  We study the effects of the ground state entropy of colored quarks upon the
bag pressure at low temperatures. The vacuum expectation values of the quark
and gluon fields are used to express the interactions in QCD ground state in
the limit of low temperatures and chemical potentials. Apparently, the
inclusion of this entropy in the equation of state provides the hadron
constituents with an additional heat which causes a decrease in the effective
latent heat inside the hadronic bag and consequently decreases the
non-perturbative bag pressure. We have considered two types of baryonic bags,
$\Delta$ and $\Omega^-$. In both cases we have found that the bag pressure
decreases with the temperature. On the other hand, when the colored quark
ground state entropy is not considered, the bag pressure as conventionally
believed remains constant for finite temperature.


  In this paper we study subsets $E$ of ${\Bbb Z}_p^d$ such that any function
$f: E \to {\Bbb C}$ can be written as a linear combination of characters
orthogonal with respect to $E$. We shall refer to such sets as spectral. In
this context, we prove the Fuglede Conjecture in ${\Bbb Z}_p^2$ which says that
$E \subset {\Bbb Z}_p^2$ is spectral if and only if $E$ tiles ${\Bbb Z}_p^2$ by
translation. Arithmetic properties of the finite field Fourier transform,
elementary Galois theory and combinatorial geometric properties of direction
sets play the key role in the proof.


  Recently, the vector-like leptons (VLLs) as a simple extension to the
standard model (SM) have attracted widespread attention both in theory and
experiments. The present collider searches mainly focus on the studies of their
prompt decays, which prefer a relatively large coupling. In this paper, we
concentrate on searches for long-lived signatures of the singlet VLLs $F$ or
their accompanying scalar particles $\phi$ both in the hadronic and electronic
colliders. The long-lived signatures are naturally induced from small chiral
mass mixing between VLLs and SM leptons. Two specific models distinguished by
whether the VLLs couple to scalar particles are introduced to realize the
aforementioned features. For long-lived VLLs case, we find that with the kink
track method the sensitivities at future HL-LHC with $\sqrt{s}=14~\text{TeV}$
can reach the regions for VLL mass $m_F \in [200,1100]~\text{GeV}$ and the mass
mixing parameter $\theta_L \in [10^{-10},3\times 10^{-8}]$. For the long-lived
accompanying scalar particle case, by fixing VLLs or scalar mass, or the mass
ratio between VLL and the accompanying scalar, we explore the projected
sensitivities through the time delay and displaced vertex strategies, which can
probe the regions for $m_F \in [200,1200]~\text{GeV}$ and coupling
$y\theta_L\in [10^{-11},10^{-6}]$. Furthermore, we also explore the long-lived
accompanying scalars at the future CEPC provided that the VLLs can couple to
the SM first-generation leptons. We find that CEPC has good performances for
$m_\phi < 120~\text{GeV}$ and $m_F<1200~\text{GeV}$. These long-lived searches
are complementary to previous studies, which opens the door towards the smaller
coupling regions.


  The X-ray emission from the central region of the Galactic plane, |l|<45 deg
and |b|<0.4 deg, was studied in the 0.7-10 keV energy band with a spatial
resolution of ~3' with the ASCA observatory. We developed a new analysis method
for the ASCA data to resolve discrete sources from the extended Galactic ridge
X-ray emission (GRXE). We resolved 163 discrete sources with a flux down to
10^-12.5 ergs cm^-2 s^-1 and determined the intensity variations of the GRXE as
a function of the Galactic longitude with a spatial resolution of ~1 deg. The
longitudinal variation of the GRXE in the energy band above 4 keV shows a large
enhancement within |l|<30 deg. This suggests a strong enhancement of X-ray
emissivity inside the 4-kpc arms. Searches for identifications of the resolved
sources with cataloged X-ray sources and optical stars show that the 66% are
unidentified. Spectral analysis of each source shows that a large number of the
unidentified sources have hard X-ray spectra. We classified the sources into
several groups according to the spectra and analyzed the spectra summed within
each group. Possible X-ray origins of these sources are discussed based on the
grouping analysis. Also, we derived the LogN-LogS relations of the resolved
sources in the energy bands below and above 2 keV. The obtained LogN-LogS
relation of the Galactic X-ray sources above 2 keV is represented by a
power-law with an index of -0.79+/-0.07. This flat LogN-LogS relation suggests
that the spatial distribution of the sources should have an arm-like structure
in which the Solar system is included. The integrated surface brightness of the
resolved sources is about 10% of the total GRXE in both energy bands. The
approximately 90% of the emission remaining is still unresolved.


  Strong-motion duration and number of cycles of motion are the well-known
parameters with which earthquake engineers currently use to take into account
the influence of motion duration for the dynamic analysis of structures. In
addition to these prevalent means, using the number of nonlinear cycles, which
a structure experiences during the motion, is also one another method to find
the influence of the motion duration in the response of structures. In this
study, a new approach is proposed to determine the number of nonlinear cycles.
Moreover, a parameter based on both hysteretic energy and number of nonlinear
cycles is likewise suggested to precisely reflect the shaking characteristics
of the motion. The proposed parameter is expected to demonstrate a high
correlation with the response of structures even for the structures having
deteriorative and degraded behaviour during the motion. This matter is examined
and confirmed with the application of this parameter to predict the influence
of motion duration in the response of several degrading RC frames. It is
revealed that the proposed parameter is able to well predict the influence of
motion duration and can be used as a criterion to select ground motions for
dynamic analysis.


  Reinforcement Learning (RL) has enabled social robots to generate
trajectories without human-designed rules or interventions, which makes it more
effective than hard-coded systems for generalizing to complex real-world
scenarios. However, social navigation is a safety-critical task that requires
robots to avoid collisions with pedestrians while previous RL-based solutions
fall short in safety performance in complex environments. To enhance the safety
of RL policies, to the best of our knowledge, we propose the first algorithm,
SoNIC, that integrates adaptive conformal inference (ACI) with constrained
reinforcement learning (CRL) to learn safe policies for social navigation. More
specifically, our method augments RL observations with ACI-generated
nonconformity scores and provides explicit guidance for agents to leverage the
uncertainty metrics to avoid safety-critical areas by incorporating safety
constraints with spatial relaxation. Our method outperforms state-of-the-art
baselines in terms of both safety and adherence to social norms by a large
margin and demonstrates much stronger robustness to out-of-distribution
scenarios. Our code and video demos are available on our project website:
https://sonic-social-nav.github.io/.


  In this paper, we present novel deterministic algorithms for multiplying two
$n \times n$ matrices approximately. Given two matrices $A,B$ we return a
matrix $C'$ which is an \emph{approximation} to $C = AB$. We consider the
notion of approximate matrix multiplication in which the objective is to make
the Frobenius norm of the error matrix $C-C'$ arbitrarily small. Our main
contribution is to first reduce the matrix multiplication problem to solving a
set of linear equations and then use standard techniques to find an approximate
solution to that system in $\tilde{O}(n^2)$ time. To the best of our knowledge
this the first examination into designing quadratic time deterministic
algorithms for approximate matrix multiplication which guarantee arbitrarily
low \emph{absolute error} w.r.t. Frobenius norm.


  We investigate $4+d$ dimensional fermionic models in which the system in
codimension-$d$ supports a topologically stable solution, and in which the
fermion may be localised to the brane, with power law in 'instanton'
backgrounds and exponentially in 'soliton' backgrounds. When the fermions are
isoscalars, the mechanism fails, while for isospinor fermions it is successful.
As backgrounds we consider instantons of Yang--Mills and sigma models in even
codimensions, solitons of sigma models in odd codimensions, as well as solitons
of Higgs and Goldstone models in all codimensions.


  Uniswap v3 is the largest decentralized exchange for digital currencies. A
novelty of its design is that it allows a liquidity provider (LP) to allocate
liquidity to one or more closed intervals of the price of an asset instead of
the full range of possible prices. An LP earns fee rewards proportional to the
amount of its liquidity allocation when prices move in this interval. This
induces the problem of {\em strategic liquidity provision}: smaller intervals
result in higher concentration of liquidity and correspondingly larger fees
when the price remains in the interval, but with higher risk as prices may exit
the interval leaving the LP with no fee rewards. Although reallocating
liquidity to new intervals can mitigate this loss, it comes at a cost, as LPs
must expend gas fees to do so. We formalize the dynamic liquidity provision
problem and focus on a general class of strategies for which we provide a
neural network-based optimization framework for maximizing LP earnings. We
model a single LP that faces an exogenous sequence of price changes that arise
from arbitrage and non-arbitrage trades in the decentralized exchange. We
present experimental results informed by historical price data that demonstrate
large improvements in LP earnings over existing allocation strategy baselines.
Moreover we provide insight into qualitative differences in optimal LP
behaviour in different economic environments.


  Using the classical Lazard's elimination theorem, we obtain a decomposition
theorem for Lie algebras defined by generators and relations of a certain type.
This is a preprint version of the paper appearing in Communications in Algebra
Volume 32, Issue 10, 2004.


  We propose a group-theoretical approach to the generalized oscillator algebra
Ak recently investigated in J. Phys. A: Math. Theor. 43 (2010) 115303. The case
k > or 0 corresponds to the noncompact group SU(1,1) (as for the harmonic
oscillator and the Poeschl-Teller systems) while the case k < 0 is described by
the compact group SU(2) (as for the Morse system). We construct the phase
operators and the corresponding temporally stable phase eigenstates for Ak in
this group-theoretical context. The SU(2) case is exploited for deriving
families of mutually unbiased bases used in quantum information. Along this
vein, we examine some characteristics of a quadratic discrete Fourier transform
in connection with generalized quadratic Gauss sums and generalized Hadamard
matrices.


  We consider the pairing Hamiltonian and systematically construct its density
functional in the strong-coupling limit and in the limit of large particle
number. In the former limit, the functional is an expansion into central
moments of occupation numbers. In the latter limit, the functional is known
from BCS theory. Both functionals are nonlocal in structure, and the
nonlocalities are in the form of simple products of local functionals. We also
derive the relation between the occupation numbers and the Kohn-Sham density.


