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We calculate diffractive $D^{0}$ photoproduction in ultraperipheral lead--lead (Pb--Pb) collisions at the Large Hadron Collider (LHC) within the recently developed G$γ$A--FONLL framework, where photon--lead diffraction is modeled using nuclear diffractive parton distributions obtained in the leading twist shadowing approach and the photon fluxes include corrections for independent electromagnetic dissociation accompanying the hard photoproduction process. We then use the predicted diffractive cross section to quantify the coherent diffractive contribution rejected by the $Xn0n$ neutron-tagged event selection adopted in the first measurement of $D^0$ photoproduction in Pb-Pb collisions at the LHC, which requires neutron emission from only one of the two lead nuclei. In this work, we also extend the G$γ$A--FONLL framework to proton--lead ($p$--Pb) UPCs and present predictions for inclusive and diffractive $D^{0}$ photoproduction at the LHC. In this case, the dominant configuration is photon emission from the lead ion followed by photon--proton scattering, and the diffractive contribution is evaluated using proton diffractive parton distributions constrained by HERA data.

Sales lead conversion in high-stakes domains (e.g., automotive, real estate) differs fundamentally from e-commerce recommendation due to prolonged decision cycles and multi-stage funnels. Traditional lead scoring methods rule-based scorecards, machine learning, or pointwise CTR models face severe challenges: sparse supervision, a semantic gap in unstructured CRM logs, and inability to capture relative lead priority. While Large Language Models(LLMs) offer superior semantic understanding of customer interactions, general-purpose LLMs are ill-suited for lead ranking: they generate text rather than comparable scores, and lack alignment with the hierarchical priorities of sales funnels. We introduce an LLM-based discriminative framework for sales lead scoring, which supports joint modeling of structured CRM features and unstructured customer interactions. On top of this framework, we propose HPRO (Hierarchical Preference Ranking Optimization), which augments sales lead scoring with a hierarchical preference ranking objective. HPRO employs a margin-aware Bradley-Terry formulation to transform sparse binary labels into dense, funnel-aware preference pairs, enabling lead scoring to levera

We introduce an open-source Python framework for generating synthetic ECG image datasets to advance critical deep learning-based tasks in ECG analysis, including ECG digitization, lead region and lead name detection, and pixel-level waveform segmentation. Using the PTB-XL signal dataset, our proposed framework produces four open-access datasets: (1) ECG images in various lead configurations paired with time-series signals for ECG digitization, (2) ECG images annotated with YOLO-format bounding boxes for detection of lead region and lead name, (3)-(4) cropped single-lead images with segmentation masks compatible with U-Net-based models in normal and overlapping versions. In the overlapping case, waveforms from neighboring leads are superimposed onto the target lead image, while the segmentation masks remain clean. The open-source Python framework and datasets are publicly available at https://github.com/rezakarbasi/ecg-image-and-signal-dataset and https://doi.org/10.5281/zenodo.15484519, respectively.

Lead sheets have become commonplace in generative music research, being used as an initial compressed representation for downstream tasks like multitrack music generation and automatic arrangement. Despite this, researchers have often fallen back on deterministic reduction methods (such as the skyline algorithm) to generate lead sheets when seeking paired lead sheets and full scores, with little attention being paid toward the quality of the lead sheets themselves and how they accurately reflect their orchestrated counterparts. To address these issues, we propose the problem of conditional lead sheet generation (i.e. generating a lead sheet given its full score version), and show that this task can be formulated as an unsupervised music compression task, where the lead sheet represents a compressed latent version of the score. We introduce a novel model, called Lead-AE, that models the lead sheets as a discrete subselection of the original sequence, using a differentiable top-k operator to allow for controllable local sparsity constraints. Across both automatic proxy tasks and direct human evaluations, we find that our method improves upon the established deterministic baseline and

The chemical evolution of neutron capture elements in the Milky Way is still a matter of debate. Although more and more studies investigate their chemical behaviour, there is still a lack of a significant large sample of abundances of a key heavy element: lead. Lead is the final product of the s-process nucleosynthesis channel and is one of the most stable heavy elements. We analysed high-resolution spectra from the ESO UVES and FEROS archives. Atmospheric parameters were taken from the AMBRE parametrisation. We used the automated abundance method GAUGUIN to derive lead abundances in 653 slow-rotating FGK-type stars from the 368.34nm Pb I line. We present the largest catalogue of homogeneous LTE and non-LTE lead abundances ever published with metallicities ranging from -2.9 to 0.6dex and [Pb/Fe] from -0.7 to 3.3dex. Within this sample, no lead-enhanced Asymptotic Giant Branch (AGB) stars were found, but nine lead-enhanced metal-poor stars ([Pb/Fe] > 1.5) were detected. Most of them were already identified as carbon-enhanced metal-poor stars with enrichments in other s-process species. The lead abundance of 13 Gaia Benchmark Stars are also provided. We then investigated the Pb co

