  We show that any proper coloring of a Kneser graph $KG_{n,k}$ with $n-2k+2$
colors contains a trivial color (i.e., a color consisting of sets that all
contain a fixed element), provided $n>(2+\varepsilon)k^2$, where
$\varepsilon\to 0$ as $k\to \infty$. This bound is essentially tight. This is a
consequence of a more general result on the minimum number of non-trivial
colors needed to properly color $KG_{n,k}$.


  A method is presented to calculate the spin relaxation times T1, T2 due to a
non-uniform magnetic field, and the linear-in-electric-field precession
frequency shift {\delta}{\omega}E when an electric field is present, in the
diffusion approximation for spins confined to a rectangular cell. It is found
that the rectangular cell geometry admits of a general result for T1, T2, and
{\delta}{\omega}E in terms of the spatial cosine-transform components of the
magnetic field.


  Density functional theory (DFT) is used in thousands of papers each year, yet
lack of universality reduces DFT's predictive capacity, and functionals may
produce energy-density imbalances. The absolute electronegativity (\chi) and
hardness (\eta) directly reflect the energy-density relationship via the
chemical potential dE/dN and we thus hypothesized that they probe universality.
We studied \chi and \eta for atoms Z = 1-36 using 50 diverse functionals
covering all major classes. Very few functionals describe both \chi and \eta
well. \eta benefits from error cancelation whereas \chi is marred by error
propagation from IP and EA; thus almost all standard GGA and hybrid functionals
display a plateau in the MAE at 0.2-0.3 eV for \eta. In contrast, variable
performance for \chi indicates problems in describing the chemical potential by
DFT. The accuracy and precision of a functional is far from linearly related,
yet for a universal functional we expect linearity. Popular functionals such as
B3LYP, PBE, and revPBE, perform poorly for both properties. Density sensitivity
calculations indicate large density-derived errors as occupation of degenerate
p- and d-orbitals causes "non-universality" and large dependency on exact
exchange. Thus, we argue that performance for \chi for the same systems is a
hallmark of universality by probing dE/dN. With this metric, B98, B97-1,
PW6B95D3, APFD are the most "universal" tested functionals. B98 and B97-1 are
accurate for very diverse metal-ligand bonds, supporting that a balanced
description of dE/dN and dE2/dN2, via \chi and \eta, is probably a first simple
probe of universality.


  R. P. Stanley proved the Upper Bound Conjecture in 1975. We imitate his proof
for the Ehrhart rings. We give some upper bounds for the volume of integrally
closed lattice polytopes. We derive some inequalities for the delta-vector of
integrally closed lattice polytopes. Finally we apply our results for reflexive
integrally closed and order polytopes.


  In this paper we give a new, less restrictive condition for removability of
singular sets, $E$, of smooth solutions to the m-Hessian equation (and also for
more general fully nonlinear elliptic equations) in $\Omega \setminus E$,
$\Omega \subset \mathbb R^n$. Besides the existence and regularity results for
these equations, the proof only makes use of the classical elliptic theory,
i.e. the classical maximum principles and a Hopf lemma.


  In a wide range of statistical learning problems such as ranking, clustering
or metric learning among others, the risk is accurately estimated by
$U$-statistics of degree $d\geq 1$, i.e. functionals of the training data with
low variance that take the form of averages over $k$-tuples. From a
computational perspective, the calculation of such statistics is highly
expensive even for a moderate sample size $n$, as it requires averaging
$O(n^d)$ terms. This makes learning procedures relying on the optimization of
such data functionals hardly feasible in practice. It is the major goal of this
paper to show that, strikingly, such empirical risks can be replaced by
drastically computationally simpler Monte-Carlo estimates based on $O(n)$ terms
only, usually referred to as incomplete $U$-statistics, without damaging the
$O_{\mathbb{P}}(1/\sqrt{n})$ learning rate of Empirical Risk Minimization (ERM)
procedures. For this purpose, we establish uniform deviation results describing
the error made when approximating a $U$-process by its incomplete version under
appropriate complexity assumptions. Extensions to model selection, fast rate
situations and various sampling techniques are also considered, as well as an
application to stochastic gradient descent for ERM. Finally, numerical examples
are displayed in order to provide strong empirical evidence that the approach
we promote largely surpasses more naive subsampling techniques.


  Comprehensive investigations on ZnSe/GaAs and GaAs/ZnSe interfaces were
carried out by photoluminescence (PL) and transmission electron microscopy
(TEM), as a part of realizing high quality ZnSe-GaAs (100) hetero-valent
structures (HS). The nature of ZnSe/GaAs interface under different surface
terminations of GaAs was examined. The ZnSe/Ga-terminated GaAs was found to
have a superior optical and microstructural quality, with a chemical interface
consisting of a mixture of both the GaAs and ZnSe atomic constituents. For
GaAs/ZnSe interface studies, a low-temperature migration enhanced epitaxy
(LT-MEE) growth technique was used to grow GaAs layers under the conditions
compatible to the growth of ZnSe. Both Ga and As-initialized LT-MEE GaAs/ZnSe
interfaces were investigated. A defective transition layer was observed along
the As-initialized GaAs/ZnSe interface, which may be attributed to the
formation Zn$_3$As$_2$ compound. The correlation between the observed optical
as well as structural properties of both the (GaAs/ZnSe and ZnSe/GaAs)
interfaces and the growth conditions used in this study are discussed in
detail. This study could provide a valuable insight on the interface nature of
ZnSe-GaAs HS.


  One prediction of particle acceleration in the supernova remnants in the
magnetic wind of exploding Wolf Rayet and Red Super Giant stars is that the
final spectrum is a composition of a spectrum $E^{-7/3}$ and a polar cap
component of $E^{-2}$ at the source. This polar cap component contributes to
the total energy content with only a few percent, but dominates the spectrum at
higher energy. The sum of both components gives spectra which curve upwards.
The upturn was predicted to occur always at the same rigidity. An additional
component of cosmic rays from acceleration by supernovae exploding into the
Inter-Stellar Medium (ISM) adds another component for Hydrogen and for Helium.
After transport the predicted spectra $J(E)$ for the wind-SN cosmic rays are
$E^{-8/3}$ and $E^{-7/3}$; the sum leads to an upturn from the steeper
spectrum. An upturn has now been seen. Here, we test the observations against
the predictions, and show that the observed properties are consistent with the
predictions. Hydrogen can be shown to also have a noticeable wind-SN-component.
The observation of the upturn in the heavy element spectra being compatible
with the same rigidity for all heavy elements supports the magneto-rotational
mechanism for these supernovae. This interpretation predicts the observed
upturn to continue to curve upwards and approach the $E^{-7/3}$ spectrum. If
confirmed, this would strengthen the case that supernovae of very massive stars
with magnetic winds are important sources of Galactic cosmic rays.


  Inflationary correlation functions are potentially affected by infrared
divergences. For example, the two-point correlator of curvature perturbation at
momentum k receives corrections ~ln(kL), where L is the size of the region in
which the measurement is performed. We define infrared-safe correlation
functions which have no sensitivity to the size L of the box used for the
observation. The conventional correlators with their familiar log-enhanced
corrections (both from scalar and tensor long-wavelength modes) are easily
recovered from our IR-safe correlation functions. Among other examples, we
illustrate this by calculating the corrections to the non-Gaussianity parameter
f_NL coming from long-wavelength tensor modes. In our approach, the IR
corrections automatically emerge in a resummed, all-orders form. For the scalar
corrections, the resulting all-orders expression can be evaluated explicitly.


  The zero sets of harmonic polynomials play a crucial role in the study of the
free boundary regularity problem for harmonic measure. In order to understand
the fine structure of these free boundaries a detailed study of the singular
points of these zero sets is required. In this paper we study how "degree $k$
points" sit inside zero sets of harmonic polynomials in $\mathbb R^n$ of degree
$d$ (for all $n\geq 2$ and $1\leq k\leq d$) and inside sets that admit
arbitrarily good local approximations by zero sets of harmonic polynomials. We
obtain a general structure theorem for the latter type of sets, including sharp
Hausdorff and Minkowski dimension estimates on the singular set of "degree $k$
points" ($k\geq 2$) without proving uniqueness of blowups or aid of PDE methods
such as monotonicity formulas. In addition, we show that in the presence of a
certain topological separation condition, the sharp dimension estimates improve
and depend on the parity of $k$. An application is given to the two-phase free
boundary regularity problem for harmonic measure below the continuous threshold
introduced by Kenig and Toro.


  From the consideration of measuring bipartite mixed states by separable pure
states, we introduce algebraic sets in complex projective spaces for bipartite
mixed states as the degenerating locus of the measurement. These algebraic sets
are independent of the eigenvalues and only measure the "position" of
eigenvectors of bipartite mixed states. They are nonlocal invariants ie.,
remaining invariant after local unitary transformations. The algebraic sets
have to be the sum of the linear subspaces if the mixed states are separable,
and thus we give a "eigenvalue-free" criterion of separability.Based on our
criterion, examples are given to illustrate that entangled mixed states which
are invariant under partial transposition or fufill entropy and disorder
criterion of separability can be constructed systematically.We reveal that a
large part of quantum entanglement is independent of eigenvalue spectra and
develop a method to measure this part of quantum enatnglement.The results are
extended to multipartite case.


  We report high-resolution optical spectroscopy of the low-mass X-ray binary
V395 Car/2S 0921-630 obtained with the MIKE echelle spectrograph on the
Magellan-Clay telescope. Our spectra are obtained near superior conjunction of
the mass donor star and we exploit the absorption lines originating from the
back-side of the K-type object to accurately derive its rotational velocity.
Using K0-K1 III templates, we find vsini=32.9 +/- 0.8 km/s. We show that the
choice of template star and the assumed limb darkening coefficient has little
impact on the derived rotational velocity. This value is a significant revision
downwards compared to previously published values. We derive new system
parameter constraints in the light of our much lower rotational velocity. We
find M_1=1.44 +/- 0.10 Msun, M_2=0.35 +/- 0.03 Msun, and q=0.24 +/- 0.02 where
the errors have been estimated through a Monte-Carlo simulation. A possible
remaining systematic effect is the fact that we may be over-estimating the
orbital velocity of the mass donor due to irradiation effects. However, any
correction for this effect will only reduce the compact object mass further,
down to a minimum mass of M_1=1.05 +/- 0.08 Msun. There is thus strong evidence
that the compact object in this binary is a neutron star of rather typical mass
and that the previously reported mass values of 2-4Msun were too high due to an
over-estimate of the rotational broadening.


