  Let $\mathfrak{H}$ be the Heisenberg group. From the standard CR structure
$\mathcal{H}$ of $\mathfrak{H}$ we construct the complex hyperbolic structure
of the Siegel domain. Additionally, using the same minimal data for
$\mathfrak{H}$, that is, its Sasakian structure, we provide the Siegel domain
with yet another K\"ahler structure: this structure is of unbounded negative
sectional curvature, and its complex structure does not commute with the
standard complex structure. However, we show that those two K\"ahler structures
are {\rm PCR} K\"ahler equivalent, that is to say, essentially the same when
restricted to $\mathcal{H}$.


  In the last decades, important progress has been achieved in the
understanding of the neurotrophic effects of intermittent fasting (IF), caloric
restriction (CR) and exercise. Improved neuroprotection, synaptic plasticity
and adult neurogenesis (NSPAN) are essential examples of these neurotrophic
effects. The importance in this respect of the metabolic switch from glucose to
ketone bodies as cellular fuel has been highlighted. More recently, calorie
restriction mimetics (CRMs; resveratrol and other polyphenols in particular)
have been investigated thoroughly in relation to NSPAN. In the narrative review
sections of this manuscript, recent findings on these essential functions are
synthesized and the most important molecules involved are presented. The most
researched signaling pathways (PI3K, Akt, mTOR, AMPK, GSK3$\beta$, ULK, MAPK,
PGC-1$\alpha$, NF-$\kappa$B, sirtuins, Notch, Sonic hedgehog and Wnt) and
processes (e.g., anti-inflammation, autophagy, apoptosis) that support or
thwart neuroprotection, synaptic plasticity and neurogenesis are then briefly
presented. This provides an accessible entry point to the literature. In the
annotated bibliography section of this contribution, brief summaries are
provided of about 30 literature reviews relating to the neurotrophic effects of
interest in relation to IF, CR, CRMs and exercise. Most of the selected reviews
address these essential functions from the perspective of healthier aging
(sometimes discussing epigenetic factors) and the reduction of the risk for
neurodegenerative diseases (Alzheimer's disease, Huntington's disease,
Parkinson's disease) and depression or the improvement of cognitive function.


  We explore the band structure and ballistic electron transport in twisted
bilayer $\textrm{MoS}_2$ using Density Functional Theory (DFT). The sphagetti
like bands are unfolded to generate band structures in the primitive unit cell
of the original un-twisted $\textrm{MoS}_2$ bilayer and projected onto an
individual layer. The corresponding twist angle dependent indirect bandedges
are extracted from the unfolded band structures. Based on a comparison within
the same primitive unit cell, an efficient two band effective mass model for
indirect conduction and valence valleys is created and parameterized by fitting
the unfolded band structures. With the two band effective mass model, transport
properties - specifically, we calculate the ballistic transmission in
arbitrarily twisted bilayer $\textrm{MoS}_2$.


  Despite the vast literature on Human Activity Recognition (HAR) with wearable
inertial sensor data, it is perhaps surprising that there are few studies
investigating semisupervised learning for HAR, particularly in a challenging
scenario with class imbalance problem. In this work, we present a new
benchmark, called A*HAR, towards semisupervised learning for class-imbalanced
HAR. We evaluate state-of-the-art semi-supervised learning method on A*HAR, by
combining Mean Teacher and Convolutional Neural Network. Interestingly, we find
that Mean Teacher boosts the overall performance when training the classifier
with fewer labelled samples and a large amount of unlabeled samples, but the
classifier falls short in handling unbalanced activities. These findings lead
to an interesting open problem, i.e., development of semi-supervised HAR
algorithms that are class-imbalance aware without any prior knowledge on the
class distribution for unlabeled samples. The dataset and benchmark evaluation
are released at https://github.com/I2RDL2/ASTAR-HAR for future research.


  This paper presents the first data-driven analysis of Gettr, a new social
network platform launched by former US President Donald Trump's team. Among
other things, we find that users on the platform heavily discuss politics, with
a focus on the Trump campaign in the US and Bolsonaro's in Brazil. Activity on
the platform has steadily been decreasing since its launch, although a core of
verified users and early adopters kept posting and become central to it.
Finally, although toxicity has been increasing over time, the average level of
toxicity is still lower than the one recently observed on other fringe social
networks like Gab and 4chan. Overall, we provide a first quantitative look at
this new community, observing a lack of organic engagement and activity.


  The emission of multi-MeV ($\gamma$-ray) photons from the interaction of a
high-powered laser pulse with a dense plasma target is studied using
particle-in-cell simulations. A new set of diagnostic techniques is presented
and applied to analyze the intense field and ultra-relativistic electrons. Such
methods elucidate the dominant processes responsible for efficient
laser-to-$\gamma$ energy conversion, which include nonlinear Compton scattering
and magneto-bremsstrahlung radiation, and provide a clear picture of the
interaction on a microscopic level. We identify regions in the plasma target of
high photon energy-density and obtain an energy conversion efficiency as high
as 30%. The essential characteristics of the interaction are validated with
full-3D simulations.


  The presence of amorphous oxide layers can significantly affect the coherent
time of superconducting qubits due to their high dielectric loss. Typically,
the surface oxides of superconductor films exhibit lossy and unstable behavior
when exposed to air. To increase the coherence time, it is essential for qubits
to have stable and low dielectric loss oxides, either as barrier or passivation
layers. In this study, we highlight the robust and stable nature of an
amorphous tantalum oxide layer formed on {\alpha}-Ta (110) film by employing
chemical and structural analyses. Such kind of oxide layer forms in a
self-limiting process on the surface of {\alpha}-Ta (110) film in piranha
solution, yielding stable thickness and steady chemical composition. Quarter
wavelength coplanar waveguide resonators are made to study the loss of this
oxide. One resonator has a Qi of 3.0x10^6 in the single photon region. The Qi
of most devices are higher than 2.0x10^6. Moreover, most of them are still over
1x10^6 even after exposed to air for months. Based on these findings, we
propose an all-tantalum superconducting qubit utilizing such oxide as
passivation layers, which possess low dielectric loss and improved stability.


  We present an avatar of the Euler obstruction to foliated structures on
certain non-metric surfaces. This adumbrates (at least for the simplest
2D-configurations) that the standard mechanism---to the effect that the devil
of algebra sometimes barricades the existence of angelic geometric structures
(obstruction theory more-or-less)---propagates slightly beyond the usual
metrical proviso. Alas, the game is much more conservative than revolutionary:
in particular we enjoyed retrospecting at Poincar\'e's argument of 1885
(announced in 1881).


  We present an explicit supersymmetric deformation of supergravity backgrounds
describing D3-branes on Calabi-Yau cones. From the geometrical point of view,
it corresponds to blowing up a 4-cycle in the Calabi-Yau and can be done
universally. In the field theory, we identify this deformation with motion on
non-mesonic directions in the full moduli space of vacua. For the case of a Z_2
orbifold of the conifold, we discuss an explicit gravity solution with two
deformation parameters: one corresponding to blowing up a 2-cycle and one
corresponding to blowing up a 4-cycle. The generic case where the Calabi-Yau is
toric is also discussed in detail. Quite generally, the order parameter of
these 4-cycle deformations is a dimension six operator. We also consider probe
strings which show linear confinement and probe D7 branes which help in
understanding the behavior far in the infrared.


  Energetic plasma is everywhere in the Universe. The terrestrial
Magnetospheric System is a key case where direct measures of plasma
energization and energy transport can be made in situ at high resolution.
Despite the large amount of available observations, we still do not fully
understand how plasma energization and energy transport work. Key physical
processes driving much plasma energization and energy transport occur where
plasma on fluid scales couple to the smaller ion kinetic scales. These scales
(1 RE) are strongly related to the larger mesoscales (several RE) at which
large-scale plasma energization and energy transport structures form. All these
scales and processes need to be resolved experimentally, however existing
multi-point in situ observations do not have a sufficient number of measurement
points. New multiscale observations simultaneously covering scales from
mesoscales to ion kinetic scales are needed. The implementation of these
observations requires a strong international collaboration in the coming years
between the major space agencies. The Plasma Observatory is a mission concept
tailored to resolve scale coupling in plasma energization and energy transport
at fluid and ion scales. It targets the two ESA-led Medium Mission themes
Magnetospheric Systems and Plasma Cross-scale Coupling of the ESA Voyage 2050
report and is currently under evaluation as a candidate for the ESA M7 mission.
MagCon (Magnetospheric Constellation) is a mission concept being studied by
NASA aiming at studying the flow of mass, momentum, and energy through the
Earth magnetosphere at mesoscales. Coordination between Plasma Observatory and
MagCon missions would allow us for the first time to simultaneously cover from
mesoscales to ion kinetic scales leading to a paradigm shift in the
understanding of the Earth Magnetospheric System.


  This work introduces an approach to enhancing the computational efficiency of
3D atmospheric simulations by integrating a machine-learned surrogate model
into the OASIS global circulation model (GCM). Traditional GCMs, which are
based on repeatedly numerically integrating physical equations governing
atmospheric processes across a series of time-steps, are time-intensive,
leading to compromises in spatial and temporal resolution of simulations. This
research improves upon this limitation, enabling higher resolution simulations
within practical timeframes. Speeding up 3D simulations holds significant
implications in multiple domains. Firstly, it facilitates the integration of 3D
models into exoplanet inference pipelines, allowing for robust characterisation
of exoplanets from a previously unseen wealth of data anticipated from JWST and
post-JWST instruments. Secondly, acceleration of 3D models will enable higher
resolution atmospheric simulations of Earth and Solar System planets, enabling
more detailed insights into their atmospheric physics and chemistry. Our method
replaces the radiative transfer module in OASIS with a recurrent neural
network-based model trained on simulation inputs and outputs. Radiative
transfer is typically one of the slowest components of a GCM, thus providing
the largest scope for overall model speed-up. The surrogate model was trained
and tested on the specific test case of the Venusian atmosphere, to benchmark
the utility of this approach in the case of non-terrestrial atmospheres. This
approach yields promising results, with the surrogate-integrated GCM
demonstrating above 99.0% accuracy and 101 factor GPU speed-up of the entire
simulation compared to using the matched original GCM under Venus-like
conditions.


