  Doped SrTiO3 becomes a metal at extremely low doping concentrations n and is
even superconducting with the superconducting transition temperature adopting a
dome-like shape with the carrier concentration. It is shown here within the
polarizability model that up to a well-defined carrier concentration transverse
optic mode softening takes place together with polar nano-domain formation
which evidences inhomogeneity and a two-component type behavior with
metallicity coexisting with polarity. Beyond this region a conventional metal
is formed where superconductivity as well as mode softening is absent. For this
regime the effective electron-phonon coupling follows the superconducting
transition temperature. Effusion measurements as well as macroscopic and
nanoscopic conductivity measurements indicate that the distribution of oxygen
vacancies is local and inhomogeneous from which it is concluded that
metallicity stems from filaments which are embedded in a polar matrix as long
as the carrier concentration is less than the critical one.


  Structured illumination can reject out-of-focus signal from a sample,
enabling high-speed and high-contrast imaging over large areas with widefield
detection optics. Currently, this optical-sectioning technique is limited by
image reconstruction artefacts and the need for sequential imaging of multiple
colour channels. We combine multicolour interferometric pattern generation with
machine-learning processing, permitting high-contrast, real-time reconstruction
of image data. The method is insensitive to background noise and unevenly
phase-stepped illumination patterns. We validate the method in silico and
demonstrate its application on diverse specimens, ranging from fixed and live
biological cells to synthetic biosystems, imaging at up to 37 Hz across a 44 x
44 $\mu m^2$ field of view.


  One of the remaining challenges in reinforcement learning is to develop
agents that can generalise to novel scenarios they might encounter once
deployed. This challenge is often framed in a multi-task setting where agents
train on a fixed set of tasks and have to generalise to new tasks. Recent work
has shown that in this setting increased exploration during training can be
leveraged to increase the generalisation performance of the agent. This makes
sense when the states encountered during testing can actually be explored
during training. In this paper, we provide intuition why exploration can also
benefit generalisation to states that cannot be explicitly encountered during
training. Additionally, we propose a novel method Explore-Go that exploits this
intuition by increasing the number of states on which the agent trains.
Explore-Go effectively increases the starting state distribution of the agent
and as a result can be used in conjunction with most existing on-policy or
off-policy reinforcement learning algorithms. We show empirically that our
method can increase generalisation performance in an illustrative environment
and on the Procgen benchmark.


  We investigate the dynamics of a single, overdamped colloidal particle, which
is driven by a constant force through a one-dimensional periodic potential. We
focus on systems with large barrier heights where the lowest-order cumulants of
the density field, that is, average position and the mean-squared displacement,
show nontrivial (non-diffusive) short-time behavior characterized by the
appearance of plateaus. We demonstrate that this "cage-like" dynamics can be
well described by a discretized master equation model involving two states
(related to two positions) within each potential valley. Non-trivial
predictions of our approach include analytic expressions for the plateau
heights and an estimate of the "de-caging time" obtained from the study of
deviations from Gaussian behaviour. The simplicity of our approach means that
it offers a minimal model to describe the short-time behavior of systems with
hindered dynamics.


  We prove that if a linear equation, whose coefficients are continuous
rational functions on a nonsingular real algebraic surface, has a continuous
solution, then it also has a continuous rational solution. This is known to
fail in higher dimensions.


  The global fit of the Standard Model to electroweak precision data, routinely
performed by the LEP electroweak working group and others, demonstrated
impressively the predictive power of electroweak unification and quantum loop
corrections. We have revisited this fit in view of (i) the development of the
new generic fitting package Gfitter, (ii) the insertion of constraints from
direct Higgs searches at LEP and the Tevatron, and (iii) a more thorough
statistical interpretation of the results. Gfitter is a modular fitting
toolkit, which features predictive theoretical models as independent plugins,
and a statistical analysis of the fit results using toy Monte Carlo techniques.
The state-of-the-art electroweak Standard Model is fully implemented, as well
as generic extensions to it. This paper introduces the Gfitter project, and
presents state-of-the-art results for the global electroweak fit in the
Standard Model, and for a model with an extended Higgs sector (2HDM). Numerical
and graphical results for fits with and without including the constraints from
the direct Higgs searches at LEP and Tevatron are given. Perspectives for
future colliders are analysed and discussed. Including the direct Higgs
searches, we find M_H=(116.4 +18.3 -1.3) GeV, and the 2sigma and 3sigma allowed
regions [114,145] GeV and [[113,168] and [180,225]] GeV, respectively. For the
strong coupling strength at fourth perturbative order we obtain
alpha_S(M_Z)=0.1193 +0.0028 -0.0027(exp) +- 0.0001(theo). Finally, for the mass
of the top quark, excluding the direct measurements, we find m_t=(178.2 +9.8
-4.2) GeV. In the 2HDM we exclude a charged-Higgs mass below 240 GeV at 95%
confidence level. This limit increases towards larger tan(beta), where e.g.,
M_H+-<780 GeV is excluded for tan(beta)=70.


  We present the submission of Samsung AI Centre Cambridge to the CVPR2020
EPIC-Kitchens Action Recognition Challenge. In this challenge, action
recognition is posed as the problem of simultaneously predicting a single
`verb' and `noun' class label given an input trimmed video clip. That is, a
`verb' and a `noun' together define a compositional `action' class. The
challenging aspects of this real-life action recognition task include small
fast moving objects, complex hand-object interactions, and occlusions. At the
core of our submission is a recently-proposed spatial-temporal video attention
model, called `W3' (`What-Where-When') attention~\cite{perez2020knowing}. We
further introduce a simple yet effective contextual learning mechanism to model
`action' class scores directly from long-term temporal behaviour based on the
`verb' and `noun' prediction scores. Our solution achieves strong performance
on the challenge metrics without using object-specific reasoning nor extra
training data. In particular, our best solution with multimodal ensemble
achieves the 2$^{nd}$ best position for `verb', and 3$^{rd}$ best for `noun'
and `action' on the Seen Kitchens test set.


  The Fermi Gamma-ray Space Telescope has greatly expanded the number and
energy window of observations of gamma-ray bursts (GRBs). However, the coarse
localizations of tens to a hundred square degrees provided by the Fermi GRB
Monitor instrument have posed a formidable obstacle to locating the bursts'
host galaxies, measuring their redshifts, and tracking their panchromatic
afterglows. We have built a target-of-opportunity mode for the intermediate
Palomar Transient Factory in order to perform targeted searches for Fermi
afterglows. Here, we present the results of one year of this program: 8
afterglow discoveries out of 35 searches. Two of the bursts with detected
afterglows (GRBs 130702A and 140606B) were at low redshift (z=0.145 and 0.384
respectively) and had spectroscopically confirmed broad-line Type Ic
supernovae. We present our broadband follow-up including spectroscopy as well
as X-ray, UV, optical, millimeter, and radio observations. We study possible
selection effects in the context of the total Fermi and Swift GRB samples. We
identify one new outlier on the Amati relation. We find that two bursts are
consistent with a mildly relativistic shock breaking out from the progenitor
star, rather than the ultra-relativistic internal shock mechanism that powers
standard cosmological bursts. Finally, in the context of the Zwicky Transient
Facility, we discuss how we will continue to expand this effort to find optical
counterparts of binary neutron star mergers that may soon be detected by
Advanced LIGO and Virgo.


  Strong decays of X(3915) and X(4350) have been studied by assuming them as
P-wave charmonium states. We estimate the {\Gamma}(X(3915) \to
J/{\psi}{\omega}) and {\Gamma}(X(4350) \to J/{\psi}\phi) via D(*)D(*)open-charm
intermediate states. Our calculation supports that the assignment of X(4350) to
the charmonium state while the assignment of X(3915) is disfavored.


  In this paper, a distributed tracking control scheme with distributed
estimators has been developed for a leader-follower multi-agent system with
measurement noises and directed interconnection topology. It is supposed that
each follower can only measure relative positions of its neighbors in a noisy
environment, including the relative position of the second-order active leader.
A neighbor-based tracking protocol together with distributed estimators is
designed based on a novel velocity decomposition technique. It is shown that
the closed loop tracking control system is stochastically stable in mean square
and the estimation errors converge to zero in mean square as well. A simulation
example is finally given to illustrate the performance of the proposed control
scheme.


  Depression is debilitating, and not uncommon. Indeed, studies of excessive
social media users show correlations with depression, ADHD, and other mental
health concerns. Given that there is a large number of people with excessive
social media usage, then there is a significant population of potentially
undiagnosed users and posts that they create. In this paper, we propose a
depression severity detection system using a semi-supervised learning technique
to predict if a post is from a user who is experiencing severe, moderate, or
low (non-diagnostic) levels of depression. Namely, we use a trained model to
classify a large number of unlabelled social media posts from Reddit, then use
these generated labels to train a more powerful classifier. We demonstrate our
framework on Detecting Signs of Depression from Social Media Text -
LT-EDI@RANLP 2023 shared task, where our framework ranks 3rd overall.


  In a paper by Umarov, Tsallis and Steinberg (2008), a generalization of the
Fourier transform, called the $q$-Fourier transform, was introduced and applied
for the proof of a $q$-generalized central limit theorem ($q$-CLT).
Subsequently, Hilhorst illustrated (2009 and 2010) that the $q$-Fourier
transform for $q>1$ is not invertible in the space of density functions.
Indeed, using an invariance principle, he constructed a family of densities
with the same $q$-Fourier transform and noted that "as a consequence, the
$q$-central limit theorem falls short of achieving its stated goal". The
distributions constructed there have compact support. We prove now that the
limit distribution in the $q$-CLT is unique and can not have a compact support.
This result excludes all the possible counterexamples which can be constructed
using the invariance principle and fills the gap mentioned by Hilhorst.


  It has been suggested recently to study the dynamics of a gravitating gluon
condensate q in the context of a spatially flat Friedmann-Robertson-Walker
universe. The expansion of the Universe (or, more generally, the presence of a
nonvanishing Ricci curvature scalar R) perturbs the gluon condensate and may
induce a nonanalytic term \tilde{h}(R,q) in the effective gravitational action.
The aim of this article is to explore the cosmological implications of a
particular nonanalytic term \tilde{h} \propto \eta |R|^{1/2} |q|^{3/4}. With a
quadratic approximation of the gravitating gluon-condensate vacuum energy
density \rho_{V}(q) near the equilibrium value q_{0} and a small coupling
constant \eta of the modified-gravity term \tilde{h}, an "accelerating
universe" is obtained which resembles the present Universe, both qualitatively
and quantitatively. The unknown component X of this model universe (here,
primarily due to modified-gravity effects) has an effective equation-of-state
parameter \bar{w}_{X} which is found to evolve toward the value -1 from above.