  In the last years, social media has gained an unprecedented amount of
attention, playing a pivotal role in shaping the contemporary landscape of
communication and connection. However, Coordinated Inhautentic Behaviour (CIB),
defined as orchestrated efforts by entities to deceive or mislead users about
their identity and intentions, has emerged as a tactic to exploit the online
discourse. In this study, we quantify the efficacy of CIB tactics by defining a
general framework for evaluating the influence of a subset of nodes in a
directed tree. We design two algorithms that provide optimal and greedy
post-hoc placement strategies that lead to maximising the configuration
influence. We then consider cascades from information spreading on Twitter to
compare the observed behaviour with our algorithms. The results show that,
according to our model, coordinated accounts are quite inefficient in terms of
their network influence, thus suggesting that they may play a less pivotal role
than expected. Moreover, the causes of these poor results may be found in two
separate aspects: a bad placement strategy and a scarcity of resources.


  In this paper, we first show for a slightly degenerate pre-modular fusion
category $\mathcal{C}$ that squares of dimensions of simple objects divide half
of the dimension of $\mathcal{C}$, and that slightly degenerate fusion
categories of FP-dimensions $2p^nd$ and $4p^nd$ are nilpotent, where $p$ is an
odd prime and $d$ is an odd square-free integer. Then we classify slightly
degenerate generalized Tambara-Yamagami fusion categories and weakly integral
slightly degenerate fusion categories of particular dimensions.


  Dynamical Einstein cluster is a spherical self-gravitating system of
counterrotating particles, which may expand, oscillate and collapse. This
system exhibits critical behaviour in its collapse at the threshold of black
hole formation. It appears when the specific angular momentum of particles is
tuned finely to the critical value. We find the unique exact self-similar
solution at the threshold. This solution begins with a regular surface,
involves timelike naked singularity formation and asymptotically approaches a
static self-similar cluster.


  Light field presents a rich way to represent the 3D world by capturing the
spatio-angular dimensions of the visual signal. However, the popular way of
capturing light field (LF) via a plenoptic camera presents spatio-angular
resolution trade-off. Computational imaging techniques such as compressive
light field and programmable coded aperture reconstruct full sensor resolution
LF from coded projections obtained by multiplexing the incoming spatio-angular
light field. Here, we present a unified learning framework that can reconstruct
LF from a variety of multiplexing schemes with minimal number of coded images
as input. We consider three light field capture schemes: heterodyne capture
scheme with code placed near the sensor, coded aperture scheme with code at the
camera aperture and finally the dual exposure scheme of capturing a
focus-defocus pair where there is no explicit coding. Our algorithm consists of
three stages 1) we recover the all-in-focus image from the coded image 2) we
estimate the disparity maps for all the LF views from the coded image and the
all-in-focus image, 3) we then render the LF by warping the all-in-focus image
using disparity maps and refine it. For these three stages we propose three
deep neural networks - ViewNet, DispairtyNet and RefineNet. Our reconstructions
show that our learning algorithm achieves state-of-the-art results for all the
three multiplexing schemes. Especially, our LF reconstructions from
focus-defocus pair is comparable to other learning-based view synthesis
approaches from multiple images. Thus, our work paves the way for capturing
high-resolution LF (~ a megapixel) using conventional cameras such as DSLRs.
Please check our supplementary materials
$\href{https://docs.google.com/presentation/d/1Vr-F8ZskrSd63tvnLfJ2xmEXY6OBc1Rll3XeOAtc11I/}{online}$
to better appreciate the reconstructed light fields.


  We provide quantitative evidence for our previous conjecture which states an
equivalence of the partition function of a 3d N=2 gauge theory on a duality
wall and that of the SL(2,R) Chern-Simons theory on a mapping torus, for a
class of examples associated with once-punctured torus. In particular, we
demonstrate that a limit of the 3d N=2 partition function reproduces the
hyperbolic volume and the Chern-Simons invariant of the mapping torus. This is
shown by analyzing the classical limit of the trace of an element of the
mapping class group in the Hilbert space of the quantum Teichmuller theory. We
also show that the subleading correction to the partition function reproduces
the Reidemeister torsion.


  A toolkit for interacting with the Elekta Monaco (Clements et al 2018)
treatment planning system (TPS) has been designed without the need of a
dedicated Application Programming Interface (API). It provides automatization
of the radiotherapy planning procedure allowing the TPS to calculate or
optimize plans during non-working hours. The software is based on an open
source library that mimics human interaction with Microsoft Windows
applications. The impact on the clinical workflow is important not only
providing better efficiency but also increasing treatment quality. Successful
inverse planning depends on the tweaking of many parameters that can be
explored more exhaustively with this tool without significantly increasing
planning time. Furthermore, a simple way to analyze calculated plans and the
impact of the cost functions in the optimization has been implemented. The
Autoflow sequence has been developed allowing fully automated planning; it
works analyzing the relative impact of different cost functions in the
optimization and modifying constraints accordingly. It also changes
segmentation parameters to fit more complex treatments. A prostate study has
been conducted, comparing automatically created plans with already treated
ones. The Autoflow sequence has proved to reduce dose to organs at risk with a
negligible decrease in target coverage. Implementing this tool has enabled an
efficient use of the TPS, allowing research and clinical use to coexist in a
friendly way. It is surely of great use in clinics with little resources. It
provides consistency and efficiency throughout the treatment planning process.


  We present a unique method for solving for the Reynolds stress in turbulent
canonical flows, based on the momentum balance for a control volume moving at
the local mean velocity. A differential transform converts this momentum
balance to a solvable form. Comparisons with experimental and computational
data in simple geometries show quite good agreements. An alternate picture for
the turbulence momentum transport is offered, as verified with data, where the
turbulence momentum is transported by the mean velocity while being dissipated
by viscosity. The net momentum transport is the Reynolds stress. This
turbulence momentum balance is verified using DNS and experimental data.
Implications of this work are that the Reynolds stress can be written
explicitly in terms of basic turbulence parameters in a simple form, derived
from pure fluid physics, and that potential exists for applications of this
concept to more complex turbulent flows.


  Mutual Information (MI) is often used for feature selection when developing
classifier models. Estimating the MI for a subset of features is often
intractable. We demonstrate, that under the assumptions of conditional
independence, MI between a subset of features can be expressed as the
Conditional Mutual Information (CMI) between pairs of features. But selecting
features with the highest CMI turns out to be a hard combinatorial problem. In
this work, we have applied two unique global methods, Truncated Power Method
(TPower) and Low Rank Bilinear Approximation (LowRank), to solve the feature
selection problem. These algorithms provide very good approximations to the
NP-hard CMI based feature selection problem. We experimentally demonstrate the
effectiveness of these procedures across multiple datasets and compare them
with existing MI based global and iterative feature selection procedures.


  Defect formation in LiF, which is used as an observation window in ramp and
shock experi- ments, has significant effects on its transmission properties.
Given the extreme conditions of the experiments it is hard to measure the
change in transmission directly. Using molecular dynam- ics, we estimate the
change in conductivity as a function of the concentration of likely point and
extended defects using a Green-Kubo technique with careful treatment of
size-effects. With this data, we form a model of the mean behavior and its
estimated error; then, we use this model to predict the conductivity of a large
sample of defective LiF resulting from a direct simulation of ramp compression
as a demonstration of the accuracy of its predictions. Given estimates of
defect densities in a LiF window used in an experiment, the model can be used
to correct the observations of thermal energy through the window. In addition,
the methodology we develop is extensible to modelling, with quantified
uncertainty, the effects of a variety of defects on thermal conductivity of
solid materials.


  We prove an avoidance principle for expanding translates of unipotent orbits
for some semisimple homogeneous spaces. In addition, we prove a quantitative
isolation result of closed orbits and give an upper bound on the number of
closed orbits of bounded volume. The proofs of our results rely on the
construction of a Margulis function and the theory of finite dimensional
representations of semisimple Lie groups.


  This paper investigates the classical statistical signal processing problem
of detecting a signal in the presence of colored noise with an unknown
covariance matrix. In particular, we consider a scenario where m-dimensional p
possible signal-plus-noise samples and m-dimensional n noise-only samples are
available at the detector. Then the presence of a signal can be detected using
the largest generalized eigenvalue (l.g.e.) of the so called whitened sample
covariance matrix. This amounts to statistically characterizing the maximum
eigenvalue of the deformed Jacobi unitary ensemble (JUE). To this end, we
employ the powerful orthogonal polynomial approach to determine a new finite
dimensional expression for the cumulative distribution function (c.d.f.) of the
l.g.e. of the deformed JUE. This new c.d.f. expression facilitates the further
analysis of the receiver operating characteristics (ROC) of the detector. It
turns out that, for m=n, when m and p increase such that m/p attains a fixed
value, there exists an optimal ROC profile corresponding to each fixed
signal-to-noise ratio (SNR). In this respect, we have established a tight
approximation for the corresponding optimal ROC profile.


  Recent studies have demonstrated that detailed monitoring of gravitational
microlensing events can reveal the presence of planets orbiting the microlensed
source stars. With the potential of probing planets in the Galactic Bulge and
Magellanic Clouds, such detections greatly increase the volume over which
planets can be found. This paper expands on these original studies by
considering the effect of planetary phase on the form of the resultant
microlensing light curve. It is found that crescent-like sources can undergo
substantially more magnification than a uniformly illuminated disk, the model
typically employed in studying such planets. In fact, such a circularly
symmetric model is found to suffer a minimal degree of magnification when
compared to the crescent models. The degree of magnification is also a strong
function of the planet's orientation with respect to the microlensing caustic.
The form of the magnification variability is also strongly dependent on the
planetary phase and from which direction it is swept by the caustic, providing
further clues to the geometry of the planetary system. As the amount of light
reflected from a planet also depends on its phase, the detection of extreme
crescent-like planets requires the advent of 30-m class telescopes, while light
curves of planets at more moderate phases can be determined with today's 10-m
telescopes.


  We consider critical points of master functions associated with integral
dominant weights of Kac-Moody algebras and introduce a generating procedure
constructing new critical points starting from a given one. The set of all
critical points constructed from a given one is called a population. We
formulate a conjecture that a population is isomorphic to the flag variety of
the Langlands dual Kac-Moody algebra and prove the conjecture for algebras
$sl_{N+1}, so_{2N+1}$, and $sp_{2N}$.
  We show that populations associated with a collection of integral dominant
$sl_{N+1}$-weights are in one to one correspondence with intersection points of
suitable Schubert cycles in a Grassmannian variety.


