  Answer retrieval is to find the most aligned answer from a large set of
candidates given a question. Learning vector representations of
questions/answers is the key factor. Question-answer alignment and
question/answer semantics are two important signals for learning the
representations. Existing methods learned semantic representations with dual
encoders or dual variational auto-encoders. The semantic information was
learned from language models or question-to-question (answer-to-answer)
generative processes. However, the alignment and semantics were too separate to
capture the aligned semantics between question and answer. In this work, we
propose to cross variational auto-encoders by generating questions with aligned
answers and generating answers with aligned questions. Experiments show that
our method outperforms the state-of-the-art answer retrieval method on SQuAD.


  Network Science provides a universal formalism for modelling and studying
complex systems based on pairwise interactions between agents. However, many
real networks in the social, biological or computer sciences involve
interactions among more than two agents, having thus an inherent structure of a
simplicial complex. We propose new notions of higher-order degrees of adjacency
for simplices in a simplicial complex, allowing any dimensional comparison
among them and their faces, which as far as we know were lacked in the
literature. We introduce multi-parameter boundary and coboundary operators in
an oriented simplicial complex and also a novel multi-combinatorial Laplacian
is defined, which generalises the graph and combinatorial Laplacian. To
illustrate the potential applications of these theoretical results, we perform
a structural analysis of higher-order connectivity in simplicial-complex
networks by studying the associated distributions with these simplicial degrees
in 17 real-world datasets coming from different domains such as coauthor
networks, cosponsoring Congress bills, contacts in schools, drug abuse warning
networks, e-mail networks or publications and users in online forums. We find
rich and diverse higher-order connectivity structures and observe that datasets
of the same type reflect similar higher-order collaboration patterns.
Furthermore, we show that if we use what we have called the maximal simplicial
degree (which counts the distinct maximal communities in which our simplex and
all its strict sub-communities are contained), then its degree distribution is,
in general, surprisingly different from the classical node degree distribution.


  Diffusion probabilistic models have been recently used in a variety of tasks,
including speech enhancement and synthesis. As a generative approach, diffusion
models have been shown to be especially suitable for imputation problems, where
missing data is generated based on existing data. Phase retrieval is inherently
an imputation problem, where phase information has to be generated based on the
given magnitude. In this work we build upon previous work in the speech domain,
adapting a speech enhancement diffusion model specifically for STFT phase
retrieval. Evaluation using speech quality and intelligibility metrics shows
the diffusion approach is well-suited to the phase retrieval task, with
performance surpassing both classical and modern methods.


  We present a revised lens model of MACS J1149+2223, in which the first
resolved multiply-imaged lensed supernova was discovered. The lens model is
based on the model of Johnson et al. (2014) with some modifications. We include
more lensing constraints from the host galaxy of the newly discovered
supernova, and increase the flexibility of the model in order to better
reproduce the lensing signal in the vicinity of this galaxy. The revised model
accurately reconstructs the positions of the lensed supernova, provides
magnifications, and predicts the time delay between the instances of the
supernova. Finally, we reconstruct the source image of the host galaxy, and
position the supernova on one of its spiral arms. Products of this lens model
are available to the community through MAST.


  In this work, we tackle the problem of car license plate detection and
recognition in natural scene images. We propose a unified deep neural network
which can localize license plates and recognize the letters simultaneously in a
single forward pass. The whole network can be trained end-to-end. In contrast
to existing approaches which take license plate detection and recognition as
two separate tasks and settle them step by step, our method jointly solves
these two tasks by a single network. It not only avoids intermediate error
accumulation, but also accelerates the processing speed. For performance
evaluation, three datasets including images captured from various scenes under
different conditions are tested. Extensive experiments show the effectiveness
and efficiency of our proposed approach.


  Physical systems characterized by stick-slip dynamics often display
avalanches. Regardless of the diversity of their microscopic structure, these
systems are governed by a power-law distribution of avalanche size and
duration. Here we focus on the interevent times between avalanches and show
that, unlike their distributions of size and duration, the interevent time
distributions are able to distinguish different mechanical states of the
system, characterized by different volume fractions or confining pressures. We
use experiments on granular systems and numerical simulations of emulsions to
show that systems having the same probability distribution for avalanche size
and duration can have different interevent time distributions. Remarkably, for
large packing ratios, these interevent time distributions look similar to those
for earthquakes and are indirect evidence of large space-time correlations in
the system. Our results therefore indicate that interevent time statistics are
more informative to characterize the dynamics of avalanches.


  We introduce a new method of detecting when the fundamental group of a Dehn
surgery on a knot admits a left-ordering, a method which is particularly useful
for 2-bridge knots. As an illustration of this method, we show that all Dehn
surgeries on the knot $6_2$ with slope in the interval $(-4, 8)\cap\mathbb{Q}$
have left-orderable fundamental groups by exhibiting a family of hyperbolic
$\widetilde{PSL}(2,\mathbb{R})$-representations of the knot complement group.


  Cosmologies of the lower Bianchi types, i.e. except those of type VIII or IX,
admit a two-dimensional Abelian subgroup of the isometry group, the $G_2$. In
orthogonal perfect fluid cosmologies of all lower Bianchi types except for type
VI$_{-1/9}$ the $G_2$ acts orthogonally-transitively, which is closely related
to an eventual cessation of the oscillations and thus to a quiescent
singularity. But due to a degeneracy in the momentum constraints, such
cosmologies of type VI$_{-1/9}$ do not necessarily have this property. As a
consequence, the dynamics of type VI$_{-1/9}$ orthogonal perfect fluid
cosmologies have the same degrees of freedom as those of the higher types VIII
and IX and their dynamics are expected to be markedly different compared to
those of the other lower Bianchi types.
  In this article we take a different approach to quiescence, namely the
presence of an orthogonal stiff fluid. On the one hand, this completes the
analysis of the initial singularity for all Bianchi orthogonal stiff fluid
cosmologies. On the other hand, this allows us to get a grasp of the underlying
dynamics of type VI$_{-1/9}$ perfect fluid cosmologies, in particular the
effect of orthogonal transitivity as well as possible (asymptotic) polarization
conditions. In particular, we show that a generic type VI$_{-1/9}$ cosmology
with an orthogonal stiff fluid has similar asymptotics as a generic Bianchi
type VIII or IX cosmology with an orthogonal stiff fluid. The only exceptions
to this genericity are solutions satisfying (asymptotic) polarization
conditions, and solutions for which the $G_2$ acts orthogonally-transitively.
Only in those cases may the limits of the eigenvalues of the
expansion-normalized Weingarten map be negative.


  We investigate the component sizes of the critical configuration model, as
well as the related problem of critical percolation on a supercritical
configuration model. We show that, at criticality, the finite third moment
assumption on the asymptotic degree distribution is enough to guarantee that
the sizes of the largest connected components are of the order $n^{2/3}$ and
the re-scaled component sizes (ordered in a decreasing manner) converge to the
ordered excursion lengths of an inhomogeneous Brownian Motion with a parabolic
drift. We use percolation to study the evolution of these component sizes while
passing through the critical window and show that the vector of percolation
cluster-sizes, considered as a process in the critical window, converge to the
multiplicative coalescent process in the sense of finite dimensional
distributions. This behavior was first observed for Erd\H{o}s-R\'enyi random
graphs by Aldous (1997) and our results provide support for the empirical
evidences that the nature of the phase transition for a wide array of
random-graph models are universal in nature. Further, we show that the
re-scaled component sizes and surplus edges converge jointly under a strong
topology, at each fixed location of the scaling window.


  Context-aware Human Activity Recognition (HAR) is a hot research area in
mobile computing, and the most effective solutions in the literature are based
on supervised deep learning models. However, the actual deployment of these
systems is limited by the scarcity of labeled data that is required for
training. Neuro-Symbolic AI (NeSy) provides an interesting research direction
to mitigate this issue, by infusing common-sense knowledge about human
activities and the contexts in which they can be performed into HAR deep
learning classifiers. Existing NeSy methods for context-aware HAR rely on
knowledge encoded in logic-based models (e.g., ontologies) whose design,
implementation, and maintenance to capture new activities and contexts require
significant human engineering efforts, technical knowledge, and domain
expertise. Recent works show that pre-trained Large Language Models (LLMs)
effectively encode common-sense knowledge about human activities. In this work,
we propose ContextGPT: a novel prompt engineering approach to retrieve from
LLMs common-sense knowledge about the relationship between human activities and
the context in which they are performed. Unlike ontologies, ContextGPT requires
limited human effort and expertise. An extensive evaluation carried out on two
public datasets shows how a NeSy model obtained by infusing common-sense
knowledge from ContextGPT is effective in data scarcity scenarios, leading to
similar (and sometimes better) recognition rates than logic-based approaches
with a fraction of the effort.


  Training and validating models for semantic segmentation require datasets
with pixel-wise annotations, which are notoriously labor-intensive. Although
useful priors such as foundation models or crowdsourced datasets are available,
they are error-prone. We hence propose an effective framework of active label
correction (ALC) based on a design of correction query to rectify pseudo labels
of pixels, which in turn is more annotator-friendly than the standard one
inquiring to classify a pixel directly according to our theoretical analysis
and user study. Specifically, leveraging foundation models providing useful
zero-shot predictions on pseudo labels and superpixels, our method comprises
two key techniques: (i) an annotator-friendly design of correction query with
the pseudo labels, and (ii) an acquisition function looking ahead label
expansions based on the superpixels. Experimental results on PASCAL,
Cityscapes, and Kvasir-SEG datasets demonstrate the effectiveness of our ALC
framework, outperforming prior methods for active semantic segmentation and
label correction. Notably, utilizing our method, we obtained a revised dataset
of PASCAL by rectifying errors in 2.6 million pixels in PASCAL dataset.


  Finite Lorentz groups acting on 4-dimensional vector spaces coordinatized by
finite fields with a prime number of elements are represented as homomorphic
images of countable, rational subgroups of the Lorentz group acting on real
4-dimensional space-time. Bounded subsets of the real Lorentz group are
retractable with arbitrary accuracy to finite subsets of such rational
subgroups. These finite retracts correspond, via local isomorphisms, to
well-behaved subsets of Lorentz groups over finite fields. This establishes a
relationship of approximation between the real Lorentz group and Lorentz groups
over very large finite fields.


