  We consider skew products $$T_\phi:X\times G\to X\times
G,~~(x,g)\mapsto(F_1(x),g\;\!\phi(x)),$$ where $X$ is a compact manifold with
probability measure, $G$ a compact Lie group with Lie algebra $\frak g$,
$F_1:X\to X$ the time-one map of a measure-preserving flow, and $\phi\in
C^1(X,G)$ a cocycle. Then, we define the degree of $\phi$ as a suitable
function $P_\phi M_\phi:X\to\frak g$, we show that it transforms in a natural
way under Lie group homomorphisms and under the relation of $C^1$-cohomology,
and we explain how it generalises previous definitions of degree of a cocycle.
For each finite-dimensional irreducible representation $\pi$ of $G$, and $\frak
g_\pi$ the Lie algebra of $\pi(G)$, we define in an analogous way the degree of
$\pi\circ\phi$ as a suitable function
$P_{\pi\circ\phi}M_{\pi\circ\phi}:X\to\frak g_\pi$. If $F_1$ is uniquely
ergodic and the functions $\pi\circ\phi$ diagonal, or if $T_\phi$ is uniquely
ergodic, then the degree of $\phi$ reduces to a constant in $\frak g$ given by
an integral over $X$. As a by-product, we obtain that there is no uniquely
ergodic skew product $T_\phi$ with nonzero degree if $G$ is a connected
semisimple compact Lie group.
  Next, we show that $T_\phi$ is mixing in the orthocomplement of the kernel of
$P_{\pi\circ\phi}M_{\pi\circ\phi}$, and under some additional assumptions we
show that $U_\phi$ has purely absolutely continuous spectrum in that
orthocomplement if $(iP_{\pi\circ\phi}M_{\pi\circ\phi})^2$ is strictly
positive. Summing up these results for each $\pi$, one obtains a global result
for the mixing and the absolutely continuous spectrum of $T_\phi$. As an
application, we present four explicit cases: when $G$ is a torus, $G=SU(2)$,
$G=SO(3,\mathbb R)$, and $G=U(2)$. In each case, the results we obtain are new,
or generalise previous results.
  Our proofs rely on new results on positive commutator methods for unitary
operators.


  We develop an efficient algorithm for Synthetic Aperture Sonar imaging based
on the one-way wave equations. The algorithm utilizes the operator-splitting
method to integrate the one-way wave equations. The well-posedness of the
one-way wave equations and the proposed algorithm is shown. A computational
result against real field data is reported and the resulting image is enhanced
by the BV-like regularization.


  The Atacama Large Millimeter/submillimeter Array (ALMA) was used in 2021 to
image the carbon-rich evolved star R Lep in Bands 8-10 (397-908 GHz) with
baselines up to 16 km. The goal was to validate the calibration, using
band-to-band (B2B) phase referencing with a close phase calibrator J0504-1512,
1.2 deg from R Lep in this case, and the imaging procedures required to obtain
the maximum angular resolution achievable with ALMA. Images of the continuum
emission and the hydrogen cyanide (HCN) maser line at 890.8 GHz, from the
J=10-9 transition between the (1110) and (0400) vibrationally excited states,
achieved angular resolutions of 13, 6, and 5 mas in Bands 8-10, respectively.
Self-calibration (self-cal) was used to produce ideal images as to compare with
the B2B phase referencing technique. The continuum emission was resolved in
Bands 9 and 10, leaving too little flux for self-cal of the longest baselines,
so these comparisons are made at coarser resolution. Comparisons showed that
B2B phase referencing provided phase corrections sufficient to recover 92%,
83%, and 77% of the ideal image continuum flux densities. The HCN maser was
sufficiently compact to obtain self-cal solutions in Band 10 for all baselines
(up to 16 km). In Band 10, B2B phase referencing as compared to the ideal
images recovered 61% and 70% of the flux density for the HCN maser and
continuum, respectively.


  We investigate the properties of circumstellar discs (CDs) produced in
hydrodynamical simulations of gravoturbulent core collapse considering
Kolmogorov and Burger-type turbulence. We report that massive discs are more
prevalent in the Kolmogorov regime than for Burger-type turbulence. A
significant number of discs are formed with a radius of $\sim$ 15 au in both
cases. However, the number of extended discs with radii $>$ 15 au is
significantly larger in case of Kolmogorov turbulence. The two regimes of
turbulence, in general, yield disc radii in the ranges of 7 au $-$ 30 au, and
13 au $-$ 39 au, respectively. The corresponding ranges of the disc masses are
30.37 $M_{\rm Jup}$ $-$ 0.92 M$_{\odot}$, and 2.09 $M_{\rm Jup}$ $-$ 0.13
M$_{\odot}$, respectively. Moreover, the ratio $M_{\rm disc}$/$M_{\rm star}$ is
higher in models of Kolmogorov-type turbulence than in models of Burgers-type
turbulence. We do not find any correlation between $R_{\rm disc}$ and $M_{\rm
disc}$ over the explored range of initial temperatures (8 K $-$ 14 K) and the
type of turbulence. Also, for these initial thermal variations, the turbulent
circumstellar disc structures do not exhibit signs of turbulent diffusion.
Nonetheless, both sub and supersonic velocity dispersions cause variations in
the specific angular momentum (AM) of infalling gas, especially for CDs with
radii $\sim$ 16 au $-$ 21 au. The radial profiles of CDs do not correlate with
the initial conditions.


  Monte Carlo simulation is an essential tool in emission tomography that can
assist in the design of new medical imaging devices, the optimization of
acquisition protocols, and the development or assessment of image
reconstruction algorithms and correction techniques. GATE, the Geant4
Application for Tomographic Emission, encapsulates the Geant4 libraries to
achieve a modular, versatile, scripted simulation toolkit adapted to the field
of nuclear medicine. In particular, GATE allows the description of
time-dependent phenomena such as source or detector movement, and source decay
kinetics. This feature makes it possible to simulate time curves under
realistic acquisition conditions and to test dynamic reconstruction algorithms.
A public release of GATE licensed under the GNU Lesser General Public License
can be downloaded at the address http://www-lphe.epfl.ch/GATE/.


  Purpose: Enhanced health literacy has been linked to better health outcomes;
however, few interventions have been studied. We investigate whether large
language models (LLMs) can serve as a medium to improve health literacy in
children and other populations.
  Methods: We ran 288 conditions using 26 different prompts through
ChatGPT-3.5, Microsoft Bing, and Google Bard. Given constraints imposed by rate
limits, we tested a subset of 150 conditions through ChatGPT-4. The primary
outcome measurements were the reading grade level (RGL) and word counts of
output.
  Results: Across all models, output for basic prompts such as "Explain" and
"What is (are)" were at, or exceeded, a 10th-grade RGL. When prompts were
specified to explain conditions from the 1st to 12th RGL, we found that LLMs
had varying abilities to tailor responses based on RGL. ChatGPT-3.5 provided
responses that ranged from the 7th-grade to college freshmen RGL while
ChatGPT-4 outputted responses from the 6th-grade to the college-senior RGL.
Microsoft Bing provided responses from the 9th to 11th RGL while Google Bard
provided responses from the 7th to 10th RGL.
  Discussion: ChatGPT-3.5 and ChatGPT-4 did better in achieving lower-grade
level outputs. Meanwhile Bard and Bing tended to consistently produce an RGL
that is at the high school level regardless of prompt. Additionally, Bard's
hesitancy in providing certain outputs indicates a cautious approach towards
health information. LLMs demonstrate promise in enhancing health communication,
but future research should verify the accuracy and effectiveness of such tools
in this context.
  Implications: LLMs face challenges in crafting outputs below a sixth-grade
reading level. However, their capability to modify outputs above this threshold
provides a potential mechanism to improve health literacy and communication in
a pediatric population and beyond.


  This paper investigates the construction of rank-metric codes with specified
Ferrers diagram shapes. These codes play a role in the multilevel construction
for subspace codes. A conjecture from 2009 provides an upper bound for the
dimension of a rank-metric code with given specified Ferrers diagram shape and
rank distance. While the conjecture in its generality is wide open, several
cases have been established in the literature. This paper contributes further
cases of Ferrers diagrams and ranks for which the conjecture holds true. In
addition, the proportion of maximal Ferrers diagram codes within the space of
all rank-metric codes with the same shape and dimension is investigated.
Special attention is being paid to MRD codes. It is shown that for growing
field size the limiting proportion depends highly on the Ferrers diagram. For
instance, for $[m\times 2]$-MRD codes with rank~$2$ this limiting proportion is
close to $1/e$.


  We present a rigorous calculation of the dynamical friction force exerted on
a spherical massive perturber moving through an infinite homogenous system of
field stars. By calculating the shape and mass of the polarization cloud
induced by the perturber in the background system, which decelerates the motion
of the perturber, we recover Chandrasekhar's drag force law with a modified
Coulomb logarithm. As concrete examples we calculate the drag force exerted on
a Plummer sphere or a sphere with the density distribution of a Hernquist
profile. It is shown that the shape of the perturber affects only the exact
form of the Coulomb logarithm. The latter converges on small scales, because
encounters of the test and field stars with impact parameters less than the
size of the massive perturber become inefficient. We confirm this way earlier
results based on the impulse approximation of small angle scatterings.


  We propose a gradient-based method for quadratic programming problems with a
single linear constraint and bounds on the variables. Inspired by the GPCG
algorithm for bound-constrained convex quadratic programming [J.J. Mor\'e and
G. Toraldo, SIAM J. Optim. 1, 1991], our approach alternates between two phases
until convergence: an identification phase, which performs gradient projection
iterations until either a candidate active set is identified or no reasonable
progress is made, and an unconstrained minimization phase, which reduces the
objective function in a suitable space defined by the identification phase, by
applying either the conjugate gradient method or a recently proposed spectral
gradient method. However, the algorithm differs from GPCG not only because it
deals with a more general class of problems, but mainly for the way it stops
the minimization phase. This is based on a comparison between a measure of
optimality in the reduced space and a measure of bindingness of the variables
that are on the bounds, defined by extending the concept of proportioning,
which was proposed by some authors for box-constrained problems. If the
objective function is bounded, the algorithm converges to a stationary point
thanks to a suitable application of the gradient projection method in the
identification phase. For strictly convex problems, the algorithm converges to
the optimal solution in a finite number of steps even in case of degeneracy.
Extensive numerical experiments show the effectiveness of the proposed
approach.