  We prove the existence of a close connection between spaces with measured
walls and median metric spaces. We then relate properties (T) and Haagerup
(a-T-menability) to actions on median spaces and on spaces with measured walls.
This allows us to explore the relationship between the classical properties (T)
and Haagerup and their versions using affine isometric actions on $L^p$-spaces.
It also allows us to answer an open problem on a dynamical characterization of
property (T), generalizing results of Robertson-Steger.


  This work begins with a review of complexification and realification of Hopf
algebras. We emphasize the notion of multiplier Hopf algebras for the
description of different classes of functions (compact supported, bounded,
unbounded) on complex quantum groups and the construction of the associated
left and right Haar measure. Using a continuation of $6j$ symbols of $SU_q (2)$
with complex spins, we give a new description of the unitary representations of
$SL_q (2,\CC)_{\RR}$ and find explicit expressions for the characters of $SL_q
(2,\CC)_{\RR}$. The major theorem of this article is the Plancherel theorem for
the Quantum Lorentz Group.


  In this paper a procedure for large-eddy simulation (LES) has been devised
for fluid and magnetohydrodynamic turbulence in Fourier space using the
renormalized parameters. The parameters calculated using field theory have been
taken from recent papers by Verma [Phys. Rev. E, 2001; Phys. Plasmas, 2001]. We
have carried out LES on $64^3$ grid. These results match quite well with direct
numerical simulations of $128^3$. We show that proper choice of parameter is
necessary in LES.


  In a data stream environment, classification models must handle concept drift
efficiently and effectively. Ensemble methods are widely used for this purpose;
however, the ones available in the literature either use a large data chunk to
update the model or learn the data one by one. In the former, the model may
miss the changes in the data distribution, and in the latter, the model may
suffer from inefficiency and instability. To address these issues, we introduce
a novel ensemble approach based on the Broad Learning System (BLS), where mini
chunks are used at each update. BLS is an effective lightweight neural
architecture recently developed for incremental learning. Although it is fast,
it requires huge data chunks for effective updates, and is unable to handle
dynamic changes observed in data streams. Our proposed approach named Broad
Ensemble Learning System (BELS) uses a novel updating method that significantly
improves best-in-class model accuracy. It employs an ensemble of output layers
to address the limitations of BLS and handle drifts. Our model tracks the
changes in the accuracy of the ensemble components and react to these changes.
We present the mathematical derivation of BELS, perform comprehensive
experiments with 20 datasets that demonstrate the adaptability of our model to
various drift types, and provide hyperparameter and ablation analysis of our
proposed model. Our experiments show that the proposed approach outperforms
nine state-of-the-art baselines and supplies an overall improvement of 13.28%
in terms of average prequential accuracy.


  Cyclic pre-proofs can be represented as sets of finite tree derivations with
back-links. In the frame of the first-order logic with inductive definitions,
the nodes of the tree derivations are labelled by sequents and the back-links
connect particular terminal nodes, referred to as buds, to other nodes labelled
by a same sequent. However, only some back-links can constitute sound
pre-proofs. Previously, it has been shown that special ordering and
derivability conditions, defined along the minimal cycles of the digraph
representing a particular normal form of the cyclic pre-proof, are sufficient
for validating the back-links. In that approach, a same constraint could be
checked several times when processing different minimal cycles, hence one may
require additional recording mechanisms to avoid redundant computation in order
to downgrade the time complexity to polynomial.
  We present a new approach that does not need to process minimal cycles. It
based on a normal form that allows to define the validation conditions by
taking into account only the root-bud paths from the non-singleton strongly
connected components of its digraph.


  A new criterion, based on noncontextuality, is derived to discriminate
between separable and nonseparable states in classical wave optics where no
discreteness is involved. An experiment is proposed to test the violation of
noncontextuality by a nonseparable state. Such states have only recently begun
to be explored. The significance of their nonseparability or entanglement as
well as their similarities with and differences from entangled quantum states
are discussed.


  (1-x)PbMg1/3Nb2/3O3-(x)PbSc1/2Nb1/2O3 (PMN-PSN) solid solution crystals have
been grown by the flux method in the whole concentration range. X-ray supercell
reflections due to B-cation ordering were observed for as-grown crystals from
the 0.1 < x < 0.65 compositional range. Though the ordered domains are rather
large (~50 nm) the relaxor-like dielectric behavior is observed for
compositions with x < 0.6. The diffusion of the dielectric permittivity maximum
in as-grown crystals is the lowest at x = 0.6 and increases towards the end
members of solid solution. Such behavior is explained within a Bragg-Williams
approach by employing the random layer model. At x ~ 0.6 the excitation energy
determined from the Vogel-Fulcher relation exhibits a jump which we regard to
changing the kind of the polar regions from PbMg1/3Nb2/3O3 to PbSc1/2Nb1/2O3
related type.


  In machine learning (ML), efficient asset management, including ML models,
datasets, algorithms, and tools, is vital for resource optimization, consistent
performance, and a streamlined development lifecycle. This enables quicker
iterations, adaptability, reduced development-to-deployment time, and reliable
outputs. Despite existing research, a significant knowledge gap remains in
operational challenges like model versioning, data traceability, and
collaboration, which are crucial for the success of ML projects. Our study aims
to address this gap by analyzing 15,065 posts from developer forums and
platforms, employing a mixed-method approach to classify inquiries, extract
challenges using BERTopic, and identify solutions through open card sorting and
BERTopic clustering. We uncover 133 topics related to asset management
challenges, grouped into 16 macro-topics, with software dependency, model
deployment, and model training being the most discussed. We also find 79
solution topics, categorized under 18 macro-topics, highlighting software
dependency, feature development, and file management as key solutions. This
research underscores the need for further exploration of identified pain points
and the importance of collaborative efforts across academia, industry, and the
research community.


  The aim of the paper is to evaluate the miss probability of a Least Recently
Used (LRU) cache, when it is offered a non-stationary request process given by
a Poisson cluster point process. First, we construct a probability space using
Palm theory, describing how to consider a tagged document with respect to the
rest of the request process. This framework allows us to derive a general
integral formula for the expected number of misses of the tagged document.
Then, we consider the limit when the cache size and the arrival rate go to
infinity proportionally, and use the integral formula to derive an asymptotic
expansion of the miss probability in powers of the inverse of the cache size.
This enables us to quantify and improve the accuracy of the so-called Che
approximation.


  We propose an explanation of features of spiral galaxies: spiral arms and
observed flat rotation curves, without the presence of an exotic form of
matter. The formalism is based on Boltzmanns transport equation for the
collisional matter and the very-low-velocity post-Newtonian approximation of
the general relativity equations expressed in the Maxwell-like form. The
Maxwell-like formulation provides the base for the explanation of the above
phenomena in the language of dynamically created gravitoelectromagnetic fields
by the movement of mass streams in the plane of the galaxy disc. In the model
we use radical simplifications expressed as neglect of the gravitational
interaction between neighbors and approximation of the incompressible mass
flow. In this frame we show that if the galaxy disc is fuelled constantly by
collisional mass carriers, then the amplification of the gravitomagnetic field
can be large enough to create the rotational velocity pattern and spiral arms.
In this framework the collisional part of the mass gas in the galaxy disc
plane, i.e. molecules and atoms, is responsible for the creation of the
gravitomagnetic field. According to the model the spiral pattern of arms is
static and determined by a direction of the mass flow. The model reproduces
qualitatively the observed spiral arms and reproduces well the shape of the
rotational velocity pattern. As an example of the usability of the proposed
mechanism, we reproduce qualitatively the above features for the IC 342 and NGC
4321 galaxies.


  While self-supervised representation learning (SSL) has proved to be
effective in the large model, there is still a huge gap between the SSL and
supervised method in the lightweight model when following the same solution. We
delve into this problem and find that the lightweight model is prone to
collapse in semantic space when simply performing instance-wise contrast. To
address this issue, we propose a relation-wise contrastive paradigm with
Relation Knowledge Distillation (ReKD). We introduce a heterogeneous teacher to
explicitly mine the semantic information and transferring a novel relation
knowledge to the student (lightweight model). The theoretical analysis supports
our main concern about instance-wise contrast and verify the effectiveness of
our relation-wise contrastive learning. Extensive experimental results also
demonstrate that our method achieves significant improvements on multiple
lightweight models. Particularly, the linear evaluation on AlexNet obviously
improves the current state-of-art from 44.7% to 50.1%, which is the first work
to get close to the supervised 50.5%. Code will be made available.


  We examine a wavefunction ansatz in which a doped hole can experience a
quantum transition from a charge $+e$ Landau quasiparticle to a neutral spinon
as a function of the underlying spin-spin correlation. As shown variationally,
such a wavefunction accurately captures all the essential features revealed by
exact diagonalization and density matrix renormalization group simulations in a
two-leg $t$-$J$ ladder. Hence its analytic form can provide an explicit
understanding of the mechanism for the unconventional ground state. The
transition in the phase diagram is accompanied by a change of the hole
composite from a tight charge-spin binding to a loosely-bound hole-spin pair.
In the latter, the hole carries a \emph{finite} spin current but with vanishing
charge current in the degenerate ground states. We show that the charge of the
hole composite here is dynamically diminished due to an internal relative
hole-spin motion, which is fundamentally distinct from a simple charge-spin
separation in a one-dimensional case. We further show that the same effect is
also responsible for a strong pairing between two doped holes in such a
non-Landau quasiparticle regime.


  I describe how superstring theory may violate spin-statistics in an
experimentally observable manner. Reviewing the basics of superstring
interactions and how to utilize these to produce a statistical phase, I then
apply these ideas to two specific examples. The first is the case of heterotic
worldsheet linkings, whereby one small closed string momentarily enlarges
sufficiently to pass over another, producing such a statistical phase. The
second is the braneworld model with noncommutative geometry, whereby matter
composed of open strings may couple to a background in which spacetime
coordinates do not commute, modifying the field (anti)commutator algebra. I
conclude with ways to sharpen and experimentally test these exciting avenues to
possibly verify superstring theory.