  We prove a distributional limit theorem conjectured in [Journal of
Statistical Physics 174, No. 6, 1372-1403 (2019)] for partition functions
defining models of directed polymers on diamond hierarchical graphs with
disorder variables placed at the graphical edges. The limiting regime involves
a joint scaling in which the number of hierarchical layers, $n\in \mathbb{N}$,
of the graphs grows as the inverse temperature, $\beta\equiv \beta(n)$,
vanishes with a fine-tuned dependence on $n$. The conjecture pertains to the
marginally relevant disorder case of the model wherein the branching parameter
$b \in \{2,3,\ldots\}$ and the segmenting parameter $s \in \{2,3,\ldots\}$
determining the hierarchical graphs are equal, which coincides with the diamond
fractal embedding the graphs having Hausdorff dimension two. Unlike the
analogous weak-disorder scaling limit for random polymer models on hierarchical
graphs in the disorder relevant $b<s$ case (or for the (1+1)-dimensional
polymer on the rectangular lattice), the distributional convergence of the
partition function when $b=s$ cannot be approached through a term-by-term
convergence to a Wiener chaos expansion, which does not exist for the continuum
model emerging in the limit. The analysis proceeds by controlling the
distributional convergence of the partition functions in terms of the
Wasserstein distance through a perturbative generalization of Stein's method at
a critical step. In addition, we prove that a similar limit theorem holds for
the analogous model with disorder variables placed at the vertices of the
graphs.


  Software developers and maintainers need to read and understand source
programs and other software artifacts. The increase in size and complexity of
software drastically affects several quality attributes, especially
understandability and maintainability. False interpretation often leads to
ambiguities, misunderstanding and hence to faulty development results. Despite
the fact that software understandability is vital and one of the most
significant components of the software development process, it is poorly
managed. This is mainly due to the lack of its proper management and control.
The paper highlights the importance of understandability in general and as a
factor of software testability. Two major contributions are made in the paper.
A relation between testability factors and object oriented characteristics has
been established as a first contribution. In second contribution, a model has
been proposed for estimating understandability of object oriented software
using design metrics. In addition, the proposed model has been validated using
experimental try-out.


  In the applications related to airborne radars, simulation has always played
an important role. This is mainly because of the two fold reason of the
unavailability of desired data and the difficulty associated with the
collection of data under controlled environment. A simple example will be
regarding the collection of pure multipolar radar data. Even after phenomenal
development in the field of radar hardware design and signal processing, till
now the collection of pure multipolar data is a challenge for the radar system
designers. Till very recently, the power of computer simulation of radar signal
return was available to a very selected few. This was because of the heavy cost
associated with some of the main line electro magnetic (EM) simulators for
radar signal simulation, and secondly because many such EM simulators are for
restricted marketting. However, because of the fast progress made in the field
of EM simulation, many of the current generic EM simulators can be used to
simulate radar returns from realistic targets. The current article expounds the
steps towards generating a synthetic aperture radar (SAR) image database of
ground targets, using a eneric EM g simulator. It also demonstrates by the help
of some example images, the quality of the SAR mage generated i using a general
purpose EM simulator.


  Defect-free atom arrays are an important precursor for quantum information
processing and quantum simulation. Yet, large-scale defect-free atom arrays can
be challenging to realize, due to the losses encountered when rearranging
stochastically loaded atoms to achieve a desired target array. Here, we
demonstrate a novel parallel rearrangement algorithm that uses multiple mobile
tweezers to independently sort and compress atom arrays in a way that naturally
avoids atom collisions. With a high degree of parallelism, our algorithm offers
a reduced move complexity compared to both single-tweezer algorithms and
existing multi-tweezer algorithms. We further determine the optimal degree of
parallelism to be a balance between an algorithmic speedup and multi-tweezer
inhomogeneity effects. The defect-free probability for a 225-atom array is
demonstrated to be as high as 33(1)% in a room temperature setup after multiple
cycles of rearrangement. The algorithm presented here can be implemented for
any target array geometry with an underlying periodic structure.


  PCANet was proposed as a lightweight deep learning network that mainly
leverages Principal Component Analysis (PCA) to learn multistage filter banks
followed by binarization and block-wise histograming. PCANet was shown worked
surprisingly well in various image classification tasks. However, PCANet is
data-dependence hence inflexible. In this paper, we proposed a
data-independence network, dubbed DCTNet for face recognition in which we adopt
Discrete Cosine Transform (DCT) as filter banks in place of PCA. This is
motivated by the fact that 2D DCT basis is indeed a good approximation for high
ranked eigenvectors of PCA. Both 2D DCT and PCA resemble a kind of modulated
sine-wave patterns, which can be perceived as a bandpass filter bank. DCTNet is
free from learning as 2D DCT bases can be computed in advance. Besides that, we
also proposed an effective method to regulate the block-wise histogram feature
vector of DCTNet for robustness. It is shown to provide surprising performance
boost when the probe image is considerably different in appearance from the
gallery image. We evaluate the performance of DCTNet extensively on a number of
benchmark face databases and being able to achieve on par with or often better
accuracy performance than PCANet.


  Very Large Array observations of the Galactic Center at 7 mm have produced an
image of the 30 arcseconds surrounding Sgr A* with a resolution of 82x42
milliarcseconds (mas). A comparison with IR images taken simultaneously with
the Very Large Telescope (VLT) identifies 41 radio sources with L-band (3.8
microns) stellar counterparts. The well-known young, massive stars in the
central Sgr A* cluster (e.g., IRS 16C, IRS 16NE, IRS 16SE2, IRS 16NW, IRS 16SW,
AF, AFNW, IRS 34W and IRS 33E) are detected with peak flux densities between
0.2 and 1.3 mJy. The origin of the stellar radio emission in the central
cluster is discussed in terms of ionized stellar winds with mass-loss rates in
the range 0.8-5x10^{-5} solar mass per year. Radio emission from eight massive
stars is used as a tool for registration between the radio and infrared frames
with mas precision within a few arcseconds of Sgr A*. This is similar to the
established technique of aligning SiO masers and evolved stars except that
radio stars lie within a few arcseconds of Sgr A*. Our data show a scatter of
~6.5 mas in the positions of the eight radio sources that appear in both the
L-band and 7 mm images. Lastly, we use the radio and IR data to argue that
members of IRS 13N are Young Stellar Objects rather than dust clumps,
supporting the hypothesis that recent star formation has occurred near Sgr A*.


  We study the temporal fluctuations of the flux of surface potential energy in
Surface Quasi-Geostrophic (SQG) turbulence. By means of high-resolution, direct
numerical simulations of the SQG model in the regime of forced and dissipated
cascade of temperature variance, we show that the instantaneous imbalance in
the energy budget originates a subleading correction to the spectrum of the
turbulent cascade. Using a multiple-scale approach combined with a dimensional
closure we derive a theoretical prediction for the power-law behavior of the
corrections, which holds for a class of turbulent transport equations known as
{\alpha}-turbulence. Further, we develop and apply a method to disentangle the
equilibrium and non-equilibrium contribution in the instantaneous spectra,
which can be generalized to other turbulent systems.


  Sustainable development is a framework for achieving human development goals.
It provides natural systems' ability to deliver natural resources and ecosystem
services. Sustainable development is crucial for the economy and society.
Artificial intelligence (AI) has attracted increasing attention in recent
years, with the potential to have a positive influence across many domains. AI
is a commonly employed component in the quest for long-term sustainability. In
this study, we explore the impact of AI on three pillars of sustainable
development: society, environment, and economy, as well as numerous case
studies from which we may deduce the impact of AI in a variety of areas, i.e.,
agriculture, classifying waste, smart water management, and Heating,
Ventilation, and Air Conditioning (HVAC) systems. Furthermore, we present
AI-based strategies for achieving Sustainable Development Goals (SDGs) which
are effective for developing countries like Bangladesh. The framework that we
propose may reduce the negative impact of AI and promote the proactiveness of
this technology.


  We extend the BMN duality between IIB superstring theory on a pp-wave
background and a sector of N=4 super Yang-Mills theory to the
non-supersymmetric and unstable background built by Romans as a
compactification on a U(1) bundle over CP2 with 3-form and 5-form field
strength fluxes. We obtain a stable theory with the fewest number of
supercharges (e.g. 16) allowed by this kind of solutions and make conjectures
on the dual gauge theory.


  We give estimates on the intrinsic and the extrinsic curvature of manifolds
that are isometrically immersed as cylindrically bounded submanifolds of warped
products. We also address extensions of the results in the case of submanifolds
of the total space of a Riemannian submersion.


  We give an overview of relative tensor products (RTPs) for von Neumann
algebra modules. For background, we start with the categorical definition and
go on to examine its algebraic formulation, which is applied to Morita
equivalence and index. Then we consider the analytic construction, with
particular emphasis on explaining why the RTP is not generally defined for
every pair of vectors. We also look at recent work justifying a representation
of RTPs as composition of unbounded operators, noting that these ideas work
equally well for L^p modules. Finally, we prove some new results characterizing
preclosedness of the map (\xi, \eta) \mapsto \xi \otimes_\phi \eta.


  This paper presents a framework to incorporate ride-pooling from a mesoscopic
point of view, within time-invariant network flow models of Mobility-on-Demand
systems. The resulting problem structure remains identical to a standard
network flow model, a linear problem, which can be solved in polynomial time
for a given ride-pooling request assignment. In order to compute such a
ride-pooling assignment, we devise a polynomial-time knapsack-like algorithm
that is optimal w.r.t. the minimum user travel time instance of the original
problem. Finally, we conduct two case studies of Sioux Falls and Manhattan,
where we validate our models against state-of-the-art time-varying results, and
we quantitatively highlight the effects that maximum waiting time and maximum
delay thresholds have on the vehicle hours traveled, overall pooled rides and
actual delay experienced. We show that for a sufficient number of requests,
with a maximum waiting time and delay of 5 minutes, it is possible to ride-pool
more than 80% of the requests for both case studies. Last, allowing for four
people ride-pooling can significantly boost the performance of the system.


