  'Internal spacetime' is a modification of general relativity that was
recently introduced as an approximate spacetime geometric model of quantum
nonlocality. In an internal spacetime, time is stationary along the worldlines
of fundamental (dust) particles. Consequently, the dimensions of tangent spaces
at different points of spacetime vary, and spin wavefunction collapse is
modeled by the projection from one tangent space to another. In this article we
develop spinors on an internal spacetime, and construct a new Dirac-like
Lagrangian $\mathcal{L} = \bar{\psi}(i \slashed \partial - \hat{\omega}) \psi$
whose equations of motion describe their couplings and interactions.
Furthermore, we show that hidden within $\mathcal{L}$ is the entire standard
model: $\mathcal{L}$ contains precisely three generations of quarks and
leptons, the electroweak gauge bosons, the Higgs boson, and one new massive
spin-$2$ boson; gluons are considered in a companion article. Specifically, we
are able to derive the correct spin, electric charge, and color charge of each
standard model particle, as well as predict the existence of a new boson.


  A new reference design is introduced for holographic coherent diffraction
imaging. This consists in two references - "block" and "pinhole" shaped regions
- placed adjacent to the imaging specimen. An efficient recovery algorithm is
provided for the resulting holographic phase retrieval problem, which is based
on solving a structured, overdetermined linear system. Analysis of the expected
recovery error on noisy data, which is contaminated by Poisson shot noise,
shows that this simple modification synergizes the individual references and
hence leads to uniformly superior performance over single-reference schemes.
Numerical experiments on simulated data confirm the theoretical prediction, and
the proposed dual-reference scheme achieves a smaller recovery error than
leading single-reference schemes.


  It is shown, that specific properties of the instanton induced interaction
between quarks leads to the anomalous violation of the OZI-rule in the $N\bar
N\to \Phi\Phi$, $N\bar N\to \Phi\gamma$ reactions. In the framework of
instanton model of the QCD vacuum, the energy dependence of the cross sections
of these reactions is calculated.


  For probability measures on countable spaces we derive distributional limits
for empirical entropic optimal transport quantities. More precisely, we show
that the empirical optimal transport plan weakly converges to a centered
Gaussian process and that the empirical entropic optimal transport value is
asymptotically normal. The results are valid for a large class of cost
functions and generalize distributional limits for empirical entropic optimal
transport quantities on finite spaces. Our proofs are based on a sensitivity
analysis with respect to norms induced by suitable function classes, which
arise from novel quantitative bounds for primal and dual optimizers, that are
related to the exponential penalty term in the dual formulation. The
distributional limits then follow from the functional delta method together
with weak convergence of the empirical process in that respective norm, for
which we provide sharp conditions on the underlying measures. As a byproduct of
our proof technique, consistency of the bootstrap for statistical applications
is shown.


  This paper studies the problem of maximizing expected utility from terminal
wealth in a semi-static market composed of derivative securities, which we
assume can be traded only at time zero, and of stocks, which can be traded
continuously in time and are modeled as locally-bounded semi-martingales.
  Using a general utility function defined on the positive real line, we first
study existence and uniqueness of the solution, and then we consider the
dependence of the outputs of the utility maximization problem on the price of
the derivatives, investigating not only stability but also differentiability,
monotonicity, convexity and limiting properties.


  Quantum computing and quantum communications are exciting new frontiers in
computing and communications. Indeed, the massive investments made by the
governments of the US, China, and EU in these new technologies are not a secret
and are based on the expected potential of these technologies to revolutionize
communications, computing, and security. In addition to several field trials
and hero experiments, a number of companies such as Google and IBM are actively
working in these areas and some have already reported impressive demonstrations
in the past few years. While there is some skepticism about whether quantum
cryptography will eventually replace classical cryptography, the advent of
quantum computing could necessitate the use of quantum cryptography as the
ultimate frontier of secure communications. This is because, with the amazing
speeds demonstrated with quantum computers, breaking cryptographic keys might
no longer be a daunting task in the next decade or so. Hence, quantum
cryptography as the ultimate frontier in secure communications might not be
such a far-fetched idea. It is well known that Heisenberg's Uncertainty
Principle is essentially a "negative result" in Physics and Quantum Mechanics.
It turns out that Heisenberg's Uncertainty Principle, one of the most
interesting results in Quantum Mechanics, could be the theoretical basis and
the main scientific principle behind the ultimate frontier in quantum
cryptography or secure communications in conjunction with Quantum Entanglement.


  Power transformers are an important component of a nuclear power plant (NPP).
Currently, the NPP operates a lot of power transformers with extended service
life, which exceeds the designated 25 years. Due to the extension of the
service life, the task of monitoring the technical condition of power
transformers becomes urgent. An important method for monitoring power
transformers is Chromatographic Analysis of Dissolved Gas. It is based on the
principle of controlling the concentration of gases dissolved in transformer
oil. The appearance of almost any type of defect in equipment is accompanied by
the formation of gases that dissolve in oil, and specific types of defects
generate their gases in different quantities. At present, at NPPs, the
monitoring systems for transformer equipment use predefined control limits for
the concentration of dissolved gases in the oil. This study describes the
stages of developing an algorithm to detect defects and faults in transformers
automatically using machine learning and data analysis methods. Among machine
learning models, we trained Logistic Regression, Decision Trees, Random Forest,
Gradient Boosting, Neural Networks. The best of them were then combined into an
ensemble (StackingClassifier) showing F1-score of 0.974 on a test sample. To
develop mathematical models, we used data on the state of transformers,
containing time series with values of gas concentrations (H2, CO, C2H4, C2H2).
The datasets were labeled and contained four operating modes: normal mode,
partial discharge, low energy discharge, low-temperature overheating.


  We offer to the star formation community the third installment of the Massive
Star-forming Regions (MSFRs) Omnibus X-ray Catalog (MOXC3), a compilation of
X-ray point sources detected in 50 archival Chandra/ACIS observations of 14
Galactic MSFRs and surrounding fields. The MOXC3 MSFRs are NGC 2264, NGC 6193,
RCW 108-IR, Aur OB1, DR15, NGC 6231, Berkeley 87, NGC 6357, AFGL 4029, h Per
(NGC 869), NGC 281, Onsala 2S, G305, and RCW 49 (Wd 2); they have distances of
0.7 kpc to 4.2 kpc. Most exhibit clumped or clustered young stellar
populations; several contain at least two distinct massive young stellar
clusters. The total MOXC3 catalog includes 27,923 X-ray point sources. We take
great care to identify even the faintest X-ray point sources across these
fields. This allows us to remove this point source light, revealing diffuse
X-ray structures that pervade and surround MSFRs, often generated by hot
plasmas from massive star feedback. As we found in MOXC1 and MOXC2, diffuse
X-ray emission is traceable in all MOXC3 MSFRs; here we perform spectral
fitting to investigate the origins of selected diffuse regions. Once again,
MOXC3 shows the value of high spatial resolution X-ray studies of MSFRs enabled
by Chandra.


  Modern approaches to sound synthesis using deep neural networks are hard to
control, especially when fine-grained conditioning information is not
available, hindering their adoption by musicians.
  In this paper, we cast the generation of individual instrumental notes as an
inpainting-based task, introducing novel and unique ways to iteratively shape
sounds. To this end, we propose a two-step approach: first, we adapt the
VQ-VAE-2 image generation architecture to spectrograms in order to convert
real-valued spectrograms into compact discrete codemaps, we then implement
token-masked Transformers for the inpainting-based generation of these
codemaps.
  We apply the proposed architecture on the NSynth dataset on masked resampling
tasks. Most crucially, we open-source an interactive web interface to transform
sounds by inpainting, for artists and practitioners alike, opening up to new,
creative uses.


  Hyperdimensional computing (HD), also known as vector symbolic architectures
(VSA), is a framework for computing with distributed representations by
exploiting properties of random high-dimensional vector spaces. The commitment
of the scientific community to aggregate and disseminate research in this
particularly multidisciplinary area has been fundamental for its advancement.
Joining these efforts, we present Torchhd, a high-performance open source
Python library for HD/VSA. Torchhd seeks to make HD/VSA more accessible and
serves as an efficient foundation for further research and application
development. The easy-to-use library builds on top of PyTorch and features
state-of-the-art HD/VSA functionality, clear documentation, and implementation
examples from well-known publications. Comparing publicly available code with
their corresponding Torchhd implementation shows that experiments can run up to
100x faster. Torchhd is available at:
https://github.com/hyperdimensional-computing/torchhd.


  We introduce meta-prompting, an effective scaffolding technique designed to
enhance the functionality of language models (LMs). This approach transforms a
single LM into a multi-faceted conductor, adept at managing and integrating
multiple independent LM queries. By employing high-level instructions,
meta-prompting guides the LM to break down complex tasks into smaller, more
manageable subtasks. These subtasks are then handled by distinct "expert"
instances of the same LM, each operating under specific, tailored instructions.
Central to this process is the LM itself, in its role as the conductor, which
ensures seamless communication and effective integration of the outputs from
these expert models. It additionally employs its inherent critical thinking and
robust verification processes to refine and authenticate the end result. This
collaborative prompting approach empowers a single LM to simultaneously act as
a comprehensive orchestrator and a panel of diverse experts, significantly
enhancing its performance across a wide array of tasks. The zero-shot,
task-agnostic nature of meta-prompting greatly simplifies user interaction by
obviating the need for detailed, task-specific instructions. Furthermore, our
research demonstrates the seamless integration of external tools, such as a
Python interpreter, into the meta-prompting framework, thereby broadening its
applicability and utility. Through rigorous experimentation with GPT-4, we
establish the superiority of meta-prompting over conventional scaffolding
methods: When averaged across all tasks, including the Game of 24,
Checkmate-in-One, and Python Programming Puzzles, meta-prompting, augmented
with a Python interpreter functionality, surpasses standard prompting by 17.1%,
expert (dynamic) prompting by 17.3%, and multipersona prompting by 15.2%.


