  Previous work on novel object detection considers zero or few-shot settings
where none or few examples of each category are available for training. In real
world scenarios, it is less practical to expect that 'all' the novel classes
are either unseen or {have} few-examples. Here, we propose a more realistic
setting termed 'Any-shot detection', where totally unseen and few-shot
categories can simultaneously co-occur during inference. Any-shot detection
offers unique challenges compared to conventional novel object detection such
as, a high imbalance between unseen, few-shot and seen object classes,
susceptibility to forget base-training while learning novel classes and
distinguishing novel classes from the background. To address these challenges,
we propose a unified any-shot detection model, that can concurrently learn to
detect both zero-shot and few-shot object classes. Our core idea is to use
class semantics as prototypes for object detection, a formulation that
naturally minimizes knowledge forgetting and mitigates the class-imbalance in
the label space. Besides, we propose a rebalanced loss function that emphasizes
difficult few-shot cases but avoids overfitting on the novel classes to allow
detection of totally unseen classes. Without bells and whistles, our framework
can also be used solely for Zero-shot detection and Few-shot detection tasks.
We report extensive experiments on Pascal VOC and MS-COCO datasets where our
approach is shown to provide significant improvements.


  Two apparently unrelated fields -- normalizing flows and causality -- have
recently received considerable attention in the machine learning community. In
this work, we highlight an intrinsic correspondence between a simple family of
autoregressive normalizing flows and identifiable causal models. We exploit the
fact that autoregressive flow architectures define an ordering over variables,
analogous to a causal ordering, to show that they are well-suited to performing
a range of causal inference tasks, ranging from causal discovery to making
interventional and counterfactual predictions. First, we show that causal
models derived from both affine and additive autoregressive flows with fixed
orderings over variables are identifiable, i.e. the true direction of causal
influence can be recovered. This provides a generalization of the additive
noise model well-known in causal discovery. Second, we derive a bivariate
measure of causal direction based on likelihood ratios, leveraging the fact
that flow models can estimate normalized log-densities of data. Third, we
demonstrate that flows naturally allow for direct evaluation of both
interventional and counterfactual queries, the latter case being possible due
to the invertible nature of flows. Finally, throughout a series of experiments
on synthetic and real data, the proposed method is shown to outperform current
approaches for causal discovery as well as making accurate interventional and
counterfactual predictions.


  We explore the tail of various waiting time datasets of processes that follow
a nonstationary Poisson distribution with a sinusoidal driver. Analytically, we
find that the distribution of large waiting times of such processes can be
described using a power law slope of -2.5. We show that this result applies
more broadly to any nonstationary Poisson process driven periodically. Examples
of such processes include solar flares, coronal mass ejections, geomagnetic
storms, and substorms. We also discuss how the power law specifically relates
to the behavior of driver near its minima.


  Due to the acceleration of the black hole, the circular orbits of the photons
will deviate from the equatorial plane and the property of the black hole
shadow will change. We find that the latitude of the circular orbit increases
with the increasing acceleration and then show that the observer's inclination
angles which make the shadow radius and the shadow distortion maximum increase
with the increasing acceleration for the accelerating Kerr black hole.


  We show how to achieve both fast and hyperuniform dispersions of particles in
viscous fluids. To do so, we first extend the concept of critical random
organization to chaotic drives. We show how palindromic sequences of chaotic
advection cause microscopic particles to effectively interact at long range
thereby inhibiting critical self-organization. Based on this understanding we
go around this limitation and design sequences of stirring and unstirring which
simultaneously optimize the speed of particle spreading and the homogeneity of
the resulting dispersions.


  Non-reciprocal charge transport that is strongly associated with the
structural or magnetic chirality of the quantum materials system is one of the
most exotic properties of condensed matter physics. Here, using magnetic alloys
film Pt2MnGe, we have realized the large non-reciprocal charge transport up to
room temperature, which roots in the organic combination of chirality dependent
carrier scattering and special magnetic configurations. In this framework, the
conduction electrons are scattered asymmetrically by the emerging non-zero
vector spin chirality under in-plane magnetic field, resulting in robust
non-reciprocal response. More astonishingly, the vector spin chirality in
Pt2MnGe film can be reversed by a spin-polarized current through spin Hall
effect in a junction with Pt layer. Our work resolves the general limitation of
non-reciprocal charge transport to cryogenic temperatures, and paves the way
for extending its applications in the emerging field of chiral spintronics.


  We present an analytical model that enables throughput evaluation of
Opportunistic Spectrum Orthogonal Frequency Division Multiple Access (OS-OFDMA)
networks. The core feature of the model, based on a discrete time Markov chain,
is the consideration of different channel and subchannel allocation strategies
under different Primary and Secondary user types, traffic and priority levels.
The analytical model also assesses the impact of different spectrum sensing
strategies on the throughput of OS-OFDMA network. The analysis applies to the
IEEE 802.22 standard, to evaluate the impact of two-stage spectrum sensing
strategy and varying temporal activity of wireless microphones on the IEEE
802.22 throughput. Our study suggests that OS-OFDMA with subchannel notching
and channel bonding could provide almost ten times higher throughput compared
with the design without those options, when the activity and density of
wireless microphones is very high. Furthermore, we confirm that OS-OFDMA
implementation without subchannel notching, used in the IEEE 802.22, is able to
support real-time and non-real-time quality of service classes, provided that
wireless microphones temporal activity is moderate (with approximately one
wireless microphone per 3,000 inhabitants with light urban population density
and short duty cycles). Finally, two-stage spectrum sensing option improves
OS-OFDMA throughput, provided that the length of spectrum sensing at every
stage is optimized using our model.


  Given positive integers $n\ge s$, we let ${\mathrm{ex}}(n,s)$ denote the
maximum number of edges in a triangle-free graph $G$ on $n$ vertices with
$\alpha(G)\le s$. In the early sixties Andr\'{a}sfai conjectured that for
$n/3<s<n/2$ the function ${\mathrm{ex}}(n, s)$ is piecewise quadratic with
critical values at $s/n={k}/({3k-1})$. We confirm that this is indeed the case
whenever $s/n$ is slightly larger than a critical value, thus determining
${\mathrm{ex}}(n,s)$ for all $n$ and $s$ such that $s/n\in [{k}/({3k-1}),
{k}/({3k-1})+\gamma_k]$, where $\gamma_k=\Theta(k^{-6})$.


  We study the effects that the constant periodic data condition have on
topological entropy of Anosov diffeomorphisms. Under constant periodic data
condition we prove that Anosov diffeomorphism has finitely many measures of
maximal entropy and each one of them is absolutely continuous with respect to
Lebesgue. From this, in the setting of $C^{\infty}-$Anosov diffeomorphisms
satisfying constant periodic data, we provide a characterization of
transitivity property via measures of maximal entropy.


  The non-thermal production of dark matter (DM) usually requires very tiny
couplings of the dark sector with the visible sector and therefore is
notoriously challenging to hunt in laboratory experiments. Here we propose a
novel pathway to test such a production in the context of a non-standard
cosmological history, using both gravitational wave (GW) and laboratory
searches. We investigate the formation of DM from the decay of a scalar field
that we dub as the reheaton, as it also reheats the Universe when it decays. We
consider the possibility that the Universe undergoes a phase %of kination with
\textit{kination-like} stiff equation-of-state ($w_{\rm kin}>1/3$) before the
reheaton dominates the energy density of the Universe and eventually decays
into Standard Model and DM particles. We then study how first-order tensor
perturbations generated during inflation, the amplitude of which may get
amplified during the kination era and lead to detectable GW signals. Demanding
that the reheaton produces the observed DM relic density, we show that the
reheaton's lifetime and branching fractions are dictated by the cosmological
scenario. In particular, we show that it is long-lived and can be searched by
various experiments such as DUNE, FASER, FASER-II, MATHUSLA, SHiP, etc. We also
identify the parameter space which leads to complementary observables for GW
detectors such as LISA and u-DECIGO. In particular we find that a kination-like
period with an equation-of-state parameter $w_{\rm kin}\approx 0.5$ and a
reheaton mass $\mathcal O(0.5-5)$ GeV and a DM mass of $\mathcal O (10-100)$
keV may lead to sizeable imprints in both kinds of searches.


  We assess the viability of successful reconstruction of the evolution of the
dark energy equation of state using varying fundamental couplings, such as the
fine structure constant or the proton-to-electron mass ratio. We show that the
same evolution of the dark energy equation of state parameter with cosmic time
may be associated with arbitrary variations of the fundamental couplings.
Various examples of models with the same (different) background evolution and
different (the same) time variation of fundamental couplings are studied in the
letter. Although we demonstrate that, for a broad family of models, it is
possible to redefine the scalar field in such a way that its dynamics is that
of a standard quintessence scalar field, in general such redefinition leads to
the breakdown of the linear relation between the scalar field and the variation
of fundamental couplings. This implies that the assumption of a linear coupling
is not sufficient to guarantee a successful reconstruction of the dark energy
dynamics and consequently additional model dependent assumptions about the
scalar field responsible for the dark energy need to be made.


  We consider functional equations driven by linear fractional transformations,
which are special cases of de Rham's functional equations. We consider
Hausdorff dimension of the measure whose distribution function is the solution.
We give a necessary and sufficient condition for singularity. We also show that
they have a relationship with stationary measures.


  The explosive growth in the development of Traditional Chinese Medicine (TCM)
has resulted in the continued increase in clinical and research data. The lack
of standardised terminology, flaws in data quality planning and management of
TCM informatics are preventing clinical decision-making, drug discovery and
education. This paper argues that the introduction of data warehousing
technologies to enhance the effectiveness and durability in TCM is paramount.
To showcase the role of data warehousing in the improvement of TCM, this paper
presents a practical model for data warehousing with detailed explanation,
which is based on the structured electronic records, for TCM clinical
researches and medical knowledge discovery.


  This work explores the ability of classical electronic structure methods to
efficiently represent (compress) the information content of full configuration
interaction (FCI) wave functions. We introduce a benchmark set of four hydrogen
model systems of different dimensionality and distinctive electronic
structures: a 1D chain, a 1D ring, a 2D triangular lattice, and a 3D
close-packed pyramid. To assess the ability of a computational method to
produce accurate and compact wave functions, we introduce the accuracy volume,
a metric that measures the number of variational parameters necessary to
achieve a target energy error. Using this metric and the hydrogen models, we
examine the performance of three classical deterministic methods: i) selected
configuration interaction (sCI) realized both via an a posteriori and
variational selection of the most important determinants, ii) rank-reduced FCI,
obtained by an a posteriori singular value decomposition of the FCI tensor
(SVD-FCI), and iii) the matrix product state representation obtained via the
density matrix renormalization group (DMRG). We find that DMRG generally gives
the most efficient wave function representation for all systems, particularly
in the 1D chain with a localized basis. For the 2D and 3D systems, all methods
perform best with a delocalized basis, and the efficiency of sCI is closer to
that of DMRG, with the former having and accuracy volume approximately twice as
large in the strong correlation regime. Compared to sCI, the SVD-FCI scheme is
generally found to require a slightly larger number of parameters to achieve
the same energy accuracy.


