  Due to the ever-growing concerns on air pollution and energy security, many
cities have started to update their taxi fleets with electric ones. Although
environmentally friendly, the rapid promotion of electric taxis raises problems
to both taxi drivers and governments, e.g., prolonged waiting/charging time,
unbalanced utilization of charging infrastructures and reduced taxi supply due
to the long charging time. In this paper, we make the first effort to
understand the long-term evolving patterns through a five-year study on one of
the largest electric taxi networks in the world, i.e., the Shenzhen electric
taxi network in China. In particular, we perform a comprehensive measurement
investigation called ePat to explore the evolving mobility and charging
patterns of electric vehicles. Our ePat is based on 4.8 TB taxi GPS data, 240
GB taxi transaction data, and metadata from 117 charging stations, during an
evolving process from 427 electric taxis in 2013 to 13,178 in 2018. Moreover,
ePat also explores the impacts of various contexts and benefits during the
evolving process. Our ePat as a comprehensive investigation of the electric
taxi network mobility and charging evolving has the potential to advance the
understanding of the evolving patterns of electric taxi networks and pave the
way for analyzing future shared autonomous vehicles.


  We study the interplay between Coulomb blockade and superconductivity in a
tunable superconductor-superconductor-normal metal single-electron transistor.
The device is realized by connecting the superconducting island via an oxide
barrier to the normal metal lead and with a break junction to the
superconducting lead. The latter enables Cooper pair transport and (multiple)
Andreev reflection. We show that those processes are relevant also far above
the superconducting gap and that signatures of Coulomb blockade may reoccur at
high bias while they are absent for small bias in the strong-coupling regime.
Our experimental findings agree with simulations using a master equation
approach in combination with the full counting statistics of multiple Andreev
reflection.


  The presence of H3+ in the interstellar medium was forecast almost four
decades ago. Almost three decades ago it was asserted that its reactions with
neutral molecular and atomic species directly lead to the production of many of
the interstellar molecules that have been discovered by radio and infrared
astronomers. With the recent detection of H3+ in interstellar space,
astronomers finally have direct confirmation of H3+ as the foundation of
ion-molecule interstellar chemistry. Although many questions remain to be
answered, it is clear that H3+is a unique tool for understanding the properties
of interstellar clouds.


  We have performed low temperature scanning tunnelling spectroscopy (STS)
measurements on graphene epitaxially grown on Ru(0001). An inelastic feature,
related to the excitation of a vibrational breathing mode of the graphene
lattice, was found at 360 meV. The change in the differential electrical
conductance produced by this inelastic feature, which is associated with the
electron-phonon interaction strength, varies spatially from one position to
other of the graphene supercell. This inhomogeneity in the electronic
properties of graphene on Ru(0001) results from local variations of the
carbon-ruthenium interaction due to the lattice mismatch between the graphene
and the Ru(0001) lattices.


  The left-right symmetric model (LRSM) is a well-motivated framework to
restore parity and implement seesaw mechanisms for the tiny neutrino masses at
or above the TeV-scale, and has a very rich phenomenology at both the
high-energy and high-precision frontiers. In this paper we examine the phase
transition and resultant gravitational waves (GWs) in the minimal version of
LRSM. Taking into account all the theoretical and experimental constraints on
LRSM, we identify the parameter regions with strong first-order phase
transition and detectable GWs in the future experiments. It turns out in a
sizeable region of the parameter space, GWs can be generated in the phase
transition with the strength of $10^{-17}$ to $10^{-12}$ at the frequency of
0.1 to 10 Hz, which can be detected by BBO and DECIGO. Furthermore, GWs in the
LRSM favor a relatively light $SU(2)_R$-breaking scalar $H_3^0$, which is
largely complementary to the direct searches of a long-lived neutral scalar at
the high-energy colliders. It is found that the other heavy scalars and the
right-handed neutrinos in the LRSM also play an important part for GW signal
production in the phase transition.


  In order to better comprehend the global carbon cycle and predict the
prognosis for the response to climate change, accurate assessment of sea-air
CO2 flux is necessary. Comparing to the relative homogeneously distribution of
atmospheric CO2 , the pCO2 in the sea surface water is exposed to huge
spatio-temporal variability, which leaves a prominent uncertainty resource.
Many regional studies typically divided the observational pCO2 data into grid
boxes so as to obtain enough data points statistically for their calculatio.
However, using the data inside the grid box areas to represent its holistic
property (such as standard deviation to represent spatial variance) will mix up
three different uncertainty sources. First, the analytical error in the pCO2
determination and the associated environmental parameters used in deriving pCO2
. Second, the spatial variance because of inhomogenous spatial pattern of sea
surface pCO2 , especially the region with a dramatically dynamic circumstance
like: coastal areas, boundaries or fronts and etc. Third, the estimation
process in undersampling condition, specifically, this kind of uncertainty
origins from the process that using a sparse data to represent its holistic
property of the box area. Common uncertainty quantification by Standard
Deviation will mix up the different sources of uncertainty. In this paper, it
introduces an optimized procedure to determine three sources of uncertainty
(1st analytical error, 2nd spatial variance, 3rd bias from undersampling.)
using the combined remote sensing-derived and field-measured pCO2 data. In
order to provide a comprehensive error assessment report.


  Newman's conjecture (proved by Rodgers and Tao in 2018) concerns a certain
family of deformations $\{\xi_t(s)\}_{t \in \mathbb{R}}$ of the Riemann xi
function for which there exists an associated constant $\Lambda \in \mathbb{R}$
(called the de Bruijn-Newman constant) such that all the zeros of $\xi_t$ lie
on the critical line if and only if $t \geq \Lambda$. The Riemann hypothesis is
equivalent to the statement that $\Lambda \leq 0$, and Newman's conjecture
states that $\Lambda \geq 0$.
  In this paper we give a new proof of Newman's conjecture which avoids many of
the complications in the proof of Rodgers and Tao. Unlike the previous best
methods for bounding $\Lambda$, our approach does not require any information
about the zeros of the zeta function, and it can be readily be applied to a
wide variety of $L$-functions. In particular, we establish that any
$L$-function in the extended Selberg class has an associated de Bruijn-Newman
constant and that all of these constants are nonnegative.
  Stated in the Riemann xi function case, our argument proceeds by showing that
for every $t < 0$ the function $\xi_t$ can be approximated in terms of a
Dirichlet series $\zeta_t(s)=\sum_{n=1}^{\infty}\exp(\frac{t}{4} \log^2
n)n^{-s}$ whose zeros then provide infinitely many zeros of $\xi_t$ off the
critical line.


  We show how the neutrino mixing angles and oscillation phase can be predicted
from tri-bimaximal neutrino mixing, corrected by charged lepton mixing angles
which are related to quark mixing angles via quark-lepton unification. The
tri-bimaximal neutrino mixing can naturally originate from the see-saw
mechanism via constrained sequential dominance (CSD), where CSD can result from
the vacuum alignment of a non-Abelian family symmetry such as SO(3). We
construct a realistic model of quark and lepton masses and mixings based on
SO(3) family symmetry with quark-lepton unification based on the Pati-Salam
gauge group. The atmospheric angle is predicted to be approximately maximal
$\theta_{23}= 45^\circ$, corrected by the quark mixing angle
$\theta_{23}^{\mathrm{CKM}}\approx 2.4^\circ$, with the correction controlled
by an undetermined phase in the quark sector. The solar angle is predicted by
the tri-bimaximal complementarity relation: $\theta_{12}+
\frac{1}{\sqrt{2}}\frac{\theta_{\mathrm{C}}}{3} \cos (\delta - \pi) \approx
35.26^\circ $, where $\theta_{\mathrm{C}}$ is the Cabibbo angle and $\delta$ is
the neutrino oscillation phase. The reactor angle is predicted to be
$\theta_{13} \approx \frac{1}{\sqrt{2}}\frac{\theta_{\mathrm{C}}}{3}\approx
3.06^\circ$. The MNS neutrino oscillation phase $\delta$ is predicted in terms
of the solar angle to be $\cos (\delta - \pi) \approx (35.26^\circ -
\theta_{12}^\circ)/3.06^\circ $. These predictions can all be tested by future
high precision neutrino oscillation experiments, thereby probing the nature of
high energy quark-lepton unification.


  In this paper, we define and study a kind of Steinberg representation for
linear algebraic groups of a particular kind, called groups of parahoric type,
defined overa finite field; in particular, when G is the group of F-points of a
connected reductive quasisplit group defined over F which splits over an
unramified extension of F, the quotients of parahoric subgroups of G by their
congruence subgroups are groups of parahoric type. In particular, under certain
conditions on the residual characteristic p of F, we determine the irreducible
factors of the Steinberg representation of a group of parahoric type associated
to a pseudo-Borel subgroup of this group in the special case, that is when this
group os a quotient of a maximal special parahoric subgroup of G.


  We develop number theoretic tools that allow to perform computations relevant
for the quantum mechanics over finite fields of arbitrary, odd size, with the
same speedup that is enjoyed by the Fast Fourier Transform.


  This paper presents a detailed symbolic approach to the study of self-similar
tilings. It uses properties of addresses associated with graph-directed
iterated function systems to establish conjugacy properties of tiling spaces.
Tiles may be fractals and the tiled set may be a complicated unbounded subset
of $\mathbb{R}^{M}$.


  We discuss the reaction rate of the muon-catalysed $t$-$t$ fusion. The
reaction rate is determined as a function of the temperature using the model of
"in flight" fusion. We especially take into account the effect of the
fluctuation of the cross section due to the existence of the muon. The obtained
reaction rate 5.0$\times10^{-3} \mu$s$^{-1}$ is a factor of 10$^{-3}$ smaller
than the experimental muonic cycling rate in the solid tritium target.


  We study the natural property of projectability of a torsion-free connection
along a foliation on the underlying manifold, which leads to a projected
torsion-free connection on a local leaf space, focusing on projectability of
Levi-Civita connections of pseudo-Riemannian metric along foliations tangent to
null parallel distributions. For the neutral metric signature and
mid-dimensional distributions, Afifi showed in 1954 that projectability of the
Levi-Civita connection characterizes, locally, the case of Patterson and
Walker's Riemann extension metrics. We extend this correspondence to null
parallel distributions of any dimension, introducing a suitable generalization
of Riemann extensions.