  This work presents comprehensive energy management and in-depth energy
footprint analysis of an electrified strong parallel commercial vehicle. We use
the PS3 framework, validated real-world powertrain system models, and
Pareto-optimal analysis to optimize fuel consumption and harmful pollutant
emissions. The approach involves dynamic optimization of 13 states and 4
control levers with complex interactions between multiple subsystems for a
parallel hybrid electric pick-up and delivery truck. These subsystems exhibit
thermal, electrical, and mechanical dynamics at different time scales, and
contain kinematic and combinatorial constraints, integer- and real-valued
variables, interpolated look-up tables, and data maps. A Pareto-optimal
solution is found by carefully optimizing fuel and NOx emissions to understand
the energy footprint of the electrified powertrain. The presented results
exhibit rich analysis and complex interactions among the powertrain subsystems
to unearth a 7% improvement in its fuel consumption and 29% pollutant NOx
reduction when compared to solution from a coarsely modeled powertrain system.


  Transmission of the scalar field through the random medium, represented by
the system of randomly distributed dielectric cylinders is calculated
numerically. System is mapped to the problem of electronic transport in
disordered two-dimensional systems. Universality of the statistical
distribution of transmission parameters is analyzed in the metallic and in the
localized regimes.In the metallic regime the universality of the transmission
statistics in all transparent channels is observed. In the band gaps, we
distinguish the disorder induced (Anderson) localization from the tunneling
through the system due to the gap in the density of states. We show also that
absorption causes rapid decrease of the mean conductance, but, contrary to the
localized regime, the conductance is self-averaged with a
  Gaussian distribution.


  We carefully develop the framework required to model the dynamical tidal
response of a spinning neutron star in an inspiralling binary system, in the
context of Newtonian gravity, making sure to include all relevant details and
connections to the existing literature. The tidal perturbation is decomposed in
terms of the normal oscillation modes, used to derive an expression for the
effective Love number which is valid for any rotation rate. In contrast to
previous work on the problem, our analysis highlights subtle issues relating to
the orthogonality condition required for the mode-sum representation of the
dynamical tide and shows how the prograde and retrograde modes combine to
provide the overall tidal response. Utilising a slow-rotation expansion, we
show that the dynamical tide (the effective Love number) is corrected at first
order in rotation, whereas in the case of the static tide (the static Love
number) the rotational corrections do not enter until second order.


  Since the introduction of the lasso in regression, various sparse methods
have been developed in an unsupervised context like sparse principal component
analysis (s-PCA), sparse canonical correlation analysis (s-CCA) and sparse
singular value decomposition (s-SVD). These sparse methods combine feature
selection and dimension reduction. One advantage of s-PCA is to simplify the
interpretation of the (pseudo) principal components since each one is expressed
as a linear combination of a small number of variables. The disadvantages lie
on the one hand in the difficulty of choosing the number of non-zero
coefficients in the absence of a well established criterion and on the other
hand in the loss of orthogonality for the components and/or the loadings. In
this paper we propose sparse variants of correspondence analysis (CA)for large
contingency tables like documents-terms matrices used in text mining, together
with pPMD, a deation technique derived from projected deflation in s-PCA. We
use the fact that CA is a double weighted PCA (for rows and columns) or a
weighted SVD, as well as a canonical correlation analysis of indicator
variables. Applying s-CCA or s-SVD allows to sparsify both rows and columns
weights. The user may tune the level of sparsity of rows and columns and
optimize it according to some criterium, and even decide that no sparsity is
needed for rows (or columns) by relaxing one sparsity constraint. The latter is
equivalent to apply s-PCA to matrices of row (or column) profiles.


  In this paper we derive a representation of the deterministic 3-dimensional
Navier-Stokes equations based on stochastic Lagrangian paths. The particle
trajectories obey SDEs driven by a uniform Wiener process; the inviscid Weber
formula for the Euler equations of ideal fluids is used to recover the velocity
field. This method admits a self-contained proof of local existence for the
nonlinear stochastic system, and can be extended to formulate stochastic
representations of related hydrodynamic-type equations, including viscous
Burgers equations and LANS-alpha models.


  People are witnessing quantum computing revolutions nowadays. Progress in the
number of qubits, coherence times and gate fidelities are happening. Although
quantum error correction era has not arrived, the research and development of
quantum computing have inspired insights and breakthroughs in quantum
technologies, both in theories and in experiments. In this review, we introduce
the basic principles of quantum computing and the multilayer architecture for a
quantum computer. There are different experimental platforms for implementing
quantum computing. In this review, based on a mature experimental platform, the
Nuclear Magnetic Resonance (NMR) platform, we introduce the basic steps to
experimentally implement quantum computing, as well as common challenges and
techniques.


  One-sided matching mechanisms are fundamental for assigning a set of
indivisible objects to a set of self-interested agents when monetary transfers
are not allowed. Two widely-studied randomized mechanisms in multiagent
settings are the Random Serial Dictatorship (RSD) and the Probabilistic Serial
Rule (PS). Both mechanisms require only that agents specify ordinal preferences
and have a number of desirable economic and computational properties. However,
the induced outcomes of the mechanisms are often incomparable and thus there
are challenges when it comes to deciding which mechanism to adopt in practice.
In this paper, we first consider the space of general ordinal preferences and
provide empirical results on the (in)comparability of RSD and PS. We analyze
their respective economic properties under general and lexicographic
preferences. We then instantiate utility functions with the goal of gaining
insights on the manipulability, efficiency, and envyfreeness of the mechanisms
under different risk-attitude models. Our results hold under various preference
distribution models, which further confirm the broad use of RSD in most
practical applications.


  In this work, we present an early prototype of NeVer 2.0, a new system for
automated synthesis and analysis of deep neural networks.NeVer 2.0borrows its
design philosophy from NeVer, the first package that integrated learning,
automated verification and repair of (shallow) neural networks in a single
tool. The goal of NeVer 2.0 is to provide a similar integration for deep
networks by leveraging a selection of state-of-the-art learning frameworks and
integrating them with verification algorithms to ease the scalability challenge
and make repair of faulty networks possible.


  We study the absorption probability and Hawking radiation of the scalar field
in the rotating black holes on codimension-2 branes. We find that finite brane
tension modifies the standard results in Hawking radiation if compared with the
case when brane tension is completely negligible. We observe that the rotation
of the black hole brings richer physics. Nonzero angular momentum triggers the
super-radiance which becomes stronger when the angular momentum increases. We
also find that rotations along different angles influence the result in
absorption probability and Hawking radiation. Compared with the black hole
rotating orthogonal to the brane, in the background that black hole spins on
the brane, its angular momentum brings less super-radiance effect and the brane
tension increases the range of frequency to accommodate super-radiance. These
information can help us know more about the rotating codimension-2 black holes.


  The discussion on time-reversal in quantum mechanics exists at least since
Wigner's ``Uber die Operation der Zeitumkehr in der Quantenmechanik'' paper in
1932. If and how the dynamics of the quantum world is time-reversible has been
the subject of many controversies. Some have seen quantum mechanics as
fundamentally time-irreversible, see for example von Neumann, and some have
seen in that the ultimate cause of time's arrow and second law behavior. In his
best-selling book ``The Emperor's New Mind'', Roger Penrose argues similarly
and concludes that ``our sought-for quantum gravity must be a time-asymmetric
theory'' [p.351]. Not so long ago, we read yet about another project in
Physicalia 26: to extend quantum mechanics into new fundamentally irreversible
equations, thus proposing a new theory giving ``... une description
fondamentale irreversible de tout systeme physique''.


  We extend a recently derived higher-dimensional Cardy formula to include
angular momenta, which we use to obtain the Bekensten-Hawking entropy of AdS
black branes, compactified rotating branes, and large Schwarzschild/Kerr black
holes. This is the natural generalization of Strominger's microscopic
derivation of the BTZ black hole entropy to higher dimensions. We propose an
extension to include $U(1)$ charge, which agrees with the Bekenstein-Hawking
entropy of large Reissner-Nordstrom/Kerr-Newman black holes at high
temperature. We extend the results to arbitrary hyperscaling violation exponent
(this captures the case of black D$p$-branes as a subclass) and reproduce
logarithmic corrections.


  The majority game, modelling a system of heterogeneous agents trying to
behave in a similar way, is introduced and studied using methods of statistical
mechanics. The stationary states of the game are given by the (local) minima of
a particular Hopfield like hamiltonian. On the basis of a replica symmetric
calculations, we draw the phase diagram, which contains the analog of a
retrieval phase. The number of metastable states is estimated using the
annealed approximation. The results are confronted with extensive numerical
simulations.


  We discuss the time-dependent formulation of perturbation theory in the
context of the interacting zeroth-order Hamiltonians that appear in
multi-reference situations. As an example, we present a time-dependent
formulation and implementation of second-order n-electron valence perturbation
theory. The resulting t-NEVPT2 method yields the fully uncontracted n-electron
valence perturbation wavefunction and energy, but has a lower computational
scaling than the usual contracted variants, and also avoids the construction of
high-order density matrices and the diagonalization of metrics. We present
results of t-NEVPT2 for the water, nitrogen, carbon, and chromium molecules,
and outline directions for the future.


  Assessing whether an agent has abandoned a goal or is actively pursuing it is
important when multiple agents are trying to achieve joint goals, or when
agents commit to achieving goals for each other. Making such a determination
for a single goal by observing only plan traces is not trivial as agents often
deviate from optimal plans for various reasons, including the pursuit of
multiple goals or the inability to act optimally. In this article, we develop
an approach based on domain independent heuristics from automated planning,
landmarks, and fact partitions to identify sub-optimal action steps - with
respect to a plan - within a plan execution trace. Such capability is very
important in domains where multiple agents cooperate and delegate tasks among
themselves, e.g. through social commitments, and need to ensure that a
delegating agent can infer whether or not another agent is actually progressing
towards a delegated task. We demonstrate how an agent can use our technique to
determine - by observing a trace - whether an agent is honouring a commitment.
We empirically show, for a number of representative domains, that our approach
infers sub-optimal action steps with very high accuracy and detects commitment
abandonment in nearly all cases.