  We present observations of V838 Mon and its close vicinity in the three
lowest rotational transitions of CO. The J = 2-1 and 3-2 data were obtained
using the 3 m KOSMA telescope. They include on-the-fly maps covering a large
area (~3.4 sq. deg.) around V838 Mon and long integrations on the star
position. Complementary observations in the CO J = 1-0 transition were obtained
using the 13.7 m Delingha telescope. The star position as well as 25 other
points preselected in the near vicinity of the object have been measured in
this transition.
  We report on a detection of two narrow emission components in J = 2-1 and 3-2
transitions at the position of V838 Mon. Lines were found at radial velocities
of V_lsr=53.3 km/s and V_lsr=-11.0 km/s. Their origin is unclear. We also
shortly discuss results of the observations of the vicinity of V838 Mon.


  We propose a novel method, StyLitGAN, for relighting and resurfacing
generated images in the absence of labeled data. Our approach generates images
with realistic lighting effects, including cast shadows, soft shadows,
inter-reflections, and glossy effects, without the need for paired or CGI data.
  StyLitGAN uses an intrinsic image method to decompose an image, followed by a
search of the latent space of a pre-trained StyleGAN to identify a set of
directions. By prompting the model to fix one component (e.g., albedo) and vary
another (e.g., shading), we generate relighted images by adding the identified
directions to the latent style codes. Quantitative metrics of change in albedo
and lighting diversity allow us to choose effective directions using a forward
selection process. Qualitative evaluation confirms the effectiveness of our
method.


  In this master's thesis, we will go into the (projective) blow-up
construction for Lie groupoids and Lie algebroids. In the literature, there are
different methods to be found on how to do this, especially for Lie groupoids.
The main goal of the thesis is to explain, in detail, the Lie groupoid and the
Lie algebroid blow-up constructions, but also to examine and compare different
points of view. More explicitly, we will rigorously explain the blow-up
construction for Lie groupoids by Claire Debord and Georges Skandalis.
Moreover, we will show that the blow-up construction for Lie groupoids by
Songhao Li and Marco Gualtieri, and the construction by Kirsten Wang, fit into
this setting. Also, we will show that, analogously, we obtain a general
geometric blow-up construction for Lie algebroids. This construction for Lie
algebroids coincides with the construction of lower elementary modification in
the codimension one case (by e.g. Songhao Li and Marco Gualtieri, Melinda
Lanius, or Ralph Klaasse). Examples that are discussed include the blow-up of a
pair groupoid (resp. tangent bundle) along a pair groupoid (resp. tangent
bundle), the blow-up of a groupoid (resp. algebroid) along the groupoid (resp.
algebroid) restricted to a saturated submanifold, and the blow-up of a
regularly foliated manifold along a leaf. The blow-up construction by Debord
and Skandalis uses the theory of deformation to the normal cones. We will give
a broad introduction to this theory and go into some of its known applications.
We also discuss the observation made by Debord and Skandalis about Morita
invariance of the construction of blow-up and of deformation to the normal
cone.


  We introduce and analyze a quantum spin/Majorana chain with a tricritical
Ising point separating a critical phase from a gapped phase with order-disorder
coexistence. We show that supersymmetry is not only an emergent property of the
scaling limit, but manifests itself on the lattice. Namely, we find explicit
lattice expressions for the supersymmetry generators and currents. Writing the
Hamiltonian in terms of these generators allows us to find the ground states
exactly at a frustration-free coupling. These confirm the coexistence between
two (topologically) ordered ground states and a disordered one in the gapped
phase. Deforming the model by including explicit chiral symmetry breaking, we
find the phases persist up to an unusual chiral phase transition where the
supersymmetry becomes exact even on the lattice.


  Fuzzy ordered linear spaces, Riesz spaces, fuzzy Archimedean spaces and
$\sigma$-complete fuzzy Riesz spaces were defined and studied in several works.
Following the efforts along this line, we define fuzzy Riesz subspaces, fuzzy
ideals, fuzzy bands and fuzzy band projections and establish their fundamental
properties.


  This paper focuses on scattered data fitting problems on spheres. We study
the approximation performance of a class of weighted spectral filter
algorithms, including Tikhonov regularization, Landaweber iteration, spectral
cut-off, and iterated Tikhonov, in fitting noisy data with possibly unbounded
random noise. For the analysis, we develop an integral operator approach that
can be regarded as an extension of the widely used sampling inequality approach
and norming set method in the community of scattered data fitting. After
providing an equivalence between the operator differences and quadrature rules,
we succeed in deriving optimal Sobolev-type error estimates of weighted
spectral filter algorithms. Our derived error estimates do not suffer from the
saturation phenomenon for Tikhonov regularization in the literature,
native-space-barrier for existing error analysis and adapts to different
embedding spaces. We also propose a divide-and-conquer scheme to equip weighted
spectral filter algorithms to reduce their computational burden and present the
optimal approximation error bounds.


  Cross-modality synthesis (CMS), super-resolution (SR), and their combination
(CMSR) have been extensively studied for magnetic resonance imaging (MRI).
Their primary goals are to enhance the imaging quality by synthesizing the
desired modality and reducing the slice thickness. Despite the promising
synthetic results, these techniques are often tailored to specific tasks,
thereby limiting their adaptability to complex clinical scenarios. Therefore,
it is crucial to build a unified network that can handle various image
synthesis tasks with arbitrary requirements of modality and resolution
settings, so that the resources for training and deploying the models can be
greatly reduced. However, none of the previous works is capable of performing
CMS, SR, and CMSR using a unified network. Moreover, these MRI reconstruction
methods often treat alias frequencies improperly, resulting in suboptimal
detail restoration. In this paper, we propose a Unified Co-Modulated Alias-free
framework (Uni-COAL) to accomplish the aforementioned tasks with a single
network. The co-modulation design of the image-conditioned and stochastic
attribute representations ensures the consistency between CMS and SR, while
simultaneously accommodating arbitrary combinations of input/output modalities
and thickness. The generator of Uni-COAL is also designed to be alias-free
based on the Shannon-Nyquist signal processing framework, ensuring effective
suppression of alias frequencies. Additionally, we leverage the semantic prior
of Segment Anything Model (SAM) to guide Uni-COAL, ensuring a more authentic
preservation of anatomical structures during synthesis. Experiments on three
datasets demonstrate that Uni-COAL outperforms the alternatives in CMS, SR, and
CMSR tasks for MR images, which highlights its generalizability to wide-range
applications.


  We develop a quantitative version of Aubry duality and use it to obtain
several sharp estimates for the dynamics of Schr\"odinger cocycles associated
to a non-perturbatively small analytic potential and Diophantine frequency. In
particular, we establish the full version of Eliasson's reducibility theory in
this regime (our approach actually leads to improvements even in the
perturbative regime: we are able to show, for all energies, ``almost
reducibility'' in some band of analyticity). We also prove 1/2-H\"older
continuity of the integrated density of states. For the almost Mathieu
operator, our results hold through the entire regime of sub-critical coupling
and imply also the dry version of the Ten Martini Problem for the concerned
parameters.


  Most of the Active Galactic Nuclei (AGN) are radio-quiet (RQ) and,
differently from radio-loud (RL) AGN, do not show signature of large-scale and
powerful jets. The physical origin of their radio emission remains then broadly
unclear. The observation of flat/inverted radio spectra at GHz frequencies
seems to support however the presence of an unresolved synchrotron
self-absorbed region in the close environment of the supermassive black hole.
Its size could be as small as that of the X-ray corona. Since synchrotron self
absorption decreases strongly with frequency, these sources need to be observed
in the millimetric (mm) domain. We report here a 12h simultaneous mm-X-ray
observation of the RQ AGN MCG+08-11-11 by NOEMA and NuSTAR. The mm flux shows a
weak but clear increase along the pointing with a fractional variability of
$2.0\pm0.1$\%. The 3-10 keV flux of NuSTAR also increases and shows a
fractional variability of $7.0\pm1.5$\%. A structure function analysis shows a
local maximum in the mm light curve corresponding to 2-3\% of variability on
timescale of $\sim2\times10^4$ seconds (100-300 $R_g$ light crossing time).
Assuming an optically thick mm emitting medium, this translates into an upper
limit of its size of $\sim$1300 $R_g$. The observation of fast variability in
radio-mm and X-ray wavelengths, as well as a similar variability trend, well
support the mm emission to be emitted by a region close, and potentially
related to, the X-ray corona like an outflow/weak jet.


  The ability to actively ground task instructions from an egocentric view is
crucial for AI agents to accomplish tasks or assist humans virtually. One
important step towards this goal is to localize and track key active objects
that undergo major state change as a consequence of human actions/interactions
to the environment without being told exactly what/where to ground (e.g.,
localizing and tracking the `sponge` in video from the instruction "Dip the
`sponge` into the bucket."). While existing works approach this problem from a
pure vision perspective, we investigate to which extent the textual modality
(i.e., task instructions) and their interaction with visual modality can be
beneficial. Specifically, we propose to improve phrase grounding models'
ability on localizing the active objects by: (1) learning the role of `objects
undergoing change` and extracting them accurately from the instructions, (2)
leveraging pre- and post-conditions of the objects during actions, and (3)
recognizing the objects more robustly with descriptional knowledge. We leverage
large language models (LLMs) to extract the aforementioned action-object
knowledge, and design a per-object aggregation masking technique to effectively
perform joint inference on object phrases and symbolic knowledge. We evaluate
our framework on Ego4D and Epic-Kitchens datasets. Extensive experiments
demonstrate the effectiveness of our proposed framework, which leads to>54%
improvements in all standard metrics on the TREK-150-OPE-Det localization +
tracking task, >7% improvements in all standard metrics on the TREK-150-OPE
tracking task, and >3% improvements in average precision (AP) on the Ego4D SCOD
task.


  Games such as go, chess and checkers have multiple equivalent game states,
i.e. multiple board positions where symmetrical and opposite moves should be
made. These equivalences are not exploited by current state of the art neural
agents which instead must relearn similar information, thereby wasting
computing time. Group equivariant CNNs in existing work create networks which
can exploit symmetries to improve learning, however, they lack the
expressiveness to correctly reflect the move embeddings necessary for games. We
introduce Finite Group Neural Networks (FGNNs), a method for creating agents
with an innate understanding of these board positions. FGNNs are shown to
improve the performance of networks playing checkers (draughts), and can be
easily adapted to other games and learning problems. Additionally, FGNNs can be
created from existing network architectures. These include, for the first time,
those with skip connections and arbitrary layer types. We demonstrate that an
equivariant version of U-Net (FGNN-U-Net) outperforms the unmodified network in
image segmentation.