  Class-imbalance is an inherent characteristic of multi-label data which
affects the prediction accuracy of most multi-label learning methods. One
efficient strategy to deal with this problem is to employ resampling techniques
before training the classifier. Existing multilabel sampling methods alleviate
the (global) imbalance of multi-label datasets. However, performance
degradation is mainly due to rare subconcepts and overlapping of classes that
could be analysed by looking at the local characteristics of the minority
examples, rather than the imbalance of the whole dataset. We propose a new
method for synthetic oversampling of multi-label data that focuses on local
label distribution to generate more diverse and better labeled instances.
Experimental results on 13 multi-label datasets demonstrate the effectiveness
of the proposed approach in a variety of evaluation measures, particularly in
the case of an ensemble of classifiers trained on repeated samples of the
original data.


This paper has been withdrawn.


  Let $K$ be a compact set with connected complement on the half-plane
Re$(s)>0$, and let $f$ be a continuous function on $K$ which is analytic in its
interior. We prove that for any parameter $0<\alpha<1, \alpha \neq \frac 1 2$
then $f(s)$ may be uniformly approximated arbitrarily closely by
$\zeta(1+iT+i\delta s,\alpha)$ on $K$ for some $T,\delta>0$, where
$\zeta(s,\alpha)$ denote the Hurwitz zeta-function. This is the first known
universality result that is also known to hold for the Hurwitz zeta-function
with an algebraic irrational parameter.


  In quantum optics, it is common to assume that atoms are point-like objects
compared to the wavelength of the electromagnetic field they interact with.
However, this dipole approximation is not always valid, e.g., if atoms couple
to the field at multiple discrete points. Previous work has shown that
superconducting qubits coupled to a one-dimensional waveguide can behave as
such "giant atoms" and then can interact through the waveguide without
decohering, a phenomenon that is not possible with small atoms. Here, we show
that this decoherence-free interaction is also possible when the coupling to
the waveguide is chiral, i.e., when the coupling depends on the propagation
direction of the light. Furthermore, we derive conditions under which the giant
atoms in such chiral architectures exhibit dark states. In particular, we show
that unlike small atoms, giant atoms in a chiral waveguide can reach a dark
state even without being excited by a coherent drive. We also show that in the
driven-dissipative regime, dark states can be populated faster in giant atoms.
The results presented here lay a foundation for applications based on giant
atoms in quantum simulations and quantum networks with chiral settings.


  A complete study of the one-loop induced decay Z -> neutrino antineutrino
photon is presented within the framework of the Standard Model. The advantages
of using a nonlinear gauge are stressed. We have found that the main
contributions come from the electric dipole and the magnetic dipole transitions
of the Z gauge boson and the neutrino, respectively. We obtain a branching
ratio B=7.16E-10, which is about four orders of magnitude smaller than the
bound recentely obtained by the L3 collaboration and thus it leaves open a
window to search for new physics effects in single-photon decays of the Z
boson.


  Rarefied gas dynamics is usually described by the Boltzmann equation.
Unfortunately, the expense of evaluating this operator can be very prohibitive.
This made it worthwhile to look for approximations that convey essentially an
equivalent amount of physical information. One widely known approximative
collision operator is the Bathnagar-Gross-Krook (BGK) operator. However,
recently, the Foker-Planck approximation has become increasingly popular.
Nevertheless, the modeling of gas mixtures in the context of the kinetic
Fokker-Planck equation has so far only been addressed in a very few papers. In
this paper, we propose a general multi-species Fokker-Planck model. We prove
consistency of our model: conservation properties, positivity of all
temperatures, H-Theorem and the shape of equilibrium as Maxwell distributions
with the same mean velocity and temperature. Moreover, we derive the usual
macroscopic equations.


  Simultaneous localization and mapping (SLAM) is one of the essential
techniques and functionalities used by robots to perform autonomous navigation
tasks. Inspired by the rodent hippocampus, this paper presents a biologically
inspired SLAM system based on a LiDAR sensor using a hippocampal model to build
a cognitive map and estimate the robot pose in indoor environments. Based on
the biologically inspired models mimicking boundary cells, place cells, and
head direction cells, the SLAM system using LiDAR point cloud data is capable
of leveraging the self-motion cues from the LiDAR odometry and the boundary
cues from the LiDAR boundary cells to build a cognitive map and estimate the
robot pose. Experiment results show that with the LiDAR boundary cells the
proposed SLAM system greatly outperforms the camera-based brain-inspired method
in both simulation and indoor environments, and is competitive with the
conventional LiDAR-based SLAM methods.


  Many multi-agent scenarios require message sharing among agents to promote
coordination, hastening the robustness of multi-agent communication when
policies are deployed in a message perturbation environment. Major relevant
works tackle this issue under specific assumptions, like a limited number of
message channels would sustain perturbations, limiting the efficiency in
complex scenarios. In this paper, we take a further step addressing this issue
by learning a robust multi-agent communication policy via multi-view message
certification, dubbed CroMAC. Agents trained under CroMAC can obtain guaranteed
lower bounds on state-action values to identify and choose the optimal action
under a worst-case deviation when the received messages are perturbed.
Concretely, we first model multi-agent communication as a multi-view problem,
where every message stands for a view of the state. Then we extract a
certificated joint message representation by a multi-view variational
autoencoder (MVAE) that uses a product-of-experts inference network. For the
optimization phase, we do perturbations in the latent space of the state for a
certificate guarantee. Then the learned joint message representation is used to
approximate the certificated state representation during training. Extensive
experiments in several cooperative multi-agent benchmarks validate the
effectiveness of the proposed CroMAC.


  Hyperspectral images (HSI) with abundant spectral information reflected
materials property usually perform low spatial resolution due to the hardware
limits. Meanwhile, multispectral images (MSI), e.g., RGB images, have a high
spatial resolution but deficient spectral signatures. Hyperspectral and
multispectral image fusion can be cost-effective and efficient for acquiring
both high spatial resolution and high spectral resolution images. Many of the
conventional HSI and MSI fusion algorithms rely on known spatial degradation
parameters, i.e., point spread function, spectral degradation parameters,
spectral response function, or both of them. Another class of deep
learning-based models relies on the ground truth of high spatial resolution HSI
and needs large amounts of paired training images when working in a supervised
manner. Both of these models are limited in practical fusion scenarios. In this
paper, we propose an unsupervised HSI and MSI fusion model based on the cycle
consistency, called CycFusion. The CycFusion learns the domain transformation
between low spatial resolution HSI (LrHSI) and high spatial resolution MSI
(HrMSI), and the desired high spatial resolution HSI (HrHSI) are considered to
be intermediate feature maps in the transformation networks. The CycFusion can
be trained with the objective functions of marginal matching in single
transform and cycle consistency in double transforms. Moreover, the estimated
PSF and SRF are embedded in the model as the pre-training weights, which
further enhances the practicality of our proposed model. Experiments conducted
on several datasets show that our proposed model outperforms all compared
unsupervised fusion methods. The codes of this paper will be available at this
address: https: //github.com/shuaikaishi/CycFusion for reproducibility.


  Microscopic spin interaction processes are fundamental for global static and
dynamical magnetic properties of many-body systems. Quantum gases as pure and
well isolated systems offer intriguing possibilities to study basic magnetic
processes including non-equilibrium dynamics. Here, we report on the
realization of a well-controlled fermionic spinor gas in an optical lattice
with tunable effective spin ranging from 1/2 to 9/2. We observe long-lived
intrinsic spin oscillations and investigate the transition from two-body to
many-body dynamics. The latter results in a spin-interaction driven melting of
a band insulator. Via an external magnetic field we control the system's
dimensionality and tune the spin oscillations in and out of resonance. Our
results open new routes to study quantum magnetism of fermionic particles
beyond conventional spin 1/2 systems.


  In the magic angle twisted bilayer graphene (MATBG), one of the most
remarkable observations is the $C_3$-symmetry-breaking nematic state. We
identify that the nematicity in MATBG is the $E$-symmetry ferro bond order,
which is the modulation of correlated hopping integrals owing to the
$E$-symmetry particle-hole pairing condensation. The nematicity in MATBG
originates from prominent quantum interference among $SU(4)$ valley+spin
composite fluctuations. This novel "valley + spin fluctuation interference
mechanism" is revealed by the density wave equation analysis for realistic
multiorbital Hubbard model for MATBG. \color{black}We find that the nematic
state is robust once three van Hove singularity points exist in each valley.
This interference mechanism also causes novel time-reversal-symmetry-broken
valley polarization accompanied by a charge loop current. We discuss
interesting similarities and differences between MATBG and Fe-based
superconductors.


  The totally asymmetric simple exclusion process (TASEP) with a localized
defect is revisited in this article with attention paid to the power spectra of
the particle occupancy N(t). Intrigued by the oscillatory behaviors in the
power spectra of an ordinary TASEP in high/low density phase(HD/LD) observed by
Adams et al. (2007 Phys. Rev. Lett. 99 020601), we introduce a single slow site
with hopping rate q<1 to the system. As the power spectrum contains
time-correlation information of the particle occupancy of the system, we are
particularly interested in how the defect affects fluctuation in particle
number of the left and right subsystems as well as that of the entire system.
Exploiting Monte Carlo simulations, we observe the disappearance of
oscillations when the defect is located at the center of the system. When the
defect is off center, oscillations are restored. To explore the origin of such
phenomenon, we use a linearized Langevin equation to calculate the power
spectrum for the sublattices and the whole lattice. We provide insights into
the interactions between the sublattices coupled through the defect site for
both simulation and analytical results.


  Radar echoes from bird flocks contain modulation signals, which we find are
produced by the flapping gaits of birds in the flock, resulting in a group of
spectral peaks with similar amplitudes spaced at a specific interval. We call
this the formation wing-beat modulation (FWM) effect. FWM signals are
micro-Doppler modulated by flapping wings and are related to the bird number,
wing-beat frequency, and flight phasing strategy. Our X-band radar data show
that FWM signals exist in radar signals of a seagull flock, providing tools for
quantifying the bird number and estimating the mean wingbeat rate of birds.
This new finding could aid in research on the quantification of bird migration
numbers and estimation of bird flight behavior in radar ornithology and
aero-ecology.


  Relying on the mathematical analogy of the pure states of a two-qubit system
with four-component Dirac spinors, we provide an alternative consideration of
quantum entanglement using the mathematical formulation of Cartan's pure
spinors. A result of our analysis is that the Cartan equation of two qubits
state is entanglement sensitive in a way that the Dirac equation for fermions
is mass sensitive. The Cartan equation for unentangled qubits is reduced to a
pair of Cartan equations for single qubits as the Dirac equation for massless
fermions separates into two Weyl equations. Finally, we establish a
correspondance between the separability condition in qubit geometry and the
separability condition in spinor geometry.


