  Colloidal quantum dots (cQDs) are now a mature nanomaterial with optical
properties customizable through varying size and composition. However, their
use in optical devices is limited as they are not widely available in
convenient forms such as optical fibers. With advances in polymerization
methods incorporating nanocrystals, nanocomposite materials suitable for
processing into high quality hybrid active fibers can be achieved. We
demonstrate a plastic optical fiber fabrication method which ensures
homogeneous dispersion of cQDs within a polymer core matrix. Loading
concentrations between 10$^{11}$-10$^{13}$ CdSe/CdS cQDs per cm$^{3}$ in
polystyrene were electronically imaged, confirming only sporadic sub-wavelength
aggregates. Rayleigh scattering losses are therefore dominant at energies below
the semiconductors' band gap, but are overtaken by a sharp CdS-related
absorption onset around 525 nm facilitating cQD excitation. The redshifted
photoluminescence emission is then minimally reabsorbed along the fiber with a
spectrum barely affected by the polymerization and a quantum yield staying at
$\sim$65$\%$ of its initial value. The latter, along with the glass transition
temperature and refractive index, is independent of the cQD concentration hence
yielding a proportionally increasing light output. Our cQD-doped fibers are
photostable to within 5$\%$ over days showing great promise for functional
material applications.


  Spin backflow and spin-memory loss have been well established to considerably
lower the interfacial spin transmissivity of metallic magnetic interfaces and
thus the energy efficiency of spin-orbit torque technologies. Here we report
that spin backflow and spin-memory loss at Pt-based heavy metal/ferromagnet
interfaces can be effectively eliminated by inserting an insulating
paramagnetic NiO layer of optimum thickness. The latter enables the thermal
magnon-mediated essentially unity spin-current transmission at room temperature
due to considerably enhanced effective spin-mixing conductance of the
interface. As a result, we obtain dampinglike spin-orbit torque efficiency per
unit current density of up to 0.8 as detected by the standard technology
ferromagnet FeCoB and others, which reaches the expected upper-limit spin Hall
ratio of Pt. We establish that Pt/NiO and Pt-Hf/NiO are two energy-efficient,
integration-friendly, and high-endurance spin-current generators that provide
>100 times greater energy efficiency than sputter-deposited topological
insulators BiSb and BiSe. Our finding will benefit spin-orbitronic research and
advance spin-torque technologies.


  In this paper, we propose a new class of quantized message-passing decoders
for LDPC codes over the BSC. The messages take values (or levels) from a finite
set. The update rules do not mimic belief propagation but instead are derived
using the knowledge of trapping sets. We show that the update rules can be
derived to correct certain error patterns that are uncorrectable by algorithms
such as BP and min-sum. In some cases even with a small message set, these
decoders can guarantee correction of a higher number of errors than BP and
min-sum. We provide particularly good 3-bit decoders for 3-left-regular LDPC
codes. They significantly outperform the BP and min-sum decoders, but more
importantly, they achieve this at only a fraction of the complexity of the BP
and min-sum decoders.


  The Bipolar Spherical Harmonics (BipoSH) form a natural basis to study the
CMB two point correlation function in a non-statistically isotropic (non-SI)
universe. The coefficients of expansion in this basis are a generalisation of
the well known CMB angular power spectrum and contain complete information of
the statistical properties of a non-SI but Gaussian random CMB sky. We use
these coefficients to describe the weak lensing of CMB photons in a non-SI
universe. Finally we show that the results reduce to the standard weak lensing
results in the isotropic limit.


  In the first scenario, we revise the upper bound of the cutoff scale of the
dimension-six potential Higgs operator required for a successful electroweak
baryogenesis in the case of a modified expansion history caused by the
existence of a non-interacting scalar field at the time phase transition
happens. The upper bound 860 GeV of the cutoff scale in the conventional case
can be improved to 1 TeV in the modified expansion case under certain
conditions. In the second scenario, we consider the Randall-Sundrum type II
model. We show that the lower bound of the five-dimensional Planck scale in
this model, which is determined from the validity of Newtonian gravitational
potential at small distance, turns out to be crucial in eliminating this model
as a viable candidate to satisfy the sphaleron bound; however, again modifying
the expansion history by including a non-interacting scalar field at the
electroweak scale can then make this model satisfy the sphaleron bound with a
certain parameter space.


  With the assistance of sophisticated training methods applied to single
labeled datasets, the performance of fully-supervised person re-identification
(Person Re-ID) has been improved significantly in recent years. However, these
models trained on a single dataset usually suffer from considerable performance
degradation when applied to videos of a different camera network. To make
Person Re-ID systems more practical and scalable, several cross-dataset domain
adaptation methods have been proposed, which achieve high performance without
the labeled data from the target domain. However, these approaches still
require the unlabeled data of the target domain during the training process,
making them impractical. A practical Person Re-ID system pre-trained on other
datasets should start running immediately after deployment on a new site
without having to wait until sufficient images or videos are collected and the
pre-trained model is tuned. To serve this purpose, in this paper, we
reformulate person re-identification as a multi-dataset domain generalization
problem. We propose a multi-dataset feature generalization network (MMFA-AAE),
which is capable of learning a universal domain-invariant feature
representation from multiple labeled datasets and generalizing it to `unseen'
camera systems. The network is based on an adversarial auto-encoder to learn a
generalized domain-invariant latent feature representation with the Maximum
Mean Discrepancy (MMD) measure to align the distributions across multiple
domains. Extensive experiments demonstrate the effectiveness of the proposed
method. Our MMFA-AAE approach not only outperforms most of the domain
generalization Person Re-ID methods, but also surpasses many state-of-the-art
supervised methods and unsupervised domain adaptation methods by a large
margin.


  If gamma-ray bursts are cosmological or in a halo distribution their
properties are expected to be isotropic (at least to 1st order). However, our
motion with respect to the burst parent population (whose proper frame is
expected to be that of the Cosmic Microwave Background (CMB), or that of a
static halo) will cause a dipole effect in the distribution of bursts and in
their photon number counts (together termed a Compton-Getting effect). We argue
that the photon number count information is necessary to distinguish a genuine
Compton-Getting effect from some other anisotropy and to fully test the proper
frame isotropy of the bursts. Using the 2B burst catalogue and the dipole
determined from the CMB, we find the surprising result that although the number
weighted distribution is consistent with isotropy, the fluence weighted dipole
has a correlation with the CMB dipole that has a probability of occuring only
10% of the time for an isotropic photon distribution. Furthermore, the photon
and number dipoles are inconsistent under the hypothesis of isotropy, at the
2-sigma level. This could be an indication that a non-negligible fraction of
gamma-ray bursts originate in the local, anisotropic universe. (shortened
Abstract)


  Irrespective of the fact that a complete theoretical description of critical
phenomena in connection with phase transition has been well-established through
the renormalization group formalism, the understanding of the phase transition
itself remains incomplete. For example, the questions like why and how the
phase transition happens are still unclear. Here we provide a pattern picture
to dissect the quantum phase transition occurring in the transverse Ising model
for a finite lattice. After the validity of the pattern formulation obtained is
confirmed, the energy contributions of different patterns to the ground state
energy provide a sufficient detail to show why and how the phase transition
takes place. Furthermore, a histogram of patterns' occupancy calculated by the
projections of ground state wavefunction on the patterns also shows the
detailed process of the phase transition. Our results are not only fundamental
in understanding the mechanism of phase transition, but also of practical
interest in quantum simulation platforms.


  This work describes the first observations of the ionisation of neon in a
metastable atomic state utilising a strong-field, few-cycle light pulse. We
compare the observations to theoretical predictions based on the
Ammosov-Delone-Krainov (ADK) theory and a solution to the time-dependent
Schrodinger equation (TDSE). The TDSE provides better agreement with the
experimental data than the ADK theory. We optically pump the target atomic
species and demonstrate that the ionisation rate depends on the spin state of
the target atoms and provide physically transparent interpretation of such a
spin dependence in the frameworks of the spin-polarised Hartree-Fock and
random-phase approximations.


  We analyze the properties of a nonlinear metamaterial formed by integrating
nonlinear components or materials into the capacitive regions of metamaterial
elements. A straightforward homogenization procedure leads to general
expressions for the nonlinear susceptibilities of the composite metamaterial
medium. The expressions are convenient, as they enable inhomogeneous system of
scattering elements to be described as a continuous medium using the standard
notation of nonlinear optics. We illustrate the validity and accuracy of our
theoretical framework by performing measurements on a fabricated metamaterial
sample composed of an array of split ring resonators (SRRs) with packaged
varactors embedded in the capacitive gaps in a manner similar to that of Wang
et al. [Opt. Express 16, 16058 (2008)]. Because the SRRs exhibit a predominant
magnetic response to electromagnetic fields, the varactor-loaded SRR composite
can be described as a magnetic material with nonlinear terms in its effective
magnetic susceptibility. Treating the composite as a nonlinear effective
medium, we can quantitatively assess the performance of the medium to enhance
and facilitate nonlinear processes, including second harmonic generation,
three- and four-wave mixing, self-focusing and other well-known nonlinear
phenomena. We illustrate the accuracy of our approach by predicting the
intensity-dependent resonance frequency shift in the effective permeability of
the varactor-loaded SRR medium and comparing with experimental measurements.


  I review recent approaches to constructing supersymmetric lattice theories
focusing in particular on the concept of topological twisting. The latter
technique is shown to expose a nilpotent, scalar supersymmetry which can be
implemented exactly in the lattice theory. Using these ideas a lattice action
for $\mathcal{N}=4$ super Yang-Mills in four dimensions can be written down
which is gauge invariant, free of fermion doublers and respects one out of a
total of 16 continuum supersymmetries. It is shown how these exact symmetries
together with the large point group symmetry of the lattice strongly constrain
the possible counterterms needed to renormalize the theory and hence determine
how much residual fine tuning will be needed to restore all supersymmetries in
the continuum limit. We report on progress to study these renormalization
effects at one loop. We go on to give examples of applications of these
supersymmetric lattice theories to explore the connections between gauge
theories and gravity.