  We give a purely topological definition of the perturbative quantum
invariants of links and 3-manifolds associated with Chern-Simons field theory.
Our definition is as close as possible to one given by Kontsevich. We will also
establish some basic properties of these invariants, in particular that they
are universally finite type with respect to algebraically split surgery and
with respect to Torelli surgery. Torelli surgery is a mutual generalization of
blink surgery of Garoufalidis and Levine and clasper surgery of Habiro.


  We report the results of our follow-up campaign of the peculiar supernova
ASASSN-15no, based on optical data covering ~300 days of its evolution.
Initially the spectra show a pure blackbody continuum. After few days, the HeI
5876 A transition appears with a P-Cygni profile and an expansion velocity of
about 8700 km/s. Fifty days after maximum, the spectrum shows signs typically
seen in interacting supernovae. A broad (FWHM~8000 km/s) Halpha becomes more
prominent with time until ~150 days after maximum and quickly declines later
on. At these phases Halpha starts to show an intermediate component, which
together with the blue pseudo-continuum are clues that the ejecta begin to
interact with the CSM. The spectra at the latest phases look very similar to
the nebular spectra of stripped-envelope SNe. The early part (the first 40 days
after maximum) of the bolometric curve, which peaks at a luminosity
intermediate between normal and superluminous supernovae, is well reproduced by
a model in which the energy budget is essentially coming from ejecta
recombination and 56Ni decay. From the model we infer a mass of the ejecta Mej
= 2.6 Msun; an initial radius of the photosphere R0 = 2.1 x 10^14 cm; and an
explosion energy Eexpl = 0.8 x 10^51 erg. A possible scenario involves a
massive and extended H-poor shell lost by the progenitor star a few years
before explosion. The shell is hit, heated and accelerated by the supernova
ejecta. The accelerated shell+ejecta rapidly dilutes, unveiling the unperturbed
supernova spectrum below. The outer ejecta start to interact with a H-poor
external CSM lost by the progenitor system about 9 -- 90 years before the
explosion.


  We perform N-body simulations of young triple systems consisting of two
low-mass companions orbiting around a significantly more massive primary. We
find that, when the orbits of the companions are coplanar and not too widely
separated, the chance of a collision between the two companions can be as high
as 20 per cent. Collisions between one of the companions (always the less
massive) and the primary can also occur in systems with unequal-mass
companions. The chance of collisions is a few per cent in systems with more
realistic initial conditions, such as with slightly non-coplanar orbits and
unequal-mass companions. If the companions start widely separated then
collision are very rare except in some cases when the total mass of the
companions is large. We suggest that collisions between members of young
multiple systems may explain some unusual young multiple systems such as
apparently non-coeval companions.


  Homomorphic encryption (HE) is pivotal for secure computation on encrypted
data, crucial in privacy-preserving data analysis. However, efficiently
processing high-dimensional data in HE, especially for machine learning and
statistical (ML/STAT) algorithms, poses a challenge. In this paper, we present
an effective acceleration method using the kernel method for HE schemes,
enhancing time performance in ML/STAT algorithms within encrypted domains. This
technique, independent of underlying HE mechanisms and complementing existing
optimizations, notably reduces costly HE multiplications, offering near
constant time complexity relative to data dimension. Aimed at accessibility,
this method is tailored for data scientists and developers with limited
cryptography background, facilitating advanced data analysis in secure
environments.


  Combining Pati-Salam (PS) and flavour symmetries in a renormalisable setup,
we devise a scenario which produces realistic masses for the charged leptons.
Flavour-symmetry breaking scalar fields in the adjoint representations of the
PS gauge group are responsible for generating different flavour structures for
up- and down-type quarks as well as for leptons. The model is characterised by
new heavy fermions which mix with the Standard Model quarks and leptons. In
particular, the partners for the third fermion generation induce sizeable
sources of flavour violation. Focusing on the charged-lepton sector, we
scrutinise the model with respect to its implications for lepton-flavour
violating processes such as $\mu \rightarrow e\gamma$, $\mu\rightarrow 3e$ and
muon conversion in nuclei.


  Neural networks have proven to be effective at solving machine learning tasks
but it is unclear whether they learn any relevant causal relationships, while
their black-box nature makes it difficult for modellers to understand and debug
them. We propose a novel method overcoming these issues by allowing a two-way
interaction whereby neural-network-empowered machines can expose the
underpinning learnt causal graphs and humans can contest the machines by
modifying the causal graphs before re-injecting them into the machines. The
learnt models are guaranteed to conform to the graphs and adhere to expert
knowledge, some of which can also be given up-front. By building a window into
the model behaviour and enabling knowledge injection, our method allows
practitioners to debug networks based on the causal structure discovered from
the data and underpinning the predictions. Experiments with real and synthetic
tabular data show that our method improves predictive performance up to 2.4x
while producing parsimonious networks, up to 7x smaller in the input layer,
compared to SOTA regularised networks.


  We study doubly-periodic instantons, i.e. instantons on the product of a
1-dimensional complex torus T with a complex line C, with quadratic curvature
decay. We determine the asymptotic behaviour of these instantons, constructing
new asymptotic invariants. We show that the underlying holomorphic bundle
extends to TxP1. The converse statement is also true, namely a holomorphic
bundle on TxP1 which is flat on the torus at infinity, and satisfies a
stability condition, comes from a doubly-periodic instanton. Finally, we study
the hyperkahler geometry of the moduli space of doubly-periodic instantons, and
prove that the Nahm transform previously defined by the second author is a
hyperkahler isometry with the moduli space of certain meromorphic Higgs bundles
on the dual torus.


  Boson-Fermion mixture confined on an optical lattice is numerically studied.
Mott-plateau like behavior is observed under certain conditions in the density
profile of the bosons and fermions as one sees in bosonic atoms in a trap
potential. In the Mott plateau area, the total number of the bosons and
fermions at each site is kept to an integer value but both local density of the
bosons and that of the fermions take some intermediate values. Unlike bosonic
or fermionic Mott insulators, the bosons and fermions can move even in the Mott
plateau area by exchanging their positions. With Monte Carlo simulations we
demonstrate the formation of the Mott plateau, examine the property of the
bosons and fermions by measuring the densities and local density fluctuations,
and give discussion on this rather new state.


  We construct exact cosmological scaling solutions in N=8 gauged supergravity.
We restrict to solutions for which the scalar fields trace out geodesic curves
on the scalar manifold. Under these restrictions it is shown that the axionic
scalars are necessarily constant. The potential is then a sum of exponentials
and has a very specific form that allows for scaling solutions. The scaling
solutions describe eternal accelerating and decelerating power-law universes,
which are all unstable. An uplift of the solutions to 11-dimensional
supergravity is carried out and the resulting timedependent geometries are
discussed. In the discussion we briefly comment on the fact that N=2 gauged
supergravity allows stable scaling solutions.


  Imputation of data with general structures (e.g., data with continuous,
binary, unordered categorical, and ordinal variables) is commonly performed
with fully conditional specification (FCS) instead of joint modeling. A key
drawback of FCS is that it does not invoke an appropriate data augmentation
mechanism and as such convergence of the resulting Markov chain Monte Carlo
procedure is not assured. Methods that use joint modeling lack these drawbacks
but have not been efficiently implemented in data of general structures. We
address these issues by developing a new method, the so-called GERBIL
algorithm, that draws imputations from a latent joint multivariate normal model
that underpins the generally structured data. This model is constructed using a
sequence of flexible conditional linear models that enables the resulting
procedure to be efficiently implemented on high dimensional datasets in
practice. Simulations show that GERBIL performs well when compared to those
that utilize FCS. Furthermore, the new method is computationally efficient
relative to existing FCS procedures.


  We present an error analysis for the discontinuous Galerkin method applied to
the discrete-ordinate discretization of the steady-state radiative transfer
equation. Under some mild assumptions, we show that the DG method converges
uniformly with respect to a scaling parameter $\varepsilon$ which characterizes
the strength of scattering in the system. However, the rate is not optimal and
can be polluted by the presence of boundary layers. In one-dimensional slab
geometries, we demonstrate optimal convergence when boundary layers are not
present and analyze a simple strategy for balance interior and boundary layer
errors. Some numerical tests are also provided in this reduced setting.


  Context. The determination of meteor shower or parent body associations is
inherently a statistical problem. Traditional methods, primarily the similarity
discriminants, have limitations, particularly in handling the increasing volume
and complexity of meteoroid orbit data.
  Aims. We aim to introduce a new, more statistically robust and generalizable
method for estimating false positive detections in meteor shower
identification, leveraging Kernel Density Estimation (KDE).
  Methods. Utilizing a dataset of 824 fireballs observed by the European
Fireball Network, we apply a multivariate Gaussian kernel within KDE and
z-score data normalization. Our method analyzes the parameter space of
meteoroid orbits and geocentric impact characteristics, focusing on four
different similarity discriminants: DSH, D', DH, and DN.
  Results. The KDE methodology consistently converges towards a true
established shower-associated fireball rate within the EFN dataset of 18-25%
for all criteria. This indicates that the approach provides a more
statistically robust estimate of the shower-associated component.
  Conclusions. Our findings highlight the potential of KDE, combined with
appropriate data normalization, in enhancing the accuracy and reliability of
meteor shower analysis. This method addresses the existing challenges posed by
traditional similarity discriminants and offers a versatile solution adaptable
to varying datasets and parameters.


  We develop sheaf-theoretic methods to deal with non-smooth objects in
symplectic geometry. We show the completeness of a derived category of sheaves
with respect to the interleaving distance and construct a sheaf quantization of
a Hamiltonian homeomorphism. We also develop Lusternik--Schnirelmann theory in
the microlocal theory of sheaves. With these new sheaf-theoretic methods, we
prove an Arnold-type theorem for the image of a compact exact Lagrangian
submanifold under a Hamiltonian homeomorphism.


