  We introduce a class of generative network models that insert edges by
connecting the starting and terminal vertices of a random walk on the network
graph. Within the taxonomy of statistical network models, this class is
distinguished by permitting the location of a new edge to explicitly depend on
the structure of the graph, but being nonetheless statistically and
computationally tractable. In the limit of infinite walk length, the model
converges to an extension of the preferential attachment model---in this sense,
it can be motivated alternatively by asking what preferential attachment is an
approximation to. Theoretical properties, including the limiting degree
sequence, are studied analytically. If the entire history of the graph is
observed, parameters can be estimated by maximum likelihood. If only the final
graph is available, its history can be imputed using MCMC. We develop a class
of sequential Monte Carlo algorithms that are more generally applicable to
sequential network models, and may be of interest in their own right. The model
parameters can be recovered from a single graph generated by the model.
Applications to data clarify the role of the random walk length as a length
scale of interactions within the graph.


  In this paper, we construct incomplete versions of the equivariant stable
category; i.e., equivariant stabilization of the category of $G$-spaces with
respect to incomplete systems of transfers encoded by an $N_\infty$ operad
$\mathcal{O}$. These categories are built from the categories of
$\mathcal{O}$-algebras in $G$-spaces. Using this operadic formulation, we
establish incomplete versions of the usual structural properties of the
equivariant stable category, notably the tom Dieck splitting. Our work is
motivated in part by the examples arising from the equivariant units and Picard
space functors.


  We show that dynamical quantum phase transitions (DQPTs) in the quench
dynamics of two-dimensional topological systems can be characterized by a
dynamical topological invariant defined along an appropriately chosen closed
contour in momentum space. Such a dynamical topological invariant reflects the
vorticity of dynamical vortices responsible for the DQPTs, and thus serves as a
dynamical topological order parameter in two dimensions. We demonstrate that
when the contour crosses topologically protected fixed points in the quench
dynamics, an intimate connection can be established between the dynamical
topological order parameter in two dimensions and those in one dimension. We
further define a reduced rate function of the Loschmidt echo on the contour,
which features non-analyticities at critical times and is sufficient to
characterize DQPTs in two dimensions. We illustrate our results using the
Haldane honeycomb model and the quantum anomalous Hall model as concrete
examples, both of which have been experimentally realized using cold atoms.


  The Latitude Density Search utilized Hyper Suprime-Cam on Subaru Telescope to
discover 60 moving objects in the outer Solar System, 54 of which have
semi-major axes beyond 30 AU. The images were acquired in exceptional seeing
(0.4") and reached a detection limit of m_r~=25.2. The two night arcs were used
to calculate orbits which are poorly constrained, however, the distance and
inclination are the parameters best constrained by short arcs and a reasonable
determination can be made of which objects are cold classical TNOs and which
are dynamically excited. We identify 10 objects as likely cold classical
objects. We searched all of the detections for binary sources using a trailed
Point Spread Function subtraction method, and identified one binary object with
a separation of 0.34" and a secondary with 17% the brightness of the primary
(2.0 magnitudes fainter). This is the brightest TNO in the sample, the
previously known object 471165 (2010 HE79), which has a dynamically excited
orbit. Because of the excellent seeing, this search was sensitive to binaries
with 0.34" separation and a brightness of >=50% the primary brightness for 7
objects, including one cold classical. This gives an intrinsic binary fraction
of ~17% (1 of 6) for the dynamically excited objects within our detection
limits. The trailed point spread function subtraction method to identify
binaries, fit the two components, and determine the sensitivity limits, used in
the Latitude Density Search is a useful tool that could be more broadly applied
to identify binary TNOs and track known binary TNO orbits.


  We consider the M-theory lifts of configurations of type IIA D6-branes
intersecting at angles. In supersymmetry preserving cases, the lifts correspond
to special holonomy geometries, like conifolds and $G_2$ holonomy
singularities. Transitions in which D6-branes approach and recombine lift to
topology changing transition in these geometries. In some instances
non-supersymmetric configurations can be reliably lifted, leading to the same
topological manifolds, but endowed with non-supersymmetric metrics. In these
cases the phase transitions are driven dynamically, due to instabilities
localized at the singularities. Even though in non-compact setups the
instabilities relax to infinity, in compact situations there exist nearby
minima where the instabilities dissappear and the decay reaches a well-defined
(in general supersymmetric) endpoint.


  In "On the super edge graceful trees of even orders," Chung, Lee, Gao, and
Schaffer posed the following problem: Characterize trees of diameter 4 which
are super edge-graceful. In this paper, we provide super edge-graceful
labelings for all caterpillars and even size lobsters of diameter 4 which
permit such labelings. We also provide super edge-graceful labelings for
several families of odd size lobsters of diameter 4.


  We present a simple numerical procedure for calculating the irrotational
hydrodynamic flow in a helium solvation structure around a spherical solute in
linear motion through superfluid helium. The calculation requires only the
radial helium density around the impurity as input. From the resulting
irrotational flow, the helium contribution to the effective mass of the solute
is calculated. The results for alkali cations are compared to recent many-body
Variational Monte Carlo (VMC) calculations by M. Buzzacchi, D. E. Galli, and L.
Reatto (Phys. Rev. B., {\bf 64} 094512 (2001)). The helium contribution to the
effective masses calculated by the two methods are 12.9(4.6) versus 9.4 u for
Li$^+$, 48.2(5.6) versus 52.1 u for Na$^+$, 69.6(4.8) versus 70.1 u for K$^+$,
and 6.4(8.8) versus 6.8 for Cs$^+$, with the VMC result listed first (with one
$\sigma$ statistical error estimate) and the hydrodynamic result listed second.
For the cases of Na$^+$ and K$^+$, the hydrodynamic calculation treated the
first helium solvation shell as a rigid solid, as suggested by the VMC
calculations; treating the first solvent layer as part of the superfluid lead
to considerable underestimate, $\approx 50%$, of the mass increase in both
cases. In all cases, the agreement of the two results are in within the error
estimate of the VMC calculation, demonstrating the accuracy of hydrodynamic
treatment of helium motion even on the atomic length scale.


  Since its first appearance, transformers have been successfully used in wide
ranging domains from computer vision to natural language processing.
Application of transformers in Reinforcement Learning by reformulating it as a
sequence modelling problem was proposed only recently. Compared to other
commonly explored reinforcement learning problems, the Rubiks cube poses a
unique set of challenges. The Rubiks cube has a single solved state for
quintillions of possible configurations which leads to extremely sparse
rewards. The proposed model CubeTR attends to longer sequences of actions and
addresses the problem of sparse rewards. CubeTR learns how to solve the Rubiks
cube from arbitrary starting states without any human prior, and after move
regularisation, the lengths of solutions generated by it are expected to be
very close to those given by algorithms used by expert human solvers. CubeTR
provides insights to the generalisability of learning algorithms to higher
dimensional cubes and the applicability of transformers in other relevant
sparse reward scenarios.


  When particulate suspensions are sheared, perturbations in the shear flows
around the rigid particles increase the local energy dissipation, so that the
viscosity of the suspension is effectively higher than that of the solvent. For
bulk (three-dimensional) fluids, understanding this viscosity enhancement is a
classic problem in hydrodynamics that originated over a century ago with
Einstein's study of a dilute suspension of spherical particles.
\cite{Einstein1} In this paper, we investigate the analogous problem of the
effective viscosity of a suspension of disks embedded in a two-dimensional
membrane or interface. Unlike the hydrodynamics of bulk fluids, low-Reynolds
number membrane hydrodynamics is characterized by an inherent length scale
generated by the coupling of the membrane to the bulk fluids that surround it.
As a result, we find that the size of the particles in the suspension relative
to this hydrodynamic length scale has a dramatic effect on the effective
viscosity of the suspension. Our study also helps to elucidate the mathematical
tools needed to solve the mixed boundary value problems that generically arise
when considering the motion of rigid inclusions in fluid membranes.


  Learning methods in Banach spaces are often formulated as regularization
problems which minimize the sum of a data fidelity term in a Banach norm and a
regularization term in another Banach norm. Due to the infinite dimensional
nature of the space, solving such regularization problems is challenging. We
construct a direct sum space based on the Banach spaces for the data fidelity
term and the regularization term, and then recast the objective function as the
norm of a suitable quotient space of the direct sum space. In this way, we
express the original regularized problem as an unregularized problem on the
direct sum space, which is in turn reformulated as a dual optimization problem
in the dual space of the direct sum space. The dual problem is to find the
maximum of a linear function on a convex polytope, which may be solved by
linear programming. A solution of the original problem is then obtained by
using related extremal properties of norming functionals from a solution of the
dual problem. Numerical experiments are included to demonstrate that the
proposed duality approach leads to an implementable numerical method for
solving the regularization learning problems.


  Maximum entropy models are the least structured probability distributions
that exactly reproduce a chosen set of statistics measured in an interacting
network. Here we use this principle to construct probabilistic models which
describe the correlated spiking activity of populations of up to 120 neurons in
the salamander retina as it responds to natural movies. Already in groups as
small as 10 neurons, interactions between spikes can no longer be regarded as
small perturbations in an otherwise independent system; for 40 or more neurons
pairwise interactions need to be supplemented by a global interaction that
controls the distribution of synchrony in the population. Here we show that
such "K-pairwise" models--being systematic extensions of the previously used
pairwise Ising models--provide an excellent account of the data. We explore the
properties of the neural vocabulary by: 1) estimating its entropy, which
constrains the population's capacity to represent visual information; 2)
classifying activity patterns into a small set of metastable collective modes;
3) showing that the neural codeword ensembles are extremely inhomogenous; 4)
demonstrating that the state of individual neurons is highly predictable from
the rest of the population, allowing the capacity for error correction.