  Spherical convolutional neural networks (Spherical CNNs) learn nonlinear
representations from 3D data by exploiting the data structure and have shown
promising performance in shape analysis, object classification, and planning
among others. This paper investigates the properties that Spherical CNNs
exhibit as they pertain to the rotational structure inherent in spherical
signals. We build upon the rotation equivariance of spherical convolutions to
show that Spherical CNNs are stable to general structure perturbations. In
particular, we model arbitrary structure perturbations as diffeomorphism
perturbations, and define the rotation distance that measures how far from
rotations these perturbations are. We prove that the output change of a
Spherical CNN induced by the diffeomorphism perturbation is bounded
proportionally by the perturbation size under the rotation distance. This
stability property coupled with the rotation equivariance provide theoretical
guarantees that underpin the practical observations that Spherical CNNs exploit
the rotational structure, maintain performance under structure perturbations
that are close to rotations, and offer good generalization and faster learning.


  I show that W-like entangled quantum states are not a necessary quantum
resource for totally correct anonymous leader election protocols. This is
proven by defining a symmetric quantum state that is n-partite SLOCC
inequivalent to the W state, and then constructing a totally correct anonymous
leader election protocol using this state. This result, which contradicts the
previous necessity result of D'Hondt and Panangaden, furthers our understanding
of how non-local quantum states can be used as a resource for distributed
computation.


  We propose a new improved model-independent method for calculating the pion
charge radius. In a recently-proposed model-independent method for the pion
charge radius, we find it difficult to compute the pion charge radius for small
pole mass $M_{\rm{pole}}^2$ and volume due to systematic errors coming from
finite volume effect and higher-order contamination of the Taylor expansion of
the form factor. We circumvent this difficulty by introducing a new appropriate
function and propose a modified method that can calculate the pion charge
radius with less systematic errors in the small $M_{\rm{pole}}^2$ and volume
cases. As preliminary results, we check that our improved model-independent
method works well on a mockup data and also an actual lattice QCD data at the
pion mass of 0.51 GeV.


  In this paper we consider the following question. What is the maximum number
of pairwise disjoint $k$-spreads which exist in PG(n,q)? We prove that if k+1
divides n+1 and n>k then there exist at least two disjoint k-spreads in PG(n,q)
and there exist at least $2^{k+1}-1$ pairwise disjoint $k$-spreads in PG(n,2).
We also extend the known results on parallelism in a projective geometry from
which the points of a given subspace were removed.


  Optical parametric oscillations (OPOs) - a non-linear process involving the
coherent coupling of an optically excited two particle pump state to a signal
and an idler states with different energies - is a relevant mechanism for
optical amplification as well as for the generation of correlated photons. OPOs
require states with well-defined symmetries and energies: the fine-tuning of
material properties and structural dimensions to create these states remains a
challenge for the realization of scalable OPO-based functionalities in
semiconductor nanostructures. Here, we demonstrate a pathway towards this goal
based on the control of confined microcavity exciton-polaritons modulated by
the spatially and time varying dynamical potentials produced by a surface
acoustic waves (SAW). The exciton-polariton are confined in um-sized
intra-cavity traps fabricated by structuring a planar semiconductor microcavity
during the epitaxial growth process. OPOs in these structures benefit from the
enhanced non-linearities of confined systems. We show that SAW fields induce
state-dependent and time-varying energy shifts, which enable the energy
alignment of the confined levels with the appropriate symmetry for OPO
triggering. Furthermore, the dynamic acoustic tuning, which is fully described
by a theoretical model for the modulation of the confined polaritons by the
acoustic field, compensates for fluctuations in symmetry and dimensions of the
confinement potential thus enabling a variety of dynamic OPO regimes. The
robustness of the acoustic tuning is demonstrated by the synchronous excitation
of an array of confined OPOs using a single acoustic beam, thus opening the way
for the realization of scalable non-linear on-chip systems.


  The interaction of two resonant impurities in graphene has been predicted to
have a long-range character with weaker repulsion when the two adatoms reside
on the same sublattice and stronger attraction when they are on different
sublattices. We reveal that this attraction results from a single energy level.
This opens up a possibility of controlling the sign of the impurity interaction
via the adjustment of the chemical potential. For many randomly distributed
impurities (adatoms or vacancies) this may offer a way to achieve a controlled
transition from aggregation to dispersion.


  Lorentz-violating Yukawa couplings lying within the renormalizable part of
the SME generate Lorentz-invariant one-loop contributions to electromagnetic
moments of fermions. These proceedings provide a discussion on such
contributions and present bounds on SME coefficients from experimental data on
electromagnetic moments. Constraints as restrictive as $\sim10^{-14}$ are
found.


  An RNA-seq experiment with 48 biological replicates in each of 2 conditions
was performed to determine the number of biological replicates ($n_r$)
required, and to identify the most effective statistical analysis tools for
identifying differential gene expression (DGE). When $n_r=3$, seven of the nine
tools evaluated give true positive rates (TPR) of only 20 to 40 percent. For
high fold-change genes ($|log_{2}(FC)|\gt2$) the TPR is $\gt85$ percent. Two
tools performed poorly; over- or under-predicting the number of differentially
expressed genes. Increasing replication gives a large increase in TPR when
considering all DE genes but only a small increase for high fold-change genes.
Achieving a TPR $\gt85$% across all fold-changes requires $n_r\gt20$. For
future RNA-seq experiments these results suggest $n_r\gt6$, rising to
$n_r\gt12$ when identifying DGE irrespective of fold-change is important. For
$6 \lt n_r \lt 12$, superior TPR makes edgeR the leading tool tested. For $n_r
\ge12$, minimizing false positives is more important and DESeq outperforms the
other tools.


  The planar $(n+1)$-body problem models the motion of $n+1$ bodies in the
plane under their mutual Newtonian gravitational attraction forces. When $n\ge
3$, the question about final motions, that is, what are the possible limit
motions in the planar $(n+1)$-body problem when $t\to \infty$, ceases to be
completely meaningful due to the existence of non-collision singularities.
  In this paper we prove the existence of solutions of the planar $(n+1)$-body
problem which are defined for all forward time and tend to a parabolic motion,
that is, that one of the bodies reaches infinity with zero velocity while the
rest perform a bounded motion.
  These solutions are related to whiskered parabolic tori at infinity, that is,
parabolic tori with stable and unstable invariant manifolds which lie at
infinity. These parabolic tori appear in cylinders which can be considered
`normally parabolic'.
  The existence of these whiskered parabolic tori is a consequence of a general
theorem on parabolic tori developed here. Another application of our theorem is
a conjugation result for a class of skew product maps with a parabolic torus
with its normal form generalizing results of Takens and Voronin.


  We define a positive operator valued measure $E$ on $[0,2\pi]\times R$
describing the measurement of randomly sampled quadratures in quantum homodyne
tomography, and we study its probabilistic properties. Moreover, we give a
mathematical analysis of the relation between the description of a state in
terms of $E$ and the description provided by its Wigner transform.


  Context. The extremely low density of several long-period exoplanets in
mature systems is still unexplained -- with HIP 41378 f being archetypical of
this category. It has been proposed that such planets could actually have
normal densities but be surrounded by a ring observed approximately face on,
mimicking the transit depth of a puffy planet. This would imply that the
equator of the planet is nearly perpendicular to its orbit plane, which is at
odds with the formation process of gas giants. Yet, in the context of the Solar
System planets, it has been shown that after gigayears of evolution, the tidal
migration of a moon can naturally lead to a very tilted planet with a ring.
  Aims. As exomoons are expected to be ubiquitous around giant exoplanets, this
mechanism may be responsible for the anomalous radii of some observed
exoplanets. In preparation for the future discoveries of the PLATO mission, we
present a simple method for checking the plausibility of this mechanism for a
given exoplanet.
  Methods. Analytical formulas give the probability density function of the
relevant precession harmonics of the planet. For each harmonic, simple criteria
set the moon mass and other properties required for the mechanism to operate.
  Results. We applied this methodology to HIP 41378 f, and we show that in
order to reproduce the observed configuration, a hypothetical former moon
should have had a moon-to-planet mass ratio of a few times 1e-4 (i.e. roughly
the mass of our Moon) and have migrated over a distance of a few planet's radii
on a gigayear timescale. These orders of magnitude match the properties of
moons expected to exist around gaseous exoplanets.
  Conclusions. We conclude that the migration of a former moon is a viable
formation pathway for the proposed ring and tilt of HIP 41378 f. This example
strengthens the ring hypothesis and motivates its application to other targets.


  Visual Question Answering (VQA) is one of the most important tasks in
autonomous driving, which requires accurate recognition and complex situation
evaluations. However, datasets annotated in a QA format, which guarantees
precise language generation and scene recognition from driving scenes, have not
been established yet. In this work, we introduce Markup-QA, a novel dataset
annotation technique in which QAs are enclosed within markups. This approach
facilitates the simultaneous evaluation of a model's capabilities in sentence
generation and VQA. Moreover, using this annotation methodology, we designed
the NuScenes-MQA dataset. This dataset empowers the development of vision
language models, especially for autonomous driving tasks, by focusing on both
descriptive capabilities and precise QA. The dataset is available at
https://github.com/turingmotors/NuScenes-MQA.


  We consider two consecutive eons $\hat{M}$ and $\check{M}$ from Penrose's
Conformal Cyclic Cosmology and study how the matter content of the past eon
($\hat{M}$) determines the matter content of the present eon ($\check{M}$) by
means of the reciprocity hypothesis. We assume that the only matter content in
the final stages of the past eon is a spherical wave described by Einstein's
equations with the pure radiation energy momentum tensor $$\hat{T}^{ij} =
\hat{\Phi}K^iK^j, \quad \hat{g}_{ij} K^iK^j = 0,$$ and with cosmological
constant $\hat{\Lambda}$ . We solve these Einstein's equations associating to
$\hat{M}$ the metric $\hat{g}=t^{-2}\big(-d t^2+h_t\big)$, which is a
Lorentzian analog of the Poincar\'e-Einstein metric known from the theory of
conformal invariants. The solution is obtained under the assumption that the
3-dimensional conformal structure $[h]$ on the $\mathscr{I}^+$ of $\hat{M}$ is
flat, that the metric $\hat{g}$ admits a power series expansion in the time
variable $t$, and that $h_0\in [h]$. Such solution depends on one real
arbitrary function of the radial variable $r$. Applying the reciprocal
hypothesis, $\hat{g}\to \check{g}=t^4\hat{g}$, we show that the new eon
$(\check{M},\check{g})$ created from the one containing a single spherical
wave, is filled at its initial state with three types of radiation: (i) the
damped spherical wave which continues its life from the previous eon, (ii) the
in-going spherical wave obtained as a result of a collision of the wave from
the past eon with the Bang hypersurface and (3) randomly scattered waves that
could be interpreted as perfect fluid with the energy density $\check{\rho}$
and the isotropic pressure $\check{p}$ such that
$\check{p}=\tfrac13\check{\rho}$.