  We introduce an analytical framework to understand the path for scaling
nanophotonic interconnects to meet the energy and footprint requirements of
CMOS global interconnects. We derive the device requirements for sub 100
fJ/cm/bit interconnects including tuning power, serialization-deserialization
energy, optical insertion losses, extinction ratio and bit error rates. Using
CMOS with integrated nanophotonics as an example platform, we derive the
energy/bit, linear and areal bandwidth density of optical interconnects. We
also derive the targets for device performance which indicate the need for
continued improvements in insertion losses (<8dB), laser efficiency,
operational speeds (>40 Gb/s), tuning power (<100 {\mu}W/nm),
serialization-deserialization (< 10 fJ/bit/Operation) and necessity for
spectrally selective devices with wavelength multiplexing (> 6 channels).


  Prompt fission neutron spectra from $^{239}$Pu($n,f$) were measured for
incident neutron energies from $0.7$ to $700\,$MeV at the Weapons Neutron
Research facility (WNR) of the Los Alamos Neutron Science Center. A newly
designed high-efficiency fission chamber was coupled to the highly segmented
Chi-Nu array to detect neutrons emitted in fission events. The double
time-of-flight technique was used to deduce the incident-neutron energies from
the spallation target and the outgoing-neutron energies from the fission
chamber. Prompt fission neutron spectra (PFNS) were measured with respect to
$^{252}$Cf spontaneous fission down to $200\,$keV and up to about $12\,$MeV for
all the incident neutron energies with typical uncertainties well below $2\%$
up to about $10\,$MeV outgoing-neutron energy. The general trend of PFNS is
well reproduced by JEFF3.3 and ENDF-B\VIII.0 evaluations. Discrepancies were
however observed for the low-energy part of the spectra, where evaluations
overestimate the number of emitted neutrons. Neutron multiplicities and average
kinetic energies as a function of incident-neutron energy are obtained
experimentally with reported uncertainties below $0.4\%$. Neutron
multiplicities disagree with some older datasets above $6\,$ MeV, indicating
the need of using a high-efficiency fission detector, which does not bias the
data. The measured mean kinetic energies agree with the most recent data.
Evaluations fairly reproduce the trend, but fail to reproduce the experimental
values within their uncertainties.


  It is believed that young massive stars orbiting Sgr A* in two stellar discs
on scales of 0.1-0.2 parsecs were formed either farther out in the Galaxy and
then quickly migrated inward, or in situ in a massive self-gravitating disc.
Comparing N-body evolution of stellar orbits with observational constraints, we
set upper limits on the masses of the two stellar systems. These masses turn
out to be few times lower than the expected total stellar mass estimated from
the observed young high-mass stellar population and the standard galactic IMF.
If these stars were formed in situ, in a massive self-gravitating disc, our
results suggest that the formation of low-mass stars was suppressed by a factor
of at least a few, requiring a top-heavy initial mass function (IMF) for stars
formed near sgr A*.


  Let $K$ be a compact subset of $\mathbb C^n$, $K^\ast K$ a closed subset. In
this paper we are dealing with evolution $E_t(K,K^\ast)$ of $K$ with fixed part
$K^\ast$ by Levi form. This amounts to solve a parabolic problem for an
elliptic operator. We prove existence and unicity for such a problem and the
solution $u(z,t)$ exists for any time $t\ge 0$.If $K$ is a smooth graph
$\Gamma$ and $K^\ast={\rm b}\Gamma$ the the evolution $E_t(\Gamma,{\rm
b}\Gamma)$ is still a graph. In particular, if ${\rm b}\Gamma$ bounds a Levi
flat hypersurface $M$ then $E_t(\Gamma,{\rm b}\Gamma)\to M$ as $t\to+\infty$.


  It is well known that periodic potentials can be used to induce freezing and
melting in colloids. Here, we transfer this concept to active systems and find
the emergence of a so-far unknown active matter phase in between the frozen
solid-like phase and the molten phase. This phase of "active adaptolates"
adopts the geometry of the underlying lattice like the frozen phase, maintains
ballistic dynamics like the molten phase, and percolates. In particular, this
finding creates a route to use external fields for designing the intrinsic
structure of active systems without qualitatively affecting their dynamics.


  Based on an isospin and momentum-dependent transport model, we studied the
effect of momentum-dependent symmetry potential on the free neutron to proton
ratio of preequilibrium nucleon emission. It is found that for
$^{132}Sn+^{124}Sn$ reaction at 400 MeV/nucleon incident beam energy, free n/p
ratio of preequilibrium nucleon emission mainly probe the momentum-dependence
of symmetry potential at nucleon momentum around 400 $\sim$ 600 MeV/c. Whereas
for 200 MeV/nucleon incident beam energy, this observable mainly probe the
momentum-dependence of symmetry potential at nucleon momentum around 200 $\sim$
400 MeV/c. To probe the symmetry energy/potential using free n/p ratio, not all
the details of the momentum-dependence of the symmetry potential are important,
the values of symmetry potential at only certain momentum range are crucial for
an observable. It is important to input reasonable density- and
momentum-dependence of the symmetry potential according to the magnitude of
incident beam energy of heavy-ion collisions. The present experimental data on
the symmetry potential are not enough for probing the density-dependent
symmetry energy. More experimental data (such as nucleon and nuclei's
scattering experiments at different nucleonic momenta and densities) on the
symmetry potential are therefore needed to pin down the density-dependent
symmetry energy.


  In this paper we consider a class of discrete time prey-predator models with
three interacting species defined on the two-dimensional simplex. For some
choices of parameters of the operator describing the evolution of the relative
frequencies, we show that the ergodic hypothesis does not hold. Moreover, we
prove that any order Ces\`aro mean of the trajectories diverges. For another
class of parameters, we show that all orbits starting from the interior of the
simplex converge to the unique fixed point of the operator while for the
remaining choices of parameters all orbits converge to one of the vertices of
the simplex. Contrary to many authors we study discrete time models but we
include a speed function $f$ in the dynamics which allows us to approximate the
continuous-time case arbitrarily well when $f$ is small.


  Magnetotransport measurements were performed in a ultra-high mobility
GaAs/AlGaAs quantum well of density $\sim 3.0 \times 10^{11}$ $cm^{-2}$. The
temperature dependence of the magnetoresistance $R_{xx}$ was studied in detail
in the vicinity of $\nu={9/2}$. In particular, we discovered new minima in
$R_{xx}$ at filling factor $\nu\simeq 4{1/5}$ and $4{4/5}$, but only at
intermediate temperatures $80\lesssim T\lesssim 120$ mK. We interpret these as
evidence for a fractional quantum Hall liquid forming in the N=2 Landau level
and competing with bubble and Wigner crystal phases favored at lower
temperatures. Our data suggest that a magnetically driven insulator-insulator
quantum phase transition occurs between the bubble and Wigner crystal phases at
T=0.


  Legionella are gram-negative, facultative intracellular, and pathogenic
bacteria that pose a risk for human health and cause significant energy losses
due to extensive preventive heating-up of water installations. We investigate
acoustically-driven motion - acoustophoresis - of several Legionella species,
Escherichia coli, Pseudomonas aeruginosa, and Acanthamoeba castellanii, a
common Legionella host in water. All the investigated cells can be acoustically
manipulated in an ultrasonic standing wave in water, as they possess a non-zero
acoustic contrast that is positive for all of the cells, leading to the
focusing into pressure nodes of the standing wave. Multi-body simulations
indicate that an increase in cell concentration could significantly accelerate
the rate of focusing due to hydrodynamic interactions - a phenomenon that we
call collective hydrodynamic focusing (CHF). The results form a foundation for
acoustic manipulation of bacteria and could pave a path towards
acoustically-aided detection of Legionella in water.


  Motivated by MacKenzie's observation of a negative force couple near to
impact, this paper explores a model for how the golf club moves near to impact.
It assumes the golf club is moving as the distal arm of a double pendulum. At
impact the club head is moving straight down the target line, at its maximum
speed, on a path with a specified radius of curvature. From this model the
forces and torques required to move the club near to impact are calculated. The
negative couple near to impact is a robust feature of this model, balancing the
torque associated with the force that drives the center of mass of the golf
club. The negative couple allows the golfer to maintain a larger radius of
curvature of the path of the club head as it moves through impact.
  Because the negative couple can also serve to reduce the rotational speed of
the club, the presence of a negative couple at impact in the golf swing
manifests a trade between distance and direction.


  The contact graph of an arbitrary finite packing of unit balls in Euclidean
3-space is the (simple) graph whose vertices correspond to the packing elements
and whose two vertices are connected by an edge if the corresponding two
packing elements touch each other. One of the most basic questions on contact
graphs is to find the maximum number of edges that a contact graph of a packing
of n unit balls can have. In this paper, improving earlier estimates, we prove
that the number of touching pairs in an arbitrary packing of n unit balls in
Euclidean 3-space is always less than $6n-0.926n^{2/3}$. Moreover, as a natural
extension of the above problem, we propose to study the maximum number of
touching triplets (resp., quadruples) in an arbitrary packing of n unit balls
in Euclidean 3-space. In particular, we prove that the number of touching
triplets (resp., quadruples) in an arbitrary packing of n unit balls in
Euclidean 3-space is at most $25/3n$ (resp., $11/4n$).


  The transport properties of dense QCD matter play a crucial role in the
physics of neutron stars and their mergers, but are notoriously difficult to
study with traditional quantum field theory tools. Specializing to the case of
unpaired quark matter in beta equilibrium, we approach the problem through the
machinery of holography, in particular the V-QCD and D3-D7 models, and derive
results for the electrical and thermal conductivities and the shear and bulk
viscosities. In addition we compare the bulk to shear viscosity ratio to the
speed of sound and find that it violates the so-called Buchel bound. Our
results differ dramatically from earlier predictions of perturbative QCD, the
root causes and implications of which we analyze in detail.


  Micro-pattern gas detectors, such as the Gas Electron Multiplier (GEM) and
the Micromegas need narrow high density anode readout elements to achieve good
spatial resolution. A high-density anode readout would require an unmanageable
number of electronics channels for certain potential micro-detector
applications such as the Time Projection Chamber. We describe below a new
technique to achieve good spatial resolution without increasing the electronics
channel count in a modified micro-detector outfitted with a high surface
resistivity anode readout structure. The concept and preliminary measurements
of spatial resolution from charge dispersion in a modified GEM detector with a
resistive anode are described below.