  Hierarchically structured agent plans are important for efficient planning
and acting, and they also serve (among other things) to produce "richer"
classical plans, composed not just of a sequence of primitive actions, but also
"abstract" ones representing the supplied hierarchies. A crucial step for this
and other approaches is deriving precondition and effect "summaries" from a
given plan hierarchy. This paper provides mechanisms to do this for more
pragmatic and conventional hierarchies than in the past. To this end, we
formally define the notion of a precondition and an effect for a hierarchical
plan; we present data structures and algorithms for automatically deriving this
information; and we analyse the properties of the presented algorithms. We
conclude the paper by detailing how our algorithms may be used together with a
classical planner in order to obtain abstract plans.


  Extending and unifying concepts extensively used in the literature, we
introduce the notion of approximable interpolation sets for algebras of
functions on locally compact groups, especially for weakly almost periodic
functions and for uniformly continuous functions. We characterize approximable
interpolation sets both in combinatorial terms and in terms of the
$\mathscr{LUC}$- and $\mathscr{WAP}$-compactifications and analyze some of
their properties.


  We show how three-dimensional superconformal theories for any number N <= 8
of supersymmetries can be obtained by taking a conformal limit of the
corresponding three-dimensional gauged supergravity models. The superconformal
theories are characterized by an embedding tensor that satisfies a linear and
quadratic constraint. We analyze these constraints and give the general
solutions for all cases. We find new N = 4,5 superconformal theories based on
the exceptional Lie superalgebras F(4), G(3) and D(2|1;\alpha). Using the
supergravity connection we discuss which massive deformations to expect. As an
example we work out the details for the case of N = 6 supersymmetry.


  We describe a practical algorithm for computing normal forms for semigroups
and monoids with finite presentations satisfying so-called small overlap
conditions. Small overlap conditions are natural conditions on the relations in
a presentation, which were introduced by J. H. Remmers and subsequently studied
extensively by M. Kambites. Presentations satisfying these conditions are
ubiquitous; Kambites showed that a randomly chosen finite presentation
satisfies the $C(4)$ condition with probability tending to 1 as the sum of the
lengths of relation words tends to infinity. Kambites also showed that several
key problems for finitely presented semigroups and monoids are tractable in
$C(4)$ monoids: the word problem is solvable in $O(\min\{|u|, |v|\})$ time in
the size of the input words $u$ and $v$; the uniform word problem for $\langle
A|R\rangle$ is solvable in $O(N ^ 2 \min\{|u|, |v|\})$ where $N$ is the sum of
the lengths of the words in $R$; and a normal form for any given word $u$ can
be found in $O(|u|)$ time. Although Kambites' algorithm for solving the word
problem in $C(4)$ monoids is highly practical, it appears that the coefficients
in the linear time algorithm for computing normal forms are too large in
practice.
  In this paper, we present an algorithm for computing normal forms in $C(4)$
monoids that has time complexity $O(|u| ^ 2)$ for input word $u$, but where the
coefficients are sufficiently small to allow for practical computation.
Additionally, we show that the uniform word problem for small overlap monoids
can be solved in $O(N \min\{|u|, |v|\})$ time.


  Solidification pattern during nonequilibrium crystallization is among the
most important microstructures in the nature and technical realms. Phase field
crystal (PFC) model could simulate the pattern formation during equilibrium
crystallization at atom scale, but cannot grasp the nonequilibrium ones due to
the absence of proper elastic-relaxation time scale. In this work, we propose a
minimal classical-density-functional-theory-based model for crystal growth in
supercooled liquid. Growth front nucleation (GFN) and various nonequilibrium
patterns, including the faceting growth, spherulite, dendrite and the
columnar-to-equiaxed transition (CET) among others, are grasped at atom scale.
It is amazing that, except for undercooling and seed spacing, seed distribution
is key factor that determines the CET. Overall, two-stage growth process, i.e.,
the diffusion-controlled growth and the GFN-dominated growth, are identified.
But, compared with the second stage, the first stage becomes too short to be
noticed under the high undercooling. The distinct feature for the second stage
is the dramatic increments of dislocations, which explains the amorphous
nucleation precursor in the supercooled liquid. Transition time between the two
stages at different undercooling are investigated. Crystal growth of BCC
structure further confirms our conclusions.


  It has recently been shown how to break SO(10) down to the Standard Model in
a realistic way with only one adjoint Higgs. The expectation value of this
adjoint must point in the B-L direction. This has consequences for the possible
form of the quark and lepton mass matrices. These consequences are explored in
this paper, and it is found that one is naturally led to consider a particular
form for the masses of the heavier generations. This form implies typically
that there should be large (nearly maximal) mixing of the mu- and
tau-neutrinos. An explanation that does not involve large tan beta also emerges
for the fact that b and tau are light compared to the top quark.


  Cryogenic detectors have a long history of success in the field of rare event
searches. In particular scintillating calorimeters are very suitable detectors
for this task since they provide particle discrimination: the simultaneous
detection of the thermal and the light signal produced by a particle
interaction in scintillating crystals allows to identify the nature of
particle, as the light yield depends thereon. We investigate the performance of
two large CsI (undoped) crystals ($\sim$122 g each) operated as scintillating
calorimeters at milli-Kelvin temperatures in terms of calorimetric properties
and background rejection capabilities. Furthermore, we discuss the feasibility
of this detection approach towards a background-free future dark matter
experiment based on alkali halides crystals, with active particle
discrimination via the two-channel detection.


  This is an exposition of homotopical results on the geometric realization of
semi-simplicial spaces. We then use these to derive basic foundational results
about classifying spaces of topological categories, possibly without units. The
topics considered include: fibrancy conditions on topological categories; the
effect on classifying spaces of freely adjoining units; approximate notions of
units; Quillen's Theorems A and B for non-unital topological categories; the
effect on classifying spaces of changing the topology on the space of objects;
the Group-Completion Theorem.


  Excitation of two identical species in a cluster by the absorption of two
photons of the same energy is strongly suppressed since the excitation of one
subunit blocks the excitation of the other one due to the binding Coulomb
interaction. Here, we propose a very efficient way to overcome this blockade in
producing doubly-excited homoatomic clusters by a single intense laser pulse.
For Ne$_2$ it is explicitly demonstrated that the optimal carrier frequency of
the pulse is given by half of the energy of the target state, which allows one
to doubly excite more than half of the dimers at moderate field intensities.
These dimers then undergo ultrafast interatomic decay bringing one Ne to its
ground state and ionizing the other one. The reported \emph{ab initio} electron
spectra present reliable predictions for future experiments by strong laser
pulses.


  We determine the border ranks of tensors that could potentially advance the
known upper bound for the exponent $\omega$ of matrix multiplication. The
Kronecker square of the small $q=2$ Coppersmith-Winograd tensor equals the
$3\times 3$ permanent, and could potentially be used to show $\omega=2$. We
prove the negative result for complexity theory that its border rank is $16$,
resolving a longstanding problem. Regarding its $q=4$ skew cousin in $
C^5\otimes C^5\otimes C^5$, which could potentially be used to prove
$\omega\leq 2.11$, we show the border rank of its Kronecker square is at most
$42$, a remarkable sub-multiplicativity result, as the square of its border
rank is $64$. We also determine moduli spaces $\underline{VSP}$ for the small
Coppersmith-Winograd tensors.


  I review recent progresses in heavy quarks physics on the lattice. I focus on
decay constants and form factors relevant for the extraction of CKM matrix
elements from experimental data. B-\bar{B} mixing is also discussed. In the
last part of the paper I describe phenomenological applications of Heavy Quark
Effective Theory (HQET) on the lattice, presenting in some detail the recent
non-perturbative determination of the b-quark mass including O(1/m_b)
corrections.


  It has been a long-standing challenge to accurately predict the properties of
light nuclei and nuclear matter simultaneously in nuclear $ab\ initio$
calculations. In this Letter, we develop the relativistic quantum Monte Carlo
methods for the nuclear $ab\ initio$ problem, and calculate the ground-state
energies of $A\leq4$ nuclei using the two-nucleon Bonn force with an
unprecedented high accuracy. For $A=3,4$ nuclei, the present relativistic
results significantly outperforms the nonrelativistic results with only
two-nucleon forces. Combining the present results for light nuclei and the
previous results for nuclear matter with the same Bonn force, a correlation
between the properties of light $A\leq4$ nuclei and the nuclear saturation is
revealed, and both systems are well described simultaneously, even without
introducing three-nucleon forces. This provides a quantitative understanding of
the connection between the light nuclei and nuclear matter saturation
properties, which has been an outstanding problem in nuclear $ab\ initio$
calculations for decades.


  The next generations of wireless networks will work in frequency bands
ranging from sub-6 GHz up to 100 GHz. Radio signal propagation differs here in
several critical aspects from the behaviour in the microwave frequencies
currently used. With wavelengths in the millimeter range (mmWave), both
penetration loss and free-space path loss increase, while specular reflection
will dominate over diffraction as an important propagation channel. Thus,
current channel model protocols used for the generation of mobile networks and
based on statistical parameter distributions obtained from measurements become
insufficient due to the lack of deterministic information about the
surroundings of the base station and the receiver-devices. These challenges
call for new modelling tools for channel modelling which work in the short
wavelength/high-frequency limit and incorporate site-specific details -- both
indoors and outdoors. Typical high-frequency tools used in this context --
besides purely statistical approaches -- are based on ray-tracing techniques.
Ray-tracing can become challenging when multiple reflections dominate. In this
context, mesh-based energy flow methods have become popular in recent years. In
this study, we compare the two approaches both in terms of accuracy and
efficiency and benchmark them against traditional power balance methods.


  For a system with three identical atoms, the dependence of the $s-$wave
virtual state energy on the weakly bound dimer and trimer binding energies is
calculated in a form of a universal scaling function. The scaling function is
obtained from a renormalizable three-body model with a pairwise Dirac-delta
interaction. It was also discussed the threshold condition for the appearance
of the trimer virtual state.


