  The presence of young stars in the immediate vicinity and strong tidal field
of SgrA* remains unexplained. One currently popular idea for their origin
posits that the stars were bused in by an Intermediate-Mass Black Hole (IMBH)
which has inspiraled into the Galactic Center a few million years ago.
  Yu and Tremaine (2003) have argued that in this case some of the old stars in
the SgrA* cusp would be ejected by hard gravitational collisions with the IMBH.
Here we derive a general expression for the phase-space distribution of the
ejected high-velocity stars, given the distribution function of the stars in
the cusp. We compute it explicitly for the Peebles-Young distribution function
of the cusp, and make a detailed model for the time-dependent ejection of stars
during the IMBH inspiral. We find that (1) the stars are ejected in a burst
lasting a few dynamical friction timescales; if the ejected stars are detected
by Gaia they are likely to be produced by a single inspiral event, (2) if the
inspiral is circular than in the beginning of the burst the velocity vectors of
the ejected stars cluster around the inspiral plane, but rapidly isotropise as
the burst proceeds, (3) if the inspiral is eccentric, then the stars are
ejected in a broad jet roughly perpendicular to the Runge-Lenz vector of the
IMBH orbit. In a typical cusp the orbit will precess with a period of \sim 10^5
years, and the rate of ejection into our part of the Galaxy (as defined by e.g.
the Gaia visibility domain) will be modulated periodically. Gaia, together with
the ground-based follow-up observations, will be able to clock many
high-velocity stars back to their ejection from the Galactic Center, thus
measuring some of the above phenomena. This would provide a clear signature of
the IMBH inspiral in the past 10--20 Myr.


  Formal methods have provided approaches for investigating software
engineering fundamentals and also have high potential to improve current
practices in dependability assurance. In this article, we summarise known
strengths and weaknesses of formal methods. From the perspective of the
assurance of robots and autonomous systems (RAS), we highlight new
opportunities for integrated formal methods and identify threats to the
adoption of such methods. Based on these opportunities and threats, we develop
an agenda for fundamental and empirical research on integrated formal methods
and for successful transfer of validated research to RAS assurance.
Furthermore, we outline our expectations on useful outcomes of such an agenda.


  Nanotransistors typically operate in far-from-equilibrium (FFE) conditions,
that cannot be described neither by drift-diffusion, nor by purely ballistic
models. In carbonbased nanotransistors, source and drain contacts are often
characterized by the formation of Schottky Barriers (SBs), with strong
influence on transport. Here we present a model for onedimensional field-effect
transistors (FETs), taking into account on equal footing both SB contacts and
FFE transport regime. Intermediate transport is introduced within the Buttiker
probe approach to dissipative transport, in which a non-ballistic transistor is
seen as a suitable series of individually ballistic channels. Our model permits
the study of the interplay of SBs and ambipolar FFE transport, and in
particular of the transition between SB-limited and dissipation-limited
transport.


  We introduce an ensemble learning scheme for community detection in complex
networks. The scheme uses a Machine Learning algorithmic paradigm we call
Extremal Ensemble Learning. It uses iterative extremal updating of an ensemble
of network partitions, which can be found by a conventional base algorithm, to
find a node partition that maximizes modularity. At each iteration, core groups
of nodes that are in the same community in every ensemble partition are
identified and used to form a reduced network. Partitions of the reduced
network are then found and used to update the ensemble. The smaller size of the
reduced network makes the scheme efficient. We use the scheme to analyze the
community structure in a set of commonly studied benchmark networks and find
that it outperforms all other known methods for finding the partition with
maximum modularity.


  Theoretical studies of localization, anomalous diffusion and ergodicity
breaking require solving the electronic structure of disordered systems. We use
free probability to approximate the ensemble- averaged density of states
without exact diagonalization. We present an error analysis that quantifies the
accuracy using a generalized moment expansion, allowing us to distinguish
between different approximations. We identify an approximation that is accurate
to the eighth moment across all noise strengths, and contrast this with the
perturbation theory and isotropic entanglement theory.


  In this paper we discuss how to generate inductive invariants for safety
verification of hybrid systems. A hybrid symbolic-numeric method is presented
to compute inequality inductive invariants of the given systems. A numerical
invariant of the given system can be obtained by solving a parameterized
polynomial optimization problem via sum-of-squares (SOS) relaxation. And a
method based on Gauss-Newton refinement and rational vector recovery is
deployed to obtain the invariants with rational coefficients, which exactly
satisfy the conditions of invariants. Several examples are given to illustrate
our algorithm.


  We propose a search for Higgs decays with as many as eight leptons in the
final state. This signal can arise in a simple model with a hidden vector
($A_d$) that gets mass via a hidden scalar ($h_d$) vacuum expectation value.
The 125 GeV Higgs can then decay $H\rightarrow h_d h_d \rightarrow
4A_d\rightarrow 8f$, where $f$ are Standard Model fermions. We recast current
searches and show that a branching ratio of $H\rightarrow h_dh_d$ as large as
10% is allowed. We also describe a dedicated search that could place bounds on
BR($H\rightarrow h_dh_d$) as low as $10^{-5}$ using only 36 fb$^{-1}$ of data,
with significant improvements coming from greater integrated luminosity.


  A fascinating type of symmetry-protected topological states of matter are
topological Kondo insulators, where insulating behavior arises from Kondo
screening of localized moments via conduction electrons, and non-trivial
topology emerges from the structure of the hybridization between the
local-moment and conduction bands. Here we study the physics of Kondo holes,
i.e., missing local moments, in three-dimensional topological Kondo insulators,
using a self-consistent real-space mean-field theory. Such Kondo holes quite
generically induce in-gap states which, for Kondo holes at or near the surface,
hybridize with the topological surface state. In particular, we study the
surface-state quasiparticle interference (QPI) induced by a dilute
concentration of surface Kondo holes and compare this to QPI from conventional
potential scatterers. We treat both strong and weak topological-insulator
phases and, for the latter, specifically discuss the contributions to QPI from
inter-Dirac-cone scattering.


  It is shown that if $\{H_n\}_{n \in \omega}$ is a sequence of groups without
involutions, with $1 < |H_n| \leq 2^{\aleph_0}$, then the topologist's product
modulo the finite words is (up to isomorphism) independent of the choice of
sequence. This contrasts with the abelian setting: if $\{A_n\}_{n \in \omega}$
is a sequence of countably infinite torsion-free abelian groups, then the
isomorphism class of the product modulo sum $\prod_{n \in \omega}
A_n/\bigoplus_{n \in \omega} A_n$ is dependent on the sequence.


  Whereas in standard quantum state tomography one estimates an unknown state
by performing various measurements with known devices, and whereas in detector
tomography one estimates the POVM elements of a measurement device by
subjecting to it various known states, we consider here the case of SPAM (state
preparation and measurement) tomography where neither the states nor the
measurement device are assumed known. For $d$-dimensional systems measured by
$d$-outcome detectors, we find there are at most $d^2(d^2-1)$ "gauge"
parameters that can never be determined by any such experiment, irrespective of
the number of unknown states and unknown devices. For the case $d=2$ we find
new gauge-invariant quantities that can be accessed directly experimentally and
that can be used to detect and describe SPAM errors. In particular, we identify
conditions whose violations detect the presence of correlations between SPAM
errors. From the perspective of SPAM tomography, standard quantum state
tomography and detector tomography are protocols that fix the gauge parameters
through the assumption that some set of fiducial measurements is known or that
some set of fiducial states is known, respectively.


  Operating large-scale scientific facilities often requires fast tuning and
robust control in a high dimensional space. In this paper we introduce a new
physics-informed optimization algorithm based on Gaussian process regression.
Our method takes advantage of the existing domain knowledge in the form of
realizations of a physics model of the observed system. We have applied a
physics-informed Gaussian Process method experimentally at the SPEAR3 storage
ring to demonstrate online accelerator optimization. This method outperforms
Gaussian Process trained on data as well as the standard approach routinely
used for operation, in terms of convergence speed and optimal point. The
proposed method could be applicable to automatic tuning and control of other
complex systems, without a prerequisite for any observed data.


  Nuclear beta decays between (J^pi,T) = (0^+,1) analog states yield the best
value for the Vud element of the Cabibbo-Kobayashi-Maskawa matrix. Current
world data establish the corrected Ft values of 14 separate superallowed
transitions to a precision of order 0.1% or better. The validity of the small
theoretical correction terms is confirmed by excellent consistency among the 14
Ft values and by recent measurements that compare pairs of mirror superallowed
transitions. With consistency established, the results now yield |Vud| =
0.97420(21). This value is consistent with the considerably less precise
results obtained from beta decays of the neutron, the pion and T=1/2 mirror
nuclei, which are hampered by experimental challenges.


  Detecting changes in data streams is a vital task in many applications. There
is increasing interest in changepoint detection in the online setting, to
enable real-time monitoring and support prompt responses and informed
decision-making. Many approaches assume stationary sequences before
encountering an abrupt change in the mean or variance. Notably less attention
has focused on the challenging case where the monitored sequences exhibit
trend, periodicity and seasonality. Dynamic mode decomposition is a data-driven
dimensionality reduction technique that extracts the essential components of a
dynamical system. We propose a changepoint detection method that leverages this
technique to sequentially model the dynamics of a moving window of data and
produce a low-rank reconstruction. A change is identified when there is a
significant difference between this reconstruction and the observed data, and
we provide theoretical justification for this approach. Extensive simulations
demonstrate that our approach has superior detection performance compared to
other methods for detecting small changes in mean, variance, periodicity, and
second-order structure, among others, in data that exhibits seasonality.
Results on real-world datasets also show excellent performance compared to
contemporary approaches.


  We use the N-terminal scheme for studying the edge state transportin in
two-dimensional topological insulators. We find the universal nonlocal response
in the ballistic transport approach. This macroscopic exhibition of the
topological order offers different areas for applications.