  Building extraction aims to segment building pixels from remote sensing
images and plays an essential role in many applications, such as city planning
and urban dynamic monitoring. Over the past few years, deep learning methods
with encoder-decoder architectures have achieved remarkable performance due to
their powerful feature representation capability. Nevertheless, due to the
varying scales and styles of buildings, conventional deep learning models
always suffer from uncertain predictions and cannot accurately distinguish the
complete footprints of the building from the complex distribution of ground
objects, leading to a large degree of omission and commission. In this paper,
we realize the importance of uncertain prediction and propose a novel and
straightforward Uncertainty-Aware Network (UANet) to alleviate this problem. To
verify the performance of our proposed UANet, we conduct extensive experiments
on three public building datasets, including the WHU building dataset, the
Massachusetts building dataset, and the Inria aerial image dataset. Results
demonstrate that the proposed UANet outperforms other state-of-the-art
algorithms by a large margin.


  In this article, we study the final-state rescattering effects in the decay
$B^0 \to \eta_cK^*$, the numerical results indicate the corrections are
comparable with the contribution from the naive factorizable amplitude, and the
total amplitudes can accommodate the experimental data.


  We consider the optimal cloning of quantum coherent states with single-clone
and joint fidelity as figures of merit. Both optimal fidelities are attained
for phase space translation covariant cloners. Remarkably, the joint fidelity
is maximized by a Gaussian cloner, whereas the single-clone fidelity can be
enhanced by non-Gaussian operations: a symmetric non-Gaussian 1-to-2 cloner can
achieve a single-clone fidelity of approximately 0.6826, perceivably higher
than the optimal fidelity of 2/3 in a Gaussian setting. This optimal cloner can
be realized by means of an optical parametric amplifier supplemented with a
particular source of non-Gaussian bimodal states. Finally, we show that the
single-clone fidelity of the optimal 1-to-infinity cloner, corresponding to a
measure-and-prepare scheme, cannot exceed 1/2. This value is achieved by a
Gaussian scheme and cannot be surpassed even with supplemental bound entangled
states.


  This paper proves that there are no compact forms for a large class of
homogeneous spaces admitting actions by higher-rank semisimple Lie groups. It
builds on Zimmer's approach for studying such spaces using cocycle
superrigidity. The proof involves cocycle superrigidity, measure rigidity for
unipotent flows, techniques from partially hyperbolic dynamics, and the
geometry and pseudo-Riemannian structure of the homogeneous space.


  Practically all of the planning research is limited to states represented in
terms of Boolean and numeric state variables. Many practical problems, for
example, planning inside complex software systems, require far more complex
data types, and even real-world planning in many cases requires concepts such
as sets of objects, which are not convenient to express in modeling languages
with scalar types only. In this work, we investigate a modeling language for
complex software systems, which supports complex data types such as sets,
arrays, records, and unions. We give a reduction of a broad range of complex
data types and their operations to Boolean logic, and then map this
representation further to PDDL to be used with domain-independent PDDL
planners. We evaluate the practicality of this approach, and provide solutions
to some of the issues that arise in the PDDL translation.


  We construct a basis for a modified quantum group of finite type, extending
the PBW bases of positive and negative halves of a quantum group. Generalizing
Lusztig's classic results on PBW bases, we show that this basis is orthogonal
with respect to its natural bilinear form (and hence called a PBW basis), and
moreover, the matrix for the PBW-expansion of the canonical basis is unital
triangular. All these follow by a new construction of the modified quantum
group of arbitrary type, which is built on limits of sequences of elements in
tensor products of lowest and highest weight modules. Explicitly formulas are
worked out in the rank one case.


  General formulation of hydrodynamics describing transversally thermalized
matter created at the early stages of ultra-relativistic heavy-ion collisions
is presented. Similarities and differences with the standard
three-dimensionally thermalized relativistic hydrodynamics are discussed. The
role of the conservation laws as well as the thermodynamic consistency of
two-dimensional thermodynamic variables characterizing transversally
thermalized matter is emphasized.


  Contact matrices provide a coarse grained description of the configuration
omega of a linear chain (polymer or random walk) on Z^n: C_{ij}(omega)=1 when
the distance between the position of the i-th and j-th step are less than or
equal to some distance "a" and C_{ij}(omega)=0 otherwise. We consider models in
which polymers of length N have weights corresponding to simple and
self-avoiding random walks, SRW and SAW, with "a" the minimal permissible
distance. We prove that to leading order in N, the number of matrices equals
the number of walks for SRW, but not for SAW. The coarse grained Shannon
entropies for SRW agree with the fine grained ones for n <= 2, but differs for
n >= 3.


  Dynamical networks with time delays can pose a considerable challenge for
mathematical analysis. Here, we extend the approach of generalized modeling to
investigate the stability of large networks of delay-coupled delay oscillators.
When the local dynamical stability of the network is plotted as a function of
the two delays then a pattern of tongues is revealed. Exploiting a link between
structure and dynamics, we identify conditions under which perturbations of the
topology have a strong impact on the stability. If these critical regions are
avoided the local stability of large random networks can be well approximated
analytically.


  We compare the grand canonical partition function at fixed chemical potential
mu with the canonical partition function at fixed baryon number B, formally and
by numerical simulations at mu=0 and B=0 with four flavours of staggered
quarks. We verify that the free energy densities are equal in the thermodynamic
limit, and show that they can be well described by the hadron resonance gas at
T < T_c and by the free fermion gas at T>T_c.
  Small differences between the two ensembles, for thermodynamic observables
characterising the deconfinement phase transition, vanish with increasing
lattice size. These differences are solely caused by contributions of non-zero
baryon density sectors, which are exponentially suppressed with increasing
volume. The Polyakov loop shows a different behaviour: for all temperatures and
volumes, its expectation value is exactly zero in the canonical formulation,
whereas it is always non-zero in the commonly used grand-canonical formulation.
We clarify this paradoxical difference, and show that the non-vanishing
Polyakov loop expectation value is due to contributions of non-zero triality
states, which are not physical, because they give zero contribution to the
partition function.


  Consider a Frobenius kernel G in a split semisimple algebraic group, in very
good characteristic. We provide an analysis of support for the Drinfeld center
Z(rep(G)) of the representation category for G, or equivalently for the
representation category of the Drinfeld double of kG. We show that thick ideals
in the corresponding stable category are classified by cohomological support,
and calculate the Balmer spectrum of the stable category of Z(rep(G)). We also
construct a $\pi$-point style rank variety for the Drinfeld double, identify
$\pi$-point support with cohomological support, and show that both support
theories satisfy the tensor product property. Our results hold, more generally,
for Drinfeld doubles of Frobenius kernels in any smooth algebraic group which
admits a quasi-logarithm, such as a Borel subgroup in a split semisimple group
in very good characteristic.


  Audio-visual learning, aimed at exploiting the relationship between audio and
visual modalities, has drawn considerable attention since deep learning started
to be used successfully. Researchers tend to leverage these two modalities
either to improve the performance of previously considered single-modality
tasks or to address new challenging problems. In this paper, we provide a
comprehensive survey of recent audio-visual learning development. We divide the
current audio-visual learning tasks into four different subfields: audio-visual
separation and localization, audio-visual correspondence learning, audio-visual
generation, and audio-visual representation learning. State-of-the-art methods
as well as the remaining challenges of each subfield are further discussed.
Finally, we summarize the commonly used datasets and performance metrics.


  We consider the statistics of the scattering coefficient S of a chaotic
microwave cavity coupled to a single port. We remove the non-universal effects
of the coupling from the experimental S data using the radiation impedance
obtained directly from the experiments. We thus obtain the normalized, complex
scattering coefficient whose Probability Density Function (PDF) is predicted to
be universal in that it depends only on the loss (quality factor) of the
cavity. We compare experimental PDFs of the normalized scattering coefficients
with those obtained from Random Matrix Theory (RMT), and find excellent
agreement. The results apply to scattering measurements on any wave chaotic
system.


  A method for ray-tracing through n-body simulations has been recently
proposed. It is based on a periodic universe covered by simulation boxes.
Photons move along appropriate directions to avoid periodicity effects. Here,
an improved version of this method is applied to simulate lensed CMB maps and
maps of lens deformations. Particle mesh n-body simulations with appropriate
boxes and resolutions are used to evolve the nonlinear inhomogeneities until
present time. The resulting maps are statistically analyzed to look for
deviations from Gaussianity. These deviations are measured --for the first
time-- using correlations for configurations of n directions (n being smaller
than 7). A wide range of angular scales are considered. Some interesting
prospects are outlined.


  These notes concern the nonlinear geometry of Banach spaces, asymptotic
uniform smoothness and several Banach-Saks-like properties. We study the
existence of certain concentration inequalities in asymptotically uniformly
smooth Banach spaces as well as weakly sequentially continuous coarse
(Lipschitz) embeddings into those spaces. Some results concerning the
descriptive set theoretical complexity of those properties are also obtained.
We finish the paper with a list of open problem.


  Let $\mathcal{C}$ be a finitely complete small category. In this paper, first
we construct two weak (Lawvere-Tierney) topologies on the category of
presheaves. One of them is established by means of a subfunctor of the Yoneda
functor and the other one, is constructed by an admissible class on
$\mathcal{C}$ and the internal existential quantifier in the presheaf topos
$\widehat{\mathcal{C}}$. Moreover, by using an admissible class on
$\mathcal{C},$ we are able to define an action on the subobject classifier
$\Omega$ of $\widehat{\mathcal{C}}$. Then we find some necessary conditions for
that the two weak topologies and also the double negation topology $\neg\neg$
on $\widehat{\mathcal{C}}$ to be action preserving maps. Finally, among other
things, we constitute an action preserving weak topology on
$\widehat{\mathcal{C}}$.


