  In this article, we introduce novel tactile-based motion primitives termed
"tactile-traversal", "tactile-turning" and "ricocheting" for unmanned aerial
vehicles (UAVs). These primitives enable contact-rich UAV missions such as
tactile-based exploration, mapping, and collision-inclusive navigation. We
begin by introducing XPLORER, a passive deformable UAV that sustains collisions
and establishes smooth contacts by exploiting its spring-augmented chassis.
Next, an improved and fast converging external force estimation algorithm is
proposed to detect contacts/collisions. We also design three distinct reaction
controllers for (i) static-wrench application, (ii) disturbance rejection, and
(iii) collision recovery. Finally, the three new tactile-based motion
primitives are proposed by leveraging the reactions obtained from deploying
these controllers to interact with surroundings. We showcase the effectiveness
of these primitives to facilitate efficient exploration and rapid navigation in
unknown environments by capitalizing on collisions and contacts.


  Quantum anomalous Hall effect (QAHE) was discovered a decade ago, but is
still not utilized beyond a handful of research groups, due to numerous
limitations such as extremely low temperature, electric field-effect gating
requirement, small sample sizes and environmental aging effect. Here, we
present a robust platform that provides effective solutions to these problems.
Specifically, on this platform, we observe QAH signatures at record high
temperatures, with the Hall conductance of 1.00 e2/h at 2.0 K, 0.98 e2/h at 4.2
K, and 0.92 e2/h at 10 K, on centimeter-scale substrates, without
electric-field-effect gating. The key ingredient is an active CrOx capping
layer, which substantially boosts the ferromagnetism while suppressing
environmental degradation. With this development, QAHE will now be accessible
to much broader applications than before.


  We propose a novel approach for navigating in polygonal environments by
synthesizing controllers that take as input relative displacement measurements
with respect to a set of landmarks. Our algorithm is based on solving a
sequence of robust min-max Linear Programming problems on the elements of a
cell decomposition of the environment. The optimization problems are formulated
using linear Control Lyapunov Function (CLF) and Control Barrier Function (CBF)
constraints, to provide stability and safety guarantees, respectively. The
inner maximization problem ensures that these constraints are met by all the
points in each cell, while the outer minimization problem balances the
different constraints in a robust way. We show that the min-max optimization
problems can be solved efficiently by transforming it into regular linear
programming via the dualization of the inner maximization problem. We test our
algorithm to agents with first and second-order integrator dynamics, although
our approach is in principle applicable to any system with piecewise linear
dynamics. Through our theoretical results and simulations, we show that the
resulting controllers: are optimal (with respect to the criterion used in the
formulation), are applicable to linear systems of any order, are robust to
changes to the start location (since they do not rely on a single nominal
path), and to significant deformations of the environment.


  We study the dynamics of radiation pressure supported tori around
Schwarzschild black holes, focusing on their oscillatory response to an
external perturbation. Using KORAL, a general relativistic radiation
hydrodynamics code capable of modeling all radiative regimes from the optically
thick to the optically thin, we monitor a sample of models at different initial
temperatures and opacities, evolving them in two spatial dimensions for $\sim
165$ orbital periods. The dynamics of models with high opacity is very similar
to that of purely hydrodynamics models, and it is characterized by regular
oscillations which are visible also in the light curves. As the opacity is
decreased, the tori quickly and violently migrate towards the gas-pressure
dominated regime, collapsing towards the equatorial plane. When the spectra of
the $L_2$ norm of the mass density are considered, high frequency
inertial-acoustic modes of oscillations are detected (with the fundamental mode
at a frequency $68 M_{\rm BH}^{-1}\,\rm Hz$), in close analogy to the
phenomenology of purely hydrodynamic models. An additional mode of oscillation,
at a frequency $129 M_{\rm BH}^{-1}\,\rm Hz$, is also found, which can be
unambiguously attributed to the radiation. The spectra extracted from the light
curves are typically more noisy, indicating that in a real observation such
modes would not be easily detected.


  The emergence of noisy medium-scale quantum devices has led to
proof-of-concept applications for quantum computing in various domains.
Examples include Natural Language Processing (NLP) where sentence
classification experiments have been carried out, as well as procedural
generation, where tasks such as geopolitical map creation, and image
manipulation have been performed. We explore applications at the intersection
of these two areas by designing a hybrid quantum-classical algorithm for
sentence generation.
  Our algorithm is based on the well-known simulated annealing technique for
combinatorial optimisation. An implementation is provided and used to
demonstrate successful sentence generation on both simulated and real quantum
hardware. A variant of our algorithm can also be used for music generation.
  This paper aims to be self-contained, introducing all the necessary
background on NLP and quantum computing along the way.


  The universal centralizer of a semisimple algebraic group is the family of
centralizers of regular elements, parametrized by their conjugacy classes. When
the group is of adjoint type, we construct a smooth, log-symplectic fiberwise
compactification of the universal centralizer by taking the closure of each
fiber in the wonderful compactification. We use the geometry of the wonderful
compactification to give an explicit description of its symplectic leaves. We
also show that the compactified centralizer fibers are isomorphic to certain
Hessenberg varieties -- we apply this connection to compute the singular
cohomology of the partial compactification, and to study the geometry of the
corresponding universal Hessenberg family.


  We apply our three dimensional photoionization code to model Wisconsin
H-alpha Mapper observations of the H II region surrounding the O9.5V star Zeta
Oph. Our models investigate the porosity of the interstellar medium around zeta
Oph and the effects of 3D densities on the H-alpha surface brightness and
variation in the [N II]/H-alpha line ratio. The Zeta Oph H II region has a well
characterized ionizing source, so it is an excellent starting point for 3D
models of diffuse ionized gas. We investigate various hierarchically clumped
density structures, varying the overall smoothness within the clumping
algorithm. By simulating the observations, we can estimate the porosity of the
medium in the vicinity of Zeta Oph and find that within the context of our
hierarchically clumped models, around 50% to 80% of the volume is occupied by
clumps surrounded by a low density smooth medium. We also conclude that in
order for O stars to ionize the diffuse Warm Ionized Medium, the O star
environment must be more porous than that surrounding Zeta Oph, with clumps
occupying less than one half of the interstellar volume. Our clumpy models have
irregular boundaries, similar to observed H II regions. However, in observed H
II regions it is difficult to identify the precise location of the boundary
because of the foreground and/or background emission from the widespread Warm
Ionized Medium. This complicates the interpretation of the predicted rapid rise
of some emission line ratios near the edge of uniform density H II regions and
combined with the three dimensional clumpy nature of the interstellar medium
may explain the apparent lack of distinctive emission line ratios near H I -- H
II interfaces.


  This paper proposes a quantum approximate optimization algorithm (QAOA)
method for wireless scheduling problems. The QAOA is one of the promising
hybrid quantum-classical algorithms for many applications and it provides
highly accurate optimization solutions in NP-hard problems. QAOA maps the given
problems into Hilbert spaces, and then it generates Hamiltonian for the given
objectives and constraints. Then, QAOA finds proper parameters from classical
optimization approaches in order to optimize the expectation value of generated
Hamiltonian. Based on the parameters, the optimal solution to the given problem
can be obtained from the optimum of the expectation value of Hamiltonian.
Inspired by QAOA, a quantum approximate optimization for scheduling (QAOS)
algorithm is proposed. First of all, this paper formulates a wireless
scheduling problem using maximum weight independent set (MWIS). Then, for the
given MWIS, the proposed QAOS designs the Hamiltonian of the problem. After
that, the iterative QAOS sequence solves the wireless scheduling problem. This
paper verifies the novelty of the proposed QAOS via simulations implemented by
Cirq and TensorFlow-Quantum.


  Almost all previous deep learning-based multi-view stereo (MVS) approaches
focus on improving reconstruction quality. Besides quality, efficiency is also
a desirable feature for MVS in real scenarios. Towards this end, this paper
presents a Fast-MVSNet, a novel sparse-to-dense coarse-to-fine framework, for
fast and accurate depth estimation in MVS. Specifically, in our Fast-MVSNet, we
first construct a sparse cost volume for learning a sparse and high-resolution
depth map. Then we leverage a small-scale convolutional neural network to
encode the depth dependencies for pixels within a local region to densify the
sparse high-resolution depth map. At last, a simple but efficient Gauss-Newton
layer is proposed to further optimize the depth map. On one hand, the
high-resolution depth map, the data-adaptive propagation method and the
Gauss-Newton layer jointly guarantee the effectiveness of our method. On the
other hand, all modules in our Fast-MVSNet are lightweight and thus guarantee
the efficiency of our approach. Besides, our approach is also memory-friendly
because of the sparse depth representation. Extensive experimental results show
that our method is 5$\times$ and 14$\times$ faster than Point-MVSNet and
R-MVSNet, respectively, while achieving comparable or even better results on
the challenging Tanks and Temples dataset as well as the DTU dataset. Code is
available at https://github.com/svip-lab/FastMVSNet.


  Following the experimental observation of bright matter-wave solitons [L.
Khaykovich et al., Science v. 296, 1290 (2002); K. E. Strecker et al., Nature
(London) v. 417, 150 (2002)], we develop a semi-phenomenological theory for
soliton thermodynamics and find the condensation temperature. Under a modified
thermodynamic limit, the condensate occupation at the critical temperature
undergoes a sudden jump to a nonzero value, indicating a discontinuous phase
transition. Treating the condensation as a diffusion over a barrier shows that
the condensation time is exponentially long as one approaches the thermodynamic
limit, and the longest near the critical temperature.


  In this paper we use some ideas from \cite{FG-97, G-06} and consider the
description of H\"{o}rmander type pseudo-differential operators on
$\mathbb{R}^d$ ($d\geq1$), including the case of the magnetic
pseudo-differential operators introduced in \cite{IMP-1, IMP-19}, with respect
to a tight Gabor frame. We show that all these operators can be identified with
some infinitely dimensional matrices whose elements are strongly localized near
the diagonal. Using this matrix representation, one can give short and elegant
proofs to classical results like the Calder{\'o}n-Vaillancourt theorem and
Beals' commutator criterion, and also establish local trace-class criteria.


