  Intertwining of different quantum degrees of freedom manifests exotic quantum
phenomena in many-body systems, especially in reduced dimensionality. Here we
show that monolayered NbTe2 serves as an ideal platform where lattice, charge,
and spin degrees of freedom manifest cooperatively, leading to a new and
threading order of chirality. By using spin-polarized scanning tunneling
microscopy/spectroscopy, we reveal that the root19 * root19 phase of NbTe2 is
encoded with both alternating-chiral atomic displacements and charge density
waves, characterized by two chiral units of opposite handedness within the
reconstructed cell. We show unambiguous evidence for emergent spin
polarizations spreading over the primitive cell, with the magnetization
orientation synchronized with alternating handedness of chiral order. Our
first-principles studies identify the origin of intertwined orders being
correlation driven, with the threading order of chirality emerging when the
on-site Coulomb repulsion exceeds a critical value. The spin ordering is
further shown to be of hybrid ferrimagnetic nature, contributed by the
itinerant electrons and localized d-orbitals. Collectively, these findings
expand the realm of chiral order in correlated electron systems, and facilitate
an appealing platform for chiral spintronic and related applications.


  Optimizing multiple competing objectives is a common problem across science
and industry. The inherent inextricable trade-off between those objectives
leads one to the task of exploring their Pareto front. A meaningful quantity
for the purpose of the latter is the hypervolume indicator, which is used in
Bayesian Optimization (BO) and Evolutionary Algorithms (EAs). However, the
computational complexity for the calculation of the hypervolume scales
unfavorably with increasing number of objectives and data points, which
restricts its use in those common multi-objective optimization frameworks. To
overcome these restrictions we propose to approximate the hypervolume function
with a deep neural network, which we call DeepHV. For better sample efficiency
and generalization, we exploit the fact that the hypervolume is
scale-equivariant in each of the objectives as well as permutation invariant
w.r.t. both the objectives and the samples, by using a deep neural network that
is equivariant w.r.t. the combined group of scalings and permutations. We
evaluate our method against exact, and approximate hypervolume methods in terms
of accuracy, computation time, and generalization. We also apply and compare
our methods to state-of-the-art multi-objective BO methods and EAs on a range
of synthetic benchmark test cases. The results show that our methods are
promising for such multi-objective optimization tasks.


  Point cloud videos effectively capture real-world spatial geometries and
temporal dynamics, which are essential for enabling intelligent agents to
understand the dynamically changing 3D world we live in. Although static 3D
point cloud processing has witnessed significant advancements, designing an
effective 4D point cloud video backbone remains challenging, mainly due to the
irregular and unordered distribution of points and temporal inconsistencies
across frames. Moreover, recent state-of-the-art 4D backbones predominantly
rely on transformer-based architectures, which commonly suffer from large
computational costs due to their quadratic complexity, particularly when
processing long video sequences. To address these challenges, we propose a
novel 4D point cloud video understanding backbone based on the recently
advanced State Space Models (SSMs). Specifically, our backbone begins by
disentangling space and time in raw 4D sequences, and then establishing
spatio-temporal correlations using our newly developed Intra-frame Spatial
Mamba and Inter-frame Temporal Mamba blocks. The Intra-frame Spatial Mamba
module is designed to encode locally similar or related geometric structures
within a certain temporal searching stride, which can effectively capture
short-term dynamics. Subsequently, these locally correlated tokens are
delivered to the Inter-frame Temporal Mamba module, which globally integrates
point features across the entire video with linear complexity, further
establishing long-range motion dependencies. Experimental results on human
action recognition and 4D semantic segmentation tasks demonstrate the
superiority of our proposed method. Especially, for long video sequences, our
proposed Mamba-based method has an 87.5% GPU memory reduction, 5.36 times
speed-up, and much higher accuracy (up to +10.4%) compared with
transformer-based counterparts on MSR-Action3D dataset.


  In this paper we consider a class of Einstein warped product semi-Riemannian
manifolds $\widehat{M} = M^{n}\times_{f}N^{m}$ with $n\geq 3$ and $m\geq 2$.
For $\widehat{M}$ with compact base and Ricci-flat fiber, we prove that
$\widehat{M}$ is simply a Riemannian product space. Then, when the base $M$ is
conformal to a pseudo-Euclidean space which is invariant under the action of a
$(n-1)$-dimensional translation group, we classify all such spaces.
Furthermore, we get new examples of complete Einstein warped products
Riemannian manifolds.


  The dependence of macroscopic radiation pressure on the velocity of the
object being pushed is commonly attributed to the Doppler effect. This need not
be the case, and here we highlight velocity dependent radiation pressure terms
that have their origins in the mixing of s and p polarizations brought about by
the Lorentz transformation between the lab and the material rest frame, rather
than in the corresponding transformation of frequency and wavevector. The
theory we develop is relevant to the nano-optomechanics of moving bodies.


  We present the three-body decay of the Higgs boson into two leptons and a
photon to dimension-eight in the Standard Model Effective Field Theory (SMEFT).
In order to obtain this result we interfere the full one-loop Standard Model
result with the tree-level result in the SMEFT. This is the first calculation
of the partial width of the Higgs boson into two leptons and a photon in the
SMEFT to incorporate the full one-loop dependence for the Standard Model as
well as the full tree level dimension-eight dependence in the SMEFT. We find
that this channel can aid in distinguishing strongly interacting and weakly
interacting UV completions of the SMEFT under standard assumptions. We also
find that this channel presents the opportunity to distinguish different
operator Classes within the SMEFT, potentially including contact
$H\bar\ell\ell\gamma$ operators which are first generated only at
dimension-eight in the SMEFT.


  We consider a MIMO fading broadcast channel and compute achievable ergodic
rates when channel state information is acquired at the receivers via downlink
training and it is provided to the transmitter by channel state feedback.
Unquantized (analog) and quantized (digital) channel state feedback schemes are
analyzed and compared under various assumptions. Digital feedback is shown to
be potentially superior when the feedback channel uses per channel state
coefficient is larger than 1. Also, we show that by proper design of the
digital feedback link, errors in the feedback have a minor effect even if
simple uncoded modulation is used on the feedback channel. We discuss first the
case of an unfaded AWGN feedback channel with orthogonal access and then the
case of fading MIMO multi-access (MIMO-MAC). We show that by exploiting the
MIMO-MAC nature of the uplink channel, a much better scaling of the feedback
channel resource with the number of base station antennas can be achieved.
Finally, for the case of delayed feedback, we show that in the realistic case
where the fading process has (normalized) maximum Doppler frequency shift 0 < F
< 1/2, a fraction 1 - 2F of the optimal multiplexing gain is achievable. The
general conclusion of this work is that very significant downlink throughput is
achievable with simple and efficient channel state feedback, provided that the
feedback link is properly designed.


  Certain zero-bias anomalies (ZBAs) in the voltage, temperature and magnetic
field dependence of the conductance $G(V,T,H)$ of quenched Cu point contacts
have previously been interpreted to be due to non-magnetic 2-channel Kondo
(2CK) scattering from near-degenerate atomic two-level tunneling systems (Ralph
and Buhrman, 1992; Ralph et al. 1994), and hence to represent an experimental
realization of the non-Fermi-liquid physics of the T=0 fixed point of the
2-channel Kondo model. In this, the first in a series of three papers
(I,II,III) devoted to 2-channel Kondo physics, we present a comprehensive
review of the quenched Cu ZBA experiments and their 2CK interpretation,
including new results on ZBAs in constrictions made from Ti or from metallic
glasses. We first review the evidence that the ZBAs are due to electron
scattering from stuctural defects that are not static, but possess internal
dynamics. In order to distinguish between several mechanisms proposed to
explain the experiments, we then analyze the scaling properties of the
conductance at low temperature and voltage and extract from the data a
universal scaling function $\Gamma(v)$. The theoretical calculation of the
corresponding scaling function within the 2CK model is the subject of papers II
and III. The main conclusion of our work is that the properties of the ZBAs,
and most notably their scaling behavior, are in good agreement with the 2CK
model and clearly different from several other proposed mechanisms.


  A model for cooperative sequential adsorption that incorporates
nearest-neighbor exclusion and next-nearest neighbor interaction is presented.
It is analyzed for the case of one-dimensional dimer and two-dimensional
monomer adsorption. Analytic solutions found for certain values of the
interaction strength are used to investigate jamming coverage and temporal
approach to jamming in the one-dimensional case. In two dimensions, the series
expansion of the coverage $\theta(t)$ is presented and employed to provide
estimates for the jamming coverage as a function of interaction strength. These
estimates are supported by Monte Carlo simulation results.


  A formal derivation of linear hydrodynamics for a granular fluid is given.
The linear response to small spatial perturbations of the homogeneous reference
state is studied in detail using methods of non-equilibrium statistical
mechanics. A transport matrix for macroscopic excitations in the fluid is
defined in terms of the response functions. An expansion in the wavevector to
second order allows identification of all phenomenological susceptibilities and
transport coefficients through Navier-Stokes order, in terms of appropriate
time correlation functions. The transport coefficients in this representation
are the generalization to granular fluids of the familiar Helfand and
Green-Kubo relations for normal fluids. The analysis applies to a wide range of
collision rules. Important differences in both the analysis and results from
those for normal fluids are identified and discussed. A scaling limit is
described corresponding to the conditions under which idealized inelastic hard
sphere models can apply. Further details and interpretation are provided in the
paper following this one, by specialization to the case of smooth, inelastic
hard spheres with constant coefficient of restitution.


  We address a linearity problem for differentiable vectors in representations
of infinite-dimensional Lie groups on locally convex spaces, which is similar
to the linearity problem for the directional derivatives of functions.


  We discuss the systematic uncertainties in the recovery of dark energy
properties from the use of baryon acoustic oscillations as a standard ruler. We
demonstrate that while unknown relativistic components in the universe prior to
recombination would alter the sound speed, the inferences for dark energy from
low-redshift surveys are unchanged so long as the microwave background
anisotropies can measure the redshift of matter-radiation equality, which they
can do to sufficient accuracy. The mismeasurement of the radiation and matter
densities themselves (as opposed to their ratio) would manifest as an incorrect
prediction for the Hubble constant at low redshift. In addition, these
anomalies do produce subtle but detectable features in the microwave
anisotropies.


