  In this work, we make a detailed discussion on the phenomenology of the
scotogenic Dirac model, which could accommodate the Dirac neutrino mass and
dark matter. We have studied the LFV processes in this model, which are
mediated by the charged scalar $\phi^\pm$ and heavy fermions $N_i$. The
experimental bounds, especially given by decays $\mu\to e\gamma$ and $\mu\to
3e$, have put severe constraints on Yukawa $y_\Phi$ and masses $m_{N1}$,
$m_\phi$. We select the heavy fermion $N_1$ as dark matter candidate and find
the correct relic density is basically by annihilating through another Yukawa
$y_\chi$. After satisfying LFV and dark matter relic density constraints, we
consider the indirect detections of dark matter annihilating into leptons. But
the constraints are relatively loose. Then we make a detailed discussion on the
dark matter direct detections. Although two Yukawa can both contribute to the
direct detection processes, more attention has been paid on the
$y_\Phi$-related processes as the $y_\chi$-related process is bounded loosely.
The current and future direct detection experiments have been used to set
constraints on the Yukawas and masses. The current direct detections bounds are
relatively loose and can barely exclude more parameter region beyond the LFV.
For the future direct detection experiments, the excluding capacities can be
improved due to larger exposures. The detecting capabilities in the large mass
region have not been weakened as the existence of mass enhancement from the
magnetic dipole operator $\mathcal{O}_{\rm mag.}$. At last, we briefly discuss
the collider signal searching in this model, the most promising signature is
pair produced $\phi^+\phi^-$ and decay into signal of
$\ell^+\ell^-+\not{\!\!E}_T$. The exclusion limits from collider on $m_{N1}$
and $m_\phi$ have provided a complementary detecting capability compared to the
LFV and dark matter detections.


  At certain quantum critical points in metals an entire Fermi surface may
disappear. A crucial question is the nature of the electronic excitations at
the critical point. Here we provide arguments showing that at such quantum
critical points the Fermi surface remains sharply defined even though the
Landau quasiparticle is absent. The presence of such a critical Fermi surface
has a number of consequences for the universal phenomena near the quantum
critical point which are discussed. In particular the structure of scaling of
the universal critical singularities can be significantly modified from more
familiar criticality. Scaling hypotheses appropriate to a critical fermi
surface are proposed. Implications for experiments on heavy fermion critical
points are discussed. Various phenomena in the normal state of the cuprates are
also examined from this perspective. We suggest that a phase transition that
involves a dramatic reconstruction of the Fermi surface might underlie a number
of strange observations in the metallic states above the superconducting dome.


  Agglomeration economies are a persistent subject of debate among economists
and urban planners. Their definition turns on whether or not larger cities and
regions are more efficient and more productive than smaller ones. We complement
existing discussion on agglomeration economies and the urban wage premium here
by providing a sensitivity analysis of estimated coefficients to different
delineations of urban agglomeration as well as to different definitions of the
economic measure that summarises the urban premium. This quantity can consist
of total wages measured at the place of work, or of income registered at the
place of residence. The chosen option influences the scaling behaviour of city
size as well as the spatial distribution of the phenomenon at the city level.
Spatial discrepancies between the distribution of jobs and the distribution of
households at different economic levels makes city definitions crucial to the
estimation of economic relations which vary with city size. We argue this point
by regressing measures of income and wage over about five thousands different
definitions of cities in France, based on our algorithmic aggregation of
administrative spatial units at regular cutoffs which reflect density,
population thresholds and commuting flows. We also go beyond aggregated
observations of wages and income by searching for evidence of larger
inequalities and economic segregation in the largest cities. This paper
therefore considers the spatial and economic complexity of cities with respect
to discussion about how we measure agglomeration economies. It provides a basis
for reflection on alternative ways to model the processes which lead to
observed variations, and this can provide insights for more comprehensive
regional planning.


  Relative positional embeddings (RPE) have received considerable attention
since RPEs effectively model the relative distance among tokens and enable
length extrapolation. We propose KERPLE, a framework that generalizes relative
position embedding for extrapolation by kernelizing positional differences. We
achieve this goal using conditionally positive definite (CPD) kernels, a class
of functions known for generalizing distance metrics. To maintain the inner
product interpretation of self-attention, we show that a CPD kernel can be
transformed into a PD kernel by adding a constant offset. This offset is
implicitly absorbed in the Softmax normalization during self-attention. The
diversity of CPD kernels allows us to derive various RPEs that enable length
extrapolation in a principled way. Experiments demonstrate that the logarithmic
variant achieves excellent extrapolation performance on three large language
modeling datasets. Our implementation and pretrained checkpoints are released
at https://github.com/chijames/KERPLE.git.


  Hexagonal boron nitride (hBN) is a prototypical high-quality two-dimensional
insulator and an ideal material to study tunneling phenomena, as it can be
easily integrated in vertical van der Waals devices. For spintronic devices,
its potential has been demonstrated both for efficient spin injection in
lateral spin valves and as a barrier in magnetic tunnel junctions (MTJs). Here
we reveal the effect of point defects inevitably present in mechanically
exfoliated hBN on the tunnel magnetoresistance of Co-hBN-NiFe MTJs. We observe
a clear enhancement of both the conductance and magnetoresistance of the
junction at well-defined bias voltages, indicating resonant tunneling through
magnetic (spin-polarized) defect states. The spin polarization of the defect
states is attributed to exchange coupling of a paramagnetic impurity in the
few-atomic-layer thick hBN to the ferromagnetic electrodes. This is confirmed
by excellent agreement with theoretical modelling. Our findings should be taken
into account in analyzing tunneling processes in hBN-based magnetic devices.
More generally, our study shows the potential of using atomically thin hBN
barriers with defects to engineer the magnetoresistance of MTJs and to achieve
spin filtering, opening the door towards exploiting the spin degree of freedom
in current studies of point defects as quantum emitters.


  We summarize the results of the investigation of the $IJ^{PC}=00^{++}$ wave
which is devoted to the search for the lightest scalar glueball. In the
framework of the K-matrix formalism, the $q\bar q$-nonet classification of the
scalar/isoscalar states is performed in the mass region below 1900 MeV basing
on the following data: GAMS data for $\pi^-p\to\pi^0\pi^0n$, $\eta\eta n$,
$\eta\eta'n$; CERN-M\"unich data for $\pi^-p\to\pi^+\pi^-n$; Crystal Barrel
data for $p\bar p\to\pi^0\pi^0\pi^0$, $\pi^0\pi^0\eta$, $\pi^0\eta\eta$; BNL
data for $\pi\pi\to K\overline K$. It is shown the existence of an extra state
for $q\bar q$ classification: this state is a good candidate for the lightest
scalar glueball. Analysis of the glueball/$q\bar q$ mixing shows that the
glueball state has mainly dispersed over three real resonances: $f_0(1300)$,
$f_0(1500)$ and $f_0(1530^{+90}_{-250})$. The broad resonance with complex mass
$m=1530^{+90}_{-250}-i(560\pm 140)$ MeV is descendant of the pure glueball. The
restored mass of the pure glueball is equal to 1695 MeV. This value agrees well
with the results of lattice gluodynamics.


  A peer-to-peer system is a distributed system in which equal nodes (in terms
of role and usage) exchange information and services directly. This paper
describes a distributed peer-to-peer protocol that allows wifi-enabled smart
devices (especially Android smartphones) to exchange data using only wifi. The
protocol is designed to allow the automatic establishment of a distributed
peer-to-peer network of any size without geography constraint. It is applied to
file sharing between devices, but can easily be adapted to support any data
sharing. The protocol defines two layers: (1) a kernel layer responsible for
creating, routing, establishing and maintaining links between nodes (peer),
addressing a node and adding and removing nodes; and an (2) application layer
to support data sharing (file sharing in this case). The structure of the
peer-to-peer network is hybrid. Regarding the file sharing use case, the
application allows a node to (i) search for a file in the catalog of its subnet
that is held by the root node, and (ii) download a file: if the file is not in
the subnet, the root node delegates a node to make vouchers in neighboring
subnetworks to get the file and make it available. A proof of concept was made
on Android.


  High-mass stars are thought to accumulate much of their mass via short,
infrequent bursts of disk-aided accretion. Such accretion events are rare and
difficult to observe directly but are known to drive enhanced maser emission.
In this Letter we report high-resolution, multi-epoch methanol maser
observations toward G358.93-0.03 which reveal an interesting phenomenon; the
sub-luminal propagation of a thermal radiation "heat-wave" emanating from an
accreting high-mass proto-star. The extreme transformation of the maser
emission implies a sudden intensification of thermal infrared radiation from
within the inner (40 mas, 270 au) region. Subsequently, methanol masers trace
the radial passage of thermal radiation through the environment at $\geq$ 4-8\%
the speed of light. Such a high translocation rate contrasts with the $\leq$ 10
km s$^{-1}$ physical gas motions of methanol masers typically observed using
very long baseline interferometry (VLBI). The observed scenario can readily be
attributed to an accretion event in the high-mass proto-star G358.93-0.03-MM1.
While being the third case in its class, G358.93-0.03-MM1 exhibits unique
attributes hinting at a possible `zoo' of accretion burst types. These results
promote the advantages of maser observations in understanding high-mass star
formation, both through single-dish maser monitoring campaigns and via their
international cooperation as VLBI arrays.


  This paper generalizes the GGP conjectures which were earlier formulated for
tempered or more generally generic L-packets to Arthur packets, especially for
the nongeneric L-packets arising from Arthur parameters. The paper introduces
the key notion of a relevant pair of A-parameters which governs the branching
laws for $GL_n$ and all classical groups over both local fields and global
fields. It plays a role for all the branching problems studied in our earlier
work including Bessel models and Fourier-Jacobi models.


  In this talk we discuss the possibility of constructing a fluctuation theory
for structural glasses in the non-equilibrium aging state. After reviewing well
known results in a toy model we discuss some of the key assumptions which
support the validity of this theory, in particular the role of the
configurational entropy and its relation to the effective temperature. Recent
numerical results for mean-field finite-size glasses agree with this scenario.


  The structure of domain walls delimiting magnetic bubbles in L10 FePd thin
layers is described on the basis of Lorentz transmission electron microscopy
(LTEM) and multiscale magnetic simulations. Images obtained by high resolution
LTEM show the existence of magnetization reversal areas inside domain walls,
called vertical Bloch lines (VBLs). Combining these observations and multiscale
simulations on various geometries, we can identify the structure of these VBLs,
notably the presence or not of magnetic singularities.


