  Hawkes processes are a particularly interesting class of stochastic process
that have been applied in diverse areas, from earthquake modelling to financial
analysis. They are point processes whose defining characteristic is that they
'self-excite', meaning that each arrival increases the rate of future arrivals
for some period of time. Hawkes processes are well established, particularly
within the financial literature, yet many of the treatments are inaccessible to
one not acquainted with the topic. This survey provides background, introduces
the field and historical developments, and touches upon all major aspects of
Hawkes processes.


  Photoinduced nonequilibrium states can provide new insight into dynamical
properties of strongly correlated electron systems. One of the typical and
extensively studied systems is the half-filled one-dimensional extended Hubbard
model (1DEHM). Here, we propose that the supervised machine learning (ML) can
provide useful information for characterizing photoexcited states in 1DEHM.
Using entanglement spectra as a training dataset, we construct neural network.
Judging from the trained network, we find that bond-spin-density wave (BSDW)
order can be enhanced in photoexcited states if the frequency of a driving
pulse nearly resonates with gap. We separately calculate the time evolution of
local and non-local order parameters and confirm that the correlation functions
of BSDW are actually enhanced by photoexcitation as predicted by ML. The
successful prediction of BSDW demonstrates the advantage of ML to assist
characterizing photoexcited quantum states.


  Unidirectional radiation is of particular interest in high-power lasing and
optics. Commonly, however, it is difficult to achieve a unidirectional profile
in such a system without breaking reciprocity. Recently, assisted by
metamaterials without structural symmetry, antennas that radiate asymmetrically
have been developed, hence providing the possibility of achieving
unidirectionality. Nevertheless, it has been challenging to achieve extremely
high radiation asymmetry in such antennas. Here, we demonstrate that this
radiation asymmetry is further enhanced when magnetic plasmons are present in
the metamaterials. Experimentally, we show that a thin metamaterial with a
thickness of approximately {\lambda}_0/8 can exhibit a forward-to-backward
emission asymmetry of up to 1:32 without any optimization. Our work paves the
way for manipulating asymmetric radiation by means of metamaterials and may
have a variety of promising applications, such as directional optical and
quantum emitters, lasers, and absorbers.


  Financial markets are a complex dynamical system. The complexity comes from
the interaction between a market and its participants, in other words, the
integrated outcome of activities of the entire participants determines the
markets trend, while the markets trend affects activities of participants.
These interwoven interactions make financial markets keep evolving. Inspired by
stochastic recurrent models that successfully capture variability observed in
natural sequential data such as speech and video, we propose CLVSA, a hybrid
model that consists of stochastic recurrent networks, the sequence-to-sequence
architecture, the self- and inter-attention mechanism, and convolutional LSTM
units to capture variationally underlying features in raw financial trading
data. Our model outperforms basic models, such as convolutional neural network,
vanilla LSTM network, and sequence-to-sequence model with attention, based on
backtesting results of six futures from January 2010 to December 2017. Our
experimental results show that, by introducing an approximate posterior, CLVSA
takes advantage of an extra regularizer based on the Kullback-Leibler
divergence to prevent itself from overfitting traps.


  Recent advances in decentralized deep learning algorithms have demonstrated
cutting-edge performance on various tasks with large pre-trained models.
However, a pivotal prerequisite for achieving this level of competitiveness is
the significant communication and computation overheads when updating these
models, which prohibits the applications of them to real-world scenarios. To
address this issue, drawing inspiration from advanced model merging techniques
without requiring additional training, we introduce the Decentralized Iterative
Merging-And-Training (DIMAT) paradigm--a novel decentralized deep learning
framework. Within DIMAT, each agent is trained on their local data and
periodically merged with their neighboring agents using advanced model merging
techniques like activation matching until convergence is achieved. DIMAT
provably converges with the best available rate for nonconvex functions with
various first-order methods, while yielding tighter error bounds compared to
the popular existing approaches. We conduct a comprehensive empirical analysis
to validate DIMAT's superiority over baselines across diverse computer vision
tasks sourced from multiple datasets. Empirical results validate our
theoretical claims by showing that DIMAT attains faster and higher initial gain
in accuracy with independent and identically distributed (IID) and non-IID
data, incurring lower communication overhead. This DIMAT paradigm presents a
new opportunity for the future decentralized learning, enhancing its
adaptability to real-world with sparse and light-weight communication and
computation.


  As the revolutionary improvement being made on the performance of smartphones
over the last decade, mobile photography becomes one of the most common
practices among the majority of smartphone users. However, due to the limited
size of camera sensors on phone, the photographed image is still visually
distinct to the one taken by the digital single-lens reflex (DSLR) camera. To
narrow this performance gap, one is to redesign the camera image signal
processor (ISP) to improve the image quality. Owing to the rapid rise of deep
learning, recent works resort to the deep convolutional neural network (CNN) to
develop a sophisticated data-driven ISP that directly maps the phone-captured
image to the DSLR-captured one. In this paper, we introduce a novel network
that utilizes the attention mechanism and wavelet transform, dubbed AWNet, to
tackle this learnable image ISP problem. By adding the wavelet transform, our
proposed method enables us to restore favorable image details from RAW
information and achieve a larger receptive field while remaining high
efficiency in terms of computational cost. The global context block is adopted
in our method to learn the non-local color mapping for the generation of
appealing RGB images. More importantly, this block alleviates the influence of
image misalignment occurred on the provided dataset. Experimental results
indicate the advances of our design in both qualitative and quantitative
measurements. The code is available publically.


  A major challenge in computational research in 3D medical imaging is the lack
of comprehensive datasets. Addressing this issue, our study introduces CT-RATE,
the first 3D medical imaging dataset that pairs images with textual reports.
CT-RATE consists of 25,692 non-contrast chest CT volumes, expanded to 50,188
through various reconstructions, from 21,304 unique patients, along with
corresponding radiology text reports. Leveraging CT-RATE, we developed CT-CLIP,
a CT-focused contrastive language-image pre-training framework. As a versatile,
self-supervised model, CT-CLIP is designed for broad application and does not
require task-specific training. Remarkably, CT-CLIP outperforms
state-of-the-art, fully supervised methods in multi-abnormality detection
across all key metrics, thus eliminating the need for manual annotation. We
also demonstrate its utility in case retrieval, whether using imagery or
textual queries, thereby advancing knowledge dissemination. The open-source
release of CT-RATE and CT-CLIP marks a significant advancement in medical AI,
enhancing 3D imaging analysis and fostering innovation in healthcare.


  This work addresses the scarcity of annotated hyperspectral data required to
train deep neural networks. Especially, we investigate generative adversarial
networks and their application to the synthesis of consistent labeled spectra.
By training such networks on public datasets, we show that these models are not
only able to capture the underlying distribution, but also to generate
genuine-looking and physically plausible spectra. Moreover, we experimentally
validate that the synthetic samples can be used as an effective data
augmentation strategy. We validate our approach on several public
hyper-spectral datasets using a variety of deep classifiers.


  Foundation models have enormous potential in advancing Earth and climate
sciences, however, current approaches may not be optimal as they focus on a few
basic features of a desirable Earth and climate foundation model. Crafting the
ideal Earth foundation model, we define eleven features which would allow such
a foundation model to be beneficial for any geoscientific downstream
application in an environmental- and human-centric manner.We further shed light
on the way forward to achieve the ideal model and to evaluate Earth foundation
models. What comes after foundation models? Energy efficient adaptation,
adversarial defenses, and interpretability are among the emerging directions.


  It is a common practice in natural language processing to pre-train a single
model on a general domain and then fine-tune it for downstream tasks. However,
when it comes to Large Language Models, fine-tuning the entire model can be
computationally expensive, resulting in very intensive energy consumption. As a
result, several Parameter Efficient Fine-Tuning (PEFT) approaches were recently
proposed. One of the most popular approaches is low-rank adaptation (LoRA),
where the key insight is decomposing the update weights of the pre-trained
model into two low-rank matrices. However, the proposed approaches either use
the same rank value across all different weight matrices, which has been shown
to be a sub-optimal choice, or do not use any quantization technique, one of
the most important factors when it comes to a model's energy consumption. In
this work, we propose Bayesian-LoRA which approaches low-rank adaptation and
quantization from a Bayesian perspective by employing a prior distribution on
both quantization levels and rank values. As a result, B-LoRA is able to
fine-tune a pre-trained model on a specific downstream task, finding the
optimal rank values and quantization levels for every low-rank matrix. We
validate the proposed model by fine-tuning a pre-trained DeBERTaV3 on the GLUE
benchmark. Moreover, we compare it to relevant baselines and present both
qualitative and quantitative results, showing how the proposed approach is able
to learn optimal-rank quantized matrices. B-LoRA performs on par with or better
than the baselines while reducing the total number of bit operations by roughly
70% compared to the baseline methods.


  We report a new structure, called the Shielded Ohmic Contact (ShOC) rectifier
which utilizes trenches filled with a high barrier metal to shield an Ohmic
contact during the reverse bias. When the device is forward biased, the Ohmic
contact conducts with a low forward drop. However, when reverse biased, the
Ohmic contact is completely shielded by the high barrier Schottky contact
resulting in a low reverse leakage current. Two dimensional numerical
simulation is used to evaluate and explain the superior performance of the
proposed ShOC rectifier.


  In this proceeding we consider QCD radiative corrections to the production of
colourless high-mass systems in hadron collisions. At small transverse momentum
the logarithmically-enhanced contributions can be organized to all perturbative
orders by a universal resummation formula that depends on a single
process-dependent hard factor. We show that the hard factor is directly related
to the all-order virtual amplitude of the corresponding partonic process by a
universal (process independent) formula, which we explicitly evaluate up to
two-loop level. Once the next-to-next-to-leading order (NNLO) scattering
amplitude is available, the corresponding hard factor is directly determined.
It can be used in fully-exclusive perturbative calculations (via q$_T$
subtraction formalism) up to NNLO, in resummed calculations at full
next-to-next-to-leading logarithmic (NNLL) accuracy, and also, it's a necessary
ingredient to the next subsequent logarithmic order (N$^3$LL).


