  We derive new formulas for the number of unordered (distinct) factorizations
with $k$ parts of a positive integer $n$ as sums over the partitions of $k$ and
an auxiliary function, the number of partitions of the prime exponents of $n$,
where the parts have a specific number of colors. As a consequence, some new
relations between partitions, Bell numbers and Stirling number of the second
kind are derived. We also derive a recursive formula for the number of
unordered factorizations with $k$ different parts and a simple recursive
formula for the number of partitions with $k$ different parts.


  We examine possible pairing mechanisms of superconductivity in PuCoGa$_5$
based on spin-fluctuations or phonons as mediating bosons. We consider
experimental data of specific heat C(T) and resistivity $\rho(T)$ as input to
determine a consistent scattering boson with the superconducting transition
temperature of 18.5K in PuCoGa$_5$. Irrespective to the type of boson, the
characteristic boson frequency is found to be $\sim 150 K$ from the resistivity
fitting. The spin fluctuation model is most consistent with the experimental
resistivity, successfully explaining the anomalous temperature dependence
($\sim \frac{T^2}{150 K +T}$) at low temperatures as well as the saturation
behavior at high temperatures. Assuming that the pairing state is non s-wave,
the large residual resistivity $\rho_{imp} \sim 20 \mu \Omega cm \sim 120 K$
suggests that an ideally pure sample of PuCoGa$_5$ would have a maximum T$_c$
of 39 K.


  Recent novel observations have probed the baryonic fraction of the galactic
dark matter that has eluded astronomers for decades. Late in 1993, the MACHO
and EROS collaborations announced in this journal the detection of transient
and achromatic brightenings of a handful of stars in the Large Magellanic Cloud
that are best interpreted as gravitational microlensing by low-mass foreground
objects (MACHOS). This tantalized astronomers, for it implied that the
population of cool, compact objects these lenses represent could be the elusive
dark matter of our galactic halo. A year later in 1994, Sackett et al. reported
the discovery of a red halo in the galaxy NGC 5907 that seems to follow the
inferred radial distribution of its dark matter. This suggested that dwarf
stars could constitute its missing component. Since NGC 5907 is similar to the
Milky Way in type and radius, some surmised that the solution of the galactic
dark matter problem was an abundance of ordinary low-mass stars. Now Bahcall et
al., using the Wide-Field Camera of the recently repaired Hubble Space
Telescope, have dashed this hope.


  We construct a partial compactification of the moduli space, M_k, of SU(2)
magnetic monopoles on R^3, wherein monopoles of charge k decompose into widely
separated 'monopole clusters' of lower charge going off to infinity at
comparable rates. The hyperKahler metric on M_k has a complete asymptotic
expansion up to the boundary, the leading term of which generalizes the
asymptotic metric discovered by Bielawski, Gibbons and Manton in the case that
each lower charge is 1.


  Rule-based temporal query languages provide the expressive power and
flexibility required to capture in a natural way complex analysis tasks over
streaming data. Stream processing applications, however, typically require near
real-time response using limited resources. In particular, it becomes essential
that the underpinning query language has favourable computational properties
and that stream processing algorithms are able to keep only a small number of
previously received facts in memory at any point in time without sacrificing
correctness. In this paper, we propose a recursive fragment of temporal Datalog
with tractable data complexity and study the properties of a generic stream
reasoning algorithm for this fragment. We focus on the window validity problem
as a way to minimise the number of time points for which the stream reasoning
algorithm needs to keep data in memory at any point in time.


  We present a method to model the interaction and the dynamics of atoms
excited to Rydberg states. We show a way to solve the optical Bloch equations
for laser excitation of the frozen gas in good agreement with the experiment. A
second method, the Kinetic Monte Carlo method gives an exact solution of rate
equations. Using a simple N-body integrator (Verlet), we are able to describe
dynamical processes in space and time. Unlike more sophisticated methods, the
Kinetic Monte Carlo simulation offers the possibility of numerically following
the evolution of tens of thousands of atoms within a reasonable computation
time. The Kinetic Monte Carlo simulation gives good agreement with
dipole-blockade type of experiment. The role of ions and the individual
particle effects are investigated.


  Let $(A,\m)$ be a Gorenstein local ring of dimension $d \geq 1$. Let
$\CMS(A)$ be the stable category of maximal \CM \ $A$-modules and let
$\ICMS(A)$ denote the set of isomorphism classes in $\CMS(A)$. We define a
function $\xi \colon \ICMS(A) \rt \ZZ$ which behaves well with respect to exact
triangles in $\CMS(A)$. We then apply this to (Gorenstein) liason theory. We
prove that if $\dim A \geq 2$ and $A$ is not regular then the even liason
classes of $\m^n; n\geq 1$ is an infinite set. We also prove that if $A$ is an
complete equi-characteristic simple singularity with $A/\m$ uncountable then
for each $m \geq 1$ the set $\mathcal{C}_m = \{I \mid I \ \text{is a codim 2
CM-ideal with} \ e_0(A/I) \leq m \}$ is contained in finitely many even liason
classes $L_1,\ldots,L_r$ (here $r$ may depend on $m$).


  We formulate the problem of defogging as state estimation and future state
prediction from previous, partial observations in the context of real-time
strategy games. We propose to employ encoder-decoder neural networks for this
task, and introduce proxy tasks and baselines for evaluation to assess their
ability of capturing basic game rules and high-level dynamics. By combining
convolutional neural networks and recurrent networks, we exploit spatial and
sequential correlations and train well-performing models on a large dataset of
human games of StarCraft: Brood War. Finally, we demonstrate the relevance of
our models to downstream tasks by applying them for enemy unit prediction in a
state-of-the-art, rule-based StarCraft bot. We observe improvements in win
rates against several strong community bots.


  The phase stability of the various crystalline structures of the super-heavy
element Copernicium was determined based on the first-principles calculations
with different levels of the relativistic effects. We utilized the Darwin term,
mass-velocity, and spin-orbit interaction with the single electron framework of
the density functional theory while treating the exchange and correlation
effects using local density approximations. It is found that the spin-orbit
coupling is the key component to stabilize the body-centered cubic ($bcc$)
structure over the hexagonal closed packed ($hcp$) structure, which is in
accord with Sol. Stat. Comm. 152 (2012) 530, but in contrast to Sol. Stat.
Comm. 201 (2015) 88, Angew. Chem. 46 (2007) 1663, Handbook of Elemental Solids
Z=104-112 (Springer 2015). It seems that the main role here is the correct
description of the semi-core relativistic $6p_{1/2}$ orbitals. The all other
investigated structures, i.e. face-centered cubic ($fcc$), simple cubic ($sc$)
as well as rhombohedral ($rh$) structures are higher in energy. The criteria of
mechanical stability were investigated based on the calculated elastic
constants, identifying the phase instability of $fcc$ and $rh$ structures, but
surprisingly confirm the stability of the energetically higher $sc$ structure.
In addition, the pressure-induced structural transition between two stable $sc$
and $bcc$ phases has been detected. The ground-state $bcc$ structure exhibits
the highest elastic anisotropy from single elements of the Periodic table. At
last, we support the experimental findings that Copernicium is a metal.


  Stochastic variance reduced optimization methods are known to be globally
convergent while they suffer from slow local convergence, especially when
moderate or high accuracy is needed. To alleviate this problem, we propose an
optimization algorithm -- which we refer to as a hybrid acceleration scheme --
for a class of proximal variance reduced stochastic optimization algorithms.
The proposed optimization scheme combines a fast locally convergent algorithm,
such as a quasi--Newton method, with a globally convergent variance reduced
stochastic algorithm, for instance SAGA or L--SVRG. Our global convergence
result of the hybrid acceleration method is based on specific safeguard
conditions that need to be satisfied for a step of the locally fast convergent
method to be accepted.
  We prove that the sequence of the iterates generated by the hybrid
acceleration scheme converges almost surely to a solution of the underlying
optimization problem. We also provide numerical experiments that show
significantly improved convergence of the hybrid acceleration scheme compared
to the basic stochastic variance reduced optimization algorithm.


  We compute quasinormal mode frequencies for static limits of physical black
holes - semi-classical black hole solutions to Einstein-Hilbert gravity
characterized by the finite formation time of an apparent horizon and its weak
regularity. These assumptions lead to a highly constrained yet non-trivial form
of the metric and components of the energy-momentum tensor near the horizon,
which contain as a special case many known models of black holes. Using a
two-point M-fraction approximation to construct an interpolating metric which
captures the essential near-horizon and asymptotic properties of black holes,
we explore a large part of the parameter space that characterizes the
near-horizon geometry. We cast the perturbation problem as a discretized
homogeneous eigensystem and compute the low-lying quasinormal mode frequencies
for perturbations of a massless scalar field. Working in spherical symmetry, we
provide rough constraints on leading and subleading deviations from the
Schwarzschild solution which arise in a semi-classical setting, and demonstrate
the potential for mimicking signatures of axially-symmetric geometries from a
spherically-symmetric source.


  This review mainly focuses on the relation between antioxidants with cancer
therapy. Antioxidants have been reported to play an essential role to reduce
free radical species. Free radicals commonly cause oxidative damage which is a
common factor in the aging process, and also the vital factor of formation, and
development of major disease specially cancer. Although, since last many
decades several antioxidants belong to natural and synthetic origin have been
tested in clinical trials against oxidative stress, however these clinical
trials end up with undesirable effects. This review also complied with the most
recent findings of oxidative stress, highlighting of free racial production,
and its related oxidative damage at cellular and molecular level, with the new
and existing natural and synthetic classes of free radical scavenger and their
related clinical trials.


  Nonlinear classical dissipative systems present a rich phenomenology in their
"route to chaos", including period-doubling, i.e. the system evolves with a
period which is twice that of the driving. However, typically the attractor of
a periodically driven quantum open system evolves with a period which exactly
matches that of the driving. Here we analyze a manybody open quantum system
whose classical correspondent presents period-doubling. We show that by
analysing the spectrum of the periodic propagator and by studying the dynamical
correlations, it is possible to show the occurrence of period-doubling in the
quantum (period-$1$) steady state. We also discuss that such systems are
natural candidates for clean Floquet time crystals.