  We discuss the aspects of the little Higgs model with the $SU(4)_L \times
U(1)_X$ electroweak gauge group as an alternative solution to the naturalness
and fine-tuning issues. We introduce anomaly-free fermion spectra, and present
their interactions with the physical gauge bosons. We also discuss some
phenomenological implications of these fermions and the extra gauge bosons
based on recent experimental results.


  The flow of cerebrospinal fluid through the perivascular spaces of the brain
is believed to play a crucial role in eliminating toxic waste proteins. While
the driving forces of this flow have been enigmatic, experiments have shown
that arterial wall motion is central. In this work, we present a network model
for simulating pulsatile fluid flow in perivascular networks. We establish the
well-posedness of this model in the primal and dual mixed variational settings,
and show how it can be discretized using mixed finite elements. Further, we
utilize this model to investigate fundamental questions concerning the physical
mechanisms governing perivascular fluid flow. Notably, our findings reveal that
arterial pulsations can induce directional flow in branching perivascular
networks.


  The ability to reduce energy loss at semiconductor surfaces through
passivation or surface field engineering has become an essential step in the
manufacturing of efficient photovoltaic (PV) and optoelectronic devices.
Similarly, surface modification of emerging halide perovskites with quasi-2D
heterostructures is now ubiquitous to achieve PV power conversion efficiencies
(PCEs) > 22% and has enabled single-junction PV devices to reach 25.7%, yet a
fundamental understanding to how these treatments function is still generally
lacking. This has established a bottleneck for maximizing beneficial
improvements as no concrete selection and design rules currently exist. Here we
uncover a new type of tunable passivation strategy and mechanism found in
perovskite PV devices that were the first to reach the > 25% PCE milestone,
which is enabled by surface treating a bulk perovskite layer with hexylammonium
bromide (HABr). We uncover the simultaneous formation of an iodide-rich 2D
layer along with a Br halide gradient achieved through partial halide exchange
that extends from defective surfaces and grain boundaries into the bulk layer.
We demonstrate and directly visualize the tunability of both the 2D layer
thickness, halide gradient, and band structure using a unique combination of
depth-sensitive nanoscale characterization techniques. We show that the
optimization of this interface can extend the charge carrier lifetime to values
> 30 {\mu}s, which is the longest value reported for a direct bandgap
semiconductor (GaAs, InP, CdTe) over the past 50 years. Importantly, this work
reveals an entirely new strategy and knob for optimizing and tuning
recombination and charge transport at semiconductor interfaces and will likely
establish new frontiers in achieving the next set of perovskite device
performance records.


  Let $p$ be a large prime, and let $k\ll \log p$. A new proof of the existence
of any pattern of $k$ consecutive quadratic residues and quadratic nonresidues
is introduced in this note. Further, an application to the least quadratic
nonresidues $n_p$ modulo $p$ shows that $n_p\ll (\log p)(\log \log p)$.


  We explore further the idea that the lattice QCD data for hadron properties
in the region m_\pi^2 > 0.2 GeV^2 can be described by the constituent quark
model. This leads to a natural explanation of the fact that nucleon excited
states are generally stable for pion masses greater than their physical
excitation energies. Finally we apply these same ideas to the problem of how
pentaquarks might behave in lattice QCD, with interesting conclusions.


  Biphoton process is an essential benchmark for quantum information science
and technologies, while great efforts have been made to improve the coherence
of the system for better quantum correlations. Nevertheless, we find that the
non-Hermitian features induced by the atomic quantum interference could be well
employed for the direct control of entanglement. We report the demonstration of
exceptional point (EP) in biphotons by measuring the light-atom interaction as
a natural non-Hermitian system, in which the electromagnetically induced
transparency regime provides a powerful mechanism to precisely tune the
non-Hermitian coupling strength. Such biphoton correlation is tuned within an
unprecedented large range from Rabi oscillation to
antibunching-exponential-decay, also indicating high-dimensional entanglement
within the strong and weak light-matter coupling regimes. The EP at the
transition point between the two regimes is clearly observed with the biphoton
quantum correlation measurements, exhibiting a single exponential decay and
manifesting the coalesced single eigenstate. Our results provide a unique
method to realize the controllability of natural non-Hermitian processes
without the assistance of artificial photonic structures, and paves the way for
quantum control by manipulating the non-Hermitian features of the light-matter
interaction.


  In this paper we propose a new refined shear deformation plate theory which
possesses a series of desirable features, the most salient of which are as
follows: (i) The loads, which are generally considered to be applied on the
middle surface of the plate, act on the upper surface of the plate; (ii) The
equations are applicable to the calculation of the stresses in isotropic plates
and provide the same order of accuracy as several theories with second order
shear deformation effects; (iii) It constitutes a theory, in the sense defined
by Love, since it gives easy expressions for application to problems in
different fields in architecture and civil engineering


  Study and design of magneto-optically active single point defects in
semiconductors are rapidly growing fields due to their potential in quantum bit
and single photon emitter applications. Detailed understanding of the
properties of candidate defects is essential for these applications, and
requires the identification of the defects microscopic configuration and
electronic structure. Multi-component semiconductors often host two or more
non-equivalent configurations of point defects. These configurations generally
exhibit similar electronic structure and basic functionalities, however, they
differ in details that are of great importance whenever single defect
applications are considered. Identification of non-equivalent configurations of
point defects is thus essential for successful single defect manipulation and
application. A promising way to identify defects is via comparison of
experimental measurements and results of first-principle calculations. We
investigate a possibility to produce accurate ab initio data for zero-phonon
lines and hyperfine coupling parameters that are required for systematic
quantum bit search. We focus on properties relevant for the possible use of the
divacancy defect in quantum bits in 4H-SiC. We provide a decisive
identification of divacancy configurations in 4H-SiC and clarify differences in
prior predictions of 4H-SiC divacancy zero-phonon photoluminescence lines.


  Due to the difficulty of obtaining ground-truth labels, learning from
virtual-world datasets is of great interest for real-world applications like
semantic segmentation. From domain adaptation perspective, the key challenge is
to learn domain-agnostic representation of the inputs in order to benefit from
virtual data. In this paper, we propose a novel trident-like architecture that
enforces a shared feature encoder to satisfy confrontational source and target
constraints simultaneously, thus learning a domain-invariant feature space.
Moreover, we also introduce a novel training pipeline enabling self-induced
cross-domain data augmentation during the forward pass. This contributes to a
further reduction of the domain gap. Combined with a self-training process, we
obtain state-of-the-art results on benchmark datasets (e.g. GTA5 or Synthia to
Cityscapes adaptation). Code and pre-trained models are available at
https://github.com/HMRC-AEL/TridentAdapt


  In this paper we discuss the existence and regularity of solutions of
strongly indefinite systems involving fractional elliptic operators on a smooth
bounded domain $\Omega$ in $\R^n$.


  We give a general description of gauge symmetry breaking using vertical and
remainder fluxes in 4D F-theory models. The fluxes can break a geometric gauge
group to a smaller group and induce chiral matter, even when the larger group
admits no chiral matter representations. We focus specifically on applications
to realizations of the Standard Model gauge group and chiral matter spectrum
through breaking of rigid exceptional gauge groups $E_7, E_6$, which are
ubiquitous in the 4D F-theory landscape. Supplemented by an intermediate
$\mathrm{SU}(5)$ group, these large classes of models give natural
constructions of Standard Model-like theories with small numbers of generations
of matter in F-theory.


  We study the change of the effective fine structure constant in the
cosmological models of a scalar field with a non-vanishing coupling to the
electromagnetic field. Combining cosmological data and terrestrial observations
we place empirical constraints on the size of the possible coupling and explore
a large class of models that exhibit tracking behavior. The change of the fine
structure constant implied by the quasar absorption spectra together with the
requirement of tracking behavior impose a lower bound of the size of this
coupling. Furthermore, the transition to the quintessence regime implies a
narrow window for this coupling around $10^{-5}$ in units of the inverse Planck
mass. We also propose a non-minimal coupling between electromagnetism and
quintessence which has the effect of leading only to changes of alpha
determined from atomic physics phenomena, but leaving no observable
consequences through nuclear physics effects. In doing so we are able to
reconcile the claimed cosmological evidence for a changing fine structure
constant with the tight constraints emerging from the Oklo natural nuclear
reactor.


  We consider the classification problem for finite-dimensional irreducible
representations of the Yangians associated with the orthosymplectic Lie
superalgebras ${\frak{osp}}_{2n|2m}$ with $n\geqslant 2$. We give necessary
conditions for an irreducible highest weight representation to be
finite-dimensional. We conjecture that these conditions are also sufficient and
prove the conjecture for a class of representations with linear highest
weights. The arguments are based on a new type of odd reflections for the
Yangian associated with ${\frak{osp}}_{2|2}$. In the Appendix, we construct an
isomorphism between the Yangians associated with the Lie superalgebras
${\frak{osp}}_{2|2}$ and ${\frak{gl}}_{1|2}$.


  We study Online Convex Optimization in the unbounded setting where neither
predictions nor gradient are constrained. The goal is to simultaneously adapt
to both the sequence of gradients and the comparator. We first develop
parameter-free and scale-free algorithms for a simplified setting with hints.
We present two versions: the first adapts to the squared norms of both
comparator and gradients separately using $O(d)$ time per round, the second
adapts to their squared inner products (which measure variance only in the
comparator direction) in time $O(d^3)$ per round. We then generalize two prior
reductions to the unbounded setting; one to not need hints, and a second to
deal with the range ratio problem (which already arises in prior work). We
discuss their optimality in light of prior and new lower bounds. We apply our
methods to obtain sharper regret bounds for scale-invariant online prediction
with linear models.


  Solution of some boundary value problems and initial problems in unique ball
leads to the convergence and sumability problems of Fourier series of given
function by eigenfunctions of Laplace operator on a sphere - spherical
harmonics. Such a series are called as Fourier-Laplace series on sphere. There
are a number of works devoted investigation of these expansions in different
topologies and for the functions from the various functional spaces. In the
present work we consider only localization problems in both usual and
generalized (almost everywhere localization) senses in the classes of summable
functions. We use Chezaro means of the partial sums for study of summability
problems. In order to prove the main theorems we obtaine estimations for so
called maximal operator estimating it by Hardy-Littlwood's maximal function.
Significance of this function is that it majors of many important operators of
mathematical physics. For instance, Hardy-Littlewood's maximal function majors
Poisson's integral in the space.