Social and collaborative platforms emit multivariate time-series traces in which early interactions -- such as views, likes, or downloads -- are followed, sometimes months or years later, by higher impact like citations, sales, or reviews. We formalize this setting as Lead-Lag Forecasting (LLF): given an early usage channel (the lead), predict a correlated but temporally shifted outcome channel (the lag). Despite the ubiquity of such patterns, LLF has not been treated as a unified forecasting problem within the time-series community, largely due to the absence of standardised datasets. To anchor research in LLF, here we present two high-volume benchmark datasets: arXiv (accesses -> citations of 2.3M papers) and GitHub (pushes/stars -> forks of 3M repositories). Our datasets provide ideal testbeds for lead-lag forecasting, by capturing long-horizon dynamics across years, spanning the full spectrum of outcomes, and avoiding survivorship bias in sampling. We documented all technical details of data curation and cleaning, verified the presence of lead-lag dynamics through statistical and classification tests, and benchmarked parametric and non-parametric baselines for regression.

Algebraic matrix multiplication algorithms are designed by bounding the rank of matrix multiplication tensors, and then using a recursive method. However, designing algorithms in this way quickly leads to large constant factors: if one proves that the tensor for multiplying $n \times n$ matrices has rank $\leq t$, then the resulting recurrence shows that $M \times M$ matrices can be multiplied using $O(n^2 \cdot M^{\log_n t})$ operations, where the leading constant scales proportionally to $n^2$. Even modest increases in $n$ can blow up the leading constant too much to be worth the slight decrease in the exponent of $M$. Meanwhile, the asymptotically best algorithms use very large $n$, such that $n^2$ is larger than the number of atoms in the visible universe! In this paper, we give new ways to use tensor rank bounds to design matrix multiplication algorithms, which lead to smaller leading constants than the standard recursive method. Our main result shows that, if the tensor for multiplying $n \times n$ matrices has rank $\leq t$, then $M \times M$ matrices can be multiplied using only $n^{O(1/(\log n)^{0.33})} \cdot M^{\log_n t}$ operations. In other words, we improve the leading

Human exposure to lead (Pb) is a global health concern, yet existing technologies for detecting lead in our environment remain prohibitively expensive for widespread deployment. Here we present a new concept towards lead screening using X-ray fluorescence (XRF) in an unconventional geometry we coin transmission XRF in which the sample is placed between the source and detector. For cost reduction, we then show that $^{241}$Am found in ionizing smoke detectors is spectrally suitable for Pb L-shell XRF generation and can thus replace X-ray tubes used in conventional XRF devices. Exploring soil screening as the first application, we demonstrate with Monte Carlo simulations that a configuration with 7$\times$ $^{241}$Am sources and a standard silicon drift detector can enable screening-relevant detection limits (100 ppm Pb) in soil within practical measurement times (<30 min). We believe this concept opens a route toward low-cost and scalable XRF instrumentation for democratizing lead screening across a wide range of samples.

We follow the nonequilibrium Green's function formalism to study time-dependent thermal transport in a linear chain system consisting of two semi-infinite leads connected together by a coupling that is harmonically modulated in time. The modulation is driven by an external agent that can absorb and emit energy. We determine the energy current flowing out of the leads exactly by solving numerically the Dyson equation for the contour-ordered Green's function. The amplitude of the modulated coupling is of the same order as the interparticle coupling within each lead. When the leads have the same temperature, our numerical results show that modulating the coupling between the leads may direct energy to either flow into the leads simultaneously or flow out of the leads simultaneously, depending on the values of the driving frequency and temperature. A special combination of values of the driving frequency and temperature exists wherein no net energy flows into or out of the leads, even for long times. When one of the leads is warmer than the other, net energy flows out of the warmer lead. For the cooler lead, however, the direction of the energy current flow depends on the values of the