  We propose an algorithm for Bayesian functional optimisation - that is,
finding the function to optimise a process - guided by experimenter beliefs and
intuitions regarding the expected characteristics (length-scale, smoothness,
cyclicity etc.) of the optimal solution encoded into the covariance function of
a Gaussian Process. Our algorithm generates a sequence of finite-dimensional
random subspaces of functional space spanned by a set of draws from the
experimenter's Gaussian Process. Standard Bayesian optimisation is applied on
each subspace, and the best solution found used as a starting point (origin)
for the next subspace. Using the concept of effective dimensionality, we
analyse the convergence of our algorithm and provide a regret bound to show
that our algorithm converges in sub-linear time provided a finite effective
dimension exists. We test our algorithm in simulated and real-world
experiments, namely blind function matching, finding the optimal
precipitation-strengthening function for an aluminium alloy, and learning rate
schedule optimisation for deep networks.


  We compute the leading-order low-energy constants of the DeltaS=1 effective
weak Hamiltonian in the quenched approximation of QCD with up, down, strange,
and charm quarks degenerate and light. They are extracted by comparing the
predictions of finite volume chiral perturbation theory with lattice QCD
computations of suitable correlation functions carried out with quark masses
ranging from a few MeV up to half of the physical strange mass. We observe a
large DeltaI=1/2 enhancement in this corner of the parameter space of the
theory. Although matching with the experimental result is not observed for the
DeltaI=1/2 amplitude, our computation suggests large QCD contributions to the
physical DeltaI=1/2 rule in the GIM limit, and represents the first step to
quantify the role of the charm quark-mass in K-->pipi amplitudes.


  As an alternative gravity model we consider an extended Einstein-Maxwell
gravity containing a gauge invariance property. Extension is assumed to be
addition of a directional coupling between spatial electromagnetic fields with
the Ricci tensor. We will see importance of the additional term in making a
compact stellar object and value of its radius. As an application of this model
we substitute ansatz of magnetic field of a hypothetical magnetic monopole
which has just time independent radial component and for matter part we assume
a perfect fluid stress tensor. To obtain spherically symmetric internal metric
of the perfect fluid stellar compact object we solve Tolman-Oppenheimer-Volkoff
equation with a polytropic form of equation of state as $p(\rho)=a\rho^2$.
Using dynamical system approach we study stability of the solutions for which
arrow diagrams show saddle (quasi stable) for $a<0$ (dark stars) and sink
(stable) for $a>0$ (normal visible stars). We check also the energy conditions,
speed of sound and Harrison-Zeldovich-Novikov static stability criterion for
obtained solution and confirm that they make stable state.


  We calculate the size distribution of quark nuggets, which could be formed
due to first order QCD phase transition in the early universe. We find that
there are a large number of stable Quark Nuggets which could be a viable
candidate for cosmological dark matter.


  The Magellanic Clouds (MCs) offer an outstanding variety of young stellar
associations, in which large samples of low-mass stars (with masses less than 1
solar mass) currently in the act of formation can be resolved and explored
sufficiently with the Hubble Space Telescope. These pre-main sequence (PMS)
stars provide a unique snapshot of the star formation process, as it is being
recorded for the last 20 Myr, and they give important information on the
low-mass Initial Mass Function (IMF) of their host environments. We present the
latest results from observations with the Advanced Camera for Surveys (ACS) of
such star-forming regions in the MCs, and discuss the importance of Hubble}for
a comprehensive collection of substantial information on the most recent
low-mass star formation and the low-mass IMF in the MCs.


  With the growing digital transformation of the worldwide economy, cyber risk
has become a major issue. As 1 % of the world's GDP (around $1,000 billion) is
allegedly lost to cybercrime every year, IT systems continue to get
increasingly interconnected, making them vulnerable to accumulation phenomena
that undermine the pooling mechanism of insurance. As highlighted in the
literature, Hawkes processes appear to be suitable models to capture contagion
phenomena and clustering features of cyber events. This paper extends the
standard Hawkes modeling of cyber risk frequency by adding external shocks,
modelled by the publication of cyber vulnerabilities that are deemed to
increase the likelihood of attacks in the short term. The aim of the proposed
model is to provide a better quantification of contagion effects since, while
the standard Hawkes model allocates all the clustering phenomena to
self-excitation, our model allows to capture the external common factors that
may explain part of the systemic pattern. We propose a Hawkes model with two
kernels, one for the endogenous factor (the contagion from other cyber events)
and one for the exogenous component (cyber vulnerability publications). We use
parametric exponential specifications for both the internal and exogenous
intensity kernels, and we compare different methods to tackle the inference
problem based on public datasets containing features of cyber attacks found in
the Hackmageddon database and cyber vulnerabilities from the Known Exploited
Vulnerability database and the National Vulnerability Dataset. By refining the
external excitation database selection, the degree of endogeneity of the model
is nearly halved. We illustrate our model with simulations and discuss the
impact of taking into account the external factor driven by vulnerabilities.
Once an attack has occurred, response measures are implemented to limit the
effects of an attack. These measures include patching vulnerabilities and
reducing the attack's contagion. We use an augmented version of the model by
adding a second phase modeling a reduction in the contagion pattern from the
remediation measures. Based on this model, we explore various scenarios and
quantify the effect of mitigation measures of an insurance company that aims to
mitigate the effects of a cyber pandemic in its insured portfolio.


  We consider an integrable model describing an Anderson-like impurity coupled
to an open $t$--$J$ chain. Both the hybridization (i.e. its coupling to bulk
chain) and the local spectrum can be controlled without breaking the
integrability of the model. As the hybridization is varied, holon and spinon
bound states appear in the many body ground state. Based on the exact solution
we study the state of the impurity and its contribution to thermodynamic
quantities as a function of an applied magnetic field. Kondo behaviour in the
magnetic response of the impurity can be observed provided that its parameters
have been adjusted properly to the energy scales of the holon and spinon
excitations of the one-dimensional bulk.


  Mobile app stores produce a tremendous amount of data in the form of user
reviews, which is a huge source of user requirements and sentiments; such
reviews allow app developers to proactively address issues in their apps.
However, only a small number of reviews capture common issues and sentiments
which creates a need for automatically identifying prominent reviews.
Unfortunately, most existing work in text ranking and popularity prediction
focuses on social contexts where other signals are available, which renders
such works ineffective in the context of app reviews. In this work, we propose
a new framework, PPrior, that enables proactive prioritization of app issues
through identifying prominent reviews (ones predicted to receive a large number
of votes in a given time window). Predicting highly-voted reviews is
challenging given that, unlike social posts, social network features of users
are not available. Moreover, there is an issue of class imbalance, since a
large number of user reviews receive little to no votes. PPrior employs a
pre-trained T5 model and works in three phases. Phase one adapts the
pre-trained T5 model to the user reviews data in a self-supervised fashion. In
phase two, we leverage contrastive training to learn a generic and
task-independent representation of user reviews. Phase three uses radius
neighbors classifier t o m ake t he final predictions. This phase also uses
FAISS index for scalability and efficient search. To conduct extensive
experiments, we acquired a large dataset of over 2.1 million user reviews from
Google Play. Our experimental results demonstrate the effectiveness of the
proposed framework when compared against several state-of-the-art approaches.
Moreover, the accuracy of PPrior in predicting prominent reviews is comparable
to that of experienced app developers.


  Based on Minkowski's work on critical lattices of 3-dimensional convex bodies
we present an efficient algorithm for computing the density of a densest
lattice packing of an arbitrary 3-polytope. As an application we calculate
densest lattice packings of all regular and Archimedean polytopes.


  Cortical plasticity is one of the main features that enable our ability to
learn and adapt in our environment. Indeed, the cerebral cortex self-organizes
itself through structural and synaptic plasticity mechanisms that are very
likely at the basis of an extremely interesting characteristic of the human
brain development: the multimodal association. In spite of the diversity of the
sensory modalities, like sight, sound and touch, the brain arrives at the same
concepts (convergence). Moreover, biological observations show that one
modality can activate the internal representation of another modality when both
are correlated (divergence). In this work, we propose the Reentrant
Self-Organizing Map (ReSOM), a brain-inspired neural system based on the
reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose
and compare different computational methods for unsupervised learning and
inference, then quantify the gain of the ReSOM in a multimodal classification
task. The divergence mechanism is used to label one modality based on the
other, while the convergence mechanism is used to improve the overall accuracy
of the system. We perform our experiments on a constructed written/spoken
digits database and a DVS/EMG hand gestures database. The proposed model is
implemented on a cellular neuromorphic architecture that enables distributed
computing with local connectivity. We show the gain of the so-called hardware
plasticity induced by the ReSOM, where the system's topology is not fixed by
the user but learned along the system's experience through self-organization.


  Multi-messenger astronomy combining Gravitational Wave (GW) and
Electromagnetic Wave (EM) observation brings huge impact on physics,
astrophysics and cosmology. However, the majority of sources to be detected
with currently running ground-based GW observatories are binary black hole
(BBH) mergers, which are expected disappointedly to have no EM counterparts. In
this letter, we propose that if the BBH merger happens in a gaseous disk around
a supermassive black hole, the merger can be accompanied by a transient radio
flare alike a fast radio burst (FRB). We argue that the total mass and the
effective spin derived from GW detection can be used to distinguish such a
source from other channels of BBH mergers. If the prediction is confirmed with
future observation, multi-messenger astronomy can be brought to a distance
which is one order of magnitude farther than present. The mystery of the origin
of FRBs can also be revealed partially.


  We give a characterization of groups with twisted p-periodic cohomology in
terms of group actions on mod p homology spheres. An equivalent algebraic
characterization of such groups is also presented.


  With the Akeno Giant Air Shower Array (AGASA), 581 cosmic rays above 10^19eV,
47 above 4 x 10^19eV, and 7 above 10^20eV are observed until August 1998.
Arrival direction distribution of these extremely high energy cosmic rays has
been studied. While no significant large-scale anisotropy is found on the
celestial sphere, some interesting clusters of cosmic rays are observed. Above
4 x 10^19eV, there are one triplet and three doublets within separation angle
of 2.5^o and the probability of observing these clusters by a chance
coincidence under an isotropic distribution is smaller than 1 %. Especially the
triplet is observed against expected 0.05 events. The cos(\theta_GC)
distribution expected from the Dark Matter Halo model fits the data as well as
an isotropic distribution above 2 x 10^19eV and 4 x 10^19eV, but is a poorer
fit than isotropy above 10^19eV. Arrival direction distribution of seven
10^20eV cosmic rays is consistent with that of lower energy cosmic rays and is
uniform. Three of seven are members of doublets above about 4 x 10^19eV.