  We study small-scale brightenings in Ca II 8542 \AA\ line-core images to
determine their nature and effect on localized heating and mass transfer in
active regions. High-resolution 2D spectroscopic observations of an active
region in the Ca II 8542 \AA\ line were acquired with the GFPI attached to the
1.5-meter GREGOR telescope. Inversions of the spectra were carried out using
NICOLE. We identified three brightenings of sizes up to 2"x2". We found
evidence that the brightenings belonged to the footpoints of a microflare (MF).
The properties of the observed brightenings disqualified the scenarios of
Ellerman bombs or IRIS bombs. However, this MF shared some common properties
with flaring active-region fibrils or flaring arch filaments (FAFs): (1) FAFs
and MFs are both apparent in chromospheric and coronal layers according to the
AIA channels, and (2) both show flaring arches with lifetimes of about 3.0-3.5
min and lengths of about 20". The inversions revealed heating by 600 K at the
footpoint location in the ambient chromosphere during the impulsive phase.
Connecting the footpoints, a dark filamentary structure appeared in the Ca II
line-core images. Before the start of the MF, the spectra of this structure
already indicated average blueshifts, meaning upward motions of the plasma
along the LOS. During the impulsive phase, these velocities increased up to
-2.2 km/s. Downflows dominated at the footpoints. However, in the upper
photosphere, slight upflows occurred during the impulsive phase. Hence,
bidirectional flows are present in the footpoints of the MF. Conclusions: We
detected Ca II brightenings that coincided with the footpoint location of an
MF. The MF event led to a rise of plasma in the upper photosphere, both before
and during the impulsive phase. Excess mass, previously raised to at most
chromospheric layers, slowly drained downward along arches toward the
footpoints of the MF.


  We present a determination of the LEP beam energy using "radiative return"
fermion-pair events recorded at centre-of-mass energies from 183 GeV to 209
GeV. We find no evidence of a disagreement between the OPAL data and the LEP
Energy Workings Group's standard calibration. Including the energy- averaged 11
MeV uncertainty in the standard determination, the beam energy we obtain from
the OPAL data is higher than that obtained from the LEP calibration by
0+-34(stat.)+-27(syst.)MeV


  We show that the onset and rise of QCD mini-jets provide the dynamical
mechanism behind the appearance of a soft edge in pp collisions around ISR
energies and thus such a soft edge is built in our mini-jet model with soft
gluon resummation. Here the model is optimized for LHC at $\sqrt{s} = 7, 8 TeV$
and predictions made for higher LHC and cosmic ray energies. Further, we
provide a phenomenological picture to discuss the breakup of the total
cross-section into its elastic, uncorrelated and correlated inelastic pieces in
the framework of a one-channel eikonal function.


  The challenges in operational flood forecasting lie in producing reliable
forecasts given constrained computational resources and within processing times
that are compatible with near-real-time forecasting. Flood hydrodynamic models
exploit observed data from gauge networks, e.g. water surface elevation (WSE)
and/or discharge that describe the forcing time-series at the upstream and
lateral boundary conditions of the model. A chained hydrologic-hydraulic model
is thus interesting to allow extended lead time forecasts and overcome the
limits of forecast when using only observed gauge measurements. This research
work focuses on comprehensively reducing the uncertainties in the model
parameters, hydraulic state and especially the forcing data in order to improve
the overall flood reanalysis and forecast performance. It aims at assimilating
two main complementary EO data sources, namely in-situ WSE and SAR-derived
flood extent observations.


  3D visual grounding aims to locate the referred target object in 3D point
cloud scenes according to a free-form language description. Previous methods
mostly follow a two-stage paradigm, i.e., language-irrelevant detection and
cross-modal matching, which is limited by the isolated architecture. In such a
paradigm, the detector needs to sample keypoints from raw point clouds due to
the inherent properties of 3D point clouds (irregular and large-scale), to
generate the corresponding object proposal for each keypoint. However, sparse
proposals may leave out the target in detection, while dense proposals may
confuse the matching model. Moreover, the language-irrelevant detection stage
can only sample a small proportion of keypoints on the target, deteriorating
the target prediction. In this paper, we propose a 3D Single-Stage Referred
Point Progressive Selection (3D-SPS) method, which progressively selects
keypoints with the guidance of language and directly locates the target.
Specifically, we propose a Description-aware Keypoint Sampling (DKS) module to
coarsely focus on the points of language-relevant objects, which are
significant clues for grounding. Besides, we devise a Target-oriented
Progressive Mining (TPM) module to finely concentrate on the points of the
target, which is enabled by progressive intra-modal relation modeling and
inter-modal target mining. 3D-SPS bridges the gap between detection and
matching in the 3D visual grounding task, localizing the target at a single
stage. Experiments demonstrate that 3D-SPS achieves state-of-the-art
performance on both ScanRefer and Nr3D/Sr3D datasets.


  Future service robots working in human environments, such as kitchens, will
face situations where they need to improvise. The usual tool for a given task
might not be available and the robot will have to use some substitute tool. The
robot needs to select an appropriate alternative tool from the candidates
available, and also needs to know where to grasp it, how to orient it and what
part to use as the end-effector. We present a system which takes as input a
candidate tool's point cloud and weight, and outputs a score for how effective
that tool is for a task, and how to use it. Our key novelty is in taking a
task-driven approach, where the task exerts a top-down influence on how low
level vision data is interpreted. This facilitates the type of 'everyday
creativity' where an object such as a wine bottle could be used as a rolling
pin, because the interpretation of the object is not fixed in advance, but
rather results from the interaction between the bottom-up and top-down
pressures at run-time. The top-down influence is implemented by transfer: prior
knowledge of geometric features that make a tool good for a task is used to
seek similar features in a candidate tool. The prior knowledge is learned by
simulating Web models performing the tasks. We evaluate on a set of fifty
household objects and five tasks. We compare our system with the closest one in
the literature and show that we achieve significantly better results


  Eigenmode coalescence imparts remarkable properties to non-hermitian time
evolution, culminating in a purely non-hermitian spectral degeneracy known as
an exceptional point (EP). Here, we revisit time evolution at the EP and
classify two-level non-hermitian Hamiltonians in terms of the M\"obius group.
We then leverage that classification to study dynamical EP encircling, by
applying it to periodically-modulated (Floquet) Hamiltonians. This reveals that
Floquet non-hermitian systems exhibit rich physics whose complexity is not
captured by an EP-encircling rule. For example, Floquet EPs can occur without
encircling and vice-versa. Instead, we show that the elaborate interplay
between non-hermitian and modulation instabilities is better understood through
the lens of parametric resonance.


  The ultimate miniaturization of any optical system relies on the reduction or
removal of free-space gaps between optical elements. Recently, nonlocal flat
optic components named spaceplates were introduced to effectively compress
space for light propagation. However, space compression over the visible
spectrum remains beyond the reach of current spaceplate designs due to their
inherently limited operating bandwidth and functional inefficiencies in the
visible range. Here, we introduce multi-color spaceplates performing achromatic
space compression at three distinct color channels across the visible spectrum
to markedly miniaturize color imaging systems. In this approach, we first
design monochromatic spaceplates with high compression factors and high
transmission amplitudes at visible wavelengths based on a scalable structure
and dielectric materials widely used in the fabrication of meta-optical
components. We then show that the dispersion-engineered combination of
monochromatic spaceplates with suitably designed transmission responses forms
multi-color spaceplates that function achromatically. The proposed multi-color
spaceplates, composed of amorphous titanium dioxide and silicon dioxide layers,
efficiently replace free-space volumes with compression ratios as high as 4.6,
beyond what would be achievable by a continuously broadband spaceplate made of
the same materials. Our strategy for designing monochromatic and multi-color
spaceplates along with the presented results show that strong space-compression
effects can be achieved in the visible range, and may ultimately enable a new
generation of ultra-thin optical devices for various applications.


  Single-view 3D object reconstruction has seen much progress, yet methods
still struggle generalizing to novel shapes unseen during training. Common
approaches predominantly rely on learned global shape priors and, hence,
disregard detailed local observations. In this work, we address this issue by
learning a hierarchy of priors at different levels of locality from ground
truth input depth maps. We argue that exploiting local priors allows our method
to efficiently use input observations, thus improving generalization in visible
areas of novel shapes. At the same time, the combination of local and global
priors enables meaningful hallucination of unobserved parts resulting in
consistent 3D shapes. We show that the hierarchical approach generalizes much
better than the global approach. It generalizes not only between different
instances of a class but also across classes and to unseen arrangements of
objects.


  We study scalar-linear and vector-linear solutions of the generalized
combination network. We derive new upper and lower bounds on the maximum number
of nodes in the middle layer, depending on the network parameters and the
alphabet size. These bounds improve and extend the parameter range of known
bounds. Using these new bounds we present a lower bound and an upper bound on
the gap in the alphabet size between optimal scalar-linear and optimal
vector-linear network coding solutions. For a fixed network structure, while
varying the number of middle-layer nodes $r$, the asymptotic behavior of the
upper and lower bounds shows that the gap is in $\Theta(\log(r))$.


  Dynamical models are often corrupted by model uncertainties, external
disturbances, and measurement noise. These factors affect the performance of
model-based observers and as a result, affect the closed-loop performance.
Therefore, it is critical to develop robust model-based estimators that
reconstruct both the states and the model disturbances while mitigating the
effect of measurement noise in order to ensure good system monitoring and
closed-loop performance when designing controllers. In this article, a robust
step by step non-asymptotic observer for triangular nonlinear systems for the
joint estimation of the state and the disturbance is developed. The proposed
approach provides a sequential estimation of the states and the disturbance in
finite time using smooth modulating functions. The robustness of the proposed
observer is both in the sense of model disturbances and measurement noise. In
fact, the structure of triangular systems combined with the modulating
function-based method allows the estimation of the states independently of
model disturbances and the integral operator involved in the modulating
function-based method mitigates the noise. Additionally, the modulating
function method shifts the derivative from the noisy output to the smooth
modulating function which strengthens its robustness properties. The
applicability of the proposed modulating function-based estimator is
illustrated in numerical simulations and compared to a second-order sliding
mode super twisting observer under different measurement noise levels.