  We prove some regularity properties (convexity, closedness, compactness and
preservation of upper hemicontinuity) for distribution and regular conditional
distribution of correspondences under the nowhere equivalence condition. We
show the necessity of such a condition for any of these properties to hold. As
an application, we demonstrate that the nowhere equivalence condition is
satisfied on the underlying agent space if and only if pure-strategy Nash
equilibria exist in general large games with any fixed uncountable compact
action space.


  Despite the success of deep neural networks on facial action unit (AU)
detection, better performance depends on a large number of training images with
accurate AU annotations. However, labeling AU is time-consuming, expensive, and
error-prone. Considering AU detection and facial expression recognition (FER)
are two highly correlated tasks, and facial expression (FE) is relatively easy
to annotate, we consider learning AU detection and FER in a multi-task manner.
However, the performance of the AU detection task cannot be always enhanced due
to the negative transfer in the multi-task scenario. To alleviate this issue,
we propose a Meta Auxiliary Learning method (MAL) that automatically selects
highly related FE samples by learning adaptative weights for the training FE
samples in a meta learning manner. The learned sample weights alleviate the
negative transfer from two aspects: 1) balance the loss of each task
automatically, and 2) suppress the weights of FE samples that have large
uncertainties. Experimental results on several popular AU datasets demonstrate
MAL consistently improves the AU detection performance compared with the
state-of-the-art multi-task and auxiliary learning methods. MAL automatically
estimates adaptive weights for the auxiliary FE samples according to their
semantic relevance with the primary AU detection task.


  By analyzing a large data set of daily returns with data clustering
technique, we identify economic sectors as clusters of assets with a similar
economic dynamics. The sector size distribution follows Zipf's law. Secondly,
we find that patterns of daily market-wide economic activity cluster into
classes that can be identified with market states. The distribution of
frequencies of market states shows scale-free properties and the memory of the
market state process extends to long times ($\sim 50$ days). Assets in the same
sector behave similarly across states. We characterize market efficiency by
analyzing market's predictability and find that indeed the market is close to
being efficient. We find evidence of the existence of a dynamic pattern after
market's crashes.


  This paper is part of the Global Income Dynamics Project cross-country
comparison of earnings inequality, volatility, and mobility. Using data from
the U.S. Census Bureau's Longitudinal Employer-Household Dynamics (LEHD)
infrastructure files we produce a uniform set of earnings statistics for the
U.S. From 1998 to 2019, we find U.S. earnings inequality has increased and
volatility has decreased. The combination of increased inequality and reduced
volatility suggest earnings growth differs substantially across different
demographic groups. We explore this further by estimating 12-year average
earnings for a single cohort of age 25-54 eligible workers. Differences in
labor supply (hours paid and quarters worked) are found to explain almost 90%
of the variation in worker earnings, although even after controlling for labor
supply substantial earnings differences across demographic groups remain
unexplained. Using a quantile regression approach, we estimate counterfactual
earnings distributions for each demographic group. We find that at the bottom
of the earnings distribution differences in characteristics such as hours paid,
geographic division, industry, and education explain almost all the earnings
gap, however above the median the contribution of the differences in the
returns to characteristics becomes the dominant component.


  In this paper we briefly review the most general form of GUP motivated from
nonlocal quantum mechanics discussed in detail by S. Masood, {\em et al}. We
apply the special limit of this GUP form to the one-dimension linear potential
to calculate the energy correction and the modified wave function. We also
discuss the deformation effect on one dimensional delta potential well and one
dimensional delta potential barrier. Moreover, we pay attention to the
deformation effect on one dimensional hydrogen atom and investigate the Stark
effect on the atom separately. Finally, we make a short conclusion.


  Viscoelastic material properties at high strain rates are needed to model
many biological and medical systems. Bubble cavitation can induce such strain
rates, and the resulting bubble dynamics are sensitive to the material
properties. Thus, in principle, these properties can be inferred via
measurements of the bubble dynamics. Estrada et al. (2018) demonstrated such
bubble-dynamic high-strain-rate rheometry by using least-squares shooting to
minimize the difference between simulated and experimental bubble radius
histories. We generalize their technique to account for additional
uncertainties in the model, initial conditions, and material properties needed
to uniquely simulate the bubble dynamics. Ensemble-based data assimilation
minimizes the computational expense associated with the bubble cavitation
model. We test an ensemble Kalman filter (EnKF), an iterative ensemble Kalman
smoother (IEnKS), and a hybrid ensemble-based 4D--Var method (En4D--Var) on
synthetic data, assessing their estimations of the viscosity and shear modulus
of a Kelvin--Voigt material. Results show that En4D--Var and IEnKS provide
better moduli estimates than EnKF. Applying these methods to the experimental
data of Estrada et al. (2018) yields similar material property estimates to
those they obtained, but provides additional information about uncertainties.
In particular, the En4D--Var yields lower viscosity estimates for some
experiments, and the dynamic estimators reveal a potential mechanism that is
unaccounted for in the model, whereby the viscosity is reduced in some cases
due to material damage occurring at bubble collapse.


  Deep Neural Networks (DNNs) have recently made significant progress in many
fields. However, studies have shown that DNNs are vulnerable to adversarial
examples, where imperceptible perturbations can greatly mislead DNNs even if
the full underlying model parameters are not accessible. Various defense
methods have been proposed, such as feature compression and gradient masking.
However, numerous studies have proven that previous methods create detection or
defense against certain attacks, which renders the method ineffective in the
face of the latest unknown attack methods. The invisibility of adversarial
perturbations is one of the evaluation indicators for adversarial example
attacks, which also means that the difference in the local correlation of
high-frequency information in adversarial examples and normal examples can be
used as an effective feature to distinguish the two. Therefore, we propose an
adversarial example detection framework based on a high-frequency information
enhancement strategy, which can effectively extract and amplify the feature
differences between adversarial examples and normal examples. Experimental
results show that the feature augmentation module can be combined with existing
detection models in a modular way under this framework. Improve the detector's
performance and reduce the deployment cost without modifying the existing
detection model.


  For several decades the dominant techniques for integer linear programming
have been branching and cutting planes. Recently, several authors have
developed core point methods for solving symmetric integer linear programs
(ILPs). An integer point is called a core point if its orbit polytope is
lattice-free. It has been shown that for symmetric ILPs, optimizing over the
set of core points gives the same answer as considering the entire space.
Existing core point techniques rely on the number of core points (or
equivalence classes) being finite, which requires special symmetry groups. In
this paper we develop some new methods for solving symmetric ILPs (based on
outer approximations of core points) that do not depend on finiteness but are
more efficient if the group has large disjoint cycles in its set of generators.


  For single-particle nonrelativistic quantum mechanics, a Gamow state is an
eigenfunction of the Hamiltonian with complex eigenvalue. Gamow states are not
normalizable; they depend on time via the usual multiplier exp(-iEt)
supplemented by a cutoff at an expanding wavefront. Gamow states have been used
to extend nuclear shell models; they are to metastable states as normalizable
eigenfunctions are to bound states. In this paper we generalize Gamow states to
decays with multiple outgoing particles. We derive the exact form of the
expanding wavefront, even for relativistic outgoing particles. In the
non-relativistic limit we derive the exact form of the multi-particle Gamow
state contribution to the system propagator (Green's function).


  Scintillation efficiency of low-energy nuclear recoils in noble liquids plays
a crucial role in interpreting results from some direct searches for Weakly
Interacting Massive Particle (WIMP) dark matter. However, the cause of a
reduced scintillation efficiency relative to electronic recoils in noble
liquids remains unclear at the moment. We attribute such a reduction of
scintillation efficiency to two major mechanisms: 1) energy loss and 2)
scintillation quenching. The former is commonly described by Lindhard's theory
and the latter by Birk's saturation law. We propose to combine these two to
explain the observed reduction of scintillation yield for nuclear recoils in
noble liquids. Birk's constants $kB$ for argon, neon and xenon determined from
existing data are used to predict noble liquid scintillator's response to
low-energy nuclear recoils and low-energy electrons. We find that energy loss
due to nuclear stopping power that contributes little to ionization and
excitation is the dominant reduction mechanism in scintillation efficiency for
nuclear recoils, but that significant additional quenching results from the
nonlinear response of scintillation to the ionization density.


  In a recent paper (Phys. Rev. Lett. 102, 015301 (2009), arXiv:0810.3763)
Rousseau and Denteneer claim that an unconventional "super-Mott" (SM) phase is
realized by bosons trapped in an optical lattice close to a Feshbach resonance
with a molecular state. The supposed SM phase, observed via quantum Monte Carlo
(QMC) simulations of an atom-molecule Bose-Hubbard model, is an incompressible
phase developing spontaneous atomic/molecular supercurrents which are perfectly
anticorrelated. Here we show that the identification of this phase is based on
a misinterpretation of the estimators of superfluidity in QMC, which break down
in the presence of coherent atom/molecule conversion. Our conclusion is that
the supposed SM phase is in fact a fully normal insulator.


  In this paper we propose a Hamiltonian of the n-level system by making use of
generalized Pauli matrices.


  The counting of alternating tangles in terms of their crossing number, number
of external legs and connected components is presented here in a unified
framework using quantum field-theoretic methods applied to a matrix model of
colored links. The overcounting related to topological equivalence of diagrams
is removed by means of a renormalization scheme of the matrix model; the
corresponding ``renormalization equations'' are derived. Some particular cases
are studied in detail and solved exactly.


