  In this paper we consider partial linear spaces induced on the point set of a
polar space, but with as lines the hyperbolic lines of this polar space. We
give some geometric characterizations of these and related spaces. The results
have applications in group theory, in the theory of Lie algebras and in graph
theory.


  In the cores of young dense star clusters repeated stellar collisions
involving the same object can occur, which has been suggested to lead to the
formation of an intermediate-mass black hole. In order to verify this scenario
we compute the detailed evolution of the merger remnant of three sequences. We
follow the evolution until the onset of carbon burning and estimate the final
remnant mass to determine the ultimate fate of a runaway merger sequence.
  We use a detailed stellar evolution code to follow the evolution of the
collision product. At each collision, we mix the two colliding stars, taking
account of mass loss during the collision. During the stellar evolution we
apply mass loss rates from the literature, as appropriate for the evolutionary
stage of the merger remnant. We compute models for high ($Z=0.02$) and low
($Z=0.001$) metallicity to quantify metallicity effects.
  We find that the merger remnant becomes a Wolf-Rayet star before the end of
core hydrogen burning. Mass loss from stellar winds dominates over the mass
increase due to repeated mergers for all three merger sequences that we
consider. In none of our high metallicity models an intermediate-mass black
hole is formed, instead our models have a mass of 10--14 \Msun{} at the onset
of carbon burning. For low metallicity we expect the final remnant of the
merger sequence to explode as a pair creation supernova. We find that our
metal-rich models become inflated as a result of developing an extended
low-density envelope. This may increase the probability of further collisions,
but self-consistent $N$-body calculations with detailed evolution of runaway
mergers are required to verify this.


  A simple heuristic proof of an integral identity recently derived (Glasser ML
2011 J. Phys. A: Math. Theor. 44 225202) is presented.


  Both Faradaic and non-Faradaic processes can take place at an electrode. The
difference between the two processes is clearly discussed in several classical
sources, starting with Grahame (1952). However, later reference to charge
transfer across the metal-solution interface as a defining feature of a
Faradaic process, has led to ambiguities. Following Grahame, in a Faradaic
process, charged particles transfer across the electrode, from one bulk phase
to another. Thus, in a Faradaic process, after applying a constant current, the
electrode charge, voltage and composition go to constant values. Instead, in a
non-Faradaic (capacitive) process, charge is progressively stored. We
characterize the intercalation material nickel hexacyanoferrate by two
electrochemical methods and compare with theory. Data for the capacitance of
this material is well described by the extended Frumkin isotherm. This data,
and the correspondence with theory, demonstrates that this is a capacitive
material and ion and charge storage in this material a non-Faradaic electrode
process. Cyclic Voltammetry (CV) diagrams for this material have broad peaks
for certain potential windows, and rectangular shapes for other conditions,
both experimentally and in theoretical calculations based on a RC network model
that includes how capacitance is a function of charge. Measured and predicted
CV diagrams are in perfect agreement with one another. This shows that (broad)
peaks in CV diagrams do not establish whether an electrode material is Faradaic
or not.


  Although deep reinforcement learning methods can learn effective policies for
challenging problems such as Atari games and robotics tasks, algorithms are
complex, and training times are often long. This study investigates how
Evolution Strategies perform compared to gradient-based deep reinforcement
learning methods. We use Evolution Strategies to optimize the weights of a
neural network via neuroevolution, performing direct policy search. We
benchmark both deep policy networks and networks consisting of a single linear
layer from observations to actions for three gradient-based methods, such as
Proximal Policy Optimization. These methods are evaluated against three
classical Evolution Strategies and Augmented Random Search, which all use
linear policy networks. Our results reveal that Evolution Strategies can find
effective linear policies for many reinforcement learning benchmark tasks,
unlike deep reinforcement learning methods that can only find successful
policies using much larger networks, suggesting that current benchmarks are
easier to solve than previously assumed. Interestingly, Evolution Strategies
also achieve results comparable to gradient-based deep reinforcement learning
algorithms for higher-complexity tasks. Furthermore, we find that by directly
accessing the memory state of the game, Evolution Strategies can find
successful policies in Atari that outperform the policies found by Deep
Q-Learning. Evolution Strategies also outperform Augmented Random Search in
most benchmarks, demonstrating superior sample efficiency and robustness in
training linear policy networks.