  Given a simplicial idempotent augmented endofunctor $F$ on a simplicial
combinatorial model category $M$, under the assumption of Vopenka's principle,
we exhibit a set $A$ of cofibrant objects in $M$ such that $F$ is equivalent to
$\CW_A$, the cellularization with respect to $A$.


  We present analytical and numerical studies of the behaviour of the
$\alpha$-Renyi entropies in the Toric code in presence of several types of
perturbations aimed at studying the simulability of these perturbations to the
parent Hamiltonian using local operations and classical communications (LOCC) -
a property called local-convertibility. In particular, the derivatives, with
respect to the perturbation parameter, present different signs for different
values of $\alpha$ within the topological phase. From the information-theoretic
point of view, this means that such ground states cannot be continuously
deformed within the topological phase by means of catalyst assisted local
operations and classical communications (LOCC). Such LOCC differential
convertibility is on the other hand always possible in the trivial disordered
phase. The non-LOCC convertibility is remarkable because it can be computed on
a system whose size is independent of correlation length. This method can
therefore constitute an experimentally feasible witness of topological order.


  This paper derives a \emph{distributed} Kalman filter to estimate a sparsely
connected, large-scale, $n-$dimensional, dynamical system monitored by a
network of $N$ sensors. Local Kalman filters are implemented on the
($n_l-$dimensional, where $n_l\ll n$) sub-systems that are obtained after
spatially decomposing the large-scale system. The resulting sub-systems
overlap, which along with an assimilation procedure on the local Kalman
filters, preserve an $L$th order Gauss-Markovian structure of the centralized
error processes. The information loss due to the $L$th order Gauss-Markovian
approximation is controllable as it can be characterized by a divergence that
decreases as $L\uparrow$. The order of the approximation, $L$, leads to a lower
bound on the dimension of the sub-systems, hence, providing a criterion for
sub-system selection. The assimilation procedure is carried out on the local
error covariances with a distributed iterate collapse inversion (DICI)
algorithm that we introduce. The DICI algorithm computes the (approximated)
centralized Riccati and Lyapunov equations iteratively with only local
communication and low-order computation. We fuse the observations that are
common among the local Kalman filters using bipartite fusion graphs and
consensus averaging algorithms. The proposed algorithm achieves full
distribution of the Kalman filter that is coherent with the centralized Kalman
filter with an $L$th order Gaussian-Markovian structure on the centralized
error processes. Nowhere storage, communication, or computation of
$n-$dimensional vectors and matrices is needed; only $n_l \ll n$ dimensional
vectors and matrices are communicated or used in the computation at the
sensors.


  Recently issued data privacy regulations like GDPR (General Data Protection
Regulation) grant individuals the right to be forgotten. In the context of
machine learning, this requires a model to forget about a training data sample
if requested by the data owner (i.e., machine unlearning). As an essential step
prior to machine unlearning, it is still a challenge for a data owner to tell
whether or not her data have been used by an unauthorized party to train a
machine learning model. Membership inference is a recently emerging technique
to identify whether a data sample was used to train a target model, and seems
to be a promising solution to this challenge. However, straightforward adoption
of existing membership inference approaches fails to address the challenge
effectively due to being originally designed for attacking membership privacy
and suffering from several severe limitations such as low inference accuracy on
well-generalized models. In this paper, we propose a novel membership inference
approach inspired by the backdoor technology to address the said challenge.
Specifically, our approach of Membership Inference via Backdooring (MIB)
leverages the key observation that a backdoored model behaves very differently
from a clean model when predicting on deliberately marked samples created by a
data owner. Appealingly, MIB requires data owners' marking a small number of
samples for membership inference and only black-box access to the target model,
with theoretical guarantees for inference results. We perform extensive
experiments on various datasets and deep neural network architectures, and the
results validate the efficacy of our approach, e.g., marking only 0.1% of the
training dataset is practically sufficient for effective membership inference.


  In the evolving landscape of terahertz communication, the behavior of
channels within indoor environments, particularly through glass doors, has
garnered significant attention. This paper comprehensively investigates
terahertz channel performance under such conditions, employing a measurement
setup operational between 113 and 170 GHz. Analyzing scenarios frequently
induced by human activity and environmental factors, like door movements, we
established a comprehensive theoretical model. This model seamlessly integrates
transmission, reflection, absorption, and diffraction mechanisms, leveraging
the Fresnel formula, multi-layer transmission paradigm, and knife-edge
diffraction theory. Our experimental results and theoretical predictions
harmoniously align, revealing intricate dependencies, such as increased power
loss at higher frequencies and larger incident angles. Furthermore, door
interactions, whether opening or oscillations, significantly impact the
terahertz channel. Notably, door edges lead to a power blockage surpassing the
transmission loss of the glass itself but remaining inferior to metallic handle
interferences. This paper's insights are pivotal for the design and fabrication
of terahertz communication systems within indoor settings, pushing the
boundaries of efficient and reliable communication.


  It is demonstrated theoretically that the circularly polarized irradiation of
two-dimensional conducting systems can produce the composite bosons consisting
of two electrons with different effective masses (different charge carriers),
which are stable due to the Fermi sea of conduction electrons. As a result, the
optically induced mixture of paired electrons and normal conduction electrons
(the hybrid Bose-Fermi system) appears. Elementary excitations in such a hybrid
system are analyzed and possible manifestations of the light-induced electron
pairing are discussed for semiconductor quantum wells.


  To fully leverage the multi-task optimization paradigm for accelerating the
solution of expensive scheduling problems, this study has effectively tackled
three vital concerns. The primary issue is identifying auxiliary tasks that
closely resemble the original expensive task. We suggested a sampling strategy
based on job importance, creating a compact matrix by extracting crucial rows
from the entire problem specification matrix of the expensive task. This matrix
serves as an economical auxiliary task. Mathematically, we proved that this
economical auxiliary task bears similarity to its corresponding expensive task.
The subsequent concern revolves around making auxiliary tasks more
cost-effective. We determined the sampling proportions for the entire problem
specification matrix through factorial design experiments, resulting in a more
compact auxiliary task. With a reduced search space and shorter function
evaluation time, it can rapidly furnish high-quality transferable information
for the primary task. The last aspect involves designing transferable deep
information from auxiliary tasks. We regarded the job priorities in the (sub-)
optimal solutions to the economical auxiliary task as transferable invariants.
By adopting a partial solution patching strategy, we augmented specificity
knowledge onto the common knowledge to adapt to the target expensive task. The
strategies devised for constructing task pairs and facilitating knowledge
transfer, when incorporated into various evolutionary multitasking algorithms,
were utilized to address expensive instances of permutation flow shop
scheduling. Extensive experiments and statistical comparisons have validated
that, with the collaborative synergy of these strategies, the performance of
evolutionary multitasking algorithms is significantly enhanced in handling
expensive scheduling tasks.


  We study Dirac commutators of canonical variables on D-branes with a constant
Neveu-Schwarz 2-form field by using the Dirac constraint quantization method,
and point out some subtleties appearing in previous works in analyzing
constraint structure of the brane system. Overcoming some ad hoc procedures, we
obtain desirable noncommutative coordinates exactly compatible with the result
of the conformal field theory in recent literatures. Furthermore, we find
interesting commutator relations of other canonical variables.


  Let $A$ be an unital alternative $*$-algebra. Assume that $A$ contains a
nontrivial symmetric idempotent element $e$ which satisfies $xA \cdot e = 0$
implies $x = 0$ and $xA \cdot (1_A - e) = 0$ implies $x = 0$. In this paper, it
is shown that $\Phi$ is a nonlinear $*$-Jordan-type derivation on A if and only
if $\Phi$ is an additive $*$-derivation. As application, we get a result on
alternative $W^{*}$-algebras.


  We present a first study of the charmonium spectrum on N_f=2 dynamical,
anisotropic lattices. We take advantage of all-to-all quark propagators to
build spatially extended interpolating operators to increase the overlap with
states not easily accessible with point propagators such as radially excited
states of eta_c, psi, and chi_c, D-waves and hybrid states.


  In the present paper we give a historical account -ranging from classical to
modern results- of the problem of rolling two Riemannian manifolds one on the
other, with the restrictions that they cannot instantaneously slip or spin one
with respect to the other. On the way we show how this problem has profited
from the development of intrinsic Riemannian geometry, from geometric control
theory and sub-Riemannian geometry. We also mention how other areas -such as
robotics and interpolation theory- have employed the rolling model.


  Semi-supervised video object segmentation has made significant progress on
real and challenging videos in recent years. The current paradigm for
segmentation methods and benchmark datasets is to segment objects in video
provided a single annotation in the first frame. However, we find that
segmentation performance across the entire video varies dramatically when
selecting an alternative frame for annotation. This paper address the problem
of learning to suggest the single best frame across the video for user
annotation-this is, in fact, never the first frame of video. We achieve this by
introducing BubbleNets, a novel deep sorting network that learns to select
frames using a performance-based loss function that enables the conversion of
expansive amounts of training examples from already existing datasets. Using
BubbleNets, we are able to achieve an 11% relative improvement in segmentation
performance on the DAVIS benchmark without any changes to the underlying method
of segmentation.


  We consider a special class of linear and quadratic Poisson brackets related
to ODE systems with matrix variables. We investigate general properties of such
brackets, present an example of a compatible pair of quadratic and linear
brackets and found the corresponding hierarchy of integrable models, which
generalizes the two-component Manakov's matrix system in the case of arbitrary
number of matrices.


