  Linear Vector AutoRegressive (VAR) models where the innovations could be
unconditionally heteroscedastic and serially dependent are considered. The
volatility structure is deterministic and quite general, including breaks or
trending variances as special cases. In this framework we propose Ordinary
Least Squares (OLS), Generalized Least Squares (GLS) and Adaptive Least Squares
(ALS) procedures. The GLS estimator requires the knowledge of the time-varying
variance structure while in the ALS approach the unknown variance is estimated
by kernel smoothing with the outer product of the OLS residuals vectors.
Different bandwidths for the different cells of the time-varying variance
matrix are also allowed. We derive the asymptotic distribution of the proposed
estimators for the VAR model coefficients and compare their properties. In
particular we show that the ALS estimator is asymptotically equivalent to the
infeasible GLS estimator. This asymptotic equivalence is obtained uniformly
with respect to the bandwidth(s) in a given range and hence justifies
data-driven bandwidth rules. Using these results we build Wald tests for the
linear Granger causality in mean which are adapted to VAR processes driven by
errors with a non stationary volatility. It is also shown that the commonly
used standard Wald test for the linear Granger causality in mean is potentially
unreliable in our framework. Monte Carlo experiments illustrate the use of the
different estimation approaches for the analysis of VAR models with stable
innovations.


  This paper has been withdraw by the auhor due to a mistake in classification
of the algebra


  We study a model of congestible resources, where pricing and scheduling are
intertwined. Motivated by the problem of pricing cloud instances, we model a
cloud computing service as linked $GI/GI/\cdot$ queuing systems where the
provider chooses to offer a fixed pricing service, a dynamic market based
service, or a hybrid of both, where jobs can be preempted in the market-based
service. Users (jobs), who are heterogeneous in both the value they place on
service and their cost for waiting, then choose between the services offered.
Combining insights from auction theory with queuing theory we are able to
characterize user equilibrium behavior, and show its insensitivity to the
precise market design mechanism used. We then provide theoretical and
simulation based evidence suggesting that a fixed price typically, though not
always, generates a higher expected revenue than the hybrid system for the
provider.


  The canonical structure of pure Yang-Mills theory is analysed in the case
when Gauss' law is satisfied identically by construction. It is shown that the
theory has a canonical structure in this case, provided one uses a special
gauge condition, which is a natural generalisation of the Coulomb gauge
condition of electrodynamics. The emergence of a canonical structure depends
critically also on the boundary conditions used for the relevant field
variables. Possible boundary conditions are analysed in detail. A comparison of
the present formulation in the generalised Coulomb gauge with the well known
Weyl gauge ($A_{0} = 0$) formulation is made. It appears that the Hamiltonians
in these two formulations differ from one another in a non-trivial way. It is
still an open question whether these differences give rise to truly different
structures upon quantisation. An extension of the formalism to include coupling
to fermionic fields is briefly discussed.


  We report the discovery of the partially eclipsing binary LP 133-373. Nearly
identical eclipses along with observed photometric colors and spectroscopy
indicate that it is a pair of chromospherically active dM4 stars in a circular
1.6 d orbit. Light and velocity curve modeling to our differential photometry
and velocity data show that each star has a mass and radius of 0.340+/-0.014
M_sun and 0.33+/-0.02 R_sun. The binary is itself part of a common proper
motion pair with LP 133-374 a cool DC or possible DA white dwarf with a mass of
0.49-0.82 M_sun, which would make the system at least 3 Gyr old.


  A system consisting of two neutral spin 1/2 particles is analyzed for two
magnetic field perturbations: 1) an inhomogeneous magnetic field over all
space, and 2) external fields over a half space containing only one of the
particles. The field is chosen to point from one particle to the other, which
results in essentially a one-dimensional problem. A number of interesting
features are revealed for the first case: the singlet, which has zero potential
energy in the unperturbed case, remains unstable in the perturbing field. The
spin zero component of the triplet evolves into a bound state with a double
well potential, with the possibility of tunneling. Superposition states can be
constructed which oscillate between entangled and unentangled states. For the
second case, we show that changes in the magnetic field around one particle
affect measurements of the spin of the entangled particle not in the magnetic
field nonlocally. By using protective measurements, we show it is possible in
principle to establish a nonlocal interaction using the two particles, provided
the dipole-dipole potential energy does not vanish and is comparable to the
potential energy of the particle in the external field.


  Finding efficient descriptions of how an environment affects a collection of
discrete quantum systems would lead to new insights into many areas of modern
physics. Markovian, or time-local, methods work well for individual systems,
but for groups a question arises: does system-bath or inter-system coupling
dominate the dissipative dynamics? The answer has profound consequences for the
long-time quantum correlations within the system. We consider two bosonic modes
coupled to a bath. By comparing an exact solution to different Markovian master
equations, we find that a smooth crossover of the equations-of-motion between
dominant inter-system and system-bath coupling exists -- but requires a
non-secular master equation. We predict a singular behaviour of the dynamics,
and show that the ultimate failure of non-secular equations of motion is
essentially a failure of the Markov approximation. Our findings justify the use
of time-local theories throughout the crossover between system-bath dominated
and inter-system-coupling dominated dynamics.


  In this paper we present a multi-rate control architecture for safety
critical systems. We consider a high level planner and a low level controller
which operate at different frequencies. This multi-rate behavior is described
by a piecewise nonlinear model which evolves on a continuous and a discrete
level. First, we present sufficient conditions which guarantee recursive
constraint satisfaction for the closed-loop system. Afterwards, we propose a
control design methodology which leverages Control Barrier Functions (CBFs) for
low level control and Model Predictive Control (MPC) policies for high level
planning. The control barrier function is designed using the full nonlinear
dynamical model and the MPC is based on a simplified planning model. When the
nonlinear system is control affine and the high level planning model is linear,
the control actions are computed by solving convex optimization problems at
each level of the hierarchy. Finally, we show the effectiveness of the proposed
strategy on a simulation example, where the low level control action is updated
at a higher frequency than the high level command.


  This paper proposes and analyzes a new operator splitting method for
stochastic Maxwell equations driven by additive noise, which not only
decomposes the original multi-dimensional system into some local
one-dimensional subsystems, but also separates the deterministic and stochastic
parts. This method is numerically efficient, and preserves the symplecticity,
the multi-symplecticity as well as the growth rate of the averaged energy. A
detailed $H^2$-regularity analysis of stochastic Maxwell equations is obtained,
which is a crucial prerequisite of the error analysis. Under the regularity
assumptions of the initial data and the noise, the convergence order one in
mean square sense of the operator splitting method is established.


  The contracting boundary of a proper geodesic metric space generalizes the
Gromov boundary of a hyperbolic space. It consists of contracting geodesics up
to bounded Hausdorff distances. Another generalization of the Gromov boundary
is the $\kappa$-Morse boundary with a sublinear function $\kappa$. The two
generalizations model the Gromov boundary based on different characteristics of
geodesics in Gromov hyperbolic spaces. It was suspected that the $\kappa$-Morse
boundary contains the contracting boundary. We will prove this conjecture: when
$\kappa =1$ is the constant function, the 1-Morse boundary and the contracting
boundary are equivalent as topological spaces.


  We study linear perturbations of a rotating black hole solution that has been
recently discovered in degenerate higher-order scalar-tensor (DHOST) theories.
We find a parametrization which permits the explicit resolution of the scalar
perturbation while the tensor perturbation is obtained as a Teukolsky equation
supplemented by an effective source term. The effective source term is related
to the black hole hair and can be computed exactly for any value of the black
hole spin. We discuss how the perturbations of the geometry and thus the
emitted gravitational waves could be modified in comparison with general
relativity.


  In this work, we use algebraic methods for studying distance computation and
subgraph detection tasks in the congested clique model. Specifically, we adapt
parallel matrix multiplication implementations to the congested clique,
obtaining an $O(n^{1-2/\omega})$ round matrix multiplication algorithm, where
$\omega < 2.3728639$ is the exponent of matrix multiplication. In conjunction
with known techniques from centralised algorithmics, this gives significant
improvements over previous best upper bounds in the congested clique model. The
highlight results include:
  -- triangle and 4-cycle counting in $O(n^{0.158})$ rounds, improving upon the
$O(n^{1/3})$ triangle detection algorithm of Dolev et al. [DISC 2012],
  -- a $(1 + o(1))$-approximation of all-pairs shortest paths in $O(n^{0.158})$
rounds, improving upon the $\tilde{O} (n^{1/2})$-round $(2 +
o(1))$-approximation algorithm of Nanongkai [STOC 2014], and
  -- computing the girth in $O(n^{0.158})$ rounds, which is the first
non-trivial solution in this model.
  In addition, we present a novel constant-round combinatorial algorithm for
detecting 4-cycles.


  We discuss the structure of negacyclic codes of odd length over the ring
$\mathbb{F}_p[u, v]/ \langle u^2, v^2, uv-vu \rangle$. We find the unique
generating set, the rank and the minimum distance for these negacyclic codes.


  We consider the small deformation of the point-like Wilson loop in the
3-dimensional $\mathcal{N}=6$ superconformal Chern-Simons theory. By Taylor
expansion of the point-like Wilson loop in powers of the loop variables, we
obtain the BPS operators that correspond to the excited string states of the
dual IIA string theory on the pp wave background. The BPS conditions of the
Wilson loop constrain both the loop variables and the forms of the operators
obtained in the Taylor expansion.


  We introduce an algebraic system which can be used as a model for spaces with
geodesic paths between any two of their points. This new algebraic structure is
based on the notion of mobility algebra which has recently been introduced as a
model for the unit interval of real numbers. Mobility algebras consist on a set
$A$ together with three constants and a ternary operation. In the case of the
closed unit interval $A=[0,1]$, the three constants are 0, 1 and 1/2 while the
ternary operation is $p(x,y,z)=x-yx+yz$. A mobility space is a set $X$ together
with a map $q\colon{X\times A\times X\to X}$ with the meaning that $q(x,t,y)$
indicates the position of a particle moving from point $x$ to point $y$ at the
instant $t\in A$, along a geodesic path within the space $X$. A mobility space
is thus defined with respect to a mobility algebra, in the same way as a module
is defined over a ring. We introduce the axioms for mobility spaces,
investigate the main properties and give examples. We also establish the
connection between the algebraic context and the one of spaces with geodesic
paths. The connection with affine spaces is briefly mentioned.