  QJT (Quantum Jet Theory) is the quantum theory of jets, which can be
canonically identified with truncated Taylor series. Ultralocality requires a
novel quantization scheme, where dynamics is treated as a constraint in the
history phase space. QJT differs from QFT since it involves a new datum: the
expansion point. This difference is substantial because it leads to new gauge
and diff anomalies, which are necessary to combine background independence with
locality. Physically, the new ingredient is that the observer's trajectory is
explicitly introduced and quantized together with the fields. In this paper the
harmonic oscillator and free fields are treated within QJT, correcting previous
flaws. The standard Hilbert space is recovered for the harmonic oscillator, but
there are interesting modifications already for the free scalar field, due to
quantization of the observer's trajectory. Only free fields are treated in
detail, but the complications when interactions are introduced are briefly
discussed. We also explain why QJT is necessary to resolve the conceptual
problems of quantum gravity.


  In this paper, we propose to dynamically select a MIMO detector using neural
network for each resource element (RE) in the transport block of 5G NR/LTE
communication system. The objective is to minimize the computational complexity
of MIMO detection while keeping the transport block error rate (BLER) close to
the BLER when dimension-reduced maximum-likelihood (DR-ML) detection is used. A
detector selection problem is formulated to achieve this objective. However,
since the problem is high dimensional and NP-hard, we first decompose the
problem into smaller problems and train a multi-layer perceptron (MLP) network
to obtain the solution. The MLP network is trained to select a low-complexity,
yet reliable, detector using instantaneous channel condition in the RE. We
first propose a method to generate a labeled dataset to select a low-complexity
detector. Then, the MLP is trained twice using quasi-Newton method to select a
reliable detector for each RE. The performance of online detector selection is
evaluated in 5G NR link level simulator in terms of BLER and the complexity is
quantified in terms of the number of Euclidean distance (ED) computations and
the number of real additions and multiplication. Results show that the
computational complexity in the MIMO detector can be reduced by ~10X using the
proposed method.


  The importance of cosmic-ray experimental measurements of proton-proton total
cross sections to understand the underlying fundamental dynamics is discussed.
It is shown that early discovered global structure of proton-proton total cross
section [5,17] is completely compatible with the values obtained from
cosmic-ray experiments.


  Blended courses have become the norm in post-secondary education.
Universities use large-scale learning management systems to manage class
content. Instructors deliver readings, lectures, and office hours online;
students use intelligent tutors, web forums, and online submission systems; and
classes communicate via web forums. These online tools allow students to form
new social networks or bring social relationships online. They also allow us to
collect data on students' social relationships. In this paper we report on our
research on community formation in blended courses based on online forum
interactions. We found that it was possible to group students into communities
using standard community detection algorithms via their posts and reply
structure and that the students' grades are significantly correlated with their
closest peers.


  We review recent results obtained in the scattering theory of dissipative
quantum systems representing the long-time evolution of a system $S$
interacting with another system $S'$ and susceptible of being absorbed by $S'$.
The effective dynamics of $S$ is generated by an operator of the form $H = H_0
+ V - \mathrm{i} C^* C$ on the Hilbert space of the pure states of $S$, where
$H_0$ is the self-adjoint generator of the free dynamics of $S$, $V$ is
symmetric and $C$ is bounded. The main example is a neutron interacting with a
nucleus in the nuclear optical model. We recall the basic objects of the
scattering theory for the pair $(H,H_0)$, as well as the results, proven in
arXiv:1703.09018 and arXiv:1808.09179, on the spectral singularities of $H$ and
the asymptotic completeness of the wave operators. Next, for the nuclear
optical model, we show that asymptotic completeness generically holds.


  For the Lagrange spectrum and other applications, we determine the smallest
accumulation point of binary sequences that are maximal in their shift orbits.
This problem is trivial for the lexicographic order, and its solution is the
fixed point of a substitution for the alternating lexicographic order. For
orders defined by cylinders, we show that the solutions are $S$-adic sequences,
where $S$ is a certain infinite set of substitutions that includes Sturmian
morphisms. We also consider a similar problem for symmetric ternary shifts,
which is applicable to the multiplicative version of the Markoff-Lagrange
spectrum.


  We report an {\it in-situ} scanning tunneling microscopy study of atomically
thin films of CeCoIn$_5$, a $d$-wave heavy-fermion superconductor. Both
hybridization and superconducting gaps are observed even in monolayer
CeCoIn$_5$, providing direct evidence of superconductivity of heavy
quasiparticles mediated by purely two-dimensional bosonic excitations. In these
atomically thin films, $T_c$ is suppressed to nearly half of the bulk, but is
similar to CeCoIn$_5$/YbCoIn$_5$ superlattices containing CeCoIn$_5$ layers
with the same thickness as the thin films. Remarkably, the out-of-plane upper
critical field $\mu_0H_{c2\perp}$ at zero temperature is largely enhanced from
those of bulk and superlattices. The enhanced $H_{c2\perp}$ well exceeds the
Pauli and bulk orbital limits, suggesting the possible emergence of unusual
superconductivity with parity mixing caused by the inversion symmetry breaking.


  In this work, we study the SM-like Higgs pair productions in the framework of
the general CP-violating two-Higgs-doublet model. Several constraints are
imposed to the model sequentially, including the SM-like Higgs boson signal
fits, the precise measurements of the electric dipole moments, the perturbative
unitarity and stability bounds to the Higgs potential, and the most recent LHC
searches for the heavy Higgs bosons. We show how are the CP-violating mixing
angles related to the Higgs cubic self couplings in this setup. Afterwards, we
estimate the cross sections of the future LHC/SppC searches for the Higgs pair
productions, as well as other possible decay modes for the heavy Higgs bosons.


  Rayleigh-B\'enard convection (RBC) is a recurrent phenomenon in several
industrial and geoscience flows and a well-studied system from a fundamental
fluid-mechanics viewpoint. However, controlling RBC, for example by modulating
the spatial distribution of the bottom-plate heating in the canonical RBC
configuration, remains a challenging topic for classical control-theory
methods. In the present work, we apply deep reinforcement learning (DRL) for
controlling RBC. We show that effective RBC control can be obtained by
leveraging invariant multi-agent reinforcement learning (MARL), which takes
advantage of the locality and translational invariance inherent to RBC flows
inside wide channels. The MARL framework applied to RBC allows for an increase
in the number of control segments without encountering the curse of
dimensionality that would result from a naive increase in the DRL action-size
dimension. This is made possible by the MARL ability for re-using the knowledge
generated in different parts of the RBC domain. We show in a case study that
MARL DRL is able to discover an advanced control strategy that destabilizes the
spontaneous RBC double-cell pattern, changes the topology of RBC by coalescing
adjacent convection cells, and actively controls the resulting coalesced cell
to bring it to a new stable configuration. This modified flow configuration
results in reduced convective heat transfer, which is beneficial in several
industrial processes. Therefore, our work both shows the potential of MARL DRL
for controlling large RBC systems, as well as demonstrates the possibility for
DRL to discover strategies that move the RBC configuration between different
topological configurations, yielding desirable heat-transfer characteristics.
These results are useful for both gaining further understanding of the
intrinsic properties of RBC, as well as for developing industrial applications.


  This paper is a sequel of the series of papers [gr-qc/9409010, gr-qc/9505034,
gr-qc/9603022], being an immediate continuation and development of the latter
of them. In the Friedmann universe, the equation $\Omega_0=2q_0$ holds
($\Omega$ is density parameter, $q$ is deceleration parameter, and subscript 0
indicates present-day values), which gives rise to the problem of the missing
matter as observational data give $\Omega_0<2q_0$. In the cosmic-length
universe, $\Omega_0=2q_0- L/R_0^3H_0^2$ ($R_0$ is the radius of the universe,
$H_0$ is Hubble constant), which lifts the problem. The cosmic length, $L=const
\approx 1/H_0$, is the infimum of the set of maximal radii of a closed
universe.


  The foundations of environment-induced decoherence theory are discussed and
the role of unphysical assumptions is pointed out. An alternative
interpretation of decoherence is proposed.


  Dombi et al. (2019) introduced a three parameter omega distribution and
showed that its asymptotic distribution is the Weibull model. We propose a new
record-based transmuted generalization of the unit omega distribution by
considering Balakrishnan and He (2021) approach. We call it the RTUOMG
distribution. We derive expressions for some statistical quantities, like,
probability density function, distribution, hazard function, quantile function,
moments, incomplete moments, inverted moments, moment generating function,
Lorenz curve, and Bonferroni curve of the proposed distribution. The numerical
values of various measures of central tendency and coefficient of skewness and
kurtosis are also presented. Concepts of stochastic ordering and some results
related to ordered statistics of the RTUOMG distribution are discussed. The
parameters of the RTUOMG distribution are estimated using five distinct
estimators. Additionally, the Monte Carlo simulations are performed to assess
the performance of these estimators. Finally, two real data sets are analyzed
to demonstrate the utility of the RTUOMG distribution.


  A common optical design for a coma-corrected, 6-meter aperture,
crossed-Dragone telescope has been adopted for the CCAT-prime telescope of CCAT
Observatory, Inc., and for the Large Aperture Telescope of the Simons
Observatory. Both are to be built in the high altitude Atacama Desert in Chile
for submillimeter and millimeter wavelength observations, respectively. The
design delivers a high throughput, relatively flat focal plane, with a field of
view 7.8 degrees in diameter for 3 mm wavelengths, and the ability to
illuminate >100k diffraction-limited beams for < 1 mm wavelengths. The optics
consist of offset reflecting primary and secondary surfaces arranged in such a
way as to satisfy the Mizuguchi-Dragone criterion, suppressing first-order
astigmatism and maintaining high polarization purity. The surface shapes are
perturbed from their standard conic forms in order to correct coma aberrations.
We discuss the optical design, performance, and tolerancing sensitivity. More
information about CCAT-prime can be found at ccatobservatory.org and about
Simons Observatory at simonsobservatory.org.