  This paper is devoted to the study of quasi-periodic properties of fractional
order integrals and derivatives of periodic functions. Considering
Riemann-Liouville and Caputo definitions, we discuss when the fractional
derivative and when the fractional integral of a certain class of periodic
functions satisfies particular properties. We study concepts close to the well
known idea of periodic function, such as S-asymptotically periodic,
asymptotically periodic or almost periodic function. Boundedness of fractional
derivative and fractional integral of a periodic function is also studied.


  We here present an extension of the CKKW-L multi-jet merging technique to
so-called sector showers as implemented in the Vincia antenna shower. The
bijective nature of sector showers allows for efficient multi-jet merging at
high multiplicities, as any given configuration possesses only a single
"history", while retaining the accuracy of the CKKW-L technique. Our method
reduces the factorial scaling of the number of parton shower histories to a
constant of a single history per colour-ordered final state. We show that the
complexity of constructing shower histories is reduced to an effective linear
scaling with the number of final-state particles. Moreover, we demonstrate that
the overall event generation time and the memory footprint of our
implementation remain approximately constant when including additional jets. We
compare both to the conventional CKKW-L implementation in Pythia and gain a
first estimate of renormalisation scale uncertainties at high merged
multiplicities. As a proof of concept, we show parton-level predictions for
vector boson production in proton-proton collisions with up to nine hard jets
using the new implementation. Despite its much simpler nature, we dub the new
technique MESS, in analogy to the conventional MEPS nomenclature.


  We consider families of codes obtained by "lifting" a base code $\mathcal{C}$
through operations such as $k$-XOR applied to "local views" of codewords of
$\mathcal{C}$, according to a suitable $k$-uniform hypergraph. The $k$-XOR
operation yields the direct sum encoding used in works of [Ta-Shma, STOC 2017]
and [Dinur and Kaufman, FOCS 2017].
  We give a general framework for list decoding such lifted codes, as long as
the base code admits a unique decoding algorithm, and the hypergraph used for
lifting satisfies certain expansion properties. We show that these properties
are satisfied by the collection of length $k$ walks on an expander graph, and
by hypergraphs corresponding to high-dimensional expanders. Instantiating our
framework, we obtain list decoding algorithms for direct sum liftings on the
above hypergraph families. Using known connections between direct sum and
direct product, we also recover the recent results of Dinur et al. [SODA 2019]
on list decoding for direct product liftings.
  Our framework relies on relaxations given by the Sum-of-Squares (SOS) SDP
hierarchy for solving various constraint satisfaction problems (CSPs). We view
the problem of recovering the closest codeword to a given word, as finding the
optimal solution of a CSP. Constraints in the instance correspond to edges of
the lifting hypergraph, and the solutions are restricted to lie in the base
code $\mathcal{C}$. We show that recent algorithms for (approximately) solving
CSPs on certain expanding hypergraphs also yield a decoding algorithm for such
lifted codes.
  We extend the framework to list decoding, by requiring the SOS solution to
minimize a convex proxy for negative entropy. We show that this ensures a
covering property for the SOS solution, and the "condition and round" approach
used in several SOS algorithms can then be used to recover the required list of
codewords.


  We consider Guth's approach to the Fourier restriction problem via polynomial
partitioning. By writing out his induction argument as a recursive algorithm
and introducing new geometric information, known as the polynomial Wolff
axioms, we obtain improved bounds for the restriction conjecture, particularly
in high dimensions. Consequences for the Kakeya conjecture are also considered.


  Recently, Bauke and Mertens conjectured that the local statistics of energies
in random spin systems with discrete spin space should in most circumstances be
the same as in the random energy model. This was proven in a large class of
models for energies that do not grow too fast with the system size. Considering
the example of the generalized random energy model, we show that the conjecture
breaks down for energies proportional to the volume of the system, and describe
the far more complex behavior that then sets in.


  Simultaneous, high-quality measurements of the neutral helium triplet
features at 5876~\AA\ and 10830~\AA, respectively, in a sample of solar-type
stars are presented. The observations were made with ESO telescopes at the La
Silla Paranal Observatory under program ID 088.D-0028(A) and MPG Utility Run
for FEROS 088.A-9029(A). The equivalent widths of these features combined with
chromospheric models are utilized to infer the fractional area coverage, or
filling factor, of magnetic regions outside of spots. We find that the majority
of the sample is characterized by filling factors less than unity. However,
discrepancies occur among the coolest K-type and warmest and most rapidly
rotating F-type dwarf stars. We discuss these apparently anomalous results and
find that in the case of K-type stars they are an artifact of the application
of chromospheric models best suited to the Sun than to stars with significantly
lower $T_\mathrm{eff}$. The case of the F-type rapid rotators can be explained
with the measurement uncertainties of the equivalent widths, but they may also
be due to a non-magnetic heating component in their atmospheres. With the
exceptions noted above, preliminary results suggest that the average heating
rates in the active regions are the same from one star to the other, differing
in the spatially integrated, observed level of activity due to the area
coverage. Hence, differences in activity in this sample are mainly due to the
filling factor of active regions.


  The propagation of localized solitons in the presence of large-scale waves is
a fundamental problem, both physically and mathematically, with applications in
fluid dynamics, nonlinear optics and condensed matter physics. Here, the
evolution of a soliton as it interacts with a rarefaction wave or a dispersive
shock wave, examples of slowly varying and rapidly oscillating dispersive mean
fields, for the Korteweg-de Vries equation is studied. Step boundary conditions
give rise to either a rarefaction wave (step up) or a dispersive shock wave
(step down). When a soliton interacts with one of these mean fields, it can
either transmit through (tunnel) or become embedded (trapped) inside, depending
on its initial amplitude and position. A comprehensive review of three separate
analytical approaches is undertaken to describe these interactions. First, a
basic soliton perturbation theory is introduced that is found to capture the
solution dynamics for soliton-rarefaction wave interaction in the small
dispersion limit. Next, multiphase Whitham modulation theory and its finite-gap
description are used to describe soliton-rarefaction wave and
soliton-dispersive shock wave interactions. Lastly, a spectral description and
an exact solution of the initial value problem is obtained through the Inverse
Scattering Transform. For transmitted solitons, far-field asymptotics reveal
the soliton phase shift through either type of wave mentioned above. In the
trapped case, there is no proper eigenvalue in the spectral description,
implying that the evolution does not involve a proper soliton solution. These
approaches are consistent, agree with direct numerical simulation, and
accurately describe different aspects of solitary wave-mean field interaction


  Ghys and Sergiescu proved in the $80$s that Thompson's group $T$, and hence
$F$, admits actions by $C^{\infty}$ diffeomorphisms of the circle . They proved
that the standard actions of these groups are topologically conjugate to a
group of $C^\infty$ diffeomorphisms. Monod defined a family of groups of
piecewise projective homeomorphisms, and Lodha-Moore defined finitely
presentable groups of piecewise projective homeomorphisms. These groups are of
particular interest because they are nonamenable and contain no free subgroup.
In contrast to the result of Ghys-Sergiescu, we prove that the groups of Monod
and Lodha-Moore are not topologically conjugate to a group of $C^1$
diffeomorphisms.
  Furthermore, we show that the group of Lodha-Moore has no nonabelian $C^1$
action on the interval. We also show that many Monod's groups $H(A)$, for
instance when $A$ is such that $\mathsf{PSL}(2,A)$ contains a rational
homothety $x\mapsto \tfrac{p}{q}x$, do not admit a $C^1$ action on the
interval. The obstruction comes from the existence of hyperbolic fixed points
for $C^1$ actions. With slightly different techniques, we also show that some
groups of piecewise affine homeomorphisms of the interval or the circle are not
smoothable.


  In the present paper, we introduce and study counterparts of Rickart
involutive algebras, i.e., almost inner Rickart algebras. We prove that a
nilpotent associative algebra, which has no nilpotent elements with nonzero
square roots, is an almost inner Rickart algebra. A nilpotent associative
algebra, which has no nilpotent elements with a square root $b$ such that
$b^3\neq 0$, is not an almost inner Rickart algebra if there exists a nonzero
element $a$ such that $a^2\neq 0$. As a main result of the paper, we describe a
finite-dimensional almost inner Rickart algebra $\mathcal{A}$ over a field
$\mathbb{F}$, isomorphic to $\mathbb{F}^n\dot{+} \mathcal{N}$, $n=1,2$, with a
nilradical $\mathcal{N}$. Also, we classify finite-dimensional almost inner
Rickart algebras over the real or complex numbers with a nonzero nilradical
$\mathcal{N}$.


  We present magnetotransport evidence for antiferromagnetism in films of the
electron-doped cuprates Pr$_{2-x}$Ce$_x$CuO$_4$. Our results show clear
signature of static antiferromagnetism up to optimal doping x=0.15, with a
quantum phase transition close to x=0.16, and a coexistence of static
antiferromagnetism and superconductivity for 0.12$\le$x$\le$0.15.


  Most recent semi-supervised deep learning (deep SSL) methods used a similar
paradigm: use network predictions to update pseudo-labels and use pseudo-labels
to update network parameters iteratively. However, they lack theoretical
support and cannot explain why predictions are good candidates for
pseudo-labels. In this paper, we propose a principled end-to-end framework
named deep decipher (D2) for SSL. Within the D2 framework, we prove that
pseudo-labels are related to network predictions by an exponential link
function, which gives a theoretical support for using predictions as
pseudo-labels. Furthermore, we demonstrate that updating pseudo-labels by
network predictions will make them uncertain. To mitigate this problem, we
propose a training strategy called repetitive reprediction (R2). Finally, the
proposed R2-D2 method is tested on the large-scale ImageNet dataset and
outperforms state-of-the-art methods by 5 percentage points.


  By exploiting the role of the horizontal symmetry SL(2,R), we extend the
analysis and classification of two-centered extremal black hole charge
configurations to the case of "small" single-centered constituents. These
latter are seen to decrease the number of independent horizontal-invariant
polynomials from four to one, depending on the rank of the charge orbit
supporting each of the two centers. Within U-duality groups of type E7, both
reducible and irreducible symmetric supergravity models in four space-time
dimensions are considered, thus encompassing N = 2 and N = 8 theories.