  We apply the stationary phase method developed in (Assier, Shanin \&
Korolkov, QJMAM, 76(1), 2022) to the problem of wave diffraction by a
quarter-plane. The wave field is written as a double Fourier transform of an
unknown spectral function. We make use of the analytical continuation results
of (Assier \& Shanin, QJMAM, 72(1), 2018) to uncover the singularity structure
of this spectral function. This allows us to provide a closed-form far-field
asymptotic expansion of the field by estimating the double Fourier integral
near some special points of the spectral function. All the known results on the
far-field asymptotics of the quarter-plane problem are recovered, and new
mathematical expressions are derived for the secondary diffracted waves in the
plane of the scatterer.


  The weak lensing (WL) distortions of distant galaxy images are sensitive to
neutrino masses by probing the suppression effect on clustering strengths of
total matter in large-scale structure. We use the latest measurement of WL
correlations, the CFHTLS data, to explore constraints on neutrino masses. We
find that, while the WL data alone cannot place a stringent limit on neutrino
masses due to parameter degeneracies, the constraint can be significantly
improved when combined with other cosmological probes, the WMAP 5-year (WMAP5)
data and the distance measurements of type-Ia supernovae (SNe) and baryon
acoustic oscillations (BAO). The upper bounds on the sum of neutrino masses are
m_tot = 1.1, 0.76 and 0.54 eV (95% CL) for WL+WMAP5, WMAP5+SNe+BAO, and
WL+WMAP5+SNe+BAO, respectively, assuming a flat LCDM model with finite-mass
neutrinos. In deriving these constraints, our analysis includes the
non-Gaussian covariances of the WL correlation functions to properly take into
account significant correlations between different angles.


  The free-space optical (FSO) communications can achieve high capacity with
huge unlicensed optical spectrum and low operational costs. The corresponding
performance analysis of FSO systems over turbulence channels is very limited,
especially when using multiple apertures at both transmitter and receiver
sides. This paper aim to provide the ergodic capacity characterization of
multiple-input multiple-output (MIMO) FSO systems over atmospheric
turbulence-induced fading channels. The fluctuations of the irradiance of
optical channels distorted by atmospheric conditions is usually described by a
gamma-gamma ($\Gamma \Gamma$) distribution, and the distribution of the sum of
$\Gamma \Gamma$ random variables (RVs) is required to model the MIMO optical
links. We use an $\alpha$-$\mu$ distribution to efficiently approximate the
probability density function (PDF) of the sum of independent and identical
distributed $\Gamma\Gamma$ RVs through moment-based estimators. Furthermore,
the PDF of the sum of independent, but not necessarily identically distributed
$\Gamma \Gamma$ RVs can be efficiently approximated by a finite weighted sum of
PDFs of $\Gamma \Gamma$ distributions. Based on these reliable approximations,
novel and precise analytical expressions for the ergodic capacity of MIMO FSO
systems are derived. Additionally, we deduce the asymptotic simple expressions
in high signal-to-noise ratio regimes, which provide useful insights into the
impact of the system parameters on the ergodic capacity. Finally, our proposed
results are validated via Monte-Carlo simulations.


  COMET is a leading experiment to search for coherent conversion of $\mu^-
\mathrm{N}\to e^- \mathrm{N}$ with a high-intensity pulsed muon beamline,
produced by the innovative slow extraction techniques. Therefore, it is
critical to measure the characteristics of the muon beam. We set up a Muon Beam
Monitor (MBM), where scintillation fibers (SciFi) weaved in the cross shape are
coupled to silicon photomultipliers (SiPM), to measure the spatial profile and
timing structure of the extracted muon beam for COMET. The MBM detector has
been tested successfully with a proton beamline in China Spallation Neutron
Source (CSNS) and taken data with good performance in the commissioning run
called COMET Phase-$\alpha$. Experience of the MBM development, such as the
mechanical structure and electronics readout, and its beam measurement results
will be shared.


  Black-box checking (BBC)} is a testing method for cyber-physical systems
(CPSs) as well as software systems. BBC consists of active automata learning
and model checking; a Mealy machine is learned from the system under test
(SUT), and the learned Mealy machine is verified against a specification using
model checking. When the Mealy machine violates the specification, the model
checker returns an input witnessing the specification violation of the Mealy
machine. We use it to refine the Mealy machine or conclude that the SUT
violates the specification. Otherwise, we conduct equivalence testing to find
an input witnessing the difference between the Mealy machine and the SUT. In
the BBC for CPSs, equivalence testing tends to be time-consuming due to the
time for the system execution. In this paper, we enhance the BBC utilizing
model checking with strengthened specifications. By model checking with a
strengthened specification, we have more chance to obtain an input witnessing
the specification violation than model checking with the original
specification. The refinement of the Mealy machine with such an input tends to
reduce the number of equivalence testing, which improves the efficiency. We
conducted experiments with an automotive benchmark. Our experiment results
demonstrate the merit of our method.


  It is shown that the patches of complex d_{xy} component are generated around
magnetic impurity in the presence of the coupling between orbital moment of the
condensate and impurity spin S. Locally induced d_{xy} gap leads to the fully
gapped quasiparticle spectrum near impurity. The phase of the induced i d_{xy}
component is determined by impurity spin and averages to zero at high
temperatures. It is suggested that at low temperature the well defined patches
of d_{xy} are formed and they can undergo a phase transition into phase locked
state via Josephson effect. Violation of time-reversal symmetry and parity
occurs spontaneously via the second order transition. In the ordered phase both
impurity magnetization and d_{xy} component of the order parameter develop and
are proportional to each other.


  We give correct explicit formulas for the probabilities of rooted binary
trees and cladograms under Ford's $\alpha$-model.


  This papper aims to present and demonstrate Clifford's version for a
generalization of Miquel's theorem with the use of Euclidean geometry arguments
only.


  Previous studies have shown that dark matter deficient galaxies (DMDG) such
as NGC1052-DF2 (hereafter DF2) can result from tidal stripping. An important
question, though, is whether such a stripping scenario can explain DF2's large
specific frequency of globular clusters (GCs). After all, tidal stripping and
shocking preferentially remove matter from the outskirts. We examine this using
idealized, high-resolution simulations of a regular dark matter dominated
galaxy that is accreted onto a massive halo. As long as the initial
(pre-infall) dark matter halo of the satellite is cored, which is consistent
with predictions of cosmological, hydrodynamical simulations, the tidal remnant
can be made to resemble DF2 in all its properties, including its GC population.
The required orbit has a peri-centre at the 8.3 percentile of the distribution
for subhaloes at infall, and thus is not particularly extreme. On this orbit
the satellite loses 98.5 (30) percent of its original dark matter (stellar)
mass, and thus evolves into a DMDG. The fraction of GCs that is stripped off
depends on the initial radial distribution. If, at infall, the median projected
radius of the GC population is roughly two times that of the stars, consistent
with observations of isolated galaxies, only $\sim 20$ percent of the GCs are
stripped off. This is less than for the stars, which is due to dynamical
friction counteracting the tidal stirring. We predict that, if indeed DF2 was
crafted by strong tides, its stellar outskirts should have a very shallow
metallicity gradient.


  We prove a quantitative statement of the quantum ergodicity for Hecke--Maass
cusp forms on the modular surface. As an application of our result, along a
density $1$ subsequence of even Hecke--Maass cusp forms, we obtain a sharp
lower bound for the $L^2$-norm of the restriction to a fixed compact geodesic
segment of $\eta=\{iy~:~y>0\} \subset \mathbb{H}$. We also obtain an upper
bound of $O_\epsilon\left(t_\phi^{3/8+\epsilon}\right)$ for the $L^\infty$ norm
along a density $1$ subsequence of Hecke--Maass cusp forms; for such forms,
this is an improvement over the upper bound of
$O_\epsilon\left(t_\phi^{5/12+\epsilon}\right)$ given by Iwaniec and Sarnak.
  In a recent work of Ghosh, Reznikov, and Sarnak, the authors proved for all
even Hecke--Maass forms that the number of nodal domains, which intersect a
geodesic segment of $\eta$, grows faster than $t_\phi^{1/12-\epsilon}$ for any
$\epsilon>0$, under the assumption that the Lindel{\"o}f Hypothesis is true and
that the geodesic segment is long enough. Upon removing a density zero subset
of even Hecke--Maass forms, we prove without making any assumptions that the
number of nodal domains grows faster than $t_\phi^{1/8-\epsilon}$ for any
$\epsilon>0$.


  An electronically excited atom or molecule located outside but near a planar
optical waveguide can decay by spontaneous emission of a photon into a guided
mode of the waveguide. We outline a QED theory for calculating the probability
for this process and describe general physical insights from that theory. A
couple of representative examples are discussed in detail.


  This study introduces a novel Supervised Info-enhanced Contrastive Learning
framework for EEG based Emotion Recognition (SICLEER). SI-CLEER employs
multi-granularity contrastive learning to create robust EEG contextual
representations, potentiallyn improving emotion recognition effectiveness.
Unlike existing methods solely guided by classification loss, we propose a
joint learning model combining self-supervised contrastive learning loss and
supervised classification loss. This model optimizes both loss functions,
capturing subtle EEG signal differences specific to emotion detection.
Extensive experiments demonstrate SI-CLEER's robustness and superior accuracy
on the SEED dataset compared to state-of-the-art methods. Furthermore, we
analyze electrode performance, highlighting the significance of central frontal
and temporal brain region EEGs in emotion detection. This study offers an
universally applicable approach with potential benefits for diverse EEG
classification tasks.


  We introduce the model of communicating timed automata (CTA) that extends the
classical models of finite-state processes communicating through FIFO perfect
channels and timed automata, in the sense that the finite-state processes are
replaced by timed automata, and messages inside the perfect channels are
equipped with clocks representing their ages. In addition to the standard
operations (resetting clocks, checking guards of clocks) each automaton can
either (1) append a message to the tail of a channel with an initial age or (2)
receive the message at the head of a channel if its age satisfies a set of
given constraints. In this paper, we show that the reachability problem is
undecidable even in the case of two timed automata connected by one
unidirectional timed channel if one allows global clocks (that the two automata
can check and manipulate). We prove that this undecidability still holds even
for CTA consisting of three timed automata and two unidirectional timed
channels (and without any global clock). However, the reachability problem
becomes decidable (in $\mathsf{EXPTIME}$) in the case of two automata linked
with one unidirectional timed channel and with no global clock. Finally, we
consider the bounded-context case, where in each context, only one timed
automaton is allowed to receive messages from one channel while being able to
send messages to all the other timed channels. In this case we show that the
reachability problem is decidable.