  BERT and IndoBERT have achieved impressive performance in several NLP tasks.
There has been several investigation on its adaption in specialized domains
especially for English language. We focus on financial domain and Indonesian
language, where we perform post-training on pre-trained IndoBERT for financial
domain using a small scale of Indonesian financial corpus. In this paper, we
construct an Indonesian self-supervised financial corpus, Indonesian financial
sentiment analysis dataset, Indonesian financial topic classification dataset,
and release a family of BERT models for financial NLP. We also evaluate the
effectiveness of domain-specific post-training on sentiment analysis and topic
classification tasks. Our findings indicate that the post-training increases
the effectiveness of a language model when it is fine-tuned to domain-specific
downstream tasks.


  Measurements of the surface velocity and surface texture of a freely
propagating shear jamming front in a dense suspension are compared. The
velocity fields are captured with particle image velocimetry (PIV), while the
surface texture is captured in a separated experiment by observing a direct
reflection on the suspension surface with high-speed cameras. A method for
quantifying the surface features and their orientation is presented based on
the fast Fourier transform of localised windows. The region that exhibits
strong surface features corresponds to the the solid-like jammed region
identified via the PIV measurements. Moreover, the surface features within the
jammed region are predominantly oriented in the same direction as the
eigenvectors of the strain tensor. Thus, from images of the free surface, our
analysis is able to show that the surface texture contains information on the
principle strain directions and the propagation of the jamming front.


  Given any n-tuple of complex numbers, one can canonically define a polynomial
of degree n+1 that has the entries of this n-tuple as its critical points. In
2002, Beardon, Carne, and Ng studied a map $\theta\colon \mathbb{C}^n\to
\mathbb{C}^n$ which outputs the critical values of the canonical polynomial
constructed from the input, and they proved that this map is onto. Along the
way, they showed that $\theta$ is a local homeomorphism whenever the entries of
the input are distinct and nonzero, and, implicitly, they produced a polynomial
expression for the Jacobian determinant of $\theta$. In this article we extend
and generalize both the local homeomorphism result and the elegant determinant
identity to analogous situations where the critical points occur with
multiplicities. This involves stratifying $\mathbb{C}^n$ according to which
coordinates are equal and generalizing $\theta$ to a similar map
$\mathbb{C}^\ell \to \mathbb{C}^\ell$ where $\ell$ is the number of distinct
critical points. The more complicated determinant identity that we establish is
closely connected to the multinomial identity known as Dyson's conjecture.


  We present proof-of-operation for a new method of electron thermometry using
microwave impedance of a hafnium micro-absorber. The new method leads to an
ultimate THz-range detector suitable for microwave readout and frequency
division multiplexing. The sensing part of the device is a hot-electron-gas
absorber responding to the incident radiation by variation of its impedance
measured at probing frequency about 1.5 GHz. The absorber is a microbridge made
from hafnium (Tc = 375 mK, RN = 30 Ohm) sized 2.5 um by 2.5 um by 50 nm and
integrated with a planar 600-700 GHz antenna placed near the open end of a
quarter-wave CPW resonator (Q-factor about 10^4). All elements of the circuit,
except the microbridge, are made from 100-nm thick Nb, including the resonator,
which is weakly coupled to a throughput line. The device was tested at 50-350
mK smoothly responding with its transmission coefficient S21 to applied
microwave power at the resonance frequency. We have found that the power
absorbed by the bridge fits to the model of hot electron gas, P=k(Te^n-Tph^n)
(n = 5...6). The idle NEP down to about 10^-18 W/Hz^(-1/2) and the
corresponding cross-over temperature for photon background about 5 K are
estimated from the measured data. The saturation power of about 1 pW and
possibility of moderate gain are anticipated for a practicable device operating
at temperature 200 mK. Since the optimum readout frequency is found exactly at
the resonance, the detector is insensitive to most phase instabilities at the
probing frequency.


  In this paper, we investigated the thermodynamic properties of strange quark
matter using Nambu-Jona-Lasinio (NJL) model at finite temperatures where we
considered the dynamical mass as the effective interaction between quarks.
  By considering the pressure of strange quark matter (SQM) at finite
temperatures, we showed that the equation of state of this system gets stiffer
by increasing temperature. In addition, we investigated the energy conditions
and stability of the equation of state and showed that the equation of state of
SQM satisfy the conditions of stability. Finally, we computed the structure
properties of hot strange quark stars (SQS) including the gravitational mass,
radius, Schwarzschild radius, average density, compactness and gravitational
redshift. Our calculations showed that in this model, the maximum mass and
radius of SQS increase by increasing temperature. Furthermore it was shown that
the average density of SQS is greater than the normal nuclear density, and it
is an increasing function of temperature. We also discussed the temperature
dependence of the maximum gravitational mass calculated from different methods.


  Stochasticity is both exploited and controlled by cells. Although the
intrinsic stochasticity inherent in biochemistry is relatively well understood,
cellular variation, or 'noise', is predominantly generated by interactions of
the system of interest with other stochastic systems in the cell or its
environment. Such extrinsic fluctuations are nonspecific, affecting many system
components, and have a substantial lifetime, comparable to the cell cycle (they
are 'colored'). Here, we extend the standard stochastic simulation algorithm to
include extrinsic fluctuations. We show that these fluctuations affect mean
protein numbers and intrinsic noise, can speed up typical network response
times, and can explain trends in high-throughput measurements of variation. If
extrinsic fluctuations in two components of the network are correlated, they
may combine constructively (amplifying each other) or destructively
(attenuating each other). Consequently, we predict that incoherent feedforward
loops attenuate stochasticity, while coherent feedforwards amplify it. Our
results demonstrate that both the timescales of extrinsic fluctuations and
their nonspecificity substantially affect the function and performance of
biochemical networks.


  The one and two-particle form factors of the energy operator in the
two-dimensional Ising model in a magnetic field at $T=T_c$ are exactly computed
within the form factor bootstrap approach. Together with the matrix elements of
the magnetisation operator already computed in ref.\,\cite{immf}, they are used
to write down the large distance expansion for the correlators of the two
relevant fields of the model.


  The measurable degree of entanglement from a quantum dot via the
biexciton-exciton cascade depends crucially on the bright exciton
fine-structure splitting and on the detection time resolution. Here, we propose
an optical approach with fast rotating waveplates to erase this fine-structure
splitting and therefore obtain a high degree of entanglement with near-unity
efficiency. Our optical approach is possible with current technology and is
also compatible with any quantum dot showing fine-structure splitting.


  Based on the combinatorial description of the moduli spaces of curves
provided by Strebel differentials, Witten and Kontsevich have introduced
combinatorial cohomology classes $W_{(m_0,m_1,m_2,\dots),n}$, and conjectured
that these can be expressed in terms of Mumford-Morita-Miller classes. It is
argued that this link should be provided by a theorem of Di Francesco, Itzykson
and Zuber which relates the derivatives of the Witten-Kontsevich partition
function with respect to one set of variables to the derivatives with respect
to the other set of variables. Two things are shown. First of all that this
works in complex codimension 1. Secondly that in all the cases when it has been
possible to make the Di Francesco, Itzykson and Zuber correpondence explicit
this translates into identities of the type $$
\int_{W_{(m_0,m_1,m_2,\dots),n}}\prod\psi_i^{d_i}
=\int_{\overline{\cal{M}}_{g,n}} X_{(m_0,m_1,m_2,\dots),n}\prod\psi_i^{d_i} $$
where the $X_{(m_0,m_1,m_2,\dots),n}$ are explicit polynomials in the
algebro-geometric classes and the $\psi_i$ are the Chern classes of the point
bundles, for any choice of $d_1,\dots,d_n$.


  The scattered starlight which contaminates the spectrum of a reddened star
depends on l as 1/lambda^n (n*4), and must be attributed to small particles.
Surprisingly these particules, which have a quasi-isotropic phase function, do
not participate to the brightness of the nebulae. The importance of the
intensity of the scattered light present in the spectrum of reddened stars can
be explained only if the scattering is coherent. The intensity of the scattered
light is then proportional to the square of the number of particles, and is
concentrated within an angle of 10^{-8}" from the star. The scatterers can be
small grains, or, atoms or molecules from the gas. Hydrogen satisfies the
conditions imposed to the scatterers.
  --
  La lumiere diffusee qui contamine le spectre des etoiles rougies a une
dependance en 1/lambda^n (n>=4), caracteristique de particules petites devant
la longueur d'onde. Il est surprenant que ces particules, qui doivent diffuser
de facon quasi-isotrope, ne contribuent pas a la brillance des nebuleuses.
L'importance de la lumiere diffusee par ces petites particules dans le spectre
d'une etoile rougie s'explique seulement si la diffusion est coherente.
L'intensite diffusee est alors proportionnelle au carre du nombre de
diffuseurs. Vue par l'observateur, elle est concentree dans un angle inferieur
a 10{-8}'' de l'etoile. Les diffuseurs peuvent etre des petits grains, ou, des
atomes ou molecules du gaz. L'hydrogene satisfait remarquablement aux
conditions que les observations imposent aux diffuseurs.


  We generalize the idea of boundary states to the open string channel. They
describe emission and absorption of open strings in the presence of
intersecting D-branes. We construct the explicit oscillator representation for
the free boson and fermionic ghost. The inner product of such states describes
a disk amplitude of rectangular shape and possesses modular covariance with a
nontrivial conformal weight. We compare the result obtained here with those
obtained using two different methods, one employing the path integral formalism
and one employing the conformal anomaly. We find that all these methods give
consistent results. In our method, we must be careful in our treatment of the
singularity of the CFT near the corners. Specifically, we derive the correction
to the conformal weight of the primary field inserted at the corner, and it
gives the modular weight of the rectangle amplitude. We also carry out explicit
computations of the correlation functions.


  Deep neural networks (DNNs) are threatened by adversarial examples.
Adversarial detection, which distinguishes adversarial images from benign
images, is fundamental for robust DNN-based services. Image transformation is
one of the most effective approaches to detect adversarial examples. During the
last few years, a variety of image transformations have been studied and
discussed to design reliable adversarial detectors. In this paper, we
systematically synthesize the recent progress on adversarial detection via
image transformations with a novel classification method. Then, we conduct
extensive experiments to test the detection performance of image
transformations against state-of-the-art adversarial attacks. Furthermore, we
reveal that each individual transformation is not capable of detecting
adversarial examples in a robust way, and propose a DNN-based approach referred
to as \emph{AdvJudge}, which combines scores of 9 image transformations.
Without knowing which individual scores are misleading or not misleading,
AdvJudge can make the right judgment, and achieve a significant improvement in
detection rate. Finally, we utilize an explainable AI tool to show the
contribution of each image transformation to adversarial detection.
Experimental results show that the contribution of image transformations to
adversarial detection is significantly different, the combination of them can
significantly improve the generic detection ability against state-of-the-art
adversarial attacks.