  We propose a discrete traffic flow model with discrete time. Continuum limit
of this model is equivalent to the optimal velocity model. It has also an
ultradiscrete limit and a piecewise-linear type of traffic flow model is
obtained. Both models show phase transition from free flow to jam in a
fundamental diagram. Moreover, the ultradiscrete model includes the
Fukui-Ishibashi model in a special case.


  This paper presents scale-adaptive filters that optimize the
detection/separation of compact sources on a background. We assume that the
sources have a multiquadric profile, i. e. $\tau (x) = {[1 +
{(x/r_c)}^2]}^{-\lambda}, \lambda \geq {1/2}, x\equiv |\vec{x}|$, and a
background modeled by a homogeneous and isotropic random field, characterized
by a power spectrum $P(q)\propto q^{-\gamma}, \gamma \geq 0, q\equiv
|\vec{q}|$. We make an n-dimensional treatment but consider two interesting
astrophysical applications related to clusters of galaxies (Sunyaev-Zel'dovich
effect and X-ray emission).


  We analyse N-body simulations of star-forming regions to investigate the
effects of external far and extreme ultra-violet photoevaporation from massive
stars on protoplanetary discs. By varying the initial conditions of simulated
star-forming regions, such as the spatial distribution, net bulk motion (virial
ratio), and density, we investigate which parameters most affect the rate at
which discs are dispersed due to external photoevaporation. We find that disc
dispersal due to external photoevaporation is faster in highly substructured
star-forming regions than in smooth and centrally concentrated regions.
Sub-virial star-forming regions undergoing collapse also show higher rates of
disc dispersal than regions that are in virial equilibrium or are expanding. In
moderately dense ($\sim$100 M$_{\odot}$ pc$^{-3}$) regions, half of all
protoplanetary discs with radii $\geq$ 100 AU are photoevaporated within 1 Myr,
three times faster than is currently suggested by observational studies. Discs
in lower-density star-forming regions ($\sim$10 M$_{\odot}$ pc$^{-3}$) survive
for longer, but half are still dispersed on short timescales ($\sim$2 Myr).
This demonstrates that the initial conditions of the star forming regions will
greatly impact the evolution and lifetime of protoplanetary discs. These
results also imply that either gas giant planet formation is extremely rapid
and occurs before the gas component of discs is evaporated, or gas giants only
form in low-density star-forming regions where no massive stars are present to
photoevaporate gas from protoplanetary discs.


  Pre-training and transfer learning are an important building block of current
computer vision systems. While pre-training is usually performed on large
real-world image datasets, in this paper we ask whether this is truly
necessary. To this end, we search for a minimal, purely synthetic pre-training
dataset that allows us to achieve performance similar to the 1 million images
of ImageNet-1k. We construct such a dataset from a single fractal with
perturbations. With this, we contribute three main findings. (i) We show that
pre-training is effective even with minimal synthetic images, with performance
on par with large-scale pre-training datasets like ImageNet-1k for full
fine-tuning. (ii) We investigate the single parameter with which we construct
artificial categories for our dataset. We find that while the shape differences
can be indistinguishable to humans, they are crucial for obtaining strong
performances. (iii) Finally, we investigate the minimal requirements for
successful pre-training. Surprisingly, we find that a substantial reduction of
synthetic images from 1k to 1 can even lead to an increase in pre-training
performance, a motivation to further investigate ''scaling backwards''.
Finally, we extend our method from synthetic images to real images to see if a
single real image can show similar pre-training effect through shape
augmentation. We find that the use of grayscale images and affine
transformations allows even real images to ''scale backwards''.


  A massively parallel kinetic Monte Carlo (kMC) approach is proposed for
simulating ionic migration in a crystal system by introducing the atomic
fragmentation scheme (fragment kMC). The fragment kMC method achieved a
reasonable parallel efficiency with 1728 central processing unit (CPU) cores,
and the method enables the simulation of ionic diffusion in $\mu$m-scale
perovskite crystals. To demonstrate the feasibility of the proposed approach,
the fragment kMC method was applied to predict the diffusion coefficients of
hydrogen and oxygen in SrTiO$_{(3-x)}$H$_x$ and BaTiO$_{(3-x)}$H$_x$ system.
Finally, the fragment kMC method was customized for $\mu$-scale BaTiO$_3$
simulation under an applied bias voltage, and oxygen diffusion in BaTiO$_3$
model was evaluated. The respective grain sizes are sub-nanometre, and we
conclude that the proposed fragment kMC method can be applied to calculate the
extent of ionic migration in $\mu$-scale materials with fully atomistic
simulation models at a reasonable computational cost.


  We calculate the one-quark-loop amplitude for the low energy
$\gamma\gamma\to\pi\pi$ collision in the context of the Nambu and Jona-Lasinio
model with scalar and pseudoscalar four quark couplings to all orders in the
external momenta. We show that the NJL predictions for the
$\gamma\gamma\to\pi^+\pi^-$ reaction are not far from the Born amplitude, which
is close to the data, and is compatible with the chiral perturbation theory
estimations. We determine the corrections given by the NJL model in leading
order of $1/N_c$ to the chiral loop amplitude for $\gamma\gamma\to\pi^0\pi^0$.
Numerical results for the $\gamma\gamma\rightarrow\pi\pi$ cross sections and
for pion polarizabilities are given.


  Recently, Marian-Oprea-Pandharipande established (a generalization of) Lehn's
conjecture for Segre numbers associated to Hilbert schemes of points on
surfaces. Extending work of Johnson, they provided a conjectural correspondence
between Segre and Verlinde numbers. For surfaces with holomorphic 2-form, we
propose conjectural generalizations of their results to moduli spaces of stable
sheaves of any rank.
  Using Mochizuki's formula, we derive a universal function which expresses
virtual Segre and Verlinde numbers of surfaces with holomorphic 2-form in terms
of Seiberg-Witten invariants and intersection numbers on products of Hilbert
schemes of points. We prove that certain canonical virtual Segre and Verlinde
numbers of general type surfaces are topological invariants and we verify our
conjectures in examples.
  The power series in our conjectures are algebraic functions, for which we
find expressions in several cases and which are permuted under certain Galois
actions. Our conjectures imply an algebraic analog of the Mari\~{n}o-Moore
conjecture for higher rank Donaldson invariants. For ranks $3$ and $4$, we
obtain explicit expressions for Donaldson invariants in terms of Seiberg-Witten
invariants.


  It was proposed that a dilute semimetal is unstable against the formation of
an exciton insulator, however experimental confirmations have remained elusive.
We investigate the origin of bulk energy gap in inverted InAs/GaSb quantum
wells (QWs) which naturally host spatially-separated electrons and holes, using
charge-neutral point density (no~po) in gated-device as a tuning parameter. We
find two distinct regimes of gap formation, that for I), no >> 5x1010/cm2, a
soft gap opens predominately by electron-hole hybridization; and for II),
approaching the dilute limit no~ 5x1010/cm2, a hard gap opens leading to a true
bulk insulator with quantized edge states. Moreover, the gap is dramatically
reduced as the QWs are tuned to less dilute. We further examine the response of
gaps to in-plane magnetic fields, and find that for I) the gap closes at B// >
~ 10T, consistent with hybridization while for II) the gap opens continuously
for B// as high as 35T. Our analyses show that the hard gap in II) cannot be
explained by single-particle hybridization. The data are remarkably consistent
with the formation of a nontrivial exciton insulator in very dilute InAs/GaSb
QWs.


  We have studied the influence of a square array of pinning centers on the
dynamics of vortex avalanches in Pb thin films by means of ac- and dc-
magnetization measurements. Close to the superconducting transition Tc the
commensurability between the vortex lattice and the pinning array leads to the
well known local increments of the critical current. As temperature T
decreases, matching features progressively fade out and eventually disappear.
Further down in temperature vortex avalanches develop and dominate the magnetic
response. These avalanches manifest themselves as jumps in the dc-magnetization
and produce a lower ac-shielding giving rise to a paramagnetic reentrance in
the ac-screening x'(T). Within the flux jump regime two subregimes can be
identified. Close to the boundary where vortex avalanches develope, the field
separation between consecutive jumps follow the periodicity of the pinning
array and a field and temperature dependent screening is observed. In this
regime, the response also depends on frequency f in agreement with theoretical
models for magnetothermal instabilities. At low enough temperatures and fields,
the screening saturates to a constant value independent of T, H, and f, where
jumps are randomly distributed. We have also found that vortex instabilities
occupy a larger portion of the H-T diagram in patterned samples than in films
without nanoengineered pinning sites. Finally, we discuss the possible origin
of the vortex avalanches and compare our results with previous experimental and
theoretical studies.


  We introduce a new family of Poisson vertex algebras
$\mathcal{W}(\mathfrak{a})$ analogous to the classical $\mathcal{W}$-algebras.
The algebra $\mathcal{W}(\mathfrak{a})$ is associated with the centralizer
$\mathfrak{a}$ of an arbitrary nilpotent element in $\mathfrak{gl}_N$. We show
that $\mathcal{W}(\mathfrak{a})$ is an algebra of polynomials in infinitely
many variables and produce its free generators in an explicit form. This
implies that $\mathcal{W}(\mathfrak{a})$ is isomorphic to the center at the
critical level of the affine vertex algebra associated with $\mathfrak{a}$.


  A recent X-ray observation has shown that the radio pulsar PSR B0943+10, with
clear drifting subpulses, has a much smaller polar cap area than that of
conventional pulsars with mass of $\sim\msun$ and radius of $\sim10$ km. Zhang
et al. (2005) addressed then that this new result conflicts with the standard
vacuum gap model. Nonetheless, the discrepancy could be explained if PSR
B0943+10 is actually a low-mass quark star. It is found that the potential drop
in the open-field-line region of oblique pulsars (i.e., inclination angle
$\alpha\neq 0$) might be $\sim 10^2$ times that of aligned pulsars, and that
PSR B0943+10 with $\alpha = 12.4^{\rm o}$ could be well above the deathline. We
thus conclude that the Ruderman-Sutherland-type vacuum gap model still works
well for this pulsar if it is a bare quark star with a mass of $\sim
0.02M_\odot$ and a radius of $\sim 2.6$ km.


  The objectives of this work are to revisit the experimental measurements on
dam break flow over a dry horizontal bed and to provide a detailed insight into
the dynamics of the dam break wave impacting a vertical wall downstream the
dam, with emphasis on the pressure loads. The measured data are statistically
analyzed and critically discussed. As a result, an extensive set of data for
validation of computational tools is provided.