  Roughly a dozen X-ray binaries are presently known in which the compact
accreting primary stars are too massive to be neutron stars. These primaries
are identified as black holes, though there is as yet no definite proof that
any of the candidate black holes actually possesses an event horizon. We
discuss how Type I X-ray bursts may be used to verify the presence of the event
horizon in these objects. Type I bursts are caused by thermonuclear explosions
when gas accretes onto a compact star. The bursts are commonly seen in many
neutron star X-ray binaries, but they have never been seen in any black hole
X-ray binary. Our model calculations indicate that black hole candidates ought
to burst frequently if they have surfaces. Based on this, we argue that the
lack of bursts constitutes strong evidence for the presence of event horizons
in these objects.


  Photothermal effects can alter the response of an optical cavity, for
example, by inducing self-locking behavior or unstable anomalies. The
consequences of these effects are often regarded as parasitic and generally
cause limited operational performance of the cavity. Despite their importance,
however, photothermal parameters are usually hard to characterize precisely. In
this work we use an optical cavity strongly coupled to photothermal effects to
experimentally observe an optical back-action on the photothermal relaxation
rate. This effect, reminiscent of the radiation-pressure-induced optical spring
effect in cavity optomechanical systems, uses optical detuning as a fine
control to change the photothermal relaxation process. The photothermal
relaxation rate of the system can be accordingly modified by more than an order
of magnitude. This approach offers an opportunity to obtain precise in-situ
estimations of the parameters of the cavity, in a way that is compatible with a
wide range of optical resonator platforms. Through this back-action effect we
are able to determine the natural photothermal relaxation rate and the
effective thermal conductivity of the cavity mirrors with unprecedented
resolution.


  Holographic soft-wall model is successful in the phenomenology of hadrons.
Here with the use of generalized parton distributions (GPDs) obtained from
AdS/QCD, perturbative effects are entered into the formalism. Perturbations are
incorporated in the formalism through the evolution of GPDs according to the
DGLAP like equations. Evolved proton GPDs are compared with a phenomenological
model to show that we can get good improvements of the holographic model. It
seems that combining the holographic soft-wall model with perturbative effects
to some extent, gives the correct physics of GPDs.


  We present the results of the covariant spectator quark model applied to the
nucleon structure function $f(x)$ measured in unpolarized deep inelastic
scattering, and the structure functions $g_1(x)$ and $g_2(x)$ measured in deep
inelastic scattering using polarized beams and targets ($x$ is the Bjorken
scaling variable). The nucleon is modeled by a valence quark-diquark structure
with $S,P$ and $D$ components. The shape of the wave functions and the relative
strength of each component are fixed by making fits to the deep inelastic
scattering data for the structure functions $f(x)$ and $g_1(x)$. The model is
then used to make predictions on the function $g_2(x)$ for the proton and
neutron.


  The problem of detecting and identifying sensor faults is critical for
efficient, safe, regulatory-compliant and sustainable operations of modern
systems. Their increasing complexity brings new challenges for the Sensor Fault
Detection and Isolation (SFD-SFI) tasks. One of the key enablers for any
SFD-SFI methods employed in modern complex sensor systems, is the so-called
analytical redundancy, which is nothing but building an analytical model of the
sensors observations (either derived from first principles or identified from
historical data in a data-driven fashion). In a nutshell, SFD amounts to
generate and to monitor residuals by comparing the sensor measurements with the
model predictions with the idea that the faulty sensors will result in large
residuals (i.e. the defective sensors generate measurement that are
inconsistent with their expected behavior represented by the model). In this
paper we introduce a disentangled Recurrent Neural Network (RNN) with the
objective to cope with the \textit{smearing-out} effect, i.e. the propagation
of a sensor fault to the non-faulty sensors resulting in large misleading
residuals. Moreover, the introduction of a probabilistic model for the residual
generation allows us to develop a novel procedure for the identification of the
faulty sensors. The computational complexity of the proposed algorithm is
linear in the number of sensors as opposed to the combinatorial nature of the
SFI problem. Finally, we empirically verify the performances of the proposed
SFD-SFI architecture using a real data set collected at a petrochemical plant.


  We present an Imitation Learning approach for the control of dynamical
systems with a known model. Our policy search method is guided by solutions
from MPC. Typical policy search methods of this kind minimize a distance metric
between the guiding demonstrations and the learned policy. Our loss function,
however, corresponds to the minimization of the control Hamiltonian, which
derives from the principle of optimality. Therefore, our algorithm directly
attempts to solve the optimality conditions with a parameterized class of
control laws. Additionally, the proposed loss function explicitly encodes the
constraints of the optimal control problem and we provide numerical evidence
that its minimization achieves improved constraint satisfaction. We train a
mixture-of-expert neural network architecture for controlling a quadrupedal
robot and show that this policy structure is well suited for such multimodal
systems. The learned policy can successfully stabilize different gaits on the
real walking robot from less than 10 min of demonstration data.


  We search for a linearity in the ratio of dark matter to baryonic matter as a
function of radius for galaxy clusters, motivated by a recent result by Lovas
(arXiv:2206.11431), who has discovered such a linearity for a diverse suite of
galaxies in the SPARC sample. For our analysis, we used a sample of 54 non-cool
core clusters from the HIFLUGCS sample. We do not find any evidence for a
linear trend in the aforementioned ratio as a function of radius for individual
clusters. We then repeat this analysis for the stacked sample, which also does
not show this linearity. Therefore, the linear scaling found by Lovas is not a
universal property of dark matter haloes at all scales.


  Generative models of code, pretrained on large corpora of programs, have
shown great success in translating natural language to code (Chen et al., 2021;
Austin et al., 2021; Li et al., 2022, inter alia). While these models do not
explicitly incorporate program semantics (i.e., execution results) during
training, they are able to generate correct solutions for many problems.
However, choosing a single correct program from a generated set for each
problem remains challenging. In this work, we introduce execution result--based
minimum Bayes risk decoding (MBR-EXEC) for program selection and show that it
improves the few-shot performance of pretrained code models on
natural-language-to-code tasks. We select output programs from a generated
candidate set by marginalizing over program implementations that share the same
semantics. Because exact equivalence is intractable, we execute each program on
a small number of test inputs to approximate semantic equivalence. Across
datasets, execution or simulated execution significantly outperforms the
methods that do not involve program semantics. We find that MBR-EXEC
consistently improves over all execution-unaware selection methods, suggesting
it as an effective approach for natural language to code translation. We
open-source our code at github.com/facebookresearch/mbr-exec and data at
dl.fbaipublicfiles.com/mbr-exec/mbr-exec-release.zip


  How does the quantum-to-classical transition of measurement occur? This
question is vital for both foundations and applications of quantum mechanics.
Here, we develop a new measurement-based framework for characterizing the
classical and quantum free electron-photon interactions and then experimentally
test it. We first analyze the transition from projective to weak measurement in
generic light-matter interactions and show that any classical
electron-laser-beam interaction can be represented as an outcome of a weak
measurement. In particular, the appearance of classical point-particle
acceleration is an example of an amplified weak value resulting from weak
measurement. A universal factor quantifies the measurement regimes and their
transition from quantum to classical, where Gamma corresponds to the ratio
between the electron wavepacket size and the optical wavelength. This
measurement-based formulation is experimentally verified in both limits of
photon-induced near-field electron microscopy and the classical acceleration
regime using a dielectric laser accelerator. Our results shed new light on the
transition from quantum to classical electrodynamics, enabling to employ the
essence of wave-particle duality of both light and electrons in quantum
measurement for exploring and applying many quantum and classical light-matter
interactions.


  The advent of data science has provided an increasing number of challenges
with high data complexity. This paper addresses the challenge of space-time
data where the spatial domain is not a planar surface, a sphere, or a linear
network, but a generalized network (termed a graph with Euclidean edges).
Additionally, data are repeatedly measured over different temporal instants. We
provide new classes of nonseparable space-time stationary covariance functions
where {\em space} can be a generalized network, a Euclidean tree, or a linear
network, and where time can be linear or circular (seasonal). Because the
construction principles are technical, we focus on illustrations that guide the
reader through the construction of statistically interpretable examples. A
simulation study demonstrates that we can recover the correct model when
compared to misspecified models. In addition, our simulation studies show that
we effectively recover simulation parameters. In our data analysis, we consider
a traffic accident dataset that shows improved model performance based on
covariance specifications and network-based metrics.


  The physics of highly excited Rydberg atoms is governed by blockade or
exclusion interactions that hinder the excitation of atoms in the proximity of
a previously excited one. This leads to cooperative effects and a relaxation
dynamics displaying space-time heterogeneity similar to what is observed in the
relaxation of glass-forming systems. Here we establish theoretically the
existence of a glassy dynamical regime in an open Rydberg gas, associated with
phase coexistence at a first-order transition in dynamical large deviation
functions. This transition occurs between an active phase of low density in
which dynamical processes take place on short timescales, and an inactive phase
in which excited atoms are dense and the dynamics is highly arrested. We
perform a numerically exact study and develop a mean-field approach that allows
to understand the mechanics of this phase transition. We show that radiative
decay --- which becomes experimentally relevant for long times --- moves the
system away from dynamical phase coexistence. Nevertheless, the dynamical phase
transition persists and causes strong fluctuations in the observed dynamics.


  This paper concerns the explicit construction of extremal Kaehler metrics on
total spaces of projective bundles, which have been studied in many places. We
present a unified approach, motivated by the theory of hamiltonian 2-forms (as
introduced and studied in previous papers in the series) but this paper is
largely independent of that theory.
  We obtain a characterization, on a large family of projective bundles, of
those `admissible' Kaehler classes (i.e., the ones compatible with the bundle
structure in a way we make precise) which contain an extremal Kaehler metric.
In many cases, such as on geometrically ruled surfaces, every Kaehler class is
admissible. In particular, our results complete the classification of extremal
Kaehler metrics on geometrically ruled surfaces, answering several
long-standing questions.
  We also find that our characterization agrees with a notion of K-stability
for admissible Kaehler classes. Our examples and nonexistence results therefore
provide a fertile testing ground for the rapidly developing theory of stability
for projective varieties, and we discuss some of the ramifications. In
particular we obtain examples of projective varieties which are destabilized by
a non-algebraic degeneration.