  In order to understand the nature of magnetic reconnection in ``free space''
which is free from any influence of external circumstances, I have studied the
structure of spontaneous reconnection outflow using a shock tube approximation.
The reconnection system of this case continues to expand self-similarly. This
work aims 1) to solve the structure of reconnection outflow and 2) to clarify
the determination mechanism of reconnection rate of the ``self-similar
evolution model'' of fast reconnection.
  Many cases of reconnection in astrophysical phenomena are characterized by a
huge dynamic range of expansion in size ($\sim 10^7$ for typical solar flares).
Although such reconnection is intrinsically time dependent, a specialized model
underlying the situation has not been established yet.
  The theoretical contribution of this paper is in obtaining a solution for
outflow structure which is absent in our previous papers proposing the above
new model. The outflow has a shock tube-like structure, i.e., forward slow
shock, reverse fast shock and contact discontinuity between them. By solving
the structure in a sufficiently wide range of plasma-$\beta$: $0.001 \leq \beta
\leq 100$, we obtain an almost constant reconnection rate ($\sim 0.05$: this
value is the maximum for spontaneous reconnection and is consistent with
previous models) and a boundary value along the edge of the outflow (good
agreement with our simulation result) which is important to solve the inflow
region. Note that everything, including the reconnection rate, is spontaneously
determined by the reconnection system itself in our model.


  Many modern computational approaches to classical problems in quantitative
finance are formulated as empirical loss minimization (ERM), allowing direct
applications of classical results from statistical machine learning. These
methods, designed to directly construct the optimal feedback representation of
hedging or investment decisions, are analyzed in this framework demonstrating
their effectiveness as well as their susceptibility to generalization error.
Use of classical techniques shows that over-training renders trained investment
decisions to become anticipative, and proves overlearning for large hypothesis
spaces. On the other hand, non-asymptotic estimates based on Rademacher
complexity show the convergence for sufficiently large training sets. These
results emphasize the importance of synthetic data generation and the
appropriate calibration of complex models to market data. A numerically studied
stylized example illustrates these possibilities, including the importance of
problem dimension in the degree of overlearning, and the effectiveness of this
approach.


  In this article we investigate the structure of the four-point functions of
the $AdS_3$-WZNW model. We consider the integral expression for the unflowed
four-point correlator involving at least one state in the discrete part of the
spectrum derived by analytic continuation from the $H_3^+$-WZNW model and we
show that the conformal blocks can be obtained from those with an
extremal-weight state by means of an intertwining operator. We adapt the
procedure for dealing with correlators with a single unit of spectral flow
charge and we get a factorized integral expression for the corresponding
four-point function. We finally transform the formulas back to the space-time
picture.


  We examine the validity of the Fermi-liquid description of the dilute 2D hole
gas in the crossover from 'metallic'-to-'insulating' behaviour of R(T).It has
been established that, at r_s as large as 29, negative magnetoresistance does
exist and is well described by weak localisation. The dephasing time extracted
from the magnetoresistance is dominated by the T^2 -term due to Landau
scattering in the clean limit. The effect of hole-hole interactions, however,
is suppressed when compared with the theory for small r_s.


  A novel, compact, quasi-axisymmetric configuration is presented which
exhibits low fast-particle losses and is stable to ideal MHD instabilities. The
design has fast-particle loss rates below 8\% for flux surfaces within the
half-radius, and is shown to have an MHD-stability limit of a normalised
pressure of $\langle\beta\rangle=3\%$ where $\langle\beta\rangle$ is volume
averaged. The flux surfaces at various plasma betas and currents as calculated
using the SPEC equilibrium code are presented. Neoclassical transport
coefficients are shown to be similar to an equivalent tokamak, with a distinct
banana regime at half-radius. An initial coil design study is presented to
assess the feasibility of this configuration as a fusion-relevant experiment.


  The connection between the out of equilibrium linear response function and
static properties established by Franz, Mezard, Parisi and Peliti for slowly
relaxing systems is analyzed in the context of phase ordering processes.
Separating the response in the bulk of domains from interface response, we find
that in order for the connection to hold the interface contribution must be
asymptotically negligible. How fast this happens depends on the competition
between interface curvature and the perturbing external field in driving domain
growth. This competition depends on space dimensionality and there exists a
critical value $d_c=3$ below which the interface response becomes increasingly
important eventually invalidating the connection between statics and dynamics
as the limit $d=1$ is reached. This mechanism is analyzed numerically for the
Ising model with $d$ ranging from 1 to 4 and analytically for a continuous spin
model with arbitrary dimensionality.


  Let $A$ and $B$ be independent, central Wishart matrices in $p$ variables
with common covariance and having $m$ and $n$ degrees of freedom, respectively.
The distribution of the largest eigenvalue of $(A+B)^{-1}B$ has numerous
applications in multivariate statistics, but is difficult to calculate exactly.
Suppose that $m$ and $n$ grow in proportion to $p$. We show that after
centering and scaling, the distribution is approximated to second-order,
$O(p^{-2/3})$, by the Tracy--Widom law. The results are obtained for both
complex and then real-valued data by using methods of random matrix theory to
study the largest eigenvalue of the Jacobi unitary and orthogonal ensembles.
Asymptotic approximations of Jacobi polynomials near the largest zero play a
central role.


  The results of the experimental investigations of double beta processes in
Zinc and Tungsten isotopes with the help of middle volume (117 g, 168 g and 699
g) low-background ZnWO_4 crystal scintillators are presented. The experiment
was carried out in the low-background "DAMA/R&D" set-up at the Gran Sasso
National Laboratories of the INFN (Italy) at a depth of \approx3600 m w.e. The
total measurement time exceeds ten thousand hours. New improved half-life
limits on double electron capture and electron capture with positron emission
in ^{64}Zn have been set: T^{2\nu 2K}_{1/2}(^{64}Zn) \geq 6.2(6.3) \times
10^{18} yr, T^{0\nu 2\epsilon}_{1/2} (^{64}Zn) \geq 1.1(2.8) \times 10^{20} yr,
T^{2\nu \epsilon \beta^+}_{1/2}(^{64}Zn) \geq 0.7(2.1) \times 10^{21} yr, and
T^{0\nu \epsilon \beta^+}_{1/2}(^{64}Zn) \geq 4.3(5.7) \times 10^{20} yr, all
the limits are at 90% (68%) C.L. The positive indication on the \epsilon
\beta^+ decay of ^{64}Zn with T^{(2\nu + 0\nu) \epsilon \beta^+}_{1/2}
(^{64}Zn) = (1.1 \pm 0.9) \times 10^{19} yr suggested in [Appl. Radiat. Isot.
46 (1995) 455-456] is fully discarded by the present experiment. The half-life
limits on the 2\beta processes in ^{70}Zn, ^{180}W, and two neutrino mode of
2\beta^- decay in ^{186}W established in the present work on the level of
10^{17}-10^{20} yr are one order of magnitude higher than those set in previous
experiments.
  Energy resolution, relative light output, \alpha/\beta ratio, decay time,
pulse-shape discrimination between \alpha particles and \gamma rays (\beta
particles), and radioactive contamination of CdWO_4, PbWO_4 (undoped, and doped
by F, Eu, Mo, Gd, S), and ZnWO_4 crystal scintillators were studied.
Applicability of these scintillators to search for double beta decay was
proved.


  We propose a method for multi-person detection and 2-D pose estimation that
achieves state-of-art results on the challenging COCO keypoints task. It is a
simple, yet powerful, top-down approach consisting of two stages.
  In the first stage, we predict the location and scale of boxes which are
likely to contain people; for this we use the Faster RCNN detector. In the
second stage, we estimate the keypoints of the person potentially contained in
each proposed bounding box. For each keypoint type we predict dense heatmaps
and offsets using a fully convolutional ResNet. To combine these outputs we
introduce a novel aggregation procedure to obtain highly localized keypoint
predictions. We also use a novel form of keypoint-based Non-Maximum-Suppression
(NMS), instead of the cruder box-level NMS, and a novel form of keypoint-based
confidence score estimation, instead of box-level scoring.
  Trained on COCO data alone, our final system achieves average precision of
0.649 on the COCO test-dev set and the 0.643 test-standard sets, outperforming
the winner of the 2016 COCO keypoints challenge and other recent state-of-art.
Further, by using additional in-house labeled data we obtain an even higher
average precision of 0.685 on the test-dev set and 0.673 on the test-standard
set, more than 5% absolute improvement compared to the previous best performing
method on the same dataset.


  Let $M$ be a compact orientable 3-manifold. The set of characters of
$SL_2({\mathbb C})$ representations of the fundamental group of $M$ forms a
closed affine algebraic set. We show that its coordinate ring is isomorphic to
a specialization of the Kauffman bracket skein module modulo its nilradical.
This is accomplished by making the module into a combinatorial analog of the
ring, in which tools of skein theory are exploited to illuminate relations
among characters. We conclude with an application, proving that a small
manifold's specialized module is necessarily finite dimensional.


  Dynamic contrast-enhanced (DCE) MRI is an evolving imaging technique that
provides a quantitative measure of pharmacokinetic (PK) parameters in body
tissues, in which series of T1-weighted images are collected following the
administration of a paramagnetic contrast agent. Unfortunately, in many
applications, conventional clinical DCE-MRI suffers from low spatiotemporal
resolution and insufficient volume coverage. In this paper, we propose a novel
deep learning based approach to directly estimate the PK parameters from
undersampled DCE-MRI data. Specifically, we design a custom loss function where
we incorporate a forward physical model that relates the PK parameters to
corrupted image-time series obtained due to subsampling in k-space. This allows
the network to directly exploit the knowledge of true contrast agent kinetics
in the training phase, and hence provide more accurate restoration of PK
parameters. Experiments on clinical brain DCE datasets demonstrate the efficacy
of our approach in terms of fidelity of PK parameter reconstruction and
significantly faster parameter inference compared to a model-based iterative
reconstruction method.


  Balancing oneself using the spine is a physiological alignment of the body
posture in the most efficient manner by the muscular forces for mammals. For
this reason, we can see many disabled quadruped animals can still stand or walk
even with three limbs. This paper investigates the optimization of dynamic
balance during trot gait based on the spatial relationship between the center
of mass (CoM) and support area influenced by spinal flexion. During trotting,
the robot balance is significantly influenced by the distance of the CoM to the
support area formed by diagonal footholds. In this context, lateral spinal
flexion, which is able to modify the position of footholds, holds promise for
optimizing balance during trotting. This paper explores this phenomenon using a
rat robot equipped with a soft actuated spine. Based on the lateral flexion of
the spine, we establish a kinematic model to quantify the impact of spinal
flexion on robot balance during trot gait. Subsequently, we develop an
optimized controller for spinal flexion, designed to enhance balance without
altering the leg locomotion. The effectiveness of our proposed controller is
evaluated through extensive simulations and physical experiments conducted on a
rat robot. Compared to both a non-spine based trot gait controller and a trot
gait controller with lateral spinal flexion, our proposed optimized controller
effectively improves the dynamic balance of the robot and retains the desired
locomotion during trotting.


