  Emission line galaxies (ELGs), more generally star-forming galaxies, are
valuable tracers of large-scale structure and therefore main targets of
upcoming wide-area spectroscopic galaxy surveys. We propose a fixed-aperture
shape estimator of each ELG for extracting the intrinsic alignment (IA) signal,
and assess the performance of the method using image simulations of ELGs
generated from the IllustrisTNG simulation including observational effects such
as the sky background noise. We show that our method enables a significant
detection of the IA power spectrum with the linear-scale coefficient $A_{\rm
IA}\simeq (13$--$15)\pm 3.0$ up to $z=2$, even from the small simulation volume
$\sim0.009\,(h^{-1}{\rm Gpc})^3$, in contrast to the null detection with the
standard method. Thus the ELG IA signal, measured with our method, opens up
opportunities to exploit cosmology and galaxy physics in high-redshift
universe.


  It is shown that if $A$ is a uniform algebra generated by real-analytic
functions on a suitable compact subset $K$ of a real-analytic variety such that
the maximal ideal space of $A$ is $K$, and every continuous function on $K$ is
locally a uniform limit of functions in $A$, then $A=C(K)$. This gives an
affirmative answer to a special case of a question from the Proceedings of the
Symposium on Function Algebras held at Tulane University in 1965.


  Robust environment perception is essential for decision-making on robots
operating in complex domains. Intelligent task execution requires principled
treatment of uncertainty sources in a robot's observation model. This is
important not only for low-level observations (e.g., accelerometer data), but
also for high-level observations such as semantic object labels. This paper
formalizes the concept of macro-observations in Decentralized Partially
Observable Semi-Markov Decision Processes (Dec-POSMDPs), allowing scalable
semantic-level multi-robot decision making. A hierarchical Bayesian approach is
used to model noise statistics of low-level classifier outputs, while
simultaneously allowing sharing of domain noise characteristics between
classes. Classification accuracy of the proposed macro-observation scheme,
called Hierarchical Bayesian Noise Inference (HBNI), is shown to exceed
existing methods. The macro-observation scheme is then integrated into a
Dec-POSMDP planner, with hardware experiments running onboard a team of dynamic
quadrotors in a challenging domain where noise-agnostic filtering fails. To the
best of our knowledge, this is the first demonstration of a real-time,
convolutional neural net-based classification framework running fully onboard a
team of quadrotors in a multi-robot decision-making domain.


  The Eisenbud-Green-Harris conjecture states that a homogeneous ideal in
k[x_1,...,x_n] containing a homogeneous regular sequence f_1,...,f_n with
deg(f_i)=a_i has the same Hilbert function as an ideal containing x_i^{a_i} for
1 \leq i \leq n. In this paper we prove the Eisenbud-Green-Harris conjecture
when a_j> sum_{i=1}^{j-1} (a_i-1) for all j>1. This result was independently
obtained by the two authors.


  We present the HI content of galaxies in nearby groups and clusters as
measured by the 70% complete Arecibo Legacy Fast-ALFA (ALFALFA) survey,
including constraints from ALFALFA detection limits. Our sample includes 22
systems at distances between 70-160 Mpc over the mass range 12.5<log
M/M_sun<15.0, for a total of 1986 late-type galaxies. We find that late-type
galaxies in the centers of groups lack HI at fixed stellar mass relative to the
regions surrounding them. Larger groups show evidence of a stronger dependence
of HI properties on environment, despite a similar dependence of color on
environment at fixed stellar mass. We compare several environment variables to
determine which is the best predictor of galaxy properties; group-centric
distance r and r/R_200 are similarly effective predictors, while local density
is slightly more effective and group size and halo mass are slightly less
effective. While both central and satellite galaxies in the blue cloud exhibit
a significant dependence of HI content on local density, only centrals show a
strong dependence on stellar mass, and only satellites show a strong dependence
on halo mass. Finally, we see evidence that HI is deficient for blue cloud
galaxies in denser environments even when both stellar mass and color are
fixed. This is consistent with a picture where HI is removed or destroyed,
followed by reddening within the blue cloud. Our results support the existence
of pre-processing in isolated groups, along with an additional rapid mechanism
for gas removal within larger groups and clusters, perhaps ram-pressure
stripping.


  The crossing resolution of a non-planar drawing of a graph is the value of
the minimum angle formed by any pair of crossing edges. Recent experiments have
shown that the larger the crossing resolution is, the easier it is to read and
interpret a drawing of a graph. However, maximizing the crossing resolution
turns out to be an NP-hard problem in general and only heuristic algorithms are
known that are mainly based on appropriately adjusting force-directed
algorithms.
  In this paper, we propose a new heuristic algorithm for the crossing
resolution maximization problem and we experimentally compare it against the
known approaches from the literature. Our experimental evaluation indicates
that the new heuristic produces drawings with better crossing resolution, but
this comes at the cost of slightly higher aspect ratio, especially when the
input graph is large.


  The Narrow Line Seyfert 1 Galaxy 1H0707-495 went in to a low state from 2010
December to 2011 February, discovered by a monitoring campaign using the X-Ray
Telescope on the Swift satellite. We triggered a 100 ks XMM-Newton observation
of the source in 2011 January, revealing the source to have dropped by a factor
of ten in the soft band, below 1 keV, and a factor of 2 at 5 keV, compared with
a long observation in 2008. The sharp spectral drop in the source usually seen
around 7 keV now extends to lower energies, below 6 keV in our frame. The 2011
spectrum is well fit by a relativistically-blurred reflection spectrum similar
to that which fits the 2008 data, except that the emission is now concentrated
solely to the central part of the accretion disc. The irradiating source must
lie within 1 gravitational radius of the event horizon of the black hole, which
spins rapidly. Alternative models are briefly considered but none has any
simple physical interpretation.


  A semiconductor transmon with an epitaxial Al shell fully surrounding an InAs
nanowire core is investigated in the low $E_J/E_C$ regime. Little-Parks
oscillations as a function of flux along the hybrid wire axis are destructive,
creating lobes of reentrant superconductivity separated by a metallic state at
a half-quantum of applied flux. In the first lobe, phase winding around the
shell can induce topological superconductivity in the core. Coherent qubit
operation is observed in both the zeroth and first lobes. Splitting of parity
bands by coherent single-electron coupling across the junction is not resolved
beyond line broadening, placing a bound on Majorana coupling, $E_M/h$ < 10 MHz,
much smaller than the Josephson coupling $E_J/h$ ~ 4.7 GHz.


  We show that the notion of asymptotic lift generalizes naturally to normal
positive maps $\phi$ acting on von Neumann algebras M.
  We focus on cases in which the domain of the asymptotic lift can be embedded
as an operator subsystem of M, and characterize when that subsystem is a Jordan
subalgebra of M in terms of the asymptotic multiplicative properties of $\phi$.


  In this paper, we define new unfitted finite element methods for numerically
approximating the solution of surface partial differential equations using bulk
finite elements. The key idea is that the $n$-dimensional hypersurface, $\Gamma
\subset \mathbb{R}^{n+1}$, is embedded in a polyhedral domain in $\mathbb
R^{n+1}$ consisting of a union, $\mathcal{T}_h$, of $(n+1)$-simplices. The
finite element approximating space is based on continuous piece-wise linear
finite element functions on $\mathcal{T}_h$. Our first method is a sharp
interface method, \emph{SIF}, which uses the bulk finite element space in an
approximating weak formulation obtained from integration on a polygonal
approximation, $\Gamma_{h}$, of $\Gamma$. The full gradient is used rather than
the projected tangential gradient and it is this which distinguishes \emph{SIF}
from the method of [42]. The second method, \emph{NBM}, is a narrow band method
in which the region of integration is a narrow band of width $O(h)$. \emph{NBM}
is similar to the method of [13]. but again the full gradient is used in the
discrete weak formulation. The a priori error analysis in this paper shows that
the methods are of optimal order in the surface $L^{2}$ and $H^{1}$ norms and
have the advantage that the normal derivative of the discrete solution is small
and converges to zero. Our third method combines bulk finite elements, discrete
sharp interfaces and narrow bands in order to give an unfitted finite element
method for parabolic equations on evolving surfaces. We show that our method is
conservative so that it preserves mass in the case of an advection diffusion
conservation law. Numerical results are given which illustrate the rates of
convergence.


  This paper describes a practical model for the polarization of Be stars which
can be used to estimate roughly the physical parameters for optically thin
circumstellar envelopes from broadband UBVRI photopolarimetry data. Analysis of
long-term variability in terms of these parameters is a promising approach
toward understanding the Be phenomenon. An interesting result from fitting the
model to observations of eight Be stars is that all of them may have
geometrically thin disks, with opening half-angles on the order of ten degrees
or less. This contributes to the growing evidence that most Be disks are
geometrically thin.


  We investigate the liquid-vapor interface of the restricted primitive model
(RPM) for an ionic fluid using a density-functional approximation based on
correlation functions of the homogeneous fluid as obtained from the
mean-spherical approximation (MSA). In the limit of a homogeneous fluid our
approach yields the well-known MSA (energy) equation of state. The ionic
interfacial density profiles, which for the RPM are identical for both species,
have a shape similar to those of simple atomic fluids in that the decay towards
the bulk values is more rapid on the vapor side than on the liquid side. This
is the opposite asymmetry of the decay to that found in earlier calculations
for the RPM based on a square-gradient theory. The width of the interface is,
for a wide range of temperatures, approximately four times the second moment
correlation length of the liquid phase. We discuss the magnitude and
temperature dependence of the surface tension, and argue that for temperatures
near the triple point the ratio of the dimensionless surface tension and
critical temperature is much smaller for the RPM than for simple atomic fluids.


  In recent years, thanks to the continuous surveys performed by INTEGRAL and
Swift satellites, our knowledge of the hard X-ray/soft gamma-ray sky has
greatly improved. As a result it is now populated with about 2000 sources, both
Galactic and extra-galactic, mainly discovered by IBIS and BAT instruments.
Many different follow-up campaigns have been successfully performed by using a
multi-wavelength approach, shedding light on the nature of a number of these
new hard X-ray sources. However, a fraction are still of a unidentified nature.
This is mainly due to the lack of lower energy observations, which usually
deliver a better constrained position for the sources, and the unavailability
of the key observational properties, needed to obtain a proper physical
characterization. Here we report on the classification of two poorly studied
Galactic X-ray transients IGR J20155+3827 and Swift J1713.4-4219, for which the
combination of new and/or archival X-ray and Optical/NIR observations have
allowed us to pinpoint their nature. In particular, thanks to XMM\Newton
archival data together with new optical spectroscopic and archival Optical/NIR
photometric observations, we have been able to classify IGR J20155+3827 as a
distant HMXB. The new INTEGRAL and Swift data collected during the 2019 X-ray
outburst of Swift J1713.4-4219, in combination with the archival optical/NIR
observations, suggest a LMXB classification for this source.