  It is well-known that all Feynman integrals within a given family can be
expressed as a finite linear combination of master integrals. The master
integrals naturally group into sectors. Starting from two loops, there can
exist sectors made up of more than one master integral. In this paper we show
that such sectors may have additional symmetries. First of all, self-duality,
which was first observed in Feynman integrals related to Calabi--Yau
geometries, often carries over to non-Calabi--Yau Feynman integrals. Secondly,
we show that in addition there can exist Galois symmetries relating integrals.
In the simplest case of two master integrals within a sector, whose definition
involves a square root $r$, we may choose a basis $(I_1,I_2)$ such that $I_2$
is obtained from $I_1$ by the substitution $r \rightarrow -r$. This pattern
also persists in sectors, which a priori are not related to any square root
with dependence on the kinematic variables. We show in several examples that in
such cases a suitable redefinition of the integrals introduces constant square
roots like $\sqrt{3}$. The new master integrals are then again related by a
Galois symmetry, for example the substitution $\sqrt{3} \rightarrow -\sqrt{3}$.
To handle the case where the argument of a square root would be a perfect
square we introduce a limit Galois symmetry. Both self-duality and Galois
symmetries constrain the differential equation.


  The results of a numerical study of the growth of solid particles, ranging
from 1 micron to 1 millimeter in size, in the vicinity of an azimuthally
symmetric density enhancement of a protostellar disk are presented. It is shown
that the combined effect of gas drag and pressure gradients, which causes solid
objects to rapidly migrate toward the location of the local maximum density,
can also enhance the rate of growth of dust particles to larger objects. The
results of numerical simulations of such growth processes are presented and the
effects of the changes of the physical properties of particles on the rates of
their growth are also discussed.


  We report on an energy-sensitive imaging detector for studying the
fragmentation of polyatomic molecules in the dissociative recombination of fast
molecular ions with electrons. The system is based on a large area (10 cm x 10
cm) position-sensitive, double-sided Si-strip detector with 128 horizontal and
128 vertical strips, whose pulse height information is read out individually.
The setup allows to uniquely identify fragment masses and is thus capable of
measuring branching ratios between different fragmentation channels, kinetic
energy releases, as well as breakup geometries, as a function of the relative
ion-electron energy. The properties of the detection system, which has been
installed at the TSR storage ring facility of the Max-Planck Institute for
Nuclear Physics in Heidelberg, is illustrated by an investigation of the
dissociative recombination of the deuterated triatomic hydrogen cation D2H+. A
huge isotope effect is observed when comparing the relative branching ratio
between the D2+H and the HD+D channel; the ratio 2B(D2+H)/B(HD+D), which is
measured to be 1.27 +/- 0.05 at relative electron-ion energies around 0 eV, is
found to increase to 3.7 +/- 0.5 at ~5 eV.


  In this paper, we study the problem of exact community recovery in the
symmetric stochastic block model, where a graph of $n$ vertices is randomly
generated by partitioning the vertices into $K \ge 2$ equal-sized communities
and then connecting each pair of vertices with probability that depends on
their community memberships. Although the maximum-likelihood formulation of
this problem is discrete and non-convex, we propose to tackle it directly using
projected power iterations with an initialization that satisfies a partial
recovery condition. Such an initialization can be obtained by a host of
existing methods. We show that in the logarithmic degree regime of the
considered problem, the proposed method can exactly recover the underlying
communities at the information-theoretic limit. Moreover, with a qualified
initialization, it runs in $\mathcal{O}(n\log^2n/\log\log n)$ time, which is
competitive with existing state-of-the-art methods. We also present numerical
results of the proposed method to support and complement our theoretical
development.


  The two-photon correlation of the light pulse emitted from a sonoluminescence
bubble is discussed. It is shown that several important information about the
mechanism of light emission, such as the time-scale and the shape of the
emission region could be obtained from the HBT interferometry. We also argue
that such a measurement may serve to reject one of the two currently suggested
emission mechanisms, i.e., thermal process versus dynamical Casimir effect.


  Predation driven Allee effects play an important role in the dynamics of
small population, however, such predation-driven Allee effects cannot occur for
the model with type I functional response. It generally occurs when a
generalist predator targets some specific prey. However, apart from the lethal
effects of predation, there are some non-lethal effects in the presence of
predator. Due to the fear of predation, positive density dependence growth may
be observed at low population density, because of reduced foraging activities.
Moreover, this non-lethal effect can be carried over generations. In the
present manuscript, we investigate the role of predation fear and its
carry-over effects in prey-predator model. First, we study the single species
model in global perspective. We have shown that depending on the birth rate,
our single species model describes three types of growth dynamics, namely,
strong Allee dynamics, weak Allee dynamics and logistic dynamics. Then we
consider the explicit dynamics of predator, with type I functional response.
Basic dynamical properties, as well as global stability of each equilibria have
been discussed. From our analysis, we can observe that both the fear and its
carry-over effects have significant role in the stability of the coexistence
equilibrium, even if for the model with type I functional response. The
phenomenon paradox of enrichment can be observed in our model, which cannot be
observed in the classical prey-predator model with type I functional response.
However, we can see that such phenomenon can be ruled out by choosing suitable
non-lethal effect parameters. Therefore, our study shows how non-lethal effects
change the dynamics of a competition model, and has important biological
insights, specially for the understanding of the dynamics of small populations.


  RPL is the emerging routing standard for low power and lossy networks (LLNs).
LLN is a key component of the Internet of Things (IoT), hence its security is
imperative for the age of IoT. In this work, we present the DAO induction
attack, a novel attack against RPL. In this attack, a malicious insider or a
compromised node periodically increments its DTSN number. Each such increment
can trigger/induce a large number of control message transmissions in the
network. We show that this degrades the network performance in terms of
end-to-end latency, packet loss ratio, and power consumption. To mitigate, we
propose a lightweight solution to detect the DAO induction attack. Our solution
imposes nearly no overhead on IoT devices, which is important as these devices
are typically constrained in terms of power, memory and processing.


  Nighttime sky transparency statistics on Mauna Kea are reported based on data
from the Canada-France-Hawaii Telescope SkyProbe monitor. We focus on the
period beginning with the start of MegaCam wide-field optical imager operations
in 2003, and continuing for almost three years. Skies were clear enough to
observe on 76% of those nights; attenuations were less than 0.2 magnitudes up
to 60% of the time. An empirical model of cloud attenuation and duration is
presented allowing us to further characterize the photometric conditions. This
is a good fit tothe SkyProbe data, and indicates that Mauna Kea skies are truly
photometric (without cloud) an average of 56% of the time, with moderate
seasonal variation. Continuous monitoring of transparency during the night is
necessary to overcome fluctuations in attenuation due to thin cloud.


  Using Braun-Chuang-Lazarev's derived quotient, we enhance the contraction
algebra of Donovan-Wemyss to an invariant valued in differential graded
algebras. Given an isolated contraction $X \to X_\mathrm{con}$ of an
irreducible rational curve $C$ to a point $p$, we show that its derived
contraction algebra controls the derived noncommutative deformations of $C$. We
use dg singularity categories to prove that, when $X$ is smooth, the derived
contraction algebra recovers the geometry of $X_\mathrm{con}$ complete locally
around $p$, establishing a positive answer to a derived version of a conjecture
of Donovan and Wemyss. When $X \to X_\mathrm{con}$ is a simple threefold
flopping contraction, it is known that the Bridgeland-Chen flop-flop
autoequivalence of $D^b(X)$ is a `noncommutative twist' around the contraction
algebra. We show that the derived contraction algebra controls an analogous
autoequivalence in more general settings, and in particular for partial
resolutions of Kleinian singularities.


  We demonstrate via a muon spin rotation experiment that the electronic ground
state of the iridium spinel compound, CuIr$_2$S$_4$, is not the presumed
spin-singlet state but a novel paramagnetic state, showing a quasistatic spin
glass-like magnetism below ~100 K. Considering the earlier indication that
IrS$_6$ octahedra exhibit dimerization associated with the metal-to-insulator
transition below 230 K, the present result suggests that a strong spin-orbit
interaction may be playing an important role in determining the ground state
that accompanies magnetic frustration.


  We show that, in general, the translational average over a spatial
variable---discussed by Backus \cite{backus}, and referred to as the
equivalent-medium average---and the rotational average over a symmetry group at
a point---discussed by Gazis et al. \cite{gazis}, and referred to as the
effective-medium average---do not commute. However, they do commute in special
cases of particular symmetry classes, which correspond to special relations
among the elasticity parameters. We also show that this noncommutativity is a
function of the strength of anisotropy. Surprisingly, a perturbation of the
elasticity parameters about a point of weak anisotropy results in the
commutator of the two types of averaging being of the order of the {\it
square\/} of this perturbation. Thus, these averages nearly commute in the case
of weak anisotropy, which is of interest in such disciplines as quantitative
seismology, where the weak-anisotropy assumption results in empirically
adequate models.


  Graph neural networks (GNNs) continue to achieve state-of-the-art performance
on many graph learning tasks, but rely on the assumption that a given graph is
a sufficient approximation of the true neighborhood structure. When a system
contains higher-order sequential dependencies, we show that the tendency of
traditional graph representations to underfit each node's neighborhood causes
existing GNNs to generalize poorly. To address this, we propose a novel Deep
Graph Ensemble (DGE), which captures neighborhood variance by training an
ensemble of GNNs on different neighborhood subspaces of the same node within a
higher-order network structure. We show that DGE consistently outperforms
existing GNNs on semisupervised and supervised tasks on six real-world data
sets with known higher-order dependencies, even under a similar parameter
budget. We demonstrate that learning diverse and accurate base classifiers is
central to DGE's success, and discuss the implications of these findings for
future work on ensembles of GNNs.