  Petri nets are an established graphical formalism for modeling and analyzing
the behavior of systems. An important consideration of the value of Petri nets
is their use in describing both the syntax and semantics of modeling
formalisms. Describing a modeling notation in terms of a formal technique such
as Petri nets provides a way to minimize ambiguity. Accordingly, it is
imperative to develop a deep and diverse understanding of Petri nets. This
paper is directed toward a new, but preliminary, exploration of the semantics
of such an important tool. Specifically, the concern in this paper is with the
semantics of Petri nets interpreted in a modeling language based on the notion
of machines of things that flow. The semantics of several Petri net diagrams
are analyzed in terms of flow of things. The results point to the viability of
the approach for exploring the underlying assumptions of Petri nets.


  Analytical expressions are derived for the number of fractions with equal
numerators in the Farey sequence of order $n$, $F_n$, and in the truncated
Farey sequence $F_n^{1/k}$ containing all Farey fractions below $1/k$, with
$1\leq k \leq n$. These developments lead to an expression for the rank of
$1/k$ in $F_n$, or equivalently $\left|F_n^{1/k}\right|$, and to remarkable
relations between the ranks of different unit fractions. Furthermore, the
results are extended to Farey fractions of the form $2/k$.


  We present Herschel observations of the isolated, low-mass star-forming Bok
globule CB244. It contains two cold sources, a low-mass Class 0 protostar and a
starless core, which is likely to be prestellar in nature, separated by 90
arcsec (~ 18000 AU). The Herschel data sample the peak of the Planck spectrum
for these sources, and are therefore ideal for dust-temperature and column
density modeling. With these data and a near-IR extinction map, the MIPS 70
micron mosaic, the SCUBA 850 micron map, and the IRAM 1.3 mm map, we model the
dust-temperature and column density of CB244 and present the first measured
dust-temperature map of an entire star-forming molecular cloud. We find that
the column-averaged dust-temperature near the protostar is ~ 17.7 K, while for
the starless core it is ~ 10.6K, and that the effect of external heating causes
the cloud dust-temperature to rise to ~ 17 K where the hydrogen column density
drops below 10^21 cm^-2. The total hydrogen mass of CB244 (assuming a distance
of 200 pc) is 15 +/- 5 M_sun. The mass of the protostellar core is 1.6 +/- 0.1
M_sun and the mass of the starless core is 5 +/- 2 M_sun, indicating that ~ 45%
of the mass in the globule is participating in the star-formation process.


  We propose a four-dimensional N = 1 supergravity-based Starobinsky-type
inflationary model in terms of a single massive vector multiplet, whose action
includes the Dirac-Born-Infeld-type kinetic terms and a generalized (new)
Fayet-Iliopolulos-type term without gauging the R-symmetry. The bosonic action
and the scalar potential are computed. Inflaton is the superpartner of
goldstino in our model, and supersymmetry is spontaneously broken after
inflation by the D-type mechanism, whose scale is related to the value of the
cosmological constant.


  The way diseases spread through schools, epidemics through countries, and
viruses through the Internet is crucial in determining their risk. Although
each of these threats has its own characteristics, its underlying network
determines the spreading. To restrain the spreading, a widely used approach is
the fragmentation of these networks through immunization, so that epidemics
cannot spread. Here we develop an immunization approach based on optimizing the
susceptible size, which outperforms the best known strategy based on immunizing
the highest-betweenness links or nodes. We find that the network's
vulnerability can be significantly reduced, demonstrating this on three
different real networks: the global flight network, a school friendship
network, and the internet. In all cases, we find that not only is the average
infection probability significantly suppressed, but also for the most relevant
case of a small and limited number of immunization units the infection
probability can be reduced by up to 55%.


  We present the dynamic propensity distribution as an explicit measure of the
degree to which the dynamics in a liquid over the time scale of structural
relaxation is determined by the initial configuration. We then examine, for a
binary mixture of soft discs in two dimensions, the correlation between the
spatial distribution of propensity and that of two localmeasures of
configuration structure: the local composition and local free volume. While the
small particles dominate the high propensity population,we find no strong
correlation between either the local composition or the local free volume and
the propensity. It is argued that this is a generic failure of purely local
structural measures to capture the inherently non-local character of collective
behaviour.


  We find that scale-free random networks are excellently modeled by a
deterministic graph. This graph has a discrete degree distribution (degree is
the number of connections of a vertex) which is characterized by a power-law
with exponent $\gamma=1+\ln3/\ln2$. Properties of this simple structure are
surprisingly close to those of growing random scale-free networks with $\gamma$
in the most interesting region, between 2 and 3. We succeed to find exactly and
numerically with high precision all main characteristics of the graph. In
particular, we obtain the exact shortest-path-length distribution. For the
large network ($\ln N \gg 1$) the distribution tends to a Gaussian of width
$\sim \sqrt{\ln N}$ centered at $\bar{\ell} \sim \ln N$. We show that the
eigenvalue spectrum of the adjacency matrix of the graph has a power-law tail
with exponent $2+\gamma$.


  Positive thinking is thought to be an important component of self-motivation
in various practical fields such as education and the workplace. Previous work,
including sentiment transfer and positive reframing, has focused on the
positive side of language. However, self-motivation that drives people to reach
their goals has not yet been studied from a computational perspective.
Moreover, negative feedback has not yet been explored, even though positive and
negative feedback are both necessary to grow self-motivation. To facilitate
self-motivation, we propose CArrot and STICk (CASTIC) dataset, consisting of
12,590 sentences with 5 different strategies for enhancing self-motivation. Our
data and code are publicly available at here.


  Modern interferometric imaging relies on advanced calibration that
incorporates direction-dependent effects. Their increasing number of antennas
(e.g. in LOFAR, VLA, MeerKAT/SKA) and sensitivity are often tempered with the
accuracy of their calibration. Beam accuracy drives particularly the capability
for high dynamic range imaging (HDR - contrast > 1:$10^6$). The Radio
Interferometric Measurement Equation (RIME) proposes a refined calibration
framework for wide field of views (i.e. beyond the primary lobe and first null)
using beam models. We have used holography data taken on 12 antennas of the
Very Large Array (VLA) with two different approaches: a `data-driven'
representation derived from Principal Component Analysis (PCA) and a projection
on the Zernike polynomials. We determined sparse representations of the beam to
encode its spatial and spectral variations. For each approach, we compressed
the spatial and spectral distribution of coefficients using low-rank
approximations. The spectral behaviour was encoded with a Discrete Cosine
Transform (DCT). We compared our modelling to that of the Cassbeam software
which provides a parametric model of the antenna and its radiated field. We
present comparisons of the beam reconstruction fidelity vs. `compressibility'.
We found that the PCA method provides the most accurate model. In the case of
VLA antennas, we discuss the frequency ripple over L-band which is associated
with a standing wave between antenna reflectors. The results are a series of
coefficients that can easily be used `on-the-fly' in calibration pipelines to
generate accurate beams at low computing costs.


  An exact Lagrangian submanifold $L$ in the symplectization of standard
contact $(2n-1)$-space with Legendrian boundary $\Sigma$ can be glued to itself
along $\Sigma$. This gives a Legendrian embedding $\Lambda(L,L)$ of the double
of $L$ into contact $(2n+1)$-space. We show that the Legendrian isotopy class
of $\Lambda(L,L)$ is determined by formal data: the manifold $L$ together with
a trivialization of its complexified tangent bundle. In particular, if $L$ is a
disk then $\Lambda(L,L)$ is the Legendrian unknot.


  Gravity plays an important part in the experiments and discoveries of the
modern world. But how was it discovered? Surely Newton and Einstein were not
the only people to observe it and account for it. It had been a long path
before the full theory for Gravitation could be formulated with open ends for
more add-ons and modifications. All the contributions from across the world and
different eras helped in the discovery of gravity as a whole new concept and
area of research with a major contribution from the Greeks. This 3 article
series lists out the important curves in the carefully carved path of
gravitational discovery. The first article summarises the development of
interest in the cosmos and the growth of scientific knowledge through ancient
theories and observations.


  In the paper we investigate the structure of $i$-components of two classes of
codes: Kerdock codes and the duals of the primitive cyclic BCH code with
designed distance 5 of length $n=2^m-1$, for odd $m$. We prove that for any
admissible length a punctured Kerdock code consists of two $i$-components and
the dual of BCH code is a $i$-component for any $i$. We give an alternative
proof for the fact presented by De Caen and van Dam in 1999 that the
restriction of the Hamming scheme to a doubly shortened Kerdock code is an
association scheme.


  The low-frequency instability of a cylindrical poorly magnetized plasma with
an inward-directed radial electric field is studied changing the gas pressure
and the ion cyclotron frequency. The unstable frequency always decreases when
the gas pressure is increased indicating collisional effects. At a fixed
pressure, the unstable frequency increases with the magnetic field when the
B-field is low and decreases at larger magnetic field strength. We find that
the transition between these two regimes is obtained when the ion cyclotron
frequency equals the ion-neutrals collision frequency. This is in agreement
with the theory of the slow-ion drift instability induced by the collisional
slowing of the electric ion drift (A. Simon, Phys. Fluids 6, 382, (1963).


  The Kitaev-Heisenberg (KH) model has been proposed to capture magnetic
interactions in iridate Mott insulators on the honeycomb lattice. We show that
analogous interactions arise in many other geometries built from edge-sharing
IrO_6 octahedra, including the pyrochlore and hyperkagome lattices relevant to
Ir2O4 and Na4Ir3O8 respectively. The Kitaev spin liquid exact solution does not
generalize to these lattices. However, a different exactly soluble point of the
honeycomb lattice KH model, obtained by a four-sublattice transformation to a
ferromagnet, generalizes to all these lattices. A Klein four-group =Z2xZ2
structure is associated with this mapping (hence Klein duality). A finite
lattice admits the duality if a simple geometrical condition is met. This
duality predicts fluctuation free ordered states on these different 2D and 3D
lattices, which are analogs of the honeycomb lattice KH stripy order. This
result is used in conjunction with a semiclassical Luttinger-Tisza
approximation to obtain phase diagrams for KH models on the different lattices.
We also discuss a Majorana fermion based mean field theory at the Kitaev point,
which is exact on the honeycomb lattice, for the KH models on the different
lattices. We attribute the rich behavior of these models to the interplay of
geometric frustration and frustration induced by spin-orbit coupling.