  Let $X$ be a fs logarithmic scheme that is generically logarithmically
smooth, and that admits a strict closed embedding into a logarithmically smooth
scheme $Y$ over a field $\kk$ of characteristic zero. We construct a simple and
fast procedure to functorial logarithmic resolution of $X$, where the end
result is in particular a stack-theoretic modification $X' \rightarrow X$ such
that $X'$ is logarithmically smooth over $k$. In particular, if $X$ is a closed
subscheme of a smooth $k$-scheme $Y$, the procedure not only shares the same
desirable features as the 'dream resolution algorithm' of
Abramovich-Temkin-Wlodarczyk (arXiv:1906.07106), but also accounts for a key
feature of Hironaka's Main Theorem I, which was not addressed in
arXiv:1906.07106. As a consequence, we recover a different and simpler approach
to Hironaka's resolution of singularities in characteristic zero.


  Data-driven analysis of complex networks has been in the focus of research
for decades. An important area of research is to study how well real networks
can be described with a small selection of metrics, furthermore how well
network models can capture the relations between graph metrics observed in real
networks. In this paper, we apply machine learning techniques to investigate
the aforementioned problems. We study 500 real-world networks along with 2,000
synthetic networks generated by four frequently used network models with
previously calibrated parameters to make the generated graphs as similar to the
real networks as possible. This paper unifies several branches of data-driven
complex network analysis, such as the study of graph metrics and their
pair-wise relationships, network similarity estimation, model calibration, and
graph classification. We find that the correlation profiles of the structural
measures significantly differ across network domains and the domain can be
efficiently determined using a small selection of graph metrics. The structural
properties of the network models with fixed parameters are robust enough to
perform parameter calibration. The goodness-of-fit of the network models highly
depends on the network domain. By solving classification problems, we find that
the models lack the capability of generating a graph with a high clustering
coefficient and relatively large diameter simultaneously. On the other hand,
models are able to capture exactly the degree-distribution-related metrics.


  We start in this work the study of the relation between the theory of
regularity structures and paracontrolled calculus. We give a paracontrolled
representation of the reconstruction operator and provide a natural
parametrization of the space of admissible models.


  The oscillation frequency Delta-md of B0 anti-B0 mixing is measured using the
partially reconstructed semileptonic decay anti-B0 -> l- nubar D*+ X. The data
sample was collected with the CDF detector at the Fermilab Tevatron collider
during 1992 - 1995 by triggering on the existence of two lepton candidates in
an event, and corresponds to about 110 pb-1 of pbar p collisions at sqrt(s) =
1.8 TeV. We estimate the proper decay time of the anti-B0 meson from the
measured decay length and reconstructed momentum of the l- D*+ system. The
charge of the lepton in the final state identifies the flavor of the anti-B0
meson at its decay. The second lepton in the event is used to infer the flavor
of the anti-B0 meson at production. We measure the oscillation frequency to be
Delta-md = 0.516 +/- 0.099 +0.029 -0.035 ps-1, where the first uncertainty is
statistical and the second is systematic.


  Within a formulation of a Dynamical AdS/QCD model we calculate the spectrum
of light flavored mesons. The background fields of the model correspond to an
IR deformed Anti de Sitter metric coupled to a dilaton field. Confinement comes
as a consequence of the dilaton dynamics coupled to gravity. Additionally to
the Regge-like spectrum of light- scalar, vector and higher spin mesons, we
obtain the decay width of scalar mesons into two pions.


  In the past, Kepler painstakingly derived laws of planetary motion using
difficult to understand and hard to follow techniques. In 1843 William Hamilton
created and described the quaternions, which extend the complex numbers and can
easily describe rotations in three dimensional space. In this article, we will
harness this system to provide a new and intuitive way to derive Kepler's laws.
This will include using a quaternionic version of the spatial Kepler problem
differential equation, and using the general solution to describe the motion of
planets orbiting a central body. We use the standard method for regularizing
celestial mechanics, but this article will be solely focused on showing the
validity of Kepler's laws.


  Dimensionally reduced supersymmetric theories retain a great deal of
information regarding their higher dimensional origins. In superspace, this
"memory" allows us to restore the action governing a reduced theory to that
describing its higher-dimensional progenitor. We illustrate this by restoring
four-dimensional N=4 Yang-Mills to its six-dimensional parent, N=(1,1)
Yang-Mills. Supersymmetric truncation is introduced into this framework and
used to obtain the N=1 action in six dimensions. We work in light-cone
superspace, dealing exclusively with physical degrees of freedom.


  Dynamic monopolies were already defined and studied for the formulation of
the phenomena of the spread of influence in social networks such as disease,
opinion, adaptation of new product and etc. The elements of the network which
have been influenced (e.g. infected or adapted an opinion) are called active
vertices. It is assumed in these models that when an element is activated, it
remains active until the end of the process. But in some phenomena of the
spread of influence this property does not hold. For example in some diseases
the infection lasts only a limited period of time or consider the spread of
disease or propagation of computer virus together with some quarantination or
decontamination methods. Dynamic monopolies are not useful for the study of
these latter phenomena. For this purpose, we introduce a new model for such
diffusions of influence and call it weak dynamic monopoly. A social network is
represented by a graph $G$. Assume that any vertex $v$ of $G$ has a threshold
$\tau(v)\in \Bbb{N}$. Then a subset $D\subseteq V(G)$ is said to be a weak
dynamic monopoly if $V(G)$ can be partitioned into $D_0=D, D_1, \ldots, D_k$
such that for any $i$, any vertex $v$ of $D_i$ has at least $\tau(v)$ neighbors
in $D_{i-1}$. In this definition, by the size and the processing time of $D$ we
mean $|D|$ and $k$, respectively. We first investigate the relationships
between weak dynamic monopolies and other related concepts and then obtain some
bounds for the smallest size of weak dynamic monopolies. Next we obtain some
results concerning the processing time of weak dynamic monopolies in terms of
some graph parameters. Finally, a hardness result concerning inapproximibility
of the determining the smallest size of weak dynamic monopolies in general
graphs is obtained.


  The person re-identification (Re-ID) task requires to robustly extract
feature representations for person images. Recently, part-based representation
models have been widely studied for extracting the more compact and robust
feature representations for person images to improve person Re-ID results.
However, existing part-based representation models mostly extract the features
of different parts independently which ignore the relationship information
between different parts. To overcome this limitation, in this paper we propose
a novel deep learning framework, named Part-based Hierarchical Graph
Convolutional Network (PH-GCN) for person Re-ID problem. Given a person image,
PH-GCN first constructs a hierarchical graph to represent the pairwise
relationships among different parts. Then, both local and global feature
learning are performed by the messages passing in PH-GCN, which takes other
nodes information into account for part feature representation. Finally, a
perceptron layer is adopted for the final person part label prediction and
re-identification. The proposed framework provides a general solution that
integrates local, global and structural feature learning simultaneously in a
unified end-to-end network. Extensive experiments on several benchmark datasets
demonstrate the effectiveness of the proposed PH-GCN based Re-ID approach.


  We present a classification of strict limits of planar BV homeomorphisms. The
authors and S. Hencl showed in a previous work \cite{CHKR} that such mappings
allow for cavitations and fractures singularities but fulfill a suitable
generalization of the INV condition. As pointed out by J. Ball \cite{B}, these
features are physically expected by limit configurations of elastic
deformations. In the present work we develop a suitable generalization of the
\emph{no-crossing} condition introduced by De Philippis and Pratelli in
\cite{PP} to describe weak limits of planar Sobolev homeomorphisms that we call
\emph{BV no-crossing} condition, and we show that a planar mapping satisfies
this property if and only if it can be approximated strictly by homeomorphisms
of bounded variations.


  The citations to a set of academic articles are typically unevenly shared,
with many articles attracting few citations and few attracting many. It is
important to know more precisely how citations are distributed in order to help
statistical analyses of citations, especially for sets of articles from a
single discipline and a small range of years, as normally used for research
evaluation. This article fits discrete versions of the power law, the lognormal
distribution and the hooked power law to 20 different Scopus categories, using
citations to articles published in 2004 and ignoring uncited articles. The
results show that, despite its popularity, the power law is not a suitable
model for collections of articles from a single subject and year, even for the
purpose of estimating the slope of the tail of the citation data. Both the
hooked power law and the lognormal distributions fit best for some subjects but
neither is a universal optimal choice and parameter estimates for both seem to
be unreliable. Hence only the hooked power law and discrete lognormal
distributions should be considered for subject-and-year-based citation analysis
in future and parameter estimates should always be interpreted cautiously.


  Lung nodule proposals generation is the primary step of lung nodule detection
and has received much attention in recent years . In this paper, we first
construct a model of 3-dimension Convolutional Neural Network (3D CNN) to
generate lung nodule proposals, which can achieve the state-of-the-art
performance. Then, we analyze a series of key problems concerning the training
performance and efficiency. Firstly, we train the 3D CNN model with data in
different resolutions and find out that models trained by high resolution input
data achieve better lung nodule proposals generation performances especially
for nodules in too small sizes, while consumes much more memory at the same
time. Then, we analyze the memory consumptions on different platforms and the
experimental results indicate that CPU architecture can provide us with larger
memory and enables us to explore more possibilities of 3D applications. We
implement the 3D CNN model on CPU platform and propose an Intel Extended-Caffe
framework which supports many highly-efficient 3D computations, which is opened
source at https://github.com/extendedcaffe/extended-caffe.