  A fuzzy controller is usually designed by formulating the knowledge of a
human expert into a set of linguistic variables and fuzzy rules. Among the most
successful methods to automate the fuzzy controllers development process are
evolutionary algorithms. In this work, we propose the Recurrent Fuzzy Voronoi
(RFV) model, a representation for recurrent fuzzy systems. It is an extension
of the FV model proposed by Kavka and Schoenauer that extends the application
domain to include temporal problems. The FV model is a representation for fuzzy
controllers based on Voronoi diagrams that can represent fuzzy systems with
synergistic rules, fulfilling the $\epsilon$-completeness property and
providing a simple way to introduce a priory knowledge. In the proposed
representation, the temporal relations are embedded by including internal units
that provide feedback by connecting outputs to inputs. These internal units act
as memory elements. In the RFV model, the semantic of the internal units can be
specified together with the a priori rules. The geometric interpretation of the
rules allows the use of geometric variational operators during the evolution.
The representation and the algorithms are validated in two problems in the area
of system identification and evolutionary robotics.


  Geometrical Computation as a new model of computation is the counterpart of
Cellular Automata that has Turing computing ability. In this paper we provide
an algorithm to simulate Alternating Turing Machine in the context of Signal
Machine using techniques adopted from the features of Signal Machine to set up
and manage the copies/branches of Alternating Turing Machine. We show that our
algorithm can simulate Alternating Turing Machine in Signal Machine as same
functionality as classic family of Turing Machines. Time complexity of the
algorithm is linear as ordinary simulated Turing Machines. Depending on the
computation tree space complexity is exponential order of d, where d is the
depth of the computation tree.


  The key of sequential recommendation lies in the accurate item correlation
modeling. Previous models infer such information based on item co-occurrences,
which may fail to capture the real causal relations, and impact the
recommendation performance and explainability. In this paper, we equip
sequential recommendation with a novel causal discovery module to capture
causalities among user behaviors. Our general idea is firstly assuming a causal
graph underlying item correlations, and then we learn the causal graph jointly
with the sequential recommender model by fitting the real user behavior data.
More specifically, in order to satisfy the causality requirement, the causal
graph is regularized by a differentiable directed acyclic constraint.
Considering that the number of items in recommender systems can be very large,
we represent different items with a unified set of latent clusters, and the
causal graph is defined on the cluster level, which enhances the model
scalability and robustness. In addition, we provide theoretical analysis on the
identifiability of the learned causal graph. To the best of our knowledge, this
paper makes a first step towards combining sequential recommendation with
causal discovery. For evaluating the recommendation performance, we implement
our framework with different neural sequential architectures, and compare them
with many state-of-the-art methods based on real-world datasets. Empirical
studies manifest that our model can on average improve the performance by about
7% and 11% on f1 and NDCG, respectively. To evaluate the model explainability,
we build a new dataset with human labeled explanations for both quantitative
and qualitative analysis.


  The variability of quasars across multiple wavelengths is a useful probe of
physical conditions in active galactic nuclei. In particular, variable
accretion rates, instabilities, and reverberation effects in the accretion disk
of a supermassive black hole (SMBH) are expected to produce correlated flux
variations in UV and optical bands. Recent work has further argued that binary
quasars should exhibit strongly correlated UV and optical periodicities. Strong
UV-optical correlations have indeed been established in small samples of up to
approximately 30 quasars with well-sampled light curves, and have extended the
"bluer-when-brighter" trend previously found within the optical bands. Here we
further test the nature of quasar variability by examining the observed-frame
UV-optical correlations in a large sample of 1,315 bright quasars with
overlapping UV and optical light curves for the Galaxy Evolution Explorer
(GALEX) and the Catalina Real-time Transient Survey (CRTS), respectively. We
find that strong correlations exist in this much larger sample, but we rule
out, at approximately 95% confidence, the simple hypothesis that the intrinsic
UV and optical variations of all quasars are fully correlated. Our results
therefore imply the existence of physical mechanism(s) that can generate
uncorrelated optical and UV flux variations.


  Black holes are believed to have the fast scrambling properties of random
matrices. If the fuzzball proposal is to be a viable model for quantum black
holes, it should reproduce this expectation. This is considered challenging,
because it is natural for the modes on a fuzzball microstate to follow Poisson
statistics. In a previous paper, we noted a potential loophole here, thanks to
the modes depending not just on the $n$-quantum number, but also on the
$J$-quantum numbers of the compact dimensions. For a free scalar field $\phi$,
by imposing a Dirichlet boundary condition $\phi=0$ at the stretched horizon,
we showed that this $J$-dependence leads to a linear ramp in the Spectral Form
Factor (SFF). Despite this, the status of level repulsion remained mysterious.
In this letter, motivated by the profile functions of BPS fuzzballs, we
consider a generic profile $\phi = \phi_0(\theta)$ instead of $\phi=0$ at the
stretched horizon. For various notions of genericity (eg. when the Fourier
coefficients of $\phi_0(\theta)$ are suitably Gaussian distributed), we find
that the $J$-dependence of the spectrum exhibits striking evidence of level
repulsion, along with the linear ramp. We also find that varying the profile
leads to natural interpolations between Poisson and Wigner-Dyson(WD)-like
spectra. The linear ramp in our previous work can be understood as arising via
an extreme version of level repulsion in such a limiting spectrum. We also
explain how the stretched horizon/fuzzball is different in these aspects from
simply putting a cut-off in flat space or AdS (ie., without a horizon).


  This paper studies the problem of extracting planar regions in uneven
terrains from unordered point cloud measurements. Such a problem is critical in
various robotic applications such as robotic perceptive locomotion. While
existing approaches have shown promising results in effectively extracting
planar regions from the environment, they often suffer from issues such as low
computational efficiency or loss of resolution. To address these issues, we
propose a multi-resolution planar region extraction strategy in this paper that
balances the accuracy in boundaries and computational efficiency. Our method
begins with a pointwise classification preprocessing module, which categorizes
all sampled points according to their local geometric properties to facilitate
multi-resolution segmentation. Subsequently, we arrange the categorized points
using an octree, followed by an in-depth analysis of nodes to finish
multi-resolution plane segmentation. The efficiency and robustness of the
proposed approach are verified via synthetic and real-world experiments,
demonstrating our method's ability to generalize effectively across various
uneven terrains while maintaining real-time performance, achieving frame rates
exceeding 35 FPS.


  We study the Inexact Langevin Dynamics (ILD), Inexact Langevin Algorithm
(ILA), and Score-based Generative Modeling (SGM) when utilizing estimated score
functions for sampling. Our focus lies in establishing stable biased
convergence guarantees in terms of the Kullback-Leibler (KL) divergence. To
achieve these guarantees, we impose two key assumptions: 1) the target
distribution satisfies the log-Sobolev inequality (LSI), and 2) the score
estimator exhibits a bounded Moment Generating Function (MGF) error. Notably,
the MGF error assumption we adopt is more lenient compared to the $L^\infty$
error assumption used in existing literature. However, it is stronger than the
$L^2$ error assumption utilized in recent works, which often leads to unstable
bounds. We explore the question of how to obtain a provably accurate score
estimator that satisfies the MGF error assumption. Specifically, we demonstrate
that a simple estimator based on kernel density estimation fulfills the MGF
error assumption for sub-Gaussian target distribution, at the population level.


Invited contribution to Annalen der Physik (Expert Opinion).


  Economic interactions often occur in networks where heterogeneous agents
(such as workers or firms) sort and produce. However, most existing estimation
approaches either require the network to be dense, which is at odds with many
empirical networks, or they require restricting the form of heterogeneity and
the network formation process. We show how the functional differencing approach
introduced by Bonhomme (2012) in the context of panel data, can be applied in
network settings to derive moment restrictions on model parameters and average
effects. Those restrictions are valid irrespective of the form of
heterogeneity, and they hold in both dense and sparse networks. We illustrate
the analysis with linear and nonlinear models of matched employer-employee
data, in the spirit of the model introduced by Abowd, Kramarz, and Margolis
(1999).


  Adapting the Diffusion Probabilistic Model (DPM) for direct image
super-resolution is wasteful, given that a simple Convolutional Neural Network
(CNN) can recover the main low-frequency content. Therefore, we present
ResDiff, a novel Diffusion Probabilistic Model based on Residual structure for
Single Image Super-Resolution (SISR). ResDiff utilizes a combination of a CNN,
which restores primary low-frequency components, and a DPM, which predicts the
residual between the ground-truth image and the CNN predicted image. In
contrast to the common diffusion-based methods that directly use LR images to
guide the noise towards HR space, ResDiff utilizes the CNN's initial prediction
to direct the noise towards the residual space between HR space and
CNN-predicted space, which not only accelerates the generation process but also
acquires superior sample quality. Additionally, a frequency-domain-based loss
function for CNN is introduced to facilitate its restoration, and a
frequency-domain guided diffusion is designed for DPM on behalf of predicting
high-frequency details. The extensive experiments on multiple benchmark
datasets demonstrate that ResDiff outperforms previous diffusion based methods
in terms of shorter model convergence time, superior generation quality, and
more diverse samples.


  Let G be a finite group. By Riemann's Existence Theorem, braid orbits of
generating systems of G with product 1 correspond to irreducible families of
covers of the Riemann sphere with monodromy group G. Thus many problems on
algebraic curves require the computation of braid orbits. In this paper we
describe an implementation of this computation. We discuss several
applications, including the classification of irreducible families of
indecomposable rational functions with exceptional monodromy group.


  We construct examples of ergodic vertical flows in periodic configurations of
Eaton lenses of fixed radius. We achieve this by studying a family of infinite
translation surfaces that are $\mathbb{Z}^2$-covers of slit tori. We show that
the Hausdorff dimension of lattices for which the vertical flow is ergodic is
bigger than 3/2. Moreover, the lattices are explicitly constructed.


  Timely stress detection is crucial for protecting vulnerable groups from
long-term detrimental effects by enabling early intervention. Wearable devices,
by collecting real-time physiological signals, offer a solution for accurate
stress detection accommodating individual differences. This position paper
introduces an adaptive framework for personalized stress detection using PPG
and EDA signals. Unlike traditional methods that rely on a generalized model,
which may suffer performance drops when applied to new users due to domain
shifts, this framework aims to provide each user with a personalized model for
higher stress detection accuracy. The framework involves three stages:
developing a generalized model offline with an initial dataset, adapting the
model to the user's unlabeled data, and fine-tuning it with a small set of
labeled data obtained through user interaction. This approach not only offers a
foundation for mobile applications that provide personalized stress detection
and intervention but also has the potential to address a wider range of mental
health issues beyond stress detection using physiological signals.