  We investigate the problem of whether one can anticipate any features of the
graviton without a detailed knowledge of a full quantum gravity. Assuming that
in linearized gravity the graviton is in a sense similar to the photon, we
derive a curious large number coincidence between the number of gravitons
emitted by a solar planet during its orbital period and the number of its
nucleons. In Einstein's GR the analogy between the graviton and the photon is
ill founded. A generic relationship between quanta of a quantum field and plane
waves of the corresponding classical field is broken in the case of GR. The
graviton cannot be classically approximated by a generic pp wave nor by the
exact plane wave. Most important, the ADM energy is a zero frequency
characteristic of any asymptotically flat spacetime and this means that any
general relationship between energy and frequency is a priori impossible. In
particular the formula $E=\hbar \omega$ does not hold. The graviton must have
features different from those of the photon and these cannot be predicted from
classical general relativity.


  Speckle artifacts degrade image quality in virtually all modalities that
utilize coherent energy, including optical coherence tomography, reflectance
confocal microscopy, ultrasound, and widefield imaging with laser illumination.
We present an adversarial deep learning framework for laser speckle reduction,
called DeepLSR (https://durr.jhu.edu/DeepLSR), that transforms images from a
source domain of coherent illumination to a target domain of speckle-free,
incoherent illumination. We apply this method to widefield images of objects
and tissues illuminated with a multi-wavelength laser, using light emitting
diode-illuminated images as ground truth. In images of gastrointestinal
tissues, DeepLSR reduces laser speckle noise by 6.4 dB, compared to a 2.9 dB
reduction from optimized non-local means processing, a 3.0 dB reduction from
BM3D, and a 3.7 dB reduction from an optical speckle reducer utilizing an
oscillating diffuser. Further, DeepLSR can be combined with optical speckle
reduction to reduce speckle noise by 9.4 dB. This dramatic reduction in speckle
noise may enable the use of coherent light sources in applications that require
small illumination sources and high-quality imaging, including medical
endoscopy.


  It is proved that for any two subsets $A$ and $B$ of an arbitrary finite
field $\Fq$ such that $|A||B|>q$ the identity $16AB=\Fq$ holds. Moreover, it is
established that for every subsets $X, Y\subset \Fq$ with the property
$|X||Y|\geqslant 2q$ the equality $8XY=\Fq$ holds.


  Chain-of-thought (CoT) is capable of eliciting models to explicitly generate
reasoning paths, thus promoting reasoning accuracy and attracting increasing
attention. Specifically, zero-shot CoT achieves remarkable improvements in a
wide range of reasoning tasks by simply instructing the LLM with the prompt
"Let's think step by step!". Despite the success of zero-shot CoT, the existing
zero-shot prompting techniques remain limited to a single language, making it
challenging to generalize to other languages and hindering global development.
In this work, we introduce cross-lingual prompting (CLP), aiming to improve
zero-shot CoT reasoning across languages. Specifically, CLP consists of two
main components: (1) cross-lingual alignment prompting and (2) task-specific
solver prompting. The cross-lingual alignment prompting is responsible for
aligning representations across different languages, whereas the task-specific
solver prompting is used to generate the final chain of thoughts and results
for the reasoning task. In addition, we further introduce cross-lingual
self-consistent prompting (CLSP) to ensemble different reasoning paths across
languages. Our experimental evaluations on several benchmarks demonstrate that
CLP and CLSP significantly outperform the existing prompting methods and
achieve state-of-the-art performance. We hope this work will inspire further
breakthroughs in cross-lingual CoT.


  The study of global event shape variables can provide sensitive tests of
predictions for multijet production in proton-proton collisions. This paper
presents a study of several event shape variables calculated using jet four
momenta in proton-proton collisions at a centre-of-mass energy of 13 TeV and
uses data recorded with the CMS detector at the LHC corresponding to an
integrated luminosity of 2.2 fb$^{-1}$. After correcting for detector effects,
the resulting distributions are compared with several theoretical predictions.
The agreement generally improves as the energy, represented by the average
transverse momentum of the two leading jets, increases.


  The concept of Wireless Network-on-Chip (WNoC) has emerged as a potential
solution to address the escalating communication demands of modern computing
systems due to their low-latency, versatility, and reconfigurability. However,
for WNoC to fulfill its potential, it is essential to establish multiple
high-speed wireless links across chips. Unfortunately, the compact and enclosed
nature of computing packages introduces significant challenges in the form of
Co-Channel Interference (CCI) and Inter-Symbol Interference (ISI), which not
only hinder the deployment of multiple spatial channels but also severely
restrict the symbol rate of each individual channel. In this paper, we posit
that Time Reversal (TR) could be effective in addressing both impairments in
this static scenario thanks to its spatiotemporal focusing capabilities even in
the near field. Through comprehensive full-wave simulations and bit error rate
analysis in multiple scenarios and at multiple frequency bands, we provide
evidence that TR can increase the symbol rate by an order of magnitude,
enabling the deployment of multiple concurrent links and achieving aggregate
speeds exceeding 100 Gb/s. Finally, we evaluate the impact of reducing the
sampling rate of the TR filter on the achievable speeds, paving the way to
practical TR-based wireless communications at the chip scale.


  Bibliographic analysis considers the author's research areas, the citation
network and the paper content among other things. In this paper, we combine
these three in a topic model that produces a bibliographic model of authors,
topics and documents, using a nonparametric extension of a combination of the
Poisson mixed-topic link model and the author-topic model. This gives rise to
the Citation Network Topic Model (CNTM). We propose a novel and efficient
inference algorithm for the CNTM to explore subsets of research publications
from CiteSeerX. The publication datasets are organised into three corpora,
totalling to about 168k publications with about 62k authors. The queried
datasets are made available online. In three publicly available corpora in
addition to the queried datasets, our proposed model demonstrates an improved
performance in both model fitting and document clustering, compared to several
baselines. Moreover, our model allows extraction of additional useful knowledge
from the corpora, such as the visualisation of the author-topics network.
Additionally, we propose a simple method to incorporate supervision into topic
modelling to achieve further improvement on the clustering task.


  Considering hybrid F-term inflation in RSII model and using the most recent
data from Planck 2018 and in comparison with Planck 2015and WMAP 9-year , we
can obtain some interesting constraints on main parameters of the model. Also,
we attain convenient compatibility between this model and observational data.
We show that this setup provides a successful hybrid inflation with high enough
Reheating Temperature, T_R, to have a successful thermalization. After
inflation, particles are created in the process of preheating. Inflaton
field(s) oscillated and fermionic field(s) interacted with it in a
non-perturbative regime of parametric resonance. We apply theory of fermionic
preheating coupling to the inflaton, without expansion of the universe, to
calculate the occupation number of created particles analytically, and some
interesting results are achieved.


  People often interact repeatedly: with relatives, through file sharing, in
politics, etc. Many such interactions are reciprocal: reacting to the actions
of the other. In order to facilitate decisions regarding reciprocal
interactions, we analyze the development of reciprocation over time. To this
end, we propose a model for such interactions that is simple enough to enable
formal analysis, but is sufficient to predict how such interactions will
evolve. Inspired by existing models of international interactions and arguments
between spouses, we suggest a model with two reciprocating attitudes where an
agent's action is a weighted combination of the others' last actions (reacting)
and either i) her innate kindness, or ii) her own last action (inertia). We
analyze a network of repeatedly interacting agents, each having one of these
attitudes, and prove that their actions converge to specific limits.
Convergence means that the interaction stabilizes, and the limits indicate the
behavior after the stabilization. For two agents, we describe the interaction
process and find the limit values. For a general connected network, we find
these limit values if all the agents employ the second attitude, and show that
the agents' actions then all become equal. In the other cases, we study the
limit values using simulations. We discuss how these results predict the
development of the interaction and can be used to help agents decide on their
behavior.


  We propose an effective theory of non-Abelian superconductivity, an
SU(2)xU(1) extension of the Abelian Landau-Ginzburg theory, which could be
viewed as an effective theory of ferromagnetic superconductivity made of
spin-up and spin-down doublet Cooper pair. Just like the Abelian
Landau-Ginzburg theory it has the U(1) electromagnetic interaction, but the new
ingredient is the non-Abelian SU(2) gauge interaction between the spin doublet
Cooper pair. A remarkable feature of the theory is the mixing between the
photon and the diagonal part of the SU(2) gauge boson. After the mixing it has
massless gauge boson (the massless non-Abelian spinon) and massive gauge boson
(the massive photon), in addition to the massive off-diagonal gauge bosons (the
massive non-Abelian spinons) which induces the spin-flip interaction between
the spin up and down components of the Cooper pair. So, unlike the ordinary
Landau-Ginzburg theory it has a long range interaction mediated by the massless
non-Abelian spinon, which could be responsible for the long range magnetic
order and spin waves observed in ferromagnetic superconductors. The theory is
characterized by three scales. In addition to the correlation length fixed by
the mass of the Higgs field it has two different penetration lengths, the one
fixed by the mass of the photon (which generates the well known Meissner
effect) and the other fixed by the mass of the off-diagonal spinons (which
determines the scale of the spin flip interaction). The non-Abelian structure
of the theory naturally accommodates new topological objects, the non-Abrikosov
quantized spin vortex (as well as the well known Abrikosov vortex) and
non-Abelian spin monopole. We discuss the physical implications of the
non-Abelian Landau-Ginzburg theory.


  A light echo around SN 1993J was observed 8.2 years after explosion by a HST
WFPC2 observation, adding to the small family of supernovae with light echoes.
The light echo was formed by supernova light scattered from a dust sheet, which
lies 220 parsecs away from the supernova, 50 parsecs thick along the line of
sight, as inferred from radius and width of the light echo. The dust inferred
from the light echo surface brightness is 1000 times denser than the intercloud
dust. The graphite to silicate fraction can not be determined by our BVI
photometric measurements, however, a pure graphite model can be excluded based
on comparison with the data. With future observations, it will be possible to
measure the expansion rate of the light echo, from which an independent
distance to M81 can be obtained.