  Often, the unknown diffusivity in diffusive processes is structured by
piecewise constant patches. This paper is devoted to efficient methods for the
determination of such structured diffusion parameters by exploiting shape
calculus. A novel shape gradient is derived in parabolic processes. Furthermore
quasi-Newton techniques are used in order to accelerate shape gradient based
iterations in shape space. Numerical investigations support the theoretical
results.


  Objective: Probe-based confocal endomicroscopy is an emerging
high-magnification optical imaging technique that provides in vivo and in situ
cellular-level imaging for real-time assessment of tissue pathology.
Endomicroscopy could potentially be used for intraoperative surgical guidance,
but it is challenging to assess a surgical site using individual microscopic
images due to the limited field-of-view and difficulties associated with
manually manipulating the probe.
  Methods: In this paper, a novel robotic device for large-area endomicroscopy
imaging is proposed, demonstrating a rapid, but highly accurate, scanning
mechanism with image-based motion control which is able to generate
histology-like endomicroscopy mosaics. The device also includes, for the first
time in robotic-assisted endomicroscopy, the capability to ablate tissue
without the need for an additional tool.
  Results: The device achieves pre-programmed trajectories with positioning
accuracy of less than 30 um, while the image-based approach demonstrated that
it can suppress random motion disturbances up to 1.25 mm/s. Mosaics are
presented from a range of ex vivo human and animal tissues, over areas of more
than 3 mm^2, scanned in approximate 10 seconds.
  Conclusion: This work demonstrates the potential of the proposed instrument
to generate large-area, high-resolution microscopic images for intraoperative
tissue identification and margin assessment.
  Significance: This approach presents an important alternative to current
histology techniques, significantly reducing the tissue assessment time, while
simultaneously providing the capability to mark and ablate suspicious areas
intraoperatively.


  Transformers have recently been shown to be capable of reliably performing
logical reasoning over facts and rules expressed in natural language, but
abductive reasoning - inference to the best explanation of an unexpected
observation - has been underexplored despite significant applications to
scientific discovery, common-sense reasoning, and model interpretability.
  We present AbductionRules, a group of natural language datasets designed to
train and test generalisable abduction over natural-language knowledge bases.
We use these datasets to finetune pretrained Transformers and discuss their
performance, finding that our models learned generalisable abductive techniques
but also learned to exploit the structure of our data. Finally, we discuss the
viability of this approach to abductive reasoning and ways in which it may be
improved in future work.


  This chapter presents recent advances in content based image search and
retrieval (CBIR) systems in remote sensing (RS) for fast and accurate
information discovery from massive data archives. Initially, we analyze the
limitations of the traditional CBIR systems that rely on the hand-crafted RS
image descriptors. Then, we focus our attention on the advances in RS CBIR
systems for which deep learning (DL) models are at the forefront. In
particular, we present the theoretical properties of the most recent DL based
CBIR systems for the characterization of the complex semantic content of RS
images. After discussing their strengths and limitations, we present the deep
hashing based CBIR systems that have high time-efficient search capability
within huge data archives. Finally, the most promising research directions in
RS CBIR are discussed.


  We report on the first detailed study of the mechanisms involved in knockout
reactions, via a coincidence measurement of the residue and fast proton in
one-proton knockout reactions, using the S800 spectrograph in combination with
the HiRA detector array at the NSCL. Results on the reactions
$^9$Be($^9$C,$^8$B+X)Y and $^9$Be($^8$B,$^7$Be+X)Y are presented. They are
compared with theoretical predictions for both the diffraction and stripping
reaction mechanisms, as calculated in the eikonal model. The data shows a clear
distinction between the two reaction mechanisms, and the observed respective
proportions are very well reproduced by the reaction theory. This agreement
supports the results of knockout reaction analyses and their applications to
the spectroscopy of rare isotopes.


  Data are presented on the production of jets and pi^0 mesons at low Bjorken x
in a kinematic region where standard DGLAP evolution in Q^2 gives little phase
space for high p_t particle and jet production.
  The data are compared with various QCD models based on different treatments
of parton emissions at small x.


  Unique and flexible properties of non-Hermitian photonic systems attract
ever-increasing attention via delivering a whole bunch of novel optical effects
and allowing for efficient tuning light-matter interactions on nano- and
microscales. Together with an increasing demand for the fast and spatially
compact methods of light governing, this peculiar approach paves a broad avenue
to novel optical applications. Here, unifying the approaches of disordered
metamaterials and non-Hermitian photonics, we propose a conceptually new and
simple architecture driven by disordered loss-gain multilayers and, therefore,
providing a powerful tool to control both the passage time and the wave-front
shape of incident light with different switching times. For the first time we
show the possibility to switch on and off kink formation by changing the level
of disorder in the case of adiabatically raising wave fronts. At the same time,
we deliver flexible tuning of the output intensity by using the nonlinear
effect of loss and gain saturation. Since the disorder strength in our system
can be conveniently controlled with the power of the external pump, our
approach can be considered as a basis for different active photonic devices.


  We establish a quantum cluster algebra structure on the quantum Grothendieck
ring of a certain monoidal subcategory of the category of finite-dimensional
representations of a simply-laced quantum affine algebra. Moreover, the
(q,t)-characters of certain irreducible representations, among which
fundamental representations, are obtained as quantum cluster variables. This
approach gives a new algorithm to compute these (q,t)-characters. As an
application, we prove that the quantum Grothendieck ring of a larger category
of representations of the Borel subalgebra of the quantum affine algebra,
defined in a previous work as a quantum cluster algebra, contains indeed the
well-known quantum Grothendieck ring of the category of finite-dimensional
representations. Finally, we display our algorithm on a concrete example.


  We have studied the effects of oxygen on hydrogenated amorphous/crystalline
silicon films in terms of their structural and optical properties. Different
hydrogenated silicon oxide (SiO:H) and silicon (Si:H) films are fabricated
between microcrystalline and amorphous transition region. X-ray diffraction,
Raman, FTIR and UV-Vis emission spectrometry have been used to characterize
different films. A comparison of the results with those of different types of
films like hydrogenated amorphous silicon oxide (a-SiO:H), hydrogenated
amorphous silicon (a-Si:H) and microcrystalline silicon ($\mu$c-Si:H) films
reveal their superiority as an excellent substance for solar cell. X-ray
diffraction, FTIR and Raman spectral analysis show that difference of the H
dilution effect has a major effect on the structure of the film and the optical
properties. Photoluminescence analysis of amorphous silicon-oxygen and
silicon-hydride alloy films has established their efficient application
appropriate as Si based light emitting devices. A large optical band gap of
1.83 eV and appearance of strong photo luminescence at 2.0 eV validates the
applicability of a-SiO:H film as a better alternative for the solar cells.


  We present results on the two-loop anomalous dimensions of the heavy baryon
HQET currents $J=(q^TC\Gamma\tau q)\Gamma'Q$ with arbitrary Dirac matrices
$\Gamma$ and $\Gamma'$. From our general result we obtain the two-loop
anomalous dimensions for currents with quantum numbers of the ground state
heavy baryons $\Lambda_Q$, $\Sigma_Q$ and $\Sigma_Q^*$. As a by-product of our
calculation and as an additional check we rederive the known two-loop anomalous
dimensions of mesonic scalar, pseudoscalar, vector, axial vector and tensor
currents $(J=\bar q\Gamma q)$ in massless QCD as well as in HQET.


  We report on Differential Evolution for Analytic Continuation (DEAC): a
parameter-free evolutionary algorithm to generate the dynamic structure factor
from imaginary time correlation functions. Our approach to this long-standing
problem in quantum many-body physics achieves enhanced spectral fidelity while
using fewer compute (CPU) hours. The need for fine-tuning of algorithmic
control parameters is eliminated by embedding them within the genome to be
optimized for this evolutionary computation based algorithm. Benchmarks are
presented for models where the dynamic structure factor is known exactly, and
experimentally relevant results are included for quantum Monte Carlo
simulations of bulk $^4$He below the superfluid transition temperature.


  We study torsion in the integral cohomology of a certain family of
$2n$-dimensional orbifolds $X$ with actions of the $n$-dimensional compact
torus. Compact simplicial toric varieties are in our family. For a prime number
$p$, we find a necessary condition for the integral cohomology of $X$ to have
no $p$-torsion. Then we prove that the necessary condition is sufficient in
some cases. We also give an example of $X$ which shows that the necessary
condition is not sufficient in general.


  If $X$ is the attractor set of a conformal IFS in dimension two or three, we
prove that there exists a quasiregular semigroup $G$ with Julia set equal to
$X$. We also show that in dimension two, with a further assumption similar to
the open set condition, the same result can be achieved with a semigroup
generated by one element. Consequently, in this case the attractor set is
quasiconformally equivalent to the Julia set of a rational map.


  Object detection tasks, crucial in safety-critical systems like autonomous
driving, focus on pinpointing object locations. These detectors are known to be
susceptible to backdoor attacks. However, existing backdoor techniques have
primarily been adapted from classification tasks, overlooking deeper
vulnerabilities specific to object detection. This paper is dedicated to
bridging this gap by introducing Detector Collapse} (DC), a brand-new backdoor
attack paradigm tailored for object detection. DC is designed to instantly
incapacitate detectors (i.e., severely impairing detector's performance and
culminating in a denial-of-service). To this end, we develop two innovative
attack schemes: Sponge for triggering widespread misidentifications and
Blinding for rendering objects invisible. Remarkably, we introduce a novel
poisoning strategy exploiting natural objects, enabling DC to act as a
practical backdoor in real-world environments. Our experiments on different
detectors across several benchmarks show a significant improvement
($\sim$10\%-60\% absolute and $\sim$2-7$\times$ relative) in attack efficacy
over state-of-the-art attacks.