  We present the star cluster catalogs for 17 dwarf and irregular galaxies in
the $HST$ Treasury Program "Legacy ExtraGalactic UV Survey" (LEGUS). Cluster
identification and photometry in this subsample are similar to that of the
entire LEGUS sample, but special methods were developed to provide robust
catalogs with accurate fluxes due to low cluster statistics. The colors and
ages are largely consistent for two widely used aperture corrections, but a
significant fraction of the clusters are more compact than the average training
cluster. However, the ensemble luminosity, mass, and age distributions are
consistent suggesting that the systematics between the two methods are less
than the random errors. When compared with the clusters from previous dwarf
galaxy samples, we find that the LEGUS catalogs are more complete and provide
more accurate total fluxes. Combining all clusters into a composite dwarf
galaxy, we find that the luminosity and mass functions can be described by a
power law with the canonical index of $-2$ independent of age and global SFR
binning. The age distribution declines as a power law, with an index of
$\approx-0.80\pm0.15$, independent of cluster mass and global SFR binning. This
decline of clusters is dominated by cluster disruption since the combined star
formation histories and integrated-light SFRs are both approximately constant
over the last few hundred Myr. Finally, we find little evidence for an
upper-mass cutoff ($<2\sigma$) in the composite cluster mass function, and can
rule out a truncation mass below $\approx10^{4.5}$M$_{\odot}$ but cannot rule
out the existence of a truncation at higher masses.


  As robotics systems become more distributed, the communications between
different robot modules play a key role for the reliability of the overall
robot control. In this paper, we present a study of the Linux communication
stack meant for real-time robotic applications. We evaluate the real-time
performance of UDP based communications in Linux on multi-core embedded devices
as test platforms. We prove that, under an appropriate configuration, the Linux
kernel greatly enhances the determinism of communications using the UDP
protocol. Furthermore, we demonstrate that concurrent traffic disrupts the
bounded latencies and propose a solution by separating the real-time
application and the corresponding interrupt in a CPU.


  The resonant substructure of the decay $D^{0} \rightarrow \pi^{+} \pi^{-}
\pi^{+} \pi^{-}$ is studied by performing a full five-dimensional amplitude
analysis. Preliminary results based on data collected by the CLEO-c detector
are presented. This is the largest dataset of $D^{0} \rightarrow \pi^{+}
\pi^{-} \pi^{+} \pi^{-}$ decays analysed in this way to-date. The two most
significant contributions are $D^{0} \rightarrow a_{1}(1260)^{+} \pi^{-}$ and
$D^{0} \rightarrow \rho(770)^{0}\rho(770)^{0}$. The line shape, mass and width
of the $a_{1}(1260)$ resonance are determined, and model-independent studies of
the line shapes of several resonant contributions are preformed.


  The discovery of supernovae associated with long-duration gamma ray burst
observations is primary evidence that the progenitors of these outbursts are
massive stars. One of the principle mysteries in understanding these
progenitors has been the fact that all of these gamma-ray burst associated
supernovae are type Ic supernovae, with no evidence of helium in the stellar
atmosphere. Many studies have focused on whether or not this helium is simply
hidden from spectral analyses. In this paper, we show results from recent
stellar models using new convection algorithms based on our current
understanding of stellar mixing. We demonstrate that enhanced convection may
lead to severe depletion of stellar helium layers, suggesting that the helium
is not observed simply because it is not in the star. We also present
light-curves and spectra of these compact helium-depleted stars, compared to
models with more conventional helium layers.


  This paper proves a combinatorial rule giving all maximal and minimal
partitions $\lambda$ such that the Schur function $s_\lambda$ appears in a
plethysm of two arbitrary Schur functions. Determining the decomposition of
these plethysms has been identified by Stanley as a key open problem in
algebraic combinatorics. As corollaries we prove three conjectures of Agaoka on
the partitions labelling the lexicographically greatest and least Schur
functions appearing in an arbitrary plethysm. We also show that the
multiplicity of the Schur function labelled by the lexicographically least
constituent may be arbitrarily large. The proof is carried out in the symmetric
group and gives an explicit non-zero homomorphism corresponding to each maximal
or minimal partition.


  Most explainable AI (XAI) techniques are concerned with the design of
algorithms to explain the AI's decision. However, the data that is used to
train these algorithms may contain features that are often incomprehensible to
an end-user even with the best XAI algorithms. Thus, the problem of
explainability has to be addressed starting right from the data creation step.
In this paper, we studied this problem considering the use-case of explaining
loan denials to end-users as opposed to AI engineers or domain experts.
Motivated by the lack of datasets that are representative of user-friendly
explanations, we build the first-of-its-kind dataset that is representative of
user-friendly explanations for loan denials. The paper shares some of the
insights gained in curating the dataset. First, existing datasets seldom
contain features that end users consider as acceptable in understanding a
model's decision. Second, understanding of the explanation's context such as
the human-in-the-loop seeking the explanation, and the purpose for which an
explanation is sought, aids in the creation of user-friendly datasets. Thus,
our dataset, which we call Xnet, also contains explanations that serve
different purposes: those that educate the loan applicants, and help them take
appropriate action towards a future approval. We hope this work will trigger
the creation of new user friendly datasets, and serve as a guide for the
curation of such datasets.


  We show that the presence of a localized curvilinear defect drastically
changes magnetic properties of a thin perpendicularly magnetized ferromagnetic
film. For a large enough defect amplitude a discrete set of equilibrium
magnetization states appears forming a ladder of energy levels. Each
equilibrium state has either zero or unit topological charge, i.e.
topologically trivial and skyrmion multiplets generally appear. Transitions
between the levels with the same topological charge are allowed and can be
utilized to encode and switch a bit of information. There is a wide range of
geometrical and material parameters, where the skyrmion level has the lowest
energy. As a result, periodically arranged curvilinear defects generate a
skyrmion lattice as the ground state.


  An approximate model of the spacetime foam is offered in which a quantum
handle (wormhole) is a 5D wormhole-like solution. Neglecting the linear sizes
of the wormhole throat we can introduce a spinor field for an approximate and
effective description of the foam. The definition of the spinor field can be
made by a dynamic and non-dynamic ways. In the first case some field equations
are used and the second case leads to superspace. It is shown that : the
spacetime with the foam is similar to a dielectric with dipoles and
supergravity theories with a non-minimal interaction between spinor and
electromagnetic fields can be considered as an effective model for the
spacetime foam.


  AI-based monitoring has become crucial for cloud-based services due to its
scale. A common approach to AI-based monitoring is to detect causal
relationships among service components and build a causal graph. Availability
of domain information makes cloud systems even better suited for such causal
detection approaches. In modern cloud systems, however, auto-scalers
dynamically change the number of microservice instances, and a load-balancer
manages the load on each instance. This poses a challenge for off-the-shelf
causal structure detection techniques as they neither incorporate the system
architectural domain information nor provide a way to model distributed compute
across varying numbers of service instances. To address this, we develop
CausIL, which detects a causal structure among service metrics by considering
compute distributed across dynamic instances and incorporating domain knowledge
derived from system architecture. Towards the application in cloud systems,
CausIL estimates a causal graph using instance-specific variations in
performance metrics, modeling multiple instances of a service as independent,
conditional on system assumptions. Simulation study shows the efficacy of
CausIL over baselines by improving graph estimation accuracy by ~25% as
measured by Structural Hamming Distance whereas the real-world dataset
demonstrates CausIL's applicability in deployment settings.


  Astronomy has a long history of acquiring, systematizing, and interpreting
large quantities of data. Starting from the earliest sky atlases through the
first major photographic sky surveys of the 20th century, this tradition is
continuing today, and at an ever increasing rate.
  Like many other fields, astronomy has become a very data-rich science, driven
by the advances in telescope, detector, and computer technology. Numerous large
digital sky surveys and archives already exist, with information content
measured in multiple Terabytes, and even larger, multi-Petabyte data sets are
on the horizon. Systematic observations of the sky, over a range of
wavelengths, are becoming the primary source of astronomical data. Numerical
simulations are also producing comparable volumes of information. Data mining
promises to both make the scientific utilization of these data sets more
effective and more complete, and to open completely new avenues of astronomical
research.
  Technological problems range from the issues of database design and
federation, to data mining and advanced visualization, leading to a new toolkit
for astronomical research. This is similar to challenges encountered in other
data-intensive fields today.
  These advances are now being organized through a concept of the Virtual
Observatories, federations of data archives and services representing a new
information infrastructure for astronomy of the 21st century. In this article,
we provide an overview of some of the major datasets in astronomy, discuss
different techniques used for archiving data, and conclude with a discussion of
the future of massive datasets in astronomy.


  Na0.5CoO2 exhibits a metal-insulator transition at 53 K upon cooling. The
nature of another transition at 88 K has not been fully clarified yet. We
report the results of measurements of the electrical conductivity, the magnetic
susceptibility and 23Na NMR on a powder sample of Na0.5CoO2, including the
mapping of NMR spectra, as well as probing the spin-lattice relaxation rate and
the spin-spin relaxation rate, in the temperature range between 30 K and 305 K.
The NMR data reflect the transition at T_X very well but provide less evidence
for the metal-insulator transition at T_MI. The temperature evolution of the
shape of the spectra implies the formation of a staggered internal field below
T_X, not accompained by a rearrangement of the electric charge distribution.
Our results thus indicate that in Na0.5CoO2, an unusual type of magnetic
ordering in the metallic phase precedes the onset of charge ordering, which
finally induces an insulating ground state.


  An experimental formula, sometimes named as Newton-collision-formula, (v1-v2)
= - e.(u1-u2) relating relative-velocities before & after impact of two bodies
under linear-collision, is commonly used successfully for study of
collision-dynamics. Although it seems possible as shown in text-books [1,2] to
derive this relation, assuming (defining) e as ratio of impulses during
restitution & deformation; but as yet, neither a good theoretical-basis nor a
general-derivation of this formula based on obvious choice of
energy-consideration, has been reported. In this brief-paper a
relativistic-theoretical-basis is adopted and a general formula-derivation is
given. It is shown, interestingly, that proof of the Newton-collision-formula
is hidden in Einstein-special-relativity.