  We prove that free boundary incompressible Euler equations are locally well
posed in a class of solutions in which the interfaces can exhibit corners and
cusps. Contrary to what happens in all the previously known non-$C^1$ water
waves, the angle of these crests can change in time.


  Thin sheets that are forced at their boundaries develop a variety of shapes
aimed at minimising elastic energy by curving spontaneously in ways that break
the symmetry of the sheet and the forcing. Characterising such buckling
generally requires detailed analysis. Here, we follow a complementary approach,
applicable to axisymmetric systems. It aims to predict the qualitative nature
of any buckling using only the simple axisymmetric geometry of the initial
system. We view an axisymmetric deformation according to the displacement it
imposes on the radial lines making up the sheet. Thus, e.g., in-plane tensile
loads on a disk-shaped annulus move radial lines along their length. Any
compressional strain present in this deformed state then implies
symmetry-breaking buckling out of the original plane. Using only these radial
motions, we account for the buckling predicted by more detailed analysis: axial
wrinkling confined to the inner part of the annulus. A second type of imposed
deformation on radial lines is motion normal to the sheet. Considering the
strains induced by rotating these lines through some common angle $\alpha$, one
may similarly account for both the buckling of d-cones and that of curved
creases. Our framework thus unifies three previously unconnected buckled
shapes.


  We prove a simple equivalence between the virtual count of rational curves in
the total space of an anti-nef line bundle and the virtual count of rational
curves maximally tangent to a smooth section of the dual line bundle. We
conjecture a generalization to direct sums of line bundles.


  We describe a procedure for accurately determining luminosity distances to
Type Ia supernovae (SNe Ia) without knowledge of redshift. This procedure,
which may be used as an extension of any of the various distance determination
methods currently in use, is based on marginalizing over redshift, removing the
requirement of knowing $z$ a priori. We demonstrate that the Hubble diagram
scatter of distances measured with this technique is approximately equal to
that of distances derived from conventional redshift-specific methods for a set
of 60 nearby SNe Ia. This indicates that accurate distances for cosmological
SNe Ia may be determined without the requirement of spectroscopic redshifts,
which are typically the limiting factor for the number of SNe that modern
surveys can collect. Removing this limitation would greatly increase the number
of SNe for which current and future SN surveys will be able to accurately
measure distance. The method may also be able to be used for high-$z$ SNe Ia to
determine cosmological density parameters without redshift information.


  Let R be a commutative ring with identity and Specs(M) denote the set all
second submodules of an R-module M. In this paper, we construct and study a
sheaf of modules, denoted by O(N; M), on Specs(M) equipped with the dual
Zariski topology of M, where N is an R-module. We give a characterization of
the sections of the sheaf O(N; M) in terms of the ideal transform module. We
present some interrelations between algebraic properties of N and the sections
of O(N; M). We obtain some morphisms of sheaves induced by ring and module
homomorphisms.


  In this paper we analyze the spin-wave excitations (magnons) of an
inhomogeneous spin system within the Boltzmann-Gibbs framework and then connect
the results with the nonextensive approach (in the sense of Tsallis
statistics). Considering an equivalence between those two frameworks, we could
connect the entropic parameter q with moments of the distribution of exchange
integrals of the inhomogenous system. It ratifies the idea that the entropic
parameter is connected to the microscopic properties of the system.


  Using simulation to train robot manipulation policies holds the promise of an
almost unlimited amount of training data, generated safely out of harm's way.
One of the key challenges of using simulation, to date, has been to bridge the
reality gap, so that policies trained in simulation can be deployed in the real
world. We explore the reality gap in the context of learning a contextual
policy for multi-fingered robotic grasping. We propose a Grasping Objects
Approach for Tactile (GOAT) robotic hands, learning to overcome the reality gap
problem. In our approach we use human hand motion demonstration to initialize
and reduce the search space for learning. We contextualize our policy with the
bounding cuboid dimensions of the object of interest, which allows the policy
to work on a more flexible representation than directly using an image or point
cloud. Leveraging fingertip touch sensors in the hand allows the policy to
overcome the reduction in geometric information introduced by the coarse
bounding box, as well as pose estimation uncertainty. We show our learned
policy successfully runs on a real robot without any fine tuning, thus bridging
the reality gap.


  Learning a single universal policy that can perform a diverse set of
manipulation tasks is a promising new direction in robotics. However, existing
techniques are limited to learning policies that can only perform tasks that
are encountered during training, and require a large number of demonstrations
to learn new tasks. Humans, on the other hand, often can learn a new task from
a single unannotated demonstration. In this work, we propose the
Invariance-Matching One-shot Policy Learning (IMOP) algorithm. In contrast to
the standard practice of learning the end-effector's pose directly, IMOP first
learns invariant regions of the state space for a given task, and then computes
the end-effector's pose through matching the invariant regions between
demonstrations and test scenes. Trained on the 18 RLBench tasks, IMOP achieves
a success rate that outperforms the state-of-the-art consistently, by 4.5% on
average over the 18 tasks. More importantly, IMOP can learn a novel task from a
single unannotated demonstration, and without any fine-tuning, and achieves an
average success rate improvement of $11.5\%$ over the state-of-the-art on 22
novel tasks selected across nine categories. IMOP can also generalize to new
shapes and learn to manipulate objects that are different from those in the
demonstration. Further, IMOP can perform one-shot sim-to-real transfer using a
single real-robot demonstration.


  We study the asymptotic behaviour of the statistic (des+ides) which assigns
to an element w of a finite Coxeter group W the number of descents of w plus
the number of descents of its inverse. Our main result is a central limit
theorem for the probability distributions associated to this statistic. This
answers a question of Kahle-Stump and generalises work of Chatterjee-Diaconis,
\"Ozdemir and R\"ottger.


  We consider the scenario of classical communication over a finite-dimensional
quantum channel with memory using a separable-state input ensemble and local
output measurements. We propose algorithms for estimating the information rate
of such communication setups, along with algorithms for bounding the
information rate based on so-called auxiliary channels. Some of the algorithms
are extensions of their counterparts for (classical) finite-state-machine
channels. Notably, we discuss suitable graphical models for doing the relevant
computations. Moreover, the auxiliary channels are learned in a data-driven
approach; i.e., only input/output sequences of the true channel are needed, but
not the channel model of the true channel.


  The Lanczos algorithm for matrix tridiagonalisation suffers from strong
numerical instability in finite precision arithmetic when applied to evaluate
matrix eigenvalues. The mechanism by which this instability arises is well
documented in the literature. A recent application of the Lanczos algorithm
proposed by Bai, Fahey and Golub allows quadrature evaluation of inner products
of the form $\psi^\dagger g(A) \psi$. We show that this quadrature evaluation
is numerically stable and explain how the numerical errors which are such a
fundamental element of the finite precision Lanczos tridiagonalisation
procedure are automatically and exactly compensated in the Bai, Fahey and Golub
algorithm. In the process, we shed new light on the mechanism by which roundoff
error corrupts the Lanczos procedure


  We analyze the turbulence driven particle transport in Texas Helimak (K. W.
Gentle and Huang He, Plasma Sci. and Technology, 10, 284 (2008)), a toroidal
plasma device with one-dimensional equilibrium with magnetic curvature and
shear. Alterations on the radial electric field, through an external voltage
bias, change spectral plasma characteristics inducing a dominant frequency for
negative bias values and a broad band frequency spectrum for positive bias
values. For negative biased plasma discharges, the transport is high where the
waves propagate with phase velocities near the plasma flow velocity, an
indication that the transport is strongly affected by a wave particle resonant
interaction. On the other hand, for positive bias the plasma has a reversed
shear flow and we observe that the transport is almost zero in the shearless
radial region, an evidence of a transport barrier in this region.


  Hybrid systems comprising superconducting and semiconducting materials are
promising architectures for quantum computing. Superconductors induce
long-range interactions between the spin degrees of freedom of semiconducting
quantum dots. These interactions are widely anisotropic when the semiconductor
material has strong spin-orbit interactions. We show that this anisotropy is
tunable and enables fast and high-fidelity two-qubit gates between
singlet-triplet (ST) spin qubits. Our design is immune to leakage of the
quantum information into noncomputational states and removes always-on
interactions between the qubits, thus resolving key open challenges for these
architectures. Our ST qubits do not require additional technologically
demanding components nor fine-tuning of parameters. They operate at low
magnetic fields of a few millitesla and are fully compatible with
superconductors. By suppressing systematic errors in realistic devices, we
estimate infidelities below $10^{-3}$, which could pave the way toward
large-scale hybrid superconducting-semiconducting quantum processors.


  The rich polymorphism exhibited by inorganic/organic interfaces is a major
challenge for materials design. In this work we present a method to efficiently
explore the potential energy surface and predict the formation energies of
polymorphs and defects. This is achieved by training a machine learning model
on a list of only 100 candidate structures that are evaluated via
dispersion-corrected Density Functional Theory (DFT) calculations. We
demonstrate the power of this approach for tetracyanoethylene on Ag(100) and
explain the anisotropic ordering that is observed experimentally.


  Mobile Crowd Sensing (MCS) is the special case of crowdsourcing, which
leverages the smartphones with various embedded sensors and user's mobility to
sense diverse phenomenon in a city. Task allocation is a fundamental research
issue in MCS, which is crucial for the efficiency and effectiveness of MCS
applications. In this article, we specifically focus on the task allocation in
MCS systems. We first present the unique features of MCS allocation compared to
generic crowdsourcing, and then provide a comprehensive review for diversifying
problem formulation and allocation algorithms together with future research
opportunities.


  In this paper, we study distortion in the group $\mathcal A$ of Affine
Interval Exchange Transformations (AIET). We prove that any distorted element
$f$ of $\mathcal A$, has an iterate $f^ k$ that is conjugate by an element of
$\mathcal A$ to a product of infinite order restricted rotations, with pairwise
disjoint supports. As consequences we prove that no Baumslag-Solitar group,
$BS(m,n)$ with $\vert m \vert \neq \vert n \vert$, acts faithfully by elements
of $\mathcal A$, every finitely generated nilpotent group of $\mathcal A$ is
virtually abelian and there is no distortion element in $\mathcal A_{\mathbb
Q}$, the subgroup of $\mathcal A$ consisting of rational AIETs.


