  At the nano-scale, surface phenomena such as attractive VdW forces strongly
dominate; explosions may well be thought impossible. We confirm nano explosions
that are important for a fractal (hierarchical, scale invariant) pore
structure, greatly increasing pore access. Analyzing only microscope images,
image analysis and statistical error reduction algorithms alone provide
conclusive evidence of explosively expelled material. The results reconfirm
that computer image recognition and statistical analysis are a widely
applicable and inexpensive technique for determining parameters which are
otherwise unavailable, such as the densities of single nano shapes. The
explosion mechanism explains optimum calcination temperatures for
cobalt-hydroxide and why slow heating optimizes porosity. Slow heating
increases the accessible surface area by 60% with this supercapacitor material.


  Surface brightness profiles of globular clusters with shallow central cusps
(Sigma ~ R^v with -0.3<~ v <~ -0.05) have been associated by several recent
studies with the presence of a central intermediate mass black hole (IMBH).
Such shallow slopes are observed in several globular clusters thanks to the
high angular resolution of Hubble Space Telescope imaging. In this Letter we
evaluate whether shallow cusps are a unique signature of a central IMBH by
analyzing a sample of direct N-body simulations of star clusters with and
without a central IMBH. We ``observe'' the simulations as if they were HST
images. Shallow cusps are common in our simulation sample: star clusters
without an IMBH have v >~ -0.3 in the pre-core-collapse and core-collapse
phases. Post-core-collapse clusters without an IMBH transition to steeper
cusps, -0.7<~ v <~ -0.4, only if the primordial binary fraction is very small,
f_{bin}< 3 per cent, and if there are few stellar-mass black holes remaining.
Otherwise v values overlap the range usually ascribed to the presence of an
IMBH throughout the entire duration of the simulations. In addition, measuring
v is intrinsically prone to significant uncertainty, therefore typical
measurement errors may lead to v > -0.3 even when <v> <~ -0.4. Overall our
analysis shows that a shallow cusp is not an unequivocal signature of a central
IMBH and casts serious doubts on the usefulness of measuring v in the context
of the hunt for IMBHs in globular clusters.


  Nakajima introduced a resolution of singularities of the Donaldson-Uhlenbeck
completion of the moduli space of based instantons over $S^4$. For $k\leq 4$,
we extend this result to $\mathbb P^2$ and compute, in the rank-stable limit,
the homotopy type of these spaces by showing that, in this limit, these
constructions yield universal bundles.


  We propose to learn word embeddings from visual co-occurrences. Two words
co-occur visually if both words apply to the same image or image region.
Specifically, we extract four types of visual co-occurrences between object and
attribute words from large-scale, textually-annotated visual databases like
VisualGenome and ImageNet. We then train a multi-task log-bilinear model that
compactly encodes word "meanings" represented by each co-occurrence type into a
single visual word-vector. Through unsupervised clustering, supervised
partitioning, and a zero-shot-like generalization analysis we show that our
word embeddings complement text-only embeddings like GloVe by better
representing similarities and differences between visual concepts that are
difficult to obtain from text corpora alone. We further evaluate our embeddings
on five downstream applications, four of which are vision-language tasks.
Augmenting GloVe with our embeddings yields gains on all tasks. We also find
that random embeddings perform comparably to learned embeddings on all
supervised vision-language tasks, contrary to conventional wisdom.


  We consider a Heegaard splitting M=H_1 \cup_S H_2 of a 3-manifold M having an
essential disk D in H_1 and an essential surface F in H_2 with |D \cap F|=1.
(We require that boundary of F is in S when H_2 is a compressionbody with
non-empty "minus" boundary.) Let F be a genus g surface with n boundary
components. From S, we obtain a genus g(S)+2g+n-2 Heegaard splitting M=H'_1
\cup_S' H'_2 by cutting H_2 along F and attaching F \times [0,1] to H_1. As an
application, by using a theorem due to Casson and Gordon, we give examples of
3-manifolds having two Heegaard splittings of distinct genera where one of the
two Heegaard splittings is a strongly irreducible non-minimal genus splitting
and it is obtained from the other by the above construction.


  Machine learning holds the promise of learning the energy functional via
examples, bypassing the need to solve complicated quantum-chemical equations
and realizing efficient computing of molecular electronic properties.


  The determination of hydrogenous content in commercial cargoes is an
important challenge in cargo security. Prior work has shown the feasibility of
hydrogenous cargo classification in radiographic applications. This result was
achieved by using the significant material to material differences in the
energy dependence of their neutron scattering cross-sections. The work
presented here details the application of this technique to
multiple-monoenergetic neutron transmission measurements of several
heterogeneous cargo mock-ups with the goal of quantifying hydrogenous content.
It demonstrates the ability to determine the hydrogenous content of a cargo
container by performing analysis of pulse-height data of transmitted neutrons.
The set-up used for these feasibility studies was able to detect and quantify
areal densities of up to 30 g/cm$^2$ of high density polyethylene (HDPE), even
when mixed with metallic materials. Accurate determination of the hydrogenous
content of the cargo has two important uses: it can allow for better imaging
and discrimination of conventional contraband during cargo screening; it may
also allow the unfolding of the individual elemental contributions from an
effective atomic number, $Z_{\mathrm{eff}}$, as determined from multiple
monoenergetic gamma radiography (MMGR). This combination of capabilities could
make multiple monoenergetic neutron radiography applications a significant
contribution to cargo security.


  Theoretical results are reported, concerning the reflection and transmission
of few-cycle laser pulses on a very thin conducting layer, which may represent
the surface current density of the massless charges of graphene. It is shown
that the pulse may undergo violent distortions, even at moderate intensities,
to that extent, that the scattered radiation contains rectangular trains, which
are approximate realizations of Rademacher functions in the optical or
terahertz regime.


  Two major deviations from causality in the existing formulations of quantum
mechanics, related respectively to quantum chaos and indeterminate wave
reduction, are eliminated within the new, universal concept of dynamic
complexity. The analysis involves a new paradigm for description of a system
with interaction, the principle of dynamic multivaluedness (redundance), and
the ensuing concept of the fundamental dynamic uncertainty. It is shown that
both the wave reduction and truly unpredictable (chaotic) behaviour in quantum
systems can be completely and causally understood as a higher sublevel of the
same dynamic complexity that provides the causally complete picture of the
unified wave-particle duality and relativity at its lowest level
(quant-ph/9902015,16). The presentation is divided into five parts. The first
three parts deal with intrinsic randomness in Hamiltonian (isolated) quantum
systems as the basic case of dynamical chaos. In the last two parts a causal
solution to the problem of quantum indeterminacy and wave reduction is
proposed. Part I introduces the method of effective dynamical functions as a
generalisation of the optical potential formalism. The method provides a legal
transformation of the Schroedinger equation revealing the hidden
multivaluedness of interaction process, i. e. its self-consistent, dynamical
splitting into many equally real, but mutually incompatible branches, called
'realisations'. Each realisation incorporates the usual "complete" set of
eigenfunctions and eigenvalues for the entire problem. The method is presented
in detail for the Hamiltonian system with periodic (not small) perturbation,
both in its time-independent and time-dependent versions.


  Nowadays game engines are imperative for building 3D applications and games.
This is for the reason that the engines appreciably reduce resources for
employing obligatory but intricate utilities. This paper elucidates about a
game engine and its foremost elements. It portrays a number of special kinds of
contemporary game engines by way of their aspects, procedure and deliberates
their stipulations with comparison.


  The decay of kinetic helicity is studied in numerical models of forced
turbulence using either an externally imposed forcing function as an
inhomogeneous term in the equations or, alternatively, a term linear in the
velocity giving rise to a linear instability. The externally imposed forcing
function injects energy at the largest scales, giving rise to a turbulent
inertial range with nearly constant energy flux while for linearly forced
turbulence the spectral energy is maximum near the dissipation wavenumber.
Kinetic helicity is injected once a statistically steady state is reached, but
it is found to decay on a turbulent time scale regardless of the nature of the
forcing and the value of the Reynolds number.


  Type IIA flux compactifications with O6-planes have been argued from a four
dimensional effective theory point of view to admit stable, moduli free
solutions. We discuss in detail the ten dimensional description of such vacua
and present exact solutions in the case when the O6-charge is smoothly
distributed. In the localised case, the solution is a half-flat, non-Calabi-Yau
metric. Finally, using the ten dimensional description we show how all moduli
are stabilised and reproduce precisely the results of de Wolfe et al.


  This paper describes the realization of a two terminal-pair digital impedance
bridge and the test measurements performed with it. The bridge, with a very
simple architecture, is based on a commercial two-channel digital signal
synthesizer and a synchronous detector. The bridge can perform comparisons
between impedances having arbitrary phase and magnitude ratio: its balance is
achieved automatically in less than a minute. $R$-$C$ comparisons with
calibrated standards, at kHz frequency and 100 kohm magnitude level, give ratio
errors of the order of $10^{-6}$, with potential for further improvements.


  We investigate the geometry of smooth hyperelliptic curves that possess
additional involutions, especially from the point of view of the Prym theory.
Our main result is the injectivity of the Prym map for hyperelliptic
$\mathbb{Z}_2^2$-coverings over hyperelliptic curves of positive genus.


  We first give a general error estimate for the nonconforming approximation of
a problem for which a Banach-Ne{\v c}as-Babu{\v s}ka (BNB) inequality holds.
This framework covers parabolic problems with general conditions in time
(initial value problems as well as periodic problems) under minimal regularity
assumptions. We consider approximations by two types of space-time
discretizations, both based on a conforming Galerkin method in space. The first
one is the Euler $\theta$--scheme. In this case, we show that the BNB
inequality is always satisfied, and may require an extra condition on the time
step for $\theta$ $\le$ 1 2. The second one is the time discontinuous Galerkin
method, where the BNB condition holds without any additional condition.