  We present a study of globular clusters (GCs) and other small stellar systems
(SSSs) in the field of NGC3115, observed as part of the VEGAS imaging survey,
carried out with the VST telescope. We use deep g and i data of NGC3115, a
well-studied lenticular galaxy with excellent scientific literature. This is
fundamental to test the methodologies, verify the results, and probe the
capabilities of the VEGAS-SSS. Leveraging the large field of view of the VST
allow us to accurately study of the distribution and properties of SSSs as a
function of galactocentric distance Rgc, well beyond ~20 galaxy effective
radii, in a way not often possible. Our analysis of colors, magnitudes and
sizes of SSS candidates confirms the results from existing studies, some of
which carried out with 8-10m class telescopes, and further extends them to
unreached Rgc distances, with similar accuracy. We find a color bimodality for
the GC population and a r1/4 profile for the surface density of GCs as for the
galaxy light profile. The radial color gradient of blue and red GCs previously
found, for instance by the SLUGGS survey, is further extended out to the
largest Rgc inspected, ~65 kpc. The surface density profiles of blue and red
GCs taken separately are well approximated by a r1/4 density profile, with the
fraction of blue GCs being slightly larger at larger Rgc. We do not find hints
of a trend for the red GC and for the GC turnover magnitude to vary with
radius, but we observe a ~0.2 mag difference in the turnover magnitude of the
blue and red GCs subpopulations. Inspecting SSS sizes and colors we obtained a
list of UCDs and GC candidates suitable for future spectroscopic follow-up. In
conclusion, our study shows the reliability of the methodologies developed to
study SSSs in the field of bright early-type galaxies, and the great potential
of the VEGAS survey to produce original results on SSSs science.


  NLO QCD corrections to H, W and Z production via vector boson fusion have
recently been calculated in the form of flexible parton level Monte Carlo
programs. This allows for the calculation of distributions and cross sections
with cuts at NLO accuracy. Some features of the calculation, as well as results
for the LHC, are reviewed.


  Transformers have demonstrated impressive results for 3D point cloud semantic
segmentation. However, the quadratic complexity of transformer makes
computation cost high, limiting the number of points that can be processed
simultaneously and impeding the modeling of long-range dependencies. Drawing
inspiration from the great potential of recent state space models (SSM) for
long sequence modeling, we introduce Mamba, a SSM-based architecture, to the
point cloud domain and propose Mamba24/8D, which has strong global modeling
capability under linear complexity. Specifically, to make disorderness of point
clouds fit in with the causal nature of Mamba, we propose a multi-path
serialization strategy applicable to point clouds. Besides, we propose the
ConvMamba block to compensate for the shortcomings of Mamba in modeling local
geometries and in unidirectional modeling. Mamba24/8D obtains state of the art
results on several 3D point cloud segmentation tasks, including ScanNet v2,
ScanNet200 and nuScenes, while its effectiveness is validated by extensive
experiments.


  Cluster detection plays a fundamental role in the analysis of data. In this
paper, we focus on the use of s-defective clique models for network-based
cluster detection and propose a nonlinear optimization approach that
efficiently handles those models in practice. In particular, we introduce an
equivalent continuous formulation for the problem under analysis, and we
analyze some tailored variants of the Frank-Wolfe algorithm that enable us to
quickly find maximal s-defective cliques. The good practical behavior of those
algorithmic tools, which is closely connected to their support identification
properties, makes them very appealing in practical applications. The reported
numerical results clearly show the effectiveness of the proposed approach.


  We present a generalized version of the Saturation in the Null Space (SNS)
algorithm for the task control of redundant robots when hard inequality
constraints are simultaneously present both in the joint and in the Cartesian
space. These hard bounds should never be violated, are treated equally and in a
unified way by the algorithm, and may also be varied, inserted or deleted
online. When a joint/Cartesian bound saturates, the robot redundancy is
exploited to continue fulfilling the primary task. If no feasible solution
exists, an optimal scaling procedure is applied to enforce directional
consistency with the original task. Simulation and experimental results on
different robotic systems demonstrate the efficiency of the approach. The
proposed algorithm can be viewed as a generic platform that is easily
applicable to any robotic application in which robots operate in an
unstructured environment and online handling of joint and Cartesian constraints
is critical.


  We prove stability and convergence of a full discretization for a class of
stochastic evolution equations with super-linearly growing operators appearing
in the drift term. This is done using the recently developed tamed Euler
method, which uses a fully explicit time stepping, coupled with a Galerkin
scheme for the spatial discretization.