  Deciphering visual content from functional Magnetic Resonance Imaging (fMRI)
helps illuminate the human vision system. However, the scarcity of fMRI data
and noise hamper brain decoding model performance. Previous approaches
primarily employ subject-specific models, sensitive to training sample size. In
this paper, we explore a straightforward but overlooked solution to address
data scarcity. We propose shallow subject-specific adapters to map
cross-subject fMRI data into unified representations. Subsequently, a shared
deeper decoding model decodes cross-subject features into the target feature
space. During training, we leverage both visual and textual supervision for
multi-modal brain decoding. Our model integrates a high-level perception
decoding pipeline and a pixel-wise reconstruction pipeline guided by high-level
perceptions, simulating bottom-up and top-down processes in neuroscience.
Empirical experiments demonstrate robust neural representation learning across
subjects for both pipelines. Moreover, merging high-level and low-level
information improves both low-level and high-level reconstruction metrics.
Additionally, we successfully transfer learned general knowledge to new
subjects by training new adapters with limited training data. Compared to
previous state-of-the-art methods, notably pre-training-based methods (Mind-Vis
and fMRI-PTE), our approach achieves comparable or superior results across
diverse tasks, showing promise as an alternative method for cross-subject fMRI
data pre-training. Our code and pre-trained weights will be publicly released
at https://github.com/YulongBonjour/See_Through_Their_Minds.


  We define a Hopf cyclic (co)homology theory in an arbitrary symmetric strict
monoidal category. Thus we unify all different types of Hopf cyclic
(co)homologies under one single universal theory. We recover Hopf cyclic
(co)homology of module algebras, comodule algebras and module coalgebras along
with Hopf-Hochschild (co)homology of module algebras. This universal theory
also allows us to define the missing Hopf cyclic theory for comodule
coalgebras.


  The GDH sum rule is discussed for the Delta(1232) resonance. It is shown that
apart from ordinary excitations to higher-energy states, the sum rule contains
a large negative contribution due to de-excitation into the nucleon state.
Therefore, a fulfillment of the sum rule assumes a strong coupling of Delta^+
and Delta^0 to resonances of spin 5/2 and higher. Calculations performed in
quark models suggest that D15(1675) may be such a resonance. However, its
strength is found to be not sufficient for bringing the GDH sum rule to a
theoretically expected positive magnitude.


  Motivated by emergent phenomena at oxide surfaces and interfaces,
particularly those involving transition metal oxides with perovskite crystal
structure such as LaTiO3/SrTiO3, we examine the ferromagnetic Kondo lattice
model (FKLM) in the presence of a Rashba spin-orbit coupling (RSOC). Using
numerical techniques, under the assumption that the electrons on localized
orbitals may be treated as classical continuum spins, we compute various
charge, spin and transport properties on square clusters at zero temperature.
We find that the main effect of the RSOC is the destruction of the
ferromagnetic state present in the FKLM at low electron fillings, with the
consequent suppression of conductivity. In addition, near half-filling the RSOC
leads to a departure of the antiferromagnetic state of the FKLM with a
consequent reduction to the intrinsic tendency to electronic phase separation.
The interplay between phase separation on one side, and magnetic and transport
properties on the other, is carefully analyzed as a function of the
RSOC/hopping ratio.


  Young stars on their way to the ZAMS evolve in significantly different ways
depending on mass. While the theoretical and observational properties of low-
and intermediate-mass stars are rather well understood and/or empirically
tested, the situation for massive stars (>10-15 Msun) is, to say the least,
still elusive. On theoretical grounds, the PMS evolution of these objects
should be extremely short, or nonexistent at all. Observationally, despite a
great deal of effort, the simple (or bold) predictions of simplified models of
massive star formation/evolution have proved more difficult to be checked.
After a brief review of the theoretical expectations, I will highlight some
critical test on young stars of various masses.


  We report studies of low-field conductivity, sigma, of the orthorhombic TaS_3
samples as a function of strain, epsilon. In the Peierls state the
sigma(epsilon) dependencies show hysteresis. A similar hysteresis loop is
observed for K_{0.3}MoO_3. For nano-sized TaS_3 samples the sigma(epsilon)
curves show step-like changes associated with the "quantization" of the wave
vector, q, of the charge-density wave (CDW). The dependences clearly reveal the
change of the q-vector with strain. In contrast with the traditional concept, q
is found to increase with sample expansion. This means that the stretch-induced
anomalies cannot be explained by the transition of the CDW to fourfold
commensurability with the pristine lattice (lock-in transition). Alternatively,
we suppose, that at the critical stretch a CDW with larger amplitude and
modified q-vector forms. Further, the models describing metastable length
states and drop of the Young modulus on CDW depinning in terms of longitudinal
CDW strain, require reconsideration. Presumably, transverse effects should be
taken into account.


  We prove the existence of a cuspidal automorphic representation $\pi$ for
$GL_{79}/\mathbf{Q}$ of level one and weight zero. We construct $\pi$ using
symmetric power functoriality and a change of weight theorem, using Galois
deformation theory. As a corollary, we construct the first known cuspidal
cohomology classes in $H^*(GL_{n}(\mathbf{Z}),\mathbf{C})$ for any $n > 1$.


  Given a generic $K3$ surface $Y_k$ of the Ap\'ery-Fermi pencil, we use the
Kneser-Nishiyama technique to determine all its non isomorphic elliptic
fibrations. These computations lead to determine those fibrations with
2-torsion sections T. We classify the fibrations such that the translation by T
gives a Shioda-Inose structure. The other fibrations correspond to a K3 surface
identified by it transcendental lattice. The same problem is solved for a
singular member $Y_2$ of the family showing the differences with the generic
case. In conclusion we put our results in the context of relations between
$2$-isogenies and isometries on the singular surfaces of the family.


  A Pervin space is a set equipped with a bounded sublattice of its powerset,
while its pointfree version, called Frith frame, consists of a frame equipped
with a generating bounded sublattice. It is known that the dual adjunction
between topological spaces and frames extends to a dual adjunction between
Pervin spaces and Frith frames, and that the latter may be seen as
representatives of certain quasi-uniform structures. As such, they have an
underlying bitopological structure and inherit a natural notion of completion.
In this paper we start by exploring the bitopological nature of Pervin spaces
and of Frith frames, proving some categorical equivalences involving
zero-dimensional structures. We then provide a conceptual proof of a duality
between the categories of $T_0$ complete Pervin spaces and of complete Frith
frames. This enables us to interpret several Stone-type dualities as a
restriction of the dual adjunction between Pervin spaces and Frith frames along
full subcategory embeddings. Finally, we provide analogues of Banaschewski and
Pultr's characterizations of sober and $T_D$ topological spaces in the setting
of Pervin spaces and of Frith frames, highlighting the parallelism between the
two notions.


  Some of the new experimental results and theoretical developments presented
at the Workshop on Deep Inelastic Scattering and Related Phenomena (Rome, April
1996) are reviewed.


  We study the ground state properties of the critical Lipkin-Meshkov-Glick
model. Using the Holstein-Primakoff boson representation, and the continuous
unitary transformation technique, we compute explicitly the finite-size scaling
exponents for the energy gap, the ground state energy, the magnetization, and
the spin-spin correlation functions. Finally, we discuss the behavior of the
two-spin entanglement in the vicinity of the phase transition.


  Determining effective elastic properties of rocks from their pore-scale
digital images is a key goal of digital rock physics (DRP). Direct numerical
simulation (DNS) of elastic behavior, however, incurs high computational cost;
and surrogate machine learning (ML) model, particularly convolutional neural
network (CNN), show promises to accelerate homogenization process. 3D CNN
models, however, are unable to handle large images due to memory issues. To
address this challenge, we propose a novel method that combines 3D CNN with
hierarchical homogenization method (HHM). The surrogate 3D CNN model
homogenizes only small subimages, and a DNS is used to homogenize the
intermediate image obtained by assembling small subimages. The 3D CNN model is
designed to output the homogenized elastic constants within the
Hashin-Shtrikman (HS) bounds of the input images. The 3D CNN model is first
trained on data comprising equal proportions of five sandstone (quartz
mineralogy) images, and, subsequently, fine-tuned for specific rocks using
transfer learning. The proposed method is applied to homogenize the rock images
of size 300x300x300 and 600x600x600 voxels, and the predicted homogenized
elastic moduli are shown to agree with that obtained from the brute-force DNS.
The transferability of the trained 3D CNN model (using transfer learning) is
further demonstrated by predicting the homogenized elastic moduli of a
limestone rock with calcite mineralogy. The surrogate 3D CNN model in
combination with the HHM is thus shown to be a promising tool for the
homogenization of large 3D digital rock images and other random media


  The ANTARES project aims to build a deep-sea Cherenkov Telescope for High
Energy Neutrino Astronomy located in the Mediterranean Sea. The experiment,
currently in the construction phase, has recently achieved an important
milestone : the operation of a prototype line and of a line with monitoring
instruments. These deployments allowed a thorough understanding of
environmental parameters.


  In this paper, the determinant representation of the n-fold Darboux
transformation (DT) of the Kundu-DNLS equation is given. Based on our analysis,
the soliton solutions, positon solutions and breather solutions of the
Kundu-DNLS equation are given explicitly. Further, we also construct the rogue
wave solutions which are given by using the Taylor expansion of the breather
solution. Particularly, these rogue wave solutions possess several free
parameters. With the help of these parameters, these rogue waves constitute
several patterns, such as fundamental pattern, triangular pattern, circular
pattern.


  In the framework of the QCD shock-wave approach, we review our results on the
description of diffractive production of various final states (jets, meson) at
next-to-leading order. This is applied to exclusive diffractive dijet
electroproduction at HERA.


  Random exploration is one of the main mechanisms through which reinforcement
learning (RL) finds well-performing policies. However, it can lead to
undesirable or catastrophic outcomes when learning online in safety-critical
environments. In fact, safe learning is one of the major obstacles towards
real-world agents that can learn during deployment. One way of ensuring that
agents respect hard limitations is to explicitly configure boundaries in which
they can operate. While this might work in some cases, we do not always have
clear a-priori information which states and actions can lead dangerously close
to hazardous states. Here, we present an approach where an additional policy
can override the main policy and offer a safer alternative action. In our
instinct-regulated RL (IR^2L) approach, an "instinctual" network is trained to
recognize undesirable situations, while guarding the learning policy against
entering them. The instinct network is pre-trained on a single task where it is
safe to make mistakes, and transferred to environments in which learning a new
task safely is critical. We demonstrate IR^2L in the OpenAI Safety gym domain,
in which it receives a significantly lower number of safety violations during
training than a baseline RL approach while reaching similar task performance.