  This letter reports high-performance $\mathrm{\beta} Ga2O3 thin channel
MOSFETs with T-gate and degenerately doped source/drain contacts regrown by
MOCVD. Gate length scaling (LG= 160-200 nm) leads to a peak drain current
(ID,MAX) of 285 mA/mm and peak trans-conductance (gm) of 52 mS/mm at 10 V drain
bias with 23.5 Ohm mm on resistance (Ron). A low metal/n+ contact resistance of
0.078 Ohm mm was extracted from TLM measurement. Ron is dominated by interface
resistance between channel and regrown layer. A gate-to-drain breakdown voltage
of 192 V is measured for LGD = 355 nm resulting in average breakdown field
(E_AVG) of 5.4 MV/cm. This E_AVG is the highest reported among all sub-micron
gate length lateral FETs. RF measurements on 200 nm Silicon Nitride (Si3N4)
passivated device shows a current gain cut off frequency (f_T) of 11 GHz and
record power gain cut off frequency (f_MAX) of 48 GHz. The f_T.V_Br product is
2.11 THz.V for 192 V breakdown voltage. The switching figure of merit exceeds
that of silicon and is comparable to mature wide-band gap devices.


  We consider Fair Principal Component Analysis (FPCA) and search for a low
dimensional subspace that spans multiple target vectors in a fair manner. FPCA
is defined as a non-concave maximization of the worst projected target norm
within a given set. The problem arises in filter design in signal processing,
and when incorporating fairness into dimensionality reduction schemes. The
state of the art approach to FPCA is via semidefinite relaxation and involves a
polynomial yet computationally expensive optimization. To allow scalability, we
propose to address FPCA using naive sub-gradient descent. We analyze the
landscape of the underlying optimization in the case of orthogonal targets. We
prove that the landscape is benign and that all local minima are globally
optimal. Interestingly, the SDR approach leads to sub-optimal solutions in this
simple case. Finally, we discuss the equivalence between orthogonal FPCA and
the design of normalized tight frames.


  We establish a limit formula for the median of the distance between two
leaves in a fully resolved unrooted phylogenetic tree with n leaves. More
precisely, we prove that this median is equal, in the limit, to the square root
of 4*ln(2)*n.


  We generalize the Ruh-Vilms problem by characterizing the submanifolds in
Euclidean spaces with proper biharmonic Gauss map and we construct examples of
such hypersurfaces.


  We present phase-resolved spectroscopy and extensive survey photometry of the
detached eclipsing binary system XY Cet, which is composed of two
metallic-lined stars. We measure their masses to be 1.773 +/- 0.016 and 1.615
+/- 0.014 Msun and their radii to be 1.873 +/- 0.035 and 1.773 +/- 0.029 Rsun,
resulting in logarithmic surface gravities of 4.142 +/- 0.016 and 4.149 +/-
0.014 (cgs). We determine effective temperatures of 7870 +/- 115 and 7620 +/-
125 K. The projected rotational velocities are 34.4 +/- 0.4 and 34.1 +/- 0.4
km/s, which are close to synchronous. Theoretical models cannot match all of
these properties, but come closest for a solar helium and metal abundance and
an age in the region of 850 Myr. We obtain the individual spectra of the two
stars by the spectral disentangling method, and compare them to synthetic
spectra calculated for the measured effective temperatures and a solar chemical
composition. Both stars show enhanced abundances of iron-group elements and
clear deficiencies of Ca I and Sc II, confirming their classification as Am
stars. We also find strong overabundances of Zr II and the rare-earth species
La II, Ce II and Nd II, a hallmark of chemically peculiar A-stars. XY Cet is a
prime candidate for detailed spectroscopic analyses of metallic-lined stars
whose masses and radii are known to accuracies of 1-2%.


  Recent observations have demonstrated that waves which are capable of
carrying large amounts of energy are ubiquitous throughout the solar corona.
However, the question of how this wave energy is dissipated (on which time and
length scales) and released into the plasma remains largely unanswered. Both
analytic and numerical models have previously shown that Alfv\'enic turbulence
may play a key role not only in the generation of the fast solar wind, but in
the heating of coronal loops. In an effort to bridge the gap between theory and
observations, we expand on a recent study [De Moortel et al., ApJL, 782:L34,
2014] by analyzing thirty-seven clearly isolated coronal loops using data from
the Coronal Multi-channel Polarimeter (CoMP) instrument. We observe Alfv\'enic
perturbations with phase speeds which range from 250-750 km/s and periods from
140-270 s for the chosen loops. While excesses of high frequency wave-power are
observed near the apex of some loops (tentatively supporting the onset of
Alfv\'enic turbulence), we show that this excess depends on loop length and the
wavelength of the observed oscillations. In deriving a proportional
relationship between the loop length/wavelength ratio and the enhanced wave
power at the loop apex, and from the analysis of the line-widths associated
with these loops, our findings are supportive of the existence of Alfv\'enic
turbulence in coronal loops.


  We demonstrate that scattering of particles strongly interacting in three
dimensions (3D) can be suppressed at low energies in a quasi-one-dimensional
(1D) confinement. The underlying mechanism is the interference of the s- and
p-wave scattering contributions with large s- and p-wave 3D scattering lengths
being a necessary prerequisite. This low-dimensional quantum scattering effect
might be useful in "interacting" quasi-1D ultracold atomic gases, guided atom
interferometry, and impurity scattering in strongly confined quantum wire-based
electronic devices.


  We investigate the dynamical one-armed spiral instability in differentially
rotating stars with both eigenmode analysis and hydrodynamic simulations in
Newtonian gravity. We find that the one-armed spiral mode is generated around
the corotation radius of the star, and the distribution of angular momentum
shifts inwards the corotation radius during the growth of one-armed spiral
mode. We also find by investigating the distribution of the canonical angular
momentum density that the low T/|W| dynamical instability for both m=1 and m=2
mode, where T is the rotational kinetic energy and W is the gravitational
potential energy, is generated around the corotation point. Finally, we discuss
the feature of gravitational waves generated from these modes.


  We consider a class of dissipative stochastic differential equations (SDE's)
with time-periodic coefficients in finite dimension, and the response of
time-asymptotic probability measures induced by such SDE's to sufficiently
regular, small perturbations of the underlying dynamics. Understanding such a
response provides a systematic way to study changes of statistical observables
in response to perturbations, and it is often very useful for sensitivity
analysis, uncertainty quantification, and for improving probabilistic
predictions of nonlinear dynamical systems, especially in high dimensions.
Here, we are concerned with the linear response to small perturbations in the
case when the time-asymptotic probability measures are time-periodic. First, we
establish sufficient conditions for the existence of stable random
time-periodic orbits generated by the underlying SDE. Ergodicity of
time-periodic probability measures supported on these random periodic orbits is
subsequently discussed. Then, we derive the so-called fluctuation-dissipation
relations which allow to describe the linear response of statistical
observables to small perturbations away from the time-periodic ergodic regime
in a manner which only exploits the unperturbed dynamics. The results are
formulated in an abstract setting but they apply to problems ranging from
aspects of climate modelling, to molecular dynamics, to the study of
approximation capacity of neural~networks and robustness of their estimates.


  We are motivated by the need, in some applications, for impromptu or
as-you-go deployment of wireless sensor networks. A person walks along a line,
starting from a sink node (e.g., a base-station), and proceeds towards a source
node (e.g., a sensor) which is at an a priori unknown location. At equally
spaced locations, he makes link quality measurements to the previous relay, and
deploys relays at some of these locations, with the aim to connect the source
to the sink by a multihop wireless path. In this paper, we consider two
approaches for impromptu deployment: (i) the deployment agent can only move
forward (which we call a pure as-you-go approach), and (ii) the deployment
agent can make measurements over several consecutive steps before selecting a
placement location among them (which we call an explore-forward approach). We
consider a light traffic regime, and formulate the problem as a Markov decision
process, where the trade-off is among the power used by the nodes, the outage
probabilities in the links, and the number of relays placed per unit distance.
We obtain the structures of the optimal policies for the pure as-you-go
approach as well as for the explore-forward approach. We also consider natural
heuristic algorithms, for comparison. Numerical examples show that the
explore-forward approach significantly outperforms the pure as-you-go approach.
Next, we propose two learning algorithms for the explore-forward approach,
based on Stochastic Approximation, which asymptotically converge to the set of
optimal policies, without using any knowledge of the radio propagation model.
We demonstrate numerically that the learning algorithms can converge (as
deployment progresses) to the set of optimal policies reasonably fast and,
hence, can be practical, model-free algorithms for deployment over large
regions.


  The microstructure of a CoNiAl FSMA was determined by conventional
transmission electron microscopy, electron diffraction studies together with
advanced microscopy techniques and in-situ Lorentz microscopy. 10 to 60 nm
sized rod-like precipitates of hcp $\epsilon$-Co were confirmed to be present
by HRTEM. The orientation relationship between the precipitates and B2 matrix
is described by the Burgers orientation relationship. The crystal structure of
the martensite obtained after cooling is tetragonal L10 with a (1-11) twinning
plane. The magnetic domain structure was determined during an in-situ cooling
experiment using the Fresnel mode of Lorentz microscopy. While transformation
proceeds from B2 austenite to L10 martensite, new domains are nucleated leading
to a decrease in domain width, with the magnetization lying predominantly along
a single direction. It was possible to completely describe the relationship
between magnetic domains and crystallographic directions in the austenite phase
though complications existed for the martensite phase.


  As part of our program to develop the description of three-body effects in
quantum vacuum phenomena, we study the three-body interaction of two
anisotropically polarizable atoms with a perfect electrically conducting plate,
a generalization of earlier work. Three- and four-scattering effects are
important, and lead to nonmonotonic behavior.


  The control of thermal radiation by shaping its spatial and spectral emission
characteristics plays a key role in many areas of science and engineering.
Conventional approaches to tailor thermal emission using metamaterials are
severely hampered both by the limited spatial resolution of the required
sub-wavelength material structures and by the materials' strong absorption in
the infrared. Here, we demonstrate a promising new approach based on the
concept of topology. By changing a single parameter of a multilayer coating, we
control the reflection topology of a surface, with the critical point of zero
reflection being topologically protected. As a result, the boundaries between
sub-critical and super-critical spatial domains host topological interface
states with near-unity thermal emissivity. Our experimental demonstration of
this effect shows that topological concepts enable unconventional manipulation
of thermal light with promising applications for thermal management, energy
harvesting and thermal camouflage.