  Riboswitches, structured elements in the untranslated regions of messenger
RNAs, regulate gene expression by binding specific metabolites. We introduce a
kinetic network model that describes the functions of riboswitches at the
systems level. Using experimental data for flavin mono nucleotide riboswitch as
a guide we show that efficient function, implying a large dynamic range without
compromising the requirement to suppress transcription, is determined by a
balance between the transcription speed, the folding and unfolding rates of the
aptamer, and the binding rates of the metabolite. We also investigated the
effect of negative feedback accounting for binding to metabolites, which are
themselves the products of genes that are being regulated. For a range of
transcription rates negative feedback suppresses gene expression by nearly 10
fold. Negative feedback speeds the gene expression response time, and
suppresses the change of steady state protein concentration by half relative to
that without feedback, when there is a modest spike in DNA concentration. A
dynamic phase diagram expressed in terms of transcription speed, folding rates,
and metabolite binding rates predicts different scenarios in
riboswitch-mediated transcription regulation.


  Graphs have been widely used in real-world applications, in which
investigating relations between vertices is an important task. In this paper,
we study the problem of generating the k-hop-constrained s-t simple path graph,
i.e., the subgraph consisting of all simple paths from vertex s to vertex t of
length no larger than k. To our best knowledge, we are the first to formalize
this problem and prove its NP-hardness on directed graphs. To tackle this
challenging problem, we propose an efficient algorithm named EVE, which
exploits the paradigm of edge-wise examination rather than exhaustively
enumerating all paths. Powered by essential vertices appearing in all simple
paths between vertex pairs, EVE distinguishes the edges that are definitely (or
not) contained in the desired simple path graph, producing a tight upper-bound
graph in the time cost $\mathcal{O}(k^2|E|)$. Each remaining undetermined edge
is further verified to deliver the exact answer. Extensive experiments are
conducted on 15 real networks. The results show that EVE significantly
outperforms all baselines by several orders of magnitude. Moreover, by taking
EVE as a built-in block, state-of-the-art for hop-constrained simple path
enumeration can be accelerated by up to an order of magnitude.


  Small teams in the field can benefit from the capabilities provided by small
Uncrewed Aerial Systems (sUAS) for missions such as reconnaissance, hostile
attribution, remote emplacement, and search and rescue. The mobility,
communications, and flexible payload capacity of sUAS can offer teams new
levels of situational awareness and enable more highly coordinated missions
than previously possible. However, piloting such aircraft for specific missions
draws personnel away from other mission-critical tasks, increasing the load on
remaining personnel while also increasing complexity of operations. For wider
adoption and use of sUAS for security and humanitarian missions, safe and
robust autonomy must be employed to reduce this burden on small teams. In this
paper, we present the development of the Collaborative-UAS for Hostile
Attribution, Surveillance, Emplacement, and Reconnaissance (CHASER) testbed,
for rapidly prototyping capabilities that will reduce strain on small teams
through sensor-guided autonomous control. We attempt to address autonomy needs
unfilled by commercial sUAS platforms by creating and testing a series of
composable modules that can be configured to support multiple missions. Methods
implemented and presented here include radar track correlation, on-board
computer vision target detection, target position estimation, closed-loop
relative position control, and efficient search of a 3D volume for target
acquisition. We configure and test a series of these modules in an example
mission, executing a fully autonomous chase of an intruding sUAS in live
flight, and demonstrating the success of the modularized autonomy approach. We
present performance results from simulation or live flight tests for each
module. Lastly, we describe the software architecture that we have developed
for flexible controls and comment on how the capabilities presented may enable
additional missions.


  Let $\mu$ be the M\"{o}bius function and let $k \geq 1$. We prove that the
Gowers $U^k$-norm of $\mu$ restricted to progressions $\{n \leq X: n\equiv
a_q\pmod{q}\}$ is $o(1)$ on average over $q\leq X^{1/2-\sigma}$ for any $\sigma
> 0$, where $a_q\pmod{q}$ is an arbitrary residue class with $(a_q,q) = 1$.
This generalizes the Bombieri-Vinogradov inequality for $\mu$, which
corresponds to the special case $k=1$.


  The multifractal nature of drop breakup in air-blast nozzle atomization
process has been studied. We apply the multiplier method to extract the
negative and the positive parts of the f(alpha) curve with the data of drop
size distribution measured using Dual PDA. A random multifractal model with the
multiplier triangularly distributed is proposed to characterize the breakup of
drops. The agreement of the left part of the multifractal spectra between the
experimental result and the model is remarkable. The cause of the distinction
of the right part of the f(alpha) curve is argued. The fact that negative
dimensions arise in the current system means that the spatial distribution of
the drops yielded by the high-speed jet fluctuates from sample to sample. On
other words, the spatial concentration distribution of the disperse phase in
the spray zone fluctuates momentarily showing intrinsic randomness.


  In our recent paper (Yadav et al. 2007) we described a fast cubic
(bispectrum) estimator of the amplitude of primordial non-Gaussianity of local
type, f_{NL}, from a combined analysis of the Cosmic Microwave Background (CMB)
temperature and E-polarization observations. In this paper we generalize the
estimator to deal with a partial sky coverage as well as inhomogeneous noise.
Our generalized estimator is still computationally efficient, scaling as
O(N^3/2) compared to the O(N^5/2) scaling of the brute force bispectrum
calculation for sky maps with N pixels. Upcoming CMB experiments are expected
to yield high-sensitivity temperature and E-polarization data. Our generalized
estimator will allow us to optimally utilize the combined CMB temperature and
E-polarization information from these realistic experiments, and to constrain
primordial non-Gaussianity.


  Abstraction -- the process of generalizing specific examples into broad
reusable patterns -- is central to how people efficiently process and store
information and apply their knowledge to new data. Promisingly, research has
shown that ML models learn representations that span levels of abstraction,
from specific concepts like "bolo tie" and "car tire" to more general concepts
like "CEO" and "model". However, existing techniques analyze these
representations in isolation, treating learned concepts as independent
artifacts rather than an interconnected web of abstraction. As a result,
although we can identify the concepts a model uses to produce its output, it is
difficult to assess if it has learned a human-aligned abstraction of the
concepts that will generalize to new data. To address this gap, we introduce
abstraction alignment, a methodology to measure the agreement between a model's
learned abstraction and the expected human abstraction. We quantify abstraction
alignment by comparing model outputs against a human abstraction graph, such as
linguistic relationships or medical disease hierarchies. In evaluation tasks
interpreting image models, benchmarking language models, and analyzing medical
datasets, abstraction alignment provides a deeper understanding of model
behavior and dataset content, differentiating errors based on their agreement
with human knowledge, expanding the verbosity of current model quality metrics,
and revealing ways to improve existing human abstractions.


  Implicit processes (IPs) are a generalization of Gaussian processes (GPs).
IPs may lack a closed-form expression but are easy to sample from. Examples
include, among others, Bayesian neural networks or neural samplers. IPs can be
used as priors over functions, resulting in flexible models with
well-calibrated prediction uncertainty estimates. Methods based on IPs usually
carry out function-space approximate inference, which overcomes some of the
difficulties of parameter-space approximate inference. Nevertheless, the
approximations employed often limit the expressiveness of the final model,
resulting, e.g., in a Gaussian predictive distribution, which can be
restrictive. We propose here a multi-layer generalization of IPs called the
Deep Variational Implicit process (DVIP). This generalization is similar to
that of deep GPs over GPs, but it is more flexible due to the use of IPs as the
prior distribution over the latent functions. We describe a scalable
variational inference algorithm for training DVIP and show that it outperforms
previous IP-based methods and also deep GPs. We support these claims via
extensive regression and classification experiments. We also evaluate DVIP on
large datasets with up to several million data instances to illustrate its good
scalability and performance.


  We list more than 200 new examples of minor minimal intrinsically knotted
graphs and describe many more that are intrinsically knotted and likely minor
minimal.


  We derive an equation for the cooling dynamics of the quantum motion of an
atom trapped by an external potential inside an optical resonator. This
equation has broad validity and allows us to identify novel regimes where the
motion can be efficiently cooled to the potential ground state. Our result
shows that the motion is critically affected by quantum correlations induced by
the mechanical coupling with the resonator, which may lead to selective
suppression of certain transitions for the appropriate parameters regimes,
thereby increasing the cooling efficiency.


  We show a near optimal direct-sum theorem for the two-party randomized
communication complexity. Let $f\subseteq X \times Y\times Z$ be a relation,
$\varepsilon> 0$ and $k$ be an integer. We show,
$$\mathrm{R}^{\mathrm{pub}}_\varepsilon(f^k) \cdot
\log(\mathrm{R}^{\mathrm{pub}}_\varepsilon(f^k)) \ge \Omega(k \cdot
\mathrm{R}^{\mathrm{pub}}_\varepsilon(f)) \enspace,$$ where $f^k= f \times
\ldots \times f$ ($k$-times) and $\mathrm{R}^{\mathrm{pub}}_\varepsilon(\cdot)$
represents the public-coin randomized communication complexity with worst-case
error $\varepsilon$. Given a protocol $\mathcal{P}$ for $f^k$ with
communication cost $c \cdot k$ and worst-case error $\varepsilon$, we exhibit a
protocol $\mathcal{Q}$ for $f$ with external-information-cost $O(c)$ and
worst-error $\varepsilon$. We then use a message compression protocol due to
Barak, Braverman, Chen and Rao [2013] for simulating $\mathcal{Q}$ with
communication $O(c \cdot \log(c\cdot k))$ to arrive at our result. To show this
reduction we show some new chain-rules for capacity, the maximum information
that can be transmitted by a communication channel. We use the powerful concept
of Nash-Equilibrium in game-theory, and its existence in suitably defined
games, to arrive at the chain-rules for capacity. These chain-rules are of
independent interest.


  Full-dimensional (FD) multi-user massive multiple input multiple output
(m-MIMO) systems employ large two-dimensional (2D) rectangular antenna arrays
to control both the azimuth and elevation angles of signal transmission. We
introduce the sum of two outer products of the azimuth and elevation
beamforming vectors having moderate dimensions as a new class of FD
beamforming. We show that this low-complexity class is capable of outperforming
2D beamforming relying on the single outer product of the azimuth and elevation
beamforming vectors. It is also capable of performing close to its FD
counterpart of massive dimensions in terms of either the users minimum rate or
their geometric mean rate (GM-rate), or sum rate (SR). Furthermore, we also
show that even FD beamforming may be outperformed by our outer product-based
improper Gaussian signaling solution. Explicitly, our design is based on
low-complexity algorithms relying on convex problems of moderate dimensions for
max-min rate optimization or on closed-form expressions for GM-rate and SR
maximization.