The presence of unreacted lead iodide in organic-inorganic lead halide perovskite solar cells is widely correlated with an increase in power conversion efficiency. We investigate the mechanism for this increase by identifying the role of surfaces and interfaces present between methylammonium lead iodide perovskite films and excess lead iodide. We show how type I and II band alignments arising under different conditions result in either passivation of surface defects or hole injection. Through first-principles simulations of solid-solid interfaces, we find that lead iodide captures holes from methylammonium lead iodide and modulates the formation of defects in the perovskite, affecting recombination. Using surface-sensitive optical spectroscopy techniques, such as transient reflectance and time-resolved photoluminescence, we show how excess lead iodide affects the diffusion and surface recombination velocity of charge carriers in methylammonium lead iodide films. Our coupled experimental and theoretical results elucidate the role of excess lead iodide in perovskite solar cells.

Real-time monocular 3D object detection remains challenging due to severe depth ambiguity, viewpoint shifts, and the high computational cost of 3D reasoning. Existing approaches either rely on LiDAR or geometric priors to compensate for missing depth or sacrifice efficiency to achieve competitive accuracy. We introduce LeAD-M3D, a monocular 3D detector that achieves state-of-the-art accuracy and real-time inference without extra modalities. Our method is enabled by three key components. Asymmetric Augmentation Denoising Distillation (A2D2) transfers geometric knowledge from a clean-image teacher to a MixUp-noised student via a quality- and importance-weighted depth-feature loss, enabling stronger depth reasoning without LiDAR. 3D-aware Consistent Matching (CM$_{\text{3D}}$) improves prediction-to-ground truth assignment by integrating 3D MGIoU into the matching score, yielding stable and precise supervision. Finally, Confidence-Gated 3D Inference (CGI$_{\text{3D}}$) accelerates inference by restricting expensive 3D regression to confident regions. Together, these contributions set a new Pareto frontier for monocular 3D detection: LeAD-M3D achieves state-of-the-art accuracy on KITTI

Heavy metal subdwarfs are a class of hot subdwarfs with very high abundances of heavy elements, typically around 10 000 times solar. They include stars which are strongly enhanced in either lead or zirconium, as well as other elements. Vertical stratification of the enhanced elements, where the element is concentrated in a thin layer of the atmosphere, has been proposed as a mechanism to explain the apparent high abundances. This paper explores the effects of the vertical stratification of lead on theoretical spectra of hot subdwarfs. The concentration of lead in different regions of the model atmosphere is found to affect individual lines in a broadly wavelength-dependent manner, with the potential for lines to display modified profiles depending on the location of lead enhancement in the atmosphere. This wavelength dependence highlights the importance of observations in both the optical and the UV for determining whether stratification is present in real stars.

We investigate various aspects of the statistics of leaders in growing network models defined by stochastic attachment rules. The leader is the node with highest degree at a given time (or the node which reached that degree first if there are co-leaders). This comprehensive study includes the full distribution of the degree of the leader, its identity, the number of co-leaders, as well as several observables characterizing the whole history of lead changes: number of lead changes, number of distinct leaders, lead persistence probability. We successively consider the following network models: uniform attachment, linear attachment (the Barabasi-Albert model), and generalized preferential attachment with initial attractiveness.

This paper presents a multi-lead fusion method for the accurate and automated detection of the QRS complex location in 12 lead ECG (Electrocardiogram) signals. The proposed multi-lead fusion method accurately delineates the QRS complex by the fusion of detected QRS complexes of the individual 12 leads. The proposed algorithm consists of two major stages. Firstly, the QRS complex location of each lead is detected by the single lead QRS detection algorithm. Secondly, the multi-lead fusion algorithm combines the information of the QRS complex locations obtained in each of the 12 leads. The performance of the proposed algorithm is improved in terms of Sensitivity and Positive Predictivity by discarding the false positives. The proposed method is validated on the ECG signals with various artifacts, inter and intra subject variations. The performance of the proposed method is validated on the long duration recorded ECG signals of St. Petersburg INCART database with Sensitivity of 99.87% and Positive Predictivity of 99.96% and on the short duration recorded ECG signals of CSE (Common Standards for Electrocardiography) multi-lead database with 100% Sensitivity and 99.13% Positive Predictiv