  The universal bifurcation property of the H\'enon map in parameter space is
studied with symbolic dynamics. The universal-$L$ region is defined to
characterize the bifurcation universality. It is found that the universal-$L$
region for relative small $L$ is not restricted to very small $b$ values. These
results show that it is also a universal phenomenon that universal sequences
with short period can be found in many nonlinear dissipative systems.


  Many people now watch health videos, such as diet, exercise, mental health,
COVID-19, and chronic disease videos, on social media. Most existing studies
focused on video creators, leaving the motivations and practices of viewers
underexplored. We interviewed 18 participants, surveyed 121 respondents, and
derived a model characterizing consumers' video consumption practices on social
media. The practices include five main activities: deciding to watch videos
driven by various motivations, accessing videos on social media through a
socio-technical ecosystem, watching videos to meet informational, emotional,
and entertainment needs, evaluating the credibility and interestingness of
videos, and using videos to achieve health goals. Through an iterative video
consumption process, individuals strategically navigate across multiple
platforms, seeking better accessibility, higher reliability, and cultivating a
stronger motivation. They actively look for longer and more in-depth videos. We
further identified challenges consumers face while consuming health videos on
social media and discussed design implications and directions for future
research.


  The flavor puzzle of the Standard Model quark sector is formulated in a
non-perturbative way, using basis invariants that are independent of the choice
of quark field basis. To achieve this, we first derive the algebraic ring of 10
CP even (primary) and 1 CP odd (secondary) basis invariants, using the Hilbert
series and plethystic logarithm. An orthogonal basis in the ring of basis
invariants is explicitly constructed, using hermitian projection operators
derived via birdtrack diagrams. The thereby constructed invariants have well
defined CP transformation behavior and give the most direct access to the
flavor symmetric alignments of basis covariants. We firstly "measure" the
orthogonal basis invariants from experimental data and characterize their
location in the available parameter space. The experimentally observed
orthogonal basis invariants take very close to maximal values and are highly
correlated. Explaining the location of the invariants at close to maximal
points, including the associated miniscule and highly correlated deviations,
corresponds to solving the flavor puzzle in the invariant language. Once
properly normalized, the orthogonal basis invariants are close to scale (RGE)
invariant, hence, provide exquisite targets for fits of both, low- and
high-scale (bottom-up and top-down) flavor models. Our result provides an
entirely new angle on the flavor puzzle, and opens up ample opportunities for
its ultimate exploration.


  Automated legal reasoning and its application in smart contracts and
automated decisions are increasingly attracting interest. In this context,
ethical and legal concerns make it necessary for automated reasoners to justify
in human-understandable terms the advice given. Logic Programming, specially
Answer Set Programming, has a rich semantics and has been used to very
concisely express complex knowledge. However, modelling discretionality to act
and other vague concepts such as ambiguity cannot be expressed in top-down
execution models based on Prolog, and in bottom-up execution models based on
ASP the justifications are incomplete and/or not scalable. We propose to use
s(CASP), a top-down execution model for predicate ASP, to model vague concepts
following a set of patterns. We have implemented a framework, called s(LAW), to
model, reason, and justify the applicable legislation and validate it by
translating (and benchmarking) a representative use case, the criteria for the
admission of students in the "Comunidad de Madrid".


  We numerically demonstrate an all-optical nonlinearity pre-compensation
module for state-of-the-art long-reach Raman-amplified unrepeatered links. The
compensator design is optimized in terms of propagation symmetry to maximize
the performance gains under WDM transmission, achieving 4.0dB and 2.6dB of SNR
improvement for 250-km and 350-km links.


  In this paper, a general framework for deep learning-based power control
methods for max-min, max-product and max-sum-rate optimization in uplink
cell-free massive multiple-input multiple-output (CF mMIMO) systems is
proposed. Instead of using supervised learning, the proposed method relies on
unsupervised learning, in which optimal power allocations are not required to
be known, and thus has low training complexity. More specifically, a deep
neural network (DNN) is trained to learn the map between fading coefficients
and power coefficients within short time and with low computational complexity.
It is interesting to note that the spectral efficiency of CF mMIMO systems with
the proposed method outperforms previous optimization methods for max-min
optimization and fits well for both max-sum-rate and max-product optimizations.


  We first discuss recent progress in using the Milky Way globular cluster (GC)
system as a `test-bed' for properties derived from integrated spectra and
stellar population models. Standard techniques may give rise to spuriously high
alpha-element ratios at low metallicities. We then discuss evidence for early
epoch (z > 2) formation for most GCs in galaxies today. Recent accretions of
GCs (and their host galaxy) make a small contribution but recent mergers form
few if any new GCs in today's elliptical galaxies. The early formation of
metal-poor GCs and the bimodality seen in GC specific frequency requires a
`truncation' which may be due to reionization.