  Derivatives and integrals of non-integer order were introduced more than
three centuries ago, but only recently gained more attention due to their
application on nonlocal phenomena. In this context, the Caputo derivatives are
the most popular approach to fractional calculus among physicists, since
differential equations involving Caputo derivatives require regular boundary
conditions. Motivated by several applications in physics and other sciences,
the fractional calculus of variations is currently in fast development.
However, all current formulations for the fractional variational calculus fail
to give an Euler-Lagrange equation with only Caputo derivatives. In this work,
we propose a new approach to the fractional calculus of variations by
generalizing the DuBois-Reymond lemma and showing how Euler-Lagrange equations
involving only Caputo derivatives can be obtained.


  The VRI passbands light curves of V344 Lac were presented and analyzed by
using the latest version of the W-D code. The observed spectrum reveals that
V344 Lac is not an A3 type but would be a later F type star according to the
yielded temperature. The results of solution show that V344 Lac is an A-subtype
contact binary, with a mediate photometric mass ratio of 0.387(0.003) and a
mediate contact factor of 44.6(3.0)%. Based on the parallax given by Gaia, the
parameters of the components are estimated as: M1 = 1.16 Ms, M2 = 0.45 Ms, R1 =
1.31 Rs, R2 = 0.88 Rs, L1 = 2.512 Ls, L2 = 1.057 Ls. The period investigation
indicates that V344 Lac may have an eccentric orbital oscillation, with P3 =
12.4(0.5) yr, A3 = 0.0020(0.0002) d, and e = 0.38(0.16). Analysis shows such
oscillation would be caused by a magnetic activity which can be explained by
the Applegate mechanism. Meanwhile, according to the value of l3 and the
estimated physical parameters of V344 Lac, the mass of the third companion may
be 0.79 Ms. This third body could be a wide company.


  We present a critical review of the determination of fundamental parameters
of white dwarfs discovered by the Gaia mission. We first reinterpret
color-magnitude and color-color diagrams using photometric and spectroscopic
information contained in the Montreal White Dwarf Database (MWDD), combined
with synthetic magnitudes calculated from a self-consistent set of model
atmospheres with various atmospheric compositions. The same models are then
applied to measure the fundamental parameters of white dwarfs using the
so-called photometric technique, which relies on the exquisite Gaia
trigonometric parallax measurements, and photometric data from Pan-STARRS,
SDSS, and Gaia. In particular, we discuss at length the systematic effects
induced by these various photometric systems. We then study in great detail the
mass distribution as a function of effective temperature for the white dwarfs
spectroscopically identified in the MWDD, as well as for the white dwarf
candidates discovered by Gaia. We pay particular attention to the assumed
atmospheric chemical composition of cool, non-DA stars. We also briefly revisit
the validity of the mass-radius relation for white dwarfs, and the recent
discovery of the signature of crystallization in the Gaia color-magnitude
diagram for DA white dwarfs. We finally present evidence that the core
composition of most of these white dwarfs is, in bulk, a mixture of carbon and
oxygen, an expected result from stellar evolution theory, but never empirically
well established before.


  Isospin e ffects on multifragmentation properties were studied thanks to
nuclear collisions between di fferent isotopes of xenon beams and tin targets.
It is shown that, in central collisions leading to multifragmentation, the mean
number of fragments and their mean kinetic energy increase with the
neutron-richness of the total system. Comparisons with a stochastic transport
model allow to attribute the multiplicity increase to the multifragmentation
stage, before secondary decay. The total charge bound in fragments is proposed
as an alternate variable to quantify preequilibrium emission and to investigate
symmetry energy e ffects.


  The radial profiles of stars in disc galaxies are observed to be either
purely exponential (Type-I), truncated (Type-II) or anti-truncated (Type-III)
exponentials. Controlled formation simulations of isolated galaxies can
reproduce all of these profile types by varying a single parameter, the initial
halo spin. In this paper we examine these simulations in more detail in an
effort to identify the physical mechanism that leads to the formation of
Type-III profiles. The stars in the anti-truncated outskirts of such discs are
now on eccentric orbits, but were born on near-circular orbits at much smaller
radii. We show that, and explain how, they were driven to the outskirts via
non-linear interactions with a strong and long-lived central bar, which greatly
boosted their semi-major axis but also their eccentricity. While bars have been
known to cause radial heating and outward migration to stellar orbits, we link
this effect to the formation of Type-III profiles. This predicts that the
anti-truncated parts of galaxies have unusual kinematics for disc-like stellar
configurations: high radial velocity dispersions and slow net rotation. Whether
such discs exist in nature, can be tested by future observations.


  The rise in popularity of permissioned blockchain platforms in recent time is
significant. Hyperledger Fabric is one such permissioned blockchain platform
and one of the Hyperledger projects hosted by the Linux Foundation. The Fabric
comprises various components such as smart-contracts, endorsers, committers,
validators, and orderers. As the performance of blockchain platform is a major
concern for enterprise applications, in this work, we perform a comprehensive
empirical study to characterize the performance of Hyperledger Fabric and
identify potential performance bottlenecks to gain a better understanding of
the system. We follow a two-phased approach. In the first phase, our goal is to
understand the impact of various configuration parameters such as block size,
endorsement policy, channels, resource allocation, state database choice on the
transaction throughput & latency to provide various guidelines on configuring
these parameters. In addition, we also aim to identify performance bottlenecks
and hotspots. We observed that (1) endorsement policy verification, (2)
sequential policy validation of transactions in a block, and (3) state
validation and commit (with CouchDB) were the three major bottlenecks. In the
second phase, we focus on optimizing Hyperledger Fabric v1.0 based on our
observations. We introduced and studied various simple optimizations such as
aggressive caching for endorsement policy verification in the cryptography
component (3x improvement in the performance) and parallelizing endorsement
policy verification (7x improvement). Further, we enhanced and measured the
effect of an existing bulk read/write optimization for CouchDB during state
validation & commit phase (2.5x improvement). By combining all three
optimizations1, we improved the overall throughput by 16x (i.e., from 140 tps
to 2250 tps).


  Context-aware recommender systems (CARSs) apply sensing and analysis of user
context in order to provide personalized services. Adding context to a
recommendation model is challenging, since the addition of context may
increases both the dimensionality and sparsity of the model. Recent research
has shown that modeling contextual information as a latent vector may address
the sparsity and dimensionality challenges. We suggest a new latent modeling of
sequential context by generating sequences of contextual information and
reducing their contextual space to a compressed latent space.We train a long
short-term memory (LSTM) encoder-decoder network on sequences of contextual
information and extract sequential latent context from the hidden layer of the
network in order to represent a compressed representation of sequential data.
We propose new context-aware recommendation models that extend the neural
collaborative filtering approach and learn nonlinear interactions between
latent features of users, items, and contexts which take into account the
sequential latent context representation as part of the recommendation process.
We deployed our approach using two context-aware datasets with different
context dimensions. Empirical analysis of our results validates that our
proposed sequential latent context-aware model (SLCM), surpasses state of the
art CARS models.


  The charge doped into a semiconductor in a field effect transistor (FET) is
generally confined to the interface of the semiconductor. A planar step at the
interface causes a potential drop due to the strong electric field of the FET,
which in turn is screened by the doped carriers. We analyze the dipolar
electronic structure of a single step in the Thomas-Fermi approximation and
find that the transmission coefficient through the step is exponentially
suppressed by the electric field and the induced carrier density as well as by
the step height. In addition, the field enhancement at the step edge can
facilitate the electric breakthrough of the insulating layer. We suggest that
these two effects may lead to severe problems when engineering FET devices with
very high doping. On the other hand steps can give rise to interesting physics
in superconducting FETs by forming weak links and potentially creating atomic
size Josephson junctions.


  We studied the phase structures of N=1 supersymmetric USp(2N_c) gauge theory
with N_f flavors in the fundamental representation as we deformed the N=2
supersymmetric QCD by adding the superpotential for adjoint chiral scalar
field. We determined the most general factorization curves for various breaking
patterns, for example, the two different breaking patterns of quartic
superpotential. We observed all kinds of smooth transitions for quartic
superpotential. Finally we discuss the intriguing role of USp(0) in the phase
structure and the possible connection with observations made recently in
hep-th/0304271 (Aganagic, Intriligator, Vafa and Warner) and in hep-th/0307063
(Cachazo).


  An internally thermalized dark matter (DM) with only gravitational
interaction with the standard model (SM) particles at low temperatures, may
undergo number-changing self-scatterings in the early Universe, eventually
freezing out to the observed DM abundance. If these reactions, such as a $3
\rightarrow 2$ process, take place when the DM is non-relativistic, DM
cannibalizes itself to cool much slower than standard non-relativistic matter
during the cannibal phase. As shown in earlier studies, if the cannibal phase
takes place during the matter-dominated epoch, there are very strong
constraints from structure formation. Considering scenarios in which the
cannibal phase freezes out in the radiation-dominated epoch instead, we show
that cannibal DM decoupled from the SM can be viable, consistent with all
present cosmological constraints. To this end, we solve the coupled evolution
equations of the DM temperature and density, and determine its abundance for
different DM self-couplings. We then evaluate the constraints on these
parameters from the cosmic-microwave background power spectrum, the big-bang
nucleosynthesis limits on the relativistic degrees of freedom, the
Lyman-$\alpha$ limits on the DM free-streaming length and the theoretical upper
bound on the $3 \rightarrow 2$ annihilation rate from $S-$matrix unitarity. We
find that depending upon the DM self-couplings, a scalar cannibal DM with mass
in the range of around 80 eV to 700 TeV can make up the observed DM density and
satisfy all the constraints, when the initial DM temperature ($T_{\rm DM}$) is
lower than the SM one ($T_{\rm SM}$), with $T_{\rm SM}/9100 \lesssim T_{\rm DM}
\lesssim \,T_{\rm SM}/1.1$.