  The recent experimental program with the out-of-plane spectrometer system
(OOPS) at MIT-Bates encompassed an extensive set of
$\mathrm{d}(\vec\mathrm{e},\mathrm{e}'\mathrm{p})$ measurements, investigations
of the $\mathrm{N}\to\Delta$ transition using
$\mathrm{p}(\vec\mathrm{e},\mathrm{e}'\mathrm{p})\pi^0$ and
$\mathrm{p}(\vec\mathrm{e},\mathrm{e}'\pi^+)\mathrm{n}$ reaction channels, and
studies of virtual Compton scattering (VCS)
$\mathrm{p}(\mathrm{e},\mathrm{e}'\mathrm{p})\gamma$ below the pion threshold.
Preliminary results are presented.


  We study a class of exactly solvable models for strongly correlated
electrons, defined on a set of N cells, and with infinite on-site repulsion on
part of the sites of each cell. For 2N or more electrons the exact ground state
is known. We construct a tractable (2N-1)-particle state which becomes
asymptotically degenerate to the ground state in the thermodynamic limit for
one special D-dimensional model. For other models, that state may be used to
calculate variational upper bounds on the ground-state energy. For a Hubbard
chain with three sites per unit cell, the analytical bound is compared to
numerically exact results.


  We show that a connection with skew-symmetric torsion satisfying the Einstein
metricity condition exists on an almost contact metric manifold exactly when it
is D-homothetic to a cosymplectic manifold. In dimension five, we get that the
existence of a connection with skew torsion satisfying the Einstein metricity
condition is equivalent to the existence of a Sasaki-Einstein 5-manifold and
vice versa, any Sasaki-Einstein 5-manifold generates a two parametric family of
connections with skew torsion satisfying the Einstein metricity condition.
Formulas for the curvature and the Ricci tensors of these connections are
presented in terms of the Sasaki-Einstein SU(2) structures.


  One major obstacle that precludes the success of reinforcement learning in
real-world applications is the lack of robustness, either to model
uncertainties or external disturbances, of the trained policies. Robustness is
critical when the policies are trained in simulations instead of real world
environment. In this work, we propose a risk-aware algorithm to learn robust
policies in order to bridge the gap between simulation training and real-world
implementation. Our algorithm is based on recently discovered distributional RL
framework. We incorporate CVaR risk measure in sample based distributional
policy gradients (SDPG) for learning risk-averse policies to achieve robustness
against a range of system disturbances. We validate the robustness of
risk-aware SDPG on multiple environments.


  Superconductor/ferromagnet proximity effect has been studied for
Pb/Co$_2$Cr$_{1-x}$Fe$_x$Al bilayers. Different substrate temperatures allowed
us to prepare the Heusler alloy Co$_2$Cr$_{1-x}$Fe$_x$Al films with different
degree of spin polarization of conduction band of the Heusler layer. We obtain
a strong correlation between the dependence of the superconducting transition
temperature on the Pb-layer thickness at fixed thickness of the Heusler layer
and the degree of spin polarization in the ferromagnetic layer.


  Monitoring dietary intake is a crucial aspect of promoting healthy living. In
recent years, advances in computer vision technology have facilitated dietary
intake monitoring through the use of images and depth cameras. However, the
current state-of-the-art image-based food portion estimation algorithms assume
that users take images of their meals one or two times, which can be
inconvenient and fail to capture food items that are not visible from a
top-down perspective, such as ingredients submerged in a stew. To address these
limitations, we introduce an innovative solution that utilizes stationary
user-facing cameras to track food items on utensils, not requiring any change
of camera perspective after installation. The shallow depth of utensils
provides a more favorable angle for capturing food items, and tracking them on
the utensil's surface offers a significantly more accurate estimation of
dietary intake without the need for post-meal image capture. The system is
reliable for estimation of nutritional content of liquid-solid heterogeneous
mixtures such as soups and stews. Through a series of experiments, we
demonstrate the exceptional potential of our method as a non-invasive,
user-friendly, and highly accurate dietary intake monitoring tool.


  We construct the tensor product for f-algebras, including proving a universal
property for it, and investigate how it preserves algebraic properties of the
factors.


  There exist memoryless zero-capacity quantum channels that when used jointly
result in the channel with positive capacity. This phenomenon is called
superactivation. Making use of Parrondo's paradox, we exhibit examples of
superactivation-like effect for the capacity of classical communication
channels as well as quantum and private capacity of quantum channels with
memory. There are several ingredients necessary for superactivation of quantum
capacity to occur in memoryless case. The first one is the requirement for the
quantum channels which are amenable for superactivation to come from two
distinct families - binding entanglement channels and erasure channels. The
second one is the ability to utilize inputs which are entangled across the uses
of the channels. Our construction uses a single family of erasure channels with
classical memory to achieve the same superactivation-like effect for quantum
capacity without any of the ingredients above.


  Electrical impedance tomography (EIT) is a noninvasive imaging method whereby
electrical measurements on the boundary of a conductive medium (the data) are
taken according to a prescribed protocol set and inverted to map the internal
conductivity (the model). This paper introduces a sensitivity analysis method
and corresponding inversion and protocol optimization that generalizes the
criteria for tomographic inversion to minimize the model-space dimensionality
and maximize data importance. Sensitivity vectors, defined as rows of the
Jacobian matrix in the linearized forward problem, are used to map targeted
conductivity features from model-space to data-space, and a volumetric
outer-product of these vectors in model-space called the sensitivity
parallelotope volume provides a figure-of-merit for data protocol optimization.
Orthonormal basis functions that accurately constrain the model-space to
features of interest can be defined from a priori information. By increasing
the contact number to expand the number of possible measurements Dmax, and by
reducing the model-space to a minimal number M0 of basis functions that
describe only the features of interest, the M0 << Dmax sensitivity vectors of
greatest length and maximal orthogonality that span this model-space can be
identified. The reduction in model-space dimensionality accelerates the
inversion by several orders of magnitude, and the enhanced sensitivity can
tolerate noise levels up to 1,000 times larger than standard protocols.


  Nowadays, a significant share of the Business-to-Consumer sector is based on
online platforms like Amazon and Alibaba and uses Artificial Intelligence for
pricing strategies. This has sparked debate on whether pricing algorithms may
tacitly collude to set supra-competitive prices without being explicitly
designed to do so. Our study addresses these concerns by examining the risk of
collusion when Reinforcement Learning algorithms are used to decide on pricing
strategies in competitive markets. Prior research in this field focused on
Tabular Q-learning (TQL) and led to opposing views on whether learning-based
algorithms can lead to supra-competitive prices. Our work contributes to this
ongoing discussion by providing a more nuanced numerical study that goes beyond
TQL by additionally capturing off- and on-policy Deep Reinforcement Learning
(DRL) algorithms. We study multiple Bertrand oligopoly variants and show that
algorithmic collusion depends on the algorithm used. In our experiments, TQL
exhibits higher collusion and price dispersion phenomena compared to DRL
algorithms. We show that the severity of collusion depends not only on the
algorithm used but also on the characteristics of the market environment. We
further find that Proximal Policy Optimization appears to be less sensitive to
collusive outcomes compared to other state-of-the-art DRL algorithms.


  A direct relation between two types of topological field theories,
Chern-Simons theory and BF theory, is presented by using ``Generalized
Differential Calculus'', which extends an ordinary p-form to an ordered pair of
p and (p+1)-form. We first establish the generalized Chern-Weil homomormism for
generalized curvature invariant polynomials in general even dimensional
manifolds, and then show that BF gauge theory can be obtained from the action
which is the generalized second Chern class with gauge group G. Particularly
when G is taken as SL(2,C) in four dimensions, general relativity with
cosmological constant can be derived by constraining the topological BF theory.


  We present observations of SDSSp J104433.04--012502.2, a luminous quasar at
z=5.80 discovered from Sloan Digital Sky Survey (SDSS) multicolor imaging data.
This object was selected as an i'-band dropout object, with i*=21.8 +/- 0.2,
z*=19.2 +/- 0.1. It has an absolute magnitude M1450 = -27.2 (H_0 =50 km/s/Mpc,
q0 = 0.5). The spectrum shows a strong and broad Ly alpha emission line, strong
Ly alpha forest absorption lines with a mean continuum decrement D_A = 0.91,
and a Lyman Limit System at z=5.72. The spectrum also shows strong OI and SiIV
emission lines similar to those of quasars at z<= 5, suggesting that these
metals were produced at redshift beyond six. The lack of a Gunn-Peterson trough
in the spectrum indicates that the universe is already highly ionized at z ~
5.8. Using a high-resolution spectrum in the Ly alpha forest region, we place a
conservative upper limit of the optical depth due to the Gunn-Peterson effect
of tau < 0.5 in regions of minimum absorption. The Ly alpha forest absorption
in this object is much stronger than that in quasars at z<= 5. The object is
unresolved in a deep image with excellent seeing, implying that it is unlensed.
The black hole mass of this quasar is ~3 x 10^9 M_solar if we assume that it is
radiating at the Eddington luminosity and no lensing amplification, implying
that it resides in a very massive dark matter halo. The discovery of one quasar
at M_1450 < -27 in a survey area of 600 deg^2 is consistent with an
extrapolation of the observed luminosity function at lower redshift. The
abundance and evolution of such quasars can provide sensitive tests of models
of quasar and galaxy formation.


  Lower bounds for the first and the second eigenvalue of uniform hypergraphs
which are regular and linear are obtained. One of these bounds is a
generalization of the Alon-Boppana Theorem to hypergraphs.


  In this article, we show that solving the system of linear equations by
manipulating the kernel and the range space is equivalent to solving the
problem of least squares error approximation. This establishes the ground for a
gradient-free learning search when the system can be expressed in the form of a
linear matrix equation. When the nonlinear activation function is invertible,
the learning problem of a fully-connected multilayer feedforward neural network
can be easily adapted for this novel learning framework. By a series of kernel
and range space manipulations, it turns out that such a network learning boils
down to solving a set of cross-coupling equations. By having the weights
randomly initialized, the equations can be decoupled and the network solution
shows relatively good learning capability for real world data sets of small to
moderate dimensions. Based on the structural information of the matrix
equation, the network representation is found to be dependent on the number of
data samples and the output dimension.