  IaaS clouds invest substantial capital in operating their data centers.
Reducing the cost of resource provisioning, is their forever pursuing goal.
Computing resource trading among multiple IaaS clouds provide a potential for
IaaS clouds to utilize cheaper resources to fulfill their jobs, by exploiting
the diversities of different clouds' workloads and operational costs. In this
paper, we focus on studying the IaaS clouds' cost reduction through computing
resource trading among multiple IaaS clouds. We formulate the global cost
minimization problem among multiple IaaS clouds under cooperative scenario
where each individual cloud's workload and cost information is known. Taking
into consideration jobs with disparate lengths, a non-preemptive approximation
algorithm for leftover job migration and new job scheduling is designed. Given
to the selfishness of individual clouds, we further design a randomized double
auction mechanism to elicit clouds' truthful bidding for buying or selling
virtual machines. We evaluate our algorithms using trace-driven simulations.


  We present the structured average intersection-over-union ratio (STRUCT-IOU),
a similarity metric between constituency parse trees motivated by the problem
of evaluating speech parsers. STRUCT-IOU enables comparison between a
constituency parse tree (over automatically recognized spoken word boundaries)
with the ground-truth parse (over written words). To compute the metric, we
project the ground-truth parse tree to the speech domain by forced alignment,
align the projected ground-truth constituents with the predicted ones under
certain structured constraints, and calculate the average IOU score across all
aligned constituent pairs. STRUCT-IOU takes word boundaries into account and
overcomes the challenge that the predicted words and ground truth may not have
perfect one-to-one correspondence. Extending to the evaluation of text
constituency parsing, we demonstrate that STRUCT-IOU can address token-mismatch
issues, and shows higher tolerance to syntactically plausible parses than
PARSEVAL (Black et al., 1991).


  Determining extrinsic calibration parameters is a necessity in any robotic
system composed of actuators and cameras. Once a system is outside the lab
environment, parameters must be determined without relying on outside artifacts
such as calibration targets. We propose a method that relies on structured
motion of an observed arm to recover extrinsic calibration parameters. Our
method combines known arm kinematics with observations of conics in the image
plane to calculate maximum-likelihood estimates for calibration extrinsics.
This method is validated in simulation and tested against a real-world model,
yielding results consistent with ruler-based estimates. Our method shows
promise for estimating the pose of a camera relative to an articulated arm's
end effector without requiring tedious measurements or external artifacts.
  Index Terms: robotics, hand-eye problem, self-calibration, structure from
motion


  We consider the problem of estimating multiple principal components using the
recently-proposed Sparse and Functional Principal Components Analysis (SFPCA)
estimator. We first propose an extension of SFPCA which estimates several
principal components simultaneously using manifold optimization techniques to
enforce orthogonality constraints. While effective, this approach is
computationally burdensome so we also consider iterative deflation approaches
which take advantage of existing fast algorithms for rank-one SFPCA. We show
that alternative deflation schemes can more efficiently extract signal from the
data, in turn improving estimation of subsequent components. Finally, we
compare the performance of our manifold optimization and deflation techniques
in a scenario where orthogonality does not hold and find that they still lead
to significantly improved performance.


  Given a completely positive (CP) map $T$, there is a theorem of the
Radon-Nikodym type [W.B. Arveson, Acta Math. {\bf 123}, 141 (1969); V.P.
Belavkin and P. Staszewski, Rep. Math. Phys. {\bf 24}, 49 (1986)] that
completely characterizes all CP maps $S$ such that $T-S$ is also a CP map. This
theorem is reviewed, and several alternative formulations are given along the
way. We then use the Radon-Nikodym formalism to study the structure of order
intervals of quantum operations, as well as a certain one-to-one correspondence
between CP maps and positive operators, already fruitfully exploited in many
quantum information-theoretic treatments. We also comment on how the
Radon-Nikodym theorem can be used to derive norm estimates for differences of
CP maps in general, and of quantum operations in particular.


  Evolutionary game theory examines how strategies spread and persist in
populations through reproduction and imitation based on their fitness.
Traditionally, models assume instantaneous dynamics where fitness depends on
the current population state. However, some real-world processes unfold over
time, with outcomes emerging from history. This motivates incorporating time
delays into evolutionary game models, where fitness relies on the past. We
study the impact of time delays on mutant fixation in a Moran Birth-death
process with two strategies in a well-mixed population. At each time step of
the process, an individual reproduces proportionally to fitness coming from the
past. We model this as an absorbing Markov chain, allowing computational
calculation of the fixation probability and time. We focus on three important
games: the Stag-Hunt, Snowdrift, and Prisoner's Dilemma. We will show time
delays reduce the fixation probability in the Stag-Hunt and the Prisoner's
Dilemma but increase it in the Snowdrift. For the Stag-Hunt and the Prisoner's
Dilemma, time delays lengthen the fixation time until a critical point, then
reduce it. The Snowdrift exhibits the opposite trend.


  Early diagnosis of retinal diseases such as diabetic retinopathy has had the
attention of many researchers. Deep learning through the introduction of
convolutional neural networks has become a prominent solution for image-related
tasks such as classification and segmentation. Most tasks in image
classification are handled by deep CNNs pretrained and evaluated on imagenet
dataset. However, these models do not always translate to the best result on
other datasets. Devising a neural network manually from scratch based on
heuristics may not lead to an optimal model as there are numerous
hyperparameters in play. In this paper, we use two nature-inspired swarm
algorithms: particle swarm optimization (PSO) and ant colony optimization (ACO)
to obtain TDCN models to perform classification of fundus images into severity
classes. The power of swarm algorithms is used to search for various
combinations of convolutional, pooling, and normalization layers to provide the
best model for the task. It is observed that TDCN-PSO outperforms imagenet
models and existing literature, while TDCN-ACO achieves faster architecture
search. The best TDCN model achieves an accuracy of 90.3%, AUC ROC of 0.956,
and a Cohen kappa score of 0.967. The results were compared with the previous
studies to show that the proposed TDCN models exhibit superior performance.


  EuRhAl4Si2, crystallizes in tetragonal crystal structure and orders
antiferromagnetically at ~12 K. The isothermal magnetization along the two
principle directions is highly anisotropic despite Eu2+ being an S-state ion.
The variation of entropy change, which is a measure of MCE, with field and
temperature, calculated from the isothermal magnetization data taken at various
temperatures along the principal crystallographic directions present
interesting behavior in EuRhAl4Si2. In the magnetically ordered state the
entropy change is non-monotonic below spin flip fields; however, in the
paramagnetic region, it is negative irrespective of the strength of applied
magnetic field. For H || [001] the maximum entropy change at 7 T is -21 J/Kg K
around TN, which is large and comparable to the largest known values in this
temperature range. The variation of the MCE with field strongly depends upon
the direction of the applied magnetic field. Magnetic phase diagram of
EuRhAl4Si2 derived from M(H) data is also constructed.


  Data breaches-mass leakage of stored information-are a major security
concern. Encryption can provide confidentiality, but encryption depends on a
key which, if compromised, allows the attacker to decrypt everything,
effectively instantly. Security of encrypted data thus becomes a question of
protecting the encryption keys. In this paper, we propose using keyless
encryption to construct a mass leakage resistant archiving system, where
decryption of a file is only possible after the requester, whether an
authorized user or an adversary, completes a proof of work in the form of
solving a cryptographic puzzle. This proposal is geared towards protection of
infrequently-accessed archival data, where any one file may not require too
much work to decrypt, decryption of a large number of files-mass
leakage-becomes increasingly expensive for an attacker. We present a prototype
implementation realized as a user-space file system driver for Linux. We report
experimental results of system behaviour under different file sizes and puzzle
difficulty levels. Our keyless encryption technique can be added as a layer on
top of traditional encryption: together they provide strong security against
adversaries without the key and resistance against mass decryption by an
attacker.


  We use the standard Runge-Kutta method to solve the set of basic equations
describing black hole accretion flows composed of two-temperature plasma. We do
not invoke any extra energy transport mechanism such as thermal conduction and
do not specify any ad hoc outer boundary condition for the advection-dominated
accretion flow (ADAF) solution. We find that in the case of high viscosity and
non-zero radiative cooling, the ADAF solution can have an asymptotic approach
to the Shakura-Sunyaev disk (SSD) solution, and the SSD-ADAF transition radius
is close to the central black hole. Our results further prove the mechanism of
thermal instability-triggered SSD-ADAF transition suggested previously by
Takeuchi & Mineshige and Gu & Lu.


  We present deep MUSE integral-field unit (IFU) spectroscopic observations of
the giant (~150 x 80 kpc) Ly-alpha halo around the z=4.1 radio galaxy TNJ
J1338-1942. This 9-hr observation maps the two-dimensional kinematics of the
Ly-alpha emission across the halo. We identify two HI absorbers which are seen
against the Ly-alpha emission, both of which cover the full 150 x 80 kpc extent
of the halo and so have covering fractions ~1. The stronger and more
blue-shifted absorber (dv~1200 km/s) has dynamics that mirror that of the
underlying halo emission and we suggest that this high column material (n(HI) ~
10^19.4 /cm^2), which is also seen in CIV absorption, represents an out-flowing
shell that has been driven by the AGN (or star formation) within the galaxy.
The weaker (n(HI)~10^14 /cm^2) and less blue shifted (dv~500 km/s) absorber
most likely represents material in the cavity between the out-flowing shell and
the Ly-alpha halo. We estimate that the mass in the shell must be of order
10^10 Msol -- a significant fraction of the ISM from a galaxy at z=4. The large
scales of these coherent structures illustrate the potentially powerful
influence of AGN feedback on the distribution and energetics of material in
their surroundings. Indeed, the discovery of high-velocity (~1000 km/s),
group-halo-scale (i.e. >150 kpc) and mass-loaded winds in the vicinity of the
central radio source are broadly in agreement with the requirements of models
that invoke AGN-driven outflows to regulate star formation and black-hole
growth in massive galaxies at early times.


  Coronary artery disease (CAD) is the most common cause of death globally, and
its diagnosis is usually based on manual myocardial segmentation of Magnetic
Resonance Imaging (MRI) sequences. As the manual segmentation is tedious,
time-consuming and with low applicability, automatic myocardial segmentation
using machine learning techniques has been widely explored recently. However,
almost all the existing methods treat the input MRI sequences independently,
which fails to capture the temporal information between sequences, e.g., the
shape and location information of the myocardium in sequences along time. In
this paper, we propose a myocardial segmentation framework for sequence of
cardiac MRI (CMR) scanning images of left ventricular cavity, right ventricular
cavity, and myocardium. Specifically, we propose to combine conventional
networks and recurrent networks to incorporate temporal information between
sequences to ensure temporal consistent. We evaluated our framework on the
Automated Cardiac Diagnosis Challenge (ACDC) dataset. Experiment results
demonstrate that our framework can improve the segmentation accuracy by up to
2% in Dice coefficient.