  The formation of Bose condensates far from equilibrium can play an important
role in our understanding of collision experiments of heavy nuclei or for the
evolution of the early universe. In the relativistic quantum world particle
number changing processes can counteract Bose condensation, and there is a
considerable debate about the relevance of this phenomenon in this context. We
show that the involved question of Bose condensation from initial
over-population can be answered for the example of scalar field theories.
Condensate formation occurs as a consequence of an inverse particle cascade
with a universal power-law spectrum. This particle transport towards low
momenta is part of a dual cascade, in which energy is also transfered by weak
wave turbulence towards higher momenta. To highlight the importance of number
changing processes for the subsequent decay of the condensate, we also compare
to non-relativistic theories with exact number conservation. We discuss the
relevance of these results for nonabelian gauge theories.


  In this article, we present an effective encoding of dendrograms by embedding
them into the Bruhat-Tits trees associated to $p$-adic number fields. As an
application, we show how strings over a finite alphabet can be encoded in
cyclotomic extensions of $\mathbb{Q}_p$ and discuss $p$-adic DNA encoding. The
application leads to fast $p$-adic agglomerative hierarchic algorithms similar
to the ones recently used e.g. by A. Khrennikov and others. From the viewpoint
of $p$-adic geometry, to encode a dendrogram $X$ in a $p$-adic field $K$ means
to fix a set $S$ of $K$-rational punctures on the $p$-adic projective line
$\mathbb{P}^1$. To $\mathbb{P}^1\setminus S$ is associated in a natural way a
subtree inside the Bruhat-Tits tree which recovers $X$, a method first used by
F. Kato in 1999 in the classification of discrete subgroups of
$\textrm{PGL}_2(K)$.
  Next, we show how the $p$-adic moduli space $\mathfrak{M}_{0,n}$ of
$\mathbb{P}^1$ with $n$ punctures can be applied to the study of time series of
dendrograms and those symmetries arising from hyperbolic actions on
$\mathbb{P}^1$. In this way, we can associate to certain classes of dynamical
systems a Mumford curve, i.e. a $p$-adic algebraic curve with totally
degenerate reduction modulo $p$.
  Finally, we indicate some of our results in the study of general discrete
actions on $\mathbb{P}^1$, and their relation to $p$-adic Hurwitz spaces.


  A model of a device is proposed and related theoretical calculation is
performed to study the weak interactions among neutral atoms and molecules. In
this model 3-body collisions among the neutral particles occur repeatedly in a
trap. Results of calculation demonstrate that information on interaction can be
obtained by observing the time-dependent densities of the system.


  The degree-based entropy of a graph is defined as the Shannon entropy based
on the information functional that associates the vertices of the graph with
the corresponding degrees. In this paper, we study extremal problems of finding
the graphs attaining the minimum degree-based graph entropy among graphs and
bipartite graphs with a given number of vertices and edges. We characterize the
unique extremal graph achieving the minimum value among graphs with a given
number of vertices and edges and present a lower bound for the degree-based
entropy of bipartite graphs and characterize all the extremal graphs which
achieve the lower bound. This implies the known result due to Cao et al. (2014)
that the star attains the minimum value of the degree-based entropy among trees
with a given number of vertices.


  Raman scattering in relaxor ferroelectric PbMg1/3Ta2/3O3 (PMT) was
investigated in the single crystalline form in the temperature range of 20 -
295 K. Anomalous temperature dependence of the integrated intensity and the
Raman line contours were found at the diffuse phase transition. A correlation
between the anomalies in the integrated intensities and the dispersion of the
dielectric response was observed. The distortions of Raman lines with
decreasing temperature are discussed.


  Galactic and extragalactic objects in the universe are sources of high-energy
neutrinos {that may contribute to the astrophysical neutrino signal seen by
IceCube.} Recently, a study done using cascade-like events seen by IceCube
reported neutrino emission from the Galactic plane with $>$4$\sigma$
significance. In this work, we put a lower limit on the number of Galactic
sources required to explain this emission. To achieve this, we {use} a
simulation package created to simulate point sources in the Galaxy along with
the neutrino and gamma-ray flux emissions originating from them. Along with
{using} past IceCube discovery potential curves, we also account for Eddington
bias effects due to Poisson fluctuations in the number of detected neutrino
events.{We present a toy Monte Carlo simulation to show that there should be at
least 8 sources, each with luminosities $1.6 \times 10^{35}$erg/s responsible
for the Galactic neutrino emission. } Our results constrain the number of
individual point-like emission regions, which applies both to discrete
astrophysical sources and to individual points of diffuse emission.


  This is the revised version of the second paper in a series introducing a
generalized Fredholm theory in a new class of smooth spaces called polyfolds.
The theory will be illustrated in upcoming papers by applications to Floer
Theory, Gromov-Witten Theory and Symplectic Field Theory.
  Some proofs have been improved and a glossary added.


  We address the 'plane of satellites problem' by studying planar
configurations around two disc galaxies with no late major mergers, formed in
zoom-in hydro-simulations. Due to the current lack of good quality kinematic
data for M31 satellites, we use only positional information. So far, positional
analyses of simulations are unable to find planes as thin and populated as the
observed ones. Moreover, they miss systematicity and detail in the
plane-searching techniques, as well as in the study of the properties and
quality of planes, both in simulations or real data. To fill this gap, i) we
extend the 4-galaxy-normal density plot method (Pawlowski et al. 2013) in a way
designed to efficiently identify the best quality planes (i.e., thin and
populated) without imposing extra constraints on their properties, and ii), we
apply it for the first time to simulations. Using zoom-in simulations allows us
to mimic MW/M31-like systems regarding the number of satellites involved as
well as the galactic disc mass and morphology, in view of possible disc
effects. At all timesteps analyzed in both simulations we find satellite planar
configurations that are compatible, along given time intervals, with all the
spatial characteristics of observed planes identified using the same
methodology. However, the fraction of co-orbiting satellites within them is in
general low, suggesting time-varying satellite membership. We conclude that
high-quality positional planes of satellites are not infrequent in LCDM-formed
disc galaxies with a quiet assembly history. Detecting kinematically-coherent,
time-persistent planes demands considering the full six-dimensional phase-space
information of satellites.


  After a short introduction to the characteristic geometry underlying weakly
hyperbolic systems of partial differential equations we review the notion of
symmetric hyperbolicity of first-order systems and that of regular
hyperbolicity of second-order systems. Numerous examples are provided, mainly
taken from nonrelativistic and relativistic continuum mechanics.


  Let ${\mathscr G}$ be a linear algebraic group over $k$, where $k$ is an
algebraically closed field, a pseudo-finite field or the valuation ring of a
nonarchimedean local field. Let $G= {\mathscr G}(k)$. We prove that if $\gamma,
\delta\in G$ such that $\gamma$ is a commutator and $\langle \delta\rangle=
\langle \gamma\rangle$ then $\delta$ is a commutator. This generalises a result
of Honda for finite groups. Our proof uses the Lefschetz Principle from
first-order model theory.


  We obtain sharp error rates in the local limit theorem for the Sinai billiard
map (one and two dimensional) with infinite horizon. This result allows us to
further obtain higher order terms and thus, sharp mixing rates in the speed of
mixing of dynamically H\"older observables for the planar and tubular infinite
horizon Lorentz gases in the map (discrete time) case. We also obtain an
asymptotic estimate for the tail probability of the first return time to the
initial cell. In the process, we study families of transfer operators for
infinite horizon Sinai billiards perturbed with the free flight function and
obtain higher order expansions for the associated families of eigenvalues and
eigenprojectors.


  A previous paper presented Witscript, a system for generating conversational
jokes that rely on wordplay. This paper extends that work by presenting
Witscript 2, which uses a large language model to generate conversational jokes
that rely on common sense instead of wordplay. Like Witscript, Witscript 2 is
based on joke-writing algorithms created by an expert comedy writer. Human
evaluators judged Witscript 2's responses to input sentences to be jokes 46% of
the time, compared to 70% of the time for human-written responses. This is
evidence that Witscript 2 represents another step toward giving a chatbot a
humanlike sense of humor.


  Canonical orders, introduced in the minimal model program for orders, are
simultaneous generalisations of Kleinian singularities and their associated
skew group rings. In this paper, we construct minimal resolutions of canonical
orders via non-commutative cyclic covers and skew group rings. This allows us
to exhibit a derived equivalence between minimal resolutions of canonical
orders and the skew group ring form of the canonical order in all but one case.
The Fourier-Mukai transform used to construct this equivalence allows us to
make explicit, the numerical version of the McKay correspondence for canonical
orders which, relates the exceptional curves of the minimal resolution to the
indecomposable reflexive modules of the canonical order.


  Human motion copy is an intriguing yet challenging task in artificial
intelligence and computer vision, which strives to generate a fake video of a
target person performing the motion of a source person. The problem is
inherently challenging due to the subtle human-body texture details to be
generated and the temporal consistency to be considered. Existing approaches
typically adopt a conventional GAN with an L1 or L2 loss to produce the target
fake video, which intrinsically necessitates a large number of training samples
that are challenging to acquire. Meanwhile, current methods still have
difficulties in attaining realistic image details and temporal consistency,
which unfortunately can be easily perceived by human observers. Motivated by
this, we try to tackle the issues from three aspects: (1) We constrain
pose-to-appearance generation with a perceptual loss and a theoretically
motivated Gromov-Wasserstein loss to bridge the gap between pose and
appearance. (2) We present an episodic memory module in the pose-to-appearance
generation to propel continuous learning that helps the model learn from its
past poor generations. We also utilize geometrical cues of the face to optimize
facial details and refine each key body part with a dedicated local GAN. (3) We
advocate generating the foreground in a sequence-to-sequence manner rather than
a single-frame manner, explicitly enforcing temporal inconsistency. Empirical
results on five datasets, iPER, ComplexMotion, SoloDance, Fish, and Mouse
datasets, demonstrate that our method is capable of generating realistic target
videos while precisely copying motion from a source video. Our method
significantly outperforms state-of-the-art approaches and gains 7.2% and 12.4%
improvements in PSNR and FID respectively.