  Enlightened by the similar equation form between the GCHS \footnote{GCHS:
Generalized Covariant Hamilton System\\GSPB:Generalized structural Poisson
bracket} defined by the GSPB and the geodesic equation expressed by geospin
variable, we find a deep connection between the geospin matrix and S-dynamics.
In this contrastive way, we actually proves that the GCHS is a compatible
theory suitable for the curved spacetime as primitively stated. By contrast,
geospin matrix in Riemannian geometry has the same physical nature as
S-dynamics in GCHS. We obtain a fact that geodesic equation can be naturally
derived by the GCHS in terms of the velocity field.
  We strictly prove that the geometrio $\hat{S}{{\left( {{x}_{k}},{{p}_{i}},H
\right)}^{T}}={{\left( {{b}_{k}},{{A}_{i}},w \right)}^{T}}$ holds by using
structural operator $\hat{S}$ directly induced by structural derivative
${A}_{i}$ in terms of position ${x}_{k}$, momentum ${p}_{i}$ and Hamiltonian
$H$ respectively. It evidently proves that the GCHS on the Riemannian manifold
is certainly determined by the Christoffel symbols. As an application, we
consider the GCHS on Riemannian geometry.


  Loop groups G as families of mappings of the complex manifold M into another
complex manifold N preserving marked points $s_0\in M$ and $y_0\in N$ are
investigated. Quasi-invariant measures $\mu $ on G relative to dense subgroups
$G'$ are constructed. These measures are used for the studying of irreducible
representations of such groups.


  Scenario-based development and test processes are a promising approach for
verifying and validating automated driving functions. For this purpose,
scenarios have to be generated during the development process in a traceable
manner. In early development stages, the operating scenarios of the item to be
developed are usually described in an abstract, linguistic way.Within the scope
of a simulation-assisted test process, these linguistically described scenarios
have to be transformed into a state space representation and converted into
data formats which can be used with the respective simulation environment.
Currently, this step of detailing scenarios takes a considerable manual effort.
Furthermore, a standardized interpretation of the linguistically described
scenarios and a consistent transformation into the data formats are not
guaranteed due to multiple authors as well as many constraints between the
scenario parameters. In this paper, the authors present an approach to
automatically detail a keyword-based scenario description for execution in a
simulation environment and provide a basis for test case generation. As a first
step, the keyword-based description is transformed into a parameter space
representation. At the same time, constraints regarding the selection and
combination of parameter values are documented for the following process steps
(e. g. evolutionary or stochastic test methods). As a second step, the
parameter space representation is converted into data formats required by the
simulation environment. As an example, the authors use scenarios on German
freeways and convert them into the data formats OpenDRIVE (description of the
road) and OpenSCENARIO (description of traffic participants and environmental
conditions) for execution in the simulation environment Virtual Test Drive.


  Social Security and other public policies can be viewed as a series of cash
in and outflows that depend on parameters such as the age distribution of the
population and the retirement age. Given forecasts of these parameters,
policies can be designed to be financially stable, i.e., to terminate with a
zero balance. If reality deviates from the forecasts, policies normally
terminate with a surplus or a deficit. We derive constraints on the cash flows
of robust policies that terminate with zero balance even in the presence of
forecasting errors. Social Security and most similar policies are not robust.
We show that non-trivial robust policies exist and provide a recipe for
constructing robust extensions of non-robust policies. An example illustrates
our results.


  We outline a procedure for obtaining the electron-phonon spectral density by
inversion of optical conductivity data, a process very similar in spirit to the
McMillan-Rowell inversion of tunelling data. We assume both electron-impurity
(elastic) and electron-phonon (inelastic) scattering processes. This procedure
has the advantage that it can be utilized in the normal state. Furthermore, a
very good qualitative result can be obtained explicitly, without iteration. We
illustrate this technique on recently acquired far-infrared data in
K$_3$C$_{60}$. We show that the electron-phonon interaction is most likely
responsible for superconductivity in these materials.


  The salient information of an infrared image and the abundant texture of a
visible image can be fused to obtain a comprehensive image. As can be known,
the current fusion methods based on Transformer techniques for infrared and
visible (IV) images have exhibited promising performance. However, the
attention mechanism of the previous Transformer-based methods was prone to
extract common information from source images without considering the
discrepancy information, which limited fusion performance. In this paper, by
reevaluating the cross-attention mechanism, we propose an alternate Transformer
fusion network (ATFuse) to fuse IV images. Our ATFuse consists of one
discrepancy information injection module (DIIM) and two alternate common
information injection modules (ACIIM). The DIIM is designed by modifying the
vanilla cross-attention mechanism, which can promote the extraction of the
discrepancy information of the source images. Meanwhile, the ACIIM is devised
by alternately using the vanilla cross-attention mechanism, which can fully
mine common information and integrate long dependencies. Moreover, the
successful training of ATFuse is facilitated by a proposed segmented pixel loss
function, which provides a good trade-off for texture detail and salient
structure preservation. The qualitative and quantitative results on public
datasets indicate our ATFFuse is effective and superior compared to other
state-of-the-art methods.


  In the framework of the littlest Higgs model with T-parity (LHT), we study
the production processes of T-even (T_+) and T-odd (T_-) partners of the top
quark at the Large Hadron Collider (LHC). We show that the signal events can be
distinguished from the standard-model backgrounds, and that information about
mass and mixing parameters of the top partners can be measured with relatively
good accuracies. With the measurements of these parameters, we show that a
non-trivial test of the LHT can be performed. We also discuss a possibility to
reconstruct the thermal relic density of the lightest T-odd particle A_H using
the LHC results, and show that the scenario where A_H becomes dark matter may
be checked.


  New formulation of relativistic wave equations (RWE) for massive particles
with arbitrary half-integer spins s interacting with external electromagnetic
fields are proposed. They are based on wave functions which are irreducible
tensors of rank $n ($n=s-\frac12$) antisymmetric w.r.t. n pairs of indices,
whose components are bispinors. The form of RWE is straightforward and free of
inconsistencies associated with the other approaches to equations describing
interacting higher spin particles.


  Mobile app user interfaces (UIs) are rich with action, text, structure, and
image content that can be utilized to learn generic UI representations for
tasks like automating user commands, summarizing content, and evaluating the
accessibility of user interfaces. Prior work has learned strong visual
representations with local or global captioning losses, but fails to retain
both granularities. To combat this, we propose Textual Foresight, a novel
pretraining objective for learning UI screen representations. Textual Foresight
generates global text descriptions of future UI states given a current UI and
local action taken. Our approach requires joint reasoning over elements and
entire screens, resulting in improved UI features: on generation tasks, UI
agents trained with Textual Foresight outperform state-of-the-art by 2% with
28x fewer images. We train with our newly constructed mobile app dataset,
OpenApp, which results in the first public dataset for app UI representation
learning. OpenApp enables new baselines, and we find Textual Foresight improves
average task performance over them by 5.7% while having access to 2x less data.


  We first present chemodynamical simulations to investigate how stellar winds
of massive stars influence early dynamical and chemical evolution of forming
globular clusters (GCs). In our numerical models, GCs form in
turbulent,high-density giant molecular clouds (GMCs), which are embedded in a
massive dark matter halo at high redshifts. We show how high-density, compact
stellar systems are formed from GMCs influenced both by physical processes
associated with star formation and by tidal fields of their host halos. We also
show that chemical pollution of GC-forming GMCs by stellar winds from massive
stars can result in star-to-star abundance inhomogeneities among light elements
(e.g., C, N, and O) of stars in GCs. The present model with a canonical initial
mass function (IMF) also shows a C-N anticorrelation that stars with smaller
[C/Fe] have larger [N/Fe] in a GC. Although these results imply that
``self-pollution'' of GC-forming GMCs by stellar winds from massive stars can
cause abundance inhomogeneities of GCs, the present models with different
parameters and canonical IMFs can not show N-rich stars with [N/Fe] ~ 0.8
observed in some GCs (e.g., NGC 6752). We discuss this apparent failure in the
context of massive star formation preceding low-mass one within GC-forming GMCs
(``bimodal star formation scenario''). We also show that although almost all
stars (~97%) show normal He abundances (Y) of ~0.24 some stars later formed in
GMCs can have Y as high as ~0.3 in some models. The number fraction of He-rich
stars with Y >0.26 is however found to be small (~10^-3) for most models.


  Label switching is a well-known and fundamental problem in Bayesian
estimation of mixture or hidden Markov models. In case that the prior
distribution of the model parameters is the same for all states, then both the
likelihood and posterior distribution are invariant to permutations of the
parameters. This property makes Markov chain Monte Carlo (MCMC) samples
simulated from the posterior distribution non-identifiable. In this paper, the
\pkg{label.switching} package is introduced. It contains one probabilistic and
seven deterministic relabelling algorithms in order to post-process a given
MCMC sample, provided by the user. Each method returns a set of permutations
that can be used to reorder the MCMC output. Then, any parametric function of
interest can be inferred using the reordered MCMC sample. A set of user-defined
permutations is also accepted, allowing the researcher to benchmark new
relabelling methods against the available ones


  The impurity photonic drag effect (PDE), theory for semiconductive quantum
wire (QW) in a longitudinal (along the quantum wire axis) magnetic field B, has
been developed. The PDE is due to the photon longitudinal momentum transmission
to localized electrons, under optical transitions from D(-)-states to QW
hybrid-quantum states, if the QW is described by the parabolic confinement
potential. The analytical expression for the drag current (DC) density has been
obtained within the framework of zero-range potential model and in the
effective mass approximation. The drag current spectral dependence has been
investigated for various values of B and QW parameters, under electron
scattering on the dotty-impurities system. The drag current spectral dependence
is characterized by Zeeman doublet with a pronounced beak-type peak. This peak
is related to electron optical transitions from D(-)-states to the states with
the magnetic quantum number m=1. With an increase of the magnetic field B the
beak-type peak is shifted to short-wave spectrum region, and the peak height
considerably increases. We discuss the possibility of using of the
one-dimensional drag current effect, in a longitudinal magnetic field, to
develop a new type of laser radiation detectors.