  In this work, a part of the Research and Development effort of the PICOSEC
detector is presented. The PICOSEC detector is a novel gas-filled detector,
based on the Micromegas detector, which has been developed by the RD-51 PICOSEC
collaboration. Instead of relying on traditional direct ionization, the PICOSEC
detector takes advantage of the prompt timing characteristics of Cherenkov
radiation by converting the Cherenkov photons into electrons through the use of
a photocathode. The detector has been put into two type of tests where
experimental data are collected. One involves a laser beam and a single
photoelectron response at CEA-SACLAY, while the other involves a test beam of
150 GeV muons at the CERN SPS H4 secondary beamline, with multiple
photoelectrons. The methods employed in the statistical analysis of the
detector's timing properties are summarized and in their application an optimal
time resolution of 76 ps is achieved for single photoelectrons. A strange
dependence of the mean timing on the size of the electron peak is observed,
which mimics the behaviour of the "time walk" effect. A simulation of the
detector is developed to study the timing properties, which is also the main
scope of the thesis. After a detailed investigation, it is found that the
electrons in the gaseous mixture move with a different drift velocity before
and after the first multiplication, introducing this "time walk" effect, and a
phenomenological explanation of this effect is provided. Finally, a maximum
likelihood statistical method for the estimation of the mean number of
photoelectron extracted per particle is developed and an optimal time
resolution of 24 ps is achieved with a mean number of photoelectrons per
particle equal to 10.4. In this work, a deep understanding is acquired on a new
detector which has brought upon unprecedented results in the field of
gas-filled detectors.


  We present a self-contained development of Gauss' Arithmetic-Geometric Mean
(AGM) and the work of A.E. Ingham who obtained rigorous error bounds for the
AGM approximations to the period of a simple pendulum


  In this letter we show that the field of Operator Space Theory provides a
general and powerful mathematical framework for arbitrary Bell inequalities, in
particular regarding the scaling of their violation within quantum mechanics.
We illustrate the power of this connection by showing that bipartite quantum
states with local Hilbert space dimension n can violate a Bell inequality by a
factor of order $\frac{\sqrt{n}}{\log^2n}$ when observables with n possible
outcomes are used. Applications to resistance to noise, Hilbert space dimension
estimates and communication complexity are given.


  We count the asymptotic number of triangles in uniform random graphs where
the degree distribution follows a power law with degree exponent
$\tau\in(2,3)$. We also analyze the local clustering coefficient $c(k)$, the
probability that two random neighbors of a vertex of degree $k$ are connected.
We find that the number of triangles, as well as the local clustering
coefficient, scale similarly as in the erased configuration model, where all
self-loops and multiple edges of the configuration model are removed.
Interestingly, uniform random graphs contain more triangles than erased
configuration models with the same degree sequence. The number of triangles in
uniform random graphs is closely related to that in a version of the rank-1
inhomogeneous random graph, where all vertices are equipped with weights, and
the edge probabilities are moderated by asymptotically linear functions of the
products of these vertex weights.


  We present a framework to generate and evaluate thematic recommendations
based on multilayer network representations of knowledge graphs (KGs). In this
representation, each layer encodes a different type of relationship in the KG,
and directed interlayer couplings connect the same entity in different roles.
The relative importance of different types of connections is captured by an
intuitive salience matrix that can be estimated from data, tuned to incorporate
domain knowledge, address different use cases, or respect business logic.
  We apply an adaptation of the personalised PageRank algorithm to multilayer
models of KGs to generate item-item recommendations. These recommendations
reflect the knowledge we hold about the content and are suitable for thematic
and/or cold-start recommendation settings. Evaluating thematic recommendations
from user data presents unique challenges that we address by developing a
method to evaluate recommendations relying on user-item ratings, yet respecting
their thematic nature. We also show that the salience matrix can be estimated
from user data. We demonstrate the utility of our methods by significantly
improving consumption metrics in an AB test where collaborative filtering
delivered subpar performance. We also apply our approach to movie
recommendation using publicly-available data to ensure the reproducibility of
our results. We demonstrate that our approach outperforms existing thematic
recommendation methods and is even competitive with collaborative filtering
approaches.