  The rapid advancement of quantum computing has increasingly highlighted its
potential in the realm of machine learning, particularly in the context of
natural language processing (NLP) tasks. Quantum machine learning (QML)
leverages the unique capabilities of quantum computing to offer novel
perspectives and methodologies for complex data processing and pattern
recognition challenges. This paper introduces a novel Quantum Mixed-State
Attention Network (QMSAN), which integrates the principles of quantum computing
with classical machine learning algorithms, especially self-attention networks,
to enhance the efficiency and effectiveness in handling NLP tasks. QMSAN model
employs a quantum attention mechanism based on mixed states, enabling efficient
direct estimation of similarity between queries and keys within the quantum
domain, leading to more effective attention weight acquisition. Additionally,
we propose an innovative quantum positional encoding scheme, implemented
through fixed quantum gates within the quantum circuit, to enhance the model's
accuracy. Experimental validation on various datasets demonstrates that QMSAN
model outperforms existing quantum and classical models in text classification,
achieving significant performance improvements. QMSAN model not only
significantly reduces the number of parameters but also exceeds classical
self-attention networks in performance, showcasing its strong capability in
data representation and information extraction. Furthermore, our study
investigates the model's robustness in different quantum noise environments,
showing that QMSAN possesses commendable robustness to low noise.


  Many social programs attempt to allocate scarce resources to people with the
greatest need. Indeed, public services increasingly use algorithmic risk
assessments motivated by this goal. However, targeting the highest-need
recipients often conflicts with attempting to evaluate the causal effect of the
program as a whole, as the best evaluations would be obtained by randomizing
the allocation. We propose a framework to design randomized allocation rules
which optimally balance targeting high-need individuals with learning treatment
effects, presenting policymakers with a Pareto frontier between the two goals.
We give sample complexity guarantees for the policy learning problem and
provide a computationally efficient strategy to implement it. We then apply our
framework to data from human services in Allegheny County, Pennsylvania.
Optimized policies can substantially mitigate the tradeoff between learning and
targeting. For example, it is often possible to obtain 90% of the optimal
utility in targeting high-need individuals while ensuring that the average
treatment effect can be estimated with less than 2 times the samples that a
randomized controlled trial would require. Mechanisms for targeting public
services often focus on measuring need as accurately as possible. However, our
results suggest that algorithmic systems in public services can be most
impactful if they incorporate program evaluation as an explicit goal alongside
targeting.


  Acoustic echo cancellation (AEC), noise suppression (NS) and automatic gain
control (AGC) are three often required modules for real-time communications
(RTC). This paper proposes a neural network supported algorithm for RTC, namely
NN3A, which incorporates an adaptive filter and a multi-task model for residual
echo suppression, noise reduction and near-end speech activity detection. The
proposed algorithm is shown to outperform both a method using separate models
and an end-to-end alternative. It is further shown that there exists a
trade-off in the model between residual suppression and near-end speech
distortion, which could be balanced by a novel loss weighting function. Several
practical aspects of training the joint model are also investigated to push its
performance to limit.


  The emergence of large-margin softmax cross-entropy losses in training deep
speaker embedding neural networks has triggered a gradual shift from parametric
back-ends to a simpler cosine similarity measure for speaker verification.
Popular parametric back-ends include the probabilistic linear discriminant
analysis (PLDA) and its variants. This paper investigates the properties of
margin-based cross-entropy losses leading to such a shift and aims to find
scoring back-ends best suited for speaker verification. In addition, we revisit
the pre-processing techniques which have been widely used in the past and
assess their effectiveness on large-margin embeddings. Experiments on the
state-of-the-art ECAPA-TDNN networks trained with various large-margin softmax
cross-entropy losses show a substantial increment in intra-speaker compactness
making the conventional PLDA superfluous. In this regard, we found that
constraining the within-speaker covariance matrix could improve the performance
of the PLDA. It is demonstrated through a series of experiments on the
VoxCeleb-1 and SITW core-core test sets with 40.8% equal error rate (EER)
reduction and 35.1% minimum detection cost (minDCF) reduction. It also
outperforms cosine scoring consistently with reductions in EER and minDCF by
10.9% and 4.9%, respectively.


  Recent elastic and inelastic neutron scattering studies of the highly
frustrated pyrochlore antiferromagnet Tb2Ti2O7 have shown some very intriguing
features that cannot be modeled by the local <111> classical Ising model,
naively expected to describe this system at low temperatures. Using the random
phase approximation to take into account fluctuations between the ground state
doublet and the first excited doublet, we successfully describe the elastic
neutron scattering pattern and dispersion relations in Tb2Ti2O7,
semi-quantitatively consistent with experimental observations.


  We present new, streamlined proofs of certain maximality principles studied
by Hamkins and Woodin. Moreover, we formulate an intermediate maximality
principle, which is shown here to be equiconsistent with the existence of a
weakly compact cardinal $\kappa$ such that $V_{\kappa}\prec V$.


  We present and discuss the development of an unconditionally stable algorithm
used to solve the evolution equations of the Phase Field Crystal (PFC) model.
This algorithm allows for an arbitrarily large algorithmic time step. As the
basis for our analysis of the accuracy of this algorithm, we determine an
effective time step in Fourier space. We then compare our calculations with a
set of representative numerical results, and demonstrate that this algorithm is
an effective approach for the study of the PFC models, yielding a time step
effectively 180 times larger than the Euler algorithm for a representative set
of material parameters. As the PFC model is just a simple example of a wide
class of density functional theories, we expect this method will have wide
applicability to modeling systems of considerable interest to the materials
modeling communities.


  We present an efficient adaptive refinement procedure that preserves
analysis-suitability of the T-mesh, this is, the linear independence of the
T-spline blending functions. We prove analysis-suitability of the overlays and
boundedness of their cardinalities, nestedness of the generated T-spline
spaces, and linear computational complexity of the refinement procedure in
terms of the number of marked and generated mesh elements.


  We investigate the description of the region behind the event horizon in
rotating black holes in the AdS/CFT correspondence, using the rotating BTZ
black hole as a concrete example. We extend a technique introduced by Kraus,
Ooguri and Shenker [hep-th/0212277], based on analytically continuing
amplitudes defined in a Euclidean space, to include rotation. In the rotating
case, boundary amplitudes again have two different bulk descriptions, involving
either integration only over the regions outside the black holes' event
horizon, or integration over this region and the region between the event
horizon and the Cauchy horizon (inner horizon). We argue that generally, the
holographic map will relate the field theory to the region bounded by the
Cauchy horizons in spacetime. We also argue that these results suggest that the
holographic description of black holes will satisfy strong cosmic censorship.


  We present a model for smectic elastomer membranes which includes elastic and
liquid crystalline degrees of freedom. Based on our model, we determined the
qualitative phase diagram of a smectic elastomer membrane using mean-field
theory. This phase diagram is found to comprise five phases, viz.
smectic-A--flat, smectic-A--crumpled, smectic-C--flat, smectic-C--crumpled and
smectic-C--tubule, where in the latter phase, the membrane is flat in the
direction of mesogenic tilt and crumpled in the perpendicular direction. The
transitions between adjacent phases are second order phase transitions. We
study in some detail the elasticity of the smectic-C--flat and the
smectic-C--tubule phases which are associated with a spontaneous breaking of
in-plane rotational symmetry. As a consequence of the Goldstone theorem, these
phases exhibit soft elasticity characterized by the vanishing of in-plane shear
moduli.


  In this paper we survey a number of recent results concerning the existence
and moduli spaces of solutions of various geometric problems on noncompact
manifolds. The three problems which we discuss in detail are:
  I. Complete properly immersed minimal surfaces in $\RR^3$ with finite total
curvature.
  II. Complete embedded surfaces of constant mean curvature in $\RR^3$ with
finite topology.
  III. Complete conformal metrics of constant positive scalar curvature on $M^n
\setminus \Lambda$, where $M^n$ is a compact Riemannian manifold, $n\geq3$ and
$\Lam \subset M$ is closed.
  The existence results we discuss for each of these problems are ones whereby
known solutions (sometimes satisfying certain nondegeneracy hypotheses) are
glued together to produce new solutions. Although this sort of procedure is
quite well-known, there have been some recent advances on which we wish to
report here. We also discuss what has been established about the moduli spaces
of all solutions to these problems, and report on some work in progress
concerning global aspects of these moduli spaces. In the final section we
present a new compactness result for the `unmarked moduli spaces' for problem
III.


  A central challenge in computational modeling of dynamic biological systems
is parameter inference from experimental time course measurements. However, one
would not only like to infer kinetic parameters but also study their
variability from cell to cell. Here we focus on the case where single-cell
fluorescent protein imaging time series data are available for a population of
cells. Based on van Kampen's linear noise approximation, we derive a dynamic
state space model for molecular populations which is then extended to a
hierarchical model. This model has potential to address the sources of
variability relevant to single-cell data, namely, intrinsic noise due to the
stochastic nature of the birth and death processes involved in reactions and
extrinsic noise arising from the cell-to-cell variation of kinetic parameters.
In order to infer such a model from experimental data, one must also quantify
the measurement process where one has to allow for nonmeasurable molecular
species as well as measurement noise of unknown level and variance. The
availability of multiple single-cell time series data here provides a unique
testbed to fit such a model and quantify these different sources of variation
from experimental data.


  Recently there has been increasing interest in probabilistic solvers for
ordinary differential equations (ODEs) that return full probability measures,
instead of point estimates, over the solution and can incorporate uncertainty
over the ODE at hand, e.g. if the vector field or the initial value is only
approximately known or evaluable. The ODE filter proposed in recent work models
the solution of the ODE by a Gauss-Markov process which serves as a prior in
the sense of Bayesian statistics. While previous work employed a Wiener process
prior on the (possibly multiple times) differentiated solution of the ODE and
established equivalence of the corresponding solver with classical numerical
methods, this paper raises the question whether other priors also yield
practically useful solvers. To this end, we discuss a range of possible priors
which enable fast filtering and propose a new prior--the Integrated Ornstein
Uhlenbeck Process (IOUP)--that complements the existing Integrated Wiener
process (IWP) filter by encoding the property that a derivative in time of the
solution is bounded in the sense that it tends to drift back to zero. We
provide experiments comparing IWP and IOUP filters which support the belief
that IWP approximates better divergent ODE's solutions whereas IOUP is a better
prior for trajectories with bounded derivatives.