  High-resolution mapping of cells and tissue structures provides a foundation
for developing interpretable machine-learning models for computational
pathology. Deep learning algorithms can provide accurate mappings given large
numbers of labeled instances for training and validation. Generating adequate
volume of quality labels has emerged as a critical barrier in computational
pathology given the time and effort required from pathologists. In this paper
we describe an approach for engaging crowds of medical students and
pathologists that was used to produce a dataset of over 220,000 annotations of
cell nuclei in breast cancers. We show how suggested annotations generated by a
weak algorithm can improve the accuracy of annotations generated by non-experts
and can yield useful data for training segmentation algorithms without
laborious manual tracing. We systematically examine interrater agreement and
describe modifications to the MaskRCNN model to improve cell mapping. We also
describe a technique we call Decision Tree Approximation of Learned Embeddings
(DTALE) that leverages nucleus segmentations and morphologic features to
improve the transparency of nucleus classification models. The annotation data
produced in this study are freely available for algorithm development and
benchmarking at: https://sites.google.com/view/nucls.


  We have grown lead iron niobate thin films with composition Pb(Fe1/2Nb1/2)O3
(PFN) on (0 0 1) SrTiO3 substrates by pulsed laser deposition. The influence of
the deposition conditions on the phase purity was studied. Due to similar
thermodynamic stability spaces, a pyrochlore phase often coexists with the PFN
perovskite phase. By optimizing the kinetic parameters, we succeeded in
identifying a deposition window which resulted in epitaxial perovskite-phase
PFN thin films with no identifiable trace of impurity phases appearing in the
X-ray diffractograms. PFN films having thicknesses between 20 and 200 nm were
smooth and epitaxially oriented with the substrate and as demonstrated by RHEED
streaks which were aligned with the substrate axes. X-ray diffraction showed
that the films were completely c-axis oriented and of excellent crystalline
quality with low mosaicity (X-ray rocking curve FWHM<0.09). The surface
roughness of thin films was also investigated by atomic force microscopy. The
root-mean-square roughness varies between 0.9 nm for 50-nm-thick films to 16 nm
for 100-nm-thick films. We also observe a correlation between grain size,
surface roughness and film thickness.


  Recent availability of geo-localized data capturing individual human activity
together with the statistical data on international migration opened up
unprecedented opportunities for a study on global mobility. In this paper we
consider it from the perspective of a multi-layer complex network, built using
a combination of three datasets: Twitter, Flickr and official migration data.
Those datasets provide different but equally important insights on the global
mobility: while the first two highlight short-term visits of people from one
country to another, the last one - migration - shows the long-term mobility
perspective, when people relocate for good. And the main purpose of the paper
is to emphasize importance of this multi-layer approach capturing both aspects
of human mobility at the same time. So we start from a comparative study of the
network layers, comparing short- and long- term mobility through the
statistical properties of the corresponding networks, such as the parameters of
their degree centrality distributions or parameters of the corresponding
gravity model being fit to the network. We also focus on the differences in
country ranking by their short- and long-term attractiveness, discussing the
most noticeable outliers. Finally, we apply this multi-layered human mobility
network to infer the structure of the global society through a community
detection approach and demonstrate that consideration of mobility from a
multi-layer perspective can reveal important global spatial patterns in a way
more consistent with other available relevant sources of international
connections, in comparison to the spatial structure inferred from each network
layer taken separately.


  This paper builds on the theory of generalised functions begun in [1]. The
Colombeau theory of generalised scalar fields on manifolds is extended to a
nonlinear theory of generalised tensor fields which is diffeomorphism invariant
and has the sheaf property. The generalised Lie derivative for generalised
tensor fields is introduced and it is shown that this commutes with the
embedding of distributional tensor fields. It is also shown that the covariant
derivative of generalised tensor fields commutes with the embedding at the
level of association. The concept of generalised metric is introduced and used
to develop a nonsmooth theory of differential geometry. It is shown that the
embedding of a continuous metric results in a generalised metric with well
defined connection and curvature. It is also shown that a twice continuously
differentiable metric which is a solution of the vacuum Einstein equations may
be embedded into the algebra of generalised tensor fields and has generalised
Ricci curvature associated to zero. Thus, the embedding preserves the Einstein
equations at the level of association. Finally, we consider an example of a
metric which lies outside the Geroch-Traschen class and show that in our
diffeomorphism invariant theory the curvature of a cone is associated to a
delta function.


  A large set of Spitzer-selected transitional disks in the Ophiuchus molecular
cloud was recently examined by Cieza et al (2010), and 4 of the targets were
identified as (giant) planet-forming candidates based on the morphology of
their Spectral Energy Distributions (SEDs), the apparent lack of stellar
companions, and evidence for accretion. Here we characterize the structures of
these disks modeling their optical, infrared and (sub)millimeter SEDs. We use
the Monte Carlo radiative transfer package RADMC to construct a parametric
model of the dust distribution in a flared disk with an inner cavity and
calculate the temperature structure consistent with the density profile, in
thermal equilibrium with the irradiating star. For each object, we conducted a
Bayesian exploration of the parameter space generating Monte Carlo Markov
Chains (MCMC) that allow the identification of the best fit parameters and to
constrain their range of statistical confidence. Our calculations point to the
presence of evacuated cavities with radii ~2-8 AU, consistent with having been
carved by embedded giant planets. We found parameter values consistent with
those previously given in the literature, indicating a mild degree of grain
growth and dust settling, which deserves to be investigated with further
modeling and follow up observations. Resolved images with (sub)millimeter
interferometers are required to break some of the degeneracies of the models
and better constrain the physical properties of these fascinating disks.


  Using in-situ transmission electron microscopy, we have directly observed
nano-scale defects formed in ultra-high purity tungsten by low-dose high energy
self-ion irradiation at 30K. At cryogenic temperature lattice defects have
reduced mobility, so these microscope observations offer a window on the
initial, primary damage caused by individual collision cascade events. Electron
microscope images provide direct evidence for a power-law size distribution of
nano-scale defects formed in high-energy cascades, with an upper size limit
independent of the incident ion energy, as predicted by Sand et al. [Eur. Phys.
Lett., 103:46003, (2013)]. Furthermore, the analysis of pair distribution
functions of defects observed in the micrographs shows significant
intra-cascade spatial correlations consistent with strong elastic interaction
between the defects.


  A novel kind of crystal order in high-density nanoconfined bilayer ice is
proposed from molecular dynamics and density-functional theory simulations. A
first-order transition is observed between a low-temperature proton-ordered
solid and a high-temperature proton-disordered solid. The latter is shown to
possess crystalline order for the oxygen positions, arranged on a close-packed
triangular lattice with AA stacking. Uniquely amongst the ice phases, the
triangular bilayer is characterized by two levels of disorder (for the bonding
network and for the protons) which results in a configurational entropy twice
that of bulk ice.


  Since its outbreak, the ongoing COVID-19 pandemic has caused unprecedented
losses to human lives and economies around the world. As of 18th July 2020, the
World Health Organization (WHO) has reported more than 13 million confirmed
cases including close to 600,000 deaths across 216 countries and territories.
Despite several government measures, India has gradually moved up the ranks to
become the third worst-hit nation by the pandemic after the US and Brazil, thus
causing widespread anxiety and fear among her citizens. As majority of the
world's population continues to remain confined to their homes, more and more
people have started relying on social media platforms such as Twitter for
expressing their feelings and attitudes towards various aspects of the
pandemic. With rising concerns of mental well-being, it becomes imperative to
analyze the dynamics of public affect in order to anticipate any potential
threats and take precautionary measures. Since affective states of human mind
are more nuanced than meager binary sentiments, here we propose a deep
learning-based system to identify people's emotions from their tweets. We
achieve competitive results on two benchmark datasets for multi-label emotion
classification. We then use our system to analyze the evolution of emotional
responses among Indians as the pandemic continues to spread its wings. We also
study the development of salient factors contributing towards the changes in
attitudes over time. Finally, we discuss directions to further improve our work
and hope that our analysis can aid in better public health monitoring.


  We show, by means of low-energy muon spin rotation measurements, that
few-unit-cells thick La(2)CuO(4) layers synthesized digitally by molecular beam
epitaxy synthesis are antiferromagnetically ordered. Below a thickness of about
5 CuO(2) layers the long-range ordered state breaks down, and a magnetic state
appears with enhanced quantum fluctuations and a reduced spin stiffness. This
magnetic state can exist in close proximity (few Angstrom) to high-temperature
superconducting layers, without transmitting supercurrents.


  Glitches are sudden jumps in the spin frequency of pulsars believed to
originate in the superfluid interior of neutron stars. Superfluid flow in a
model neutron star is simulated by solving the equations of motion of a
two-component superfluid consisting of a viscous proton-electron plasma and an
inviscid neutron condensate in a spherical Couette geometry. We examine the
response of our model neutron star to glitches induced in three different ways:
by instantaneous changes of the spin frequency of the inner and outer
boundaries, and by instantaneous recoupling of the fluid components in the
bulk. All simulations are performed with strong and weak mutual friction. It is
found that the maximum size of a glitch that originates in the bulk decreases
as the mutual friction strengthens. It is also found that mutual friction
determines the fraction of the frequency jump which is later recovered, a
quantity known as the 'healing parameter'. These behaviours may explain some of
the diversity in observed glitch recoveries.


  We present an encoding technique that reduces the effects of noise on quantum
spin systems whose operation is driven by Hamiltonian evolution. This technique
is widely applicable, being most relevant to the scenarios where there are
insufficient qubits to permit full scale error correction. Instead, our
technique can be implemented over small numbers of qubits and still leads to
noticeable improvements in the fidelity of operations. The encoding scheme is
easy to implement, flexible with respect to choice of Hamiltonian, and close to
optimal.