  In speaker recognition, where speech segments are mapped to embeddings on the
unit hypersphere, two scoring backends are commonly used, namely cosine scoring
or PLDA. Both have advantages and disadvantages, depending on the context.
Cosine scoring follows naturally from the spherical geometry, but for PLDA the
blessing is mixed -- length normalization Gaussianizes the between-speaker
distribution, but violates the assumption of a speaker-independent
within-speaker distribution. We propose PSDA, an analogue to PLDA that uses Von
Mises-Fisher distributions on the hypersphere for both within and between-class
distributions. We show how the self-conjugacy of this distribution gives
closed-form likelihood-ratio scores, making it a drop-in replacement for PLDA
at scoring time. All kinds of trials can be scored, including single-enroll and
multi-enroll verification, as well as more complex likelihood-ratios that could
be used in clustering and diarization. Learning is done via an EM-algorithm
with closed-form updates. We explain the model and present some first
experiments.


  We have constructed models of quasi-periodic variability of X-ray emission
from accreting compact objects. Assuming a general scenario of a propagation
model of variability, with inverse Compton upscatering as the emission
mechanism, we have considered a number of cases for the periodic modulation:
modulation of the plasma heating rate, cooling rate by external soft photons,
and the amplitude of the reprocessed component. We have computed various
observational characteristics which can be compared to good quality data.These
include Fourier-frequency resolved spectra and results of cross-correlation
analysis between light-curves at different energies. Each model of modulation
predicts specific observational signatures, which help in identifying the
physical processes driving QPO emission in accreting sources.


  We extend the concept of galaxy environment from the local galaxy number
density to the gravitational potential and its functions like the shear tensor.
For this purpose we examine whether or not one can make an accurate estimation
of the gravitational potential from an observational sample which is finite in
volume, biased due to galaxy biasing, and subject to redshift space distortion.
Dark halos in a $\Lambda$CDM simulation are used in this test. We find that one
needs to stay away from the sample boundaries by more than 30$h^{-1}$Mpc to
reduce the error within 20% of the root mean square values of the potential or
the shear tensor. The error due to the galaxy biasing can be significantly
reduced by using the galaxy mass density field instead of the galaxy number
density field. The error caused by the redshift space distortion can be
effectively removed by correcting galaxy positions for the peculiar velocity
effects. We inspect the dependence of dark matter halo properties on four
environmental parameters; local density, gravitational potential, and the
ellipticity and prolateness of the shear tensor. We find the local density has
the strongest correlation with halo properties. This is evidence that the
internal physical properties of dark halos are mainly controlled by small-scale
physics. In high density regions dark halos are on average more massive and
spherical, and have higher spin parameter and velocity dispersion. In high
density regions dark halos are on average more massive and spherical, and have
higher spin parameter and velocity dispersion. We also study the relation
between the environmental parameters and the subtypes of dark halos. The spin
parameter of satellite halos depends only weakly on the local density for all
mass ranges studied while that of isloated or central halos depends more
sensitively on the local density. (abridged)


  We report the discovery of superconductivity at high pressure in SrFe2As2 and
BaFe2As2. The superconducting transition temperatures are up to 27 K in
SrFe2As2 and 29 K in BaFe2As2, making these the highest pressure-induced
superconducting materials discovered thus far.


  We explore the use of orbital-angular-momentum (OAM)-multiplexing to increase
the capacity of free-space data transmission to moving platforms, with an added
potential benefit of decreasing the probability of data intercept.
Specifically, we experimentally demonstrate and characterize the performance of
an OAM-multiplexed, free-space optical (FSO) communications link between a
ground station and a moving unmanned-aerial-vehicle (UAV). We achieve a total
capacity of 80 Gbit/s up to 100-m-roundtrip link by multiplexing 2 OAM beams,
each carrying a 40-Gbit/s quadrature-phase-shift-keying (QPSK) signal.
Moreover, we investigate for static, hovering, and moving conditions the
effects of channel impairments, including: tracking errors, propeller-induced
airflows, power loss, intermodal crosstalk, and system bit error rate (BER). We
find the following: (a) when the UAV hovers in the air, the power on the
desired mode fluctuates by 2.1 dB, while the crosstalk to the other mode is -19
dB below the power on the desired mode; and (b) when the UAV moves in the air,
the power fluctuation on the desired mode increases to 4.3 dB and the crosstalk
to the other mode increases to -10 dB. Furthermore, the channel crosstalk
decreases with an increase in OAM mode spacing.