  We investigate the existence, uniqueness, and $L^1$-contractivity of weak
solutions to a porous medium equation with fractional diffusion on an evolving
hypersurface. To settle the existence, we reformulate the equation as a local
problem on a semi-infinite cylinder, regularise the porous medium nonlinearity
and truncate the cylinder. Then we pass to the limit first in the truncation
parameter and then in the nonlinearity, and the identification of limits is
done using the theory of subdifferentials of convex functionals.
  In order to facilitate all of this, we begin by studying (in the setting of
closed Riemannian manifolds and Sobolev spaces) the fractional
Laplace--Beltrami operator which can be seen as the Dirichlet-to-Neumann map of
a harmonic extension problem. A truncated harmonic extension problem will also
be examined and convergence results to the solution of the harmonic extension
will be given. For a technical reason, we will also consider some related
extension problems on evolving hypersurfaces which will provide us with the
minimal time regularity required on the harmonic extensions in order to
properly formulate the moving domain problem. This functional analytic theory
is of course independent of the fractional porous medium equation and will be
of use generally in the analysis of fractional elliptic and parabolic problems
on manifolds.


  Due to the challenges of satisfying the demands for communication efficiency
and intelligent connectivity, sixth-generation (6G) wireless network requires
new communication frameworks to enable effective information exchange and the
integrated Artificial Intelligence (AI) and communication. The Deep Learning
(DL) based semantic communication, which can integrate application requirements
and the data meanings into data processing and transmission, is expected to
become a new paradigm in 6G wireless networks. However, existing semantic
communications frameworks rely on sending full semantic feature, which can
maximize the semantic fidelity but fail to achieve the efficient semantic
communications. In this article, we introduce a novel Scalable Extraction based
Semantic Communication (SE-SC) model to support the potential applications in
6G wireless networks and then analyze its feasibility. Then, we propose a
promising the SE-SC framework to highlight the potentials of SE-SC model in 6G
wireless networks. Numerical results show that our proposed SE-SC scheme can
offer an identical Quality of Service (QoS) for the downstream task with much
fewer transmission symbols than the full semantic feature transmission and the
traditional codec scheme. Finally, we discuss several challenges for further
investigating the scalable extraction based semantic communications.


  For the GMRT, we calculate the expected signal from redshifted HI emission at
two frequency bands centered at 610 and 325 MHz. The study focuses on the
visibility-visibility cross-correlations, proposed earlier as the optimal
statistical estimator for detecting and analyzing this signal. These
correlations directly probe the power spectrum of density fluctuations at the
redshift where the radiation originated, and thereby provide a method for
studying the large scale structures at large redshifts. We present detailed
estimates of the correlations expected between the visibilities measured at
different baselines and frequencies. Analytic fitting formulas representing the
salient features of the expected signal are also provided. These will be useful
in planning observations and deciding an optimal strategy for detecting this
signal.


  Certain problems in the field of stellar atmospheres, polarized radiative
transfer and magnetic field diagnostics cannot be addressed by means of
traditional sequential programming techniques because CPU times become
prohibitive on even the fastest single processor machines when realistic
physics and accurate numerical methods are employed. This contribution
discusses the question of what kind of supercomputing approach is best suited
for the modelling of Ap stars, pointing out the superiority of parallel
computing with Ada95 over High Performance Fortran in all of the
above-mentioned fields.


  The highlights of the recent activities and physics results leading up to the
summer of 2018 from the Compact Muon Solenoid (CMS) experiment at the CERN
Large Hadron Collider (LHC) are presented here. The CMS experiment has a very
wide-ranging physics program, and only a very limited subset of the physics
analyses being performed at CMS are discussed here, consisting of several
important results from the analysis of proton-proton collision data at
center-of-mass energy of 13 TeV. These include important analyses of Higgs
boson physics, with the highlight being the first observation of the
$\mathrm{t\bar{t}H}$ production of the Higgs boson, along with analyses
pertaining to precision standard model measurements, top quark physics, with
the single top production cross-section measurement, and flavor physics, with
the important observation of $\chi_{b}$(3P) states. Additionally, important
searches for physics beyond the standard model are also presented.


  Compact groups of galaxies recently have been discovered in association with
several strong gravitational lens systems. These groups provide additional
convergence to the lensing potential and thus affect the value of H_0 derived
from the systems. Lens system time delays are now being measured with
uncertainties of only a few percent or better. Additionally, vast improvements
are being made in incorporating observational constraints such as Einstein ring
structures and stellar velocity dispersions into the lens models. These
advances are reducing the uncertainties on H_0 to levels at which the the
effects of associated galaxy groups may contribute significantly to the overall
error budget. We describe a dedicated multiwavelength program, using Keck, HST,
and Chandra, to find such groups and measure their properties. We present, as a
case study, results obtained from observations of the CLASS lens system
B1608+656 and discuss the implications for the value of H_0 derived from this
system.


  The COVID-19 can cause severe pneumonia and is estimated to have a high
impact on the healthcare system. The standard image diagnosis tests for
pneumonia are chest X-ray (CXR) and computed tomography (CT) scan. CXR are
useful in because it is cheaper, faster and more widespread than CT. This study
aims to identify pneumonia caused by COVID-19 from other types and also healthy
lungs using only CXR images. In order to achieve the objectives, we have
proposed a classification schema considering the multi-class and hierarchical
perspectives, since pneumonia can be structured as a hierarchy. Given the
natural data imbalance in this domain, we also proposed the use of resampling
algorithms in order to re-balance the classes distribution. Our classification
schema extract features using some well-known texture descriptors and also
using a pre-trained CNN model. We also explored early and late fusion
techniques in order to leverage the strength of multiple texture descriptors
and base classifiers at once. To evaluate the approach, we composed a database,
named RYDLS-20, containing CXR images of pneumonia caused by different
pathogens as well as CXR images of healthy lungs. The classes distribution
follows a real-world scenario in which some pathogens are more common than
others. The proposed approach achieved a macro-avg F1-Score of 0.65 using a
multi-class approach and a F1-Score of 0.89 for the COVID-19 identification in
the hierarchical classification scenario. As far as we know, we achieved the
best nominal rate obtained for COVID-19 identification in an unbalanced
environment with more than three classes. We must also highlight the novel
proposed hierarchical classification approach for this task, which considers
the types of pneumonia caused by the different pathogens and lead us to the
best COVID-19 recognition rate obtained here.


  We study the spin wave in the S=1/2 multiple-spin exchange model on a
triangular lattice in a magnetic field within the linear spin-wave theory. We
take only two-, three- and four-spin exchange interactions into account and
restrict ourselves to the region where a coplanar three-sublattice state is the
mean-field ground state. We found that the Y-shape ground state survives
quantum fluctuations and the phase transition to a phase with a 6-sublattice
structure occurs with softening of the spin wave. We estimated the quantum
corrections to the ground state sublattice magnetizations due to zero-point
spin-wave fluctuations.


  We suggest that holography can be formulated in terms of the information
capacity of the St\"uckelberg degrees of freedom that maintain gauge invariance
of the theory in the presence of an information boundary. These St\"uckelbergs
act as qubits that account for a certain fraction of quantum information. Their
information capacity is measured by the ratio of the inverse St\"uckelberg
energy gap to the size of the system. Systems with the smallest gap are
maximally holographic. For massless gauge systems this information measure is
universally equal to the inverse coupling evaluated at the systems' length
scale. In this language it becomes very transparent why the St\"uckelberg
information capacity of black holes saturates the Bekenstein bound and accounts
for the entire information of the system. The physical reason is that the
strength of quantum interaction is bounded from below by the gravitational
coupling, which scales as area. Observing the striking similarity between the
scalings of the energy gap of the boundary St\"uckelberg modes and the
Bogoliubov modes of critical many-body systems, we establish a connection
between holography and quantum criticality through the correspondence between
these modes.


  Traditional pulsar timing techniques involve averaging large numbers of
single pulses to obtain a high signal-to-noise (S/N) profile, which is matched
to a template to measure a time of arrival (TOA). However, the morphology of
individual single pulses varies greatly due to pulse jitter. Pulses of
different fluence contribute differently to the S/N of the pulse average. Our
study proposes a method that accounts for these variations by identifying a
range of states and timing each separately. We selected two 1-hour observations
of PSR J2145-0750, each in a different frequency band with the Green Bank
Telescope. We normalized the pulse amplitudes to account for scintillation
effects and probed different excision algorithms to reduce radio-frequency
interference. We then measured four pulse parameters (amplitude, position,
width, and energy) to classify the single pulses using automated clustering
algorithms. For each cluster, we calculated an average pulse profile and
template and used both to obtain a TOA and TOA error. Finally, we computed the
weighted average TOA and TOA error, the latter as a metric of the total timing
precision for the epoch. The TOA is shifted relative to the one obtained
without clustering, and we can estimate the shift with this weighting using the
same data. For the 820 MHz and 1400 MHz bands, we obtained TOA uncertainties of
0.057 microseconds and 0.46 microseconds, compared to 0.066 microseconds and
0.74 microseconds when no clustering is applied. We conclude that tailoring
this method could help improve the timing precision for certain bright pulsars
in NANOGrav's dataset.


  Through a collaborative effort, the Fermilab Information Resources Department
and Computing Division have created a "virtual library" of technical
publications that provides public access to electronic full-text documents.
This paper will discuss the vision, planning and milestones of the project, as
well as the hardware, software and interdepartmental cooperation components.


  In this paper we study the type IV Knorr Held space time models. Such models
typically apply intrinsic Markov random fields and constraints are imposed for
identifiability. INLA is an efficient inference tool for such models where
constraints are dealt with through a conditioning by kriging approach. When the
number of spatial and/or temporal time points become large, it becomes
computationally expensive to fit such models, partly due to the number of
constraints involved. We propose a new approach, HyMiK, dividing constraints
into two separate sets where one part is treated through a mixed effect
approach while the other one is approached by the standard conditioning by
kriging method, resulting in a more efficient procedure for dealing with
constraints. The new approach is easy to apply based on existing
implementations of INLA.
  We run the model on simulated data, on a real data set containing dengue
fever cases in Brazil and another real data set of confirmed positive test
cases of Covid-19 in the counties of Norway. For all cases we get very similar
results when comparing the new approach with the tradition one while at the
same time obtaining a significant increase in computational speed, varying on a
factor from 2 to 4, depending on the sizes of the data sets.