  We show that if $A$ is an $n\times n$-matrix, then the diagonal entries of
each power $A^{m}$ are uniquely determined by the principal minors of $A$, and
can be written as universal (integral) polynomials in the latter. Furthermore,
if the latter all equal $1$, then so do the former. These results are inspired
by Problem B5 on the Putnam contest 2021, and shed a new light on the behavior
of minors under matrix multiplication.


  Reinforcement learning algorithms have performed well in playing challenging
board and video games. More and more studies focus on improving the
generalisation ability of reinforcement learning algorithms. The General Video
Game AI Learning Competition aims to develop agents capable of learning to play
different game levels that were unseen during training. This paper summarises
the five years' General Video Game AI Learning Competition editions. At each
edition, three new games were designed. The training and test levels were
designed separately in the first three editions. Since 2020, three test levels
of each game were generated by perturbing or combining two training levels.
Then, we present a novel reinforcement learning technique with dual-observation
for general video game playing, assuming that it is more likely to observe
similar local information in different levels rather than global information.
Instead of directly inputting a single, raw pixel-based screenshot of the
current game screen, our proposed general technique takes the encoded,
transformed global and local observations of the game screen as two
simultaneous inputs, aiming at learning local information for playing new
levels. Our proposed technique is implemented with three state-of-the-art
reinforcement learning algorithms and tested on the game set of the 2020
General Video Game AI Learning Competition. Ablation studies show the
outstanding performance of using encoded, transformed global and local
observations as input.


  The nature of the `inner engine' that accelerate and collimate the
relativistic flow at the cores of GRBs is the most interesting current puzzle
concerning GRBs. Numerical simulations have shown that the internal shocks'
light curve reflects the activity of this inner engine. Using a simple analytic
toy model we clarify the relations between the observed $ \gamma $-rays light
curve and the inner engine's activity and the dependence of the light curves on
the inner engine's parameters. This simple model also explains the observed
similarity between the observed distributions of pulses widths and the
intervals between pulses and the correlation between the width of a pulse and
the length of the preceding interval. Our analysis suggests that the
variability in the wind's Lorentz factors arises due to a modulation of the
mass injected into a constant energy flow.


  We present several results concerning the free energy of odd-dimensional
conformal field theories (CFTs) on squashed spheres. First, we propose a
formula which computes this quantity for holographic CFTs dual to
higher-curvature gravities with second-order linearized equations of motion. As
opposed to standard on-shell action methods for Taub geometries, our formula is
automatically UV-finite and only involves a simple evaluation of the
corresponding bulk Lagrangian on an auxiliary pure-AdS space. The expression is
closely related to the function determining the possible AdS vacua of the bulk
theory in question, which we argue to act as a generating functional from which
correlation functions of the boundary stress tensor can be easily
characterized. Finally, based on holographic results and free-field numerical
calculations, we conjecture that the subleading term in the squashing-parameter
free-energy expansion is universally controlled by the stress-tensor
three-point function charge $t_4$ for general $(2+1)$-dimensional CFTs.


  We have annealed two dimensional lattice model of Coulomb glass using Monte
Carlo simulations to obtain the ground state. We have shown that the energy
required to create a domain of linear size L in d dimensions is proportional to
$L^{d-1}$. Using Imry-Ma arguments given for RFIM, one gets $d_{c}\geq 2$ for
Coulomb glass. The investigations in the transition region shows that the
domain wall of the metastable state in the charge-ordered phase shifts as
disorder is increased to give disordered ground state at higher disorder
strength indicating phase coexistence. This coupled with discontinuity in
magnetization is an indication of first-order type transition from
charge-ordered phase to disordered phase. The structure and nature of Random
field fluctuations of the domain in Coulomb glass are inconsistent with the
assumptions of Imry and Ma as was also reported for RFIM.


  We have investigated the Holmberg effect of Main galaxy pairs of the SDSS
Data Release 4 (SDSS4). It is found that except i-z color the color indices
between the two components of Main galaxy pairs clearly have significantly
larger correlation coefficients. Further analyses also show that the Holmberg
Effect of galaxies not only depends on the color indices but also on the
morphological type for two components of pairs.


  In this note, we investigate an infinite one parameter family of circle
packings, each with a set of three mutually tangent circles. We use these to
generate an infinite set of circle packings with the Apollonian property. That
is, every circle in the packing is a member of a cluster of four mutually
tangent circles.


  A study of $D^+\KS$ and $D^0K^+$ final states is performed in a sample of
1.0/fb of $pp$ collision data collected at a centre-of-mass energy of
$\sqrt{s}=7$ TeV with the \lhcb detector. We confirm the existence of the
$D_{s1}^*(2700)^+$ and $D_{sJ}^*(2860)^+$ excited states and measure their
masses and widths to be {eqnarray} m(D_{s1}^*(2700)^+) &=& 2709.2 \pm
1.9({stat})\pm\,\,\,4.5({syst}) {MeV}/c^2,\cr \Gamma(D_{s1}^*(2700)^+) &=&
\,\,\,115.8 \pm 7.3({stat}) \pm12.1({syst}) {MeV}/c^2,\cr m(D_{sJ}^*(2860)^+)
&=& 2866.1 \pm 1.0({stat}) \pm\,\,\,6.3({syst}) {MeV}/c^2,\cr
\Gamma(D_{sJ}^*(2860)^+) &=& \,\,\,\,\,\,69.9 \pm 3.2({stat})
\pm\,\,\,6.6({syst}) {MeV}/c^2.\cr {eqnarray}


  We present simultaneous optical and X-ray data from Swift for a sample of
FSRQs selected from the EMSS survey. We present also a complete analysis of
Swift and INTEGRAL data on 4 blazars recently discussed as possibly challenging
the trends of the "blazar spectral sequence". The SEDs of all these objects are
modelled in terms of a general theoretical scheme leading to an estimate of the
jets' physical parameters. Our results show that, in the case of the EMSS broad
line blazars, X-ray selection does not lead to find sources with synchrotron
peaks in the UV/X-ray range, as was the case for X-ray selected BL Lacs.
Instead, for a wide range of radio powers all the sources with broad emission
lines show similar SEDs, with synchrotron components peaking below the
optical/UV range. Of the remaining 4 "anomalous" blazars, two highly luminous
sources with broad lines, claimed to possibly emit synchrotron X-rays, are
shown to be better described with IC models for their X-ray emission. For one
source with weak emission lines (a BL Lac object) a synchrotron peak in the
soft X-ray range is confirmed, while for the fourth source, exhibiting lines
typical of NLSy1s, no evidence of X-ray emission from a relativistic jet is
found. We reexamine the original "blazar spectral sequence" and suggest that
the photon ambient, in which the particle acceleration and emission occur, is
likely the main factor determining the shape of the blazar SED.


  We derive statistical equilibrium solutions of the truncated inviscid surface
quasi-geostrophic (SQG) equations, and verify the validity of these solutions
at late times in numerical simulations of the truncated SQG equations. The
results indicate enstrophy thermalizes while energy can condense at the gravest
modes, in agreement with previous indications of a direct cascade of enstrophy
and an inverse cascade of energy in forced-dissipative SQG systems. At early
times, the truncated inviscid SQG simulations show a behavior reminiscent of
forced-dissipative SQG turbulence, and we identify spectral scaling laws for
the energy and enstrophy spectra. Finally, a comparison between viscous and
inviscid simulations allows us to identify free-decay similarity laws for the
enstrophy in SQG turbulence at very large Reynolds number.


  The purpose of this article is to study generalized quasi Yamabe gradient
solitons on warped product manifolds. First, we obtain some necessary and
sufficient conditions for the existence of generalized quasi Yamabe gradient
solitons equipped with the warped product structure. Then we study three
important applications in the Lorentzian and the neutral settings for the
particular class, called as gradient Yamabe soliton: We proved the existence of
the non-trivial gradient Yamabe soliton on generalized Robertson-Walker
spacetimes, standard static spacetimes and Walker manifolds.


  In this paper we implement a numerical algorithm to compute codimension-one
tori in three-dimensional, volume-preserving maps. A torus is defined by its
conjugacy to rigid rotation, which is in turn given by its Fourier series. The
algorithm employs a quasi-Newton scheme to find the Fourier coefficients of a
truncation of the series. This technique is based upon the theory developed in
the accompanying article by Blass and de la Llave. It is guaranteed to converge
assuming the torus exists, the initial estimate is suitably close, and the map
satisfies certain nondegeneracy conditions. We demonstrate that the growth of
the largest singular value of the derivative of the conjugacy predicts the
threshold for the destruction of the torus. We use these singular values to
examine the mechanics of the breakup of the tori, making comparisons to
Aubry-Mather and anti-integrability theory when possible.


  The adaptation to situations of sequential choice under uncertainty of
decision criteria which deviate from (subjective) expected utility raises the
problem of ensuring the selection of a nondominated strategy. In particular,
when following the suggestion of Machina and McClennen of giving up
separability (also known as consequentialism), which requires the choice of a
substrategy in a subtree to depend only on data relevant to that subtree, one
must renounce to the use of dynamic programming, since Bellman's principle is
no longer valid. An interpretation of McClennen's resolute choice, based on
cooperation between the successive Selves of the decision maker, is proposed.
Implementations of resolute choice which prevent Money Pumps negative prices of
information or, more generally, choices of dominated strategies, while
remaining computationally tractable, are proposed.


  The pedal of a curve in the Euclidean plane is a classical subject which has
a singular point at the inflection point of the original curve or the pedal
point. The primitive of a curve is a curve given by the inverse construction
for making the pedal. In this paper we consider the pedal of a quadratic curve.
On of the main results gives a characterization of such curves, which is one of
the generalizations of limacons of Pascal.


  In epidemiological studies, zero-inflated and hurdle models are commonly used
to handle excess zeros in reported infectious disease cases. However, they can
not model the persistence (from presence to presence) and reemergence (from
absence to presence) of a disease separately. Covariates can sometimes have
different effects on the reemergence and persistence of a disease. Recently, a
zero-inflated Markov switching negative binomial model was proposed to
accommodate this issue. We present a Markov switching negative binomial hurdle
model as a competitor of that approach, as hurdle models are often also used as
alternatives to zero-inflated models for accommodating excess zeroes. We begin
the comparison by inspecting the underlying assumptions made by both models.
Hurdle models assume perfect detection of the disease cases while zero-inflated
models implicitly assume the case counts can be under-reported, thus we
investigate when a negative binomial distribution can approximate the true
distribution of reported counts. A comparison of the fit of the two types of
Markov switching models is undertaken on chikungunya cases across the
neighborhoods of Rio de Janeiro. We find that, among the fitted models, the
Markov switching negative binomial zero-inflated model produces the best
predictions and both Markov switching models produce remarkably better
predictions than more traditional negative binomial hurdle and zero-inflated
models.