  We study directional mean dimension of $\mathbb{Z}^k$-actions (where $k$ is a
positive integer). On the one hand, we show that there is a
$\mathbb{Z}^2$-action whose directional mean dimension (considered as a
$[0,+\infty]$-valued function on the torus) is not continuous. On the other
hand, we prove that if a $\mathbb{Z}^k$-action is continuum-wise expansive,
then the values of its $(k-1)$-dimensional directional mean dimension are
bounded. This is a generalization (with a view towards Meyerovitch and
Tsukamoto's theorem on mean dimension and expansive multiparameter actions) of
a classical result due to Ma\~n\'e: Any compact metrizable space admitting an
expansive homeomorphism (with respect to a compatible metric) is
finite-dimensional.


  Anisotropic pairing interactions mediated by phonons are examined in layer
systems. It is shown that the screening effects become weaker when the layer
spacing increases. Then the anisotropic components of the pairing interactions
increase with the screening length since the momentum dependence changes. As a
result, various types of anisotropic superconductivity occur depending on the
parameter region. For example, p-wave superconductivity occurs when the
short-range part of Coulomb repulsion is strong and the layer spacing is large.
Two kinds of inter-layer pairing may occur when the layer spacing is not too
large. Although the phonon contribution to the d-wave pairing interaction is
weaker than the p-wave interaction, it increases with the layer spacing.
Relevance of the present results to organic superconductors, high-T_c cuprates,
and Sr_2RuO_4 is discussed.


  We outline, on a few instructive examples, the characteristic features of the
approach to superbranes and super Born-Infeld theories based on the concept of
partial spontaneous breaking of global supersymmetry (PBGS). The examples
include the N=1, D=4 supermembrane and the ``space-filling'' D2- and D3-branes.
Besides giving a short account of the available results for these systems, we
present some new developments. For the supermembrane we prove the equivalence
of the equation of motion following from the off-shell Goldstone superfield
action and the one derived directly from the nonlinear realizations formalism.
We give a new derivation of the off-shell Goldstone superfield actions for the
considered systems, using a universal procedure inspired by the relationship
between linear and nonlinear realizations of PBGS.


  We discuss two theorems in analytic number theory and combinatory analysis
that have seen increased use in recent years. A corollary to a Tauberian
theorem of Ingham allows one to quickly prove asymptotic formulas for
arithmetic sequences, so long as the corresponding generating function exhibits
exponential growth of a certain form near its radius of convergence. Two common
methods for proving the required analytic behavior are modular transformations
and Euler-Maclaurin summation.
  However, these results are sometimes stated without certain technical
conditions that are necessary for the complex analytic techniques that appear
in Ingham's proof. We carefully examine the precise statements and proofs of
these results, and find that in practice, the technical conditions are
satisfied for those cases appearing in recent applications. We also generalize
the classical approach of Euler-Maclaurin summation in order to prove
asymptotic expansions for series with complex values, simple poles, or
multi-dimensional summation indices.


  Abstract: This article offers a comprehensive analysis of Artificial General
Intelligence (AGI) development through a humanistic lens. Utilizing a wide
array of academic and industry resources, it dissects the technological and
ethical complexities inherent in AGI's evolution. Specifically, the paper
underlines the societal and individual implications of AGI and argues for its
alignment with human values and interests.
  Purpose: The study aims to explore the role of human-centered design in AGI's
development and governance.
  Design/Methodology/Approach: Employing content analysis and literature
review, the research evaluates major themes and concepts in human-centered
design within AGI development. It also scrutinizes relevant academic studies,
theories, and best practices.
  Findings: Human-centered design is imperative for ethical and sustainable
AGI, emphasizing human dignity, privacy, and autonomy. Incorporating values
like empathy, ethics, and social responsibility can significantly influence
AGI's ethical deployment. Talent development is also critical, warranting
interdisciplinary initiatives.
  Research Limitations/Implications: There is a need for additional empirical
studies focusing on ethics, social responsibility, and talent cultivation
within AGI development.
  Practical Implications: Implementing human-centered values in AGI development
enables ethical and sustainable utilization, thus promoting human dignity,
privacy, and autonomy. Moreover, a concerted effort across industry, academia,
and research sectors can secure a robust talent pool, essential for AGI's
stable advancement.
  Originality/Value: This paper contributes original research to the field by
highlighting the necessity of a human-centered approach in AGI development, and
discusses its practical ramifications.


  We present the spectra of 36 Supernovae (SNe) of various types, obtained by
the European Supernova Collaboration. Because of the spectral classification
and the phase determination at their discovery the SNe did not warrant further
study, and the spectra we present are the only available for the respective
objects. In this paper we present and discuss this material using a new
software for the automated classification of SNe spectra.
  As a validation of the software, we verify the classification and phase
estimate reported for these objects in their discovery / classification
circulars. For the comparison, the software uses the library of template
spectra of Padova-Asiago Supernova Archive (ASA).
  For each spectrum of our sample we present a brief, individual discussion,
highlighting the main characteristics and possible peculiarities. The
comparison with ASA spectra confirms the previous classification of all objects
and refines the age estimates. For our software we determine numerical limits
of "safe" spectral classification and the uncertainties of the phase
determination.


  We identify nine young stellar objects (YSOs) in the NGC 2264 star-forming
region with optical {\em CoRoT} light curves exhibiting short-duration,
shallow, periodic flux dips. All of these stars have infrared (IR) excesses
that are consistent with their having inner disk walls near the Keplerian
co-rotation radius. The repeating photometric dips have FWHM generally less
than one day, depths almost always less than 15%, and periods (3<P<11 days)
consistent with dust near the Keplerian co-rotation period. The flux dips vary
considerably in their depth from epoch to epoch, but usually persist for
several weeks and, in two cases, were present in data collected on successive
years. For several of these stars, we also measure the photospheric rotation
period and find that the rotation and dip periods are the same, as predicted by
standard "disk-locking" models. We attribute these flux dips to clumps of
material in or near the inner disk wall, passing through our line of sight to
the stellar photosphere. In some cases, these dips are also present in
simultaneous {\em Spitzer} IRAC light curves at 3.6 and 4.5 microns. We
characterize the properties of these dips, and compare the stars with light
curves exhibiting this behavior to other classes of YSO in NGC 2264. A number
of physical mechanisms could locally increase the dust scale height near the
inner disk wall, and we discuss several of those mechanisms; the most plausible
mechanisms are either a disk warp due to interaction with the stellar magnetic
field or dust entrained in funnel-flow accretion columns arising near the inner
disk wall.


  Oscillating solid bodies have frequently been used for studying the
properties of normal and superfluid helium. In particular, the transition from
laminar flow to turbulence has attracted much interest recently. The purpose of
this note is to review several central features of this transition in
oscillatory flow, which have been inaccurately formulated in some recent work.


  If $R$-parity is not conserved, the lightest supersymmetric particle (LSP)
could decay via lepton number violating or baryon number violating
interactions. The latter case is particularly insidious since it leads to a
reduction of the $\eslt$ as well as leptonic signals for supersymmetry. We
evaluate cross sections for jets plus $\eslt$, 1$\ell$, $2\ell$ (same-sign and
opposite sign) and $3\ell$ event topologies that result from the simultaneous
production of all sparticles at the CERN Large Hadron Collider (LHC), assuming
that the lightest supersymmetric particle $\tz_1$ decays hadronically inside
the detector via $R$-parity violating interactions. We assume that these
interactions do not affect the production rates or decays of other sparticles.
We examine the SUSY reach of the LHC for this ``pessimistic'' scenario, and
show that experiments at the LHC will still be able to search for gluinos and
squarks as heavy as 1 TeV, given just 10 fb$^{-1}$ of integrated luminosity,
even with cuts designed to explore the canonical SUSY framework with a {\it
conserved} $R$-parity.


  Today, enterprises are faced with increased global competition in an
environment where customers are demanding faster delivery, better service and
also want to gain significant and immediate business value by increasing
productivity and reducing operational cost. Spurred by unprecedented customer
demand, each Industry cluster has developed its own source of comparative
advantage. Even within a single organization, the business value chain is
geographically fragmented. Such diversification and fragmentation of value
chain drives the need for cross-platform Web applications over mobile channel.
Mobile Web is the next logical transition in this evolutionary process and
Mobile Web applications will continue to gain more prominence in the
enterprises not just to improve the return on investment in their existing
system landscape, but also to expand global reach and improve operational
efficiency of their mobile workforce. This paper outlines the critical business
needs to rapidly create flexible Mobile web solutions across all lines of
business. The paper enlightens the benefits offered by enabling web
applications on Mobile devices and also addresses the current business
challenges in developing Mobile Web applications. This paper is intended for
all business domains irrespective of application portfolios.


  We have simulated the pure gauge compact QED with fixed boundary conditions,
on lattices from $6^4$ to $16^4$. We argue that a lattice with this fixed
boundary imposition corresponds actually to a lattice with spherical topology.
We have found the presence of a phase transition without any trace of
discontinuity. Moreover, the specific heat and the Binder cumulant are
qualitatively consistent with a second order phase transition. The implications
of this observation on the nature of the compact lattice QED are discussed.


  We study quantum effects in hybrid atomic optomechanics in a system
comprising a cloud of atoms and a mobile mirror mediated by a single-mode
cavity. Tripartite nonlocality is observed in the atom-light-mirror system, as
demonstrated by the violation of the Mermin-Klyshko (MK) inequality. It has
been shown [C. Genes, et al., PRA 77, 050307 (R) (2008)] that tripartite
entanglement is optimized when the cavity is resonant with the anti-Stokes
sideband of the driving laser and the atomic frequency matches the Stokes one.
However, we show that this is not the case for the nonlocality. The MK function
achieves {\it minima} when the atoms are resonant with both the Stokes and
anti-Stokes sidebands, and unexpectedly, we find violation of the MK inequality
only in a parameter region where entanglement is far from being maximum. A
negative relation exists between nonlocality and entanglement with
consideration of the possibility of bipartite nonlocality in the violation of
the MK inequality. We also study the non-classicality of the mirror by
post-selected measurements, e.g. Geiger-like detection, on the cavity and/or
the atoms. We show that with feasible parameters Geiger-like detection on the
atoms can effectively induce mechanical non-classicality.