  Low-energy electron diffraction (LEED) is a widely used technique in
surface-science. Yet, it is rarely used to its full potential. The quantitative
information about the surface structure, contained in the modulation of the
intensities of the diffracted beams as a function of incident electron energy,
LEED I(V), is underutilized. To acquire these data, minor adjustments would be
required in most experimental setups, but existing analysis software is
cumbersome to use. ViPErLEED (Vienna package for Erlangen LEED) lowers these
barriers, introducing a combined solution for data acquisition, extraction, and
computational analysis. These parts are discussed in three separate
publications. Here, the focus is on the computational part of ViPErLEED, which
performs automated LEED-I(V) calculations and structural optimization. Minimal
user input is required, and the functionality is significantly enhanced
compared to existing solutions. Computation is performed by embedding the
Erlangen tensor-LEED package (TensErLEED). ViPErLEED manages parallelization,
monitors convergence, and processes input and output. This makes LEED I(V) more
accessible to new users while minimizing the potential for errors and the
manual labor. Added functionality includes structure-dependent defaults,
automatic detection of bulk and surface symmetries and their relationship,
automated symmetry-preserving search procedures, adjustments to the TensErLEED
code to handle larger systems, as well as parallelization and optimization.
Modern file formats are used as input and output, and there is a direct
interface to the Atomic Simulation Environment (ASE) package. The software is
implemented primarily in Python (version >=3.7) and provided as an open-source
package (GNU GPLv3 or later). A structure determination of the
$\alpha$-Fe2O3(1-102)-(1x1) surface is presented as an example for the
application of the software.


  One of the most common things that a genealogist is tasked with is the
gathering of a person's initial family history, normally via in-person
interviews or with the use of a platform such as ancestry.com, as this can
provide a strong foundation upon which a genealogist may build. However, the
ability to conduct these interviews can often be hindered by both geographical
constraints and the technical proficiency of the interviewee, as the
interviewee in these types of interviews is most often an elderly person with a
lower than average level of technical proficiency. With this in mind, this
study presents what we believe, based on prior research, to be the first
chatbot geared entirely towards the gathering of family histories, and explores
the viability of utilising such a chatbot by comparing the performance and
usability of such a method with the aforementioned alternatives. With a
chatbot-based approach, we show that, though the average time taken to conduct
an interview may be longer than if the user had used ancestry.com or
participated in an in-person interview, the number of mistakes made and the
level of confusion from the user regarding the UI and process required is lower
than the other two methods. Note that the final metric regarding the user's
confusion is not applicable for the in-person interview sessions due to its
lack of a UI. With refinement, we believe this use of a chatbot could be a
valuable tool for genealogists, especially when dealing with interviewees who
are based in other countries where it is not possible to conduct an in-person
interview.


  Internet of Things (IoT) has been emerging as one of the use cases permeating
our daily lives in 5th Generation wireless networks, where status update
packages are usually required to be timely delivered for many IoT based
intelligent applications. Enabling the collected raw data to be preprocessed
before transmitted to the destination can provider users with better
context-aware services and lighten the transmission burden. However, the effect
from data preprocessing on the overall information freshness is an essential
yet unrevealed issue. In this work we study the joint effects of data
preprocessing and transmission procedures on information freshness measured by
peak age of information (PAoI). Particularity, we formulate the considered
multi-source preprocessing and transmission enabled IoT system as a tandem
queue where a priority M/G/1 queue is followed by a G/G/1 queue. Then, we
respectively derive the closed-form and an information theoretic approximation
of the expectation of waiting time for the formulated processing queue and
transmission queue, and further get the analytical expressions of the average
PAoI for packages from different sources. Finally, the accuracy of our analysis
is verified with simulation results.


  Weakly Interacting Massive Particles (WIMPs) and axions are arguably the most
compelling dark matter candidates in the literature. Could they coexist as dark
matter particles? More importantly, can they be incorporated in a well
motivated framework in agreement with experimental data? In this work, we show
that this two component dark matter can be realized in the Inert Doublet Model
in an elegant and natural manner by virtue of the spontaneous breaking of a
Peccei-Quinn $U(1)_{PQ}$ symmetry into a residual $Z_2$ symmetry. The WIMP
stability is guaranteed by the $Z_{2}$ symmetry and a new dark matter
component, the axion, arises. There are two interesting outcomes: (i)
vector-like quarks needed to implement the Peccei-Quinn symmetry in the model
act as a portal between the dark sector and the SM fields with a
supersymmetry-type phenomenology at colliders; (ii) two-component Inert Doublet
Model re-opens the phenomenologically interesting 100-500 GeV mass region. We
show that the model can plausibly have two component dark matter and at the
same time avoid low and high energy physics constraints such as monojet and
dijet plus missing energy, as well as indirect and direct dark matter detection
bounds.


  The Milky Way nuclear star cluster (MW NSC) has been used as a template to
understand the origin and evolution of galactic nuclei and the interaction of
nuclear star clusters with supermassive black holes. It is the only nuclear
star cluster with a supermassive black hole where we can resolve individual
stars to measure their kinematics and metal abundance to reconstruct its
formation history. Here, we present results of the first chemo-dynamical model
of the inner 1 pc of the MW NSC using metallicity and radial velocity data from
the KMOS spectrograph on the Very Large Telescope. We find evidence for two
kinematically and chemically distinct components in this region. The majority
of the stars belong to a previously known super-solar metallicity component
with a rotation axis perpendicular to the Galactic plane. However, we identify
a new kinematically distinct sub-solar metallicity component which contains
about 7\% of the stars and appears to be rotating faster than the main
component with a rotation axis that may be misaligned. This second component
may be evidence for an infalling star cluster or remnants of a dwarf galaxy,
merging with the MW NSC. These measurements show that the combination of
chemical abundances with kinematics is a promising method to directly study the
MW NSC's origin and evolution.


  The development of robotic grippers is driven by the need to execute
particular manual tasks or meet specific objectives in handling operations.
Grippers with specific functions vary from being small, accurate and highly
controllable such as the surgical tool effectors of the Da Vinci robot
(designed to be used as non-invasive grippers controlled by a human operator
during keyhole surgeries) to larger, highly controllable grippers like the
Shadow Dexterous Hand (designed to recreate the hand motions of a human).
Additionally, there are less finely controllable grippers, such as the
iRobot-Harvard-Yale (iHY) Hand or Istituto Italiano di Tecnoglia-Pisa
(IIT-Pisa) Softhand, which instead leverage natural motions during grasping via
designs inspired by observed bio-mechanical systems.
  As robotic systems become more autonomous and widely used, it is becoming
increasingly important to consider the design, form and function of robotic
grippers.


  Large transformer-based language models, e.g. BERT and GPT-3, outperform
previous architectures on most natural language processing tasks. Such language
models are first pre-trained on gigantic corpora of text and later used as
base-model for finetuning on a particular task. Since the pre-training step is
usually not repeated, base models are not up-to-date with the latest
information. In this paper, we update RobBERT, a RoBERTa-based state-of-the-art
Dutch language model, which was trained in 2019. First, the tokenizer of
RobBERT is updated to include new high-frequent tokens present in the latest
Dutch OSCAR corpus, e.g. corona-related words. Then we further pre-train the
RobBERT model using this dataset. To evaluate if our new model is a plug-in
replacement for RobBERT, we introduce two additional criteria based on concept
drift of existing tokens and alignment for novel tokens.We found that for
certain language tasks this update results in a significant performance
increase. These results highlight the benefit of continually updating a
language model to account for evolving language use.


  Most quantum information tasks based on Bell tests relie on the assumption of
measurement independence. However, it is difficult to ensure that the
assumption of measurement independence is always met in experimental
operations, so it is crucial to explore the effects of relaxing this assumption
on Bell tests. In this paper, we discuss the effects of relaxing the assumption
of measurement independence on 1-parameter family of Bell (1-PFB) tests. For
both general and factorizable input distributions, we establish the
relationship among measurement dependence, guessing probability, and the
maximum value of 1-PFB correlation function that Eve can fake. The
deterministic strategy when Eve fakes the maximum value is also given. We
compare the unknown information rate of Chain inequality and 1-PFB inequality,
and find the range of the parameter in which it is more difficult for Eve to
fake the maximum quantum violation in 1-PFB inequality than in Chain
inequality.


  The paper suggests a family of paired comparison-based scoring procedures for
ranking the participants of a Swiss system chess team tournament. We present
the challenges of ranking in Swiss system, the features of individual and team
competitions as well as the failures of the official rankings based on
lexicographical order. The tournament is represented as a ranking problem such
that the linearly-solvable row sum (score), generalized row sum, and least
squares methods have favourable axiomatic properties.
  Two chess team European championships are analysed as case studies. Final
rankings are compared by their distances and visualized with multidimensional
scaling (MDS). Differences to the official ranking are revealed by the
decomposition of the least squares method. Rankings are evaluated by prediction
power, retrodictive performance, and stability. The paper argues for the use of
least squares method with a results matrix favouring match points on the basis
of its relative insensitivity to the choice between match and board points,
retrodictive accuracy, and robustness.


  In [M. Walter et al., Science 340, 1205, 7 June (2013)], they gave a
sufficient condition for genuinely entangled pure states and discussed SLOCC
classification via polytopes and the eigenvalues of the single-particle states.
In this paper, for $4n$ qubits, we show the invariance of algebraic
multiplicities (AMs) and geometric multiplicities (GMs) of eigenvalues and the
invariance of sizes of Jordan blocks (JBs) of the coefficient matrices under
SLOCC. We explore properties of spectra, eigenvectors, generalized
eigenvectors, standard Jordan normal forms (SJNFs), and Jordan chains of the
coefficient matrices. The properties and invariance permit a reduction of SLOCC
classification of $4n$ qubits to integer partitions (in number theory) of the
number $2^{2n}-k$ and the AMs.


  We explain the necessity of application of semi-metric in general relativity.
A detailed discussion on the energy-momentum conservation in the general
relativity is presented using the mathematical tool of semi-metric. By means of
the general covariant spacetime translation transformation, the most general
covariant conservation law of energy-momentum is obtained, which is valid for
any coordinates and overcomes the flaws of the expressions of Einstain, Landau
and Moller.