  Updating and querying on a range is a classical algorithmic problem with a
multitude of applications. The Segment Tree data structure is particularly
notable in handling the range query and update operations. A Segment Tree
divides the range into disjoint segments and merges them together to perform
range queries and range updates elegantly. Although this data structure is
remarkably potent for 1-dimensional problems, it falls short in higher
dimensions. Lazy Propagation enables the operations to be computed in $O(logn)$
time in a single dimension. However, the concept of lazy propagation could not
be translated to higher-dimensional cases, which imposes a time complexity of
$O(n^{k-1} \; logn)$ for operations on $k$-dimensional data. In this work, we
have made an attempt to emulate the idea of lazy propagation differently so
that it can be applied for 2-dimensional cases. Moreover, the proposed
modification should be capable of performing most general aggregate functions
similar to the original Segment Tree, and can also be extended to even higher
dimensions. Our proposed algorithm manages to perform range sum queries and
updates in $O(\log^2 n)$ time for a 2-dimensional problem, which becomes
$O(\log^d n)$ for a $d$-dimensional situation.


  Fully 3D cosmological simulations of scalar field dark matter with
self-interactions, also known as Bose-Einstein condensate dark matter, are
performed using a set of effective hydrodynamic equations. These are derived
from the non-linear Schr\"odinger equation by performing a smoothing operation
over scales larger than the de Broglie wavelength, but smaller than the
self-interaction Jeans' length. The dynamics on the de Broglie scale become an
effective thermal energy in the hydrodynamic approximation, which is assumed to
be subdominant in the initial conditions, but become important as structures
collapse and the fluid is shock-heated. The halos that form have
Navarro-Frenk-White envelopes, while the centers become cored due to the fluid
pressures (thermal + self-interaction). The core radii are mostly determined by
the self-interaction Jeans' length, even though the effective thermal energy
eventually dominates over the self-interaction energy everywhere, a result that
is insensitive to the initial smallness of the thermal energy. Scaling
relations for the simulated population of halos are compared with Milky Way
dwarf spheroidals and nearby galaxies, assuming a Burkert halo profile, and are
found to not match, also for core radii $R_c\approx 1\text{kpc}$, which has
generally been the value expected to resolve the cusp-core issue. However, the
simulations have a limited volume, and therefore a limited halo mass range,
include no baryonic physics, and use fiducial cold dark matter initial
conditions with a cut-off near the Jeans' length at $z=50$, all of which can
affect the halo properties. [Abridged]


  Employing a conformal map to hyperbolic space cross a circle, we compute the
universal contribution to the vacuum entanglement entropy (EE) across a sphere
in even-dimensional conformal field theory. Previous attempts to derive the EE
in this way were hindered by a lack of knowledge of the appropriate boundary
terms in the trace anomaly. In this paper we show that the universal part of
the EE can be treated as a purely boundary effect. As a byproduct of our
computation, we derive an explicit form for the A-type anomaly contribution to
the Wess-Zumino term for the trace anomaly, now including boundary terms. In
d=4 and 6, these boundary terms generalize earlier bulk actions derived in the
literature.


  We study optimal eavesdropping in quantum cryptography with three-dimensional
systems, and show that this scheme is more secure than protocols using
two-dimensional states. We generalize the according eavesdropping
transformation to arbitrary dimensions, and discuss the connection with optimal
quantum cloning.


  It is demonstrated that an understanding of the 5/2 fractional quantum Hall
effect can be achieved within the composite fermion theory without appealing to
the Pfaffian wave function. The residual interaction between composite fermions
plays a crucial role in establishing incompressibility at this filling factor.
This approach has the advantage of being amenable to systematic perturbative
improvements, and produces ground as well as excited states. It, however, does
not relate to non-Abelian statistics in any obvious manner.


  We focus on short--period eclipsing binaries that belong to a class of
Cataclysmic Variables (CVs). They are known as polars and intermediate polars,
closely resembling their prototype AM Herculis. These binaries consist of a red
dwarf and a strongly magnetic white dwarf, having orbital periods of only a few
hours. Monitoring eclipses of these typically faint sources demands high-time
resolution photometry. We describe the very recent results obtained for two
CVs, HU Aqr and DQ Her, which were observed with the Optical Pulsar Timing
Analyzer (OPTIMA). The new observations of HU Aqr confirm that the O--C
(Observed minus Calculated) diagrams exhibit variations known for this binary
which can be explained by a single, massive Jupiter--like planet, possibly
accompanied by a very distant companion.


  We study dark matter, assumed to be composed by weak interacting massive
particles (WIMPs), scattering off ${}^2$H and ${}^4$He nuclei. In order to
parameterize the WIMP-nucleon interaction the chiral effective field theory
approach is used. Considering only interactions invariant under parity, charge
conjugation and time reversal, we examine five interaction types: scalar,
pseudoscalar, vector, axial and tensor. Scattering amplitudes between two
nucleons and a WIMP are determined up to second order of chiral perturbation
theory. We apply this program to calculate the interaction rate as function of
the WIMP mass and of the magnitude of the WIMP-quark coupling constants. From
our study, we conclude that the scalar nuclear response functions result much
greater than the others due to theirs large combination of low energy
constants. We verify that the leading order contributions are dominant in this
low energy processes. We also provide an estimate for the background due to
atmospheric neutrinos.


  The influence of a staggered molecular field in frustrated rare-earth
pyrochlores, produced via the magnetic iridium occupying the transition metal
site, can generate exotic ground states, such as the fragmentation of the
magnetization in the Ho compound. At variance with the Ising Ho$^{3+}$ moment,
we focus on the behavior of the quasi isotropic magnetic moment of the
Gd$^{3+}$ ion at the rare-earth site. By means of macroscopic measurements and
neutron scattering, we find a complex situation where different components of
the magnetic moment contribute to two antiferromagnetic non-collinear
arrangements: a high temperature all in - all out order induced by the Ir
molecular field, and Palmer and Chalker correlations that tend to order at much
lower temperatures. This is enabled by the anisotropic nature of the Gd-Gd
interactions and requires a weak easy-plane anisotropy of the Gd$^{3+}$ moment
due to the mixing of the ground state with multiplets of higher spectral terms.


  Speaker verification performance in neutral talking environment is usually
high, while it is sharply decreased in emotional talking environments. This
performance degradation in emotional environments is due to the problem of
mismatch between training in neutral environment while testing in emotional
environments. In this work, a three-stage speaker verification architecture has
been proposed to enhance speaker verification performance in emotional
environments. This architecture is comprised of three cascaded stages: gender
identification stage followed by an emotion identification stage followed by a
speaker verification stage. The proposed framework has been evaluated on two
distinct and independent emotional speech datasets: in-house dataset and
Emotional Prosody Speech and Transcripts dataset. Our results show that speaker
verification based on both gender information and emotion information is
superior to each of speaker verification based on gender information only,
emotion information only, and neither gender information nor emotion
information. The attained average speaker verification performance based on the
proposed framework is very alike to that attained in subjective assessment by
human listeners.


  We investigate the effect of the anisotropy of a harmonic trap on the
behaviour of a fast rotating Bose-Einstein condensate. This is done in the
framework of the 2D Gross-Pitaevskii equation and requires a symplectic
reduction of the quadratic form defining the energy. This reduction allows us
to simplify the energy on a Bargmann space and study the asymptotics of large
rotational velocity. We characterize two regimes of velocity and anisotropy; in
the first one where the behaviour is similar to the isotropic case, we
construct an upper bound: a hexagonal Abrikosov lattice of vortices, with an
inverted parabola profile. The second regime deals with very large velocities,
a case in which we prove that the ground state does not display vortices in the
bulk, with a 1D limiting problem. In that case, we show that the coarse grained
atomic density behaves like an inverted parabola with large radius in the
deconfined direction but keeps a fixed profile given by a Gaussian in the other
direction. The features of this second regime appear as new phenomena.


  This paper concerns the structural stability of subsonic flows with a contact
discontinuity in a finitely long axisymmetric cylinder. We establish the
existence and uniqueness of axisymmetric subsonic flows with a contact
discontinuity by prescribing the horizontal mass flux distribution, the swirl
velocity, the entropy and the Bernoulli's quantity at the entrance and the
radial velocity at the exit. It can be formulated as a free boundary problem
with the contact discontinuity to be determined simultaneously with the flows.
Compared with the two-dimensional case, a new difficulty arises due to the
singularity near the axis. One of the key points in the analysis is the
introduction of an invertible modified Lagrangian transformation which can
overcome this difficulty and straighten the contact discontinuity. Another one
is to utilize the deformation-curl decomposition for the steady Euler system
introduced in \cite{WX19} to effectively decouple the hyperbolic and elliptic
modes. Finally, the contact discontinuity will be located by using the implicit
function theorem.


  A new spin wavelet transform on the sphere is proposed to analyse the
polarisation of the cosmic microwave background (CMB), a spin $\pm 2$ signal
observed on the celestial sphere. The scalar directional scale-discretised
wavelet transform on the sphere is extended to analyse signals of arbitrary
spin. The resulting spin scale-discretised wavelet transform probes the
directional intensity of spin signals. A procedure is presented using this new
spin wavelet transform to recover E- and B-mode signals from partial-sky
observations of CMB polarisation.


  Conventional prior for Variational Auto-Encoder (VAE) is a Gaussian
distribution. Recent works demonstrated that choice of prior distribution
affects learning capacity of VAE models. We propose a general technique
(embedding-reparameterization procedure, or ER) for introducing arbitrary
manifold-valued variables in VAE model. We compare our technique with a
conventional VAE on a toy benchmark problem. This is work in progress.


  One can compute the local $\mathbb{A}^1$-degree at points with separable
residue field by base changing, working rationally, and post-composing with the
field trace. We show that for endomorphisms of the affine line, one can compute
the local $\mathbb{A}^1$-degree at points with inseparable residue field by
taking a suitable lift of the polynomial and transferring its local degree. We
also discuss the general set-up and strategy in terms of the six functor
formalism. As an application, we show that trace forms of number fields are
local $\mathbb{A}^1$-degrees.


  We investigate the presence of static solutions in models described by real
scalar field in two-dimensional spacetime. After taking advantage of a
procedure introduced sometime ago, we solve intricate nonlinear ordinary
differential equations and illustrate how to find compact structures in models
engendering standard kinematics. In particular, we study linear stability and
show that all the static solutions we have found are linearly stable.