  Society's reliance on social media as a primary source of news has spawned a
renewed focus on the spread of misinformation. In this work, we identify the
differences in how social media accounts identified as bots react to news
sources of varying credibility, regardless of the veracity of the content those
sources have shared. We analyze bot and human responses annotated using a
fine-grained model that labels responses as being an answer, appreciation,
agreement, disagreement, an elaboration, humor, or a negative reaction. We
present key findings of our analysis into the prevalence of bots, the variety
and speed of bot and human reactions, and the disparity in authorship of
reaction tweets between these two sub-populations. We observe that bots are
responsible for 9-15% of the reactions to sources of any given type but
comprise only 7-10% of accounts responsible for reaction-tweets; trusted news
sources have the highest proportion of humans who reacted; bots respond with
significantly shorter delays than humans when posting answer-reactions in
response to sources identified as propaganda. Finally, we report significantly
different inequality levels in reaction rates for accounts identified as bots
vs not.


  A crucial issue in fluid dynamics is related to the knowledge of the fluid
pressure. A new general pressure equation is derived from compressible
Navier-Stokes equation. It is argued that this new pressure equation allows
unifying compressible, low-Mach and incompressible approaches. Moreover, this
equation should be able to replace the Poisson equation in isothermal
incompressible fluids. For computational fluid dynamics, it can be seen as an
alternative to Lattice Boltzmann methods and as the physical justification of
artificial com-pressibility.


  From SUSY ladder operators in momentum space of a neutron in the magnetic
field of a linear current, we construct $2\times 2$ matrix operators that
together with the z-component of the angular momentum satisfy the su(2) Lie
algebra. We use this fact to explain the degeneracy of the energy spectrum.


  We present a linearizable, non-blocking $k$-ary search tree ($k$-ST) that
supports fast searches and range queries. Our algorithm uses single-word
compare-and-swap (CAS) operations, and tolerates any number of crash failures.
Performance experiments show that, for workloads containing small range
queries, our $k$-ST significantly outperforms other algorithms which support
these operations, and rivals the performance of a leading concurrent skip-list,
which provides range queries that cannot always be linearized.


  We generalize the nullity theorem of Gustafson [Linear Algebra Appl. (1984)]
from matrix inversion to principal pivot transform. Several special cases of
the obtained result are known in the literature, such as a result concerning
local complementation on graphs. As an application, we show that a particular
matrix polynomial, the so-called nullity polynomial, is invariant under
principal pivot transform.


  Neutron star (NS) equation of state (EoS) insensitive relations or universal
relations (UR) involving neutron star bulk properties play a crucial role in
gravitational-wave astronomy. Considering a wide range of equations of state
originating from (i) phenomenological relativistic mean field models, (ii)
realistic EoS models based on different physical motivations, and (iii)
polytropic EoSs described by spectral decomposition method, we update the
EoS-insensitive relations involving NS tidal deformability (Multipole Love
relation) and the UR between f-mode frequency and tidal deformability (f-Love
relation). We analyze the binary neutron star (BNS) event GW170817 using the
frequency domain TaylorF2 waveform model with updated universal relations and
find that the additional contribution of the octupolar electric tidal parameter
and quadrupolar magnetic tidal parameter or the change of multipole Love
relation has no significant impact on the inferred NS properties. However,
adding the f-mode dynamical phase lowers the 90% upper bound on
$\tilde{\Lambda}$ by 16-20% as well as lowers the upper bound of NSs radii by
$\sim$500m. The combined URs (multipole Love and f-Love) developed in this work
predict a higher median (also a higher 90% upper bound) for $\tilde{\Lambda}$
by 6% and also predict higher radii for the binary components of GW170817 by
200-300m compared to the URs used previously in the literature. We further
perform injection and recovery studies on simulated events with different EoSs
in $\rm A+$ detector configuration as well as with third generation (3G)
Einstein telescope. In agreement with the literature, we find that neglecting
f-mode dynamical tides can significantly bias the inferred NS properties,
especially for low mass NSs. However, we also find that the impact of the URs
is within statistical errors.


  In-memory computing with crosspoint resistive memory arrays has been shown to
accelerate data-centric computations such as the training and inference of deep
neural networks, thanks to the high parallelism endowed by physical rules in
the electrical circuits. By connecting crosspoint arrays with negative feedback
amplifiers, it is possible to solve linear algebraic problems such as linear
systems and matrix eigenvectors in just one step. Based on the theory of
feedback circuits, we study the dynamics of the solution of linear systems
within a memory array, showing that the time complexity of the solution is free
of any direct dependence on the problem size N, rather it is governed by the
minimal eigenvalue of an associated matrix of the coefficient matrix. We show
that, when the linear system is modeled by a covariance matrix, the time
complexity is O(logN) or O(1). In the case of sparse positive-definite linear
systems, the time complexity is solely determined by the minimal eigenvalue of
the coefficient matrix. These results demonstrate the high speed of the circuit
for solving linear systems in a wide range of applications, thus supporting
in-memory computing as a strong candidate for future big data and machine
learning accelerators.


  Epoxy resins are widely used polymer matrices for numerous applications.
Despite substantial advances, the molecular-level knowledge-base required to
exploit these materials to their full potential remains limited. A deeper
comprehension of structure/property relationships in epoxy resins at the
molecular level is critical to progressing these efforts. It can be laborious,
if not impractical, to elucidate these relationships based on experiments
alone. Here, molecular dynamics simulations are used to calculate and compare
thermal conductivities and mechanical properties of an exemplar epoxy resin,
Bisphenol F cross-linked with Diethyl Toluene Diamine, revealing these
inter-relationships. Both elastic modulus and thermal transport of the epoxy
resin show an increase with greater cross-linking. Specifically, decomposition
of the thermal conductivity into different force contributions suggests that
the bonded term contributes to an increase in the heat flux. These outcomes
provide a foundation for designing and fabricating customized epoxy resins with
desirable thermal and mechanical attributes.


  In the paper, we consider a completely integrable Hamiltonian system with
three degrees of freedom found by V.V.Sokolov and A.V.Tsiganov. This system is
known as the generalized two-field gyrostat. For the case of only gyroscopic
forces present, we find new invariant four-dimensional submanifolds such that
the induced dynamical systems are almost everywhere Hamiltonian with two
degrees of freedom.


  We study the injection mechanism of a single electron from a static quantum
dot into a moving quantum dot created in a long depleted channel with surface
acoustic waves (SAWs). We demonstrate that such a process is characterized by
an activation law with a threshold that depends on the SAW amplitude and the
dot-channel potential gradient. By increasing sufficiently the SAW modulation
amplitude, we can reach a regime where the transfer is unitary and potentially
adiabatic. This study points at the relevant regime to use moving dots in
quantum information protocols.


  The origin of room temperature (RT) ferromagneticlike behavior in ZnO-based
diluted magnetic semiconductors is still an unclear topic. The present work
concentrates on the appearance of RT magnetic moments in just mixed ZnO/Co3O4
mixtures without thermal treatment. In this study, it is shown that the
magnetism seems to be related to surface reduction of the Co3O4 nanoparticles,
in which, an antiferromagnetic Co3O4 nanoparticle (core) is surrounded by a
CoO-like shell. This singular superficial magnetism has also been found in
other mixtures with semiconductors such as TiO2 and insulators such as Al2O3.


  The essence of self-supervised image denoising is to restore the signal from
the noisy image alone. State-of-the-art solutions for this task rely on the
idea of masking pixels and training a fully-convolutional neural network to
impute them. This most often requires multiple forward passes, information
about the noise model, and intricate regularization functions. In this paper,
we propose a Swin Transformer-based Image Autoencoder (SwinIA), the first
convolution-free architecture for self-supervised denoising. It can be trained
end-to-end with a simple mean squared error loss without masking and does not
require any prior knowledge about clean data or noise distribution. Despite its
simplicity, SwinIA establishes state-of-the-art on several common benchmarks.


  We demonstrate that the classification of boosted, hadronically-decaying weak
gauge bosons can be significantly improved over traditional cut-based and
BDT-based methods using deep learning and the jet charge variable. We construct
binary taggers for $W^+$ vs. $W^-$ and $Z$ vs. $W$ discrimination, as well as
an overall ternary classifier for $W^+$/$W^-$/$Z$ discrimination. Besides a
simple convolutional neural network (CNN), we also explore a composite of two
CNNs, with different numbers of layers in the jet $p_{T}$ and jet charge
channels. We find that this novel structure boosts the performance particularly
when considering the $Z$ boson as signal. The methods presented here can
enhance the physics potential in SM measurements and searches for new physics
that are sensitive to the electric charge of weak gauge bosons.


  This paper proposes a fusion-based cooperative support identification scheme
for distributed compressive sparse signal recovery via resource-constrained
wireless sensor networks. The proposed support identification protocol
involves: (i) local sparse sensing for economizing data gathering and storage,
(ii) local binary decision making for partial support knowledge inference,
(iii) binary information exchange among active nodes, and (iv) binary data
aggregation for support estimation. Then, with the aid of the estimated signal
support, a refined local decision is made at each node. Only the measurements
of those informative nodes will be sent to the fusion center, which employs a
weighted $\ell_1$-minimization for global signal reconstruction. The design of
a Bayesian local decision rule is discussed, and the average communication cost
is analyzed. Computer simulations are used to illustrate the effectiveness of
the proposed scheme.