  We use the English model of BERT and explore how a deletion of one word in a
sentence changes representations of other words. Our hypothesis is that
removing a reducible word (e.g. an adjective) does not affect the
representation of other words so much as removing e.g. the main verb, which
makes the sentence ungrammatical and of "high surprise" for the language model.
We estimate reducibilities of individual words and also of longer continuous
phrases (word n-grams), study their syntax-related properties, and then also
use them to induce full dependency trees.


  Task-specific scores are often used to optimize for and evaluate the
performance of conditional text generation systems. However, such scores are
non-differentiable and cannot be used in the standard supervised learning
paradigm. Hence, policy gradient methods are used since the gradient can be
computed without requiring a differentiable objective.
  However, we argue that current n-gram overlap based measures that are used as
rewards can be improved by using model-based rewards transferred from tasks
that directly compare the similarity of sentence pairs. These reward models
either output a score of sentence-level syntactic and semantic similarity
between entire predicted and target sentences as the expected return, or for
intermediate phrases as segmented accumulative rewards.
  We demonstrate that using a \textit{Transferable Reward Learner} leads to
improved results on semantical evaluation measures in policy-gradient models
for image captioning tasks. Our InferSent actor-critic model improves over a
BLEU trained actor-critic model on MSCOCO when evaluated on a Word Mover's
Distance similarity measure by 6.97 points, also improving on a Sliding Window
Cosine Similarity measure by 10.48 points. Similar performance improvements are
also obtained on the smaller Flickr-30k dataset, demonstrating the general
applicability of the proposed transfer learning method.


  We define and study Poissonian actions of Polish groups as a framework to
Poisson suspensions, characterize them spectrally, and provide a complete
characterization of their ergodicity. We further construct 'spatial' Poissonian
actions, answering partially a question of Glasner, Tsirelson & Weiss about
L\'evy groups. We also construct for every diffeomorphism group an ergodic free
spatial probability preserving actions. This constitutes a new class of Polish
groups admitting non-essentially countable orbit equivalence relations,
obtaining progress on a problem of Kechris.


  In the context of learning to map an input $I$ to a function
$h_I:\mathcal{X}\to \mathbb{R}$, two alternative methods are compared: (i) an
embedding-based method, which learns a fixed function in which $I$ is encoded
as a conditioning signal $e(I)$ and the learned function takes the form $h_I(x)
= q(x,e(I))$, and (ii) hypernetworks, in which the weights $\theta_I$ of the
function $h_I(x) = g(x;\theta_I)$ are given by a hypernetwork $f$ as
$\theta_I=f(I)$. In this paper, we define the property of modularity as the
ability to effectively learn a different function for each input instance $I$.
For this purpose, we adopt an expressivity perspective of this property and
extend the theory of Devore et al. 1996 and provide a lower bound on the
complexity (number of trainable parameters) of neural networks as function
approximators, by eliminating the requirements for the approximation method to
be robust. Our results are then used to compare the complexities of $q$ and
$g$, showing that under certain conditions and when letting the functions $e$
and $f$ be as large as we wish, $g$ can be smaller than $q$ by orders of
magnitude. This sheds light on the modularity of hypernetworks in comparison
with the embedding-based method. Besides, we show that for a structured target
function, the overall number of trainable parameters in a hypernetwork is
smaller by orders of magnitude than the number of trainable parameters of a
standard neural network and an embedding method.


  We prove that any complete metric on R^3 minus a ball with non-negative Ricci
curvature and quadratic Ricci-curvature decay, has cubic volume growth.


  We establish necessary and sufficient conditions for a semigroup identity to
hold in the monoid of $n\times n$ upper triangular tropical matrices, in terms
of equivalence of certain tropical polynomials. This leads to an algorithm for
checking whether such an identity holds, in time polynomial in the length of
the identity and size of the alphabet. It also allows us to answer a question
of Izhakian and Margolis, by showing that the identities which hold in the
monoid of $2\times 2$ upper triangular tropical matrices are exactly the same
as those which hold in the bicyclic monoid. Our results extend to a broader
class of "chain structured tropical matrix semigroups"; we exhibit a faithful
representation of the free monogenic inverse semigroup within such a semigroup,
which leads also to a representation by $3\times 3$ upper triangular matrix
semigroups, and a new proof of the fact that this semigroup satisfies the same
identities as the bicyclic monoid.


  Let $M$ be a Seifert fiber space with non-abelian fundamental group and
admitting a triangulation with $t$ tetrahedra. We show that there is a
non-abelian $\text{PSL}(2, \mathbb{F})$ quotient where $|\mathbb F| <
c(2^{20t}3^{120t})$ for an absolute constant $c>0$ and use this to show that
the lens space recognition problem lies in coNP for Seifert fiber space input.
We end with a discussion of our results in the context of distinguishing lens
spaces from other $3$--manifolds more generally.


  We describe a nef divisor $D_P$ on $\bar{M}_{0,12}$ that is not numerically
equivalent to an effective sum of boundary divisors.


  We propose a composite multilayer structure consist of graphene Tamm plasmon
and Bragg reflector with defect layer to realize the low threshold and tunable
optical bistability (OB) at the terahertz frequencies. This low-threshold OB
originates from the couple of the Tamm plasmon (TP) and the defect mode (DM).
We discuss the influence of graphene and the DM on the hysteretic response of
the TM-polarized reflected light. It is found that the switch-up and
switch-down threshold required to observe the optical bistable behavior are
lowered markedly due to the excitation of the TP and DM. Besides, the switching
threshold value can be further reduced by coupling the TP and DM. We believe
these results will provide a new avenue for realizing the low threshold and
tunable optical bistable devices and other nonlinear optical devices.


  Using free world-sheet fermions, we construct and classify all the N=2, Z2 X
Z2 four-dimensional orbifolds of the type IIA/B strings for which the orbifold
projections act symmetrically on the left and right movers. We study the
deformations of these models out of the fermionic point, deriving the partition
functions at a generic point in the moduli of the internal torus T6=T2 X T2 X
T2. We investigate some of their perturbative and non-perturbative dualities
and construct new dual pairs of type IIA/type II asymmetric orbifolds, which
are related non-perturbatively and allow us to gain insight into some of the
non-perturbative properties of the type IIA/B strings in four dimensions. In
particular, we consider some of the (non-)perturbative gravitational
corrections.


  Existing algorithms for human body part segmentation have shown promising
results on challenging datasets, primarily relying on end-to-end supervision.
However, these algorithms exhibit severe performance drops in the face of
domain shifts, leading to inaccurate segmentation masks. To tackle this issue,
we introduce POSTURE: \underline{Po}se Guided Un\underline{s}upervised Domain
Adap\underline{t}ation for H\underline{u}man Body Pa\underline{r}t
S\underline{e}gmentation - an innovative pseudo-labelling approach designed to
improve segmentation performance on the unlabeled target data. Distinct from
conventional domain adaptive methods for general semantic segmentation, POSTURE
stands out by considering the underlying structure of the human body and uses
anatomical guidance from pose keypoints to drive the adaptation process. This
strong inductive prior translates to impressive performance improvements,
averaging 8\% over existing state-of-the-art domain adaptive semantic
segmentation methods across three benchmark datasets. Furthermore, the inherent
flexibility of our proposed approach facilitates seamless extension to
source-free settings (SF-POSTURE), effectively mitigating potential privacy and
computational concerns, with negligible drop in performance.


  We construct a non-Hamiltonian symplectic circle action on a closed,
connected, six-dimensional symplectic manifold with exactly 32 fixed points.


  The major source of noise in high-contrast imaging is the presence of slowly
evolving speckles that do not average with time. The temporal stability of the
point-spread-function (PSF) is therefore critical to reach a high contrast with
extreme adaptive optics (xAO) instruments. Understanding on which timescales
the PSF evolves and what are the critical parameters driving the speckle
variability allow to design an optimal observing strategy and data reduction
technique to calibrate instrumental aberrations and reveal faint astrophysical
sources. We have obtained a series of 52 min, AO-corrected, coronagraphically
occulted, high-cadence (1.6Hz), H-band images of the star HR 3484 with the
SPHERE (Spectro-Polarimeter High-contrast Exoplanet REsearch instrument on the
VLT. This is a unique data set from an xAO instrument to study its stability on
timescales as short as one second and as long as several tens of minutes. We
find different temporal regimes of decorrelation. We show that residuals from
the atmospheric turbulence induce a fast, partial decorrelation of the PSF over
a few seconds, before a transition to a regime with a linear decorrelation with
time, at a rate of several tens parts per million per second (ppm/s). We
analyze the spatial dependence of this decorrelation, within the well-corrected
radius of the adaptive optics system and show that the linear decorrelation is
faster at short separations. Last, we investigate the influence of the distance
to the meridian on the decorrelation.


  The rapid variability of X-ray binaries produces a wide range of X-ray states
that are linked to activity across the electromagnetic spectrum. It is
particularly challenging to study a sample of sources large enough to include
all types in their various states, and to cover the full range of frequencies
that show flux density variations. Simultaneous observations with many
telescopes are necessary. In this project we monitor 48 X-ray binaries with
seven telescopes across the electromagnetic spectrum from 5 x 10^9 Hz to 10^19
Hz, including ground-based radio, IR, and optical observatories and five
instruments on two spacecraft over a one-week period. We construct spectral
energy distributions and matching X-ray color-intensity diagrams for 20 sources
that have the most extensive detections. Our observations are consistent with
several models of expected behavior proposed for the different classes: we
detect no significant radio emission from pulsars or atoll sources, but we do
detect radio emission from Z sources in the normal or horizontal branch, and
from black holes in the high/soft, low/hard and quiescent states. The survey
data provide useful constraints for more detailed models predicting behavior
from the different classes of sources.


  For the sub-Riemannian problem on the group of motions of Euclidean space we
present explicit formulas for extremal controls in a special case, when one of
the initial momenta is fixed.