  We report our investigation on the input signal amplification in
unidirectionally coupled monostable Duffing oscillators in one- and
two-dimensions with first oscillator alone driven by a weak periodic signal.
Applying a perturbation theory we obtain a set of nonlinear equations for the
response amplitude of the coupled oscillators. We identify the conditions for
undamped signal propagation with enhanced amplitude through the coupled
oscillators. When the number of oscillators increases the response amplitude
approaches a limiting value. We determine this limiting value. Also, we analyse
the signal amplification in the coupled oscillators in two-dimensions with
fraction of oscillators chosen randomly for coupling and forcing.


  Colombeau's generalized functions are used to adapt the distributional
approach to singular hypersurfaces in general relativity with signature change.
Equations governing the dynamics of singular hypersurface is obtained and it is
shown that the stress-energy tensor of the surface can be non-vanishing.


  It is revealed that there is a link between the quantization approach
employed and the dimension of the vector parameter which can be accurately
estimated by a quantized estimation system. A critical quantity called
inestimable dimension for quantized data (IDQD) is introduced, which doesn't
depend on the quantization regions and the statistical models of the
observations but instead depends only on the number of sensors and on the
precision of the vector quantizers employed by the system. It is shown that the
IDQD describes a quantization induced fundamental limitation on the estimation
capabilities of the system. To be specific, if the dimension of the desired
vector parameter is larger than the IDQD of the quantized estimation system,
then the Fisher information matrix for estimating the desired vector parameter
is singular, and moreover, there exist infinitely many nonidentifiable vector
parameter points in the vector parameter space. Furthermore, it is shown that
under some common assumptions on the statistical models of the observations and
the quantization system, a smaller IDQD can be obtained, which can specify an
even more limiting quantization induced fundamental limitation on the
estimation capabilities of the system.


  We study a two-point flux approximation finite volume scheme for a
cross-diffusion system. The scheme is shown to preserve the key properties of
the continuous systems, among which the decay of the entropy. The convergence
of the scheme is established thanks to compactness properties based on the
discrete entropy - entropy dissipation estimate. Numerical results illustrate
the behavior of our scheme.


  In this paper we give the answers to two open questions on complex symmetric
composition operators. By doing this, we give a complete description of complex
symmetric composition operators whose symbols are linear fractional.


  Space observatories have provided unprecedented depictions of the many
variability behaviors typical of low-mass, young stars. However, those studies
have so far largely omitted more massive objects ($\sim$2 $M_\odot$ to 4-5
$M_\odot$), and were limited by the absence of simultaneous, multi-wavelength
information. We present a new study of young star variability in the $\sim$1-2
Myr-old, massive Lagoon Nebula region. Our sample encompasses 278 young, late-B
to K-type stars, monitored with Kepler/K2. Auxiliary $u,g,r,i,H\alpha$ time
series photometry, simultaneous with K2, was acquired at the Paranal
Observatory. We employed this comprehensive dataset and archival infrared
photometry to determine individual stellar parameters, assess the presence of
circumstellar disks, and tie the variability behaviors to inner disk dynamics.
We found significant mass-dependent trends in variability properties, with B/A
stars displaying substantially reduced levels of variability compared to G/K
stars for any light curve morphology. These properties suggest different
magnetic field structures at the surface of early-type and later-type stars. We
also detected a dearth of some disk-driven variability behaviors, particularly
dippers, among stars earlier than G. This indicates that their higher surface
temperatures and more chaotic magnetic fields prevent the formation and
survival of inner disk dust structures co-rotating with the star. Finally, we
examined the characteristic variability timescales within each light curve, and
determined that the day-to-week timescales are predominant over the K2 time
series. These reflect distinct processes and locations in the inner disk
environment, from intense accretion triggered by instabilities in the innermost
disk regions, to variable accretion efficiency in the outer magnetosphere.


  In specific types of partially rectangular billiards we estimate the mass of
an eigenfunction of energy $E$ in the region outside the rectangular set in the
high-energy limit. We use the adiabatic ansatz to compare the Dirichlet energy
form with a second quadratic form for which separation of variables applies.
This allows us to use sharp one-dimensional control estimates and to derive the
bound assuming that $E$ is not resonating with the Dirichlet spectrum of the
rectangular part.


  Understanding human affective behaviour, especially in the dynamics of
real-world settings, requires Facial Expression Recognition (FER) models to
continuously adapt to individual differences in user expression, contextual
attributions, and the environment. Current (deep) Machine Learning (ML)-based
FER approaches pre-trained in isolation on benchmark datasets fail to capture
the nuances of real-world interactions where data is available only
incrementally, acquired by the agent or robot during interactions. New learning
comes at the cost of previous knowledge, resulting in catastrophic forgetting.
Lifelong or Continual Learning (CL), on the other hand, enables adaptability in
agents by being sensitive to changing data distributions, integrating new
information without interfering with previously learnt knowledge. Positing CL
as an effective learning paradigm for FER, this work presents the Continual
Facial Expression Recognition (ConFER) benchmark that evaluates popular CL
techniques on FER tasks. It presents a comparative analysis of several CL-based
approaches on popular FER datasets such as CK+, RAF-DB, and AffectNet and
present strategies for a successful implementation of ConFER for Affective
Computing (AC) research. CL techniques, under different learning settings, are
shown to achieve state-of-the-art (SOTA) performance across several datasets,
thus motivating a discussion on the benefits of applying CL principles towards
human behaviour understanding, particularly from facial expressions, as well
the challenges entailed.


  We study the problem of partitioning the vertex set of a given graph so that
each part induces a graph with components of bounded order; we are also
interested in restricting these components to be paths. In particular, we say a
graph $G$ admits an $({\cal I}, {\cal O}_k)$-partition if its vertex set can be
partitioned into an independent set and a set that induces a graph with
components of order at most $k$. We prove that every graph $G$ with
$\operatorname{mad}(G)<\frac 52$ admits an $({\cal I}, {\cal O}_3)$-partition.
This implies that every planar graph with girth at least $10$ can be
partitioned into an independent set and a set that induces a graph whose
components are paths of order at most 3. We also prove that every graph $G$
with $\operatorname{mad}(G) < \frac{8k}{3k+1} = \frac{8}{3}\left( 1 -
\frac{1}{3k+1} \right)$ admits an $({\cal I}, {\cal O}_k)$-partition. This
implies that every planar graph with girth at least $9$ can be partitioned into
an independent set and a set that induces a graph whose components are paths of
order at most 9.


  We derive a general expression that quantifies the total entanglement
production rate in continuous variable systems, where a source emits two
entangled Gaussian beams with arbitrary correlators.This expression is
especially useful for situations where the source emits an arbitrary frequency
spectrum,e.g. when cavities are involved. To exemplify its meaning and
potential, we apply it to a four-mode optomechanical setup that enables the
simultaneous up- and down-conversion of photons from a drive laser into
entangled photon pairs. This setup is efficient in that both the drive and the
optomechanical up- and down-conversion can be fully resonant.


  Causal discovery serves a pivotal role in mitigating model uncertainty
through recovering the underlying causal mechanisms among variables. In many
practical domains, such as healthcare, access to the data gathered by
individual entities is limited, primarily for privacy and regulatory
constraints. However, the majority of existing causal discovery methods require
the data to be available in a centralized location. In response, researchers
have introduced federated causal discovery. While previous federated methods
consider distributed observational data, the integration of interventional data
remains largely unexplored. We propose FedCDI, a federated framework for
inferring causal structures from distributed data containing interventional
samples. In line with the federated learning framework, FedCDI improves privacy
by exchanging belief updates rather than raw samples. Additionally, it
introduces a novel intervention-aware method for aggregating individual
updates. We analyze scenarios with shared or disjoint intervened covariates,
and mitigate the adverse effects of interventional data heterogeneity. The
performance and scalability of FedCDI is rigorously tested across a variety of
synthetic and real-world graphs.


  Let $(S,E)$ be a log Calabi-Yau surface pair with $E$ a smooth divisor. We
define new conjecturally integer-valued counts of $\mathbb{A}^1$-curves in
$(S,E)$. These log BPS numbers are derived from genus 0 log Gromov-Witten
invariants of maximal tangency along $E$ via a formula analogous to the
multiple cover formula for disk counts. A conjectural relationship to genus 0
local BPS numbers is described and verified for del Pezzo surfaces and curve
classes of arithmetic genus up to 2. We state a number of conjectures and
provide computational evidence.


  We investigate the interaction between the existence of reproducing kernels
on infinite-dimensional Hermitian vector bundles and the positivity properties
of the corresponding bundles. The positivity refers to the curvature form of
certain covariant derivatives associated to reproducing kernels on the vector
bundles under consideration. The values of the curvature form are Hilbert space
operators, and its positivity is thus understood in the usual sense from
operator theory.


  Reconfigurable intelligent surfaces (RIS) as an effective technique for
intelligently manipulating channel paths through reflection to serve desired
users. Full-duplex (FD) systems, enabling simultaneous transmission and
reception from a base station (BS), offer the theoretical advantage of doubled
spectrum efficiency. However, the presence of strong self-interference (SI) in
FD systems significantly degrades performance, which can be mitigated by
leveraging the capabilities of RIS. In this work, we consider joint BS and RIS
beamforming for maximizing the downlink (DL) transmission rate while
guaranteeing uplink (UL) rate requirement. We propose an FD-RIS beamforming
(FRIS) scheme by adopting penalty convex-concave programming. Simulation
results demonstrate the UL/DL rate improvements achieved by considering various
levels of imperfect CSI. The proposed FRIS scheme validates their effectiveness
across different RIS deployments and RIS/BS configurations. FRIS has achieved
the highest rate compared to the other approximation method, conventional
beamforming techniques, HD systems, and deployment without RIS.