  In this paper, combining Nash-Moser iteration and Sallof-Coste type Sobolev
ineualities, we establish fundamental and concise $C^0$ and $C^1$ estimates for
solutions to a class of nonlinear elliptic equations of the form $$\Delta
u(x)+a(x)u(x)\ln u(x)+b(x)u(x)=0,$$ which possesses abundant geometric
backgrounds. Utilizing these estimates which retrieve more geometric
information, we obtain some further properties of such solutions. Especially,
we prove a local Liouville type theorem of corresponding constant coefficient
equation.


  Time series modeling has entered an era of unprecedented growth in the size
and complexity of data which require new modeling approaches. While many new
general purpose machine learning approaches have emerged, they remain poorly
understand and irreconcilable with more traditional statistical modeling
approaches. We present a general class of exponential smoothed recurrent neural
networks (RNNs) which are well suited to modeling non-stationary dynamical
systems arising in industrial applications. In particular, we analyze their
capacity to characterize the non-linear partial autocorrelation structure of
time series and directly capture dynamic effects such as seasonality and
trends. Application of exponentially smoothed RNNs to forecasting electricity
load, weather data, and stock prices highlight the efficacy of exponential
smoothing of the hidden state for multi-step time series forecasting. The
results also suggest that popular, but more complicated neural network
architectures originally designed for speech processing, such as LSTMs and
GRUs, are likely over-engineered for industrial forecasting and light-weight
exponentially smoothed architectures, trained in a fraction of the time,
capture the salient features while being superior and more robust than simple
RNNs and ARIMA models. Additionally uncertainty quantification of the
exponential smoothed recurrent neural networks, provided by Bayesian
estimation, is shown to provide improved coverage.


  In sequential decision-making problems involving sensitive attributes like
race and gender, reinforcement learning (RL) agents must carefully consider
long-term fairness while maximizing returns. Recent works have proposed many
different types of fairness notions, but how unfairness arises in RL problems
remains unclear. In this paper, we address this gap in the literature by
investigating the sources of inequality through a causal lens. We first analyse
the causal relationships governing the data generation process and decompose
the effect of sensitive attributes on long-term well-being into distinct
components. We then introduce a novel notion called dynamics fairness, which
explicitly captures the inequality stemming from environmental dynamics,
distinguishing it from those induced by decision-making or inherited from the
past. This notion requires evaluating the expected changes in the next state
and the reward induced by changing the value of the sensitive attribute while
holding everything else constant. To quantitatively evaluate this
counterfactual concept, we derive identification formulas that allow us to
obtain reliable estimations from data. Extensive experiments demonstrate the
effectiveness of the proposed techniques in explaining, detecting, and reducing
inequality in reinforcement learning. We publicly release code at
https://github.com/familyld/InsightFair.


  A few FRI radio galaxies were detected at GeV gamma-rays with CGRO EGRET,
with peroperties suggesting that the gamma-ray flux originates from the core.
Here we discuss the possibility that the extended radio features of radio
galaxies could be detected with the LAT, focusing on the particularly promising
case of the nearby giant radio galaxy Fornax A.


  We report a new implementation for axisymmetric simulation in full general
relativity. In this implementation, the Einstein equations are solved using the
Nakamura-Shibata formulation with the so-called cartoon method to impose an
axisymmetric boundary condition, and the general relativistic hydrodynamic
equations are solved using a high-resolution shock-capturing scheme based on an
approximate Riemann solver. As tests, we performed the following simulations:
(i) long-term evolution of non-rotating and rapidly rotating neutron stars,
(ii) long-term evolution of neutron stars of a high-amplitude damping
oscillation accompanied with shock formation, (iii) collapse of unstable
neutron stars to black holes, and (iv) stellar collapses to neutron stars. The
tests (i)--(iii) were carried out with the $\Gamma$-law equation of state, and
the test (iv) with a more realistic parametric equation of state for
high-density matter. We found that this new implementation works very well: It
is possible to perform the simulations for stable neutron stars for more than
10 dynamical time scales, to capture strong shocks formed at stellar core
collapses, and to accurately compute the mass of black holes formed after the
collapse and subsequent accretion. In conclusion, this implementation is robust
enough to apply to astrophysical problems such as stellar core collapse of
massive stars to a neutron star and black hole, phase transition of a neutron
star to a high-density star, and accretion-induced collapse of a neutron star
to a black hole. The result for the first simulation of stellar core collapse
to a neutron star started from a realistic initial condition is also presented.


  In this paper we focus on strong solutions of some heat-like problems with a
non-local derivative in time induced by a Bernstein function and an elliptic
operator given by the generator or the Fokker-Planck operator of a Pearson
diffusion. Such kind of non-local equations naturally arise in the treatment of
particle motion in heterogeneous media. In particular, we use spectral
decomposition results for the usual Pearson diffusion to exploit explicit
solutions of the aforementioned equations. Moreover, we provide stochastic
representation of such solutions in terms of time-changed Pearson diffusions.
Finally, we exploit some further properties of these processes, such as limit
distributions and long/short-range dependence.


  We perform a detailed analysis of a large class of effective holographic
models with broken translations at finite charge density and magnetic field. We
exhaustively discuss the dispersion relations of the hydrodynamic modes at zero
magnetic field and successfully match them to the predictions from charged
hydrodynamics. At finite magnetic field, we identify the presence of an
expected type-B Goldstone boson $\mathrm{Re}[\omega]\sim k^2$, known as
magnetophonon and its gapped partner -- the magnetoplasmon. We discuss their
properties in relation to the effective field theory and hydrodynamics
expectations. Finally, we compute the optical conductivities and the
quasinormal modes at finite magnetic field. We observe that the pinning
frequency of the magneto-resonance peak increases with the magnetic field, in
agreement with experimental data on certain 2D materials, revealing the quantum
nature of the holographic pinning mechanism.


  Revenue-optimal auction design is a challenging problem with significant
theoretical and practical implications. Sequential auction mechanisms, known
for their simplicity and strong strategyproofness guarantees, are often limited
by theoretical results that are largely existential, except for certain
restrictive settings. Although traditional reinforcement learning methods such
as Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) are
applicable in this domain, they struggle with computational demands and
convergence issues when dealing with large and continuous action spaces. In
light of this and recognizing that we can model transitions differentiable for
our settings, we propose using a new reinforcement learning framework tailored
for sequential combinatorial auctions that leverages first-order gradients. Our
extensive evaluations show that our approach achieves significant improvement
in revenue over both analytical baselines and standard reinforcement learning
algorithms. Furthermore, we scale our approach to scenarios involving up to 50
agents and 50 items, demonstrating its applicability in complex, real-world
auction settings. As such, this work advances the computational tools available
for auction design and contributes to bridging the gap between theoretical
results and practical implementations in sequential auction design.


  High-bunch-charge photoemission electron-sources operating in a continuous
wave (CW) mode are required for many advanced applications of particle
accelerators, such as electron coolers for hadron beams, electron-ion
colliders, and free-electron lasers (FELs). Superconducting RF (SRF) has
several advantages over other electron-gun technologies in CW mode as it offers
higher acceleration rate and potentially can generate higher bunch charges and
average beam currents. A 112 MHz SRF electron photoinjector (gun) was developed
at Brookhaven National Laboratory (BNL) to produce high-brightness and
high-bunch-charge bunches for the Coherent electron Cooling Proof-of-Principle
(CeC PoP) experiment. The gun utilizes a quarter-wave resonator (QWR) geometry
for assuring beam dynamics, and uses high quantum efficiency (QE) multi-alkali
photocathodes for generating electrons.


  We report results of a systematic analysis of matter-wave gap solitons (GSs)
in three-dimensional self-repulsive Bose-Einstein condensates (BECs) loaded
into a combination of a cigar-shaped trap and axial optical-lattice (OL)
potential. Basic cases of the strong, intermediate, and weak radial
(transverse) confinement are considered, as well as settings with shallow and
deep OL potentials. Only in the case of the shallow lattice combined with tight
radial confinement, which actually has little relevance to realistic
experimental conditions, does the usual one-dimensional (1D) cubic
Gross-Pitaevskii equation (GPE) furnish a sufficiently accurate description of
GSs. However, the effective 1D equation with the nonpolynomial nonlinearity,
derived in Ref. [Phys. Rev. A \textbf{77}, 013617 (2008)], provides for quite
an accurate approximation for the GSs in all cases, including the situation
with weak transverse confinement, when the soliton's shape includes a
considerable contribution from higher-order transverse modes, in addition to
the usual ground-state wave function of the respective harmonic oscillator.
Both fundamental GSs and their multipeak bound states are considered. The
stability is analyzed by means of systematic simulations. It is concluded that
almost all the fundamental GSs are stable, while their bound states may be
stable if the underlying OL potential is deep enough.


  In nonisothermal plasma at temperature T_e>> T_i diffusion plays decisive
role at conditions of smooth inhomogeneity when the inhomogeneity size is
larger than the Debye radius by more than {T_e/T_i}^1/2 times. When the
inhomogeneity is rather abrupt and the condition is violated, then during the
spreading process the Maxwellian relaxation of ion charges becomes significant.
Here, we consider these two phenomena together and refer to the anomalous
character of diffusion, i.e. anomalous diffusion.


  We have studied the antiferromagnetic order -- disorder transition occurring
at $T=0$ in a 2-layer quantum Heisenberg antiferromagnet as the inter-plane
coupling is increased. Quantum Monte Carlo results for the staggered structure
factor in combination with finite-size scaling theory give the critical ratio
$J_c = 2.51 \pm 0.02$ between the inter-plane and in-plane coupling constants.
The critical behavior is consistent with the 3D classical Heisenberg
universality class. Results for the uniform magnetic susceptibility and the
correlation length at finite temperature are compared with recent predictions
for the 2+1-dimensional nonlinear $\sigma$-model. The susceptibility is found
to exhibit quantum critical behavior at temperatures significantly higher than
the correlation length.