  We study the structure and evolution of dark matter halos from z = 300 to z =
6 for two cosmological N-body simulation initialization techniques. While the
second order Lagrangian perturbation theory (2LPT) and the Zel'dovich
approximation (ZA) both produce accurate present day halo mass functions,
earlier collapse of dense regions in 2LPT can result in larger mass halos at
high redshift. We explore the differences in dark matter halo mass and
concentration due to initialization method through three 2LPT and three ZA
initialized cosmological simulations. We find that 2LPT induces more rapid halo
growth, resulting in more massive halos compared to ZA. This effect is most
pronounced for high mass halos and at high redshift. Halo concentration is, on
average, largely similar between 2LPT and ZA, but retains differences when
viewed as a function of halo mass. For both mass and concentration, the
difference between typical individual halos can be very large, highlighting the
shortcomings of ZA-initialized simulations for high-z halo population studies.


  This paper has been withdrawn by the author due to a crucial sign error in
equation 1


  We generalize the decomposition theorem for perverse sheaves to Artin stacks
with affine stabilizers over finite fields.


  The b-chromatic number of a graph $G$, denoted by $\phi(G)$, is the largest
integer $k$ that $G$ admits a proper $k$-coloring such that each color class
has a vertex that is adjacent to at least one vertex in each of the other color
classes. We prove that for each $d$-regular graph $G$ which contains no
4-cycle, $\phi(G)\geq\lfloor\frac{d+3}{2}\rfloor$ and if $G$ has a triangle,
then $\phi(G)\geq\lfloor\frac{d+4}{2}\rfloor$. Also, if $G$ is a $d$-regular
graph which contains no 4-cycle and $diam(G)\geq6$, then $\phi(G)=d+1$.
Finally, we show that for any $d$-regular graph $G$ which does not contain
4-cycle and $\kappa(G)\leq\frac{d+1}{2}$, $\phi(G)=d+1$.


  We present a Karchmer-Wigderson game to study the complexity of hazard-free
formulas. This new game is both a generalization of the monotone
Karchmer-Wigderson game and an analog of the classical Boolean
Karchmer-Wigderson game. Therefore, it acts as a bridge between the existing
monotone and general games.
  Using this game, we prove hazard-free formula size and depth lower bounds
that are provably stronger than those possible by the standard technique of
transferring results from monotone complexity in a black-box fashion. For the
multiplexer function we give (1) a hazard-free formula of optimal size and (2)
an improved low-depth hazard-free formula of almost optimal size and (3) a
hazard-free formula with alternation depth $2$ that has optimal depth. We then
use our optimal constructions to obtain an improved universal worst-case
hazard-free formula size upper bound. We see our results as a significant step
towards establishing hazard-free computation as an independent missing link
between Boolean complexity and monotone complexity.


  One of the obstacles hindering the scaling-up of the initial successes of
machine learning in practical engineering applications is the dependence of the
accuracy on the size of the database that "drives" the algorithms.
Incorporating the already-known physical laws into the training process can
significantly reduce the size of the required database. In this study, we
establish a neural network-based computational framework to characterize the
finite deformation of elastic plates, which in classic theories is described by
the F\"oppl--von K\'arm\'an (FvK) equations with a set of boundary conditions
(BCs). A neural network is constructed by taking the spatial coordinates as the
input and the displacement field as the output to approximate the exact
solution of the FvK equations. The physical information (PDEs, BCs, and
potential energies) is then incorporated into the loss function, and a pseudo
dataset is sampled without knowing the exact solution to finally train the
neural network. The prediction accuracy of the modeling framework is carefully
examined by applying it to four different loading cases: in-plane tension with
non-uniformly distributed stretching forces, in-plane central-hole tension,
out-of-plane deflection, and buckling under compression. \hl{Three ways of
formulating the loss function are compared: 1) purely data-driven, 2)
PDE-based, and 3) energy-based. Through the comparison with the finite element
simulations, it is found that all the three approaches can characterize the
elastic deformation of plates with a satisfactory accuracy if trained properly.
Compared with incorporating the PDEs and BCs in the loss, using the total
potential energy shows certain advantage in terms of the simplicity of
hyperparameter tuning and the computational efficiency.


  The majority of public transport vehicles are fitted with Automatic Vehicle
Location (AVL) systems generating a continuous stream of data. The availability
of this data has led to a substantial body of literature addressing the
development of algorithms to predict Estimated Times of Arrival (ETA). Here
research literature reporting the development of ETA prediction systems
specific to busses is reviewed to give an overview of the state of the art.
Generally, reviews in this area categorise publications according to the type
of algorithm used, which does not allow an objective comparison. Therefore this
survey will categorise the reviewed publications according to the input data
used to develop the algorithm. The review highlighted inconsistencies in
reporting standards of the literature. The inconsistencies were found in the
varying measurements of accuracy preventing any comparison and the frequent
omission of a benchmark algorithm. Furthermore, some publications were lacking
in overall quality. Due to these highlighted issues, any objective comparison
of prediction accuracies is impossible. The bus ETA research field therefore
requires a universal set of standards to ensure the quality of reported
algorithms. This could be achieved by using benchmark datasets or algorithms
and ensuring the publication of any code developed.


  We discuss the strong interaction regime of the nonlinear Landau-Zener
problem coming up at coherent photo- and magneto-association of ultracold
atoms. We apply a variational approach to an exact third-order nonlinear
differential equation for the molecular state probability and construct an
accurate approximation describing the whole time dynamics of the coupled
atom-molecular system. The resultant solution improves the accuracy of the
previous approximation by A. Ishkhanyan et al. [J. Phys. A 39, 14887 (2006)].
The obtained results reveal a remarkable observation that in the strong
coupling limit the resonance crossing is mostly governed by the nonlinearity
while the coherent atom-molecular oscillations coming up soon after the
resonance has been crossed are principally of linear nature. This observation
is supposed to be general for all the nonlinear quantum systems having the same
generic quadratic nonlinearity, due to the basic attributes of the resonance
crossing processes in such systems. The constructed approximation turns out to
have a larger applicability range (than it was initially expected) covering the
whole moderate coupling regime for which the proposed solution accurately
describes all the main characteristics of the system's evolution except the
amplitude of the coherent atom-molecule oscillation, which is rather
overestimated.


  Let $D$ be the set of $\beta \in (1, 2]$ such that $f_\beta$ is a symmetric
tent map with finite critical orbit. For $\beta \in D$, by operating Denjoy
like surgery on $f_{\beta}$, we constructed a $C^1$ unimodal map
$\tilde{g}_\beta$ admitting a thick hyperbolic repelling invariant Cantor set
which contains a wild Cantor attractor. The smoothness of $\tilde{g}_\beta$ is
ensured by the effective estimation of the preimages of the critical point as
well as the prescribed lengths of the inserted intervals. Furthermore, $D$ is
dense in $(1, 2]$, and $\tilde{g}_\beta$ can not be $C^{1+\alpha}$ because the
hyperbolic repelling invariant Cantor set of $C^{1+\alpha}$ map has Lebesgue
measure equal to zero.


  We address the controversy over the proximity effect between topological
materials and high T$_{c}$ superconductors. Junctions are produced between
Bi$_{2}$Sr$_{2}$CaCu$_{2}$O$_{8+\delta}$ and materials with different Fermi
surfaces (Bi$_{2}$Te$_{3}$ \& graphite). Both cases reveal tunneling spectra
consistent with Andreev reflection. This is confirmed by magnetic field that
shifts features via the Doppler effect. This is modeled with a single parameter
that accounts for tunneling into a screening supercurrent. Thus the tunneling
involves Cooper pairs crossing the heterostructure, showing the Fermi surface
mis-match does not hinder the ability to form transparent interfaces, which is
accounted for by the extended Brillouin zone and different lattice symmetries.


  TianQin is a proposed space-based gravitational wave observatory. It is
designed to detect the gravitational wave signals in the frequency range of 0.1
mHz -- 1 Hz. At a geocentric distance of $10^5$ km, the plasma in the earth
magnetosphere will contribute as the main source of environmental noises. Here,
we analyze the acceleration noises that are caused by the magnetic field of
space plasma for the test mass of TianQin. The real solar wind data observed by
the Advanced Composition Explorer are taken as the input of the
magnetohydrodynamic simulation. The Space Weather Modeling Framework is used to
simulate the global magnetosphere of the earth, from which we obtain the plasma
and magnetic field parameters on the detector's orbits. We calculate the time
series of the residual accelerations and the corresponding amplitude spectral
densities on these orbit configurations. We find that the residual acceleration
produced by the interaction between the TM's magnetic moment induced by the
space magnetic field and the spacecraft magnetic field ($\bm{a}_{\rm M1}$) is
the dominant term, which can approach $10^{-15}$ m/s$^2$/Hz$^{1/2}$ at $f
\approx$ 0.2 mHz for the nominal values of the magnetic susceptibility
($\chi_{\rm m} = 10^{-5}$) and the magnetic shielding factor ($\xi_{\rm m} =
10$) of the test mass. The ratios between the amplitude spectral density of the
acceleration noise caused by the space magnetic field and the preliminary goal
of the inertial sensor are 0.38 and 0.08 at 1 mHz and 10 mHz, respectively. We
discuss the further reduction of this acceleration noise by decreasing
$\chi_{\rm m}$ and/or increasing $\xi_{\rm m}$ in the future instrumentation
development for TianQin.


  We show that the infrared reflectance of the quasi-one dimensional
charge-density-wave (CDW) conductor K0.3MoO3 (blue bronze) varies with position
when a voltage greater than the CDW depinning threshold is applied. The spatial
dependence and spectra associated with these changes are generally as expected
from the electro-transmission [B.M. Emerling, et al, Eur. Phys. J. B 16, 295
(2000)], but there are some differences which might be associated with changes
in the CDW properties on the surface. We have examined the electro-reflectance
spectrum associated with CDW current investigation for light polarized parallel
to the conducting chains for signs of expected current-induced intragap states,
and conclude that the density of any such states is at least an order of
magnitude lower than expected.