  We present a sample of 150 narrow-line Seyfert 1s (NLS1s) found within the
Sloan Digital Sky Survey Early Data Release (EDR), only two of which were
previously identified as such. This substantially increases the known number of
NLS1s, and provides a basic method by which to identify many more with
subsequent releases of SDSS data. With its large size and homogeneous,
well-defined selection criteria, this sample will help alleviate two major
problems which have plauged NLS1 research in the past; namely, their relative
rarity and significant differences in selection algorithms between the known
samples. 45 of these SDSS-selected NLS1s are detected at energies of 0.1-2 keV
in the ROSAT All-Sky Survey (RASS), and are found to have ultrasoft X-ray
spectra with photon indices of Gamma >~ 2, in agreement with previous results
for NLS1s. However, about 10-20 of those NLS1s that were not detected by ROSAT
have optical properties very similar to the detected objects, and so should
also have been detected by the RASS. This may be due to either significant
intrinsic absorption in many NLS1s, or a significant sub-class of NLS1s that
have uncharacteristic, intrinsically flatter (hence harder) X-ray spectral
energy distributions.


  We report on the detection (10 \sigma) of 917 events of long-period (3 to 16
hours) intensity pulsations in the 19.5 nm passband of the SOHO Extreme
ultraviolet Imaging Telescope. The data set spans from January 1997 to July
2010, i.e the entire solar cycle 23 and the beginning of cycle 24. The events
can last for up to six days and have relative amplitudes up to 100%. About half
of the events (54%) are found to happen in active regions, and 50% of these
have been visually associated with coronal loops. The remaining 46% are
localized in the quiet Sun. We performed a comprehensive analysis of the
possible instrumental artifacts and we conclude that the observed signal is of
solar origin. We discuss several scenarios which could explain the main
characteristics of the active region events. The long periods and the
amplitudes observed rule out any explanation in terms of magnetohydrodynamic
waves. Thermal nonequilibrium could produce the right periods, but it fails to
explain all the observed properties of coronal loops and the spatial coherence
of the events. We propose that moderate temporal variations of the heating term
in the energy equation, so as to avoid a thermal nonequilibrium state, could be
sufficient to explain those long-period intensity pulsations. The large number
of detections suggests that these pulsations are common in active regions. This
would imply that the measurement of their properties could provide new
constraints on the heating mechanisms of coronal loops.


  The rapid progress in quantum information processing leads to a rising demand
for devices to control the propagation of electromagnetic wave pulses and to
ultimately realize a universal and efficient quantum memory. While in recent
years significant progress has been made to realize slow light and quantum
memories with atoms at optical frequencies, superconducting circuits in the
microwave domain still lack such devices. Here, we demonstrate slowing down
electromagnetic waves in a superconducting metamaterial composed of eight
qubits coupled to a common waveguide, forming a waveguide quantum
electrodynamics system. We analyze two complementary approaches, one relying on
dressed states of the Autler-Townes splitting, and the other based on a
tailored dispersion profile using the qubits tunability. Our time-resolved
experiments show reduced group velocities of down to a factor of about 1500
smaller than in vacuum. Depending on the method used, the speed of light can be
controlled with an additional microwave tone or an effective qubit detuning.
Our findings demonstrate high flexibility of superconducting circuits to
realize custom band structures and open the door to microwave dispersion
engineering in the quantum regime.


  At the quantization of fields, due to the non-linear character of the time
reversal, the creation-annihilation operators for the negative frequency modes
should be replaced to the operators of antiparticles not directly in the field
operators, but in the operator products. For the standard minimal Lagrangians
(asymmetrical under the complex conjugated fields) it is shown that the charge
conjugation (C-) symmetry conditions for the Hamiltonian and the charge
operator lead to the identities for the operator products allowing one to
replace the negative frequency operator products to the positive frequency
ones. At the same time the operators in observables become normal ordered and
the zero-point energy does not appear. Only the symmetrized under the field
operators Lagrangians lead to the zero-point energy. The confrontation by the
experiments of the such C-symmetric quantization of fields and the solution
some of the vacuum energy problems are discussed.


  We introduce a framework for efficient Markov Chain Monte Carlo (MCMC)
algorithms targeting discrete-valued high-dimensional distributions, such as
posterior distributions in Bayesian variable selection (BVS) problems. We show
that many recently introduced algorithms, such as the locally informed sampler
and the Adaptively Scaled Individual adaptation sampler (ASI), can be viewed as
particular cases within the framework. We then describe a novel algorithm, the
Adaptive Random Neighbourhood Informed sampler (ARNI), by combining ideas from
both of these existing approaches. We show using several examples of both real
and simulated datasets that a computationally efficient point-wise
implementation (PARNI) leads to relatively more reliable inferences on a range
of variable selection problems, particularly in the very large $p$ setting.


  Optical second harmonic generation is a second-order nonlinear process that
combines two photons of a given frequency into a third photon at twice the
frequency. Due to the symmetry constraints, it is widely used as a sensitive
probe to detect broken inversion symmetry and local polar order. Analytical
modeling of the electric-dipole SHG response is essential to extract
fundamental properties of materials from experiments. However, complexity
builds up dramatically in the analytical model when the probed crystal is of a
low bulk crystal symmetry, with a low-symmetry surface orientation, exhibits
absorption and dispersion, and consists of multiple interfaces. As a result,
there is a largely uneven landscape in the literature on the SHG modeling of
new materials, involving numerous approximations and a wide range of
(in)accuracies, leading to a rather scattered dataset of reported SHG nonlinear
susceptibility. Towards streamlining the reliability and accuracy of this
process, we have developed an open-source package called the Second Harmonic
Analysis of Anisotropic Rotational Polarimetry (SHAARP) which derives
analytical solutions and performs numerical simulations of reflection SHG from
a single interface for homogeneous crystals. Five key generalizations in SHG
modeling are implemented, including all crystal symmetries down to triclinic,
any crystal orientation, complex dielectric tensor (refractive indices) with
frequency dispersion, and general polarization states of the light. SHAARP
enables accurate anisotropic modeling of SHG response for a broad range of
materials systems. The method is extendible to multiple interfaces. The code is
free to download from https://github.com/Rui-Zu/SHAARP


  The stringent requirements of mobile edge computing (MEC) applications and
functions fathom the high capacity and dense deployment of MEC hosts to the
upcoming wireless networks. However, operating such high capacity MEC hosts can
significantly increase energy consumption. Thus, a base station (BS) unit can
act as a self-powered BS. In this paper, an effective energy dispatch mechanism
for self-powered wireless networks with edge computing capabilities is studied.
First, a two-stage linear stochastic programming problem is formulated with the
goal of minimizing the total energy consumption cost of the system while
fulfilling the energy demand. Second, a semi-distributed data-driven solution
is proposed by developing a novel multi-agent meta-reinforcement learning
(MAMRL) framework to solve the formulated problem. In particular, each BS plays
the role of a local agent that explores a Markovian behavior for both energy
consumption and generation while each BS transfers time-varying features to a
meta-agent. Sequentially, the meta-agent optimizes (i.e., exploits) the energy
dispatch decision by accepting only the observations from each local agent with
its own state information. Meanwhile, each BS agent estimates its own energy
dispatch policy by applying the learned parameters from meta-agent. Finally,
the proposed MAMRL framework is benchmarked by analyzing deterministic,
asymmetric, and stochastic environments in terms of non-renewable energy
usages, energy cost, and accuracy. Experimental results show that the proposed
MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the
energy cost (with 95.8% prediction accuracy), compared to other baseline
methods.


  Object pose estimation is a critical task in robotics for precise object
manipulation. However, current techniques heavily rely on a reference 3D
object, limiting their generalizability and making it expensive to expand to
new object categories. Direct pose predictions also provide limited information
for robotic grasping without referencing the 3D model. Keypoint-based methods
offer intrinsic descriptiveness without relying on an exact 3D model, but they
may lack consistency and accuracy. To address these challenges, this paper
proposes ShapeShift, a superquadric-based framework for object pose estimation
that predicts the object's pose relative to a primitive shape which is fitted
to the object. The proposed framework offers intrinsic descriptiveness and the
ability to generalize to arbitrary geometric shapes beyond the training set.


  We study one-loop corrections to the two-point correlation function of tensor
perturbations in primordial cosmology induced by massless spectator matter
fields. Using the Schwinger-Keldysh formalism in cosmological perturbation
theory, we employ dimensional regularization and cutoff regularization to study
the finite quantum corrections at one-loop arising from isocurvature fields of
the massless scalar, fermion and abelian gauge field which are freely
propagating on the FRW spacetime. For all cases, we find a logarithmic running
of the form $\frac{C}{q^3} \, \frac{H^4}{M^4_p}
  \log \left( \frac{H}{\mu } \right)$ where $C$ is a negative constant related
to the beta function, $H$ is the Hubble parameter at horizon exit and $\mu$ is
the renormalization scale.


  We consider the task of evaluating policies of algorithmic resource
allocation through randomized controlled trials (RCTs). Such policies are
tasked with optimizing the utilization of limited intervention resources, with
the goal of maximizing the benefits derived. Evaluation of such allocation
policies through RCTs proves difficult, notwithstanding the scale of the trial,
because the individuals' outcomes are inextricably interlinked through resource
constraints controlling the policy decisions. Our key contribution is to
present a new estimator leveraging our proposed novel concept, that involves
retrospective reshuffling of participants across experimental arms at the end
of an RCT. We identify conditions under which such reassignments are
permissible and can be leveraged to construct counterfactual trials, whose
outcomes can be accurately ascertained, for free. We prove theoretically that
such an estimator is more accurate than common estimators based on sample means
-- we show that it returns an unbiased estimate and simultaneously reduces
variance. We demonstrate the value of our approach through empirical
experiments on synthetic, semi-synthetic as well as real case study data and
show improved estimation accuracy across the board.


  We consider a graph observability problem: how many edge colors are needed
for an unlabeled graph so that an agent, walking from node to node, can
uniquely determine its location from just the observed color sequence of the
walk?
  Specifically, let G(n,d) be an edge-colored subgraph of d-dimensional
(directed or undirected) lattice of size n^d = n * n * ... * n. We say that
G(n,d) is t-observable if an agent can uniquely determine its current position
in the graph from the color sequence of any t-dimensional walk, where the
dimension is the number of different directions spanned by the edges of the
walk. A walk in an undirected lattice G(n,d) has dimension between 1 and d, but
a directed walk can have dimension between 1 and 2d because of two different
orientations for each axis.
  We derive bounds on the number of colors needed for t-observability. Our main
result is that Theta(n^(d/t)) colors are both necessary and sufficient for
t-observability of G(n,d), where d is considered a constant.
  This shows an interesting dependence of graph observability on the ratio
between the dimension of the lattice and that of the walk. In particular, the
number of colors for full-dimensional walks is Theta(n^(1/2)) in the directed
case, and Theta(n) in the undirected case, independent of the lattice
dimension.
  All of our results extend easily to non-square lattices: given a lattice
graph of size N = n_1 * n_2 * ... * n_d, the number of colors for
t-observability is Theta (N^(1/t)).