  Let $E(s, Q)$ be the Epstein zeta function attached to a positive definite
quadratic form of discriminant $D<0$, such that $h(D)\geq 2$, where $h(D)$ is
the class number of the imaginary quadratic field $\mathbb{Q}(\sqrt{D})$. We
denote by $N_E(\sigma_1, \sigma_2, T)$ the number of zeros of $E(s, Q)$ in the
rectangle $\sigma_1 <\text{Re}(s)\leq \sigma_2$ and $T\leq \text{Im}(s)\leq
2T$, where $1/2<\sigma_1<\sigma_2<1$ are fixed real numbers. In this paper, we
improve the asymptotic formula of Gonek and Lee for $N_E(\sigma_1, \sigma_2,
T)$, obtaining a saving of a power of $\log T$ in the error term.


  Multi-Task Evolutionary Optimization (MTEO), an important field focusing on
addressing complex problems through optimizing multiple tasks simultaneously,
has attracted much attention. While MTEO has been primarily focusing on task
similarity, there remains a hugely untapped potential in harnessing the shared
characteristics between different domains to enhance evolutionary optimization.
For example, real-world complex systems usually share the same characteristics,
such as the power-law rule, small-world property, and community structure, thus
making it possible to transfer solutions optimized in one system to another to
facilitate the optimization. Drawing inspiration from this observation of
shared characteristics within complex systems, we set out to extend MTEO to a
novel framework - multi-domain evolutionary optimization (MDEO). To examine the
performance of the proposed MDEO, we utilize a challenging combinatorial
problem of great security concern - community deception in complex networks as
the optimization task. To achieve MDEO, we propose a community-based
measurement of graph similarity to manage the knowledge transfer among domains.
Furthermore, we develop a graph representation-based network alignment model
that serves as the conduit for effectively transferring solutions between
different domains. Moreover, we devise a self-adaptive mechanism to determine
the number of transferred solutions from different domains and introduce a
novel mutation operator based on the learned mapping to facilitate the
utilization of knowledge from other domains. Experiments on eight real-world
networks of different domains demonstrate MDEO superiority in efficacy compared
to classical evolutionary optimization. Simulations of attacks on the community
validate the effectiveness of the proposed MDEO in safeguarding community
security.


  We study the photo-physical stability of ensemble near-surface nitrogen
vacancy (NV) centers in diamond under vacuum and air. The optically detected
magnetic resonance contrast of the NV centers was measured following exposure
to laser illumination, showing opposing trends in air compared to vacuum
(increasing by up to 9% and dropping by up to 25%, respectively).
Characterization using Raman and X-ray photoelectron spectroscopies suggests a
surface reconstruction: In air, atmospheric oxygen adsorption on surface leads
to an increase in NV- fraction, whereas in vacuum, net oxygen desorption
increases the NV0 fraction. NV charge state switching is confirmed by
photoluminescence spectroscopy. Deposition of ~ 2 nm alumina (Al2O3) over the
diamond surface was shown to stabilize the NV charge state under illumination
in either environment, attributed to a more stable surface electronegativity.
The use of alumina coating on diamond is therefore a promising approach to
improve the resilience of NV sensors.


  Metasurfaces, the two-dimensional analogues of metamaterials, are ideal
platforms for sensing molecular chirality at the nanoscale, e.g. of inclusions
of natural optically active molecules, as they offer large accessible areas
(they are essentially surfaces) and can accommodate the necessary strong
resonances for coupling the probing radiation with the chiral inclusions. Here,
we examine theoretically achiral isotropic metasurfaces, and we treat them as
polarizable surfaces that support resonant electric and magnetic currents,
which are coupled via the chiral inclusions. We derive analytically, and verify
numerically, expressions that provide insight to the enhancement mechanism of
the magneto-electric coupling and explain why circular dichroism signals
(difference in absorption between left- and right- circularly polarized waves)
can arise from both the real and the imaginary part of the chirality parameter
$\kappa$. Our analysis demonstrates distinct chiroptical signals where the
contributions from both the real and imaginary part of $\kappa$ can be
independently observed and, based on such measurements, we propose a scheme for
the unambiguous determination of an unknown chirality.


  Weighted Fekete points are defined as those that maximize the weighted
version of the Vandermonde determinant over a fixed set. They can also be
viewed as the equilibrium distribution of the unit discrete charges in an
external electrostatic field. While these points have many applications, they
are very difficult to find explicitly, and are only known in a few (unweighted)
classical cases. We give two rare explicit examples of weighted Fekete points.
The first one is for the weights $w(x)=|x-ai|^{-s}$ on the real line, with
$s\ge 1$ and $a\neq 0,$ while the second is for the weights $w(z)=1/|z-b|$ on
the unit circle, with $b\in\mathbb{R},\ b\neq\pm 1.$ In both cases, we provide
solutions of the continuous energy problems with external fields that express
the limit versions of considered weighted Fekete points problems.


  The view of a node in a port-labeled network is an infinite tree encoding all
walks in the network originating from this node. We prove that for any integers
$n\geq D\geq 1$, there exists a port-labeled network with at most $n$ nodes and
diameter at most $D$ which contains a pair of nodes whose (infinite) views are
different, but whose views truncated to depth $\Omega(D\log (n/D))$ are
identical.


  Ranks of subspaces of vector spaces satisfy all linear inequalities satisfied
by entropies (including the standard Shannon inequalities) and an additional
inequality due to Ingleton. It is known that the Shannon and Ingleton
inequalities generate all such linear rank inequalities on up to four
variables, but it has been an open question whether additional inequalities
hold for the case of five or more variables. Here we give a list of 24
inequalities which, together with the Shannon and Ingleton inequalities,
generate all linear rank inequalities on five variables. We also give a partial
list of linear rank inequalities on six variables and general results which
produce such inequalities on an arbitrary number of variables; we prove that
there are essentially new inequalities at each number of variables beyond four
(a result also proved recently by Kinser).


  We discuss theoretically the behavior of the velocity autocorrelation
function in the Dissipative Particle Dynamics model. Two dynamical regimes are
identified depending on the dimensionless model parameters. For low frictions a
mean field behavior is observed in which the kinetic theory for DPD provides
good predictions. For high frictions, collective hydrodynamic effects are
dominant. We have performed numerical simulations that validate the theory
presented.


  We explore the task of language-guided video segmentation (LVS). Previous
algorithms mostly adopt 3D CNNs to learn video representation, struggling to
capture long-term context and easily suffering from visual-linguistic
misalignment. In light of this, we present Locater (local-global context aware
Transformer), which augments the Transformer architecture with a finite memory
so as to query the entire video with the language expression in an efficient
manner. The memory is designed to involve two components -- one for
persistently preserving global video content, and one for dynamically gathering
local temporal context and segmentation history. Based on the memorized
local-global context and the particular content of each frame, Locater
holistically and flexibly comprehends the expression as an adaptive query
vector for each frame. The vector is used to query the corresponding frame for
mask generation. The memory also allows Locater to process videos with linear
time complexity and constant size memory, while Transformer-style
self-attention computation scales quadratically with sequence length. To
thoroughly examine the visual grounding capability of LVS models, we contribute
a new LVS dataset, A2D-S+, which is built upon A2D-S dataset but poses
increased challenges in disambiguating among similar objects. Experiments on
three LVS datasets and our A2D-S+ show that Locater outperforms previous
state-of-the-arts. Further, we won the 1st place in the Referring Video Object
Segmentation Track of the 3rd Large-scale Video Object Segmentation Challenge,
where Locater served as the foundation for the winning solution. Our code and
dataset are available at: https://github.com/leonnnop/Locater


  Discrete Boltzmann model (DBM) is a type of coarse-grained mesoscale kinetic
model derived from the Boltzmann equation. Physically, it is roughly equivalent
to a hydrodynamic model supplemented by a coarse-grained model for the relevant
thermodynamic non-equilibrium (TNE) behaviours. The Navier-Stokes (NS) model is
a traditional macroscopic hydrodynamic model based on continuity hypothesis and
conservation laws. In this study, the two models are compared from two aspects,
physical capability and computational cost, by simulating two kinds of flow
problems including the thermal Couette flow and a Mach 3 step problem. In the
cases where the TNE effects are weak, both the two models give accurate results
for the hydrodynamic behaviour. Besides, DBM can provide more detailed
non-equilibrium information, while the NS is more efficient if concern only the
density, momentum, energy and their derived quantities. It is concluded that,
if the TNE effects are strong or are to be investigated, the NS is insufficient
while DBM is a good choice. While in the cases where the TNE effects are weak
and only the macro flow fields are to be studied, the NS is more preferable.


  Let $R$ be a ring. An $R$-module $M$ is said to be an absolutely $w$-pure
module if and only if $\Ext^1_R(F,M)$ is a GV-torsion module for any finitely
presented module $F$. In this paper, we introduce and study the concept of
$w$-FP-projective module which is in some way a generalization of the notion of
$\rm FP$-projective module. An $R$-module $M$ is said to be $w$-$\rm
FP$-projective if $\Ext^1_R(M,N)=0$ for any absolutely $w$-pure module $N$.
This new class of modules will be used to characterize (Noetherian) $DW$ rings.
Hence, we introduce the $w$-$\rm FP$-projective dimensions of modules and
rings. The relations between the introduced dimensions and other (classical)
homological dimensions are discussed. Illustrative examples are given.


  We propose a novel hue-preserving tone mapping scheme. Various tone mapping
operations have been studied so far, but there are very few works on color
distortion caused in image tone mapping. First, LDR images produced from HDR
ones by using conventional tone mapping operators (TMOs) are pointed out to
have some distortion in hue values due to clipping and rounding quantization
processing. Next,we propose a novel method which allows LDR images to have the
same maximally saturated color values as those of HDR ones. Generated LDR
images by the proposed method have smaller hue degradation than LDR ones
generated by conventional TMOs. Moreover, the proposed method is applicable to
any TMOs. In an experiment, the proposed method is demonstrated not only to
produce images with small hue degradation but also to maintain well-mapped
luminance, in terms of three objective metrics: TMQI, hue value in CIEDE2000,
and the maximally saturated color on the constant-hue plane in the RGB color
space.