  This paper considers over-the-air federated learning (OTA-FL). OTA-FL
exploits the superposition property of the wireless medium, and performs model
aggregation over the air for free. Thus, it can greatly reduce the
communication cost incurred in communicating model updates from the edge
devices. In order to fully utilize this advantage while providing comparable
learning performance to conventional federated learning that presumes model
aggregation via noiseless channels, we consider the joint design of
transmission scaling and the number of local iterations at each round, given
the power constraint at each edge device. We first characterize the training
error due to such channel noise in OTA-FL by establishing a fundamental lower
bound for general functions with Lipschitz-continuous gradients. Then, by
introducing an adaptive transceiver power scaling scheme, we propose an
over-the-air federated learning algorithm with joint adaptive computation and
power control (ACPC-OTA-FL). We provide the convergence analysis for
ACPC-OTA-FL in training with non-convex objective functions and heterogeneous
data. We show that the convergence rate of ACPC-OTA-FL matches that of FL with
noise-free communications.


  Low-frequency radio astronomy is limited by severe ionospheric distortions
below 50 MHz and complete reflection of radio waves below 10-30 MHz. Shielding
of man-made interference from long-range radio broadcasts, strong natural radio
emission from the Earth's aurora, and the need for setting up a large
distributed antenna array make the lunar far side a supreme location for a
low-frequency radio array. A number of new scientific drivers for such an
array, such as the study of the dark ages and epoch of reionization,
exoplanets, and ultra-high energy cosmic rays, have emerged and need to be
studied in greater detail. Here we review the scientific potential and
requirements of these and other new scientific drivers and discuss the
constraints for various lunar surface arrays. In particular we describe
observability constraints imposed by the interstellar and interplanetary
medium, calculate the achievable resolution, sensitivity, and confusion limit
of a dipole array using general scaling laws, and apply them to various
scientific questions. Whichever science is deemed most important, pathfinder
arrays are needed to test the feasibility of these experiments in the not too
distant future. Lunar low-frequency arrays are thus a timely option to
consider, offering the potential for significant new insights into a wide range
of today's crucial scientific topics. This would open up one of the last
unexplored frequency domains in the electromagnetic spectrum.


  We consider Schr\"odinger operators on a class of periodic quantum graphs
with randomly distributed Kirchhoff coupling constants at all vertices. Using
the technique of self-adjoint extensions we obtain conditions for localization
on quantum graphs in terms of finite volume criteria for some energy-dependent
discrete Hamiltonians. These conditions hold in the strong disorder limit and
at the spectral edges.


  The problem of recovering a signal from its low frequency components occurs
often in practical applications due to the lowpass behavior of many physical
systems. Here we study in detail conditions under which a signal can be
determined from its low-frequency content. We focus on signals in
shift-invariant spaces generated by multiple generators. For these signals, we
derive necessary conditions on the cutoff frequency of the lowpass filter as
well as necessary and sufficient conditions on the generators such that signal
recovery is possible. When the lowpass content is not sufficient to determine
the signal, we propose appropriate pre-processing that can improve the
reconstruction ability. In particular, we show that modulating the signal with
one or more mixing functions prior to lowpass filtering, can ensure the
recovery of the signal in many cases, and reduces the necessary bandwidth of
the filter.


  We describe the construction of trigonometric R-matrices corresponding to the
(multiplicity-free) tensor product of any two irreducible representations of a
quantum algebra $U_q(\G)$. Our method is a generalization of the tensor product
graph method to the case of two different representations. It yields the
decomposition of the R-matrix into projection operators. Many new examples of
trigonometric R-matrices (solutions to the spectral parameter dependent
Yang-Baxter equation) are constructed using this approach.


  High-performance autonomy often must operate at the boundaries of safety.
When external agents are present in a system, the process of ensuring safety
without sacrificing performance becomes extremely difficult. In this paper, we
present an approach to stress test such systems based on the rapidly exploring
random tree (RRT) algorithm.
  We propose to find faults in such systems through adversarial agent
perturbations, where the behaviors of other agents in an otherwise fixed
scenario are modified. This creates a large search space of possibilities,
which we explore both randomly and with a focused strategy that runs RRT in a
bounded projection of the observable states that we call the objective space.
The approach is applied to generate tests for evaluating overtaking logic and
path planning algorithms in autonomous racing, where the vehicles are driving
at high speed in an adversarial environment. We evaluate several autonomous
racing path planners, finding numerous collisions during overtake maneuvers in
all planners. The focused RRT search finds several times more crashes than the
random strategy, and, for certain planners, tens to hundreds of times more
crashes in the second half of the track.


  In this paper the application of the multi-level Monte Carlo (MLMC) method on
numerical simulations of turbulent flows with uncertain parameters is
investigated. Several strategies for setting up the MLMC method are presented,
and the advantages and disadvantages of each strategy are also discussed. A
numerical experiment is carried out using the Antarctic Circumpolar Current
(ACC) with uncertain, small-scale bottom topographic features. It is
demonstrated that, unlike the pointwise solutions, the averaged volume
transports are correlated across grid resolutions, and the MLMC method could
increase simulation efficiency without losing accuracy in uncertainty
assessment.


  Optical spectroscopy has been the primary tool to study the electronic
structure of molecules. However the strict spin selection rule has severely
limited its ability to access states of different spin multiplicities. Here we
propose a new strategy to activate spin-forbidden transitions in molecules by
introducing spatially highly inhomogeneous plasmonic field. The giant
enhancement of the magnetic field strength resulted from the curl of the
inhomogeneous vector potential makes the transition between states of different
spin multiplicities naturally feasible. The dramatic effect of the
inhomogeneity of the plasmonic field on the spin and symmetry selection rules
is well illustrated by first principles calculations of C60. Remarkably, the
intensity of singlet-triplet transitions can even be stronger than that of
singlet-singlet transitions when the plasmon spatial distribution is comparable
with the molecular size. This approach offers a powerful means to completely
map out all excited states of molecules and to actively control their
photochemical processes. The same concept can also be applied to study nano and
biological systems.


  Recently, a circuits-based approach for modeling the mutual coupling of
reconfigurable surfaces, which comprise sub-wavelength spaced passive
scattering elements coupled with electronic circuits for enabling the
reconfiguration of the surface, has been introduced. The approach is based on a
finite-length discrete dipole representation of a reconfigurable surface, and
on the assumption that the current distribution on each thin wire dipole is a
sinusoidal function. Under these assumptions, the voltages at the ports of a
multi-antenna receiver can be formulated in terms of the voltage generators at
a multi-antenna transmitter through a transfer function matrix that explicitly
depends on the mutual coupling and the tuning circuits through the mutual
impedances between every pair of thin wire dipoles. In currently available
works, the mutual impedances are formulated in an integral form. In this paper,
we show that they can be formulated in a closed-form expression in terms of
exponential integral functions.


  In this paper, we describe general characteristics of the MathPartner
computer algebra system (CAS) and Mathpar programming language thereof.
MathPartner can be used for scientific and engineering calculations, as well as
in high schools and universities. It allows one to carry out both simple
calculations (acting as a scientific calculator) and complex calculations with
large-scale mathematical objects. Mathpar is a procedural language; it supports
a large number of elementary and special functions, as well as matrix and
polynomial operators. This service allows one to build function images and
animate them. MathPartner also makes it possible to solve some symbolic
computation problems on supercomputers with distributed memory. We highlight
main differences of MathPartner from other CASs and describe the Mathpar
language along with the user service provided.


  Low-lying collective states involving many nucleons interacting by a random
ensemble of two-body interactions (TBRE) are investigated in a collective
SD-pair subspace, with the collective pairs defined dynamically from the
two-nucleon system. It is found that in this truncated pair subspace collective
vibrations arise naturally for a general TBRE hamiltonian whereas collective
rotations do not. A hamiltonian restricted to include only a few randomly
generated separable terms is able to produce collective rotational behavior, as
long as it includes a reasonably strong quadrupole-quadrupole component.
Similar results arise in the full shell model space. These results suggest that
the structure of the hamiltonian is key to producing generic collective
rotation.


  Two types of novel graphene-based components, namely, filters and
electro-optical switches in guided wave configuration are suggested and
analysed. The filters differ from the known ones with collinear orientation of
the input and output waveguides by geometry and symmetry. The components
consist of a circular graphene disk and two nanoribbons oriented at $90^\circ$
to each other in the plane of the graphene layer. The graphene elements are
placed on a dielectric substrate. We show that change in symmetry leads to a
drastic change in the properties of the components. The physical principle of
the devices is based on the dipole, quadrupole and hexapole resonances in the
graphene disk which define stop-band, pass-band and stop-band frequency
characteristics, respectively. A combination of stop-band and pass-band filter
properties by shifing the electronic Fermi level allows one to design a switch.
Numerical simulations show that the suggested components have very small
dimensions, good characteristics and provide a dynamic control of their central
frequency via electrostatic gating.


  We investigate the Universe at the late stage of its evolution and inside the
cell of uniformity 150-370 Mpc. We consider the Universe to be filled at these
scales with dust like matter, a minimally coupled Galileon field and radiation.
We use the mechanical approach. Therefore, the peculiar velocities of the
inhomogeneities as well as the fluctuations of the other perfect fluids can be
considered nonrelativistic. Such fluids are said to be coupled because they are
concentrated around the inhomogeneities. We investigate the conditions under
which the physical Galileon field, i.e. compatible with results of the latest
gravitational wave experiments GW150914, GW151226, GW170104, GW170814, GW170817
and GW170608, can become coupled. We know that at the background level coupled
scalar fields behave as a two-component perfect fluids: one which mimics a
network of frustrated cosmic string and an another one which corresponds to an
effective cosmological constant. We found a correction for the Galileon field,
which behaves like a matter component.