  Large-scale sparse precision matrix estimation has attracted wide interest
from the statistics community. The convex partial correlation selection method
(CONCORD) developed by Khare et al. (2015) has recently been credited with some
theoretical properties for estimating sparse precision matrices. The CONCORD
obtains its solution by a coordinate descent algorithm (CONCORD-CD) based on
the convexity of the objective function. However, since a coordinate-wise
update in CONCORD-CD is inherently serial, a scale-up is nontrivial. In this
paper, we propose a novel parallelization of CONCORD-CD, namely, CONCORD-PCD.
CONCORD-PCD partitions the off-diagonal elements into several groups and
updates each group simultaneously without harming the computational convergence
of CONCORD-CD. We guarantee this by employing the notion of edge coloring in
graph theory. Specifically, we establish a nontrivial correspondence between
scheduling the updates of the off-diagonal elements in CONCORD-CD and coloring
the edges of a complete graph. It turns out that CONCORD-PCD simultaneously
updates off-diagonal elements in which the associated edges are colorable with
the same color. As a result, the number of steps required for updating
off-diagonal elements reduces from p(p-1)/2 to p-1 (for even p) or p (for odd
p), where p denotes the number of variables. We prove that the number of such
steps is irreducible In addition, CONCORD-PCD is tailored to single-instruction
multiple-data (SIMD) parallelism. A numerical study shows that the
SIMD-parallelized PCD algorithm implemented in graphics processing units (GPUs)
boosts the CONCORD-CD algorithm multiple times.


  We consider the sectoral composition of a country's GDP, i.e. the
partitioning into agrarian, industrial, and service sectors. Exploring a simple
system of differential equations we characterize the transfer of GDP shares
between the sectors in the course of economic development. The model fits for
the majority of countries providing 4 country-specific parameters. Relating the
agrarian with the industrial sector, a data collapse over all countries and all
years supports the applicability of our approach. Depending on the parameter
ranges, country development exhibits different transfer properties. Most
countries follow 3 of 8 characteristic paths. The types are not random but show
distinct geographic and development patterns.


  1I/'Oumuamua is the first interstellar object observed passing through the
Solar System. Understanding the nature of these objects will provide crucial
information about the formation and evolution of planetary systems, and the
chemodynamical evolution of the Galaxy as a whole. We obtained the galactic
orbital parameters of this object, considering 8 different models for the
Galaxy, and compared it to those of stars of different ages from the
Geneva-Copenhagen Survey (GCS). Assuming that the galactic orbital evolution of
this object is similar to that of stars, we applied a Bayesian analyses and
used the distribution of stellar velocities, as a function of age, to obtain a
probability density function for the age of 'Oumuamua. We considered two models
for the age-velocity dispersion relation (AVR): the traditional power law,
fitted using data from the GCS; and a model that implements a second power law
for younger ages, which we fitted using a sample of 153 Open Clusters (OCs). We
find that the slope of the AVR is smaller for OCs than it is for field stars.
Using these AVRs, we constrained an age range of 0.01-1.87 Gyr for 'Oumuamua
and characterized a most likely age ranging between 0.20-0.45 Gyr, depending on
the model used for the AVR. We also estimated the intrinsic uncertainties of
the method due to not knowing the exact value of the Solar motion and the
particularities of 1I/'Oumuamua's ejection.


  This paper concerns a general class of PDE-ODE reaction-diffusion systems,
which features a singular fast-reaction limit towards a reaction-diffusion
equation coupled to a scalar hysteresis operator.
  As prototypical application, we present a PDE model for the growth of a
population according to a given food supply coupled to an ODE for the turnover
of a food stock. Under realistic conditions the stock turnover is much faster
than the population growth yielding an intrinsic scaling parameter. We present
two models of consume rate functions such that the resulting food stock
dynamics converges to a generalised play operator in the associated
fast-reaction-limit. We emphasise that the structural assumptions on the
considered PDE-ODE models are quite general and that analogue systems might
describe e.g. cell-biological buffer mechanisms, where proteins are stored and
used in parallel.
  Finally, we discuss an explicit example showing that nonlinear coupling with
a scalar generalised play operator can lead to spatially inhomogeneous
large-time behaviour in a kind of hysteresis-diffusion driven instability.


  A digital frequency multiplexing (DfMUX) system has been developed and used
to tune large arrays of transition edge sensor (TES) bolometers read out with
SQUID arrays for mm-wavelength cosmology telescopes. The DfMUX system
multiplexes the input bias voltages and output currents for several bolometers
on a single set of cryogenic wires. Multiplexing reduces the heat load on the
camera's sub-Kelvin cryogenic detector stage. In this paper we describe the
algorithms and software used to set up and optimize the operation of the
bolometric camera. The algorithms are implemented on soft processors embedded
within FPGA devices operating on each backend readout board. The result is a
fully parallelized implementation for which the setup time is independent of
the array size.


  Generative approaches have been recently shown to be effective for both
Entity Disambiguation and Entity Linking (i.e., joint mention detection and
disambiguation). However, the previously proposed autoregressive formulation
for EL suffers from i) high computational cost due to a complex (deep) decoder,
ii) non-parallelizable decoding that scales with the source sequence length,
and iii) the need for training on a large amount of data. In this work, we
propose a very efficient approach that parallelizes autoregressive linking
across all potential mentions and relies on a shallow and efficient decoder.
Moreover, we augment the generative objective with an extra discriminative
component, i.e., a correction term which lets us directly optimize the
generator's ranking. When taken together, these techniques tackle all the above
issues: our model is >70 times faster and more accurate than the previous
generative method, outperforming state-of-the-art approaches on the standard
English dataset AIDA-CoNLL. Source code available at
https://github.com/nicola-decao/efficient-autoregressive-EL


  We report a discovery of a proto-cluster in vigorous assembly and hosting
strong star forming activities, associated to a radio galaxy USS 1558-003 at
z=2.53, as traced by a wide-field narrow-band H_alpha imaging with MOIRCS on
Subaru Telescope. We find 68 H_alpha emitters with dust-uncorrected SFRs down
to 8.6 Msun/yr. Their spatial distribution indicates that there are three
prominent clumps of H_alpha emitters, one surrounding the radio galaxy and
another located at ~1.5 Mpc away to the south-west, and the other located in
between the two. These contiguous three systems are very likely to merge
together in the near future and may grow to a single more massive cluster at
later times. Whilst most H_alpha emitters reside in the "blue cloud" on the
color--magnitude diagram, some emitters have very red colors with
J-Ks>1.38(AB). Interestingly, such red H_alpha emitters are located towards the
faint end of the red sequence, and they tend to be located in the high density
clumps. We do not see any statistically significant difference in the
distributions of individual star formation rates or stellar masses of the
H_alpha emitters between the dense clumps and the other regions, suggesting
that this is one of the notable sites where the progenitors of massive galaxies
in the present-day clusters were in their vigorous formation phase. Finally, we
find that H_alpha emission of the radio galaxy is fairly extended spatially
over ~4.5 arcsec. However it is not as widespread as its Lya halo, meaning that
the Lya emission is indeed severely extended by resonant scattering.


  Fine-tuning pre-trained Vision Transformers (ViTs) has showcased significant
promise in enhancing visual recognition tasks. Yet, the demand for
individualized and comprehensive fine-tuning processes for each task entails
substantial computational and memory costs, posing a considerable challenge.
Recent advancements in Parameter-Efficient Transfer Learning (PETL) have shown
potential for achieving high performance with fewer parameter updates compared
to full fine-tuning. However, their effectiveness is primarily observed in
simple tasks like image classification, while they encounter challenges with
more complex vision tasks like dense prediction. To address this gap, this
study aims to identify an effective tuning method that caters to a wider range
of visual tasks. In this paper, we introduce Hierarchical Side-Tuning (HST), an
innovative PETL method facilitating the transfer of ViT models to diverse
downstream tasks. Diverging from existing methods that focus solely on
fine-tuning parameters within specific input spaces or modules, HST employs a
lightweight Hierarchical Side Network (HSN). This network leverages
intermediate activations from the ViT backbone to model multi-scale features,
enhancing prediction capabilities. To evaluate HST, we conducted comprehensive
experiments across a range of visual tasks, including classification, object
detection, instance segmentation, and semantic segmentation. Remarkably, HST
achieved state-of-the-art performance in 13 out of the 19 tasks on the VTAB-1K
benchmark, with the highest average Top-1 accuracy of 76.1%, while fine-tuning
a mere 0.78M parameters. When applied to object detection and semantic
segmentation tasks on the COCO and ADE20K testdev benchmarks, HST outperformed
existing PETL methods and even surpassed full fine-tuning.


  A lattice-based model exhibits an unusual conductivity when it is subjected
to both a static magnetic field and electromagnetic radiation. This
conductivity anomaly may explain some aspects of the recently observed
"zero-resistance states".
  PACS: 72.40+w, 73.40-c, 73.63 Keywords: Zero-resistance states, negative
conductivity, lattice model


  Neutron star models with maximum mass close to $2 \ M_{\odot}$ reach high
central densities, which may activate nucleonic and hyperon direct Urca
neutrino emission. To alleviate the tension between fast theoretical cooling
rates and thermal luminosity observations of moderately magnetized, isolated
thermally-emitting stars (with $L_{\gamma} \gtrsim 10^{31}$ erg s$^{-1}$ at $t
\gtrsim 10^{5.3}$ yr), some internal heating source is required. The power
supplied by the internal heater is estimated for both a phenomenological source
in the inner crust and Joule heating due to magnetic field decay, assuming
different superfluidity models and compositions of the outer stellar envelope.
It is found that a thermal power of $W(t) \approx 10^{34}$ erg s$^{-1}$ allows
neutron star models to match observations of moderately magnetized, isolated
stars with ages $t \gtrsim 10^{5.3}$ yr. The requisite $W(t)$ can be supplied
by Joule heating due to crust-confined initial magnetic configurations with (i)
mixed poloidal-toroidal fields, with surface strength $B_{\textrm{dip}} =
10^{13}$ G at the pole of the dipolar poloidal component and $\sim 90$ per cent
of the magnetic energy stored in the toroidal component; and (ii) poloidal-only
configurations with $B_{\textrm{dip}} = 10^{14}$ G.