  In this paper, we give a mechanism for controlling speedup of a single-qubit
open quantum system by exclusively manipulating the system-reservoir bound
states using additional non-interacting qubits. It is demonstrated that
providing stronger bound states in the system-reservoir spectrum makes the
single-qubit evolution with higher speed. We examine the performance of the
mechanism for different spectral densities such as Lorentzian and Ohmic and
find out the decisive role of bound states manipulation in speeding up of
quantum evolution.


  This paper considers homogeneous order preserving continuous maps on the
normal cone of an ordered normed vector space. It is shown that certain
operators of that kind which are not necessarily compact themselves but have a
compact power have a positive eigenvector that is associated with the cone
spectral radius. We also derive conditions for the existence of homogeneous
order preserving eigenfunctionals. Our results are illustrated in a model for
spatially distributed two-sex populations.


  A technique for constructing an infinite tower of pairs of PT-symmetric
Hamiltonians, $\hat{H}_n$ and $\hat{K}_n$ (n=2,3,4,...), that have exactly the
same eigenvalues is described. The eigenvalue problem for the first Hamiltonian
$\hat{H}_n$ of the pair must be posed in the complex domain, so its
eigenfunctions satisfy a complex differential equation and fulfill homogeneous
boundary conditions in Stokes' wedges in the complex plane. The eigenfunctions
of the second Hamiltonian $\hat{K}_n$ of the pair obey a real differential
equation and satisfy boundary conditions on the real axis. This equivalence
constitutes a proof that the eigenvalues of both Hamiltonians are real.
Although the eigenvalue differential equation associated with $\hat{K}_n$ is
real, the Hamiltonian $\hat{K}_n$ exhibits quantum anomalies (terms
proportional to powers of $\hbar$). These anomalies are remnants of the complex
nature of the equivalent Hamiltonian $\hat{H}_n$. In the classical limit in
which the anomaly terms in $\hat{K}_n$ are discarded, the pair of Hamiltonians
$H_{n,classical}$ and $K_{n,classical}$ have closed classical orbits whose
periods are identical.


  A novel realization of the gravity-mediated SUSY breaking is presented taking
into account a continuous global R symmetry. Consistently with it, we employ a
linear superpotential for the hidden sector superfield and a Kaehler potential
parameterizing the SU(1,1)/U(1) Kaehler manifold with constant curvature -1/2.
The classical vacuum energy vanishes without unnatural fine tuning and
non-vanishing soft SUSY-breaking parameters, of the order of the gravitino
mass, arise. A solution to the mu problem of MSSM may be also achieved by
conveniently applying the Giudice-Masiero mechanism. The potentially
troublesome R axion may acquire acceptably large mass by explicitly breaking
the R symmetry in the Kaehler potential through a quartic term which does not
affect, though, the achievements above.


  The measurement of a quantum two-level system, or a qubit in modern
terminology, often involves an electromagnetic field that interacts with the
qubit, before the field is measured continuously and the qubit state is
inferred from the noisy field measurement. During the measurement, the qubit
may undergo spontaneous transitions, further obscuring the initial qubit state
from the observer. Taking advantage of some well known techniques in stochastic
detection theory, here we propose a novel signal processing protocol that can
infer the initial qubit state optimally from the measurement in the presence of
noise and qubit dynamics. Assuming continuous quantum-nondemolition
measurements with Gaussian or Poissonian noise and a classical Markov model for
the qubit, we derive analytic solutions to the protocol in some special cases
of interest using It\={o} calculus. Our method is applicable to
multi-hypothesis testing for robust qubit readout and relevant to experiments
on qubits in superconducting microwave circuits, trapped ions, nitrogen-vacancy
centers in diamond, semiconductor quantum dots, or phosphorus donors in
silicon.


  We report a measurement of the exclusive e+e- -->Lambda_c+ Lambda_c- cross
section as a function of center-of-mass energy near the Lambda_c+ Lambda_c-
threshold. A clear peak with a significance of 8.2 sigma is observed in the
Lambda_c+ Lambda_c- invariant mass distribution just above threshold. With an
assumption of a resonance origin for the observed peak, a mass and width of
M=(4634^{+8}_{-7}(stat.)^{+5}_{-8}(sys.))Mev/c2 and
Gamma_tot=(92^{+40}_{-24}(stat.)}^{+10}_{-21}(sys.))MeV are determined. The
analysis is based on a study of events with initial-state-radiation photons in
a data sample collected with the Belle detector at the \Upsilon(4S) resonance
and nearby continuum with an integrated luminosity of 695/fb at the KEKB
asymmetric-energy e+e- collider.


  In this paper we compare the heave, surge, and pitch dynamics of a submerged
cylindrical point absorber, simulated using potential flow and fully-resolved
computational fluid dynamics (CFD) models. The potential flow model is based on
the time-domain Cummins equation, whereas the CFD model uses the fictitious
domain Brinkman penalization (FD/BP) technique. The submerged cylinder is
tethered to the seabed using a power take-off (PTO) unit which restrains the
heave, surge, and pitch motions of the converter, and absorbs energy from all
three modes. It is demonstrated that the potential theory over-predicts the
amplitudes of heave and surge motions, whereas it results in an insignificant
pitch for a fully-submerged axisymmetric converter. It also under-estimates the
slow drift of the buoy, which the CFD model is able to capture reliably.
Further, we use fully-resolved CFD simulations to study the performance of a
three degrees of freedom (DOF) cylindrical buoy under varying PTO coefficients,
mass density of the buoy, and incoming wave heights. It is demonstrated that
the PTO coefficients predicted by the linear potential theory are sub-optimal
for waves of moderate and high steepness. The wave absorption efficiency
improves significantly when higher than the predicted value of the PTO damping
is selected. Simulations with different mass densities of the buoy show that
converters with low mass densities have an increased tension in their PTO and
mooring lines. Moreover, the mass density also influences the range of
resonance periods of the device. Finally, simulations with different wave
heights show that at higher heights, the wave absorption efficiency of the
converter decreases and a large portion of available wave power remains
unabsorbed.


  Graph neural networks (GNNs) have recently exploded in popularity thanks to
their broad applicability to graph-related problems such as quantum chemistry,
drug discovery, and high energy physics. However, meeting demand for novel GNN
models and fast inference simultaneously is challenging due to the gap between
developing efficient accelerators and the rapid creation of new GNN models.
Prior art focuses on accelerating specific classes of GNNs, such as Graph
Convolutional Networks (GCN), but lacks generality to support a wide range of
existing or new GNN models. Furthermore, most works rely on graph
pre-processing to exploit data locality, making them unsuitable for real-time
applications. To address these limitations, in this work, we propose a generic
dataflow architecture for GNN acceleration, named FlowGNN, which is
generalizable to the majority of message-passing GNNs. The contributions are
three-fold. First, we propose a novel and scalable dataflow architecture, which
generally supports a wide range of GNN models with message-passing mechanism.
The architecture features a configurable dataflow optimized for simultaneous
computation of node embedding, edge embedding, and message passing, which is
generally applicable to all models. We also propose a rich library of
model-specific components. Second, we deliver ultra-fast real-time GNN
inference without any graph pre-processing, making it agnostic to dynamically
changing graph structures. Third, we verify our architecture on the Xilinx
Alveo U50 FPGA board and measure the on-board end-to-end performance. We
achieve a speed-up of up to 24-254x against CPU (6226R) and 1.3-477x against
GPU (A6000) (with batch sizes 1 through 1024); we also outperform the SOTA GNN
accelerator I-GCN by 1.26x speedup and 1.55x energy efficiency over four
datasets. Our implementation code and on-board measurement are publicly
available on GitHub.


  A very brief introduction to tropical and idempotent mathematics is
presented. Tropical mathematics can be treated as a result of a dequantization
of the traditional mathematics as the Planck constant tends to zero taking
imaginary values. In the framework of idempotent mathematics usually
constructions and algorithms are more simple with respect to their traditional
analogs. We especially examine algorithms of tropical/idempotent mathematics
generated by a collection of basic semiring (or semifield) operations and other
"good" operations. Every algorithm of this type has an interval version. The
complexity of this interval version coincides with the complexity of the
initial algorithm. The interval version of an algorithm of this type gives
exact interval estimates for the corresponding output data. Algorithms of
linear algebra over idempotent and semirings are examined. In this case, basic
algorithms are polynomial as well as their interval versions. This situation is
very different from the traditional linear algebra, where basic algorithms are
polynomial but the corresponding interval versions are NP-hard and interval
estimates are not exact.


  We revisit the method of using the photon-graviton conversion mechanism in
the presence of the external magnetic field to probe small-scale primordial
magnetic fields that may exist between the last scattering surface and present.
Specifically, we investigate impacts on the conversion efficiency due to the
presence of matter, including the plasma collective effect and the atomic
polarizability. In general, these effects tend to reduce the conversion
probability. Under this more realistic picture and based on the precision of
COBE's measurement of CMB (cosmic microwave background) blackbody spectrum, we
find an upper bound for the primordial magnetic field strength, B < 30G, at the
time of recombination. Although at present the bound based on the
photon-graviton conversion mechanism is not as tight as that obtained by the
direct use of CMB temperature anisotropy, it nevertheless provides an important
independent constraint on primordial magnetic fields and at epochs in addition
to the recombination. The bound can be significantly improved if the CMB
blackbody spectrum measurement becomes more precise in future experiments such
as PIXIE.


  This paper is devoted to a study of observability estimate for the wave
equation with variable coefficients $(h^{jk}(x))_{n\times n}$
($n\in\mathbb{N})$. We consider both the observation point lies outside the
domain and the observation point lies inside the domain. Based on a Carleman
estimate for the ultra-hyperbolic operator and a delicate treatment of
observation region, we obtain two observability estimates with explicit
observability constants. The key improvements are: (1) we improve the
requirement of waiting time $T$; (2) we improve the size of the observation
region (see Fingure 1 and Fingure 2 for the case of $(h^{jk}(x))_{n\times
n}=I_n)$ .