  Young supernova remnants show a characteristic ejecta-dominated X-ray
emission that allows us to probe the products of the explosive nucleosynthesis
processes and to ascertain important information about the physics of the
supernova explosions. Hard X-ray observations have recently revealed the
radioactive decay lines of 44Ti at ~67.9 keV and ~78.4 keV in the Tycho's SNR.
We here analyze the set of XMM-Newton archive observations of the Tycho's SNR.
We produce equivalent width maps of the Fe K and Ca XIX emission lines and find
indications for a stratification of the abundances of these elements and
significant anisotropies. We then perform a spatially resolved spectral
analysis by identifying five different regions characterized by high/low values
of the Fe K equivalent width. We find that the spatial distribution of the Fe K
emission is correlated with that of the Cr XXII. We also detect the Ti K-line
complex in the spectra extracted from the two regions with the highest values
of the Fe and Cr equivalent widths. The Ti line emissions remains undetected in
regions where the Fe and Cr equivalent widths are low. Our results indicate
that the post-shock Ti is spatially co-located with other iron-peak nuclei in
Tycho's SNR, in agreement with the predictions of multi-D models of Type Ia
supernovae.


  We give a combinatorial proof of the transcendence of $L(1,\chi_s)/\Pi$,
where $L(1,\chi_s)$ (resp. $\Pi$) is the analogue in characteristic $p$ of the
function $L$ of Dirichlet (resp. $\pi$). This result has been proven by G.
Damamme using the criteria of de Mathan. Our proof is based on the Theorem of
Christol and another property of $k$-automatic sequences.


  Diarization of audio recordings from ad-hoc mobile devices using spatial
information is considered in this paper. A two-channel synchronous recording is
assumed for each mobile device, which is used to compute directional statistics
separately at each device in a frame-wise manner. The recordings across the
mobile devices are asynchronous, but a coarse synchronization is performed by
aligning the signals using acoustic events, or real-time clock. Direction
statistics computed for all the devices, are then modeled jointly using a
Dirichlet mixture model, and the posterior probability over the mixture
components is used to derive the diarization information. Experiments on real
life recordings using mobile phones show a diarization error rate of less than
14%.


  It is the second article of a series whose final goal is the stabilization of
the twisted trace formula. Over a non archimedean field, we define (after
Arthur) wheighted orbital integrals and their stable and endoscopic versions.
We state the main theorems that will be proved in the following articles. We
begin to study the local properties of our integrals, for instance expansion in
Shalika germs.


  User response prediction makes a crucial contribution to the rapid
development of online advertising system and recommendation system. The
importance of learning feature interactions has been emphasized by many works.
Many deep models are proposed to automatically learn high-order feature
interactions. Since most features in advertising system and recommendation
system are high-dimensional sparse features, deep models usually learn a
low-dimensional distributed representation for each feature in the bottom
layer. Besides traditional fully-connected architectures, some new operations,
such as convolutional operations and product operations, are proposed to learn
feature interactions better. In these models, the representation is shared
among different operations. However, the best representation for different
operations may be different. In this paper, we propose a new neural model named
Operation-aware Neural Networks (ONN) which learns different representations
for different operations. Our experimental results on two large-scale
real-world ad click/conversion datasets demonstrate that ONN consistently
outperforms the state-of-the-art models in both offline-training environment
and online-training environment.


  Generating and editing a 3D scene guided by natural language poses a
challenge, primarily due to the complexity of specifying the positional
relations and volumetric changes within the 3D space. Recent advancements in
Large Language Models (LLMs) have demonstrated impressive reasoning,
conversational, and zero-shot generation abilities across various domains.
Surprisingly, these models also show great potential in realizing and
interpreting the 3D space. In light of this, we propose a novel language-guided
interactive 3D generation system, dubbed LI3D, that integrates LLMs as a 3D
layout interpreter into the off-the-shelf layout-to-3D generative models,
allowing users to flexibly and interactively generate visual content.
Specifically, we design a versatile layout structure base on the bounding boxes
and semantics to prompt the LLMs to model the spatial generation and reasoning
from language. Our system also incorporates LLaVA, a large language and vision
assistant, to provide generative feedback from the visual aspect for improving
the visual quality of generated content. We validate the effectiveness of LI3D,
primarily in 3D generation and editing through multi-round interactions, which
can be flexibly extended to 2D generation and editing. Various experiments
demonstrate the potential benefits of incorporating LLMs in generative AI for
applications, e.g., metaverse. Moreover, we benchmark the layout reasoning
performance of LLMs with neural visual artist tasks, revealing their emergent
ability in the spatial layout domain.


  An exact mapping is established between sequence alignment, one of the most
commonly used tools of computational biology, and the asymmetric exclusion
process, one of the few exactly solvable models of nonequilibrium physics. The
statistical significance of sequence alignments is characterized through
studying the total hopping current of the discrete time and space version of
the asymmetric exclusion process.


  Understanding the role of higher derivatives is probably one of the most
relevant questions in quantum gravity theory. Already at the semiclassical
level, when gravity is a classical background for quantum matter fields, the
action of gravity should include fourth derivative terms to provide
renormalizability in the vacuum sector. The same situation holds in the quantum
theory of metric. At the same time, including the fourth derivative terms means
the presence of massive ghosts, which are gauge-independent massive states with
negative kinetic energy. At both classical and quantum level such ghosts
violate stability and hence the theory becomes inconsistent. Several approaches
to solve this contradiction were invented and we are proposing one more, which
looks simpler than those what were considered before. We explore the dynamics
of the gravitational waves on the background of classical solutions and give
certain arguments that massive ghosts produce instability only when they are
present as physical particles. At least on the cosmological background one can
observe that if the initial frequency of the metric perturbations is much
smaller than the mass of the ghost, no instabilities are present.


  In this paper, we extend existing results about simulation and intrinsic
universality in a model of tile-based self-assembly. Namely, we work within the
2-Handed Assembly Model (2HAM), which is a model of self-assembly in which
assemblies are formed by square tiles that are allowed to combine, using glues
along their edges, individually or as pairs of arbitrarily large assemblies in
a hierarchical manner, and we explore the abilities of these systems to
simulate each other when the simulating systems have a higher "temperature"
parameter, which is a system wide threshold dictating how many glue bonds must
be formed between two assemblies to allow them to combine. It has previously
been shown that systems with lower temperatures cannot simulate arbitrary
systems with higher temperatures, and also that systems at some higher
temperatures can simulate those at particular lower temperatures, creating an
infinite set of infinite hierarchies of 2HAM systems with strictly increasing
simulation power within each hierarchy. These previous results relied on two
different definitions of simulation, one (strong simulation) seemingly more
restrictive than the other (standard simulation), but which have previously not
been proven to be distinct. Here we prove distinctions between them by first
fully characterizing the set of pairs of temperatures such that the high
temperature systems are intrinsically universal for the lower temperature
systems (i.e. one tile set at the higher temperature can simulate any at the
lower) using strong simulation. This includes the first impossibility result
for simulation downward in temperature. We then show that lower temperature
systems which cannot be simulated by higher temperature systems using the
strong definition, can in fact be simulated using the standard definition,
proving the distinction between the types of simulation.


  We prove some sharp Hardy inequalities for domains with a spherical symmetry.
In particular, we prove an inequality for domains of the unit $n$-dimensional
sphere with a point singularity, and an inequality for functions defined on the
half-space $\R_+^{n+1}$} vanishing on the hyperplane $\{x_{n+1}=0\}$, with
singularity along the $x_{n+1}$-axis. The proofs rely on a one-dimensional
Hardy inequality involving a weight function related to the volume element on
the sphere, as well as on symmetrization arguments. The one-dimensional
inequality is derived in a general form.


  A decomposition of a graph $G$ is a family of subgraphs of $G$ whose edge
sets form a partition of $E(G)$. In this paper, we prove that every
triangle-free planar graph $G$ can be decomposed into a $2$-degenerate graph
and a matching. Consequently, every triangle-free planar graph $G$ has a
matching $M$ such that $G-M$ is online 3-DP-colorable. This strengthens an
earlier result in [R. \v{S}krekovski, {\em A Gr\"{o}tzsch-Type Theorem for List
Colourings with Impropriety One}, Combin. Prob. Comput. 8 (1999), 493-507] that
every triangle-free planar graph is $1$-defective $3$-choosable.


  The 1-D mean-field equation describing the evolution of the subsurface
toroidal field can be used together with the observed surface radial field to
model the subsurface toroidal flux density. We aim to test this model and
determine the relationship between the observationally inferred surface
toroidal field (as a proxy for flux emergence), and the modelled subsurface
toroidal flux density. We use a combination of sunspot area observations, the
surface toroidal field inferred from WSO line-of-sight magnetic field
observations, and compare with the results of a 1-D mean-field evolution
equation for the subsurface toroidal field, driven by the observed radial field
from the National Solar Observatory/Kitt Peak and SOLIS observations. We derive
calibration curves relating the subsurface toroidal flux density to the
observed surface toroidal field strengths and sunspot areas. The calibration
curves are for two regimes, one corresponding to ephemeral region emergence
outside of the butterfly wings, the other to active region emergence in the
butterfly wings. We discuss this in terms of the size and vertical velocity
associated with the two types of flux emergence.


  The resonance Cherenkov radiation generated from a train of equally-spaced
unidimensional electron bunches travelling along the axis of a hollow channel
inside an infinite cylindrical waveguide filled with (weakly dispersing)
transparent dielectric has been investigated. It was shown that its excitation
might be stimulated at a large number of neighboring modes of the waveguide. A
visual explanation of this effect is given and the possibility of its
observation in the range of terahertz radiation is discussed.


  Let $\mathcal{H}$ denote a connected component of a stratum of translation
surfaces. We show that the Siegel-Veech transform of a bounded compactly
supported function on $\mathbb{R}^2$ is in $L^2(\mathcal{H}, \mu)$, where $\mu$
is Lebesgue measure on $\mathcal{H}$, and give applications to bounding error
terms for counting problems for saddle connections. We also propose a new
invariant associated to $SL(2, \mathbb{R})$-invariant measures on strata
satisfying certain integrability conditions.


  People employ their knowledge to recognize things. This paper is concerned
with how to measure people's knowledge for recognition and how it changes. The
discussion is based on three assumptions. Firstly, we construct two evolution
process equations, of which one is for uncertainty and knowledge, and the other
for uncertainty and ignorance. Secondly, by solving the equations, formulas for
measuring the levels of knowledge and the levels of ignorance are obtained in
two particular cases. Thirdly, a new concept of knowledge entropy is
introduced. Its similarity with Boltzmann's entropy and its difference with
Shannon's Entropy are examined. Finally, it is pointed out that the obtained
formulas of knowledge and knowledge entropy reflect two fundamental principles:
(1) The knowledge level of a group is not necessarily a simple sum of the
individuals' knowledge levels; and (2) An individual's knowledge entropy never
increases if the individual's thirst for knowledge never decreases.