  The present paper proves that if for a power sum $\alpha$ over $\ZZ$ the
length of the period of the continued fraction for $\sqrt{\alpha(n)}$ is
constant for infinitely many even (resp. odd) $n$, then $\sqrt{\alpha(n)}$
admits a functional continued fraction expansion for all even (resp. odd) $n$,
except finitely many; in particular, for such $n$, the partial quotients can be
expressed by power sums of the same kind.


  A sample of 196 AGNs and ULIRGs observed by the Infrared Spectrograph (IRS)
on Spitzer is analyzed to study the distribution of the strength of the 9.7
micron silicate feature. Average spectra are derived for quasars, Seyfert 1 and
Seyfert 2 AGNs, and ULIRGs. We find that quasars are characterized by silicate
features in emission and Seyfert 1s equally by emission or weak absorption.
Seyfert 2s are dominated by weak silicate absorption, and ULIRGs are
characterized by strong silicate absorption (mean apparent optical depth about
1.5). Luminosity distributions show that luminosities at rest frame 5.5 micron
are similar for the most luminous quasars and ULIRGs and are almost 10^5 times
more luminous than the least luminous AGN in the sample. The distributions of
spectral characteristics and luminosities are compared to those of optically
faint infrared sources at z~2 being discovered by the IRS, which are also
characterized by strong silicate absorption. It is found that local ULIRGs are
a similar population, although they have lower luminosities and somewhat
stronger absorption compared to the high redshift sources.


  It is often stated that the removal of gas by ram pressure stripping of a
galaxy disk is not a common process in galaxy groups. In this study, with the
aid of an observational classification of galaxies and a simple physical model,
we show that this may not be true. We examined and identified 45 ram-pressure
stripped galaxy candidates from a sample of 1311 galaxy group members within
125 spectroscopically-selected galaxy groups. 13 of these galaxies are the most
secure candidates with multiple distinct features. These candidate ram pressure
stripped galaxies have similar properties to those found in clusters -- they
occur at a range of stellar masses, are largely blue and star-forming and have
phase-space distributions consistent with being first infallers into their
groups. The only stand-out feature of these candidates is they exist not in
clusters, but in groups, with a median halo mass of 10$^{13.5}$ M$_\odot$.
Although this may seem surprising, we employ an analytic model of the expected
ram pressure stripping force in groups and find that reasonable estimates of
the relevant infall speeds and intragroup medium content would result in ram
pressure stripped galaxies at these halo masses. Finally, given the
considerable uncertainty on the lifetime of the ram-pressure phase, this
physical mechanism could be the dominant quenching mechanism in galaxy groups,
if our ram pressure stripped candidates can be confirmed.


  In this paper, we present CaveSeg - the first visual learning pipeline for
semantic segmentation and scene parsing for AUV navigation inside underwater
caves. We address the problem of scarce annotated training data by preparing a
comprehensive dataset for semantic segmentation of underwater cave scenes. It
contains pixel annotations for important navigation markers (e.g. caveline,
arrows), obstacles (e.g. ground plane and overhead layers), scuba divers, and
open areas for servoing. Through comprehensive benchmark analyses on cave
systems in USA, Mexico, and Spain locations, we demonstrate that robust deep
visual models can be developed based on CaveSeg for fast semantic scene parsing
of underwater cave environments. In particular, we formulate a novel
transformer-based model that is computationally light and offers near real-time
execution in addition to achieving state-of-the-art performance. Finally, we
explore the design choices and implications of semantic segmentation for visual
servoing by AUVs inside underwater caves. The proposed model and benchmark
dataset open up promising opportunities for future research in autonomous
underwater cave exploration and mapping.


  We present Ego3DPose, a highly accurate binocular egocentric 3D pose
reconstruction system. The binocular egocentric setup offers practicality and
usefulness in various applications, however, it remains largely under-explored.
It has been suffering from low pose estimation accuracy due to viewing
distortion, severe self-occlusion, and limited field-of-view of the joints in
egocentric 2D images. Here, we notice that two important 3D cues, stereo
correspondences, and perspective, contained in the egocentric binocular input
are neglected. Current methods heavily rely on 2D image features, implicitly
learning 3D information, which introduces biases towards commonly observed
motions and leads to low overall accuracy. We observe that they not only fail
in challenging occlusion cases but also in estimating visible joint positions.
To address these challenges, we propose two novel approaches. First, we design
a two-path network architecture with a path that estimates pose per limb
independently with its binocular heatmaps. Without full-body information
provided, it alleviates bias toward trained full-body distribution. Second, we
leverage the egocentric view of body limbs, which exhibits strong perspective
variance (e.g., a significantly large-size hand when it is close to the
camera). We propose a new perspective-aware representation using trigonometry,
enabling the network to estimate the 3D orientation of limbs. Finally, we
develop an end-to-end pose reconstruction network that synergizes both
techniques. Our comprehensive evaluations demonstrate that Ego3DPose
outperforms state-of-the-art models by a pose estimation error (i.e., MPJPE)
reduction of 23.1% in the UnrealEgo dataset. Our qualitative results highlight
the superiority of our approach across a range of scenarios and challenges.


  We reexamine the conventional physical description of the neutrino evolution
inside the Sun. We point out that the traditional resonance condition has
physical meaning only in the limit of small values of the neutrino mixing
angle, theta<<1. For large values of theta, the resonance condition specifies
neither the point of the maximal violation of adiabaticity in the nonadiabatic
case, nor the point where the flavor conversion occurs at the maximal rate in
the adiabatic case. The corresponding correct conditions, valid for all values
of theta including theta>pi/4, are presented. An adiabaticity condition valid
for all values of theta is also described. The results of accurate numerical
computations of the level jumping probability in the Sun are presented. These
calculations cover a wide range of Delta m^2, from the vacuum oscillation
region to the region where the standard exponential approximation is good. A
convenient empirical parametrization of these results in terms of elementary
functions is given. The matter effects in the so-called "quasi-vacuum
oscillation regime" are discussed. Finally, it is shown how the known
analytical results for the exponential, 1/x, and linear matter distributions
can be simply obtained from the formula for the hyperbolic tangent profile. An
explicit formula for the jumping probability for the distribution N_e ~
(coth(x/l) +- 1) is obtained.


  Unit test environments are today's state of the art for many programming
languages to keep the software's quality above a certain level. However, the
software's syntactic quality necessary for the developers themselves is not
covered by the aforementioned frameworks. This paper presents a tool realized
using the DSL framework MontiCore for automatically validating easily
extensible coding guidelines for any domain specific language or even general
purpose languages like C++ and its application in an automotive R&D project
where a German OEM and several suppliers were involved. Moreover, it was
exemplary applied on UML/P-based sequence charts as well.


  Clustering aims to divide a set of points into groups. The current paradigm
assumes that the grouping is well-defined (unique) given the probability model
from which the data is drawn. Yet, recent experiments have uncovered several
high-dimensional datasets that form different binary groupings after projecting
the data to randomly chosen one-dimensional subspaces. This paper describes a
probability model for the data that could explain this phenomenon. It is a
simple model to serve as a proof of concept for understanding the geometry of
high-dimensional data. We start by building a rescaled multivariate Bernouilli
model (stretched hypercube) so to create several overlapping grouping
structures in the data. The size of each scaling parameter is related to the
likelihood of uncovering the corresponding grouping by random 1D projection.
Clusters in the original space are then created by adding noise to this
cluster-free model. In high dimension, these clusters would hardly be
observable given a sample set from the distribution because of the curse of
dimensionality, but the binary groupings are clear. Our construction makes it
clear that one needs to make a distinction between "groupings" and "clusters"
in the original space. It also highlights the need to interpret any clustering
found in projected data as merely one among potentially many other groupings in
a dataset.


  We study the physical propagating modes in a massive gravity model in curved
cosmological backgrounds, which we have found as classical solutions in our
previous paper. We show that, generically, there exist such the cosmological
background solutions consistent with the equations of motion where we assume
the ghost condensation ansatzes. Using the (1+3)-parametrization of the metric
fluctuations with 'unitary' gauge, we find that there is neither a scalar ghost
nor a tachyon in the spectrum of the propagating modes, the tensor modes become
massive owing to gravitational Higgs mechanism, and the model is free of the
Boulware-Deser instability. The price we have to pay is that the scalar sector
breaks the Lorentz-invariance, but there are no pathologies in the spectrum and
lead to interesting phenomenology. Moreover, we present a proof of the absence
of non-unitary modes for a specific ghost condensation model in a cosmological
background.


  The outer stellar halos of galaxies contain vital information about the
formation history of galaxies, since the relaxation timescales in the outskirts
are long enough to keep the memory, while the information about individual
formation events in the central parts has long been lost due to mixing, star
formation and relaxation. To unveil some of the information encoded in these
faint outer halo regions, we study the stellar outskirts of galaxies selected
from a fully hydrodynamical high resolution cosmological simulation, called
Magneticum. We find that the density profiles of the outer stellar halos of
galaxies over a broad mass range can be well described by an Einasto profile.
For a fixed total mass range, the free parameters of the Einasto fits are
closely correlated. Galaxies which had more (dry) merger events tend to have
lesser curved outer stellar halos, however, we find no indication that the
amount of curvature is correlated with galaxy morphology. The Einasto-like
shape of the outer stellar halo densities can also explain the observed
differences between the Milky Way and Andromeda outer stellar halos.


  Motivated by a variety of theories that predict new effects, we tested the
gravitational 1/r^2 law at separations between 10.77 mm and 137 microns using
two different 10-fold azimuthally symmetric torsion pendulums and rotating
10-fold symmetric attractors. Our work improves upon other experiments by up to
a factor of about 100. We found no deviation from Newtonian physics at the 95%
confidence level and interpret these results as constraints on extensions of
the Standard Model that predict Yukawa or power-law forces. We set a constraint
on the largest single extra dimension (assuming toroidal compactification and
that one extra dimension is significantly larger than all the others) of R <=
160 microns, and on two equal-sized large extra dimensions of R <= 130 microns.
Yukawa interactions with |alpha| >= 1 are ruled out at 95% confidence for
lambda >= 197 microns. Extra-dimensions scenarios stabilized by radions are
restricted to unification masses M >= 3.0 TeV/c^2, regardless of the number of
large extra dimensions. We also provide new constraints on power-law potentials
V(r)\propto r^{-k} with k between 2 and 5 and on the gamma_5 couplings of
pseudoscalars with m <= 10 meV/c^2.


