  The treatment of equality as a type in type theory gives rise to an
interesting type-theoretic structure known as `identity type'. The idea is
that, given terms $a,b$ of a type $A$, one may form the type $Id_{A}(a,b)$,
whose elements are proofs that $a$ and $b$ are equal elements of type $A$. A
term of this type, $p : Id_{A}(a,b)$, makes up for the grounds (or proof) that
establishes that $a$ is indeed equal to $b$. Based on that, a proof of equality
can be seen as a sequence of substitutions and rewrites, also known as a
`computational path'. One interesting fact is that it is possible to rewrite
computational paths using a set of reduction rules arising from an analysis of
redundancies in paths. These rules were mapped by De Oliveira in 1994 in a term
rewrite system known as $LND_{EQ}-TRS$. Here we use computational paths and
this term rewrite system to develop the main foundations of homotopy type
theory, i.e., we develop the lemmas and theorems connected to the main types of
this theory, types such as products, coproducts, identity type, transport and
many others. We also show that it is possible to directly construct path spaces
through computational paths. To show this, we construct the natural numbers and
the fundamental group of the circle, showing results connected to these
structures.


  We study Galois points for a hypersurface $X$ with $\dim {\rm Sing}(X) \le
\dim X-2$. The purpose of this article is to determine the set $\Delta(X)$ of
Galois points in characteristic zero: Indeed, we give a sharp upper bound of
the number of Galois points in terms of $\dim X$ and $\dim {\rm Sing}(X)$ if
$\Delta(X)$ is a finite set, and prove that $X$ is a cone if $\Delta(X)$ is
infinite. To achieve our purpose, we need a certain hyperplane section theorem
on Galois point. We prove this theorem in arbitrary characteristic. On the
other hand, the hyperplane section theorem has other important applications:
For example, we can classify the Galois group induced from a Galois point in
arbitrary characteristic and determine the distribution of Galois points for a
Fermat hypersurface of degree $p^e+1$ in characteristic $p>0$.


  Garbage Collection in concurrent data structures, especially lock-free ones,
pose multiple design and consistency challenges. In this instance, we consider
the case of concurrent sets. A set is a collection of elements, where the
elements are ordered and distinct. These two invariants are always maintained
at every point in time. Currently, multiple implementations of concurrent sets
already exist. LazyList, Hand-over-hand List and Harris List are some of the
well-known implementations. However none of these implementations employ, or
are concerned with garbage collection of deleted nodes. Instead each
implementation ignores deleted nodes or depends on the garbage collector of the
language to handle them. Additionally, Garbage collection in concurrent lists,
that use optimistic traversals or that are lock-free, is not trivial. For
example, in LazyList and Harris List, they allow a thread to traverse a node or
a sequence of nodes after these nodes have already been removed from the list,
and hence possibly deleted. If deleted nodes are to be reused, this will
potentially lead to the ABA problem. Moreover, some languages like C++ do not
have an inbuilt garbage collector. Some constructs like Shared Pointers provide
a limited garbage collection facility, but it degrades performance by a large
scale. Integrating Shared Pointers into a concurrent code is also not a trivial
task. In this paper, we propose a new representation of a concurrent set,
GCList, which employs inbuilt garbage collection. We propose a novel garbage
collection scheme that implements in-built memory reclamation whereby it reuses
deleted nodes from the list. We propose both lock-based and lock-free
implementations of GCList. The garbage collection scheme works in parallel with
the Set operations.


  We describe some theoretical results on triangulations of surfaces and we
develop a theory on roots, decompositions and genus-surfaces. We apply this
theory to describe an algorithm to list all triangulations of closed surfaces
with at most a fixed number of vertices. We specialize the theory for the case
where the number of vertices is at most 11 and we get theoretical restrictions
on genus-surfaces allowing us to get the list of triangulations of closed
surfaces with at most 11 vertices.


  Process mining is a discipline which concerns the analysis of execution data
of operational processes, the extraction of models from event data, the
measurement of the conformance between event data and normative models, and the
enhancement of all aspects of processes. Most approaches assume that event data
is accurately capture behavior. However, this is not realistic in many
applications: data can contain uncertainty, generated from errors in recording,
imprecise measurements, and other factors. Recently, new methods have been
developed to analyze event data containing uncertainty; these techniques
prominently rely on representing uncertain event data by means of graph-based
models explicitly capturing uncertainty. In this paper, we introduce a new
approach to efficiently calculate a graph representation of the behavior
contained in an uncertain process trace. We present our novel algorithm, prove
its asymptotic time complexity, and show experimental results that highlight
order-of-magnitude performance improvements for the behavior graph
construction.


  A contact interaction is used to calculate an array of pion twist-two, -three
and -four generalised transverse light-front momentum dependent parton
distribution functions (GTMDs). Despite the interaction's simplicity, many of
the results are physically relevant, amongst them a statement that GTMD size
and shape are largely prescribed by the scale of emergent hadronic mass.
Moreover, proceeding from GTMDs to generalised parton distributions (GPDs), it
is found that the pion's mass distribution form factor is harder than its
electromagnetic form factor, which is harder than the gravitational pressure
distribution form factor; the pressure in the neighbourhood of the pion's core
is commensurate with that at the centre of a neutron star; the shear pressure
is maximal when confinement forces become dominant within the pion; and the
spatial distribution of transversely polarised quarks within the pion is
asymmetric. Regarding transverse momentum dependent distribution functions
(TMDs), their magnitude and domain of support decrease with increasing twist.
The simplest Wigner distribution associated with the pion's twist-two
dressed-quark GTMD is sharply peaked on the kinematic domain associated with
valence-quark dominance; has a domain of negative support; and broadens as the
transverse position variable increases in magnitude.


  Explosive energy release is a common phenomenon occurring in magnetized
plasma systems ranging from laboratories, Earth's magnetosphere, the solar
corona and astrophysical environments. Its physical explanation is usually
attributed to magnetic reconnection in a thin current sheet. Here we report the
important role of magnetic flux rope structure, a volumetric current channel,
in producing explosive events. The flux rope is observed as a hot channel prior
to and during a solar eruption from the Atmospheric Imaging Assembly (AIA)
telescope on board the Solar Dynamic Observatory (SDO). It initially appears as
a twisted and writhed sigmoidal structure with a temperature as high as 10 MK
and then transforms toward a semi-circular shape during a slow rise phase,
which is followed by fast acceleration and onset of a flare. The observations
suggest that the instability of the magnetic flux rope trigger the eruption,
thus making a major addition to the traditional magnetic-reconnection paradigm.


  The ability to understand spatial-temporal patterns for crowds of people is
crucial for achieving long-term autonomy of mobile robots deployed in human
environments. However, traditional historical data-driven memory models are
inadequate for handling anomalies, resulting in poor reasoning by robot in
estimating the crowd spatial distribution. In this article, a Receding Horizon
Optimization (RHO) formulation is proposed that incorporates a
Probability-related Partially Updated Memory (PPUM) for robot path planning in
crowded environments with uncertainties. The PPUM acts as a memory layer that
combines real-time sensor observations with historical knowledge using a
weighted evidence fusion theory to improve robot's adaptivity to the dynamic
environments. RHO then utilizes the PPUM as a informed knowledge to generate a
path that minimizes the likelihood of encountering dense crowds while reducing
the cost of local motion planning. The proposed approach provides an innovative
solution to the problem of robot's long-term safe interaction with human in
uncertain crowded environments. In simulation, the results demonstrate the
superior performance of our approach compared to benchmark methods in terms of
crowd distribution estimation accuracy, adaptability to anomalies and path
planning efficiency.


  We analyze the effect of color superconductivity in the transition from hot
hadron matter to quark matter in the presence of a gas of trapped electron
neutrinos. To describe strongly interacting matter we adopt a two-phase picture
in which the hadronic phase is described by means of a non-linear Walecka model
and just deconfined matter through the MIT bag model including color
superconductivity. We impose flavor conservation during the transition in such
a way that just deconfined quark matter is transitorily out of equilibrium with
respect to weak interactions. Our results show that color superconductivity
facilitates the transition for temperatures below $T_c$. This effect may be
strong if the superconducting gap is large enough. As in previous work we find
that trapped neutrinos increase the critical density for deconfinement;
however, if the just deconfined phase is color superconducting this effect is
weaker than if deconfined matter is unpaired. We also explore the effect of
different parametrizations of the hadronic equation of state (GM1 and NL3) and
the effect of hyperons in the hadronic phase. We compare our results with those
previously obtained employing the Nambu-Jona-Lasinio model in the description
of just deconfined matter and show that they are in excellent agreement if the
bag constant $B$ is properly chosen.


  We explore leptogenesis within the E6 inspired U(1) extension of the MSSM in
which exact custodial symmetry forbids tree-level flavour-changing transitions
and the most dangerous baryon and lepton number violating operators. This
supersymmetric (SUSY) model involves extra exotic matter beyond the MSSM. In
the simplest phenomenologically viable scenarios the lightest exotic fermions
are neutral and stable. These states should be substantially lighter than 1 eV
forming hot dark matter in the Universe. The low-energy effective Lagrangian of
the SUSY model under consideration possesses an approximate global U(1)_E
symmetry associated with the exotic states. The U(1)_E symmetry is explicitly
broken because of the interactions between the right-handed neutrino
superfields and exotic matter supermultiplets. As a consequence the decays of
the lightest right-handed neutrino/sneutrino give rise to both U(1)_E and
U(1)_{B-L} asymmetries. When all right-handed neutrino/sneutrino are relatively
light \sim 10^6-10^7 GeV the appropriate amount of the baryon asymmetry can be
induced via these decays if the Yukawa couplings of the lightest right-handed
neutrino superfields to the exotic matter supermultiplets vary between
10^{-4}-10^{-3}.