  We exploit the deep resolved Halpha kinematic data from the KMOS^3D and
SINS/zC-SINF surveys to examine the largely unexplored outer disk kinematics of
star-forming galaxies (SFGs) out to the peak of cosmic star formation. Our
sample contains 101 SFGs representative of the more massive (9.3 < log(M*/Msun)
< 11.5) main sequence population at 0.6<z<2.6. Through a novel stacking
approach we are able to constrain a representative rotation curve extending out
to ~4 effective radii. This average rotation curve exhibits a significant drop
in rotation velocity beyond the turnover, with a slope of Delta(V)/Delta(R) =
$-0.26^{+0.10}_{-0.09}$ in units of normalized coordinates V/V_max and
R/R_turn. This result confirms that the fall-off seen previously in some
individual galaxies is a common feature of our sample of high-z disks. We show
that this outer fall-off strikingly deviates from the flat or mildly rising
rotation curves of local spiral galaxies of similar masses. We furthermore
compare our data with models including baryons and dark matter demonstrating
that the falling stacked rotation curve can be explained by a high mass
fraction of baryons relative to the total dark matter halo (m_d>~0.05) in
combination with a sizeable level of pressure support in the outer disk. These
findings are in agreement with recent studies demonstrating that star-forming
disks at high redshift are strongly baryon dominated within the disk scale, and
furthermore suggest that pressure gradients caused by large turbulent gas
motions are present even in their outer disks. We demonstrate that these
results are largely independent of our model assumptions such as the presence
of a central stellar bulge, the effect of adiabatic contraction at fixed m_d,
and variations in the concentration parameter.


  Validating whether a quantum device confers a computational advantage often
requires classical simulation of its outcomes. The worst-case sampling cost of
$L_1$-norm based simulation has plateaued at $\le(2+\sqrt{2})\xi_t \delta^{-1}$
in the limit that $t \rightarrow \infty$, where $\delta$ is the additive error
and $\xi_t$ is the stabilizer extent of a $t$-qubit magic state. We reduce this
prefactor 68-fold by a leading-order reduction in $t$ through correlated
sampling. The result exceeds even the average-case of the prior
state-of-the-art and current simulators accurate to multiplicative error.
Numerical demonstrations support our proofs. The technique can be applied
broadly to reduce the cost of $L_1$ minimization.


  M\"obius-ring resonators stem from a well-studied and fascinating geometrical
structure that features a one-sided topology; the M\"obius strip, and have been
shown to exhibit fermion rotational symmetry with respect to a ring resonator
with no twist (which exhibits boson rotational symmetry) (see
PhysRevLett.101.247701). Here, we present a new type of resonator through the
formation of twisted hollow structures using equilateral triangular
cross-sections, which leads to the realization of a cavity with anyon
rotational symmetry. Unlike all previous cavity resonators, the anyon resonator
permits the existence of bulk resonant modes that exhibit non-zero
electromagnetic helicity in vacuo, with a non-zero overlap of the electric and
magnetic mode eigenvectors, $\int \mathbf{E}_p\cdot\mathbf{B}_p~d\tau$,
integrated over the cavity volume. In the upconversion limit, we show that
these non-zero helical modes couple naturally to ultra-light dark matter axions
within the bandwidth of the resonator by adding amplitude-modulated sidebands
through the axion-photon chiral anomaly. Thus, we show a sensitive ultra-light
dark matter experiment may be realized by implementing such a resonator in an
ultra-stable oscillator configuration and searching for signals in the Fourier
spectrum of amplitude fluctuations. This removes the typical requirement for an
external magnetic field and therefore permits the use of superconducting
materials to reduce surface losses and enhance sensitivity to axions.


  We study the entropy production of a microscopic model for nonequilibrium
wetting. We show that, in contrast to the equilibrium case, a bound interface
in a nonequilibrium steady state produces entropy. Interestingly, in some
regions of the phase diagram a bound interface produces more entropy than a
free interface. Moreover, by solving exactly a four-site system, we find that
the first derivative of the entropy production with respect to the control
parameter displays a discontinuity at the critical point of the wetting
transition.


  Distributed scheduling algorithms for throughput or utility maximization in
dense wireless multi-hop networks can have overwhelmingly high overhead,
causing increased congestion, energy consumption, radio footprint, and security
vulnerability. For wireless networks with dense connectivity, we propose a
distributed scheme for link sparsification with graph convolutional networks
(GCNs), which can reduce the scheduling overhead while keeping most of the
network capacity. In a nutshell, a trainable GCN module generates node
embeddings as topology-aware and reusable parameters for a local decision
mechanism, based on which a link can withdraw itself from the scheduling
contention if it is not likely to win. In medium-sized wireless networks, our
proposed sparse scheduler beats classical threshold-based sparsification
policies by retaining almost $70\%$ of the total capacity achieved by a
distributed greedy max-weight scheduler with $0.4\%$ of the point-to-point
message complexity and $2.6\%$ of the average number of interfering neighbors
per link.


  This dissertation examines the impact of a drift {\mu} on Brownian Bees,
which is a type of branching Brownian motion that retains only the N closest
particles to the origin. The selection effect in the 0-drift system ensures
that it remains recurrent and close to the origin. The study presents two novel
findings that establish a threshold for {\mu}: below this value, the system
remains recurrent, and above it, the system becomes transient. Moreover, the
paper proves convergence to a unique invariant distribution for the small drift
case. The research also explores N-BBM, a variant of branching Brownian motion
where the N leftmost particles are retained, and presents one new result and
further discussion on this topic.


  With the agile approach to managing software development projects comes an
increased dependability on well functioning teams, since many of the practices
are built on teamwork. The objective of this study was to investigate if, and
how, team development from a group psychological perspective is related to some
work practices of agile teams. Data were collected from 34 agile teams (200
individuals) from six software development organizations and one university in
both Brazil and Sweden using the Group Development Questionnaire (Scale IV) and
the Perceptive Agile Measurement (PAM). The result indicates a strong
correlation between levels of group maturity and the two agile practices
\emph{iterative development} and \emph{retrospectives}. We, therefore, conclude
that agile teams at different group development stages adopt parts of team
agility differently, thus confirming previous studies but with more data and by
investigating concrete and applied agile practices. We thereby add evidence to
the hypothesis that an agile implementation and management of agile projects
need to be adapted to the group maturity levels of the agile teams.


  We give a randomized O(n polylog n)-time approximation scheme for the Steiner
forest problem in the Euclidean plane. For every fixed eps > 0 and given n
terminals in the plane with connection requests between some pairs of
terminals, our scheme finds a (1 + eps)-approximation to the minimum-length
forest that connects every requested pair of terminals.


  The aim of this paper is to determine criteria of being integral curve for
the geodesic spray of the natural lift curves of the spherical indicatrices of
the involutes of a given spacelike curve with a timelike binormal in Minkowski
3-space. Furthermore, some interesting results about the spacelike evolute
curve with timelike binormal and spacelike or timelike Darboux vector were
obtained, depending on the assumption that the natural lift curves of the
spherical indicatrices of the involute curve should be the integral curve on
the tangent bundle. Additionally we illustrate an example of our main results.


  We introduce a framework for automatically defining and learning deep
generative models with problem-specific structure. We tackle problem domains
that are more traditionally solved by algorithms such as sorting, constraint
satisfaction for Sudoku, and matrix factorization. Concretely, we train
diffusion models with an architecture tailored to the problem specification.
This problem specification should contain a graphical model describing
relationships between variables, and often benefits from explicit
representation of subcomputations. Permutation invariances can also be
exploited. Across a diverse set of experiments we improve the scaling
relationship between problem dimension and our model's performance, in terms of
both training time and final accuracy. Our code can be found at
https://github.com/plai-group/gsdm.


  A large number (67) of the compact/ultra-compact H II regions identified in
the Coordinated Radio and Infrared Survey for High-Mass Star Formation
catalogue were determined to be powered by a Lyman continuum flux in excess of
what was expected given their corresponding luminosity. In this study we
attempt to reasonably explain the Lyman excess phenomenon in as many of the 67
H II regions as possible through a variety of observational and astrophysical
means including new luminosity estimates, new Herschel photometry, new distance
determinations, the use of different models for dust and ionized gas covering
factors, and the use of different stellar calibrations. This phenomenon has
been observed before; however, the objects shown to exhibit this behaviour in
the literature have decidedly different physical properties than the regions in
our sample, and thus the origin of the excess is not the same. We find that the
excess can be reproduced using OB stellar atmosphere models that have been
slightly modified in the extreme ultraviolet. Though the exact mechanism
producing the excess is still uncertain, we do find that a scaled up
magnetospheric accretion model, often used to explain similar emission from T
Tauri stars, is unable to match our observations. Our results suggest that the
Lyman excess may be associated with younger H II regions, and that it is more
commonly found in early B-type stars. Our refined sample of 24 Lyman excess H
II regions provides an ideal sample for comparative studies with regular H II
regions, and can act as the basis for the further detailed study of individual
regions.


  This paper presents a novel approach to Single-Positive Multi-label Learning.
In general multi-label learning, a model learns to predict multiple labels or
categories for a single input image. This is in contrast with standard
multi-class image classification, where the task is predicting a single label
from many possible labels for an image. Single-Positive Multi-label Learning
(SPML) specifically considers learning to predict multiple labels when there is
only a single annotation per image in the training data. Multi-label learning
is in many ways a more realistic task than single-label learning as real-world
data often involves instances belonging to multiple categories simultaneously;
however, most common computer vision datasets predominantly contain single
labels due to the inherent complexity and cost of collecting multiple high
quality annotations for each instance. We propose a novel approach called
Vision-Language Pseudo-Labeling (VLPL), which uses a vision-language model to
suggest strong positive and negative pseudo-labels, and outperforms the current
SOTA methods by 5.5% on Pascal VOC, 18.4% on MS-COCO, 15.2% on NUS-WIDE, and
8.4% on CUB-Birds. Our code and data are available at
https://github.com/mvrl/VLPL.


  In this article, we consider a $n$-dimensional random walk $X_t$, whose error
terms are linear processes generated by $n$-dimensional noise vectors, and each
component of these noise vectors is independent and may not be identically
distributed with uniformly bounded 8th moment and densities. Given $T$
observations such that the dimension $n$ and sample size $T$ going to infinity
proportionally, define $\boldsymbol{X}$ and $\hat{\boldsymbol{R}}$ as the data
matrix and the sample correlation matrix of $\boldsymbol{X}$ respectively. This
article establishes the central limit theorem (CLT) of the first $K$ largest
eigenvalues of $n^{-1}\hat{\boldsymbol{R}}$. Subsequently, we propose a new
unit root test for the panel high-dimensional nonstationary time series based
on the CLT of the largest eigenvalue of $n^{-1}\hat{\boldsymbol{R}}$. A
numerical experiment is undertaken to verify the power of our proposed unit
root test.