  Speaker embeddings extracted from voice recordings have been proven valuable
for dementia detection. However, by their nature, these embeddings contain
identifiable information which raises privacy concerns. In this work, we aim to
anonymize embeddings while preserving the diagnostic utility for dementia
detection. Previous studies rely on adversarial learning and models trained on
the target attribute and struggle in limited-resource settings. We propose a
novel approach that leverages domain knowledge to disentangle prosody features
relevant to dementia from speaker embeddings without relying on a dementia
classifier. Our experiments show the effectiveness of our approach in
preserving speaker privacy (speaker recognition F1-score .01%) while
maintaining high dementia detection score F1-score of 74% on the ADReSS
dataset. Our results are also on par with a more constrained
classifier-dependent system on ADReSSo (.01% and .66%), and have no impact on
synthesized speech naturalness.


  For an isotropic two-dimensional system, when the temperature is lowered
toward T=0, it has been found recently that, for the transition from the normal
to the superfluid state in the paramagnetic limit, the order parameter
describing the Fulde-Ferrell-Larkin-Ovchinnikov phase has an increasingly
complex structure with contributions from an increasing number of wavevectors.
This cascade of phase transitions is directly linked to the fact that, due to
the rotational invariance, all the wavevectors directions which can enter the
order parameter are degenerate. We study how this cascade of phase transitions
is modified when one takes into account the anisotropy arising in a real solid
state compound. For a simple model of anisotropy with elliptical dispersion
relation, we find surprisingly that the cascade of phase transitions is not
modified and the degeneracy with respect to the wavevector direction is still
present. When we take into account a deviation with respect to this elliptical
model, which is treated to first order in perturbation, we find that the
degeneracy is lifted and that, basically, a single wavevector is favored right
at the transition. However when one enters the superfluid phase, additional
wavevectors come in the order parameter and the cascade of transitions is still
present, though in a modified form.


  In this fith part, (with the notations of the preceding parts) we make the
following hypothesis: $(W,S)$ is a Coxeter system, irreducible, $2$-spherical
and $S$ is of cardinality $3$. Let $R:W\to GL(M)$ be a reducible reflection
representation of $W$. Let $G:= Im\,R$. Each sub-space of $M$ $(\neq M)$
stabilize by $G$ is contained in $C_{M}(G)$. Let $M':=M/C_{M}(G)$ and
$N(G):=\{g|g\in G,g\, \text{acts trivially on}\,M'\}$. We call $N(G)$ the
translation sub-group of $G$. One of the goals of this part is to study $M'$
and $N(G)$. In particular it is shown that $N(G)$ is an $\mathcal{O}G-module$.


  In this paper we describe the Nearby Optical Galaxy (NOG) sample, which is a
complete, distance-limited ($cz\leq$6000 km/s) and magnitude-limited
(B$\leq$14) sample of $\sim$7000 optical galaxies. The sample covers 2/3 (8.27
sr) of the sky ($|b|>20^{\circ}$) and appears to have a good completeness in
redshift (98%). We select the sample on the basis of homogenized corrected
total blue magnitudes in order to minimize systematic effects in galaxy
sampling.
  We identify the groups in this sample by means of both the hierarchical and
the percolation {\it friends of friends} methods. The resulting catalogs of
loose groups appear to be similar and are among the largest catalogs of groups
presently available. Most of the NOG galaxies ($\sim$60%) are found to be
members of galaxy pairs ($\sim$580 pairs for a total of $\sim$15% of objects)
or groups with at least three members ($\sim$500 groups for a total of
$\sim$45% of objects). About 40% of galaxies are left ungrouped (field
galaxies). We illustrate the main features of the NOG galaxy distribution.
Compared to previous optical and IRAS galaxy samples, the NOG provides a denser
sampling of the galaxy distribution in the nearby universe. Given its large sky
coverage, the identification of groups, and its high-density sampling, the NOG
is suited for the analysis of the galaxy density field of the nearby universe,
especially on small scales.


  This paper proposes a new method for manipulating unknown objects through a
sequence of non-prehensile actions that displace an object from its initial
configuration to a given goal configuration on a flat surface. The proposed
method leverages recent progress in differentiable physics models to identify
unknown mechanical properties of manipulated objects, such as inertia matrix,
friction coefficients and external forces acting on the object. To this end, a
recently proposed differentiable physics engine for two-dimensional objects is
adopted in this work and extended to deal forces in the three-dimensional
space. The proposed model identification technique analytically computes the
gradient of the distance between forecasted poses of objects and their actual
observed poses and utilizes that gradient to search for values of the
mechanical properties that reduce the reality gap. Experiments with real
objects using a real robot to gather data show that the proposed approach can
identify the mechanical properties of heterogeneous objects on the fly.


  I consider the effect of the gravitational deflection of light upon the light
curves of eclipsing binary stars, focussing mainly upon systems containing at
least one white dwarf component. In absolute terms the effects are small,
however they are strongest at the time of secondary eclipse when the white
dwarf transits its companion, and act to reduce the depth of this feature. If
not accounted for, this may lead to under-estimation of the radius of the white
dwarf compared to that of its companion. I show that the effect is significant
for plausible binary parameters, and that it leads to ~25% reduction in the
transit depth in the system KPD 1930+2752. The reduction of eclipse depth is
degenerate with the stellar radius ratio, and therefore cannot be used to
establish the existence of lensing. A second order effect of the light bending
is to steepen the ingress and egress features of the secondary eclipse relative
to the primary eclipse, although it will be difficult to see this in practice.
I consider also binaries containing neutron stars and black-holes. I conclude
that, although relatively large effects are possible in such systems, a
combination of rarity, faintness and intrinsic variability make it unlikely
that lensing will be detectable in them.


  This paper presents the Speech Technology Center (STC) systems submitted to
Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof)
Challenge 2015. In this work we investigate different acoustic feature spaces
to determine reliable and robust countermeasures against spoofing attacks. In
addition to the commonly used front-end MFCC features we explored features
derived from phase spectrum and features based on applying the multiresolution
wavelet transform. Similar to state-of-the-art ASV systems, we used the
standard TV-JFA approach for probability modelling in spoofing detection
systems. Experiments performed on the development and evaluation datasets of
the Challenge demonstrate that the use of phase-related and wavelet-based
features provides a substantial input into the efficiency of the resulting STC
systems. In our research we also focused on the comparison of the linear (SVM)
and nonlinear (DBN) classifiers.


  This is an English (annotated) translation of the Thesis report (in German)
of Max Planck at the University of Munich (1879)


  By integrating domain knowledge with labeled samples, informed machine
learning has been emerging to improve the learning performance for a wide range
of applications. Nonetheless, rigorous understanding of the role of injected
domain knowledge has been under-explored. In this paper, we consider an
informed deep neural network (DNN) with over-parameterization and domain
knowledge integrated into its training objective function, and study how and
why domain knowledge benefits the performance. Concretely, we quantitatively
demonstrate the two benefits of domain knowledge in informed learning -
regularizing the label-based supervision and supplementing the labeled samples
- and reveal the trade-off between label and knowledge imperfectness in the
bound of the population risk. Based on the theoretical analysis, we propose a
generalized informed training objective to better exploit the benefits of
knowledge and balance the label and knowledge imperfectness, which is validated
by the population risk bound. Our analysis on sampling complexity sheds lights
on how to choose the hyper-parameters for informed learning, and further
justifies the advantages of knowledge informed learning.


  I review the unwrapping story the SN 1987A explosion event, and the main
discoveries associated with it. I will show that, although this supernova is
somewhat peculiar, the study of SN 1987A has clarified quite a number of
important aspects of the nature and the properties of supernovae in general.


  This paper presents theoretical star formation and chemical enrichment
histories for the stellar halo of the Milky Way based on new chemodynamical
modeling. The goal of this study is to assess the extent to which metal-poor
stars in the halo reflect the star formation conditions that occurred in halo
progenitor galaxies at high redshift, before and during the epoch of
reionization. Simple prescriptions that translate dark-matter halo mass into
baryonic gas budgets and star formation histories yield models that resemble
the observed Milky Way halo in its total stellar mass, metallicity
distribution, and the luminosity function and chemical enrichment of dwarf
satellite galaxies. These model halos in turn allow an exploration of how the
populations of interest for probing the epoch of reionization are distributed
in physical and phase space, and of how they are related to lower-redshift
populations of the same metallicity. The fraction of stars dating from before a
particular time or redshift depends strongly on radius within the galaxy,
reflecting the "inside-out" growth of cold-dark-matter halos, and on
metallicity, reflecting the general trend toward higher metallicity at later
times. These results suggest that efforts to discover stars from z > 6 - 10
should select for stars with [Fe/H] <~ -3 and favor stars on more tightly bound
orbits in the stellar halo, where the majority are from z > 10 and 15 - 40% are
from z > 15. The oldest, most metal-poor stars - those most likely to reveal
the chemical abundances of the first stars - are most common in the very center
of the Galaxy's halo: they are in the bulge, but not of the bulge. These models
have several implications for the larger project of constraining the properties
of the first stars and galaxies using data from the local Universe.


  We report Mo isotopic data for 16 15N-rich presolar SiC grains of type AB
(14N/15N<solar, AB1) and their correlated Sr and Ba isotope ratios when
available. Eight of the 16 AB1 grains show s-process Mo isotopic compositions,
together with s-process Ba and/or Sr isotopic compositions. We found that a
higher percentage of AB1 grains show anomalous isotopic compositions than that
of AB2 grains (14N/15N>solar), thus providing further support to the division
of the two AB subgroups recently proposed by Liu et al. (2017a), who showed
that AB1 grains most likely originated from Type II supernovae (SNe) with
explosive H burning. Comparison of the Sr, Mo, and Ba isotopic compositions of
the AB1 grains with SN model predictions indicates that the s-process isotopic
compositions of AB1 grains resulted from neutron-capture processes occurring
during the progenitor massive stars' pre-SN evolution rather than from an
explosive neutron-capture process. In addition, the observations of (1)
explosive H burning occurring in the C-rich regions of the progenitor SNe of SN
X grains as suggested by the isotopic compositions of X grains, and (2)
explosive H burning occurring both at the bottom of the He/C zone and at the
top of the He/N zone as suggested by model simulations, imply that explosive H
burning is a common phenomenon in outer SN zones.