  We consider Bayesian shrinkage predictions for the Normal regression problem
under the frequentist Kullback-Leibler risk function.
  Firstly, we consider the multivariate Normal model with an unknown mean and a
known covariance. While the unknown mean is fixed, the covariance of future
samples can be different from training samples. We show that the Bayesian
predictive distribution based on the uniform prior is dominated by that based
on a class of priors if the prior distributions for the covariance and future
covariance matrices are rotation invariant.
  Then, we consider a class of priors for the mean parameters depending on the
future covariance matrix. With such a prior, we can construct a Bayesian
predictive distribution dominating that based on the uniform prior.
  Lastly, applying this result to the prediction of response variables in the
Normal linear regression model, we show that there exists a Bayesian predictive
distribution dominating that based on the uniform prior. Minimaxity of these
Bayesian predictions follows from these results.


  Can information theory provide insights into whether exoplanets are
habitable? Here we apply information theory to a range of simulated exoplanet
transmission spectra as a diagnostic tool to search for potential signatures of
life on Earth-analog planets. We test the algorithms on three epochs of
evolution for Earth-like planets orbiting a range of host stars. The James Webb
Space Telescope and upcoming ground- and space-based missions promise to
achieve sufficient high-resolution data that information theory can be applied
to assess habitability. This approach provides a framework and a tool for
observers to assess whether an exoplanet shows signs of habitability.


  BACKGROUND: 155Tb represents a potentially useful radionuclide for diagnostic
medical applications, but its production remains a challenging problem. A
recent experimental campaign, conducted with low-energy proton beams impinging
on a 155Gd target with 91.9% enrichment, demonstrated a significant
co-production of 156gTb, a contaminant of great concern since its half-life is
comparable to that of 155Tb and its high-energy gamma emissions severely impact
on the dose released and on the quality of the SPECT images. Herein, the
isotopic purity of the enriched 155Gd target necessary to minimize the
co-production of contaminant radioisotopes, in particular 156gTb, was explored
using various computational simulations. RESULTS: Starting from the recent data
obtained with the 155Gd-enriched target, the co-production of other Tb
radioisotopes besides 155Tb has been evaluated using the TALYS code. It was
found that 156Gd, with an isotopic content of 5.87%, was the principal
contributor to the co-production of 156gTb. The analysis also demonstrated that
the maximum amount of 156Gd admissible for 155Tb production with a
radionuclidic purity higher than 99% was 1%. A less stringent condition was
obtained through computational dosimetry analysis, suggesting that a 2% content
of 156Gd in the target can be tolerated to limit the dose increase to the
patient below the 10% limit. Moreover, it has been demonstrated that the
imaging properties of the produced 155Tb are not severely affected by this
level of impurity in the target. CONCLUSIONS: 155Tb can be produced with a
quality suitable for medical applications using low-energy proton beams and
155Gd-enriched targets if the 156Gd impurity content does not exceed 2%. Under
these conditions, the dose increase due to the presence of contaminant
radioisotopes remains below the 10% limit and good quality images, comparable
to those of 111In, are guaranteed.


  It is shown how a selection of prominent results in singularity theory and
differential geometry can be deduced from one theorem, the Rank Theorem for
maps between spaces of power series.


  Akin to many subareas of computer vision, the recent advances in deep
learning have also significantly influenced the literature on optical flow.
Previously, the literature had been dominated by classical energy-based models,
which formulate optical flow estimation as an energy minimization problem.
However, as the practical benefits of Convolutional Neural Networks (CNNs) over
conventional methods have become apparent in numerous areas of computer vision
and beyond, they have also seen increased adoption in the context of motion
estimation to the point where the current state of the art in terms of accuracy
is set by CNN approaches. We first review this transition as well as the
developments from early work to the current state of CNNs for optical flow
estimation. Alongside, we discuss some of their technical details and compare
them to recapitulate which technical contribution led to the most significant
accuracy improvements. Then we provide an overview of the various optical flow
approaches introduced in the deep learning age, including those based on
alternative learning paradigms (e.g., unsupervised and semi-supervised methods)
as well as the extension to the multi-frame case, which is able to yield
further accuracy improvements.


  Eigenstates of 2D charged electron-hole complexes in magnetic fields are
considered. The operator formalism that allows one to partially separate the
center-of-mass motion from internal degrees of freedom is presented. The scheme
using magnetic translations is developed for calculating in strong magnetic
fields the eigenspectra of negatively charged excitons X^-, a bound state of
two electrons and one hole.


  Generative art is a rules-driven approach to creating artistic outputs in
various mediums. For example, a fluid simulation can govern the flow of colored
pixels across a digital display or a rectangle placement algorithm can yield a
Mondrian-style painting. Previously, we investigated how genetic improvement, a
sub-field of genetic programming, can automatically create and optimize
generative art drawing programs. One challenge of applying genetic improvement
to generative art is defining fitness functions and their interaction in a
many-objective evolutionary algorithm such as Lexicase selection. Here, we
assess the impact of each fitness function in terms of the their individual
effects on generated images, characteristics of generated programs, and impact
of bloat on this specific domain. Furthermore, we have added an additional
fitness function that uses a classifier for mimicking a human's assessment as
to whether an output is considered as "art." This classifier is trained on a
dataset of input images resembling the glitch art aesthetic that we aim to
create. Our experimental results show that with few fitness functions,
individual generative techniques sweep across populations. Moreover, we found
that compositions tended to be driven by one technique with our current fitness
functions. Lastly, we show that our classifier is best suited for filtering out
noisy images, ideally leading towards more outputs relevant to user preference.


  The mixed Hegselmann-Krause (HK) model consists of a finite number of agents
characterized by their opinion, a vector in $\mathbf{R^d}$. For the
deterministic case, each agent updates its opinion by the rule: decide its
degree of stubbornness and mix its opinion with the average opinion of its
neighbors, the agents whose opinion differs by at most some confidence
threshold from its opinion at each time step. The mixed model is studied
deterministically in \cite{mHK}. In this paper, we study it
nondeterministically and involve a social relationship among the agents which
can vary over time. We investigate circumstances under which asymptotic
stability holds. Furthermore, we indicate the mixed model covers not only the
HK model but also the Deffuant model.


  Sgr A lobes are a pair of 15-pc-sized bipolar bubbles with co-aligned major
axes perpendicular to the Galactic plane found in X-ray and radio observations
of the Galactic center (GC). Their elusive origin is a vital ingredient in
understanding the ongoing high energy processes at the GC. Here we perform a
suite of hydrodynamic simulations to explore the tidal disruption event (TDE)
outflow model for the origin of the Sgr A lobes. By following the outflow
evolution in the circumnuclear medium, we find that TDE outflows naturally
produce bipolar lobes delimited by forward shocks, and the dense postshock
shell contributes significantly to the lobe's X-ray emission. Our fiducial run
reproduces the morphology, density, temperature, and X-ray surface brightness
distribution of the observed Sgr A lobes reasonably well. The current lobe age
is ~3300 yr. Our model further predicts that the uplifted wake flow breaks
through the ejecta-induced shock front, producing a shock-enclosed head region
which is relatively dim in X-rays compared to other lobe regions. Both the dim
head region and the predicted limb-brightening feature of the lobes are
slightly inconsistent with current X-ray observations, and may be used to
further test our model with more sensitive future X-ray observations. While
light narrow jets and massive wide winds from TDE events usually do not
reproduce the observed oval-shaped morphology of the lobes, the TDE outflow in
our fiducial run is massive and yet narrow. Whether it is a jet or wind remains
uncertain and future simulations covering both the outflow acceleration region
and its pc-scale evolution would be very helpful in determining whether the Sgr
A lobes indeed originate from a pair of TDE jets or winds.


  Robin Forman's highly influential 2002 paper titled A User's Guide to
Discrete Morse Theory presents an overview of the subject in a very readable
manner. As a proof of concept, the author determines the topology (homotopy
type) of the abstract simplicial complex of disconnected graphs of order $n$
(which was previously done by Victor Vassiliev using classical topological
methods) using discrete Morse theoretic techniques, which are purely
combinatorial in nature. The techniques involve the construction (and
verification) of a discrete gradient vector field on the complex. However, the
verification part relies on a claim that doesn't seem to hold. In this note, we
provide a couple of counterexamples against this specific claim and also an
alternative proof of the claim that the constructed discrete vector field is
indeed a gradient vector field.


  Astro-WISE is the Astronomical Wide-field Imaging System for Europe. It is a
scientific information system which consists of hardware and software federated
over about a dozen institutes throughout Europe. It has been developed to
exploit the ever increasing avalanche of data produced by astronomical surveys
and data intensive scientific experiments in general.
  The demo explains the architecture of the Astro-WISE information system and
shows the use of Astro-WISE interfaces. Wide-field astronomical images are
derived from the raw image to the final catalog according to the user's
request. The demo is based on the standard Astro-WISE guided tour, which can be
accessed from the Astro-WISE website.
  The typical Astro-WISE data processing chain is shown, which can be used for
data handling for a variety of different instruments, currently 14, including
OmegaCAM, MegaCam, WFI, WFC, ACS/HST, etc.


  Predicting cancer dynamics under treatment is challenging due to high
inter-patient heterogeneity, lack of predictive biomarkers, and sparse and
noisy longitudinal data. Mathematical models can summarize cancer dynamics by a
few interpretable parameters per patient. Machine learning methods can then be
trained to predict the model parameters from baseline covariates, but do not
account for uncertainty in the parameter estimates. Instead, hierarchical
Bayesian modeling can model the relationship between baseline covariates to
longitudinal measurements via mechanistic parameters while accounting for
uncertainty in every part of the model.
  The mapping from baseline covariates to model parameters can be modeled in
several ways. A linear mapping simplifies inference but fails to capture
nonlinear covariate effects and scale poorly for interaction modeling when the
number of covariates is large. In contrast, Bayesian neural networks can
potentially discover interactions between covariates automatically, but at a
substantial cost in computational complexity.
  In this work, we develop a hierarchical Bayesian model of subpopulation
dynamics that uses baseline covariate information to predict cancer dynamics
under treatment, inspired by cancer dynamics in multiple myeloma (MM), where
serum M protein is a well-known proxy of tumor burden. As a working example, we
apply the model to a simulated dataset and compare its ability to predict M
protein trajectories to a model with linear covariate effects. Our results show
that the Bayesian neural network covariate effect model predicts cancer
dynamics more accurately than a linear covariate effect model when covariate
interactions are present. The framework can also be applied to other types of
cancer or other time series prediction problems that can be described with a
parametric model.