  Let $X^n\subset C^{n+a}$ or $X^n\subset P^{n+a}$ be a patch of an analytic
submanifold of an affine or projective space, let $x\in X$ be a general point,
and let L^k be a linear space of dimension k osculating to order m at x. If m
is large enough, one expects L to be contained in X and thus X contains a
linear space of dimension kthrough almost every point. We show that $L\subset
X$ in the following cases: k=1 and m=n+1; k=n-1, $a\geq 2$, and m=2; $n\geq 4$,
k=n-2 and m=4. We prove these results by first deriving the order of osculation
that generically implies containment and then showing that in these cases
containment must occur.
  If X is a patch of a projective variety, we address the question as to
whether X can be a smooth variety. We show that if there is a P^k through each
point and $codim(X)<\frac{k}{n-k}$ then X cannot be a smooth variety.


  The purpose of this paper consists in using variational methods to establish
the existence of heteroclinic solutions for some classes of prescribed mean
curvature equations of the type
  $$
  -div\left(\frac{\nabla u}{\sqrt{1+|\nabla u|^2}}\right) + A(\epsilon
x,y)V'(u)=0~~\text{ in }~~\mathbb{R}^2,
  $$
  where $\epsilon>0$ and $V$ is a double-well potential with minima at
$t=\alpha$ and $t=\beta$ with $\alpha<\beta$. Here, we consider some class of
functions $A(x,y)$ that are oscillatory in the variable $y$ and satisfy
different geometric conditions such as periodicity in all variables or
asymptotically periodic at infinity.


  We analyze the spatial correlations in the spin density of an electron gas in
the vicinity of a Kondo impurity. Our analysis extends to the spin-anisotropic
regime, which was not investigated in the literature. We use an original and
numerically exact method, based on a systematic coherent-state expansion of the
ground state of the underlying spin-boson Hamiltonian, which we apply to the
computation of observables that are specific to the fermionic Kondo model. We
also present an important technical improvement to the method, that obviates
the need to discretize modes of the Fermi sea, and allows one to tackle the
problem in the thermodynamic limit. One can thus obtain excellent spatial
resolution over arbitrary length scales, for a relatively low computational
cost, a feature that gives the method an advantage over popular techniques such
as NRG and DMRG. We find that the anisotropic Kondo model shows rich universal
scaling behavior in the spatial structure of the entanglement cloud. First,
SU(2) spin-symmetry is dynamically restored in a finite domain in parameter
space in vicinity of the isotropic line, as expected from poor man's scaling.
We are also able to obtain in closed analytical form a set of different, yet
universal, scaling curves for strong exchange asymmetry, which are parametrized
by the longitudinal exchange coupling. Deep inside the cloud, i.e. for
distances smaller than the Kondo length, the correlation between the electron
spin density and the impurity spin oscillates between ferromagnetic and
antiferromagnetic values at the scale of the Fermi wavelength, an effect that
is drastically enhanced at strongly anisotropic couplings. Our results also
provide further numerical checks and alternative analytical approximations for
the recently computed Kondo overlaps [PRL 114, 080601 (2015)].


  We propose a primordial-origin model for the composite configurations of
global magnetic fields in spiral galaxies. We show that uniform tilted magnetic
field wound up into a rotating disk galaxy can evolve into composite magnetic
configurations comprising bisymmetric spiral (S=BSS), axisymmetric spiral
(A=ASS), plane-reversed spiral (PR), and/or ring (R) fields in the disk, and
vertical (V) fields in the center. By MHD simulations we show that these
composite galactic fields are indeed created from weak primordial uniform
field, and that the different configurations can co-exist in the same galaxy.
We show that spiral fields trigger the growth of two-armed gaseous arms. The
centrally accumulated vertical fields are twisted and produce jet toward the
halo. We find that the more vertical was the initial uniform field, the
stronger is the formed magnetic field in the galactic disk.


  In the framework of ion-trap quantum computing, we develop a characterization
of experimentally realistic imperfections which may affect the Cirac-Zoller
implementation of the CNOT gate. The CNOT operation is performed by applying a
protocol of five laser pulses of appropriate frequency and polarization. The
laser-pulse protocol exploits auxiliary levels, and its imperfect
implementation leads to unitary as well as non-unitary errors affecting the
CNOT operation. We provide a characterization of such imperfections, which are
physically realistic and have never been considered before to the best of our
knowledge. Our characterization shows that imperfect laser pulses unavoidably
cause a leak of information from the states which alone should be transformed
by the ideal gate, into the ancillary states exploited by the experimental
implementation.


  In this article, a new integrable (2+1)-dimensional Kundu-Mukherjee-Naskar
model which is a variant of the well known nonlinear Schr\"odinger equation is
investigated. Bright-dark optical solitons along with periodic waves,
complexiton and rational solutions are constructed by employing a generalized
traveling wave analysis, Jacobian-elliptic function, Riccati equation and
ansatz approach. Further, the dynamics of these bright/dark optical solitons
and complexiton/periodic waves are studied by exploring the importance of
arbitrary physical parameters with graphical demonstrations for a clear
understanding. The obtained higher dimensional nonlinear wave solutions of this
integrable system shall have useful applications in different physical systems
including the dynamics of beam propagation in optical fibers, ion-acoustic
waves in magnetized plasma and deep water oceanic rogue waves.


  This paper addresses the problem of axisymetric rotating flows bounded by a
fixed horizontal plate and subject to a permanent, uniform, vertical magnetic
field (the so-called B\"odewadt-Hartmann problem). The aim is to find out which
one of the Coriolis or the Lorentz force dominates the dynamics (and hence the
boundary layer thickness) when their ratio, represented by the Elsasser number
$A$, varies. After a short review of existing linear solutions of the semi
infinite Ekman-Hartmann problem, weakly non-linear analytical solutions as well
as fully non-linear numerical solutions are given.
  The case of a rotating vortex in a finite depth fluid layer is then studied,
first when the flow is steady under a forced rotation and second for spin-down
from some initial state. The angular velocity in the first case and decay time
in the second are obtained analytically as a function of $A$ using the weakly
non linear results of the semi-infinite B\"{o}dewadt-Hartmann problem.


  Dozens of high-energy neutrinos have been detected by the IceCube neutrino
telescope, but no clear association with any classes of astrophysical sources
has been identified so far. Recently, Kadler et al. (2016) report that a PeV
cascade-like neutrino event occurs in positional and temporal coincidence with
a giant gamma-ray flare of the blazar PKS B1424-418. Since IceCube track-like
events have much better angular resolution, we here search for possible
short-term gamma-ray flares that are associated with the IceCube track-like
events with Fermi Large Area Telescope (LAT) observations. Among them, three
track-like neutrino events occur within the field of view of Fermi-LAT at the
time of the detection, so search for the {\em prompt} gamma-ray emission
associated with neutrinos are possible. Assuming a point source origin and a
single power law spectrum for the possible gamma-ray sources associated with
neutrinos, a likelihood analysis of 0.2-100 GeV photons observed by Fermi-LAT
on the timescales of $\sim 12$ hours and one year are performed, and for the
three special neutrinos, the analysis are also performed on the timescales of
thousand of seconds before and after the neutrino detection. No significant GeV
excesses over the background are found and the upper limit fluxes at 95\%
confidence level are obtained for different timescales. We also search for
possible hard X-ray transient sources associated with the IceCube track-like
neutrino events, but the search also yields null results. We discuss the
implication of the non-detection of gamma-ray flares for the constraints on the
neutrino source density.


  This technical report describes the CONE approach for Ego4D Natural Language
Queries (NLQ) Challenge in ECCV 2022. We leverage our model CONE, an efficient
window-centric COarse-to-fiNE alignment framework. Specifically, CONE
dynamically slices the long video into candidate windows via a sliding window
approach. Centering at windows, CONE (1) learns the inter-window
(coarse-grained) semantic variance through contrastive learning and speeds up
inference by pre-filtering the candidate windows relevant to the NL query, and
(2) conducts intra-window (fine-grained) candidate moments ranking utilizing
the powerful multi-modal alignment ability of the contrastive vision-text
pre-trained model EgoVLP. On the blind test set, CONE achieves 15.26 and 9.24
for<EMAIL_ADDRESS>and<EMAIL_ADDRESS>respectively.


  Within context of quantum logic, it is possible to assign dispersion-free
probabilities to experimental propositions pertaining to qubits. This makes
qubits distinct from the rest of quantum systems since the latter do not admit
probabilities having only values 0 and 1. The present paper shows that erasing
qubit discrimination leads to a model of computation which permits execution of
many primitive operations in a massive parallel way. In the paper, it is
demonstrated that such a model (that can be called a quantum parallel
random-access machine, QPRAM) is quantum mechanically plausible.


  We consider a fluid-structure interaction problem with Navier-slip boundary
conditions in which the fluid is considered as a non-Newtonian fluid and the
structure is described by a nonlinear multi-layered model. The fluid domain is
driven by a nonlinear elastic shell and thus is not fixed. To simplify the
problem, we map the moving fluid domain into a fixed domain by applying an
arbitrary Lagrange Euler mapping. Unlike the classical method by which we can
consider the problem as its entirety, we utilize the time-discretization and
split the problem into a fluid subproblem and a structure subproblem by an
operator splitting scheme. Since the structure subproblem is nonlinear,
Lax-Milgram lemma does not hold. Here we prove the existence and uniqueness by
means of the traditional semigroup theory. Noticing that the Non-Newtonian
fluid possesses a $ p- $Laplacian structure, we show the existence and
uniqueness of solutions to the fluid subproblem by considering the
Browder-Minty theorem. With the uniform energy estimates, we deduce the weak
and weak* convergence respectively. By a generalized Aubin-Lions-Simon Lemma
proposed by Muha and Cani\'c [J. Differential Equations {\bf 266} (2019),
8370--8418], we obtain the strong convergence. Finally, we construct the test
functions and pass the approximate weak formulation to the limit as time step
goes to zero with the convergence results.