  Millimeter-wave (mmWave) communications rely on directional transmissions to
overcome severe path loss. Nevertheless, the use of narrow beams complicates
the initial access procedure and increase the latency as the transmitter and
receiver beams should be aligned for a proper link establishment. In this
paper, we investigate the feasibility of random beamforming for the cell-search
phase of initial access. We develop a stochastic geometry framework to analyze
the performance in terms of detection failure probability and expected latency
of initial access as well as total data transmission. Meanwhile, we compare our
scheme with the widely used exhaustive search and iterative search schemes, in
both control plane and data plane. Our numerical results show that, compared to
the other two schemes, random beamforming can substantially reduce the latency
of initial access with comparable failure probability in dense networks. We
show that the gain of the random beamforming is more prominent in light
traffics and low-latency services. Our work demonstrates that developing
complex cell-discovery algorithms may be unnecessary in dense mmWave networks
and thus shed new lights on mmWave network design.


  This paper presents a closed-loop controller for wind farms to provide active
power control services using a high-fidelity computational fluid dynamics based
wind plant simulator. The proposed design enhances power tracking stability and
allows for simple understanding, where each turbine is considered as a pure
time-delay system. The paper investigates the control performance with
different nominal power distributions in a fully waked condition and limited
power availability. Results demonstrate the improvement in power production
obtained by closing the control loop, compared to greedy operation.
Additionally, power tracking capabilities are enhanced with a nominal power
distribution favored by axial-induction, as well as the occurrence of turbine
saturation and the distribution of loads.


  We report the broad-band spectral properties of the X-ray pulsar Cep X-4 by
using a Suzaku observation in 2014 July. The 0.8-70 keV spectrum was found to
be well described by three continuum models - Negative and Positive power-law
with Exponential cutoff (NPEX), high energy cutoff power-law and CompTT models.
Additional components such as a cyclotron line at $\sim$28 keV and two Gaussian
components for iron lines at 6.4 and 6.9 keV were required in the spectral
fitting. Apart from these, an additional absorption feature at $\sim$45 keV was
clearly detected in residuals obtained from the spectral fitting. This
additional feature at $\sim$45 keV was clearly seen in phase-resolved spectra
of the pulsar. We identified this feature as the first harmonic of the
fundamental cyclotron line at $\sim$28 keV. The ratio between the first
harmonic and fundamental line energies (1.7) was found to be in disagreement
with the conventional factor of 2, indicating that the heights of line forming
regions are different or viewed at larger angles. The phase-resolved
spectroscopy of the fundamental and first harmonic cyclotron lines shows
significant pulse-phase variation of the line parameters. This can be
interpreted as the effect of viewing angle or the role of complicated magnetic
field of the pulsar.


  The Nambu--Jona-Lasinio model is widely used to study strong-interaction
phenomena in vacuum and quark matter. Since the model is nonrenormalizable, one
needs to work within a specific regularization scheme to obtain finite results.
Here we show that a commonly used cutoff regularization scheme leads to
unphysical results, such as superluminal speed of sound and wrong
high-temperature behavior of the specific heat and other thermodynamical
quantities. Such a troublesome feature of the cutoff regularization invalidates
the model for temperature and baryon density values relevant to the
phenomenology of heavy-ion collisions and compact stars. We show that the
source of the problems stems from cutting off momentum modes in finite
integrals depending on thermal distribution functions in the grand canonical
potential. The problems go away when taking into account the full momentum
range of those integrals. Explicit examples are worked out in the SU(2)-flavor
version of the model.


  The measurements of the possible gravitational microlensing events are
analysed with a simple yet accurate disc--halo model of the Milky Way Galaxy.
This comprises a luminous exponential disc embedded in a flattened dark matter
halo with density varying like ${\rm distance}^{-1.8}$. Including a disc has
the important effect of lowering the implied masses of the dark matter objects.
For the possible detection reported by Alcock et al (1993), the inferred mass
of the lens lies in the range $\sim 0.01 - 0.15 M_\odot$. The candidate events
of Aubourg et al (1993) have slightly larger implied masses of $\sim 0.025 -
0.35\ M_\odot$ and $\sim 0.03 - 0.45\ M_\odot$ respectively. These are
consistent with the deflecters being either brown dwarfs or low mass stars. If
there is no disc dark matter and the halo is completely composed of baryonic
dark objects of typical mass $ \sim 0.08\ M_\odot$, then the monitoring of $1.8
\times 10^6$ stars in the Large Magellanic Cloud will provide at least $8-9$
detections a year, in the limit of $100 \%$ efficiency.


  (shortened) The SMC is ideally suited to investigating the recent star
formation history from X-ray source population studies. It harbours a large
number of Be/X-ray binaries, and the supernova remnants can be easily resolved
with imaging X-ray instruments. We search for new supernova remnants in the SMC
and in particular for composite remnants with a central X-ray source. We study
the morphology of newly found candidate supernova remnants using radio, optical
and X-ray images and investigate their X-ray spectra. Here we report on the
discovery of the new supernova remnant around the recently discovered Be/X-ray
binary pulsar SXP 1062 in radio and X-ray images. The Be/X-ray binary system is
found near the centre of the supernova remnant, which is located at the outer
edge of the eastern wing of the SMC. The remnant is oxygen-rich, indicating
that it developed from a type Ib event. From XMM-Newton observations we find
that the neutron star with a spin period of 1062 s shows a very high average
spin-down rate of 0.26 s per day over the observing period of 18 days. From the
currently accepted models, our estimated age of around 10000-25000 years for
the supernova remnant is not long enough to spin down the neutron star from a
few 10 ms to its current value. Assuming an upper limit of 25000 years for the
age of the neutron star and the extreme case that the neutron star was spun
down by the accretion torque that we have measured during the XMM-Newton
observations since its birth, a lower limit of 0.5 s for the birth spin period
is inferred. For more realistic, smaller long-term average accretion torques
our results suggest that the neutron star was born with a correspondingly
longer spin period. This implies that neutron stars in Be/X-ray binaries with
long spin periods can be much younger than currently anticipated.


  Model-based reinforcement learning (RL) is considered to be a promising
approach to reduce the sample complexity that hinders model-free RL. However,
the theoretical understanding of such methods has been rather limited. This
paper introduces a novel algorithmic framework for designing and analyzing
model-based RL algorithms with theoretical guarantees. We design a
meta-algorithm with a theoretical guarantee of monotone improvement to a local
maximum of the expected reward. The meta-algorithm iteratively builds a lower
bound of the expected reward based on the estimated dynamical model and sample
trajectories, and then maximizes the lower bound jointly over the policy and
the model. The framework extends the optimism-in-face-of-uncertainty principle
to non-linear dynamical models in a way that requires \textit{no explicit}
uncertainty quantification. Instantiating our framework with simplification
gives a variant of model-based RL algorithms Stochastic Lower Bounds
Optimization (SLBO). Experiments demonstrate that SLBO achieves
state-of-the-art performance when only one million or fewer samples are
permitted on a range of continuous control benchmark tasks.


  Green and Sisask showed that the maximal number of $3$-term arithmetic
progressions in $n$-element sets of integers is $\lceil n^2/2\rceil$; it is
easy to see that the same holds if the set of integers is replaced by the real
line or by any Euclidean space. We study this problem in general metric spaces,
where a triple $(a,b,c)$ of points in a metric space is considered a $3$-term
arithmetic progression if $d(a,b)=d(b,c)=\frac{1}{2}d(a,c)$. In particular, we
show that the result of Green and Sisask extends to any Cartan--Hadamard
manifold (in particular, to the hyperbolic spaces), but does not hold in
spherical geometry or in the $r$-regular tree, for any $r\geq 3$.


  The complete magnetic and multiferroic phase diagram of
Mn$_{1-x}$Co$_{x}$WO$_4$ single crystals is investigated by means of magnetic,
heat capacity, and polarization experiments. We show that the ferroelectric
polarization $\overrightarrow{P}$ in the multiferroic state abruptly changes
its direction twice upon increasing Co content, x. At x$_{c1}$=0.075,
$\overrightarrow{P}$ rotates from the $b-$axis into the $a-c$ plane and at
x$_{c2}$=0.15 it flips back to the $b-$axis. The origin of the multiple
polarization flops is identified as an effect of the Co anisotropy on the
orientation and shape of the spin helix leading to thermodynamic instabilities
caused by the decrease of the magnitude of the polarization in the
corresponding phases. A qualitative description of the ferroelectric
polarization is derived by taking into account the intrachain ($c-$axis) as
well as the interchain ($a-$axis) exchange pathways connecting the magnetic
ions. In a narrow Co concentration range (0.1$\leq$x$\leq$0.15), an
intermediate phase, sandwiched between the collinear high-temperature and the
helical low-temperature phases, is discovered. The new phase exhibits a
collinear and commensurate spin modulation similar to the low-temperature
magnetic structure of MnWO$_4$.


  A d-dimensional framework is a graph and a map from its vertices to E^d. Such
a framework is globally rigid if it is the only framework in E^d with the same
graph and edge lengths, up to rigid motions. For which underlying graphs is a
generic framework globally rigid? We answer this question by proving a
conjecture by Connelly, that his sufficient condition is also necessary: a
generic framework is globally rigid if and only if it has a stress matrix with
kernel of dimension d+1, the minimum possible.
  An alternate version of the condition comes from considering the geometry of
the length-squared mapping l: the graph is generically locally rigid iff the
rank of l is maximal, and it is generically globally rigid iff the rank of the
Gauss map on the image of l is maximal.
  We also show that this condition is efficiently checkable with a randomized
algorithm, and prove that if a graph is not generically globally rigid then it
is flexible one dimension higher.


  In this paper we study sum-free sets of order $m$ in finite Abelian groups.
We prove a general theorem on 3-uniform hypergraphs, which allows us to deduce
structural results in the sparse setting from stability results in the dense
setting. As a consequence, we determine the typical structure and asymptotic
number of sum-free sets of order $m$ in Abelian groups $G$ whose order is
divisible by a prime $q$ with $q \equiv 2 \pmod 3$, for every $m \ge C(q)
\sqrt{n \log n}$, thus extending and refining a theorem of Green and Ruzsa. In
particular, we prove that almost all sum-free subsets of size $m$ are contained
in a maximum-size sum-free subset of $G$. We also give a completely
self-contained proof of this statement for Abelian groups of even order, which
uses spectral methods and a new bound on the number of independent sets of size
$m$ in an $(n,d,\lambda)$-graph.