  Relations are given for the correction of bias when mean absolute magnitudes
are derived by the method of reduced parallaxes. The bias in the case of the
derivation of the absolute magnitudes of individual objects is also considered.


  A recent proposal to experimentally test quantum mechanics against
noncontextual hidden-variable theories [Phys. Rev. Lett. 80, 1797 (1998)] is
shown to be related with the smallest proof of the Kochen-Specker theorem
currently known [Phys. Lett. A 212, 183 (1996)]. This proof contains eighteen
yes-no questions about a four-dimensional physical system, combined in nine
mutually incompatible tests. When these tests are considered as tests about a
two-part two-state system, then quantum mechanics and non-contextual hidden
variables make the same predictions for eight of them, but make different
predictions for the ninth. Therefore, this ninth test would allow us to
discriminate between quantum mechanics and noncontextual hidden-variable
theories in a (gedanken) single run experiment.


  In $1735$ Euler \cite{1} proved that for each positive integer $k$, the
series $\zeta(2k) = \sum_{\ell=1}^{\infty} \ell^{-2k}$ converges to a rational
multiple of $\pi^{2k}$. Many demonstrations of this fact are now known, and
Euler's discovery is traditionally proven using non-elementary techniques, such
as Fourier series or the calculus of residues \cite{2}. We give an elementary
proof, similar to Cauchy's \cite{3} proof of the identity $\zeta(2) = \pi^2/6$,
only extended recursively for all values $\zeta(2k)$. Our main formula
$$\zeta(2k)=-\dfrac{(-\pi^2)^{k}}{4^{2k}-4^{k}}\left[\dfrac{4^{k}k}{(2k)!}+{\displaystyle
\sum_{\ell=1}^{k-1}}(4^{2\ell}-4^{\ell})\dfrac{4^{k-\ell}}{(2k-2\ell)!}\dfrac{\zeta(2\ell)}{(-\pi^2)^{\ell}}\right]
\phantom{spa}k = 1,2,3,\dots$$ may be derived from previously known formulae
\cite{4}. Remarkably, Apostol \cite{5} discovered a proof similar to ours, yet
arrived at a different formula, relating $\zeta(2k)$ to the Bernoulli numbers,
\`a la Euler.


  We present a fully perturbative mechanism that naturally generates mass
hierarchies for the Standard Model (SM) fermions in a flavor-blind sector. The
dynamics generating the mass hierarchies can therefore be independent from the
source of flavor violation, and hence this dynamics may operate at a much lower
scale. This mechanism works by dynamically enforcing simultaneous
diagonalization - alignment - among a set of flavor-breaking spurions, as well
as generating highly singular spectra for them. It also has general
applications in model building beyond the SM, wherever alignment between exotic
and SM sources of flavor violation is desired.


  The electric field integral equation is a well known workhorse for obtaining
fields scattered by a perfect electric conducting (PEC) object. As a result,
the nuances and challenges of solving this equation have been examined for a
while. Two recent papers motivate the effort presented in this paper. Unlike
traditional work that uses equivalent currents defined on surfaces, recent
research proposes a technique that results in well conditioned systems by
employing generalized Debye sources (GDS) as unknowns. In a complementary
effort, some of us developed a method that exploits the same representation for
both the geometry (subdivision surface representations) and functions defined
on the geometry, also known as isogeometric analysis (IGA). The challenge in
generalizing GDS method to a discretized geometry is the complexity of the
intermediate operators. However, thanks to our earlier work on subdivision
surfaces, the additional smoothness of geometric representation permits
discretizing these intermediate operations. In this paper, we employ both ideas
to present a well conditioned GDS-EFIE. Here, the intermediate surface
Laplacian is well discretized by using subdivision basis. Likewise, using
subdivision basis to represent the sources, results in an efficient and
accurate IGA framework. Numerous results are presented to demonstrate the
efficacy of the approach.


  Throughout this paper we study the existence of irreducible curves C on
smooth projective surfaces S with singular points of prescribed topological
types S_1,...,S_r. There are necessary conditions for the existence of the type
\sum_{i=1}^r \mu(S_i) < aC^2+bC.K+c+1 for some fixed divisor K on S and
suitable coefficients a, b and c, and the main sufficient condition that we
find is of the same type, saying it is asymptotically optimal. Even for the
case where S is the projective plane, ten years ago general results of this
quality have not been known.
  An important ingredient for the proof is a vanishing theorem for invertible
sheaves on the blown up S of the form O_{S'}(\pi^*D-\sum_{i=1}^r m_iE_i),
deduced from the Kawamata-Vieweg Vanishing Theorem. Its proof covers the first
part of the paper, while the middle part is devoted to the existence theorems.
In the last part we investigate our conditions on ruled surfaces, products of
elliptic curves, surfaces in projective 3-space, and K3-surfaces.


  This paper examines synchronization of computer clocks connected via a data
network and proposes a skewless algorithm to synchronize them. Unlike existing
solutions, which either estimate and compensate the frequency difference (skew)
among clocks or introduce offset corrections that can generate jitter and
possibly even backward jumps, our solution achieves synchronization without
these problems. We first analyze the convergence property of the algorithm and
provide explicit necessary and sufficient conditions on the parameters to
guarantee synchronization. We then study the effect of noisy measurements
(jitter) and frequency drift (wander) on the offsets and synchronization
frequency, and further optimize the parameter values to minimize their
variance. Our study reveals a few insights, for example, we show that our
algorithm can converge even in the presence of timing loops and noise, provided
that there is a well defined leader. This marks a clear contrast with current
standards such as NTP and PTP, where timing loops are specifically avoided.
Furthermore, timing loops can even be beneficial in our scheme as it is
demonstrated that highly connected subnetworks can collectively outperform
individual clients when the time source has large jitter. The results are
supported by experiments running on a cluster of IBM BladeCenter servers with
Linux.


  This paper presents a simulation platform, namely CIMulator, for quantifying
the efficacy of various synaptic devices in neuromorphic accelerators for
different neural network architectures. Nonvolatile memory devices, such as
resistive random-access memory, ferroelectric field-effect transistor, and
volatile static random-access memory devices, can be selected as synaptic
devices. A multilayer perceptron and convolutional neural networks (CNNs), such
as LeNet-5, VGG-16, and a custom CNN named C4W-1, are simulated to evaluate the
effects of these synaptic devices on the training and inference outcomes. The
dataset used in the simulations are MNIST, CIFAR-10, and a white blood cell
dataset. By applying batch normalization and appropriate optimizers in the
training phase, neuromorphic systems with very low-bit-width or binary weights
could achieve high pattern recognition rates that approach software-based CNN
accuracy. We also introduce spiking neural networks with RRAM-based synaptic
devices for the recognition of MNIST handwritten digits.


  Evolutionary game theory predicts that cooperation in social dilemma games is
promoted when agents are connected as a network. However, when networks are
fixed over time, humans do not necessarily show enhanced mutual cooperation.
Here we show that reinforcement learning (specifically, the so-called
Bush-Mosteller model) approximately explains the experimentally observed
network reciprocity and the lack thereof in a parameter region spanned by the
benefit-to-cost ratio and the node's degree. Thus, we significantly extend
previously obtained numerical results.


  Particle statistics is a fundamental part of quantum physics, and yet its
role and use in the context of quantum information have been poorly explored so
far. After briefly introducing particle statistics and the Symmetrization
Postulate, I will argue that this fundamental aspect of Nature can be seen as a
resource for quantum information processing and I will present examples showing
how it is possible to do useful and efficient quantum information processing
using only the effects of particles statistics.


  We discuss various aspects of noncommutative geometry of a smooth subalgebra
of the Toeplitz algebra. In particular, we study the structure of derivations
on this subalgebra.


  Let $R$ be a Cohen-Macaulay local ring of dimension $d$ with infinite residue
field. Let $I$ be an $R$-ideal that has analytic spread $\ell(I)=d$, $G_d$
condition and the Artin-Nagata property $AN^-_{d-2}$. We provide a formula
relating the length $\lambda(I^{n+1}/JI^{n})$ to the difference
$P_I(n)-H_I(n)$, where $J$ is a general minimal reduction of $I$, $P_I(n)$ and
$H_I(n)$ are the generalized Hilbert-Samuel polynomial and the generalized
Hilbert-Samuel function in the sense of C. Polini and Y. Xie. We then use it to
establish formulas to compute the higher generalized Hilbert coefficients of
$I$. As an application, we extend Northcott's inequality to non
$\mathfrak{m}$-primary ideals. When equality holds in the generalized
Northcott's inequality, the ideal $I$ enjoys nice properties. Indeed, in this
case, we prove that the reduction number of $I$ is at most one and the
associated graded ring of $I$ is Cohen-Macaulay. We also recover results of G.
Colom$\acute{{\rm e}}$-Nin, C. Polini, B. Ulrich and Y. Xie on the positivity
of the generalized first Hilbert coefficient $j_1(I)$. Our work extends that of
S. Huckaba, C. Huneke and A. Ooishi to ideals that are not necessarily
$\mathfrak{m}$-primary.


  The ease of using a Large Language Model (LLM) to answer a wide variety of
queries and their high availability has resulted in LLMs getting integrated
into various applications. LLM-based recommenders are now routinely used by
students as well as professional software programmers for code generation and
testing. Though LLM-based technology has proven useful, its unethical and
unattributed use by students and professionals is a growing cause of concern.
As such, there is a need for tools and technologies which may assist teachers
and other evaluators in identifying whether any portion of a source code is LLM
generated.
  In this paper, we propose a neural network-based tool that instructors can
use to determine the original effort (and LLM's contribution) put by students
in writing source codes. Our tool is motivated by minimum description length
measures like Kolmogorov complexity. Our initial experiments with moderate
sized (up to 500 lines of code) have shown promising results that we report in
this paper.


  The determination of the Fermi coupling constant, G_F, is examined in the
light of recently calculated 2-loop QED corrections and planned experiments to
measure the muon lifetime to a level below 1ppm. The methods used in the
calculation of the QED corrections are described in detail. Sources of the
dominant theoretical and experimental uncertainties are identified. Finally the
incorporation of G_F into analyses using the full electroweak Standard Model is
discussed.