  Manipulation of thin sheets by folding and cutting offers opportunity to
engineer structures with novel mechanical properties, and to prescribe complex
force-displacement relationships via material elasticity in combination with
the trajectory imposed by the fold topology. We study the mechanics of cellular
Kirigami that rotates upon compression, which we call Flexigami; the addition
of diagonal cuts to an equivalent closed cell permits its reversible collapse
without incurring significant tensile strains in its panels. Using
finite-element modeling and experiment we show how the mechanics of flexigami
is governed by the coupled rigidity of the panels and hinges and we design
flexigami to achieve reversible force response ranging from smooth
mono-stability to sharp bi-stability. We then demonstrate the use of flexigami
to construct laminates with multi-stable behavior, a rotary-linear boom
actuator, and self-deploying cells with activated hinges. Advanced digital
fabrication methods can enable the practical use of flexigami and other
metamaterials that share its underlying principles, for applications such as
morphing structures, soft robotics and medical devices.


  Weakly interacting massive particles (WIMPs) are one of the leading
candidates for dark matter. Currently, the most promising method to detect many
different WIMP candidates is the direct detection of the recoil energy
deposited in a low-background laboratory detector due to elastic WIMP-nucleus
scattering. So far the usual procedure has been to predict the event rate of
direct detection of WIMPs based on some model(s) of the galactic halo. The aim
of our work is to invert this process. That is, we study what future direct
detection experiment can teach us about the WIMP halo. As the first step we
consider a time-averaged recoil spectrum, assuming that no directional
information exists. We develop a method to construct the (time-averaged)
one-dimensional velocity distribution function from this spectrum. Moments of
this function, such as the mean velocity and velocity dispersion of WIMPs, can
also be obtained directly from the recoil spectrum. The only input needed in
addition to a measured recoil spectrum is the mass of the WIMP; no information
about the scattering cross section or WIMP density is required.


  Entity linking is a standard component in modern retrieval system that is
often performed by third-party toolkits. Despite the plethora of open source
options, it is difficult to find a single system that has a modular
architecture where certain components may be replaced, does not depend on
external sources, can easily be updated to newer Wikipedia versions, and, most
important of all, has state-of-the-art performance. The REL system presented in
this paper aims to fill that gap. Building on state-of-the-art neural
components from natural language processing research, it is provided as a
Python package as well as a web API. We also report on an experimental
comparison against both well-established systems and the current
state-of-the-art on standard entity linking benchmarks.


  We present a general framework to model strategic aspects and stable and fair
resource allocations in networks via variants and generalizations of path
coalitional games. In these games, a coalition of edges or vertices is
successful if it can enable an s-t path. We present polynomial-time algorithms
to compute and verify least core payoffs of cost-based generalizations of path
coalitional games and their duals, thereby settling a number of open problems.
The least core payoffs of path coalitional games are completely characterized
and a polynomial-time algorithm for computing the nucleolus of edge path
coalitional games on undirected series-parallel graphs is presented.


  Using conformal invariance of gravitational waves, we show that for a luminal
modified gravity theory, the gravitational-wave propagation and luminosity
distance are the same as in general relativity. The relation between the
gravitational-wave and electromagnetic-wave luminosity distance gets however
modified for electromagnetism minimally coupled to the Jordan frame metric.
Using effective field theory we show that the modified relation obtained for
luminal theories is also valid for non-luminal theories with Jordan frame
matter-gravity coupling.We generalise our analysis to a time-dependent speed of
gravitational waves with matter minimally coupled to either the Jordan or
Einstein frame metrics.


  A cosmological first order electroweak phase transition could explain the
origin of the matter-antimatter asymmetry in the Universe. Such a phase
transition does not occur in the Standard Model, while it becomes possible with
the existence of a second Higgs doublet in Nature. We obtain the properties of
the new scalars $H_0$, $A_0$ and $H^{\pm}$ that lead to such a phase
transition, showing that its characteristic signature at LHC would be the
observation of the decay $A_0 \rightarrow H_0 Z$. We analyze the LHC search
prospects for this decay in the $\ell \ell b\bar{b}$ and $\ell \ell W^{+}
W^{-}$ final states, showing that either one is promising at the early stages
of the 14 TeV run.


  In this note we (in particular) prove an almost sure invariance principle
(ASIP) for non-stationary and uniformly bounded sequences of random variables
which are exponentially fast $\phi$-mixing. The obtained rate is of order
$o(V_n^{\frac14+\del})$ for an arbitrary $\del>0$, where $V_n$ is the variance
of the underlying partial sums $S_n$. For certain classes of inhomogeneous
Markov chains we also prove a vector-valued ASIP with similar rates.


  We study McKay's observation on the Monster simple group, which relates the
2A-involutions of the Monster simple group to the extended E_8 diagram, using
the theory of vertex operator algebras (VOAs). We first consider the
sublattices L of the E_8 lattice obtained by removing one node from the
extended E_8 diagram at each time. We then construct a certain coset (or
commutant) subalgebra U associated with L in the lattice VOA V_{\sqrt{2}E_8}.
There are two natural conformal vectors of central charge 1/2 in U such that
their inner product is exactly the value predicted by Conway. The Griess
algebra of U coincides with the algebra described in Conway's paper. There is a
canonical automorphism of U of order |E_8/L|. Such an automorphism can be
extended to the Leech lattice VOA V_\Lambda and it is in fact a product of two
Miyamoto involutions. In the sequel [LYY] to this article we shall develop the
representation theory of $U$. It is expected that if U is actually contained in
the Moonshine VOA V^\natural, the product of two Miyamoto involutions is in the
desired conjugacy class of the Monster simple group.


  We establish new results and introduce new methods in the theory of
measurable orbit equivalence, using bounded cohomology of group
representations. Our rigidity statements hold for a wide (uncountable) class of
groups arising from negative curvature geometry. Amongst our applications are
(a) measurable Mostow-type rigidity theorems for products of negatively curved
groups; (b) prime factorization results for measure equivalence; (c)
superrigidity for orbit equivalence; (d) the first examples of continua of type
II_1 equivalence relations with trivial outer automorphism group that are
mutually not stably isomorphic.


  A data-driven block thresholding procedure for wavelet regression is proposed
and its theoretical and numerical properties are investigated. The procedure
empirically chooses the block size and threshold level at each resolution level
by minimizing Stein's unbiased risk estimate. The estimator is sharp adaptive
over a class of Besov bodies and achieves simultaneously within a small
constant factor of the minimax risk over a wide collection of Besov Bodies
including both the ``dense'' and ``sparse'' cases. The procedure is easy to
implement. Numerical results show that it has superior finite sample
performance in comparison to the other leading wavelet thresholding estimators.


  We propose a simple explanation for the connection between chiral symmetry
restoration and deconfinement in QCD at high temperature. In the Higgs
description of the QCD vacuum both spontaneous chiral symmetry breaking and
effective gluon masses are generated by the condensate of a color octet
quark-antiquark pair. The transition to the high temperature state proceeds by
the melting of this condensate. Quarks and gluons become (approximately)
massless at the same critical temperature. For instanton-dominated effective
multiquark interactions and three light quarks with equal mass we find a first
order phase transition at a critical temperature around 170 MeV.


  Predictive biomarkers of treatment response are lacking for metastatic clear
cell renal cell carcinoma (ccRCC), a tumor type that is treated with
angiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a
HIF2 inhibitor. The Angioscore, an RNA-based quantification of angiogenesis, is
arguably the best candidate to predict anti-angiogenic (AA) response. However,
the clinical adoption of transcriptomic assays faces several challenges
including standardization, time delay, and high cost. Further, ccRCC tumors are
highly heterogenous, and sampling multiple areas for sequencing is impractical.
Here we present a novel deep learning (DL) approach to predict the Angioscore
from ubiquitous histopathology slides. To overcome the lack of
interpretability, one of the biggest limitations of typical DL models, our
model produces a visual vascular network which is the basis of the model's
prediction. To test its reliability, we applied this model to multiple cohorts
including a clinical trial dataset. Our model accurately predicts the RNA-based
Angioscore on multiple independent cohorts (spearman correlations of 0.77 and
0.73). Further, the predictions help unravel meaningful biology such as
association of angiogenesis with grade, stage, and driver mutation status.
Finally, we find our model can predict response to AA therapy, in both a
real-world cohort and the IMmotion150 clinical trial. The predictive power of
our model vastly exceeds that of CD31, a marker of vasculature, and nearly
rivals the performance (c-index 0.66 vs 0.67) of the ground truth RNA-based
Angioscore at a fraction of the cost. By providing a robust yet interpretable
prediction of the Angioscore from histopathology slides alone, our approach
offers insights into angiogenesis biology and AA treatment response.


  We show that if a Banach space X has the weak fixed point property for
nonexpansive mappings and Y has the generalized Gossez-Lami Dozo property or is
uniformly convex in every direction, then the direct sum of X and Y with a
strictly monotone norm has the weak fixed point property. The result is new
even if Y is finite-dimensional.


  ALDO2 is a multi-function, adjustable, low dropout linear regulator designed
in onsemi I3T80 0.35~$\mu$m HV CMOS technology for use in HEP detectors that
adopt silicon photomultipliers (SiPMs). The chip features four independent
regulators, two low voltage channels (max 3.3 V) used to filter and stabilize
the power supply of front-end chips, and two HV channels (max 70 V),
specifically designed to provide the bias voltage to arrays of SiPMs. Each
regulator can be independently shut down and is protected for over-current and
over-temperature. The HV regulators also implement a circuit to monitor the
bias current of the SiPM arrays, allowing to perform I-V curves and thus to
fine-tune the working point of the SiPM arrays during the detector lifetime.
The chip adopts radiation hardening techniques and has been fully qualified up
to a TID of 20 Mrad, a 1-MeV-equivalent neutron fluence of $10^{15}$ cm$^{-2}$,
and with heavy ions up to 40 MeV cm$^2$ mg$^{-1}$ LET and $10^{10}$ cm$^{-2}$
cumulative fluence. The chip will be installed in two CMS detectors in the
HL-LHC phase, the Barrel Timing Detector (BTL) and the High Granularity
Calorimeter (HGCAL).