  This three section report can be regarded as an extended appendix to (Bueler,
Brown, and Lingle 2006). First we give the detailed construction of an exact
solution to a standard continuum model of a cold, shallow, and thermocoupled
ice sheet. The construction is by calculation of compensatory accumulation and
heat source functions which make a chosen pair of functions for thickness and
temperature into exact solutions of the coupled system. The solution we
construct here is ``TestG'' in (Bueler and others, 2006) and the steady state
solution ``Test F'' is a special case. In the second section we give a
reference C implementation of these exact solutions. In the last section we
give an error analysis of a finite difference scheme for the temperature
equation in the thermocoupled model. The error analysis gives three results,
first the correct form of the Courant-Friedrichs-Lewy (CFL) condition for
stability of the advection scheme, second an equation for error growth which
contributes to understanding the famous ``spokes'' of (Payne and others, 2000),
and third a convergence theorem under stringent fixed geometry and smoothness
assumptions.


  Supernova remnants (SNRs) have long been considered the leading candidates
for the accelerators of cosmic rays within the Galaxy through the process of
diffusive shock acceleration. The connection between SNRs and cosmic rays is
supported by the detection of high energy (HE; 100 MeV to 100 GeV) and very
high energy (VHE; 100 GeV to 100 TeV) gamma rays from young and middle-aged
SNRs. However, the interpretation of the gamma-ray observations is not unique.
This is because gamma rays can be produced both by electrons through
non-thermal Bremsstrahlung and inverse Compton scattering, and by protons
through proton-proton collisions and subsequent neutral-pion decay. To
disentangle and quantify the contributions of electrons and protons to the
gamma-ray flux, it is necessary to measure precisely the spectra and morphology
of SNRs over a broad range of gamma-ray energies. Cassiopeia A (Cas A) is one
such young SNR (~ 350 years) which is bright in radio and X-rays. It has been
detected as a bright point source in HE gamma rays by Fermi-LAT and in VHE
gamma rays by HEGRA, MAGIC and VERITAS. Cas A has been observed with VERITAS
for more than 60 hours, tripling the published exposure. The observations span
2007-2013, and half of the data were taken at large zenith angles to boost the
effective area above few TeV. We will present the detailed spectral and
morphological results from the complete dataset.


  We provide the first example of a sequence of {\em nondegenerate}, in the
sense of Duyckaerts-Kenig-Merle \cite{DKM}, nodal nonradial solutions to the
critical Yamabe problem $$ -\Delta Q= |Q|^{\frac{2}{n-2}} Q, \ \ Q \in
{\mathcal D}^{1,2} (\R^n). $$


  We present the spatially resolved measurements of a cool galactic outflow in
the gravitationally lensed galaxy RCS0327 at $z \approx 1.703$ using VLT/MUSE
IFU observations. We probe the cool outflowing gas, traced by blueshifted Mg II
and Fe II absorption lines, in 15 distinct regions of the same galaxy in its
image-plane. Different physical regions, 5 to 7 kpc apart within the galaxy,
drive the outflows at different velocities ($V_{out} \sim $ $-161$ to $-240$ km
s$^{-1}$), and mass outflow rates ($\dot{M}_{out} \sim$ 183 to 527 $M_{\odot}\
yr^{-1}$). The outflow velocities from different regions of the same galaxy
vary by 80 km s$^{-1}$, which is comparable to the variation seen in a large
sample of star-burst galaxies in the local Universe. Using multiply lensed
images of RCS0327, we probe the same star-forming region at different spatial
scales (0.5 kpc$^2$-25 kpc$^2$), we find that outflow velocities vary between $
\sim $ $-120$ to $-242$ km s$^{-1}$, and the mass outflow rates vary between
$\sim$ 37 to 254 $M_{\odot}\ yr^{-1}$. The outflow momentum flux in this galaxy
is $\geq$ 100% of the momentum flux provided by star-formation in individual
regions, and outflow energy flux is $\approx$ 10% of the total energy flux
provided by star-formation. These estimates suggest that the outflow in RCS0327
is energy driven. This work shows the importance of small scale variations of
outflow properties due to the variations of local stellar properties of the
host galaxy in the context of galaxy evolution.


  Navigation services utilized by autonomous vehicles or ordinary users require
the availability of detailed information about road-related objects and their
geolocations, especially at road intersections. However, these road
intersections are mainly represented as point elements without detailed
information, or are even not available in current versions of crowdsourced
mapping databases including OpenStreetMap(OSM). This study develops an approach
to automatically detect road objects and place them to right location from
street-level images. Our processing pipeline relies on two convolutional neural
networks: the first segments the images, while the second detects and
classifies the specific objects. Moreover, to locate the detected objects, we
establish an attributed topological binary tree(ATBT) based on urban grammar
for each image to depict the coherent relations of topologies, attributes and
semantics of the road objects. Then the ATBT is further matched with map
features on OSM to determine the right placed location. The proposed method has
been applied to a case study in Berlin, Germany. We validate the effectiveness
of our method on two object classes: traffic signs and traffic lights.
Experimental results demonstrate that the proposed approach provides
near-precise localization results in terms of completeness and positional
accuracy. Among many potential applications, the output may be combined with
other sources of data to guide autonomous vehicles


  While deep AUC maximization (DAM) has shown remarkable success on imbalanced
medical tasks, e.g., chest X-rays classification and skin lesions
classification, it could suffer from severe overfitting when applied to small
datasets due to its aggressive nature of pushing prediction scores of positive
data away from that of negative data. This paper studies how to improve
generalization of DAM by mixup data augmentation -- an approach that is widely
used for improving generalization of the cross-entropy loss based deep learning
methods. %For overfitting issues arising from limited data, the common approach
is to employ mixup data augmentation to boost the models' generalization
performance by enriching the training data. However, AUC is defined over
positive and negative pairs, which makes it challenging to incorporate mixup
data augmentation into DAM algorithms. To tackle this challenge, we employ the
AUC margin loss and incorporate soft labels into the formulation to effectively
learn from data generated by mixup augmentation, which is referred to as the
AUC-mixup loss. Our experimental results demonstrate the effectiveness of the
proposed AUC-mixup methods on imbalanced benchmark and medical image datasets
compared to standard DAM training methods.


  We report a study of the high-pressure structural behavior of ZnV2O6,
Zn2V2O7, and Zn3V2O8, which has been explored by means of synchrotron powder
x-ray diffraction. We found that ZnV2O6 and Zn3V2O8 remain in the
ambient-pressure structure up to 15 GPa. In contrast, in the same pressure
range, Zn2V2O7 undergoes three phase transitions at 0.7, 3.0, and 10.8 GPa,
respectively. Possible crystal structures for the first and second
high-pressure phases are proposed. Reasons for the distinctive behavior of
Zn2V2O7 are discussed. The compressibility of the different polymorphs has been
determined. The response to pressure is found to be anisotropic in all the
considered compounds and the room-temperature equations of state have been
determined. The bulk moduli of ZnV2O6 (129(2) GPa) and Zn3V2O8 (120(2) GPa) are
consistent with a structural framework composed of compressible ZnO6 octahedra
and uncompressible VO4 tetrahedra. In contrast, Zn2V2O7 is highly compressible
with a bulk modulus of 58(9) GPa, which is almost half of the bulk modulus of
the other two vanadates. The large compressibility of Zn2V2O7 and its sequence
of structural transitions are related to the fact that this material is less
dense than the other zinc vanadates and to the penta-coordination of Zn atoms
by oxygen atoms in Zn2V2O7. A comparison to the high-pressure behavior of
related compounds is presented.


  It has been recently argued, via very clever arguments, that the MaxEnt
variational problem would not adequately work for Renyi's and Tsallis'
entropies. We constructively show here that this is not so if one formulates
the associated variational problem in a more orthodox functional fashion.


  By formally comparing the geodesic equation with the Schr\"{o}dinger equation
on Riemannian manifold, we come up with the geometric Hamiltonian matrix on
Riemannian manifold based on the geospin matrix, and we discuss its eigenvalue
equation as well. Meanwhile, we get the geometric Hamiltonian function only
related to the scalar curvature.


  The correspondence between classical and quantum invariants is established.
The Ermakov Lewis quantum invariant of the time dependent harmonic oscillator
is translated from the coordinate and momentum operators into amplitude and
phase operators. In doing so, Turski's phase operator as well as
Susskind-Glogower operators are generalized to the time dependent harmonic
oscillator case. A quantum derivation of the Manley-Rowe relations is shown as
an example.


  We investigate the effect of correlated disorder on Majorana zero modes
(MZMs) bound to magnetic vortices in two-dimensional topological
superconductors. By starting from a lattice model of interacting fermions with
a $p_x \pm i p_y$ superconducting ground state in the disorder-free limit, we
use perturbation theory to describe the enhancement of the Majorana
localization length at weak disorder and a self-consistent numerical solution
to understand the breakdown of the MZMs at strong disorder. We find that
correlated disorder has a much stronger effect on the MZMs than uncorrelated
disorder and that it is most detrimental if the disorder correlation length
$\ell$ is on the same order as the superconducting coherence length $\xi$. In
contrast, MZMs can survive stronger disorder for $\ell \ll \xi$ as random
variations cancel each other within the length scale of $\xi$, while an MZM may
survive up to very strong disorder for $\ell \gg \xi$ if it is located in a
favorable domain of the given disorder realization.


  This work proposes Autonomous Iterative Motion Learning (AI-MOLE), a method
that enables systems with unknown, nonlinear dynamics to autonomously learn to
solve reference tracking tasks. The method iteratively applies an input
trajectory to the unknown dynamics, trains a Gaussian process model based on
the experimental data, and utilizes the model to update the input trajectory
until desired tracking performance is achieved. Unlike existing approaches, the
proposed method determines necessary parameters automatically, i.e., AI-MOLE
works plug-and-play and without manual parameter tuning. Furthermore, AI-MOLE
only requires input/output information, but can also exploit available state
information to accelerate learning.
  While other approaches are typically only validated in simulation or on a
single real-world testbed using manually tuned parameters, we present the
unprecedented result of validating the proposed method on three different
real-world robots and a total of nine different reference tracking tasks
without requiring any a priori model information or manual parameter tuning.
Over all systems and tasks, AI-MOLE rapidly learns to track the references
without requiring any manual parameter tuning at all, even if only input/output
information is available.