  Using first-principles calculations based on the density functional theory,
we show a strong strain dependence of magnetic order in
(LaMnO$_{3}$)$_n$/(SrMnO$_{3}$)$_{2n}$ (001) superlattices with $n=1,2$. The
epitaxial strain lifts the degeneracy of Mn $e_{g}$ orbitals, thus inducing an
inherent orbital order, which in turn strongly affects the ferromagnetic double
exchange of itinerant $e_{g}$ electrons, competing with the antiferromagnetic
superexchange of localized $t_{2g}$ electrons. For the case of tensile strain
induced by SrTiO$_3$ (001) substrate, we find that the ground state is A-type
antiferromagnetic and $d_{x^2-y^2}$ orbital ordered, which is in excellent
agreement with recent experiments [S. J. May {\it et al.}, Nature Materials
{\bf 8}, 892 (2009)]. Instead, for the case of compressive strain induced by
LaAlO$_3$ (001) substrate, we predict that the ground state is C-type
antiferromagnetic and $d_{3z^2-r^2}$ orbital ordered.


  The effective Lagrangian for the seesaw model is derived including effects
due to CP violation. Besides the usual dimension-5 operator responsible for
light neutrino masses, a dimension-6 operator is obtained. For three or less
heavy neutrino generations, the inclusion of both operators is necessary and
sufficient for all independent physical parameters of the high-energy seesaw
Lagrangian to appear in the low-energy effective theory, including the CP-odd
phases relevant for leptogenesis. The dimension-6 operator implies exotic
low-energy couplings for light neutrinos, providing a link between the
high-energy physics and low-energy observables.


  We report the discovery of two planetary systems around comoving stars;
TOI-2076 (TIC 27491137) and TOI-1807 (TIC 180695581). TOI-2076 is a nearby
(41.9 pc) multi-planetary system orbiting a young (204$\pm$50 Myr), bright (K =
7.115 in TIC v8.1). TOI-1807 hosts a single transiting planet, and is similarly
nearby (42.58pc), similarly young (180$\pm$40 Myr), and bright. Both targets
exhibit significant, periodic variability due to star spots, characteristic of
their young ages. Using photometric data collected by TESS we identify three
transiting planets around TOI-2076 with radii of R$_b$=3.3$\pm$0.04$R_\oplus$,
R$_c$=4.4$\pm$0.05$R_\oplus$, and R$_d$=4.1$\pm$0.07$R_\oplus$. Planet
TOI-2076b has a period of P$_b$=10.356 d. For both TOI 2076c and d, TESS
observed only two transits, separated by a 2-year interval in which no data
were collected, preventing a unique period determination. A range of long
periods (>17d) are consistent with the data. We identify a short-period planet
around TOI-1807 with a radius of R$_b$=1.8$\pm$0.04$R_\oplus$ and a period of
P$_b$=0.549 d. Their close proximity, and bright, cool host stars, and young
ages, make these planets excellent candidates for follow-up. TOI-1807b is one
of the best known small ($R<2R_\oplus$) planets for characterization via
eclipse spectroscopy and phase curves with JWST. TOI-1807b is the youngest
ultra-short period planet discovered to date, providing valuable constraints on
formation time-scales of short period planets. Given the rarity of young
planets, particularly in multiple planet systems, these planets present an
unprecedented opportunity to study and compare exoplanet formation, and young
planet atmospheres, at a crucial transition age for formation theory.


  An avoidance problem of configurations in 4-cycle systems is investigated by
generalizing the notion of sparseness, which is originally from Erd\H{o}s'
r-sparse conjecture on Steiner triple systems. A 4-cycle system of order v,
4CS(v), is said to be r-sparse if for every integer j satisfying 1 < j < r+1 it
contains no configurations consisting of j 4-cycles whose union contains
precisely j+3 vertices. If an r-sparse 4CS(v) is also free from copies of a
configuration on two 4-cycles sharing a diagonal, called the double-diamond, we
say it is strictly r-sparse. In this paper, we show that for every admissible
order v there exists a strictly 4-sparse 4CS(v). We also prove that for any
positive integer r > 1 and sufficiently large integer v there exists a constant
number c such that there exists a strictly r-sparse 4-cycle packing of order v
with cv^2 4-cycles.


  We discuss the possibility to observe hadron modification in hot matter via
the correlation of identical particles. We find that the hadron modification
causes a new type of correlation, back-to-back correlation.


  Reparameterization (RP) and likelihood ratio (LR) gradient estimators are
used to estimate gradients of expectations throughout machine learning and
reinforcement learning; however, they are usually explained as simple
mathematical tricks, with no insight into their nature. We use a first
principles approach to explain that LR and RP are alternative methods of
keeping track of the movement of probability mass, and the two are connected
via the divergence theorem. Moreover, we show that the space of all possible
estimators combining LR and RP can be completely parameterized by a flow field
$u(x)$ and an importance sampling distribution $q(x)$. We prove that there
cannot exist a single-sample estimator of this type outside our characterized
space, thus, clarifying where we should be searching for better Monte Carlo
gradient estimators.


  In this exposition, I discuss several developments in the theory of vertex
operator algebras, and I include motivation for the definition.


  We propose a protocol for conditional suppression of losses in direct quantum
state transmission over a lossy quantum channel. The method works by
noiselessly attenuating the input state prior to transmission through a lossy
channel followed by noiseless amplification of the output state. The procedure
does not add any noise hence it keeps quantum coherence. We experimentally
demonstrate it in the subspace spanned by vacuum and single-photon states, and
consider its general applicability.


  We present new EVLA continuum observations at 7 mm of the 253-1536 binary
disk system in the Orion Nebula Cluster. The measured fluxes were combined with
data in the sub-mm to derive the millimeter spectral index of each individual
disk component. We show how these observations can be used to test the models
of dust evolution and early growth of solids in protoplanetary disks. Our
analysis indicates that the disk with lower density and higher temperature
hosts larger grains than the companion disk. This result is the opposite of
what predicted by the dust evolution models. The models and observational
results can be reconciled if the viscosity $\alpha$-parameter differs by more
than a factor of ten in the two disks, or if the distribution of solids in the
disks is strongly affected by radial motions. This analysis can be applied to
future high-angular resolution observations of young disks with EVLA and ALMA
to provide even stronger observational constraints to the models of dust
evolution in protoplanetary disks.


  We present a new method to adapt an RGB-trained water segmentation network to
target-domain aerial thermal imagery using online self-supervision by
leveraging texture and motion cues as supervisory signals. This new thermal
capability enables current autonomous aerial robots operating in near-shore
environments to perform tasks such as visual navigation, bathymetry, and flow
tracking at night. Our method overcomes the problem of scarce and
difficult-to-obtain near-shore thermal data that prevents the application of
conventional supervised and unsupervised methods. In this work, we curate the
first aerial thermal near-shore dataset, show that our approach outperforms
fully-supervised segmentation models trained on limited target-domain thermal
data, and demonstrate real-time capabilities onboard an Nvidia Jetson embedded
computing platform. Code and datasets used in this work will be available at:
https://github.com/connorlee77/uav-thermal-water-segmentation.


  We report the first search for CP violating decays of the $\Upsilon(4S)$
using a data sample that contains 535 million $\Upsilon(4S)$ mesons with the
Belle detector at the KEKB asymmetric-energy e^+ e^- collider. A partial
reconstruction technique is employed to enhance the signal sensitivity. No
significant signals were observed. We obtain an upper limit of $4 \times
10^{-7}$ at the 90 % confidence level for the branching fractions of the $CP$
violating modes, $\Upsilon(4S) \to B^0\bar{B}^0 \to J/\psi K^0_S +
J/\psi(\eta_c) K^0_S$. Extrapolating the result, we find that an observation
with 5$\sigma$ significance is expected with a 30 ab^{-1} data sample, which is
within the reach of a future super B factory.


  A yield surface of a material is a set of critical stress conditions beyond
which macroscopic plastic deformation begins. For crystalline solids, plastic
deformation occurs through the motion of dislocations, which can be captured by
discrete dislocation dynamics (DDD) simulations. In this paper, we predict the
yield surfaces and strain-hardening behaviors using DDD simulations and a
geometric manifold learning approach. The yield surfaces in the
three-dimensional space of plane stress are constructed for single-crystal
copper subjected to uniaxial loading along the $[100]$ and $[110]$ directions,
respectively. With increasing plastic deformation under $[100]$ loading, the
yield surface expands nearly uniformly in all directions, corresponding to
isotropic hardening. In contrast, under $[110]$ loading, latent hardening is
observed, where the yield surface remains nearly unchanged in the orientations
in the vicinity of the loading direction itself, but expands in other
directions, resulting in an asymmetric shape. This difference in hardening
behaviors is attributed to the different dislocation multiplication behaviors
on various slip systems under the two loading conditions.


  In recent years, enhanced light-matter interactions through a plethora of
dipole-type polaritonic excitations have been observed in two-dimensional (2D)
layered materials. In graphene, electrically tunable and highly confined
plasmon-polaritons were predicted and observed, opening up opportunities for
optoelectronics, bio-sensing and other mid-infrared applications. In hexagonal
boron nitride (hBN), low-loss infrared-active phonon-polaritons exhibit
hyperbolic behavior for some frequencies, allowing for ray-like propagation
exhibiting high quality factors and hyperlensing effects. In transition metal
dichalcogenides (TMDs), reduced screening in the 2D limit leads to optically
prominent excitons with large binding energy, with these polaritonic modes
having been recently observed with scanning near field optical microscopy
(SNOM). Here, we review recent progress in state-of-the-art experiments, survey
the vast library of polaritonic modes in 2D materials, their optical spectral
properties, figures-of-merit and application space. Taken together, the
emerging field of 2D material polaritonics and their hybrids provide enticing
avenues for manipulating light-matter interactions across the visible, infrared
to terahertz spectral ranges, with new optical control beyond what can be
achieved using traditional bulk materials.


  Let $\eps >0$. We prove that there exists an operator
$T_\eps:\ell_2\to\ell_2$, such that for any polynomial $P$ we have $\|{P(T)}\|
\leq(1+\eps)\|{P}\|_\infty$, but which is not similar to a contraction, {\it
i.e.} there does not exist an invertible operator $S:\ \ell_2\to\ell_2$ such
that $\|{S^{-1}T_\eps S}\|\leq 1$. This answers negatively a question
attributed to Halmos after his well known 1970 paper (``Ten problems in Hilbert
space").