  We present modifications to the Athena++ framework to enable use of general
equations of state (EOS). Part of our motivation for doing so is to model
transient astrophysics phenomena, as these types of events are often not well
approximated by an ideal gas. This necessitated changes to the Riemann solvers
implemented in Athena++. We discuss the adjustments made to the HLLC, and HLLD
solvers and EOS calls required for arbitrary EOS. We demonstrate the
reliability of our code in a number of tests which utilize a relatively simple,
but non-trivial EOS based on hydrogen ionization, appropriate for the
transition from atomic to ionized hydrogen. Additionally, we perform tests
using an electron-positron Helmholtz EOS, appropriate for regimes where nuclear
statistical equilibrium is a good approximation. These new complex EOS tests
overall show that our modifications to Athena++ accurately solve the Riemann
problem with linear convergence and linear-wave tests with quadratic
convergence. We provide our test solutions as a means to check the accuracy of
other hydrodynamic codes. Our tests and additions to Athena++ will enable
further research into (magneto)hydrodynamic problems where realistic treatments
of the EOS are required.


  Partitioning of red blood cells (RBCs) at the level of bifurcations in the
microcirculatory system affects many physiological functions yet it remains
poorly understood. We address this problem by using T-shaped microfluidic
bifurcations as a model. Our computer simulations and in vitro experiments
reveal that the hematocrit ($\phi_0$) partition depends strongly on RBC
deformability, as long as $\phi_0 <20$% (within the normal range in
microcirculation), and can even lead to complete deprivation of RBCs in a child
branch. Furthermore, we discover a deviation from the Zweifach-Fung effect
which states that the child branch with lower flow rate recruits less RBCs than
the higher flow rate child branch. At small enough $\phi_0$, we get the inverse
scenario, and the hematocrit in the lower flow rate child branch is even higher
than in the parent vessel. We explain this result by an intricate up-stream RBC
organization and we highlight the extreme dependence of RBC transport on
geometrical and cell mechanical properties. These parameters can lead to
unexpected behaviors with consequences on the microcirculatory function and
oxygen delivery in healthy and pathological conditions.


  One of the ways of achieving improved capacity in mobile cellular networks is
via network densification. Even though densification increases the capacity of
the network, it also leads to increased energy consumption which can be curbed
by dynamically switching off some base stations (BSs) during periods of low
traffic. However, dynamic cell switching has the challenge of spectrum
under-utilizationas the spectrum originally occupied by the BSs that are turned
off remains dormant. This dormant spectrum can be leased by the primary network
(PN) operators, who hold the license, to the secondary network (SN) operators
who cannot afford to purchase the spectrum license. Thus enabling the PN to
gain additional revenue from spectrum leasing as well as from electricity cost
savings due to reduced energy consumption. Therefore, in this work, we propose
a cell switching and spectrum leasing framework based on simulated annealing
(SA) algorithm to maximize the revenue of the PN while respecting the
quality-of-service constraints. The performance evaluation reveals that the
proposed method is very close to optimal exhaustive search method with a
significant reduction in the computation complexity.


  For many years it has been speculated that excited nuclei would undergo a
liquid to vapor phase transition. For even longer, it has been known that
clusterization in a vapor carries direct information on the liquid- vapor
equilibrium according to Fisher's droplet model. Now the thermal component of
the 8 GeV/c pion + 197Au multifragmentation data of the ISiS Collaboration is
shown to follow the scaling predicted by Fisher's model, thus providing the
strongest evidence yet of the liquid to vapor phase transition.


  Further to a recent controversy on whether the differential transformation
method (DTM) for solving a differential equation is purely and solely the
traditional Taylor series method, it is emphasized that the DTM is currently
used, often only, as a technique for (analytically) calculating the power
series of the solution (in terms of the initial value parameters). Sometimes, a
piecewise analytic continuation process is implemented either in a numerical
routine (e.g., within a shooting method) or in a semi-analytical procedure
(e.g., to solve a boundary value problem). Emphasized also is the fact that, at
the time of its invention, the currently-used basic ingredients of the DTM
(that transform a differential equation into a difference equation of same
order that is iteratively solvable) were already known for a long time by the
"traditional"-Taylor-method users (notably in the elaboration of software
packages --numerical routines-- for automatically solving ordinary differential
equations). At now, the defenders of the DTM still ignore the, though much
better developed, studies of the "traditional"-Taylor-method users who, in
turn, seem to ignore similarly the existence of the DTM. The DTM has been given
an apparent strong formalization (set on the same footing as the Fourier,
Laplace or Mellin transformations). Though often used trivially, it is easily
attainable and easily adaptable to different kinds of differentiation
procedures. That has made it very attractive. Hence applications to various
problems of the Taylor method, and more generally of the power series method
(including noninteger powers) has been sketched. It seems that its potential
has not been exploited as it could be. After a discussion on the reasons of the
"misunderstandings" which have caused the controversy, the preceding topics are
concretely illustrated.


  Lamb waves are trapped acoustic-gravity waves that propagate energy over
great distances along a solid boundary in density stratified, compressible
fluids. They constitute useful indicators of explosions in planetary
atmospheres. When the density stratification exceeds a threshold, or when the
impermeability condition at the boundary is relaxed, atmospheric Lamb waves
suddenly disappear. Here we use topological arguments to predict the possible
existence of new trapped Lamb-like waves in the absence of a solid boundary,
depending on the stratification profile. The topological origin of the
Lamb-like waves is emphasized by relating their existence to two-band crossing
points carrying opposite Chern numbers. The existence of these band crossings
coincides with a restoration of the vertical mirror symmetry that is in general
broken by gravity. From this perspective, Lamb-like waves also bear strong
similarities with boundary modes encountered in quantum valley Hall effect. Our
study shows that the presence of Lamb-like waves encode essential information
on the underlying stratification profile in astrophysical and geophysical
flows, which is often poorly constrained by observations.


  The Colonel Blotto game, first introduced by Borel in 1921, is a well-studied
game theory classic. Two colonels each have a pool of troops that they divide
simultaneously among a set of battlefields. The winner of each battlefield is
the colonel who puts more troops in it and the overall utility of each colonel
is the sum of weights of the battlefields that s/he wins. Over the past
century, the Colonel Blotto game has found applications in many different forms
of competition from advertisements to politics to sports.
  Two main objectives have been proposed for this game in the literature: (i)
maximizing the guaranteed expected payoff, and (ii) maximizing the probability
of obtaining a minimum payoff $u$. The former corresponds to the conventional
utility maximization and the latter concerns scenarios such as elections where
the candidates' goal is to maximize the probability of getting at least half of
the votes (rather than the expected number of votes). In this paper, we
consider both of these objectives and show how it is possible to obtain
(almost) optimal solutions that have few strategies in their support.
  One of the main technical challenges in obtaining bounded support strategies
for the Colonel Blotto game is that the solution space becomes non-convex. This
prevents us from using convex programming techniques in finding optimal
strategies which are essentially the main tools that are used in the
literature. However, we show through a set of structural results that the
solution space can, interestingly, be partitioned into polynomially many
disjoint convex polytopes that can be considered independently. Coupled with a
number of other combinatorial observations, this leads to polynomial time
approximation schemes for both of the aforementioned objectives.


  We examine two associative products over the ring of symmetric functions
related to the intransitive and Cartesian products of permutation groups. As an
application, we give an enumeration of some Feynman type diagrams arising in
Bender's QFT of partitions. We end by exploring possibilities to construct
noncommutative analogues.


  For a dynamical system $(X,T)$, $d\in\mathbb{N}$ and distinct non-constant
integral polynomials $p_1,\ldots, p_d$ vanishing at $0$, the notion of
regionally proximal relation along $C=\{p_1,\ldots,p_d\}$ (denoted by
$RP_C^{[d]}(X,T)$) is introduced.
  It turns out that for a minimal system, $RP_C^{[d]}(X,T)=\Delta$ implies that
$X$ is an almost one-to-one extension of $X_k$ for some $k\in\mathbb{N}$ only
depending on a set of finite polynomials associated with $C$ and has zero
entropy, where $X_k$ is the maximal $k$-step pro-nilfactor of $X$.
  Particularly, when $C$ is a collection of linear polynomials, it is proved
that $RP_C^{[d]}(X,T)=\Delta$ implies $(X,T)$ is a $d$-step pro-nilsystem,
which answers negatively a conjecture in \cite{5p}. The results are obtained by
proving a refined saturation theorem for polynomials.


  Low-precision quantization is recognized for its efficacy in neural network
optimization. Our analysis reveals that non-quantized elementwise operations
which are prevalent in layers such as parameterized activation functions, batch
normalization, and quantization scaling dominate the inference cost of
low-precision models. These non-quantized elementwise operations are commonly
overlooked in SOTA efficiency metrics such as Arithmetic Computation Effort
(ACE). In this paper, we propose ACEv2 - an extended version of ACE which
offers a better alignment with the inference cost of quantized models and their
energy consumption on ML hardware. Moreover, we introduce PikeLPN, a model that
addresses these efficiency issues by applying quantization to both elementwise
operations and multiply-accumulate operations. In particular, we present a
novel quantization technique for batch normalization layers named QuantNorm
which allows for quantizing the batch normalization parameters without
compromising the model performance. Additionally, we propose applying Double
Quantization where the quantization scaling parameters are quantized.
Furthermore, we recognize and resolve the issue of distribution mismatch in
Separable Convolution layers by introducing Distribution-Heterogeneous
Quantization which enables quantizing them to low-precision. PikeLPN achieves
Pareto-optimality in efficiency-accuracy trade-off with up to 3X efficiency
improvement compared to SOTA low-precision models.


  Experimental data have revealed that neuronal connection efficacy exhibits
two forms of short-term plasticity, namely, short-term depression (STD) and
short-term facilitation (STF). They have time constants residing between fast
neural signaling and rapid learning, and may serve as substrates for neural
systems manipulating temporal information on relevant time scales. The present
study investigates the impact of STD and STF on the dynamics of continuous
attractor neural networks (CANNs) and their potential roles in neural
information processing. We find that STD endows the network with slow-decaying
plateau behaviors-the network that is initially being stimulated to an active
state decays to a silent state very slowly on the time scale of STD rather than
on the time scale of neural signaling. This provides a mechanism for neural
systems to hold sensory memory easily and shut off persistent activities
gracefully. With STF, we find that the network can hold a memory trace of
external inputs in the facilitated neuronal interactions, which provides a way
to stabilize the network response to noisy inputs, leading to improved accuracy
in population decoding. Furthermore, we find that STD increases the mobility of
the network states. The increased mobility enhances the tracking performance of
the network in response to time-varying stimuli, leading to anticipative neural
responses. In general, we find that STD and STP tend to have opposite effects
on network dynamics and complementary computational advantages, suggesting that
the brain may employ a strategy of weighting them differentially depending on
the computational purpose.