  We investigate numerically and analytically the heating process in ergodic
clusters of interacting spins 1/2 subjected to periodic pulses of external
magnetic field. Our findings indicate that there is a threshold for the pulse
strength below which the heating is suppressed. This threshold decreases with
the increase of the cluster size, approaching zero in the thermodynamic limit;
yet it should be observable in clusters with fairly large Hilbert spaces. We
obtain the above threshold quantitatively as a condition for the breakdown of
the golden rule in the second-order perturbation theory. It is caused by the
phenomenon of dynamic localization.


  There has recently been great progress in automatic segmentation of medical
images with deep learning algorithms. In most works observer variation is
acknowledged to be a problem as it makes training data heterogeneous but so far
no attempts have been made to explicitly capture this variation. Here, we
propose an approach capable of mimicking different styles of segmentation,
which potentially can improve quality and clinical acceptance of automatic
segmentation methods. In this work, instead of training one neural network on
all available data, we train several neural networks on subgroups of data
belonging to different segmentation variations separately. Because a priori it
may be unclear what styles of segmentation exist in the data and because
different styles do not necessarily map one-on-one to different observers, the
subgroups should be automatically determined. We achieve this by searching for
the best data partition with a genetic algorithm. Therefore, each network can
learn a specific style of segmentation from grouped training data. We provide
proof of principle results for open-sourced prostate segmentation MRI data with
simulated observer variations. Our approach provides an improvement of up to
23% (depending on simulated variations) in terms of Dice and surface Dice
coefficients compared to one network trained on all data.


  High-time-resolution X-ray observations of compact objects provide direct
access to strong field gravity, black hole masses and spins, and the equation
of state of ultra-dense matter. LOFT, the large observatory for X-ray timing,
is specifically designed to study the very rapid X-ray flux and spectral
variability that directly probe the motion of matter down to distances very
close to black holes and neutron stars. A 10 m^2-class instrument in
combination with good spectral resolution (<260 eV @ 6 keV) is required to
exploit the relevant diagnostics and holds the potential to revolutionise the
study of collapsed objects in our Galaxy and of the brightest supermassive
black holes in active galactic nuclei. LOFT will carry two main instruments: a
Large Area Detector (LAD), to be built at MSSL/UCL with the collaboration of
the Leicester Space Research Centre for the collimator) and a Wide Field
Monitor (WFM). The ground-breaking characteristic of the LAD (that will work in
the energy range 2-30 keV) is a mass per unit surface in the range of ~10
kg/m^2, enabling an effective area of ~10 m^2 (@10 keV) at a reasonable weight
and improving by a factor of ~20 over all predecessors. This will allow timing
measurements of unprecedented sensitivity, allowing the capability to measure
the mass and radius of neutron stars with ~5% accuracy, or to reveal blobs
orbiting close to the marginally stable orbit in active galactic nuclei. In
this contribution we summarise the characteristics of the LOFT instruments and
give an overview of the expectations for its capabilities.


  Development of optimal control procedures for congested networks is a key
factor in maintaining efficient network utilization. The absence of congestion
control mechanism or its failure can lead to the lack of availability for
certain network segments, and in severe cases -- for the entire network. The
paper presents an analytical model describing the operation of the TCP Reno
congestion control algorithm in terms of differential calculus and queuing
systems. The purpose of this research is to explore the possibilities and ways
of increasing the virtual channel capacity utilization efficiency in a lossy
environment.


  Electrostatically defined quantum dots in bilayer graphene offer a promising
platform for spin qubits with presumably long coherence times due to low
spin-orbit coupling and low nuclear spin density. We demonstrate two different
experimental approaches to measure the decay times of excited states. The first
is based on direct current measurements through the quantum device. Pulse
sequences are applied to control the occupation of ground and excited states.
We observe a lower bound for the excited state decay on the order of hundred
microseconds. The second approach employs a capacitively coupled charge sensor
to study the time dynamics of the excited state using the Elzerman technique.
We find that the relaxation time of the excited state is of the order of
milliseconds. We perform single-shot readout of our two-level system with a
visibility of $87.1\%$, which is an important step for developing a quantum
information processor in graphene.


  We verify the Kosterlitz Thouless scenario for three different SOS
(solid-on-solid) models, including the dual transforms of XY-models with
Villain and with cosine action. The method is based on a matching of the
renormalization group (RG) flow of the candidate models with the flow of a bona
fide KT model, the exactly solvable BCSOS model. We obtain high precision
estimates for the critical couplings and other non-universal quantities.


  Identifying, measuring and reporting lesions accurately and comprehensively
from patient CT scans are important yet time-consuming procedures for
physicians. Computer-aided lesion/significant-findings detection techniques are
at the core of medical imaging, which remain very challenging due to the
tremendously large variability of lesion appearance, location and size
distributions in 3D imaging. In this work, we propose a novel deep anchor-free
one-stage VULD framework that incorporates (1) P3DC operators to recycle the
architectural configurations and pre-trained weights from the off-the-shelf 2D
networks, especially ones with large capacities to cope with data variance, and
(2) a new SPR method to effectively regress the 3D lesion spatial extents by
pinpointing their representative key points on lesion surfaces. Experimental
validations are first conducted on the public large-scale NIH DeepLesion
dataset where our proposed method delivers new state-of-the-art quantitative
performance. We also test VULD on our in-house dataset for liver tumor
detection. VULD generalizes well in both large-scale and small-sized tumor
datasets in CT imaging.


  In this paper, the deconfinement and chiral restoration transitions in strong
magnetic field is realized at zero temperature in the Polyakov
Nambu$-$Jona-Lasinio model. We provide the thermodynamic treatment to mimic the
deconfinement phase transition at zero temperature together with the entangled
scalar and vector interactions coupled with the Polyakov loop. The magnetic
catalysis is found by a rising behavior of the critical chemical potential for
the first-order deconfinement phase transition. While the magnetic catalysis on
the chiral restoration could convert to inverse magnetic catalysis under the
running coupling interaction ansatz. Furthermore, the stronger magnetic field
makes the possible quarkyonic phase window to be enlarged under the running
coupling interaction.


  We have studied the $J/\psi\phi$ mass distribution of the process $B^+\to
J/\psi\phi K^+$ from the threshold to about 4250 MeV, by considering the
contribution of the $X(4140)$ with a narrow width, together with the $X(4160)$
state. Our results show that the cusp structure at the $D^*_s\bar{D}^*_s$
threshold is tied to the molecular nature of the $X(4160)$ state.


  A strong X-ray illumination of the optical star atmosphere in Her X-1,
asymmetric because of a partial shadowing by the tilted twisted accretion disk
around central neutron star, leads to the formation of matter flows coming out
of the orbital plane and crossing the line of sight before entering the disk.
We suggest that the absorption of X-ray emission by this flow leads to the
formation of pre-eclipse and anomalous dips of type I. These dips are observed
during several orbits after turn-on both in the main-on and short-on state.
Almost coherent action of tidal torques and matter streams enhances the disk
wobbling which causes the disk edge to shield the X-ray source after the
turn-on. Anomalous dips of type II and post-eclipse recovery appear due to this
process only on the first orbit after turn-on.


  The investigation of directed acyclic graphs (DAGs) encoding the same Markov
property, that is the same conditional independence relations of multivariate
observational distributions, has a long tradition; many algorithms exist for
model selection and structure learning in Markov equivalence classes. In this
paper, we extend the notion of Markov equivalence of DAGs to the case of
interventional distributions arising from multiple intervention experiments. We
show that under reasonable assumptions on the intervention experiments,
interventional Markov equivalence defines a finer partitioning of DAGs than
observational Markov equivalence and hence improves the identifiability of
causal models. We give a graph theoretic criterion for two DAGs being Markov
equivalent under interventions and show that each interventional Markov
equivalence class can, analogously to the observational case, be uniquely
represented by a chain graph called interventional essential graph (also known
as CPDAG in the observational case). These are key insights for deriving a
generalization of the Greedy Equivalence Search algorithm aimed at structure
learning from interventional data. This new algorithm is evaluated in a
simulation study.


  We find strong evidence for intermittency in forced two dimensional (2D)
turbulence in a flowing soap film experiment. In the forward enstrophy cascade
the structure function scaling exponents are nearly indistinguishable from 3D
studies. Intermittency corrections are present in the inverse energy cascade as
well, but weaker. Stretched exponential tails of the velocity difference
probability distribution functions and shock like events at large velocity
differences also resemble 3D studies. For decaying turbulence, where only the
forward enstrophy cascade remains, all signs of intermittency disappear.


  The paper is devoted to numerical study of stability of nonlinear localized
modes ("gap solitons") for the spatially one-dimensional Gross-Pitaevskii
equation (1D GPE) with periodic potential and repulsive interparticle
interactions. We use the Evans function approach combined with the exterior
algebra formulation in order to detect and describe weak oscillatory
instabilities. We show that the simplest ("fundamental") gap solitons in the
first and in the second spectral gaps can undergo oscillatory instabilities for
certain values of the frequency parameter (i.e., the chemical potential). The
number of unstable eigenvalues and the associated instability rates are
described. Several stable and unstable more complex (non-fundamental) gap
solitons are also discussed. The results obtained from the Evans function
approach are independently confirmed using the direct numerical integration of
the GPE.


  A simple undirected graph is said to be {\em semisymmetric} if it is regular
and edge-transitive but not vertex-transitive. Every semisymmetric graph is a
bipartite graph with two parts of equal size. It was proved in [{\em J. Combin.
Theory Ser. B} {\bf 3}(1967), 215-232] that there exist no semisymmetric graphs
of order $2p$ and $2p^2$, where $p$ is a prime. The classification of
semisymmetric graphs of order $2pq$ was given in [{\em Comm. in Algebra} {\bf
28}(2000), 2685-2715], for any distinct primes $p$ and $q$. Our long term goal
is to determine all the semisymmetric graphs of order $2p^3$, for any prime
$p$. All these graphs $\G$ are divided into two subclasses: (I) $\Aut(\G)$ acts
unfaithfully on at least one bipart; and (II) $\Aut(\G)$ acts faithfully on
both biparts. This paper gives a group theoretical characterization for
Subclass (I) and based on this characterization, we shall give a complete
classification for this subclass in our further research.


  We address the total Lagrangian finite element implementation of the
Flory-Rehner free-energy function in the framework of a hyperelastic material
model. We explicitly give all the equations required to implement this material
model in an implicit nonlinear finite element analysis, particularly, we show
how to derive the so-called algorithmic or consistent linearized tangent
modulus in the Lagrangian description. Some analytical and numerical results
for different boundary-value problems are presented to validate the
implementation.