  We define an algebraic setup of homology for hypergraphs, which defaults to
simplicial homology in the case of graphs, and study its basic properties. As
part of our study we define algebraic spanning trees of hypergraphs, along with
fundamental cuts and cycles playing their usual roles.


  Visual Question Answering (VQA) has attracted much attention since it offers
insight into the relationships between the multi-modal analysis of images and
natural language. Most of the current algorithms are incapable of answering
open-domain questions that require to perform reasoning beyond the image
contents. To address this issue, we propose a novel framework which endows the
model capabilities in answering more complex questions by leveraging massive
external knowledge with dynamic memory networks. Specifically, the questions
along with the corresponding images trigger a process to retrieve the relevant
information in external knowledge bases, which are embedded into a continuous
vector space by preserving the entity-relation structures. Afterwards, we
employ dynamic memory networks to attend to the large body of facts in the
knowledge graph and images, and then perform reasoning over these facts to
generate corresponding answers. Extensive experiments demonstrate that our
model not only achieves the state-of-the-art performance in the visual question
answering task, but can also answer open-domain questions effectively by
leveraging the external knowledge.


  Gaze redirection aims at manipulating the gaze of a given face image with
respect to a desired direction (i.e., a reference angle) and it can be applied
to many real life scenarios, such as video-conferencing or taking group photos.
However, previous work on this topic mainly suffers of two limitations: (1)
Low-quality image generation and (2) Low redirection precision. In this paper,
we propose to alleviate these problems by means of a novel gaze redirection
framework which exploits both a numerical and a pictorial direction guidance,
jointly with a coarse-to-fine learning strategy. Specifically, the coarse
branch learns the spatial transformation which warps input image according to
desired gaze. On the other hand, the fine-grained branch consists of a
generator network with conditional residual image learning and a multi-task
discriminator. This second branch reduces the gap between the previously warped
image and the ground-truth image and recovers finer texture details. Moreover,
we propose a numerical and pictorial guidance module~(NPG) which uses a
pictorial gazemap description and numerical angles as an extra guide to further
improve the precision of gaze redirection. Extensive experiments on a benchmark
dataset show that the proposed method outperforms the state-of-the-art
approaches in terms of both image quality and redirection precision. The code
is available at https://github.com/jingjingchen777/CFGR


  Port-based teleportation (PBT) is a protocol in which Alice teleports an
unknown quantum state to Bob using measurements on a shared entangled
multipartite state called the port state and forward classical communication.
In this paper, we give an explicit proof that the so-called pretty good
measurement, or square-root measurement, is optimal for the PBT protocol with
independent copies of maximally entangled states as the port state. We then
show that the very same measurement remains optimal even when the port state is
optimized to yield the best possible PBT protocol. Hence, there is one
particular pretty good measurement achieving the optimal performance in both
cases. The following well-known facts are key ingredients in the proofs of
these results: (i) the natural symmetries of PBT, leading to a description in
terms of representation-theoretic data; (ii) the operational equivalence of PBT
with certain state discrimination problems, which allows us to employ duality
of the associated semidefinite programs. Along the way, we rederive the
representation-theoretic formulas for the performance of PBT protocols proved
in [Studzi\'nski et al., 2017] and [Mozrzymas et al., 2018] using only standard
techniques from the representation theory of the unitary and symmetric groups.
Providing a simplified derivation of these beautiful formulas is one of the
main goals of this paper.


  Software debloating seeks to mitigate security risks and improve performance
by eliminating unnecessary code. In recent years, a plethora of debloating
tools have been developed, creating a dense and varied landscape. Several
studies have delved into the literature, focusing on comparative analysis of
these tools. To build upon these efforts, this paper presents a comprehensive
systematization of knowledge (SoK) of the software debloating landscape. We
conceptualize the software debloating workflow, which serves as the basis for
developing a multilevel taxonomy. This framework classifies debloating tools
according to their input/output artifacts, debloating strategies, and
evaluation criteria. Lastly, we apply the taxonomy to pinpoint open problems in
the field, which, together with the SoK, provide a foundational reference for
researchers aiming to improve software security and efficiency through
debloating.


  The persistence properties of a set of random walkers obeying the A+B -> 0
reaction, with equal initial density of particles and homogeneous initial
conditions, is studied using two definitions of persistence. The probability,
P(t), that an annihilation process has not occurred at a given site has the
asymptotic form $P(t) -> const + t^{-\theta}$, where $\theta$ is the
persistence exponent (``type I persistence''). We argue that, for a density of
particles $\rho >> 1$, this non-trivial exponent is identical to that governing
the persistence properties of the one-dimensional diffusion equation, where
$\theta \approx 0.1207$. In the case of an initially low density, $\rho_0 <<
1$, we find $\theta \approx 1/4$ asymptotically. The probability that a site
remains unvisited by any random walker (``type II persistence'') is also
investigated and found to decay with a stretched exponential form, $P(t) \sim
\exp(-const \rho_0^{1/2}t^{1/4})$, provided $\rho_0 << 1$. A heuristic argument
for this behavior, based on an exactly solvable toy model, is presented.


  Routinely operating since July 2012, the APACHE survey has celebrated its
second birthday. While the main goal of the Project is the detection of
transiting planets around a large sample of bright, nearby M dwarfs in the
northern hemisphere, the APACHE large photometric database for hundreds of
different fields represents a relevant resource to search for and provide a
first characterization of new variable stars. We celebrate here the conclusion
of the second year of observations by reporting the discovery of 14 new
variables.


  In this paper we present a short survey of fuzzy and Semantic approaches to
Knowledge Extraction. The goal of such approaches is to define flexible
Knowledge Extraction Systems able to deal with the inherent vagueness and
uncertainty of the Extraction process. In this survey we address if and how
some approaches met their goal.


  A fluid dynamics video was created using data from a Direct Numerical
Simulation of a premixed n-heptane flame at high Karlovitz number. The
magnitude of vorticity and progress variable(a monotonically increasing
variable through the flame) illustrate the turbulence-flame interaction.


  We propose protocols for determining the distances in Hilbert space between
pure and mixed quantum states prepared on a quantum computer. In the case of
pure quantum states, the protocol is based on measuring the square of modulus
of scalar product between certain states. Determination of the distance between
mixed quantum states is reduced to measuring the squares of modules of scalar
products between all pure states included in the mixed states. In addition, we
develop a protocol that allows one to determine the speed of evolution of the
spin system simulated by a quantum computer. These protocols we apply to
measure distances and speeds of evolution of different quantum systems
implemented on the ibmq-santiago quantum computer.


  We develop a general framework for the construction of probabilities for the
time of arrival in quantum systems. The time of arrival is identified with the
time instant when a transition in the detector's degrees of freedom takes
place. Thus, its definition is embedded within the larger issue of defining
probabilities with respect to time for general quantum transitions. The key
point in our analysis is that we manage to reduce the problem of defining a
quantum time observable to a mathematical model where time is associated to a
transition from a subspace of the Hilbert space of the total system to its
complementary subspace. This property makes it possible to derive a general
expression for the probability for the time of transition, valid for any
quantum system, with the only requirement that the time of transition is
correlated with a definite macroscopic record.
  The framework developed here allows for the consideration of any experimental
configuration for the measurement of the time of arrival and it also applies to
relativistic systems with interactions described by quantum field theory. We
use the method in order to describe time-of-arrival measurements in high-energy
particle reactions and for a rigorous derivation of the time-integrated
probabilities in particle oscillations.


  Generative models for graphs have been typically committed to strong prior
assumptions concerning the form of the modeled distributions. Moreover, the
vast majority of currently available models are either only suitable for
characterizing some particular network properties (such as degree distribution
or clustering coefficient), or they are aimed at estimating joint probability
distributions, which is often intractable in large-scale networks. In this
paper, we first propose a novel network statistic, based on the Laplacian
spectrum of graphs, which allows to dispense with any parametric assumption
concerning the modeled network properties. Second, we use the defined statistic
to develop the Fiedler random graph model, switching the focus from the
estimation of joint probability distributions to a more tractable conditional
estimation setting. After analyzing the dependence structure characterizing
Fiedler random graphs, we evaluate them experimentally in edge prediction over
several real-world networks, showing that they allow to reach a much higher
prediction accuracy than various alternative statistical models.


  We present an unsupervised training approach for a neural network-based mask
estimator in an acoustic beamforming application. The network is trained to
maximize a likelihood criterion derived from a spatial mixture model of the
observations. It is trained from scratch without requiring any parallel data
consisting of degraded input and clean training targets. Thus, training can be
carried out on real recordings of noisy speech rather than simulated ones. In
contrast to previous work on unsupervised training of neural mask estimators,
our approach avoids the need for a possibly pre-trained teacher model entirely.
We demonstrate the effectiveness of our approach by speech recognition
experiments on two different datasets: one mainly deteriorated by noise (CHiME
4) and one by reverberation (REVERB). The results show that the performance of
the proposed system is on par with a supervised system using oracle target
masks for training and with a system trained using a model-based teacher.


  The $\Delta$-scaling method has been applied to ultra-relativistic p+p, C+C
and Pb+Pb collision data simulated using a high energy Monte Carlo package,
LUCIAE 3.0. The $\Delta$-scaling is found to be valid for some physical
variables, such as charged particle multiplicity, strange particle multiplicity
and number of binary nucleon-nucleon collisions from these simulated
nucleus-nucleus collisions over an extended energy ranging from $E_{lab}$ = 20
to 200 A GeV. In addition we derived information entropy from the multiplicity
distribution as a function of beam energy for these collisions.


  We obtain Calder\'on-Zygmund type estimates for parabolic equations with
Orlicz growth, where nonlinearities involved in the equations may be
discontinuous for the space and time variables. In addition, we consider
parabolic systems with the Uhlenbeck structure.


  The resolution of linear system with positive integer variables is a basic
yet difficult computational problem with many applications. We consider sparse
uncorrelated random systems parametrised by the density $c$ and the ratio
$\alpha=N/M$ between number of variables $N$ and number of constraints $M$. By
means of ensemble calculations we show that the space of feasible solutions
endows a Van-Der-Waals phase diagram in the plane ($c$, $\alpha$). We give
numerical evidence that the associated computational problems become more
difficult across the critical point and in particular in the coexistence
region.