  This article considers evidence from physical and biological sciences to show
machines are deficient compared to biological systems at incorporating
intelligence. Machines fall short on two counts: firstly, unlike brains,
machines do not self-organize in a recursive manner; secondly, machines are
based on classical logic, whereas Nature's intelligence may depend on quantum
mechanics.


  This paper considers a cloud radio access network (C-RAN) where spatially
distributed remote radio heads (RRHs) communicate with a full-duplex user. In
order to reflect a realistic scenario, the uplink (UL) and downlink (DL) RRHs
are assumed to be equipped with multiple antennas and distributed according to
a Poisson point process. We consider all participate and nearest RRH
association schemes with distributed beamforming in the form of maximum ratio
combining/maximal ratio transmission (MRC/MRT) and zero-forcing/MRT(ZF/MRT)
processing. We derive analytical expressions useful to compare the average sum
rate among association schemes as a function of the number of RRHs antennas and
density of the UL and DL RRHs. Numerical results show that significant
performance improvements can be achieved by using the full-duplex mode as
compared to the half-duplex mode, while the choice of the beamforming design as
well as the RRH association scheme plays a critical role in determining the
full-duplex gains.


  We report on the first measurements of the drag effect between coupled
2D-hole gases. We investigate the coupling by changing the carrier densities in
the quantum wells, the widths of the barriers between the gases and the
perpendicular magnetic field. From the data we are able to attribute the
frictional drag to phonon coupling, because the non-parabolicity allows to tune
the Fermi wavevector and the Fermi velocity separately and, thereby, to
distinguish between phonon- and plasmon-dominated coupling.


  The isotropic limit of spin systems with orbital degeneracy is shown to have
global SU(4) symmetry. On many 2D lattices, the ground state does not posses
long range order, which may explain the observed spin liquid properties of
$LiNiO_2$. In the SU(4) Neel ordered state, spin-spin correlations can be
antiferromagneitc between two neighboring sites with parallel magnetic moments.


  The space Weak L^1 consists of all measurable functions on [0,1] such that
  q(f) = sup_{c>0} c \lambda{t : |f(t)| > c}
  is finite, where \lambda denotes Lebesgue measure. Let \rho be the gauge
functional of the unit ball {f : q(f) \leq 1} of the quasi- norm q, and let N
be the null space of \rho. The normed envelope of Weak L^1, which we denote by
W, is the space (Weak L^1/N, \rho). The Banach envelope of Weak L^1,
\overline{W}, is the completion of W. We show that \overline{W} is
isometrically lattice isomorphic to a sublattice of W. It is also shown that
all rearrangement invariant Banach function spaces are isometrically isomorphic
to a sublattice of W.


  We propose and demonstrate that the behaviors of long-range, two-site quantum
resources can effectively diagnose quantum phases. In an XX spin chain with
symmetry-breaking quantum phase transitions, we reveal that the asymptotic and
oscillating decay modes of quantum coherence or quantum discord, along with
two-site distance, can identify two spin-liquid phases. Furthermore, based on
our analytical results of spin correlation functions, we confirm the existence
of long-range entanglement in the system and establish a connection between
two-site entanglement and quantum phases. Additionally, for the extended Ising
model with topological phase transitions, we find that coherence and quantum
discord behaviors can also signify topological quantum phases. In particular,
we discover the quantum resource freezing phenomenon, where topologically
protected long-range quantum resources may have potential applications in
quantum information processing.


  After a decade of gravitational microlensing experiments, 13 to 17 events by
MACHO (depending on quality) and two events by EROS have been detected. All of
those have been observed in the direction of Large Magellanic Cloud. We use
Evans spherically symmetric halo model to study the rate of microlensing
events. The expected number of events in this model obtain by using EROS and
MACHO observational efficiencies. We compare our numbers with the observed
events to obtain the fraction of halo that is made by compact objects. It is
shown that results derived from two experiments are in good agreement with each
other and MACHOs, comprise only a fraction (depending on the model) of Milky
Way halo. The results are also compatible with the White Dwarfs population
studies in the Hubble Deep Field.


  The article is devoted to a new type of measures which are hypercomplex
generalizations of Gaussian-type measures. The considered such measures are
related with solutions of high order hyperbolic PDEs and related Markov
processes. Their characteristic functionals are investigated. Cylindrical
distributions of these measures are studied.


  The current-driven domain wall dynamics along ferrimagnetic systems are here
theoretically analyzed as a function of the temperature by means of
micromagnetic simulations and a one dimensional model. Contrarily to
conventional effective approaches, our model takes into account the two coupled
ferromagnetic sublattices forming the ferrimagnetic system. Although the model
is suitable for systems with asymmetric exchange interaction and spin-orbit
coupling effects due to adjacent heavy metal layers, we here focus our
attention on the case of single-layer ferrimagnetic strips where domain walls
adopt achiral Bloch configurations at rest. Such domain walls can be driven by
either out-of-plane fields or spin transfer torques upon bulk current
injection. Our results indicate that the domain wall velocity is optimized at
the angular compensation temperature for both field-driven and current-driven
cases. Our advanced models allow us to infer that the precession of the
internal domain wall moments is suppressed at such compensation temperature,
and they will be useful to interpret state-of-the art experiments on these
systems.


  Neural-symbolic integration aims to combine the connectionist subsymbolic
with the logical symbolic approach to artificial intelligence. In this paper,
we first define the answer set semantics of (boolean) neural nets and then
introduce from first principles a class of neural logic programs and show that
nets and programs are equivalent.


  How much is 131 million US dollars? To help readers put such numbers in
context, we propose a new task of automatically generating short descriptions
known as perspectives, e.g. "$131 million is about the cost to employ everyone
in Texas over a lunch period". First, we collect a dataset of numeric mentions
in news articles, where each mention is labeled with a set of rated
perspectives. We then propose a system to generate these descriptions
consisting of two steps: formula construction and description generation. In
construction, we compose formulae from numeric facts in a knowledge base and
rank the resulting formulas based on familiarity, numeric proximity and
semantic compatibility. In generation, we convert a formula into natural
language using a sequence-to-sequence recurrent neural network. Our system
obtains a 15.2% F1 improvement over a non-compositional baseline at formula
construction and a 12.5 BLEU point improvement over a baseline description
generation.


  A unified linear tearing-mode formulation is given incorporating both
resistivity and Hall effects. A variational method is used that appears to be
best suited to deal with the difficulties peculiar to the {\it triple-deck}
structure associated with the Hall resistive tearing mode but also to lead to a
convenient analytical dispersion relation for the Hall resisitive tearing mode.
  This analytical dispersion relation -
  * recovers the Furth-Killeen-Rosenbluth[15] result for the resistive branch;
  * gives a growth rate for the Hall branch which appears to be consistent with
the growth rate of the electron-inertia driven tearing mode given previously
(Coppi [19]);
  * recovers the scaling relation for the transition from the resisitive regime
to the Hall regime numerically established by Fitzpatrick[20] in a driven Hall
resistive reconnection situation.


  We formulate a strong equivalence between machine learning, artificial
intelligence methods and the formulation of statistical data assimilation as
used widely in physical and biological sciences. The correspondence is that
layer number in the artificial network setting is the analog of time in the
data assimilation setting. Within the discussion of this equivalence we show
that adding more layers (making the network deeper) is analogous to adding
temporal resolution in a data assimilation framework.
  How one can find a candidate for the global minimum of the cost functions in
the machine learning context using a method from data assimilation is
discussed. Calculations on simple models from each side of the equivalence are
reported.
  Also discussed is a framework in which the time or layer label is taken to be
continuous, providing a differential equation, the Euler-Lagrange equation,
which shows that the problem being solved is a two point boundary value problem
familiar in the discussion of variational methods. The use of continuous layers
is denoted "deepest learning". These problems respect a symplectic symmetry in
continuous time/layer phase space. Both Lagrangian versions and Hamiltonian
versions of these problems are presented. Their well-studied implementation in
a discrete time/layer, while respected the symplectic structure, is addressed.
The Hamiltonian version provides a direct rationale for back propagation as a
solution method for the canonical momentum.


  Primordial black holes (PBHs) that form in the early Universe in the modified
Affleck-Dine (AD) mechanism of baryogenesis should have intrinsic log-normal
mass distribution of PBHs. We show that the parameters of this distribution
adjusted to provide the required spatial density of massive seeds ($\ge 10^4
M_\odot$) for early galaxy formation and not violating the dark matter density
constraints, predict the existence of the population of intermediate-mass PBHs
with a number density of $\sim 1000$~Mpc$^{-3}$. We argue that the population
of intermediate-mass AD PBHs can also seed the formation of globular clusters
in galaxies. In this scenario, each globular cluster should host an
intermediate-mass black hole with a mass of a few thousand solar masses, and
should not obligatorily be immersed in a massive dark matter halo.


  Characteristic class relations in Dolbeault cohomology follow from the
existence of a holomorphic geometric structure (for example, holomorphic
conformal structures, holomorphic Engel distributions, holomorphic projective
connections, and holomorphic foliations). These relations can be calculated
directly from the representation theory of the structure group, without
selecting any metric or connection or having any knowledge of the Dolbeault
cohomology groups of the manifold. This paper improves on its predecessor [17]
by allowing infinite type geometric structures.


  Melanoma is a prevalent lethal type of cancer that is treatable if diagnosed
at early stages of development. Skin lesions are a typical indicator for
diagnosing melanoma but they often led to delayed diagnosis due to high
similarities of cancerous and benign lesions at early stages of melanoma. Deep
learning (DL) can be used as a solution to classify skin lesion pictures with a
high accuracy, but clinical adoption of deep learning faces a significant
challenge. The reason is that the decision processes of deep learning models
are often uninterpretable which makes them black boxes that are challenging to
trust. We develop an explainable deep learning architecture for melanoma
diagnosis which generates clinically interpretable visual explanations for its
decisions. Our experiments demonstrate that our proposed architectures matches
clinical explanations significantly better than existing architectures.