  In this paper we give a proof of the fact, that the motivic Hodge-Chern class
transformation MHC_y and Hirzebruch class transformation MHT_y* for mixed Hodge
modules and strictly specializable filtered D-modules commute with
specialization in the algebraic and in a suitable complex analytic context.
Here specialization in the Hodge- and D-module context means the corresponding
nearby cycles defined in terms of the $V$-filtration of Malgrange-Kashiwara.
This generalizes a corresponding specialization result of Verdier about
MacPherson's Chern class transformation c_*.


  Self-assembly into target structures is an efficient material design
strategy. Combining analytical calculations and computational techniques of
evolutionary and Monte Carlo types, we report about a remarkable structural
variability of Wigner bilayer ground states, when charges are confined between
parallel charged plates. Changing the inter-layer separation, or the plate
charge asymmetry, a cascade of ordered patterns emerges. At variance with the
symmetric case phenomenology, the competition between commensurability features
and charge neutralization leads to long range attraction, appearance of
macroscopic charges, exotic phases, and non conventional phase transitions with
distinct critical indices, offering the possibility of a subtle, but precise
and convenient control over patterns.


  Observations suggest that in normal period radio pulsars, coherent curvature
radiation is excited within 10$\%$ of the light cylinder. The coherence is
attributed to Langmuir mode instability in a relativistically streaming
one-dimensional plasma flow along the open magnetic field lines. In this work,
we use a hot plasma treatment to solve the hydrodynamic dispersion relation of
Langmuir mode for realistic pulsar parameters. The solution involves three
scenarios of two-stream instability viz., driven by high energy beams, due to
longitudinal drift that leads to a separation of electron-positron distribution
functions in the secondary plasma and due to cloud-cloud interaction causing
spatial overlap of two successive secondary plasma clouds. We find that
sufficient amplification can be obtained only for the latter two scenarios. Our
analysis shows that longitudinal drift is characterized by high growth rates
only for certain multi-polar surface field geometry. For these configurations,
very high growth rates are obtained starting from a few tens of km from the
neutron star surface, which then falls monotonically with increasing distance.
For cloud-cloud overlap, growth rates become high starting only after a few
hundred km from the surface, which first increases and then decreases with
increasing distance. A spatial window of up to around a 1000 km above the
neutron star surface has been found where large amplitude Langmuir waves can be
excited while the pair plasma is dense enough to account for high brightness
temperature.


  We employ concepts and tools from the theory of finite permutation groups in
order to analyse the Hidden Subgroup Problem via Quantum Fourier Sampling (QFS)
for the symmetric group. We show that under very general conditions both the
weak and the random-strong form (strong form with random choices of basis) of
QFS fail to provide any advantage over classical exhaustive search. In
particular we give a complete characterisation of polynomial size subgroups,
and of primitive subgroups, that can be distinguished from the identity
subgroup with the above methods. Furthermore, assuming a plausible group
theoretic conjecture for which we give supporting evidence, we show that weak
and random-strong QFS for the symmetric group have no advantage whatsoever over
classical search.


  The stellar wind around the compact object in luminous wind-accreting high
mass X-ray binaries is expected to be strongly ionized with the X-rays coming
from the compact object. The stellar wind of hot stars is mostly driven by
light absorption in lines of heavier elements, and X-ray photo-ionization
significantly reduces the radiative force within the so-called Stroemgren
region leading to wind stagnation around the compact object. In close binaries
like Vela X-1 this effect might alter the wind structure throughout the system.
Using the spectral data from Monitor of All-sky X-ray Image (MAXI), we study
the observed dependence of the photoelectric absorption as function of orbital
phase in Vela X-1, and find that it is inconsistent with expectations for a
spherically-symmetric smooth wind. Taking into account previous investigations
we develop a simple model for wind structure with a stream-like photoionization
wake region of slower and denser wind trailing the neutron star responsible for
the observed absorption curve.


  In this paper, the generalized finite element method (GFEM) for solving
second order elliptic equations with rough coefficients is studied. New optimal
local approximation spaces for GFEMs based on local eigenvalue problems
involving a partition of unity are presented. These new spaces have advantages
over those proposed in [I. Babuska and R. Lipton, Multiscale Model.\;\,Simul.,
9 (2011), pp.~373--406]. First, in addition to a nearly exponential decay rate
of the local approximation errors with respect to the dimensions of the local
spaces, the rate of convergence with respect to the size of the oversampling
region is also established. Second, the theoretical results hold for problems
with mixed boundary conditions defined on general Lipschitz domains. Finally,
an efficient and easy-to-implement technique for generating the discrete
$A$-harmonic spaces is proposed which relies on solving an eigenvalue problem
associated with the Dirichlet-to-Neumann operator, leading to a substantial
reduction in computational cost. Numerical experiments are presented to support
the theoretical analysis and to confirm the effectiveness of the new method.


  In supersymmetric scenarios with broken R-parity, baryon number violating
sparticle decays become possible. In order to search for such decays, a good
understanding of expected event properties is essential. We here develop a
complete framework that allows detailed studies. Special attention is given to
the hadronization phase, wherein the baryon number violating vertex is
associated with the appearance of a junction in the colour confinement field.
This allows us to tell where to look for the extra (anti)baryon directly
associated with the baryon number violating decay.


  Let $G$ be a reductive group over a local field $F$ of characteristic $0$. By
Harish-Chandra's regularity theorem, every global character $\Theta_{\pi}$ of
an irreducible, admissible representation $\pi$ of $G$ is given by a locally
integrable function $\theta_{\pi}$ on $G$. It is a natural question whether
$\theta_{\pi}$ has better integrability properties, namely, whether it is
locally $L^{1+\epsilon}$-integrable for some $\epsilon>0$. It follows from
Harish-Chandra's work that the answer is positive, and this gives rise to a new
singularity invariant of representations $\epsilon_{\star}(\pi):=\sup\left\{
\epsilon:\theta_{\pi}\in L_{\mathrm{Loc}}^{1+\epsilon}(G)\right\} $, which we
explore in this paper.
  We provide a lower bound on $\epsilon_{\star}(\pi)$ for any $G$, and
determine $\epsilon_{\star}(\pi)$ in the case of a $p$-adic $\mathrm{GL}_{n}$.
This is done by studying integrability properties of the Fourier transforms
$\widehat{\xi_{\mathcal{O}}}$ of stable Richardson nilpotent orbital integrals
$\xi_{\mathcal{O}}$. We express $\epsilon_{\star}(\widehat{\xi_{\mathcal{O}}})$
as the log-canonical threshold of a suitable relative Weyl discriminant, and
use a resolution of singularities algorithm coming from the theory of
hyperplane arrangements, to compute it in terms of the partition associated
with the orbit.
  As an application, we obtain bounds on the multiplicities of $K$-types in
irreducible representations of $G$ in the $p$-adic case, where $K$ is an open
compact subgroup.


  This paper presents regional attraction of line segment maps, and hereby
poses the problem of line segment detection (LSD) as a problem of region
coloring. Given a line segment map, the proposed regional attraction first
establishes the relationship between line segments and regions in the image
lattice. Based on this, the line segment map is equivalently transformed to an
attraction field map (AFM), which can be remapped to a set of line segments
without loss of information. Accordingly, we develop an end-to-end framework to
learn attraction field maps for raw input images, followed by a squeeze module
to detect line segments. Apart from existing works, the proposed detector
properly handles the local ambiguity and does not rely on the accurate
identification of edge pixels. Comprehensive experiments on the Wireframe
dataset and the YorkUrban dataset demonstrate the superiority of our method. In
particular, we achieve an F-measure of 0.831 on the Wireframe dataset,
advancing the state-of-the-art performance by 10.3 percent.


  Two pairs of disjoint bases $\mathbf{P}_1=(R_1,B_1)$ and
$\mathbf{P}_2=(R_2,B_2)$ of a matroid $M$ are called equivalent if
$\mathbf{P}_1$ can be transformed into $\mathbf{P}_2$ by a series of symmetric
exchanges. In 1980, White conjectured that such a sequence always exists
whenever $R_1\cup B_1=R_2\cup B_2$. A strengthening of the conjecture was
proposed by Hamidoune, stating that minimum length of an exchange is at most
the rank of the matroid.
  We propose a weighted variant of Hamidoune's conjecture, where the weight of
an exchange depends on the weights of the exchanged elements. We prove the
conjecture for several matroid classes: strongly base orderable matroids, split
matroids, graphic matroids of wheels, and spikes.


  Planet formation processes or evolution mechanisms are surmised to be at the
origin of the hot Neptune desert. Studying exoplanets currently living within
or at the edge of this desert could allow disentangling the respective roles of
formation and evolution. We present the HARPS transmission spectrum of the
bloated super-Neptune WASP-166b, located at the outer rim of the Neptune
desert. Neutral sodium is detected at the 3.4 $\sigma$ level ($0.455 \pm 0.135
\%$), with a tentative indication of line broadening, which could be caused by
winds blowing sodium farther into space, a possible manifestation of the
bloated character of these highly irradiated worlds. We put this detection into
context with previous work claiming a non-detection of sodium in the same
observations and show that the high noise in the trace of the discarded stellar
sodium lines was responsible for the non-detection. We highlight the impact of
this low signal-to-noise remnant on detections for exoplanets similar to
WASP-166b.


  Quantum Communications Networks using the properties of qubits, namely state
superposition, no-cloning and entanglement, can enable the exchange of
information in a very secure manner across optical links or free space. New
innovations enable the use of optical repeaters as well as multi-cast
communication in the networks. Some types of quantum communications mechanisms
can be implemented at room-temperature instead of requiring super-cooled
systems. This makes it likely that business impact from quantum communications
will be realized sooner than that from quantum computers.
  Quantum networks need to be integrated into the ecosystem of currently
deployed classical networks and augment them with new capabilities. Classical
computers and networks need to be able to use the new secure communication
capabilities offered by quantum networks. To provide this interoperability,
appropriate software abstractions on the usage of quantum networks need to be
developed. In this paper, we examine what the type of software abstractions
quantum networks can provide, and the type of applications that the new
abstractions can support.