  The major sources of the Soft X-ray Background (SXRB), besides distinct
structures as supernovae and superbubbles (e.g. Loop I), are: (i) an absorbed
extragalactic emission following a power law, (ii) an absorbed thermal
component ~2x10^6 K) from the galactic disk and halo, (iii) an unabsorbed
thermal component, supposedly at 10^6 K, attributed to the Local Bubble and
(iv) the very recently identified unabsorbed Solar Wind Charge-eXchange (SWCX)
emission from the heliosphere and the geocorona. We study the SWCX heliospheric
component and its contribution to observed data. In a first part, we apply a
SWCX heliospheric simulation to model the oxygen lines (3/4 keV) local
intensities during shadowing observations of the MBM12 molecular cloud and a
dense filament in the south galactic hemisphere with Chandra, XMM-Newton, and
Suzaku telescopes. In a second part, we present a preliminary comparison of
SWCX model results with ROSAT and Wisconsin surveys data in the 1/4 keV band.
We conclude that, in the 3/4 keV band, the total local intensity is entirely
heliospheric, while in the 1/4 keV band, the heliospheric component seems to
contribute significantly to the local SXRB intensity and has potentially a
strong influence on the interpretation of the ROSAT and Wisconsin surveys data
in terms of Local Bubble hot gas temperature.


  Conformance checking is a major function of process mining, which allows
organizations to identify and alleviate potential deviations from the intended
process behavior. To fully leverage its benefits, it is important that
conformance checking results are visualized in a way that is approachable and
understandable for non-expert users. However, the visualization of conformance
checking results has so far not been widely considered in research. Therefore,
the goal of this paper is to develop an understanding of how conformance
checking results are visualized by process mining tools to provide a foundation
for further research on this topic. We conduct a systematic study, where we
analyze the visualization capabilities of nine academic and seven commercial
tools by means of a visual analytics framework. In this study, we find that the
''Why?'' aspect of conformance checking visualization seems already be
well-defined, but the ''What?'' and ''How?'' aspects require future research.


  In an aircraft electric power system (EPS), a supervisory control unit must
actuate a set of switches to distribute power from generators to loads, while
satisfying safety, reliability and real-time performance requirements. To
reduce expensive re-design steps in current design methodologies, such a
control problem is generally addressed based on minor incremental changes on
top of consolidated solutions, since it is difficult to estimate the impact of
earlier design decisions on the final implementation. In this paper, we
introduce a methodology for the design space exploration and virtual
prototyping of EPS supervisory control protocols, following the platform-based
design (PBD) paradigm. Moreover, we describe the modeling infrastructure that
supports the methodology. In PBD, design space exploration is carried out as a
sequence of refinement steps from the initial specification towards a final
implementation, by mapping higher-level behavioral models into a set of library
components at a lower level of abstraction. In our flow, the system
specification is captured using SysML requirement and structure diagrams.
State-machine diagrams enable verification of the control protocol at a high
level of abstraction, while lowerlevel hybrid models, implemented in Simulink,
are used to verify properties related to physical quantities, such as time,
voltage and current values. The effectiveness of our approach is illustrated on
a prototype EPS control protocol design.


  Let $\mathcal{J}^1$ be the real form of a complex simple Jordan algebra such
that the automorphism group is $\mathrm{F}_{4(-20)}$. By using some orbit types
of $\mathrm{F}_{4(-20)}$ on $\mathcal{J}^1$, for $\mathrm{F}_{4(-20)}$,
explicitly, we give the Iwasawa decomposition, the Oshima--Sekiguchi's
$K_{\epsilon}-$Iwasawa decomposition, the Matsuki decomposition, and the Bruhat
and Gauss decompositions.


  We give mean square bounds for the remainder in the lattice point counting
problem, counting the number of lattice points in a large ball in
$\mathbb{R}^d$, when averaged over families of shears of the lattice.


  Let $R$ be a commutative local $k$-algebra of Krull dimension one, where $k$
is a field. Let $\alpha$ be a $k$-algebra automorphism of $R$, and define $S$
to be the skew polynomial algebra $R[\theta; \alpha]$. We offer, under some
additional assumptions on $R$, a criterion for $S$ to have injective hulls of
all simple $S$-modules locally Artinian - that is, for $S$ to satisfy property
$(\diamond)$. It is easy and well known that if $\alpha$ is of finite order,
then $S$ has this property, but in order to get the criterion when $\alpha$ has
infinite order we found it necessary to classify all cyclic (Krull) critical
$S$-modules in this case, a result which may be of independent interest. With
the help of the above we show that $\hat{S}=k[[X]][\theta, \alpha]$ satisfies
$(\diamond)$ for all $k$-algebra automorphisms $\alpha$ of $k[[X]]$.


  State-of-the-art object detection models are frequently trained offline using
available datasets, such as ImageNet: large and overly diverse data that are
unbalanced and hard to cluster semantically. This kind of training drops the
object detection performance should the change in illumination, in the
environmental conditions (e.g., rain), or in the lens positioning (out-of-focus
blur) occur. We propose a decentralized hierarchical multi-agent deep
reinforcement learning approach for intelligently controlling the camera and
the lens focusing settings, leading to a significant improvement beyond the
capacity of the popular detection models (YOLO, Faster R-CNN, and Retina are
considered). The algorithm relies on the latent representation of the camera's
stream and, thus, it is the first method to allow a completely no-reference
tuning of the camera, where the system trains itself to auto-focus itself.


  The three-body problem is reexamined in the framework of general relativity.
The Newtonian three-body problem admits Euler's collinear solution, where three
bodies move around the common center of mass with the same orbital period and
always line up. The solution is unstable. Hence it is unlikely that such a
simple configuration would exist owing to general relativistic forces dependent
not only on the masses but also on the velocity of each body. However, we show
that the collinear solution remains true with a correction to the spatial
separation between masses. Relativistic corrections to the Sun-Jupiter Lagrange
points L1, L2 and L3 are also evaluated.


  We extend our discussion of effective actions for stochastic partial
differential equations to systems that give rise to a Martin-Siggia-Rose (MSR)
type of action. This type of action naturally arises when one uses the
many-body formalism of Doi and Peliti to describe reaction-diffusion models
which undergo transitions into the absorbing state and which are described by a
Master equation. These models include predator prey models, and directed
percolation models as well as chemical kinetic models. For classical dynamical
systems with external noise it is always possible to construct an MSR action.
  Using a path integral representation for the generator of the correlation
functions, we show how, by introducing a composite auxiliary field, one can
generate an auxiliary field loop expansion for the effective action for both
types of systems. As a specific example of the Doi-Peliti formalism we
determine the effective action for the chemical reaction annihilation and
diffusion process $A+A \rightarrow 0$. For the external noise problem we
evaluate the effective action for the Cole-Hopf form of the Kardar-Parisi Zhang
(KPZ) equation as well as for the Ginzburg Landau model of spin relaxation. We
determine for arbitrary spatial dimension $d$, the renormalized effective
potential in leading order in the auxiliary field loop expansion (LOAF) and
also determine the renormalization group equation for the running of the
reaction rate (coupling constant) for arbitrary $d$. We compare our results
with known perturbative and non-perturbative results for the renormalization
group equations.


  It is often said that a sign of a great player is that he makes the players
around him better. The player may or may not score much himself, but his
teammates perform better when he plays. One way a hockey player can improve his
or her teammates' performance is to create goal scoring opportunities.
Unfortunately, in hockey goal scoring is relatively infrequent, and statistics
like assists can be unreliable as a measure of a player's playmaking ability.
Assists also depend on playing time, power play usage, the strength of a
player's linemates, and other factors. In this paper we develop a metric for
quantifying playmaking ability that addresses these issues. Our playmaking
metric has two benefits over assists for which we can provide statistical
evidence: it is more consistent than assists, and it is better than assists at
predicting future assists. Quantifying player contributions using this measure
can assist decision-makers in identifying, acquiring, and integrating
successful playmakers into their lineups.


  Federated learning systems are vulnerable to attacks from malicious clients.
As the central server in the system cannot govern the behaviors of the clients,
a rogue client may initiate an attack by sending malicious model updates to the
server, so as to degrade the learning performance or enforce targeted model
poisoning attacks (a.k.a. backdoor attacks). Therefore, timely detecting these
malicious model updates and the underlying attackers becomes critically
important. In this work, we propose a new framework for robust federated
learning where the central server learns to detect and remove the malicious
model updates using a powerful detection model, leading to targeted defense. We
evaluate our solution in both image classification and sentiment analysis tasks
with a variety of machine learning models. Experimental results show that our
solution ensures robust federated learning that is resilient to both the
Byzantine attacks and the targeted model poisoning attacks.


  In order to obtain solutions to problem $$ {{array}{c} -\Delta
u=\dfrac{A+h(x)} {|x|^2}u+k(x)u^{2^*-1}, x\in {\mathbb R}^N, u>0
\hbox{in}{\mathbb R}^N, {and}u\in {\mathcal D}^{1,2}({\mathbb R}^N), {array}.
$$ $h$ and $k$ must be chosen taking into account not only the size of some
norm but the shape. Moreover, if $h(x)\equiv 0$, to reach multiplicity of
solution, some hypotheses about the local behaviour of $k$ close to the points
of maximum are needed.


  We propose that underdoped high $T_c$ superconductors are described by an
algebraic spin liquid (ASL) at high energies, which undergoes a spin-charge
recombination transition at low energies. The spin correlation in the ASL is
calculated via its effective theory - a system of massless Dirac fermions
coupled to a U(1) gauge field. We find that without fine tuning any parameters
the gauge interaction strongly enhances the staggered spin correlation even in
the presence of a large single particle pseudo-gap. This allows us to show that
the ASL plus spin-charge recombination picture can explain many highly unusual
properties of underdoped high $T_c$ superconductors.