  Using single crystal neutron diffraction, we present a systematic
investigation of the crystal structure and magnetism of van der Waals
topological insulators MnBi$_2$Te$_4$ and MnBi$_4$Te$_7$, where rich
topological quantum states have been recently predicted and observed.
Structural refinements reveal that considerable Bi atoms occupied on the Mn
sites in both materials, distinct from the previously reported antisite
disorder. We show unambiguously that MnBi$_{2}$Te$_{4}$ orders
antiferromagnetically below 24 K featured by a magnetic symmetry $R_I$-${3c}$
while MnBi$_{4}$Te$_{7}$ is antiferromagnetic below 13 K with a magnetic space
group $P_c$-${3c1}$. They both present antiferromagnetically coupled
ferromagnetic layers with spins along the $c$-axis. We put forward a stacking
rule for the crystal structure of an infinitely adaptive series
MnBi$_{2n}$Te$_{3n+1}$ (n$\geq$1) with the building unit of [Bi$_2$Te$_3$]. A
comparison of magnetic properties between MnBi$_{2}$Te$_{4}$ and
MnBi$_{4}$Te$_{7}$, together with the recent density-functional theory
calculations, enables us to draw that a two-dimensional magnetism limit might
be realized in the derivatives. Our work may promote the theoretical studies of
topological magnetic states in the series of MnBi$_{2n}$Te$_{3n+1}$.


  We study stability of superdeformed oblate (SDO) superheavy $Z\geq 120$
nuclei predicted by systematic macroscopic-microscopic calculations in 12D
deformation space and confirmed by the Hartree-Fock calculations with the
realistic SLy6 force. We include into consideration high-$K$ isomers that very
likely form at the SDO shape. Although half-lives $T_{1/2}\lesssim10^{-5}$ s
are calclulated or estimated for even-even spin zero systems, decay hindrances
known for high-$K$ isomers suggest that some SDO superheavy nuclei may be
detectable by the present experimental technique.


  We make progress on a conjecture of Cilleruelo on the growth of the least
common multiple of consecutive values of an irreducible polynomial $f$ on the
additional hypothesis that the polynomial be even. This strengthens earlier
work of Rudnick--Maynard and Sah subject to that additional hypothesis when the
degree of $f$ exceeds two. The improvement rests upon a different treatment of
`large' prime divisors of $Q_f(N) = f(1)\cdots f(N)$ by means of certain zero
sums amongst the roots of $f$. A similar argument was recently used by Baier
and Dey with regard to another problem. The same method also allows for further
improvements on a related conjecture of Sah on the size of the radical of
$Q_f(N)$.


  Using the recently introduced declination function, we estimate the net
number of seats won in the US House of Representatives due to asymmetries in
vote distributions. Such asymmetries can arise from combinations of partisan
gerrymandering and inherent geographic advantage. Our estimates show
significant biases in favor of the Democrats prior to the mid 1990s and
significant biases in favor of Republicans since then. We find net differences
of 28, 20 and 25 seats in favor of the Republicans in the years 2012, 2014 and
2016, respectively. The validity of our results is supported by the technique
of simulated packing and cracking. We also use this technique to show that the
presidential-vote logistic regression model is insensitive to the packing and
cracking by which partisan gerrymanders are achieved.


  The sample-function regularity of the random-field solution to a stochastic
partial differential equation (SPDE) depends naturally on the roughness of the
external noise, as well as on the properties of the underlying
integro-differential operator that is used to define the equation. In this
paper, we consider parabolic and hyperbolic SPDEs on
$0,\infty)\times\mathbb{R}^d$ of the form $\partial_t u = L u + g(u) + \dot{F}
\qquad\text{and}\qquad \partial^2_t u = L u + c + \dot{F}, $ with suitable
initial data, forced with a space-time homogeneous Gaussian noise $\dot{F}$
that is white in its time variable and correlated in its space variable, and
driven by the generator $L$ of a genuinely $d$-dimensional L\'evy process $X$.
We find optimal H\"older conditions for the respective random-field solutions
to these SPDEs. Our conditions are stated in terms of indices that describe
thresholds on the integrability of some functionals of the characteristic
exponent of the process $X$ with respect to the spectral measure of the spatial
covariance of $\dot F$. Those indices are suggested by references [45, 46] on
the particular case that $L$ is the Laplace operator on $\mathbb{R}^d$.


  We give a rigorous description of a model of the quantized electromagnetic
field interacting with quantized current fields. In the special case of
classical currents our results agree with common knowledge about the problem. A
toy model of a quantum current is studied as well.