  Beamforming techniques that employ Singular Value Decomposition (SVD) are
commonly used in Multi-Input Multi-Output (MIMO) wireless communication
systems. In the absence of channel coding, when a single symbol is transmitted,
these systems achieve the full diversity order provided by the channel; whereas
when multiple symbols are simultaneously transmitted, this property is lost.
When channel coding is employed, full diversity order can be achieved. For
example, when Bit-Interleaved Coded Modulation (BICM) is combined with this
technique, full diversity order of NM in an MxN MIMO channel transmitting S
parallel streams is possible, provided a condition on S and the BICM
convolutional code rate is satisfied. In this paper, we present constellation
precoded multiple beamforming which can achieve the full diversity order both
with BICM-coded and uncoded SVD systems. We provide an analytical proof of this
property. To reduce the computational complexity of Maximum Likelihood (ML)
decoding in this system, we employ Sphere Decoding (SD). We report an SD
technique that reduces the computational complexity beyond commonly used
approaches to SD. This technique achieves several orders of magnitude reduction
in computational complexity not only with respect to conventional ML decoding
but also, with respect to conventional SD.


  Large language model (LLM)-based recommender models that bridge users and
items through textual prompts for effective semantic reasoning have gained
considerable attention. However, few methods consider the underlying rationales
behind interactions, such as user preferences and item attributes, limiting the
reasoning capability of LLMs for recommendations. This paper proposes a
rationale distillation recommender (RDRec), a compact model designed to learn
rationales generated by a larger language model (LM). By leveraging rationales
from reviews related to users and items, RDRec remarkably specifies their
profiles for recommendations. Experiments show that RDRec achieves
state-of-the-art (SOTA) performance in both top-N and sequential
recommendations. Our source code is released at
https://github.com/WangXFng/RDRec.


  We explain when and why symmetries enhance in fermionic rational conformal
field theories. In order to achieve the goal, we first clarify invariants under
renormalization group flows. In particular, we find the Ocneanu rigidity is not
enough to protect some quantities. Concretely, while (double) braidings are
subject to the rigidity, they jump at conformal fixed points. The jump happens
in a specific way, so the double braiding relation further constrains
renormalization group flows. The new constraints enable us three things; 1) to
predict infrared conformal dimensions in massless flow, 2) to reveal some
structures of the theory space, and 3) to obtain a necessary condition for a
flow to be massless. We also find scaling dimensions ``monotonically'' decrease
along massless flows. Combining the discovery with predictions, sometimes, we
can uniquely fix infrared conformal dimensions.


  Selection of appropriate template matching algorithms to run effectively on
real-time low-cost systems is always major issue. This is due to unpredictable
changes in image scene which often necessitate more sophisticated real-time
algorithms to retain image consistency. Inefficiency of low cost auxiliary
hardware and time limitations are the major constraints in using these sorts of
algorithms. The real-time system introduced here copes with these problems
utilising a fast running template matching algorithm, which makes use of best
colour band selection. The system uses fast running real-time algorithms to
achieve template matching and vehicle classification at about 4 frames /sec. on
low-cost hardware. The colour image sequences have been taken by a fixed CCTV
camera overlooking a busy multi-lane road


  We present a new method for the geometric reconstruction of elastic surfaces
simulated by the immersed boundary method with the goal of simulating the
motion and interactions of cells in whole blood. Our method uses parameter-free
radial basis functions for high-order meshless parametric reconstruction of
point clouds and the elastic force computations required by the immersed
boundary method. This numerical framework allows us to consider the effect of
endothelial geometry and red blood cell motion on the motion of platelets. We
find red blood cells to be crucial for understanding the motion of platelets,
to the point that the geometry of the vessel wall has a negligible effect in
the presence of RBCs. We describe certain interactions that force the platelets
to remain near the endothelium for extended periods, including a novel platelet
motion that can be seen only in 3-dimensional simulations that we term
"unicycling." We also observe red blood cell-mediated interactions between
platelets and the endothelium for which the platelet has reduced speed. We
suggest that these behaviors serve as mechanisms that allow platelets to better
maintain vascular integrity.


  We investigate the formation and evolution of giant molecular clouds (GMCs)
in a Milky-Way-like disk galaxy with a flat rotation curve. We perform a series
of 3D adaptive mesh refinement (AMR) numerical simulations that follow both the
global evolution on scales of ~20kpc and resolve down to scales ~<10pc with a
multiphase atomic interstellar medium (ISM). In this first study, we omit star
formation and feedback, and focus on the processes of gravitational instability
and cloud collisions and interactions. We define clouds as regions with
n_H>=100cm^-3 and track the evolution of individual clouds as they orbit
through the galaxy from their birth to their eventual destruction via merger or
via destructive collision with another cloud. After ~140Myr a large fraction of
the gas in the disk has fragmented into clouds with masses ~10^6 Msun and a
mass spectrum similar to that of Galactic GMCs. The disk settles into a quasi
steady state in which gravitational scattering of clouds keeps the disk near
the threshold of global gravitational instability. The cloud collision time is
found to be a small fraction, ~1/5, of the orbital time, and this is an
efficient mechanism to inject turbulence into the clouds. This helps to keep
clouds only moderately gravitationally bound, with virial parameters of order
unity. Many other observed GMC properties, such as mass surface density,
angular momentum, velocity dispersion, and vertical distribution, can be
accounted for in this simple model with no stellar feedback.


  The data by Q.Yang et al suggest absence of "ultrafast flow" of solvent
across graphene oxide (GO) membranes. The "ultrafast flow" is result of using
unrealistic geometrical model with close packed hole-free micrometer sized GO
flakes providing only 0.1 % of area in each layer available for permeation. The
data by Q.Yang et all demonstrate that at least 3-5 % of total layer area are
available for permeation in real GO membranes due to holes between irregularly
shaped flakes. At least 2-3 percent of area also needs to be added to the
permeation cross section due to holes and cracks in GO flakes, especially
abundant due to prolonged sonication of dispersions. Permeation of solutions
mostly through pinholes penetrating tens of GO layers suggests that "graphene
capillaries" are not required to explain water flow across the membrane. Taking
into account realistic packing of GO flakes with holes between the flakes and
across the flakes, 2-3 orders of magnitude shorter permeation path should be
formed. Considering shorter zigzag permeation path between GO sheets and 5-10 %
of area in each layer available for permeation due to holes across and between
GO flakes, permeation rates across GO membranes can be explained by trivial
diffusion. In absence of "graphene capillaries", the "ultra-precise sieving"
related to "cutoff" value of 4.5 \AA{} (hydration diameter) provided by
"graphene capillaries" has little meaning. As it is well known from earlier
studies and now demonstrated by Q.Yang et al using their own samples the
swelling of GO membranes is different for different solutions, depends on
concentration of solutes and results in delamination in many solutions. Q.Yang
et al provide controversial data for stability of their GO membranes in water
citing use of surfactant to prevent dissolving. Oxidation state of GO membranes
remains to be unknown due to incorrect analysis of XPS spectra.


  The optimal learner for prediction modeling varies depending on the
underlying data-generating distribution. Super Learner (SL) is a generic
ensemble learning algorithm that uses cross-validation to select among a
"library" of candidate prediction models. The SL is not restricted to a single
prediction model, but uses the strengths of a variety of learning algorithms to
adapt to different databases. While the SL has been shown to perform well in a
number of settings, it has not been thoroughly evaluated in large electronic
healthcare databases that are common in pharmacoepidemiology and comparative
effectiveness research. In this study, we applied and evaluated the performance
of the SL in its ability to predict treatment assignment using three electronic
healthcare databases. We considered a library of algorithms that consisted of
both nonparametric and parametric models. We also considered a novel strategy
for prediction modeling that combines the SL with the high-dimensional
propensity score (hdPS) variable selection algorithm. Predictive performance
was assessed using three metrics: the negative log-likelihood, area under the
curve (AUC), and time complexity. Results showed that the best individual
algorithm, in terms of predictive performance, varied across datasets. The SL
was able to adapt to the given dataset and optimize predictive performance
relative to any individual learner. Combining the SL with the hdPS was the most
consistent prediction method and may be promising for PS estimation and
prediction modeling in electronic healthcare databases.


  Cheapfake is a recently coined term that encompasses non-AI (``cheap'')
manipulations of multimedia content. Cheapfakes are known to be more prevalent
than deepfakes. Cheapfake media can be created using editing software for
image/video manipulations, or even without using any software, by simply
altering the context of an image/video by sharing the media alongside
misleading claims. This alteration of context is referred to as out-of-context
(OOC) misuse of media. OOC media is much harder to detect than fake media,
since the images and videos are not tampered. In this challenge, we focus on
detecting OOC images, and more specifically the misuse of real photographs with
conflicting image captions in news items. The aim of this challenge is to
develop and benchmark models that can be used to detect whether given samples
(news image and associated captions) are OOC, based on the recently compiled
COSMOS dataset.


  Inspired by the paper [1] of V. Bergelson, John H.Johnson Jr., J. Moreira, we
formulate an abstract version of image partition regularity. To establish the
result we have used a variant of first entry condition and for infinite case we
contained our work to Milliken-Taylor system.


  Constrained Forest Problems (CFPs) as introduced by Goemans and Williamson in
1995 capture a wide range of network design problems with edge subsets as
solutions, such as Minimum Spanning Tree, Steiner Forest, and Point-to-Point
Connection. While individual CFPs have been studied extensively in individual
computational models, a unified approach to solving general CFPs in multiple
computational models has been lacking. Against this background, we present the
shell-decomposition algorithm, a model-agnostic meta-algorithm that efficiently
computes a $(2+\epsilon)$-approximation to CFPs for a broad class of forest
functions. To demonstrate the power and flexibility of this result, we
instantiate our algorithm for 3 fundamental, NP-hard CFPs in 3 different
computational models. For example, for constant $\epsilon$, we obtain the
following $(2+\epsilon)$-approximations in the Congest model:
  1. For Steiner Forest specified via input components, where each node knows
the identifier of one of $k$ disjoint subsets of $V$, we achieve a
deterministic $(2+\epsilon)$-approximation in $O(\sqrt{n}+D+k)$ rounds, where
$D$ is the hop diameter of the graph.
  2. For Steiner Forest specified via symmetric connection requests, where
connection requests are issued to pairs of nodes, we leverage randomized
equality testing to reduce the running time to $O(\sqrt{n}+D)$, succeeding with
high probability.
  3. For Point-to-Point Connection, we provide a $(2+\epsilon)$-approximation
in $O(\sqrt{n}+D)$ rounds.
  4. For Facility Placement and Connection, a relative of non-metric Facility
Location, we obtain a $(2+\epsilon)$-approximation in $O(\sqrt{n}+D)$ rounds.
  We further show how to replace the $\sqrt{n}+D$ term by the complexity of
solving Partwise Aggregation, achieving (near-)universal optimality in any
setting in which a solution to Partwise Aggregation in near-shortcut-quality
time is known.