  There are many methods for image enhancement. Image inpainting is one of them
which could be used in reconstruction and restoration of scratch images or
editing images by adding or removing objects. According to its application,
different algorithmic and learning methods are proposed. In this paper, the
focus is on applications, which enhance the old and historical scratched
images. For this purpose, we proposed an adaptive spline interpolation. In this
method, a different number of neighbors in four directions are considered for
each pixel in the lost block. In the previous methods, predicting the lost
pixels that are on edges is the problem. To address this problem, we consider
horizontal and vertical edge information. If the pixel is located on an edge,
then we use the predicted value in that direction. In other situations,
irrelevant predicted values are omitted, and the average of rest values is used
as the value of the missing pixel. The method evaluates by PSNR and SSIM
metrics on the Kodak dataset. The results show improvement in PSNR and SSIM
compared to similar procedures. Also, the run time of the proposed method
outperforms others.


  To understand the computations of our visual system, it is important to
understand also the natural environment it evolved to interpret. Unfortunately,
existing models of the visual environment are either unrealistic or too complex
for mathematical description. Here we describe a naturalistic image model and
present a mathematical solution for the statistical relationships between the
image features and model variables. The world described by this model is
composed of independent, opaque, textured objects which occlude each other.
This simple structure allows us to calculate the joint probability distribution
of image values sampled at multiple arbitrarily located points, without
approximation. This result can be converted into probabilistic relationships
between observable image features as well as between the unobservable
properties that caused these features, including object boundaries and relative
depth. Using these results we explain the causes of a wide range of natural
scene properties, including highly non-gaussian distributions of image features
and causal relations between pairs of edges. We discuss the implications of
this description of natural scenes for the study of vision.


  Proprioception is crucial in intuitive control of prosthetic limbs and
therefor contributes to intuitive prosthetic use. The agonist-antagonist
myoneural interface (AMI) is an prosthetic innovation with enhanced control,
reduced pain, and heightened proprioceptive sensation in clinical experiments.
Furthermore, studies have addressed surgical techniques to make this
myoelectric interface available to a larger group of patients. A narrative
review on AMI developmental process, surgical implementations, and validation
has been conducted for clinical and pre-clinical experimental research,
embedded in a theoretical background on feedback control and proprioception.
The closing chapter on magnetomicrometery serves to illustrate how motor
control reading, as an example component, can benefit from technical
innovations to enhance intuitive prosthetic control in AMI.


  Because of its large angular size and proximity to the Milky Way, NGC 253, an
archetypal starburst galaxy, provides an excellent laboratory to study the
intricacies of this intense episode of star formation. We aim to characterize
the excitation mechanisms driving the emission in NGC 253. Specifically we aim
to distinguish between shock excitation and UV excitation as the dominant
driving mechanism, using Br\gamma, H_2 and [FeII] as diagnostic emission line
tracers. Using SINFONI observations, we create linemaps of Br\gamma,
[FeII]_{1.64}, and all detected H_2 transitions. By using symmetry arguments of
the gas and stellar gas velocity field, we find a kinematic center in agreement
with previous determinations. The ratio of the 2-1 S(1) to 1-0 S(1) H_2
transitions can be used as a diagnostic to discriminate between shock and
fluorescent excitation. Using the 1-0 S(1)/2-1 S(1) line ratio as well as
several other H_2 line ratios and the morphological comparison between H_2 and
Br\gamma and [FeII], we find that excitation from UV photons is the dominant
excitation mechanisms throughout NGC 253. We employ a diagnostic energy level
diagram to quantitatively differentiate between mechanisms. We compare the
observed energy level diagrams to PDR and shock models and find that in most
regions and over the galaxy as a whole, fluorescent excitation is the dominant
mechanism exciting the H_2 gas. We also place an upper limit of the percentage
of shock excited H_2 at 29%. We find that UV radiation is the dominant
excitation mechanism for the H_2 emission. The H_2 emission does not correlate
well with Br\gamma but closely traces the PAH emission, showing that not only
is H_2 fluorescently excited, but it is predominately excited by slightly lower
mass stars than O stars which excite Br\gamma, such as B stars.


  This work establishes a structure theorem for compact K\"ahler manifolds with
semipositive anticanonical bundle. Up to finite \'etale cover, it is proved
that such manifolds split holomorphically and isometrically as a product of
Ricci flat varieties and of rationally connected manifolds. The proof is based
on a characterization of rationally connected manifolds through the non
existence of certain twisted contravariant tensor products of the tangent
bundle, along with a generalized holonomy principle for pseudoeffective line
bundles. A crucial ingredient for this is the characterization of uniruledness
by the property that the anticanonical bundle is not pseudoeffective.


  The results of an extremely deep, 8-day long observation of the central kpc
of the nearby starburst galaxy M82 using MERLIN (Multi-Element Radio Linked
Interferometer Network) at 5 GHz are presented. The 17E-06 Jy/beam, rms noise
level in the naturally weighted image make it the most sensitive high
resolution radio image of M82 made to date. Over 50 discrete sources are
detected, the majority of which are supernova remnants, but with 13 identified
as HII regions. Sizes, flux densities and radio brightnesses are given for all
of the detected sources, which are all well resolved with a majority showing
shell or partial shell structures. Those sources within the sample which are
supernova remnants have diameters ranging from 0.3 to 6.7 pc, with a mean size
of 2.9 pc.
  From a comparison with previous MERLIN 5 GHz observations made in July 1992,
which gives a 9.75 year timeline, it has been possible to measure the expansion
velocities of ten of the more compact sources, eight of which have not been
measured before. These derived expansion velocities range between 2200 and
10500 km/s.


  In the coldest (10--20 K) regions of the interstellar medium, the icy
surfaces of interstellar grains serve as solid-state supports for chemical
reactions. Among their plausible roles, that of third body is advocated, in
which the reaction energies of surface reactions dissipate throughout the
grain, stabilizing the product. This energy dissipation process is poorly
understood at the atomic scale, although it can have a high impact on
Astrochemistry. Here, we study, by means of quantum mechanical simulations, the
formation of NH3 via successive H-additions to atomic N on water ice surfaces,
paying special attention to the third body role. We first characterize the
hydrogenation reactions and the possible competitive processes (i.e.,
H-abstractions), in which the H-additions are more favourable than the
H-abstractions. Subsequently, we study the fate of the hydrogenation reaction
energies by means of ab initio molecular dynamics simulations. Results show
that around 58--90\% of the released energy is quickly absorbed by the ice
surface, inducing a temporary increase of the ice temperature. Different energy
dissipation mechanisms are distinguished. One mechanism, more general, is based
on the coupling of the highly excited vibrational modes of the newly formed
species and the libration modes of the icy water molecules. A second mechanism,
exclusive during the NH$_3$ formation, is based on the formation of a transient
H$_3$O$^+$/NH$_2^-$ ion pair, which significantly accelerates the energy
transfer to the surface. Finally, the astrophysical implications of our
findings relative to the interstellar synthesis of NH$_3$ and its chemical
desorption into the gas are discussed.


  We calculate polarization phenomena for virtual Compton scattering on
protons, at relatively large momentum transfer 1 GeV$^2$ $\le -q^2\le$ 5
GeV$^2$ on the basis of a model for $\gamma^*+ p\to \gamma+p$ with two main
contributions: $\pi^0$-exchange in $t-$channel and $\Delta$-excitation in
$s$-channel. This model applies from threshold to $\Delta$ region. The
parameters entering in this model, such as coupling constants and
electromagnetic form factors are well known. The analyzing powers for
$\gamma^*+\vec p\to \gamma+ p$ and the components of the final proton
polarization in $\gamma^*+ p\to \gamma+ \vec p$ are large in absolute value and
show strong sensitivity to $\pi\bigotimes\Delta$ interference. These results
can be applied to the calculation of radiative corrections to polarization
phenomena in elastic $ep$-scattering.


  In this paper, we investigate the generalized degrees-of-freedom (GDoF) of
the asymmetric interference channel with delayed channel state information at
the transmitter (CSIT), where each transmitter has two antennas, each receiver
has one antenna, and the strength for each interfering link can vary. The
optimal sum-GDoF is characterized by matched converse and achievability proof.
Through our results, we also reveal that in our antenna setting, the symmetric
GDoF lower bound in [Mohanty et. al, TIT 2019] can be elevated, and the
symmetric GDoF upper bound in [Mohanty et. al, TIT 2019] is tight in fact.


  Context. There are more than 3000 confirmed and probable known Galactic
planetary nebulae, but central star spectroscopic information is available for
only 13% of them. Aims. We undertook a spectroscopic survey of central stars of
PNe to identify their spectral types. Methods. We performed spectroscopic
observations, at low resolution, with the 2-m telescope at CASLEO, Argentina.
Results. We present the spectra of 46 central stars of PNe, most of them are
OB-type and emission-line stars.


  A MOSFET threshold voltage extraction method covering the entire range of
drain-to-source voltage, from linear to saturation modes, is presented.
Transconductance-to-current ratio is obtained from MOSFET transfer
characteristics measured at low to high drain voltage. Based on the
charge-based modeling approach, a near-constant value of threshold voltage is
obtained over the whole range of drain voltage for ideal, long-channel MOSFETs.
The method reveals a distinct increase of threshold voltage versus drain
voltage for halo-implanted MOSFETs in the low drain voltage range. The method
benefits from moderate inversion operation, where high-field effects, such as
vertical field mobility reduction and series resistances, are minimal. The
present method is applicable over the full range of drain voltage, is fully
analytical, easy to be implemented, and provides more consistent results when
compared to existing methods.


  Scalar fields play a crucial role in the Standard model. On the other hand,
in the weak-coupling regime there is an unsolved problem of the quadratic
divergence of scalar masses. Thus, it is natural to turn to composite, or
effective scalar fields in the strong-coupling regime. Lattice simulations
provide information on the "actually existing" scalar fields in Yang-Mills
theories. On the continuum-theory side, dual models predict existence of
various low-dimension vacuum defects, including probably scalar particles. We
are looking for correspondence between the two frameworks, of lattice
phenomenology and dual models, and discuss possible applications to the theory
of the Yang-Mills plasma in the deconfining phase. It is not ruled out that the
effective scalars play important role in the plasma dynamics.