  Open responses form a rich but underused source of information in educational
data mining and intelligent tutoring systems. One of the major obstacles is the
difficulty of clustering short texts automatically. In this paper, we
investigate the problem of clustering free-formed questionnaire answers. We
present comparative experiments on clustering ten sets of open responses from
course feedback queries in English and Finnish. We also evaluate how well the
main topics could be extracted from clusterings with the HITS algorithm. The
main result is that, for English data, affinity propagation performed well
despite frequent outliers and considerable overlapping between real clusters.
However, for Finnish data, the performance was poorer and none of the methods
clearly outperformed the others. Similarly, topic extraction was very
successful for the English data but only satisfactory for the Finnish data. The
most interesting discovery was that stemming could actually deteriorate the
clustering quality significantly.


  IRC +10420 is one of the extreme hypergiant stars that define the empirical
upper luminosity boundary in the HR diagram. During their post--RSG evolution,
these massive stars enter a temperature range (6000-9000 K) of increased
dynamical instability, high mass loss, and increasing opacity, a
semi--forbidden region, that de Jager and his collaborators have called the
`yellow void'. We report HST/STIS spatially resolved spectroscopy of IRC +10420
and its reflection nebula with some surprising results. Long slit spectroscopy
of the reflected spectrum allows us to effectively view the star from different
directions. Measurements of the double--peaked Halpha emission profile show a
uniform outflow of gas in a nearly spherical distribution, contrary to previous
models with an equatorial disk or bipolar outflow. Based on the temperature and
mass loss rate estimates that are usually quoted for this object, the wind is
optically thick to the continuum at some and possibly all wavelengths.
Consequently the observed variations in apparent spectral type and inferred
temperature are changes in the wind and do not necessarily mean that the
underlying stellar radius and interior structure are evolving on such a short
timescale. To explain the evidence for simultaneous outflow and infall of
material near the star, we propose a `rain' model in which blobs of gas
condense in regions of lowered opacity outside the dense wind. With the
apparent warming of its wind, the recent appearance of strong emission, and a
decline in the mass loss rate, IRC +10420 may be about to shed its opaque wind,
cross the `yellow void', and emerge as a hotter star.


  An important task in structural design is to quantify the structural
performance of an object under the external forces it may experience during its
use. The problem proves to be computationally very challenging as the external
forces' contact locations and magnitudes may exhibit significant variations. We
present an efficient analysis approach to determine the most critical force
contact location in such problems with force location uncertainty. Given an
input 3D model and regions on its boundary where arbitrary normal forces may
make contact, our algorithm predicts the worst-case force configuration
responsible for creating the highest stress within the object. Our approach
uses a computationally tractable experimental design method to select number of
sample force locations based on geometry only, without inspecting the stress
response that requires computationally expensive finite-element analysis. Then,
we construct a simple regression model on these samples and corresponding
maximum stresses. Combined with a simple ranking based post-processing step,
our method provides a practical solution to worst-case structural analysis
problem. The results indicate that our approach achieves significant
improvements over the existing work and brute force approaches. We demonstrate
that further speed- up can be obtained when small amount of an error tolerance
in maximum stress is allowed.


  The power graph $\mathcal{P}(G)$ is a graph with group elements as vertex set
and two elements are adjacent if one is a power of the other. The order
supergraph $\mathcal{S}(G)$ of the power graph $\mathcal{P}(G)$ is a graph with
vertex set $G$ in which two elements $x, y \in G$ are joined if $o(x) | o(y)$
or $o(y) | o(x)$. The purpose of this paper is to study certain properties of
this new graph together with the relationship between $\mathcal{P}(G)$ and
$\mathcal{S}(G)$.


  Many networks do not live in isolation but are strongly interacting, with
profound consequences on their dynamics. Here, we consider the case of two
interacting social networks and, in the context of a simple model, we address
the case of political elections. Each network represents a competing party and
every agent on the election day can choose to be either active in one of the
two networks (vote for the corresponding party) or to be inactive in both (not
vote). The opinion dynamics during the election campaign is described through a
simulated annealing algorithm. We find that for a large region of the parameter
space the result of the competition between the two parties allows for the
existence of pluralism in the society, where both parties have a finite share
of the votes. The central result is that a densely connected social network is
key for the final victory of a party. However, small committed minorities can
play a crucial role, and even reverse the election outcome.


  Self-play via online learning is one of the premier ways to solve large-scale
two-player zero-sum games, both in theory and practice. Particularly popular
algorithms include optimistic multiplicative weights update (OMWU) and
optimistic gradient-descent-ascent (OGDA). While both algorithms enjoy $O(1/T)$
ergodic convergence to Nash equilibrium in two-player zero-sum games, OMWU
offers several advantages including logarithmic dependence on the size of the
payoff matrix and $\widetilde{O}(1/T)$ convergence to coarse correlated
equilibria even in general-sum games. However, in terms of last-iterate
convergence in two-player zero-sum games, an increasingly popular topic in this
area, OGDA guarantees that the duality gap shrinks at a rate of
$O(1/\sqrt{T})$, while the best existing last-iterate convergence for OMWU
depends on some game-dependent constant that could be arbitrarily large. This
begs the question: is this potentially slow last-iterate convergence an
inherent disadvantage of OMWU, or is the current analysis too loose? Somewhat
surprisingly, we show that the former is true. More generally, we prove that a
broad class of algorithms that do not forget the past quickly all suffer the
same issue: for any arbitrarily small $\delta>0$, there exists a $2\times 2$
matrix game such that the algorithm admits a constant duality gap even after
$1/\delta$ rounds. This class of algorithms includes OMWU and other standard
optimistic follow-the-regularized-leader algorithms.


  Reset controllers have demonstrated their effectiveness in enhancing
performance in precision motion systems. To further exploiting the potential of
reset controllers, this study introduces a parallel-partial reset control
structure. Frequency response analysis is effective for the design and
fine-tuning of controllers in industries. However, conducting frequency
response analysis for reset control systems poses challenges due to their
nonlinearities. We develop frequency response analysis methods for both the
open-loop and closed-loop parallel-partial reset systems. Simulation results
validate the accuracy of the analysis methods, showcasing precision
enhancements exceeding 100% compared to the traditional describing function
method. Furthermore, we design a parallel-partial reset controller within the
Proportional-Integral-Derivative (PID) control structure for a
mass-spring-damper system. The frequency response analysis of the designed
system indicates that, while maintaining the same bandwidth and phase margin of
the first-order harmonics, the new system exhibits lower magnitudes of
higher-order harmonics, compared to the traditional reset system. Moreover,
simulation results demonstrate that the new system achieves lower overshoot and
quicker settling time compared to both the traditional reset and linear
systems.


  Laser angle-resolved photoemission spectroscopy (ARPES) is employed to
investigate the temperature (T) dependence of the electronic structure in
BaFe2As2 across the magneto-structural transition at TN ~ 140 K. A drastic
transformation in Fermi surface (FS) shape across TN is observed, as expected
by first-principles band calculations. Polarization-dependent ARPES and band
calculations consistently indicate that the observed FSs at kz ~ pi in the
low-T antiferromagnetic (AF) state are dominated by the Fe3dzx orbital, leading
to the two-fold electronic structure. These results indicate that
magneto-structural transition in BaFe2As2 accompanies orbital-dependent
modifications in the electronic structure.


  By considering the realization of the emergent universe scenario in
Eddington-inspired Born-Infeld (EiBI) theory, we study the stability of the
Einstein static universe filled with perfect fluid in EiBI theory against both
the homogeneous and inhomogeneous scalar perturbations in this work. We find
that in both the spatially flat and closed cases, the emergent universe
scenario is no longer viable, since the Einstein static universe cannot be
stable against both the homogeneous and inhomogeneous scalar perturbations
simultaneously. However, the emergent universe scenario survives in the
spatially open case, while the Einstein static universe can be stable under
some conditions.


  Growing networks have a causal structure. We show that the causality strongly
influences the scaling and geometrical properties of the network. In particular
the average distance between nodes is smaller for causal networks than for
corresponding homogeneous networks. We explain the origin of this effect and
illustrate it using as an example a solvable model of random trees. We also
discuss the issue of stability of the scale-free node degree distribution. We
show that a surplus of links may lead to the emergence of a singular node with
the degree proportional to the total number of links. This effect is closely
related to the backgammon condensation known from the balls-in-boxes model.


  In this paper, we propose an active perception method for recognizing object
categories based on the multimodal hierarchical Dirichlet process (MHDP). The
MHDP enables a robot to form object categories using multimodal information,
e.g., visual, auditory, and haptic information, which can be observed by
performing actions on an object. However, performing many actions on a target
object requires a long time. In a real-time scenario, i.e., when the time is
limited, the robot has to determine the set of actions that is most effective
for recognizing a target object. We propose an MHDP-based active perception
method that uses the information gain (IG) maximization criterion and lazy
greedy algorithm. We show that the IG maximization criterion is optimal in the
sense that the criterion is equivalent to a minimization of the expected
Kullback--Leibler divergence between a final recognition state and the
recognition state after the next set of actions. However, a straightforward
calculation of IG is practically impossible. Therefore, we derive an efficient
Monte Carlo approximation method for IG by making use of a property of the
MHDP. We also show that the IG has submodular and non-decreasing properties as
a set function because of the structure of the graphical model of the MHDP.
Therefore, the IG maximization problem is reduced to a submodular maximization
problem. This means that greedy and lazy greedy algorithms are effective and
have a theoretical justification for their performance. We conducted an
experiment using an upper-torso humanoid robot and a second one using synthetic
data. The experimental results show that the method enables the robot to select
a set of actions that allow it to recognize target objects quickly and
accurately. The results support our theoretical outcomes.