  In recent years, the exploration of node centrality has received significant
attention and extensive investigation, primarily fuelled by its applications in
diverse domains such as product recommendations, opinion propagation, disease
spread, and other scenarios requiring the maximization of node influence.
Despite various perspectives emphasizing the indispensability of higher-order
networks, research specifically delving into node centrality within the realm
of hypergraphs has been relatively constrained. This paper focuses on the
problem of influence maximization on the Simplicial Contagion Model (SCM),
using the susceptible-infected-recovered (SIR) model as an example. To find
practical solutions to this optimization problem, we have developed a
theoretical framework based on message passing process and conducted stability
analysis of equilibrium solutions for the self-consistent equations.
Furthermore, we introduce a metric called collective influence and propose an
adaptive algorithm, known as the Collective Influence Adaptive (CIA), to
identify influential propagators in the spreading process. Notably, our
algorithm distinguishes itself by prioritizing collective influence over
individual influence, resulting in demonstrably superior performance, a
characteristic substantiated by a comprehensive array of experiments.


  Zero-shot learning on 3D point cloud data is a related underexplored problem
compared to its 2D image counterpart. 3D data brings new challenges for ZSL due
to the unavailability of robust pre-trained feature extraction models. To
address this problem, we propose a prompt-guided 3D scene generation and
supervision method that augments 3D data to learn the network better, exploring
the complex interplay of seen and unseen objects. First, we merge point clouds
of two 3D models in certain ways described by a prompt. The prompt acts like
the annotation describing each 3D scene. Later, we perform contrastive learning
to train our proposed architecture in an end-to-end manner. We argue that 3D
scenes can relate objects more efficiently than single objects because popular
language models (like BERT) can achieve high performance when objects appear in
a context. Our proposed prompt-guided scene generation method encapsulates data
augmentation and prompt-based annotation/captioning to improve 3D ZSL
performance. We have achieved state-of-the-art ZSL and generalized ZSL
performance on synthetic (ModelNet40, ModelNet10) and real-scanned
(ScanOjbectNN) 3D object datasets.


  Strongly coupled hidden sector theories predict collider production of
invisible, composite dark matter candidates mixed with standard model hadrons
in the form of semivisible jets. Classical mass reconstruction techniques may
not be optimal for these unusual topologies, in which the missing transverse
momentum comes from massive particles and has a nontrivial relationship to the
visible jet momentum. We apply the artificial event variable network, a
semisupervised, interpretable machine learning technique that uses an
information bottleneck, to derive superior mass reconstruction functions for
several cases of resonant semivisible jet production. We demonstrate that the
technique can extrapolate to unknown signal model parameter values. We further
demonstrate the viability of conducting an actual search for new physics using
this method, by applying the learned functions to standard model background
events from quantum chromodynamics.


  Uncertainty quantification during atmospheric chemistry modeling is
computationally expensive as it typically requires a large number of
simulations using complex models. As large-scale modeling is typically
performed with simplified chemical mechanisms for computational tractability,
we describe a probabilistic surrogate modeling method using principal
components analysis (PCA) and Ensemble Sparse Identification of Nonlinear
Dynamics (E-SINDy) to both automatically simplify a gas-phase chemistry
mechanism and to quantify the uncertainty introduced when doing so. We
demonstrate the application of this method on a small photochemical box model
for ozone formation. With 100 ensemble members, the calibration $R$-squared
value is 0.96 among the three latent species on average and 0.98 for ozone,
demonstrating that predicted model uncertainty aligns well with actual model
error. In addition to uncertainty quantification, this probabilistic method
also improves accuracy as compared to an equivalent deterministic version, by
$\sim$60% for the ensemble prediction mean or $\sim$50% for deterministic
prediction by the best-performing single ensemble member. Overall, the ozone
testing root mean square error (RMSE) is 15.1% of its root mean square (RMS)
concentration. Although our probabilistic ensemble simulation ends up being
slower than the reference model it emulates, we expect that use of a more
complex reference model in future work will result in additional opportunities
for acceleration. Versions of this approach applied to full-scale chemical
mechanisms may result in improved uncertainty quantification in models of
atmospheric composition, leading to enhanced atmospheric understanding and
improved support for air quality control and regulation.


  The increasing demand on precision and throughput within high-precision
mechatronics industries requires a new generation of feedforward controllers
with higher accuracy than existing, physics-based feedforward controllers. As
neural networks are universal approximators, they can in principle yield
feedforward controllers with a higher accuracy, but suffer from bad
extrapolation outside the training data set, which makes them unsafe for
implementation in industry. Motivated by this, we develop a novel
physics-guided neural network (PGNN) architecture that structurally merges a
physics-based layer and a black-box neural layer in a single model. The
parameters of the two layers are simultaneously identified, while a novel
regularization cost function is used to prevent competition among layers and to
preserve consistency of the physics-based parameters. Moreover, in order to
ensure stability of PGNN feedforward controllers, we develop sufficient
conditions for analyzing or imposing (during training) input-to-state stability
of PGNNs, based on novel, less conservative Lipschitz bounds for neural
networks. The developed PGNN feedforward control framework is validated on a
real-life, high-precision industrial linear motor used in lithography machines,
where it reaches a factor 2 improvement with respect to physics-based
mass-friction feedforward and it significantly outperforms alternative neural
network based feedforward controllers.


  This paper analyses the multiplexing gain (MG) achievable over Wyner's
symmetric network with random user activity and random arrival of mixed-delay
traffic. The mixed-delay traffic is composed of delay-tolerant traffic and
delay-sensitive traffic where only the former can benefit from transmitter and
receiver cooperation since the latter is subject to stringent decoding delays.
The total number of cooperation rounds at transmitter and receiver sides is
limited to $\D$ rounds. We derive inner and outer bounds on the MG region. In
the limit as $\D\to \infty$, the bounds coincide and the results show that
transmitting delay-sensitive messages does not cause any penalty on the sum MG.
For finite $\D$ our bounds are still close and prove that the penalty caused by
delay-sensitive transmissions is small.


  We report a global effect induced by the local complex field, associated with
the spin-exchange interaction. High-order exceptional point up to ($N+1$)-level
coalescence is created at the critical local complex field applied to the
$N$-size quantum spin chain. The ($N+1$)-order coalescent level is a saturated
ferromagnetic ground state in the isotropic spin system. Remarkably, the final
state always approaches the ground state for an arbitrary initial state with
any number of spin flips; even if the initial state is orthogonal to the ground
state. Furthermore, the switch of macroscopic magnetization is solely driven by
the time and forms a hysteresis loop in the time domain. The retentivity and
coercivity of the hysteresis loop mainly rely on the non-Hermiticity. Our
findings highlight the cooperation of non-Hermiticity and the interaction in
quantum spin system, suggest a dynamical framework to realize magnetization,
and thus pave the way for the non-Hermitian quantum spin system.


  We give an introductory overview of the classical Poincar\'e gauge theory of
gravity formulated on the spacetime manifold that carries the Riemann-Cartan
geometry with nontrivial curvature and torsion. After discussing the basic
mathematical structures at an elementary level in the framework of the standard
tensor analysis, we formulate the general dynamical scheme of Poincar\'e gauge
gravity for the class of Yang-Mills type models, and consider a selected number
of physically interesting consequences of this theory.


  We study the dispersion relation obtained from the semiclassical loop quantum
gravity. This dispersion relation is considered for a photon system at finite
temperatures and the changes to the Planck's radiation law, the Wien and
Boltzmann laws are discussed. Corrections to the equation of state of the black
body radiation are also obtained.


  Charged lepton mixing as a consequence of neutrino mixing is studied for two
generations $e,\mu$ in the temperature regime $m_\mu \ll T \ll M_W$ in the
early Universe. We state the general criteria for charged lepton mixing,
critically reexamine aspects of neutrino equilibration and provide arguments to
suggest that neutrinos may equilibrate as mass eigenstates in the temperature
regime \emph{prior} to flavor equalization. We assume this to be the case, and
that neutrino mass eigenstates are in equilibrium with different chemical
potentials. Charged lepton self-energies are obtained to leading order in the
electromagnetic and weak interactions. The upper bounds on the neutrino
asymmetry parameters from CMB and BBN without oscillations, combined with the
fit to the solar and KamLAND data for the neutrino mixing angle, suggest that
for the two generation case there is resonant \emph{charged lepton} mixing in
the temperature range $T \sim 5 \mathrm{GeV}$. In this range the charged lepton
oscillation frequency is of the same order as the electromagnetic damping rate.


  With big data becoming increasingly available, IoT hardware becoming widely
adopted, and AI capabilities becoming more powerful, organizations are
continuously investing in sensing. Data coming from sensor networks are
currently combined with sensor fusion and AI algorithms to drive innovation in
fields such as self-driving cars. Data from these sensors can be utilized in
numerous use cases, including alerts in safety systems of urban settings, for
events such as gun shots and explosions. Moreover, diverse types of sensors,
such as sound sensors, can be utilized in low-light conditions or at locations
where a camera is not available. This paper investigates the potential of the
utilization of sound-sensor data in an urban context. Technically, we propose a
novel approach of classifying sound data using the Wigner-Ville distribution
and Convolutional Neural Networks. In this paper, we report on the performance
of the approach on open-source datasets. The concept and work presented is
based on my doctoral thesis, which was performed as part of the Engineering
Doctorate program in Data Science at the University of Eindhoven, in
collaboration with the Dutch National Police. Additional work on real-world
datasets was performed during the thesis, which are not presented here due to
confidentiality.


  We introduce two natural notions for the occupation measure of a function $V$
with finite variation. The first yields a signed measure, and the second a
positive measure. By comparing two versions of the change-of-variables formula,
we show that both measures are absolutely continuous with respect to Lebesgue
measure. Occupation densities can be thought of as local times of $V$, and are
described by a Meyer-Tanaka like formula.