  A lack of sufficient training data, both in terms of variety and quantity, is
often the bottleneck in the development of machine learning (ML) applications
in any domain. For agricultural applications, ML-based models designed to
perform tasks such as autonomous plant classification will typically be coupled
to just one or perhaps a few plant species. As a consequence, each
crop-specific task is very likely to require its own specialized training data,
and the question of how to serve this need for data now often overshadows the
more routine exercise of actually training such models. To tackle this problem,
we have developed an embedded robotic system to automatically generate and
label large datasets of plant images for ML applications in agriculture. The
system can image plants from virtually any angle, thereby ensuring a wide
variety of data; and with an imaging rate of up to one image per second, it can
produce lableled datasets on the scale of thousands to tens of thousands of
images per day. As such, this system offers an important alternative to time-
and cost-intensive methods of manual generation and labeling. Furthermore, the
use of a uniform background made of blue keying fabric enables additional image
processing techniques such as background replacement and plant segmentation. It
also helps in the training process, essentially forcing the model to focus on
the plant features and eliminating random correlations. To demonstrate the
capabilities of our system, we generated a dataset of over 34,000 labeled
images, with which we trained an ML-model to distinguish grasses from
non-grasses in test data from a variety of sources. We now plan to generate
much larger datasets of Canadian crop plants and weeds that will be made
publicly available in the hope of further enabling ML applications in the
agriculture sector.


  Large scale Monte Carlo simulation results for the two-orbital model for
manganites, including Jahn-Teller lattice distortions, are here presented. At
hole density x=1/4 and in the vicinity of the region of competition between the
ferromagnetic metallic and spin-charge-orbital ordered insulating phases, the
colossal magnetoresistance (CMR) phenomenon is observed with a
magnetoresistance ratio ~10,000% Our main result is that this CMR transition is
found to be of first order in some portions of the phase diagram, in agreement
with early results from neutron scattering, specific heat, and magnetization,
thus solving a notorious discrepancy between experiments and previous
theoretical studies. The first-order characteristics of the transition survive,
and are actually enhanced, when weak quenched disorder is introduced.


  The model order of a wireless channel plays an important role for a variety
of applications in communications engineering, e.g., it represents the number
of resolvable incident wavefronts with non-negligible power incident from a
transmitter to a receiver. Areas such as direction of arrival estimation
leverage the model order to analyze the multipath components of channel state
information. In this work, we propose to use a variational autoencoder to group
unlabeled channel state information with respect to the model order in the
variational autoencoder latent space in an unsupervised manner. We validate our
approach with simulated 3GPP channel data. Our results suggest that, in order
to learn an appropriate clustering, it is crucial to use a more flexible
likelihood model for the variational autoencoder decoder than it is usually the
case in standard applications.


  We are developing a novel high-brightness atomic beam, comprised of a
two-body exotic atom called muonium (M $ = \mu^+ + e^-$), for next-generation
atomic physics and gravitational interaction measurements. This M source
originates from a thin sheet of superfluid helium (SFHe), hence diagnostics and
later measurements require a detection system which is operational in a
dilution cryostat at temperatures below 1 K. In this paper, we describe the
operation and characterization of silicon photomultipliers (SiPMs) at ultra-low
temperatures in SFHe targets. We show the temperature dependence of the signal
shape, breakdown voltage, and single photon detection efficiency, concluding
that single photon detection with SiPMs below 0.85 K is feasible. Furthermore,
we show the development of segmented scintillation detectors, where 16 channels
at 1.7 K and one channel at 170 mK were commissioned using a muon beam.


  In this paper, after a telegraphic introduction to modern immunology, we
present a simple model for the idiotypic network among antibodies and we study
its relevance for the maintenance of immunological memory. We also consider the
problem of computing the memory capacity of such a model.


  We present the Scalable Nucleotide Alignment Program (SNAP), a new short and
long read aligner that is both more accurate (i.e., aligns more reads with
fewer errors) and 10-100x faster than state-of-the-art tools such as BWA.
Unlike recent aligners based on the Burrows-Wheeler transform, SNAP uses a
simple hash index of short seed sequences from the genome, similar to BLAST's.
However, SNAP greatly reduces the number and cost of local alignment checks
performed through several measures: it uses longer seeds to reduce the false
positive locations considered, leverages larger memory capacities to speed
index lookup, and excludes most candidate locations without fully computing
their edit distance to the read. The result is an algorithm that scales well
for reads from one hundred to thousands of bases long and provides a rich error
model that can match classes of mutations (e.g., longer indels) that today's
fast aligners ignore. We calculate that SNAP can align a dataset with 30x
coverage of a human genome in less than an hour for a cost of $2 on Amazon EC2,
with higher accuracy than BWA. Finally, we describe ongoing work to further
improve SNAP.


  Flying robots such as the quadrotor could provide an efficient approach for
medical treatment or sensor placing of wild animals. In these applications,
continuously targeting the moving animal is a crucial requirement. Due to the
underactuated characteristics of the quadrotor and the coupled kinematics with
the animal, nonlinear optimal tracking approaches, other than smooth feedback
control, are required. However, with severe nonlinearities, it would be
time-consuming to evaluate control inputs, and real-time tracking may not be
achieved with generic optimizers onboard. To tackle this problem, a novel
efficient egocentric regulation approach with high computational efficiency is
proposed in this paper. Specifically, it directly formulates the optimal
tracking problem in an egocentric manner regarding the quadrotor's body
coordinates. Meanwhile, the nonlinearities of the system are peeled off through
a mapping of the feedback states as well as control inputs, between the
inertial and body coordinates. In this way, the proposed efficient egocentric
regulator only requires solving a quadratic performance objective with linear
constraints and then generate control inputs analytically. Comparative
simulations and mimic biological experiment are carried out to verify the
effectiveness and computational efficiency. Results demonstrate that the
proposed control approach presents the highest and stablest computational
efficiency than generic optimizers on different platforms. Particularly, on a
commonly utilized onboard computer, our method can compute the control action
in approximately 0.3 ms, which is on the order of 350 times faster than that of
generic nonlinear optimizers, establishing a control frequency around 3000 Hz.


  This is a mostly self-contained survey article about bundle gerbes and some
of their recent applications in geometry, field theory, and quantisation. We
cover the definition of bundle gerbes with connection and their morphisms, and
explain the classification of bundle gerbes with connection in terms of
differential cohomology. We then survey how the surface holonomy of bundle
gerbes combines with their transgression line bundles to yield a smooth
bordism-type field theory. Finally, we exhibit the use of bundle gerbes in
geometric quantisation of 2-plectic as well as 1- and 2-shifted symplectic
forms. This generalises earlier applications of gerbes to the prequantisation
of quasi-symplectic groupoids.


  Extreme debris discs (EDDs) are bright and warm circumstellar dusty
structures around main sequence stars. They may represent the outcome of giant
collisions occuring in the terrestrial region between large planetesimals or
planetary bodies, and thus provide a rare opportunity to peer into the
aftermaths of these events. Here, we report on results of a mini-survey we
conducted with the aim to increase the number of known EDDs, investigate the
presence of solid-state features around 10{\mu}m in eight EDDs, and classify
them into the silica or silicate dominated groups. We identify four new EDDs
and derive their fundamental properties. For these, and for four other
previously known discs, we study the spectral energy distribution around
10{\mu}m by means of VLT/VISIR photometry in three narrow-band filters and
conclude that all eight objects likely exhibit solid-state emission features
from sub-micron grains. We find that four discs probably belong to the silicate
dominated subgroup. Considering the age distribution of the entire EDD sample,
we find that their incidence begins to decrease only after 300 Myr, suggesting
that the earlier common picture that these objects are related to the formation
of rocky planets may not be exclusive, and that other processes may be involved
for older objects (>100 Myr). Because most of the older EDD systems have wide,
eccentric companions, we suggest that binarity may play a role in triggering
late giant collisions.


  Using samples drawn from the Sloan Digital Sky Survey, we study the
relationship between local galaxy density and the properties of galaxies on the
red sequence. After removing the mean dependence of average overdensity (or
"environment") on color and luminosity, we find that there remains a strong
residual trend between luminosity-weighted mean stellar age and environment,
such that galaxies with older stellar populations favor regions of higher
overdensity relative to galaxies of like color and luminosity (and hence of
like stellar mass). Even when excluding galaxies with recent star-formation
activity (i.e., younger mean stellar ages) from the sample, we still find a
highly significant correlation between stellar age and environment at fixed
stellar mass. This residual age-density relation provides direct evidence for
an assembly bias on the red sequence such that galaxies in higher-density
regions formed earlier than galaxies of similar mass in lower-density
environments. We discuss these results in the context of the age-metallicity
degeneracy and in comparison to previous studies at low and intermediate
redshift. Finally, we consider the potential role of assembly bias in
explaining recent results regarding the evolution of post-starburst (or
post-quenching) galaxies and the environmental dependence of the type Ia
supernova rate.


  A binocular system developed by the author in terms of projective Fourier
transform (PFT) of the conformal camera, which numerically integrates the head,
eyes, and visual cortex, is used to process visual information during saccadic
eye movements. Although we make three saccades per second at the eyeball's
maximum speed of 700 deg/sec, our visual system accounts for these incisive eye
movements to produce a stable percept of the world. This visual constancy is
maintained by neuronal receptive field shifts in various retinotopically
organized cortical areas prior to saccade onset, giving the brain access to
visual information from the saccade's target before the eyes' arrival. It
integrates visual information acquisition across saccades. Our modeling
utilizes basic properties of PFT. First, PFT is computable by FFT in complex
logarithmic coordinates that approximate the retinotopy. Second, a translation
in retinotopic (logarithmic) coordinates, modeled by the shift property of the
Fourier transform, remaps the presaccadic scene into a postsaccadic reference
frame. It also accounts for the perisaccadic mislocalization observed by human
subjects in laboratory experiments. Because our modeling involves
cross-disciplinary areas of conformal geometry, abstract and computational
harmonic analysis, computational vision, and visual neuroscience, we include
the corresponding background material and elucidate how these different areas
interwove in our modeling of primate perception. In particular, we present the
physiological and behavioral facts underlying the neural processes related to
our modeling. We also emphasize the conformal camera's geometry and discuss how
it is uniquely useful in the intermediate-level vision computational aspects of
natural scene understanding.