  Perching on {the surface} of moving objects, like vehicles, could extend the
flight {time} and range of quadrotors. Suction cups are usually adopted for
{surface attachment} due to their durability and large adhesive force. To seal
on {a surfaces}, suction cups {must} be aligned with {the surface} and {possess
proper relative tangential velocity}. {However, quadrotors' attitude and
relative velocity errors would become significant when the object surface is
moving and inclined. To address this problem, we proposed a real-time
trajectory planning algorithm. The time-optimal aggressive trajectory is
efficiently generated through multimodal search in a dynamic time-domain. The
velocity errors relative to the moving surface are alleviated.} To further
adapt to the residual errors, we design a compliant gripper using self-sealing
cups. Multiple cups in different directions are integrated into a wheel-like
mechanism to increase the tolerance to attitude errors. The wheel mechanism
also eliminates the requirement of matching the attitude and tangential
velocity. {Extensive tests are conducted to perch on static and moving surfaces
at various inclinations.} Results demonstrate that our proposed system enables
a quadrotor to reliably perch on moving inclined surfaces (up to $1.07m/s$ and
$90^\circ$) with a success rate of $70\%$ or higher. {The efficacy of the
trajectory planner is also validated. Our gripper has larger adaptability to
attitude errors and tangential velocities than conventional suction cup
grippers.} The success rate increases by 45\% in dynamic perches.


  The Bardeen-Cooper-Schrieffer (BCS) mean-field theory of the pairing
interaction breaks down for nuclei and ultra-small metallic grains
(nanoparticles). Finite-temperature pairing correlations in such finite-size
systems can be calculated beyond the BCS theory in an auxiliary-field Monte
Carlo approach. We identify thermal signatures of pairing correlations in both
nuclei and nanoparticles that depend on the particle-number parity.


  We reply to the Comment of Challet et al. [cond-mat/0004308] on our paper
[Phys. Rev. Lett. 83, 4429 (1999)]. We show that the claim of the Comment that
the effects of the temperature in the Thermal Minority Game ``can be eliminated
by time rescaling'' and consequently the behaviour is ``independent of T'' has
no general validity.


  We study the effect of disorder on the intrinsic anomalous Hall (AH)
conductivity in a spin-polarized two-dimensional electron gas with a
Rashba-type spin-orbit interaction. We find that AH conductivity vanishes
unless the lifetime is spin-dependent, similar to the spin Hall (SH)
conductivity in the non-magnetic system. In addition, we find that the SH
conductivity does not vanish in the presence of magnetic scatterers. We show
that the SH conductivity can be controlled by changing the amount of the
magnetic impurities.


  Tiled spatial architectures have proved to be an effective solution to build
large-scale DNN accelerators. In particular, interconnections between tiles are
critical for high performance in these tile-based architectures. In this work,
we identify the inefficiency of the widely used traditional on-chip networks
and the opportunity of software-hardware co-design. We propose METRO with the
basic idea of decoupling the traffic scheduling policies from hardware fabrics
and moving them to the software level. METRO contains two modules working in
synergy: METRO software scheduling framework to coordinate the traffics and
METRO hardware facilities to deliver the data based on software configurations.
We evaluate the co-design using different flit sizes for synthetic study,
illustrating its effectiveness under various hardware resource constraints, in
addition to a wide range of DNN models selected from real-world workloads. The
results show that METRO achieves 56.3% communication speedup on average and up
to 73.6% overall processing time reduction compared with traditional on-chip
network designs.


  In the context of a project aimed at characterizing the properties of the
so-called Bulge Fossil Fragments (the fossil remnants of the bulge formation
epoch), here we present the first determination of the metallicity distribution
of Liller 1. For a sample of 64 individual member stars we used ESO- MUSE
spectra to measure the equivalent width of the CaII triplet and then derive the
iron abundance. To test the validity of the adopted calibration in the
metal-rich regime, the procedure was first applied to three reference bulge
globular clusters (NGC 6569, NGC 6440, and NGC 6528). In all the three cases,
we found single-component iron distributions, with abundance values fully in
agreement with those reported in the literature. The application of the same
methodology to Liller 1 yielded, instead, a clear bimodal iron distribution,
with a sub-solar component at $\text{[Fe/H]}= -0.48\,$dex ($\sigma = 0.22$) and
a super-solar component at $\text{[Fe/H]}= +0.26\,$dex ($\sigma = 0.17$). The
latter is found to be significantly more centrally concentrated than the
metal-poor population, as expected in a self-enrichment scenario and in
agreement with what found in another bulge system, Terzan 5. The obtained
metallicity distribution is astonishingly similar to that predicted by the
reconstructed star formation history of Liller 1, which is characterized by
three main bursts and a low, but constant, activity of star formation over the
entire lifetime. These findings provide further support to the possibility
that, similar to Terzan 5, also Liller 1 is a Bulge Fossil Fragment.


  We numerically demonstrate two types of metasurface absorbers to efficiently
absorb digital signals. First, we show that the digital waveforms used in this
study contain not only a fundamental wave but also nonnegligible harmonic
waves, which limits the absorption performance of a conventional metasurface
absorber operating in only a single, finite frequency band. The first type of
the proposed absorbers is designed using two kinds of unit cells, each of which
absorbs either a fundamental frequency or third harmonic of an incident digital
waveform. This dual-band metasurface absorber exhibits absorption performance
exceeding that of the conventional metasurface absorber and more strongly
dissipates the energy of a digital waveform. In addition, the second type of
absorber exploits the concept of nonlinear analogous circuits to convert an
incoming wave to a different waveform, specifically, a triangular waveform that
has a larger magnitude at a fundamental frequency. Therefore, the incoming
waveform is more effectively absorbed by this waveform-conversion metasurface
absorber as well. Although still there remain some issues to put these digital
signal absorbers into practice, including experimental validation, our results
contribute to mitigating electromagnetic interference issues caused by digital
noise and realising physically smaller, lighter digital signal processing
products for the next generation.


  Choropleth maps are a common visual representation for region-specific
tabular data and are used in a number of different venues (newspapers,
articles, etc). These maps are human-readable but are often challenging to deal
with when trying to extract data for screen readers, analyses, or other related
tasks. Recent research into Visual-Question Answering (VQA) has studied
question answering on human-generated charts (ChartQA), such as bar, line, and
pie charts. However, little work has paid attention to understanding maps;
general VQA models, and ChartQA models, suffer when asked to perform this task.
To facilitate and encourage research in this area, we present MapQA, a
large-scale dataset of ~800K question-answer pairs over ~60K map images. Our
task tests various levels of map understanding, from surface questions about
map styles to complex questions that require reasoning on the underlying data.
We present the unique challenges of MapQA that frustrate most strong baseline
algorithms designed for ChartQA and general VQA tasks. We also present a novel
algorithm, Visual Multi-Output Data Extraction based QA (V-MODEQA) for MapQA.
V-MODEQA extracts the underlying structured data from a map image with a
multi-output model and then performs reasoning on the extracted data. Our
experimental results show that V-MODEQA has better overall performance and
robustness on MapQA than the state-of-the-art ChartQA and VQA algorithms by
capturing the unique properties in map question answering.


  We show a strong parallel between the Hawking, Beckenstein black hole
Thermodynamics and electromagnetism: When the gravitational coupling constant
transform into the electromagnetic coupling constant, the Schwarzchild radius,
the Beckenstein temperature, the Beckenstein decay time and the Planck mass
transform to respectively the Compton wavelength, the Hagedorn temperature, the
Compton time and a typical elementary particle mass. The reasons underlying
this parallalism are then discussed in detail.


  We introduce a new class of intersection graphs, the edge intersection graphs
of paths on a triangular grid, called EPGt graphs. We show similarities and
differences from this new class to the well-known class of EPG graphs. A turn
of a path at a grid point is called a bend. An EPGt representation in which
every path has at most $k$ bends is called a B$_k$-EPGt representation and the
corresponding graphs are called B$_k$-EPGt graphs. We provide examples of
B$_{2}$-EPG graphs that are B$_{1}$-EPGt. We characterize the representation of
cliques with three vertices and chordless 4-cycles in B$_{1}$-EPGt
representations. We also prove that B$_{1}$-EPGt graphs have Strong Helly
number $3$. Furthermore, we prove that B$_{1}$-EPGt graphs are $7$-clique
colorable.


  Subsystem codes protect quantum information by encoding it in a tensor factor
of a subspace of the physical state space. Subsystem codes generalize all major
quantum error protection schemes, and therefore are especially versatile. This
paper introduces numerous constructions of subsystem codes. It is shown how one
can derive subsystem codes from classical cyclic codes. Methods to trade the
dimensions of subsystem and co-subystem are introduced that maintain or improve
the minimum distance. As a consequence, many optimal subsystem codes are
obtained. Furthermore, it is shown how given subsystem codes can be extended,
shortened, or combined to yield new subsystem codes. These subsystem code
constructions are used to derive tables of upper and lower bounds on the
subsystem code parameters.


  We study singular radially symmetric solution to the Lin-Ni-Takagi equation
for a supercritical power non-linearity in dimension $N\geq 3$. It is shown
that for any ball and any $k \geq 0$, there is a singular solution that
satisfies Neumann boundary condition and oscillates at least $k$ times around
the constant equilibrium. Moreover, we show that the Morse index of the
singular solution is finite or infinite if the exponent is respectively larger
or smaller than the Joseph--Lundgren exponent.


  We examine the structure and dynamics of the galaxies in the Coma cluster
using a catalog of 552 redshifts including 243 new measurements. The velocity
distribution is shown to be non-Gaussian due to structure associated with the
group of galaxies around NGC4839, 40 arcmin SW of the cluster core. We apply a
mixture-modelling algorithm to the galaxy sample and obtain a robust partition
into two subclusters which we use to examine the system's dynamics. We find
that the late-type galaxies are freely-falling into a largely virialised
cluster core dominated by early types. We obtain a virial mass for the main
cluster in close agreement with the estimates derived from recent X-ray data.
The mass of the NGC4839 group is about 5-10% the mass of the main cluster.
Assuming the main cluster and the NGC4839 group follow a linear two-body orbit,
the favored solution has the two clusters lying at 74 degrees to the line of
sight at a true separation of 0.8 Mpc and moving together at 1700 km/s. The
cluster core shows evidence of an ongoing merger between two subclusters
centered in projection on the dominant galaxies NGC4874 and NGC4889 but offset
in velocity by 300 km/s and 1100 km/s respectively. Combining these results
with X-ray and radio observations, and an interpretation of the presence or
lack of an extended halo around the dominant galaxies, we develop a merger
history for the Coma cluster.