  In order to understand the flow profiles of complex fluids, a crucial issue
concerns the emergence of spatial correlations among plastic rearrangements
exhibiting cooperativity flow behaviour at the macroscopic level. In this
paper, the rate of plastic events in a Poiseuille flow is experimentally
measured on a confined foam in a Hele-Shaw geometry. The correlation with
independently measured velocity profiles is quantified. To go beyond a
limitation of the experiments, namely the presence of wall friction which
complicates the relation between shear stress and shear rate, we compare the
experiments with simulations of emulsion droplets based on the
lattice-Boltzmann method, which are performed both with, and without, wall
friction. Our results indicate a correlation between the localisation length of
the velocity profiles and the localisation length of the number of plastic
events. Finally, unprecedented results on the distribution of the orientation
of plastic events show that there is a non-trivial correlation with the
underlying local shear strain. These features, not previously reported for a
confined foam, lend further support to the idea that cooperativity mechanisms,
originally invoked for concentrated emulsions (Goyon et al. 2008), have
parallels in the behaviour of other soft-glassy materials.


  While recurrent neural networks still largely define state-of-the-art speech
recognition systems, the Transformer network has been proven to be a
competitive alternative, especially in the offline condition. Most studies with
Transformers have been constrained in a relatively small scale setting, and
some forms of data argumentation approaches are usually applied to combat the
data sparsity issue. In this paper, we aim at understanding the behaviors of
Transformers in the large-scale speech recognition setting, where we have used
around 65,000 hours of training data. We investigated various aspects on
scaling up Transformers, including model initialization, warmup training as
well as different Layer Normalization strategies. In the streaming condition,
we compared the widely used attention mask based future context lookahead
approach to the Transformer-XL network. From our experiments, we show that
Transformers can achieve around 6% relative word error rate (WER) reduction
compared to the BLSTM baseline in the offline fashion, while in the streaming
fashion, Transformer-XL is comparable to LC-BLSTM with 800 millisecond latency
constraint.


  We prove a D=1 analytic versal deformation theorem for WKB expansions. We
define the spectrum of an operator in local analytic terms. We use the Morse
lemma to show that the perturbation series arising in a perturbed harmonic
oscillator become analytic after a formal Borel transform.


  The metallic transition-metal dichalcogenides (TMDCs) are benchmark systems
for studying and controlling intertwined electronic orders in solids, with
superconductivity developing upon cooling from a charge density wave state. The
interplay between such phases is thought to play a critical role in the
unconventional superconductivity of cuprates, Fe-based, and heavy-fermion
systems, yet even for the more moderately-correlated TMDCs, their nature and
origins have proved highly controversial. Here, we study a prototypical
example, $2H$-NbSe$_2$, by spin- and angle-resolved photoemission and
first-principles theory. We find that the normal state, from which its hallmark
collective phases emerge, is characterised by quasiparticles whose spin is
locked to their valley pseudospin. This results from a combination of strong
spin-orbit interactions and local inversion symmetry breaking. Non-negligible
interlayer coupling further drives a rich three-dimensional momentum-dependence
of the underlying Fermi surface spin texture. Together, these findings
necessitate a fundamental re-investigation of the nature of charge order and
superconducting pairing in NbSe$_2$ and related TMDCs.


  A biological system is a complex network of heterogeneous molecular entities
and their interactions contributing to various biological characteristics of
the system. However, current biological networks are noisy, sparse, and
incomplete, limiting our ability to create a holistic view of the biological
system and understand the biological phenomena. Experimental identification of
such interactions is both time-consuming and expensive. With the recent
advancements in high-throughput data generation and significant improvement in
computational power, various computational methods have been developed to
predict novel interactions in the noisy network. Recently, deep learning
methods such as graph neural networks have shown their effectiveness in
modeling graph-structured data and achieved good performance in biomedical
interaction prediction. However, graph neural networks-based methods require
human expertise and experimentation to design the appropriate complexity of the
model and significantly impact the performance of the model. Furthermore, deep
graph neural networks face overfitting problems and tend to be poorly
calibrated with high confidence on incorrect predictions. To address these
challenges, we propose Bayesian model selection for graph convolutional
networks to jointly infer the most plausible number of graph convolution layers
(depth) warranted by data and perform dropout regularization simultaneously.
Experiments on four interaction datasets show that our proposed method achieves
accurate and calibrated predictions. Our proposed method enables the graph
convolutional networks to dynamically adapt their depths to accommodate an
increasing number of interactions.


  The polarimetric observations on the protoplanetary disk around HL Tau have
shown the scattering-induced polarization at ALMA Band 7, which indicates that
the maximum dust size is $\sim 100~{\rm \mu m}$, while the Spectral Energy
Distribution (SED) has suggested that the maximum dust size is $\sim$ mm. To
solve the contradiction, we investigate the impact of differential settling of
dust grains on the SED and polarization. If the disk is optically thick, longer
observing wavelength traces more interior layer which would be dominated by
larger grains. We find that, the SED of the center part of the HL Tau disk can
be explained with mm-sized grains for a broad range of turbulence strength,
while $160~{\rm \mu m}$-sized grains can explain barely only if the turbulence
strength parameter $\alpha_{\rm t}$ is lower than $10^{-5}$. We also find that
the observed polarization fraction can be potentially explained with the
maximum dust size of $1~{\rm mm}$ if $\alpha_{\rm t}\lesssim10^{-5}$, although
models with $160~{\rm \mu m}$-sized grains are also acceptable. However, if the
maximum dust size is $\sim3~{\rm mm}$, the simulated polarization fraction is
too low to explain the observations even if the turbulence strength is
extremely small, indicating the maximum dust size of $\lesssim1$ mm. The
degeneracy between 100 ${\rm \mu m}$-sized and mm-sized grains can be solved by
improving the ALMA calibration accuracy or polarimetric observations at
(sub-)cm wavelengths.


  The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the
U.S. cotton industry that has cost more than 16 billion USD in damages since it
entered the United States from Mexico in the late 1800s. This pest has been
nearly eradicated; however, southern part of Texas still faces this issue and
is always prone to the pest reinfestation each year due to its sub-tropical
climate where cotton plants can grow year-round. Volunteer cotton (VC) plants
growing in the fields of inter-seasonal crops, like corn, can serve as hosts to
these pests once they reach pin-head square stage (5-6 leaf stage) and
therefore need to be detected, located, and destroyed or sprayed . In this
paper, we present a study to detect VC plants in a corn field using YOLOv3 on
three band aerial images collected by unmanned aircraft system (UAS). The
two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be
used for VC detection in a corn field using RGB (red, green, and blue) aerial
images collected by UAS and (ii) to investigate the behavior of YOLOv3 on
images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512,
S3 pixels) based on average precision (AP), mean average precision (mAP) and
F1-score at 95% confidence level. No significant differences existed for mAP
among the three scales, while a significant difference was found for AP between
S1 and S3 (p = 0.04) and S2 and S3 (p = 0.02). A significant difference was
also found for F1-score between S2 and S3 (p = 0.02). The lack of significant
differences of mAP at all the three scales indicated that the trained YOLOv3
model can be used on a computer vision-based remotely piloted aerial
application system (RPAAS) for VC detection and spray application in near
real-time.


  The robust adaptive beamforming (RAB) problem for general-rank signal model
with an additional positive semi-definite constraint is considered. Using the
principle of the worst-case performance optimization, such RAB problem leads to
a difference-of-convex functions (DC) optimization problem. The existing
approaches for solving the resulted non-convex DC problem are based on
approximations and find only suboptimal solutions. Here we solve the non-convex
DC problem rigorously and give arguments suggesting that the solution is
globally optimal. Particularly, we rewrite the problem as the minimization of a
one-dimensional optimal value function whose corresponding optimization problem
is non-convex. Then, the optimal value function is replaced with another
equivalent one, for which the corresponding optimization problem is convex. The
new one-dimensional optimal value function is minimized iteratively via
polynomial time DC (POTDC) algorithm.We show that our solution satisfies the
Karush-Kuhn-Tucker (KKT) optimality conditions and there is a strong evidence
that such solution is also globally optimal. Towards this conclusion, we
conjecture that the new optimal value function is a convex function. The new
RAB method shows superior performance compared to the other state-of-the-art
general-rank RAB methods.


  In this poster I briefly review several articles on astrophysics of old
isolated neutron stars, which were published in 1994-99 by my co-authors and
myself.


  As the new-generation precision experiments such as MOLLER and P2 look for
physics beyond Standard Model, it is becoming increasingly important to
evaluate the higher-order electroweak radiative corrections to a sub-percent
level of uncertainty. However, due to propagators with different masses and
higher-order tensor Feynman integrals, the two-loop calculations involving
thousands of Feynman graphs become a demanding task requiring novel
computational approaches. In this paper, we describe our dispersive sub-loop
insertion approach and develop two-loop integrals using two-point functions
basis which is applicable to wide range of processes.


  The procedure used to "do physics" in the macroscopic world is familiar: You
take an object, start it off with a particular position and velocity, subject
it to known forces (say gravity or friction, or both), and follow its
trajectory. You then measure the dynamical properties (say position or energy)
of that object at a later time and compare those measurements with the
prediction using Isaac Newton's laws of motion. Newton's laws directly predict
what those quantities should be at that later time, so the comparison is
straightforward. However, the microscopic laws of physics, quantum mechanics,
aren't so simple. The quantum concepts are so alien and counterintuitive that
the language used to describe the mathematics and physics is often ambiguous
and confusing. Therefore, for you to \emph{really} understand what is going on,
some mathematics must be used. This is my attempt to explain some "weird facts"
using a minimum of mathematics, yet eliminating as much ambiguity as possible.


  Cecotti and Vafa introduced the tt*-equation (topological-antitopological
fusion equation), whose solutions describe massive deformations of
supersymmetric conformal field theories. We describe some solutions of the
tt*-equation constructed from the SU(2)$_k$-fusion algebra. The idea of the
construction is due to Cecotti and Vafa, but we give a precise mathematical
formulation and a description of the "holomorphic data" corresponding to the
solutions by using the DPW method. Furthermore, we give a relation between the
solutions and the representations of SU(2). As a special case, we consider the
solutions corresponding to the supersymmetric A$_k$-minimal model.


  Without the faithful assumption, we prove that every Jordan derivation on a
class of path algebras of quivers without oriented cycles is a derivation and
that every Lie derivation on such kinds of algebras is of the standard form.