  Recent hype surrounding the increasing sophistication of language processing
models has renewed optimism regarding machines achieving a human-like command
of natural language. Research in the area of natural language understanding
(NLU) in artificial intelligence claims to have been making great strides in
this area, however, the lack of conceptual clarity/consistency in how
'understanding' is used in this and other disciplines makes it difficult to
discern how close we actually are. In this interdisciplinary research thesis, I
integrate insights from cognitive science/psychology, philosophy of mind, and
cognitive linguistics, and evaluate it against a critical review of current
approaches in NLU to explore the basic requirements--and remaining
challenges--for developing artificially intelligent systems with human-like
capacities for language use and comprehension.


  The Lie product and the order relation are viewed as defining structures for
Hamiltonian dynamical systems. Their admissible combinations are singled out by
the requirement that the group of the Lie automorphisms be contained in the
group of the order automorphisms (Lie algebras with invariant cones). Taking
advantage of the reciprocal independence of the relevant structures, the
inclusion relation between the two automorphism groups can be reversed; a
procedure which leads to an entirely new formal language (ordered linear spaces
with invariant Lie products). Presumably it offers an alternative description
for quantum systems, radically different from the conventional algebraic
models.


  The decay $B^{\pm}\to DK^{\pm}$ followed by the subsequent decay of the $D$
meson into final states involving a neutral kaon can be used to determine the
CKM angle $\gamma$. We study CP violation effects due to mixing and decay of
the final state kaon. We find that ignoring these effects produce a shift in
$\gamma$ of order $\epsilon_{K}/r_{B}$, an enhancement of $1/r_B$ compared to
the naive expectation. We then show how to take these effects into account such
that, in principle, they will not introduce any theoretical error in the
extraction of $\gamma$.


  This paper presents a massively parallel and scalable neuromorphic cortex
simulator designed for simulating large and structurally connected spiking
neural networks, such as complex models of various areas of the cortex. The
main novelty of this work is the abstraction of a neuromorphic architecture
into clusters represented by minicolumns and hypercolumns, analogously to the
fundamental structural units observed in neurobiology. Without this approach,
simulating large-scale fully connected networks needs prohibitively large
memory to store look-up tables for point-to-point connections. Instead, we use
a novel architecture, based on the structural connectivity in the neocortex,
such that all the required parameters and connections can be stored in on-chip
memory. The cortex simulator can be easily reconfigured for simulating
different neural networks without any change in hardware structure by
programming the memory. A hierarchical communication scheme allows one neuron
to have a fan-out of up to 200k neurons. As a proof-of-concept, an
implementation on one Altera Stratix V FPGA was able to simulate 20 million to
2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified
the system by emulating a simplified auditory cortex (with 100 million
neurons). This cortex simulator achieved a low power dissipation of 1.62 {\mu}W
per neuron. With the advent of commercially available FPGA boards, our system
offers an accessible and scalable tool for the design, real-time simulation,
and analysis of large-scale spiking neural networks.


  Tail-sitter vertical takeoff and landing (VTOL) unmanned aerial vehicles
(UAVs) have the capability of hovering and performing efficient level flight
with compact mechanical structures. We present a unified controller design for
such UAVs, based on recurrent neural networks. An advantage of this design
method is that the various flight modes (i.e., hovering, transition and level
flight) of a VTOL UAV are controlled in a unified manner, as opposed to
treating them separately and in the runtime switching one from another. The
proposed controller consists of an outer-loop position controller and an
inner-loop attitude controller. The inner-loop controller is composed of a
proportional attitude controller and a loop-shaping linear angular rate
controller. For the outer-loop controller, we propose a nonlinear solver to
compute the desired attitude and thrust, based on the UAV dynamics and an
aerodynamic model, in addition to a cascaded PID controller for the position
and velocity tracking. We employ a recurrent neural network (RNN) to
approximate the behavior of the nonlinear solver, which suffers from high
computational complexity. The proposed RNN has negligible approximation errors,
and can be implemented in real-time (e.g., 50 Hz). Moreover, the RNN generates
much smoother outputs than the nonlinear solver. We provide an analysis of the
stability and robustness of the overall closed-loop system. Simulation and
experiments are also presented to demonstrate the effectiveness of the proposed
method.


  Entanglement serves as the resource to empower quantum computing. Recent
progress has highlighted its positive impact on learning quantum dynamics,
wherein the integration of entanglement into quantum operations or measurements
of quantum machine learning (QML) models leads to substantial reductions in
training data size, surpassing a specified prediction error threshold. However,
an analytical understanding of how the entanglement degree in data affects
model performance remains elusive. In this study, we address this knowledge gap
by establishing a quantum no-free-lunch (NFL) theorem for learning quantum
dynamics using entangled data. Contrary to previous findings, we prove that the
impact of entangled data on prediction error exhibits a dual effect, depending
on the number of permitted measurements. With a sufficient number of
measurements, increasing the entanglement of training data consistently reduces
the prediction error or decreases the required size of the training data to
achieve the same prediction error. Conversely, when few measurements are
allowed, employing highly entangled data could lead to an increased prediction
error. The achieved results provide critical guidance for designing advanced
QML protocols, especially for those tailored for execution on early-stage
quantum computers with limited access to quantum resources.


  Deep radio observations at 1.4GHz for the Extended Chandra Deep Field South
were performed in June through September of 2007 and presented in a first data
release (Miller et al. 2008). The survey was made using six separate pointings
of the Very Large Array (VLA) with over 40 hours of observation per pointing.
In the current paper, we improve on the data reduction to produce a second data
release (DR2) mosaic image. This DR2 image covers an area of about a third of a
square degree and reaches a best rms sensitivity of 6 uJy and has a typical
sensitivity of 7.4 uJy per 2.8" by 1.6" beam. We also present a more
comprehensive catalog, including sources down to peak flux densities of five or
more times the local rms noise along with information on source sizes and
relevant pointing data. We discuss in some detail the consideration of whether
sources are resolved under the complication of a radio image created as a
mosaic of separate pointings each suffering some degree of bandwidth smearing,
and the accurate evaluation of the flux densities of such sources. Finally, the
radio morphologies and optical/near-IR counterpart identifications (Bonzini et
al. 2012) are used to identify 17 likely multiple-component sources and arrive
at a catalog of 883 radio sources, which is roughly double the number of
sources contained in the first data release.


  We discuss the dynamics of multiple scalar fields and the possibility of
realistic inflation in the maximal gauged supergravity. In this paper, we
address this problem in the framework of recently discovered 1-parameter
deformation of ${\rm SO}(4,4)$ and ${\rm SO}(5,3)$ dyonic gaugings, for which
the base point of the scalar manifold corresponds to an unstable de Sitter
critical point. In the gauge-field frame where the embedding tensor takes the
value in the sum of the {\bf 36} and {\bf 36'} representations of ${\rm
SL}(8)$, we present a scheme that allows us to derive an analytic expression
for the scalar potential. With the help of this formalism, we derive the full
potential and gauge coupling functions in analytic forms for the ${\rm
SO}(3)\times {\rm SO}(3)$-invariant subsectors of ${\rm SO}(4,4)$ and ${\rm
SO}(5,3)$ gaugings, and argue that there exist no new critical points in
addition to those discovered so far. For the ${\rm SO}(4,4)$ gauging, we also
study the behavior of 6-dimensional scalar fields in this sector near the
Dall'Agata-Inverso de Sitter critical point at which the negative eigenvalue of
the scalar mass square with the largest modulus goes to zero as the deformation
parameter approaches a critical value. We find that when the deformation
parameter is taken sufficiently close to the critical value, inflation lasts
more than 60 e-folds even if the initial point of the inflaton allows an
$O(0.1)$ deviation in Planck units from the Dall'Agata-Inverso critical point.
It turns out that the spectral index $n_s$ of the curvature perturbation at the
time of the 60 e-folding number is always about 0.96 and within the $1\sigma$
range $n_s=0.9639\pm0.0047$ obtained by Planck, irrespective of the value of
the $\eta$ parameter at the critical saddle point. The tensor-scalar ratio
predicted by this model is around $10^{-3}$ and is close to the value in the
Starobinsky model.


  We numerically investigate whether and how gaseous ejecta from AGB stars can
be converted into new stars within originally massive star clusters (MSCs) in
order to understand the origin of multiple stellar populations in globular
clusters (GCs). We adopt a scenario in which (i) MSCs with masses of M_s can be
formed from high-mass, high-density giant molecular clouds (GMCs) in their host
galactic building blocks embedded in dark matter halos at high redshifts and
(ii) their evolution therefore can be significantly influenced by M_s, their
initial locations, and physical properties of their hosts. Our 3D
hydrodynamical simulations show that gaseous ejecta from AGB stars can be
retained within MSCs and consequently converted into new stars very efficiently
in the central regions of MSCs, only if M_s exceed a threshold mass (M_th) of
~10^6 M_sun. The new stars can correspond to the ``second generation (SG)'' of
stars with higher Na and lower O abundances observed in GCs. Star formation
efficiencies during the formation of SG stars within MSCs with M_s > M_th can
be rather high (0.3-0.9) so that very compact new clusters within original MSCs
can be formed. M_s should be as large as 10^6-10^7 M_sun to explain the
observed large fraction of SG stars in the present ordinary Galactic GCs,
because new stars can consist of only 1-4% among all stars for the standard
IMF. Nuclear MSCs are found to retain much more effectively the AGB ejecta and
convert more efficiently the gas into new stars owing to much deeper
gravitational potential of their hosts. We suggest that both M_s and their
locations within their hosts can determine whether abundance spread can be seen
only in light elements or even in heavy ones.


  Electron transfer (ET) through proteins, a fundamental element of many
biochemical reactions, has been studied intensively in solution. We report the
results of electron transport (ETp) measurements across proteins, sandwiched
between two solid electrodes with a long-range goal of understanding in how far
protein properties are expressed (and can be utilized) in such a configuration.
While most such studies to date were conducted with one or just a few molecules
in the junction, we present the high yield, reproducible preparation of large
area monolayer junctions of proteins from three different families: Azurin
(Az), a blue-copper ET protein, Bacteriorhodopsin (bR), a membrane
protein-chromophore complex with a proton pumping function, and Bovine Serum
Albumin (BSA). Surprisingly, the current-voltage (I-V) measurements on such
junctions, which are highly reproducible, show relatively minor differences
between Az and bR, even though the latter lacks a known ET function. ETp across
both Az and bR is much more efficient than across BSA, but also for the latter
the currents are still high, and the decay coefficients too low to be
consistent with coherent tunneling. Rather, inelastic hopping is proposed to
dominate ETp in these junctions. Other features such as asymmetrical I-V curves
and distinct behavior of different proteins can be viewed as molecular
signatures in the solid-state conductance.