  In this article, we present an overview of the new developments in problems
of the plasma influence on the effects of gravitational lensing, complemented
by pieces of new material and relevant discussions. Deflection of light in the
presence of gravity and plasma is determined by a complex combination of
various physical phenomena: gravity, dispersion, refraction. In particular, the
gravitational deflection itself, in a homogeneous plasma without refraction,
differs from the vacuum one and depends on the frequency of the photon. In an
inhomogeneous plasma, chromatic refraction also takes place. We describe
chromatic effects in strong lens systems including a shift of angular position
of image and a change in magnification. We also investigate high-order images
that arise when lensing on a black hole surrounded by homogeneous plasma. The
recent results of analytical studies of the effect of plasma on the shadow of
the Schwarzschild and Kerr black holes are presented.


  A mixed list of ads and organic items is usually displayed in feed and how to
allocate the limited slots to maximize the overall revenue is a key problem.
Meanwhile, modeling user preference with historical behavior is essential in
recommendation and advertising (e.g., CTR prediction and ads allocation). Most
previous works for user behavior modeling only model user's historical
point-level positive feedback (i.e., click), which neglect the page-level
information of feedback and other types of feedback. To this end, we propose
Deep Page-level Interest Network (DPIN) to model the page-level user preference
and exploit multiple types of feedback. Specifically, we introduce four
different types of page-level feedback as input, and capture user preference
for item arrangement under different receptive fields through the multi-channel
interaction module. Through extensive offline and online experiments on Meituan
food delivery platform, we demonstrate that DPIN can effectively model the
page-level user preference and increase the revenue for the platform.


  In this article, we propose the outcome-adjusted balance measure to perform
model selection for the generalized propensity score (GPS), which serves as an
essential component in estimation of the pairwise average treatment effects
(ATEs) in observational studies with more than two treatment levels. The
primary goal of the balance measure is to identify the GPS model specification
such that the resulting ATE estimator is consistent and efficient. Following
recent empirical and theoretical evidence, we establish that the optimal GPS
model should only include covariates related to the outcomes. Given a
collection of candidate GPS models, the outcome-adjusted balance measure
imputes all baseline covariates by matching on each candidate model, and
selects the model that minimizes a weighted sum of absolute mean differences
between the imputed and original values of the covariates. The weights are
defined to leverage the covariate-outcome relationship, so that GPS models
without optimal variable selection are penalized. Under appropriate
assumptions, we show that the outcome-adjusted balance measure consistently
selects the optimal GPS model, so that the resulting GPS matching estimator is
asymptotically normal and efficient. We compare its finite sample performance
with existing measures in a simulation study. We illustrate an application of
the proposed methodology in the analysis of the Tutoring data.


  A point particle of mass $\mu$ moving on a geodesic creates a perturbation
$h_{ab}$, of the spacetime metric $g_{ab}$, that diverges at the particle.
Simple expressions are given for the singular $\mu/r$ part of $h_{ab}$ and its
distortion caused by the spacetime. This singular part $h^\SS_{ab}$ is
described in different coordinate systems and in different gauges. Subtracting
$h^\SS_{ab}$ from $h_{ab}$ leaves a regular remainder $h^\R_{ab}$. The
self-force on the particle from its own gravitational field adjusts the world
line at $\Or(\mu)$ to be a geodesic of $g_{ab}+h^\R_{ab}$; this adjustment
includes all of the effects of radiation reaction. For the case that the
particle is a small non-rotating black hole, we give a uniformly valid
approximation to a solution of the Einstein equations, with a remainder of
$\Or(\mu^2)$ as $\mu\to0$.
  An example presents the actual steps involved in a self-force calculation.
Gauge freedom introduces ambiguity in perturbation analysis. However,
physically interesting problems avoid this ambiguity.


  Multi-modal populations of networks arise in many scenarios including in
large-scale multi-modal neuroimaging studies that capture both functional and
structural neuroimaging data for thousands of subjects. A major research
question in such studies is how functional and structural brain connectivity
are related and how they vary across the population. we develop a novel
PCA-type framework for integrating multi-modal undirected networks measured on
many subjects. Specifically, we arrange these networks as semi-symmetric
tensors, where each tensor slice is a symmetric matrix representing a network
from an individual subject. We then propose a novel Joint, Integrative
Semi-Symmetric Tensor PCA (JisstPCA) model, associated with an efficient
iterative algorithm, for jointly finding low-rank representations of two or
more networks across the same population of subjects. We establish one-step
statistical convergence of our separate low-rank network factors as well as the
shared population factors to the true factors, with finite sample statistical
error bounds. Through simulation studies and a real data example for
integrating multi-subject functional and structural brain connectivity, we
illustrate the advantages of our method for finding joint low-rank structures
in multi-modal populations of networks.


  Synthesizing novel views for urban environments is crucial for tasks like
autonomous driving and virtual tours. Compared to object-level or indoor
situations, outdoor settings present unique challenges, such as inconsistency
across frames due to moving vehicles and camera pose drift over lengthy
sequences. In this paper, we introduce a method that tackles these challenges
on view synthesis for outdoor scenarios. We employ a neural point light field
scene representation and strategically detect and mask out dynamic objects to
reconstruct novel scenes without artifacts. Moreover, we simultaneously
optimize camera pose along with the view synthesis process, and thus, we
simultaneously refine both elements. Through validation on real-world urban
datasets, we demonstrate state-of-the-art results in synthesizing novel views
of urban scenes.


  In frequency-division duplexing systems, the downlink channel state
information (CSI) acquisition scheme leads to high training and feedback
overheads. In this paper, we propose an uplink-aided downlink channel
acquisition framework using deep learning to reduce these overheads. Unlike
most existing works that focus only on channel estimation or feedback modules,
to the best of our knowledge, this is the first study that considers the entire
downlink CSI acquisition process, including downlink pilot design, channel
estimation, and feedback. First, we propose an adaptive pilot design module by
exploiting the correlation in magnitude among bidirectional channels in the
angular domain to improve channel estimation. Next, to avoid the bit allocation
problem during the feedback module, we concatenate the complex channel and
embed the uplink channel magnitude to the channel reconstruction at the base
station. Lastly, we combine the above two modules and compare two popular
downlink channel acquisition frameworks. The former framework estimates and
feeds back the channel at the user equipment subsequently. The user equipment
in the latter one directly feeds back the received pilot signals to the base
station. Our results reveal that, with the help of uplink, directly feeding
back the pilot signals can save approximately 20% of feedback bits, which
provides a guideline for future research.


  Spectra of the QSO pair Q0307-195A,B have been obtained in the Ly-alpha
Forest (3660 - 3930 A) and C IV (4720 - 4850 A) regions with a FWHM resolution
between 0.7 and 0.5 A. 46 lines have been detected in the spectrum of object A
while 36 in the spectrum of object B, of them 29 and 20 were identified as
Ly-alpha absorptions respectively. The present observations have been
supplemented with data of comparable quality on other 7 QSO pairs available in
the literature to give an enlarged sample of 217 Ly-alpha lines with rest
equivalent width W_o > 0.3 A. The analysis of the hits (i.e. when an absorption
line appears in both QSO spectra) and misses (i.e. when a line is seen in any
of the QSO spectra, but no line is seen in the other), carried out with an
improved statistical approach, indicates that the absorbers have typically a
large size (h = H_0/100 km/s/Mpc, q_0 = 0.5): R = 362 h^{-1} kpc, with 95 %
confidence limits 298< R <426 h^{-1} kpc and R = 412 h^{-1} kpc, with 95 %
confidence limits 333< R <514 h^{-1} kpc for the radius in idealized spherical
and disc geometries, respectively. The present data do not allow to establish
any correlation of the typical inferred size with the proper separation or with
the redshift of the pairs. The correlation between the observed equivalent
widths of the absorbers in the adjacent lines of sight becomes poorer and
poorer with increasing proper separation. A disc geometry with a column density
profile N(r) \propto (r/R_0)^{-gamma}, gamma=4, is found to reasonably
reproduce the data with R_0 ~ 100-200 h^{-1} kpc, but also spherical clouds
with the same column density profile and a power-law distribution of radii may
give a satisfactory representation of the observations.


  \emph{TxGraffiti} is a machine learning and heuristic based artificial
intelligence designed to automate the task of conjecturing in mathematics.
Since its inception, TxGraffiti has generated many surprising conjectures
leading to publication in respectable mathematical journals. In this paper we
outline the machine learning and heuristic techniques implemented by
TxGraffiti. We also recall its contributions to the mathematical literature and
announce a new online version of the program available for anyone curious to
explore conjectures in graph theory.


  Compact symmetric spaces are probably one of the most prominent class of
formal spaces, i.e. of spaces where the rational homotopy type is a formal
consequence of the rational cohomology algebra. As a generalisation, it is even
known that their isotropy action is equivariantly formal.
  In this article we show that $(\mathbb{Z}_2\oplus \mathbb{Z}_2)$-symmetric
spaces are equivariantly formal and formal in the sense of Sullivan, in
particular. Moreover, we give a short alternative proof of equivariant
formality in the case of symmetric spaces with our new approach.


  A formalism is developed to calculate the bubble nucleation rate in theories
where the symmetry breaking is by radiative corrections. Although to leading
and next-to-leading order the result can be expressed in terms of the effective
action of the theory, this is not true of the higher order corrections. In
particular, the imaginary part of the effective potential does not explicitly
enter the calculation and the problems of interpretation which it would entail
are avoided. Based on a talk given at the NATO Advanced Research Workshop
Electroweak Physics and the Early Universe, Sintra, Portugal, March 25, 1994


  We introduce a mathematical model, based on networks, for the elasticity and
plasticity of materials. We define the tension tensor for a periodic graph in a
Euclidean space, and we show that the tension tensor expresses elasticity under
deformation. Plasticity is induced by local moves on a graph. The graph is
described in terms of the weights of edges, and we discuss how these weights
affect the plasticity.