  Lie-Yamaguti algebras generalize both the notions of Lie algebras and Lie
triple systems. In this paper, we consider the inducibility problem for
automorphisms of extensions of Lie-Yamaguti algebras. More precisely, given an
abelian extension $$0 \to V \xrightarrow[]{i} \widetilde{L} \xrightarrow[]{p} L
\to 0$$ of a Lie-Yamaguti algebra $L$, we are interested in finding the pairs
$(\phi, \psi)\in \mathrm{Aut}(V)\times \mathrm{Aut}(L)$, which are inducible by
an automorphism in $\mathrm{Aut}(\widetilde{L})$. We connect the inducibility
problem to the $(2,3)$-cohomology of Lie-Yamaguti algebra. In particular, we
show that the obstruction for a pair of automorphism in $\mathrm{Aut}(V)\times
\mathrm{Aut}(L)$ to be inducible lies in the $(2,3)$-cohomology group
$\mathrm{H}^{(2,3)}(L,V)$. We develop the Wells exact sequence for Lie-Yamaguti
algebra extensions, which relates the space of derivations, automorphism
groups, and $(2,3)$-cohomology groups of Lie-Yamaguti algebras. As an
application, we describe certain automorphism groups of semi-direct product
Lie-Yamaguti algebras. In the sequel, we apply our results to discuss
inducibility problem for nilpotent Lie-Yamaguti algebras of index $2$. We give
examples of infinite families of such nilpotent Lie-Yamaguti algebras and
characterize the inducible pairs of automorphisms for extensions arising from
these examples. Finally, we write an algorithm to find out all the inducible
pairs of automorphisms for extensions arising from nilpotent Lie-Yamaguti
algebras of index $2$.


  Many recent observational studies have concluded that planetary systems
commonly exist in multiple-star systems. At least ~20%, and presumably a larger
fraction of the known extrasolar planetary systems are associated with one or
more stellar companions. These stellar companions normally exist at large
distances from the planetary systems (typical projected binary separations are
on the orders 100-10000AU) and are often faint (ranging from F to T spectral
types). Yet, secular cyclic angular momentum exchange with these distant
stellar companions can significantly alter the orbital configuration of the
planets around the primaries. One of the most interesting and fairly common
outcomes seen in numerical simulations is the opening of a large mutual
inclination angle between the planetary orbits, forced by differential nodal
precessions caused by the binary companion. The growth of the mutual
inclination angle between planetary orbits induces additional large-amplitude
eccentricity oscillations of the inner planet due to the quadrupole
gravitational perturbation by the outer planet. This eccentricity oscillation
may eventually result in the orbital decay of the inner planet through tidal
friction, as previously proposed as Kozai migration or Kozai cycles with tidal
friction (KCTF). This orbital decay mechanism induced by the binary
perturbation and subsequent tidal dissipation may stand as an alternative
formation channel for close-in extrasolar planets.


  In this paper we show that Sun-viewing images obtained by the Mars Science
Laboratory (MSL) Navigation Cameras (Navcam) can be used for retrieving the
dust optical depth and constrain the aerosol physical properties at Gale Crater
by evaluating the sky brightness as a function of the scattering angle. We have
used 65 Sun-pointing images covering a period of almost three Martian years,
from MSL mission sol 21 to sol 1646 (MY 31 to 33). Radiometric calibration and
geometric reduction were performed on MSL Navcam raw image data records to
provide the observed sky radiance as a function of the scattering angle for the
near-Sun region (scattering angle from 4{\deg} to 30{\deg}). These curves were
fitted with a multiple scattering radiative transfer model for a plane-parallel
Martian atmosphere model using the discrete ordinates method. Modelled sky
brightness curves were generated as a function of two parameters: the aerosol
particle size distribution effective radius and the dust column optical depth
at the surface. A retrieval scheme was implemented for deriving the parameters
that generated the best fitting curve under a least-square error criterion. The
obtained results present a good agreement with previous work, showing the
seasonal dependence of both dust column optical depth and the effectiveparticle
radius.


  We address the problem of coding for classical multiple-access channels
(MACs) with the assistance of non-signaling correlations between parties. It is
well-known that non-signaling assistance does not change the capacity of
classical point-to-point channels. However, it was recently observed that one
can construct MACs from two-player non-local games while relating the winning
probability of the game to the capacity of the MAC. By considering games for
which entanglement increases the winning probability, this shows that for some
specific kinds of channels, entanglement between the senders can increase the
capacity.
  We make several contributions towards understanding the capacity region for
MACs with the assistance of non-signaling correlations. We develop a linear
program computing the optimal success probability for coding over $n$ copies of
a MAC $W$ with size growing polynomially in $n$. Solving this linear program
allows us to achieve inner bounds for MACs. Applying this method to the binary
adder channel, we show that using non-signaling assistance, the sum-rate
$1.5425$ can be reached even with zero error, which beats the maximum sum-rate
capacity of $1.5$ in the unassisted case. For noisy channels, where the
zero-error non-signaling assisted capacity region is trivial, we can use
concatenated codes to obtain achievable points in the capacity region. Applied
to a noisy version of the binary adder channel, we show that non-signaling
assistance still improves the sum-rate capacity. Complementing these
achievability results, we give an outer bound on the non-signaling assisted
capacity region that has the same expression as the unassisted region except
that the channel inputs are not required to be independent. Finally, we show
that the capacity region with non-signaling assistance shared only between each
sender and the receiver independently is the same as without assistance.


  Banks, Douglas, Horowitz and Martinec [hep-th/9808016] recently argued that
in the microcanonical ensemble for string theory on AdS_m xS^n, there is a
phase transition between a black hole solution extended over the S^n and a
solution localized on the S^n. If we think of this AdS_m x S^n geometry as
arising from the near-horizon limit of a black m-2 brane, the existence of this
phase transition is puzzling. We present a resolution of this puzzle, and
discuss its significance from the point of view of the dual m-1 dimensional
field theory. We also discuss multi-black hole solutions in AdS.


  We have investigated how helium atoms are distributed within a mixed
$^3$He$_{N_3}$-$^4$He$_{N_4}$ large drop with $N_3 \gg N_4$. For drops doped
with a SF$_6$ molecule or a Xe atom, we have found that the number of $^3$He
atoms within the volume containing the first two solvation shells increases
when $N_4$ decreases in a way such that these dopants may be in a superfluid
environment for $N_4 \geq$ 60, which gradually disappears as $N_4$ decreases.
The result is in qualitative agreement with recent experimental data.


  In this paper, we consider the number of both arrivals and departures seen by
a tagged customer while in service in a classical $M/M/1$ processor sharing
queue. By exploiting the underlying orthogonal structure of this queuing system
revealed in an earlier study, we compute the distributions of these two
quantities and prove that they are equal in distribution. We moreover derive
the asymptotic behavior of this common distribution. The knowledge of the
number of departures seen by a tagged customer allows us to test the validity
of an approximation, which consists of assuming that the tagged customer is
randomly served among those customers in the residual busy period of the queue
following the arrival of the tagged customer. A numerical evidence shows that
this approximation is reasonable for moderate values of the number of
departures, given that the asymptotic behaviors of the distributions are very
different even if the exponential decay rates are equal.


  We present the novel concept of an Advanced de-Inclination Device (AID). This
technique holds the promise of instantaneously solving almost all issues
concerning the observed projection of astronomical sources on the plane of the
sky. Along with basic design considerations, we also outline several examples
for which this technique can be extremely useful. Simply put, the concept is
based on (abridged)


  High-throughput blockchains require efficient transaction broadcast
mechanisms that can deliver transactions to most network nodes with low
bandwidth overhead and latency. Existing schemes coordinate transmissions
across peers to avoid sending redundant data, but they either incur a high
latency or are not robust against adversarial network nodes. We present
Strokkur, a new transaction broadcasting mechanism that provides both low
bandwidth overhead and low latency. The core idea behind Strokkur is to avoid
explicit coordination through randomized transaction coding. Rather than
forward individual transactions. Strokkur nodes send out codewords -- XOR sums
of multiple transactions selected at random. Since almost every codeword is
useful for the receiver to decode new transactions, Strokkur nodes do not
require coordination, for example, to determine which transactions the receiver
is missing. Strokkur's coding strategy builds on LT codes, a popular class of
rateless erasure codes, and extends them to support multiple uncoordinated
senders with partially-overlapping continual streams of transaction data.
Strokkur introduces mechanisms to cope with adversarial senders that may send
corrupt codewords, and a simple rate control algorithm that enables each node
to independently determine an appropriate sending rate of codewords for each
peer. Our implementation of Strokkur in Golang supports 647k transactions per
second using only one CPU core. Our evaluation across a 19-node Internet
deployment and large-scale simulation show that Strokkur consumes 2--7.6x less
bandwidth than the existing scheme in Bitcoin, and 9x lower latency that Shrec
when only 4% of nodes are adversarial.


  We propose and analyze a method for improving quantum chemical energy
calculations on a quantum computer impaired by decoherence and shot noise. The
error mitigation approach relies on the fact that the one- and two-particle
reduced density matrices (1- and 2-RDM) of a chemical system need to obey
so-called N-representability constraints. We post-process the result of an RDM
measurement by projecting it into the subspace where certain N-representability
conditions are fulfilled. Furthermore, we utilize that such constraints also
hold in the hole and particle-hole sector and perform projections in these
sectors as well. We expand earlier work by conducting a careful analysis of the
method's performance in the context of quantum computing. Specifically, we
consider typical decoherence channels (dephasing, damping, and depolarizing
noise) as well as shot noise due to a finite number of projective measurements.
We provide analytical considerations and examine numerically three example
systems, \ch{H2}, \ch{LiH}, and \ch{BeH2}. From these investigations, we derive
our own practical yet effective method to best employ the various projection
options. Our results show the approach to significantly lower energy errors and
measurement variances of (simulated) quantum computations.


  Thermoacoustic instabilities observed in turbulent combustion systems have
disastrous consequences and are notoriously challenging to model, predict and
control. Here, we introduce a mean-field model of thermoacoustic transitions,
where the nonlinear flame response is modeled as the amplitude weighted
response of an ensemble of phase oscillators constrained to collectively evolve
at the rhythm of acoustic fluctuations. Starting from the acoustic wave
equation coupled with the phase oscillators, we derive the evolution equations
for the amplitude and phase and obtain the limit cycle solution. We show that
the model captures abrupt and continuous transition to thermoacoustic
instability observed in disparate combustors. We obtain quantitative insights
into the model by estimating the model parameters from the experimental data
using parameter optimisation. Importantly, our approach provides an explanation
of spatiotemporal synchronization and pattern-formation underlying the
transition to thermoacoustic instability while encapsulating the statistical
properties of desynchronization, chimeras, and global phase synchronization. We
further show using the model that continuous and abrupt transitions to limit
cycle oscillations in turbulent combustors corresponds to synchronization
transitions of \textit{second-order} and \textit{first-order}, respectively.
The present formulation provides a highly interpretable model of thermoacoustic
transitions: changes in empirical bifurcation parameters which lead to limit
cycle oscillations amounts to an increase in the coupling strength of the phase
oscillators, promoting global phase synchronization. The generality of the
model in capturing different types of transitions and states of
pattern-formation highlights the possibility of extending the present model to
a broad range of fluid-dynamical phenomena beyond thermoacoustics.