  Approximate Bayesian computation (ABC) methods permit approximate inference
for intractable likelihoods when it is possible to simulate from the model.
However they perform poorly for high dimensional data, and in practice must
usually be used in conjunction with dimension reduction methods, resulting in a
loss of accuracy which is hard to quantify or control. We propose a new ABC
method for high dimensional data based on rare event methods which we refer to
as RE-ABC. This uses a latent variable representation of the model. For a given
parameter value, we estimate the probability of the rare event that the latent
variables correspond to data roughly consistent with the observations. This is
performed using sequential Monte Carlo and slice sampling to systematically
search the space of latent variables. In contrast standard ABC can be viewed as
using a more naive Monte Carlo estimate. We use our rare event probability
estimator as a likelihood estimate within the pseudo-marginal
Metropolis-Hastings algorithm for parameter inference.
  We provide asymptotics showing that RE-ABC has a lower computational cost for
high dimensional data than standard ABC methods. We also illustrate our
approach empirically, on a Gaussian distribution and an application in
infectious disease modelling.


  We propose the concept of pair-wise coherence to study the relation between
the l1 norm of coherence and the quantum interference in Young's multi-photon
multi-path experiments, where the input photons may be entangled each other. We
find that only the local coherence of each single photon can make quantum
interference and the collective coherence between photons has no contribution
to quantum interference. The visibility of interference fringe is commonly less
than the l1norm of coherence of the corresponding input state, suggesting that
the l1 norm of coherence is only the necessary but not sufficient condition for
quantum interference. We also find that the maximal fringe visibility can reach
one. The optimal input states for producing the maximal visibility are
presented.


  To manipulate orbital angular momentum (OAM) carried by light beams, there is
a great interest in designing various optical elements from the
deep-ultraviolet to the microwave. Normally, the OAM variation introduced by
optical elements can be attributed to two terms, namely the dynamic and
geometric phases. Up till now, the dynamic contribution induced by optical
elements has been clearly recognized. However, the contribution of geometric
phase still seems obscure, especially considering the vector vortex beams. In
this work, an analytical formula is derived to fully describe the OAM variation
introduced by the nonabsorbing optical elements, which perform space-variant
polarization-state manipulations. It is found that the geometric contribution
can be further divided into two parts: one is directly related to optical
elements and the other one explicitly relies solely on the vortices before and
after the transformations. Based on this result, the same OAM variation can be
achieved with different combinations of the dynamic and/or geometric
contributions. With numerical simulations, it is shown that transformation of
the optical vortices can be fully and flexibly designed with a family of
optical elements. We believe that these results are helpful to understand the
effect of optical elements and offer a new perspective to design the optical
elements for manipulating the OAM carried by light beams.


  In the present article, we prove the sharp local well-posedness and
ill-posedness results for the "good" Boussinesq equation on $\mathbb{T}$; the
initial value problem is locally well-posed in $H^{-1/2}(\mathbb{T})$ and
ill-posed in $H^s(\mathbb{T})$ for $s<-1/2$. Well-posedness result is obtained
from reduction of the problem into a quadratic nonlinear Schr\"odinger equation
and the contraction argument in suitably modified $X^{s,b}$ spaces. The proof
of the crucial bilinear estimates in these spaces, especially in the lowest
regularity, rely on some bilinear estimates for one dimensional periodic
functions in $X^{s,b}$ spaces, which are generalization of the bilinear
refinement of the $L^4$ Strichartz estimate on $\mathbb{R}$. Our result
improves the known local well-posedness in $H^s(\mathbb{T})$ with $s>-3/8$
given by Oh and Stefanov (2012) to the regularity threshold
$H^{-1/2}(\mathbb{T})$. Similar ideas also establish the sharp local
well-posedness in $H^{-1/2}(\mathbb{R})$ and ill-posedness below $H^{-1/2}$ for
the nonperiodic case, which improves the result of Tsugawa and the author
(2010) in $H^s(\mathbb{R})$ with $s>-1/2$ to the limiting regularity.


  This research seeks to explore how Augmented Reality (AR) can support
learning psychomotor tasks that involve complex manipulation and reasoning
processes. The AR prototype was created using Unity and used on HoloLens 2
headsets. Here, we explore the potential of AR as a training or assistive tool
for spatial tasks and the need for intelligent mechanisms to enable adaptive
and personalized interactions between learners and AR. The paper discusses how
integrating AR with Artificial Intelligence (AI) can adaptably scaffold the
learning of complex tasks to accelerate the development of expertise in
psychomotor domains.


  In the present study, the anisotropic resistivity of the monolayer graphene
has been obtained in semiclassical regime beyond the Dirac point approximation.
In particular, detailed investigations were made on the dependence of
conductivity on the Fermi energy. At low energies, in the vicinity of the Dirac
points, band energy of the monolayer graphene is isotropic at the Fermi level.
Meanwhile, at the intermediate Fermi energies anisotropic effects such as
trigonal warping is expected to be the origin of the anisotropic resistivity.
However, besides the band anisotropy there also exists an other source of
anisotropic resistivity which was introduced by scattering matrix. At high
energies it was shown that the band anisotropy is less effective than the
anisotropy generated by the scattering matrix. It was also shown that there
exist two distinct regimes of anisotropic resistivity corresponding the
trigonal warping and connected Fermi curve at intermediate and high energies
respectively.


  "Secondary doping" in
poly(3,4-ethylenedioxy-thiophene):poly(styrenesulfonate) (PEDOT:PSS) is quite
effective and a frequently used method for the conductivity enhancement. This
simple approach, the addition of co-solvents to the PEDOT:PSS solution before
film fabrication, is an essential way to derive the high electrical performance
of PEDOT:PSS, but the mechanism still remains unclear. In this study, nanoscale
structural changes synchronized with the conductivity enhancement via secondary
doping in PEDOT:PSS films were investigated. During secondary doping with
ethylene glycol near the critical dopant concentration, non-crystalized PEDOT
molecules uncoupled from PSS chains and then underwent nano-crystallization.
These structural changes might be the key driving force for conductivity
enhancement via secondary doping.


  We theoretically investigate the impact of weak perturbations of a flat
potential on the density of a quasi-two-dimensional dipolar Bose gas. We use a
mean-field perturbative treatment of the potential defects and derive their
effects at first order in the mean-field stable regime. We first focus on
defects containing a single spatial frequency and study the wavevector
dependence of the density perturbation. A qualitative modification of the
wavenumber dependence with the interaction parameters and a sensitivity in the
excitation direction reveal the long-range and anisotropic dipolar effects.
These effects are found to be most important at intermediate wavenumbers and
can give rise to a local maximum in the density perturbation reminiscent of the
roton mode softening and local instabilities. The dependence on the gas and
interaction parameters is studied. The case of a flat potential perturbed with
white noise on a certain momentum range is then examined. Here it is found that
the strength perturbation becomes independent of the mean density when
sufficiently large. Our study touches upon experimentally relevant issues,
giving hints on how flat a uniform potential should be to achieve uniform
quasi-two-dimensional dipolar Bose gases.


  In existing joint detection and tracking methods, pairwise relational
features are used to match previous tracklets to current detections. However,
the features may not be discriminative enough for a tracker to identify a
target from a large number of detections. Selecting only high-scored detections
for tracking may lead to missed detections whose confidence score is low.
Consequently, in the online setting, this results in disconnections of
tracklets which cannot be recovered. In this regard, we present Sparse Graph
Tracker (SGT), a novel online graph tracker using higher-order relational
features which are more discriminative by aggregating the features of
neighboring detections and their relations. SGT converts video data into a
graph where detections, their connections, and the relational features of two
connected nodes are represented by nodes, edges, and edge features,
respectively. The strong edge features allow SGT to track targets with tracking
candidates selected by top-K scored detections with large K. As a result, even
low-scored detections can be tracked, and the missed detections are also
recovered. The robustness of K value is shown through the extensive
experiments. In the MOT16/17/20 and HiEve Challenge, SGT outperforms the
state-of-the-art trackers with real-time inference speed. Especially, a large
improvement in MOTA is shown in the MOT20 and HiEve Challenge. Code is
available at https://github.com/HYUNJS/SGT.


  In this paper, a notion of affine walled Brauer-Clifford superalgebras
$BC_{r, t}^{\rm aff} $ is introduced over an arbitrary integral domain $R$
containing $2^{-1}$. These superalgebras can be considered as affinization of
walled Brauer superalgebras in \cite{JK}. By constructing infinite many
homomorphisms from $BC_{r, t}^{\rm aff}$ to a class of level two walled
Brauer-Clifford superagebras over $\mathbb C$, we prove that $BC_{r, t}^{\rm
aff} $ is free over $R$ with infinite rank. We explain that any finite
dimensional irreducible $BC_{r, t}^{\rm aff} $-module over an algebraically
closed field $F$ of characteristic not $2$ factors through a cyclotomic
quotient of $BC_{r, t}^{\rm aff} $, called a cyclotomic (or level $k$) walled
Brauer-Clifford superalgebra $ BC_{k, r, t}$. Using a previous method on
cyclotomic walled Brauer algebras in \cite{RSu1}, we prove that $BC_{k, r, t}$
is free over $R$ with super rank $(k^{r+t}2^{r+t-1} (r+t)!, k^{r+t}2^{r+t-1}
(r+t)!)$ if and only if it is admissible in the sense of Definition~6.4.
Finally, we prove that the degenerate affine (resp., cyclotomic) walled
Brauer-Clifford superalgebras defined by Comes-Kujawa in \cite{CK} are
isomorphic to our affine (resp., cyclotomic) walled Brauer-Clifford
superalgebras.


  We present experimental observations of a non-resonant dynamic Stark shift in
strongly coupled microcavity quantum well exciton-polaritons - a system which
provides a rich variety of solid-state collective phenomena. The Stark effect
is demonstrated in a GaAs/AlGaAs system at 10K by femtosecond pump-probe
measurements, with the blue shift approaching the meV scale for a pump fluence
of 2 mJcm^-2 and 50 meV red detuning, in good agreement with theory. The energy
level structure of the strongly coupled polariton Rabi-doublet remains
unaffected by the blue shift. The demonstrated effect should allow generation
of ultrafast density-independent potentials and imprinting well-defined phase
profiles on polariton condensates, providing a powerful tool for manipulation
of these condensates, similar to dipole potentials in cold atom systems.