  Type migration is the process of adding types to untyped code to gain
assurance at compile time. TypeScript and other gradual type systems facilitate
type migration by allowing programmers to start with imprecise types and
gradually strengthen them. However, adding types is a manual effort and several
migrations on large, industry codebases have been reported to have taken
several years. In the research community, there has been significant interest
in using machine learning to automate TypeScript type migration. Existing
machine learning models report a high degree of accuracy in predicting
individual TypeScript type annotations. However, in this paper we argue that
accuracy can be misleading, and we should address a different question: can an
automatic type migration tool produce code that passes the TypeScript type
checker?
  We present TypeWeaver, a TypeScript type migration tool that can be used with
an arbitrary type prediction model. We evaluate TypeWeaver with three models
from the literature: DeepTyper, a recurrent neural network; LambdaNet, a graph
neural network; and InCoder, a general-purpose, multi-language transformer that
supports fill-in-the-middle tasks. Our tool automates several steps that are
necessary for using a type prediction model, (1) importing types for a
project's dependencies; (2) migrating JavaScript modules to TypeScript
notation; (3) inserting predicted type annotations into the program to produce
TypeScript when needed; and (4) rejecting non-type predictions when needed.
  We evaluate TypeWeaver on a dataset of 513 JavaScript packages, including
packages that have never been typed before. With the best type prediction
model, we find that only 21% of packages type check, but more encouragingly,
69% of files type check successfully.


  This paper describes an investigation of the early evolution of extragalactic
radio sources using samples of faint and bright Gigahertz Peaked Spectrum (GPS)
and Compact Steep Spectrum (CSS) radio galaxies. Correlations found between
their peak frequency, peak flux density and angular size provide strong
evidence that synchrotron self absorption is the cause of the spectral
turnovers, and indicate that young radio sources evolve in a self-similar way.
In addition, the data seem to suggest that the sources are in equipartition
while they evolve. If GPS sources evolve to large size radio sources, their
redshift dependent birth-functions should be the same. Therefore, since the
lifetimes of radio sources are thought to be short compared to the Hubble time,
the observed difference in redshift distribution between GPS and large size
sources must be due to a difference in slope of their luminosity functions. We
argue that this slope is strongly affected by the luminosity evolution of the
individual sources. A scenario for the luminosity evolution is proposed in
which GPS sources increase in luminosity and large scale radio sources decrease
in luminosity with time. This evolution scenario is expected for a ram-pressure
confined radio source in a surrounding medium with a King profile density. In
the inner parts of the King profile, the density of the medium is constant and
the radio source builds up its luminosity, but after it grows large enough the
density of th e surrounding medium declines and the luminosity of the radio
source decreases. A comparison of the local luminosity function (LLF) of GPS
galaxies with that of extended sources is a good test for this evolution
scenario [abridged].


  We show that a general solution of the Einstein equations that describes
approach to an inhomogeneous and anisotropic sudden spacetime singularity does
not experience geodesic incompleteness. This generalises the result established
for isotropic and homogeneous universes. Further discussion of the weakness of
the singularity is also included.


  The effective adhesive properties of heterogeneous thin films are
characterized through a combined experimental and theoretical investigation. By
bridging scales, we show how variations of elastic or adhesive properties at
the microscale can significantly affect the effective peeling behavior of the
adhesive at the macroscale. Our study reveals three elementary mechanisms in
heterogeneous systems involving front propagation: (i) patterning the elastic
bending stiffness of the film produces fluctuations of the driving force
resulting in dramatically enhanced resistance to peeling; (ii) optimized
arrangements of pinning sites with large adhesion energy are shown to control
the effective system resistance, allowing the design of highly anisotropic and
asymmetric adhesives; (iii) heterogeneities of both types result in front
motion instabilities producing sudden energy releases that increase the overall
adhesion energy. These findings open potentially new avenues for the design of
thin films with improved adhesion properties, and motivate new investigation of
other phenomena involving front propagation.


  The eccentric luminosity variation of quasars is still a mystery. Analytic
results of this behavior ranged from multi-periodic behavior to a purely random
process. Recently, we have used nonlinear time-series analysis to analyze the
light curve of 3C 273 and found its eccentric behavior may be chaos [L. Liu,
Chin. J. Astron. Astrophys. \textbf{6}, 663 (2006)]. This result induces us to
look for some nonlinear mechanism to explain the eccentric luminosity
variation. In this paper, we propose a simple non-linear accretion disk model
and find it shows a kind of chaotic behavior under some circumstances. Then we
compute the outburst energy $\triangle F$, defined as the difference of the
maximum luminosity and the minimum luminosity, and the mean luminosity $<F>$.
We find that $\triangle F\sim < F >^{\alpha}$ in the chaotic domain, where
$\alpha\approx 1$. In this domain, we also find that $<F > \sim M^{0.5}$, where
$M$ is the mass of central black hole. These results are confirmed by or
compatible with some results from the observational data analysis [A. J. Pica
and A. G. Smith, Astrophys. J. \textbf{272}, 11 (1983); M. Wold, M. S.
Brotherton and Z. Shang, Mon. Not. R. Astron. Soc. \textbf{375}, 989 (2007)].


  We study non-expanding random walks on the space of affine lattices and
establish a new classification theorem for stationary measures. Further, we
prove a theorem that relates the genericity with respect to these random walks
to Birkhoff genericity. Finally, we apply these theorems to obtain several
results in inhomogeneous Diophantine approximation, especially on fractals.


  We study the effectiveness of using multiple phases for maximizing the extent
of information diffusion through a social network, and present insights while
considering various aspects. In particular, we focus on the independent cascade
model with the possibility of adaptively selecting seed nodes in multiple
phases based on the observed diffusion in preceding phases, and conduct a
detailed simulation study on real-world network datasets and various values of
seeding budgets. We first present a negative result that more phases do not
guarantee a better spread, however the adaptability advantage of more phases
generally leads to a better spread in practice, as observed on real-world
datasets. We study how diffusing in multiple phases affects the mean and
standard deviation of the distribution representing the extent of diffusion. We
then study how the number of phases impacts the effectiveness of multiphase
diffusion, how the diffusion progresses phase-by-phase, and what is an optimal
way to split the total seeding budget across phases. Our experiments suggest a
significant gain when we move from single phase to two phases, and an
appreciable gain when we further move to three phases, but the marginal gain
thereafter is usually not very significant. Our main conclusion is that, given
the number of phases, an optimal way to split the budget across phases is such
that the number of nodes influenced in each phase is almost the same.


  Popular extensions of the standard model of particle physics feature new
fields and symmetries which could, for example, dynamically generate neutrino
masses from $B-L$ spontaneous symmetry breaking. If a new light scalar that
decays into dark radiation appears in the spectrum of the theory, it could
significantly modify the cosmological observables. In this case, cold dark
matter could have a stable and a decaying component and limits on its decay
rate $\Gamma_{\rm dcdm}$ can be used to put constraints on the new energy
scales of a given model. We illustrate this idea using a gauged $B-L$ model
where the dark radiation is in the form of light neutrinos.


  Strange particles have been a very important observable in the search for a
deconfined state of strongly interacting matter, the quark-gluon plasma (QGP),
which is expected to be formed in ultra-relativistic heavy ion collisions. We
review the main experimental observations made at the Super Proton Synchrotron
(SPS) at CERN, Geneva, and at the Relativistic Heavy Ion Collider (RHIC) at
Brookhaven National Laboratory (BNL). The large amount of recently collected
data allows for a comprehensive study of strangeness production as a function
of energy and system size. We review results on yields, transverse mass and
rapidity spectra, as well as elliptic flow. The measurements are interpreted in
the context of various theoretical concepts and their implications are
discussed. Of particular interest is the question whether strange particles are
in any way sensitive to a partonic phase. Finally, a compilation of
experimental data is provided.


  We put a probabilistic framework on the Demographic Prisoner's Dilemma. In
this model, cooperating and defecting individuals are placed on a torus to move
and play prisoner's dilemma game, if they are on the same site. Each individual
accumulates its payoff into a quantity called wealth. If an individual becomes
wealthy enough, it can have an offspring. If its wealth becomes negative, it
disappears. In this framework we prove that if if the Sucker payoff is far
greater than the Reward then for all initial state almost surely all
cooperators will die. Moreover if the Temptation payoff (resp. Reward) are far
greater than the Punition (resp. Sucker payoff) then for all initial state with
positive probability cooperators and defectors live \emph{ad vitam eternam}. We
also set a Mean Field model on the demographic prisoner's dilemma and prove on
a linearized version of the Mean Field model that with weaker assumptions with
positive probability Cooperators live \emph{ad vitam eternam}.


  Magnetostatic fields in accelerators are conventionally described in terms of
multipoles. We show that in two dimensions, multipole fields do provide
solutions of Maxwell's equations, and we consider the distributions of electric
currents and geometries of ferromagnetic materials required (in idealized
situations) to generate specified multipole fields. Then, we consider how to
determine the multipole components in a given field. Finally, we show how the
two-dimensional multipole description may be extended to three dimensions; this
allows fringe fields, or the main fields in such devices as undulators and
wigglers, to be expressed in terms of a set of modes, where each mode provides
a solution to Maxwell's equations.


  We consider two classes of random graphs: $(a)$ Poissonian random graphs in
which the $n$ vertices in the graph have i.i.d.\ weights distributed as $X$,
where $\mathbb{E}(X) = \mu$. Edges are added according to a product measure and
the probability that a vertex of weight $x$ shares and edge with a vertex of
weight $y$ is given by $1-e^{-xy/(\mu n)}$. $(b)$ A thinned configuration model
in which we create a ground-graph in which the $n$ vertices have i.i.d.\
ground-degrees, distributed as $D$, with $\mathbb{E}(D) = \mu$. The graph of
interest is obtained by deleting edges independently with probability $1-p$.
  In both models the fraction of vertices in the largest connected component
converges in probability to a constant $1-q$, where $q$ depends on $X$ or $D$
and $p$.
  We investigate for which distributions $X$ and $D$ with given $\mu$ and $p$,
$1-q$ is maximized. We show that in the class of Poissonian random graphs, $X$
should have all its mass at 0 and one other real, which can be explicitly
determined. For the thinned configuration model $D$ should have all its mass at
0 and two subsequent positive integers.


  This paper demonstrates the efficiency of using Edgeworth and Gram-Charlier
expansions in the calibration of the Libor Market Model with Stochastic
Volatility and Displaced Diffusion (DD-SV-LMM). Our approach brings together
two research areas; first, the results regarding the SV-LMM since the work of
Wu and Zhang (2006), especially on the moment generating function, and second
the approximation of density distributions based on Edgeworth or Gram-Charlier
expansions. By exploring the analytical tractability of moments up to fourth
order, we are able to perform an adjustment of the reference Bachelier model
with normal volatilities for skewness and kurtosis, and as a by-product to
derive a smile formula relating the volatility to the moneyness with
interpretable parameters. As a main conclusion, our numerical results show a
98% reduction in computational time for the DD-SV-LMM calibration process
compared to the classical numerical integration method developed by Heston
(1993).