  We describe a theoretical and effective algorithm which enables us to prove
that rather general hypergeometric series and integrals can be decomposed as
linear combinations of multiple zeta values, with rational coefficients.


  We have observed the S- and Q-branch Raman spectra of H2, HD, and D2 adsorbed
at 85K and pressures up to 8 atm on single-walled, carbon nanotubes (SWNT).
Comparative data for H2 on graphite and C60 were also collected. For each
adsorbate, we observed a small shift in the Q-branch frequencies relative to
the gas-phase values. To aid in interpreting this result, we constructed an
H2-surface potential, including van der Waals and electrostatic terms. Computed
shifts based on this potential are in good agreement with our data.


  The study of the additive volume of sets can be reduced to the case of
one-dimensional sets. The exact values of the volume of extremal sets are given
as a conjecture.


  The prominence of figurative language devices, such as sarcasm and irony,
poses serious challenges for Arabic Sentiment Analysis (SA). While previous
research works tackle SA and sarcasm detection separately, this paper
introduces an end-to-end deep Multi-Task Learning (MTL) model, allowing
knowledge interaction between the two tasks. Our MTL model's architecture
consists of a Bidirectional Encoder Representation from Transformers (BERT)
model, a multi-task attention interaction module, and two task classifiers. The
overall obtained results show that our proposed model outperforms its
single-task counterparts on both SA and sarcasm detection sub-tasks.


  A detailed understanding of wind turbine performance status classification
can improve operations and maintenance in the wind energy industry. Due to
different engineering properties of wind turbines, the standard supervised
learning models used for classification do not generalize across data sets
obtained from different wind sites. We propose two methods to deal with the
transferability of the trained models: first, data normalization in the form of
power curve alignment, and second, a robust method based on convolutional
neural networks and feature-space extension. We demonstrate the success of our
methods on real-world data sets with industrial applications.


  Analysis of SFG vibrational spectra of OH stretching bands in four
experimental configurations shows that orientational motion of water molecule
at air/water interface is libratory within a limited angular range. This
picture is significantly different from the previous conclusion that the
interfacial water molecule orientation varies over a broad range within the
vibrational relaxation time, the only direct experimental evidence for
ultrafast and broad orientational motion of a liquid interface by Wei et al.
[Phys. Rev. Lett. 86, 4799, (2001)] using single SFG experimental
configuration.


  A novel biologically motivated face recognition algorithm based on polar
frequency is presented. Polar frequency descriptors are extracted from face
images by Fourier-Bessel transform (FBT). Next, the Euclidean distance between
all images is computed and each image is now represented by its dissimilarity
to the other images. A Pseudo-Fisher Linear Discriminant was built on this
dissimilarity space. The performance of Discrete Fourier transform (DFT)
descriptors, and a combination of both feature types was also evaluated. The
algorithms were tested on a 40- and 1196-subjects face database (ORL and FERET,
respectively). With 5 images per subject in the training and test datasets,
error rate on the ORL database was 3.8, 1.25 and 0.2% for the FBT, DFT, and the
combined classifier, respectively, as compared to 2.6% achieved by the best
previous algorithm. The most informative polar frequency features were
concentrated at low-to-medium angular frequencies coupled to low radial
frequencies. On the FERET database, where an affine normalization
pre-processing was applied, the FBT algorithm outperformed only the PCA in a
rank recognition test. However, it achieved performance comparable to
state-of-the-art methods when evaluated by verification tests. These results
indicate the high informative value of the polar frequency content of face
images in relation to recognition and verification tasks, and that the
Cartesian frequency content can complement information about the subjects'
identity, but possibly only when the images are not pre-normalized. Possible
implications for human face recognition are discussed.


  In this paper, we show the existence of real-analytic stationary
Navier-Stokes flows with isotropic streamlines in all latitudes in some
simply-connected flow region on a rotating round sphere. We also exclude the
possibility of having a Poiseuille's flow profile to be one of these stationary
Navier-Stokes flows with isotropic streamlines. When the sphere is replaced by
a 2-dimensional hyperbolic space, we also give the analog existence result for
stationary parallel laminar Navier-Stokes flows along a circular-arc boundary
portion of some compact obstacle in the 2-D hyperbolic space. The existence of
stationary parallel laminar Navier-Stokes flows along a straight boundary of
some obstacle in the 2-D hyperbolic space is also studied. In any one of these
cases, we show that a parallel laminar flow with a Poiseuille's flow profile
ceases to be a stationary Navier-Stokes flow, due to the curvature of the
background manifold.


  Some popular functions used to test global optimization algorithms have
multiple local optima, all with the same value, making them all global optima.
It is easy to make them more challenging by fortifying them via adding a
localized bump at the location of one of the optima. In previous work the
authors illustrated this for the Branin-Hoo function and the popular
differential evolution algorithm, showing that the fortified Branin-Hoo
required an order of magnitude more function evaluations. This paper examines
the effect of fortifying the Branin-Hoo function on surrogate-based
optimization, which usually proceeds by adaptive sampling. Two algorithms are
considered. The EGO algorithm, which is based on a Gaussian process (GP) and an
algorithm based on radial basis functions (RBF). EGO is found to be more frugal
in terms of the number of required function evaluations required to identify
the correct basin, but it is expensive to run on a desktop, limiting the number
of times the runs could be repeated to establish sound statistics on the number
of required function evaluations. The RBF algorithm was cheaper to run,
providing more sound statistics on performance. A four-dimensional version of
the Branin-Hoo function was introduced in order to assess the effect of
dimensionality. It was found that the difference between the ordinary function
and the fortified one was much more pronounced for the four-dimensional
function compared to the two dimensional one.


  Huygens' metasurfaces have demonstrated almost arbitrary control over the
shape of a scattered beam, however, its spatial profile is typically fixed at
fabrication time. Dynamic reconfiguration of this beam profile with tunable
elements remains challenging, due to the need to maintain the Huygens'
condition across the tuning range. In this work, we experimentally demonstrate
that a time-varying metadevice which performs frequency conversion can steer
transmitted or reflected beams in an almost arbitrary manner, with fully
dynamic control. Our time-varying Huygens' metadevice is made of both electric
and magnetic meta-atoms with independently controlled modulation, and the phase
of this modulation is imprinted on the scattered parametric waves, controlling
their shapes and directions. We develop a theory which shows how the scattering
directionality, phase and conversion efficiency of sidebands can be manipulated
almost arbitrarily. We demonstrate novel effects including all-angle beam
steering and frequency-multiplexed functionalities at microwave frequencies
around 4 GHz, using varactor diodes as tunable elements. We believe that the
concept can be extended to other frequency bands, enabling metasurfaces with
arbitrary phase pattern that can be dynamically tuned over the complete 2\pi
range.


  Algorithms are increasingly used to guide high-stakes decisions about
individuals. Consequently, substantial interest has developed around defining
and measuring the ``fairness'' of these algorithms. These definitions of fair
algorithms share two features: First, they prioritize the role of a pre-defined
group identity (e.g., race or gender) by focusing on how the algorithm's impact
differs systematically across groups. Second, they are statistical in nature;
for example, comparing false positive rates, or assessing whether group
identity is independent of the decision (where both are viewed as random
variables). These notions are facially distinct from a social welfare approach
to fairness, in particular one based on ``veil of ignorance'' thought
experiments in which individuals choose how to structure society prior to the
realization of their social identity. In this paper, we seek to understand and
organize the relationship between these different approaches to fairness. Can
the optimization criteria proposed in the algorithmic fairness literature also
be motivated as the choices of someone from behind the veil of ignorance? If
not, what properties distinguish either approach to fairness?


  We study the frame dependence/independence of cosmological observables under
disformal transformations, extending the previous results regarding conformal
transformations, and provide the correspondence between Jordan-frame and
Einstein-frame variables. We consider quantities such as the gravitational
constant in the Newtonian limit, redshift, luminosity and angular diameter
distances, as well as the distance-duality relation. Also, the Boltzmann
equation, the observed specific intensity, and the adiabaticity condition are
discussed. Since the electromagnetic action changes under disformal
transformations, photons in the Einstein frame no longer propagate along null
geodesics. As a result, several quantities of cosmological interest are
modified. Nevertheless, we show that the redshift is invariant and the
distance-duality relation (the relation between the luminosity distance and the
angular diameter distance) still holds in general spacetimes even though the
reciprocity relation (the relation between two geometrical distances) is
modified.


  The two-sided Rayleigh quotient iteration proposed by Ostrowski computes a
pair of corresponding left-right eigenvectors of a matrix $C$. We propose a
Grassmannian version of this iteration, i.e., its iterates are pairs of
$p$-dimensional subspaces instead of one-dimensional subspaces in the classical
case. The new iteration generically converges locally cubically to the pairs of
left-right $p$-dimensional invariant subspaces of $C$. Moreover, Grassmannian
versions of the Rayleigh quotient iteration are given for the generalized
Hermitian eigenproblem, the Hamiltonian eigenproblem and the skew-Hamiltonian
eigenproblem.


  The widespread of data visualisation tools on smartphones has provided end
users an easy way to track their health data, leading designers to put more
effort into delivering suitable visualisations. Both academia and industry have
developed several frameworks to guide the creation of informative and
well-designed charts, such as the visualisation and design framework and Google
Material Design.
  Despite the typical focus on design and chart types in these existing
frameworks, our study highlights the need to incorporate additional components
when developing data visualisations. The needs of non-expert users, the nature
of the data being represented, and the mobile environment are often not
prioritised in these frameworks, leading to visualisations that do not meet
user needs and expectations. To address these issues, we propose our
Six-Dimensions Data Visualisation Framework (6DVF) to assist in the design and
evaluation of visualisations on mobile devices. Finally, we present our initial
findings from a designer evaluation experiment.


  Let $G$ be an $n$-node graph without two disjoint odd cycles. The algorithm
of Artmann, Weismantel and Zenklusen (STOC'17) for bimodular integer programs
can be used to find a maximum weight stable set in $G$ in strongly polynomial
time. Building on structural results characterizing sufficiently connected
graphs without two disjoint odd cycles, we construct a size-$O(n^2)$ extended
formulation for the stable set polytope of $G$.