  We consider a graph coloring algorithm that processes vertices in order taken
uniformly at random and assigns colors to them using First-Fit strategy. We
show that this algorithm uses, in expectation, at most $(1 + o(1))\cdot \ln n
\,/\, \ln\ln n$ different colors to color any forest with $n$ vertices. We also
construct a family of forests that shows that this bound is best possible.


  Consider a pronilpotent DG (differential graded) Lie algebra over a field of
characteristic 0. In the first part of the paper we introduce the reduced
Deligne groupoid associated to this DG Lie algebra. We prove that a DG Lie
quasi-isomorphism between two such algebras induces an equivalence between the
corresponding reduced Deligne groupoids. This extends the famous result of
Goldman- Millson (attributed to Deligne) to the unbounded pronilpotent case.
  In the second part of the paper we consider the Deligne 2-groupoid. We show
it exists under more relaxed assumptions than known before (the DG Lie algebra
is either nilpotent or of quasi quantum type). We prove that a DG Lie
quasi-isomorphism between such DG Lie algebras induces a weak equivalence
between the corresponding Deligne 2-groupoids.
  In the third part of the paper we prove that an L-infinity quasi-isomorphism
between pronilpotent DG Lie algebras induces a bijection between the sets of
gauge equivalence classes of Maurer-Cartan elements. This extends a result of
Kontsevich and others to the pronilpotent case.


  The unsupervised learning of community structure, in particular the
partitioning vertices into clusters or communities, is a canonical and
well-studied problem in exploratory graph analysis. However, like most graph
analyses the introduction of immense scale presents challenges to traditional
methods. Spectral clustering in distributed memory, for example, requires
hundreds of expensive bulk-synchronous communication rounds to compute an
embedding of vertices to a few eigenvectors of a graph associated matrix.
Furthermore, the whole computation may need to be repeated if the underlying
graph changes some low percentage of edge updates. We present a method inspired
by spectral clustering where we instead use matrix sketches derived from random
dimension-reducing projections. We show that our method produces embeddings
that yield performant clustering results given a fully-dynamic stochastic block
model stream using both the fast Johnson-Lindenstrauss and CountSketch
transforms. We also discuss the effects of stochastic block model parameters
upon the required dimensionality of the subsequent embeddings, and show how
random projections could significantly improve the performance of graph
clustering in distributed memory.


  Object Detection has been a significant topic in computer vision. As the
continuous development of Deep Learning, many advanced academic and industrial
outcomes are established on localising and classifying the target objects, such
as instance segmentation, video tracking and robotic vision. As the core
concept of Deep Learning, Deep Neural Networks (DNNs) and associated training
are highly integrated with task-driven modelling, having great effects on
accurate detection. The main focus of improving detection performance is
proposing DNNs with extra layers and novel topological connections to extract
the desired features from input data. However, training these models can be
computationally expensive and laborious progress as the complicated model
architecture and enormous parameters. Besides, the dataset is another reason
causing this issue and low detection accuracy, because of insufficient data
samples or difficult instances. To address these training difficulties, this
thesis presents two different approaches to improve the detection performance
in the relatively light-weight way. As the intrinsic feature of data-driven in
deep learning, the first approach is "slot-based image augmentation" to enrich
the dataset with extra foreground and background combinations. Instead of the
commonly used image flipping method, the proposed system achieved similar mAP
improvement with less extra images which decrease training time. This proposed
augmentation system has extra flexibility adapting to various scenarios and the
performance-driven analysis provides an alternative aspect of conducting image
augmentation


  Using a Newtonian model of the Solar System with all 8 planets, we perform
extensive tests on various symplectic integrators of high orders, searching for
the best splitting scheme for long term studies in the Solar System. These
comparisons are made in Jacobi and Heliocentric coordinates and the
implementation of the algorithms is fully detailed for practical use. We
conclude that high order integrators should be privileged, with a preference
for the new $(10,6,4)$ method of (Blanes et al., 2012)


  We construct a classical solution in the GSO(-) sector in the framework of a
Wess-Zumino-Witten-like open superstring field theory on a non-BPS D-brane. We
use an su(2) supercurrent, which is obtained by compactifying a direction to a
circle with the critical radius, in order to get analytical tachyonic lump
solutions to the equation of motion. By investigating the action expanded
around a solution we find that it represents a deformation from a non-BPS
D-brane to a D-brane-anti-D-brane system at the critical value of a parameter
which is contained in classical solutions. Although such a process was
discussed in terms of boundary conformal field theory before, our study is
based on open superstring field theory including interaction terms.


  We study analytically and numerically the overdamped, deterministic dynamics
of a chain of {\it charged}, interacting particles driven by a longitudinal
alternating electric field and additionally interacting with a smooth ratchet
potential. We derive the equations of motion, analyze the general properties of
their solutions and find the drift criterion for chain motion. For ratchet
potentials of the form of a double-sine and a phase-modulated sine it is
demonstrated that both, a so-called integer and fractional transport of the
chain can occur. Explicit results for the directed chain transport for these
two classes of ratchet potentials are presented.


  In this work we develop a fully decentralized, federated, and fast solution
to the recently studied Low Rank Compressive Sensing (LRCS) problem: recover an
nxq low-rank matrix from column-wise linear projections. An important
application where this problem occurs, and a decentralized solution is
desirable is in federated sketching: efficiently compressing the vast amounts
of distributed images/videos generated by smartphones and various other devices
while respecting the users' privacy. Images from different devices, once
grouped by category, are similar and hence the matrix formed by the vectorized
images of a certain category is well-modeled as being low rank. Suppose there
are p nodes (say p smartphones), and each store a subset of the sketches of its
images. We develop a decentralized projected gradient descent (GD) based
approach to jointly reconstruct the images of all the phones/users from their
respective stored sketches. The algorithm is such that the phones/users never
share their raw data but only summaries of this data with the other phones at
each algorithm iteration. Also, the reconstructed images of user g are obtained
only locally. Other users cannot reconstruct them. Only the column span of the
matrix is reconstructed globally. By "decentralized" we mean that there is no
central node to which all nodes are connected and thus the only way to
aggregate the summaries from the various nodes is by use of an iterative
consensus algorithm that eventually provides an estimate of the aggregate at
each node, as long as the network is strongly connected. We validated the
effectiveness of our algorithm via extensive simulation experiments.


  We present Aeneas, a new verification toolchain for Rust programs based on a
lightweight functional translation. We leverage Rust's rich region-based type
system to eliminate memory reasoning for many Rust programs, as long as they do
not rely on interior mutability or unsafe code. Doing so, we relieve the proof
engineer of the burden of memory-based reasoning, allowing them to instead
focus on functional properties of their code.
  Our first contribution is a new approach to borrows and controlled aliasing.
We propose a pure, functional semantics for LLBC, a Low-Level Borrow Calculus
that captures a large subset of Rust programs. Our semantics is value-based,
meaning there is no notion of memory, addresses or pointer arithmetic. Our
semantics is also ownership-centric, meaning that we enforce soundness of
borrows via a semantic criterion based on loans rather than through a syntactic
type-based lifetime discipline. We claim that our semantics captures the
essence of the borrow mechanism rather than its current implementation in the
Rust compiler.
  Our second contribution is a translation from LLBC to a pure lambda-calculus.
This allows the user to reason about the original Rust program through the
theorem prover of their choice. To deal with the well-known technical
difficulty of terminating a borrow, we rely on a novel approach, in which we
approximate the borrow graph in the presence of function calls. This in turn
allows us to perform the translation using a new technical device called
backward functions.
  We implement our toolchain in a mixture of Rust and OCaml. Our evaluation
shows significant gains of verification productivity for the programmer.
  Rust goes to great lengths to enforce static control of aliasing; the proof
engineer should not waste any time on memory reasoning when so much already
comes "for free"!


  An emerging line of work has shown that machine-learned predictions are
useful to warm-start algorithms for discrete optimization problems, such as
bipartite matching. Previous studies have shown time complexity bounds
proportional to some distance between a prediction and an optimal solution,
which we can approximately minimize by learning predictions from past optimal
solutions. However, such guarantees may not be meaningful when multiple optimal
solutions exist. Indeed, the dual problem of bipartite matching and, more
generally, $\text{L}$-/$\text{L}^\natural$-convex function minimization have
arbitrarily many optimal solutions, making such prediction-dependent bounds
arbitrarily large. To resolve this theoretically critical issue, we present a
new warm-start-with-prediction framework for
$\text{L}$-/$\text{L}^\natural$-convex function minimization. Our framework
offers time complexity bounds proportional to the distance between a prediction
and the set of all optimal solutions. The main technical difficulty lies in
learning predictions that are provably close to sets of all optimal solutions,
for which we present an online-gradient-descent-based method. We thus give the
first polynomial-time learnability of predictions that can provably warm-start
algorithms regardless of multiple optimal solutions.


  Both deterministic and indeterministic physical laws are incompatible with
control by genuine (non-illusory) free will. We propose that an indeterministic
dynamics can be $weakly$ compatible with free will (FW), whereby the latter
acts by altering the probability distribution over allowed outcomes. In the
quantum physical world, such a FW can collapse the wave function, introducing
deviations from the Born rule. In principle, this deviation would stand in
conflict with both special relativity and (a variant of) the Strong
Church-Turing thesis, implying that the brain may be an arena of exotic,
non-standard physics. However, in practice, these deviations would not be
directly or easily observable, because they occur in sub-neuronal
superpositions in the brain, where they would be shrouded in random measurement
errors, noise and statistical fluctuations. Our result elucidates the
difference between the FW of human observers and that of observed particles in
the Free Will Theorem. This difference is a basic reason for why FW (and, in
general, consciousness) cannot be recreated by standard artificial intelligence
(AI) technology. We propose various neurobiological experiments to test our
proposed theory. We speculate that for observers to be aware of a physical
theory such as quantum mechanics, FW is necessary and that the theory must
therefore not be universal. We suggest that FW may be regarded as a primitive
principle in Nature for explaining quantum indeterminism.