  Alignments of galaxy clusters (the Binggeli effect), as well as of galaxies
themselves have long been studied both observationally and theoretically. Here
we test the influence of large-scales structures and tidal fields on the shapes
and alignments of cluster-size and galaxy-size dark matter halos. We use a
high-resolution N-body simulation of a $\Lambda$CDM universe, together with the
results of Colberg et al. (2005), who identified filaments connecting pairs of
clusters. We find that cluster pairs connected by a filament are strongly
aligned with the cluster-cluster axis, whereas unconnected ones are not. For
smaller, galaxy-size halos, there also is an alignment signal, but its strength
is independent of whether the halo is part of an obvious large-scale structure.
Additionally, we find no measureable dependence of galaxy halo shape on
membership of a filament. We also quantify the influence of tidal fields and
find that these do correlate strongly with alignments of halos. The alignments
of most halos are thus caused by tidal fields, with cluster-size halos being
strongly aligned through the added mechanism of infall of matter from
filaments.


  This article investigates the ability of graph neural networks (GNNs) to
identify risky conditions in a power grid over the subsequent few hours,
without explicit, high-resolution information regarding future generator on/off
status (grid topology) or power dispatch decisions. The GNNs are trained using
supervised learning, to predict the power grid's aggregated bus-level (either
zonal or system-level) or individual branch-level state under different power
supply and demand conditions. The variability of the stochastic grid variables
(wind/solar generation and load demand), and their statistical correlations,
are rigorously considered while generating the inputs for the training data.
The outputs in the training data, obtained by solving numerous mixed-integer
linear programming (MILP) optimal power flow problems, correspond to
system-level, zonal and transmission line-level quantities of interest (QoIs).
The QoIs predicted by the GNNs are used to conduct hours-ahead, sampling-based
reliability and risk assessment w.r.t. zonal and system-level (load shedding)
as well as branch-level (overloading) failure events. The proposed methodology
is demonstrated for three synthetic grids with sizes ranging from 118 to 2848
buses. Our results demonstrate that GNNs are capable of providing fast and
accurate prediction of QoIs and can be good proxies for computationally
expensive MILP algorithms. The excellent accuracy of GNN-based reliability and
risk assessment suggests that GNN models can substantially improve situational
awareness by quickly providing rigorous reliability and risk estimates.


  In recent years, DNS over Encrypted (DoE) methods have been regarded as a
novel trend within the realm of the DNS ecosystem. In these DoE methods, DNS
over HTTPS (DoH) provides encryption to protect data confidentiality while
providing better obfuscation to avoid censorship by multiplexing port 443 with
web services. This development introduced certain inconveniences in discovering
publicly available DoH services. In this paper, we propose the E-DoH method for
elegant and efficient DoH service detection. First, we optimized the probing
mechanism to enable a single DoH connection to accomplish multiple tasks
including service discovery, correctness validation and dependency
construction. Second, we propose an efficient DoH detection tool. This tool can
enhance probing efficiency while significantly reduce the required traffic
volume. Third, based on the above optimization methods, we conducted an
exploration of the IPv4 space and performed an in-depth analysis of DoH based
on the collected information. Through experiments, our approach demonstrates a
remarkable 80% improvement in time efficiency, and only requires 4%-20% traffic
volume to complete the detection task. In wild detection, our approach
discovered 46k DoH services, which nearly doubles the number discovered by the
state-of-the-art. Based on the collected data, we present several intriguing
conclusions about the current DoH service ecosystem.


  A classification of dynamical systems in terms of their variational
properties is reviewed. Within this classification, front propagation is
discussed in a non-gradient relaxational potential flow. The model is motivated
by transient pattern phenomena in nematics. A front propagating into an
unstable homogenous state leaves behind an unstable periodic pattern, which
decays via a second front and a second periodic state. An interface between
unstable periodic states is shown to be a source of propagating fronts in
opposite directions.


  The basic science responsible for the fascinating shapes of ice crystals and
snowflakes is still not understood. Insufficient knowledge of the interaction
potentials and the lack of relevant experimental access to the growth process
are to blame for this failure. Here, we study the growth of fractal
nanostructures in a two-dimensional (2D) system, intercalated between mica and
graphene. Based on our Scanning Tunneling Spectroscopy (STS) data we provide
compelling evidence that these fractals are 2D ice. They grow while they are in
material contact with the atmosphere at 20 $^{\circ}$C and without significant
thermal contact to the ambient. The growth is studied in-situ, in real time and
space at the nanoscale. We find that the growing 2D ice nanocrystals assume a
fractal shape, which is conventionally attributed to Diffusion Limited
Aggregation (DLA). However, DLA requires a low mass density mother phase, in
contrast to the actual currently present high mass density mother phase. Latent
heat effects and consequent transport of heat and molecules are found to be key
ingredients for understanding the evolution of the snow (ice) flakes. We
conclude that not the local availability of water molecules (DLA), but rather
them having the locally required orientation is the key factor for
incorporation into the 2D ice nanocrystal. In combination with the transport of
latent heat we attribute the evolution of fractal 2D ice nanocrystals to local
temperature dependent Rotation Limited Aggregation (RLA). The ice growth occurs
under extreme supersaturation, i.e. the conditions closely resemble the natural
ones for the growth of complex 2D snow (ice) flakes and we consider our
findings crucial for solving the 'perennial' snow (ice) flake enigma.


  The near-infrared integral field spectrograph SINFONI at the ESO VLT opens a
new window for the study of central supermassive black holes. With a near-IR
spatial resolution similar to HST optical and the ability to penetrate dust it
provides the possibility to explore the low-mass end of the M-sigma relation
(sigma<120km/s) where so far very few black hole masses were measured with
stellar dynamics. With SINFONI we observed the central region of the
low-luminosity elliptical galaxy NGC4486a at a spatial resolution of ~0.1arcsec
in the K band. The stellar kinematics was measured with a maximum penalised
likelihood method considering the region around the CO absorption band heads.
We determined a black hole mass of M_BH=1.25^{+0.75}_{-0.79} x 10^7 M_sun (90%
C.L.) using the Schwarzschild orbit superposition method including the full
2-dimensional spatial information. This mass agrees with the predictions of the
M-sigma relation, strengthening its validity at the lower sigma end.


  We report measurements of the in-plane magnetoelastic coupling in ultra-thin
Ta|CoFeB|MgO layers as a function of uniaxial strain, conducted using a
four-point bending apparatus. For annealed samples, we observe a strong
dependence on the thickness of the CoFeB layer in the range 1.3-2.0 nm, which
can be modeled as arising from a combination of effective surface and volume
contributions to the magnetoelastic coupling. We point out that if similar
thickness dependence exists for magnetoelastic coupling in response to biaxial
strain, then the standard N\'eel model for the magnetic anisotropy energy
acquires a term inversely proportional to the magnetic layer thickness. This
contribution can significantly change the overall magnetic anisotropy, and
provides a natural explanation for the strongly nonlinear dependence of
magnetic anisotropy energy on magnetic layer thickness that is commonly
observed for ultrathin annealed CoFeB|MgO films with perpendicular magnetic
anisotropy.


  This paper introduces Waste Factor (W), also denoted as Waste Figure (WF) in
dB, a promising new metric for quantifying energy efficiency in a wide range of
circuits and systems applications, including data centers and RANs. Also, the
networks used to connect data centers and AI computing engines with users for
ML applications must become more power efficient. This paper illustrates the
limitations of existing energy efficiency metrics that inadequately capture the
intricate energy dynamics of RAN components. We delineate the methodology for
applying W across various network configurations, including MISO, SIMO, and
MIMO systems, and demonstrate the effectiveness of W in identifying energy
optimization opportunities. Our findings reveal that W not only offers nuanced
insights into the energy performance of RANs but also facilitates informed
decision-making for network design and operational efficiency. Furthermore, we
show how W can be integrated with other KPIs to guide the development of
optimal strategies for enhancing network energy efficiency under different
operational conditions. Additionally, we present simulation results for a
distributed multi-user MIMO system at 3.5, 17, and 28 GHz, demonstrating
overall network power efficiency on a per square kilometer basis, and show how
overall W decreases with an increasing number of base stations and increasing
carrier frequency. This paper shows that adopting W as a figure of merit can
significantly contribute to the sustainability and energy optimization of
next-generation wireless communication networks, paving the way for greener and
more sustainable, energy-efficient 5G and 6G technologies.


  We study a 2+2 body problem introduced in a previous paper as the circular
double Sitnikov problem. Since the secondary bodies are moving on the same
perpendicular line where evolve the primaries, almost every solution is a
collision orbit. We extend the solutions beyond collisions with a symplectic
regularization and study the set of energy surfaces that contain periodic
orbits and their foliations .


  This paper investigates the influence of different broadband perturbations on
the evolution of a Richtmyer--Meshkov turbulent mixing layer initiated by a
Mach 1.84 shock traversing a perturbed interface separating gases with a
density ratio of 3:1. Both the bandwidth of modes in the interface
perturbation, as well as their relative amplitudes, are varied in a series of
carefully designed numerical simulations at grid resolutions up to
$3.2\times10^9$ cells. Three different perturbations are considered,
characterised by a power spectrum of the form $P(k)\propto k^m$ where $m=-1$,
$-2$ and $-3$. The growth of the mixing layer is shown to strongly depend on
the initial conditions, with the growth rate exponent $\theta$ found to be
$0.5$, $0.63$ and $0.75$ for each value of $m$ at the highest grid resolution.
The asymptotic values of the molecular mixing fraction $\Theta$ are also shown
to vary significantly with $m$; at the latest time considered $\Theta$ is
$0.56$, $0.39$ and $0.20$ respectively. Turbulent kinetic energy (TKE) is also
analysed in both the temporal and spectral domains. The temporal decay rate of
TKE is found not to match the predicted value of $n=2-3\theta$, which is shown
to be due to a time-varying {normalised dissipation rate $C_\epsilon$}. In
spectral space, the data follow the theoretical scaling of $k^{(m+2)/2}$ at low
wavenumbers and tend towards $k^{-3/2}$ and $k^{-5/3}$ scalings at high
wavenumbers for the spectra of transverse and normal velocity components
respectively. The results represent a significant extension of previous work on
the Richtmyer--Meshkov instability evolving from broadband initial
perturbations and provide useful benchmarks for future research.