  We discuss a fluid dynamic variant of the classical Bernoulli's
brachistochrone problem. The classical brachistochrone for a non-dissipative
particle is governed by maximization of the particle's kinetic energy resulting
in a cycloid. We consider a variant where the particle is replaced by a bottle
filled with a viscous fluid and attempt to identify the shape of a curve
connecting two points along which the bottle would move in the shortest time.
We derive the system of integro-differential equations governing system
dynamics for a given shape of the curve. Using these equations, we pose the
brachistochrone problem invoking optimal control formalism and show that (in
general) the curve deviates from a cycloid. This is due to the fact that
increasing the rate of change of bottle kinetic energy is accompanied by
increased viscous dissipation. We show that the bottle motion is governed by a
balance between the desire to minimize travel time and the need to reach the
end point in the face of increased dissipation. The trade-off between these two
physical forces plays a vital role in determining the brachistochrone of a
fluid-filled cylinder. We show that in the two limits of either vanishing or
high viscosity, the brachistochrone for this problem reduces to a cycloid. An
intermediate viscosity range is identified where the fluid brachistochrone is
non-cycloidal. Finally, we show relevance of these results to the dynamics of a
rolling liquid marble.


  Bilinear systems of equations are defined, motivated and analyzed for
solvability. Elementary structure is mentioned and it is shown that all
solutions may be obtained as rank one completions of a linear matrix polynomial
derived from elementary operations. This idea is used to identify bilinear
systems that are solvable for all right hand sides and to understand
solvability when the number of equations is large or small.


  As the fundamental phrase of collecting and analyzing data, data integration
is used in many applications, such as data cleaning, bioinformatics and pattern
recognition. In big data era, one of the major problems of data integration is
to obtain the global schema of data sources since the global schema could be
hardly derived from massive data sources directly. In this paper, we attempt to
solve such schema integration problem. For different scenarios, we develop
batch and incremental schema integration algorithms. We consider the
representation difference of attribute names in various data sources and
propose ED Join and Semantic Join algorithms to integrate attributes with
different representations. Extensive experimental results demonstrate that the
proposed algorithms could integrate schemas efficiently and effectively.


  We analyze fast and slow light transmission in a zig-zag microring resonator
chain. This novel device permits the operation in both regimes. In the
superluminal case, a new ubiquitous light transmission effect is found whereby
the input optical pulse is reproduced in an almost simultaneous manner at the
various system outputs. When the input carrier is tuned to a different
frequency, the system permits to slow down the propagating optical signal.
Between these two extreme cases, the relative delay can be tuned within a broad
range.


  We consider $B^{\pm,0} \to \rho^0(\omega)h^{\pm,0}$, where $\rho^0(\omega)$
decays to $\pi^+\pi^-$ and $h$ is any hadronic final state, such as $\pi$ or
$K$. We find a large direct CP asymmetry in B-meson decays via $\rho \omega$
interference. A possible method to determine weak phases, such as $\phi_{2,3}$,
is discussed. The experimental feasibility is also shown.


  A domain wall (DW) in a nanowire can propagate under a longitudinal magnetic
field by emitting spin waves (SWs). We numerically investigated the properties
of SWs emitted by the DW motion, such as frequency and wavenumber, and their
relation with the DW motion. For a wire with a low transverse anisotropy and in
a field above a critical value, a DW emits SWs to both sides (bow and stern),
while it oscillates and propagates at a low average speed. For a wire with a
high transverse anisotropy and in a weak field, the DW emits mostly stern
waves, while the DW distorts itself and DW center propagates forward like a
drill at a relative high speed.


  When modeling a social dynamics with an agent-oriented approach, researchers
have to describe the structure of interactions within the population. Given the
intractability of extensive network collecting, they rely on random network
generators that are supposed to explore the space of plausible networks. We
first identify the needs of modelers, including placing heterogeneous agents on
the network given their attributes and differentiating the various types of
social links that lead to different interactions. We point out the existence of
data in the form of scattered statistics and qualitative observations, that
should be used to parameter the generator. We propose a new approach peculiar
to agent-based modeling, in which we will generate social links from
individuals' observed attributes, and return them as a multiplex network.
Interdependencies between socioeconomic attributes, and generative rules, are
encoded as Bayesian networks. A methodology guides modelers through the
formalization of these parameters. This approach is illustrated by describing
the structure of interactions that supports diffusion of contraceptive
solutions in rural Kenya.


  Classifying consistent effective field theories for the gravitational
interaction has recently been the subject of intense research. Demanding the
absence of causality violation in high energy graviton scattering processes has
led to a hierarchy of constraints on higher derivative terms in the Lagrangian.
Most of these constraints have relied on analysis that is performed in general
relativistic backgrounds, as opposed to a generic solution to the equations of
motion which are perturbed by higher curvature operators. Hence, these
constraints are necessary but may not be sufficient to ensure that the theory
is consistent. In this context, we explore the so-called CEMZ causality
constraints on Quadratic Gravity in a space of shock wave solutions beyond GR.
We show that the Shapiro time delay experienced by a graviton is
polarization-independent and positive, regardless of the strength of the
gravitational couplings. Our analysis shows that as far as the causality
constraints are concerned, albeit inequivalent to General Relativity due to
additional propagating modes, Quadratic Gravity is causal as per as the
diagnostic proposed by CEMZ.


  We use gauge-gravity duality to compute entanglement entropy in a
non-conformal background with an energy scale $\Lambda$. At zero temperature,
we observe that entanglement entropy decreases by raising $\Lambda$. However,
at finite temperature, we realize that both $\frac{\Lambda}{T}$ and
entanglement entropy rise together. Comparing entanglement entropy of the
non-conformal theory, $S_{A(N)}$, and of its conformal theory at the $UV$
limit, $ S_{A(C)}$, reveals that $S_{A(N)}$ can be larger or smaller than
$S_{A(C)}$, depending on the value of $\frac{\Lambda}{T}$.


  We propose a promising hybrid quantum system, where a highly-excited atom
strongly interacts with a superconducting LC oscillator via the electric field
of capacitor. An external electrostatic field is applied to tune the energy
spectrum of atom. The atomic qubit is implemented by two eigenstates near an
avoided-level crossing in the DC Stark map of Rydberg atom. Varying the
electrostatic field brings the atomic-qubit transition on- or off-resonance to
the microwave resonator, leading to a strong atom-resonator coupling with an
extremely large cooperativity. Like the nonlinearity induced by Josephson
junctions in superconducting circuits, the large atom-resonator interface
disturbs the harmonic potential of resonator, resulting in an artificial
two-level particle. Different universal two-qubit logic gates can also be
performed on our hybrid system within the space where an atomic qubit couples
to a single photon with an interaction strength much larger than any relaxation
rates, opening the door to the cavity-mediated state transmission.


  We introduce a multispecies exclusion model where length-conserving
probabilistic fusion and fission of the hard rods are allowed. Although all
rods enter the system with the same initial length ${\ell}=1$, their length can
keep changing, because of fusion and fission, as they move in a step-by-step
manner towards the exit. Two neighboring hard rods of lengths ${\ell}_{1}$ and
${\ell}_{2}$ can fuse into a single rod of longer length
${\ell}={\ell}_{1}+{\ell}_{2}$ provided ${\ell} \leq N$. Similarly,
length-conserving fission of a rod of length ${\ell}' \leq N$ results in two
shorter daughter rods. Based on the extremum current hypothesis, we plot the
phase diagram of the model under open boundary conditions utilizing the results
derived for the same model under periodic boundary condition using mean-field
approximation. The density profile and the flux profile of rods are in
excellent agreement with computer simulations. Although the fusion and fission
of the rods are motivated by similar phenomena observed in Intraflagellar
Transport (IFT) in eukaryotic flagella, this exclusion model is too simple to
account for the quantitative experimental data for any specific organism.
Nevertheless, the concepts of `flux profile' and `transition zone' that emerge
from the interplay of fusion and fission in this model are likely to have
important implications for IFT and for other similar transport phenomena in
long cell protrusions.


  In the second part of this series we apply the ``string-inspired'' technique
to the calculation of one-loop amplitudes involving both vectors and
axialvectors, as well as a general constant electromagnetic background field.
The vector-axialvector two-point function in a constant field is calculated
explicitly.


  We investigate low-mass central galaxies with Mstar = $10^{9.5}-10^{10}$
Msun/h, located near massive groups and galaxy clusters using the TNG300 and
MDPL2-SAG simulations. We set out to study their evolution, aiming to find
hints about the large-scale conformity signal they produce. We also use a
control sample of low-mass central galaxies located far away from massive
structures. For both samples, we find a sub-population of galaxies that were
accreted by another halo in the past but are now considered central galaxies;
we refer to these objects as former satellites. The fraction of former
satellites is higher for quenched central galaxies near massive systems: 45% in
TNG300 and 17% in MDPL2-SAG. Our results in TNG300 show that former satellites
were typically hosted by massive dark matter halos (M200 $\geq 10^{13}$ Msun/h)
at z$\sim$0.3, followed by a drop in halo mass at lower redshifts. In addition,
we find a strong drop in the total gas mass at z$\leq$1 for quenched central
galaxies near galaxy groups and clusters produced by these former satellites as
well. By removing former satellites, the evolution of quenched central galaxies
is fairly similar to those of the quenched control galaxies, showing small
differences at low-z. For MDPL2-SAG, former satellites were hosted by less
massive halos, with a mean halo mass around $10^{11}$ Msun/h, and the evolution
remains equal before and after removing former satellites. We also measure the
two-halo conformity, i.e., the correlation in the specific SFR between low-mass
central galaxies and their neighbors at Mpc scales, and how former satellites
contribute to the signal at z=0, 0.3, and 1. The conformity signal decreases
from z=0 to z=1 in MDPL2-SAG but it increases in TNG300. However, after
removing former satellites in TNG300, the signal is strongly reduced but almost
does not change at z$\leq$0.3, and it disappears at z=1 (abridged).


  In this paper, we study the local spectral properties of unilateral operator
weighted shifts.


  Feature-based homography estimation approaches rely on extensive image
processing for feature extraction and matching, and do not adequately account
for the information provided by the image. Therefore, developing efficient
direct techniques to extract the homography from images is essential. This
paper presents a novel nonlinear direct homography observer that exploits the
Lie group structure of $\mathbf{SL}(3)$ and its action on the space of image
maps. Theoretical analysis demonstrates local asymptotic convergence of the
observer. The observer design is also extended for partial measurements of
velocity under the assumption that the unknown component is constant or slowly
time-varying. Finally, simulation results demonstrate the performance of the
proposed solutions on real images.