  Unpolarized 800 MeV proton inelastic scatterings from an s-d shell nucleus
$^{22}$Ne are analyzed using phenomenological optical potentials in the Dirac
coupled channel formalism. The first-order rotational collective model is used
to obtain the transition optical potentials for the low lying excited
collective states that belong to the ground state rotational band of the
nucleus. The optical potential parameters of Woods-Saxon shape and the
deformation parameters of the excited states are varied phenomenologically
using the sequential iteration method to reproduce the experimental
differential cross section data. The effective central and spin-orbit optical
potentials are obtained by reducing the Dirac equations to the
Schr\"odinger-like second-order differential equations and the surface-peaked
phenomena are observed at the real effective central potentials when the
scattering from $^{22}$Ne is considered. The obtained deformation parameters of
the excited states are compared with those of the nonrelativistic calculations
and another s-d shell nucleus $^{20}$Ne. The deformation parameters for the
$2^+$ and the $4^+$ states of the ground state rotational band at the nucleus
$^{22}$Ne are found to be smaller than those of $^{20}$Ne, indicating that the
couplings of those states to the ground state are weaker at the nucleus
$^{22}$Ne compared to those at the nucleus $^{20}$Ne. The multistep channel
coupling effect is confirmed to be important for the $4^+$ state excitation of
the ground state rotational band at the proton inelastic scattering from the
s-d shell nucleus $^{22}$Ne.


  PowerShell is a powerful and versatile task automation tool. Unfortunately,
it is also widely abused by cyber attackers. To bypass malware detection and
hinder threat analysis, attackers often employ diverse techniques to obfuscate
malicious PowerShell scripts. Existing deobfuscation tools suffer from the
limitation of static analysis, which fails to simulate the real deobfuscation
process accurately.
  In this paper, we propose PowerPeeler. To the best of our knowledge, it is
the first dynamic PowerShell script deobfuscation approach at the instruction
level. It utilizes expression-related Abstract Syntax Tree (AST) nodes to
identify potential obfuscated script pieces. Then, PowerPeeler correlates the
AST nodes with their corresponding instructions and monitors the script's
entire execution process. Subsequently, PowerPeeler dynamically tracks the
execution of these instructions and records their execution results. Finally,
PowerPeeler stringifies these results to replace the corresponding obfuscated
script pieces and reconstruct the deobfuscated script.
  To evaluate the effectiveness of PowerPeeler, we collect 1,736,669 real-world
malicious PowerShell samples with diversity obfuscation methods. We compare
PowerPeeler with five state-of-the-art deobfuscation tools and GPT-4. The
evaluation results demonstrate that PowerPeeler can effectively handle all
well-known obfuscation methods. Additionally, the deobfuscation correctness
rate of PowerPeeler reaches 95%, significantly surpassing that of other tools.
PowerPeeler not only recovers the highest amount of sensitive data but also
maintains a semantic consistency over 97%, which is also the best. Moreover,
PowerPeeler effectively obtains the largest quantity of valid deobfuscated
results within a limited time frame. Furthermore, PowerPeeler is extendable and
can be used as a helpful tool for other cyber security solutions.


  The Blanco DECam Bulge Survey (BDBS) imaged more than 200 square degrees of
the Southern Galactic bulge using the ugrizY filters of the Dark Energy Camera,
and produced point spread function photometry of approximately 250 million
unique sources. In this paper, we present details regarding the construction
and collation of survey catalogs, and also discuss the adopted calibration and
dereddening procedures. Early science results are presented with a particular
emphasis on the bulge metallicity distribution function and globular clusters.
A key result is the strong correlation (sigma ~ 0.2 dex) between (u-i)o and
[Fe/H] for bulge red clump giants. We utilized this relation to find that
interior bulge fields may be well described by simple closed box enrichment
models, but fields exterior to b ~ -6 degrees seem to require a secondary
metal-poor component. Applying scaled versions of the closed box model to the
outer bulge fields is shown to significantly reduce the strengths of any
additional metal-poor components when compared to Gaussian mixture models.
Additional results include: a confirmation that the u-band splits the sub-giant
branch in M 22 as a function of metallicity, the detection of possible
extra-tidal stars along the orbits of M 22 and FSR 1758, and additional
evidence that NGC 6569 may have a small but discrete He spread, as evidenced by
red clump luminosity variations in the reddest bands. We do not confirm
previous claims that FSR 1758 is part of a larger extended structure.


  The Renyi-Ulam game is played between two players, the Seeker and the
Obscurer. The Obscurer thinks of a number between 1 and $n$. The Seeker wishes
to identify that number. On each turn, the Seeker asks the Obscurer whether her
number belongs to a specific subset of the numbers from 1 to $n$. The Obscurer
answers either yes or no, and her answer is either true or false. The series of
truths and lies given by the Obscurer must conform to a restriction $R$ that
the players have agreed on in advance.
  We give criteria on the restrictions $R$ that allow the Seeker to win. Then
we apply our results to the study of restrictions characterized by forbidden
substrings. In particular, we give a complete classification of all such
restrictions characterized by two forbidden substrings, elaborating on
Czyzowicz, Lakshmanan and Pelc's classification of all such restrictions
characterized by one forbidden substring.


  In this paper we focus on finding high quality solutions for the problem of
maximum partitioning of graphs with supply and demand (MPGSD). There is a
growing interest for the MPGSD due to its close connection to problems
appearing in the field of electrical distribution systems, especially for the
optimization of self-adequacy of interconnected microgrids. We propose an ant
colony optimization algorithm for the problem. With the goal of further
improving the algorithm we combine it with a previously developed correction
procedure. In our computational experiments we evaluate the performance of the
proposed algorithm on both trees and general graphs. The tests show that the
method manages to find optimal solutions in more than 50% of the problem
instances, and has an average relative error of less than 0.5% when compared to
known optimal solutions.


  Chemotaxis is the process by which cells behave in a way that follows the
chemical gradient. Applications to bacteria growth, tissue inflammation, and
vascular tumors provide a focus on optimization strategies. Experiments can
characterize the form of possible chemotactic sensitivities. This paper
addresses the recovery of the chemotactic sensitivity from these experiments
while allowing for nonlinear dependence of the parameter on the state
variables. The existence of solutions to the forward problem is analyzed. The
identification of a chemotactic parameter is determined by inverse problem
techniques. Tikhonov regularization is investigated and appropriate convergence
results are obtained. Numerical results of concentration dependent chemotactic
terms are explored.


  We present the results of near continuous TESS optical observations of the
asynchronous polar CD Ind (RX J2115-5840). The 27.9 d long light curve, with 2
min resolution, reveals remarkable changes in the magnetic accretion geometry
of the system over the 7.3 d beat period. We have modelled the changes in the
optical spin period pulse shape using a cyclotron emission mapping technique.
The resulting cyclotron emission maps of the magnetic white dwarf reveal how
the accretion geometry changes from single to two pole accretion and back over
the beat cycle. Finally, we present the results from particle based numerical
magnetic accretion simulations, that agree with our interpretation of the
changing accretion scenario.


  We study the deformation and motion of an erythrocyte in fluid flows via a
lattice Boltzmann method. To this purpose, the bending rigidity and the elastic
modulus of isotropic dilation are introduced and incorporated with the lattice
Boltzmann simulation, and the membrane-flow interactions on both sides of the
membrane are carefully examined. We find that the static biconcave shape of an
erythrocyte is quite stable and can effectively resist the pathological changes
on their membrane. Further, our simulation results show that in shear flow, the
erythrocyte will be highly flattened and undergo tank tread-like motion. This
phenomenon has been observed by experiment very long time ago, but it has
feazed the boundary integral and singularity methods up to the present. Because
of its intrinsically parallel dynamics, this lattice Boltzmann method is
expected to find wide applications for both single and multi-vesicles
suspension as well as complex open membranes in various fluid flows for a wide
range of Reynolds numbers.


  By work of John Tate we can associate an epsilon factor with every
multiplicative character of a local field. In this paper we determine the
explicit signs of the epsilon factors for symplectic type characters of
$K^\times$, where $K/F$ is a wildly ramified quadratic extension of a
non-Archimedean local field $F$ of characteristic zero.


  The host-guest interaction between benzene or azine with the newly
synthesized ExBox4+ complex is studied with the help of DFT. The solvent phase
interaction energy is found to decrease with gradual substitution of methine
group of guest benzene ring with N atom in the resultant azine@ExBox4+ complex.
The nature of bonding interaction is studied with the help of newly developed
NCI plot program package along with energy decomposition analysis (EDA) and
charge decomposition analysis (CDA). The interaction is mostly pi-type van der
Waals interaction.


  We propose a compute shader based point cloud rasterizer with up to 10 times
higher performance than classic point-based rendering with the GL_POINT
primitive. In addition to that, our rasterizer offers 5 byte depth-buffer
precision with uniform or customizable distribution, and we show that it is
possible to implement a high-quality splatting method that blends together
overlapping fragments while still maintaining higher frame-rates than the
traditional approach.


  Using improved theoretical calculations of the decay form factors in the
Light Cone-QCD sum rule approach, we investigate the decay rates, dilepton
invariant mass spectra and the forward-backward (FB) asymmetry in the decays $B
\to (K,K^*) \ell^+ \ell^-$ ($\ell^\pm =e^\pm,\mu^\pm,\tau^\pm$) in the standard
model (SM) and a number of popular variants of the supersymmetric (SUSY)
models. Theoretical precision on the differential decay rates and FB-asymmetry
is estimated in these theories taking into account various parametric
uncertainties. We show that existing data on $B \to X_s \gamma$ and the
experimental upper limit on the branching ratio ${\cal B}(B \to K^* \mu^+
\mu^-)$ provide interesting bounds on the coefficients of the underlying
effective theory. We argue that the FB-asymmetry in $B \to K^* \ell^+ \ell^-$
constitutes a precision test of the SM and its measurement in forthcoming
experiments may reveal new physics. In particular, the presently allowed
large-$\tan \beta$ solutions in SUGRA models, as well as more general
flavor-violating SUSY models, yield FB-asymmetries which are characteristically
different from the corresponding ones in the SM.