  An affine vector space partition of $\operatorname{AG}(n,q)$ is a set of
proper affine subspaces that partitions the set of points. Here we determine
minimum sizes and enumerate equivalence classes of affine vector space
partitions for small parameters. We also give parametric constructions for
arbitrary field sizes.


  We compute from first principles the electronic, vibrational, and transport
properties of four known MXenes compound : Ti3C2, Ti3C2F2, Ti3C2(OH)2, and
Ti2CF2. We study the effect of different surface terminations and monosheet
thickness on the electrical conductivity, and show that the changes in
conductivity can be explained by the squared velocity density of the electronic
state, as well as their phonon scattering lifetime. We also compare the
solution of the iterative Boltzmann transport equation (IBTE) to different
linearized solutions, namely, the self-energy relaxation time approximation
(SERTA) and the momentum relaxation time approximation (MRTA), and we show that
the SERTA significantly underestimates the electrical conductivity while the
MRTA yields results in better agreement with the IBTE.
  We compute from first principles the electronic, vibrational, and transport
properties of four known MXenes: Ti3C2, Ti3C2F2, Ti3C2(OH)2, and Ti2CF2. We
study the effect of different surface terminations and monosheet thickness on
the electrical conductivity, and show that the changes in conductivity can be
explained by the squared velocity density of the electronic state, as well as
their phonon scattering lifetime. We also compare the solution of the iterative
Boltzmann transport equation (IBTE) to different linearized solutions, namely,
the self-energy relaxation time approximation (SERTA) and the momentum
relaxation time approximation (MRTA), and we show that the SERTA significantly
underestimates the electrical conductivity while the MRTA yields results in
better agreement with the IBTE. The computed monolayer conductivity at 300K is
in reasonable agreement with reported experimental measurements.


  We examine the task of privacy amplification from information-theoretic and
coding-theoretic points of view. In the former, we give a one-shot
characterization of the optimal rate of privacy amplification against classical
adversaries in terms of the optimal type-II error in asymmetric hypothesis
testing. This formulation can be easily computed to give finite-blocklength
bounds and turns out to be equivalent to smooth min-entropy bounds by Renner
and Wolf [Asiacrypt 2005] and Watanabe and Hayashi [ISIT 2013], as well as a
bound in terms of the $E_\gamma$ divergence by Yang, Schaefer, and Poor
[arXiv:1706.03866 [cs.IT]]. In the latter, we show that protocols for privacy
amplification based on linear codes can be easily repurposed for channel
simulation. Combined with known relations between channel simulation and lossy
source coding, this implies that privacy amplification can be understood as a
basic primitive for both channel simulation and lossy compression. Applied to
symmetric channels or lossy compression settings, our construction leads to
proto- cols of optimal rate in the asymptotic i.i.d. limit. Finally, appealing
to the notion of channel duality recently detailed by us in [IEEE Trans. Info.
Theory 64, 577 (2018)], we show that linear error-correcting codes for
symmetric channels with quantum output can be transformed into linear lossy
source coding schemes for classical variables arising from the dual channel.
This explains a "curious duality" in these problems for the (self-dual) erasure
channel observed by Martinian and Yedidia [Allerton 2003; arXiv:cs/0408008] and
partly anticipates recent results on optimal lossy compression by polar and
low-density generator matrix codes.


  Since Dirac stated his Large Number Hypothesis the space-time variation of
fundamental constants has been an active subject of research. Here we analyze
the possible spatial variation of two fundamental constants: the fine structure
constant {\alpha} and the speed of light c. We study the effects of such
variations on the luminosity distance and on the peak luminosity of Type Ia
supernovae (SNe Ia). For this, we consider the change of each fundamental
constant separately and discuss a dipole model for its variation. Elaborating
upon our previous work, we take into account the variation of the peak
luminosity of Type Ia supernovae resulting from the variation of each of these
fundamental constants. Furthermore, we also include the change of the energy
release during the explosion, which was not studied before in the literature.
We perform a statistical analysis to compare the predictions of the dipole
model for {\alpha} and c variation with the Union2.1 and JLA compilations of
SNe Ia. For this, we also allow the nuisance parameters of the distance
estimator $\mu_0$ and the cosmological density matter $\Omega_m$ to vary. As a
result of our analysis we obtain a first estimate of the possible spatial
variation of the speed of light c. On the other hand, we find that there is no
significant difference between the several phenomenological models studied here
and the standard cosmological model, in which fundamental constants do not vary
at all. Thus, we conclude that the actual set of data of Type Ia supernovae
does not allow to verify the hypothetical spatial variation of fundamental
constants.


  We consider the inverse problem of dynamic games, where cost function
parameters are sought which explain observed behavior of interacting players.
Maximum entropy inverse reinforcement learning is extended to the N-player case
in order to solve inverse dynamic games with continuous-valued state and
control spaces. We present methods for identification of cost function
parameters from observed data which correspond to (i) a Pareto efficient
solution, (ii) an open-loop Nash equilibrium or (iii) a feedback Nash
equilibrium. Furthermore, we give results on the unbiasedness of the estimation
of cost function parameters for each arising class of inverse dynamic game. The
applicability of the methods is demonstrated with simulation examples of a
nonlinear and a linear-quadratic dynamic game.


  We present a systematic prescription for calculating cosmological correlation
functions for models with derivative interactions through the wavefunction of
the universe and compare this result with the "in-in" formalism -- canonical
approach. The key step in this procedure is to perform the path integral over
conjugate momenta after which a straightforward generalisation of Feynman's
Rules can be applied. We show that this integral recovers the classical action
plus some additional divergent contributions which are necessary to cancel
other divergences that arise due to loop diagrams involving time derivatives.
As a side project, for the first time, we introduce the "off-shell" version of
the in-in formalism that is sometimes more straightforward, especially for the
models with derivative coupling. To examine our prescription, as a specific
example, we work out the trispectra of the scalar fluctuation in the model with
the $\lambda {\phi'}^3$ derivative coupling.


  The framework of SO(3,2) constrained BF theory applied to gravity makes it
possible to generalize formulas for gravitational diffeomorphic Noether charges
(mass, angular momentum, and entropy). It extends Wald's approach to the case
of first order gravity with a negative cosmological constant, the Holst
modification and the topological terms (Nieh-Yan, Euler, and Pontryagin).
Topological invariants play essential role contributing to the boundary terms
in the regularization scheme for the asymptotically AdS spacetimes, so that the
differentiability of the action is automatically secured. Intriguingly, it
turns out that the black hole thermodynamics does not depend on the Immirzi
parameter for the AdS-Schwarzschild, AdS-Kerr, and topological black holes,
whereas a nontrivial modification appears for the AdS-Taub-NUT spacetime, where
it impacts not only the entropy, but also the total mass.


  Massively parallel recordings of spiking activity in cortical networks show
that covariances vary widely across pairs of neurons. Their low average is well
understood, but an explanation for the wide distribution in relation to the
static (quenched) disorder of the connectivity in recurrent random networks was
so far elusive. We here derive a finite-size mean-field theory that reduces a
disordered to a highly symmetric network with fluctuating auxiliary fields. The
exposed analytical relation between the statistics of connections and the
statistics of pairwise covariances shows that both, average and dispersion of
the latter, diverge at a critical coupling. At this point, a network of
nonlinear units transits from regular to chaotic dynamics. Applying these
results to recordings from the mammalian brain suggests its operation close to
this edge of criticality.


  We describe here the package {\tt subdivision\\_solver} for the mathematical
software {\tt SageMath}. It provides a solver on real numbers for square
systems of large dense polynomials. By large polynomials we mean multivariate
polynomials with large degrees, which coefficients have large bit-size. While
staying robust, symbolic approaches to solve systems of polynomials see their
performances dramatically affected by high degree and bit-size of input
polynomials.Available numeric approaches suffer from the cost of the evaluation
of large polynomials and their derivatives.Our solver is based on interval
analysis and bisections of an initial compact domain of $\R^n$ where solutions
are sought. Evaluations on intervals with Horner scheme is performed by the
package {\tt fast\\_polynomial} for {\tt SageMath}.The non-existence of a
solution within a box is certified by an evaluation scheme that uses a Taylor
expansion at order 2, and existence and uniqueness of a solution within a box
is certified with krawczyk operator.The precision of the working arithmetic is
adapted on the fly during the subdivision process and we present a new
heuristic criterion to decide if the arithmetic precision has to be increased.


  We propose a joint channel estimation and signal detection technique for the
uplink non-orthogonal multiple access using an unsupervised clustering
approach. We apply the Gaussian mixture model to cluster received signals and
accordingly optimize the decision regions to enhance the symbol error rate
(SER). We show that when the received powers of the users are sufficiently
different, the proposed clustering-based approach with no channel state
information (CSI) at the receiver achieves an SER performance similar to that
of the conventional maximum likelihood detector with full CSI. Since the
accuracy of the utilized clustering algorithm depends on the number of the data
points available at the receiver, the proposed technique delivers a tradeoff
between the accuracy and block length.


  We prove that the linearly controlled asymptotic dimension of the fundamental
group of any 3-dimensional graph-manifold does not exceed 7. As applications we
obtain that the universal cover of such a graph-manifold is an absolute
Lipschitz retract and it admits a quasisymmetric embedding into the product of
8 metric trees.


  Let $\Lambda$ be the collection of all probability distributions for
$(X,\widetilde{X})$, where $X$ is a fixed random vector and $\widetilde{X}$
ranges over all possible knockoff copies of $X$ (in the sense of
\cite{CFJL18}). Three topics are developed in this paper: (i) A new
characterization of $\Lambda$ is proved; (ii) A certain subclass of $\Lambda$,
defined in terms of copulas, is introduced; (iii) The (meaningful) special case
where the components of $X$ are conditionally independent is treated in depth.
In real problems, after observing $X=x$, each of points (i)-(ii)-(iii) may be
useful to generate a value $\widetilde{x}$ for $\widetilde{X}$ conditionally on
$X=x$.