  The study of speech disorders can benefit greatly from time-aligned data.
However, audio-text mismatches in disfluent speech cause rapid performance
degradation for modern speech aligners, hindering the use of automatic
approaches. In this work, we propose a simple and effective modification of
alignment graph construction of CTC-based models using Weighted Finite State
Transducers. The proposed weakly-supervised approach alleviates the need for
verbatim transcription of speech disfluencies for forced alignment. During the
graph construction, we allow the modeling of common speech disfluencies, i.e.
repetitions and omissions. Further, we show that by assessing the degree of
audio-text mismatch through the use of Oracle Error Rate, our method can be
effectively used in the wild. Our evaluation on a corrupted version of the
TIMIT test set and the UCLASS dataset shows significant improvements,
particularly for recall, achieving a 23-25% relative improvement over our
baselines.


  Much of the present-day Artificial Intelligence (AI) utilizes artificial
neural networks, which are sophisticated computational models designed to
recognize patterns and solve complex problems by learning from data. However, a
major bottleneck occurs during a device's calculation of weighted sums for
forward propagation and optimization procedure for backpropagation, especially
for deep neural networks, or networks with numerous layers. Exploration into
different methods of implementing neural networks is necessary for further
advancement of the area. While a great deal of research into AI hardware in
both directions, analog and digital implementation widely exists, much of the
existing survey works lacks discussion on the progress of analog deep learning.
To this end, we attempt to evaluate and specify the advantages and
disadvantages, along with the current progress with regards to deep learning,
for analog implementations. In this paper, our focus lies on the comprehensive
examination of eight distinct analog deep learning methodologies across
multiple key parameters. These parameters include attained accuracy levels,
application domains, algorithmic advancements, computational speed, and
considerations of energy efficiency and power consumption. We also identify the
neural network-based experiments implemented using these hardware devices and
discuss comparative performance achieved by the different analog deep learning
methods along with an analysis of their current limitations. Overall, we find
that Analog Deep Learning has great potential for future consumer-level
applications, but there is still a long road ahead in terms of scalability.
Most of the current implementations are more proof of concept and are not yet
practically deployable for large-scale models.


  About $20\%$ of exoplanets discovered by radial velocity surveys reside in
stellar binaries. To clarify their origin one has to understand the dynamics of
planetesimals in protoplanetary disks within binaries. The standard
description, accounting for only gas drag and gravity of the companion star has
been challenged recently, as the gravity of the protoplanetary disk was shown
to play a crucial role in planetesimal dynamics. An added complication is the
tendency of protoplanetary disks in binaries to become eccentric, giving rise
to additional excitation of planetesimal eccentricity. Here, for the first
time, we analytically explore secular dynamics of planetesimals in binaries
such as $\alpha$ Cen and $\gamma$ Cep under the combined action of (1) gravity
of the eccentric protoplanetary disk, (2) perturbations due to the (coplanar)
eccentric companion, and (3) gas drag. We derive explicit solutions for the
behavior of planetesimal eccentricity ${\bf e}_p$ in non-precessing disks (and
in precessing disks in certain limits). We obtain the analytical form of the
distribution of relative velocities of planetesimals, which is a key input for
understanding their collisional evolution. Disk gravity strongly influences
relative velocities and tends to push sizes of planetesimals colliding with
comparable objects at the highest speed to small values, $\sim 1$ km. We also
find that planetesimals in eccentric protoplanetary disks apsidally aligned
with the binary orbit collide at lower relative velocities than in mis-aligned
disks. Our results highlight a decisive role that disk gravity plays in
planetesimal dynamics in binaries.


  Nowadays, with the booming development of the Internet, people benefit from
its convenience due to its open and sharing nature. A large volume of natural
language texts is being generated by users in various forms, such as search
queries, documents, and social media posts. As the unstructured text corpus is
usually noisy and messy, it becomes imperative to correctly identify and
accurately annotate structured information in order to obtain meaningful
insights or better understand unstructured texts. On the other hand, the
existing structured information, which embodies our knowledge such as entity or
concept relations, often suffers from incompleteness or quality-related issues.
Given a gigantic collection of texts which offers rich semantic information, it
is also important to harness the massiveness of the unannotated text corpus to
expand and refine existing structured knowledge with fewer annotation efforts.
  In this dissertation, I will introduce principles, models, and algorithms for
effective structured knowledge discovery from the massive text corpus. We are
generally interested in obtaining insights and better understanding
unstructured texts with the help of structured annotations or by
structure-aware modeling. Also, given the existing structured knowledge, we are
interested in expanding its scale and improving its quality harnessing the
massiveness of the text corpus. In particular, four problems are studied in
this dissertation: Structured Intent Detection for Natural Language
Understanding, Structure-aware Natural Language Modeling, Generative Structured
Knowledge Expansion, and Synonym Refinement on Structured Knowledge.


  Certified robustness guarantee gauges a model's robustness to test-time
attacks and can assess the model's readiness for deployment in the real world.
In this work, we critically examine how the adversarial robustness guarantees
from randomized smoothing-based certification methods change when
state-of-the-art certifiably robust models encounter out-of-distribution (OOD)
data. Our analysis demonstrates a previously unknown vulnerability of these
models to low-frequency OOD data such as weather-related corruptions, rendering
these models unfit for deployment in the wild. To alleviate this issue, we
propose a novel data augmentation scheme, FourierMix, that produces
augmentations to improve the spectral coverage of the training data.
Furthermore, we propose a new regularizer that encourages consistent
predictions on noise perturbations of the augmented data to improve the quality
of the smoothed models. We find that FourierMix augmentations help eliminate
the spectral bias of certifiably robust models enabling them to achieve
significantly better robustness guarantees on a range of OOD benchmarks. Our
evaluation also uncovers the inability of current OOD benchmarks at
highlighting the spectral biases of the models. To this end, we propose a
comprehensive benchmarking suite that contains corruptions from different
regions in the spectral domain. Evaluation of models trained with popular
augmentation methods on the proposed suite highlights their spectral biases and
establishes the superiority of FourierMix trained models at achieving
better-certified robustness guarantees under OOD shifts over the entire
frequency spectrum.


  This paper proves the $r \times r$ minors of an $n \times n$ symmetric matrix
of indeterminates are a tropical basis when $r = 2$, $r = 3$, or $r = n$, and
are not when $4 < r < n$ or $r = 4, n > 12$. In the process, it introduces two
new notions of rank for symmetric matrices coming from tropical geometry, the
symmetric tropical and the symmetric Kapranov rank, which are the symmetric
versions of their standard counterparts defined by Develin, Santos, and
Sturmfels.


  We consider categories of posets with $\mathfrak{C}$-valued structure sheaves
for any category $\mathfrak{C}$ and see how they possess poset-indexed lax
colimits that are both easy to describe and "weakly equivalent" to their
ordinary colimits in a certain sense. We employ this construction to study
descent problems on schematic spaces -- a particular scheme-like kind of ringed
poset -- , proving a general Seifert-Van Kampen Theorem for their \'etale
fundamental group that recovers and generalizes the homonym result for schemes
to the topology of flat monomorphisms. The techniques are general enough to
consider their applications in many other frameworks.


  We conduct an experimental investigation of temperature dependence of spin
diffusion length in highly-doped n-type silicon by using a non-local 3-terminal
method. Whereas an effect of spin drift is not ignorable to bias- and
temperature-dependence of spin signals in non-metallic systems except for the
case of a non-local 4-terminal method, it is not fully conclusive how the spin
drift affects spin transport properties in highly-doped Si in a non-local
3-terminal method that is often used in Si spintronics. Here, we report on
temperature dependence of spin diffusion length in the Si, and it is clarified
that the spin transport is less affected by an external electric field.


  Recent work has demonstrated the successful extraction of training data from
generative language models. However, it is not evident whether such extraction
is feasible in text classification models since the training objective is to
predict the class label as opposed to next-word prediction. This poses an
interesting challenge and raises an important question regarding the privacy of
training data in text classification settings. Therefore, we study the
potential privacy leakage in the text classification domain by investigating
the problem of unintended memorization of training data that is not pertinent
to the learning task. We propose an algorithm to extract missing tokens of a
partial text by exploiting the likelihood of the class label provided by the
model. We test the effectiveness of our algorithm by inserting canaries into
the training set and attempting to extract tokens in these canaries
post-training. In our experiments, we demonstrate that successful extraction is
possible to some extent. This can also be used as an auditing strategy to
assess any potential unauthorized use of personal data without consent.


  Haskell functions are defined as a series of clauses consisting of patterns
that are matched against the arguments in the order of definition. In case an
input is not matched by any of the clauses, an error occurs. Therefore it is
desirable to statically prove that the function is defined for all well-typed
inputs. Conversely, a clause that can never be matched also indicates a likely
defect. Analyzing these properties is challenging due to presence of GADT and
guards as well as due to Haskell's lazy evaluation. We implement a recently
proposed algorithm that unifies and extends the related analyses implemented in
version 7 of the Glasgow Haskell Compiler. By using an SMT solver to handle the
semantic constraints arising from pattern guards, we achieve a further
improvement in precision over the existing GHC 8.0.1 implementation. We present
a tool that uses the analysis to give sound, more precise, and actionable
warnings about program defects.


  Periodic frameworks with crystallographic symmetry are investigated from the
perspective of a general deformation theory of periodic bar-and-joint
structures in $R^d$. It is shown that natural parametrizations provide affine
section descriptions for families of frameworks with a specified graph and
symmetry. A simple geometric setting for diaplacive phase transitions is
obtained. Upper bounds are derived for the number of realizations of minimally
rigid periodic graphs.


  Although lane detection methods have shown impressive performance in
real-world scenarios, most of methods require post-processing which is not
robust enough. Therefore, end-to-end detectors like DEtection TRansformer(DETR)
have been introduced in lane detection.However, one-to-one label assignment in
DETR can degrade the training efficiency due to label semantic conflicts.
Besides, positional query in DETR is unable to provide explicit positional
prior, making it difficult to be optimized. In this paper, we present the
One-to-Several Transformer(O2SFormer). We first propose the one-to-several
label assignment, which combines one-to-many and one-to-one label assignment to
solve label semantic conflicts while keeping end-to-end detection. To overcome
the difficulty in optimizing one-to-one assignment. We further propose the
layer-wise soft label which dynamically adjusts the positive weight of positive
lane anchors in different decoder layers. Finally, we design the dynamic
anchor-based positional query to explore positional prior by incorporating lane
anchors into positional query. Experimental results show that O2SFormer with
ResNet50 backbone achieves 77.83% F1 score on CULane dataset, outperforming
existing Transformer-based and CNN-based detectors. Futhermore, O2SFormer
converges 12.5x faster than DETR for the ResNet18 backbone.