  We present evidence from the RAdial Velocity Experiment (RAVE) survey of
chemically separated, kinematically distinct disc components in the solar
neighbourhood. We apply probabilistic chemical selection criteria to separate
our sample into $\alpha$-low (`thin disc') and $\alpha$-high (`thick disc')
sequences. Using newly derived distances, which will be utilized in the
upcoming RAVE DR5, we explore the kinematic trends as a function of metallicity
for each of the disc components. For our $\alpha$-low disc, we find a negative
trend in the mean rotational velocity ($V_{\mathrm{\phi}}$) as a function of
iron abundance ([Fe/H]). We measure a positive gradient $\partial
V_{\mathrm{\phi}}$/$\partial$[Fe/H] for the $\alpha$-high disc, consistent with
results from high-resolution surveys. We also find differences between the
$\alpha$-low and $\alpha$-high discs in all three components of velocity
dispersion. We discuss the implications of an $\alpha$-low, metal-rich
population originating from the inner Galaxy, where the orbits of these stars
have been significantly altered by radial mixing mechanisms in order to bring
them into the solar neighbourhood. The probabilistic separation we propose can
be extended to other data sets for which the accuracy in [$\alpha$/Fe] is not
sufficient to disentangle the chemical disc components a priori. For such
datasets which will also have significant overlap with Gaia DR1, we can
therefore make full use of the improved parallax and proper motion data as it
becomes available to investigate kinematic trends in these chemical disc
components.


  A band-pass filtering scheme is proposed to mitigate spectral broadening and
channel coupling in the Nonlinear Schr\"odinger (NLS) fiber optic channel. The
scheme is modeled by modifying the NLS Equation to include an attenuation
profile with multiple brick-wall filters centered at different frequencies. It
is shown that this brick-walls profile conserves the total in-band energy of
the launch signal. Furthermore, energy fluctuations between the filtered
channels are characterized, and conditions on the channel spacings are derived
that ensure energy conservation in each channel. The maximum spectral
efficiency of such a system is derived, and a constructive rule for achieving
it using Sidon sequences is provided.


  This paper presents parametric instance classification (PIC) for unsupervised
visual feature learning. Unlike the state-of-the-art approaches which do
instance discrimination in a dual-branch non-parametric fashion, PIC directly
performs a one-branch parametric instance classification, revealing a simple
framework similar to supervised classification and without the need to address
the information leakage issue. We show that the simple PIC framework can be as
effective as the state-of-the-art approaches, i.e. SimCLR and MoCo v2, by
adapting several common component settings used in the state-of-the-art
approaches. We also propose two novel techniques to further improve
effectiveness and practicality of PIC: 1) a sliding-window data scheduler,
instead of the previous epoch-based data scheduler, which addresses the
extremely infrequent instance visiting issue in PIC and improves the
effectiveness; 2) a negative sampling and weight update correction approach to
reduce the training time and GPU memory consumption, which also enables
application of PIC to almost unlimited training images. We hope that the PIC
framework can serve as a simple baseline to facilitate future study.


  Current metropolises largely depend on a functioning transport infrastructure
and the increasing demand can only be satisfied by a well organized mass
transit. One example for a crucial mass transit system is New York City's
Staten Island Ferry, connecting the two boroughs of Staten Island and Manhattan
with a regular passenger service. Today's demand already exceeds 2500
passengers for a single cycle during peek hours, and future projections suggest
that it will further increase. One way to appraise how the system will cope
with future demand is by simulation. This contribution proposes an integrated
simulation approach to evaluate the system performance with respect to future
demand. The simulation relies on a multiscale modeling approach where the
terminal buildings are simulated by a microscopic and quantitatively valid
cellular automata (CA) and the journeys of the ferries themselves are modeled
by a mesoscopic queue simulation approach. Based on the simulation results
recommendations with respect to the future demand are given.


  Dark matter comprised of axion-like particles (ALPs) generated by the
realignment mechanism in the post-inflationary scenario leads to primordial
isocurvature fluctuations. The power spectrum of these fluctuations is flat for
small wave numbers, extending to scales accessible with cosmological surveys.
We use the latest measurements of Cosmic Microwave Background (CMB) primary
anisotropies together with CMB lensing, Baryonic Acoustic Oscillations (BAO)
and Sunyaev Zel'dovich (SZ) cluster counts to measure the amplitude and tilt of
the isocurvature component. We find preference for a white-noise isocurvature
component in the CMB primary anisotropies; this conclusion is, however,
weakened by current large-scale structure (LSS) data. Interpreting the result
as a conservative upper limit on the isocurvature component, the combined bound
on the ALP mass from all probes is $m_{a} \gtrsim 10^{-19}$ eV, with some
dependence on how $m_{a}$ evolves with temperature. The expected sensitivity of
cosmic shear and galaxy clustering from future LSS experiments and CMB lensing
suggests improved bounds of $m_{a} \gtrsim 10^{-18}$-$10^{-13}$ eV, depending
on scale cuts used to avoid non-linearities and the ALP mass-temperature
dependence.


  A new universal derivative-free optimization method CDOS (Conjugate Direction
with Orthogonal Shift) is proposed. The CDOS method was specially developed to
solve optimization tasks where the objective function and constraints are black
boxes. The method has quadratic convergence for quadratic and near quadratic
functions. An objective function can be non-differentiable and non-continuous.
For constrained optimization the constraints can also be non-differentiable and
non-continuous. The method handles inequality constraints directly, i.e., it
does not transform the objective function, nor uses numeric values of the
inequality constraints - it uses only the fact of constraint violation.


  The small-scale power spectrum of the cosmological matter distribution
together with other cosmological data provides a sensitive measure of the hot
dark matter fraction, leading to restrictive neutrino mass limits. We extend
this argument to generic cases of low-mass thermal relics. We vary the cosmic
epoch of thermal decoupling, the radiation content of the universe, and the new
particle's spin degrees of freedom. Our treatment covers various scenarios of
active plus sterile neutrinos or axion-like particles. For three degenerate
massive neutrinos, we reproduce the well-known limit of m_nu < 0.34 eV. In a
3+1 scenario of 3 massless and 1 fully thermalized sterile neutrino we find
m_nu < 1.0 eV. Thermally produced QCD axions must obey m_a < 3.0 eV,
superseding limits from a direct telescope search, but leaving room for solar
eV-mass axions to be discovered by the CAST experiment.


  Deep learning approaches are still not very common in the speaker
verification field. We investigate the possibility of using deep residual
convolutional neural network with spectrograms as an input features in the
text-dependent speaker verification task. Despite the fact that we were not
able to surpass the baseline system in quality, we achieved a quite good
results for such a new approach getting an 5.23% ERR on the RSR2015 evaluation
part. Fusion of the baseline and proposed systems outperformed the best
individual system by 18% relatively.


  We propose a novel neural label embedding (NLE) scheme for the domain
adaptation of a deep neural network (DNN) acoustic model with unpaired data
samples from source and target domains. With NLE method, we distill the
knowledge from a powerful source-domain DNN into a dictionary of label
embeddings, or l-vectors, one for each senone class. Each l-vector is a
representation of the senone-specific output distributions of the source-domain
DNN and is learned to minimize the average L2, Kullback-Leibler (KL) or
symmetric KL distance to the output vectors with the same label through simple
averaging or standard back-propagation. During adaptation, the l-vectors serve
as the soft targets to train the target-domain model with cross-entropy loss.
Without parallel data constraint as in the teacher-student learning, NLE is
specially suited for the situation where the paired target-domain data cannot
be simulated from the source-domain data. We adapt a 6400 hours
multi-conditional US English acoustic model to each of the 9 accented English
(80 to 830 hours) and kids' speech (80 hours). NLE achieves up to 14.1%
relative word error rate reduction over direct re-training with one-hot labels.


  We study the online problem of minimizing power consumption in systems with
multiple power-saving states. During idle periods of unknown lengths, an
algorithm has to choose between power-saving states of different energy
consumption and wake-up costs. We develop a learning-augmented online algorithm
that makes decisions based on (potentially inaccurate) predicted lengths of the
idle periods. The algorithm's performance is near-optimal when predictions are
accurate and degrades gracefully with increasing prediction error, with a
worst-case guarantee almost identical to the optimal classical online algorithm
for the problem. A key ingredient in our approach is a new algorithm for the
online ski rental problem in the learning augmented setting with tight
dependence on the prediction error. We support our theoretical findings with
experiments.


  In recent years, there has been an increasing awareness to traffic
localization techniques driven by the emergence of heterogeneous networks
(HetNet) with small cells deployment and the green networks. The localization
of hotspot data traffic with a very high accuracy is indeed of great interest
to know where the small cells should be deployed and how can be managed for
sleep mode concept. In this paper, we propose a new traffic localization
technique based on the combination of different key performance indicators
(KPI) extracted from the operation and maintenance center (OMC). The proposed
localization algorithm is composed with five main steps; each one corresponds
to the determination of traffic weight per area using only one KPI. These KPIs
are Timing Advance (TA), Angle of Arrival (AoA), Neighbor cell level, the load
of each cell and the Harmonic mean throughput (HMT) versus the Arithmetic mean
throughput (AMT). The five KPIs are finally combined by a function taking as
variables the values computed from the five steps. By mixing such KPIs, we show
that it is possible to lessen significantly the errors of localization in a
high precision attaining small cell dimensions.


  Key questions for any superconductor include: what is its maximum
dissipation-free electrical current (its `critical current') and can this be
used to extract fundamental thermodynamic parameters? Present models focus on
depinning of magnetic vortices and implicate materials engineering to maximise
pinning performance. But recently we showed that the self-field critical
current for thin films is a universal property, independent of microstructure,
controlled only by the penetration depth. Here we generalise this observation
to include thin films, wires or nanowires of single- or multi-band s-wave and
d-wave superconductors. Using extended BCS theory we consider dissipation-free
self-field transport currents as Meissner-London currents, avoiding the concept
of pinning altogether. We find quite generally, for type I or type II
superconductors, the current is limited by the relevant critical field divided
by the penetration depth. Our fits to 62 available data sets, from zinc
nanowires to compressed sulphur hydride with critical temperatures of 0.65 to
203 K, respectively, are excellent. Extracted London penetration depths,
superconducting energy gaps and specific heat jumps agree well with reported
bulk values. For multiband or multiphase samples we accurately recover
individual band contributions and phase fractions.