  Regression models, in which the observed features $X \in \R^p$ and the
response $Y \in \R$ depend, jointly, on a lower dimensional, unobserved, latent
vector $Z \in \R^K$, with $K< p$, are popular in a large array of applications,
and mainly used for predicting a response from correlated features. In
contrast, methodology and theory for inference on the regression coefficient
$\beta$ relating $Y$ to $Z$ are scarce, since typically the un-observable
factor $Z$ is hard to interpret. Furthermore, the determination of the
asymptotic variance of an estimator of $\beta$ is a long-standing problem, with
solutions known only in a few particular cases. To address some of these
outstanding questions, we develop inferential tools for $\beta$ in a class of
factor regression models in which the observed features are signed mixtures of
the latent factors. The model specifications are practically desirable, in a
large array of applications, render interpretability to the components of $Z$,
and are sufficient for parameter identifiability. Without assuming that the
number of latent factors $K$ or the structure of the mixture is known in
advance, we construct computationally efficient estimators of $\beta$, along
with estimators of other important model parameters. We benchmark the rate of
convergence of $\beta$ by first establishing its $\ell_2$-norm minimax lower
bound, and show that our proposed estimator is minimax-rate adaptive. Our main
contribution is the provision of a unified analysis of the component-wise
Gaussian asymptotic distribution of $\wh \beta$ and, especially, the derivation
of a closed form expression of its asymptotic variance, together with
consistent variance estimators. The resulting inferential tools can be used
when both $K$ and $p$ are independent of the sample size $n$, and when both, or
either, $p$ and $K$ vary with $n$, while allowing for $p > n$.


  Many SMT solvers implement efficient SAT-based procedures for solving
fixed-size bit-vector formulas. These approaches, however, cannot be used
directly to reason about bit-vectors of symbolic bit-width. To address this
shortcoming, we propose a translation from bit-vector formulas of non-fixed
bit-width to formulas in a logic supported by SMT solvers that includes
non-linear integer arithmetic, uninterpreted functions, and universal
quantification. While this logic is undecidable, this approach can still solve
many formulas by capitalizing on advancements in SMT solving for non-linear
arithmetic and universally quantified formulas. We provide several case studies
in which we have applied this approach with promising results, including the
bit-width independent verification of invertibility conditions, compiler
optimizations, and bit-vector rewrites.


  Let $(S,L)$ be a polarized K3 surface with $\mathrm{Pic}(S) = \mathbb{Z}[L]$
and $L\cdot L=2g-2$, let $C$ be a nonsingular curve of genus $g-1$ and let
$f:C\to S$ be such that $f(C) \in \vert L \vert$. We prove that the Gaussian
map $\Phi_{\omega_C(-T)}$ is non-surjective, where $T$ is the degree two
divisor over the singular point $x$ of $f(C)$. This generalizes a result of
Kemeny with an entirely different proof. It uses the very ampleness of $C$ on
the blown-up surface $\widetilde S$ of $S$ at $x$ and a theorem of L'vovski.


  I derive a second-order local relation between the REDSHIFT-space mass
density field and the REAL-space velocity field. This relation can be useful
for comparisons between the cosmic density and peculiar velocity fields, for a
number of reasons. First, relating the real-space velocity directly to the
redshift-space density enables one to avoid the Omega-dependent reconstruction
of the density field in real space. Secondly, the reconstruction of the
three-dimensional velocity field in redshift space, questionable because of its
vorticity, is also unnecessary. Finally, a similar relation between the GALAXY
density field and the velocity field offers a way to break the Omega-bias
degeneracy in density--velocity comparisons, when combined with an additional
measurement of the redshift-space galaxy skewness. I derive the latter relation
under the assumption of nonlinear but local bias; accounting for stochasticity
of bias is left for further study.


  In this paper we show the use of the focal underdetermined system solver to
recover sparse empirical quadrature rules for parametrized integrals from
existing data, consisting of the values of given parametric functions sampled
on a discrete set of points. This algorithm, originally proposed for image and
signal reconstruction, relies on an approximated $\ell^p$-quasi-norm
minimization. The choice of $0<p<1$ fits the nature of the constraints to which
quadrature rules are subject, thus providing a more natural formulation for
sparse quadrature recovery compared to the one based on $\ell^1$-norm
minimization. We also extend an a priori error estimate available for the
$\ell^1$-norm formulation by considering the error resulting from data
compression. Finally, we present two numerical examples to illustrate some
practical applications. The first concerns the fundamental solution of the
linear 1D Schr\"odinger equation, the second example deals with the
hyper-reduction of a partial differential equation modelling a nonlinear
diffusion process in the framework of the reduced basis method. For both the
examples we compare our method with the one based on $\ell^1$-norm minimization
and the one relaying on the use of the non-negative least square method. Matlab
codes related to the numerical examples and the algorithms described are
provided.


  We employ holographic techniques to study quantum quenches at finite
temperature, where the quenches involve varying the coupling of the boundary
theory to a relevant operator with an arbitrary conformal dimension
$2\leq\D\leq4$. The evolution of the system is studied by evaluating the
expectation value of the quenched operator and the stress tensor throughout the
process. The time dependence of the new coupling is characterized by a fixed
timescale and the response of the observables depends on the ratio of the this
timescale to the initial temperature. The observables exhibit universal scaling
behaviours when the transitions are either fast or slow, i.e. when this ratio
is very small or very large. The scaling exponents are smooth functions of the
operator dimension. We find that in fast quenches, the relaxation time is set
by the thermal timescale regardless of the operator dimension or the precise
quenching rate.


  This paper deals with subsampled spectral gradient methods for minimizing
finite sum. Subsample function and gradient approximations are employed in
order to reduce the overall computational cost of the classical spectral
gradient methods. The global convergence is enforced by a nonmonotone line
search procedure. Global convergence is proved when functions and gradients are
approximated with increasing accuracy. R-linear convergence and worst-case
iteration complexity is investigated in case of strongly convex objective
function. Numerical results on well known binary classification problems are
given to show the effectiveness of this framework and analyze the effect of
different spectral coefficient approximations arising from variable sample
nature of this procedure.


  We build a number of integrable one--scalar spatially flat cosmologies, which
play a natural role in inflationary scenarios, examine their behavior in
several cases and draw from them some general lessons on this type of systems,
whose potentials involve combinations of exponential functions, and on similar
non--integrable ones. These include the need for the scalar to emerge from the
initial singularity while climbing up sufficiently steep exponential potentials
("climbing phenomenon") and the inevitable collapse in a big Crunch whenever
the scalar tries to settle at negative extrema of the potential. We also
elaborate on the links between these types of potentials and "brane
supersymmetry breaking", a mechanism that ties together string scale and scale
of supersymmetry breaking in a class of orientifold models. We show that, under
some assumptions that are spelled out in the text, the extended objects of
these vacua can inject inflationary phases with discrete values of the spectral
index that are determined by the number of unwrapped dimensions of the branes
and by the inverse power with which the string coupling $g_s$ enters their
world--volume actions. An NS fivebrane, which is interestingly unstable in this
class of models, when wrapped on a small internal cycle would yield a spectral
index that is amusingly close to the experimentally favored PLANCK value ns ~
0.96.


  It is argued that there are two phases in QCD distinguished by different
choices of the gauge parameter. In one phase the color confinement is realized
and gluons turn out to be massive, whereas in the other phase it ceases to be
realized, but the gluons remain massless.


  It is shown that the diffusion equation and its adjoint (time reversed)
equation can be derived with only a few assumptions, using an
information-theoretic approach based on the principle of minimum Fisher
information


  Cloud computing and distributed computing are becoming ubiquitous in many
modern control systems such as smart grids, building automation, robot swarms
or intelligent transportation systems. Compared to "isolated" control systems,
the advantages of cloud-based and distributed control systems are, in
particular, resource pooling and outsourcing, rapid scalability, and high
performance. However, these capabilities do not come without risks. In fact,
the involved communication and processing of sensitive data via public networks
and on third-party platforms promote, among other cyberthreats, eavesdropping
and manipulation of data. Encrypted control addresses this security gap and
provides confidentiality of the processed data in the entire control loop. This
paper presents a tutorial-style introduction to this young but emerging field
in the framework of secure control for networked dynamical systems.


  Given the complexity and heterogeneity in Cloud computing scenarios, the
modeling approach has widely been employed to investigate and analyze the
energy consumption of Cloud applications, by abstracting real-world objects and
processes that are difficult to observe or understand directly. It is clear
that the abstraction sacrifices, and usually does not need, the complete
reflection of the reality to be modeled. Consequently, current energy
consumption models vary in terms of purposes, assumptions, application
characteristics and environmental conditions, with possible overlaps between
different research works. Therefore, it would be necessary and valuable to
reveal the state-of-the-art of the existing modeling efforts, so as to weave
different models together to facilitate comprehending and further investigating
application energy consumption in the Cloud domain. By systematically
selecting, assessing and synthesizing 76 relevant studies, we rationalized and
organized over 30 energy consumption models with unified notations. To help
investigate the existing models and facilitate future modeling work, we
deconstructed the runtime execution and deployment environment of Cloud
applications, and identified 18 environmental factors and 12 workload factors
that would be influential on the energy consumption. In particular, there are
complicated trade-offs and even debates when dealing with the combinational
impacts of multiple factors.


  We review research at the National Physical Laboratory (NPL) in the area of
trustworthy artificial intelligence (TAI), and more specifically trustworthy
machine learning (TML), in the context of metrology, the science of
measurement. We describe three broad themes of TAI: technical, socio-technical
and social, which play key roles in ensuring that the developed models are
trustworthy and can be relied upon to make responsible decisions. From a
metrology perspective we emphasise uncertainty quantification (UQ), and its
importance within the framework of TAI to enhance transparency and trust in the
outputs of AI systems. We then discuss three research areas within TAI that we
are working on at NPL, and examine the certification of AI systems in terms of
adherence to the characteristics of TAI.


  Gender inclusivity in language technologies has become a prominent research
topic. In this study, we explore gender-neutral translation (GNT) as a form of
gender inclusivity and a goal to be achieved by machine translation (MT)
models, which have been found to perpetuate gender bias and discrimination.
Specifically, we focus on translation from English into Italian, a language
pair representative of salient gender-related linguistic transfer problems. To
define GNT, we review a selection of relevant institutional guidelines for
gender-inclusive language, discuss its scenarios of use, and examine the
technical challenges of performing GNT in MT, concluding with a discussion of
potential solutions to encourage advancements toward greater inclusivity in MT.