  It will be shown that starting from a coordinate system where the 6 phase
space coordinates are linearly coupled, one can go to a new coordinate system
where the motion is uncoupled by means of a linear transformation. The original
coupled coordinates and the new uncoupled coordinates are related by a 6x6
matrix, R. R will be called the decoupling matrix. It will be shown that of the
36 elements of the 6x6 decoupling matrix R, only 12 elements are independent.
This may be contrasted with the results for motion in 4-dimensional phase
space, where R has 4 independent elements. A set of equations is given from
which the 12 elements of R can be computed from the one period transfer
matrix.This set of equations also allows the linear parameters for the
uncoupled coordinates to be computed from the one period transfer matrix. An
alternative procedure for computing the linear parameters and the 12
independent elements of the decoupling matrix R is also given which depends on
computing the eigenvectors of the one period transfer matrix. These results can
be used in a tracking program, where the one period transfer matrix can be
computed by multiplying the transfer matrices of all the elements in a period,
to compute the linear parameters and the elements of the decoupling matrix R.


  We present a convergent variational basis-set calculational scheme for
elastic scattering of positronium atom by hydrogen atom in S wave. Highly
correlated trial functions with appropriate symmetry are needed for achieving
convergence. We report convergent results for scattering lengths in atomic
units for both singlet ($=3.49\pm 0.20$) and triplet ($=2.46\pm 0.10$) states.


  A scanning helium microscope typically utilises a thermal energy helium atom
beam, with an energy and wavelength (<100 meV, ~0.05 nm) particularly sensitive
to surface structure. An angular detector stage for a scanning helium
microscope is presented that facilitates the in-situ measurement of scattering
distributions from a sample. We begin by demonstrating typical elastic and
inelastic scattering from ordered surfaces. We then go on to show the role of
topography in diffuse scattering from disordered surfaces, observing deviations
from simple cosine scattering. In total, these studies demonstrate the wealth
of information that is encoded into the scattering distributions obtained with
the technique.


  In this article, we discover a fundamental excitation called electromagnon in
the cuprate superconducting states. Doped holes render local inversion
asymmetry which produces hidden Rashba spin-orbit coupling in copper-oxide
plane. Rashba effect leads to Dzyaloshinskii-Moriya (DM) interaction between
neighboring spins, which establishes a general spin Hamitonian combined with
Heisenberg exchange interaction. The physical origin of spin fluctuations has
been revealed from Rashba-induced DM term. Electromagnon excitations are found
to result from spin fluctuations manifested as spin spiral states by hopping of
doped holes in high temperature superconducting states. The frequency of
electromagnon excitations, peak position of dielectric constant spectrum,
decreases linearly with decrease of exchange interaction in various families of
superconductors. Comparisons with superconductivity-induced B1g Raman peaks
show good agreement and thus possibly provide the experimental evidences for
electromagnon excitations in cuprates.


  This paper considers the sparse recovery with shuffled labels, i.e., $\by =
\bPitrue \bX \bbetatrue + \bw$, where $\by \in \RR^n$, $\bPi\in \RR^{n\times
n}$, $\bX\in \RR^{n\times p}$, $\bbetatrue\in \RR^p$, $\bw \in \RR^n$ denote
the sensing result, the unknown permutation matrix, the design matrix, the
sparse signal, and the additive noise, respectively. Our goal is to reconstruct
both the permutation matrix $\bPitrue$ and the sparse signal $\bbetatrue$. We
investigate this problem from both the statistical and computational aspects.
From the statistical aspect, we first establish the minimax lower bounds on the
sample number $n$ and the \emph{signal-to-noise ratio} ($\snr$) for the correct
recovery of permutation matrix $\bPitrue$ and the support set
$\supp(\bbetatrue)$, to be more specific, $n \gtrsim k\log p$ and $\log\snr
\gtrsim \log n + \frac{k\log p}{n}$. Then, we confirm the tightness of these
minimax lower bounds by presenting an exhaustive-search based estimator whose
performance matches the lower bounds thereof up to some multiplicative
constants. From the computational aspect, we impose a parsimonious assumption
on the number of permuted rows and propose a computationally-efficient
estimator accordingly. Moreover, we show that our proposed estimator can obtain
the ground-truth $(\bPitrue, \supp(\bbetatrue))$ under mild conditions.
Furthermore, we provide numerical experiments to corroborate our claims.


  We study the N=4 gauged supergravity theory which arises from the consistent
truncation of IIB supergravity on the coset T^{1,1}. We analyze three N=2
subsectors and in particular we clarify the relationship between true
superpotentials for gauged supergravity and certain fake superpotentials which
have been widely used in the literature. We derive a superpotential for the
general reduction of type I supergravity on T^{1,1} and this together with a
certain solution generating symmetry is tantamount to a superpotential for the
baryonic branch of the Klebanov-Strassler solution.


  We revisit the general topic of thermodynamical stability and critical
phenomena in black hole physics, analyzing in detail the phase diagram of the
five dimensional rotating black hole and the black rings discovered by Emparan
and Reall. First we address the issue of microcanonical stability of these
spacetimes and its relation to thermodynamics by using the so-called Poincare
(or "turning point") method, which we review in detail. We are able to prove
that one of the black ring branches is always locally unstable, showing that
there is a change of stability at the point where the two black ring branches
meet. Next we study divergence of fluctuations, the geometry of the
thermodynamic state space (Ruppeiner geometry) and compute the appropriate
critical exponents and verify the scaling laws familiar from RG theory in
statistical mechanics. We find that, at extremality, the behaviour of the
system is formally very similar to a second order phase transition.


  A permanent magnet array (PMA) is a preferred source of magnetic field for
body-part-dedicated low-field (<0.5 T) portable magnetic resonance imaging
(MRI) because it has a small footprint, no power consumption, and no need for a
cooling system. The current popular PMA is limited by the transversal field
below 100 mT, where advanced technologies developed for the long-bore MRI
systems (e.g., multi-channel techniques) cannot be applied. In this paper, a
sparse high-performance PMA is proposed based on inward-outward ring pairs and
using magnet blocks that can be bought off the shelf, targeting on portable
head imaging. Through a fast genetic algorithm (GA)-based optimization, the
proposed PMA has a longitudinal magnetic field with an average field strength
of 111.40 mT and a monotonic field pattern with inhomogeneity of 10.57 mT (an
RF bandwidth of <10%) within a Field of View (FoV) of 20 cm in diameter and 4.5
cm in length. The resulting field was validated using analytic calculations and
numerical simulations. The encoding capability of the designed PMA was examined
by checking the quality of the simulated reconstructed images. The proposed
field outperforms a linear pattern for encoding. The PMA is 57.91 cm wide, 38
cm long, has a 5-gauss range of 87x87x104 cm^3, allowing an operation in a
small space. It weights 126.08kg, comprising of a stationary main array that
supplies a strong homogeneous field, and a light rotatable sub-gradient-array
(5.12kg) that supplies a monotonic field for signal encoding. It has a magnetic
field generation efficiency of 0.88mT/kg, the highest among sparse PMAs that
offer a monotonic field pattern. The force experienced by each magnet was
calculated to validate the feasibility of physical implementation. The proposed
PMA can be a promising alternative to supply the main and gradient fields
combined for dedicated portable MRI.


  The continuum and semiclassical limits of isotropic, spatially flat loop
quantum cosmology are discussed, with an emphasis on the role played by the
Barbero-Immirzi parameter \gamma in controlling space-time discreteness. In
this way, standard quantum cosmology is shown to be the simultaneous limit
\gamma \to 0, j \to \infty of loop quantum cosmology. Here, j is a label of the
volume eigenvalues, and the simultaneous limit is technically the same as the
classical limit \hbar \to 0, l \to \infty of angular momentum in quantum
mechanics. Possible lessons for semiclassical states at the dynamical level in
the full theory of quantum geometry are mentioned.


  We search for a Gardner transition in glassy glycerol, a standard molecular
glass, measuring the third harmonics cubic susceptibility $\chi_3^{(3)}$ from
slightly below the usual glass transition temperature down to $10K$. According
to the mean field picture, if local motion within the glass were becoming
highly correlated due to the emergence of a Gardner phase then $\chi_3^{(3)}$,
which is analogous to the dynamical spin-glass susceptibility, should increase
and diverge at the Gardner transition temperature $T_G$. We find instead that
upon cooling $| \chi_3^{(3)} |$ decreases by several orders of magnitude and
becomes roughly constant in the regime $100K-10K$. We rationalize our findings
by assuming that the low temperature physics is described by localized
excitations weakly interacting via a spin-glass dipolar pairwise interaction in
a random magnetic field. Our quantitative estimations show that the spin-glass
interaction is twenty to fifty times smaller than the local random field
contribution, thus rationalizing the absence of the spin-glass Gardner phase.
This hints at the fact that a Gardner phase may be suppressed in standard
molecular glasses, but it also suggests ways to favor its existence in other
amorphous solids and by changing the preparation protocol.


  Higher-order topological insulators (HOTIs) are systems with topologically
protected in-gap boundary states localized at their $(d-n)$-dimensional
boundaries, with $d$ the system dimension and $n$ the order of the topology.
This work proposes a rather universal dynamics-based characterization of one
large class of $Z$-type HOTIs without specifically relying on any symmetry
considerations. The key element of our innovative approach is to connect
quantum quench dynamics with nested configurations of the so-called band
inversion surfaces (BISs) of momentum-space Hamiltonians as a sum of operators
from the Clifford algebra (a condition that can be relaxed), thereby making it
possible to dynamically detect each and every order of topology on an equal
footing. Given that experiments on synthetic topological matter can directly
measure the winding of certain pseudospin texture to determine topological
features of BISs, the topological invariants defined through nested BISs are
all within reach of ongoing experiments. Further, the necessity of having
nested BISs in defining higher-order topology offers a unique perspective to
investigate and engineer higher-order topological phase transitions.