  We recently formulated a rule for isomeric states for a system of 4 nucleons
with isospin T=1, namely that if the nucleons are in a single j shell then
states with angular momenta J=2 and J=(2j-1) are either isomeric or ground
states [ze12]. To show that this is a robust result, we here consider a new
interaction from the literature that was used to discuss even-even and odd-even
nuclei. We here apply it to an odd-odd nucleus.


  Let T be a C^2-expanding self-map of a compact, connected, smooth, Riemannian
manifold M. We correct a minor gap in the proof of a theorem from the
literature: the set of points whose forward orbits are nondense has full
Hausdorff dimension. Our correction allows us to strengthen the theorem.
Combining the correction with Schmidt games, we generalize the theorem in
dimension one: given a point x in M, the set of points whose forward orbit
closures miss x is a winning set.


  We classify the class $S$ theories of type $E_7$. These are four-dimensional
$\mathcal{N}=2$ superconformal field theories arising from the compactification
of the $E_7$ $(2,0)$ theory on a punctured Riemann surface, $C$. The
classification is given by listing all 3-punctured spheres ("fixtures"), and
connecting cylinders, which can arise in a pants-decomposition of $C$. We find
exactly 11,000 fixtures with three regular punctures, and an additional 48 with
one "irregular puncture" (in the sense used in our previous works). To organize
this large number of theories, we have created a web application at
https://golem.ph.utexas.edu/class-S/E7/ . Among these theories, we find 10 new
ones with a simple exceptional global symmetry group, as well as a new rank-2
SCFT and several new rank-3 SCFTs. As an application, we study the
strong-coupling limit of the $E_7$ gauge theory with 3 hypermultiplets in the
$56$. Using our results, we also verify recent conjectures that the $T^2$
compactification of certain $6d$ $(1,0)$ theories can alternatively be realized
in class $S$ as fixtures in the $E_7$ or $E_8$ theories.


  The modern power grid features the high penetration of power converters,
which widely employ a phase-locked loop (PLL) for grid synchronization.
However, it has been pointed out that PLL can give rise to small-signal
instabilities under weak grid conditions. This problem can be potentially
resolved by operating the converters in grid-forming mode, namely, without
using a PLL. Nonetheless, it has not been theoretically revealed how the
placement of grid-forming converters enhances the small-signal stability of
power systems integrated with large-scale PLL-based converters. This paper aims
at filling this gap. Based on matrix perturbation theory, we explicitly
demonstrate that the placement of grid-forming converters is equivalent to
increasing the power grid strength and thus improving the small-signal
stability of PLL-based converters. Furthermore, we investigate the optimal
locations to place grid-forming converters by increasing the smallest
eigenvalue of the weighted and Kron-reduced Laplacian matrix of the power
network. The analysis in this paper is validated through high-fidelity
simulation studies on a modified two-area test system and a modified 39-bus
test system. This paper potentially lays the foundation for understanding the
interaction between PLL-based (i.e., grid-following) converters and
grid-forming converters, and coordinating their placements in future
converter-dominated power systems.


  If expanding and contracting regions coexist in the universe, the speed of
the cosmic volume expansion can be accelerated. We construct simple
inhomogeneous dust-filled universe models in which the speed of the cosmic
volume expansion is accelerated for finite periods. These models are
constructed by removing spherical domains from the Einstein-de Sitter universe
and filling each domain with a Lemaitre-Tolman-Bondi dust sphere possessing the
same gravitational mass as the removed region. This represents an exact
solution of the Einstein equations. We find that acceleration of the cosmic
volume expansion is realized in some cases when the size of the contracting
region is comparable to the horizon radius of the Einstein-de Sitter universe
though this model is very different from the universe observed today. This
result implies that non-linear general relativistic effects of inhomogeneities
are very important to realize the acceleration of the cosmic volume expansion.


  In recent studies on sparse modeling, $l_q$ ($0<q<1$) regularization has
received considerable attention due to its superiorities on sparsity-inducing
and bias reduction over the $l_1$ regularization.In this paper, we propose a
cyclic coordinate descent (CCD) algorithm for $l_q$ regularization. Our main
result states that the CCD algorithm converges globally to a stationary point
as long as the stepsize is less than a positive constant. Furthermore, we
demonstrate that the CCD algorithm converges to a local minimizer under certain
additional conditions. Our numerical experiments demonstrate the efficiency of
the CCD algorithm.


  We examine the possibility that the experimentally observed enhancement of
superconducting (SC) fluctuations above the SC transition temperature in the
underdoped cuprates is caused by stripes -- an intrinsic electronic
inhomogeneity, common to hole-doped cuprates. By evaluating the strengths of
the diamagnetic response and the Nernst effect within the striped SC model, we
find results that are qualitatively consistent with the experimental
observations. We make a prediction for anisotropic thermopower in detwinned
samples that can be used to further test the proposed scenario.


  The reference design for the next-generation cosmic microwave background
(CMB) experiment, CMB-S4, relies on large arrays of transition edge sensor
(TES) bolometers coupled to Superconducting Quantum Interference Device
(SQUID)-based readout systems. Mapping the CMB to near cosmic variance limits
will enable the search for signatures of inflation and constrain dark energy
and neutrino physics. AlMn TESes provide simple film manufacturing and highly
uniform arrays over large areas to meet the requirements of the CMB-S4
experiment. TES parameters such as critical temperature and normal resistance
must be tuned to experiment specifications and can be varied based on geometry
and steps in the fabrication process such as deposition layering, geometry, and
baking time and temperature. Using four-terminal sensing, we measured $T_C$ and
$R_N$ of AlMn 2000 ppm films and devices of varying thicknesses fabricated at
Argonne National Laboratory to motivate device geometries and fabrication
processes to tune $T_C$ to 150-200 mK and $R_N$ to $\sim$10 mOhms. Measurements
of IV curves and time constants for the resulting devices of varying leg length
were made using time-division SQUID multiplexing, and determined $T_C$, $G$,
$k$, $f_{3db}$, and $R_N$. We present the results of these tests along with the
geometries and fabrication steps used to tune the device parameters to the
desired limits.


  We prove that every birational action of a group with Property FW can be
regularized.


  In this article I study the distribution of spiral galaxies in the Sloan
Digital Sky Survey (SDSS) to investigate whether the universe has an overall
handedness. A preference for spiral galaxies in one sector of the sky to be
left-handed or right-handed spirals would indicate a preferred handedness. The
SDSS data show a strong signal for such an asymmetry with a probability <0.2%.
The asymmetry axis is at (RA,dec) ~(202 deg,25 deg) with an uncertainty ~15
deg. The axis appears to be correlated with that of the quadrupole and octopole
moments in the WMAP microwave sky survey, an unlikely alignment that has been
dubbed "the axis of evil". Our Galaxy is aligned with its spin axis along the
same direction as the majority of the spirals.


  Several factors can contribute to the difficulty of aligning the sensors of
tracking detectors, including a large number of modules, multiple types of
detector technologies, and non-linear strip patterns on the sensors. All three
of these factors apply to the CLAS12 CVT, which is a hybrid detector consisting
of planar silicon sensors with non-parallel strips, and cylindrical micromegas
sensors with longitudinal and arc-shaped strips located within a 5~T
superconducting solenoid. To align this detector, we used the Kalman Alignment
Algorithm, which accounts for correlations between the alignment parameters
without requiring the time-consuming inversion of large matrices. This is the
first time that this algorithm has been adapted for use with hybrid
technologies, non-parallel strips, and curved sensors. We present the results
for the first alignment of the CLAS12 CVT using straight tracks from cosmic
rays and from a target with the magnetic field turned off. After running this
procedure, we achieved alignment at the level of 10~$\mu$m, and the widths of
the residual spectra were greatly reduced. These results attest to the
flexibility of this algorithm and its applicability to future use in the CLAS12
CVT and other hybrid or curved trackers, such as those proposed for the future
Electron-Ion Collider.


  We exhibit an online algorithm finding all distinct palindromes inside a
given string in time $\Theta(n\log|\Sigma|)$ over an ordered alphabet and in
time $\Theta(n|\Sigma|)$ over an unordered alphabet. Using a reduction from a
dictionary-like data structure, we prove the optimality of this algorithm in
the comparison-based computation model.


  It has been predicted theoretically that an unconventional odd-frequency
spin-triplet component of superconducting order parameter can be induced in
multilayered ferromagnetic structures with non-collinear magnetization. In this
work we study experimentally nano-scale devices, in which a ferromagnetic spin
valve is embedded into a Josephson junction. We demonstrate two ways of in-situ
analysis of such Josephson spin valves: via magnetoresistance measurements and
via in-situ magnetometry based on flux quantization in the junction. We observe
that supercurrent through the device depends on the relative orientation of
magnetization of the two ferromagnetic layers and is enhanced in the
non-collinear state of the spin valve. This provides a direct prove of
controllable generation of the spin-triplet superconducting component in a
ferromagnet.


  Thermal transport in the T -> 0 limit was measured as a function of doping in
high-quality single crystals of the cuprate superconductor YBa_2Cu_3O_y. The
residual linear term kappa_0/T is found to decrease as one moves from the
overdoped regime towards the Mott insulator region of the phase diagram. The
doping dependence of the low-energy quasiparticle gap extracted from kappa_0/T
is seen to scale closely with that of the pseudogap, arguing against a
non-superconducting origin for the pseudogap. The presence of a linear term for
all dopings is evidence against the existence of a quantum phase transition to
an order parameter with a complex (ix) component.


  Keyphrases provide semantic metadata that summarize and characterize
documents. This paper describes Kea, an algorithm for automatically extracting
keyphrases from text. Kea identifies candidate keyphrases using lexical
methods, calculates feature values for each candidate, and uses a
machine-learning algorithm to predict which candidates are good keyphrases. The
machine learning scheme first builds a prediction model using training
documents with known keyphrases, and then uses the model to find keyphrases in
new documents. We use a large test corpus to evaluate Kea's effectiveness in
terms of how many author-assigned keyphrases are correctly identified. The
system is simple, robust, and publicly available.