  We consider the twisted waveguide $\Omega_\theta$, i.e. the domain obtained
by the rotation of the bounded cross section $\omega \subset {\mathbb R}^{2}$
of the straight tube $\Omega : = \omega \times {\mathbb R}$ at angle $\theta$
which depends on the variable along the axis of $\Omega$. We study the spectral
properties of the Dirichlet Laplacian in $\Omega_\theta$, unitarily equivalent
under the diffeomorphism $\Omega_\theta \to \Omega$ to the operator
$H_{\theta'}$, self-adjoint in ${\rm L}^2(\Omega)$. We assume that $\theta' =
\beta - \epsilon$ where $\beta$ is a $2\pi$-periodic function, and $\epsilon$
decays at infinity. Then in the spectrum $\sigma(H_\beta)$ of the unperturbed
operator $H_\beta$ there is a semi-bounded gap $(-\infty, {\mathcal E}_0^+)$,
and, possibly, a number of bounded open gaps $({\mathcal E}_j^-, {\mathcal
E}_j^+)$. Since $\epsilon$ decays at infinity, the essential spectra of
$H_\beta$ and $H_{\beta - \epsilon}$ coincide. We investigate the asymptotic
behaviour of the discrete spectrum of $H_{\beta - \epsilon}$ near an arbitrary
fixed spectral edge ${\mathcal E}_j^\pm$. We establish necessary and quite
close sufficient conditions which guarantee the finiteness of $\sigma_{\rm
disc}(H_{\beta-\epsilon})$ in a neighbourhood of ${\mathcal E}_j^\pm$. In the
case where the necessary conditions are violated, we obtain the main asymptotic
term of the corresponding eigenvalue counting function. The effective
Hamiltonian which governs the the asymptotics of $\sigma_{\rm
disc}(H_{\beta-\epsilon})$ near ${\mathcal E}_j^\pm$ could be represented as a
finite orthogonal sum of operators of the form $-\mu\frac{d^2}{dx^2} - \eta
\epsilon$, self-adjoint in ${\rm L}^2({\mathbb R})$; here, $\mu > 0$ is a
constant related to the so-called effective mass, while $\eta$ is
$2\pi$-periodic function depending on $\beta$ and $\omega$.


  We calculate the most stringent constraints up to date on the parameter space
for sterile neutrino warm dark matter models possessing a radiative decay
channel into X-rays. These constraints arise from the X-ray flux observations
from the Galactic center (central parsec), taken by the XMM and NuSTAR
missions. We compare the results obtained from using different dark matter
density profiles for the Milky Way, such as NFW, Burkert or Einasto, to that
produced by the Ruffini-Arg\"uelles-Rueda (RAR) fermionic model, which has the
distinct feature of depending on the particle mass. We show that due to the
novel core-halo morphology present in the RAR profile, the allowed particle
mass window is narrowed down to $m_s\sim 10-15$ keV, when analyzed within the
$\nu$MSM sterile neutrino model. We further discuss on the possible effects in
the sterile neutrino parameter-space bounds due to a self-interacting nature of
the dark matter candidates.


  The selection of a landing site within the Artemis Exploration Zone (AEZ)
involves multiple factors and presents a complex problem. This study evaluates
potential landing sites for the Artemis III mission using a combination of
Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM)
methodologies, specifically the TOPSIS algorithm. By integrating topographic,
illumination, and mineralogy data of the Moon, we assess 1247 locations that
meet the Human Landing System (HLS) requirements within 13 candidate regions
and Site 004 near the lunar south pole. Criteria considered include surface
visibility, HLS-astronaut line of sight, Permanently Shadowed Regions (PSRs),
sunlight exposure, direct communication with Earth, geological units, and mafic
mineral abundance. Site DM2 (Nobile Rim 2), particularly the point at latitude
84{\deg}12'5.61" S and longitude 60{\deg}41'59.61" E, is the optimal location
for landing. Sensitivity analysis confirms the robustness of our approach,
validating the suitability of the best location despite the MCDM method
employed and variations in criteria weightings to prioritize illumination and
PSRs. This research demonstrates the applicability of GIS-MCDM techniques for
lunar exploration and the potential benefits they can bring to the Artemis
program.


  Whilst not all spreadsheet defects are structural in nature, poor layout
choices can compromise spreadsheet quality. These defects may be avoided at the
development stage by some simple mistake prevention and detection devices.
Poka-Yoke (Japanese for Mistake Proofing), which owes its genesis to the Toyota
Production System (the standard for manufacturing excellence throughout the
world) offers some principles that may be applied to reducing spreadsheet
defects. In this paper we examine spreadsheet structure and how it can lead to
defects and illustrate some basic spreadsheet Poka-Yokes to reduce them. These
include guidelines on how to arrange areas of cells so that whole rows and
columns can be inserted anywhere without causing errors, and rules for when to
use relative and absolute references with respect to what type of area is being
referred to.


  In this article, we study the problem of air-to-ground ultra-reliable and
low-latency communication (URLLC) for a moving ground user. This is done by
controlling multiple unmanned aerial vehicles (UAVs) in real time while
avoiding inter-UAV collisions. To this end, we propose a novel multi-agent deep
reinforcement learning (MADRL) framework, coined a graph attention exchange
network (GAXNet). In GAXNet, each UAV constructs an attention graph locally
measuring the level of attention to its neighboring UAVs, while exchanging the
attention weights with other UAVs so as to reduce the attention mismatch
between them. Simulation results corroborates that GAXNet achieves up to 4.5x
higher rewards during training. At execution, without incurring inter-UAV
collisions, GAXNet achieves 6.5x lower latency with the target 0.0000001 error
rate, compared to a state-of-the-art baseline framework.


  In bulk quantum computation one can manipulate a large number of
indistinguishable quantum computers by parallel unitary operations and measure
expectation values of certain observables with limited sensitivity. The initial
state of each computer in the ensemble is known but not pure. Methods for
obtaining effective pure input states by a series of manipulations have been
described by Gershenfeld and Chuang (logical labeling) and Cory et al. (spatial
averaging) for the case of quantum computation with nuclear magnetic resonance.
We give a different technique called temporal averaging. This method is based
on classical randomization, requires no ancilla qubits and can be implemented
in nuclear magnetic resonance without using gradient fields. We introduce
several temporal averaging algorithms suitable for both high temperature and
low temperature bulk quantum computing and analyze the signal to noise behavior
of each.


  The detection of gravitational waves and the extraction of physical
information from them requires the prediction of accurate waveforms to be used
in template banks. For that purpose, the accuracy of effective-one-body (EOB)
waveforms has been improved over the last years by calibrating them to
numerical-relativity (NR) waveforms. So far, the calibration has employed a
handful of NR waveforms with a total length of ~30 cycles, the length being
limited by the computational cost of NR simulations. Here we address the
outstanding problem of the stability of the EOB calibration with respect to the
length of NR waveforms. Performing calibration studies against NR waveforms of
nonspinning black-hole binaries with mass ratios 1, 1.5, 5, and 8, and with a
total length of ~60 cycles, we find that EOB waveforms calibrated against
either 30 or 60 cycles will be indistinguishable by the advanced detectors LIGO
and Virgo when the signal-to-noise ratio (SNR) is below 110. When extrapolating
to a very large number of cycles, using very conservative assumptions, we can
conclude that state-of-the-art nonspinning EOB waveforms of any length are
sufficiently accurate for parameter estimation with advanced detectors when the
SNR is below 20, the mass ratio is below 5 and total mass is above 20 Msun. The
results are not conclusive for the entire parameter space because of current NR
errors.


  With scanning tunneling spectroscopy (STS), we probed differential
conductance (dI/dV) images of P3HT:PCBM bulk-heterojunctions (BHJs). Since the
materials are seen energetically in dI/dV images, the imaging process provides
opportunities to view the nano-domains of the components in the BHJ. The images
were recorded at different voltages to bring out the interface region between
the two materials as well. The density of states (DOS) spectra of pristine
materials provided location of energy levels of the polymer and the fullerene
in forming energy diagram with a type-II alignment from the view-point of
charge carriers. DOS spectra recorded in the P3HT:PCBM heterojunction in
addition yielded energies that deviated from those of the components indicating
bending of energy levels at the interface region.


  To address the outstanding task of detecting entanglement in large quantum
systems, entanglement witnesses have emerged, addressing the separable nature
of a state. Yet optimizing witnesses, or accessing them experimentally, often
remains a challenge. We here introduce a family of entanglement witnesses for
open quantum systems, based on the electric field -- its quadratures and the
total fluorescence. More general than spin-squeezing inequalities, it can
detect new classes of entangled states, as changing the direction for far-field
observation opens up a continuous family of witnesses, without the need for a
state tomography. Their efficiency is demonstrated by detecting, from almost
any direction, the entanglement of collective single-photon states, such as
long-lived states generated by cooperative spontaneous emission. Able to detect
entanglement in large quantum systems, these electric-field-based witnesses can
be used on any set of emitters described by the Pauli group, such as atomic
systems (cold atoms and trapped ions), giant atoms, color centers, and
superconducting qubits.


  We analyse the sizes, colour gradients, and resolved stellar mass
distributions for 36 massive and passive galaxies in the cluster XMMUJ2235-2557
at z=1.39 using optical and near-infrared Hubble Space Telescope imaging. We
derive light-weighted S\'ersic fits in five HST bands
($i_{775},z_{850},Y_{105},J_{125},H_{160}$), and find that the size decreases
by ~20% going from $i_{775}$ to $H_{160}$ band, consistent with recent studies.
We then generate spatially resolved stellar mass maps using an empirical
relationship between $M_{*}/L_{H_{160}}$ and $(z_{850}-H_{160})$ and use these
to derive mass-weighted S\'ersic fits: the mass-weighted sizes are ~41% smaller
than their rest-frame $r$-band counterparts compared with an average of ~12% at
z~0. We attribute this evolution to the evolution in the $M_{*}/L_{H_{160}}$
and colour gradient. Indeed, as expected, the ratio of mass-weighted to
light-weighted size is correlated with the $M_{*}/L$ gradient, but is also
mildly correlated with the mass surface density and mass-weighted size. The
colour gradients $(\nabla_{z-H})$ are mostly negative, with a median value of
$\sim0.45$ mag dex$^{-1}$, twice the local value. The evolution is caused by an
evolution in age gradients along the semi-major axis ($a$), with $\nabla_{age}
= d \log(age) / d \log(a)$ $\sim-0.33$, while the survival of weaker colour
gradients in old, local galaxies implies that metallicity gradients are also
required, with $\nabla_{Z} = d \log(Z) / d \log(a)$ $\sim-0.2$. This is
consistent with recent observational evidence for the inside-out growth of
passive galaxies at high redshift, and favours a gradual mass growth mechanism,
such as minor mergers.