  We study a series of recognition tasks in two realistic scenarios requiring
the analysis of faces under strong occlusion. On the one hand, we aim to
recognize facial expressions of people wearing Virtual Reality (VR) headsets.
On the other hand, we aim to estimate the age and identify the gender of people
wearing surgical masks. For all these tasks, the common ground is that half of
the face is occluded. In this challenging setting, we show that convolutional
neural networks (CNNs) trained on fully-visible faces exhibit very low
performance levels. While fine-tuning the deep learning models on occluded
faces is extremely useful, we show that additional performance gains can be
obtained by distilling knowledge from models trained on fully-visible faces. To
this end, we study two knowledge distillation methods, one based on
teacher-student training and one based on triplet loss. Our main contribution
consists in a novel approach for knowledge distillation based on triplet loss,
which generalizes across models and tasks. Furthermore, we consider combining
distilled models learned through conventional teacher-student training or
through our novel teacher-student training based on triplet loss. We provide
empirical evidence showing that, in most cases, both individual and combined
knowledge distillation methods bring statistically significant performance
improvements. We conduct experiments with three different neural models (VGG-f,
VGG-face, ResNet-50) on various tasks (facial expression recognition, gender
recognition, age estimation), showing consistent improvements regardless of the
model or task.


  Due to the wide range of applications, theoretical models of Fe3O4 films are
found to be important. Ultra thin Fe3O4 films with ferrite structure have been
theoretically investigated using second order perturbed modified Heisenberg
Hamiltonian. Matrices for ultra thin films with two and three spin layers are
presented in this manuscript. Total magnetic energy was expressed in terms of
spin exchange interaction, magnetic dipole interaction, second order magnetic
anisotropy and stress induced magnetic anisotropy. Magnetic properties were
observed for films with two spin layers and variant second order magnetic
anisotropy. For the film with three spin layers, second order anisotropy
constant was fixed to avoid tedious derivations. Magnetic easy axis rotates
toward the in plane direction as the number of spin layers is increased from
two to three because the stress induced anisotropy energy dominates at higher
number of spin layers. According to some other experimental data, the magnetic
easy axis of thin films rotates toward the in plane direction as the thickness
is increased. For ferrite film with two spin layers, magnetic easy and hard
directions can be observed at 0.75 and 1.2 radians, respectively, when the
ratio of stress induced anisotropy to the long range dipole interaction
strength is 3.9. For ferrite film with three spin layers, magnetic easy and
hard directions can be observed at 2.4 and 2.3 radians, respectively, when the
ratio of stress induced anisotropy to the long range dipole interaction
strength is 4.2.


  In a recent article by Chapuy and Perarnau, it was shown that a uniformly
chosen automaton on $n$ states with a $2$-letter alphabet has a synchronizing
word of length $O(\sqrt{n}\log n)$ with high probability. In this note, we
improve this result by showing that, for any $\varepsilon>0$, there exists a
synchronizing word of length $O(\varepsilon^{-1}\sqrt{n \log n})$ with
probability $1-\varepsilon$. Our proof is based on two properties of random
automata. First, there are words $\omega$ of length $O(\sqrt{n \log n})$ such
that the expected number of possible states for the automaton, after inputting
$\omega$, is $O(\sqrt{n/\log n})$. Second, with high probability, each pair of
states can be synchronized by a word of length $O(\log n)$.


  Nikulin and Vinberg proved that there are only a finite number of lattices of
rank $\geq 3$ that are the N\'eron-Severi group of projective K3 surfaces with
a finite automorphism group. The aim of this paper is to provide a more
geometric description of such K3 surfaces $X$, when these surfaces have
moreover no elliptic fibrations. In that case we show that such K3 surface is
either a quartic with special hyperplane sections or a double cover of the
plane branched over a smooth sextic curve which has special tangencies
properties with some lines, conics or cuspidal cubic curves. We then study the
converse i.e. if the geometric description we obtained characterizes these
surfaces. In $4$ cases the description is sufficient, in each of the $4$ other
cases there is exactly another one possibility which we study. We obtain that
at least 5 moduli spaces of K3 surfaces (among the 8 we study) are unirational.


  We study the problem of learning a Bayesian network (BN) of a set of
variables when structural side information about the system is available. It is
well known that learning the structure of a general BN is both computationally
and statistically challenging. However, often in many applications, side
information about the underlying structure can potentially reduce the learning
complexity. In this paper, we develop a recursive constraint-based algorithm
that efficiently incorporates such knowledge (i.e., side information) into the
learning process. In particular, we study two types of structural side
information about the underlying BN: (I) an upper bound on its clique number is
known, or (II) it is diamond-free. We provide theoretical guarantees for the
learning algorithms, including the worst-case number of tests required in each
scenario. As a consequence of our work, we show that bounded treewidth BNs can
be learned with polynomial complexity. Furthermore, we evaluate the performance
and the scalability of our algorithms in both synthetic and real-world
structures and show that they outperform the state-of-the-art structure
learning algorithms.


  We construct a group field theory which realizes the sum of gravity
amplitudes over all three dimensional topologies trough a perturbative
expansion. We prove this theory to be uniquely Borel summable. This shows how
to define a non-perturbative summation over triangulations including all
topologies in the context of three dimensional discrete gravity.


  In this work the collective modes of an effective kinetic theory description
based on the Boltzmann equation in relaxation time approximation applicable to
gauge theories at weak but finite coupling and low frequencies are studied.
Real time retarded two-point correlators of the energy-momentum tensor and the
R-charge current are calculated at finite temperature in flat space-times for
large N gauge theories. It is found that the real time correlators possess
logarithmic branch cuts which in the limit of large coupling disappear and give
rise to non-hydrodynamic poles that are reminiscent of quasi-normal modes in
black holes. In addition to branch cuts, correlators can have simple
hydrodynamic poles, generalizing the concept of hydrodynamic modes to
intermediate wavelength. Surprisingly, the hydrodynamic poles cease to exist
for some critical value of the wavelength and coupling reminiscent of the
properties of onset transitions.


  We calculate the value of the coupling at the infrared zero of the beta
function of an asymptotically free SU(3) gauge theory at the five-loop level as
a function of the number of fermions. Both a direct analysis of the beta
function and analyses of Pad\'e approximants are used for this purpose. We then
calculate the value of the five-loop anomalous dimension, $\gamma_m$, of the
fermion bilinear at this IR zero of the beta function.


  In recent years, significant progress has been made in scene text recognition
by data-driven methods. However, due to the scarcity of annotated real-world
data, the training of these methods predominantly relies on synthetic data. The
distribution gap between synthetic and real data constrains the further
performance improvement of these methods in real-world applications. To tackle
this problem, a highly promising approach is to utilize massive amounts of
unlabeled real data for self-supervised training, which has been widely proven
effective in many NLP and CV tasks. Nevertheless, generic self-supervised
methods are unsuitable for scene text images due to their sequential nature. To
address this issue, we propose a Local Explicit and Global Order-aware
self-supervised representation learning method (LEGO) that accounts for the
characteristics of scene text images. Inspired by the human cognitive process
of learning words, which involves spelling, reading, and writing, we propose
three novel pre-text tasks for LEGO to model sequential, semantic, and
structural features, respectively. The entire pre-training process is optimized
by using a consistent Text Knowledge Codebook. Extensive experiments validate
that LEGO outperforms previous scene text self-supervised methods. The
recognizer incorporated with our pre-trained model achieves superior or
comparable performance compared to state-of-the-art scene text recognition
methods on six benchmarks. Furthermore, we demonstrate that LEGO can achieve
superior performance in other text-related tasks.


  We consider a quantum self-contained fridge consisting of three qubits
interacting with three separate heat reservoirs, respectively, and functioning
without any external controls. Applying the methods of endoreversible
thermodynamics, we derive explicit expressions of cooling load versus
efficiency of this fridge, which demonstrate behaviors of trade-off between
those two quantities and thus enable to discuss the thermoeconomic optimization
of performance. We also discuss a possibility for the amplification of cooling
load briefly in a simple modification from the original architecture of fridge.


  Traditional imitation learning focuses on modeling the behavioral mechanisms
of experts, which requires a large amount of interaction history generated by
some fixed expert. However, in many streaming applications, such as streaming
recommender systems, online decision-makers typically engage in online learning
during the decision-making process, meaning that the interaction history
generated by online decision-makers includes their behavioral evolution from
novice expert to experienced expert. This poses a new challenge for existing
imitation learning approaches that can only utilize data from experienced
experts. To address this issue, this paper proposes an inverse batched
contextual bandit (IBCB) framework that can efficiently perform estimations of
environment reward parameters and learned policy based on the expert's
behavioral evolution history. Specifically, IBCB formulates the inverse problem
into a simple quadratic programming problem by utilizing the behavioral
evolution history of the batched contextual bandit with inaccessible rewards.
We demonstrate that IBCB is a unified framework for both deterministic and
randomized bandit policies. The experimental results indicate that IBCB
outperforms several existing imitation learning algorithms on synthetic and
real-world data and significantly reduces running time. Additionally, empirical
analyses reveal that IBCB exhibits better out-of-distribution generalization
and is highly effective in learning the bandit policy from the interaction
history of novice experts.


  We present Mixture of Contrastive Experts (MiCE), a unified probabilistic
clustering framework that simultaneously exploits the discriminative
representations learned by contrastive learning and the semantic structures
captured by a latent mixture model. Motivated by the mixture of experts, MiCE
employs a gating function to partition an unlabeled dataset into subsets
according to the latent semantics and multiple experts to discriminate distinct
subsets of instances assigned to them in a contrastive learning manner. To
solve the nontrivial inference and learning problems caused by the latent
variables, we further develop a scalable variant of the
Expectation-Maximization (EM) algorithm for MiCE and provide proof of the
convergence. Empirically, we evaluate the clustering performance of MiCE on
four widely adopted natural image datasets. MiCE achieves significantly better
results than various previous methods and a strong contrastive learning
baseline.