  The Liouville theorem states that the phase-space volume of an ensemble in a
closed system remains constant. While gases of material particles can
efficiently be cooled by sympathetic or laser cooling techniques, allowing for
large phase-space compression, for light both the absence of an internal
structure, as well as the usual non-conservation of particle number upon
contact to matter imposes fundamental limits e.g. in fluorescence-based light
concentrators in three-dimensional systems. A different physical situation can
in principle be expected for dye-solution filled microcavities with a mirror
spacing in the wavelength range, where low dimensional photon gases with
non-vanishing, freely tunable chemical potential have been experimentally
realized. Motivated by the goal to observe phase-space compression of sunlight
by cooling the captured radiation to room temperature, we in this work
theoretically show that in a lossless system the phase space volume scales as
$(\Delta x \Delta p / T)^d = \mathrm{constant}$, where $\Delta x$ and $\Delta
p$ denote the rms position and momentum spread and $d$ the dimensionality of
the system ($d=1$ or $2$). We also experimentally realize a sunlight pumped dye
microcavity, and demonstrate thermalization of scattered sunlight to a
two-dimensional room temperature ensemble with non-vanishing chemical
potential. Prospects of phase space buildup of light by cooling, as can be
feasible in systems with a two- or three-dimensional band gap, can range from
quantum state preparation in tailored potentials up to technical applications
in diffuse sunlight collection.


  M87 is the first known radio galaxy to emit very high energy (VHE)
gamma-rays. During a monitoring program of M87, a rapid flare in VHE gamma-rays
was detected by the MAGIC telescope in early 2008. The flux was found to be
variable on a timescale as short as 1 day, reaching 15% of the Crab Nebula flux
above 350 GeV. In contrast, the flux at lower energies (150 GeV to 350 GeV) is
compatible with being constant. We present light curves and energy spectra, and
argue that the observed day-scale flux variability favours the M87 core as
source of the gamma-ray emission rather than the bright know HST-1 in the jet
of M87.


  A number of attempts have recently been made to extend the conjectured $S$
duality of Yang Mills theory to gravity. Central to these speculations has been
the belief that electrically and magnetically charged black holes, the solitons
of quantum gravity, have identical quantum properties. This is not obvious,
because although duality is a symmetry of the classical equations of motion, it
changes the sign of the Maxwell action. Nevertheless, we show that the chemical
potential and charge projection that one has to introduce for electric but not
magnetic black holes exactly compensate for the difference in action in the
semi-classical approximation. In particular, we show that the pair production
of electric black holes is not a runaway process, as one might think if one
just went by the action of the relevant instanton. We also comment on the
definition of the entropy in cosmological situations, and show that we need to
be more careful when defining the entropy than we are in an asymptotically-flat
case.


  We use multiplication maps to give a characteristic-free approach to
vanishing theorems on toric varieties. Our approach is very elementary but is
enough powerful to prove vanishing theorems.


  With the inherent benefits, such as, better cell coverage and higher area
throughput, extra-large scale massive multiple-input multiple-output (MIMO) has
great potential to be one of the key technologies for the next generation
wireless communication systems. However, in practice, when the antenna
dimensions grow large, spatial non-stationarities occur and users will only see
a portion of the base station antenna array, which we call visibility region
(VR). To assess the impact of spatial non-stationarities, in this paper, we
investigate the uplink transmission of extra-large scale massive MIMO systems
by considering VRs. In particular, we first propose a subarray-based system
architecture for extra-large scale massive MIMO systems. Then, tight
closed-form uplink spectral efficiency (SE) approximations with linear
receivers are derived. With the objective of maximizing the achievable SE, we
also propose schemes for the subarray phase coefficient design. In addition,
based on the obtained ergodic achievable SE approximations, two statistical
channel state information (CSI)-based greedy user scheduling algorithms are
developed. Our results indicate that the statistical CSI-based greedy joint
user and subarray scheduling algorithm collaborating with the on-off
switch-based subarray architecture is a promising practical solution for
extra-large scale massive MIMO systems.


  Cellular appendages conferring motility, such as flagella or cilia, are known
to synchronise their periodic beats. The origin of synchronisation is a
combination of long-range hydrodynamic interactions with physical mechanisms
allowing the phases of these biological oscillators to evolve. Two of such
mechanisms have been identified by previous work, the elastic compliance of the
periodic orbit or oscillations driven by phase-dependent biological forcing. To
help uncover the physical mechanism for hydrodynamic synchronisation most
essential overall in biology, we theoretically investigate the effect of strong
confinement on the effectiveness of hydrodynamic synchronisation. We use
minimal models where appendages are modelled as rigid spheres forced to move
along circular trajectories near a rigid surface. Strong confinement is
modelled by adding a second nearby surface, parallel to the first one, where
the distance between the surfaces is much smaller than the typical distance
between the cilia. We calculate separately the impact of confinement on the
synchronisation dynamics of the elastic compliance and the force modulation
mechanisms and compare our results to the case with no confinement. Applying
our results to the biologically-relevant situation of nodal cilia, we show that
force modulation is a mechanism that leads to phase-locked states under strong
confinement that are very similar to those without confinement as a difference
with the elastic compliance mechanism. Our results point therefore to the
robustness of force modulation for synchronisation, an important feature for
biological dynamics that suggests it could be the most essential physical
mechanism overall in arrays of nodal cilia. We further examine the distinct
situation of primary cilia and show in that case that the difference in
robustness of the mechanisms is not as pronounced but still favours the force
modulation.


  The task of preserving entanglement against noises is of crucial importance
for both quantum communication and quantum information transfer. To this aim,
quantum error correction (QEC) codes may be employed to compensate, at least
partially, the detriments induced by environmental noise that can be modelled
as a bit-flip or a phase-flip error channel. In this paper we investigate the
effects of the simple three-qubit QEC codes to restore entanglement and
nonlocality in a two-qubit system and consider two practical applications:
superdense coding and quantum teleportation. Though the considered three-qubit
QEC codes are known to perfectly work in the presence of very small noise, we
show that they can avoid the sudden death of entanglement and improve the
performance of the addressed protocols also for larger noise amplitudes.


  Consider a nonlinear Kirchhoff type equation as follows \begin{equation*}
\left\{ \begin{array}{ll} -\left( a\int_{\mathbb{R}^{N}}|\nabla
u|^{2}dx+b\right) \Delta u+u=f(x)\left\vert u\right\vert ^{p-2}u & \text{ in
}\mathbb{R}^{N}, \\ u\in H^{1}(\mathbb{R}^{N}), & \end{array}% \right.
\end{equation*}% where $N\geq 1,a,b>0,2<p<\min \left\{ 4,2^{\ast
}\right\}$($2^{\ast }=\infty $ for $N=1,2$ and $2^{\ast }=2N/(N-2)$ for $N\geq
3)$ and the function $f\in C(\mathbb{R}^{N})\cap L^{\infty }(\mathbb{R}^{N})$.
Distinguishing from the existing results in the literature, we are more
interested in the geometric properties of the energy functional related to the
above problem. Furthermore, the nonexistence, existence, unique and
multiplicity of positive solutions are proved dependent on the parameter $a$
and the dimension $N.$ In particular, we conclude that a unique positive
solution exists for $1\leq N\leq4$ while at least two positive solutions are
permitted for $N\geq5$.


  The variational approach to fracture is effective for simulating the
nucleation and propagation of complex crack patterns, but is computationally
demanding. The model is a strongly nonlinear non-convex variational inequality
that demands the resolution of small length scales. The current standard
algorithm for its solution, alternate minimization, is robust but converges
slowly and demands the solution of large, ill-conditioned linear subproblems.
In this paper, we propose several advances in the numerical solution of this
model that improve its computational efficiency. We reformulate alternate
minimization as a nonlinear Gauss-Seidel iteration and employ over-relaxation
to accelerate its convergence; we compose this accelerated alternate
minimization with Newton's method, to further reduce the time to solution; and
we formulate efficient preconditioners for the solution of the linear
subproblems arising in both alternate minimization and in Newton's method. We
investigate the improvements in efficiency on several examples from the
literature; the new solver is 5--6$\times$ faster on a majority of the test
cases


  Handling large amounts of data has become a key for developing automated
driving systems. Especially for developing highly automated driving functions,
working with images has become increasingly challenging due to the sheer size
of the required data. Such data has to satisfy different requirements to be
usable in machine learning-based approaches. Thus, engineers need to fully
understand their large image data sets for the development and test of machine
learning algorithms. However, current approaches lack automatability, are not
generic and are limited in their expressiveness. Hence, this paper aims to
analyze a state-of-the-art text and image embedding neural network and guides
through the application in the automotive domain. This approach enables the
search for similar images and the search based on a human understandable
text-based description. Our experiments show the automatability and
generalizability of our proposed method for handling large data sets in the
automotive domain.


  We show that a certain weighted mean of the Liouville function lambda(n) is
negative. In this sense, we can say that the Liouville function is negative "on
average".


  This work is concerned with the existence of mild solutions to non-linear
Fokker-Planck equations with fractional Laplace operator $(-\Delta)^s$ for
$s\in\left(\frac12,1\right)$. The uniqueness of Schwartz distributional
solutions is also proved under suitable assumptions on diffusion and drift
terms. As applications, weak existence and uniqueness of solutions to
McKean-Vlasov equations with L\'evy-Noise, as well as the Markov property for
their laws are proved.


  One-shot Network Pruning at Initialization (OPaI) is an effective method to
decrease network pruning costs. Recently, there is a growing belief that data
is unnecessary in OPaI. However, we obtain an opposite conclusion by ablation
experiments in two representative OPaI methods, SNIP and GraSP. Specifically,
we find that informative data is crucial to enhancing pruning performance. In
this paper, we propose two novel methods, Discriminative One-shot Network
Pruning (DOP) and Super Stitching, to prune the network by high-level visual
discriminative image patches. Our contributions are as follows. (1) Extensive
experiments reveal that OPaI is data-dependent. (2) Super Stitching performs
significantly better than the original OPaI method on benchmark ImageNet,
especially in a highly compressed model.