  Scene graph generation (SGG) of surgical procedures is crucial in enhancing
holistically cognitive intelligence in the operating room (OR). However,
previous works have primarily relied on multi-stage learning, where the
generated semantic scene graphs depend on intermediate processes with pose
estimation and object detection. This pipeline may potentially compromise the
flexibility of learning multimodal representations, consequently constraining
the overall effectiveness. In this study, we introduce a novel single-stage
bi-modal transformer framework for SGG in the OR, termed S^2Former-OR, aimed to
complementally leverage multi-view 2D scenes and 3D point clouds for SGG in an
end-to-end manner. Concretely, our model embraces a View-Sync Transfusion
scheme to encourage multi-view visual information interaction. Concurrently, a
Geometry-Visual Cohesion operation is designed to integrate the synergic 2D
semantic features into 3D point cloud features. Moreover, based on the
augmented feature, we propose a novel relation-sensitive transformer decoder
that embeds dynamic entity-pair queries and relational trait priors, which
enables the direct prediction of entity-pair relations for graph generation
without intermediate steps. Extensive experiments have validated the superior
SGG performance and lower computational cost of S^2Former-OR on 4D-OR
benchmark, compared with current OR-SGG methods, e.g., 3 percentage points
Precision increase and 24.2M reduction in model parameters. We further compared
our method with generic single-stage SGG methods with broader metrics for a
comprehensive evaluation, with consistently better performance achieved.


  We perform an exhaustive follow-up analysis of a subsolar-mass (SSM)
gravitational wave (GW) candidate reported by Phukon et al. from the second
observing run of Advanced LIGO. This candidate has a reported signal-to-noise
ratio (SNR) of $8.6$ and false alarm rate of $0.41$ yr which are too low to
claim a clear gravitational-wave origin. When improving on the search by using
more accurate waveforms, extending the frequency range from 45 Hz down to 20
Hz, and removing a prominent blip glitch, we find that the posterior
distribution of the network SNR lies mostly below the search value, with the
$90\%$ confidence interval being $7.94^{+0.70}_{-1.05}$. Assuming that the
origin of the signal is a compact binary coalescence (CBC), the secondary
component is $m_2 = \text{$0.76^{+0.50}_{-0.14}$} M_\odot$, with $m_2 < 1
M_\odot$ at $84\%$ confidence level, suggesting an unexpectedly light neutron
star or a black hole of primordial or exotic origin. The primary mass would be
$m_1 = \text{$4.71^{+1.57}_{-2.18}$} M_\odot$, likely in the hypothesized lower
mass gap and the luminosity distance is measured to be $D_{\rm
L}=124^{+82}_{-48}$Mpc. We then probe the CBC origin hypothesis by performing
the signal coherence tests, obtaining a log Bayes factor of $4.96 \pm 0.13$ for
the coherent vs. incoherent hypothesis. We demonstrate the capability of
performing a parameter estimation follow-up on real data for an SSM candidate
with moderate SNR. The improved sensitivity of O4 and subsequent
LIGO-Virgo-KAGRA observing runs could make it possible to observe similar
signals, if present, with a higher SNR and a more precise measurement of the
parameters of the binary.


  In this note we design a cut finite element method for a low order divergence
free element applied to a boundary value problem subject to Stokes' equations.
For the imposition of Dirichlet boundary conditions we consider either
Nitsche's method or a stabilized Lagrange multiplier method. In both cases the
normal component of the velocity is constrained using a multiplier, different
from the standard pressure approximation. The divergence of the approximate
velocities is pointwise zero over the whole mesh domain, and we derive optimal
error estimates for the velocity and pressures, where the error constant is
independent of how the physical domain intersects the computational mesh, and
of the regularity of the pressure multiplier imposing the divergence free
condition.


  A quantum theory representations of real (R) and complex (C) numbers is given
that is based on states of single, finite strings of qukits for any base k > 1.
Both unary representations and the possibility that qukits with k a prime
number are elementary and the rest composite are discussed. Cauchy sequences of
qukit string states are defined from the arithmetic properties. The
representations of R and C, as equivalence classes of these sequences, differ
from classical kit string state representations in two ways: the freedom of
choice of basis states, and the fact that each quantum theory representation is
part of a mathematical structure that is itself based on the real and complex
numbers. These aspects enable the description of 3 dimensional frame fields
labeled by different k values, different basis or gauge choices, and different
iteration stages. The reference frames in the field are based on each R and C
representation where each frame contains representations of all physical
theories as mathematical structures based on the R and C representation.
Approaches to integrating this with physics are described. It is observed that
R and C values of physical quantities, matrix elements, etc. which are viewed
in a frame as elementary and featureless, are seen in a parent frame as
equivalence classes of Cauchy sequences of qukit string states.


  The motion of a pair of micro-wedges ("carriers") in a turbulent bacterial
bath is explored using computer simulations with explicit modeling of the
bacteria and experiments. The orientation of the two micro-wedges is parallel
and fixed by an applied magnetic field but the translational coordinates can
move freely as induced by the bacterial bath. As a result, two carriers of same
orientation move such that their mutual distance decreases. Eventually the two
carriers stack on each other with no intervening bacteria exhibiting a stable
dynamical mode where the two micro-wedges follow each other with the same
velocity. These findings are in qualitative agreement with experiments on two
micro-wedges in a bacterial bath. Our results provide insight into
understanding the self-assembly of many micro-wedges in an active bath.


  Conventional state representations in reinforcement learning often omit
critical task-related details, presenting a significant challenge for value
networks in establishing accurate mappings from states to task rewards.
Traditional methods typically depend on extensive sample learning to enrich
state representations with task-specific information, which leads to low sample
efficiency and high time costs. Recently, surging knowledgeable large language
models (LLM) have provided promising substitutes for prior injection with
minimal human intervention. Motivated by this, we propose LLM-Empowered State
Representation (LESR), a novel approach that utilizes LLM to autonomously
generate task-related state representation codes which help to enhance the
continuity of network mappings and facilitate efficient training. Experimental
results demonstrate LESR exhibits high sample efficiency and outperforms
state-of-the-art baselines by an average of 29% in accumulated reward in Mujoco
tasks and 30% in success rates in Gym-Robotics tasks.


  The Munich Near-IR Cluster Survey (MUNICS) is a wide-area, medium-deep,
photometric survey selected in the K' band. The project's main scientific aims
are the identification of galaxy clusters up to redshifts of unity and the
selection of a large sample of field early-type galaxies up to z < 1.5 for
evolutionary studies. We created a Large Scale Structure catalog, using a new
structure finding technique specialized for photometric datasets, that we
developed on the basis of a friends-of-friends algorithm. We tested the
plausibility of the resulting galaxy group and cluster catalog with the help of
Color-Magnitude Diagrams (CMD), as well as a likelihood- and Voronoi-approach.


  Here are described the geometric structures of the lines of principal
curvature and the partially umbilic singularities of the tridimensional non
compact generic quadric hypersurfaces of ${\mathbb R}^4$. This includes the
ellipsoidal hyperboloids of one and two sheets and the toroidal hyperboloids.
The present study complements the analysis of the compact ellipsoidal
hypersurfaces carried out by D. Lopes, R. Garcia and J. Sotomayor.


  Although our main interest here is developing an appropriate analog, for
diffeological vector pseudo-bundles, of a Riemannian metric, a significant
portion is dedicated to continued study of the gluing operation for
pseudo-bundles introduced in arXiv:1509.03023. We give more details regarding
the behavior of this operation with respect to gluing, also providing some
details omitted from arXiv:1509.03023, and pay more attention to the relations
with the spaces of smooth maps. We also show that a usual smooth vector bundle
over a manifold that admits a finite atlas can be seen as a result of a
diffeological gluing, and thus deduce that its usual dual bundle is the same as
its diffeological dual. We then consider the notion of a pseudo-metric, the
fact that it does not always exist (which seems to be related to
non-local-triviality condition), construction of an induced pseudo-metric on a
pseudo-bundle obtained by gluing, and finally, the relation between the spaces
of all pseudo-metrics on the factors of a gluing, and on its result. We
conclude by commenting on the induced pseudo-metric on the pseudo-bundle dual
to the given one.


  This paper investigates the offline packet-delay-minimization problem for an
energy harvesting transmitter. To overcome the non-convexity of the problem, we
propose a C2-diffeomorphic transformation and provide the necessary and
sufficient condition for the transformed problem to a standard convex
optimization problem. Based on this condition, a simple choice of the
transformation is determined which allows an analytically tractable solution of
the original non-convex problem to be easily obtained once the transformed
convex problem is solved. We further study the structure of the optimal
transmission policy in a special case and find it to follow a
weighted-directional-water-filling structure. In particular, the optimal policy
tends to allocate more power in earlier time slots and less power in later time
slots. Our analytical insight is verified by simulation results.


  The entropic effects of anchored polymers on biomembranes are studied using
simulations of a meshless membrane model combined with anchored linear polymer
chains. The bending rigidity and spontaneous curvature are investigated for
anchored ideal and excluded-volume polymer chains. Our results are in good
agreement with the previous theoretical predictions. It is found that the
polymer reduces the line tension of membrane edges, as well as the interfacial
line tension between membrane domains, leading to microdomain formation.
Instead of the mixing of two phases as observed in typical binary fluids,
densely anchored polymers stabilize small domains. A mean field theory is
proposed for the edge line tension reduced by anchored ideal chains, which
reproduces our simulation results well.


  We derive an empirical effective temperature and bolometric luminosity
calibration for G and K dwarfs, by applying our own implementation of the
InfraRed Flux Method to multi-band photometry. Our study is based on 104 stars
for which we have excellent BVRIJHK photometry, excellent parallaxes and good
metallicities. Colours computed from the most recent synthetic libraries
(ATLAS9 and MARCS) are found to be in good agreement with the empirical colours
in the optical bands, but some discrepancies still remain in the infrared.
Synthetic and empirical bolometric corrections also show fair agreement. A
careful comparison to temperatures, luminosities and angular diameters obtained
with other methods in literature shows that systematic effects still exist in
the calibrations at the level of a few percent. Our InfraRed Flux Method
temperature scale is 100K hotter than recent analogous determinations in the
literature, but is in agreement with spectroscopically calibrated temperature
scales and fits well the colours of the Sun. Our angular diameters are
typically 3% smaller when compared to other (indirect) determinations of
angular diameter for such stars, but are consistent with the limb-darkening
corrected predictions of the latest 3D model atmospheres and also with the
results of asteroseismology. Very tight empirical relations are derived for
bolometric luminosity, effective temperature and angular diameter from
photometric indices. We find that much of the discrepancy with other
temperature scales and the uncertainties in the infrared synthetic colours
arise from the uncertainties in the use of Vega as the flux calibrator. Angular
diameter measurements for a well chosen set of G and K dwarfs would go a long
way to addressing this problem.