  We reformulate Dubrovin's almost duality of Frobenius structures to Saito
structures without metric. Then we formulate and study the existence and
uniqueness problem of the natural Saito structure on the orbit spaces of finite
complex reflection groups from the viewpoint of the almost duality. We give a
complete answer to the problem for the irreducible groups.


  In this paper, we prove that the dimension of the $p$-Selmer group for an
elliptic curve is controlled by certain analytic quantities associated with
modular symbols, which is conjectured by Kurihara.


  Differentially private graph analysis is a powerful tool for deriving
insights from diverse graph data while protecting individual information.
Designing private analytic algorithms for different graph queries often
requires starting from scratch. In contrast, differentially private synthetic
graph generation offers a general paradigm that supports one-time generation
for multiple queries. Although a rich set of differentially private graph
generation algorithms has been proposed, comparing them effectively remains
challenging due to various factors, including differing privacy definitions,
diverse graph datasets, varied privacy requirements, and multiple utility
metrics.
  To this end, we propose PGB (Private Graph Benchmark), a comprehensive
benchmark designed to enable researchers to compare differentially private
graph generation algorithms fairly. We begin by identifying four essential
elements of existing works as a 4-tuple: mechanisms, graph datasets, privacy
requirements, and utility metrics. We discuss principles regarding these
elements to ensure the comprehensiveness of a benchmark. Next, we present a
benchmark instantiation that adheres to all principles, establishing a new
method to evaluate existing and newly proposed graph generation algorithms.
Through extensive theoretical and empirical analysis, we gain valuable insights
into the strengths and weaknesses of prior algorithms. Our results indicate
that there is no universal solution for all possible cases. Finally, we provide
guidelines to help researchers select appropriate mechanisms for various
scenarios.


  The proximity profile in the spectra of z~3 quasars, where fluxes extend
blueward of the He II Lya wavelength 304 (1+z) A, is one of the most important
spectral features in the study of the intergalactic medium. Based on the HST
spectra of 24 He II quasars, we find that the majority of them display a
proximity profile, corresponding to an ionization radius as large as 20 Mpc in
the source's rest frame. In comparison with those in the H i spectra of the
quasars at z~6, the He II proximity effect is more prominent and is observed
over a considerably longer period of reionization. The He II proximity zone
sizes decrease at higher redshifts, particularly at z > 3.3. This trend is
similar to that for H I, signaling an onset of He II reionization at z~4.
  For quasar SDSS1253+6817 (z=3.48), the He II absorption trough displays a
gradual decline and serves a good case for modeling the He II reionization. To
model such a broad profile requires a quasar radiation field whose distribution
between 4 and 1 Rydberg is considerably harder than normally assumed. The UV
continuum of this quasar is indeed exceptionally steep, and the He II
ionization level in the quasar vicinity is higher than the average level in the
intergalactic medium. These results are evidence that a very hard EUV continuum
from this quasar produces a large ionized zone around it.
  Distinct exceptions are the two brightest He II quasars at z~2.8, for which
no significant proximity profile is present, possibly implying that they are
young.


  Most of the open-domain dialogue models tend to perform poorly in the setting
of long-term human-bot conversations. The possible reason is that they lack the
capability of understanding and memorizing long-term dialogue history
information. To address this issue, we present a novel task of Long-term Memory
Conversation (LeMon) and then build a new dialogue dataset DuLeMon and a
dialogue generation framework with Long-Term Memory (LTM) mechanism (called
PLATO-LTM). This LTM mechanism enables our system to accurately extract and
continuously update long-term persona memory without requiring multiple-session
dialogue datasets for model training. To our knowledge, this is the first
attempt to conduct real-time dynamic management of persona information of both
parties, including the user and the bot. Results on DuLeMon indicate that
PLATO-LTM can significantly outperform baselines in terms of long-term dialogue
consistency, leading to better dialogue engagingness.


  Gaussian fermionic matrix product states (GfMPS) form a class of ansatz
quantum states for 1d systems of noninteracting fermions. We show, for a simple
critical model of free hopping fermions, that: (i) any GfMPS approximation to
its ground state must have bond dimension scaling superpolynomially with the
system size, whereas (ii) there exists a non-Gaussian fermionic MPS
approximation to this state with polynomial bond dimension. This proves that,
in general, imposing Gaussianity at the level of the tensor network may
significantly alter its capability to efficiently approximate critical Gaussian
states. We also provide numerical evidence that the required bond dimension is
subexponential, and thus can still be simulated with moderate resources.


  Motivated by applications to probability and mathematical finance, we
consider a parabolic partial differential equation on a half-space whose
coefficients are suitably Holder continuous and allowed to grow linearly in the
spatial variable and which become degenerate along the boundary of the
half-space. We establish existence and uniqueness of solutions in weighted
Holder spaces which incorporate both the degeneracy at the boundary and the
unboundedness of the coefficients. In our companion article [arXiv:1211.4636],
we apply the main result of this article to show that the martingale problem
associated with a degenerate-elliptic partial differential operator is
well-posed in the sense of Stroock and Varadhan.


  Effective communication is an important skill for enabling information
exchange in multi-agent settings and emergent communication is now a vibrant
field of research, with common settings involving discrete cheap-talk channels.
Since, by definition, these settings involve arbitrary encoding of information,
typically they do not allow for the learned protocols to generalize beyond
training partners. In contrast, in this work, we present a novel problem
setting and the Quasi-Equivalence Discovery (QED) algorithm that allows for
zero-shot coordination (ZSC), i.e., discovering protocols that can generalize
to independently trained agents. Real world problem settings often contain
costly communication channels, e.g., robots have to physically move their
limbs, and a non-uniform distribution over intents. We show that these two
factors lead to unique optimal ZSC policies in referential games, where agents
use the energy cost of the messages to communicate intent. Other-Play was
recently introduced for learning optimal ZSC policies, but requires prior
access to the symmetries of the problem. Instead, QED can iteratively discovers
the symmetries in this setting and converges to the optimal ZSC policy.


  In high energy nuclear collisions, QCD jets deposit a large fraction of their
energy into the produced matter. It has been proposed that as such matter
behaves as a liquid with a very small viscosity, a fraction of this energy goes
into a collective excitation called the ``conical flow'', similar e.g. to the
sonic booms generated by the supersonic planes. In this work we study the
effect of time-dependent speed of sound on the development of the conical wave.
We show that the expansion of matter and the decrease of $c_s$ leads to an
increase of observable manifestations of the conical flow. We also show that if
the QCD phase transition is of the first order (and thus with vanishing speed
of sound in the mixed phase) the wave must split into two, with opposite
directions. We then argue that it is not the case experimentally, which
supports the conclusion that the QCD phase transition is $not$ of the first
order.


  We present non-perturbative solutions for multi-level quantum dot structures
coupled to interacting one-dimensional electrodes out of equilibrium. At a
special correlation strength the Hamiltonian can be mapped to the Kondo problem
which possesses a solvable Toulouse point, where all conductance and noise
properties can be calculated exactly. Special attention is paid to the fully
asymmetric setup when each dot level is coupled to only one of the leads and
the electron transport through the structure is accompanied by photon
absorption (emission). A relation between the optical spectra and the energy
dependent current noise power is established. Experimental implications of the
results, specifically for the Fano factor, the ratchet current, and field
emission via localised states, are discussed. In particular, we predict that
the peak in the ratchet current as function of the irradiation frequency splits
up in two due to correlation effects.


  In this paper, we present an iterative three-point method with memory based
on the family of King's methods to solve nonlinear equations. This proposed
method has eighth order convergence and costs only four function evaluations
per iteration which supports the Kung-Traub conjecture on the optimal order of
convergence. An acceleration of the convergence speed is achieved by an
appropriate variation of a free parameter in each step. This self accelerator
parameter is estimated using Newton's interpolation polynomial of fourth
degree. The order of convergence is increased from 8 to 12 without any extra
function evaluation. Consequently, this method, possesses a high computational
efficiency. Finally, a numerical comparison of the proposed method with related
methods shows its effectiveness and performance in high precision computations.


  Using several source models, we analyze the transverse momentum dependence of
HBT radii in the relativistic heavy-ion collisions. The results indicate that
the single-particle space-momentum angle distribution plays an important role
in the transverse momentum dependence of HBT radii. In a cylinder source, we
use several formulas to describe the transverse momentum dependence of HBT
radii and the single-particle space-momentum angle distribution. We also make a
numerical connection between them in the transverse plane.


  Probability interference is a fundamental characteristic of quantum
mechanics. In this paper we attempt to show with the help of some examples,
where this fundamental trait of quantum physics can be found back in a social
science environment. In order to support our thesis that interference can
possibly be found back in many other macro-scopic areas, we proceed in setting
up an experimental test.


  Ultra-dense heterogeneous networks (Ud-HetNets) have been put forward to
improve the network capacity for next-generation wireless networks. However,
counter to the 5G vision, ultra-dense deployment of networks would
significantly increase energy consumption and thus decrease network energy
efficiency suffering from the conventional worst-case network design
philosophy. This problem becomes particularly severe when Ud-HetNets meet big
data because of the traditional reactive request-transmit service mode. In view
of these, this article first develops a big-data-aware artificial intelligent
based framework for energy-efficient operations of Ud-HetNets. Based on the
framework, we then identify four promising techniques, namely big data
analysis, adaptive base station operation, proactive caching, and
interference-aware resource allocation, to reduce energy cost on both large and
small scales. We further develop a load-aware stochastic optimization approach
to show the potential of our proposed framework and techniques in energy
conservation. In a nutshell, we devote to constructing green Ud-HetNets of big
data with the abilities of learning and inferring by improving the flexibility
of control from worst-case to adaptive design and shifting the manner of
services from reactive to proactive modes.


  We present a concise review of the recent theoretical progress concerning the
standard model calculations of the rare K_L -> pi^0 nu anti-nu, K^+ -> pi^+ nu
anti-nu (gamma), anti-B -> X_s gamma, and anti-B -> X_s l^+ l^- decays. The
current status and future of the model-independent analysis of rare K- and
B-meson decays within constrained minimal-flavor-violation is also briefly
discussed.