  I prove an envelope theorem with a converse: the envelope formula is
equivalent to a first-order condition. Like Milgrom and Segal's (2002) envelope
theorem, my result requires no structure on the choice set. I use the converse
envelope theorem to extend to general outcomes and preferences the canonical
result in mechanism design that any increasing allocation is implementable, and
apply this to selling information.


  Large language models (LLMs) have exhibited their problem-solving ability in
mathematical reasoning. Solving realistic optimization (OPT) problems in
industrial application scenarios requires advanced and applied math ability.
However, current OPT benchmarks that merely solve linear programming are far
from complex realistic situations. In this work, we propose E-OPT, a benchmark
for end-to-end optimization problem-solving with human-readable inputs and
outputs. E-OPT contains rich optimization problems, including linear/nonlinear
programming with/without table data, which can comprehensively evaluate LLMs'
solving ability. In our benchmark, LLMs are required to correctly understand
the problem in E-OPT and call code solver to get precise numerical answers.
Furthermore, to alleviate the data scarcity for optimization problems, and to
bridge the gap between open-source LLMs on a small scale (e.g., Llama-2-7b and
Llama-3-8b) and closed-source LLMs (e.g., GPT-4), we further propose a novel
data synthesis method namely ReSocratic. Unlike general data synthesis methods
that proceed from questions to answers, ReSocratic first incrementally
synthesizes optimization scenarios with mathematical formulations step by step
and then back-translates the generated scenarios into questions. In such a way,
we construct the ReSocratic-29k dataset from a small seed sample pool with the
powerful open-source large model DeepSeek-V2. To demonstrate the effectiveness
of ReSocratic, we conduct supervised fine-tuning with ReSocratic-29k on
multiple open-source models. The results show that Llama3-8b is significantly
improved from 13.6% to 51.7% on E-OPT, while DeepSeek-V2 reaches 61.0%,
approaching 65.5% of GPT-4.


  In the present paper, we investigate the question if the skeleton of a
Mumford curve of genus two can be tropicalized faithfully in dimension three,
i.e. if there exists an embedding of the curve in projective three space such
that the tropicalization maps the skeleton of the curve isometrically to its
image. Baker, Payne and Rabinoff showed that the skeleton of every analytic
curve can be tropicalized faithfully. However the dimension of the ambient
space in their proof can be quite large.
  We will define a map from the skeleton to the tropicalization of the
Jacobian, which is an isometry on the cycles. It allows us to find principal
divisors and simultanously to determine the retractions of their support on the
skeleton which is necessary to calculate their tropicalization.
  It turns out that a Mumford curve of genus two whose cycles of its skeleton
are either disjoint or share an edge of length at most half of the length of
the cycles, can be tropicalized faithfully in dimension three.


  We propose a novel framework to solve PDEs on moving manifolds, where the
evolving surface is represented by a moving point cloud. This has the advantage
of avoiding the need to discretize the bulk volume around the surface, while
also avoiding the need to have a global mesh. Distortions in the point cloud as
a result of the movement are fixed by local adaptation. We first establish a
comprehensive Lagrangian framework for arbitrary movement of curves and
surfaces given by point clouds. Collision detection algorithms between point
cloud surfaces are introduced, which also allow the handling of evolving
manifolds with topological changes. We then couple this Lagrangian framework
with a meshfree Generalized Finite Difference Method (GFDM) to approximate
surface differential operators, which together give a method to solve PDEs on
evolving manifolds. The applicability of this method is illustrated with a
range of numerical examples, which include advection-diffusion equations with
large deformations of the surface, curvature dependent geometric motion, and
wave equations on evolving surfaces.


  In this paper, we study four nonlocal diffusion operators, including the
fractional Laplacian, spectral fractional Laplacian, regional fractional
Laplacian, and peridynamic operator. These operators represent the
infinitesimal generators of different stochastic processes, and especially
their differences on a bounded domain are significant. We provide extensive
numerical experiments to understand and compare their differences. We find that
these four operators collapse to the classical Laplace operator as \alpha \to
2. The eigenvalues and eigenfunctions of these four operators are different,
and the k-th (for k \in N) eigenvalue of the spectral fractional Laplacian is
always larger than those of the fractional Laplacian and regional fractional
Laplacian. For any \alpha \in (0, 2), the peridynamic operator can provide a
good approximation to the fractional Laplacian, if the horizon size \delta is
sufficiently large. We find that the solution of the peridynamic model
converges to that of the fractional Laplacian model at a rate of
O(\delta^{-\alpha}). In contrast, although the regional fractional Laplacian
can be used to approximate the fractional Laplacian as \alpha \to 2, it
generally provides inconsistent result from that of the fractional Laplacian if
\alpha \ll 2. Moreover, some conjectures are made from our numerical results,
which could contribute to the mathematics analysis on these operators.