  Meta-learning methods typically learn tasks under the assumption that all
tasks are equally important. However, this assumption is often not valid. In
real-world applications, tasks can vary both in their importance during
different training stages and in whether they contain noisy labeled data or
not, making a uniform approach suboptimal. To address these issues, we propose
the Data-Efficient and Robust Task Selection (DERTS) algorithm, which can be
incorporated into both gradient and metric-based meta-learning algorithms.
DERTS selects weighted subsets of tasks from task pools by minimizing the
approximation error of the full gradient of task pools in the meta-training
stage. The selected tasks are efficient for rapid training and robust towards
noisy label scenarios. Unlike existing algorithms, DERTS does not require any
architecture modification for training and can handle noisy label data in both
the support and query sets. Analysis of DERTS shows that the algorithm follows
similar training dynamics as learning on the full task pools. Experiments show
that DERTS outperforms existing sampling strategies for meta-learning on both
gradient-based and metric-based meta-learning algorithms in limited data budget
and noisy task settings.


  Orthogonal Calculus, first developed by Weiss in 1991, provides a calculus of
functors for functors from real inner product spaces to spaces. Many of the
functors to which Orthogonal Calculus has been applied since carry an
additional lax symmetric monoidal structure which has so far been ignored. For
instance, the functor $V \mapsto \text{BO}(V)$ admits maps $$\text{BO}(V)
\times \text{BO}(W) \to \text{BO}(V \oplus W)$$ which determine a lax symmetric
monoidal structure.
  Our first main result, Corollary 4$.$2$.$0$.$2, states that the Taylor
approximations of a lax symmetric monoidal functor are themselves lax symmetric
monoidal. We also study the derivative spectra of lax symmetric monoidal
functors, and prove in Corollary 5$.$4$.$0$.$1 that they admit
$O(n)$-equivariant structure maps of the form $$\Theta^nF \otimes \Theta^nF \to
D_{O(n)} \otimes \Theta^nF$$ where $D_{O(n)} \simeq S^{\text{Ad}_n}$ is the
Klein-Spivak dualising spectrum of the topological group $O(n)$.
  As our proof methods are largely abstract and $\infty$-categorical, we also
formulate Orthogonal Calculus in that language before proving our results.


  The finding of a Type Ic supernova connected with GRB 030329 showed a massive
star origin for this burst, supporting evidence for this association in
previous bursts with lightcurve bumps at the appropriate time for a supernova.
Here, we explore the possibility that all long bursts have massive star
progenitors, interacting with either the freely expanding wind of the
progenitor or the shocked wind. We present models for the afterglows of GRB
020405 and GRB 021211, which are a challenge to wind interaction models.
Considering sources for which wind interaction models are acceptable, a range
of wind densities is required, from values typical of Galactic Wolf-Rayet stars
to values ~100 times smaller. The reason for the low densities is unclear, but
may involve low progenitor masses and/or low metallicities. If mass is a
factor, a low density event should be associated with a low mass supernova. The
interpretation of bursts apparently interacting with constant density media as
interaction with a shocked wind requires both a range of mass loss densities
and a range of external pressures. The highest pressures, p/k > 10^8 cm^{-3} K,
may be due to an extreme starburst environment, which would imply that the
burst is superposed on an active star forming region. Although the range of
observed events can be accomodated by the shocked wind theory, special
circumstances are necessary to bring this about. Finally, we consider the high
velocity, high ionization absorption features observed in some afterglow
spectra. If the features are circumstellar, the presence of the burst in a
starburst region may be important for the formation of clumps near the burst.


  The most known of public key cryptosystem was introduced in 1978 by Rivest,
Shamir and Adleman [19] and now called the RSA public key cryptosystem in their
honor. Later, a few authors gave a simply extension of RSA over algebraic
numbers field( see [20]- [22]), but they require that the ring of algebraic
integers is Euclidean ring, this requirement is much more stronger than the
class number one condition. In this paper, we introduce a high dimensional form
of RSA by making use of the ring of algebraic integers of an algebraic number
field and the lattice theory. We give an attainable algorithm (see Algorithm I
below) of which is significant both from the theoretical and practical point of
view. Our main purpose in this paper is to show that the high dimensional RSA
is a lattice based on public key cryptosystem indeed, of which would be
considered as a new number in the family of post-quantum cryptography(see [17]
and [18]). On the other hand, we give a matrix expression for any algebraic
number fields (see Theorem 2.7 below), which is a new result even in the sense
of classical algebraic number theory.


  We report elastic scattering theory for surface electron waves in quantum
corrals defined by adatoms on the surface of noble metals. We develop a
scattering-matrix technique that allows us to account for a realistic smooth
potential profile of the scattering centers. Our calculations reproduce
quantitatively all the experimental observations, which is in contrast to
previous theories (treating the adatoms as point scatterers) that require
additional inelastic channels of scattering into the bulk in order to achieve
the agreement with the experiment. Our findings thus indicate that surface
states are not coupled to the bulk electrons.


  Supersymmetry provides a well-established theoretical framework for
extensions of the standard model of particle physics and the general
understanding of quantum field theories. We summarise here our investigations
of N=1 supersymmetric Yang-Mills theory with SU(2) gauge symmetry using the
non-perturbative first-principles method of numerical lattice simulations. The
strong interactions of gluons and their superpartners, the gluinos, lead to
confinement, and a spectrum of bound states including glueballs, mesons, and
gluino-glueballs emerges at low energies. For unbroken supersymmetry these
particles have to be arranged in supermultiplets of equal masses. In lattice
simulations supersymmetry can only be recovered in the continuum limit since it
is explicitly broken by the discretisation. We present the first continuum
extrapolation of the mass spectrum of supersymmetric Yang-Mills theory. The
results are consistent with the formation of supermultiplets and the absence of
non-perturbative sources of supersymmetry breaking. Our investigations also
indicate that numerical lattice simulations can be applied to non-trivial
supersymmetric theories.


  We prove a perturbative result concerning the uniqueness of Kerr-Newman
family of black holes: given an asymptotically flat space-time with bifurcate
horizons, if it agrees with a non-extremal Kerr-Newman space-time
asymptotically flat at infinity and it is sufficiently close to the Kerr-Newman
family, then the space-time must be one of the Kerr-Newman solutions. The
closeness to the Kerr-Newman family is measured by the smallness of a pair of
Mars-Simon type tensors, which were introduced by Wong in \cite{Wong_09} to
detect the Kerr-Newmann family.


  We construct unbounded positive $C^2$-solutions of the equation $\Delta u + K
u^{(n + 2)/(n - 2)} = 0$ in ${\R}^n$ (equipped with Euclidean metric $g_o$)
such that $K$ is bounded between two positive numbers in ${\R}^n$, the
conformal metric $g = u^{4/(n - 2)} g_o$ is complete, and the volume growth of
$g$ can be arbitrarily fast or reasonably slow according to the constructions.
By imposing natural conditions on $u$, we obtain growth estimate on the
$L^{2n/(n - 2)}$-norm of the solution and show that it has slow decay.


  Medical image synthesis has gained a great focus recently, especially after
the introduction of Generative Adversarial Networks (GANs). GANs have been used
widely to provide anatomically-plausible and diverse samples for augmentation
and other applications, including segmentation and super resolution. In our
previous work, Deep Convolutional GANs were used to generate synthetic
mammogram lesions, masses mainly, that could enhance the classification
performance in imbalanced datasets. In this new work, a deeper investigation
was carried out to explore other aspects of the generated images evaluation,
i.e., realism, feature space distribution, and observers studies. t-Stochastic
Neighbor Embedding (t-SNE) was used to reduce the dimensionality of real and
fake images to enable 2D visualisations. Additionally, two expert radiologists
performed a realism-evaluation study. Visualisations showed that the generated
images have a similar feature distribution of the real ones, avoiding outliers.
Moreover, Receiver Operating Characteristic (ROC) curve showed that the
radiologists could not, in many cases, distinguish between synthetic and real
lesions, giving 48% and 61% accuracies in a balanced sample set.


  In this work, a method for solving the constraints of general relativity is
presented, where first all geometrical objects are written in terms of a set of
orthonormal triads and a flat Weitzenbock connection, which depends on the
triads and on a flat spin connection. It is shown that the hamiltonian
constraint can be reduced from a second order equation to a first order one.
Even though the order of the equation is reduced, we do not get any extra
equations to solve by this procedure. A conformal decomposition is also
presented.


  Learning suitable and well-performing dialogue behaviour in statistical
spoken dialogue systems has been in the focus of research for many years. While
most work which is based on reinforcement learning employs an objective measure
like task success for modelling the reward signal, we use a reward based on
user satisfaction estimation. We propose a novel estimator and show that it
outperforms all previous estimators while learning temporal dependencies
implicitly. Furthermore, we apply this novel user satisfaction estimation model
live in simulated experiments where the satisfaction estimation model is
trained on one domain and applied in many other domains which cover a similar
task. We show that applying this model results in higher estimated
satisfaction, similar task success rates and a higher robustness to noise.


  Existing domain adaptation focuses on transferring knowledge between domains
with categorical indices (e.g., between datasets A and B). However, many tasks
involve continuously indexed domains. For example, in medical applications, one
often needs to transfer disease analysis and prediction across patients of
different ages, where age acts as a continuous domain index. Such tasks are
challenging for prior domain adaptation methods since they ignore the
underlying relation among domains. In this paper, we propose the first method
for continuously indexed domain adaptation. Our approach combines traditional
adversarial adaptation with a novel discriminator that models the
encoding-conditioned domain index distribution. Our theoretical analysis
demonstrates the value of leveraging the domain index to generate invariant
features across a continuous range of domains. Our empirical results show that
our approach outperforms the state-of-the-art domain adaption methods on both
synthetic and real-world medical datasets.


  The rook monoid $R_n$ is the finite monoid whose elements are the 0-1
matrices with at most one nonzero entry in each row and column. The group of
invertible elements of $R_n$ is isomorphic to the symmetric group $S_n$. The
natural extension to $R_n$ of the Bruhat-Chevalley ordering on the symmetric
group is defined in \cite{Renner86}. In this paper, we find an efficient,
combinatorial description of the Bruhat-Chevalley ordering on $R_n$. We also
give a useful, combinatorial formula for the length function on $R_n$.