  A problem in nonlinear water-wave propagation on the surface of an inviscid,
stationary fluid is presented.
  The primary surface wave, suitably initiated at some radius, is taken to be a
slowly evolving nonlinear cylindrical wave (governed by an appropriate
Korteweg-de Vries equation); the depth is assumed to be varying in a purely
radial direction.
  We consider a $\sech^2$ profile at an initial radius (which is, following our
scalings, rather large), and we describe the evolution as it propagates
radially outwards. This initial profile was chosen because its evolution over
constant depth is understood both analytically and numerically, even though it
is not an exact solitary-wave solution of the cylindrical KdV equation. The
propagation process will introduce reflected and re-reflected components which
will also be described. The precise nature of these reflections is fixed by the
requirements of mass conservation.
  The asymptotic results presented describe the evolution of the primary wave,
the development of an outward shelf and also an inward (reflected) shelf. These
results make use of specific depth variations (which were chosen to simplify
the solution of the relevant equations), and mirror those obtained for the
problem of 1-D plane waves over variable depth, although the details here are
more complex due to the axi-symmetry.


  The local magnetic properties of Yb$^{3+}$ in the layered honeycomb material
YbCl$_{3}$ were investigated by electron spin resonance on single crystals. For
in-plane and out-of-plane field orientations the $g$-factor shows a clear
anisotropy ($g_\|=2.97(8)$ and $g_\bot =1.53(4)$), whereas the low temperature
exchange coupling and the spin relaxation display a rather isotropic character.
At elevated temperatures the contribution of the first excited crystal field
level ($21\pm2$~meV) dominates the spin relaxation.


  We consider the dissipative dynamics of a qubit coupled to a nonlinear
oscillator (NO) embedded in an Ohmic environment. By treating the nonlinearity
up to first order and applying Van Vleck perturbation theory up to second order
in the qubit-NO coupling, we derive an analytical expression for the
eigenstates and eigenfunctions of the coupled qubit-NO system beyond the
rotating wave approximation. In the regime of weak coupling to the thermal
bath, analytical expressions for the time evolution of the qubit's populations
are derived: they describe a multiplicity of damped oscillations superposed to
a complex relaxation part toward thermal equilibrium. The long time dynamics is
characterized by a single relaxation rate, which is maximal when the qubit is
tuned to one of the resonances with the nonlinear oscillator.


  We derive here the orbit equations of particles in naked singularity
spacetimes, namely the Bertrand (BST) and Janis-Newman-Winicour (JNW)
geometries, and for the Schwarzschild black hole. We plot the orbit equations
and find the Perihelion precession of the orbits of particles in the BST and
JNW spacetimes and compare these with the Schwarzschild black hole spacetime.
We find and discuss different distinguishing properties in the effective
potentials and orbits of particle in BST, JNW and Schwarzschild spacetimes, and
the particle trajectories are shown for the matching of BST with an external
Schwarzschild spacetime. We show that the nature of perihelion precession of
orbits in BST and Schwarzschild spacetimes are similar, while in the JNW case
the nature of perihelion precession of orbits is opposite to that of the
Schwarzschild and BST spacetimes. Other interesting and important features of
these orbits are pointed out.


  The cosmic large-scale structure of our Universe is comprised of baryons and
cold dark matter (CDM). Yet it is customary to treat these two components as a
combined single-matter fluid with vanishing pressure, which is justified only
for sufficiently large scales and late times. Here we go beyond the
single-fluid approximation and develop the perturbation theory for two
gravitationally coupled fluids while still assuming vanishing pressure. We
mostly focus on perturbative expansions in powers of $D$ (or $D_+$), the linear
structure growth of matter in a $\Lambda$CDM Universe with cosmological
constant $\Lambda$. We derive in particular (1) explicit recursion relations
for the two fluid densities, (2) complementary all-order results in the
Lagrangian-coordinates approach, as well as (3) the associated component
wavefunctions in a semi-classical approach to cosmic large-scale structure. In
our companion paper (Hahn et al. 2020) we apply these new theoretical results
to generate novel higher-order initial conditions for cosmological
hydrodynamical simulations.


  Patch solutions for the surface quasigeostrophic (SQG) equation model sharp
temperature fronts in atmospheric and oceanic flows. We establish local
well-posedness for SQG sharp fronts of low Sobolev regularity, $H^{2+s}$ for
arbitrarily small $s>0$, allowing for fronts with unbounded curvature.


  We prove Schauder type estimates for stationary and evolution equations
driven by the classical Ornstein-Uhlenbeck operator in a separable Banach
space, endowed with a centered Gaussian measure.


  One difficulty in developing numerical methods for tsunami modeling is the
fact that solutions contain regions where much higher resolution is required
than elsewhere in the domain, particularly since the solution may contain
discontinuities or other localized features. The Clawpack software deals with
this issue by using block-structured adaptive mesh refinement to selectively
refine around propagating waves. For problems where only a target area of the
total solution is of interest (e.g. one coastal community), a method that
allows identifying and refining the grid only in regions that influence this
target area would significantly reduce the computational cost of finding a
solution.
  In this work, we show that solving the time-dependent adjoint equation and
using a suitable inner product with the forward solution allows more precise
refinement of the relevant waves. We present examples solving the shallow water
equations in one and two dimensions. To perform these simulations, the use of
the adjoint method has been integrated into the adaptive mesh refinement
strategy of the open source GeoClaw software. We also present results that show
that the accuracy of the solution is maintained and the computational time
required is significantly reduced through the integration of the adjoint method
into adaptive mesh refinement.


  In this study we show how to represent a continuous time autoregressive
moving average (CARMA) as a higher order stochastic delay differential
equation, which may be thought of as a continuous-time equivalent of the
AR($\infty$) representation. Furthermore, we show how this representation gives
rise to a prediction formula for CARMA processes. To be used in the above
mentioned results we develop a general theory for multivariate stochastic delay
differential equations, which will be of independent interest, and which will
have particular focus on existence, uniqueness and representations.


  A relation between the freezing temperature ($T^{}_{\rm g}$) and the exchange
couplings ($J^{}_{ij}$) in metallic spin-glasses is derived, taking the
spin-correlations ($G^{}_{ij}$) into account. This approach does not involve a
disorder-average. The expansion of the correlations to first order in
$J^{}_{ij}/T^{}_{\rm g}$ leads to the molecular-field result from
Thouless-Anderson-Palmer. Employing the current theory of the spin-interaction
in disordered metals, an equation for $T^{}_{\rm g}$ as a function of the
concentration of impurities is obtained, which reproduces the available data
from {\sl Au}Fe, {\sl Ag}Mn, and {\sl Cu}Mn alloys well.


  I give a simple argument that demonstrates that the state |0>|1>+|1>|0>, with
|0> denoting a state with 0 particles and |1> a 1-particle state, is entangled
in spite of recent claims to the contrary. I also discuss new viewpoints on the
old controversy about whether the above state can be said to display
single-particle or single-photon nonlocality.


  Effective and fluent close-proximity human-robot interaction requires
understanding how humans get habituated to robots and how robot motion affects
human comfort. While prior work has identified humans' preferences over robot
motion characteristics and studied their influence on comfort, we are yet to
understand how novice first-time robot users get habituated to robots and how
robot motion impacts the dynamics of comfort over repeated interactions. To
take the first step towards such understanding, we carry out a user study to
investigate the connections between robot motion and user comfort and
habituation. Specifically, we study the influence of workspace overlap,
end-effector speed, and robot motion legibility on overall comfort and its
evolution over repeated interactions. Our analyses reveal that workspace
overlap, in contrast to speed and legibility, has a significant impact on
users' perceived comfort and habituation. In particular, lower workspace
overlap leads to users reporting significantly higher overall comfort, lower
variations in comfort, and fewer fluctuations in comfort levels during
habituation.


  We present several principal bundles of embeddings of compact manifolds (with
or without boundary) whose base manifolds are nonlinear Grassmannians. We study
their infinite dimensional differential manifold structure in the Fr\'echet
category. This study is motivated by the occurrence of such objects in the
geometric Lagrangian formulation of free boundary continuum mechanics and in
the study of the associated infinite dimensional dual pairs structures.


  The element |V_{ub}| of the quark mixing matrix can be extracted with small
theoretical uncertainties by combining weighted integrals over the endpoint
regions of the lepton spectrum in B -> X_u l nu decays and the photon spectrum
in B -> X_s gamma decays. The perturbative corrections to this determination
are computed at next-to-leading order including operator mixing, which has an
important impact. The effect of Sudakov resummation is shown to be numerically
insignificant.


  In this article we present the basis of Monoid Theory from a categorical
point of view, emphasizing the analogies and differences between the theory of
modules over commutative rings. We present the generalization of afine schemes
and afine monoidal schemes. We study the relationship between the former two
with opic varieties and we set up the basis for the study of
Fontaine-Kato-Illusie's Logaritmic Geometry, widely used in Arithmetic
Geometry.
  En este art\'iculo presentamos las bases de la teor\'ia de monoides desde el
punto de vista categ\'orico, haciendo \'enfasis en las analog\'ias y
diferencias entre la teor\'ia de m\'odulos sobre anillos conmutativos. Se
presenta la generalizaci\'on de esquema af\'in a esquema af\'in monoidal;
estudiamos la relaci\'on que \'estos tienen con las variedades t\'oricas y
sentamos las bases para el estudio de la Geometr\'ia Logar\'itmica de
Fontaine-Kato-Illusie~\cite{kato1989logarithmic}, ampliamente usada en
Geometr\'ia Aritm\'etica.


  All non-human great apes are endangered in the wild, and it is therefore
important to gain an understanding of their demography and genetic diversity.
To date, however, genetic studies within these species have largely been
confined to mitochondrial DNA and a small number of other loci. Here, we
present a genome-wide survey of genetic variation in gorillas using a reduced
representation sequencing approach, focusing on the two lowland subspecies. We
identify 3,274,491 polymorphic sites in 14 individuals: 12 western lowland
gorillas (Gorilla gorilla gorilla) and 2 eastern lowland gorillas (Gorilla
beringei graueri). We find that the two species are genetically distinct, based
on levels of heterozygosity and patterns of allele sharing. Focusing on the
western lowland population, we observe evidence for population substructure,
and a deficit of rare genetic variants suggesting a recent episode of
population contraction. In western lowland gorillas, there is an elevation of
variation towards telomeres and centromeres on the chromosomal scale. On a
finer scale, we find substantial variation in genetic diversity, including a
marked reduction close to the major histocompatibility locus, perhaps
indicative of recent strong selection there. These findings suggest that
despite their maintaining an overall level of genetic diversity equal to or
greater than that of humans, population decline, perhaps associated with
disease, has been a significant factor in recent and long-term pressures on
wild gorilla populations.