  Conventionally, data driven identification and control problems for higher
order dynamical systems are solved by augmenting the system state by the
derivatives of the output to formulate first order dynamical systems in higher
dimensions. However, solution of the augmented problem typically requires
knowledge of the full augmented state, which requires numerical differentiation
of the original output, frequently resulting in noisy signals. This manuscript
develops the theory necessary for a direct analysis of higher order dynamical
systems using higher order Liouville operators. Fundamental to this theoretical
development is the introduction of signal valued RKHSs and new operators posed
over these spaces. Ultimately, it is observed that despite the added
abstractions, the necessary computations are remarkably similar to that of
first order DMD methods using occupation kernels.


  In this article, we analyze some nuclear structure aspects of the
neutrinoless double beta decay nuclear matrix elements (NME), in the framework
of the Interacting Shell Model. We give results for the decays of 48Ca, 76Ge,
82Se, 124Sn, 128Te, 130Te, and 136Xe, using improved effective interactions and
valence spaces. We examine the dependence of the NME's on the effective
interaction and the valence space, and analyze the effects of the short range
correlations and the finite size of the nucleon. Finally we study the influence
of the deformation on the values of the NME's.


  In slowly driven classical systems, work is a stochastic quantity and its
probability distribution is known to satisfy the work fluctuation-dissipation
relation, which states that the mean and variance of the dissipated work are
linearly related. Recently, it was shown that generation of quantum coherence
in the instantaneous energy eigenbasis leads to a correction to this linear
relation in the slow-driving regime. Here, we go even further by investigating
nonclassical features of work fluctuations in setups with more than one system.
To do this, we first generalize slow control protocols to encompass
multipartite systems, allowing for the generation of quantum correlations
during the driving process. Then, focussing on two-qubit systems, we show that
entanglement generation leads to a positive contribution to the dissipated
work, which is distinct from the quantum correction due to local coherence
generation known from previous work. Our results show that entanglement
generated during slow control protocols, e.g. as an unavoidable consequence of
qubit crosstalk, comes at the cost of increased dissipation.


  Hirota's bilinear approach is a very effective method to construct solutions
for soliton systems. In terms of this method, the nonlinear equations can be
transformed into linear equations, and can be solved by using perturbation
method. In this paper, we study the bilinear Boussinesq equation and obtain its
bilinear B\"{a}cklund transformation. Starting from this bilinear B\"{a}cklund
transformation, we also derive its Lax pair and test its integrability.


  We investigate the relation between on-shell and zero-momentum
non-perturbative quantities entering the parametrization of the two-point
Green's function of two-dimensional non-linear O(N) sigma models. We present
accurate estimates of ratios of mass-scales and renormalization constants,
obtained by an analysis of the strong-coupling expansion of the two-point
Green's function. These ratios allow to connect exact known on-shell results
with typical zero-momentum lattice evaluations. Our results are supported by
the 1/N-expansion.


  Formal software testing education is important for building efficient QA
professionals. Various aspects of quality assurance approaches are usually
covered in courses for training software testing students. Automated Test Tools
is one of the core courses in the software testing post-graduate curriculum due
to the high demand for automated testers in the workforce. It is important to
understand which factors are affecting student performance in the automated
testing course to be able to assist the students early on based on their needs.
Various metrics that are considered for predicting student performance in this
testing course are student engagement, grades on individual deliverables, and
prerequisite courses. This study identifies the impact of assessing students
based on individual vs. group activities, theoretical vs. practical components,
and the effect of having taken prerequisite courses in their final grade. To
carry out this research, student data was collected from the automated test
tools course of a community college-based postgraduate certificate program in
software testing. The dataset contained student records from the years 2021 to
2022 and consisted of information from five different semesters. Various
machine learning algorithms were applied to develop an effective model for
predicting students performance in the automated software testing tools course,
and finally, important features affecting the students performance were
identified. The predictive performance model of the automated test tools course
that was developed by applying the logistic regression technique, showed the
best performance, with an accuracy score of 90%.


  In this review we discuss, from a unified point of view, a variety of Monte
Carlo methods used to solve eigenvalue problems in statistical mechanics and
quantum mechanics. Although the applications of these methods differ widely,
the underlying mathematics is quite similar in that they are stochastic
implementations of the power method. In all cases, optimized trial states can
be used to reduce the errors of Monte Carlo estimates.


  We study the existence of stationary black holes with a non-canonical scalar
field as a matter source. We prove a simple black hole no-hair theorem which
rules out the existence of asymptotically flat black holes which are static or
stationary and axisymmetric possessing scalar hair for a wide class of
non-canonical scalar field theories. This applies to scalar field theories
which are of the form of K-essence theories. In particular, we rule out the
existence of such black holes in the ghost condensate model, and in large
sectors of the Dirac-Born-Infeld model.


  Consider a simple random walk on $\mathbb{Z}$ with a random coloring of
$\mathbb{Z}$. Look at the sequence of the first $N$ steps taken in the random
walk, together with the colors of the visited locations. We call this the
record. From the record one can deduce the coloring of of the interval in
$\mathbb{Z}$ that was visited, which is of size approximately $\sqrt{N}$. This
is called scenery reconstruction. Now suppose that an adversary may change
$\delta N$ entries in the record that was obtained. What can be deduced from
the record about the scenery now? In this paper we show that it is likely that
we can still reconstruct a large part of the scenery.
  More precisely, we show that for any $\theta<0.5,p>0,\epsilon>0$, there are
$N_{0}$ and $\delta_{0}$ such that if $N>N_{0}$ and $\delta<\delta_{0}$ then
with probability $>1-p$, the walk is such that we can reconstruct the coloring
of $>N^{\theta}$ integers, up to a number of possible reconstructions that is
less than $2^{\epsilon s}$, where $s$ is the number of integers whose color we
reconstruct.


  Learning from limited data has been extensively studied in machine learning,
considering that deep neural networks achieve optimal performance when trained
using a large amount of samples. Although various strategies have been proposed
for centralized training, the topic of federated learning with small datasets
remains largely unexplored. Moreover, in realistic scenarios, such as settings
where medical institutions are involved, the number of participating clients is
also constrained. In this work, we propose a novel federated learning
framework, named RepTreeFL. At the core of the solution is the concept of a
replica, where we replicate each participating client by copying its model
architecture and perturbing its local data distribution. Our approach enables
learning from limited data and a small number of clients by aggregating a
larger number of models with diverse data distributions. Furthermore, we
leverage the hierarchical structure of the client network (both original and
virtual), alongside the model diversity across replicas, and introduce a
diversity-based tree aggregation, where replicas are combined in a tree-like
manner and the aggregation weights are dynamically updated based on the model
discrepancy. We evaluated our method on two tasks and two types of data, graph
generation and image classification (binary and multi-class), with both
homogeneous and heterogeneous model architectures. Experimental results
demonstrate the effectiveness and outperformance of RepTreeFL in settings where
both data and clients are limited. Our code is available at
https://github.com/basiralab/RepTreeFL.


  Representation of coalescent process using pruning of trees has been used by
Goldschmidt and Martin for the Bolthausen-Sznitman coalescent and by Abraham
and Delmas for the $\beta(3/2,1/2)$-coalescent. By considering a pruning
procedure on stable Galton-Watson tree with $n$ labeled leaves, we give a
representation of the discrete $\beta(1+\alpha,1-\alpha)$-coalescent, with
$\alpha\in [1/2,1)$ starting from the trivial partition of the $n$ first
integers. The construction can also be made directly on the stable continuum
L{\'e}vy tree, with parameter $1/\alpha$, simultaneously for all $n$. This
representation allows to use results on the asymptotic number of coalescence
events to get the asymptotic number of cuts in stable Galton-Watson tree (with
infinite variance for the reproduction law) needed to isolate the root. Using
convergence of the stable Galton-Watson tree conditioned to have infinitely
many leaves, one can get the asymptotic distribution of blocks in the last
coalescence event in the $\beta(1+\alpha,1-\alpha)$-coalescent.


  In this paper we extend previous results concerning the behaviour of JSJ
decompositions of closed 3-manifolds with respect to the profinite completion
to the case of compact 3-manifolds with boundary.
  We also illustrate an alternative and perhaps more natural approach to part
of the original theorem, using relative cohomology to analyse the actions of
an-annular atoroidal groups on profinite trees.


  We consider certain supersymmetric configurations of intersecting branes and
branes ending on branes and analyze the duality between their open and closed
string interpretation. The examples we study are chosen such that we have the
lower dimensional brane realizing an n+1 dimensional conformal field theory on
its worldvolume and the higher dimensional one introducing a conformal
boundary. We also consider two CFTs, possibly with different central charges,
interacting along a common conformal boundary. We show with a probe calculation
that the dual closed string description is in terms of gravity in an AdS_{n+2}
bulk with an AdS_{n+1} defect or two different AdS_{n+2} spaces joined along a
defect. We also comment briefly on the expected back-reaction.


  We consider an atom inside a ring cavity, where a plane-wave cavity field
together with an external coherent laser beam induces a two-photon Raman
transition between two hyperfine ground states of the atom. This
cavity-assisted Raman transition induces effective coupling between atom's
internal degrees of freedom and its center-of-mass motion. In~the meantime,
atomic dynamics exerts a back-action to cavity photons. We investigate the
properties of this system by adopting a mean-field and a full quantum approach,
and show that the interplay between the atomic dynamics and the cavity field
gives rise to intriguing nonlinear phenomena.


  While Multi-view Graph Neural Networks (MVGNNs) excel at leveraging diverse
modalities for learning object representation, existing methods assume
identical local topology structures across modalities that overlook real-world
discrepancies. This leads MVGNNs straggles in modality fusion and
representations denoising. To address these issues, we propose adaptive
modality-wise structure learning (AMoSL). AMoSL captures node correspondences
between modalities via optimal transport, and jointly learning with graph
embedding. To enable efficient end-to-end training, we employ an efficient
solution for the resulting complex bilevel optimization problem. Furthermore,
AMoSL adapts to downstream tasks through unsupervised learning on
inter-modality distances. The effectiveness of AMoSL is demonstrated by its
ability to train more accurate graph classifiers on six benchmark datasets.