  The current paper is devoted to the study of existence and non-existence of
transition fronts for two species competition lattice system in random media,
and explore the influence of randomness of the media on the wave profiles and
wave speeds of such transition fronts. We first establish comparison principle
for sub-solutions and super-solutions of the related cooperative system. Next,
under some proper assumptions, we construct appropriate sub-solutions and
super-solutions for the cooperative system. Finally, we show that random
transition fronts exist if their least mean speed is greater than an explicit
threshold and there is no random transition front with least mean speed less
than the threshold.


  We perform a systematic investigation of Kazhdan's relative Property (T) for
pairs (G,X), where G a locally compact group and X is any subset. When G is a
connected Lie group or a p-adic algebraic group, we provide an explicit
characterization of subsets X of G such that (G,X) has relative Property (T).
In order to extend this characterization to lattices of G, a notion of
"resolutions" is introduced, and various characterizations of it are given.
Special attention is paid to subgroups of SU(2,1) and SO(4,1).


  In 2013, Cuturi [Cut13] introduced the Sinkhorn algorithm for matrix scaling
as a method to compute solutions to regularized optimal transport problems. In
this paper, aiming at a better convergence rate for a high accuracy solution,
we work on understanding the Sinkhorn algorithm under regularization
scheduling, and thus modify it with a mechanism that adaptively doubles the
regularization parameter $\eta$ periodically. We prove that such modified
version of Sinkhorn has an exponential convergence rate as iteration complexity
depending on $\log(1/\varepsilon)$ instead of $\varepsilon^{-O(1)}$ from
previous analyses [Cut13][ANWR17] in the optimal transport problems with
integral supply and demand. Furthermore, with cost and capacity scaling
procedures, the general optimal transport problem can be solved with a
logarithmic dependence on $1/\varepsilon$ as well.


  Often the best performing deep neural models are ensembles of multiple
base-level networks, nevertheless, ensemble learning with respect to domain
adaptive person re-ID remains unexplored. In this paper, we propose a multiple
expert brainstorming network (MEB-Net) for domain adaptive person re-ID,
opening up a promising direction about model ensemble problem under
unsupervised conditions. MEB-Net adopts a mutual learning strategy, where
multiple networks with different architectures are pre-trained within a source
domain as expert models equipped with specific features and knowledge, while
the adaptation is then accomplished through brainstorming (mutual learning)
among expert models. MEB-Net accommodates the heterogeneity of experts learned
with different architectures and enhances discrimination capability of the
adapted re-ID model, by introducing a regularization scheme about authority of
experts. Extensive experiments on large-scale datasets (Market-1501 and
DukeMTMC-reID) demonstrate the superior performance of MEB-Net over the
state-of-the-arts.


  Based on the shearlet transform we present a general construction of
continuous tight frames for $L^2(\mathbb{R}^2)$ from any sufficiently smooth
function with anisotropic moments. This includes for example compactly
supported systems, piecewise polynomial systems, or both. From our earlier
results it follows that these systems enjoy the same desirable approximation
properties for directional data as the previous bandlimited and very specific
constructions due to Kutyniok and Labate. We also show that the representation
formulas we derive are in a sense optimal for the shearlet transform.


  In this second paper on loop quantization of Gowdy model, we introduce the
kinematical Hilbert space on which appropriate holonomies and fluxes are well
represented. The quantization of the volume operator and the Gauss constraint
is straightforward. Imposition of the Gauss constraint can be done on the
kinematical Hilbert space to select subspace of gauge invariant states. We
carry out the quantization of the Hamiltonian constraint making specific
choices. Alternative choices are briefly discussed. It appears that to get
spatial correlations reflected in the Hamiltonian constraint, one may have to
adopt the so called `effective operator viewpoint'.


  We present the VISTA-CFHT Stripe 82 (VICS82) survey: a near-infrared (J+Ks)
survey covering 150 square degrees of the Sloan Digital Sky Survey (SDSS)
equatorial Stripe 82 to an average depth of J=21.9 AB mag and Ks=21.4 AB mag
(80% completeness limits; 5-sigma point source depths are approximately 0.5 mag
brighter). VICS82 contributes to the growing legacy of multi-wavelength data in
the Stripe 82 footprint. The addition of near-infrared photometry to the
existing SDSS Stripe 82 coadd ugriz photometry reduces the scatter in stellar
mass estimates to delta log(M_stellar)~0.3 dex for galaxies with
M_stellar>10^9M_sun at z~0.5, and offers improvement compared to optical-only
estimates out to z~1, with stellar masses constrained within a factor of
approximately 2.5. When combined with other multi-wavelength imaging of the
Stripe, including moderate-to-deep ultraviolet (GALEX), optical and
mid-infrared (Spitzer IRAC) coverage, as well as tens of thousands of
spectroscopic redshifts, VICS82 gives access to approximately 0.5 Gpc^3 of
comoving volume. Some of the main science drivers of VICS82 include (a)
measuring the stellar mass function of L^star galaxies out to z~1; (b)
detecting intermediate redshift quasars at 2<z<3.5; (c) measuring the stellar
mass function and baryon census of clusters of galaxies, and (d) performing
optical/near-infrared-cosmic microwave background lensing cross-correlation
experiments linking stellar mass to large-scale dark matter structure. Here we
define and describe the survey, highlight some early science results and
present the first public data release, which includes an SDSS-matched catalogue
as well as the calibrated pixel data itself.


  Many $z=1.5$ galaxies with a stellar mass ($M_{\star}$) $\geq
10^{10}\,\mathrm{M}_\odot$ are already quenched in both galaxy clusters ($>50$
per cent) and the field ($>20$ per cent), with clusters having a higher
quenched fraction at all stellar masses compared to the field. A puzzling issue
is that these massive quenched galaxies have stellar populations of similar age
in both clusters and the field. This suggests that, despite the higher quenched
fraction in clusters, the dominant quenching mechanism for massive galaxies is
similar in both environments. In this work, we use data from the cosmological
hydrodynamic simulations Hydrangea and EAGLE to test whether the excess
quenched fraction of massive galaxies in $z = 1.5$ clusters results from
fundamental differences in their halo properties compared to the field. We find
that (i) at $10^{10} \leq$ $M_{\star}/\,\mathrm{M}_\odot\leq 10^{11}$, quenched
fractions in the redshift range $1.5 < z < 3.5$ are consistently higher for
galaxies with higher peak maximum circular velocity of the dark matter halo
($v_{\mathrm{max, peak}}$), and (ii) the distribution of $v_{\mathrm{max,
peak}}$ is strongly biased towards higher values for cluster satellites
compared to the field centrals. Due to this difference in the halo properties
of cluster and field galaxies, secular processes alone may account for (most
of) the environmental excess of massive quenched galaxies in high-redshift
(proto) clusters. Taken at face value, our results challenge a fundamental
assumption of popular quenching models, namely that clusters are assembled from
an unbiased subset of infalling field galaxies. If confirmed, this would imply
that such models must necessarily fail at high redshift, as indicated by recent
observations.


  The zero temperature phase diagram of the mono-axial chiral helimagnet in the
magnetic field plane formed by the components parallel and perpendicular to the
helical axis is thoroughly analyzed. The nature of the transition to the
commensurate state depends on the angle between the field and the helical axis.
For field directions close to the directions parallel or perpendicular to the
helical axis the transition is continuous, while for intermediate angles the
transition is discontinuous and the incommensurate and commensurate states
coexist on the transition line. The continuous and discontinuous transition
lines are separated by two tricritical points with specific singular behaviour.
The location of the continuous and discontinuous lines and of the tricritical
points depend strongly on the easy-plane anisotropy, the effect of which is
analyzed. For large anisotropy the conical approximation locates the transition
line very accurately, although it does not predict the continuous transitions
nor the tricitical behaviour. It is shown that for large anisotropy, as in
CrNb3S6, the form of the transition line is universal, that is, independent of
the sample, and obeys a simple equation. The position of the tricritical
points, which is not universal, is theoretically estimated for a sample of
CrNb3S6


  We develop a formula (Theorem 5.1) which allows to compute top Chern classes
of vector bundles on the vanishing locus $V(s)$ of a section of this bundle.
This formula particularly applies in the case when $V(s)$ is the union of
locally complete intersections giving the individual contribution of each
component and their mutual intersections. We conclude with applications to the
enumeration of rational curves in complete intersections in projective space.


  Binary code analysis is the foundation of crucial tasks in the security
domain; thus building effective binary analysis techniques is more important
than ever. Large language models (LLMs) although have brought impressive
improvement to source code tasks, do not directly generalize to assembly code
due to the unique challenges of assembly: (1) the low information density of
assembly and (2) the diverse optimizations in assembly code. To overcome these
challenges, this work proposes a hierarchical attention mechanism that builds
attention summaries to capture the semantics more effectively, and designs
contrastive learning objectives to train LLMs to learn assembly optimization.
Equipped with these techniques, this work develops Nova, a generative LLM for
assembly code. Nova outperforms existing techniques on binary code
decompilation by up to 146.54%, and outperforms the latest binary code
similarity detection techniques by up to 6.17%, showing promising abilities on
both assembly generation and understanding tasks.


  Sports visualization has developed into an active research field over the
last decades. Many approaches focus on analyzing movement data recorded from
unstructured situations, such as soccer. For the analysis of choreographed
activities like formation dancing, however, the goal differs, as dancers follow
specific formations in coordinated movement trajectories. To date, little work
exists on how visual analytics methods can support such choreographed
performances. To fill this gap, we introduce a new visual approach for planning
and assessing dance choreographies. In terms of planning choreographies, we
contribute a web application with interactive authoring tools and views for the
dancers' positions and orientations, movement trajectories, poses, dance floor
utilization, and movement distances. For assessing dancers' real-world movement
trajectories, extracted by manual bounding box annotations, we developed a
timeline showing aggregated trajectory deviations and a dance floor view for
detailed trajectory comparison. Our approach was developed and evaluated in
collaboration with dance instructors, showing that introducing visual analytics
into this domain promises improvements in training efficiency for the future.