  The Transition-Edge Sensors (TES) is a mature, high-resolution x-ray
spectrometer technology that provides a much higher efficiency than dispersive
spectrometers such as gratings and crystal spectrometers. As larger arrays are
developed, time-division multiplexing schemes operating at MHz frequencies are
being replaced by microwave SQUID multiplexers using frequency-division
multiplexing at GHz frequencies. However, the multiplexing factor achievable
with microwave SQUIDs is limited by the high slew rate on the leading edge of
x-ray pulses. In this paper, we propose a new multiplexing scheme for
high-slew-rate TES x-ray calorimeters: the spread-spectrum SQUID multiplexer,
which has the potential to enable higher multiplexing factors, especially in
applications with lower photon arrival rates.


  We report the results of a calculation of the response of cold neutron matter
to neutral-current interactions with low energy neutrinos, carried out using an
effective interaction and effective operators consistently derived within the
formalism of Correlated Basis Functions. The neutrino mean free path obtained
from the calculated responses turns out to be strongly affected by both short
and long range correlations, leading to a sizable increase with respect to the
prediction of the Fermi gas model. The consistency between the proposed
approach and Landau theory of normal Fermi liquids also has been investigated,
using a set of Landau parameters obtained from the matrix elements of the
effective interaction.


  In this paper, we characterize Lipschitzian properties of different
multiplier-free and multiplier-dependent perturbation mappings associated with
the stationarity system of a so-called generalized nonlinear program
popularized by Rockafellar. Special emphasis is put on the investigation of the
isolated calmness property at and around a point. The latter is decisive for
the locally fast convergence of the so-called semismooth* Newton-type method by
Gfrerer and Outrata. Our central result is the characterization of the isolated
calmness at a point of a multiplier-free perturbation mapping via a combination
of an explicit condition and a rather mild assumption, automatically satisfied
e.g. for standard nonlinear programs. Isolated calmness around a point is
characterized analogously by a combination of two stronger conditions. These
findings are then related to so-called criticality of Lagrange multipliers, as
introduced by Izmailov and extended to generalized nonlinear programming by
Mordukhovich and Sarabi. We derive a new sufficient condition (a
characterization for some problem classes) of nonexistence of critical
multipliers, which has been also used in the literature as an assumption to
guarantee local fast convergence of Newton-, SQP-, or multiplier-penalty-type
methods. The obtained insights about critical multipliers seem to complement
the vast literature on the topic.


  Drug-Target binding Affinity (DTA) prediction is essential for drug
discovery. Despite the application of deep learning methods to DTA prediction,
the achieved accuracy remain suboptimal. In this work, inspired by the recent
success of retrieval methods, we propose $k$NN-DTA, a non-parametric
embedding-based retrieval method adopted on a pre-trained DTA prediction model,
which can extend the power of the DTA model with no or negligible cost.
Different from existing methods, we introduce two neighbor aggregation ways
from both embedding space and label space that are integrated into a unified
framework. Specifically, we propose a \emph{label aggregation} with
\emph{pair-wise retrieval} and a \emph{representation aggregation} with
\emph{point-wise retrieval} of the nearest neighbors. This method executes in
the inference phase and can efficiently boost the DTA prediction performance
with no training cost. In addition, we propose an extension, Ada-$k$NN-DTA, an
instance-wise and adaptive aggregation with lightweight learning. Results on
four benchmark datasets show that $k$NN-DTA brings significant improvements,
outperforming previous state-of-the-art (SOTA) results, e.g, on BindingDB
IC$_{50}$ and $K_i$ testbeds, $k$NN-DTA obtains new records of RMSE
$\bf{0.684}$ and $\bf{0.750}$. The extended Ada-$k$NN-DTA further improves the
performance to be $\bf{0.675}$ and $\bf{0.735}$ RMSE. These results strongly
prove the effectiveness of our method. Results in other settings and
comprehensive studies/analyses also show the great potential of our $k$NN-DTA
approach.


  Let $m\ge 1$ be an integer and $G$ be a graph with $m$ edges. We say that $G$
has an antimagic orientation if $G$ has an orientation $D$ and a bijection
$\tau:A(D)\rightarrow \{1,2,\ldots,m\}$ such that no two vertices in $D$ have
the same vertex-sum under $\tau$, where the vertex-sum of a vertex $v$ in $D$
under $\tau$ is the sum of labels of all arcs entering $v$ minus the sum of
labels of all arcs leaving $v$. Hefetz, M\"{u}tze and Schwartz [J. Graph
Theory, 64: 219-232, 2010] conjectured that every connected graph admits an
antimagic orientation. The conjecture was confirmed for certain classes of
graphs such as regular graphs, graphs with minimum degree at least 33,
bipartite graphs with no vertex of degree zero or two, and trees including
caterpillars and complete $k$-ary trees. We prove that every subdivided
caterpillar admits an antimagic orientation, where a subdivided caterpillar is
a subdivision of a caterpillar $T$ such that the edges of $T$ that are not on
the central path of $T$ are subdivided the same number of times.


  We present a detailed evaluation of the bosonic two-loop electroweak
corrections to the muon's anomalous magnetic moment. We study the Higgs mass
dependence and find agreement with a previous evaluation in the large Higgs
mass limit. We find $a_{\mu}^{\rm EW bos}({two-loop})=(-22.2 \pm 1.6)\times
10^{-11}$, for $114 {\rm GeV} \le M_{\rm Higgs}\le 700 {\rm GeV}$.


  As the only eclipsing asynchronous polar (AP), V1432 Aql provides an
excellent laboratory to study the interaction between the accreted matter and
the magnetic field. However, due to its complex emission, a more physical
understanding of its accretion geometry is still outstanding. Here, we report
an X-ray spectral study using contemporaneous observations from \nustar\ and
\swift. We detect significant Compton reflection and confirm earlier reports of
a high soft X-ray temperature $\sim52$ keV. We suggest that the
multi-temperature emission is due to a distribution of the specific accretion
rate over the accretion region, which leads to a continuous temperature
distribution over the heated area and explains the high temperature of the soft
X-rays. We interpret these characteristics as the results of the inefficient
accretion in APs. Thus the accretion stream can punch deeply into the
magnetosphere and feed the white dwarf (WD) over a much narrower accretion
region near its equator. Additionally, the broad-band X-rays provide details of
the accretion; the low total accretion rate of $\sim 1 \times 10^{-10}
~M_{\odot} ~yr^{-1}$ contradicts the speculation that V1432 Aql was a recent
nova, while the high specific accretion rate of $\sim 5.6 ~g ~cm^{-2} ~s^{-1}$
explains the significant reflection from the surface of the WD.


  This paper concerns the linear Landau damping for the two species
Vlasov-Poisson system for ions and electrons near Penrose stable equilibria.
The result is an extension of the result on the one species Vlasov-Poisson
equation by Mouhout and Villani. Different from their work we do not describe
the ions as a background species but as a species which is also described by a
separate Vlasov equation. We show an exponential decay of the electric energy
for the linearised system near Penrose stable equilibria.


  Despite improvements to the generalization performance of automated speech
recognition (ASR) models, specializing ASR models for downstream tasks remains
a challenging task, primarily due to reduced data availability (necessitating
increased data collection), and rapidly shifting data distributions (requiring
more frequent model fine-tuning). In this work, we investigate the potential of
leveraging external knowledge, particularly through off-policy key-value stores
generated with text-to-speech methods, to allow for flexible post-training
adaptation to new data distributions. In our approach, audio embeddings
captured from text-to-speech, along with semantic text embeddings, are used to
bias ASR via an approximate k-nearest-neighbor (KNN) based attentive fusion
step. Our experiments on LibiriSpeech and in-house voice assistant/search
datasets show that the proposed approach can reduce domain adaptation time by
up to 1K GPU-hours while providing up to 3% WER improvement compared to a
fine-tuning baseline, suggesting a promising approach for adapting production
ASR systems in challenging zero and few-shot scenarios.


  In this paper we consider the long time behavior of the weakly damped, forced
Korteweg-de Vries equation in the Sololev spaces of the negative indices in the
periodic case. We prove that the solutions are uniformly bounded in
$\dot{H}^s(\T)$ for $s>-\dfrac{1}{2}$. Moreover, we show that the solution-map
possesses a global attractor in $\dot{H}^s(\T)$ for $s>-\dfrac{1}{2}$, which is
a compact set in $H^{s+3}(\T)$.


  Recently the vacuum structure of a large class of four dimensional
(supersymmetric) quantum field theories was determined exactly. These theories
exhibit a wide range of interesting new physical phenomena. One of the main new
insights is the role of ``electric-magnetic duality.'' In its simplest form it
describes the long distance behavior of some strongly coupled, and hence
complicated, ``electric theories'' in terms of weakly coupled ``magnetic
theories.'' This understanding sheds new light on confinement and the Higgs
mechanism and uncovers new phases of four dimensional gauge theories. We review
these developments and speculate on the outlook.


  A design of a magnonic phase shifter operating without an external bias
magnetic field is proposed. The phase shifter uses a localized collective spin
wave mode propagating along a domain wall "waveguide" in a dipolarly-coupled
magnetic dot array existing in a chessboard antiferromagnetic (CAFM) ground
state. It is demonstrated numerically that remagnetization of a single magnetic
dot adjacent to the domain wall waveguide introduces a controllable phase shift
in the propagating spin wave mode without significant change of the mode
amplitude. It is also demonstrated that a logic XOR gate can be realized in the
same system.


  Gaussian periods have been studied for centuries in the realms of number
theory, field theory, cryptography, and elsewhere. However, it was only within
the last decade or so that they began to be studied from a visual perspective.
By plotting Gaussian periods in the complex plane, various interesting and
insightful patterns emerge, leading to various conjectures and theorems about
their properties. In this paper, we offer a description of Gaussian periods,
along with examples of the structure that can occur when plotting them in the
complex plane. In addition to this, we offer two ways in which this study can
be generalized to other situations -- one relating to supercharacter theory,
the other relating to class field theory -- along with discussions and visual
examples of each. We end the paper by including some code for readers to
generate images on their own.


  Using equivariant localization formulas we give a formula for conformal
blocks at level one on the sphere as suitable polynomials. Using this
presentation we give a generating set in the space of conformal blocks at any
level if the marked points on the sphere are generic.