  For a stratified symplectic space, a suitable concept of stratified Kaehler
polarization, defined in terms of an appropriate Lie-Rinehart algebra,
encapsulates Kaehler polarizations on the strata and the behaviour of the
polarizations across the strata and leads to the notion of stratified Kaehler
space. This notion establishes an intimate relationship between nilpotent
orbits, singular reduction, invariant theory, reductive dual pairs, Jordan
triple systems, symmetric domains, and pre-homogeneous spaces; in particular,
in the world of singular Poisson geometry, the closures of principal
holomorphic nilpotent orbits, positive definite hermitian JTS's, and certain
pre-homogeneous spaces appear as different incarnations of the same structure.
The space of representations of the fundamental group of a closed surface in a
compact Lie group inherits a (positive) normal (stratified) Kaehler structure,
as does the closure of a holomorphic nilpotent orbit in a semisimple Lie
algebra of hermitian type. The closure of the principal holomorphic nilpotent
orbit arises from a regular semisimple holomorphic orbit by contraction.
Symplectic reduction carries a (positive) Kaehler manifold to a (positive)
normal Kaehler space in such a way that the sheaf of germs of polarized
functions thereupon coincides with the ordinary sheaf of germs of holomorphic
functions. Symplectic reduction establishes a close relationship between
singular reduced spaces and nilpotent orbits of the dual groups.
Projectivization of holomorphic nilpotent orbits yields exotic stratified
Kaehler structures on complex projective spaces and on certain complex
projective varieties including complex projective quadrics. Physical examples
are provided by certain reduced spaces arising from angular momentum zero.


  Since the inception of Bitcoin, cryptocurrencies and the underlying
blockchain technology have attracted an increasing interest from both academia
and industry. Among various core components, consensus protocol is the defining
technology behind the security and performance of blockchain. From incremental
modifications of Nakamoto consensus protocol to innovative alternative
consensus mechanisms, many consensus protocols have been proposed to improve
the performance of the blockchain network itself or to accommodate other
specific application needs.
  In this survey, we present a comprehensive review and analysis on the
state-of-the-art blockchain consensus protocols. To facilitate the discussion
of our analysis, we first introduce the key definitions and relevant results in
the classic theory of fault tolerance which help to lay the foundation for
further discussion. We identify five core components of a blockchain consensus
protocol, namely, block proposal, block validation, information propagation,
block finalization, and incentive mechanism. A wide spectrum of blockchain
consensus protocols are then carefully reviewed accompanied by algorithmic
abstractions and vulnerability analyses. The surveyed consensus protocols are
analyzed using the five-component framework and compared with respect to
different performance metrics. These analyses and comparisons provide us new
insights in the fundamental differences of various proposals in terms of their
suitable application scenarios, key assumptions, expected fault tolerance,
scalability, drawbacks and trade-offs. We believe this survey will provide
blockchain developers and researchers a comprehensive view on the
state-of-the-art consensus protocols and facilitate the process of designing
future protocols.


  The article investigate the necessary and sufficient conditions for the
normalized Bessel-struve kernel functions belonging to the classes
$\mathcal{T}_\lambda(\alpha)$ , $\mathcal{L}_\lambda(\alpha)$. Some linear
operators involving the Bessel-Struve operator are also considered.


  We consider the clustering concentration on curves for solutions to the
problem $$
  \varepsilon^2 {\mathrm {div}}\big( \nabla_{{\mathfrak a}(y)} u\big)-
V(y)u+u^p\, =\, 0, \quad u>0 \quad\mbox{in }\Omega,
  \qquad
  \nabla_{{\mathfrak a}(y)} u\cdot \nu\, =\, 0\quad\mbox{on } \partial \Omega,
$$ where $\Omega$ is a bounded domain in $\mathbb R^2$ with smooth boundary,
the exponent $p$ is greater than $1$, $\varepsilon>0$ is a small parameter, $V$
is a uniformly positive smooth potential on $\bar{\Omega}$, and $\nu$ denotes
the outward normal of $\partial \Omega$. For two positive smooth functions
${\mathfrak a}_1(y), {\mathfrak a}_2(y)$ on $\bar\Omega$, the operator
$\nabla_{{\mathfrak a}(y)}$ is given by $$ \nabla_{{\mathfrak a}(y)}
u=\Bigg({\mathfrak a}_1(y)\frac{\partial u}{\partial y_1}, \, {\mathfrak
a}_2(y)\frac{\partial u}{\partial y_2}\Bigg). $$


  First class type equalities, in the form of generalized algebraic data types
(GADTs), are commonly found in functional programs. However, first-class
representations of other relations between types, such as subtyping, are not
yet directly supported in most functional programming languages.
  We present several encodings of first-class subtypes using existing features
of the OCaml language (made more convenient by the proposed modular implicits
extension), show that any such encodings are interconvertible, and illustrate
the utility of the encodings with several examples.


  We study the shape of the tail of a heap of granular material. A simple
theoretical argument shows that the tail adds a logarithmic correction to the
slope given by the angle of repose. This expression is in good agreement with
experiments. We present a cellular automaton that contains gravity, dissipation
and surface roughness and its simulation also gives the predicted shape.


  We present the RF Quantum Upconverter (RQU) and describe its application to
quantum metrology of electromagnetic modes between dc and the Very High
Frequency band (VHF) ($\lesssim$300MHz). The RQU uses a Josephson
interferometer made up of superconducting loops and Josephson junctions to
implement a parametric interaction between a low-frequency electromagnetic mode
(between dc and VHF) and a mode in the microwave C Band ($\sim$ 5GHz),
analogous to the radiation pressure interaction between electromagnetic and
mechanical modes in cavity optomechanics. We analyze RQU performance with
quantum amplifier theory, and show that the RQU can operate as a
quantum-limited op-amp in this frequency range. It can also use non-classical
measurement protocols equivalent to those used in cavity optomechanics,
including back-action evading (BAE) measurements, sideband cooling, and
two-mode squeezing. These protocols enable experiments using dc--VHF
electromagnetic modes as quantum sensors with sensitivity better than the
Standard Quantum Limit (SQL). We demonstrate signal upconversion from low
frequencies to microwave C band using an RQU and show a phase-sensitive gain
(extinction ratio) of $46.9$\;dB, which is a necessary step towards the
realization of full BAE.


  Galactic Internet may already exist, if all stars are exploited as
gravitational lenses. In fact, the gravitational lens of the Sun is a
well-known astrophysical phenomenon predicted by Einstein's general theory of
relativity. It implies that, if we can send a probe along any radial direction
away from the Sun up to the minimal distance of 550 AU and beyond, the Sun's
mass will act as a huge magnifying lens, letting us "see" detailed radio maps
of whatever may lie on the other side of the Sun even at very large distances.
The 2009 book by this author, ref. [1], studies such future FOCAL space
missions to 550 AU and beyond. In this paper, however, we want to study another
possibility yet: how to create the future interstellar radio links between the
solar system and any future interstellar probe by utilizing the gravitational
lens of the Sun as a huge antenna. In particular, we study the Bit Error Rate
(BER) across interstellar distances with and without using the gravitational
lens effect of the Sun (ref. [2]). The conclusion is that only when we will
exploit the Sun as a gravitational lens we will be able to communicate with our
own probes (or with nearby Aliens) across the distances of even the nearest
stars to us in the Galaxy, and that at a reasonable Bit Error Rate. We also
study the radio bridge between the Sun and any other Star that is made up by
the two gravitational lenses of both the Sun and that Star. The alignment for
this radio bridge to work is very strict, but the power-saving is enormous, due
to the huge contributions of the two stars' lenses to the overall antenna gain
of the system. We study a few cases in detail. Finally, we find the information
channel capacity for each of those radio bridges, putting thus a physical
constraint to the amount of information transfer that will be possible even by
exploiting the stars as gravitational lenses


  Axisymmetric magnetic lines of nanometer sizes (chiral vortices or skyrmions)
have been predicted to exist in a large group of noncentrosymmetric crystals
more than two decades ago. Recently these magnetic textures have been directly
observed in nanolayers of cubic helimagnets and monolayers of magnetic metals.
We develop a micromagnetic theory of chiral skyrmions in thin magnetic layers
for magnetic materials with intrinsic and induced chirality. Such particle-like
and stable micromagnetic objects can exist in broad ranges of applied magnetic
fields including zero field. Chiral skyrmions can be used as a new type of
highly mobile nanoscale data carriers.


  In high energy collisions involving small nuclei (p+p or x+Au collisions
where x=p, d, or $^3$He) the fluctuating size, shape and internal gluonic
structure of the nucleon is shown to have a strong effect on the initial size
and shape of the fireball of new matter created in the collision. A systematic
study of the eccentricity coefficients describing this initial fireball state
for several semi-realistic models of nucleon substructure and for several
practically relevant collision systems involving small nuclei is presented. The
key importance of multiplicity fluctuations in such systems is pointed out. Our
results show large differences from expectations based on conventional Glauber
model simulations of the initial state created in such collisions.


  We propose a new model of neural network. It consists of spin variables to
describe the state of neurons as in the Hopfield model and new gauge variables
to describe the state of synapses. The model possesses local gauge symmetry and
resembles lattice gauge theory of high-energy physics. Time dependence of
synapses describes the process of learning. The mean field theory predicts a
new phase corresponding to confinement phase, in which brain loses ablility of
learning and memory.


  We investigated the training of a shared model for both text-to-speech (TTS)
and voice conversion (VC) tasks. We propose using an extended model
architecture of Tacotron, that is a multi-source sequence-to-sequence model
with a dual attention mechanism as the shared model for both the TTS and VC
tasks. This model can accomplish these two different tasks respectively
according to the type of input. An end-to-end speech synthesis task is
conducted when the model is given text as the input while a
sequence-to-sequence voice conversion task is conducted when it is given the
speech of a source speaker as the input. Waveform signals are generated by
using WaveNet, which is conditioned by using a predicted mel-spectrogram. We
propose jointly training a shared model as a decoder for a target speaker that
supports multiple sources. Listening experiments show that our proposed
multi-source encoder-decoder model can efficiently achieve both the TTS and VC
tasks.