  Matrices over the dual numbers are considered. We propose an approach to
classify these matrices up to similarity. Some preliminary results on the
realization of this approach are obtained. In particular, we produce explicitly
canonical matrices of orders 2 and 3.


  In random tiling and dimer models we can get various limit shapes which gives
the boundaries between different types of phases. The shape fluctuations at
these boundaries give rise to universal limit laws, in particular the Airy
process. We survey some models which can be analyzed in detail based on the
fact that they are determinantal point processes with correlation kernels that
can be computed. We also discuss which type of limit laws that can be obtained.


  This work investigates the optimal control problem for reflected
McKean-Vlasov SDEs and the viscosity solutions to Hamilton-Jacobi-Bellman(HJB)
equations on the Wasserstein space in terms of intrinsic derivative. It follows
from the flow property of reflected McKean-Vlasov SDEs that the dynamic
programming principle holds. Applying the decoupling method and the heat kernel
estimates for parabolic equations, we show that the value function is a
viscosity solution to an appropriate HJB equation on the Wasserstein space,
where the characterization of absolutely continuous curves on the Wasserstein
space by the continuity equations plays an important role. To establish the
uniqueness of viscosity solution, we generalize the construction of a distance
like function initiated in Burzoni et al.(SICON, 2020) to the Wasserstein space
over multidimensional space and show its effectiveness to cope with HJB
equations in terms of intrinsic derivative on the Wasserstein space.


  Using PREM as a reference model for the Earth density distribution we
investigate in the present article the sensitivity of the ORCA detector to
deviations of the Earth i) outer core (OC) density, ii) inner core (IC)
density, iii) total core density, and iv) mantle density, from their respective
PREM densities. The analysis is performed by studying the effects of the Earth
matter on the oscillations of atmospheric $\nu_{\mu}$, $\nu_e$, $\bar{\nu}_\mu$
and $\bar{\nu}_e$. We present results which, in particular, illustrate the
dependence of the ORCA sensitivity to the OC, IC, core and mantle densities on
the type of systematic uncertainties used in the analysis, on the value of the
atmospheric neutrino mixing angle $\theta_{23}$, on whether the Earth mass
constraint is implemented or not, and on the way it is implemented, and on the
type - with normal ordering (NO) or inverted ordering (IO) - of the light
neutrino mass spectrum. We show, in particular, that in the "most favorable" NO
case of implemented Earth mass constraint, "minimal" systematic errors and
$\sin^2\theta_{23}=0.58$, ORCA can determine, e.g., the OC (mantle) density at
$3\sigma$ C.L. after 10 years of operation with an uncertainty of (-18\%)/+15\%
(of (-6\%)/+8\%). In the "most unfavourable" NO case of "conservative"
systematic errors and $\sin^2\theta_{23}=0.42$, the uncertainty reads
(-43\%)/+39\% ((-17\%/+20\%), while for for $\sin^2\theta_{23} = 0.50$ and 0.58
it is noticeably smaller: (-37)\%/+30\% and (-30\%)/+24\% ((-13\%)/+16\% and
(-11\%/+14\%)). We find also that the sensitivity of ORCA to the outer core,
core and mantle densities is significantly worse for IO neutrino mass spectrum.


  In this mini-review we summarize the progress of modeling, simulation and
analysis of shock responses of heterogeneous materials in our group in recent
years. The basic methodology is as below. We first decompose the problem into
different scales. Construct/Choose a model according to the scale and main
mechanisms working at that scale. Perform numerical simulations using the
relatively mature schemes. The physical information is transferred between
neighboring scales in such a way: The statistical information of results in
smaller scale contributes to establishing the constitutive equation in larger
one. Except for the microscopic Molecular Dynamics (MD) model, both the
mesoscopic and macroscopic models can be further classified into two
categories, solidic and fluidic models, respectively. The basic ideas and key
techniques of the MD, material point method and discrete Boltzmann method are
briefly reviewed. Among various schemes used in analyzing the complex fields
and structures, the morphological analysis and the home-built software, GISO,
are briefly introduced. New observations are summarized for scales from the
larger to the smaller.


  We prove that inflation is forbidden in the most well understood class of
semi-realistic type IIA string compactifications: Calabi-Yau compactifications
with only standard NS-NS 3-form flux, R-R fluxes, D6-branes and O6-planes at
large volume and small string coupling. With these ingredients, the first
slow-roll parameter satisfies epsilon >= 27/13 whenever V > 0, ruling out both
inflation (including brane/anti-brane inflation) and de Sitter vacua in this
limit. Our proof is based on the dependence of the 4-dimensional potential on
the volume and dilaton moduli in the presence of fluxes and branes. We also
describe broader classes of IIA models which may include cosmologies with
inflation and/or de Sitter vacua. The inclusion of extra ingredients, such as
NS 5-branes and geometric or non-geometric NS-NS fluxes, evades the assumptions
used in deriving the no-go theorem. We focus on NS 5-branes and outline how
such ingredients may prove fruitful for cosmology, but we do not provide an
explicit model. We contrast the results of our IIA analysis with the rather
different situation in IIB.


  We study properties of C*-algebraic deformations of homogeneous spaces
$G/\Gamma$ which are equivariant in the sense that they preserve the natural
action of $G$ by left translation. The center is shown to be isomorphic to
$C(G/G_\rho^0)$ for a certain subgroup $G_\rho^0$ of $G$, and there is a 1-1
correspondence between normalised traces and probability measures on
$G/G_\rho^0$. This makes it possible to represent the deformed algebra as
operators over $L^2(G/\Gamma)$. Applications to K-theory are also mentioned.


  This paper presents a streaming (sequential) protocol for universal
entanglement concentration at the Shannon bound. Alice and Bob begin with N
identical (but unknown) two-qubit pure states, each containing E ebits of
entanglement. They each run a reversible algorithm on their qubits, and end up
with Y perfect EPR pairs, where Y = NE +- O(\sqrt N). Our protocol is
streaming, so the N input systems are fed in one at a time, and perfect EPR
pairs start popping out almost immediately. It matches the optimal block
protocol exactly at each stage, so the average yield after n inputs is <Y> = nE
- O(log n). So, somewhat surprisingly, there is no tradeoff between yield and
lag -- our protocol optimizes both. In contrast, the optimal N-qubit block
protocol achieves the same yield, but since no EPR pairs are produced until the
entire input block is read, its lag is O(N). Finally, our algorithm runs in
O(log N) space, so a lot of entanglement can be efficiently concentrated using
a very small (e.g., current or near-future technology) quantum processor. Along
the way, we find an optimal streaming protocol for extracting randomness from
classical i.i.d. sources and a more space-efficient implementation of the Schur
transform.


  We present a revised SALT2 surface (`SALT2-2021') for fitting the light
curves of Type Ia supernovae (SNe Ia), which incorporates new measurements of
zero-point calibration offsets and Milky Way extinction. The most notable
change in the new surface occurs in the UV region. This new surface alters the
distance measurements of SNe~Ia, which can be used to investigate the nature of
dark energy by probing the expansion history of the Universe. Using the revised
SALT2 surface on public data from the first three years of the Dark Energy
Survey Supernova Program (combined with an external low-$z$ SNe Ia sample) and
combining with cosmic microwave background constraints, we find a change in the
dark energy equation of state parameter, $\Delta w = 0.015 \pm 0.004$. This
result highlights the continued importance of controlling and reducing
systematic uncertainties, particularly with the next generation of supernova
analyses aiming to improve constraints on dark energy properties.


  In this paper, we introduce a generalization of corona of graphs. This
construction generalizes the generalized corona of graphs (consequently, the
corona of graphs), the cluster of graphs, the corona-vertex subdivision graph
of graphs and the corona-edge subdivision graph of graphs. Further, it enables
to get some more variants of corona of graphs as its particular cases. To
determine the spectra of the adjacency, Laplacian and the signless Laplacian
matrices of the above mentioned graphs, we define a notion namely, the coronal
of a matrix constrained by an index set, which generalizes the coronal of a
graph matrix. Then we prove several results pertain to the determination of
this value. Then we determine the characteristic polynomials of the adjacency
and the Laplacian matrices of this graph in terms of the characteristic
polynomials of the adjacency and the Laplacian matrices of the constituent
graphs and the coronal of some matrices related to the constituent graphs.
Using these, we derive the characteristic polynomials of the adjacency and the
Laplacian matrices of the above mentioned existing variants of corona of
graphs, and some more variants of corona of graphs with some special
constraints.


  We report on tunneling experiment between two quantum Hall droplets separated
by a nearly ideal tunnel barrier. The device is produced by cleaved edge
overgrowth that laterally juxtaposes two two-dimensional electron systems
across a high quality semiconductor barrier. The dramatic evolution of the
tunneling characteristics is consistent with the magnetic field-dependent
tunneling between the coupled edge states of the quantum Hall droplets. We
identify a series of quantum critical points between successive strong and weak
tunneling regimes that are reminiscent of the plateau-transitions in quantum
Hall effect. Scaling analysis shows that the conductance near the critical
magnetic fields $B_{c}$ is a function of a single scaling argument
$|B-B_{c}|T^{-\kappa}$, where the exponent $\kappa = 0.42$. This puzzling
resemblance to a quantum Hall-insulator transition points to the significance
of interedge correlation in the lateral tunneling of quantum Hall droplets.


  Classical descriptions of enzyme kinetics ignore the physical nature of the
intracellular environment. Main implicit assumptions behind such approaches are
that reactions occur in compartment volumes which are large enough so that
molecular discreteness can be ignored and that molecular transport occurs via
diffusion. Starting from a master equation description of enzyme reaction
kinetics and assuming metabolic steady-state conditions, we derive novel
mesoscopic rate equations which take into account (i) the intrinsic molecular
noise due to the low copy number of molecules in intracellular compartments
(ii) the physical nature of the substrate transport process, i.e. diffusion or
vesicle-mediated transport. These equations replace the conventional
macroscopic and deterministic equations in the context of intracellular
kinetics. The latter are recovered in the limit of infinite compartment
volumes. We find that deviations from the predictions of classical kinetics are
pronounced (hundreds of percent in the estimate for the reaction velocity) for
enzyme reactions occurring in compartments which are smaller than approximately
200nm, for the case of substrate transport to the compartment being mediated
principally by vesicle or granule transport and in the presence of competitive
enzyme inhibitors. This has implications for the common approach of modelling
large intracellular reaction networks using ordinary differential equations and
also for the calculation of the effective dosage of competitive inhibitor
drugs.