  Given a finite group $G$, the Engel graph of $G$ is a directed graph
$\Gamma(G)$ encoding pairs of elements satisfying some Engel word. Namely,
$\Gamma(G)$ is the directed graph, where the vertices are the non-hypercentral
elements of $G$ and where there is an arc from $x$ to $y$ if and only if $[x,_
n y] = 1$ for some $n \in \mathbb{N}$. From previous work, it is known that,
except for a few exceptions, $\Gamma(G)$ is strongly connected. In this paper,
we give an absolute upper bound on the diameter of $\Gamma(G)$, when
$\Gamma(G)$ is strongly connected.


  We report the outcome of the deep optical/infrared photometric survey of the
central region (33 X 33 arcmin or 0.9 pc^2) of the eta Chamaeleontis pre-main
sequence star cluster. The completeness limits of the photometry are I = 19.1,
J = 18.2 and H = 17.6; faint enough to reveal low mass members down to the
brown dwarf and planet boundary of ~ 13 M_Jup. We found no such low mass
members in this region. Our result combined with a previous shallower (I = 17)
but larger area survey indicates that low mass objects (0.013 < M/M(solar mass)
< 0.075) either were not created in the eta Cha cluster or were lost due to the
early dynamical history of the cluster and ejected to outside the surveyed
areas.


  We establish the large deviation principle (LDP) for stochastic flows of
interacting Brownian motions. In particular, we consider smoothly correlated
flows, coalescing flows and Brownian motion stopped at a hitting moment.


  Several recent surprises appear dramatically to have improved the likelihood
that the spin of rapidly rotating, newly formed neutron stars (and, possibly,
of old stars spun up by accretion) is limited by a nonaxisymmetric instability
driven by gravitational waves. Except for the earliest part of the spin-down,
the axial l=m=2 mode (an r-mode) dominates the instability, and the emitted
waves may be observable by detectors with the sensitivity of LIGO II. A review
of these hopeful results is followed by a discussion of constraints on the
instability set by dissipative mechanisms, including viscosity, nonlinear
saturation, and energy loss to a magnetic field driven by differential
rotation.


  Recently a novel framework has been proposed for designing the molecular
structure of chemical compounds using both artificial neural networks (ANNs)
and mixed integer linear programming (MILP). In the framework, we first define
a feature vector $f(C)$ of a chemical graph $C$ and construct an ANN that maps
$x=f(C)$ to a predicted value $\eta(x)$ of a chemical property $\pi$ to $C$.
After this, we formulate an MILP that simulates the computation process of
$f(C)$ from $C$ and that of $\eta(x)$ from $x$. Given a target value $y^*$ of
the chemical property $\pi$, we infer a chemical graph $C^\dagger$ such that
$\eta(f(C^\dagger))=y^*$ by solving the MILP. In this paper, we use linear
regression to construct a prediction function $\eta$ instead of ANNs. For this,
we derive an MILP formulation that simulates the computation process of a
prediction function by linear regression. The results of computational
experiments suggest our method can infer chemical graphs with around up to 50
non-hydrogen atoms.


  We introduce a new family of MCMC samplers that combine auxiliary variables,
Gibbs sampling and Taylor expansions of the target density. Our approach
permits the marginalisation over the auxiliary variables yielding marginal
samplers, or the augmentation of the auxiliary variables, yielding auxiliary
samplers. The well-known Metropolis-adjusted Langevin algorithm (MALA) and
preconditioned Crank-Nicolson Langevin (pCNL) algorithm are shown to be special
cases. We prove that marginal samplers are superior in terms of asymptotic
variance and demonstrate cases where they are slower in computing time compared
to auxiliary samplers. In the context of latent Gaussian models we propose new
auxiliary and marginal samplers whose implementation requires a single tuning
parameter, which can be found automatically during the transient phase.
Extensive experimentation shows that the increase in efficiency (measured as
effective sample size per unit of computing time) relative to (optimised
implementations of) pCNL, elliptical slice sampling and MALA ranges from
10-fold in binary classification problems to 25-fold in log-Gaussian Cox
processes to 100-fold in Gaussian process regression, and it is on par with
Riemann manifold Hamiltonian Monte Carlo in an example where the latter has the
same complexity as the aforementioned algorithms. We explain this remarkable
improvement in terms of the way alternative samplers try to approximate the
eigenvalues of the target. We introduce a novel MCMC sampling scheme for
hyperparameter learning that builds upon the auxiliary samplers. The MATLAB
code for reproducing the experiments in the article is publicly available and a
Supplement to this article contains additional experiments and implementation
details.


  We demonstrate that local modulations of magnetic couplings have a profound
effect on the temperature dependence of the relaxation rate of optical magnons
in a wide class of antiferromagnets in which gapped excitations coexist with
acoustic spin waves. In a two-dimensional collinear antiferromagnet with an
easy-plane anisotropy, the disorder-induced relaxation rate of the gapped mode,
Gamma_imp=Gamma_0+A(TlnT)^2, greatly exceeds the magnon-magnon damping,
Gamma_m-m=BT^5, negligible at low temperatures. We measure the lifetime of
gapped magnons in a prototype XY antiferromagnet BaNi2(PO4)2 using a
high-resolution neutron-resonance spin-echo technique and find experimental
data in close accord with the theoretical prediction. Similarly strong effects
of disorder in the three-dimensional case and in noncollinear antiferromagnets
are discussed.


  Accessing the physics of strongly coupled metals in a controlled way is a
challenging problem in theoretical condensed matter physics. In this paper, we
revisit the possibility of understanding strongly coupled metals through a
holographic duality with a weakly coupled gravitational theory in one higher
dimension (i.e.\ a suitable generalization of the "AdS/CFT duality". Previous
attempts at devising holographic models of strongly coupled metals have
suffered from severe drawbacks; for example, they do not even seem to be able
to describe a Fermi surface that satisfies Luttinger's theorem, which is ought
to be a core requirement in any physically reasonable model of a metal. Here,
we propose a radically different approach to constructing holographic models of
strongly coupled metals. The idea is that for applications, it should be
sufficient to construct a holographic dual of the effective field theory that
controls the infra-red physics of the metal. We invoke recent work that has
identified a precise criterion for such an effective field theory to be
"emergeable" from a continuum ultra-violet (UV) theory at nonzero charge
density (or its equivalent in lattice models, namely an incommensurate charge
filling). We show that imposing this criterion leads to a holographic model of
a strongly coupled metal with physically reasonable properties, including a
Fermi surface satisfying Luttinger's theorem. We discuss a possible physical
interpretation of our results.


  In this paper we study the simplifying effects of supersymmetry on celestial
OPEs at both tree and loop level. We find at tree level that theories with
unbroken supersymmetry around a stable vacuum have celestial soft current
algebras satisfying the Jacobi identity, and we show at one loop that celestial
OPEs in these theories have no double poles.


  The percentage of urban areas dedicated to streets and public spaces is a
crucial feature of the spatial planning of cities. By 2050 urban mobility will
be one of the biggest confronts for global cities. This study has been carried
out to assess the ratio of public space allocated to the streets in some cities
of Bangladesh. The length, width, area, and number of street crossings have
been counted for the city core and its suburban area as an indicator of the
form and pattern of the street layout. This exercise does not consider the rest
of the public spaces, like gardens and general public spaces for amenities,
including sports. The methodology of data collection has been through Google
Earth and GIS software. For precise results, other more sophisticated software
is essential. Nevertheless, even at these levels of precision, very interesting
city patterns emerge. The findings of this study show that Bogra, Rangpur, and
Dinajpur are better cities having higher land allocation rates for streets,
including sufficient crossings. Nilphamari and Thakurgaon have lower land areas
for roads; tend to have lower connectivity and productivity. The rest of the
cities have an average land area for roads and an average number of street
crossings.


  Motivated by entropic optimal transport, time reversal of Markov jump
processes in $\mathbb{R}^n$ is investigated. Relying on an abstract integration
by parts formula for the carr\'e du champ of a Markov process recently obtained
by Cattiaux, Gentil and the authors, and using an entropic improvement strategy
discovered by F\"ollmer in the eighties, we compute the semimartingale
characteristics of the time reversed process for a wide class of jump processes
with possibly unbounded variation sample paths and singular intensities of
jump.


  In quantum metrology, the Holevo Cram\'er-Rao bound has attracted renewed
interest in recent years due to its superiority over the Helstrom Cram\'er-Rao
bound and its asymptotic attainability for multi-parameter estimation. Its
evaluation, however, is often much more difficult than that of the Helstrom
version, calling into question the actual improvement offered by the Holevo CRB
and whether it is worth the trouble. Here I prove that the Holevo bound is at
most thrice the Helstrom version, so the improvement must be limited. The
result also shows that the Helstrom version remains a pretty good bound even
for multiple parameters and can be approached asymptotically to within a factor
of 3.


  Let R* be an ideal-adic completion of a Noetherian integral domain R and let
L be a subfield of the total quotient ring of R* such that L contains R. Let A
denote the intersection of L with R*. The integral domain A sometimes inherits
nice properties from R* such as the Noetherian property. For certain fields L
it is possible to approximate A using a localzation B of a nested union of
polynomial rings over R associated to A; if B is Noetherian, then B = A. If B
is not Noetherian, we can sometimes identify the prime ideals of B that are not
finitely generated. We have obtained in this way, for each positive integer s,
a 3-dimensional local unique factorization domain B such that the maximal ideal
of B is 2-generated, B has precisely s prime ideals of height 2, each prime
ideal of B of height 2 is not finitely generated and all the other prime ideals
of B are finitely generated. We examine the map Spec A to Spec B for this
example. We also present a generalization of this example to dimension 4. We
describe a 4-dimensional local non-Noetherian UFD B such that the maximal ideal
of B is 3-generated, there exists precisely one prime ideal Q of B of height 3,
the prime ideal Q is not finitely generated. We consider the question of
whether Q is the only prime ideal of B that is not finitely generated, but have
not answered this question.