  We have used the angular Autocorrelation Function (AcF) in a pointing with
Chandra's ACIS-S instrument. However, we determined that the contribution of
Chandra's background below 1 keV is too high to achieve reasonable sensitivity.


  We consider aspects of the noncommutative approach to the standard model
based on the spectral action principle. We show that as a consequence of the
incorporation of the Clifford structures in the formalism, the spectral action
contains an extended scalar sector, with respect to the minimal Standard Model.
This may have interesting phenomenological consequences. Some of these new
scalar fields carry both weak isospin and colour indexes. We calculate the new
terms in spectral action due to the presence of these fields. Our analysis
demonstrates that the fermionic doubling in the noncommutative geometry is not
just a presence of spurious degrees of freedom, but it is an interesting and
peculiar property of the formalism, which leads to physically valuable
conclusions. Some of the new fields do not contribute to the physical fermionic
action, but they appear in the bosonic spectral action. Their contributions to
the Dirac operator correspond to couplings with the spurious fermions, which
are projected out.


  In this paper, we study the regular geometric behavior of the mean curvature
flow (MCF) of submanifolds in the standard Gaussian metric space $({\mathbb
R}^{m+p},e^{-|x|^2/m}\ol g)$ where $({\mathbb R}^{m+p},\ol g)$ is the standard
Euclidean space and $x\in{\mathbb R}^{m+p}$ denotes the position vector. Note
that, as a special Riemannian manifold, $({\mathbb R}^{m+p},e^{-|x|^2/m}\ol g)$
has an unbounded curvature. Up to a family of diffeomorphisms on $M^m$, the
mean curvature flow we considered here turns out to be equivalent to a special
variation of the ``{\em conformal mean curvature flow}\,'' which we have
introduced previously. The main theorem of this paper indicates, geometrically,
that any immersed compact submanifold in the standard Gaussian space, with the
square norm of the position vector being not equal to $m$, will blow up at a
finite time under the mean curvature flow, in the sense that either the
position or the curvature blows up to infinity; Moreover, by this main theorem,
the interval $[0,T)$ of time in which the flowing submanifolds keep regular has
some certain optimal upper bound, and it can reach the bound if and only if the
initial submanifold either shrinks to the origin or expands uniformly to
infinity under the flow. Besides the main theorem, we also obtain some other
interesting conclusions which not only play their key roles in proving the main
theorem but also characterize in part the geometric behavior of the flow, being
of independent significance.


  Hyperfine constants and anomalies of ground as well as few low lying excited
states of $^{113,115,117}$In III are studied with highly correlated
relativistic coupled-cluster theory. The ground state hyperfine splitting of
$^{115}$In III is estimated to be 106.8 GHz. A shift of almost 1.9 GHz of the
above frequency has been calculated due to modified nuclear dipole moment. This
splitting result shows its applicability as communication band and frequency
standards at $10^{-11}$ sec. Correlations study of hyperfine constants
indicates a few distinct features of many-body effects in the wave-functions in
and near the nuclear region of this ion. Astrophysically important forbidden
transition amplitudes are estimated for the first time in the literature to our
knowledge. The calculated oscillator strengths of few allowed transitions are
compared with recent experimental and theoretical results wherever available.


  We report anomalous Hall effect (AHE) in single crystals of
quasi-two-dimensional Fe$_{3-x}$GeTe$_2$ ($x \approx 0.36$) ferromagnet grown
by the flux method which induces defects on Fe site and bad metallic
resistivity. Fe K-edge x-ray absorption spectroscopy was measured to provide
information on local atomic environment in such crystals. The dc and ac
magnetic susceptibility measurements indicate a second-stage transition below
119 K in addition to the paramagnetic to ferromagnetic transition at 153 K. A
linear scaling behavior between the modified anomalous Hall resistivity
$\rho_{xy}/\mu_0H_{eff}$ and longitudinal resistivity
$\rho_{xx}^2M/\mu_0H_{eff}$ implies that the AHE in Fe$_{3-x}$GeTe$_2$ should
be dominated by the intrinsic Karplus-Luttinger mechanism rather than the
extrinsic skew-scattering and side-jump mechanisms. The observed deviation in
the linear-M Hall conductivity $\sigma_{xy}^A$ below 30 K is in line with its
transport characteristic at low temperatures, implying the scattering of
conduction electrons due to magnetic disorder and the evolution of the Fermi
surface induced by possible spin-reorientation transition.


  Micro-solid oxide fuel cells based on thin films have strong potential for
use in portable power devices. However, devices based on silicon substrates
typically involve thin-film metallic electrodes which are unstable at high
temperatures. Devices based on bulk metal substrates overcome these
limitations, though performance is hindered by the challenge of growing
state-of-the-art epitaxial materials on metals. Here, we demonstrate for the
first time the growth of epitaxial cathode materials on metal substrates
(stainless steel) commercially supplied with epitaxial electrolyte layers (1.5
{um (Y2O3)0.15(ZrO2)0.85 (YSZ) + 50 nm CeO2). We create epitaxial mesoporous
cathodes of (La0.60Sr0.40)0.95Co0.20Fe0.80O3 (LSCF) on the substrate by growing
LSCF/MgO vertically aligned nanocomposite films by pulsed laser deposition,
followed by selectively etching out the MgO. To enable valid comparison with
the literature, the cathodes are also grown on single-crystal substrates,
confirming state-of-the-art performance with an area specific resistance of
100ohmegacm2 at 500dC and activation energy down to 0.97 eV. The work marks an
important step toward the commercialization of high-performance micro-solid
oxide fuel cells for portable power applications.


  The controlled transfer of particles from one site of a spatial lattice to
another is essential for many tasks in quantum information processing and
quantum communication. Arrays of semiconductor quantum dots and ultracold atoms
held in optical lattices, provide two means of studying coherent quantum
transport in well-controlled systems. In this work we study how to induce
long-range transfer between the two ends of a dimer chain, by coupling states
that are localized just on the chain's end-points. This has the appealing
feature that the transfer occurs only between the end-points -- the particle
does not pass through the intermediate sites -- making the transfer less
susceptible to decoherence. We first show how a repulsively bound-pair of
fermions, known as a doublon, can be transferred from one end of the chain to
the other via topological edge states. We then show how non-topological surface
states of the familiar Shockley or Tamm type can be used to produce a similar
form of transfer under the action of a periodic driving potential. Finally we
show that combining these effects can produce transfer by means of more exotic
topological effects, in which the driving field can be used to switch the
topological character of the edge states, as measured by the Zak phase. Our
results demonstrate how to induce long range transfer of strongly correlated
particles by tuning both topology and driving.


  Two simple, interpolatory-like linearizations are shown for the simple
pendulum which can be used for any initial amplitude.


  We consider the nonabelian sandpile model defined on directed trees by Ayyer,
Schilling, Steinberg and Thi\'ery (Commun. Math. Phys, 2013) and restrict it to
the special case of a one-dimensional lattice of $n$ sites which has open
boundaries and disordered hopping rates. We focus on the joint distribution of
the integrated currents across each bond simultaneously, and calculate its
cumulant generating function exactly. Surprisingly, the process conditioned on
seeing specified currents across each bond turns out to be a renormalised
version of the same process. We also remark on a duality property of the large
deviation function. Lastly, all eigenvalues and both Perron eigenvectors of the
tilted generator are determined.


  Structure-based drug design aims at generating high affinity ligands with
prior knowledge of 3D target structures. Existing methods either use
conditional generative model to learn the distribution of 3D ligands given
target binding sites, or iteratively modify molecules to optimize a
structure-based activity estimator. The former is highly constrained by data
quantity and quality, which leaves optimization-based approaches more promising
in practical scenario. However, existing optimization-based approaches choose
to edit molecules in 2D space, and use molecular docking to estimate the
activity using docking predicted 3D target-ligand complexes. The misalignment
between the action space and the objective hinders the performance of these
models, especially for those employ deep learning for acceleration. In this
work, we propose MolEdit3D to combine 3D molecular generation with optimization
frameworks. We develop a novel 3D graph editing model to generate molecules
using fragments, and pre-train this model on abundant 3D ligands for learning
target-independent properties. Then we employ a target-guided self-learning
strategy to improve target-related properties using self-sampled molecules.
MolEdit3D achieves state-of-the-art performance on majority of the evaluation
metrics, and demonstrate strong capability of capturing both target-dependent
and -independent properties.


  It is by now well-known that small adversarial perturbations can induce
classification errors in deep neural networks (DNNs). In this paper, we make
the case that sparse representations of the input data are a crucial tool for
combating such attacks. For linear classifiers, we show that a sparsifying
front end is provably effective against $\ell_{\infty}$-bounded attacks,
reducing output distortion due to the attack by a factor of roughly $K / N$
where $N$ is the data dimension and $K$ is the sparsity level. We then extend
this concept to DNNs, showing that a "locally linear" model can be used to
develop a theoretical foundation for crafting attacks and defenses.
Experimental results for the MNIST dataset show the efficacy of the proposed
sparsifying front end.


  The XMM-Newton observatory shows evidence, with a 11$\sigma$ confidence
level, for seasonal variation of the X-ray background in the near-Earth
environment in the 2-6 keV energy range (Fraser et al. 2014). The authors argue
that the observed seasonal variation suggests a possible link with dark matter.
We propose an explanation which involves the Axion Quark Nugget (AQN) dark
matter model. In our proposal, AQNs can cross the Earth and emit high energy
photons at their exit. We show that the emitted spectrum is consistent with
(Fraser et al. 2014), and that our calculation is not sensitive to the specific
details of the model. Our proposal predicts a large seasonal variation, on the
level of 20-25%, much larger than conventional dark matter models (1-10%).
Since the AQN emission spectrum extends up to $\sim $ 100 keV, well beyond the
keV sensitivity of XMM-Newton, we predict the AQN contribution to the hard
X-ray and $\gamma$-ray backgrounds in the Earth's environment. The Gamma-Ray
Burst Monitor (GBM) instrument, aboard the FERMI telescope, is sensitive to the
8 keV-40 MeV energy band. The NuSTAR (Nuclear Spectroscopic Telescope Array) is
a NASA space based X ray telescope which operates in the range 3 to 79 keV is
also sensitive to higher energy bands. We suggest that the multi-year archival
data from the GBM or NuSTAR could be used to search for a seasonal variation in
the near-Earth environment up to 100 keV as a future test of the AQN framework.