  The notions of distance and similarity play a key role in many machine
learning approaches, and artificial intelligence (AI) in general, since they
can serve as an organizing principle by which individuals classify objects,
form concepts and make generalizations. While distance functions for
propositional representations have been thoroughly studied, work on distance
functions for structured representations, such as graphs, frames or logical
clauses, has been carried out in different communities and is much less
understood. Specifically, a significant amount of work that requires the use of
a distance or similarity function for structured representations of data
usually employs ad-hoc functions for specific applications. Therefore, the goal
of this paper is to provide an overview of this work to identify connections
between the work carried out in different areas and point out directions for
future work.


  We study the real-time evolution of solitary excitations in 1-d quantum spin
chains using exact diagonalization (ED) and the density-matrix renormalization
group (DMRG). The underlying question of this work is the correspondence
between classical solitons and solitons in quantum mechanics. While classical
solitons as eigensolutions of non-linear wave equations are localized and have
a sharp momentum, this is not possible in the corresponding quantum case due to
the linearity of the Schr\"odinger equation or seen in a more pictorial way,
because of the uncertainty relation. For the case of the XXZ model it is shown
that the real-time evolution of quantum wave packets accompanied by spreading
is in qualitative accordance with the one predicted by classical solitons.


  The dimensionally regularized massless on-shell planar triple box Feynman
diagram with powers of propagators equal to one is analytically evaluated for
general values of the Mandelstam variables s and t in a Laurent expansion in
the parameter \ep=(4-d)/2 of dimensional regularization up to a finite part. An
explicit result is expressed in terms of harmonic polylogarithms, with
parameters 0 and 1, up to the sixth order. The evaluation is based on the
method of Feynman parameters and multiple Mellin-Barnes representation. The
same technique can be quite similarly applied to planar triple boxes with any
numerators and integer powers of the propagators.


  The intrinsic conformality is a general property of the renormalizable gauge
theory, which ensures the scale-invariance of a fixed-order series at each
perturbative order. Following the idea of intrinsic conformality, we suggest a
novel single-scale setting approach under the principle of maximum conformality
(PMC) with the purpose of removing the conventional renormalization
scheme-and-scale ambiguities. We call this newly suggested single-scale
procedure as the PMC$_{\infty}$-s approach, in which an overall effective
$\alpha_s$, and hence an overall effective scale is achieved by identifying the
$\{\beta_0\}$-terms at each order. Its resultant conformal series is
scale-invariant and satisfies all renormalization group requirements. The
PMC$_{\infty}$-s approach is applicable to any perturbatively calculable
observables, and its resultant perturbative series provides an accurate basis
for estimating the contribution from the unknown higher-order (UHO) terms.
Using the Higgs decays into two gluons up to five-loop QCD corrections as an
example, we show how the PMC$_{\infty}$-s works, and we obtain $\Gamma_{\rm
H}\big|_{\text{PMC}_{\infty}\text{-s}}^{\rm PAA} = 334.45^{+7.07}_{-7.03}~{\rm
KeV}$ and $\Gamma_{\rm H}\big|_{\text{PMC}_{\infty}\text{-s}}^{\rm B.A.} =
334.45^{+6.34}_{-6.29}~{\rm KeV}$. Here the errors are squared averages of
those mentioned in the body of the text. The Pad$\acute{e}$ approximation
approach (PAA) and the Bayesian approach (B.A.) have been adopted to estimate
the contributions from the UHO-terms. We also demonstrate that the
PMC$_{\infty}$-s approach is equivalent to our previously suggested
single-scale setting approach (PMCs), which also follows from the PMC but
treats the $\{\beta_i\}$-terms from different point of view. Thus a proper
using of the renormalization group equation can provide a solid way to solve
the scale-setting problem.


  Using virtual residue, which is a generalization of Grothendieck residue, we
generalized Cayley- Bacharach Theorem to the cases with positive dimensions.


  We report the discovery in the Sloan Digital Sky Survey and the SDSS-III
Baryon Oscillation Spectroscopic Survey of seventeen broad absorption line
(BAL) quasars with high-ionization troughs that include absorption redshifted
relative to the quasar rest frame. The redshifted troughs extend to velocities
up to v=12,000 km/s and the trough widths exceed 3000 km/s in all but one case.
Approximately 1 in 1000 BAL quasars with blueshifted C IV absorption also has
redshifted C IV absorption; objects with C IV absorption present only at
redshifted velocities are roughly four times rarer. In more than half of our
objects, redshifted absorption is seen in C II or Al III as well as C IV,
making low-ionization absorption at least ten times more common among BAL
quasars with redshifted troughs than among standard BAL quasars. However, the C
IV absorption equivalent widths in our objects are on average smaller than
those of standard BAL quasars with low-ionization absorption. We consider
several possible ways of generating redshifted absorption. The two most likely
possibilities may be at work simultaneously, in the same objects or in
different ones. Rotationally dominated outflows seen against a quasar's
extended continuum source can produce redshifted and blueshifted absorption,
but variability consistent with this scenario is seen in only one of the four
objects with multiple spectra. The infall of relatively dense and
low-ionization gas to radii as small as 400 Schwarzschild radii can in
principle explain the observed range of trough profiles, but current models do
not easily explain the origin and survival of such gas. Whatever the origin(s)
of the absorbing gas in these objects, it must be located at small radii to
explain its large redshifted velocities, and thus offers a novel probe of the
inner regions of quasars.


  We propose an optical implementation of the Gaussian continuous-variable
quantum cloning machines. We construct a symmetric N -> M cloner which
optimally clones coherent states and we also provide an explicit design of an
asymmetric 1 -> 2 cloning machine. All proposed cloning devices can be built
from just a single non-degenerate optical parametric amplifier and several beam
splitters.


  The Fermi-LAT satellite has recently discovered a small group of radio
galaxies and steep spectrum radio sources: the misaligned AGNs (MAGNs) sample.
We present the X-ray analysis of all the sources of this sample (7 FRIs and 3
FRIIs) with a firm GeV association. This study supports the idea that FRIIs
host more efficient accretion mechanisms (m_dot >0.1) than FRIs (m_dot <0.003).
Furthermore, in objects with high accretion rates the Broad Line Regions appear
to be very active zones where, in addition to optical lines, the fluorescence
iron Kalpha feature at 6.4 keV is also produced. It seems that the FRII jets
propagate in an environment very rich in photons, explaining, at least at
zeroth order, why the External Compton is the preferred mechanism to produce
gamma-rays. In FRIs, where also the iron line is difficult to be detected, the
paucity of photons in the circumnuclear ambient seems to favor the Synchrotron
Self Compton process.


  Even for better-studied sign languages like American Sign Language (ASL),
data is the bottleneck for machine learning research. The situation is worse
yet for the many other sign languages used by Deaf/Hard of Hearing communities
around the world. In this paper, we present YouTube-SL-25, a large-scale,
open-domain multilingual corpus of sign language videos with seemingly
well-aligned captions drawn from YouTube. With >3000 hours of videos across >25
sign languages, YouTube-SL-25 is a) >3x the size of YouTube-ASL, b) the largest
parallel sign language dataset to date, and c) the first or largest parallel
dataset for many of its component languages. We provide baselines for
sign-to-text tasks using a unified multilingual multitask model based on T5 and
report scores on benchmarks across 4 sign languages. The results demonstrate
that multilingual transfer benefits both higher- and lower-resource sign
languages within YouTube-SL-25.


  We present experimental results on slow flow properties of a granular
assembly confined in a vertical column and driven upwards at a constant
velocity V. For monodisperse assemblies this study evidences at low velocities
($1<V<100 \mu m/s$) a stiffening behaviour i.e. the stress necessary to obtain
a steady sate velocity increases roughly logarithmically with velocity. On the
other hand, at very low driving velocity ($V<1 \mu m/s$), we evidence a
discontinuous and hysteretic transition to a stick-slip regime characterized by
a strong divergence of the maximal blockage force when the velocity goes to
zero. We show that all this phenomenology is strongly influenced by surrounding
humidity. We also present a tentative to establish a link between the granular
rheology and the solid friction forces between the wall and the grains. We base
our discussions on a simple theoretical model and independent grain/wall
tribology measurements. We also use finite elements numerical simulations to
confront experimental results to isotropic elasticity. A second system made of
polydisperse assemblies of glass beads is investigated. We emphasize the onset
of a new dynamical behavior, i.e. the large distribution of blockage forces
evidenced in the stick-slip regime.


  Current non-rigid object keypoint detectors perform well on a chosen kind of
species and body parts, and require a large amount of labelled keypoints for
training. Moreover, their heatmaps, tailored to specific body parts, cannot
recognize novel keypoints (keypoints not labelled for training) on unseen
species. We raise an interesting yet challenging question: how to detect both
base (annotated for training) and novel keypoints for unseen species given a
few annotated samples? Thus, we propose a versatile Few-shot Keypoint Detection
(FSKD) pipeline, which can detect a varying number of keypoints of different
kinds. Our FSKD provides the uncertainty estimation of predicted keypoints.
Specifically, FSKD involves main and auxiliary keypoint representation
learning, similarity learning, and keypoint localization with uncertainty
modeling to tackle the localization noise. Moreover, we model the uncertainty
across groups of keypoints by multivariate Gaussian distribution to exploit
implicit correlations between neighboring keypoints. We show the effectiveness
of our FSKD on (i) novel keypoint detection for unseen species, and (ii)
few-shot Fine-Grained Visual Recognition (FGVR) and (iii) Semantic Alignment
(SA) downstream tasks. For FGVR, detected keypoints improve the classification
accuracy. For SA, we showcase a novel thin-plate-spline warping that uses
estimated keypoint uncertainty under imperfect keypoint corespondences.