  The calculation of the dephasing time in electron systems is presented. By
means of the Keldysh formalism we discuss in a unifying way both weak
localization and interaction effects in disordered systems. This allows us to
show how dephasing arises both in the particle-particle channel (weak
localization) and in the particle-hole channel (interaction effect). First we
discuss dephasing by an external field. Besides reviewing previous work on how
an external oscillating field suppresses the weak localization correction, we
derive a new expression for the effect of a field on the interaction
correction. We find that the latter may be suppressed by a static electric
field, in contrast to weak localization. We then consider dephasing due to
inelastic scattering. The ambiguities involved in the definition of the
dephasing time are clarified by directly comparing the diagrammatic approach
with the path-integral approach. We show that different dephasing times appear
in the particle-particle and particle-hole channels. Finally we comment on
recent experiments.


  Although using machine learning techniques to solve computer security
challenges is not a new idea, the rapidly emerging Deep Learning technology has
recently triggered a substantial amount of interests in the computer security
community. This paper seeks to provide a dedicated review of the very recent
research works on using Deep Learning techniques to solve computer security
challenges. In particular, the review covers eight computer security problems
being solved by applications of Deep Learning: security-oriented program
analysis, defending return-oriented programming (ROP) attacks, achieving
control-flow integrity (CFI), defending network attacks, malware
classification, system-event-based anomaly detection, memory forensics, and
fuzzing for software security.


  We derive the pressure tensor and the heat flux to accompany the new
macroscopic conservation equations that we developed previously in a
volume-based kinetic framework for gas flows. This kinetic description allows
for expansion or compression of a fluid element, and is characterized by a flux
of volume that conventional modelling does not account for. A new convective
form of transport arises from this new approach, which is due purely to
macroscopic expansion or compression. It supplements the conventional transport
processes, which are transport due purely to mass motion (classical convective
transport) and transport due purely to random motion (diffusive transport). In
addition, we show that the thermodynamic parameters of the fluid (temperature
and pressure) appear with new non-equilibrium contributions that depend on
density variations in the gas. Our new model may be useful for describing gas
flows that display non-local-thermodynamic-equilibrium (rarefied gas flows),
flows with relatively large variations of macroscopic properties, and/or highly
compressible fluids and flows.


  The optimal power flow (OPF) problem is fundamental in power system
operations and planning. Large-scale renewable penetration in distribution
networks calls for real-time feedback control, and hence the need for fast and
distributed solutions for OPF. This is difficult because OPF is nonconvex and
Kirchhoff's laws are global. In this paper we propose a solution for balanced
radial distribution networks. It exploits recent results that suggest solving
for a globally optimal solution of OPF over a radial network through the
second-order cone program (SOCP) relaxation. Our distributed algorithm is based
on alternating direction method of multiplier (ADMM), but unlike standard ADMM
algorithms that often require iteratively solving optimization subproblems in
each ADMM iteration, our decomposition allows us to derive closed form
solutions for these subproblems, greatly speeding up each ADMM iteration. We
present simulations on a real-world 2,065-bus distribution network to
illustrate the scalability and optimality of the proposed algorithm.


  Despite its benefits for children's skill development and parent-child
bonding, many parents do not often engage in interactive storytelling by having
story-related dialogues with their child due to limited availability or
challenges in coming up with appropriate questions. While recent advances made
AI generation of questions from stories possible, the fully-automated approach
excludes parent involvement, disregards educational goals, and underoptimizes
for child engagement. Informed by need-finding interviews and participatory
design (PD) results, we developed StoryBuddy, an AI-enabled system for parents
to create interactive storytelling experiences. StoryBuddy's design highlighted
the need for accommodating dynamic user needs between the desire for parent
involvement and parent-child bonding and the goal of minimizing parent
intervention when busy. The PD revealed varied assessment and educational goals
of parents, which StoryBuddy addressed by supporting configuring question types
and tracking child progress. A user study validated StoryBuddy's usability and
suggested design insights for future parent-AI collaboration systems.


  In this paper, we propose a novel method that exploits music listening log
data for general-purpose music feature extraction. Despite the wealth of
information available in the log data of user-item interactions, it has been
mostly used for collaborative filtering to find similar items or users and was
not fully investigated for content-based music applications. We resolve this
problem by extending intra-domain knowledge distillation to cross-domain: i.e.,
by transferring knowledge obtained from the user-item domain to the music
content domain. The proposed system first trains the model that estimates log
information from the audio contents; then it uses the model to improve other
task-specific models. The experiments on various music classification and
regression tasks show that the proposed method successfully improves the
performances of the task-specific models.


  We consider parameter estimation of ordinary differential equation (ODE)
models from noisy observations. For this problem, one conventional approach is
to fit numerical solutions (e.g., Euler, Runge--Kutta) of ODEs to data.
However, such a method does not account for the discretization error in
numerical solutions and has limited estimation accuracy. In this study, we
develop an estimation method that quantifies the discretization error based on
data. The key idea is to model the discretization error as random variables and
estimate their variance simultaneously with the ODE parameter. The proposed
method has the form of iteratively reweighted least squares, where the
discretization error variance is updated with the isotonic regression algorithm
and the ODE parameter is updated by solving a weighted least squares problem
using the adjoint system. Experimental results demonstrate that the proposed
method attains robust estimation with at least comparable accuracy to the
conventional method by successfully quantifying the reliability of the
numerical solutions.


  We deal with stochastic differential equations with jumps. In order to obtain
an accurate approximation scheme, it is usual to replace the "small jumps" by a
Brownian motion. In this paper, we prove that for every fixed time $t$, the
approximate random variable $X^\varepsilon\_t$ converges to the original random
variable $X\_t$ in total variation distance and we estimate the error. We also
give an estimate of the distance between the densities of the laws of the two
random variables. These are done by using some integration by parts techniques
in Malliavin calculus.


  In this paper the gravity's rainbow function is derived in terms of a varying
speed of light (VSL) theory. We calculate the modified temperature, entropy and
heat capacity of the Schwarzschild, Kerr, AdS black holes, as well as spinning
black rings. Our results demonstrate that in rainbow gravity the behaviors of
various black holes have remarkably essential difference from those of standard
black holes near the Planck scale.


  The General Data Protection Regulation (GDPR) grants all natural persons the
right of access to their personal data if this is being processed by data
controllers. The data controllers are obliged to share the data in an
electronic format and often provide the data in a so called Data Download
Package (DDP). These DDPs contain all data collected by public and private
entities during the course of citizens' digital life and form a treasure trove
for social scientists. However, the data can be deeply private. To protect the
privacy of research participants while using their DDPs for scientific
research, we developed de-identification software that is able to handle
typical characteristics of DDPs such as regularly changing file structures,
visual and textual content, different file formats, different file structures
and accounting for usernames. We investigate the performance of the software
and illustrate how the software can be tailored towards specific DDP
structures.


  This study presents the analysis and design of a novel quasi-zero-stiffness
(QZS) based vibration sensor system for measuring absolute displacement of
vibrating platforms/objects. The sensor system is constructed by using positive
and negative-stiffness springs, which makes it possible to achieve an
equivalent QZS and consequently to create a broadband vibration-free point for
absolute displacement measurement in vibrating platforms. Theoretic analysis is
conducted for the analysis and design of the influence of structure parameters
on system measurement performance. A prototype is designed which can avoid the
drawback of instability in existing QZS systems with negative stiffness, and
the corresponding data-processing software is developed to fulfill time domain
and frequency domain measurements simultaneously. Both simulation and
experiment results verify the effectiveness of this novel sensor system.


  Spark gaps are likely the source of plasma in active black hole (BH)
magnetospheres. In this paper, we present results of 1D general-relativistic
particle-in-cell simulations of a starved BH magnetosphere with a realistic
treatment of inverse Compton scattering and pair production, for a broad range
of conditions, run times longer than in previous studies, and different setups.
We find that following the initial discharge, the system undergoes gradual
evolution over prolonged time until either, restoring the vacuum state or
reaching a state of quasi-periodic oscillations, depending on the spectral
shape and luminosity of the ambient radiation field. The oscillations occur
near the null charge surface in cases where the global magnetospheric current
is in the direction defined by the product of the asymptotic Goldreich-Julian
charge density and the radial velocity, while they occur near the boundary of
the simulation box when it is the opposite direction (return current). Their
amplitude and the resultant luminosity of TeV photons emitted from the gap
depend sensitively on the conditions; for the cases studied here the ratio of
TeV luminosity to the Blandford-Znajek power ranges from $10^{-5}$ to
$10^{-2}$, suggesting that strong flares may be generated by moderate changes
in disk emission. We also examined the dependence of the solution on the
initial number of particles per cell (PPC) and found convergence for PPC of
about 50 for the cases examined. At lower PPC values the pair multiplicity is
found to be artificially high, affecting the solution considerably.


  This paper aims to decompose a large dimensional vector autoregessive (VAR)
model into two components, the first one being generated by a small-scale VAR
and the second one being a white noise sequence. Hence, a reduced number of
common components generates the entire dynamics of the large system through a
VAR structure. This modelling, which we label as the dimension-reducible VAR,
extends the common feature approach to high dimensional systems, and it differs
from the dynamic factor model in which the idiosyncratic component can also
embed a dynamic pattern. We show the conditions under which this decomposition
exists. We provide statistical tools to detect its presence in the data and to
estimate the parameters of the underlying small-scale VAR model. Based on our
methodology, we propose a novel approach to identify the shock that is
responsible for most of the common variability at the business cycle
frequencies. We evaluate the practical value of the proposed methods by
simulations as well as by an empirical application to a large set of US
economic variables.


  Let kappa be the limit of <kappa_n : n<omega> (1) if each kappa_n carries an
extender of the length of the first Mahlo above kappa_n, then for every ld
above kappa there is a generic extension with power of kappa above ld. (2) if
each kappa_n carries an extender of the length of the first fixed point of the
aleph function above kappa_n of order n then for every ld between kappa and the
first inaccessible above kappa there is a generic extension satisfying
2^kappa>ld.