  We perform an exact enumeration study of polymers formed from a (quenched)
random sequence of charged monomers $\pm q_0$. Such polymers, known as
polyampholytes, are compact when completely neutral and expanded when highly
charged. Our exhaustive search included all spatial conformations and quenched
sequences for up to 12--step (13--site) walks. We investigate the behavior of
the polymer as a function of its overall excess charge $Q$, and temperature
$T$. At low temperatures there is a phase transition from compact to extended
configurations when the charge exceeds $Q_c\approx q_0 \sqrt{N}$. There are
also indications of a transition for small $Q$ between two compact states on
varying temperature. Numerical estimates are provided for the condensation
energy, surface tension, and the critical exponent $\nu$.


  We study principal curvatures of fibers and Heegaard surfaces smoothly
embedded in hyperbolic 3-manifolds. It is well known that a fiber or a Heegaard
surface in a hyperbolic 3-manifold cannot have principal curvatures everywhere
less than one in absolute value. We show that given an upper bound on the genus
of a minimally embedded fiber or Heegaard surface and a lower bound on the
injectivity radius of the hyperbolic 3-manifold, there exists a $\delta > 0$
such that the fiber or Heegaard surface must contain a point at which one of
the principal curvatures is greater than $1 + \delta$ in absolute value.


  Interpretations of exoplanetary transmission spectra have been undermined by
apparent obscuration due to clouds/hazes. Debate rages on whether weak H$_2$O
features seen in exoplanet spectra are due to clouds or inherently depleted
oxygen. Assertions of solar H$_2$O abundances have relied on making a priori
model assumptions, e.g. chemical/radiative equilibrium. In this work, we
attempt to address this problem with a new retrieval paradigm for transmission
spectra. We introduce POSEIDON, a two-dimensional atmospheric retrieval
algorithm including generalised inhomogeneous clouds. We demonstrate that this
prescription allows one to break vital degeneracies between clouds and
prominent molecular abundances. We apply POSEIDON to the best transmission
spectrum presently available, for the hot Jupiter HD 209458b, uncovering new
insights into its atmosphere at the day-night terminator. We extensively
explore the parameter space with an unprecedented 10$^8$ models, spanning the
continuum from fully cloudy to cloud-free atmospheres, in a fully Bayesian
retrieval framework. We report the first detection of nitrogen chemistry
(NH$_3$ and/or HCN) in an exoplanet atmosphere at 3.7-7.7$\sigma$ confidence,
non-uniform cloud coverage at 4.5-5.4$\sigma$, high-altitude hazes at
$>$3$\sigma$, and sub-solar H$_2$O at $\gtrsim$3-5$\sigma$, depending on the
assumed cloud distribution. We detect NH$_3$ at 3.3$\sigma$ and 4.9$\sigma$ for
fully cloudy and cloud-free scenarios, respectively. For the model with the
highest Bayesian evidence, we constrain H$_2$O at 5-15 ppm (0.01-0.03$\times$
solar) and NH$_3$ at 0.01-2.7 ppm, strongly suggesting disequilibrium chemistry
and cautioning against equilibrium assumptions. Our results herald new promise
for retrieving cloudy atmospheres using high-precision HST and JWST spectra.


  This report contains the description of two novel job shop scheduling
benchmarks that resemble instances of real scheduling problem as they appear in
industry. In particular, the aim was to provide large-scale benchmarks (up to 1
million operations) to test the state-of-the-art scheduling solutions on
problems that are closer to what occurs in a real industrial context. The first
benchmark is an extension of the well known Taillard benchmark (1992), while
the second is a collection of scheduling instances with a known-optimum
solution.


  We present the results of an infrared spectroscopic survey of 23 late-M
dwarfs with the NIRSPEC echelle spectrometer on the Keck II telescope. Using
telluric lines for wavelength calibration, we are able to achieve measurement
precisions of down to 45 m/s for our late-M dwarfs over a one to four year-long
baseline. Our sample contains two stars with RV variations of >1000 m/s. While
we require more measurements to determine whether these RV variations are due
to unseen planetary or stellar companions or are the result of starspots known
to plague the surface of M dwarfs, we can place upper limits of <40 MJsini on
the masses of any companions around those two M dwarfs with RV variations of
<160 m/s at orbital periods of 10-100 days. We have also measured the
rotational velocities for all the stars in our late-M dwarf sample and offer
our multi-order, high-resolution spectra over 2.0 to 2.4 micron to the
atmospheric modeling community to better understand the atmospheres of late-M
dwarfs.


  Problem 21 of Brauer's list of problems from 1963 asks whether for any
positive integer k there are finitely many isomorphism classes of groups that
occur as the defect group of a block with k irreducible characters. We solve
this problem for principal blocks. Another long-standing open problem (from
1982) in this area asks whether the defect group of a block with 3 irreducible
characters is necessarily the cyclic group of order 3. In most cases we reduce
this problem to a question on simple groups that is closely related to the
recent solution of Brauer's height zero conjecture.


  This paper reports the experimental determination of the phase-breaking
length L of conduction electrons in graphene using Raman spectroscopy. Based on
the double-resonance model, we extract L from the spatial dependence of the D
band susceptibility near the graphene edge. By using prior knowledge of sample
properties and the excitation point-spread function we are able to determine
the spatial variation of the Raman susceptibilities with high accuracy, and the
results reveal a phase-breaking length L~40nm near the graphene edge.


  We herewith attempt to investigate the cosmic rays behavior regarding the
scaling features of their time series. Our analysis is based on cosmic ray
observations made at four neutron monitor stations in Athens (Greece), Jung
(Switzerland) and Oulu (Finland), for the period 2000 to early 2017. Each of
these datasets was analyzed by using the Detrended Fluctuation Analysis (DFA)
and Multifractal Detrended Fluctuation Analysis (MF-DFA) in order to
investigate intrinsic properties, like self-similarity and the spectrum of
singularities. The main result obtained is that the cosmic rays time series at
all the neutron monitor stations exhibit positive long-range correlations (of
1/f type) with multifractal behavior. On the other hand, we try to investigate
the possible existence of similar scaling features in the time series of other
meteorological parameters which are closely associated with the cosmic rays,
such as parameters describing physical properties of clouds.


  We observed elastic collisions between laser-cooled fermionic lithium atoms
and calcium ions at the energy range from 100 mK to 3 K. Lithium atoms in an
optical-dipole trap were transported to the center of the ion trap using an
optical tweezer technique, and a spatial overlap of the atoms and ions was
realized in order to observe the atom-ion interactions. The elastic scattering
rate was determined from the decay of atoms due to elastic collisions with
ions. The collision-energy dependence of the elastic scattering cross-section
was consistent with semi-classical collision theory.


  In membership inference attacks (MIAs), an adversary observes the predictions
of a model to determine whether a sample is part of the model's training data.
Existing MIA defenses conceal the presence of a target sample through strong
regularization, knowledge distillation, confidence masking, or differential
privacy.
  We propose MIAShield, a new MIA defense based on preemptive exclusion of
member samples instead of masking the presence of a member. The key insight in
MIAShield is weakening the strong membership signal that stems from the
presence of a target sample by preemptively excluding it at prediction time
without compromising model utility. To that end, we design and evaluate a suite
of preemptive exclusion oracles leveraging model-confidence, exact or
approximate sample signature, and learning-based exclusion of member data
points. To be practical, MIAShield splits a training data into disjoint subsets
and trains each subset to build an ensemble of models. The disjointedness of
subsets ensures that a target sample belongs to only one subset, which isolates
the sample to facilitate the preemptive exclusion goal.
  We evaluate MIAShield on three benchmark image classification datasets. We
show that MIAShield effectively mitigates membership inference (near random
guess) for a wide range of MIAs, achieves far better privacy-utility trade-off
compared with state-of-the-art defenses, and remains resilient against an
adaptive adversary.


  Orbital angular momentum of light is a core feature in photonics. Its
confinement to surfaces using plasmonics has unlocked many phenomena and
potential applications. Here we introduce the reflection from structural
boundaries as a new degree of freedom to generate and control plasmonic orbital
angular momentum. We experimentally demonstrate plasmonic vortex cavities,
generating a succession of vortex pulses with increasing topological charge as
a function of time. We track the spatio-temporal dynamics of these angularly
decelerating plasmon pulse train within the cavities for over 300 femtoseconds
using time-resolved Photoemission Electron Microscopy, showing that the angular
momentum grows by multiples of the chiral order of the cavity. The introduction
of this degree of freedom to tame orbital angular momentum delivered by
plasmonic vortices, could miniaturize pump-probe-like quantum initialization
schemes, increase the torque exerted by plasmonic tweezers and potentially
achieve vortex lattice cavities with dynamically evolving topology.


  This paper proposes SEFGAN, a Deep Neural Network (DNN) combining maximum
likelihood training and Generative Adversarial Networks (GANs) for efficient
speech enhancement (SE). For this, a DNN is trained to synthesize the enhanced
speech conditioned on noisy speech using a Normalizing Flow (NF) as generator
in a GAN framework. While the combination of likelihood models and GANs is not
trivial, SEFGAN demonstrates that a hybrid adversarial and maximum likelihood
training approach enables the model to maintain high quality audio generation
and log-likelihood estimation. Our experiments indicate that this approach
strongly outperforms the baseline NF-based model without introducing additional
complexity to the enhancement network. A comparison using computational metrics
and a listening experiment reveals that SEFGAN is competitive with other
state-of-the-art models.


  A high NMR detection sensitivity is indispensable when dealing with mass and
volume-limited samples, or whenever a high spatial resolution is required. The
use of miniaturised RF coils is a proven way to increase sensitivity, but may
be impractical and is not applicable to every experimental situation. We
present the use of magnetic lenses, denoted as Lenz lenses due to their working
principle, to focus the magnetic flux of a macroscopic RF coil into a smaller
volume and thereby locally enhance the sensitivity of the NMR experiment - at
the expense of the total sensitive volume. Besides focusing, such lenses
facilitate re-guiding or re-shaping of magnetic fields much like optical lenses
do with light beams. For the first time we experimentally demonstrate the use
of Lenz lenses in magnetic resonance and provide a compact mathematical
description of the working principle. Through simulations we show that optimal
arrangements can be found.