  The Axiom of Choice (AC for short) is the most (in)famous axiom of the usual
foundations of mathematics, ZFC set theory. The (non-)essential use of AC in
mathematics has been well-studied and thoroughly classified. Now, fragments of
countable AC not provable in ZF have recently been used in Kohlenbach's
higher-order Reverse Mathematics to obtain equivalences between closely related
compactness and local-global principles. We continue this study and show that
NCC, a weak choice principle provable in ZF and much weaker systems, suffices
for many of these results. In light of the intimate connection between Reverse
Mathematics and computability theory, we also study realisers for NCC, i.e.
functionals that produce the choice functions claimed to exist by the latter
from the other data. Our hubris of undertaking the hitherto underdeveloped
study of the computational properties of (choice functions from) AC leads to
interesting results. For instance, using Kleene's S1-S9 computation schemes, we
show that various total realisers for NCC compute Kleene's $\exists^3$, a
functional that gives rise to full second-order arithmetic, and vice versa. By
contrast, partial realisers for NCC should be much weaker, but establishing
this conjecture remains elusive. By way of catharsis, we show that the
Continuum Hypothesis (CH for short) is equivalent to the existence of a
countably based partial realiser for NCC. The latter kind of realiser does not
compute Kleene's $\exists^3$ and is therefore strictly weaker than a total one.


  The evolution of circumstellar discs is influenced by their surroundings. The
relevant processes include external photoevaporation due to nearby stars, and
dynamical truncations. The impact of these processes on disc populations
depends on the star-formation history and on the dynamical evolution of the
region. Since star formation history and the phase-space characteristics of the
stars are important for the evolution of the discs, we start simulating the
evolution of the star cluster with the results of molecular cloud collapse
simulations. In the simulation we form stars with circumstellar discs, which
can be affected by different processes. Our models account for the viscous
evolution of the discs, internal and external photoevaporation of gas, external
photoevaporation of dust, and dynamical truncations. All these processes are
resolved together with the dynamical evolution of the cluster, and the
evolution of the stars.
  An extended period of star formation, lasting for at least 2 Myr, results in
some discs being formed late. These late formed discs have a better chance of
survival because the cluster gradually expands with time, and a lower local
stellar density reduces the effects of photoevaporation and dynamical
truncation. Late formed discs can then be present in regions of high UV
radiation, solving the proplyd lifetime problem. We also find a considerable
fraction of discs that lose their gas content, but remain sufficiently rich in
solids to be able to form a rocky planetary system.


  Attacks by Advanced Persistent Threats (APTs) have been shown to be difficult
to detect using traditional signature- and anomaly-based intrusion detection
approaches. Deception techniques such as decoy objects, often called honey
items, may be deployed for intrusion detection and attack analysis, providing
an alternative to detect APT behaviours. This work explores the use of honey
items to classify intrusion interactions, differentiating automated attacks
from those which need some human reasoning and interaction towards APT
detection. Multiple decoy items are deployed on honeypots in a virtual honey
network, some as breadcrumbs to detect indications of a structured manual
attack. Monitoring functionality was created around Elastic Stack with a Kibana
dashboard created to display interactions with various honey items. APT type
manual intrusions are simulated by an experienced pentesting practitioner
carrying out simulated attacks. Interactions with honey items are evaluated in
order to determine their suitability for discriminating between automated tools
and direct human intervention. The results show that it is possible to
differentiate automatic attacks from manual structured attacks; from the nature
of the interactions with the honey items. The use of honey items found in the
honeypot, such as in later parts of a structured attack, have been shown to be
successful in classification of manual attacks, as well as towards providing an
indication of severity of the attacks


  A theoretical model of multi-energy radiography (MER) are proposed. It is
shown that, as distinct from the conventional radiography, MER allows
identification of organic substances and control of their chemical composition.
Broad prospects are noted for MER application, specifically, for detection of
prohibited substances (explosives, drugs, etc.) during customs and
anti-terrorist safety inspection.


  We present a full calculation of the amplitudes for $B_{d[s]} \to \gamma
\gamma$ in a simple ACD model that extends an incomplete one in a previous
paper. We find cancellations between the contributions from different KK towers
and a small decrease relative to the SM predictions. It is conjectured that
radiative QCD corrections might actually lead to an enhancement in the
branching ratios and {\bf CP} asymmetries, but no more than modest ones.


  `Distribution regression' refers to the situation where a response Y depends
on a covariate P where P is a probability distribution. The model is Y=f(P) +
mu where f is an unknown regression function and mu is a random error.
Typically, we do not observe P directly, but rather, we observe a sample from
P. In this paper we develop theory and methods for distribution-free versions
of distribution regression. This means that we do not make distributional
assumptions about the error term mu and covariate P. We prove that when the
effective dimension is small enough (as measured by the doubling dimension),
then the excess prediction risk converges to zero with a polynomial rate.


  Effective exploration is a key to successful search. The recently proposed
Negatively Correlated Search (NCS) tries to achieve this by parallel
exploration, where a set of search processes are driven to be negatively
correlated so that different promising areas of the search space can be visited
simultaneously. Various applications have verified the advantages of such novel
search behaviors. Nevertheless, the mathematical understandings are still
lacking as the previous NCS was mostly devised by intuition. In this paper, a
more principled NCS is presented, explaining that the parallel exploration is
equivalent to the explicit maximization of both the population diversity and
the population solution qualities, and can be optimally obtained by partially
gradient descending both models with respect to each search process. For
empirical assessments, the reinforcement learning tasks that largely demand
exploration ability is considered. The new NCS is applied to the popular
reinforcement learning problems, i.e., playing Atari games, to directly train a
deep convolution network with 1.7 million connection weights in the
environments with uncertain and delayed rewards. Empirical results show that
the significant advantages of NCS over the compared state-of-the-art methods
can be highly owed to the effective parallel exploration ability.


  Let $\frak g$ be a reductive Lie algebra over an algebraically closed field
of characteristic 0 and $\frak k$ be a reductive in $\frak g$-subalgebra. Let
$M$ be a finitely generated (possibly, infinite-dimensional) $\frak g$-module.
We say that $M$ is a $(\frak g, \frak k)$-module if $M$ is a direct sum of a
(possibly, infinite) amount of simple finite-dimensional $\frak k$-modules. We
say that $M$ is of finite type if $M$ is a $(\frak g, \frak k)$-module and
Hom$_\frak k(V, M)<\infty$ for any simple $\frak k$-module $V$.
  Let $X$ be a variety of all Borel subalgebras of $\frak g$. Let $M$ be a
finitely generated $(\frak g, \frak k)$-module of finite type. In this article
we prove that $M$ is holonomic, i.e. $M$ is governed by some subvariety
$L_M\subset X$ and some local system $S_M$ on it. Furthermore we provide a
finite list in which L$_M$ necessarily appear.


  Training deep networks for semantic segmentation requires large amounts of
labeled training data, which presents a major challenge in practice, as
labeling segmentation masks is a highly labor-intensive process. To address
this issue, we present a framework for semi-supervised semantic segmentation,
which is enhanced by self-supervised monocular depth estimation from unlabeled
image sequences. In particular, we propose three key contributions: (1) We
transfer knowledge from features learned during self-supervised depth
estimation to semantic segmentation, (2) we implement a strong data
augmentation by blending images and labels using the geometry of the scene, and
(3) we utilize the depth feature diversity as well as the level of difficulty
of learning depth in a student-teacher framework to select the most useful
samples to be annotated for semantic segmentation. We validate the proposed
model on the Cityscapes dataset, where all three modules demonstrate
significant performance gains, and we achieve state-of-the-art results for
semi-supervised semantic segmentation. The implementation is available at
https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth.


  If a monad $T$ is monoidal, then operations on a set $X$ can be lifted
canonically to operations on $TX$. In this paper we study structural properties
under which $T$ preserves equations between those operations. It has already
been shown that any monoidal monad preserves linear equations; affine monads
preserve drop equations (where some variable appears only on one side, such as
$x\cdot y = y$) and relevant monads preserve dup equations (where some variable
is duplicated, such as $x \cdot x = x$). We start the paper by showing a
converse: if the monad at hand preserves a drop equation, then it must be
affine. From this, we show that the problem whether a given (drop) equation is
preserved is undecidable. A converse for relevance turns out to be more subtle:
preservation of certain dup equations implies a weaker notion which we call
$n$-relevance. Finally, we identify the subclass of equations such that their
preservation is equivalent to relevance.


  We report on dual-gate reflectometry in a metal-oxide-semiconductor
double-gate silicon transistor operating at low temperature as a double quantum
dot device. The reflectometry setup consists of two radio-frequency resonators
respectively connected to the two gate electrodes. By simultaneously measuring
their dispersive response, we obtain the complete charge stability diagram of
the device. Charge transitions between the two quantum dots and between each
quantum dot and either the source or the drain contact are detected through
phase shifts in the reflected radio-frequency signals. At finite bias,
reflectometry allows probing charge transitions to excited quantum-dot states
thereby enabling direct access to the energy level spectra of the quantum dots.
Interestingly, we find that in the presence of charge transport across the two
dots the reflectometry signatures of interdot transitions display a dip-peak
structure containing quantitative information on the charge relaxation rates in
the double quantum dot.


  Identifying causal structure is central to many fields ranging from strategic
decision-making to biology and economics. In this work, we propose CD-UCT, a
model-based reinforcement learning method for causal discovery based on tree
search that builds directed acyclic graphs incrementally. We also formalize and
prove the correctness of an efficient algorithm for excluding edges that would
introduce cycles, which enables deeper discrete search and sampling in DAG
space. The proposed method can be applied broadly to causal Bayesian networks
with both discrete and continuous random variables. We conduct a comprehensive
evaluation on synthetic and real-world datasets, showing that CD-UCT
substantially outperforms the state-of-the-art model-free reinforcement
learning technique and greedy search, constituting a promising advancement for
combinatorial methods.


  We provide an explicit normal distinguished boundary dilation to a pair of
commuting operators $(S,P)$ having the closed symmetrized bidisc $\Gamma$ as a
spectral set. This is called Sz.-Nagy dilation of $(S,P)$. The operator pair
that dilates $(S,P)$ is obtained by an application of Stinespring dilation of
$(S,P)$ given by Agler and Young. We further prove that the dilation is minimal
and the dilation space is no bigger than the dilation space of the minimal
unitary dilation of the contraction $P$. We also describe model space and model
operators for such a pair $(S,P)$.


  For a large class of irreducible shift spaces $X\subset\tA^{\Z^d}$, with
$\tA$ a finite alphabet, and for absolutely summable potentials $\Phi$, we
prove that equilibrium measures for $\Phi$ are weak Gibbs measures. In
particular, for $d=1$, the result holds for irreducible sofic shifts.