  This paper consists of a few results, discovered and proved during the
2012-2013 research group at Eastern Oregon University. Inertia tables are a
visual representation of the possible inertias of a given graph. The inertia of
a graph counts the number of real positive and negative eigenvalues of its
corresponding adjacency matrix. The problem of studying inertia tables is
directly related to the inverse eigenvalue problem and can be used as a tool
for the minimum rank problem. This paper describes the inverse eigenvalue
problem, and tools used. We describe a number of new general formulas for
various simple undirected graphs and improved upon an established notation for
inertia tables.


  We report experimental observation of an unexpectedly large thermopower in
mesoscopic two-dimensional (2D) electron systems on GaAs/AlGaAs
heterostructures at sub-Kelvin temperatures and zero magnetic field. Unlike
conventional non-magnetic high-mobility 2D systems, the thermopower in our
devices increases with decreasing temperature below 0.3 K, reaching values in
excess of 100 $\mu$V/K, thus exceeding the free electron estimate by more than
two orders of magnitude. With support from a parallel independent study of the
local density of states, we suggest such a phenomenon to be linked to intrinsic
localized states and many-body spin correlations in the system.


  The dynamical scaling of quantum critical systems in thermal equilibrium may
be inherited in the driven steady-state, leading to universal
out-of-equilibrium behaviour. This attractive notion has been demonstrated in
just a few cases. We demonstrate how holography - a mapping between the quantum
critical system and a gravity dual - provides an illuminating perspective and
new results. Non-trivial out-of-equilibrium universality is particularly
apparent in current noise, which is dual to Hawking radiation in the
gravitational system. We calculate this in a 2-dimensional system driven by a
strong in-plane electric field and deduce a universal scaling function
interpolating between previously established equilibrium and
far-from-equilibrium current noise. Since this applies at all fields,
out-of-equilibrium experiments no longer require very high fields for
comparison with theory.


  A method is derived to solve the massless Dirac-Weyl equation describing
electron transport in a mono-layer of graphene with a scalar potential barrier
U(x,t), homogeneous in the y-direction, of arbitrary x- and time dependence.
Resonant enhancement of both electron backscattering and currents, across and
along the barrier, is predicted when the modulation frequencies satisfy certain
resonance conditions. These conditions resemble those for Shapiro-steps of
driven Josephson junctions. Surprisingly, we find a non-zero y-component of the
current for carriers of zero momentum along the y-axis.


  Semantic Scene Completion (SSC) aims to jointly infer semantics and
occupancies of 3D scenes. Truncated Signed Distance Function (TSDF), a 3D
encoding of depth, has been a common input for SSC. Furthermore, RGB-TSDF
fusion, seems promising since these two modalities provide color and geometry
information, respectively. Nevertheless, RGB-TSDF fusion has been considered
nontrivial and commonly-used naive addition will result in inconsistent
results. We argue that the inconsistency comes from the sparsity of RGB
features upon projecting into 3D space, while TSDF features are dense, leading
to imbalanced feature maps when summed up. To address this RGB-TSDF
distribution difference, we propose a two-stage network with a 3D RGB feature
completion module that completes RGB features with meaningful values for
occluded areas. Moreover, we propose an effective classwise entropy loss
function to punish inconsistency. Extensive experiments on public datasets
verify that our method achieves state-of-the-art performance among methods that
do not adopt extra data.


  The notion of a firmly nonexpansive mapping is central in fixed point theory
because of attractive convergence properties for iterates and the
correspondence with maximal monotone operators due to Minty. In this paper, we
systematically analyze the relationship between properties of firmly
nonexpansive mappings and associated maximal monotone operators. Dual and
self-dual properties are also identified. The results are illustrated through
several examples.


  We have observed a sample of 10 white dwarf candidates in the rich open
cluster NGC 2323 (M50) with the Keck Low-Resolution Imaging Spectrometer. The
spectroscopy shows eight to be DA white dwarfs, with six of these having high
S/N appropriate for our analysis. Two of these white dwarfs are consistent with
singly evolved cluster membership, and both are high mass $\sim$1.07 M$_\odot$,
and give equivalent progenitor masses of 4.69 M$_\odot$. To supplement these
new high-mass white dwarfs and analyze the initial-final mass relation (IFMR),
we have also looked at 30 white dwarfs from publicly available data that are
mostly all high-mass ($\gtrsim$0.9 M$_\odot$). These original published data
exhibited significant scatter, and to test if this scatter is true or simply
the result of systematics, we have uniformly analyzed the white dwarf spectra
and have adopted thorough photometric techniques to derive uniform cluster
parameters for their parent clusters. The resulting IFMR scatter is
significantly reduced, arguing that mass-loss rates are not stochastic in
nature and that within the ranges of metallicity and mass analyzed in this work
mass loss is not highly sensitive to variations in metallicity. Lastly, when
adopting cluster ages based on Y$^2$ isochrones, the slope of the high-mass
IFMR remains steep and consistent with that found from intermediate-mass white
dwarfs, giving a linear IFMR from progenitor masses between 3 to 6.5 M$_\odot$.
In contrast, when adopting the slightly younger cluster ages based on PARSEC
isochrones, the high-mass IFMR has a moderate turnover near an initial mass of
4 M$_\odot$.


  A nonrelativistic unparticle can be defined as an excitation created by an
operator with a definite scaling dimension in a nonrelativistic field theory
with an approximate conformal symmetry. The point production rate of an
unparticle has power-law dependence on its total energy with an exponent
determined by its scaling dimension. We use the exact result for the 3-point
function of primary operators in a nonrelativistic conformal field theory to
derive the contribution to the point production rate of the unparticle from its
decay into another unparticle recoiling against a particle. In the case where
the conformal symmetry is broken by a large positive scattering length, we
deduce the exponent of the energy in the point production rate of the loosely
bound two-particle state recoiling against a particle with large relative
momentum.


  The topic of aspect-based sentiment analysis (ABSA) has been explored for a
variety of industries, but it still remains much unexplored in finance. The
recent release of data for an open challenge (FiQA) from the companion
proceedings of WWW '18 has provided valuable finance-specific annotations. FiQA
contains high quality labels, but it still lacks data quantity to apply
traditional ABSA deep learning architecture. In this paper, we employ
high-level semantic representations and methods of inductive transfer learning
for NLP. We experiment with extensions of recently developed domain adaptation
methods and target task fine-tuning that significantly improve performance on a
small dataset. Our results show an 8.7% improvement in the F1 score for
classification and an 11% improvement over the MSE for regression on current
state-of-the-art results.


  We investigate the expressive power and complexity questions for the LIKE
operator in SQL.


  The subspace sum graph $\mathcal{G}(\mathbb{V})$ on a finite dimensional
vector space $\mathbb{V}$ was introduced by Das [Subspace Sum Graph of a Vector
Space, arXiv:1702.08245], recently. The vertex set of $\mathcal{G}(\mathbb{V})$
consists of all the nontrivial proper subspaces of $\mathbb{V}$ and two
distinct vertices $W_1$ and $W_2$ are adjacent if and only if
$W_1+W_2=\mathbb{V}$. In that paper, some structural indices (e.g., diameter,
girth, connectivity, domination number, clique number and chromatic number)
were studied, but the characterization of automorphisms of
$\mathcal{G}(\mathbb{V})$ was left as one of further research topics. Motivated
by this, we in this paper characterize the automorphisms of
$\mathcal{G}(\mathbb{V})$ completely.


  People affected by machine learning model decisions may benefit greatly from
access to recourses, i.e. suggestions about what features they could change to
receive a more favorable decision from the model. Current approaches try to
optimize for the cost incurred by users when adopting a recourse, but they
assume that all users share the same cost function. This is an unrealistic
assumption because users might have diverse preferences about their willingness
to change certain features. In this work, we introduce a new method for
identifying recourse sets for users which does not assume that users'
preferences are known in advance. We propose an objective function, Expected
Minimum Cost (EMC), based on two key ideas: (1) when presenting a set of
options to a user, there only needs to be one low-cost solution that the user
could adopt; (2) when we do not know the user's true cost function, we can
approximately optimize for user satisfaction by first sampling plausible cost
functions from a distribution, then finding a recourse set that achieves a good
cost for these samples. We optimize EMC with a novel discrete optimization
algorithm, Cost Optimized Local Search (COLS), which is guaranteed to improve
the recourse set quality over iterations. Experimental evaluation on popular
real-world datasets with simulated users demonstrates that our method satisfies
up to 25.89 percentage points more users compared to strong baseline methods,
while, the human evaluation shows that our recourses are preferred more than
twice as often as the strongest baseline recourses. Finally, using standard
fairness metrics we show that our method can provide more fair solutions across
demographic groups than baselines. We provide our source code at:
https://github.com/prateeky2806/EMC-COLS-recourse


  Identifying the origin of a sample image in biometric systems can be
beneficial for data authentication in case of attacks against the system and
for initiating sensor-specific processing pipelines in sensor-heterogeneous
environments. Motivated by shortcomings of the photo response non-uniformity
(PRNU) based method in the biometric context, we use a texture classification
approach to detect the origin of finger vein sample images. Based on eight
publicly available finger vein datasets and applying eight classical yet simple
texture descriptors and SVM classification, we demonstrate excellent sensor
model identification results for raw finger vein samples as well as for the
more challenging region of interest data. The observed results establish
texture descriptors as effective competitors to PRNU in finger vein sensor
model identification.