  Galaxy velocity maps often show the typical pattern of a rotating disk,
consistent with the dynamical model where emitters rotate in circular orbits
around the galactic center. The simplest template used to fit these maps
consists in the rotating disk model (RDM) where the amplitude of circular
velocities is fixed by the observed velocity profile along the kinematic axis.
A more sophisticated template is the rotating tilted-ring model (RTRM) that
takes into account the presence of warps and allows a radius-dependent
orientation of the kinematic axis. In both cases, axisymmetry is assumed and
residuals between the observed and the model velocity fields are interpreted as
noncircular motions. We show that if a galaxy is not axisymmetric, there is an
intrinsic degeneracy between a rotational and a radial velocity field. We then
introduce a new galaxy template, the radial ellipse model (REM), that is not
axisymmetric and has a purely radial velocity field with an amplitude that is
correlated with the major axis of the ellipse. We show that best fits to the
observed two-dimensional velocity fields of 28 galaxies extracted from the
THINGS sample with both the REM and the RDM give residuals with similar
amplitudes, where the REM residuals trace nonradial motions. Best fits obtained
with the RTRM, because of its larger number of free parameters, give the
smallest residuals: however, we argue that this does not necessarily imply that
the RTRM gives the most accurate representation of a galaxy velocity field.
Instead, we show that this method is not able to disentangle between circular
and radial motions for the case of nonaxisymmetric systems. Finally, we
consider the physical motivation of the REM, and discuss how the interpretation
of galactic dynamics changes if one assumes that the main component of a galaxy
velocity field is modeled as a RDM/RTRM or as a REM.


  The stochastic systems without detailed balance are common in various
chemical reaction systems, such as metabolic network systems. In studies of
these systems, the concept of potential landscape is useful. However, what are
the sufficient and necessary conditions of the existence of the potential
function is still an open problem. Use Hodge decomposition theorem in
differential form theory, we focus on the general chemical Langevin equations,
which reflect complex chemical reaction systems. We analysis the conditions for
the existence of potential landscape of the systems. By mapping the stochastic
differential equations to a Hamiltonian mechanical system, we obtain the
Fokker-Planck equation of the chemical reaction systems. The obtained
Fokker-Planck equation can be used in further studies of other steady
properties of complex chemical reaction systems, such as their steady state
entropies.


  The Wigner's friend thought experiment was intended to illustrate the
difficulty one has in describing an agent as a quantum system when that agent
performs a measurement. While it does pose a challenge to the orthodox
interpretation of quantum theory, most modern interpretations have no trouble
in resolving the difficulty. Recently, a number of extensions of Wigner's ideas
have been proposed. We provide a gentle introduction to six such arguments,
modifying the specifics of many of them so that they are as simple and unified
as possible. In particular, we show that all of the arguments hinge on
assumptions about correlations between measurement outcomes that are not
accessible to any observer, even in principle. We then provide a critical
analysis of each argument, focusing especially on how well one can motivate the
required assumptions regarding these inaccessible correlations. Although we
argue that some of these assumptions are not entirely well-motivated, all of
the arguments do shed light on the nature of quantum theory, especially when
concerning the description of agents and their measurements. Although there are
other possible responses, the most compelling of these no-go theorems can be
taken to support the view that measurement outcomes are perspectival rather
than absolute.


  The number count of ionized bubbles on a map of 21 cm fluctuations with the
primordial non-Gaussianity is investigated. The existence of the primordial
non-Gaussianity modifies the reionization process, because the formation of
collapsed objects, which could be the source of reionization photons, is
affected by the primordial non-Gaussianity. In this paper, the abundance of
ionized bubbles is calculated by using a simple analytic model with the local
type of the primordial non-Gaussianity, which is parameterized by f_{NL}. In
order to take into account the dependence of the number count on the size of
ionized bubbles and the resolution of the observation instrument, a threshold
parameter B_b which is related to the the surface brightness temperature
contrast of an ionized bubble is introduced. We show the potential to put the
constraint on f_{NL} from the number count by future observations such as LOFAR
and SKA.


  In probability density function (PDF) methods a transport equation is solved
numerically to compute the time and space dependent probability distribution of
several flow variables in a turbulent flow. The joint PDF of the velocity
components contains information on all one-point one-time statistics of the
turbulent velocity field, including the mean, the Reynolds stresses and
higher-order statistics. We developed a series of numerical algorithms to model
the joint PDF of turbulent velocity, frequency and scalar compositions for
high-Reynolds-number incompressible flows in complex geometries using
unstructured grids. Advection, viscous diffusion and chemical reaction appear
in closed form in the PDF formulation, thus require no closure hypotheses. The
generalized Langevin model (GLM) is combined with an elliptic relaxation
technique to represent the non-local effect of walls on the pressure
redistribution and anisotropic dissipation of turbulent kinetic energy. The
governing system of equations is solved fully in the Lagrangian framework
employing a large number of particles representing a finite sample of all fluid
particles. Eulerian statistics are extracted at gridpoints of the unstructured
mesh. Compared to other particle-in-cell approaches for the PDF equations, this
methodology is non-hybrid, thus the computed fields remain fully consistent
without requiring any specific treatment. Two testcases demonstrate the
applicability of the algorithm: a fully developed turbulent channel flow and
the classical cavity flow both with scalars released from concentrated sources.


  An analysis of off-diagonal long-range order in superconductors shows that
the spin-spin correlation function is significantly influenced by the order if
the order parameter is anisotropic on a microscopic scale. Thus, magnetic
neutron scattering can provide a direct measurement of the condensate fraction
of a superconductor. It is also argued that recent measurements in high
temperature superconductors come very close to achieving this goal.


  We report on new high-resolution near-infrared images of the gravitationally
lensed radio source PKS1830-211, a quasar at z=2.507. These adaptive optics
observations, taken with the Very Large Telescope (VLT), are further improved
through image deconvolution. They confirm the presence of a second object along
the line of sight to the quasar, in addition to the previously known spiral
galaxy. This additional object is clearly extended in our images. However, its
faint luminosity does not allow to infer any photometric redshift. If this
galaxy is located in the foreground of PKS1830-211, it complicates the modeling
of this system and decreases the interest in using PKS1830-211 as a means to
determine H0 via the time delay between the two lensed images of the quasar.


  Convolutional neural networks (CNNs) are used in numerous real-world
applications such as vision-based autonomous driving and video content
analysis. To run CNN inference on various target devices, hardware-aware neural
architecture search (NAS) is crucial. A key requirement of efficient
hardware-aware NAS is the fast evaluation of inference latencies in order to
rank different architectures. While building a latency predictor for each
target device has been commonly used in state of the art, this is a very
time-consuming process, lacking scalability in the presence of extremely
diverse devices. In this work, we address the scalability challenge by
exploiting latency monotonicity -- the architecture latency rankings on
different devices are often correlated. When strong latency monotonicity
exists, we can re-use architectures searched for one proxy device on new target
devices, without losing optimality. In the absence of strong latency
monotonicity, we propose an efficient proxy adaptation technique to
significantly boost the latency monotonicity. Finally, we validate our approach
and conduct experiments with devices of different platforms on multiple
mainstream search spaces, including MobileNet-V2, MobileNet-V3, NAS-Bench-201,
ProxylessNAS and FBNet. Our results highlight that, by using just one proxy
device, we can find almost the same Pareto-optimal architectures as the
existing per-device NAS, while avoiding the prohibitive cost of building a
latency predictor for each device. GitHub:
https://github.com/Ren-Research/OneProxy


  Persistent oscillations are a hallmark of non-ergodic time evolution. While
time-crystalline behavior results from, e.g., many-body localization, here we
show that ever-revolving solitary waves emerge in flatband Heisenberg quantum
spin systems.


  Data recorded by gravitational wave detectors includes many non-astrophysical
transient noise bursts, the most common of which is caused by scattered-light
within the detectors. These so-called ``glitches'' in the data impact the
ability to both observe and characterize incoming gravitational wave signals.
In this work we use a scattered-light glitch waveform model to identify and
characterize scattered-light glitches in a representative stretch of
gravitational wave data. We identify $2749$ scattered-light glitches in $5.96$
days of LIGO-Hanford data and $1306$ glitches in $5.93$ days of LIGO-Livingston
data taken from the third LIGO-Virgo observing run. By subtracting identified
scattered-light glitches we demonstrate an increase in the sensitive volume of
the gravitational wave search for binary black hole signals by $\sim1\%$.


  We show that electronic-entropy effects in the size-evolutionary patterns of
relatively small (as small as 20 atoms), simple-metal clusters become prominent
already at moderate temperatures. Detailed agreement between our
finite-temperature-shell-correction-method calculations and experimental
results is obtained for certain temperatures. This agreement includes a
size-dependent smearing out of fine-structure features, accompanied by a
measurable reduction of the heights of the steps marking major-shell and
subshell closings, thus allowing for a quantitative analysis of cluster
temperatures.


  Hom shifts form a class of multidimensional shifts of finite type (SFT) and
consist of colorings of the grid Z2 where adjacent colours must be neighbors in
a fixed finite undirected simple graph G. This class includes several important
statistical physics models such as the hard square model. The gluing gap
measures how far any two square patterns of size n can be glued, which can be
seen as a measure of the range of order, and affects the possibility to compute
the entropy (or free energy per site) of a shift. This motivates a study of the
possible behaviors of the gluing gap. The class of Hom shifts has the interest
that mixing type properties can be formulated in terms of algebraic graph
theory, which has received a lot of attention recently. Improving some former
work of N. Chandgotia and B. Marcus, we prove that the gluing gap either
depends linearly on n or is dominated by log(n). We also find a Hom shift with
gap {\Theta}(log(n)), infirming a conjecture formulated by R. Pavlov and M.
Schraudner. The physical interest of these results is to better understand the
transition from short-range to long-range order (respectively sublogarithmic
and linear gluing gap), which is reflected in whether some parameter, the
square cover, is finite or infinite.