  We developed a new semi-analytic galaxy formation model: Galaxy Assembly with
Binary Evolution (GABE). For the first time we introduce binary evolution into
semi-analytic models of galaxy formation by using Yunnan-II stellar population
synthesis model, which includes various binary interactions. When implementing
our galaxy formation model onto the merger trees extracted from the Millennium
simulation, it can reproduce a large body of observational results. We find
that in the local universe the model including binary evolution reduces the
luminosity at optical and infrared wavelengths slightly, while increases the
luminosity at ultraviolet wavelength significantly, especially in $F_{\rm UV}$
band. The resulting luminosity function does not change very much over SDSS
optical bands and infrared band, but the predicted colors are bluer, especially
when $F_{\rm UV}$ band is under consideration. The new model allows us to
explore the physics of various high energy events related to the remnants of
binary stars, e.g. type Ia supernovae, short gamma-ray bursts and gravitational
wave events, and their relation with host galaxies in a cosmological context.


  This paper considers symbol-level precoding (SLP) for multiuser multi-input
single-output (MISO) downlink transmission with the aid of intelligent
reflecting surface (IRS). Specifically, by assuming one-bit transmitted signals
at the base station (BS), which arises from the use of low-resolution DACs in
the regime of massive transmit antennas, a joint design of one-bit SLP at the
BS and the phase shifts at the IRS is proposed with a goal of minimizing the
worst-case symbol error probability (SEP) of the users under the PSK
modulation. This joint design problem is essentially a mixed integer nonlinear
program (MINLP). To tackle it, we alternately optimize the one-bit signal and
the phase shifts. For the former, a dual of the relaxed one-bit SLP problem is
solved by the mirror descent (MD) method with the maximum block improvement
(MBI) heuristics. For the latter, the accelerated projected gradient (APG)
method is employed to optimize the phases. Numerical results demonstrate that
the proposed joint design can attain better SEP performance than the
conventional linear precoding and one-bit SLP.


  A new type of the gas cell for the resonance ionization laser ion source at
the Leuven Isotope Separator On Line (LISOL) has been developed and tested at
off-line and on-line conditions. Two-step selective laser ionization is applied
to produce purified beams of radioactive isotopes. The selectivity of the ion
source has been increased by more than one order of magnitude by separation of
the stopping and laser ionization regions. This allows to use electrical fields
for further ion purification.


  The new frontier in the quest for the highest contrast levels in the focal
plane of a coronagraph is now the correction of the large diffractive artifacts
effects introduced at the science camera by apertures of increasing complexity.
The coronagraph for the WFIRST/AFTA mission will be the first of such
instruments in space with a two Deformable Mirrors wavefront control system.
Regardless of the control algorithm for these multi Deformable Mirrors, they
will have to rely on quick and accurate simulation of the propagation effects
introduced by the out-of-pupil surface. In the first part of this paper, we
present the analytical description of the different approximations to simulate
these propagation effects. In Annex A, we prove analytically that, in the
special case of surfaces inducing a converging beam, the Fresnel method yields
high fidelity for simulations of these effects. We provide numerical
simulations showing this effect. In the second part, we use these tools in the
framework of the Active Compensation of Aperture Discontinuities technique
(ACAD) applied to pupil geometries similar to WFIRST-AFTA. We present these
simulations in the context of the optical layout of the High-contrast imager
for Complex Aperture Telescopes, which will test ACAD on a optical bench. The
results of this analysis show that using the ACAD method, an apodized pupil
lyot coronagraph and the performance of our current deformable mirrors, we are
able to obtain, in numerically simulations, a dark hole with an AFTA-like
pupil. Our numerical simulation shows that we can obtain contrast better than
$2.10^{-9}$ in monochromatic light and better than 3.e-8 with 10% bandwidth
between 5 and 14 lambda/D.


  A Green-function theory for the dynamic spin susceptibility in the
square-lattice spin-1/2 antiferromagnetic compass-Heisenberg model employing a
generalized mean-field approximation is presented. The theory describes
magnetic long-range order (LRO) and short-range order (SRO) at arbitrary
temperatures. The magnetization, N'eel temperature T_N, specific heat, and
uniform static spin susceptibility $\chi$ are calculated self-consistently. As
the main result, we obtain LRO at finite temperatures in two dimensions, where
the dependence of T_N on the compass-model interaction is studied. We find that
T_N is close to the experimental value for Ba2IrO4. The effects of SRO are
discussed in relation to the temperature dependence of $\chi$.


  We propose the Factorized Fourier Neural Operator (F-FNO), a learning-based
approach for simulating partial differential equations (PDEs). Starting from a
recently proposed Fourier representation of flow fields, the F-FNO bridges the
performance gap between pure machine learning approaches to that of the best
numerical or hybrid solvers. This is achieved with new representations -
separable spectral layers and improved residual connections - and a combination
of training strategies such as the Markov assumption, Gaussian noise, and
cosine learning rate decay. On several challenging benchmark PDEs on regular
grids, structured meshes, and point clouds, the F-FNO can scale to deeper
networks and outperform both the FNO and the geo-FNO, reducing the error by 83%
on the Navier-Stokes problem, 31% on the elasticity problem, 57% on the airfoil
flow problem, and 60% on the plastic forging problem. Compared to the
state-of-the-art pseudo-spectral method, the F-FNO can take a step size that is
an order of magnitude larger in time and achieve an order of magnitude speedup
to produce the same solution quality.


  Identifying local structural motifs and packing patterns of molecular solids
is a challenging task for both simulation and experiment. We demonstrate two
novel approaches to characterize local environments in different polymorphs of
molecular crystals using learning models that employ either flexibly learned or
handcrafted molecular representations. In the first case, we follow our earlier
work on graph learning in molecular crystals, deploying an atomistic graph
convolutional network, combined with molecule-wise aggregation, to enable
per-molecule environmental classification. For the second model, we develop a
new set of descriptors based on symmetry functions combined with a point-vector
representation of the molecules, encoding information about the positions as
well as relative orientations of the molecule. We demonstrate very high
classification accuracy for both approaches on urea and nicotinamide crystal
polymorphs, and practical applications to the analysis of dynamical trajectory
data for nanocrystals and solid-solid interfaces. Both architectures are
applicable to a wide range of molecules and diverse topologies, providing an
essential step in the exploration of complex condensed matter phenomena.


  The global energy fluctuations of a low density gas granular gas in the
homogeneous cooling state near its clustering instability are studied by means
of molecular dynamics simulations. The relative dispersion of the fluctuations
is shown to exhibit a power-law divergent behavior. Moreover, the probability
distribution of the fluctuations presents data collapse as the system
approaches the instability, for different values of the inelasticity. The
function describing the collapse turns out to be the same as the one found in
several molecular equilibrium and non-equilibrium systems, except for the
change in the sign of the fluctuations.


  The twisted torus link $T(p, q; r, s)$ is obtained by twisting $r$ parallel
strands of the $(p, q)$-torus link a total of $s$ full times. In this paper we
find all twisted torus links which are hyperbolic for $\vert s\vert >3$.


  With the art and practice of government policy-making, public work, and
citizen participation, many governments adopt information and communication
technologies (ICT) as a vehicle to facilitate their relationship with citizens.
This participation process is widely known as E-Participation or Electronic
Participation. This article focuses on different performance indicators and the
relevant tools for each level. Despite the growing scientific and pragmatic
significance of e-participation, that area still was not able to grow as it was
expected. Our diverse set of knowledge and e-participation policies and its
implementation is very limited. This is the key reason why e-participation
initiatives in practice often fall short of expectations. This study collects
the existing perceptions from the various interdisciplinary scientific
literature to determine a unifying definition and demonstrates the strong
abilities of e-participation and other related components which have great
potential in the coming years.


  This paper proposes a method to disentangle and quantify interactions among
words that are encoded inside a DNN for natural language processing. We
construct a tree to encode salient interactions extracted by the DNN. Six
metrics are proposed to analyze properties of interactions between constituents
in a sentence. The interaction is defined based on Shapley values of words,
which are considered as an unbiased estimation of word contributions to the
network prediction. Our method is used to quantify word interactions encoded
inside the BERT, ELMo, LSTM, CNN, and Transformer networks. Experimental
results have provided a new perspective to understand these DNNs, and have
demonstrated the effectiveness of our method.


  We obtain a representation formula for solutions to Schr\"odinger equations
with a class of homogeneous, scaling-critical electromagnetic potentials. As a
consequence, we prove the sharp $L^{1}\to L^{\infty}$ time decay estimate for
the 3D-inverse square and the 2D-Aharonov-Bohm potentials.


  Long-range magnetostatic interaction between wires strongly depends on their
spatial position. This interaction, combined with applied tensile stress,
influences the hysteresis loop of the system of wires through the stress
dependence of their coercive fields. As a result, we obtain a set of stable
magnetic states of the system, dependent on the applied field, applied stress
and mutual positions of the wires. These states can be used to encode the
system history.


  Given a knot and an SL(n,C) representation of its group that is conjugate to
its dual, the representation that replaces each matrix with its
inverse-transpose, the associated twisted Reidemeister torsion is reciprocal.
An example is given of a knot group and SL(3,Z) representation that is not
conjugate to its dual for which the twisted Reidemeister torsion is not
reciprocal.


  The aim of the project is to identify wide common proper motion companions to
a sample of spectroscopically confirmed M and L metal-poor dwarfs (also known
as subdwarfs) to investigate the impact of metallicity on the binary fraction
of low-mass metal-poor binaries and to improve the determination of their
metallicity from the higher-mass binary. We made use of Virtual Observatory
tools and large-scale public surveys to look in Gaia for common proper motion
companions to a well-defined sample of ultracool subdwarfs with spectral types
later than M5 and metallicities below or equal to $-$0.5 dex. We collected
low-resolution optical spectroscopy for our best system, which is a binary
composed of one sdM1.5 subdwarf and one sdM5.5 subdwarf located at $\sim$1,360
au, and for another two likely systems separated by more than 115,000 au. We
confirm one wide companion to an M subdwarf, and infer a multiplicity for M
subdwarfs (sdMs) of $1.0_{-1.0}^{+2.0}$% for projected physical separations of
up to 743,000 au. We also find four M$-$L systems, three of which are new
detections. No colder companion was identified in any of the 219 M and L
subdwarfs of the sample, mainly because of limitations on the detection of
faint sources with Gaia. We infer a frequency of wide systems for sdM5$-$9.5 of
$0.60_{-0.60}^{+1.17}$% for projected physical separations larger than 1,360 au
(up to 142,400 au). This study shows a multiplicity rate of
$1.0_{-1.0}^{+2.0}$% in sdMs, and $1.9_{-1.9}^{+3.7}$% in extreme M subdwarfs
(esdMs). We did not find any companion for the ultra M subdwarfs (usdMs) of our
sample, establishing an upper limit of 5.3% on binarity for these objects.