  The status of the ATLAS detector shortly before the start-up of the LHC is
presented. The progress in the commissioning of the detector, as well as plans
for early physics analysis are outlined.


  Our aim is to determine the distribution of stellar population parameters
(extinction, age, metallicity, and star formation rate) of quiescent galaxies
within the rest-frame stellar mass$-$colour and $UVJ$ colour$-$colour diagrams
corrected for extinction up to $z\sim1$. These novel diagrams reduce the
contamination in samples of quiescent galaxies owing to dust-reddened galaxies,
and they provide useful constraints on stellar population parameters. We set
constraints on the stellar population parameters of quiescent galaxies
combining the ALHAMBRA multi-filter photo-spectra with our SED-fitting code
MUFFIT, making use of composite stellar population models. The extinction
obtained by MUFFIT allowed us to remove dusty star-forming (DSF) galaxies from
the sample of red $UVJ$ galaxies. The distributions of stellar population
parameters across these rest-frame diagrams are revealed after the dust
correction and are fitted by the LOESS method to reduce uncertainty effects.
Quiescent galaxy samples defined via classical $UVJ$ diagrams are typically
contaminated by a $\sim20$% fraction of DSF galaxies. A significant part of the
galaxies in the green valley are actually obscured star-forming galaxies
($\sim30-65$%). Consequently, the transition of galaxies from the blue cloud to
the red sequence, and hence the related mechanisms for quenching, seems to be
much more efficient and faster than previously reported. The rest-frame stellar
mass$-$colour and $UVJ$ colour$-$colour diagrams are useful for constraining
the age, metallicity, extinction, and star formation rate of quiescent galaxies
by only their redshift, rest-frame colours, and/or stellar mass. Dust
correction plays an important role in understanding how quiescent galaxies are
distributed in these diagrams and is key to performing a pure selection of
quiescent galaxies via intrinsic colours.


  We compare the 230 GHz near-horizon emission from Sagittarius A* to
simulations representing three classes of accretion flows. Using the structure
function to capture the variability statistics of the light curve, we find a
noticeable discrepancy between the observations and models based on torus-fed
accretion disks, whether those disks bring in a small or large amount of net
magnetic flux. On the other hand, the simulations that are fed more
realistically by stellar winds match the observed structure function very well.
We describe the differences between models, arguing that feeding by stellar
winds may be a critical component in constructing theoretical models for
accretion in the Galactic Center.


  Two-dimensional (2D) van-der-Waals semiconductors have emerged as a class of
materials with promising device characteristics owing to the intrinsic bandgap.
For realistic applications, the ideal is to modify the bandgap in a controlled
manner by a mechanism that can be generally applied to this class of materials.
Here, we report the observation of a universally tunable bandgap in the family
of bulk 2H transition metal dichalcogenides (TMDs) by in situ surface doping of
Rb atoms. A series of angle-resolved photoemission spectra unexceptionally
shows that the bandgap of TMDs at the zone corners is modulated in the range of
0.8 ~ 2.0 eV, which covers a wide spectral range from visible to near infrared,
with a tendency from indirect to direct bandgap. A key clue to understand the
mechanism of this bandgap engineering is provided by the spectroscopic
signature of symmetry breaking and resultant spin splitting, which can be
explained by the formation of 2D electric dipole layers within the surface
bilayer of TMDs. Our results establish the surface Stark effect as a universal
mechanism of bandgap engineering based on the strong 2D nature of van-der-Waals
semiconductors.


  Energy minimization methods are a classical tool in a multitude of computer
vision applications. While they are interpretable and well-studied, their
regularity assumptions are difficult to design by hand. Deep learning
techniques on the other hand are purely data-driven, often provide excellent
results, but are very difficult to constrain to predefined physical or
safety-critical models. A possible combination between the two approaches is to
design a parametric energy and train the free parameters in such a way that
minimizers of the energy correspond to desired solution on a set of training
examples. Unfortunately, such formulations typically lead to bi-level
optimization problems, on which common optimization algorithms are difficult to
scale to modern requirements in data processing and efficiency. In this work,
we present a new strategy to optimize these bi-level problems. We investigate
surrogate single-level problems that majorize the target problems and can be
implemented with existing tools, leading to efficient algorithms without
collapse of the energy function. This framework of strategies enables new
avenues to the training of parameterized energy minimization models from large
data.


  Crowdsourcing has become an efficient paradigm for performing large scale
tasks. Truth discovery and incentive mechanism are fundamentally important for
the crowdsourcing system. Many truth discovery methods and incentive mechanisms
for crowdsourcing have been proposed. However, most of them cannot be applied
to deal with the crowdsourcing with copiers. To address the issue, we formulate
the problem of maximizing the social welfare such that all tasks can be
completed with the least confidence for truth discovery. We design an incentive
mechanism consisting truth discovery stage and reverse auction stage. In truth
discovery stage, we estimate the truth for each task based on both the
dependence and accuracy of workers. In reverse auction stage, we design a
greedy algorithm to select the winners and determine the payment. Through both
rigorous theoretical analysis and extensive simulations, we demonstrate that
the proposed mechanisms achieve computational efficiency, individual
rationality, truthfulness, and guaranteed approximation. Moreover, our truth
discovery method shows prominent advantage in terms of precision when there are
copiers in the crowdsourcing systems.


  Search for invisible final states produced at the Large Hadron Collider (LHC)
by new physics scenarios are normally carried out resorting to a variety of
probes emerging from the initial state, in the form of single-jet, -photon and
-$Z$ boson signatures. These are particularly effective for models of
Supersymmetry (SUSY) in presence of $R$-parity conservation, owing to the
presence in their spectra of a stable neutralino as dark matter candidate. We
assume here as theoretical framework Supersymmetric ($B-L$) extension of the
Standard Model (BLSSM), wherein a mediator for invisible decays can be $Z'$
boson. The peculiarity of the signal is thus that the final state objects carry
a very large (transverse) missing energy, since the $Z'$ is naturally massive
and constrained by direct searches and electro-weak precision tests to be at
least in TeV scale region. Under these circumstances the efficiency in
accessing the invisible final state and rejecting the standard model background
is very high. This somehow compensates the rather meagre production rates.
Another special feature of this invisible BLSSM signal is its composition,
which is often dominated by sneutrino decays (alongside the more traditional
neutrino and neutralino modes). Sensitivity of the CERN machine to these two
features can therefore help disentangling the BLSSM from more popular SUSY
models. We assess in this analysis the scope of the LHC in establishing the
aforementioned invisible signals through a sophisticated signal-to-background
simulation carried out in presence of parton shower, hadronisation and detector
effects. We find that significant sensitivity exists already after 300
fb$^{-1}$ during Run 2. We find that mono-jet events can be readily accessible
at the LHC, so as to enable one to claim a prompt discovery, while mono-photon
and -$Z$ signals can be used as diagnostic tools of the underlying scenario.


  The capability of making explainable inferences regarding physical processes
has long been desired. One fundamental physical process is object motion.
Inferring what causes the motion of a group of objects can even be a
challenging task for experts, e.g., in forensics science. Most of the work in
the literature relies on physics simulation to draw such infer- ences. The
simulation requires a precise model of the under- lying domain to work well and
is essentially a black-box from which one can hardly obtain any useful
explanation. By contrast, qualitative reasoning methods have the advan- tage in
making transparent inferences with ambiguous infor- mation, which makes it
suitable for this task. However, there has been no suitable qualitative theory
proposed for object motion in three-dimensional space. In this paper, we take
this challenge and develop a qualitative theory for the motion of rigid
objects. Based on this theory, we develop a reasoning method to solve a very
interesting problem: Assuming there are several objects that were initially at
rest and now have started to move. We want to infer what action causes the
movement of these objects.


  A sleepy driver is arguably much more dangerous on the road than the one who
is speeding as he is a victim of microsleeps. Automotive researchers and
manufacturers are trying to curb this problem with several technological
solutions that will avert such a crisis. This article focuses on the detection
of such micro sleep and drowsiness using neural network based methodologies.
Our previous work in this field involved using machine learning with
multi-layer perceptron to detect the same. In this paper, accuracy was
increased by utilizing facial landmarks which are detected by the camera and
that is passed to a Convolutional Neural Network (CNN) to classify drowsiness.
The achievement with this work is the capability to provide a lightweight
alternative to heavier classification models with more than 88% for the
category without glasses, more than 85% for the category night without glasses.
On average, more than 83% of accuracy was achieved in all categories. Moreover,
as for model size, complexity and storage, there is a marked reduction in the
new proposed model in comparison to the benchmark model where the maximum size
is 75 KB. The proposed CNN based model can be used to build a real-time driver
drowsiness detection system for embedded systems and Android devices with high
accuracy and ease of use.


  This thesis introduces a set of methods for testing models of modified
gravity using galaxy clusters. In particular, a technique for constraining
models with a chameleon screening is introduced. In addition, the outlined
technique is expanded to test a wider class of models, such as the theory of
emergent gravity. Finally, the first part of the thesis is concluded by
adapting the mentioned tests for model independent constraints. The obtained
results indicate that galaxy clusters can be used to obtain some of the most
powerful constraints on cosmological scales.
  The second part of the thesis is dedicated to the topic of cosmological
emulators. More specifically, a technique of emulating cosmological N-body
simulation output data based on machine learning is introduced. Generative
adversarial networks (GANs) are used to emulate dark matter-only as well as
hydrodynamical simulation data. In addition, N-body modified gravity
simulations are explored as well. The presented investigation of the GAN
algorithm shows that such emulators offer a powerful, fast and efficient way of
producing simulation output data with different cosmological parameters. The
power spectrum analysis indicates a 1-20% difference between the training and
the generated data depending on the dataset used and whether Gaussian smoothing
is applied or not.


  The simplex method is one of the most fundamental technologies for solving
linear programming (LP) problems and has been widely applied to different
practical applications. In the past literature, how to improve and accelerate
the simplex method has attracted plenty of research. One important way to
achieve this goal is to find a better initialization method for the simplex. In
this survey, we aim to provide an overview about the initialization methods in
the primal and dual simplex, respectively. We also propose several potential
future directions about how to improve the existing initialization methods with
the help of advanced learning technologies.