  Nontrivial topology of interfaces separating anti-chiral phases in helical
magnets result in new effects as they interact with spin polarized current.
These interfces or vortex walls consist of a one dimensional array of vortex
lines. Topological Hall effect comes as a natural expectation. Also Lenz's law
will result in a new damping mechanism in these systems. We study the dynamics
of these wall interacting with electric current and use fundamental
electromagntic laws to calculate this effect. On the other hand discrete nature
of these walls affects their pinning and result in low depinning current
density. We predict the value of this currnt using collective pinnng theory.
Finally throughout this study we propose possible standard experiments that may
distinguish our theoretical findings.


  Video anomaly detection aims to discover abnormal events in videos, and the
principal objects are target objects such as people and vehicles. Each target
in the video data has rich spatio-temporal context information. Most existing
methods only focus on the temporal context, ignoring the role of the spatial
context in anomaly detection. The spatial context information represents the
relationship between the detection target and surrounding targets. Anomaly
detection makes a lot of sense. To this end, a video anomaly detection
algorithm based on target spatio-temporal context fusion is proposed. Firstly,
the target in the video frame is extracted through the target detection network
to reduce background interference. Then the optical flow map of two adjacent
frames is calculated. Motion features are used multiple targets in the video
frame to construct spatial context simultaneously, re-encoding the target
appearance and motion features, and finally reconstructing the above features
through the spatio-temporal dual-stream network, and using the reconstruction
error to represent the abnormal score. The algorithm achieves frame-level AUCs
of 98.5% and 86.3% on the UCSDped2 and Avenue datasets, respectively. On the
UCSDped2 dataset, the spatio-temporal dual-stream network improves frames by
5.1% and 0.3%, respectively, compared to the temporal and spatial stream
networks. After using spatial context encoding, the frame-level AUC is enhanced
by 1%, which verifies the method's effectiveness.


  Majorana fermions often coexist with other low-energy fermionic degrees of
freedom. In such situation, topological quantum computation requires the use of
fermionic zero modes of a many-body system. We classify all such modes for
interacting fermions and show how to select the mode that maximizes the
decoherence time. We find that in a typical interacting system the maximal
decoherence time is within one order of magnitude from the decoherence time of
a qbit based on the local part of the fermion parity operator.


  Accurate and reliable prediction of individual travel mode choices is crucial
for developing multi-mode urban transportation systems, conducting
transportation planning and formulating traffic demand management strategies.
Traditional discrete choice models have dominated the modelling methods for
decades yet suffer from strict model assumptions and low prediction accuracy.
In recent years, machine learning (ML) models, such as neural networks and
boosting models, are widely used by researchers for travel mode choice
prediction and have yielded promising results. However, despite the superior
prediction performance, a large body of ML methods, especially the branch of
neural network models, is also limited by overfitting and tedious model
structure determination process. To bridge this gap, this study proposes an
enhanced multilayer perceptron (MLP; a neural network) with two hidden layers
for travel mode choice prediction; this MLP is enhanced by XGBoost (a boosting
method) for feature selection and a grid search method for optimal hidden
neurone determination of each hidden layer. The proposed method was trained and
tested on a real resident travel diary dataset collected in Chengdu, China.


  We observe $(X_i,Y_i)_{i=1}^n$ where the $Y_i$'s are real valued outputs and
the $X_i$'s are $m\times T$ matrices. We observe a new entry $X$ and we want to
predict the output $Y$ associated with it. We focus on the high-dimensional
setting, where $m T \gg n$. This includes the matrix completion problem with
noise, as well as other problems. We consider linear prediction procedures
based on different penalizations, involving a mixture of several norms: the
nuclear norm, the Frobenius norm and the $\ell_1$-norm. For these procedures,
we prove sharp oracle inequalities, using a statistical learning theory point
of view. A surprising fact in our results is that the rates of convergence do
not depend on $m$ and $T$ directly. The analysis is conducted without the
usually considered incoherency condition on the unknown matrix or restricted
isometry condition on the sampling operator. Moreover, our results are the
first to give for this problem an analysis of penalization (such nuclear norm
penalization) as a regularization algorithm: our oracle inequalities prove that
these procedures have a prediction accuracy close to the deterministic oracle
one, given that the reguralization parameters are well-chosen.


  Dark matter particles captured in the Sun would annihilate producing a
neutrino flux that could be detected at the Earth. In some channels, however,
the neutrino flux lies in the MeV range and is thus undetectable at IceCube,
namely when the dark matter particles annihilate into $e^+e^-$, $\mu^+\mu^-$ or
light quarks. On the other hand, the same interaction that mediates the
annihilations into light fermions also leads, via higher order effects, to the
production of weak gauge bosons (and in the case of quarks also gluons) that
generate a high energy neutrino flux potentially observable at IceCube. We
consider in this paper tree level annihilations into a fermion-antifermion pair
with the associated emission of one gauge boson and one loop annihilations into
two gauge bosons, and we calculate the limits on the scattering cross section
of dark matter particles with protons in scenarios where the dark matter
particle couples to electrons, muons or light quarks from the non-observation
of an excess of neutrino events in the direction of the Sun. We find that the
limits on the spin-dependent scattering cross section are, for some scenarios,
stronger than the limits from direct detection experiments.


  Limited-backhaul cell-free Massive multiple-input multiple-output (MIMO), in
which the fog radio access network (F-RAN) is implemented to exchange the
information between access points (APs) and the central processing unit (CPU),
is investigated. We introduce a novel approach where the APs estimate the
channel and send back the quantized version of the estimated channel and the
quantized version of the received signal to the central processing unit. The
Max algorithm and the Bussgang theorem are exploited to model the optimum
uniform quantization. The ergodic achievable rates are derived. We show that
exploiting microwave wireless backhaul links and using a small number of bits
to quantize the estimated channel and the received signal, the performance of
limited-backhaul cell-free Massive MIMO closely approaches the performance of
cell-free Massive MIMO with perfect backhaul links.


  Traditional recommendation models trained on observational interaction data
have generated large impacts in a wide range of applications, it faces bias
problems that cover users' true intent and thus deteriorate the recommendation
effectiveness. Existing methods tracks this problem as eliminating bias for the
robust recommendation, e.g., by re-weighting training samples or learning
disentangled representation. The disentangled representation methods as the
state-of-the-art eliminate bias through revealing cause-effect of the bias
generation. However, how to design the semantics-aware and unbiased
representation for users true intents is largely unexplored. To bridge the gap,
we are the first to propose an unbiased and semantics-aware disentanglement
learning called CaDSI (Causal Disentanglement for Semantics-Aware Intent
Learning) from a causal perspective. Particularly, CaDSI explicitly models the
causal relations underlying recommendation task, and thus produces
semantics-aware representations via disentangling users true intents aware of
specific item context. Moreover, the causal intervention mechanism is designed
to eliminate confounding bias stemmed from context information, which further
to align the semantics-aware representation with users true intent. Extensive
experiments and case studies both validate the robustness and interpretability
of our proposed model.


  We theoretically study the real-space distribution of the supercurrent that
flows under a uniform vector potential in a two-dimensional quasiperiodic
structure. This is done by considering the attractive Hubbard model on the
quasiperiodic Ammann-Beenker structure and studying the superconducting phase
within the Bogoliubov-de Gennes mean-field theory. Decomposing the local
supercurrent into the paramagnetic and diamagnetic components, we numerically
investigate their dependencies on average electron density, temperature, and
the angle of the applied vector potential. We find that the diamagnetic current
locally violates the current conservation law, necessitating compensation from
the paramagnetic current, even at zero temperature. The paramagnetic current
shows exotic behaviors in the quasiperiodic structure, such as local currents
which are oriented transversally or reversely to that of the applied vector
potential.


  We study interactive learning in a setting where the agent has to generate a
response (e.g., an action or trajectory) given a context and an instruction. In
contrast, to typical approaches that train the system using reward or expert
supervision on response, we study learning with hindsight instruction where a
teacher provides an instruction that is most suitable for the agent's generated
response. This hindsight labeling of instruction is often easier to provide
than providing expert supervision of the optimal response which may require
expert knowledge or can be impractical to elicit. We initiate the theoretical
analysis of interactive learning with hindsight labeling. We first provide a
lower bound showing that in general, the regret of any algorithm must scale
with the size of the agent's response space. We then study a specialized
setting where the underlying instruction-response distribution can be
decomposed as a low-rank matrix. We introduce an algorithm called LORIL for
this setting and show that its regret scales as $\sqrt{T}$ where $T$ is the
number of rounds and depends on the intrinsic rank but does not depend on the
size of the agent's response space. We provide experiments in two domains
showing that LORIL outperforms baselines even when the low-rank assumption is
violated.


  Steepest-entropy-ascent quantum thermodynamics, or SEAQT, is a unified
approach of quantum mechanics and thermodynamics that avoids many of the
inconsistencies that can arise between the two theories. Given a set of energy
levels, i.e., energy eigenstructure, accessible to a given physical system,
SEAQT predicts the unique kinetic path from any initial non-equilibrium state
to stable equilibrium by solving a master equation that directs the system
along the path of steepest entropy ascent. There are no intrinsic limitations
on the length and time scales the method can treat so it is well-suited for
calculations where the dynamics over multiple spacial scales need to be taken
into account within a single framework. In this paper, the theoretical
framework and its advantages are described, and several applications are
presented to illustrate the use of the SEAQT equation of motion and the
construction of a simplified, reduced-order, energy eigenstructure.


  Application migration and dynamic voltage and frequency scaling (DVFS) are
indispensable means for fully exploiting the available potential in thermal
optimization of a heterogeneous clustered multi-core processor under
user-defined quality of service (QoS) targets. However, selecting the core to
execute each application and the voltage/frequency (V/f) levels of each cluster
is a complex problem because 1) the diverse characteristics and QoS targets of
applications require different optimizations, and 2) per-cluster DVFS requires
a global optimization considering all running applications. State-of-the-art
resource management techniques for power or temperature minimization either
rely on measurements that are often not available (such as power) or fail to
consider all the dimensions of the problem (e.g., by using simplified
analytical models). Imitation learning (IL) enables to use the optimality of an
oracle policy, yet at low run-time overhead, by training a model from oracle
demonstrations. We are the first to employ IL for temperature minimization
under QoS targets. We tackle the complexity by training a neural network (NN)
and accelerate the NN inference using a neural processing unit (NPU). While
such NN accelerators are becoming increasingly widespread on end devices, they
are so far only used to accelerate user applications. In contrast, we use an
existing accelerator on a real platform to accelerate NN-based resource
management. Our evaluation on a HiKey 970 board with an Arm big.LITTLE CPU and
an NPU shows significant temperature reductions at a negligible run-time
overhead, with unseen applications and different cooling than used for
training.