  We present preliminary simulation results for the I = 0 charmonium state
$X(3872)(1^{++})$ and the I = 1 charmonium state $Z_c^+(3900)(1^{+-})$. The
study is performed on gauge field configurations with 2+1+1 flavors of highly
improved staggered sea quarks (HISQ) with clover (Fermilab interpretation)
charm quarks and HISQ light valence quarks. Since the $X(3872)$ lies very close
to the open charm $D \bar D^*$ threshold, we use a combination of $\bar c c$
and $D \bar D^* + \bar D D^*$ interpolating operators. For the $Z_c^+(3900)$ we
use a combination of $J/\psi \pi$ and $D \bar D^* + \bar D D^*$ channels. This
is the first such study with HISQ sea quarks and light valence quarks. To this
end, we describe a variational method for treating staggered quarks that
incorporates both oscillating and non-oscillating components.


  We compute next-to-next-to-leading order QCD corrections to the partonic
processes $b\to c \bar{u} d$ and $b\to c \bar{c} s$, which constitute the
dominant decay channels in standard model predictions for $B$-meson lifetimes
within the heavy quark expansion. We consider the contribution from the
four-quark operators $O_1$ and $O_2$ in the $\Delta B =1$ effective
Hamiltonian. The decay rates are obtained from the imaginary parts of four-loop
propagator-type diagrams. We compute the corresponding master integrals using
the "expand and match" approach which provides semi-analytic results for the
physical charm and bottom quark masses. We show that the dependence of the
decay rate on the renormalization scale is significantly reduced after
including the next-to-next-to-leading order corrections. Furthermore, we
compute next-to-next-to-leading order corrections to the
Cabibbo-Kobayashi-Maskawa-suppressed decay channels $b \to u \bar c s$ and $b
\to u \bar u d$.


  A simple method how to study response of solitons in dissipative systems to
external impulsive perturbations is developed. Thanks to nontrivial choice of
small parameter, the perturbative scheme captures genuine nonlinear phenomena.
The method is developed and tested by numerical simulations for kinks in 1+1
dimensions and for skyrmions in 2+1 dimensions. Extension to models including
second order time derivatives is discussed.


  The dilepton mass distribution from pre-equilibrium matter in
ultrarelativistic nuclear collisions is indistinguishable from a thermally
produced distribution.


  Black holes orbiting the Super Massive Black Hole (SMBH) Sgr A* in the
Milky-way galaxy center (GC) generate gravitational waves. The spectrum, due to
stars and black holes, is continuous below 40 nHz while individual BHs within
about 200 AU of the central SMBH stick out in the spectrum at higher
frequencies. The GWs can be detected by timing radio pulsars within a few
parsecs of this region. Future observations with the Square Kilometer Array of
such pulsars with sufficient timing accuracy may be sensitive to signals from
intermediate mass BHs (IMBH) in a 3 year observation baseline. The recent
detection of radio pulsations from the magnetar SGR J1745-29 very near the GC
opens up the possibilities of detecting millisecond pulsars (which can be used
as probes of the GWs) through lines of sight with only moderate pulse and
angular broadening due to scattering.


  Understanding the local structure of water at the interfaces of metallic
electrodes is a key problem in aqueous-based electrochemistry. Nevertheless, a
realistic simulation of such setup is challenging, particularly when the
electrodes are maintained at different potentials. To correctly compute the
effect of an external bias potential applied to truly semi-infinite surfaces,
we combine Density Functional Theory (DFT) and Non-Equilibrium Green's
Functions (NEGF) methods. This framework allows for the out-of-equilibrium
calculation of forces and dynamics, and directly correlates to the chemical
potential of the electrodes, which is the one introduced experimentally. In
this work, we apply this methodology to study the electronic properties and
atomic forces of one water molecule at the interface of gold surface. We find
that the water molecule tends to align its dipole moment with the electric
field, and it is either repelled or attracted to the metal depending on the
sign and magnitude of the applied bias, in an asymmetric fashion.


  The structure of the beta function of massless QCD is considered in analytic
perturbation theory (APT). The two-loop analytic effective coupling is written
in terms of the Lambert W function. The method of APT is applied to the gluon
propagator in the Landau gauge. It is shown that there is an nonperturbative
ambiguity in determination of the anomalous dimension function of the gluon
field. One possible resolution of the ambiguity is presented.


  Quantum key distribution (QKD) is theoretically secure using the principle of
quantum mechanics; therefore, QKD is a promising solution for the future of
secure communication. Although several experimental demonstrations of QKD have
been reported, they have not considered the polarization-dependent loss in
state preparation in the key-rate estimation. In this study, we experimentally
characterized polarization-dependent loss in realistic state-preparation
devices and verified that a considerable PDL exists in fiber- and silicon-based
polarization modulators. Hence, the security of such QKD systems is compromised
because of the secure key rate overestimation. Furthermore, we report a
decoy-state BB84 QKD experiment considering polarization-dependent loss.
Finally, we achieved rigorous finite-key security bound over up to 75 km fiber
links by applying a recently proposed security proof. This study considers more
realistic source flaws than most previous experiments; thus, it is crucial
toward a secure QKD with imperfect practical devices.


  We give a result of stability in law of the local time of the fractional
Brownian motion with respect to small perturbations of the Hurst parameter.
Concretely, we prove that the law (in the space of continuous functions) of the
local time of the fractional Brownian motion with Hurst parameter $H$ converges
weakly to that of the local time of $B^{H_0}$, when $H$ tends to $H_0$.


  Recent advances in deep generative models have led to immense progress in 3D
shape synthesis. While existing models are able to synthesize shapes
represented as voxels, point-clouds, or implicit functions, these methods only
indirectly enforce the plausibility of the final 3D shape surface. Here we
present a 3D shape synthesis framework (SurfGen) that directly applies
adversarial training to the object surface. Our approach uses a differentiable
spherical projection layer to capture and represent the explicit zero
isosurface of an implicit 3D generator as functions defined on the unit sphere.
By processing the spherical representation of 3D object surfaces with a
spherical CNN in an adversarial setting, our generator can better learn the
statistics of natural shape surfaces. We evaluate our model on large-scale
shape datasets, and demonstrate that the end-to-end trained model is capable of
generating high fidelity 3D shapes with diverse topology.


  The ethical, legal, and social challenges involved in the use of profiling
services for recruitment are the focus of many previous studies; however, the
processes vary depending on the social system and cultural practices. In August
2019, a scandal occurred in Japan in which a recruitment management company was
found to have breached users' and students' trust by selling their data to
clients. By sharing the Japanese recruitment context and associated laws, this
article contributes to our understanding of the ethical issues involved in
artificial intelligence profiling and in handling sensitive personal
information.


  The lattice operations of join and meet were defined for set partitions in
the nineteenth century, but no new logical operations on partitions were
defined and studied during the twentieth century. Yet there is a simple and
natural graph-theoretic method presented here to define any n-ary Boolean
operation on partitions. An equivalent closure-theoretic method is also
defined. In closing, the question is addressed of why it took so long for all
Boolean operations to be defined for partitions.


  Strong exciton-plasmon interaction between the layered two-dimensional (2D)
semiconductors and gap plasmons shows a great potential to implement cavity
quantum-electrodynamics in ambient condition. However, achieving a robust
plasmon-exciton coupling with nanocavity is still very challenging, because the
layer area is usually small with conventional approaches. Here, we report on a
robust strong exciton-plasmon coupling between the gap mode of bowtie and the
excitons in MoS$_2$ layers with gold-assisted mechanical exfoliation and the
nondestructive wet transfer techniques for large-area layer. Benefiting from
the ultrasmall mode volume and strong in-plane field, the estimated effective
exciton number contributing to the coupling is largely reduced. With a
corrected exciton transition dipole moment, the exciton numbers are extracted
with 40 for the case of monolayer and 48 for 8 layers. Our work paves a way to
realize the strong coupling with 2D materials with few excitons at room
temperature.


  We present the expected variability features in the context of the Synchro-
Self-Compton (SSC) model of emission from Active Galactic Nuclei (AGNs) and
show that the homogeneous SSC model describes well the observed multi-
wavelength spectrum and variability of objects such as Mkn 421 and Mkn 501;
however in order to explain the very fast flaring timescales occasionally
observed from these objects one should use a laminar, rather than spherical,
source geometry. This picture leads naturally to a shock-in- jet model which we
approach as a site of diffusive particle acceleration. We show that this
acceleration scheme can explain certain characteristic features of AGN X-ray
spectra and provides us with further observational tests.


  The theory of phase ordering kinetics for the O(2) model using the gaussian
auxiliary field approach is reexamined from two points of view. The effects of
fluctuations about the ordering field are included and we organize the theory
such that the auxiliary field correlation function is analytic in the
short-scaled distance (x) expansion. These two points are connected and we find
in the refined theory that the divergence at the origin in the defect-defect
correlation function $\tilde{g}(x)$ obtained in the original theory is removed.
Modifications to the order-parameter autocorrelation exponent $\lambda$ are
computed.


  In this paper, we give a simple proof and strengthening of a uniqueness
theorem of meromorphic functions which partially share 0, $\infty$ CM and 1 IM
with their difference operators. Meanwhile, we partial solve a conjecture given
by Chen-Yi in \cite{CY} and generalize some previous theorems in \cite{C, CX}.


  The Riemann-Roch theorem on a graph G is related to Alexander duality in
combinatorial commutive algebra. We study the lattice ideal given by chip
firing on G and the initial ideal whose standard monomials are the G-parking
functions. When G is a saturated graph, these ideals are generic and the Scarf
complex is a minimal free resolution. Otherwise, syzygies are obtained by
degeneration. We also develop a self-contained Riemann-Roch theory for artinian
monomial ideals.