  When applied to compute the density jump of a shock, the standard
magnetohydrodynamic (MHD) formalism assumes, 1) that all the upstream material
passes downstream, together with the momentum and energy it carries, and 2)
that pressures are isotropic. In a collisionless shock, shock accelerated
particles going back and forth around the front can invalid the first
assumption. In addition, an external magnetic field can sustain stable pressure
anisotropies, invaliding the second assumption. It is therefore unclear whether
the density jump of a collisionless shock fulfils the MHD jump or not.
  Here we try to clarify this issue. A literature review is conducted on 68
articles dealing with Particle-In-Cell simulations of collisionless shocks. We
analyze the factors triggering departure from the MHD density jump and quantify
their influence on $\Delta_{RH}$, the relative departure from the
Rankine-Hugoniot jump. For small departures we propose $\Delta_{RH} = +
\mathcal{O}(10^{-1-3.7\kappa})t^\kappa - \sigma \mathcal{O}(1)$ where $t$ is
the timescale of the simulation, $\sigma$ the magnetization parameter and
$\kappa$ a constant of order unity. The first term stems from the energy
leakage into accelerated particle. The second term stems from the downstream
anisotropy triggered by the field (assuming an isotropic upstream). This
relation allows to assess to which extent a collisionless shock fulfils the RH
density jump.
  In the strong field limit and for parallel shocks, the departure caused by
the field saturates at a finite, negative, value. For perpendicular shocks, the
departure goes to zero at small and high $\sigma$'s so that we find here a
departure window. The results obtained have to be checked against full 3D
simulations.


  This work introduces a novel blackbox optimization algorithm for
computationally expensive constrained multi-fidelity problems. When applying a
direct search method to such problems, the scarcity of feasible points may lead
to numerous costly evaluations spent on infeasible points. Our proposed
fidelity and interruption controlled optimization algorithm addresses this
issue by leveraging multi-fidelity information, allowing for premature
interruption of an evaluation when a point is estimated to be infeasible. These
estimations are controlled by a biadjacency matrix, for which we propose a
construction. The proposed method acts as an intermediary component bridging
any non multi-fidelity direct search solver and a multi-fidelity blackbox
problem, giving the user freedom of choice for the solver. A series of
computational tests are conducted to validate the approach. The results show a
significant improvement in solution quality when an initial feasible starting
point is provided. When this condition is not met, the outcomes are contingent
upon specific properties of the blackbox.


  We study an ensemble of diverse (inhomogeneous) thermostatically controlled
loads aggregated to provide the demand response (DR) services in a
district-level energy system. Each load in the ensemble is assumed to be
equipped with a random number generator switching heating/cooling on or off
with a Poisson rate, $r$, when the load leaves the comfort zone. Ensemble
diversity is modeled through inhomogeneity/disorder in the deterministic
dynamics of loads. Approached from the standpoint of statistical physics, the
ensemble represents a non-equilibrium system driven away from its natural
steady state by the DR. The ability of the ensemble to recover by mixing faster
to the steady state after its DR's use is advantageous. The trade-off between
the level of the aggregator's control, commanding the devices to lower the rate
$r$, and the phase-space-oscillatory deterministic dynamics is analyzed. We
discover that there exists a critical value, $r_c$, corresponding to both the
most efficient mixing and the bifurcation point where the ensemble transitions
from the oscillatory relaxation at $r>r_c$ to the pure relaxation at $r<r_c$.
Then, we study the effect of the load diversity, investigating four different
disorder probability distributions (DPDs) ranging from the case of the Gaussian
DPD to the case of the uniform with finite support DPD. Demonstrating
resemblance to the similar question of the effectiveness of Landau damping in
plasma physics, we show that stronger regularity of the DPD around its maximum
results in faster mixing. Our theoretical analysis is supported by extensive
numerical validation, which also allows us to access the effect of the
ensemble's finite size.


  Global registration of multi-view robot data is a challenging task.
Appearance-based global localization approaches often fail under drastic
view-point changes, as representations have limited view-point invariance. This
work is based on the idea that human-made environments contain rich semantics
which can be used to disambiguate global localization. Here, we present X-View,
a Multi-View Semantic Global Localization system. X-View leverages semantic
graph descriptor matching for global localization, enabling localization under
drastically different view-points. While the approach is general in terms of
the semantic input data, we present and evaluate an implementation on visual
data. We demonstrate the system in experiments on the publicly available
SYNTHIA dataset, on a realistic urban dataset recorded with a simulator, and on
real-world StreetView data. Our findings show that X-View is able to globally
localize aerial-to-ground, and ground-to-ground robot data of drastically
different view-points. Our approach achieves an accuracy of up to 85 % on
global localizations in the multi-view case, while the benchmarked baseline
appearance-based methods reach up to 75 %.


  We present a novel technique for solving the problem of safe control for a
general class of nonlinear, control-affine systems subject to parametric model
uncertainty. Invoking Lyapunov analysis and the notion of fixed-time stability
(FxTS), we introduce a parameter adaptation law which guarantees convergence of
the estimates of unknown parameters in the system dynamics to their true values
within a fixed-time independent of the initial parameter estimation error. We
then synthesize the adaptation law with a robust, adaptive control barrier
function (RaCBF) based quadratic program to compute safe control inputs despite
the considered model uncertainty. To corroborate our results, we undertake a
comparative case study on the efficacy of this result versus other recent
approaches in the literature to safe control under uncertainty, and close by
highlighting the value of our method in the context of an automobile overtake
scenario.


  We find the singular transformation between the electron operator and the
pseudoparticle operators for the Hubbard chain. We generalize the concept of
quasiparticle to one-dimensional electronic systems which in 1D refers to
many-pseudoparticle objects. We obtain explicit results for the electron
renormalization factor, self energy, and vertex functions at the Fermi points.
We also discuss the possible connection of our results to higher dimensions and
explore the possibilities of instabilities in the interacting problem such as
the formation of Cooper pairs.


  We present the first fully relativistic investigation of matter fallback in a
supernova. We investigate spherically symmetric supernova fallback using a
relativistic radiation hydrodynamics Lagrangian code that handles radiation
transport in all regimes. Our goal is to answer the fundamental question: did
SN1987A produce a black hole and, if so, when will the hole become detectable ?
We compute the light curve, assuming that a black hole has been formed during
the explosion, and compare it with the observations. Our preliminary
calculations lack radioactive energy input and adopt a very simple chemical
composition (pure hydrogen). As a result, our computed models cannot fit the
observed data of SN1987A in detail. Nevertheless, we can show that, during the
first hours, the accretion flow is self--regulated and the accretion luminosity
stays very close to the Eddington limit. The light curve is completely
dominated, during the first few weeks, by the emission of the stellar envelope
thermal energy, and resembles that obtained in ``standard'' supernova theory.
Only long after hydrogen recombination takes place is there even a chance to
actually detect radiation from the accreting black hole above the emission of
the expanding envelope. The presence of a black hole is thus not inconsistent
with observations to date. Because of the exponential decay of the $^{44}$Ti
radioactive heating rate, the date of the emergence of the black hole is not
very sensitive to the actual parameters of the models and turns out to be about
1000 years. The bulk of the emission then is expected to be in the visible
band, but will be unobservable with present instrumentation. We discuss the
implications of our results in connection with the possible emergence of a
black hole in other supernovae.


  Mobile and edge computing devices for always-on classification tasks require
energy-efficient neural network architectures. In this paper we present several
changes to neural architecture searches (NAS) that improve the chance of
success in practical situations. Our search simultaneously optimizes for
network accuracy, energy efficiency and memory usage. We benchmark the
performance of our search on real hardware, but since running thousands of
tests with real hardware is difficult we use a random forest model to roughly
predict the energy usage of a candidate network. We present a search strategy
that uses both Bayesian and regularized evolutionary search with particle
swarms, and employs early-stopping to reduce the computational burden. Our
search, evaluated on a sound-event classification dataset based upon AudioSet,
results in an order of magnitude less energy per inference and a much smaller
memory footprint than our baseline MobileNetV1/V2 implementations while
slightly improving task accuracy. We also demonstrate how combining a 2D
spectrogram with a convolution with many filters causes a computational
bottleneck for audio classification and that alternative approaches reduce the
computational burden but sacrifice task accuracy.


  We describe the radiation phenomena which can take place in the physical
vacuum such as Cherenkov-type shock waves. Their macroscopical characteristics
- cone angle, flash duration, radiation yield and spectral distribution - are
computed. It turns out that the radiation yield is proportional to the square
of the proper energy scale of the vacuum which serves also as the vacuum
instability threshold and the natural ultraviolet cutoff. While the analysis is
mainly based on the theory engaging the logarithmic nonlinear quantum wave
equation, some of the obtained results must be valid for any
Lorentz-invariance-violating theory describing the vacuum by (effectively)
continuous medium in the long-wavelength approximation.


  String/M theory inspires an uncertainty relation between space and time which
deviates from general relativity. It is possible to explore this deviation from
cosmological observations, in particular from the CMB fluctuation spectrum.
This paper extends some previous observations to more general inflation
schemes, we find that the noncommutative spacetime effects always suppress the
power spectrum, of both the scalar and tensor perturbations, and may provide a
large enough running of the spectral index to fit the WMAP data in the
inflation model.


  Automation of berthing maneuvers in shipping is a pressing issue as the
berthing maneuver is one of the most stressful tasks seafarers undertake.
Berthing control problems are often tackled via tracking a predefined
trajectory or path. Maintaining a tracking error of zero under an uncertain
environment is impossible; the tracking controller is nonetheless required to
bring vessels close to desired berths. The tracking controller must prioritize
the avoidance of tracking errors that may cause collisions with obstacles. This
paper proposes a training method based on reinforcement learning for a
trajectory tracking controller that reduces the probability of collisions with
static obstacles. Via numerical simulations, we show that the proposed method
reduces the probability of collisions during berthing maneuvers. Furthermore,
this paper shows the tracking performance in a model experiment.


  Reordering the terms of a series is a useful mathematical device, and much is
known about when it can be done without affecting the convergence or the sum of
the series. For example, if a series of real numbers absolutely converges, we
can add the even-indexed and odd-indexed terms separately, or arrange the terms
in an infinite two-dimensional table and first compute the sum of each column.
The possibility of even more intricate re-orderings prompts us to find a
general underlying principle. We identify such a principle in the setting of
Banach spaces, where we consider well-ordered series with indices beyond
{\omega}, but strictly under {\omega}_1 . We prove that for every absolutely
convergent well-ordered series indexed by a countable ordinal, if the series is
rearranged according to any countable ordinal, then the absolute convergence
and the sum of the series remain unchanged.