  We present new radial velocity (RV) measurements for 11 candidate young
very-low-mass stars and brown dwarfs, with spectral types from M7 to L7.
Candidate young objects were identified by features indicative of low surface
gravity in their optical and/or near-infrared spectra. RV measurements are
derived from high resolution (R=$\lambda$/$\Delta\lambda$=20,000) $J$ band
spectra taken with NIRSPEC at the Keck Observatory. We combine RVs with proper
motions and trigonometric distances to calculate three-dimensional space
positions and motions and to evaluate membership probabilities for nearby young
moving groups (NYMGs). We propose 2MASS J00452143+1634446 (L2$\beta$,
$J$=13.06) as an RV standard given the precision and stability of measurements
from three different studies. We test the precision and accuracy of our RV
measurements as a function of spectral type of the comparison object, finding
that RV results are essentially indistinguishable even with differences of
$\pm$5 spectral subtypes. We also investigate the strengths of
gravity-sensitive K~{\sc i} lines at 1.24--1.25 $\mu$m and evaluate their
consistency with other age indicators. We confirm or re-confirm four brown
dwarf members of NYMGs -- 2MASS J00452143+1634446, WISE J00470038+6803543,
2MASS J01174748$-$3403258, and 2MASS J19355595$-$2846343 -- and their previous
age estimates. We identify one new brown dwarf member of the Carina-Near moving
group, 2MASS J21543454$-$1055308. The remaining objects do not appear to be
members of any known NYMGs, despite their spectral signatures of youth. These
results add to the growing number of very-low-mass objects exhibiting
signatures of youth that lack likely membership in a known NYMG, thereby
compounding the mystery regarding local, low-density star formation.


  Transformer-based models generally allocate the same amount of computation
for each token in a given sequence. We develop a simple but effective "token
dropping" method to accelerate the pretraining of transformer models, such as
BERT, without degrading its performance on downstream tasks. In short, we drop
unimportant tokens starting from an intermediate layer in the model to make the
model focus on important tokens; the dropped tokens are later picked up by the
last layer of the model so that the model still produces full-length sequences.
We leverage the already built-in masked language modeling (MLM) loss to
identify unimportant tokens with practically no computational overhead. In our
experiments, this simple approach reduces the pretraining cost of BERT by 25%
while achieving similar overall fine-tuning performance on standard downstream
tasks.


  We study games of chance (e.g., pokers, dices, horse races) in the form of
agents' first-order posterior beliefs about game outcomes. We ask for any
profile of agents' posterior beliefs, is there a game that can generate these
beliefs? We completely characterize all feasible joint posterior beliefs from
these games. The characterization enables us to find a new variant of Border's
inequalities (Border, 1991), which we call a belief-based characterization of
Border's inequalities. It also leads to a generalization of Aumann's Agreement
Theorem. We show that the characterization results are powerful in bounding the
correlation of agents' joint posterior beliefs.


  With the outbreak of the COVID-19 pandemic in 2020, most colleges and
universities move to restrict campus activities, reduce indoor gatherings and
move instruction online. These changes required that students adapt and alter
their daily routines accordingly. To investigate patterns associated with these
behavioral changes, we collected smartphone sensing data using the Beiwe
platform from two groups of undergraduate students at a major North American
university, one from January to March of 2020 (74 participants), the other from
May to August (52 participants), to observe the differences in students' daily
life patterns before and after the start of the pandemic. In this paper, we
focus on the mobility patterns evidenced by GPS signal tracking from the
students' smartphones and report findings using several analytical methods
including principal component analysis, circadian rhythm analysis, and
predictive modeling of perceived sadness levels using mobility-based digital
metrics. Our findings suggest that compared to the pre-COVID group, students in
the mid-COVID group generally (1) registered a greater amount of midday
movement than movement in the morning (8-10am) and in the evening (7-9pm), as
opposed to the other way around; (2) exhibited significantly less intradaily
variability in their daily movement, and (3) had a significant lower
correlation between their mobility patterns and negative mood.


  We describe the analytical solution of the eigenvalue problem introduced in
our article mentioned in the title and relative to a punctiform electric charge
confined in an one-dimensional box in the presence of an electric field. We
also derive and discuss the analytical expressions of the external forces
acting on the punctiform charge and associated with the boundaries of the
one-dimensional box in the presence of the electric field.


  We present the evidence for X-ray variability from the core and from knot A
in the M87 jet based on data from two observations with the Einstein
Observatory High Resolution Imager (HRI) and three observations with the ROSAT
HRI. The core intensity showed a 16% increase in 17 months ('79-'80); a 12%
increase in the 3 years '92 to '95; and a 17% drop in the last half of 1995.
The intensity of knot A appears to have decreased by 16% between 92Jun and
95Dec. Although the core variability is consistent with general expectations
for AGN nuclei, the changes in knot A provide constraints on the x-ray emission
process and geometry. Thus we predict that the x-ray morphology of knot A will
differ significantly from the radio and optical structure.


  We study the implications of the Planck temperature power spectrum at low
multipoles, $\ell<1000$, and SPTPol data. We show that this combination
predicts consistent lensing-induced smoothing of acoustic peaks within
$\Lambda$CDM cosmology and yields the robust predictions of the cosmological
parameters. Combining only the Planck large-scale temperature data and the
SPTPol polarization and lensing measurements within $\Lambda$CDM model we found
substantially lower values of linear matter density perturbation $\sigma_8$
which bring the late-time parameter
$S_8=\sigma_8\sqrt{\Omega_m/0.3}=0.763\pm0.022$ into accordance with galaxy
clustering and weak lensing measurements. It also raises up the Hubble constant
$H_0=69.68\pm1.00{\rm \,\,km\,s^{-1}Mpc^{-1}}$ that reduces the Hubble tension
to the $2.5\sigma$ level. We examine the residual tension in the Early Dark
Energy (EDE) model which produces the brief energy injection prior to
recombination. We implement both the background and perturbation evolutions of
the scalar field which potential scales as $V(\phi)\propto \phi^{2n}$.
Including cosmic shear measurements (KiDS, VIKING-450, DES) and local
distance-ladder data (SH0ES) to the combined fit we found that EDE completely
alleviates the Hubble tension while not degradating the fit to large-scale
structure data. The EDE scenario significantly improves the goodness-of-fit by
$2.9\sigma$ in comparison with the concordance $\Lambda$CDM model. The account
for the intermediate-redshift data (the supernova dataset and baryon acoustic
oscillation data) fits perfectly to our parameter predictions and indicates the
preference of EDE over $\Lambda$CDM at $3\sigma$.


  Relative location prediction in computed tomography (CT) scan images is a
challenging problem. In this paper, a regression model based on one-dimensional
convolutional neural networks is proposed to determine the relative location of
a CT scan image both robustly and precisely. A public dataset is employed to
validate the performance of the study's proposed method using a 5-fold cross
validation. Experimental results demonstrate an excellent performance of the
proposed model when compared with the state-of-the-art techniques, achieving a
median absolute error of 1.04 cm and mean absolute error of 1.69 cm.


  In principle a quantum system could be used to simulate another quantum
system. The purpose of such a simulation would be to obtain information about
problems which cannot be simulated with a classical computer due to the
exponential increase of the Hilbert space with the size of the system and which
cannot be measured or controlled in an actual experiment. The system will
interact with the surrounding environment, with the other particles in the
system and be implemented using imperfect controls making it subject to noise.
It has been suggested that noise does not need to be controlled to the same
extent as it must be for general quantum computing. However the effects of
noise in quantum simulations and how to treat them are not completely
understood. In this paper we study an existing quantum algorithm for the
one-dimensional Fano-Anderson model to be simulated using a liquid-state NMR
device. We calculate the evolution of different initial states in the original
model, and then we add interacting spins to simulate a more realistic
situation. We find that states which are entangled with their environment, and
sometimes correlated but not necessarily entangled have an evolution which is
described by maps which are not completely positive. We discuss the conditions
for this to occur and also the implications.


  Dan Rudolph showed that for an amenable group $\Gamma$, the generic
measure-preserving action of $\Gamma$ on a Lebesgue space has zero entropy.
Here this is extended to nonamenable groups. In fact, the proof shows that
every action is a factor of a zero entropy action! This uses the strange
phenomena that in the presence of nonamenability, entropy can increase under a
factor map. The proof uses Seward's recent generalization of Sinai's Factor
Theorem, the Gaboriau-Lyons result and my theorem that for every nonabelian
free group, all Bernoulli shifts factor onto each other.


  In this paper, the problem of delay minimization for federated learning (FL)
over wireless communication networks is investigated. In the considered model,
each user exploits limited local computational resources to train a local FL
model with its collected data and, then, sends the trained FL model parameters
to a base station (BS) which aggregates the local FL models and broadcasts the
aggregated FL model back to all the users. Since FL involves learning model
exchanges between the users and the BS, both computation and communication
latencies are determined by the required learning accuracy level, which affects
the convergence rate of the FL algorithm. This joint learning and communication
problem is formulated as a delay minimization problem, where it is proved that
the objective function is a convex function of the learning accuracy. Then, a
bisection search algorithm is proposed to obtain the optimal solution.
Simulation results show that the proposed algorithm can reduce delay by up to
27.3% compared to conventional FL methods.


  The time-evolution of quantum meson fields in O(4)-linear sigma model is
treated approximately. It is shown that the amplification of the amplitudes of
pion modes with low momenta occurs by means of both the parametric resonance
and the forced oscillation.


  Some basic properties of the solar convection zone are considered and the use
of helioseismology as an observational tool to determine its depth and internal
angular velocity is discussed. Aspects of solar magnetism are described and
explained in the framework of dynamo theory. The main focus is on mean field
theories for the Sun's magnetic field and its differential rotation.


  A quadruple quantum-dot (QQD) cell is proposed as a spin filter. The
transport properties of the QQD cell were studied in linear response regime on
the basis of the equations of motion for retarded Green's functions. The
developed approach allowed us to take into account the influence of both intra-
and interdot Coulomb interactions on carriers' spin polarization. It was shown
that the presence of the insulating bands in the conductance due to the Coulomb
correlations results in the emergence of spin-polarized windows (SPWs) in
magnetic field leading to the high spin polarization. We demonstrated the SPWs
can be effectively manipulated by gate fields and considering the hopping
between central dots in both isotropic and anisotropic regimes.