  Results are given on the measurements of the hard diffractive interactions at
HERA $ep$ collider. The structure of the diffractive exchange in terms of
partons and the factorisation properties are discussed, in particular by
comparing the QCD predictions for dijets and $D^*$ with measurements both in
the photo and electroproduction regimes.


  Convolutional neural networks are capable of learning powerful
representational spaces, which are necessary for tackling complex learning
tasks. However, due to the model capacity required to capture such
representations, they are often susceptible to overfitting and therefore
require proper regularization in order to generalize well. In this paper, we
show that the simple regularization technique of randomly masking out square
regions of input during training, which we call cutout, can be used to improve
the robustness and overall performance of convolutional neural networks. Not
only is this method extremely easy to implement, but we also demonstrate that
it can be used in conjunction with existing forms of data augmentation and
other regularizers to further improve model performance. We evaluate this
method by applying it to current state-of-the-art architectures on the
CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results
of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at
https://github.com/uoguelph-mlrg/Cutout


  Anisotropy in the point spread function (PSF) contributes a systematic error
to weak lensing measurements. In this study we use a ray tracer that
incorporates all the optical elements of the SNAP telescope to estimate this
effect. Misalignments in the optics generates PSF anisotropy, which we
characterize by its ellipticity. The effect of three time varying effects:
thermal drift, guider jitter, and structural vibration on the PSF are estimated
for expected parameters of the SNAP telescope. Multiple realizations of a
thousand square degree mock survey are then generated to include the systematic
error pattern induced by these effects. We quantify their contribution to the
power spectrum of the lensing shear. We find that the dominant effect comes
from the thermal drift, which peaks at angular wavenumbers l ~ 10^3, but its
amplitude is over one order of magnitude smaller than the size of the expected
statistical error. While there are significant uncertainties in our modeling,
our study indicates that time-varying PSFs will contribute at a smaller level
than statistical errors in SNAP's weak lensing measurements.


  Ensuring high-quality software requires considering the social climate within
which the applications will be deployed and used. This can be done by designing
quality goals and objectives that are consistent with changing social and
ethical landscapes. Using principles of technological determinism, this article
presents three cases that illustrate why it is becoming even more important to
integrate these concerns into software design and quality assurance. With these
examples in mind, this article explains how to consider technological
determinism in software design and quality assurance practices to achieve this
enhanced sensitivity on a practical level.


  The Hall-plateau width and the activation energy were measured in the bilayer
quantum Hall state at filling factor \nu=2, 1 and 2/3, by changing the total
electron density and the density ratio in the two quantum wells. Their behavior
are remarkably different from one to another. The \nu=1 state is found stable
over all measured range of the density difference, while the \nu=2/3$ state is
stable only around the balanced point. The \nu=2 state, on the other hand,
shows a phase transition between these two types of the states as the electron
density is changed.


  (a+c) dislocations in hexagonal materials are typically observed to be
dissociated into partial dislocations. Edge (a+c) dislocations are introduced
into (0001) nitride semiconductor layers by the process of plastic relaxation.
As there is an increasing interest in obtaining relaxed InGaN buffer layers for
the deposition of high In content structures, the study of the dissociation
mechanism of misfit (a+c) dislocations laying at the InGaN/GaN interface is
then crucial for understanding their nucleation and glide mechanisms. In the
case of the presented plastically relaxed InGaN layers deposited on GaN
substrates we observe a trigonal network of (a+c) dislocations extending at the
interface with a rotation of 3 degrees from <1-100> directions. High resolution
microscopy studies show that these dislocations are dissociated into two
Frank-Shockley 1/6<2-203> partial dislocations with the I1 BSF spreading
between them. Atomistic simulations of a dissociated edge (a+c) dislocation
revealed a 3/5 atom ring structure for the cores of both partial dislocations.
The observed separation between two partial dislocations must result from the
climb of at least one of the dislocations during the dissociation process,
possibly induced by the mismatch stress in the InGaN layer.


  Extending recent N=1 and N=2 results, we propose an explicit formula for the
integration measure on the moduli space of charge-n ADHM multi-instantons in
N=4 supersymmetric SU(2) gauge theory. As a consistency check, we derive a
renormalization group relation between the N=4, N=2, and N=1 measures. We then
use this relation to construct the purely bosonic (``N=0'') measure as well, in
the classical approximation in which the one-loop small-fluctuations
determinants is not included.


  To date, more than 100 giant Jupiter-like planets have been discovered in
Doppler surveys of solar-type stars. In this paper, we perform simulations to
investigate three systems: GJ 876, HD 82943 and 55 Cnc. The former two systems
both have a pair of planets in the 2:1 Mean Motion Resonance (MMR), while the
inner two companions of the later is close to 3:1 MMR. By integrating hundreds
of the planetary orbits of three systems for million years, we find that for GJ
876 and HD 82943, the critical argument $\lambda_{1} - 2\lambda_{2} +
\varpi_{1}$ and $\lambda_{1} - 2\lambda_{2} + \varpi_{2}$ librate about
$0^{\circ}$ or $180^{\circ}$, indicating 2:1 MMR can play an important role in
stabilizing the motion of the planets so that they are protected from frequent
close encounters. As for 55 Cnc, we further show the three resonant arguments
for 3:1 MMR execute librations for millions of years respectively, which
reveals the evidence of the resonance for this system. Additionally, we should
emphasize another vital mechanism is the apsidal phase-locking between a couple
of planets for a certain system. For GJ 876 and HD 82943, we discover the
relative apsidal longitudes $\varpi_{1} - \varpi_{2}$ move about $0^{\circ}$ or
$180^{\circ}$, respectively; but for 55 Cnc, we find that there exists an
asymmetric apsidal libration between two inner planets. Finally, we made a
brief discussion about the Habitable Zones in the exoplanetary systems.


  In this paper the zero modes of fluctuation of cylindrically symmetric
self-dual vortices are analyzed and described in full detail. These BPS
topological defects arise at the critical point between Type II and Type I
superconductors, or, equivalently, when the masses of the Higgs particle and
the vector boson in the Abelian Higgs model are equal. In addition, novel bound
states of Higss and vector bosons trapped by the self-dual vortices at their
core are found and investigated.


  The statistical counterpart of the formalism of hamiltonian systems with
convex dissipation arXiv:0810.1419 , arXiv:1408.3102 is a completely open
subject. Here are described a stochastic version of the SBEN principle and a
Liouville type theorem which uses a minimal dissipation cost functional.


  We present a numerical technique to compute the gravitational lensing induced
by simulated haloes. It relies on a 2D-Tree domain decomposition in the lens
plane combined with a description of N-Body particles as extended clouds with a
non-singular density. This technique is made fully adaptive by the use of a
density-dependent smoothing which allows one to probe the lensing properties of
haloes from the densest regions in the center or in substructures to the
low-density regions in the outskirts. 'Smooth Particle Lensing' has some
promising features. First, the deflection potential, the deflection angles, the
convergence and the shear are direct and separate end-products of the SPL
calculation and can be computed at an arbitrary distribution of points on the
lens plane. Second, this flexibility avoids the use of interpolation or a
finite differentiation procedure on a grid, does not require padding the region
with zeros and focuses the computing power on relevant regions. The SPL
algorithm is tested by populating isothermal spheres and ellipsoids with
particles and then comparing the lensing calculations to the classical
FFT-based technique and analytic solutions. We assess issues related to the
resolution of the lensing code and the limitations set by the simulations
themselves. We conclude by discussing how SPL can be used to predict the impact
of substructures on strong lensing and how it can be generalized to weak
lensing and cosmic shear simulations.


  The Anderson localization transition is one of the most well studied examples
of a zero temperature quantum phase transition. On the other hand, many open
questions remain about the phenomenology of disordered systems driven far out
of equilibrium. Here we study the localization transition in the prototypical
three-dimensional, noninteracting Anderson model when the system is driven at
its boundaries to induce a current carrying non-equilibrium steady state.
Recently we showed that the diffusive phase of this model exhibits extensive
mutual information of its non-equilibrium steady-state density matrix. We show
that that this extensive scaling persists in the entanglement and at the
localization critical point, before crossing over to a short-range (area-law)
scaling in the localized phase. We introduce an entanglement witness for
fermionic states that we name the mutual coherence, which, for fermionic
Gaussian states, is also a lower bound on the mutual information. Through a
combination of analytical arguments and numerics, we determine the finite-size
scaling of the mutual coherence across the transition. These results further
develop the notion of entanglement phase transitions in open systems, with
direct implications for driven many-body localized systems, as well as
experimental studies of driven-disordered systems.


  In this paper we construct an approximation algorithm for the Minimum Vertex
Cover Problem (Min-VC) with an expected approximation ratio of 2-f(beta) for
random Power Law Graphs (PLG) in the (alpha,beta)-model of Aiello et. al.,
where f(beta) is a strictly positive function of the parameter beta. We obtain
this result by combining the Nemhauser and Trotter approach for Min-VC with a
new deterministic rounding procedure which achieves an approximation ratio of
3/2 on a subset of low degree vertices for which the expected contribution to
the cost of the associated linear program is sufficiently large.


  A virtual Schottky group is a Kleinian group $K$ containing a Schottky group
$G$ as a finite index normal subgroup. These groups correspond to those groups
of automorphisms of closed Riemann surfaces which can be realized at the level
of their Schottky uniformizations. In this paper we provides a geometrical
structural decomposition of $K$ in the particular case when $K/G$ is an abelian
group.


  Using a particle-based model, we examine the collective dynamics of skyrmions
interacting with a funnel potential under dc driving as the skyrmion density
and relative strength of the Magnus and damping terms are varied. For driving
in the easy direction, we find that increasing the skyrmion density reduces the
average skyrmion velocity due to jamming of skyrmions near the funnel opening,
while the Magnus force causes skyrmions to accumulate on one side of the funnel
array. For driving in the hard direction, there is a critical skyrmion density
below which the skyrmions become trapped. Above this critical value, a clogging
effect appears with multiple depinning and repinning states where the skyrmions
can rearrange into different clogged configurations, while at higher drives,
the velocity-force curves become continuous. When skyrmions pile up near the
funnel opening, the effective size of the opening is reduced and the passage of
other skyrmions is blocked by the repulsive skyrmion-skyrmion interactions. We
observe a strong diode effect in which the critical depinning force is higher
and the velocity response is smaller for hard direction driving. As the ratio
of Magnus force to dissipative term is varied, the skyrmion velocity varies in
a non-linear and non-monotonic way due to the pile up of skyrmions on one side
of the funnels. At high Magnus forces, the clogging effect for hard direction
driving is diminished.