  A formalism that simultaneously searches for the monopolar and dipolar
peculiar velocities is presented. The formalism is applied to (1) the Mark III
catalogue, (2) Lauer and Postman's Abell cluster catalogue, and (3) Riess et
al.'s Type Ia supernova sample. The emphasis is drawn to the monopolar peculiar
velocities, i.e., peculiar Hubble flows, within these samples. The samples show
inconsistent peculiar Hubble flows within a depth of $\sim 60h^{-1}$ Mpc.
Beyond a depth of $\sim 80h^{-1}$ Mpc, the Hubble flows of all samples converge
to the global Hubble flow to better than 10% at the $2\sigma$ level. The
results are compared with theoretical predictions. They at face value disfavor
models predicting smaller peculiar velocities such as the tilted Cold Dark
Matter model. Limitations of the catalogues are discussed, so are ways to
improve the catalogues so that an accurate map of Hubble flows in our local
universe can be drawn and be compared with theoretical predictions.


  We provide a combined theoretical and experimental study of the electronic
structure and the optical absorption edge of the orthorhombic perovskite
LaInO$_{3}$. Employing density-functional theory and many-body perturbation
theory, we predict a direct electronic quasiparticle band gap of about 5 eV and
an effective electron (hole) mass of 0.31 (0.48) m$_{0}$. We find the
lowest-energy excitation at 0.2 eV below the fundamental gap, reflecting a
sizeable electron-hole attraction. Since the transition from the valence band
maximum (VBM, $\Gamma$ point) is, however, dipole forbidden the onset is
characterized by weak excitations from transitions around it. The first intense
excitation appears about 0.32 eV above. Interestingly, this value coincides
with an experimental value obtained by ellipsometry (4.80 eV) which is higher
than the onset from optical absorption spectroscopy (4.35 eV). The latter
discrepancy is attributed to the fact that the weak transitions that define the
optical gap are not resolved by the ellipsometry measurement. The absorption
edge shows a strong dependency on the light polarization, reflecting the
character of the involved valence states. Temperature-dependent measurements
show a redshift of the optical gap by about 120 meV by increasing the
temperature from 5 to 300 K. Renormalization due to zero-point vibrations is
extrapolated from the latter measurement to amount to 150 meV. By adding the
excitonic binding energy of 0.2 eV obtained theoretically to the experimental
optical absorption onset, we determine the fundamental band gap at room
temperature to be 4.55 eV.


  The use in the action integral of a volume element of the form $\Phi d^{D}x$,
where $\Phi$ is a metric-independent measure density, can yield new interesting
results in all types of known generally coordinate-invariant theories: (1) 4-D
theories of gravity plus matter fields; (2) reparametrization invariant
theories of extended objects (strings and branes); (3) supergravity theories.
In case (1) we obtain interesting insights concerning the cosmological constant
problem, inflation and quintessence without the fifth force problem. In case
(2) the above formalism leads to dynamically induced tension and to string
models of non-abelian confinement. In case (3), we show that the
modified-measure supergravity generates an arbitrary dynamically induced
cosmological constant, i.e., a new mechanism of dynamical supersymmetry
breaking.


  We investigate the different energy regimes in the conjectured SL(2,Z)
invariant four graviton scattering amplitude that incorporates D-instanton
contributions in 10d type IIB superstring theory. We show that the infinite
product over SL(2,Z) rotations is convergent in the whole complex plane s,t.
For high energies s>> 1, fixed scattering angle, and very weak coupling g<<
1/s, the four-graviton amplitude exhibits the usual exponential suppression. As
the energy approaches 1/g, the suppression gradually diminishes until there
appears a strong amplification near a new pole coming from the exchange of a
(p,q) string. At energies s<< 1/\sqrt{g}, the pure D instanton contribution to
the scattering amplitude is found to produce a factor $A_4^{Dinst}\cong \exp (c
g^{3/2}e^{-{2\pi\over g}} s^3)$. At energies $1/\sqrt{g} << s<< 1/g $, the
D-instanton factor becomes $A_4^{Dinst}\cong \exp (2 e^{-{2\pi\over g_s}+\pi
g_s s^2})$. At higher energies s>> 1/g the D-instanton contribution becomes
very important, and one finds an oscillatory behavior which alternates
suppression and amplification. This suggests that non-perturbative effects can
lead to a high-energy behavior which is significantly different from the
perturbative string behavior.


  This paper presents an open-source miniature car-like robot with low-cost
sensing and a pipeline for optimization-based system identification, state
estimation, and control. The overall robotics platform comes at a cost of less
than $700 and thus significantly simplifies the verification of advanced
algorithms in a realistic setting. We present a modified bicycle model with
Pacejka tire forces to model the dynamics of the considered all-wheel drive
vehicle and to prevent singularities of the model at low velocities.
Furthermore, we provide an optimization-based system identification approach
and a moving horizon estimation (MHE) scheme. In extensive hardware
experiments, we show that the presented system identification approach results
in a model with high prediction accuracy, while the MHE results in accurate
state estimates. Finally, the overall closed-loop system is shown to perform
well even in the presence of sensor failure for limited time intervals. All
hardware, firmware, and control and estimation software is released under a BSD
2-clause license to promote widespread adoption and collaboration within the
community.


  Internet-based personal digital belongings present different vulnerabilities
than locally stored materials. We use responses to a survey of people who have
recovered lost websites, in combination with supplementary interviews, to paint
a fuller picture of current curatorial strategies and practices. We examine the
types of personal, topical, and commercial websites that respondents have lost
and the reasons they have lost this potentially valuable material. We further
explore what they have tried to recover and how the loss influences their
subsequent practices. We found that curation of personal digital materials in
online stores bears some striking similarities to the curation of similar
materials stored locally in that study participants continue to archive
personal assets by relying on a combination of benign neglect, sporadic
backups, and unsystematic file replication. However, we have also identified
issues specific to Internet-based material: how risk is spread by distributing
the files among multiple servers and services; the circular reasoning
participants use when they discuss the safety of their digital assets; and the
types of online material that are particularly vulnerable to loss. The study
reveals ways in which expectations of permanence and notification are violated
and situations in which benign neglect has far greater consequences for the
long-term fate of important digital assets.


  Deep learning techniques have been widely used in autonomous driving systems
for the semantic understanding of urban scenes. However, they need a huge
amount of labeled data for training, which is difficult and expensive to
acquire. A recently proposed workaround is to train deep networks using
synthetic data, but the domain shift between real world and synthetic
representations limits the performance. In this work, a novel Unsupervised
Domain Adaptation (UDA) strategy is introduced to solve this issue. The
proposed learning strategy is driven by three components: a standard supervised
learning loss on labeled synthetic data; an adversarial learning module that
exploits both labeled synthetic data and unlabeled real data; finally, a
self-teaching strategy applied to unlabeled data. The last component exploits a
region growing framework guided by the segmentation confidence. Furthermore, we
weighted this component on the basis of the class frequencies to enhance the
performance on less common classes. Experimental results prove the
effectiveness of the proposed strategy in adapting a segmentation network
trained on synthetic datasets, like GTA5 and SYNTHIA, to real world datasets
like Cityscapes and Mapillary.


  Retrieving relevant multimedia content is one of the main problems in a world
that is increasingly data-driven. With the proliferation of drones, high
quality aerial footage is now available to a wide audience for the first time.
Integrating this footage into applications can enable GPS-less geo-localisation
or location correction.
  In this paper, we present an orientation-guided training framework for
UAV-view geo-localisation. Through hierarchical localisation orientations of
the UAV images are estimated in relation to the satellite imagery. We propose a
lightweight prediction module for these pseudo labels which predicts the
orientation between the different views based on the contrastive learned
embeddings. We experimentally demonstrate that this prediction supports the
training and outperforms previous approaches. The extracted pseudo-labels also
enable aligned rotation of the satellite image as augmentation to further
strengthen the generalisation. During inference, we no longer need this
orientation module, which means that no additional computations are required.
We achieve state-of-the-art results on both the University-1652 and
University-160k datasets.


  Hadronic flux-tube solutions describing the mesonic and the baryonic states
within the dual Ginzburg-Landau (DGL) theory are investigated by using the dual
lattice formulation in the Weyl-symmetric approach. The shape of the flux tubes
is determined by placement of the color-electric Dirac-string singularity
treated as a connected stack of quantized plaquettes in the dual lattice
formulation. The Weyl symmetric profiles of the hadronic flux tubes are
obtained by using the manifestly Weyl invariant representation of the dual
gauge field.


  We consider the compressible (barotropic) Navier-Stokes system on
time-dependent domains, supplemented with slip boundary conditions. Our
approach is based on penalization of the boundary behaviour, viscosity, and the
pressure in the weak formulation. Global-in-time weak solutions are obtained.


  We present a new time integrator for articulated body dynamics. We formulate
the governing equations of the dynamics using only the position variables and
recast the position-based articulated dynamics as an optimization problem. Our
reformulation allows us to integrate the dynamics in a fully implicit manner
without computing high-order derivatives. Therefore, under arbitrarily large
timestep sizes, the stability of our time integration scheme is guaranteed
using an off-the-shelf numerical optimizer. In addition to stability, we show
that, similar to the Runge-Kutta method, the accuracy of our time integrator
can also be increased arbitrarily by using a high-order collocation method. We
provide efficient algorithms to perform time integration using our
position-based formulation. We show that each iteration of optimization has a
complexity of O(N) using Quasi-Newton method or O(N^2) using Newton's method,
where N is the number of links. Finally, our method is highly parallelizable
and can be accelerated using a Graphics Processing Unit (GPU). We highlight the
efficiency and stability of our method on different benchmarks and compare the
performance with prior articulated body dynamics simulation methods based on
the Newton-Euler's equation. Our method is stable under a timestep size as
large as 0.1s. Using a larger timestep size and less timesteps, our method
achieves up to 4 times speedup on a single-core CPU. With GPU acceleration, we
observe an additional 3-6 times speedup over a 4-core CPU.


  Continuing our previous study of modified expected dimensions for rank-2
Brill-Noether loci with prescribed special determinants, we introduce a general
framework which applies a priori for arbitrary rank, and use it to prove
modified expected dimension bounds in several new cases, applying both to rank
2 and to higher rank. The main tool is the introduction of generalized
alternating Grassmannians, which are the loci inside Grassmannians
corresponding to subspaces which are simultaneously isotropic for a family of
multilinear alternating forms on the ambient vector space. In the case of rank
2 with 2-dimensional spaces of sections, we adapt arguments due to Teixidor i
Bigas to show that our new modified expected dimensions are in fact sharp.