  Text-Attributed Graphs (TAGs) are graphs of connected textual documents.
Graph models can efficiently learn TAGs, but their training heavily relies on
human-annotated labels, which are scarce or even unavailable in many
applications. Large language models (LLMs) have recently demonstrated
remarkable capabilities in few-shot and zero-shot TAG learning, but they suffer
from scalability, cost, and privacy issues. Therefore, in this work, we focus
on synergizing LLMs and graph models with their complementary strengths by
distilling the power of LLMs to a local graph model on TAG learning. To address
the inherent gaps between LLMs (generative models for texts) and graph models
(discriminative models for graphs), we propose first to let LLMs teach an
interpreter with rich textual rationale and then let a student model mimic the
interpreter's reasoning without LLMs' textual rationale. Extensive experiments
validate the efficacy of our proposed framework.


  The proposed $\mathcal{A}$ phase and the corresponding trial wavefunction
proposed by Das \emph{et al.} (PRL 131, 056202, 2023) for 5/2 state are argued
to describe the fractional quantum Hall liquid state rather than a phase
separated or stripe or bubble state.


  Light-by-light scattering ($\gamma\gamma\rightarrow\gamma\gamma$) is a
quantum-mechanical process that is forbidden in the classical theory of
electrodynamics. This reaction is accessible at the Large Hadron Collider
thanks to the large electromagnetic field strengths generated by
ultra-relativistic colliding lead (Pb) ions. Using 480 $\mu$b$^{-1}$ of Pb+Pb
collision data recorded at a centre-of-mass energy per nucleon pair of 5.02 TeV
by the ATLAS detector, the ATLAS Collaboration reports evidence for the
$\gamma\gamma\rightarrow\gamma\gamma$ reaction. A total of 13 candidate events
are observed with an expected background of 2.6$\pm$0.7 events. After
background subtraction and analysis corrections, the fiducial cross section of
the process $\textrm{Pb+Pb}\,(\gamma\gamma)\rightarrow
\textrm{Pb}^{(\ast)}\textrm{+}\textrm{Pb}^{(\ast)}\,\gamma\gamma$, for photon
transverse energy $E_{\mathrm{T}}>$3 GeV, photon absolute pseudorapidity
$|\eta|<$2.4, diphoton invariant mass greater than 6 GeV, diphoton transverse
momentum lower than 2 GeV and diphoton acoplanarity below 0.01, is measured to
be 70 $\pm$ 24 (stat.) $\pm$ 17 (syst.) nb, which is in agreement with Standard
Model predictions.


  This survey explores the fairness of large language models (LLMs) in
e-commerce, examining their progress, applications, and the challenges they
face. LLMs have become pivotal in the e-commerce domain, offering innovative
solutions and enhancing customer experiences. This work presents a
comprehensive survey on the applications and challenges of LLMs in e-commerce.
The paper begins by introducing the key principles underlying the use of LLMs
in e-commerce, detailing the processes of pretraining, fine-tuning, and
prompting that tailor these models to specific needs. It then explores the
varied applications of LLMs in e-commerce, including product reviews, where
they synthesize and analyze customer feedback; product recommendations, where
they leverage consumer data to suggest relevant items; product information
translation, enhancing global accessibility; and product question and answer
sections, where they automate customer support. The paper critically addresses
the fairness challenges in e-commerce, highlighting how biases in training data
and algorithms can lead to unfair outcomes, such as reinforcing stereotypes or
discriminating against certain groups. These issues not only undermine consumer
trust, but also raise ethical and legal concerns. Finally, the work outlines
future research directions, emphasizing the need for more equitable and
transparent LLMs in e-commerce. It advocates for ongoing efforts to mitigate
biases and improve the fairness of these systems, ensuring they serve diverse
global markets effectively and ethically. Through this comprehensive analysis,
the survey provides a holistic view of the current landscape of LLMs in
e-commerce, offering insights into their potential and limitations, and guiding
future endeavors in creating fairer and more inclusive e-commerce environments.


  Classical Hopf manifolds are compact complex manifolds whose universal
covering is $\mathbb{C}^d \setminus \{0\}$. We investigate the tropical
analogues of Hopf manifolds, and relate their geometry to tropical contracting
germs. To do this we develop a procedure called monomialization which
transforms non-degenerate tropical germs into morphisms, up to tropical
modification. A link is provided between tropical Hopf manifolds and the
analytification of Hopf manifolds over a non-archimedean field. We conclude by
computing the tropical Picard group and $(p,q)$-homology groups.


  Although the cubic T^3 "small universe" has been ruled out by COBE/DMR
results as an interesting cosmological model, we still have the possibility of
living in a universe with a more anisotropic topology such as a rectangular T^3
"small universe" with one or two of its dimensions significantly smaller than
the present horizon (which we refer to as T^1- and T^2-models, respectively).
In order to rule out these anisotropic topologies as well, we apply a new data
analysis method that searches for the specific kind of symmetries that these
models should produce. We find that the 4 year COBE/DMR data set a lower limit
on the smallest cell size for T^1- and T^2-models of 3000h^{-1} Mpc, at 95%
confidence, for a scale invariant power spectrum (n=1). These results imply
that ALL toroidal universes (cubes and rectangles) are ruled out as interesting
cosmological models.


  Anomaly detection in time series has been widely researched and has important
practical applications. In recent years, anomaly detection algorithms are
mostly based on deep-learning generative models and use the reconstruction
error to detect anomalies. They try to capture the distribution of normal data
by reconstructing normal data in the training phase, then calculate the
reconstruction error of test data to do anomaly detection. However, most of
them only use the normal data in the training phase and can not ensure the
reconstruction process of anomaly data. So, anomaly data can also be well
reconstructed sometimes and gets low reconstruction error, which leads to the
omission of anomalies. What's more, the neighbor information of data points in
time series data has not been fully utilized in these algorithms. In this
paper, we propose RAN based on the idea of Reconstruct Anomalies to Normal and
apply it for unsupervised time series anomaly detection. To minimize the
reconstruction error of normal data and maximize this of anomaly data, we do
not just ensure normal data to reconstruct well, but also try to make the
reconstruction of anomaly data consistent with the distribution of normal data,
then anomalies will get higher reconstruction errors. We implement this idea by
introducing the "imitated anomaly data" and combining a specially designed
latent vector-constrained Autoencoder with the discriminator to construct an
adversary network. Extensive experiments on time-series datasets from different
scenes such as ECG diagnosis also show that RAN can detect meaningful
anomalies, and it outperforms other algorithms in terms of AUC-ROC.


  In this paper, we present a novel bilevel optimization-based training
approach to training acoustic models for automatic speech recognition (ASR)
tasks that we term {bi-level joint unsupervised and supervised training
(BL-JUST)}. {BL-JUST employs a lower and upper level optimization with an
unsupervised loss and a supervised loss respectively, leveraging recent
advances in penalty-based bilevel optimization to solve this challenging ASR
problem with affordable complexity and rigorous convergence guarantees.} To
evaluate BL-JUST, extensive experiments on the LibriSpeech and TED-LIUM v2
datasets have been conducted. BL-JUST achieves superior performance over the
commonly used pre-training followed by fine-tuning strategy.


  Light rays from a multiply imaged quasar usually sample different path
lengths across the deflector. Extinction in the lensing galaxy may thus lead to
a differential obscuration and reddening between the observed macro-lensed QSO
images. These effects naturally depend on the precise shape of the extinction
law and on the redshift of the lens. By means of numerical Monte-Carlo
simulations, using a least-squares fitting method and assuming an extinction
law similar to that observed in the Galaxy, we show how accurate photometric
observations of multiply imaged quasars obtained in several spectral bands
could lead to the estimate of the lens redshift, irrespective of the visibility
of the deflector. Observational requirements necessary to apply this method to
real cases are thoroughly discussed. If extinction laws turn out to be too
different from galaxy to galaxy, we find out that more promising observations
should consist in getting high signal-to-noise low resolution spectra of at
least three distinct images of a lensed quasar, over a spectral range as wide
as possible, from which it should be straightforward to extract the precise
shape of the redshifted extinction law. Very high signal-to-noise, low spectral
resolution, VLT observations of H1413+117 and MG 0414+0534 should enable one to
derive such a redshifted extinction law.


  Diproche ("Didactical Proof Checking") is an automatic system for supporting
the acquistion of elementary proving skills in the initial phase of university
education in mathematics. A key feature of Diproche - which is designed by the
example of the Naproche system developed by M. Cramer and others - is an
automated proof checker for proofs written in a controlled fragment of natural
language specifically designed to capture the language of beginners' proving
exercises in mathematics. Both the accepted language and proof methods depend
on the didactical and mathematical context and vary with the level of education
and the topic proposed. An overall presentation of the system in general was
given in Carl and Krapf 2019. Here, we briefly recall the basic architecture of
Diproche and then focus on explaining key features and the working principles
of Diproche in the sample topics of elementary number theory and axiomatic
geometry.


  Both cellular automata (CA) and lattice-gas automata (LG) provide finite
algorithmic presentations for certain classes of infinite dynamical systems
studied by symbolic dynamics; it is customary to use the term `cellular
automaton' or `lattice gas' for the dynamic system itself as well as for its
presentation. The two kinds of presentation share many traits but also display
profound differences on issues ranging from decidability to modeling
convenience and physical implementability.
  Following a conjecture by Toffoli and Margolus, it had been proved by Kari
(and by Durand--Lose for more than two dimensions) that any invertible CA can
be rewritten as an LG (with a possibly much more complex ``unit cell''). But
until now it was not known whether this is possible in general for
noninvertible CA--which comprise ``almost all'' CA and represent the bulk of
examples in theory and applications. Even circumstantial evidence--whether in
favor or against--was lacking.
  Here, for noninvertible CA, (a) we prove that an LG presentation is out of
the question for the vanishingly small class of surjective ones. We then turn
our attention to all the rest--noninvertible and nonsurjective--which comprise
all the typical ones, including Conway's `Game of Life'. For these (b) we prove
by explicit construction that all the one-dimensional ones are representable as
LG, and (c) we present and motivate the conjecture that this result extends to
any number of dimensions.
  The tradeoff between dissipation rate and structural complexity implied by
the above results have compelling implications for the thermodynamics of
computation at a microscopic scale.


  We present a novel algorithm -- convex natural evolutionary strategies
(CoNES) -- for optimizing high-dimensional blackbox functions by leveraging
tools from convex optimization and information geometry. CoNES is formulated as
an efficiently-solvable convex program that adapts the evolutionary strategies
(ES) gradient estimate to promote rapid convergence. The resulting algorithm is
invariant to the parameterization of the belief distribution. Our numerical
results demonstrate that CoNES vastly outperforms conventional blackbox
optimization methods on a suite of functions used for benchmarking blackbox
optimizers. Furthermore, CoNES demonstrates the ability to converge faster than
conventional blackbox methods on a selection of OpenAI's MuJoCo reinforcement
learning tasks for locomotion.