  We propose to measure fine-grained domain relevance - the degree that a term
is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning)
domain. Such measurement is crucial for many downstream tasks in natural
language processing. To handle long-tail terms, we build a core-anchored
semantic graph, which uses core terms with rich description information to
bridge the vast remaining fringe terms semantically. To support a fine-grained
domain without relying on a matching corpus for supervision, we develop
hierarchical core-fringe learning, which learns core and fringe terms jointly
in a semi-supervised manner contextualized in the hierarchy of the domain. To
reduce expensive human efforts, we employ automatic annotation and hierarchical
positive-unlabeled learning. Our approach applies to big or small domains,
covers head or tail terms, and requires little human effort. Extensive
experiments demonstrate that our methods outperform strong baselines and even
surpass professional human performance.


  The Distance Geometry Problem (DGP) seeks to find positions for a set of
points in geometric space when some distances between pairs of these points are
known. The so-called discretization assumptions allow to discretize the search
space of DGP instances. In this paper, we study the Discretizable Molecular
Distance Geometry Problem whose feasible solutions provide a discretization
scheme for the DGP. We propose the first constraint programming formulations as
well as a set of checks for proving infeasibility, domain reduction techniques,
symmetry breaking constraints and valid inequalities. Our computational results
indicate that our formulations outperform the state-of-the-art integer
programming formulations, both for feasible and infeasible instances.


  We consider solutions to
  $$ - \Delta_{p} u = f(x) \quad \text{in } \Omega\, ,$$
  when $p$ approaches the semilinear limiting case $p=2$ and we get third order
estimates. As a consequence we deduce improved regularity properties of the
stress field.


  We discuss the limits of the photon wavefunction (PWF) formalism, which is
experiencing a revival in these days from the new practical applications in
photonics and quantum optics. We build a Dirac-like equation for the PWF
written in a manifestly covariant form and show that, in presence of charged
matter fields, it reproduces the standard formulation of (classical)
Electrodinamics. This shows the inconsistency of the attempts to construct a
quantum theory of interacting photons, based on the so called photon
wavefunction approach, alternative to standard QED. PWF formalism can then be
used to provide an easier description of the propagation of free photons, when
the photon number remains fixed in time.


  The phase structure of the finite SU(2)xSU(2) theory with N=2 supersymmetry,
broken to N=1 by mass terms for the adjoint-valued chiral multiplets, is
determined exactly by compactifying the theory on a circle of finite radius.
The exact low-energy superpotential is constructed by identifying it as a
linear combination of the Hamiltonians of a certain symplectic reduction of the
spin generalized elliptic Calogero-Moser integrable system. It is shown that
the theory has four confining, two Higgs and two massless Coulomb vacua which
agrees with a simple analysis of the tree-level superpotential of the
four-dimensional theory. In each vacuum, we calculate all the condensates of
the adjoint-valued scalars.


  By combining neutron scattering and magnetization measurements down to 80 mK,
we determine the $(H,T)$ phase diagram of the Nd$_2$(Zr$_{1-x}$Ti$_x$)$_2$O$_7$
pyrochlore magnet compounds. In those samples, Zr is partially substituted by
Ti, hence tuning the exchange parameters and testing the robustness of the
various phases. In all samples, the ground state remains "all in / all out",
while the field induces phase transitions towards new states characterized by
"2 in - 2 out" or "1 out - 3 in / 1 in - 3 out" configurations. These
transitions manifest as metamagnetic singularities in the magnetization vs
field measurements. Strikingly, it is found that moderate substitution
reinforces the stability of the "all in / all out" phase: the N\'eel
temperature, the metamagnetic fields along with the ordered magnetic moment are
higher in substituted samples with $x <$ 10\%.


  In 1980, Gross conjectured a formula for the expected leading term at $s=0$
of the Deligne--Ribet $p$-adic $L$-function associated to a totally even
character $\psi$ of a totally real field $F$. The conjecture states that after
scaling by $L(\psi \omega^{-1}, 0)$, this value is equal to a $p$-adic
regulator of units in the abelian extension of $F$ cut out by $\psi
\omega^{-1}$. In this paper, we prove Gross's conjecture.


  Radio interferometers designed to measure the cosmological 21 cm power
spectrum require high sensitivity. Several modern low-frequency interferometers
feature drift-scan antennas placed on a regular grid to maximize the number of
instantaneously coherent (redundant) measurements. However, even for such
maximum-redundancy arrays, significant sensitivity comes through partial
coherence between baselines. Current visibility-based power-spectrum pipelines,
though shown to ease control of systematics, lack the ability to make use of
this partial redundancy. We introduce a method to leverage partial redundancy
in such power-spectrum pipelines for drift-scan arrays. Our method
cross-multiplies baseline pairs at a time lag and quantifies the sensitivity
contributions of each pair of baselines. Using the configurations and beams of
the 128-element Donald C. Backer Precision Array for Probing the Epoch of
Reionization (PAPER-128) and staged deployments of the Hydrogen Epoch of
Reionization Array, we illustrate how our method applies to different arrays
and predict the sensitivity improvements associated with pairing partially
coherent baselines. As the number of antennas increases, we find partial
redundancy to be of increasing importance in unlocking the full sensitivity of
upcoming arrays.


  A noncentrosymmetric superconductor can have a superconducting diode effect,
where the critical current in opposite directions is different when
time-reversal symmetry is also broken. We theoretically propose that a
ferroelectric superconductor with coexisting ferroelectricity and
superconductivity can support a ferroelectric reversible superconducting diode
effect. Through first-principles calculation, we predict that monolayer
CuNb$_2$Se$_4$ (i.e., bilayer NbSe$_2$ intercalated with Cu) is such a
ferroelectric superconductor, where ferroelectricity controls the layer
polarization as well as the sign of spin-orbit coupling induced spin
splittings. Because the nonreciprocal effect of the critical current is
proportional to the spin splittings, the superconducting diode effect is
reversible upon electric switch of ferroelectricity. While we use
CuNb$_2$Se$_4$ as a model system, the predicted effect can appear in a class of
two-dimensional superconducting bilayers with ferroelectricity induced by
interlayer sliding. Our work opens the door to studying the interplay between
superconductivity and ferroelectricity in two-dimensional materials.


  Topological phases of matter are defined by their nontrivial patterns of
ground-state quantum entanglement, which is irremovable so long as the
excitation gap and the protecting symmetries, if any, are maintained. Recent
studies on noninteracting electrons in crystals have unveiled a peculiar
variety of topological phases, which harbors nontrivial entanglement that can
be dissolved simply by the the addition of entanglement-free, but charged,
degrees of freedom. Such topological phases have a weaker sense of robustness
than their conventional counterparts, and are therefore dubbed "fragile
topological phases." In this work, we show that fragile topology is a general
concept prevailing beyond systems of noninteracting electrons. Fragile
topological phases can generally occur when a system has a $\mathrm{U}(1)$
charge conservation symmetry, such that only particles with one sign of the
charge are physically allowed (e.g. electrons but not positrons). We
demonstrate that fragile topological phases exist in interacting systems of
both fermions and of bosons.


  As generative models have risen in popularity, a domain that has risen
alongside is generative models for music. Our study aims to compare the
performance of a simple Markov chain model and a recurrent neural network (RNN)
model, two popular models for sequence generating tasks, in jazz music
improvisation. While music, especially jazz, remains subjective in telling
whether a composition is "good" or "bad", we aim to quantify our results using
metrics of groove pattern similarity and pitch class histogram entropy. We
trained both models using transcriptions of jazz blues choruses from
professional jazz players, and also fed musical jazz seeds to help give our
model some context in beginning the generation. Our results show that the RNN
outperforms the Markov model on both of our metrics, indicating better rhythmic
consistency and tonal stability in the generated music. Through the use of
music21 library, we tokenized our jazz dataset into pitches and durations that
our model could interpret and train on. Our findings contribute to the growing
field of AI-generated music, highlighting the important use of metrics to
assess generation quality. Future work includes expanding the dataset of MIDI
files to a larger scale, conducting human surveys for subjective evaluations,
and incorporating additional metrics to address the challenge of subjectivity
in music evaluation. Our study provides valuable insight into the use of
recurrent neural networks for sequential based tasks like generating music.


  A massively recurrent neural network responds on one side to input stimuli
and is autonomously active, on the other side, in the absence of sensory
inputs. Stimuli and information processing depends crucially on the qualia of
the autonomous-state dynamics of the ongoing neural activity. This default
neural activity may be dynamically structured in time and space, showing
regular, synchronized, bursting or chaotic activity patterns.
  We study the influence of non-synaptic plasticity on the default dynamical
state of recurrent neural networks. The non-synaptic adaption considered acts
on intrinsic neural parameters, such as the threshold and the gain, and is
driven by the optimization of the information entropy. We observe, in the
presence of the intrinsic adaptation processes, three distinct and globally
attracting dynamical regimes, a regular synchronized, an overall chaotic and an
intermittent bursting regime. The intermittent bursting regime is characterized
by intervals of regular flows, which are quite insensitive to external stimuli,
interseeded by chaotic bursts which respond sensitively to input signals. We
discuss these finding in the context of self-organized information processing
and critical brain dynamics.


  We prove a weak converse estimate for the simultaneous approximation by
several forms of the Bernstein polynomials with integer coefficients. It is
stated in terms of moduli of smoothness. In particular, it yields a big
$O$-characterization of the rate of that approximation. We also show that the
approximation process generated by these Bernstein polynomials with integer
coefficients is saturated. We identify its saturation rate and the trivial
class.