  The shortest distance between the first $n$ iterates of a typical point can
be quantified with a log rule for some dynamical systems admitting Gibbs
measures. We show this in two settings. For topologically mixing Markov shifts
with at most countably infinite alphabet admitting a Gibbs measure with respect
to a locally H\"{o}lder potential, we prove the asymptotic length of the
longest common substring for a typical point converges and the limit depends on
the R\'{e}nyi entropy. For interval maps with the Gibbs-Markov structure, we
prove a similar rule relating the correlation dimension of Gibbs measures with
the shortest distance between two iterates in the orbit generated by a typical
point.


  We analyze a general problem in a crowd-sourced setting where one user asks a
question (also called item) and other users return answers (also called labels)
for this question. Different from existing crowd sourcing work which focuses on
finding the most appropriate label for the question (the "truth"), our problem
is to determine a ranking of the users based on their ability to answer
questions. We call this problem "ability discovery" to emphasize the connection
to and duality with the more well-studied problem of "truth discovery".
  To model items and their labels in a principled way, we draw upon Item
Response Theory (IRT) which is the widely accepted theory behind standardized
tests such as SAT and GRE. We start from an idealized setting where the
relative performance of users is consistent across items and better users
choose better fitting labels for each item. We posit that a principled
algorithmic solution to our more general problem should solve this ideal
setting correctly and observe that the response matrices in this setting obey
the Consecutive Ones Property (C1P). While C1P is well understood
algorithmically with various discrete algorithms, we devise a novel variant of
the HITS algorithm which we call "HITSNDIFFS" (or HND), and prove that it can
recover the ideal C1P-permutation in case it exists. Unlike fast combinatorial
algorithms for finding the consecutive ones permutation (if it exists), HND
also returns an ordering when such a permutation does not exist. Thus it
provides a principled heuristic for our problem that is guaranteed to return
the correct answer in the ideal setting. Our experiments show that HND produces
user rankings with robustly high accuracy compared to state-of-the-art truth
discovery methods. We also show that our novel variant of HITS scales better in
the number of users than ABH, the only prior spectral C1P reconstruction
algorithm.


  Although the multi-jointed underactuated manipulator is highly dexterous, its
grasping capacity does not match that of the parallel jaw gripper. This work
introduces a fractal gripper to enhance the grasping capacity of multi-joint
underactuated manipulators, preserving their passive clamping features. We
describe in detail the working principle and manufacturing process of the
fractal gripper. This work, inspired by the 'Fractal Vise' structure, resulted
in the invention of a fractal gripper with mode switching capabilities. The
fractal gripper inherits the inherent adaptive properties of the fractal
structure and realizes the self-resetting function by integrating spring into
the original design, thereby enhancing the efficiency of object grasping tasks.
The fractal gripper prevents object damage by distributing pressure evenly and
applying it at multiple points through its fractal structure during closure.
Objects of various shapes are effectively grasped by the fractal gripper, which
ensures a safe and secure grasp. The superior performance was provided by the
force distribution characteristics of the fractal gripper. By applying the
flexible polymer PDMS, which possesses superior elasticity, to the fractal
structure's wrapping surface, potential scratching during grasping is
effectively prevented, thus protecting the object's geometric surface. Grab
experiments with objects of diverse shapes and sizes confirm fractal gripper
multi-scale adaptability and superior grasping stability.


  The past decade has seen increasing interest in applying Deep Learning (DL)
to Computational Science and Engineering (CSE). Driven by impressive results in
applications such as computer vision, Uncertainty Quantification (UQ),
genetics, simulations and image processing, DL is increasingly supplanting
classical algorithms, and seems poised to revolutionize scientific computing.
However, DL is not yet well-understood from the standpoint of numerical
analysis. Little is known about the efficiency and reliability of DL from the
perspectives of stability, robustness, accuracy, and sample complexity. In
particular, approximating solutions to parametric PDEs is an objective of UQ
for CSE. Training data for such problems is often scarce and corrupted by
errors. Moreover, the target function is a possibly infinite-dimensional smooth
function taking values in the PDE solution space, generally an
infinite-dimensional Banach space. This paper provides arguments for Deep
Neural Network (DNN) approximation of such functions, with both known and
unknown parametric dependence, that overcome the curse of dimensionality. We
establish practical existence theorems that describe classes of DNNs with
dimension-independent architecture size and training procedures based on
minimizing the (regularized) $\ell^2$-loss which achieve near-optimal algebraic
rates of convergence. These results involve key extensions of compressed
sensing for Banach-valued recovery and polynomial emulation with DNNs. When
approximating solutions of parametric PDEs, our results account for all sources
of error, i.e., sampling, optimization, approximation and physical
discretization, and allow for training high-fidelity DNN approximations from
coarse-grained sample data. Our theoretical results fall into the category of
non-intrusive methods, providing a theoretical alternative to classical methods
for high-dimensional approximation.


  The boson sampling problem has triggered a lot of interest in the scientific
community because of its potential of demonstrating the computational power of
quantum interference without the need of non-linear processes. However, the
intractability of such a problem with any classical device relies on the
realization of single photons approximately identical in their spectra. In this
paper we discuss the physics of boson sampling with non-identical single photon
sources, which is strongly relevant in view of scalable experimental
realizations and triggers fascinating questions in complexity theory.


  A large cylindrical cadmium molybdate crystal with natural isotopic abundance
has been used to fabricate a scintillating bolometer. The measurement was
performed above ground at milli-Kelvin temperature, with simultaneous readout
of the heat and the scintillation light. The energy resolution as FWHM has
achieved from 5 keV (at 238 keV) to 13 keV (at 2615 keV). We present the
results of the $\alpha$ versus $\beta$/$\gamma$ events discrimination. The low
internal trace contamination of the $\mathrm{CdMoO_4}$ crystal was evaluated as
well. The detector performance with preliminary positive indications proves
that cadmium molybdate crystal is a promising absorber for neutrinoless double
beta decay scintillating bolometric experiments with $\mathrm{{}^{116}Cd}$ and
$\mathrm{{}^{100}Mo}$ nuclides in the next-generation technique.


  We establish a new $W^{1,2\frac{n-1}{n-2}}$ estimate for the extremal
solution of $-\Delta u=\lambda f(u)$ in a smooth bounded domain $\Omega$ of
$\mathbb{R}^n$, which is convex, for arbitrary positive and increasing
nonlinearities $f\in C^1(\mathbb{R})$ satisfying $\lim_{t\rightarrow
+\infty}f(t)/t=+\infty$.


  In this paper, we propose a method to develop trustworthy reinforcement
learning systems. To ensure safety especially during exploration, we
automatically synthesize a correct-by-construction runtime enforcer, called a
shield, that blocks all actions that are unsafe with respect to a temporal
logic specification from the agent. Our main contribution is a new synthesis
algorithm for computing the shield online. Existing offline shielding
approaches compute exhaustively the safety of all states-action combinations
ahead-of-time, resulting in huge offline computation times, large memory
consumption, and significant delays at run-time due to the look-ups in a huge
database. The intuition behind online shielding is to compute during run-time
the set of all states that could be reached in the near future. For each of
these states, the safety of all available actions is analysed and used for
shielding as soon as one of the considered states is reached. Our proposed
method is general and can be applied to a wide range of planning problems with
stochastic behavior. For our evaluation, we selected a 2-player version of the
classical computer game SNAKE. The game requires fast decisions and the
multiplayer setting induces a large state space, computationally expensive to
analyze exhaustively. The safety objective of collision avoidance is easily
transferable to a variety of planning tasks.


  Measuring individual productivity (or equivalently distributing the overall
productivity) in a network structure of workers displaying peer effects has
been a subject of ongoing interest in many areas ranging from academia to
industry. In this paper, we propose a novel approach based on cooperative game
theory that takes into account the peer effects of worker productivity
represented by a complete bipartite network of interactions. More specifically,
we construct a series of cooperative games where the characteristic function of
each coalition of workers is equal to the sum of each worker intrinsic
productivity as well as the productivity of other workers within a distance
discounted by an attenuation factor. We show that these (truncated) games are
balanced and converge to a balanced game when the distance of influence grows
large. We then provide an explicit formula for the Shapley value and propose an
alternative coalitionally stable distribution of productivity which is
computationally much more tractable than the Shapley value. Lastly, we
characterize this alternative distribution based on three sensible properties
of a logistic network. This analysis enhances our understanding of
game-theoretic analysis within logistics networks, offering valuable insights
into the peer effects' impact when assessing the overall productivity and its
distribution among workers.


  We present an algorithm for active learning of deterministic timed automata
with multiple clocks. The algorithm is within the querying framework of
Angluin's $L^*$ algorithm and follows the idea proposed in existing work on the
active learning of deterministic one-clock timed automata. We introduce an
equivalence relation over the reset-clocked language of a timed automaton and
then transform the learning problem into learning the corresponding
reset-clocked language of the target automaton. Since a reset-clocked language
includes the clock reset information which is not observable, we first present
the approach of learning from a powerful teacher who can provide reset
information by answering reset information queries from the learner. Then we
extend the algorithm in a normal teacher situation in which the learner can
only ask standard membership query and equivalence query while the learner
guesses the reset information. We prove that the learning algorithm terminates
and returns a correct deterministic timed automaton. Due to the need of
guessing whether the clocks reset at the transitions, the algorithm is of
exponential complexity in the size of the target automaton.


  Estimating clinically-relevant vascular features following vessel
segmentation is a standard pipeline for retinal vessel analysis, which provides
potential ocular biomarkers for both ophthalmic disease and systemic disease.
In this work, we integrate these clinical features into a novel vascular
feature optimised loss function (VAFO-Loss), in order to regularise networks to
produce segmentation maps, with which more accurate vascular features can be
derived. Two common vascular features, vessel density and fractal dimension,
are identified to be sensitive to intra-segment misclassification, which is a
well-recognised problem in multi-class artery/vein segmentation particularly
hindering the estimation of these vascular features. Thus we encode these two
features into VAFO-Loss. We first show that incorporating our end-to-end
VAFO-Loss in standard segmentation networks indeed improves vascular feature
estimation, yielding quantitative improvement in stroke incidence prediction, a
clinical downstream task. We also report a technically interesting finding that
the trained segmentation network, albeit biased by the feature optimised loss
VAFO-Loss, shows statistically significant improvement in segmentation metrics,
compared to those trained with other state-of-the-art segmentation losses.