  We report a closed-loop control system for paramagnetic catalytically
self-propelled Janus microrobots. We achieve this control by employing
electromagnetic coils that direct the magnetic field in a desired orientation
to steer the microrobots. The microrobots move due to the catalytic
decomposition of hydrogen peroxide, during which they align themselves to the
magnetic torques applied to them. Because the angle between their direction of
motion and their magnetic orientation is a priori unknown, an algorithm is used
to determine this angular offset and adjust the magnetic field appropriately.
The microrobots are located using real-time particle tracking that integrates
with a video camera. A target location or desired trajectory can be drawn by
the user for the microrobots to follow.


  Real-time biosignal processing on wearable devices has attracted worldwide
attention for its potential in healthcare applications. However, the
requirement of low-area, low-power and high adaptability to different patients
challenge conventional algorithms and hardware platforms. In this design, a
compact online learning neuromorphic hardware architecture with ultra-low power
consumption designed explicitly for biosignal processing is proposed. A
trace-based Spiking-Timing-Dependent-Plasticity (STDP) lgorithm is applied to
realize hardware-friendly online learning of a single-layer
excitatory-inhibitory spiking neural network. Several techniques, including
event-driven architecture and a fully optimized iterative computation approach,
are adopted to minimize the hardware utilization and power consumption for the
hardware implementation of online learning. Experiment results show that the
proposed design reaches the accuracy of 87.36% and 83% for the Mixed National
Institute of Standards and Technology database (MNIST) and ECG classification.
The hardware architecture is implemented on a Zynq-7020 FPGA. Implementation
results show that the Look-Up Table (LUT) and Flip Flops (FF) utilization
reduced by 14.87 and 7.34 times, respectively, and the power consumption
reduced by 21.69% compared to state of the art.


  We suggest that the unusual events observed by the ANITA experiment originate
from axion particles traversing the Earth. Under the influence of the
geomagnetic field, the axion may oscillate into a photon and vice-versa. To
amplify the axion transition into photon, we consider that the phenomenon takes
place at resonance, where the effective photon mass is equal to the axion mass.
This requirement fixes the axion mass at 200 eV. An axion at this mass scale
reproduces the cold dark matter scenario. If our interpretation prevails, with
the help of axions we can establish an axion tomography of the Earth.


  We investigate the problem of corruption robustness in offline reinforcement
learning (RL) with general function approximation, where an adversary can
corrupt each sample in the offline dataset, and the corruption level
$\zeta\geq0$ quantifies the cumulative corruption amount over $n$ episodes and
$H$ steps. Our goal is to find a policy that is robust to such corruption and
minimizes the suboptimality gap with respect to the optimal policy for the
uncorrupted Markov decision processes (MDPs). Drawing inspiration from the
uncertainty-weighting technique from the robust online RL setting
\citep{he2022nearly,ye2022corruptionrobust}, we design a new uncertainty weight
iteration procedure to efficiently compute on batched samples and propose a
corruption-robust algorithm for offline RL. Notably, under the assumption of
single policy coverage and the knowledge of $\zeta$, our proposed algorithm
achieves a suboptimality bound that is worsened by an additive factor of
$\mathcal{O}(\zeta (C(\widehat{\mathcal{F}},\mu)n)^{-1})$ due to the
corruption. Here $\widehat{\mathcal{F}}$ is the confidence set, and the dataset
$\mathcal{Z}_n^H$, and $C(\widehat{\mathcal{F}},\mu)$ is a coefficient that
depends on $\widehat{\mathcal{F}}$ and the underlying data distribution $\mu$.
When specialized to linear MDPs, the corruption-dependent error term reduces to
$\mathcal{O}(\zeta d n^{-1})$ with $d$ being the dimension of the feature map,
which matches the existing lower bound for corrupted linear MDPs. This suggests
that our analysis is tight in terms of the corruption-dependent term.


  We present the results of the monitoring campaign of the Type IIn supernova
(SN) 2018cnf (aka ASASSN-18mr). It was discovered about 10 days before the
maximum light (on MJD = 58293.4+-5.7 in the V band, with MV = -18.13+-0.15
mag). The multiband light curves show an immediate post-peak decline with some
minor luminosity fluctuations, followed by a flattening starting about 40 days
after maximum. The early spectra are relatively blue and show narrow Balmer
lines with P Cygni profiles. Additionally, Fe II, O I, He I and Ca II are
detected. The spectra show little evolution with time, with intermediate-width
features becoming progressively more prominent, indicating stronger interaction
of the SN ejecta with the circumstellar medium. The inspection of archival
images from the Panoramic Survey Telescope and Rapid Response System
(Pan-STARRS) survey has revealed a variable source at the SN position, with a
brightest detection in December 2015 at Mr = -14.66+-0.17 mag. This was likely
an eruptive phase from the massive progenitor star started from at least
mid-2011, and that produced the circumstellar environment within which the star
exploded as a Type IIn SN. The overall properties of SN 2018cnf closely
resemble those of transients such as SN 2009ip. This similarity favours a
massive hypergiant, perhaps a luminous blue variable, as progenitor for SN
2018cnf.


  Control Barrier Functions (CBFs) that provide formal safety guarantees have
been widely used for safety-critical systems. However, it is non-trivial to
design a CBF. Utilizing neural networks as CBFs has shown great success, but it
necessitates their certification as CBFs. In this work, we leverage bound
propagation techniques and the Branch-and-Bound scheme to efficiently verify
that a neural network satisfies the conditions to be a CBF over the continuous
state space. To accelerate training, we further present a framework that embeds
the verification scheme into the training loop to synthesize and verify a
neural CBF simultaneously. In particular, we employ the verification scheme to
identify partitions of the state space that are not guaranteed to satisfy the
CBF conditions and expand the training dataset by incorporating additional data
from these partitions. The neural network is then optimized using the augmented
dataset to meet the CBF conditions. We show that for a non-linear
control-affine system, our framework can efficiently certify a neural network
as a CBF and render a larger safe set than state-of-the-art neural CBF works.
We further employ our learned neural CBF to derive a safe controller to
illustrate the practical use of our framework.


  An ion and a polar molecule interact by an anisotropic ion-dipole potential
scaling as $- \alpha \cos (\theta)/r^2$ at large distances. Due to its
long-range character, it modifies the properties of angular wave functions,
which are no longer given by spherical harmonics. In addition, an effective
centrifugal potential in the radial equation can become attractive for low
angular momenta. In this paper, we develop a general framework for an
ion-dipole reactive scattering, focusing on the regime of large $\alpha$. We
introduce modified spherical harmonics as solutions of the angular part of the
Schr\"odinger equation and derive several useful approximations in the limit of
large $\alpha$. We present a formula for the scattering amplitude expressed in
terms of the modified spherical harmonics and we derive expressions for the
elastic and reactive collision rates. The solutions of the radial equation are
given by Bessel functions, and we analyse their behaviour in two distinct
regimes corresponding, basically, to attractive and repulsive long-range
centrifugal potentials. Finally, we study reactive collisions in the universal
regime, where the short-range probability of loss or reaction is equal to
unity.


  We consider the fourth-order Schr\"odinger equation $$ i\partial_tu+\Delta^2
u+\mu\Delta u+\lambda|u|^\alpha u=0, $$ where $\alpha>0,\mu=\pm1$ or $0$ and
$\lambda\in\mathbb{C}$. Firstly, we prove local well-posedness in
$H^4\left(\R^N\right)$ in both $H^4$ subcritical and critical case: $\alpha>0$,
$(N-8)\alpha\leq8$. Then, for any given compact set $K\subset\mathbb{R}^N$, we
construct $H^4(\R^N)$ solutions that are defined on $(-T, 0)$ for some $T>0$,
and blow up exactly on $K$ at $t=0$.


  In measure theory several results are known how measure spaces are
transformed into each other. But since moment functionals are represented by a
measure we investigate in this study the effects and implications of these
measure transformations to moment funcationals. We gain characterizations of
moments functionals. Among other things we show that for a compact and path
connected set $K\subset\mathbb{R}^n$ there exists a measurable function $g:K\to
[0,1]$ such that any linear functional
$L:\mathbb{R}[x_1,\dots,x_n]\to\mathbb{R}$ is a $K$-moment functional if and
only if it has a continuous extension to some
$\overline{L}:\mathbb{R}[x_1,\dots,x_n]+\mathbb{R}[g]\to\mathbb{R}$ such that
$\tilde{L}:\mathbb{R}[t]\to\mathbb{R}$ defined by $\tilde{L}(t^d) :=
\overline{L}(g^d)$ for all $d\in\mathbb{N}_0$ is a $[0,1]$-moment functional
(Hausdorff moment problem). Additionally, there exists a continuous function
$f:[0,1]\to K$ independent on $L$ such that the representing measure
$\tilde{\mu}$ of $\tilde{L}$ provides the representing measure
$\tilde{\mu}\circ f^{-1}$ of $L$. We also show that every moment functional
$L:\mathcal{V}\to\mathbb{R}$ is represented by $\lambda\circ f^{-1}$ for some
measurable function $f:[0,1]\to\mathbb{R}^n$ where $\lambda$ is the Lebesgue on
$[0,1]$.


  Let $\pi$ be a genuine cuspidal representation of the metaplectic group of
rank $n$. We consider the theta lifts to the orthogonal group associated to a
quadratic space of dimension $2n+1$. We show a case of regularised Rallis inner
product formula that relates the pairing of theta lifts to the central value of
the Langlands $L$-function of $\pi$ twisted by a character. The bulk of this
article focuses on proving a case of regularised Siegel-Weil formula, on which
the Rallis inner product formula is based and whose proof is missing in the
literature.


  We prove that for any smooth complex projective threefold of Kodaira
dimension one, the $m$-th pluricanonical map is birational to the Iitaka
fibration for every $m\geq5868$ and divisible by $12$.


  Already in the past, hadronic tau decays have served as an interesting source
to obtain information on the parameters of the Standard Model, like the strong
coupling alpha_s or the strange quark mass. Below it will be shown that the
approach to obtain the strange mass from the hadronic tau-decay width can be
turned into a determination of the CKM matrix element V_us, once the strange
mass is used as an input from other sources. At present, we obtain V_us =
0.2179 +- 0.0045, where the error is completely dominated by the experimental
uncertainty, and can be improved through an improved measurement of the
hadronic tau-decay rate into strange particles R_tau,S.