  Mass loss is one of the key parameters that determine stellar evolution.
Despite the progress we have achieved over the last decades we still cannot
match the observational derived values with theoretical predictions. Even
worse, there are certain phases, such as the B[e] supergiants (B[e]SGs) and the
Luminous Blue Variables (LBVs), where significant mass is lost through episodic
or outburst activity. This leads to various structures around them that permit
dust formation, making these objects bright IR sources. The ASSESS project aims
to determine the role of episodic mass in the evolution of massive stars, by
examining large numbers of cool and hot objects (such as B[e]SGs/LBVs). For
this, we initiated a large observing campaign to obtain spectroscopic data for
$\sim$1000 IR selected sources in 27 nearby galaxies. Within this project we
successfully identified 7 B[e] supergiants (one candidate) and 4 Luminous Blue
Variables of which 6 and 2, respectively, are new discoveries. We used
spectroscopic, photometric, and light curve information to better constrain the
nature of the reported objects. We particularly note the presence of B[e]SGs at
metallicity environments as low as 0.14 Z$_{\odot}$.


  Transparent objects are ubiquitous in household settings and pose distinct
challenges for visual sensing and perception systems. The optical properties of
transparent objects leave conventional 3D sensors alone unreliable for object
depth and pose estimation. These challenges are highlighted by the shortage of
large-scale RGB-Depth datasets focusing on transparent objects in real-world
settings. In this work, we contribute a large-scale real-world RGB-Depth
transparent object dataset named ClearPose to serve as a benchmark dataset for
segmentation, scene-level depth completion and object-centric pose estimation
tasks. The ClearPose dataset contains over 350K labeled real-world RGB-Depth
frames and 5M instance annotations covering 63 household objects. The dataset
includes object categories commonly used in daily life under various lighting
and occluding conditions as well as challenging test scenarios such as cases of
occlusion by opaque or translucent objects, non-planar orientations, presence
of liquids, etc. We benchmark several state-of-the-art depth completion and
object pose estimation deep neural networks on ClearPose. The dataset and
benchmarking source code is available at https://github.com/opipari/ClearPose.


  There are many structures (algebras, categories, etc) with natural gradings
such that the degree 0 components are not semisimple. Particular examples
include tensor algebras with non-semisimple degree 0 parts, extension algebras
of standard modules of standardly stratified algebras. In this thesis we
develop a generalized Koszul theory for graded algebras (categories) whose
degree 0 parts may be non-semisimple. Under some extra assumption, we show that
this generalized Koszul theory preserves many classical results such as the
Koszul duality. Moreover, it has some close relation to the classical theory.
Applications of this generalized theory to finite EI categories, directed
categories, and extension algebras of standard modules of standardly stratified
algebras are described. We also study the stratification property of standardly
stratified algebras, and classify algebras standardly (resp., properly)
stratified for all linear orders.


  We present new isochrone fits to colour-magnitude diagrams of the Galactic
globular cluster NGC 5904 (M5). We utilise 29 photometric bands from the
ultraviolet to mid-infrared by use of the data from the {\it Hubble Space
Telescope}, {\it Gaia} DR2, {\it Wide-field Infrared Survey Explorer}, Sloan
Digital Sky Survey (SDSS), and other photometric data. In our isochrone fitting
we use the PAdova and TRieste Stellar Evolution Code, the MESA Isochrones and
Stellar Tracks, the Dartmouth Stellar Evolution Program, and a Bag of Stellar
Tracks and Isochrones both for the solar-scaled and enhanced He and $\alpha$
abundances with a metallicity about [Fe/H]$=-1.33$ adopted from the literature.
All tools provide us with estimates of the distance, age, and extinction law to
the cluster. The best-fit distance, true distance modulus, and age are
$7.4\pm0.3$ kpc, $14.34\pm0.09$ mag, and $12.15\pm1.00$ Gyr, respectively. The
derived distance agrees with the literature, including the {\it Gaia} DR2
parallax with its known global zero-point correction. All the data and models,
except some UV and SDSS data, agree with the extinction law of
Cardelli-Clayton-Mathis with $R_\mathrm{V}=3.60\pm0.05$ and
$A_\mathrm{V}=0.20\pm0.02$ mag. This extinction is twice as high as generally
accepted due to a rather high extinction between 625 and 2000 nm. An offset of
the model colours instead of the high extinction in this range is a less
likely, yet possible explanation of the discovered large deviations of the
isochrones from the data.


  The Southern Coalsack is located in the interior of the Upper Centaurus-Lupus
(UCL) super bubble and shows many traits that point to a much more energetic
environment than might be expected from a dark, starless molecular cloud. A
hot, X-ray emitting, envelope surrounds the cloud, it has a very strong
internal magnetic field and its darkest core seems to be on astronomical time
scales "just about" to start forming stars. In order to probe the magnetic
environment of the cloud and to compare with the optical/near infrared
polarimetry-based field estimates for the cloud, we have acquired Faraday
Rotation measurements towards the pulsar PSR J1210$-$6550, probing the magnetic
field in the vicinity of the cloud, and a comparison target, PSR J1435$-$5954,
at a similar line of sight distance but several degrees from the cloud. Both
lines of sight hence primarily probe the UCL super bubble. The earlier
estimates of the magnetic field inside the Coalsack, using the
Chandrasekhar-Fermi method on optical and near-infrared polarimetry, yield
B$_\perp$ = 64--93 $\mu$G. However, even though PSR J1210$-$6550 is located
only $\sim$30 arc minutes from the (CO) edge of the cloud, the measured field
strength is only B$_\parallel$ = 1.1$\pm$0.2 $\mu$G. While thus yielding a very
high field contrast to the cloud we argue that this might be understood as due
to the effects on the cloud by the super bubble.


  An increasing bandwidth demand has mandated a shift to the time and
wavelength division multiplexing (TWDM) techniques in optical access networks
(OAN). Typical TWDM scheduling schemes consider scheduling of the optical line
terminal receiver only. In this paper we have identified an additional
collision domain that is present in TWDM schemes that offer security, in
addition to bandwidth flexibility. Scheduling of the identified collision
domain is termed as group scheduling. We illustrate that consideration of
receiver scheduling only (as done in typical TWDM schemes) severely affects
their throughput when implemented on flexible and secure TWDM architectures. A
novel media access control protocol has been proposed in this paper that
considers the multiple collision domains. Through simulations, we are able to
illustrate that the proposed scheme achieves a high throughput. A theoretical
upper bound of throughput has also been derived to explain the simulation
results. Complexity reduction of the proposed scheme has been illustrated,
thereby making it an attractive proposal.


  This paper considers some formulations and possible approaches to the
coherent LQG and $H^\infty$ quantum control problems. Some new results for
these problems are presented in the case of annihilation operator only quantum
systems showing that in this case, the optimal controllers are trivial
controllers.


  An algebraic description of basic discrete symmetries (space inversion P,
time reversal T, charge conjugation C and their combinations PT, CP, CT, CPT)
is studied. Discrete subgroups {1,P,T,PT} of orthogonal groups of
multidimensional spaces over the fields of real and complex numbers are
considered in terms of fundamental automorphisms of Clifford algebras. The
fundamental automorphisms form a finite group of order 4. The charge
conjugation is represented by a pseudoautomorphism of the Clifford algebra.
Such a description allows one to extend the automorphism group. It is shown
that an extended automorphism group (CPT-group) forms a finite group of order
8. The group structure and isomorphisms between the extended automorphism
groups and finite groups are studied in detail. It is proved that there exist
64 different realizations of CPT-group. An extension of universal coverings
(Clifford-Lipschitz groups) of the orthogonal groups is given in terms of
CPT-structures which include well-known Shirokov-Dabrowski PT-structures as a
particular case.


  We present the first attempt of systematically investigating the effects of
shell correction energy for a dynamical process, which includes fusion,
fusion-fission and quasi-fission processes. In the superheavy mass region, for
the fusion process, shell correction energy plays a very important role and
enhances the fusion probability when the colliding partner has a strong shell
structure. By analyzing the trajectory in three-dimensional coordinate space
with the Langevin equation, we reveal the mechanism of the enhancement of the
fusion probability caused by `cold fusion valleys'. The temperature dependence
of shell correction energy is considered.


  We investigate the sample of 1175 new nonmagnetic DA white dwarfs with the
effective temperatures T_eff > 12000 K, which were extracted from the Data
Release 1 of the Sloan Digital Sky Survey. We determined masses, radii, and
bolometric luminosities of stars in the sample. The above parameters were
derived from the effective temperatures T_eff and surface gravities log g
published in the DR1, and the new theoretical M - R relations for carbon-core
and oxygen-core white dwarfs. Mass distribution of white dwarfs in this sample
exhibits the peak at M = 0.562 M_sol (carbon core stars), and the tail towards
higher masses. Both the shape of the mass distribution function and the
empirical mass - radius relation are practically identical for white dwarfs
with either pure carbon or pure oxygen cores.


  The training of artificial neural networks (ANNs) with rectified linear unit
(ReLU) activation via gradient descent (GD) type optimization schemes is
nowadays a common industrially relevant procedure. Till this day in the
scientific literature there is in general no mathematical convergence analysis
which explains the numerical success of GD type optimization schemes in the
training of ANNs with ReLU activation. GD type optimization schemes can be
regarded as temporal discretization methods for the gradient flow (GF)
differential equations associated to the considered optimization problem and,
in view of this, it seems to be a natural direction of research to first aim to
develop a mathematical convergence theory for time-continuous GF differential
equations and, thereafter, to aim to extend such a time-continuous convergence
theory to implementable time-discrete GD type optimization methods. In this
article we establish two basic results for GF differential equations in the
training of fully-connected feedforward ANNs with one hidden layer and ReLU
activation. In the first main result of this article we establish in the
training of such ANNs under the assumption that the probability distribution of
the input data of the considered supervised learning problem is absolutely
continuous with a bounded density function that every GF differential equation
admits for every initial value a solution which is also unique among a suitable
class of solutions. In the second main result of this article we prove in the
training of such ANNs under the assumption that the target function and the
density function of the probability distribution of the input data are
piecewise polynomial that every non-divergent GF trajectory converges with an
appropriate rate of convergence to a critical point and that the risk of the
non-divergent GF trajectory converges with rate 1 to the risk of the critical
point.