  Poincar\'e Gauge's theory of gravity is the most noteworthy alternative
extension of general relativity that has a correspondence between spin and
spacetime geometry. In this paper, we use Reissner-Nordstrom-de Sitter and
anti-de Sitter solutions, where torsion $\tau$ is added as an independent
field, to analyze the weak deflection angles $\hat{\alpha}$ of massive and null
particles in finite distance regime. We then apply $\hat{\alpha}$ to determine
the Einstein ring formation in M87* and Sgr. A* and determine that relative to
Earth's location from these black holes, massive torsion effects can provide
considerable deviation, while the cosmological constant's effect remains
negligible. Furthermore, we also explore how the torsion parameter affects the
shadow radius perceived by both static and co-moving (with cosmic expansion)
observers in a Universe dominated by dark energy, matter, and radiation. Our
findings indicate that torsion and cosmological constant parameters affect the
shadow radius differently between observers in static and co-moving states. We
also show how the torsion parameter affects the luminosity of the photonsphere
by studying the shadow with infalling accretion. The calculation of the
quasinormal modes, greybody bounds, and high-energy absorption cross-section
are also affected by the torsion parameter considerably.


  The random oracle methodology has proven to be a powerful tool for designing
and reasoning about cryptographic schemes. In this paper, we focus on the basic
problem of correcting faulty or adversarially corrupted random oracles, so that
they can be confidently applied for such cryptographic purposes.
  We prove that a simple construction can transform a "subverted" random oracle
which disagrees with the original one at a small fraction of inputs into an
object that is indifferentiable from a random function, even if the adversary
is made aware of all randomness used in the transformation. Our results permit
future designers of cryptographic primitives in typical kleptographic settings
(i.e., those permitting adversaries that subvert or replace basic cryptographic
algorithms) to use random oracles as a trusted black box.


  We analyze the phase diagram of QCD with two massless quark flavors in the
space of temperature, T, and chemical potential of the baryon charge, mu, using
available experimental knowledge of QCD, insights gained from various models,
as well as general and model independent arguments including continuity,
universality, and thermodynamic relations. A random matrix model is used to
describe the chiral symmetry restoration phase transition at finite T and mu.
In agreement with general arguments, this model predicts a tricritical point in
the T mu plane. Certain critical properties at such a point are universal and
can be relevant to heavy ion collision experiments.


  A locally compact group $G$ is said to be weakly amenable if the Fourier
algebra $A(G)$ admits completely bounded approximative units. Consider the
family of groups $G_n=SL(2,\Bbb R)\ltimes H_n$ where $n\ge 2$, $H_n$ is the
$2n+1$ dimensional Heisenberg group and $SL(2,\Bbb R)$ acts via the irreducible
representation of dimension $2n$ fixing the center of $H_n$. We show that these
groups fail to be weakly amenable. Following an idea of Haagerup for the case
$n=1$ one can reduce matters to the problem of obtaining nontrivial uniform
bounds for a family of singular oscillatory integral operators with product
type singularities and polynomial phases.
  The result on the family $G_n$ and various other previously known results are
used to settle the question of weak amenability for a large class of Lie
groups, including the algebraic groups; we assume that the Levi-part has finite
center.


  This paper announces the availability of a fixed point toolbox for the Matlab
compatible software package Octave. This toolbox is released under the GNU
Public License, and can be used to model the losses in algorithms implemented
in hardware. Furthermore, this paper presents as an example of the use of this
toolbox, the effects of a fixed point implementation on the precision of an
OFDM modulator.


  Low-mass, high-Q, silicon nitride nanostrings are at the cutting edge of
nanomechanical devices for sensing applications. Here we show that the addition
of a chemically functionalizable gold overlayer does not adversely affect the Q
of the fundamental out-of-plane mode. Instead the device retains its mechanical
responsiveness while gaining sensitivity to molecular bonding. Furthermore,
differences in thermal expansion within the bilayer give rise to internal
stresses that can be electrically controlled. In particular, an alternating
current excites resonant motion of the nanostring. This AC thermoelastic
actuation is simple, robust, and provides an integrated approach to sensor
actuation.


  We have fabricated NbSe$_3$ structures with widths comparable to the
Fukuyama-Lee-Rice phase-coherence length. For samples already in the
2-dimensional pinning limit, we observe a crossover from 2-dimensional to
1-dimensional collective pinning when the crystal width is less than 1.6
$\mu$m, corresponding to the phase-coherence length in this direction. Our
results show that surface pinning is negligible in our samples, and provide a
means to probe the dynamics of single domains giving access to a new regime in
charge-density wave physics.


  Till now the nanoscale focussing and imaging in the sub-diffraction limit is
achieved mainly with the help of plasmonic field enhancement assisted with
noble metal nanoparticles. Using far field imaging technique, we have recorded
polarized spectroscopic photoluminescence (PL) imaging of a single AlGaN
nanowire (NW) of diameter ~ 100 nm using confinement of polarized light. The
nanowires on the substrate have a nematic ordering. It is found that the PL
from a single NW is influenced by the proximity to other NWs with the PL
intensity scaling as 1/(lxd), where l and d are the NW length and the
separation from the neighbouring NW, respectively. We show that this proximity
induced PL intensity enhancement can be understood, if we assume the existence
of reasonably long lived photons in the intervening space between the NWs. A
nonzero non-equilibrium population of such photons causes stimulated emission
leading to the enhanced PL emission with the intensity scaling as 1/(lxd). The
effect is analogous to the Purcell enhancement of polarized optical emissions
induced by confined photons in micro-cavities. The enhancement of PL emission
facilitated the far field spectroscopic imaging of a single semiconducting
nanowire in the sub-diffraction regime.


  Machine-learned coarse-grained (MLCG) molecular dynamics is a promising
option for modeling biomolecules. However, MLCG models currently require large
amounts of data from reference atomistic molecular dynamics or substantial
computation for training. Denoising score matching -- the technology behind the
widely popular diffusion models -- has simultaneously emerged as a
machine-learning framework for creating samples from noise. Models in the first
category are often trained using atomistic forces, while those in the second
category extract the data distribution by reverting noise-based corruption. We
unify these approaches to improve the training of MLCG force-fields, reducing
data requirements by a factor of 100 while maintaining advantages typical to
force-based parameterization. The methods are demonstrated on proteins Trp-Cage
and NTL9 and published as open-source code.


  Pervasive applications are revolutionizing the perception that users have
towards the environment. Indeed, pervasive applications perform resource
intensive computations over large amounts of stream sensor data collected from
multiple sources. This allows applications to provide richer and deep insights
into the natural characteristics that govern everything that surrounds us. A
key limitation of these applications is that they have high energy footprints,
which in turn hampers the quality of experience of users. While cloud and edge
computing solutions can be applied to alleviate the problem, these solutions
are hard to adopt in existing architecture and far from become ubiquitous.
Fortunately, cloudlets are becoming portable enough, such that they can be
transported and integrated into any environment easily and dynamically. In this
article, we investigate how cloudlets can be transported by unmanned autonomous
vehicles (UAV)s to provide computation support on the edge. Based on our study,
we develop GEESE, a novel UAVbased system that enables the dynamic deployment
of an edge computing infrastructure through the cooperation of multiple UAVs
carrying cloudlets. By using GEESE, we conduct rigorous experiments to analyze
the effort to deliver cloudlets using aerial, ground, and underwater UAVs. Our
results indicate that UAVs can work in a cooperative manner to enable edge
computing in the wild.


  We present evidence that the prismatic and secondary prism facets of
ice-I$_\mathrm{h}$ crystals possess structural features that can reduce the
effective hydrophilicity of the ice/water interface. The spreading dynamics of
liquid water droplets on ice facets exhibits long-time behavior that differs
for the prismatic $\{10\bar{1}0\}$ and secondary prism $\{11\bar{2}0\}$ facets
when compared with the basal $\{0001\}$ and pyramidal $\{20\bar{2}1\}$ facets.
We also present the results of simulations of solid-liquid friction of the same
four crystal facets being drawn through liquid water, and find that the two
prismatic facets exhibit roughly half the solid-liquid friction of the basal
and pyramidal facets. These simulations provide evidence that the two prismatic
faces have a significantly smaller effective surface area in contact with the
liquid water. The ice / water interfacial widths for all four crystal facets
are similar (using both structural and dynamic measures), and were found to be
independent of the shear rate. Additionally, decomposition of orientational
time correlation functions show position-dependence for the short- and
longer-time decay components close to the interface.


  The most simplest form of quantum network is an one dimensional quantum
network with a single player in each node. In remote entanglement distribution
each of the players carry out measurement at the intermediate nodes to produce
an entangled state between initial and final node which are remotely separated.
It is imperative to say that the flow of information as well as the percolation
of entanglement in a network between the source and target node is an important
area of study. This will help us to understand the limits of the resource
states as well as the measurements that are carried out in the process of
remote entanglement distribution. In this article we investigate how the
concurrence of the final entangled state obtained is connected with the
concurrences of the initial entangled states present in a 1-D chain. We extend
the works done for the pure entangled states for mixed entangled states like
Werner states, Bell diagonal states and for general mixed states. We did not
limit ourselves to a situation where the measurements are happening perfectly.
We also investigate how these relations change when we consider imperfect
swapping. We obtain the limits on the number of swappings as well as the
success probability measurements to ensure the final state to be entangled
state after swapping. In addition to these we also investigate on how much
quantum information can be sent from the initial node to the final node (by
computing the teleportation fidelity) when the measurement is perfect and
imperfect with the same set of examples. Here also we obtain the limits on the
number of swapping and the success probability of measurement to ensure that
the final state obtained is capable of transferring the information . These
results have tremendous future applications in sending quantum information
between two quantum processors in remote entangled distribution.


  This is the first draft of a set of lecture notes developed for one-half of a
seminar on two approaches to the notion of "Abelian", namely those of universal
algebra, and of category theory. The half pertaining to the universal-algebraic
perspective is covered here. All material has been adapted from the following
sources:
  1. Commutator Theory for Congruence Modular Varieties, by Ralph Freese and
Ralph McKenzie.
  2. Geometrical Methods in Congruence Modular Algebras, by H. Peter Gumm
  3. Congruence Modular Varieties: Commutator Theory and Its Uses, by Ralph
McKenzie and John Snow