  Following upon our recent work on vector correlations in the Ar-NO collisions
[PCCP 12, 1038 (2010)], we compare model results with close-coupling
calculations for a range of channels and collision energies for the He-NO
system. The striking agreement between the model and exact polarization moments
indicates that the stereodynamics of rotationally inelastic atom-molecule
collisions at thermal energies is governed by diffraction of matter waves from
a two-dimensional repulsive core of the atom-molecule potential. Furthermore,
the model polarization moments characterizing the He-NO, He-O$_2$, He-OH, and
He-CaH stereodynamics are found to coalesce into a single, distinctive pattern,
which can serve as a "fingerprint" to identify diffraction-driven
stereodynamics in future work.


  Automatic emotion categorization has been predominantly formulated as text
classification in which textual units are assigned to an emotion from a
predefined inventory, for instance following the fundamental emotion classes
proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert
Plutchik (adding trust, anticipation). This approach ignores existing
psychological theories to some degree, which provide explanations regarding the
perception of events. For instance, the description that somebody discovers a
snake is associated with fear, based on the appraisal as being an unpleasant
and non-controllable situation. This emotion reconstruction is even possible
without having access to explicit reports of a subjective feeling (for instance
expressing this with the words "I am afraid."). Automatic classification
approaches therefore need to learn properties of events as latent variables
(for instance that the uncertainty and the mental or physical effort associated
with the encounter of a snake leads to fear). With this paper, we propose to
make such interpretations of events explicit, following theories of cognitive
appraisal of events, and show their potential for emotion classification when
being encoded in classification models. Our results show that high quality
appraisal dimension assignments in event descriptions lead to an improvement in
the classification of discrete emotion categories. We make our corpus of
appraisal-annotated emotion-associated event descriptions publicly available.


  Non-Gaussian state-space models arise in several applications, and within
this framework the binary time series setting provides a relevant example.
However, unlike for Gaussian state-space models - where filtering, predictive
and smoothing distributions are available in closed form - binary state-space
models require approximations or sequential Monte Carlo strategies for
inference and prediction. This is due to the apparent absence of conjugacy
between the Gaussian states and the likelihood induced by the observation
equation for the binary data. In this article we prove that the filtering,
predictive and smoothing distributions in dynamic probit models with Gaussian
state variables are, in fact, available and belong to a class of unified
skew-normals (SUN) whose parameters can be updated recursively in time via
analytical expressions. Also the key functionals of these distributions are, in
principle, available, but their calculation requires the evaluation of
multivariate Gaussian cumulative distribution functions. Leveraging SUN
properties, we address this issue via novel Monte Carlo methods based on
independent samples from the smoothing distribution, that can easily be adapted
to the filtering and predictive case, thus improving state-of-the-art
approximate and sequential Monte Carlo inference in small-to-moderate
dimensional studies. Novel sequential Monte Carlo procedures that exploit the
SUN properties are also developed to deal with online inference in high
dimensions. Performance gains over competitors are outlined in a financial
application.


  We consider combinatorial semi-bandits over a set of arms ${\cal X} \subset
\{0,1\}^d$ where rewards are uncorrelated across items. For this problem, the
algorithm ESCB yields the smallest known regret bound $R(T) = {\cal O}\Big( {d
(\ln m)^2 (\ln T) \over \Delta_{\min} }\Big)$, but it has computational
complexity ${\cal O}(|{\cal X}|)$ which is typically exponential in $d$, and
cannot be used in large dimensions. We propose the first algorithm which is
both computationally and statistically efficient for this problem with regret
$R(T) = {\cal O} \Big({d (\ln m)^2 (\ln T)\over \Delta_{\min} }\Big)$ and
computational complexity ${\cal O}(T {\bf poly}(d))$. Our approach involves
carefully designing an approximate version of ESCB with the same regret
guarantees, showing that this approximate algorithm can be implemented in time
${\cal O}(T {\bf poly}(d))$ by repeatedly maximizing a linear function over
${\cal X}$ subject to a linear budget constraint, and showing how to solve this
maximization problems efficiently.


  In recent ALMA surveys, the gas distributions and velocity structures of most
of the protoplanetary disks can still not be imaged at high S/N due to the
short integration time. In this work, we re-analyzed the ALMA 13CO (3-2) and
C18O (3-2) data of 88 young stellar objects in Lupus with the velocity-aligned
stacking method to enhance S/N and to study the kinematics and disk properties
traced by molecular lines. This method aligns spectra at different positions in
a disk based on the projected Keplerian velocities at their positions and then
stacks them. This method enhances the S/N ratios of molecular-line data and
allows us to obtain better detections and to constrain dynamical stellar masses
and disk orientations. We obtain 13CO detections in 41 disks and C18O
detections in 18 disks with 11 new detections in 13CO and 9 new detections in
C18O after applying the method. We estimate the disk orientations and the
dynamical stellar masses from the 13CO data. Our estimated dynamical stellar
masses correlate with the spectroscopic stellar masses, and in a subsample of
16 sources, where the inclination angles are better constrained, the two masses
are in a good agreement within the uncertainties and with a mean difference of
0.15 Msun. With more detections of fainter disks, our results show that high
gas masses derived from the 13CO and C18O lines tend to be associated with high
dust masses estimated from the continuum emission. Nevertheless, the scatter is
large (0.9 dex), implying large uncertainties in deriving the disk gas mass
from the line fluxes. We find that with such large uncertainties it is expected
that there is no correlation between the disk gas mass and the mass accretion
rate with the current data. Deeper observations to detect disks with gas masses
<1E-5 Msun in molecular lines are needed to investigate the correlation between
the disk gas mass and the mass accretion rate.


  The increasing ability to collect data from urban environments, coupled with
a push towards openness by governments, has resulted in the availability of
numerous spatio-temporal data sets covering diverse aspects of a city.
Discovering relationships between these data sets can produce new insights by
enabling domain experts to not only test but also generate hypotheses. However,
discovering these relationships is difficult. First, a relationship between two
data sets may occur only at certain locations and/or time periods. Second, the
sheer number and size of the data sets, coupled with the diverse spatial and
temporal scales at which the data is available, presents computational
challenges on all fronts, from indexing and querying to analyzing them.
Finally, it is non-trivial to differentiate between meaningful and spurious
relationships. To address these challenges, we propose Data Polygamy, a
scalable topology-based framework that allows users to query for statistically
significant relationships between spatio-temporal data sets. We have performed
an experimental evaluation using over 300 spatial-temporal urban data sets
which shows that our approach is scalable and effective at identifying
interesting relationships.


  In this paper, we investigate the thermalization of Hawking radiation from
primordial black holes (PBHs) in the early Universe, taking into account the
interference effect on thermalization of high energy particles, known as
Landau-Pomeranchuk-Migdal (LPM) effect. Small PBHs with masses $ \lesssim 10^9
\, \mathrm{g} $ completely evaporate before the big bang nucleosynthesis (BBN).
The Hawking radiation emitted from these PBHs heats up the ambient plasma with
temperature lower than the Hawking temperature, which results in a non-trivial
temperature profile around the PBHs, namely a hot spot surrounding a PBH with a
broken power-law tail. We find that the hot spot has a core with a radius much
larger than the black hole horizon and its highest temperature is independent
of the initial mass of the PBH such as $2 \times 10^{9} \, {\rm GeV} \times
(\alpha/0.1)^{19/3}$, where $\alpha$ generically represents the fine-structure
constants. We also briefly discuss the implications of the existence of the hot
spot for phenomenology.


  We study spectral lines in exceptionally bright solar limb prominences with
pronounced sodium and magnesium emission. We find that most prominences with
significant NaD2 and Mgb2 emission show centrally reversed profiles of H-alpha
and occasionally even of H-beta, which are are well reproduced by semi-infinite
models. The maximum H-alpha source function corresponds to an excitation
temperature of 3950 K, for pronounced central reversions 4000 K; the related
optical thickness exceeds 10.0. The narrow widths of the NaD2 and Mgb2 profiles
yield a non-thermal broadening of 5 km/s.


  We show that for hypersurface orthogonal Killing vectors, the corresponding
Chevreton superenergy currents will be conserved and proportional to the
Killing vectors. This holds for four-dimensional Einstein-Maxwell spacetimes
with an electromagnetic field that is sourcefree and inherits the symmetry of
the spacetime. A similar result also holds for the trace of the Chevreton
tensor. The corresponding Bel currents have previously been proven to be
conserved and our result can be seen as giving further support to the concept
of conserved mixed superenergy currents. The analogous case for a scalar field
has also previously been proven to give conserved currents and we show, for
completeness, that these currents also are proportional to the Killing vectors.


  Teaching plays a fundamental role in human learning. Typically, a human
teaching strategy would involve assessing a student's knowledge progress for
tailoring the teaching materials in a way that enhances the learning progress.
A human teacher would achieve this by tracing a student's knowledge over
important learning concepts in a task. Albeit, such teaching strategy is not
well exploited yet in machine learning as current machine teaching methods tend
to directly assess the progress on individual training samples without paying
attention to the underlying learning concepts in a learning task. In this
paper, we propose a novel method, called Knowledge Augmented Data Teaching
(KADT), which can optimize a data teaching strategy for a student model by
tracing its knowledge progress over multiple learning concepts in a learning
task. Specifically, the KADT method incorporates a knowledge tracing model to
dynamically capture the knowledge progress of a student model in terms of
latent learning concepts. Then we develop an attention pooling mechanism to
distill knowledge representations of a student model with respect to class
labels, which enables to develop a data teaching strategy on critical training
samples. We have evaluated the performance of the KADT method on four different
machine learning tasks including knowledge tracing, sentiment analysis, movie
recommendation, and image classification. The results comparing to the
state-of-the-art methods empirically validate that KADT consistently
outperforms others on all tasks.


  This paper reports on a computational study of the model error in the
LANS-alpha and NS-alpha deconvolution models of homogeneous isotropic
turbulence. The focus is on how well the model error may be characterized by a
stochastic force. Computations are also performed for a new turbulence model
obtained as a rescaled limit of the deconvolution model. The technique used is
to plug a solution obtained from direct numerical simulation of the
incompressible Navier--Stokes equations into the competing turbulence models
and to then compute the time evolution of the resulting residual. All
computations have been done in two dimensions rather than three for convenience
and efficiency. When the effective averaging length scale in any of the models
is $\alpha_0=0.01$ the time evolution of the root-mean-squared residual error
grows as $\sqrt t$. This growth rate is consistent with the hypothesis that the
model error may be characterized by a stochastic force. When $\alpha_0=0.20$
the residual error grows linearly. Linear growth suggests that the model error
possesses a systematic bias which dominates the stochastic terms. Finally, for
$\alpha_0=0.04$ the LANS-alpha model possessed systematic bias; however, for
this value of $\alpha_0$ the higher-order alpha models that were tested did
not.