  We study a cluster of quantum dots defined within silicene that host confined
electron states with spin and valley degrees of freedom. Atomistic
tight-binding and continuum Dirac approximation are applied for few-electron
system in quest for spontaneous valley polarization driven by inter-dot
tunneling and electron-electron interaction, i.e. a valley counterpart of
itinerary Nagaoka ferromagnetic ordering recently identified in GaAs square
cluster of quantum dots with three excess electrons [P. Dehollain, {\it et
al.}, Nature {\bf 579}, 528 (2020)]. We find that for Hamiltonian without
intrinsic-spin orbit coupling -- similar to the one of graphene with staggered
potential -- the valley polarization in the ground-state can be observed in a
range of inter-dot spacing provided that the spin of the system is frozen by
external magnetic field. The inter-valley scattering effects are negligible for
cluster geometry that supports the valley polarized ground-state. In presence
of a strong intrinsic spin-orbit coupling that is characteristic to graphene no
external magnetic field is necessary for observation of ground-state that is
polarized in both spin and valley. The effective magnetic field due to the
spin-orbit interaction produces a perfect anticorrelation of the spin and
valley isospin components in the degenerate ground-state.


  We comparatively investigated the transport properties for S- and
Te-substituted FeSe thin films under magnetic fields to clarify the origin of
the contrasting behavior of the superconducting transition temperature in S and
Te substitution. A classical two carrier analysis revealed that the carrier
densities of the films increased with increasing Te content, while no
significant change was observed for the S-substitution. This composition
dependence of the carrier density well corresponds to the $T_{\mathrm c}$
behavior. The clear correlation between $T_{\mathrm c}$ and the carrier
densities suggests that the structural transition affects the electronic
structure in a different manner between Fe(Se,S) and Fe(Se,Te) and that this
fact is the direct cause of the difference in the $T_{\mathrm c}$ behaviors at
the end point of the structural transition.


  Recent solar neutrino results together with the assumption of a stationary
Sun imply severe constraints on the individual components of the total neutrino
flux : $\Phi_{Be} \leq 0.7 \cdot 10^{9} cm^{-2} s^{-1}, \Phi_{CNO} \leq 0.6
\cdot 10^{9} cm^{-2} s^{-1}$ and $64 \cdot 10^{9} cm^{-2} s^{-1} \leq
\Phi_{pp+pep} \leq 65 \cdot 10^{9} cm^{-2} s^{-1}$ (at 1$ \sigma$ level), the
constraint on $\nu_{Be}$ being in strong disagreement with $\Phi_{Be}^{SSM} = 5
\cdot 10^{9} cm^{-2} s^{-1}$. We study a large variety of non-standard solar
models with low inner temperatures, finding that the temperature profiles T(m)
follow the homology relationship: T(m)=k$T(m)^{SSM}$, so that they are
specified just by the central temperature $T_{c}$. There is no value of $T_{c}$
which can account for all the available experimental results and also if we
restrict to consider just Gallium and Kamiokande results the fit is poor.
Finally we discuss what can be learned from new generation experiments, planned
for the detection of monochromatic solar neutrinos, about the properties of
neutrinos and of the Sun.


  In this paper, the null controllability in any positive time T of the
first-order equation (1) x'(t)=e^{i\theta}Ax(t)+Bu(t) (|\theta|<\pi/2 fixed) is
deduced from the null controllability in some positive time L of the
second-order equation (2) z''(t)=Az(t)+Bv(t). The differential equations (1)
and (2) are set in a Banach space, B is an admissible unbounded control
operator, and A is a generator of cosine operator function. The control
transmutation method explicits the input function u of (1) in terms of the
input function v of (2): u(t,x)=\int k(t,s)v(s)ds, where the compactly
supported kernel k depends on T and L only. It proves that the norm of a u
steering the system (1) from an initial state x_{0} to zero grows at most like
||x_{0}||\exp(\alpha_{*}L^{2}/T) as the control time T tends to zero. (The rate
\alpha_{*} is characterized independently by a one-dimensional controllability
problem.) In the applications to the cost of fast controls for the heat
equation, L is the length of the longest ray of geometric optics which does not
intersect the control region.


  We show by direct construction that a large class of quiver gauge theories
admits actions of finite Heisenberg groups. We consider various quiver gauge
theories that arise as AdS/CFT duals of orbifolds of C^3, the conifold and its
orbifolds and some orbifolds of the cone over Y(p,q). Matching the gauge theory
analysis with string theory on the corresponding spaces implies that the
operators counting wrapped branes do not commute in the presence of flux.


  This paper surveys results related to well-known works of B. Plotkin and V.
Remeslennikov on the edge of algebra, logic and geometry. We start from a brief
review of the paper and motivations. The first sections deal with model theory.
In Section 2.1 we describe the geometric equivalence, the elementary
equivalence, and the isotypicity of algebras. We look at these notions from the
positions of universal algebraic geometry and make emphasis on the cases of the
first order rigidity. In this setting Plotkin's problem on the structure of
automorphisms of (auto)endomorphisms of free objects, and auto-equivalence of
categories is pretty natural and important. Section 2.2 is dedicated to
particular cases of Plotkin's problem. Section 2.3 is devoted to Plotkin's
problem for automorphisms of the group of polynomial symplectomorphisms. This
setting has applications to mathematical physics through the use of model
theory (non-standard analysis) in the studying of homomorphisms between groups
of symplectomorphisms and automorphisms of the Weyl algebra. The last two
sections deal with algorithmic problems for noncommutative and commutative
algebraic geometry. Section 3.1 is devoted to the Gr\"obner basis in
non-commutative situation. Despite the existence of an algorithm for checking
equalities, the zero divisors and nilpotency problems are algorithmically
unsolvable. Section 3.2 is connected with the problem of embedding of algebraic
varieties; a sketch of the proof of its algorithmic undecidability over a field
of characteristic zero is given.


  With the growing popularity of Android devices, Android malware is seriously
threatening the safety of users. Although such threats can be detected by deep
learning as a service (DLaaS), deep neural networks as the weakest part of
DLaaS are often deceived by the adversarial samples elaborated by attackers. In
this paper, we propose a new semi-black-box attack framework called
one-feature-each-iteration (OFEI) to craft Android adversarial samples. This
framework modifies as few features as possible and requires less classifier
information to fool the classifier. We conduct a controlled experiment to
evaluate our OFEI framework by comparing it with the benchmark methods JSMF,
GenAttack and pointwise attack. The experimental results show that our OFEI has
a higher misclassification rate of 98.25%. Furthermore, OFEI can extend the
traditional white-box attack methods in the image field, such as fast gradient
sign method (FGSM) and DeepFool, to craft adversarial samples for Android.
Finally, to enhance the security of DLaaS, we use two uncertainties of the
Bayesian neural network to construct the combined uncertainty, which is used to
detect adversarial samples and achieves a high detection rate of 99.28%.


  We have determined the production cross sections of the $X(3872)$ state in
the reactions $\bar D D\to\pi X$, $\bar D^* D\to \pi X$ and $\bar D^* D^*\to\pi
X$, information which is useful for studies of the $X(3872)$ meson abundance in
heavy ion collisions. We construct a formalism considering $X$ as a molecular
bound state of $\bar D^0 D^{*0} - \textrm{c.c}$, $D^- D^{*+} - \textrm{c.c}$
and $D^-_s D^{*+}_s - \textrm{c.c}$. To obtain the amplitudes related to these
processes we have made use of effective field Lagrangians. The evaluation of
the cross section of the processes involving $D^*$ meson(s) requires the
calculation of an anomalous vertex, $X\bar D^* D^*$, which has been obtained by
considering triangular loops motivated by the molecular nature of $X(3872)$.
Proceeding in this way, we have evaluated the cross section for the reaction
$\bar D^* D\to \pi X$, and find that the diagrams involving the $X\bar D^* D^*$
vertex give a large contribution. Encouraged by this finding we estimate the
$X\bar D^* D^*$ coupling, which turns out to be $1.95\pm 0.22$. We then use it
to obtain the cross section for the reaction $\bar D^* D^*\to\pi X$ and find
that, in this case too, the $X\bar D^* D^*$ vertex is relevant. We also discuss
the role of the charged components of $X$ in the determination of the
production cross sections.


  We consider higher derivative corrections of the type D^{2k} R^2 in the
effective action of the D3-brane with trivial normal bundle. Based on the
perturbative disc and annulus amplitudes, and constraints of supersymmetry and
duality, we argue that these interactions are protected, at least for small
values of k. Their coefficient functions receive only a finite number of
perturbative contributions, and non-perturbative contributions from
D-instantons. We propose expressions for these modular forms for low values of
k.


  Blood transfusion is a frequently performed medical procedure in surgical and
nonsurgical contexts. Although it is frequently necessary or even life-saving,
it has been identified as one of the most overused procedures in hospitals.
Unnecessary transfusions not only waste resources but can also be detrimental
to patient outcomes. Patient blood management (PBM) is the clinical practice of
optimizing transfusions and associated outcomes. In this paper, we introduce
Sanguine, a visual analysis tool for transfusion data and related patient
medical records. Sanguine was designed with two user groups in mind: PBM
experts and clinicians who conduct transfusions. PBM experts use Sanguine to
explore and analyze transfusion practices and its associated medical outcomes.
They can compare individual surgeons, or compare outcomes or time periods, such
as before and after an intervention regarding transfusion practices. PBM
experts then curate and annotate views for communication with clinicians, with
the goal of improving their transfusion practices. Such a review session could
be in person or through a shared link. We validate the utility and
effectiveness of Sanguine through case studies.


  Let $X_i$ denote free identically-distributed random variables. This paper
investigates how the norm of products $\Pi_n=X_1 X_2 ... X_n$ behaves as $n$
approaches infinity. In addition, for positive $X_i$ it studies the asymptotic
behavior of the norm of $Y_n=X_1 \circ X_2 \circ ...\circ X_n$, where $\circ$
denotes the symmetric product of two positive operators: $A \circ
B=:A^{1/2}BA^{1/2}$.
  It is proved that if the expectation of $X_i$ is 1, then the norm of the
symmetric product $Y_{n}$ is between $c_1 n^{1/2}$ and $c_2 n$ for certain
constant $c_1$ and $c_2$. That is, the growth in the norm is at most linear.
  For the norm of the usual product $Pi_n$, it is proved that the limit of
$n^{-1}\log Norm(Pi_n)$ exists and equals $\log \sqrt{E(X_i^{\ast}X_{i})}.$ In
other words, the growth in the norm of the product is exponential and the rate
equals the logarithm of the Hilbert-Schmidt norm of operator X.
  Finally, if $\pi $ is a cyclic representation of the algebra generated by
$X_i$, and if $\xi$ is a cyclic vector, then $n^{-1}\log Norm(\pi (\Pi_{n})
\xi)=\log \sqrt{E(X_{i}^{\ast}X_{i})}$ for all $n.$ In other words, the growth
in the length of the cyclic vector is exponential and the rate coincides with
the rate in the growth of the norm of the product.
  These results are significantly different from analogous results for
commuting random variables and generalize results for random matrices derived
by Kesten and Furstenberg.