  We study the notion of joinings of W*-dynamical systems, building on ideas
from measure theoretic ergodic theory. In particular we prove sufficient and
necessary conditions for ergodicity in terms of joinings, and also briefly look
at conditional expectation operators associated with joinings.


  A general proof of the security against eavesdropping of a previously
introduced protocol for two-party quantum key distribution based on
entanglement swapping [Phys. Rev. A {\bf 61}, 052312 (2000)] is provided. In
addition, the protocol is extended to permit multiparty quantum key
distribution and secret sharing of classical information.


  We present a detailed study of the topological properties of the Kitaev chain
with long-range pairing terms and in the presence of an Aubry-Andr\'e-Harper
on-site potential. Specifically, we consider algebraically decaying
superconducting pairing amplitudes; the exponent of this decay is found to
determine a critical pairing strength, below which the chain remains
topologically trivial. Above the critical pairing, topological edge modes are
observed in the central gap. For sufficiently fast decay of the pairing, these
modes are identified as Majorana zero-modes. However, if the pairing term
decays slowly, the modes become massive Dirac modes. Interestingly, these
massive modes still exhibit a true level crossing at zero energy, which points
towards an initimate relation to Majorana physics. We also observe a clear lack
of bulk-boundary correspondence in the long-range system, where bulk
topological invariants remain constant, while dramatic changes appear in the
behavior at the edge of the system. In addition to the central gap around zero
energy, the Aubry-Andr\'e-Harper potential also leads to other energy gaps at
non-zero energy. As for the analogous short-range model, the edge modes in
these gaps can be characterized through a 2D Chern invariant. However, in
contrast to the short-range model, this topological invariant does not
correspond to the number of edge mode crossings anymore. This provides another
example for the weakening of the bulk-boundary correspondence occurring in this
model. Finally, we discuss possible realizations of the model with ultracold
atoms and condensed matter systems.


  We present the theory of modulation instability induced by spectrally
dependent losses (optical filters) in passive driven nonlinear fiber ring
resonators. Starting from an Ikeda map description of the propagation equation
and boundary conditions, we derive a mean field model - a generalised
Lugiato-Lefever equation - which reproduces with great accuracy the predictions
of the map. The effects on instability gain and comb generation of the
different control parameters such as dispersion, cavity detuning, filter
spectral position and bandwidth are discussed.


  For intelligent reflecting surface (IRS) aided downlink communication in
frequency division duplex (FDD) systems, the overhead for the base station (BS)
to acquire channel state information (CSI) is extremely high under the
conventional ``estimate-then-quantize'' scheme, where the users first estimate
and then feed back their channels to the BS. Recently, [1] revealed a strong
correlation in different users' cascaded channels stemming from their common
BS-IRS channel component, and leveraged such a correlation to significantly
reduce the pilot transmission overhead in IRS-aided uplink communication. In
this paper, we aim to exploit the above channel property for reducing the
overhead of both pilot transmission and feedback transmission in IRS-aided
downlink communication. Different from the uplink counterpart where the BS
possesses the pilot signals containing the CSI of all the users, in downlink
communication, the distributed users merely receive the pilot signals
containing their own CSI and cannot leverage the correlation in different
users' channels revealed in [1]. To tackle this challenge, this paper proposes
a novel ``quantize-then-estimate'' protocol in FDD IRS-aided downlink
communication. Specifically, the users first quantize their received pilot
signals, instead of the channels estimated from the pilot signals, and then
transmit the quantization bits to the BS. After de-quantizing the pilot signals
received by all the users, the BS estimates all the cascaded channels by
leveraging the correlation embedded in them, similar to the uplink scenario.
Furthermore, we manage to show both analytically and numerically the great
overhead reduction in terms of pilot transmission and feedback transmission
arising from our proposed ``quantize-then-estimate'' protocol.


  This article reviews the status of the field of dark matter as of summer
2017, when it was discussed at 35th International Cosmic Ray Conference (ICRC
2017) in Busan, Korea. It is the write-up of a rapporteur talk on the status of
dark matter searches given at the conference.


  We revisit the higher spin extensions of the anti de Sitter algebra in four
dimensions that incorporate internal symmetries and admit representations that
contain fermions, classified long ago by Konstein and Vasiliev. We construct
the $dS_4$, Euclidean and Kleinian version of these algebras, as well as the
corresponding fully nonlinear Vasiliev type higher spin theories, in which the
reality conditions we impose on the master fields play a crucial role. The
${\cal N}=2$ supersymmetric higher spin theory in $dS_4$, on which we elaborate
further, is included in this class of models. A subset of Konstein-Vasiliev
algebras are the higher spin extensions of the $AdS_4$ superalgebras
$osp(4|{\cal N})$ for ${\cal N}=1,2,4$ mod 4 and can be realized using
fermionic oscillators. We tensor the higher superalgebras of the latter kind
with appropriate internal symmetry groups and show that the ${\cal N}=3$ mod 4
higher spin algebras are isomorphic to those with ${\cal N}=4$ mod 4. We
describe the fully nonlinear higher spin theories based on these algebras as
well, and we elaborate further on the ${\cal N}=6$ supersymmetric theory,
providing two equivalent descriptions one of which exhibits manifestly its
relation to the ${\cal N}=8$ supersymmetric higher spin theory.


  Whilst the need for dark matter was established almost a century ago, only
its gravitational interaction has been confirmed so far, allowing for plethora
of models for dark matter. The Weakly Interacting Massive Particles (WIMPs)
category has received by far the biggest attention, however despite the
enormous experimental efforts, these particles remain elusive. The attention of
the community has hence moved on to investigate the dark matter landscape over
a much larger number of models with varying degrees of resemblances and
differences in their predictions. This calls for the need to organise the
various facets of dark matter models and their signatures, in order to maximise
the experimental sensitivity and to select the models which are compatible with
existing data. In this paper, I provide a short review of the most widespread
public codes capable of computing dark matter observables. In particular, I
discuss what is the status of each numerical tool in terms of: (i) capturing
the WIMP phenomenology and (ii) accounting for new trend dark sector models
that might be weakly coupled to ordinary matter and/or be strongly
self-interacting. This short review has the aim of guiding the user towards
selecting the best suited public code to confront his/her model with the
largest variety of theoretical predictions and experimental data in order to
determine the parameter space consistent with observations for his/her
favourite dark matter model.


  We theoretically investigate nonequilibrium spin fluctuations in a
ferromagnet induced by a light pulse. Using a Lindblad equation consistent with
the Landau-Lifshitz-Gilbert equation, we compute the autocorrelation function
of magnetization. Our analysis reveals that this function comprises both
thermal and nonequilibrium components. To examine the latter in detail, we
introduce a Fano factor similar to nonequilibrium current noise in electronic
circuits. We demonstrate that this factor encapsulates insights into the
transfer of spin units to the environment. Our findings lay the groundwork for
nonequilibrium spin noise spectroscopy, offering valuable insights into spin
relaxation dynamics.


  Chalcogen vacancies in transition metal dichalcogenides are widely
acknowledged as both donor dopants and as a source of disorder. The electronic
structure of sulphur vacancies in MoS2 however is still controversial, with
discrepancies in the literature pertaining to the origin of the in-gap features
observed via scanning tunneling spectroscopy (STS) on single sulphur vacancies.
Here we use a combination of scanning tunnelling microscopy (STM) and STS to
study embedded sulphur vacancies in bulk MoS2 crystals. We observe
spectroscopic features dispersing in real space and in energy, which we
interpret as tip position- and bias-dependent ionization of the sulphur vacancy
donor due to tip induced band bending (TIBB). The observations indicate that
care must be taken in interpreting defect spectra as reflecting in-gap density
of states, and may explain discrepancies in the literature.


  Distinct from the phononic valley pseudo-spin, the real physical spin of
elastic waves adds a novel tool-kit capable of envisaging the valley-spin
physics of topological valley phononic crystals from a local viewpoint. Here,
we report the observation of local elastic valley spin as well as the hidden
elastic spin-valley locking mechanism overlooked before. We demonstrate that
the selective one-way routing of valley phonon states along the topological
interface can be reversed by imposing the elastic spin meta-source at different
interface locations with opposite valley-spin correspondence. We unveil the
physical mechanism of selective directionality as the elastic spin controlled
chiral coupling of valley phonon states, through both analytical theory and
experimental measurement of the opposite local elastic spin density at
different interface locations for different transport directions. The elastic
spin of valley topological edge phonons can be extended to other topological
states and offers new tool to explore topological metamaterials.


  A dimerized fermion chain, described by Su-Schrieffer-Heeger (SSH) model, is
a well-known example of 1D system with a non-trivial band topology. An
interplay of disorder and topological ordering in the SSH model is of a great
interest owing to experimental advancements in synthesized quantum simulators.
In this work, we investigate a special sort of a disorder when inter-cell
hopping amplitudes are random. Using a definition for
$\mathbb{Z}_2$-topological invariant $\nu\in \{ 0; 1\}$ in terms of a
non-Hermitian part of the total Hamiltonian, we calculate $\langle\nu\rangle$
averaged by random realizations. This allows to find (i) an analytical form of
the critical surface that separates phases of distinct topological orders and
(ii) finite size fluctuations of $\nu$ for arbitrary disorder strength.
Numerical simulations of the edge modes formation and gap suppression at the
transition are provided for finite-size system. In the end, we discuss a
band-touching condition derived within the averaged Green function method for a
thermodynamic limit.