  The social network analysis (SNA), branch of complex systems can be used in
the construction of multiagent systems. This paper proposes a study of how
social network analysis can assist in modeling multiagent systems, while
addressing similarities and differences between the two theories. We built a
prototype of multi-agent systems for resolution of tasks through the formation
of teams of agents that are formed on the basis of the social network
established between agents. Agents make use of performance indicators to assess
when should change their social network to maximize the participation in teams


  We devise a protocol to build 1D time-dependent quantum walks in 1D
maximizing the spatial spread throughout the procedure. We allow only one of
the physical parameters of the coin-tossing operator to vary, i.e. the angle
$\theta$, such that for $\theta=0$ we have the $\hat\sigma_z$, while for
$\theta=\pi/4$ we obtain the Hadamard gate. The optimal $\theta$ sequences
present non-trivial patterns, with mostly $\theta\approx 0$ alternated with
$\theta\approx \pi/4$ values after increasingly long periods. We provide an
analysis of the entanglement properties, quasi-energy spectrum and survival
probability, providing a full physical picture.


  We prove that the Omega measure, which considers all moments when assessing
portfolio performance, is equivalent to the widely used Sharpe ratio under
jointly elliptic distributions of returns. Portfolio optimization of the Sharpe
ratio is then explored, with an active-set algorithm presented for markets
prohibiting short sales. When asymmetric returns are considered we show that
the Omega measure and Sharpe ratio lead to different optimal portfolios.


  Software fault-proneness prediction is an active research area, with many
factors affecting prediction performance extensively studied. However, the
impact of the learning approach (i.e., the specifics of the data used for
training and the target variable being predicted) on the prediction performance
has not been studied, except for one initial work. This paper explores the
effects of two learning approaches, useAllPredictAll and usePrePredictPost, on
the performance of software fault-proneness prediction, both within-release and
across-releases. The empirical results are based on data extracted from 64
releases of twelve open-source projects. Results show that the learning
approach has a substantial, and typically unacknowledged, impact on the
classification performance. Specifically, using useAllPredictAll leads to
significantly better performance than using usePrePredictPost learning
approach, both within-release and across-releases. Furthermore, this paper
uncovers that, for within-release predictions, this difference in
classification performance is due to different levels of class imbalance in the
two learning approaches. When class imbalance is addressed, the performance
difference between the learning approaches is eliminated. Our findings imply
that the learning approach should always be explicitly identified and its
impact on software fault-proneness prediction considered. The paper concludes
with a discussion of potential consequences of our results for both research
and practice.


  We present a detection of the Sunyaev-Zel'dovich (SZ) decrement associated
with the Luminous Red Galaxy (LRG) sample of the Sloan Digital Sky Survey. The
SZ data come from 148 GHz maps of the equatorial region made by the Atacama
Cosmology Telescope (ACT). The LRG sample is divided by luminosity into four
bins, and estimates for the central Sunyaev-Zel'dovich temperature decrement
are calculated through a stacking process. We detect and account for a bias of
the SZ signal due to weak radio sources. We use numerical simulations to relate
the observed decrement to Y200 and clustering properties to relate the galaxy
luminosity bins to mass. We also use a relation between brightest cluster
galaxy luminosity and cluster mass based on stacked gravitational lensing
measurements to estimate the characteristic halo masses. The masses are found
to be around 1e14 M_sun.


  We derive some entanglement properties of the ground states of two classes of
quantum spin chains described by the Fredkin model, for half-integer spins, and
the Motzkin model, for integer ones. Since the ground states of the two models
are known analytically, we can calculate the entanglement entropy, the
negativity and the quantum mutual information exactly. We show, in particular,
that these systems exhibit long-distance entanglement, namely two disjoint
regions of the chains remain entangled even when the separation is sent to
infinity, i.e. these systems are not affected by decoherence. This strongly
entangled behavior, occurring both for colorful versions of the models (with
spin larger than 1/2 or 1, respectively) and for colorless cases (spin 1/2 and
1), is consistent with the violation of the cluster decomposition property.
Moreover we show that this behavior involves disjoint segments located both at
the edges and in the bulk of the chains.


  We introduce a probabilistic framework for quantifying the semantic
similarity between two groups of embeddings. We formulate the task of semantic
similarity as a model comparison task in which we contrast a generative model
which jointly models two sentences versus one that does not. We illustrate how
this framework can be used for the Semantic Textual Similarity tasks using
clear assumptions about how the embeddings of words are generated. We apply
model comparison that utilises information criteria to address some of the
shortcomings of Bayesian model comparison, whilst still penalising model
complexity. We achieve competitive results by applying the proposed framework
with an appropriate choice of likelihood on the STS datasets.


  This paper presents an image-based visual servo control (IBVS) method for a
first-person-view (FPV) quadrotor to conduct aggressive aerial tracking. There
are three major challenges to maneuvering an underactuated vehicle using IBVS:
(i) finding a visual feature representation that is robust to large rotations
and is suited to be an optimization variable; (ii) keeping the target visible
without sacrificing the robot's agility; and (iii) compensating for the
rotational effects in the detected features. We propose a complete design
framework to address these problems. First, we employ a rotation on $SO(3)$ to
represent a spherical image feature on $S^{2}$ to gain singularity-free and
second-order differentiable properties. To ensure target visibility, we
formulate the IBVS as a nonlinear model predictive control (NMPC) problem with
three constraints taken into account: the robot's physical limits, target
visibility, and time-to-collision (TTC). Furthermore, we propose a novel
attitude-compensation scheme to enable formulating the visibility constraint in
the actual image plane instead of a virtual fix-orientation image plane. It
guarantees that the visibility constraint is valid under large rotations.
Extensive experimental results show that our method can track a fast-moving
target stably and aggressively without the aid of a localization system.


  The elastic $\alpha$-$^{12}$C scattering at low energies for
$l=0,1,2,3,4,5,6$ is studied in effective field theory. We discuss the
construction of the $S$ matrices of elastic $\alpha$-$^{12}$C scattering in
terms of the amplitudes of sub-threshold bound and resonant states of $^{16}$O,
which are calculated from the effective Lagrangian. The parameters appearing in
the $S$ matrices are fitted to the phase shift data below the $p$-$^{15}$N
breakup threshold energy, and we find that the phase shifts are well described
within the theory.


  Due to the fascinating generative performance of text-to-image diffusion
models, growing text-to-3D generation works explore distilling the 2D
generative priors into 3D, using the score distillation sampling (SDS) loss, to
bypass the data scarcity problem. The existing text-to-3D methods have achieved
promising results in realism and 3D consistency, but text-to-4D generation
still faces challenges, including lack of realism and insufficient dynamic
motions. In this paper, we propose a novel method for text-to-4D generation,
which ensures the dynamic amplitude and authenticity through direct supervision
provided by a video prior. Specifically, we adopt a text-to-video diffusion
model to generate a reference video and divide 4D generation into two stages:
static generation and dynamic generation. The static 3D generation is achieved
under the guidance of the input text and the first frame of the reference
video, while in the dynamic generation stage, we introduce a customized SDS
loss to ensure multi-view consistency, a video-based SDS loss to improve
temporal consistency, and most importantly, direct priors from the reference
video to ensure the quality of geometry and texture. Moreover, we design a
prior-switching training strategy to avoid conflicts between different priors
and fully leverage the benefits of each prior. In addition, to enrich the
generated motion, we further introduce a dynamic modeling representation
composed of a deformation network and a topology network, which ensures dynamic
continuity while modeling topological changes. Our method not only supports
text-to-4D generation but also enables 4D generation from monocular videos. The
comparison experiments demonstrate the superiority of our method compared to
existing methods.


  The sensitivity to the $Z^\prime$ couplings of the processes $e^+e^-\to
l^+l^-,\bar{b}b$ and $\bar{c}c$ at the linear collider with $\sqrt{s}=500 GeV$
with initial beam polarization, for typical extended model examples are
studied. To this aim, the suitable integrated, polarized, observables directly
related to the helicity cross sections that carry information on the individual
$Z^\prime$ chiral couplings to fermions are used. We discuss the derivation of
separate, model-independent limits on the couplings in the case of no observed
indirect $Z^\prime$ signal within the expected experimental accuracy. In the
hypothesis that such signals were, indeed, observed we assess the expected
accuracy on the numerical determination of such couplings and the consequent
range of $Z^\prime$ masses where the individual models can be distinguished
from each other as the source of the effect.


  Neural Radiance Fields (NeRF) have shown remarkable performance in learning
3D scenes. However, NeRF exhibits vulnerability when confronted with
distractors in the training images -- unexpected objects are present only
within specific views, such as moving entities like pedestrians or birds.
Excluding distractors during dataset construction is a straightforward
solution, but without prior knowledge of their types and quantities, it becomes
prohibitively expensive. In this paper, we propose PruNeRF, a segment-centric
dataset pruning framework via 3D spatial consistency, that effectively
identifies and prunes the distractors. We first examine existing metrics for
measuring pixel-wise distraction and introduce Influence Functions for more
accurate measurements. Then, we assess 3D spatial consistency using a
depth-based reprojection technique to obtain 3D-aware distraction. Furthermore,
we incorporate segmentation for pixel-to-segment refinement, enabling more
precise identification. Our experiments on benchmark datasets demonstrate that
PruNeRF consistently outperforms state-of-the-art methods in robustness against
distractors.


  We explicitly construct all kink solitons arising in the recent study of
Auzzi, Bolognesi, and Shifman of a monopole confinement problem in ${\cal N}=2$
supersymmetric QCD. In particular, we show that all finite-energy kink solitons
must be BPS.


  The movement of organisms is subject to a multitude of influences of widely
varying character: from the bio-mechanics of the individual, over the
interaction with the complex environment many animals live in, to evolutionary
pressure and energy constraints. As the number of factors is large, it is very
hard to build comprehensive movement models. Even when movement patterns in
simple environments are analysed, the organisms can display very complex
behaviours. While for largely undirected motion or long observation times the
dynamics can sometimes be described by isotropic random walks, usually the
directional persistence due to a preference to move forward has to be accounted
for, e.g., by a correlated random walk. In this paper we generalise these
descriptions to a model in terms of stochastic differential equations of
Langevin type, which we use to analyse experimental search flight data of
foraging bumblebees. Using parameter estimates we discuss the differences and
similarities to correlated random walks. From simulations we generate
artificial bumblebee trajectories which we use as a validation by comparing the
generated ones to the experimental data.


  The main goal of this paper is to prove the following two conjectures for
genus up to two:
  1. Witten's conjecture on the relations between higher spin curves and
Gelfand--Dickey hierarchy.
  2. Virasoro conjecture for target manifolds with conformal semisimple
Frobenius manifolds.
  The main technique used in the proof is the invariance of tautological
equations under loop group action.