  We compute the number of F_q-points on M_{4,n}, for n less than or equal to
3, and show that it is a polynomial in q, using a sieve based on Hasse-Weil
zeta functions. As an application, we prove that the rational singular
cohomology groups of moduli spaces of stable curves of genus g with n marked
points vanish in all odd degrees less than or equal to 9, for all g and n. Both
results confirm predictions of the Langlands program, via the conjectural
correspondence with polarized algebraic cuspidal automorphic representations of
conductor 1, which are classified in low weight. Our vanishing result for odd
cohomology resolves a problem posed by Arbarello and Cornalba in the 1990s.


  We present the IXPE observation of GRB 221009A which includes upper limits on
the linear polarization degree of both prompt and afterglow emission in the
soft X-ray energy band. GRB 221009A is an exceptionally bright gamma-ray burst
(GRB) that reached Earth on 2022 October 9 after travelling through the dust of
the Milky Way. The Imaging X-ray Polarimetry Explorer (IXPE) pointed at GRB
221009A on October 11 to observe, for the first time, the 2-8 keV X-ray
polarization of a GRB afterglow. We set an upper limit to the polarization
degree of the afterglow emission of 13.8% at a 99% confidence level. This
result provides constraints on the jet opening angle and the viewing angle of
the GRB, or alternatively, other properties of the emission region.
Additionally, IXPE captured halo-rings of dust-scattered photons which are
echoes of the GRB prompt emission. The 99% confidence level upper limit to the
prompt polarization degree depends on the background model assumption and it
ranges between ~55% to ~82%. This single IXPE pointing provides both the first
assessment of X-ray polarization of a GRB afterglow and the first GRB study
with polarization observations of both the prompt and afterglow phases.


  With the success of deep neural networks (NNs) in a variety of domains, the
computational and storage requirements for training and deploying large NNs
have become a bottleneck for further improvements. Sparsification has
consequently emerged as a leading approach to tackle these issues. In this
work, we consider a simple yet effective approach to sparsification, based on
the Bridge, or $L_p$ regularization during training. We introduce a novel
weight decay scheme, which generalizes the standard $L_2$ weight decay to any
$p$ norm. We show that this scheme is compatible with adaptive optimizers, and
avoids the gradient divergence associated with $0<p<1$ norms. We empirically
demonstrate that it leads to highly sparse networks, while maintaining
generalization performance comparable to standard $L_2$ regularization.


  Measuring the $M$th-order intensity correlation function of light emitted by
two statistically independent thermal light sources may display N00N-like
interferences of arbitrary order $N = M/2$. We show that via a particular
choice of detector positions one can isolate $M$-photon quantum paths where
either all $M$ photons are emitted from the same source or $M/2$ photons are
collectively emitted by both sources. The latter superposition displays
N00N-like oscillations with $N = M/2$ which may serve, e.g., in astronomy, for
imaging two distant thermal sources with $M/2$-fold increased resolution. We
also discuss slightly modified detection schemes improving the visibility of
the N00N-like interference pattern and present measurements verifying the
theoretical predictions.


  The UV-Excess Survey of the Northern Galactic Plane images a 10x185 degree
wide band, centered on the Galactic Equator using the 2.5m Isaac Newton
Telescope in four bands (U,g,r,HeI5875) down to ~21st-22nd magnitude (~20th in
HeI5875). The setup and data reduction procedures are described. Simulations of
the colours of main-sequence stars, giant, supergiants, DA and DB white dwarfs
and AM CVn stars are made, including the effects of reddening. A first look at
the data of the survey (currently 30% complete) is given.


  Next-to-leading order QCD corrections to the width difference Delta Gamma in
the B_s-meson system are presented. I further discuss how Delta Gamma can be
used to detect new physics.


  A subset K of R^n gives rise to a formal Laurent series with monomials
corresponding to lattice points in K. Under suitable hypotheses, this series
represents a rational function R(K). Michel Brion has discovered a surprising
formula relating the rational function R(P) of a lattice polytope P to the sum
of rational functions corresponding to the supporting cones subtended at the
vertices of P. The result is re-phrased and generalised in the language of
cohomology of line bundles on complete toric varieties. Brion's formula is the
special case of an ample line bundle on a projective toric variety. - The paper
also contains some general remarks on the cohomology of torus-equivariant line
bundles on complete toric varieties, valid over noetherian ground rings.


  Half-integer conductance, the signature of Majorana edge modes, has been
recently observed in a quantum anomalous Hall insulator/superconductor
heterostructure. Here, we analyze a scheme for gate-tunable control of
degenerate ground states of Majorana zero modes (MZM) in thin film topological
superconductors. Gating the top surface of a thin film magnetic topological
insulator controls the topological phase in the region underneath the gate. The
voltage of the transition depends on the gate width, and narrower gates require
larger voltages. Relatively long gates are required, on the order of a micron,
to prevent hybridization of the end modes and to allow the creation of MZMs at
low gate voltages. Applying a voltage to T{shaped and I{shaped gates localizes
the Majoranas at their ends. This scheme may provide a facile method for
implementing quantum gates for topological quantum computing.


  Although Deep Convolutional Neural Networks trained with strong pixel-level
annotations have significantly pushed the performance in semantic segmentation,
annotation efforts required for the creation of training data remains a
roadblock for further improvements. We show that augmentation of the weakly
annotated training dataset with synthetic images minimizes both the annotation
efforts and also the cost of capturing images with sufficient variety.
Evaluation on the PASCAL 2012 validation dataset shows an increase in mean IOU
from 52.80% to 55.47% by adding just 100 synthetic images per object class. Our
approach is thus a promising solution to the problems of annotation and dataset
collection.


  Nowadays, foundation models become one of fundamental infrastructures in
artificial intelligence, paving ways to the general intelligence. However, the
reality presents two urgent challenges: existing foundation models are
dominated by the English-language community; users are often given limited
resources and thus cannot always use foundation models. To support the
development of the Chinese-language community, we introduce an open-source
project, called Fengshenbang, which leads by the research center for Cognitive
Computing and Natural Language (CCNL). Our project has comprehensive
capabilities, including large pre-trained models, user-friendly APIs,
benchmarks, datasets, and others. We wrap all these in three sub-projects: the
Fengshenbang Model, the Fengshen Framework, and the Fengshen Benchmark. An
open-source roadmap, Fengshenbang, aims to re-evaluate the open-source
community of Chinese pre-trained large-scale models, prompting the development
of the entire Chinese large-scale model community. We also want to build a
user-centered open-source ecosystem to allow individuals to access the desired
models to match their computing resources. Furthermore, we invite companies,
colleges, and research institutions to collaborate with us to build the
large-scale open-source model-based ecosystem. We hope that this project will
be the foundation of Chinese cognitive intelligence.


  Shock-bubble interactions (SBI) are important across a wide range of physical
systems. In inertial confinement fusion, interactions between laser-driven
shocks and micro-voids in both ablators and foam targets generate instabilities
that are a major obstacle in achieving ignition. Experiments imaging the
collapse of such voids at high energy densities (HED) are constrained by
spatial and temporal resolution, making simulations a vital tool in
understanding these systems. In this study, we benchmark several radiation and
thermal transport models in the xRAGE hydrodynamic code against experimental
images of a collapsing mesoscale void during the passage of a 300 GPa shock. We
also quantitatively examine the role of transport physics in the evolution of
the SBI. This allows us to understand the dynamics of the interaction at
timescales shorter than experimental imaging framerates. We find that all
radiation models examined reproduce empirical shock velocities within
experimental error. Radiation transport is found to reduce shock pressures by
providing an additional energy pathway in the ablation region, but this effect
is small ($\sim$1\% of total shock pressure). Employing a flux-limited Spitzer
model for heat conduction, we find that flux limiters between 0.03 and 0.10
produce agreement with experimental velocities, suggesting that the system is
well-within the Spitzer regime. Higher heat conduction is found to lower
temperatures in the ablated plasma and to prevent secondary shocks at the
ablation front, resulting in weaker primary shocks. Finally, we confirm that
the SBI-driven instabilities observed in the HED regime are baroclinically
driven, as in the low energy case.


  Whispering gallery mode (WGM) cavity is important for exploring physics of
strong light-matter interaction. Yet it suffers from the notorious radiation
loss universally due to the light tunneling effect through the curved boundary.
In this work, we propose and demonstrate an optical black hole (OBH) cavity
based on transformation optics. The radiation loss of all WGMs in OBH cavity is
completely inhibited by an infinite wide potential barrier. Besides, the WGM
field outside the cavity is revealed to follow $1/r^\alpha$ decay rule based on
conformal mapping, which is fundamentally different from the conventional
Hankel-function distributions in a homogeneous cavity. Experimentally, a
truncated OBH cavity is achieved based on the effective medium theory, and both
the Q-factor enhancement and tightly confined WGM field are measured in the
microwave spectra which agree well with the theoretical results. The circular
OBH cavity is further applied to the arbitrary-shaped cavities including
single-core and multi-core structures with high-Q factor via the conformal
mapping. The OBH cavity design strategy can be generalized to resonant modes of
various wave systems, such as acoustic and elastic waves, and finds
applications in energy harvesting and optoelectronics.


  We compare our upper bounds on the exponential growth constant
$\phi(\Lambda)$ characterizing the asymptotic behavior of spanning forests on
Archimedean lattices $\Lambda$ with recently derived upper bounds. Our upper
bounds on $\phi(\Lambda)$, which are very close to the respective values of
$\phi(\Lambda)$ that we have calculated, are shown to be significantly better
for these lattices than the new upper bounds.