  Liouville conformal field theory (LCFT) is considered on a simply connected
domain with boundary, specializing to the case where the Liouville potential is
integrated only over the boundary of the domain. We work in the probabilistic
framework of boundary LCFT introduced by Huang-Rhodes-Vargas (2015). Building
upon the known proof of the bulk one-point function by the first author, exact
formulas are rigorously derived for the remaining basic correlation functions
of the theory, i.e., the bulk-boundary correlator, the boundary two-point and
the boundary three-point functions. These four correlations should be seen as
the fundamental building blocks of boundary Liouville theory, playing the
analogue role of the DOZZ formula in the case of the Riemann sphere. Our study
of boundary LCFT also provides the general framework to understand the
integrability of one-dimensional Gaussian multiplicative chaos measures as well
as their tail expansions. Finally these results have applications to studying
the conformal blocks of CFT and set the stage for the more general case of
boundary LCFT with both bulk and boundary Liouville potentials.


  This paper presents a novel framework for studying knotted and braided
configurations of optical fields, moving beyond the conventional Hopfion
solution based on the Hopf fibration. By employing the Seifert fibration, a
preferred framing is introduced to characterize the ``knottiness" of tubular
neighbourhoods of knots embedded in the 3-sphere. This approach yields a
specific presentation of Seifert surfaces and facilitates the description of
knotted optical fields, drawing on an enriched version of Ra\~{n}ada's
formulation. The relation between knots and braids enables one to explore the
connections between configurations generated experimentally through the
controlled over- and under-crossings of light beams, and concepts from
geometric group theory and algebraic topology. This framework, emphasizing the
significance of ``braided open books", sheds light on topological constraints
inherent in classes of braids suitable for representing interlaced optical
fields. These constraints primarily pertain to braids with $n\leq3$ strands --
aligning with existing experimental implementations -- and homogeneous braids
with any number of strands. This paper establishes a theoretical foundation for
understanding and manipulating knotted optical fields, suggesting potential
applications and avenues for further research in this domain.


  Training control policies in simulation is more appealing than on real robots
directly, as it allows for exploring diverse states in an efficient manner.
Yet, robot simulators inevitably exhibit disparities from the real-world
\rebut{dynamics}, yielding inaccuracies that manifest as the dynamical
simulation-to-reality (sim-to-real) gap. Existing literature has proposed to
close this gap by actively modifying specific simulator parameters to align the
simulated data with real-world observations. However, the set of tunable
parameters is usually manually selected to reduce the search space in a
case-by-case manner, which is hard to scale up for complex systems and requires
extensive domain knowledge. To address the scalability issue and automate the
parameter-tuning process, we introduce COMPASS, which aligns the simulator with
the real world by discovering the causal relationship between the environment
parameters and the sim-to-real gap. Concretely, our method learns a
differentiable mapping from the environment parameters to the differences
between simulated and real-world robot-object trajectories. This mapping is
governed by a simultaneously learned causal graph to help prune the search
space of parameters, provide better interpretability, and improve
generalization on unseen parameters. We perform experiments to achieve both
sim-to-sim and sim-to-real transfer, and show that our method has significant
improvements in trajectory alignment and task success rate over strong
baselines in several challenging manipulation tasks.


  Quantifying the response of marine mussel plaque attachment on wet surfaces
remains a significant challenge to a mechanistic understanding of plaque
adhesion. Here, we developed a customised microscopy system combined with
two-dimensional (2D) in-situ digital image correlation (DIC) to quantify the
in-plane deformation of a deformable substrate that interacts with a mussel
plaque while under directional tension. By analysing the strain field in the
substrate, we gained insight into how in-plane traction forces are transmitted
from the mussel plaque to the underlying substrate. Finite element (FE) models
were developed to assist the interpretation of the experimental measurement.
Our study revealed a synergistic effect of pulling angle and substrate
stiffness on plaque detachment, with mussel plaques anchoring to a 'stiff'
substrate at a smaller pulling angle having mechanical advantages with higher
load-bearing capacity and less plaque deformation. We identified two distinct
failure modes, i.e., shear traction-governed failure (STGF) mode and normal
traction-governed failure (NTGF). It was found that increasing the substrate
stiffness or reducing the pulling angle resulted in a failure mode change from
NTGF to STGF. Our findings offer new insights into the mechanistic
understanding of plaque and substrate interaction, which provides a general
plaque-inspired strategy for wet adhesion.


  At the High Energy Photon Source (HEPS) where up to 90 beamlines can be
provided in the future, minimisation of workload for individual beamlines and
maximisation of knowledge about one beamline that can be applied to other
beamlines is essential to minimise the total complexity in beamline control.
Presented in this paper are our efforts to achieve these goals by composing
relatively simple utilities and mechanisms to automate tasks, and always
remembering to keep our automation solutions simple and clear. After an
introduction to our choice of basic software in EPICS-based beamline control,
the issues encountered in introducing package management to EPICS modules, as
well as our solutions to them, are presented; then the design and
implementation of our packaging system is concisely discussed. After a
presentation of our efforts to reduce the need for self-built multi-device
EPICS IOC applications by providing reusable modular IOC executables, our
implementation of easily maintainable multi-IOC setups through the separation
and minimisation of each user's IOC configurations is given. Finally, the
ongoing project of comprehensive beamline services at HEPS to further simplify
configuration management on multiple scales, ranging from individual beamline
devices to all beamlines at HEPS, is introduced.


  We consider a local $U(1)_{B-L}$ extension of Zee-Babu model to explain the
recently observed 3.5 keV X-ray line signal. The model has three Standard model
(SM)-singlet Dirac fermions with different $U(1)_{B-L}$ charges. A complex
scalar field charged under $U(1)_{B-L}$ is introduced to break the $U(1)_{B-L}$
symmetry. After $U(1)_{B-L}$ symmetry breaking a remnant discrete symmetry
stabilizes the lightest state of the Dirac fermions, which can be a stable dark
matter (DM). The second lightest state, if mass splitting with the stable DM is
about 3.5 keV, decays dominantly to the stable DM and 3.5 keV photon through
two-loop diagrams, explaining the X-ray line signal. Two-loop suppression of
the decay amplitude makes its lifetime much longer than the age of the universe
and it can be a decaying DM candidate in large parameter region. We also
introduce a real scalar field which is singlet under both the SM and
$U(1)_{B-L}$ and can explain the current relic abundance of the Dirac fermionic
DMs. If the mixing with the SM Higgs boson is small, it does not contribute to
DM direct detection. The main contribution to the scattering of DM off atomic
nuclei comes from the exchange of $U(1)_{B-L}$ gauge boson, $Z'$, and is
suppressed below current experimental bound when $Z'$ mass is heavy ($\gtrsim
10$ TeV). If the singlet scalar mass is about 0.1-10 MeV, the DM
self-interaction can be large enough to solve small scale structure problems in
simulations with the cold DM, such as, the core-vs-cusp problem and
too-big-to-fail problem.


  We study the diagonalizability of the Atkin $U_t$-operator acting on Drinfeld
cusp forms for $\Gamma_0(t)$: starting with the slopes of eigenvalues and then
moving to the space of cusp forms for $\Gamma_1(t)$ to use Teitelbaum's
interpretation as harmonic cocycles which makes computations more explicit. We
prove $U_t$ is diagonalizable in odd characteristic for (relatively) small
weights and explicitly compute the eigenvalues. In even characteristic we show
that it is not diagonalizable when the weight is odd (except for the trivial
cases) and prove some cases of non diagonalizability in even weight as well. We
also formulate a few conjectures, supported by numerical search, about
diagonalizability of $U_t$ and the slopes of its eigenforms.


  In this paper we investigate the flexibility of matrix distributions for the
modeling of mortality. Starting from a simple Gompertz law, we show how the
introduction of matrix-valued parameters via inhomogeneous phase-type
distributions can lead to reasonably accurate and relatively parsimonious
models for mortality curves across the entire lifespan. A particular feature of
the proposed model framework is that it allows for a more direct interpretation
of the implied underlying aging process than some previous approaches.
Subsequently, towards applications of the approach for multi-population
mortality modeling, we introduce regression via the concept of proportional
intensities, which are more flexible than proportional hazard models, and we
show that the two classes are asymptotically equivalent. We illustrate how the
model parameters can be estimated from data by providing an adapted EM
algorithm for which the likelihood increases at each iteration. The practical
feasibility and competitiveness of the proposed approach, including the
right-censored case, are illustrated by several sets of mortality and survival
data.


  Contact-rich manipulation tasks in unstructured environments often require
both haptic and visual feedback. However, it is non-trivial to manually design
a robot controller that combines modalities with very different
characteristics. While deep reinforcement learning has shown success in
learning control policies for high-dimensional inputs, these algorithms are
generally intractable to deploy on real robots due to sample complexity. We use
self-supervision to learn a compact and multimodal representation of our
sensory inputs, which can then be used to improve the sample efficiency of our
policy learning. We evaluate our method on a peg insertion task, generalizing
over different geometry, configurations, and clearances, while being robust to
external perturbations. Results for simulated and real robot experiments are
presented.


  We introduce semantic form mid-tuning, an approach for transferring semantic
knowledge from semantic meaning representations into transformer-based language
encoders. In mid-tuning, we learn to align the text of general sentences -- not
tied to any particular inference task -- and structured semantic
representations of those sentences. Our approach does not require gold
annotated semantic representations. Instead, it makes use of automatically
generated semantic representations, such as from off-the-shelf PropBank and
FrameNet semantic parsers. We show that this alignment can be learned
implicitly via classification or directly via triplet loss. Our method yields
language encoders that demonstrate improved predictive performance across
inference, reading comprehension, textual similarity, and other semantic tasks
drawn from the GLUE, SuperGLUE, and SentEval benchmarks. We evaluate our
approach on three popular baseline models, where our experimental results and
analysis concludes that current pre-trained language models can further benefit
from structured semantic frames with the proposed mid-tuning method, as they
inject additional task-agnostic knowledge to the encoder, improving the
generated embeddings as well as the linguistic properties of the given model,
as evident from improvements on a popular sentence embedding toolkit and a
variety of probing tasks.