  In [OW2,OW3], the authors studied the nonlinear elliptic system $$
\overline{\partial}^\pi w = 0, \, d(w^*\lambda \circ j) = 0 $$ without
involving symplectization for each given contact triad $(Q,\lambda, J)$, and
established the a priori $W^{k,2}$ elliptic estimates and proved the asymptotic
(subsequence) convergence of the map $w: \dot \Sigma \to Q$ for any solution,
called a contact instanton, on $\dot \Sigma$ under the hypothesis
$\|w^*\lambda\|_{C^0} < \infty$ and $d^\pi w \in L^2 \cap L^4$. The asymptotic
limit of a contact instanton is a `spiraling' instanton along a `rotating' Reeb
orbit near each puncture on a punctured Riemann surface $\dot \Sigma$. Each
limiting Reeb orbit carries a `charge' arising from the integral of $w^*\lambda
\circ j$. In this article, we further develop analysis of contact instantons,
especially the $W^{1,p}$ estimate for $p > 2$ (or the $C^1$-estimate), which is
essential for the study of compactfication of the moduli space and the relevant
Fredholm theory for contact instantons. In particular, we define a Hofer-type
off-shell energy $E^\lambda(j,w)$ for any pair $(j,w)$ with a smooth map $w$
satisfying $d(w^*\lambda \circ j) = 0$, and develop the bubbling-off analysis
and prove an $\epsilon$-regularity result. We also develop the relevant
Fredholm theory and carry out index calculations (for the case of vanishing
charge).


  Single photon emitters in 2D hexagonal boron nitride (hBN) have attracted a
considerable attention because of their highly intense, stable, and
strain-tunable emission. However, the precise source of this emission, in
particular the detailed atomistic structure of the involved crystal defect,
remains unknown. In this work, we present first-principles calculations of the
vibrationally resolved optical fingerprint of the spin-triplet (2)(_^3)B_1 to
(1)(_^3)B_1 transition of the VNCB point defect in hBN. Based on the excellent
agreement with experiments for key spectroscopic quantities such as the
emission frequency and polarization, the photoluminescence (PL) line shape,
Huang-Rhys factor, Debye-Waller factor, and re-organization energy, we
conclusively assign the observed single photon emission at ~2eV to the VNCB
defect. Our work thereby resolves a long-standing debate about the exact
chemical nature of the source of single photon emission from hBN and
establishes the microscopic understanding necessary for controlling and
applying such photons for quantum technological applications.


  In this paper, we prove the existence and stability of subsonic flows for
steady full Euler-Poisson system in a two dimensional nozzle of finite length
when imposing the electric potential difference on non-insulated boundary from
a fixed point at the entrance, and prescribing the pressure at the exit of the
nozzle. The Euler-Poisson system for subsonic flow is a hyperbolic-elliptic
coupled nonlinear system. One of the crucial ingredient of this work is the
combination of Helmholtz decomposition for the velocity field and stream
function formulation together. In terms of the Helmholtz decomposition, the
Euler-Poisson system is rewritten as a second order nonlinear elliptic system
of three equations and transport equations for entropy and pseudo-Bernoulli's
invariant. The associated elliptic system in a Lipschitz domain with nonlinear
boundary conditions is solved with the help of the estimates developed in [2]
based on its nice structure. The transport equations are resolved via the flow
map induced by the stream function formulation. Furthermore, the delicate
estimates for the flow map give the uniqueness of the solutions.


  An important issue in phenomenological macroscopic electrodynamics of moving
media is the definition of the energy and momentum of the electromagnetic field
in matter. Rather surprisingly, this topic has demonstrated a remarkable
longevity, and the problem of the electromagnetic energy and momentum in matter
remained open, despite numerous theoretical and experimental investigations. We
overview the definition of the momentum of light in matter and demonstrate
that, for the correct understanding of the problem, one needs to carefully
distinguish situations when the material medium is modeled either as a
background for light or as a dynamical part of the total system. The status of
Minkowski and Abraham energy-momentum tensors of the electromagnetic field is
clarified for the two particular types of complex matter, the spinning fluid
and the liquid crystal medium, and summarized for the case of general
anisotropic moving material media with a linear constitutive law.