One of the most viable renewable energies is solar power because of its versatility, reliability, and abundance. In the market, a majority of the solar panels are made from silicon wafers. These solar panels have an efficiency of 26.4 percent and can last more than 25 years. The perovskite solar cell is a relatively new type of solar technology that has a similar maximum efficiency and much cheaper costs, the only downside is that it is less stable and the most efficient type uses lead. The name perovskite refers to the crystal structure with an ABX3 formula of the perovskite layer of the cell. All materials possess a property called a band gap. The smaller the band gap the more conductive the material, but this does not necessarily mean that the smaller the band gap the better the solar cell. The Shockley-Queisser limit provides the optimal band gap in terms of efficiency for a single junction solar cell which is 1.34 eV for single junction cells. This research focuses on tuning the band gap of lead-free perovskites through B-site cation replacement. Through this investigation, the optical band gaps of tin and lead perovskites were re-established. However, the copper-based perovsk

The recent discovery of Coherent Elastic neutrino-Nucleus Scattering (CE$ν$NS) has created new opportunities to detect and study neutrinos. The interaction cross-section in CE$ν$NS scales quadratically with the number of neutrons, making heavy-nuclei targets such as active lead-based detectors ideal. In this Letter, we discuss for the first time the potential of semiconductor lead perovskites for building neutrino detectors. Lead perovskites have emerged in the last decade as revolutionary materials for radiation detection due to their heavy and flexible element composition and their unique optoelectronic properties that result in an excellent energy resolution at an economic cost. While dedicated research and development will be necessary, we find great benefits and no inherent obstacles for the development of lead perovskites as CE$ν$NS detectors.

The recent claim of room temperature superconductivity in a copper-doped lead apatite compound, called LK-99, has sparked remarkable interest and controversy. Subsequent experiments have largely failed to reproduce the claimed superconductivity, while theoretical works have identified multiple key features including strong electronic correlation, structural instabilities, and dopability constraints. A puzzling claim of several recent theoretical studies is that both parent and copper-doped lead apatite structures are dynamically unstable at the harmonic level, questioning decades of experimental reports of the parent compound structures and the recently proposed copper-doped structures. In this work, we demonstrate that both parent and copper-doped lead apatite structures are dynamically stable at room temperature. Anharmonic phonon-phonon interactions play a key role in stabilizing some copper-doped phases, while most phases are largely stable even at the harmonic level. We also show that dynamical stability depends on both volume and correlation strength, suggesting controllable ways of exploring the copper-doped lead apatite structural phase diagram. Our results fully reconcile

The effect of a Coulombic coupling on the dynamics of a quantum dot hybridized to leads is determined. The calculation treats the interaction between charge fluctuations on the dot and the dynamically generated image charge in the leads. A formally exact solution is presented for a dot coupled to a Luttinger liquid and an approximate solution, equivalent to treating the lead dynamics within a random phase approximation, is given for a dot coupled to a two- or three-dimensional metallic lead. The leading divergences arising from the long-ranged Coulomb interaction are found to cancel, so that in the two- and three-dimensional cases the quantum-dot dynamics is equivalent to that obtained by neglecting both the dot-lead Coulomb coupling and the Coulomb renormalization of the lead electrons, while in the one-dimensional case the dot-lead mixing is enhanced relative to the non-interacting case. Explicit results are given for the short-time dynamics.

While it is widely accepted that lead-based paint and leaded gasoline are primary sources of elevated concentrations of lead in residential soils, conclusions regarding their relative contributions are mixed and generally study specific. We develop a novel nonlinear regression for soil lead concentrations over time. It is argued that this methodology provides useful insights into the partitioning of the average soil lead concentration by source and time over large residential areas. The methodology is used to investigate soil lead concentrations from the 1987 Minnesota Lead Study and the 1990 National Lead Survey. Potential litigation issues are discussed briefly.

Electrocardiography (ECG) signal generation has been heavily explored using generative adversarial networks (GAN) because the implementation of 12-lead ECGs is not always feasible. The GAN models have achieved remarkable results in reproducing ECG signals but are only designed for multiple lead inputs and the features the GAN model preserves have not been identified-limiting the generated signals use in cardiovascular disease (CVD)-predictive models. This paper presents ECGNet which is a procedure that generates a complete set of 12-lead ECG signals from any single lead input using a GAN framework with a bidirectional long short-term memory (LSTM) generator and a convolutional neural network (CNN) discriminator. Cross and auto-correlation analysis performed on the generated signals identifies features conserved during the signal generation-i.e., features that can characterize the unique-nature of each signal and thus likely indicators of CVD. Finally, by using ECG signals annotated with the CVD-indicative features detailed by the correlation analysis as inputs for a CVD-onset-predictive CNN model, we overcome challenges preventing the prediction of multiple-CVD targets. Our models