  The standard treatment of cooling in Cold Dark Matter halos assumes that all
of the gas within a ``cooling radius'' cools and contracts monolithically to
fuel galaxy formation. Here we take into account the expectation that the hot
gas in galactic halos is thermally unstable and prone to fragmentation during
cooling and show that the implications are more far-reaching than previously
expected: allowing multi-phase cooling fundamentally alters expectations about
gas infall in halos and naturally explains the bright-end cutoff in the galaxy
luminosity function. We argue that cooling should proceed via the formation of
high-density, 10^4 K clouds, pressure-confined within a hot gas background. The
background medium has a low density, and can survive as a stable corona with a
long cooling time. The fraction of baryons contained in the residual hot core
grows with halo mass because the cooling density increases, and this leads to
an upper-mass limit in quiescent, non-merged galaxies of ~10^11 Msun. In this
scenario, galaxy formation is fueled by the infall of pressure-supported
clouds. For Milky-Way-size systems, clouds of mass ~ 5x10^6 Msun that formed or
merged within the last several Gyrs should still exist as a residual population
in the halo, with a total mass in clouds of ~ 2 x 10^10 Msun. The mass of the
Milky Way galaxy is explained naturally in this model, and is a factor of two
smaller than would result in the standard treatment without feedback. We expect
clouds in galactic halos to be ~ 1 kpc in size and to extend ~150 kpc from
galactic centers. The predicted properties of clouds match well the observed
radial velocities, angular sizes, column densities, and velocity widths of High
Velocity Clouds around our Galaxy. The clouds also explain high-ion absorption
systems at z<1.


  Let A be a complex abelian variety. The moduli space ${\mathcal M}_C$ of rank
one algebraic connections on $A$ is a principal bundle over the dual abelian
variety $A^\vee=\text{Pic}^0(A)$ for the group $H^0(A, \Omega^1_A)$. Take any
line bundle $L$ on $A^\vee$; let ${\mathcal C}(L)$ be the algebraic principal
$H^0(A^\vee, \Omega^1_{A^\vee})$-bundle over $A^\vee$ given by the sheaf of
connections on $L$. The line bundle $L$ produces a homomorphism $H^0(A,
\Omega^1_A) \rightarrow H^0(A^\vee,\, \Omega^1_{A^\vee})$. We prove that
${\mathcal C}(L)$ is isomorphic to the principal $H^0(A^\vee,
\Omega^1_{A^\vee})$-bundle obtained by extending the structure group of the
principal $H^0(A,\, \Omega^1_A)$-bundle ${\mathcal M}_C$ using this
homomorphism given by $L$. We compute the ring of algebraic functions on
${\mathcal C}(L)$.


  We present an in-context learning agent for formal theorem-proving in
environments like Lean and Coq. Current state-of-the-art models for the problem
are finetuned on environment-specific proof data. By contrast, our approach,
called COPRA, repeatedly asks a high-capacity, general-purpose large language
model (GPT-4) to propose tactic applications from within a stateful
backtracking search. Proposed tactics are executed in the underlying proof
environment. Feedback from the execution is used to build the prompt for the
next model query, along with selected information from the search history and
lemmas retrieved from an external database. We evaluate our implementation of
COPRA on the miniF2F benchmark for Lean and a set of Coq tasks from the
CompCert project. On these benchmarks, COPRA significantly outperforms few-shot
invocations of GPT-4. It also compares favorably against finetuning-based
approaches, outperforming ReProver, a state-of-the-art finetuned approach for
Lean, in terms of the pass@1 metric. Our code and data are available at
https://github.com/trishullab/copra.


  The observations of the Kepler space telescope revealed that fundamental-mode
RR Lyrae stars may show various radial overtones. The presence of multiple
radial modes may allow us to conduct nonlinear asteroseismology: comparison of
mode amplitudes and frequency shifts between observations and models. Here we
report the detection of three radial modes in the star RR Lyr, the eponym of
the class, using the Kepler short cadence data: besides the fundamental mode,
both the first and the ninth overtones can be derived from the data set. RR
Lyrae shows period doubling, but switches occasionally to a state where a
pattern of six pulsation cycles repeats instead of two. We found hydrodynamic
models that show the same three modes and the period-six state, allowing for
comparison with the observations.


  Theoretical attempts to explain the origin of high temperature
superconductivity are challenged by the complexity of the normal state, which
exhibits three regimes with increasing hole doping: a pseudo-gap regime when
underdoped, strange power laws near optimal doping and more conventional
metallic behavior when heavily overdoped. We suggest that the origin of this
behavior is linked to a zero temperature quantum phase transition separating
the overdoped Fermi liquid from a spin-charge separated underdoped phase.
Central to our analysis is a new $Z_2$ gauge theory formulation, which supports
topological vortex excitations - dubbed visons. The visons are gapped in the
underdoped phase, splitting the electron's charge and Fermi statistics into two
separate excitations. Superconductivity occurs when the resulting charge $e$
boson condenses. The visons are condensed in the overdoped phase, thereby
confining the charge and statistics of the electron leading to a Fermi liquid
phase. Right at the quantum confinement transition the visons are in a critical
state, leading to power law behavior for both charge and spin.


  This paper describes the system proposed by Sabanc{\i} University Natural
Language Processing Group in the SemEval-2022 MultiCoNER task. We developed an
unsupervised entity linking pipeline that detects potential entity mentions
with the help of Wikipedia and also uses the corresponding Wikipedia context to
help the classifier in finding the named entity type of that mention. Our
results showed that our pipeline improved performance significantly, especially
for complex entities in low-context settings.


  The static structure factors of the XXZ model in the presence of uniform
field are determined from an exact computation of the groundstates at given
total spin on rings with $N=4,6,\ldots,28$ sites. In contrast to the naive
expectation a weak uniform field strengthens the antiferromagnetic order in the
transverse structure factor for the isotropic case.


  This is a summary of my lecture at Igor Frenkel's 70th birthday conference. I
give a brief review of my almost forty years of scientific interactions with
Igor. I focus here on three topics of joint interests: 2-cocycles, coadjoint
orbits and $WZW_4$. At the end of each topic I comment on new developments, if
any.


  Carbon fiber reinforced polymers are widely used due to their lightweight and
strong structural properties and understanding their mechanical behavior is
crucial for their reliable use in structural components. In this study, we
evaluated the mechanical properties of a carbon fiber reinforced composite made
of a dynamic polymer based on benzoxazine and polyetheramine Jeffamine(R) ED
600. Standard compression tests were performed on specimens with layers
oriented longitudinally or transversely to the loading direction. In-situ
mechanical testing using micro-computed tomography (micro-CT) imaging was
conducted to capture detailed images of the internal microstructure during
deformation. Changes in porosity and pore shape were observed as the composite
underwent compression. Digital Volume Correlation (DVC) analysis was applied to
quantify displacements and strains within the material and enabled the
visualization and quantification of strain patterns, which agree with kinking
failure.


  We extend the semiclassical picture for the spreading of entanglement and
correlations to quantum quenches with several species of quasiparticles that
have non-trivial pair correlations in momentum space. These pair correlations
are, for example, relevant in inhomogeneous lattice models with a
periodically-modulated Hamiltonian parameter. We provide explicit predictions
for the spreading of the entanglement entropy in the space-time scaling limit.
We also predict the time evolution of one- and two-point functions of the order
parameter for quenches within the ordered phase. We test all our predictions
against exact numerical results for quenches in the Ising chain with a
modulated transverse field and we find perfect agreement.


  We investigate matter-induced (or extrinsic) CPT violation effects in
neutrino oscillations in matter. Especially, we present approximate analytical
formulas for the CPT-violating probability differences for three flavor
neutrino oscillations in matter with an arbitrary matter density profile. Note
that we assume that the CPT invariance theorem holds, which means that the CPT
violation effects arise entirely because of the presence of matter. As special
cases of matter density profiles, we consider constant and step-function matter
density profiles, which are relevant for neutrino oscillation physics in
accelerator and reactor long baseline experiments as well as neutrino
factories. Finally, the implications of extrinsic CPT violation on neutrino
oscillations in matter for several past, present, and future long baseline
experiments are estimated.


  We classified the reddest ($r-J>2.2$) stars observed by the NASA $Kepler$
mission into main sequence dwarf or evolved giant stars and determined the
properties of 4216 M dwarfs based on a comparison of available photometry with
that of nearby calibrator stars, as well as available proper motions and
spectra. We revised the properties of candidate transiting planets using the
stellar parameters, high-resolution imaging to identify companion stars, and,
in the case of binaries, fitting light curves to identify the likely planet
host. In 49 of 54 systems we validated the primary as the host star. We
inferred the intrinsic distribution of M dwarf planets using the method of
iterative Monte Carlo simulation. We compared several models of planet orbital
geometry and clustering and found that one where planets are exponentially
distributed and almost precisely coplanar best describes the distribution of
multi-planet systems. We determined that $Kepler$ M dwarfs host an average of
$2.2 \pm 0.3$ planets with radii of 1-4$R_{\oplus}$ and orbital periods of
1.5-180 d. The radius distribution peaks at $\sim 1.2R_{\oplus}$ and is
essentially zero at $4R_{\oplus}$, although we identify three giant planet
candidates other than the previously confirmed Kepler-45b. There is suggestive
but not significant evidence that the radius distribution varies with orbital
period. The distribution with logarithmic orbital period is flat except for a
decline for orbits less than a few days. Twelve candidate planets, including
two Jupiter-size objects, experience an irradiance below the threshold level
for a runaway greenhouse on an Earth-like planet and are thus in a "habitable
zone".


  Ramsey's theorem, concerning the guarantee of certain monochromatic patterns
in large enough edge-coloured complete graphs, is a fundamental result in
combinatorial mathematics. In this work, we highlight the connection between
this abstract setting and a statistical physics problem. Specifically, we
design a classical Hamiltonian that favours configurations in a way to
establish lower bounds on Ramsey numbers. As a proof of principle we then use
Monte Carlo methods to obtain such lower bounds, finding rough agreement with
known literature values in a few cases we investigated. We discuss numerical
limitations of our approach and indicate a path towards the treatment of larger
graph sizes.


  We derive an identity for Margulis invariants of affine deformations of a
complete orientable one-ended hyperbolic sur- face following the identities of
McShane, Mirzakhani and Tan- Wong-Zhang. As a corollary, a deformation of the
surface which infinitesimally lengthens all interior simple closed curves must
in- finitesimally lengthen the boundary.


  We describe semiuniversal deformation spaces for the noncompact surfaces $Z_k
:= \operatorname{Tot} (\mathcal O_{\mathbb P^1} (-k))$ and prove that any
nontrivial deformation $Z_k (\tau)$ of $Z_k$ is affine.
  It is known that the moduli spaces of instantons of charge $j$ on $Z_k$ are
quasi-projective varieties of dimension $2j-k-2$. In contrast, our results
imply that the moduli spaces of instantons on any nontrivial deformation $Z_k
(\tau)$ are empty.