  In this paper, we study the explicit structure of the graded ring of elliptic
modular forms for the congruence subgroup $\Gamma_0(N)$ with
N=1,2,3,4,5,6,7,8,9,10,12,16,18,25. More precisely, making use of some
relations between Fourier expansions, we determine the ring by a quotient of
the polynomial ring in several variables.


  Tabulation audits for an election provide statistical evidence that a
reported contest outcome is "correct" (meaning that the tabulation of votes was
properly performed), or else the tabulation audit determines the correct
outcome.
  Stark proposed risk-limiting tabulation audits for this purpose; such audits
are effective and are beginning to be used in practice.
  We expand the study of election audits based on Bayesian methods, first
introduced by Rivest and Shen in 2012. (The risk-limiting audits proposed by
Stark are "frequentist" rather than Bayesian in character.)
  We first provide a simplified presentation of Bayesian tabulation audits. A
Bayesian tabulation audit begins by drawing a random sample of the votes in
that contest, and tallying those votes. It then considers what effect
statistical variations of this tally have on the contest outcome. If such
variations almost always yield the previously-reported outcome, the audit
terminates, accepting the reported outcome. Otherwise the audit is repeated
with an enlarged sample.
  Bayesian audits are attractive because they work with any method for
determining the winner (such as ranked-choice voting).
  We then show how Bayesian audits may be extended to handle more complex
situations, such as auditing contests that \emph{span multiple jurisdictions},
or are otherwise "stratified."
  We highlight the auditing of such multiple-jurisdiction contests where some
of the jurisdictions have an electronic cast vote record (CVR) for each cast
paper vote, while the others do not. Complex situations such as this may arise
naturally when some counties in a state have upgraded to new equipment, while
others have not. Bayesian audits are able to handle such situations in a
straightforward manner.
  We also discuss the benefits and relevant considerations for using Bayesian
audits in practice.


  This paper is an extension of Fujii et al (quant--ph/0307066) and in this one
we again treat a model of atom with n energy levels interacting with n(n-1)/2
external laser fields, which is a natural extension of usual two level system.
Then the rotating wave approximation (RWA) is assumed from the beginning.
  To solve the Schr{\" o}dinger equation we set the {\bf consistency condition}
in our terminology and reduce it to a matrix equation with symmetric matrix $Q$
consisting of coupling constants.
  However, to calculate $\exp(-itQ)$ {\bfexplicitly} is not easy. In the case
of three and four level systems we determine it in a complete manner, so our
model in these levels becomes realistic.
  In last, we make a comment on Cavity QED quantum computation based on three
energy levels of atoms as a forthcoming target.


  In this paper we study Spectral Decomposition Theorem [1] and translate it to
quantum language by means of the Wigner transform. We obtain a quantum version
of Spectral Decomposition Theorem (QSDT) which enables us to achieve three
distinct goals: First, to rank Quantum Ergodic Hierarchy levels [2,3]. Second,
to analyze the classical limit in quantum ergodic systems and quantum mixing
systems. And third, and maybe most important feature, to find a relevant and
simple connection between the first three levels of quantum ergodic hierarchy
(ergodic, exact and mixing) and quantum spectrum. Finally, we illustrate the
physical relevance of QSDT applying it to two examples: Microwave billiards
[4,5] and a phenomenological Gamow model type [6,7].


  We carry out constant volume simulations of steady-state, shear driven flow
in a simple model of athermal, bidisperse, soft-core, frictionless disks in two
dimensions, using a dissipation law that gives rise to Bagnoldian rheology.
Focusing on the small strain rate limit, we map out the rheological behavior as
a function of particle packing fraction $\phi$ and a parameter $Q$ that
measures the elasticity of binary particle collisions. We find a $Q^*(\phi)$
that marks the clear crossover from a region characteristic of strongly
inelastic collisions, $Q<Q^*$, to a region characteristic of weakly inelastic
collisions, $Q>Q^*$, and give evidence that $Q^*(\phi)$ diverges as
$\phi\to\phi_J$, the shear driven jamming transition. We thus conclude that the
jamming transition at any value of $Q$ behaves the same as the strongly
inelastic case, provide one is sufficiently close to $\phi_J$. We further
characterize the differing nature of collisions in the strongly inelastic vs
weakly inelastic regions, and recast our results into the constituent equation
form commonly used in discussions of hard granular matter.


  Recently, there has been a surge in face personalization techniques,
benefiting from the advanced capabilities of pretrained text-to-image diffusion
models. Among these, a notable method is Textual Inversion, which generates
personalized images by inverting given images into textual embeddings. However,
methods based on Textual Inversion still struggle with balancing the trade-off
between reconstruction quality and editability. In this study, we examine this
issue through the lens of initialization. Upon closely examining traditional
initialization methods, we identified a significant disparity between the
initial and learned embeddings in terms of both scale and orientation. The
scale of the learned embedding can be up to 100 times greater than that of the
initial embedding. Such a significant change in the embedding could increase
the risk of overfitting, thereby compromising the editability. Driven by this
observation, we introduce a novel initialization method, termed Cross
Initialization, that significantly narrows the gap between the initial and
learned embeddings. This method not only improves both reconstruction and
editability but also reduces the optimization steps from 5000 to 320.
Furthermore, we apply a regularization term to keep the learned embedding close
to the initial embedding. We show that when combined with Cross Initialization,
this regularization term can effectively improve editability. We provide
comprehensive empirical evidence to demonstrate the superior performance of our
method compared to the baseline methods. Notably, in our experiments, Cross
Initialization is the only method that successfully edits an individual's
facial expression. Additionally, a fast version of our method allows for
capturing an input image in roughly 26 seconds, while surpassing the baseline
methods in terms of both reconstruction and editability. Code will be made
publicly available.


  Accurate predictions of thermo-mechanically coupled process in metals can
lead to a reduction of cost and an increase of productivity in manufacturing
processes such as forming. For modeling these coupled processes with the finite
element method, accurate descriptions of both the mechanical and the thermal
responses of the material, as well as their interaction, are needed.
Conventional material modeling employs empirical macroscopic constitutive
relations but does not account for the actual thermo-mechanical mechanisms
occurring at the microscopic level. However, the consideration of the latter
might be crucial to obtain accurate predictions and a complete understanding of
the underlying physics. In this work we describe a fully coupled implicit
thermo-mechanical framework for crystal plasticity simulations. This framework
includes thermal strains, temperature dependency of the crystal behavior and
heat generation by dissipation due to plastic slip and allows the use of large
deformation steps thanks to the implicit integration of the governing
equations. Its use within computational homogenization simulations allows to
bridge the plastic deformation and temperature gradients at the macroscopic
scale with the microscopic slip at the grain scale. A series of numerical
examples are presented to validate the approach.


  The emergence and continued reliance on the Internet and related technologies
has resulted in the generation of large amounts of data that can be made
available for analyses. However, humans do not possess the cognitive
capabilities to understand such large amounts of data. Machine learning (ML)
provides a mechanism for humans to process large amounts of data, gain insights
about the behavior of the data, and make more informed decision based on the
resulting analysis. ML has applications in various fields. This review focuses
on some of the fields and applications such as education, healthcare, network
security, banking and finance, and social media. Within these fields, there are
multiple unique challenges that exist. However, ML can provide solutions to
these challenges, as well as create further research opportunities.
Accordingly, this work surveys some of the challenges facing the aforementioned
fields and presents some of the previous literature works that tackled them.
Moreover, it suggests several research opportunities that benefit from the use
of ML to address these challenges.


  Starting from a multichannel quantum-defect theory, we derive analytic
descriptions of a magnetic Feshbach resonance in an arbitrary partial wave $l$,
and the atomic interactions around it. An analytic formula, applicable to both
broad and narrow resonances of arbitrary $l$, is presented for ultracold atomic
scattering around a Feshbach resonance. Other related issues addressed include
(a) the parametrization of a magnetic Feshbach resonance of arbitrary $l$, (b)
rigorous definitions of "broad" and "narrow" resonances of arbitrary $l$ and
their different scattering characteristics, and (c) the tuning of the effective
range and the generalized effective range by a magnetic field.


  Predicting cellular metabolic states is a central problem in biophysics.
Conventional approaches, however, sensitively depend on the microscopic details
of individual metabolic systems. In this Letter, we derived a universal linear
relationship between the metabolic responses against nutrient conditions and
metabolic inhibition, with the aid of a microeconomic theory. The relationship
holds in arbitrary metabolic systems as long as the law of mass conservation
stands, as supported by extensive numerical calculations. It offers
quantitative predictions without prior knowledge of systems.


  Many researchers have explored tools that aim to recommend data insights to
users. These tools automatically communicate a rich diversity of data insights
and offer such insights for many different purposes. However, there is a lack
of structured understanding concerning what researchers of these tools mean by
"insight" and what tasks in the analysis workflow these tools aim to support.
We conducted a systematic review of existing systems that seek to recommend
data insights. Grounded in the review, we propose 12 types of automated
insights and four purposes of automating insights. We further discuss the
design opportunities emerged from our analysis.


  In this paper we study the reduction curves of a braid, and how they can be
used to decompose the braid into simpler ones in a precise way, which does not
correspond exactly to the decomposition given by Thurston theory. Then we study
how a cyclic sliding (which is a particular kind of conjugation) affects the
normal form of a braid with respect to the normal forms of its components.
Finally, using the above methods, we provide the example of a family of braids
whose sets of sliding circuits (hence ultra summit sets) have exponential size
with respect to the number of strands and also with respect to the canonical
length.