  The standard Hamiltonian machinery, being applied to field theory, leads to
infinite-dimensional phase spaces. It is not covariant. In this article, we
present covariant finite-dimensional multimomentum Hamiltonian formalism for
field theory. This is the multisymplectic generalization of the Hamiltonian
formalism in mechanics. In field theory, multimomentum canonical variables are
field functions and momenta corresponding to derivatives of fields with respect
all world coordinates, not only the time. In case of regular Lagrangian
densities, the multimomentum Hamiltonian formalism is equivalent to the
Lagrangian formalism, otherwise for degenerate Lagrangian densities. In this
case, the Euler-Lagrange equations become undetermined and require additional
conditions which remain elusive. In the framework of the multimomentum
Hamiltonian machinery, one obtaines them automatically as a part of Hamilton
equations. The key point consists in the fact that, given a degenerate
Lagrangian density, one must consider a family of associated multimomentum
Hamiltonian forms in order to exaust solutions of the Euler-Lagrange equations.
We spell out degenerate quadratic and affine Lagrangian densities. The most of
field models are of these types. As a result, we get the general procedure of
describing constraint field systems.


  Interferometric scattering microscopy (iSCAT) can image the dynamics of
nanometer-scale systems. The typical approach to analyzing interferometric
images involves intensive processing, which discards data and limits the
precision of measurements. We demonstrate an alternative approach: modeling the
interferometric point spread function (iPSF) and fitting this model to data
within a Bayesian framework. This approach yields best-fit parameters,
including the particle's three-dimensional position and polarizability, as well
as uncertainties and correlations between these parameters. Building on recent
work, we develop a model that is parameterized for rapid fitting. The model is
designed to work with Hamiltonian Monte Carlo techniques that leverage
automatic differentiation. We validate this approach by fitting the model to
interferometric images of colloidal nanoparticles. We apply the method to track
a diffusing particle in three dimensions, to directly infer the diffusion
coefficient of a nanoparticle without calculating a mean-square displacement,
and to quantify the ejection of DNA from an individual lambda phage virus,
demonstrating that the approach can be used to infer both static and dynamic
properties of nanoscale systems.


  WASP0 is a prototype for what is intended to become a collection of WASPs
whose primary aim is to detect transiting extra-solar planets across the face
of their parent star. The WASP0 instrument is a wide-field (9-degree) 6.3cm
aperture F/2.8 Apogee 10 CCD camera (2Kx2K chip, 16-arcsec pixels). The camera
is mounted piggy-back on a commercial 10-inch Meade telescope. We present some
recent results from the WASP camera, including observations from La Palma of
the known transiting planet around HD 209458 and preliminary analysis of other
stars located in the same field. We also outline further problems which
restrict the ability to achieve photon limited precision with a wide-field
commercial CCD.


  We have succeeded in establishing a cosmological model with a non-minimally
coupled scalar field $\phi$ that can account not only for the spatial
periodicity or the picket-fence structure exhibited by the galaxy $N$-$z$
relation of the 2dF survey, but also for the spatial power spectrum of the
cosmic microwave background radiation (CMB) temperature anisotropy observed by
the WMAP satellite. The scalar field of our model universe starts from an
extremely small value at around the nucleosynthesis epoch, remains in that
state for sufficiently long periods, allowing sufficient time for the CMB
temperature anisotropy to form, and then starts to grow in magnitude at the
redshift $z$ of $\sim 1$, followed by a damping oscillation which is required
to reproduce the observed picket-fence structure of the $N$-$z$ relation. To
realize such behavior of the scalar field, we have found it necessary to
introduce a new form of potential $V(\phi)\propto \phi^2\exp(-q\phi^2)$, with
$q$ being a constant. Through this parameter $q$, we can control the epoch at
which the scalar field starts growing.


  Patients with articulatory disorders often have difficulty in speaking. These
patients need several speech therapy sessions to enable them speak normally.
These therapy sessions are conducted by a specialized speech therapist. The
goal of speech therapy is to develop good speech habits as well as to teach how
to articulate sounds the right way. Speech therapy is critical for continuous
improvement to regain normal speech. Speech therapy sessions require a patient
to travel to a hospital or a speech therapy center for extended periods of time
regularly; this makes the process of speech therapy not only time consuming but
also very expensive. Additionally, there is a severe shortage of trained speech
therapists around the globe in general and in developing countries in
particular. In this paper, we propose a low cost mobile speech therapist, a
system that enables speech therapy using a mobile phone which eliminates the
need of the patient to frequently travel to a speech therapist in a far away
hospital. The proposed system, which is being built, enables both synchronous
and asynchronous interaction between the speech therapist and the patient
anytime anywhere


  In cat visual cortex, the response of a neural population to the linear
combination of two sinusoidal gratings (a plaid) can be well approximated by a
weighted sum of the population responses to the individual gratings --- a
property we refer to as {\em subspace invariance}. We tested subspace
invariance in mouse primary visual cortex by measuring the angle between the
population response to a plaid and the plane spanned by the population
responses to its individual components. We found robust violations of subspace
invariance arising from a strong, negative correlation between the responses of
neurons to individual gratings and their responses to the plaid. Contrast
invariance, a special case of subspace invariance, also failed. The responses
of some neurons decreased with increasing contrast, while others increased.
Altogether the data show that subspace and contrast invariance do not hold in
mouse primary visual cortex. These findings rule out some models of population
coding, including vector averaging, some versions of normalization and temporal
multiplexing.


  Multi-output deep neural networks(MONs) contain multiple task branches, and
these tasks usually share partial network filters that lead to the entanglement
of different task inference routes. Due to the inconsistent optimization
objectives, the task gradients used for training MONs will interfere with each
other on the shared routes, which will decrease the overall model performance.
To address this issue, we propose a novel gradient de-conflict algorithm named
DR-MGF(Dynamic Routes and Meta-weighted Gradient Fusion) in this work.
Different from existing de-conflict methods, DR-MGF achieves gradient
de-conflict in MONs by learning task-preferred inference routes. The proposed
method is motivated by our experimental findings: the shared filters are not
equally important to different tasks. By designing the learnable task-specific
importance variables, DR-MGF evaluates the importance of filters for different
tasks. Through making the dominances of tasks over filters be proportional to
the task-specific importance of filters, DR-MGF can effectively reduce the
inter-task interference. The task-specific importance variables ultimately
determine task-preferred inference routes at the end of training iterations.
Extensive experimental results on CIFAR, ImageNet, and NYUv2 illustrate that
DR-MGF outperforms the existing de-conflict methods both in prediction accuracy
and convergence speed of MONs. Furthermore, DR-MGF can be extended to general
MONs without modifying the overall network structures.


  Conversational assistive robots can aid people, especially those with
cognitive impairments, to accomplish various tasks such as cooking meals,
performing exercises, or operating machines. However, to interact with people
effectively, robots must recognize human plans and goals from noisy
observations of human actions, even when the user acts sub-optimally. Previous
works on Plan and Goal Recognition (PGR) as planning have used hierarchical
task networks (HTN) to model the actor/human. However, these techniques are
insufficient as they do not have user engagement via natural modes of
interaction such as language. Moreover, they have no mechanisms to let users,
especially those with cognitive impairments, know of a deviation from their
original plan or about any sub-optimal actions taken towards their goal. We
propose a novel framework for plan and goal recognition in partially observable
domains -- Dialogue for Goal Recognition (D4GR) enabling a robot to rectify its
belief in human progress by asking clarification questions about noisy sensor
data and sub-optimal human actions. We evaluate the performance of D4GR over
two simulated domains -- kitchen and blocks domain. With language feedback and
the world state information in a hierarchical task model, we show that D4GR
framework for the highest sensor noise performs 1% better than HTN in goal
accuracy in both domains. For plan accuracy, D4GR outperforms by 4% in the
kitchen domain and 2% in the blocks domain in comparison to HTN. The ALWAYS-ASK
oracle outperforms our policy by 3% in goal recognition and 7%in plan
recognition. D4GR does so by asking 68% fewer questions than an oracle
baseline. We also demonstrate a real-world robot scenario in the kitchen
domain, validating the improved plan and goal recognition of D4GR in a
realistic setting.


  Deep learning has been successfully adopted in mobile edge computing (MEC) to
optimize task offloading and resource allocation. However, the dynamics of edge
networks raise two challenges in neural network (NN)-based optimization
methods: low scalability and high training costs. Although conventional
node-output graph neural networks (GNN) can extract features of edge nodes when
the network scales, they fail to handle a new scalability issue whereas the
dimension of the decision space may change as the network scales. To address
the issue, in this paper, a novel link-output GNN (LOGNN)-based resource
management approach is proposed to flexibly optimize the resource allocation in
MEC for an arbitrary number of edge nodes with extremely low algorithm
inference delay. Moreover, a label-free unsupervised method is applied to train
the LOGNN efficiently, where the gradient of edge tasks processing delay with
respect to the LOGNN parameters is derived explicitly. In addition, a
theoretical analysis of the scalability of the node-output GNN and link-output
GNN is performed. Simulation results show that the proposed LOGNN can
efficiently optimize the MEC resource allocation problem in a scalable way,
with an arbitrary number of servers and users. In addition, the proposed
unsupervised training method has better convergence performance and speed than
supervised learning and reinforcement learning-based training methods. The code
is available at \url{https://github.com/UNIC-Lab/LOGNN}.


  This paper studies the stochastic nonconvex-strongly-concave minimax
optimization over a multi-agent network. We propose an efficient algorithm,
called Decentralized Recursive gradient descEnt Ascent Method (DREAM), which
achieves the best-known theoretical guarantee for finding the
$\epsilon$-stationary points. Concretely, it requires $\mathcal{O}(\min
(\kappa^3\epsilon^{-3},\kappa^2 \sqrt{N} \epsilon^{-2} ))$ stochastic
first-order oracle (SFO) calls and $\tilde{\mathcal{O}}(\kappa^2
\epsilon^{-2})$ communication rounds, where $\kappa$ is the condition number
and $N$ is the total number of individual functions. Our numerical experiments
also validate the superiority of DREAM over previous methods.


  We review the state of the art in follow-up photometry for planetary transit
searches. Three topics are discussed: (1) Photometric monitoring of planets
discovered by radial velocity to detect possible transits (2) Follow-up
photometry of candidates from photometric transit searches to weed out
eclipsing binaries and false positives (3) High-precision lightcurves of known
transiting planets to increase the accuracy on the planet parameters.


  We use monads to relax the atomicity requirement for data in a database.
Depending on the choice of monad, the database fields may contain generalized
values such as lists or sets of values, or they may contain exceptions such as
various types of nulls. The return operation for monads ensures that any
ordinary database instance will count as one of these generalized instances,
and the bind operation ensures that generalized values behave well under joins
of foreign key sequences. Different monads allow for vastly different types of
information to be stored in the database. For example, we show that classical
concepts like Markov chains, graphs, and finite state automata are each
perfectly captured by a different monad on the same schema.