  We study the problem of online learning in a two-player decentralized
cooperative Stackelberg game. In each round, the leader first takes an action,
followed by the follower who takes their action after observing the leader's
move. The goal of the leader is to learn to minimize the cumulative regret
based on the history of interactions. Differing from the traditional
formulation of repeated Stackelberg games, we assume the follower is
omniscient, with full knowledge of the true reward, and that they always
best-respond to the leader's actions. We analyze the sample complexity of
regret minimization in this repeated Stackelberg game. We show that depending
on the reward structure, the existence of the omniscient follower may change
the sample complexity drastically, from constant to exponential, even for
linear cooperative Stackelberg games. This poses unique challenges for the
learning process of the leader and the subsequent regret analysis.


  Rapidity distributions of both E895 proton data at AGS energies and NA49 net
proton data at SPS energies can be described reasonably well with a potential
version of the UrQMD in which mean-field potentials for both pre-formed hadrons
and confined baryons are considered, with the help of a traditional coalescence
afterburner in which one parameter set for both relative distance $R_0$ and
relative momentum $P_0$, (3.8 fm, 0.3 GeV$/$c), is used. Because of the large
cancellation between the expansion in $R_0$ and the shrinkage in $P_0$ through
the Lorentz transformation, the relativistic effect in clusters has little
effect on the rapidity distribution of free (net) protons. Using a
Woods-Saxon-like function instead of a pure logarithmic function as seen by
FOPI collaboration at SIS energies, one can fit well both the data at SIS
energies and the UrQMD calculation results at AGS and SPS energies. Further, it
is found that for central Au+Au or Pb+Pb collisions at top SIS, SPS and RHIC
energies, the proton fractions in clusters are about 33$\%$, 10$\%$, and
0.7$\%$, respectively.


  We classify the four dimensional perfect non-simple evolution algebras over a
field having characteristic different from 2 and in which there are roots of
orders 2, 3 and 7.


  The thermodynamic problem of a gas of static quarks carrying U(N) charges and
interacting with each other via U(N) electric gauge fields is formulated and
solved in the large N limit. In a lattice theory, the solution can be found in
any dimension. In particular, in 1+1-dimensions, the continuum model can also
be solved. In that case, and when the quarks are in the adjoint representation,
the explicit solution exhibits a first order quark confinement-deconfinement
transition at a critical temperature and density. We also show that, when there
are fundamental representation quarks, this phase transition persists until the
relative density of fundamental quarks is comparable to the density of adjoint
quarks, where it becomes a third order transition. We discuss the possible
interpretation of the third order transition as deconfinement.


  Dissipative systems can often exhibit wavelength-dependent loss rates. One
prominent example is Rydberg polaritons formed by electromagnetically-induced
transparency, which have long been a leading candidate for studying the physics
of interacting photons and also hold promise as a platform for quantum
information. In this system, dissipation is in the form of quantum diffusion,
i.e., proportional to $k^2$ ($k$ being the wavevector) and vanishing at long
wavelengths as $k\to 0$. Here, we show that one-dimensional condensates subject
to this type of loss are unstable to long-wavelength density fluctuations in an
unusual manner: after a prolonged period in which the condensate appears to
relax to a uniform state, local depleted regions quickly form and spread
ballistically throughout the system. We connect this behavior to the
leading-order equation for the nearly-uniform condensate -- a dispersive
analogue to the Kardar-Parisi-Zhang (KPZ) equation -- which develops
singularities in finite time. Furthermore, we show that the wavefronts of the
depleted regions are described by purely dissipative solitons within a pair of
hydrodynamic equations, with no counterpart in lossless condensates. We close
by discussing conditions under which such singularities and the resulting
solitons can be physically realized.


  We demonstrate that the amorphous material PAF-1, C[(C6H4)2]2, forms a
continuous random network in which tetrahedral carbon sites are connected by
4,4'-biphenyl linkers. Experimental neutron total scattering measurements on
deuterated, hydrogenous, and null-scattering samples agree with molecular
dynamics simulations based on this model. From the MD model, we are able for
the first time to interrogate the atomistic structure. The small-angle
scattering is consistent with Porod scattering from particle surfaces, of the
form Q^{-4}, where Q is the scattering vector. We measure a distinct peak in
the scattering at Q = 0.45 {\AA}^{-1}, corresponding to the first sharp
diffraction peak in amorphous silica, which indicates the structural analogy
between these two amorphous tetrahedral networks.


  We explore multi-component dark matter models where the dark sector consists
of multiple stable states with different mass scales, and dark forces coupling
these states further enrich the dynamics. The multi-component nature of the
dark matter naturally arises in supersymmetric models, where both R parity and
an additional symmetry, such as a $Z_2$, is preserved. We focus on a particular
model where the heavier component of dark matter carries lepton number and
annihilates mostly to leptons. The heavier component, which is essentially a
sterile neutrino, naturally explains the PAMELA, ATIC and synchrotron signals,
without an excess in antiprotons which typically mars other models of weak
scale dark matter. The lighter component, which may have a mass from a GeV to a
TeV, may explain the DAMA signal, and may be visible in low threshold runs of
CDMS and XENON, which search for light dark matter.


  Photon number distributions from classical and non-classical light sources
have been studied extensively, yet their impact on photoemission processes is
largely unexplored. In this article, we present measurements of electron
number-distributions from metal needle tips illuminated with ultrashort light
pulses of different photon quantum statistics. By varying the photon statistics
of the exciting light field between classical (Poissonian) and quantum
(super-Poissonian), we demonstrate that the measured electron distributions are
changed substantially. Using single-mode bright squeezed vacuum light, we
measure extreme statistics events with up to 65 electrons from one light pulse
at a mean of 0.27 electrons per pulse - the likelihood for such an event equals
$10^{-128}$ with Poissonian statistics. Changing the number of modes of the
exciting bright squeezed vacuum light, we can tailor the electron-number
distribution on demand. Most importantly, our results demonstrate that the
photon statistics is imprinted from the driving light to the emitted electrons,
opening the door to new sensor devices and to strong-field quantum optics with
quantum light.


  This paper proposes a blind detection problem for low pass graph signals.
Without assuming knowledge of the exact graph topology, we aim to detect if a
set of graph signal observations are generated from a low pass graph filter.
Our problem is motivated by the widely adopted assumption of low pass
(a.k.a.~smooth) signals required by many existing works in graph signal
processing (GSP), as well as the longstanding problem of network dynamics
identification. Focusing on detecting low pass graph signals on modular graphs
whose cutoff frequency coincides with the number of clusters in the graph, we
propose to leverage the unique spectral pattern exhibited by such low pass
graph signals. We analyze the sample complexity of these detectors considering
the effects of graph filter's properties, random delays, and other parameters.
We show novel applications of the blind detector on robustifying graph
learning, identifying antagonistic ties in opinion dynamics, and detecting
anomalies in power systems. Numerical experiments validate our findings.


  Being an effective non-orthogonal multiple access (NOMA) technique, sparse
code multiple access (SCMA) is promising for future wireless communication.
Compared with orthogonal techniques, SCMA enjoys higher overloading tolerance
and lower complexity because of its sparsity. In this paper, based on
deterministic message passing algorithm (DMPA), algorithmic simplifications
such as domain changing and probability approximation are applied for SCMA
decoding. Early termination, adaptive decoding, and initial noise reduction are
also employed for faster convergence and better performance. Numerical results
show that the proposed optimizations benefit both decoding complexity and
speed. Furthermore, efficient hardware architectures based on folding and
retiming are proposed. VLSI implementation is also given in this paper.
Comparison with the state-of-the-art have shown the proposed decoder's
advantages in both latency and throughput (multi-Gbps).


  If a quantum channel or process cannot be described by any
measure-and-prepare scheme, we may say the channel is in \textit{quantum
domain} (QD) since it can transmit quantum correlations. The concept of QD
clarifies the role of quantum channel in quantum information theory based on
the local-operation-and-classical-communication (LOCC) paradigm: The quantum
channel is only useful if it cannot be simulated by LOCC. We construct a simple
scheme to verify that a given physical process or channel is in QD by using two
non-orthogonal states. We also consider the application for the experiments
such as the transmission or storage of quantum optical coherent states,
single-photon polarization states, and squeezed vacuum states.


  Nanotechnology research requires the routine use of characterization methods
with high spatial resolution. These experiments are rather costly, not only
from the point of view of the expensive microscopes, but also considering the
need of a rather specialized equipment operator. Here, we describe the
construction of an inexpensive and simple device that allows the analysis of
nanoparticle in a FEG-SEM; images can be generated at high magnifications (ex.
x500.000) and with nanometric resolution. It is based on the acquisition of
transmitted electrons annular dark field (TE-ADF) signal; the systems can carry
up to 16 TEM samples and, it is compatible with SEM sample exchange air-lock.
Performance test have shown the measured ADF signal showed the atomic number
and thickness dependence for transition metal nanoparticle about 10 nm in
diameter. Also, the signal quality is high enough that the determination of the
histogram of size distribution can be performed using a conventional image
processing software, for gold particles in the range of 2-10 nm in diameter.
The developed ADF device allows a much faster and cheaper high spatial
resolution imaging of nanoparticle samples for routine morphological
characterization and, provides an invaluable high throughput tool for an
efficient sample screening.


  We will give a concise formula for the Hessian determinant of a smooth
function $y:\mathbb R^n\supseteq\Omega\to\mathbb R$ such that its graph is
contained in a quadratic hypersurface. The proof will make heavy use of matrix
algebra.


  Computational modeling of a complex system is limited by the parts of the
system with the least information. While detailed models and high-resolution
data may be available for parts of a system, abstract relationships are often
necessary to connect the parts and model the full system. For example, modeling
food security necessitates the interaction of climate and socioeconomic
factors, with models of system components existing at different levels of
information in terms of granularity and resolution. Connecting these models is
an ongoing challenge. In this work, we demonstrate methodology to quantize and
integrate information from data and detailed component models alongside
abstract relationships in a hybrid element-based modeling and simulation
framework. In a case study of modeling food security, we apply quantization
methods to generate (1) time-series model input from climate data and (2) a
discrete representation of a component model (a statistical emulator of crop
yield), which we then incorporate as an update rule in the hybrid element-based
model, bridging differences in model granularity and resolution. Simulation of
the hybrid element-based model recapitulated the trends of the original
emulator, supporting the use of this methodology to integrate data and
information from component models to simulate complex systems.