  Visual Place Recognition has been the subject of many endeavours utilizing
different ensemble approaches to improve VPR performance. Ideas like
multi-process fusion, Fly-Inspired Voting Units, SwitchHit or Switch-Fuse
involve combining different VPR techniques together, utilizing different
strategies. However, a major aspect often common to many of these strategies is
voting. Voting is an extremely relevant topic to explore in terms of its
application and significance for any ensemble VPR setup. This paper analyses
several voting schemes to maximise the place detection accuracy of a VPR
ensemble set up and determine the optimal voting schemes for selection. We take
inspiration from a variety of voting schemes that are widely employed in fields
such as politics and sociology and it is evident via empirical data that the
selection of the voting method influences the results drastically. The paper
tests a wide variety of voting schemes to present the improvement in the VPR
results for several data sets. We aim to determine whether a single optimal
voting scheme exists or, much like in other fields of research, the selection
of a voting technique is relative to its application and environment. We
propose a ranking of these different voting methods from best to worst which
allows for better selection. While presenting our results in terms of voting
method's performance bounds, in form of radar charts, PR curves to showcase the
difference in performance and a comparison methodology using a McNemar test
variant to determine the statistical significance of the differences. This test
is performed to further confirm the reliability of outcomes and draw
comparisons for better and informed selection a voting technique.


  We obtain heat kernel estimates for a class of fourth order non-uniformly
elliptic operators in two dimensions. Contrary to existing results, the
operators considered have symbols that are not strongly convex. This rises
certain difficulties as it is known that, as opposed to the strongly convex
case, there is no absolute exponential constant. Our estimates involve sharp
constants and Finsler-type distances that are induced by the operator symbol.
The main result is based on two general hypotheses, a weighted Sobolev
inequalitry and an interpolation inequality, which are related to the
singularity or degeneracy of the coefficients.


  We consider a variant of Cournot competition, where multiple firms allocate
the same amount of resource across multiple markets. We prove that the game has
a unique pure-strategy Nash equilibrium (NE), which is symmetric and is
characterized by the maximal point of a "potential function". The NE is
globally asymptotically stable under the gradient adjustment process, and is
not socially optimal in general. An application is in transportation, where
drivers allocate time over a street network.


  The growing availability of network data and of scientific interest in
distributed systems has led to the rapid development of statistical models of
network structure. Typically, however, these are models for the entire network,
while the data consists only of a sampled sub-network. Parameters for the whole
network, which is what is of interest, are estimated by applying the model to
the sub-network. This assumes that the model is consistent under sampling, or,
in terms of the theory of stochastic processes, that it defines a projective
family. Focusing on the popular class of exponential random graph models
(ERGMs), we show that this apparently trivial condition is in fact violated by
many popular and scientifically appealing models, and that satisfying it
drastically limits ERGM's expressive power. These results are actually special
cases of more general results about exponential families of dependent random
variables, which we also prove. Using such results, we offer easily checked
conditions for the consistency of maximum likelihood estimation in ERGMs, and
discuss some possible constructive responses.


  The Ninja data analysis challenge allowed the study of the sensitivity of
data analysis pipelines to binary black hole numerical relativity waveforms in
simulated Gaussian noise at the design level of the LIGO observatory and the
VIRGO observatory. We analyzed NINJA data with a pipeline based on the Hilbert
Huang Transform, utilizing a detection stage and a characterization stage:
detection is performed by triggering on excess instantaneous power,
characterization is performed by displaying the kernel density enhanced (KD)
time-frequency trace of the signal. Using the simulated data based on the two
LIGO detectors, we were able to detect 77 signals out of 126 above SNR 5 in
coincidence, with 43 missed events characterized by signal to noise ratio SNR
less than 10. Characterization of the detected signals revealed the merger part
of the waveform in high time and frequency resolution, free from time-frequency
uncertainty. We estimated the timelag of the signals between the detectors
based on the optimal overlap of the individual KD time-frequency maps, yielding
estimates accurate within a fraction of a millisecond for half of the events. A
coherent addition of the data sets according to the estimated timelag
eventually was used in a characterization of the event.


  Raman studies of nanocomposite SiCN thin film by sputtering showed that with
an increase of substrate temperature from room temperature to 500oC, a
transition from mostly sp2 graphitic phase to sp3 carbon took place which was
observed from the variation of ID/IG ratio and the peak shifts. This process
resulted in the growth of C3N4 and Si3N4 crystallites in the amorphous matrix
which led to an increase in hardness and modulus obtained through
nanoindentation. However, at a further higher temperature of 600oC, again an
increase of sp2 C concentration in the film was observed and the H and E values
showed a decrease due to increased growth of graphitic carbon phase. The whole
process got reflected in a modified four stage Ferrari Robertson model of Raman
spectroscopy.


  Atomic force microscopy (AFM) is a mechanical profiling technique that allows
to image surfaces with atomic resolution. Recent progress in reducing the noise
of this technique has led to a resolution level where previously undetectable
symmetries of the images of single atoms are observed. These symmetries are
related to the nature of the interatomic forces. The Si(111)-(7x7) surface is
studied by AFM with various tips and AFM images are simulated with chemical and
electrostatic model forces. The calculation of images from the tip-sample
forces is explained in detail and the implications of the imaging parameters
are discussed. Because the structure of the Si(111)-(7x7) surface is known very
well, the shape of the adatom images is used to determine the tip structure.
The observability of atomic orbitals by AFM and scanning tunneling microscopy
is discussed.


  In this paper, we find all integers $c$ having at least two representations
as a difference between a Fibonacci number and a Tribonacci number.


  Drone fleets, with counts on the order of O(100) to O(1000), will play
important and significant roles in the automation of the deployment, operation,
maintenance and repair of ubiquitous and pervasive landscape scale elongated
structures, with the longest linear spatial dimensions of O(1 mile) to O(10
mile). The organization of the drone team to support the task is considered as
a digital platform, (specifically, a digital multi-sided platform). A
computational thinking approach is used to engineer the architecture of the
platform.


  We study the effect of radiation pressure on the dust in the inner rim of
transitional disks with large inner holes. In particular, we evaluate whether
radiation pressure can be responsible for keeping the inner holes dust-free,
while allowing gas accretion to proceed. This has been proposed in a paper by
Chiang and Murray-Clay (2007, Nature Physics 3, p. 604) who explain the
formation of these holes as an inside-out evacuation due to X- ray-triggered
accretion of the innermost layer of the disk rim outside of the hole. We show
that radiation pressure is clearly incapable of stopping dust from flowing into
the hole because of dust pile-up and optical depth effects, and also because of
viscous mixing. Other mechanisms need to be found to explain the persistence of
the opacity hole in the presence of accretion, and we speculate on possible
solutions.


  The structure of the electron quantum size levels in spherical nanocrystals
is studied in the framework of an eight--band effective mass model at zero and
weak magnetic fields. The effect of the nanocrystal surface is modeled through
the boundary condition imposed on the envelope wave function at the surface. We
show that the spin--orbit splitting of the valence band leads to the
surface--induced spin--orbit splitting of the excited conduction band states
and to the additional surface--induced magnetic moment for electrons in bare
nanocrystals. This additional magnetic moment manifests itself in a nonzero
surface contribution to the linear Zeeman splitting of all quantum size energy
levels including the ground 1S electron state. The fitting of the size
dependence of the ground state electron g factor in CdSe nanocrystals has
allowed us to determine the appropriate surface parameter of the boundary
conditions. The structure of the excited electron states is considered in the
limits of weak and strong magnetic fields.


  In supersymmetric models with R-parity violation, scalar neutrinos may be
produced as s-channel resonances in e^+e^- colliders. We note that within
current constraints, the scalar neutrino may have a width of several GeV into
b\bar{b} and be produced with large cross section, leading to a novel
supersymmetry discovery signal at LEP II. In addition, if the scalar neutrino
mass approximately equals m_Z, such a resonance necessarily increases R_b and
reduces A_{FB}(b), significantly improving the fit to electroweak data. Bounds
from B meson and top quark decays are leading constraints, and we stress the
importance of future measurements.


  Traditional dark matter models, eg. WIMPs, assume dark matter is weakly
coupled to the standard model so that elastic scattering between dark matter
and baryons can be described perturbatively by Born approximation. Most direct
detection experiments are analyzed according to that assumption. We show that
when the fundamental DM-baryon interaction is attractive, dark matter-nucleus
scattering is non-perturbative in much of the relevant parameter range. The
cross section exhibits rich resonant behavior with a highly non-trivial
dependence on atomic mass; furthermore, the extended rather than point-like
nature of nuclei significantly impacts the cross sections. The repulsive case
also requires full numerical calculation. These non-perturbative effects change
existing constraints.
  Near a resonance value of the parameters, the cross section has non-trivial
velocity dependence rather than the usual $\sigma \sim v^0$; we take the
velocity dependence into account. (However doing so has little impact on
current constraints.)
  We report the corrected exclusion regions superseding previous limits from
XQC, CRESST Surface Run, CMB power spectrum and extensions with Lyman-$\alpha$
and Milky Way satellites, and Milky Way gas clouds. Some limits become weaker
than previous bounds in the literature, while others become stronger. Gaps
which open by correct treatment of some particular constraint can sometimes be
closed using a different constraint. We also discuss the dependence on mediator
mass and give approximate expressions for the velocity dependence near a
resonance. Sexaquark ($uuddss$) DM with mass around 2 GeV, which exchanges QCD
mesons with baryons, remains unconstrained for most of the parameter space of
interest.
  A statement in the literature that a DM-nucleus cross section larger than
$10^{-25}\,{\rm cm}^2$ implies dark matter is composite, is corrected.