  The identification of sources of advection-diffusion transport is based
usually on solving complex ill-posed inverse models against the available
state- variable data records. However, if there are several sources with
different locations and strengths, the data records represent mixtures rather
than the separate influences of the original sources. Importantly, the number
of these original release sources is typically unknown, which hinders
reliability of the classical inverse-model analyses. To address this challenge,
we present here a novel hybrid method for identification of the unknown number
of release sources. Our hybrid method, called HNMF, couples unsupervised
learning based on Nonnegative Matrix Factorization (NMF) and inverse-analysis
Green functions method. HNMF synergistically performs decomposition of the
recorded mixtures, finds the number of the unknown sources and uses the Green
function of advection-diffusion equation to identify their characteristics. In
the paper, we introduce the method and demonstrate that it is capable of
identifying the advection velocity and dispersivity of the medium as well as
the unknown number, locations, and properties of various sets of synthetic
release sources with different space and time dependencies, based only on the
recorded data. HNMF can be applied directly to any problem controlled by a
partial-differential parabolic equation where mixtures of an unknown number of
sources are measured at multiple locations.


  The ability to manipulate the propagation of waves on subwavelength scales is
important for many different physical applications. In this paper, we consider
a honeycomb lattice of subwavelength resonators and prove, for the first time,
the existence of a Dirac dispersion cone at subwavelength scales. As shown in
[H. Ammari et al., A high-frequency homogenization approach near the Dirac
points in bubbly honeycomb crystals, arXiv:1812.06178], near the Dirac points,
honeycomb crystals of subwavelength resonators has a great potential to be used
as near-zero materials. Here, we perform the analysis for the example of bubbly
crystals, which is a classic example of subwavelength resonance, where the
resonant frequency of a single bubble is known as the Minnaert resonant
frequency. Our first result is an asymptotic formula for the quasi-periodic
Minnaert resonant frequencies. We then prove the linear dispersion relation of
a Dirac cone. Our findings in this paper are numerically illustrated in the
case of circular bubbles, where the multipole expansion method provides an
efficient technique for computing the band structure.


  The large-scale/long-time transport of inertial particles of arbitrary mass
density under gravity is investigated by means of a formal multiple-scale
perturbative expansion in the scale-separation parametre between the carrier
flow and the particle concentration field. The resulting large-scale equation
for the particle concentration is determined, and is found to be diffusive with
a positive-definite eddy diffusivity. The calculation of the latter tensor is
reduced to the resolution of an auxiliary differential problem, consisting of a
coupled set of two differential equations in a (6+1)-dimensional coordinate
system (3 space coordinates plus 3 velocity coordinates plus time). Although
expensive, numerical methods can be exploited to obtain the eddy diffusivity,
for any desirable non-perturbative limit (e.g. arbitrary Stokes and Froude
numbers). The aforementioned large-scale equation is then specialized to deal
with two different relevant perturbative limits: i) vanishing of both Stokes
time and sedimenting particle velocity; ii) vanishing Stokes time and finite
sedimenting particle velocity. Both asymptotics lead to a greatly simplified
auxiliary differential problem, now involving only space coordinates and thus
easy to be tackled by standard numerical techniques. Explicit, exact
expressions for the eddy diffusivities have been calculated, for both
asymptotics, for the class of parallel flows, both static and time-dependent.
This allows us to investigate analytically the role of gravity and inertia on
the diffusion process by varying relevant features of the carrier flow, as e.g.
the form of its temporal correlation function. Our results exclude a universal
role played by gravity and inertia on the diffusive behaviour: regimes of both
enhanced and reduced diffusion may exist, depending on the detailed structure
of the carrier flow.


  An explicit, detailed evaluation of the classical continuum limit of the
axial anomaly/index density of the overlap Dirac operator is carried out in the
infinite volume setting, and in a certain finite volume setting where the
continuum limit involves an infinite volume limit. Our approach is based on a
novel power series expansion of the overlap Dirac operator. The correct
continuum expression is reproduced when the parameter $m_0$ is in the physical
region $0<m_0<2$. This is established for a broad range of continuum gauge
fields. An analogous result for the fermionic topological charge, given by the
index of the overlap Dirac operator, is then established for a class of
topologically non-trivial fields in the aforementioned finite volume setting.
Problematic issues concerning the index in the infinite volume setting are also
discussed.


  In recent years, several point cloud geometry compression methods that
utilize advanced deep learning techniques have been proposed, but there are
limited works on attribute compression, especially lossless compression. In
this work, we build an end-to-end multiscale point cloud attribute coding
method (MNeT) that progressively projects the attributes onto multiscale latent
spaces. The multiscale architecture provides an accurate context for the
attribute probability modeling and thus minimizes the coding bitrate with a
single network prediction. Besides, our method allows scalable coding that
lower quality versions can be easily extracted from the losslessly compressed
bitstream. We validate our method on a set of point clouds from MVUB and MPEG
and show that our method outperforms recently proposed methods and on par with
the latest G-PCC version 14. Besides, our coding time is substantially faster
than G-PCC.


  We review the status of the fourth-order (quartic in the spacetime curvature)
terms induced by superstrings/M-theory (compactified on a warped torus) in the
leading order with respect to the Regge slope parameter, and study their
(non-perturbative) impact on the evolution of the Hubble scale in the context
of the four-dimensional FRW cosmology. After taking into account the quantum
ambiguities in the definition of the off-shell superstring effective action, we
propose the generalized Friedmann equations, find the existence of their (de
Sitter) exact inflationary solutions without a spacetime singularity, and
constrain the ambiguities by demanding stability and the scale factor duality
invariance of our solutions. The most naive (Bel-Robinson tensor squared)
quartic terms are ruled out, thus giving the evidence for the necessity of
extra quartic (Ricci tensor-dependent) terms in the off-shell gravitational
effective action for superstrings. Our methods are generalizable to the higher
orders in the spacetime curvature.


  Deep-learning techniques, particularly the transformer model, have shown
great potential in enhancing the prediction performance of longitudinal health
records. While previous methods have mainly focused on fixed-time risk
prediction, time-to-event prediction (also known as survival analysis) is often
more appropriate for clinical scenarios. Here, we present a novel deep-learning
architecture we named STRAFE, a generalizable survival analysis
transformer-based architecture for electronic health records. The performance
of STRAFE was evaluated using a real-world claim dataset of over 130,000
individuals with stage 3 chronic kidney disease (CKD) and was found to
outperform other time-to-event prediction algorithms in predicting the exact
time of deterioration to stage 5. Additionally, STRAFE was found to outperform
binary outcome algorithms in predicting fixed-time risk, possibly due to its
ability to train on censored data. We show that STRAFE predictions can improve
the positive predictive value of high-risk patients by 3-fold, demonstrating
possible usage to improve targeting for intervention programs. Finally, we
suggest a novel visualization approach to predictions on a per-patient basis.
In conclusion, STRAFE is a cutting-edge time-to-event prediction algorithm that
has the potential to enhance risk predictions in large claims datasets.


  We show that the presence of a magnetic monopole in position space gives rise
to a violation of the fermion number conservation in chiral matter. Using the
chiral kinetic theory, we derive a model-independent expression of such a
violation in nonequilibrium many-body systems of chiral fermions. In local
thermal equilibrium at finite temperature and chemical potential, in
particular, this violation is proportional to the chemical potential with a
topologically quantized coefficient. These consequences are due to the
interplay between the Dirac monopole in position space and the Berry monopole
in momentum space. Our mechanism can be applied to study the roles of magnetic
monopoles in the nonequilibrium evolution of the early Universe.


  We introduce a class of neural networks derived from probabilistic models in
the form of Bayesian networks. By imposing additional assumptions about the
nature of the probabilistic models represented in the networks, we derive
neural networks with standard dynamics that require no training to determine
the synaptic weights, that perform accurate calculation of the mean values of
the random variables, that can pool multiple sources of evidence, and that deal
cleanly and consistently with inconsistent or contradictory evidence. The
presented neural networks capture many properties of Bayesian networks,
providing distributed versions of probabilistic models.


  The role of matter enhanced resonant neutrino oscillations in reviving a
stalled shock in a type-II supernova through delayed neutrino heating is
investigated. The extent of neutrino heating is estimated for the allowed
possibility of complete flavour conversion self-consistently with the changes
in nuclear equilibrium. The average internal energy per nucleon is
substantially increased indicating the possiblty of a robust explosion.


  The magnetic insulator yttrium iron garnet (YIG) with a ferrimagnetic
transition temperature of $\sim$560 K has been widely used in microwave and
spintronic devices. Anomalous features in the spin Seeback effect (SSE)
voltages have been observed in Pt/YIG and attributed to the magnon-phonon
coupling. Here we use inelastic neutron scattering to map out low-energy spin
waves and acoustic phonons of YIG at 100 K as a function of increasing magnetic
field. By comparing the zero and 9.1 T data, we find that instead of splitting
and opening up gaps at the spin wave and acoustic phonon dispersion
intersecting points, magnon-phonon coupling in YIG enhances the hybridized
scattering intensity. These results are different from expectations of
conventional spin-lattice coupling, calling for new paradigms to understand the
scattering process of magnon-phonon interactions and the resulting
magnon-polarons.


  The Nobel Prize winning Black-Scholes equation for stock options and the heat
equation can both be written in the form \[ \frac{\partial u}{\partial
t}=P_2(A)u, \] where $P_2(z)=\alpha z^2+ \beta z+\gamma$ is a quadratic
polynomial with $\alpha > 0$. In fact, taking $A = x\frac{\partial}{\partial
x}$ on functions on $[0,\infty) \times [0,\infty)$ the previous equality
reduces to the Black-Scholes equation, while taking $A =
\frac{\partial}{\partial x}$ for functions on $\mathbb{R} \times [0,\infty)$ it
becomes the heat equation. Here, we ``connect'' the two previous problems by
considering the generalized operator $A= x^a\frac{\partial}{\partial x}$ for
functions on $[0,\infty) \times [0,\infty)$ with $0<a<1$, and our main result
is that the corresponding degenerate parabolic equation is governed by a
semigroup of operators which is chaotic on a class of Banach spaces. The
relevant Banach spaces are weighted supremum norm spaces of continuous
functions on $[0,\infty)$. This paper unifies, simplifies and significantly
extends earlier results obtained for the Black-Scholes equation ($a=1$) in
\cite{EGG} and the heat equation ($a=0$) in \cite{EGG1}.