  Holographic microscopy presents challenges for color reproduction due to the
usage of narrow-band illumination sources, which especially impacts the imaging
of stained pathology slides for clinical diagnoses. Here, an accurate color
holographic microscopy framework using absorbance spectrum estimation is
presented. This method uses multispectral holographic images acquired and
reconstructed at a small number (e.g., three to six) of wavelengths, estimates
the absorbance spectrum of the sample, and projects it onto a color
tristimulus. Using this method, the wavelength selection is optimized to
holographically image 25 pathology slide samples with different tissue and
stain combinations to significantly reduce color errors in the final
reconstructed images. The results can be used as a practical guide for various
imaging applications and, in particular, to correct color distortions in
holographic imaging of pathology samples spanning different dyes and tissue
types.


  In his preprint https://arxiv.org/abs/1308.3813, Cartwright introduced the
notion of a weak tropical complex in order to generalize the concepts of
divisors and the Picard group on graphs from Baker and Norine's paper
Riemann-Roch and Abel-Jacobi Theory on a Finite Graph. A tropical complex
$\Gamma$ is a $\Delta$-complex equipped with certain algebraic data. Divisors
in a tropical complex are formal linear combinations of ridges, and
piecewise-linear functions on a tropical complex give rise in a natural way to
divisors. Divisors that arise from PL-functions are called principal, and
divisors that are locally principal are called Cartier. Two divisors that
differ by a principal divisor are said to be linearly equivalent. The linear
equivalence classes of Cartier divisors on a tropical complex $\Gamma$ form a
group called the Picard group of $\Gamma$, by analogy to the definition of the
Picard group of a variety in algebraic geometry.
  Every graph has a unique tropical complex structure. If $G$ and $H$ are
graphs, and $\Gamma$ is a triangulation of their product, then $\Gamma$ has a
weak tropical complex structure that is compatible with the tropical complex
structures on $G$ and $H$. Thus, divisors on $\Gamma$ can be thought of as
states in a higher-dimensional chip-firing game on $\Gamma$. Cartwright
conjectured that the Picard groups of $\Gamma$, $G$, and $H$ were closely
related. Let $Pic(\Gamma)$ be the tropical Picard group of $\Gamma$, and
$Pic(G)$ and $Pic(H)$ be the tropical Picard groups of $G$ and $H$. Then, it
was conjectured that there is a map $\gamma: Pic(G) \times Pic(H) \to
Pic(\Gamma)$ that is always injective and is surjective if at least one of $G$
or $H$ is a tree.
  In this paper, we prove the conjecture. In preparation, we discuss some basic
properties of tropical complexes, along with some properties specific to the
product-of-graphs case.


  Aligning distributions of view representations is a core component of today's
state of the art models for deep multi-view clustering. However, we identify
several drawbacks with na\"ively aligning representation distributions. We
demonstrate that these drawbacks both lead to less separable clusters in the
representation space, and inhibit the model's ability to prioritize views.
Based on these observations, we develop a simple baseline model for deep
multi-view clustering. Our baseline model avoids representation alignment
altogether, while performing similar to, or better than, the current state of
the art. We also expand our baseline model by adding a contrastive learning
component. This introduces a selective alignment procedure that preserves the
model's ability to prioritize views. Our experiments show that the contrastive
learning component enhances the baseline model, improving on the current state
of the art by a large margin on several datasets.


  Representation disentanglement may help AI fundamentally understand the real
world and thus benefit both discrimination and generation tasks. It currently
has at least three unresolved core issues: (i) heavy reliance on label
annotation and synthetic data -- causing poor generalization on natural
scenarios; (ii) heuristic/hand-craft disentangling constraints make it hard to
adaptively achieve an optimal training trade-off; (iii) lacking reasonable
evaluation metric, especially for the real label-free data. To address these
challenges, we propose a \textbf{C}losed-\textbf{L}oop unsupervised
representation \textbf{Dis}entanglement approach dubbed \textbf{CL-Dis}.
Specifically, we use diffusion-based autoencoder (Diff-AE) as a backbone while
resorting to $\beta$-VAE as a co-pilot to extract semantically disentangled
representations. The strong generation ability of diffusion model and the good
disentanglement ability of VAE model are complementary. To strengthen
disentangling, VAE-latent distillation and diffusion-wise feedback are
interconnected in a closed-loop system for a further mutual promotion. Then, a
self-supervised \textbf{Navigation} strategy is introduced to identify
interpretable semantic directions in the disentangled latent space. Finally, a
new metric based on content tracking is designed to evaluate the
disentanglement effect. Experiments demonstrate the superiority of CL-Dis on
applications like real image manipulation and visual analysis.


  Dark equations are defined as some kinds of integrable couplings with some
fields being homogeneously and linearly coupled to others. In this paper, dark
equations are extended in several aspects. Taking the Korteweg-de Vrise (KdV)
equation as an example, the dark KdV systems are extended to nonhomogenous
forms, nonlinear couplings and graded linear cases. The two-component
nonhomogeneous linear coupled dark KdV systems are completely classified. The
nonlinear coupled dark KdV systems may be obtained through the decompositions
from higher dimensional integrable systems like the B-type KP equation. Graded
linear coupled dark KdV systems may be produced by introducing dark parameters
(including the Grassmann parameters) to usual integrable systems. Especially,
applying the bosonization approach to the integrable systems with fermion
fields such as the supersymmetric integrable systems and super-integrable
models, infinitely many graded linear dark systems can be generated. Finally,
the dark KdV systems are extended to supersymmetric ones. The full
classifications for the supersymmetric dark KdV systems are obtained related to
two types of usual supersymmetric KdV equations.


  We study the single transverse-spin asymmetry for open charm production in
the semi-inclusive lepton-hadron deep inelastic scattering (SIDIS) and $pp$
collision. Within collinear factorization approach, we find that the asymmetry
is sensitive to the twist-3 trigluon correlation functions in the proton. With
a simple model for the trigluon correlation functions, we estimate the
asymmetry in SIDIS for both COMPASS and eRHIC kinematics, as well as in $pp$
collision at RHIC energy. We discuss the possibilities of extracting the
trigluon correlation functions in these experiments.


  With the wave interferometric approach, we study how extrinsically multiple
coherent waves excitation can dramatically alter the overall scattering states,
resulting in tailoring the energy assignment among radiation and dissipation.
To explore the concept, we derive the corresponding formulas for dissipation
and scattering powers for cylindrical passive systems encountered by general
configurations of incident waves with various illuminating directions, phases,
and intensities. We demonstrate that a linear superposition of incident waves
extrinsically interferes the target channels in a desirable way. Moreover, the
interferometric results can be irrespective to the inherent system
configurations like size, materials, and structures. The extrinsic interfering
waves pave a non-invasive solution to manipulate light and matter interaction,
with potential applications in metasurfaces, nanophotonics, and metadevices.


  The paper deals with non-linear Poisson neuron network models with bounded
memory dynamics, that can include both Hebbian learning mechanisms and
refractory periods. The state of a network is described by the times elapsed
since its neurons fired within the post-synaptic transfer kernel memory span,
and the current strengths of synaptic connections, the state spaces of our
models being hierarchies of finite-dimensional components. We establish
ergodicity of the stochastic processes describing the behaviour of the networks
and prove the existence of continuously differentiable stationary distribution
densities (with respect to the Lebesgue measures of corresponding
dimensionality) on the components of the state space and find upper bounds for
them. For the density components, we derive a system of differential equations
that can be solved in a few simplest cases only. Approaches to approximate
computation of the stationary density are discussed. One is to reduce the
dimensionality of the problem by modifying the network so that each neuron
cannot fire if the number of spikes it emitted within the post-synaptic
transfer kernel memory span reaches a given threshold. We show that the
stationary distribution of this `truncated' network converges to that of the
unrestricted one as the threshold increases, and that the convergence is at a
super-exponential rate. A complementary approach uses discrete Markov chain
approximations to the network process. We derive linear systems for the
stationary distributions of these Markov chains and prove that these
distributions converge weakly to the stationary laws for the original
processes.


  This paper proposes a zero-shot learning approach for audio classification
based on the textual information about class labels without any audio samples
from target classes. We propose an audio classification system built on the
bilinear model, which takes audio feature embeddings and semantic class label
embeddings as input, and measures the compatibility between an audio feature
embedding and a class label embedding. We use VGGish to extract audio feature
embeddings from audio recordings. We treat textual labels as semantic side
information of audio classes, and use Word2Vec to generate class label
embeddings. Results on the ESC-50 dataset show that the proposed system can
perform zero-shot audio classification with small training dataset. It can
achieve accuracy (26 % on average) better than random guess (10 %) on each
audio category. Particularly, it reaches up to 39.7 % for the category of
natural audio classes.


  Surface photometry at 3.6$\mu$m is presented for 61 low surface brightness
(LSB) galaxies ($\mu_o < 19$ 3.6$\mu$m mag arcsecs$^{-2}$). The sample covers a
range of luminosity from $-$11 to $-$22 in $M_{3.6}$ and size from 1 to 25 kpc.
The morphologies in the mid-IR are comparable to those in the optical with
3.6$\mu$m imaging reaches similar surface brightness depth as ground-based
optical imaging. A majority of the resulting surface brightness profiles are
single exponential in shape with very few displaying upward or downward breaks.
The mean $V-3.6$ color of LSB is 2.3 with a standard deviation of 0.5.
Color-magnitude and two color diagrams are well matched to models of constant
star formation, where the spread in color is due to small changes in the star
formation rate (SFR) over the last 0.5 Gyrs as also suggested by the specific
star formation rate measured by H$\alpha$.


  The 'moduli continuity method' permits an explicit algebraisation of the
Gromov-Hausdorff compactification of K\"ahler-Einstein metrics on Fano
manifolds in some fundamental examples. In this paper, we apply such method in
the 'log setting' to describe explicitly some compact moduli spaces of
K-polystable log Fano pairs. We focus on situations when the angle of
singularities is perturbed in an interval sufficiently close to one, by
considering constructions arising from Geometric Invariant Theory. More
precisely, we discuss the cases of pairs given by cubic surfaces with
anticanonical sections, and of projective space with non-Fano hypersurfaces,
and we show ampleness of the CM line bundle on their good moduli space (in the
sense of Alper). Finally, we introduce a conjecture relating K-stability (and
degenerations) of log pairs formed by a fixed Fano variety and
pluri-anticanonical sections to certain natural GIT quotients.