  In this letter, we present a study of the condensation of exciton-polaritons
in large etched pillar structures that exhibit shallow edge trapping. The
$\approx$ 100 $\mu$m $\times$ 100 $\mu$m pillars were fabricated using
photolithography and a BCl$_3$/Cl$_2$ reactive ion etch. A low energy region
emerged along the etched edge, with the minima $\approx$ 7 $\mu$m from the
outer edge. The depth of the trap was 0.5-1.5 meV relative to the level central
region, with the deepest trapping at the corners. We were able to produce a
Bose-Einstein condensate in the trap near the edges and corners by pumping
non-resonantly in the middle of the pillar. This condensate began as a set of
disconnected condensates at various points along the edges, but then became a
single mono-energetic condensate as the polariton density was increased.
Similar edge traps could be used to produce shallow 1D traps along edges or
other more complex traps using various etch geometries and scales.


  We consider a problem of finding a target located in a finite $d$-dimensional
domain, using $N$ independent random walkers, when partial information on the
target location is given as a probability distribution. When $N$ is large, the
first-passage time sensitively depends on the initial searcher distribution,
which invokes the question of what is the optimal searcher distribution that
minimizes the first-passage time. Here, we analytically derive the equation for
the optimal distribution and explore its limiting expressions. If the target
volume can be ignored, the optimal distribution is proportional to the target
distribution to the power of one-third. If we consider a target of a finite
volume and the probability of initial overlapping of searchers with the target
cannot be ignored in the large $N$ limit, the optimal distribution has a weak
dependence on the target distribution, given as a logarithm of the target
distribution. Using Langevin dynamics simulations, we numerically demonstrate
our predictions in one- and two-dimensions.


  In this paper, we propose a blockchain-based cold chain technology for
vaccine cooling track. The COVID-19 pandemic has caused the death of millions
of people. An important step towards ending the pandemic is vaccination.
Vaccines must be kept under control temperature during the whole process, from
fabrication to the hands of the health professionals who will immunize the
population. However, there are numerous reports of vaccine loss due to
temperature variations, and, currently, people getting vaccinated have no
control if their vaccine was kept safe. Blockchain is a technology solution
that can provide public and verifiable records. We review the World Health
Organization (WHO) cool chain and Blockchain technology. Moreover, we describe
current IoT temperature monitoring devices and propose Blockcoldchain to track
vaccine cold chain using blockchain, thus proving an unalterable vaccine
temperature history. Our experimental results using smart contracts demonstrate
the system's feasibility.


  The heavy-quark potential is calculated in the framework of gauge/gravity
duality using the large-D approximation, where D is the number of dimensions
transverse to the flux tube connecting a quark and an antiquark in a flat
D+2-dimensional spacetime. We find that in the large-D limit the leading
correction to the ground-state energy, as given by an effective Nambu-Goto
string, arises not from the heavy modes but from the behavior of the massless
modes in the vicinity of the quark and the antiquark. We estimate this
correction and find that it should be visible in the near-future lattice QCD
calculations of the heavy-quark potential.


  Experiments at the future colliders LHC and ILC will, for the first time,
explore electroweak reactions at energies in the TeV region, i.e. far beyond
the masses of the W and Z bosons. In exclusive processes without the emission
of massive gauge bosons double-logarithmic contributions, so-called Sudakov
logarithms, will then become relevant. Precise theoretical predictions require
the knowledge of the complete logarithmic two-loop corrections in the high
energy limit, thus exceeding the approximation through the leading powers of
logarithms known so far. This PhD thesis is focussed on the calculation of
analytic two-loop corrections to the form factor of an Abelian vector current.
It also studies with the help of evolution equations the consequences of these
results for electroweak cross sections of four-fermion processes. Contributions
from the Higgs boson and the mass difference between the W and the Z boson are
considered as well, and the factorization of the singular contributions from
the massless photon is demonstrated. The results represent theoretical two-loop
predictions for four-fermion processes in the accuracy needed for the precision
experiments at the ILC.


  Power is a RISC architecture developed by IBM, Freescale, and several other
companies and implemented in a series of POWER processors. The architecture
features a relaxed memory model providing very weak guarantees with respect to
the ordering and atomicity of memory accesses.
  Due to these weaknesses, some programs that are correct under sequential
consistency (SC) show undesirable effects when run under Power. We call these
programs not robust against the Power memory model. Formally, a program is
robust if every computation under Power has the same data and control
dependencies as some SC computation.
  Our contribution is a decision procedure for robustness of concurrent
programs against the Power memory model. It is based on three ideas. First, we
reformulate robustness in terms of the acyclicity of a happens-before relation.
Second, we prove that among the computations with cyclic happens-before
relation there is one in a certain normal form. Finally, we reduce the
existence of such a normal-form computation to a language emptiness problem.
Altogether, this yields a PSPACE algorithm for checking robustness against
Power. We complement it by a matching lower bound to show PSPACE-completeness.


  Motivated by the recent discovery of a compact object with mass in the range
$2.5{-}2.67\, \mathrm{M}_\odot$ in the binary merger GW190814, we revisit the
question of the maximum mass of neutron stars (NSs). We use a Markov Chain
Monte Carlo approach to generate about 2 million phenomenological equations of
state with and without first order phase transitions. We fix the crust equation
of state and only assume causality at higher densities. We show how a strict
upper bound on the maximum NS mass can be inferred from upcoming observation of
NS radii and masses. The derived upper bounds depend only on relativity and
causality, so it is not affected by nuclear physics uncertainties. We show how
a lower limit on the maximum mass of NSs, in combination with upcoming
measurements of NS radii by LIGO/Virgo and NICER, would constrain the equation
of state of dense matter. Finally, we discuss the implications for GW190814.


  We present a parton-level study of electro-weak production of vector-boson
pairs at the Large Hadron Collider, establishing the sensitivity to a set of
dimension-six operators in the Standard Model Effective Field Theory (SMEFT).
Different final states are statistically combined, and we discuss how the
orthogonality and interdependence of different analyses must be considered to
obtain the most stringent constraints. The main novelties of our study are the
inclusion of SMEFT effects in non-resonant diagrams and in irreducible QCD
backgrounds, and an exhaustive template analysis of optimal observables for
each operator and process considered. We also assess for the first time the
sensitivity of vector-boson-scattering searches in semileptonic final states.


  Revised 1977 seminar handout on Quillen's proof of the 1976 Quillen-Suslin
theorem -- that all finitely generated, projective $k[x_1,\ldots,x_n]$-modules
are free. This particular write-up is based on arguments of Quillen, Paul
Roberts, Vaserstein, and Moshe Roitman -- arguments that are here translated
into the language of idempotent matrices, which seems rather natural.


  If spin liquids have been famously defined by what they are not, i.e.
ordered, the past years have seen the frontier between order and spin liquid
starting to fade, with a growing number of materials whose low-temperature
physics cannot be explained without co-existence of (partial) magnetic order
and spin fluctuations. Here we study an example of such co-existence in the
presence of magnetic dipolar interactions, related to spin ice, where the order
is long range and the fluctuations support a Coulomb gauge field. Topological
defects are effectively coupled via energetic and entropic Coulomb
interactions, the latter one being stronger than for the spin-ice ground state.
Depending on whether these defects break the divergence-free condition of the
Coulomb gauge field or the long-range order, they are respectively categorized
as monopoles - as in spin ice - or monopole holes, in analogy with electron
holes in semiconductors. The long-range order plays the role of a
fully-occupied valence band, while the Coulomb spin liquid can be seen as an
empty conducting band. These results are discussed in the context of other
lattices and models which support a similar co-existence of Coulomb gauge field
and long-range order. We conclude this work by explaining how dipolar
interactions lift the spin liquid degeneracy at very low energy scale by
maximizing the number of flippable plaquettes, in light of the equivalent
quantum dimer model.


  Engineered dissipative reservoirs have the potential to steer many-body
quantum systems toward correlated steady states useful for quantum simulation
of high-temperature superconductivity or quantum magnetism. Using up to 49
superconducting qubits, we prepared low-energy states of the transverse-field
Ising model through coupling to dissipative auxiliary qubits. In one dimension,
we observed long-range quantum correlations and a ground-state fidelity of 0.86
for 18 qubits at the critical point. In two dimensions, we found mutual
information that extends beyond nearest neighbors. Lastly, by coupling the
system to auxiliaries emulating reservoirs with different chemical potentials,
we explored transport in the quantum Heisenberg model. Our results establish
engineered dissipation as a scalable alternative to unitary evolution for
preparing entangled many-body states on noisy quantum processors.


  An even (resp. odd) lollipop is the coalescence of a cycle of even (resp.
odd) length and a path with pendant vertex as distinguished vertex. It is known
that the odd lollipop is determined by its spectrum and the question is asked
by W. Haemers, X. Liu and Y. Zhang for the even lollipop. We revisit the proof
for odd lollipop, generalize it for even lollipop and therefore answer the
question. Our proof is essentially based on a method of counting closed walks.


  R. Kaufman and M. Tsujii proved that the Fourier transform of self-similar
measures has a power decay outside of a sparse set of frequencies. We present a
version of this result for homogeneous self-similar measures, with quantitative
estimates, and derive several applications: (1) non-linear smooth images of
homogeneous self-similar measures have a power Fourier decay, (2) convolving
with a homogeneous self-similar measure increases correlation dimension by a
quantitative amount, (3) the dimension and Frostman exponent of (biased)
Bernoulli convolutions tend to $1$ as the contraction ratio tends to $1$, at an
explicit quantitative rate.


  In this paper, we study backward stochastic differential equations (BSDEs
shortly) with jumps that have Lipschitz generator in a general filtration
supporting a Brownian motion and an independent Poisson random measure. Under
just integrability on the data we show that such equations admits a unique
solution which belongs to class D.