  Let $Z_\al$ be a positive $\alpha$-stable random variable and
$T_\al=(Z_\al/\tilde Z_\al)^\al,$ with independents components in the quotient.
It is known that $T_\al$ is distributed as the positive branch of a Cauchy
random variable with drift. We show that the density of the power
transformation $T_\al^\beta$ is hyperbolically completely monotone in the sense
of Thorin and Bondesson if and only if $\al\le1/2$ and $|\beta|\ge 1/(1-\al).$
This clarifies a conjecture of Bondesson (1992) on positive stable densities.


  The integrated spectra of stellar systems contain a wealth of information,
and its analysis can reveal fundamental parameters such as metallicity, age and
star formation history. Widely used methods to analyze these spectra are based
on comparing the galaxy spectra to stellar population (SP) models. Despite
being a powerful tool, SP models contain many ingredients, each with their
assumptions and uncertainties. Among the several possible sources of
uncertainties, it is not straightforward to identify which ingredient dominates
the errors in the models. In this work we propose a study of one of the SP
model ingredients -- the spectral stellar libraries -- independently of the
other ingredients. To that aim, we will use the integrated spectra of globular
clusters which have color-magnitude diagrams (CMDs) available. From these CMDs
it is possible to model the integrated spectra of these objects without having
to adopt -- or make assumptions -- on the other two main ingredients of SP
models, evolutionary tracks or an IMF. Here we tested four widely used stellar
libraries. We found that the libraries are able to reproduce the integrated
spectra of 18 of the 30 cluster spectra inside a mean flux uncertainty of 5%.
For the larger wavelength range tested, a theoretical library outperforms the
empirical ones in the comparison. Without the blue part of the spectra,
empirical libraries fare better than the theoretical, in particular when
individual features are concerned. However, the results are promising for
theoretical libraries, which are equally efficient to reproduce the whole
spectrum.


  When you first heard people speak of Piles of PCs, the first thing that came
to mind may have been a cluttered computer room with processors, monitors, and
snarls of cables all around. Collections of computers have undoubtedly become
more sophisticated than in the early days of shared drives and modem
connections. No matter what you call them, Clusters of Workstations (COW),
Networks of Workstations (NOW), Workstation Clusters (WCs), Clusters of PCs
(CoPs), clusters of computers are now filling the processing niche once
occupied by more powerful stand-alone machines. This article discusses the need
for cluster computing technology, Technologies, Components, and Applications,
Supercluster Systems and Issues, The Need for a New Task Force, and Cluster
Computing Educational Resources.


  By employing resonant X-ray microdiffraction, we image the magnetisation and
magnetic polarity domains of the Y-type hexaferrite
Ba$_{0.5}$Sr$_{1.5}$Mg$_2$Fe$_{12}$O$_{22}$. We show that the magnetic polarity
domain structure can be controlled by both magnetic and electric fields, and
that full inversion of these domains can be achieved simply by reversal of an
applied magnetic field in the absence of an electric field bias. Furthermore,
we demonstrate that the diffraction intensity measured in different X-ray
polarisation channels cannot be reproduced by the accepted model for the polar
magnetic structure, known as the 2-fan transverse conical (TC) model. We
propose a modification to this model, which achieves good quantitative
agreement with all of our data. We show that the deviations from the TC model
are large, and may be the result of an internal magnetic chirality, most likely
inherited from the parent helical (non-polar) phase.


  The appearance of the inflection point in the equation of state (EoS) is
associated with the second order phase transition. The high cumulants of
conserved quantities near the critical point are corresponding to the high
derivatives of the EoS near the inflection point. The critical behavior of high
cumulants of conserved charge near the QCD critical point, in particular, the
sign change, is closely related to the appearance of inflection point. We show
in general how the times of sign change of high cumulants relate to the order
of derivative. We also demonstrate that the character of inflection point of
EoS is as visible as the sign change of high cumulants in 3 systems, i.e., van
der Waals equation of fluid, magnetization of spin models and the baryon number
density of QCD matter. Therefore, we propose that the EoS, or the mean of
baryon number density, should be measured and studied together with its higher
cumulants in exploring QCD critical point in heavy ion collisions.


  Recent advances in deep learning have made it possible to quantify urban
metrics at fine resolution, and over large extents using street-level images.
Here, we focus on measuring urban tree cover using Google Street View (GSV)
images. First, we provide a small-scale labelled validation dataset and propose
standard metrics to compare the performance of automated estimations of street
tree cover using GSV. We apply state-of-the-art deep learning models, and
compare their performance to a previously established benchmark of an
unsupervised method. Our training procedure for deep learning models is novel;
we utilize the abundance of openly available and similarly labelled
street-level image datasets to pre-train our model. We then perform additional
training on a small training dataset consisting of GSV images. We find that
deep learning models significantly outperform the unsupervised benchmark
method. Our semantic segmentation model increased mean intersection-over-union
(IoU) from 44.10% to 60.42% relative to the unsupervised method and our
end-to-end model decreased Mean Absolute Error from 10.04% to 4.67%. We also
employ a recently developed method called gradient-weighted class activation
map (Grad-CAM) to interpret the features learned by the end-to-end model. This
technique confirms that the end-to-end model has accurately learned to identify
tree cover area as key features for predicting percentage tree cover. Our paper
provides an example of applying advanced deep learning techniques on a
large-scale, geo-tagged and image-based dataset to efficiently estimate
important urban metrics. The results demonstrate that deep learning models are
highly accurate, can be interpretable, and can also be efficient in terms of
data-labelling effort and computational resources.


  Endocytosis is an essential process by which cells internalize a piece of
plasma membrane and material from the outside. In cells with turgor, pressure
opposes membrane defor- mations, and increases the amount of force that has to
be generated by the endocytic machinery. To determine this force, and calculate
the shape of the membrane, we used physical theory to model an elastic surface
under pressure. Accurate fits of experimental profiles are obtained assuming
that the coated membrane is highly rigid and preferentially curved at the
endocytic site. The forces required from the actin machinery peaks at the onset
of deformation, indicating that once invagination has been initiated,
endocytosis is unlikely to stall before completion. Coat proteins do not lower
the initiation force but may affect the process by the curvature they induce.
In the presence of isotropic curvature inducers, pulling the tip of the
invagination can trigger the formation of a neck at the base of the
invagination. Hence direct neck constriction by actin may not be required,
while its pulling role is essential. Finally, the theory shows that anisotropic
curvature effectors stabilize membrane invaginations, and the loss of
crescent-shaped BAR domain proteins such as Rvs167 could therefore trigger
membrane scission.


  Recent work has shown that unstable D-branes in two dimensional string theory
are represented by eigenvalues in a dual matrix model. We elaborate on this
proposal by showing how to systematically include higher order effects in
string perturbation theory. The full closed string state produced by a rolling
open string tachyon corresponds to a sum of string amplitudes with any number
of boundaries and closed string vertex operators. These contributions are
easily extracted from the matrix model. As in the AdS/CFT correspondence, the
sum of planar diagrams in the open string theory is directly related to the
classical theory in the bulk, i.e. sphere diagrams. We also comment on the
description of static D-branes in the matrix model, in terms of a solution
representing a deformed Fermi sea.


  Adversarial examples have emerged as a significant threat to machine learning
algorithms, especially to the convolutional neural networks (CNNs). In this
paper, we propose two quantization-based defense mechanisms, Constant
Quantization (CQ) and Trainable Quantization (TQ), to increase the robustness
of CNNs against adversarial examples. CQ quantizes input pixel intensities
based on a "fixed" number of quantization levels, while in TQ, the quantization
levels are "iteratively learned during the training phase", thereby providing a
stronger defense mechanism. We apply the proposed techniques on undefended CNNs
against different state-of-the-art adversarial attacks from the open-source
\textit{Cleverhans} library. The experimental results demonstrate 50%-96% and
10%-50% increase in the classification accuracy of the perturbed images
generated from the MNIST and the CIFAR-10 datasets, respectively, on commonly
used CNN (Conv2D(64, 8x8) - Conv2D(128, 6x6) - Conv2D(128, 5x5) - Dense(10) -
Softmax()) available in \textit{Cleverhans} library.


  Mechanistic home range models are important tools in modeling animal dynamics
in spatially-complex environments. We introduce a class of stochastic models
for animal movement in a habitat of varying preference. Such models interpolate
between spatially-implicit resource selection analysis (RSA) and
advection-diffusion models, possessing these two models as limiting cases. We
find a closed-form solution for the steady-state (equilibrium) probability
distribution u* using a factorization of the redistribution operator into
symmetric and diagonal parts. How space use is controlled by the preference
function w then depends on the characteristic width of the redistribution
kernel: when w changes rapidly compared to this width, u* ~ w, whereas on
global scales large compared to this width, u* ~ w^2. We analyse the behavior
at discontinuities in w which occur at habitat type boundaries. We simulate the
dynamics of space use given two-dimensional prey-availability data and explore
the effect of the redistribution kernel width. Our factorization allows such
numerical simulations to be done extremely fast; we expect this to aid the
computationally-intensive task of model parameter fitting and inverse modeling.


  Fast and accurate dose predictions are one of the bottlenecks in treatment
planning for microbeam radiation therapy (MRT). In this paper, we propose a
machine learning (ML) model based on a 3D U-Net. Our approach predicts
separately the large doses of the narrow high intensity synchrotron microbeams
and the lower valley doses between them. For this purpose, a concept of macro
peak doses and macro valley doses is introduced, describing the respective
doses not on a microscopic level but as macroscopic quantities in larger
voxels. The ML model is trained to mimic full Monte Carlo (MC) data. Complex
physical effects such as polarization are therefore automatically taking into
account by the model.
  The macro dose distribution approach described in this study allows for
superimposing single microbeam predictions to a beam array field making it an
interesting candidate for treatment planning. It is shown that the proposed
approach can overcome a main obstacle with microbeam dose predictions by
predicting a full microbeam irradiation field in less than a minute while
maintaining reasonable accuracy.