  We examine two central regularization strategies for monotone variational
inequalities, the first a direct regularization of the operative monotone
mapping, and the second via regularization of the associated dual gap function.
A key link in the relationship between the solution sets to these various
regularized problems is the idea of exact regularization, which, in turn, is
fundamentally associated with the existence of Lagrange multipliers for the
regularized variational inequality. A regularization is said to be exact if a
solution to the regularized problem is a solution to the unregularized problem
for all parameters beyond a certain value. The Lagrange multipliers
corresponding to a particular regularization of a variational inequality, on
the other hand, are defined via the dual gap function. Our analysis suggests
various conceptual, iteratively regularized numerical schemes, for which we
provide error bounds, and hence stopping criteria, under the additional
assumption that the solution set to the unregularized problem is what we call
weakly sharp of order greater than one.


  IGR J17361-4441 was discovered by INTEGRAL undergoing its first detectable
X-ray outburst in 2011 and initially classified as an accreting X-ray binary in
the globular cluster NGC 6388. A reanalysis of the outburst data collected with
INTEGRAL and Swift suggested that the enhanced X-ray emission from IGR
J17361-4441 could have been due to a rare tidal disruption event of a
terrestrial- icy planet by a white dwarf. In this letter we report on the
analysis of XMM-Newton data collected in 2011 during the outburst from IGR
J17361-4441. Our analysis revealed the presence of a $\sim$100 mHz
quasi-periodic oscillation in the X-ray emission from the source and confirmed
the presence of a soft thermal component (kT$\sim$0.08 keV) in its spectrum. We
discuss these findings in the context of the different possibilities proposed
to explain the nature of IGR J17361-4441.


  We introduce Spatio-Temporal Momentum strategies, a class of models that
unify both time-series and cross-sectional momentum strategies by trading
assets based on their cross-sectional momentum features over time. While both
time-series and cross-sectional momentum strategies are designed to
systematically capture momentum risk premia, these strategies are regarded as
distinct implementations and do not consider the concurrent relationship and
predictability between temporal and cross-sectional momentum features of
different assets. We model spatio-temporal momentum with neural networks of
varying complexities and demonstrate that a simple neural network with only a
single fully connected layer learns to simultaneously generate trading signals
for all assets in a portfolio by incorporating both their time-series and
cross-sectional momentum features. Backtesting on portfolios of 46
actively-traded US equities and 12 equity index futures contracts, we
demonstrate that the model is able to retain its performance over benchmarks in
the presence of high transaction costs of up to 5-10 basis points. In
particular, we find that the model when coupled with least absolute shrinkage
and turnover regularization results in the best performance over various
transaction cost scenarios.


  We introduce the theory of operator monotone functions and employ it to
derive a new inequality relating the quantum relative entropy and the quantum
conditional entropy. We present applications of this new inequality and in
particular we prove a new lower bound on the relative entropy of entanglement
and other properties of entanglement measures.


  Evolutionary Multitasking (EMT) paradigm, an emerging research topic in
evolutionary computation, has been successfully applied in solving
high-dimensional feature selection (FS) problems recently. However, existing
EMT-based FS methods suffer from several limitations, such as a single mode of
multitask generation, conducting the same generic evolutionary search for all
tasks, relying on implicit transfer mechanisms through sole solution encodings,
and employing single-objective transformation, which result in inadequate
knowledge acquisition, exploitation, and transfer. To this end, this paper
develops a novel EMT framework for multiobjective high-dimensional feature
selection problems, namely MO-FSEMT. In particular, multiple auxiliary tasks
are constructed by distinct formulation methods to provide diverse search
spaces and information representations and then simultaneously addressed with
the original task through a multi-slover-based multitask optimization scheme.
Each task has an independent population with task-specific representations and
is solved using separate evolutionary solvers with different biases and search
preferences. A task-specific knowledge transfer mechanism is designed to
leverage the advantage information of each task, enabling the discovery and
effective transmission of high-quality solutions during the search process.
Comprehensive experimental results demonstrate that our MO-FSEMT framework can
achieve overall superior performance compared to the state-of-the-art FS
methods on 26 datasets. Moreover, the ablation studies verify the contributions
of different components of the proposed MO-FSEMT.


  Deep neural networks (DNNs) have proven their capabilities in many areas in
the past years, such as robotics, or automated driving, enabling technological
breakthroughs. DNNs play a significant role in environment perception for the
challenging application of automated driving and are employed for tasks such as
detection, semantic segmentation, and sensor fusion. Despite this progress and
tremendous research efforts, several issues still need to be addressed that
limit the applicability of DNNs in automated driving. The bad generalization of
DNNs to new, unseen domains is a major problem on the way to a safe,
large-scale application, because manual annotation of new domains is costly,
particularly for semantic segmentation. For this reason, methods are required
to adapt DNNs to new domains without labeling effort. The task, which these
methods aim to solve is termed unsupervised domain adaptation (UDA). While
several different domain shifts can challenge DNNs, the shift between synthetic
and real data is of particular importance for automated driving, as it allows
the use of simulation environments for DNN training. In this work, we present
an overview of the current state of the art in this field of research. We
categorize and explain the different approaches for UDA. The number of
considered publications is larger than any other survey on this topic. The
scope of this survey goes far beyond the description of the UDA
state-of-the-art. Based on our large data and knowledge base, we present a
quantitative comparison of the approaches and use the observations to point out
the latest trends in this field. In the following, we conduct a critical
analysis of the state-of-the-art and highlight promising future research
directions. With this survey, we aim to facilitate UDA research further and
encourage scientists to exploit novel research directions to generalize DNNs
better.


  We present a centennial review of the history of the term known as the
cosmological constant. First introduced to the general theory of relativity by
Einstein in 1917 in order to describe a universe that was assumed to be static,
the term fell from favour in the wake of the discovery of the expanding
universe, only to make a dramatic return in recent times. We consider
historical and philosophical aspects of the cosmological constant over four
main epochs: (i) the use of the term in static cosmologies (both Newtonian and
relativistic); (ii) the marginalization of the term following the discovery of
cosmic expansion; (iii) the use of the term to address specific cosmic puzzles
such as the timespan of expansion, the formation of galaxies and the redshifts
of the quasars; (iv) the re-emergence of the term in today's Lamda-CDM
cosmology. We find that the cosmological constant was never truly banished from
theoretical models of the universe, but was sidelined by astronomers for
reasons of convenience. We also find that the return of the term to the
forefront of modern cosmology did not occur as an abrupt paradigm shift due to
one particular set of observations, but as the result of a number of empirical
advances such as the measurement of present cosmic expansion using the Hubble
Space Telescope, the measurement of past expansion using type SN 1a supernovae
as standard candles, and the measurement of perturbations in the cosmic
microwave background by balloon and satellite. We give a brief overview of
contemporary interpretations of the physics underlying the cosmic constant and
conclude with a synopsis of the famous cosmological constant problem.


  The objective of this paper is to understand the variance of the far-infrared
(FIR) spectral energy distribution (SED) of the DustPedia galaxies, and its
link with the stellar and dust properties. An interesting aspect of the dust
emission is the inferred FIR colours which could inform us about the dust
content of galaxies, and how it varies with the physical conditions within
galaxies. However, the inherent complexity of dust grains as well as the
variety of physical properties depending on dust, hinder our ability to utilise
their maximum potential. We use principal component analysis (PCA) to explore
new hidden correlations with many relevant physical properties such as the dust
luminosity, dust temperature, dust mass, bolometric luminosity, star-formation
rate (SFR), stellar mass, specific SFR, dust-to-stellar mass ratio, the
fraction of absorbed stellar luminosity by dust (f_abs), and metallicity. We
find that 95% of the variance in our sample can be described by two principal
components (PCs). The first component controls the wavelength of the peak of
the SED, while the second characterises the width. The physical quantities that
correlate better with the coefficients of the first two PCs, and thus control
the shape of the FIR SED are: the dust temperature, the dust luminosity, the
SFR, and f_abs. Finally, we find a weak tendency for low-metallicity galaxies
to have warmer and broader SEDs, while on the other hand high-metallicity
galaxies have FIR SEDs that are colder and narrower.


  The ATCA has been used to measure positions with arcsecond accuracy for 379
masers at the 22-GHz transition of water. The principal observation targets
were 202 OH masers of the variety associated with star formation regions (SFR)s
in the Southern Galactic plane. At a second epoch, most of these targets were
observed again, and new targets of methanol masers were added. Many of the
water masers reported here are new discoveries. Variability in the masers is
often acute, with very few features directly corresponding to those discovered
two decades ago. Within our current observations, less than a year apart,
spectra are often dissimilar, but positions at the later epoch, even when
measured for slightly different features, mostly correspond to the detected
maser site measured earlier, to within the typical extent of the whole site, of
a few arcseconds. The precise water positions show that approximately 79% (160
of 202) of the OH maser sites show coincident water maser emission, the best
estimate yet obtained for this statistic; however, there are many instances
where additional water sites are present offset from the OH target, and
consequently less than half of the water masers coincide with a 1665-MHz
ground-state OH maser counterpart. We explore the differences between the
velocities of peak emission from the three species (OH, methanol and water),
and quantify the typically larger deviations shown by water maser peaks from
systemic velocities. Clusters of two or three distinct but nearby sites, each
showing one or several of the principal molecular masing transitions, are found
to be common. In combination with an investigation of correlations with IR
sources from the GLIMPSE catalogue, these comparative studies allow further
progress in the use of the maser properties to assign relative evolutionary
stages in star formation to individual sites.


  The core of a game $v$ on $N$, which is the set of additive games $\phi$
dominating $v$ such that $\phi(N)=v(N)$, is a central notion in cooperative
game theory, decision making and in combinatorics, where it is related to
submodular functions, matroids and the greedy algorithm. In many cases however,
the core is empty, and alternative solutions have to be found. We define the
$k$-additive core by replacing additive games by $k$-additive games in the
definition of the core, where $k$-additive games are those games whose M\"obius
transform vanishes for subsets of more than $k$ elements. For a sufficiently
high value of $k$, the $k$-additive core is nonempty, and is a convex closed
polyhedron. Our aim is to establish results similar to the classical results of
Shapley and Ichiishi on the core of convex games (corresponds to Edmonds'
theorem for the greedy algorithm), which characterize the vertices of the core.