  Over-the-air federated learning (OTA-FL) is an emerging technique to reduce
the computation and communication overload at the PS caused by the orthogonal
transmissions of the model updates in conventional federated learning (FL).
This reduction is achieved at the expense of introducing aggregation error that
can be efficiently suppressed by means of receive beamforming via large
array-antennas. This paper studies OTA-FL in massive multiple-input
multiple-output (MIMO) systems by considering a realistic scenario in which the
edge server, despite its large antenna array, is restricted in the number of
radio frequency (RF)-chains. For this setting, the beamforming for over-the-air
model aggregation needs to be addressed jointly with antenna selection. This
leads to an NP-hard problem due to the combinatorial nature of the
optimization. We tackle this problem via two different approaches. In the first
approach, we use the penalty dual decomposition (PDD) technique to develop a
two-tier algorithm for joint antenna selection and beamforming. The second
approach interprets the antenna selection task as a sparse recovery problem and
develops two iterative joint algorithms based on the Lasso and fast iterative
soft-thresholding methods. Convergence and complexity analysis is presented for
all the schemes. The numerical investigations depict that the algorithms based
on the sparse recovery techniques outperform the PDD-based algorithm, when the
number of RF-chains at the edge server is much smaller than its array size.
However, as the number of RF-chains increases, the PDD approach starts to be
superior. Our simulations further depict that learning performance with all the
antennas being active at the PS can be closely tracked by selecting less than
20% of the antennas at the PS.


  In further study of the application of crossed-product functors to the
Baum-Connes Conjecture, Buss, Echterhoff, and Willett introduced various other
properties that crossed-product functors may have. Here we introduce and study
analogues of these properties for coaction functors, making sure that the
properties are preserved when the coaction functors are composed with the full
crossed product to make a crossed-product functor. The new properties for
coaction functors studied here are functoriality for generalized homomorphisms
and the correspondence property. We particularly study the connections with the
ideal property. The study of functoriality for generalized homomorphisms
requires a detailed development of the Fischer construction of maximalization
of coactions with regard to possibly degenerate homomorphisms into multiplier
algebras. We verify that all "KLQ" functors arising from large ideals of the
Fourier-Stieltjes algebra $B(G)$ have all the properties we study, and at the
opposite extreme we give an example of a coaction functor having none of the
properties.


  We use high resolution IRAS and 20 cm radio continuum (RC) images of a sample
of 22 spiral galaxies to study the correlation between the far infra-red (FIR)
and RC emissions within the galactic disks. A combination of exponential and
gaussian profiles rather than a single exponential profile is found to be a
better representation of the observed intensity profiles in the two bands. The
gaussian component, which we show is not due to the effects of limited
beam-resolution, contains more than 60% of the total flux in majority of the
galaxies. The dominance of the gaussian component suggests that the nuclear
star forming regions and the bulge stars are more important contributors to the
emission in the two bands, rather than the outer exponential stellar disks. The
RC profile is flatter compared to the FIR profile, resulting in a decrease of
their ratio, Q60, away from the center. However, the Q60 increases in the
extreme outer parts, where the dispersion in the FIR and RC correlation is also
higher than in the central regions. The global Q60 and its dispersion match
those in the inner parts of the galaxies. These results imply that the observed
tight correlation in the global quantities reflects processes in the inner
regions only where OB stars and the associated Type II supernovae control the
FIR and RC emission. In the outer parts heating of very small dust grains by
the old disk stars provides a secondary component in the FIR emission, without
associated RC emission. The edge-on galaxy NGC3079 shows extended FIR and RC
emissions along its minor axis, probably associated with the nuclear starburst
activity.


  We described $\delta$-derivations and $\delta$-superderivations of simple
Jordan superalgebra <<KKM Double>> (also known as superalgebra of Jordan
brackets) and unital simple finite-dimensional Jordan superalgebras over
algebraic closed fields with characteristic $p\neq2$. As a consequence, we
received relationship between non-trivial $\delta$-derivations and specialty of
simple superalgebra <<KKM Double>>. We constructed new examples of non-trivial
1/2-derivations of Jordan superalgebras of vector type.


  In this paper, a regional knot invariant is constructed. Like the Wirtinger
presentation of a knot group, each planar region contributes a generator, and
each crossing contributes a relation. The invariant is call a tridle of the
link. As in the quandle theory, one can define Alexander quandle and get
Alexander polynomial from it. For link diagram, one can also define a linear
tridle and its presentation matrix. A polynomial invariant can be derive from
the matrix just like the Alexander polynomial case.


  In confirmatory clinical trials, it has been proposed to use a simple
iterative graphical approach to construct and perform intersection hypotheses
tests with a weighted Bonferroni-type procedure to control type I errors in the
strong sense. Given Phase II study results or other prior knowledge, it is
usually of main interest to find the optimal graph that maximizes a certain
objective function in a future Phase III study. In this article, we evaluate
the performance of two existing derivative-free constrained methods, and
further propose a deep learning enhanced optimization framework. Our method
numerically approximates the objective function via feedforward neural networks
(FNNs) and then performs optimization with available gradient information. It
can be constrained so that some features of the testing procedure are held
fixed while optimizing over other features. Simulation studies show that our
FNN-based approach has a better balance between robustness and time efficiency
than some existing derivative-free constrained optimization algorithms.
Compared to the traditional stochastic search method, our optimizer has
moderate multiplicity adjusted power gain when the number of hypotheses is
relatively large. We further apply it to a case study to illustrate how to
optimize a multiple testing procedure with respect to a specific study
objective.


  Previous multi-task dense prediction methods based on the Mixture of Experts
(MoE) have received great performance but they neglect the importance of
explicitly modeling the global relations among all tasks. In this paper, we
present a novel decoder-focused method for multi-task dense prediction, called
Mixture-of-Low-Rank-Experts (MLoRE). To model the global task relationships,
MLoRE adds a generic convolution path to the original MoE structure, where each
task feature can go through this path for explicit parameter sharing.
Furthermore, to control the parameters and computational cost brought by the
increase in the number of experts, we take inspiration from LoRA and propose to
leverage the low-rank format of a vanilla convolution in the expert network.
Since the low-rank experts have fewer parameters and can be dynamically
parameterized into the generic convolution, the parameters and computational
cost do not change much with the increase of experts. Benefiting from this
design, we increase the number of experts and its reception field to enlarge
the representation capacity, facilitating multiple dense tasks learning in a
unified network. Extensive experiments on the PASCAL-Context and NYUD-v2
benchmarks show that our MLoRE achieves superior performance compared to
previous state-of-the-art methods on all metrics. Our code is available at
https://github.com/YuqiYang213/MLoRE.


  Topological insulator lasers are a newly introduced kind of lasers in which
light snakes around a cavity without scattering. Like for an electron current
in a topological insulator material, a topologically protected lasing mode
travels along the cavity edge, steering neatly around corners and imperfections
without scattering or leaking out. In a recent experiment, topological
insulator lasers have been demonstrated using a square lattice of coupled
semiconductor microring resonators with a synthetic magnetic field. However,
laser arrays with slow population dynamics are likely to show dynamical
instabilities in a wide range of parameter space corresponding to realistic
experimental conditions, thus preventing stable laser operation. While
topological insulator lasers provide an interesting mean for combating disorder
and help collective oscillation of lasers at the edge of the lattice, it is not
clear whether chiral edge states are immune to dynamical instabilities. In this
work we consider a realistic model of semiconductor class-B topological
insulator laser and show that chiral edge states are not immune to dynamical
instabilities.


  This paper considers the problem of manipulating a uniformly rotating chain:
the chain is rotated at a constant angular speed around a fixed axis using a
robotic manipulator. Manipulation is quasi-static in the sense that transitions
are slow enough for the chain to be always in "rotational" equilibrium. The
curve traced by the chain in a rotating plane -- its shape function -- can be
determined by a simple force analysis, yet it possesses complex multi-solutions
behavior typical of non-linear systems. We prove that the configuration space
of the uniformly rotating chain is homeomorphic to a two-dimensional surface
embedded in $\mathbb{R}^3$. Using that representation, we devise a manipulation
strategy for transiting between different rotation modes in a stable and
controlled manner. We demonstrate the strategy on a physical robotic arm
manipulating a rotating chain. Finally, we discuss how the ideas developed here
might find fruitful applications in the study of other flexible objects, such
as elastic rods or concentric tubes.


  The rapid development of time series forecasting research has brought many
deep learning-based modules in this field. However, despite the increasing
amount of new forecasting architectures, it is still unclear if we have
leveraged the full potential of these existing modules within a properly
designed architecture. In this work, we propose a novel hierarchical neural
architecture search approach for time series forecasting tasks. With the design
of a hierarchical search space, we incorporate many architecture types designed
for forecasting tasks and allow for the efficient combination of different
forecasting architecture modules. Results on long-term-time-series-forecasting
tasks show that our approach can search for lightweight high-performing
forecasting architectures across different forecasting tasks.


  Adversarial Internet robots (botnets) represent a growing threat to the safe
use and stability of the Internet. Botnets can play a role in launching
adversary reconnaissance (scanning and phishing), influence operations
(upvoting), and financing operations (ransomware, market manipulation, denial
of service, spamming, and ad click fraud) while obfuscating tailored tactical
operations. Reducing the presence of botnets on the Internet, with the
aspirational target of zero, is a powerful vision for galvanizing policy
action. Setting a global goal, encouraging international cooperation, creating
incentives for improving networks, and supporting entities for botnet takedowns
are among several policies that could advance this goal. These policies raise
significant questions regarding proper authorities/access that cannot be
answered in the abstract. Systems analysis has been widely used in other
domains to achieve sufficient detail to enable these questions to be dealt with
in concrete terms. Defeating botnets using an observe-pursue-counter
architecture is analyzed, the technical feasibility is affirmed, and the
authorities/access questions are significantly narrowed. Recommended next steps
include: supporting the international botnet takedown community, expanding
network observatories, enhancing the underlying network science at scale,
conducting detailed systems analysis, and developing appropriate policy
frameworks.