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We present a massively parallel, GPU-accelerated implementation of the Bethe-Salpeter equation (BSE) for the calculation of the vertical excitation energies (VEEs) and optical absorption spectra of condensed and molecular systems, starting from single-particle eigenvalues and eigenvectors obtained with density functional theory. The algorithms adopted here enable one to circumvent the slowly converging sums over empty and occupied states and the inversion of large dielectric matrices, through a density matrix perturbation theory approach and a low-rank decomposition of the screened Coulomb interaction, respectively. In addition, we achieve computational savings by exploiting the nearsightedness of the density matrix of semiconductors and insulators, and we scale our calculations to thousands of GPUs with a hierarchical loop- and data-distribution strategy. We demonstrate the efficacy of our method by computing the VEEs of several spin defects in wide band-gap materials, and we show that supercells with up to 1000 atoms are necessary to obtain converged results. We discuss the validity of common approximations, e.g., the solution of the BSE with truncated sums over empty and occupied states. We then apply our GW-BSE implementation to a diamond lattice with 1727 atoms to study the symmetry breaking of triplet states caused by the interaction of a point defect with an extended line defect.

Collision avoidance and trajectory planning are crucial in multi-robot systems, particularly in environments with numerous obstacles. Although extensive research has been conducted in this field, the challenge of rapid traversal through such environments has not been fully addressed. This paper addresses this problem by proposing a novel real-time scheduling scheme designed to optimize the passage of multi-robot systems through complex, obstacle-rich maps. Inspired from network flow optimization, our scheme decomposes the environment into a network structure, enabling the efficient allocation of robots to paths based on real-time congestion data. The proposed scheduling planner operates on top of existing collision avoidance algorithms, focusing on minimizing traversal time by balancing robot detours and waiting times. Our simulation results demonstrate the efficiency of the proposed scheme. Additionally, we validated its effectiveness through real world flight tests using ten quadrotors. This work contributes a lightweight, effective scheduling planner capable of meeting the real-time demands of multi-robot systems in obstacle-rich environments.

Recent predictions suggest that oxides, such as MgO and CaO, could serve as hosts of spin defects with long coherence times and thus be promising materials for quantum applications. However, in most cases specific defects have not yet been identified. Here, by using a high-throughput first-principles framework and advanced electronic structure methods, we identify an NV-like center in MgO with favorable electronic and optical properties for hybrid quantum technologies. We show that this defect has stable triplet ground and excited states, with singlet shelving states enabling optical initialization and spin-dependent readout. We predict several properties, including absorption, emission, and zero-phonon line energies, as well as zero-field splitting tensor, and hyperfine interaction parameters, which can aid in the experimental identification of this defect. Our calculations show that due to a strong pseudo-Jahn Teller effect and low frequency phonon modes, the NV- center in MgO is subject to a substantial vibronic coupling. We discuss design strategies to reduce such coupling and increase the Debye-Waller factor, including the effect of strain and the localization of the defect states. We propose that the favorable properties of the NV- defect, along with the technological maturity of MgO, could enable hybrid classical-quantum applications, such as spintronic quantum sensors and single qubit gates., Comment: 16 pages, 5 figures

The Einstein ring of a deformed AdS-Schwarzschild black hole (BH) is investigated under the wave optics framework. When the source is fixed on the AdS boundary, we can obtain the corresponding response function generated on the opposite side of the boundary. Utilizing a specialized optical system equipped with a convex lens enables us to capture an image of the BH's holographic Einstein ring on the screen. The influence of the relevant physical parameters and the observed inclination on the characteristics of the Einstein ring can also be studied, which found that the alteration of the observer's position, the image displayed on the screen changes from an axisymmetric ring to an arc, ultimately becoming a solitary point of luminosity. Furthermore, variations in the relevant physical parameters naturally exert an influence on the Einstein ring, resulting in various changes to both its radius and brightness. We also discover the photon ring of the BH from the perspective of the geometric optics, and the numerical results demonstrate that the incident angle of the photon ring is consistent with that of the Einstein ring. It is found that the validity of studying the Einstein ring by wave optics. The investigation on the Einstein rings formed by the deformed AdS-Schwarzschild BHs is anticipated to yield more comprehensive insights into the spacetime geometry, enabling its distinction from Schwarzschild BH., Comment: 12 pages, 21 figures

We have investigated light nuclei pair photoproduction in relativistic heavy ion ultraperipheral collisions. As a first attempt, we employ our previously developed quantum electrodynamics model, which incorporates a wave-packet description of initial nuclei, to compute the cross section for proton-antiproton pair photoproduction. The effective vertex for the photon and proton interaction is chosen based on studies of two-photon exchange effects in hadron physics. We present the transverse momentum, invariant mass, and azimuthal angle distributions of proton-antiproton pairs at $\sqrt{s_{NN}}=200$ GeV in Au+Au ultraperipheral collisions. We observe a $\cos(2\phi)$ modulation and an almost negligible $\cos(4\phi)$ modulation in the azimuthal angle distribution. Our studies helps us better understand the matter generated by light., Comment: 6 pages, 3 figures

The particle-particle random phase approximation (ppRPA) within the hole-hole channel was recently proposed as an efficient tool for computing excitation energies of point defects in solids [J. Phys. Chem. Lett. 2024, 15, 2757-2764]. In this work, we investigate the application of ppRPA within the particle-particle channel for predicting correlated excited states of point defects, including the carbon-vacancy (VC) in diamond, the oxygen-vacancy (VO) in magnesium oxide (MgO), and the carbon dimer defect (C$_{\text{B}}$C$_{\text{N}}$) in two-dimensional hexagonal boron nitride (h-BN). Starting from a density functional theory calculation of the ($N-2$)-electron ground state, vertical excitation energies of the $N$-electron system are obtained as the differences between the two-electron addition energies. We show that active-space ppRPA with the B3LYP functional yields accurate excitation energies, with errors mostly smaller than 0.1 eV for tested systems compared to available experimental values. We further develop a natural transition orbital scheme within ppRPA, which provides insights into the multireference character of defect states. This study, together with our previous work, establishes ppRPA as a low-cost and accurate method for investigating excited-state properties of point defect systems.

Using the one-boson-exchange model, we investigate the interactions between the doubly charmed baryon $\Xi_{cc}(3621)$ and the $S-$wave (anti-)kaon accounting for the $S-D$ wave mixing and coupled-channel effects. We find the coupled $\Xi_{cc}K/\Xi_{cc}K^*$ state with $I(J^P)=0(1/2^-)$, the $\Xi_{cc}K^*$ state with $0(1/2^-)$, the $\Xi_{cc}\bar{K}$ state with $0(1/2^-)$, and the $\Xi_{cc}\bar{K}^*$ states with $0(1/2^-,3/2^-)$ can be recommended as good doubly charmed molecular candidates with strangeness $|S|=1$. We further examine their M1 radiative decay behaviors and magnetic moments within the constituent quark model framework. This information can enhance our understanding of their inner structures, including the distribution of electric charge and the orientation of the constituent quarks' spins., Comment: 11 pages, 5 figures, version appears in PRD

Retrieving and extracting knowledge from extensive research documents and large databases presents significant challenges for researchers, students, and professionals in today's information-rich era. Existing retrieval systems, which rely on general-purpose Large Language Models (LLMs), often fail to provide accurate responses to domain-specific inquiries. Additionally, the high cost of pretraining or fine-tuning LLMs for specific domains limits their widespread adoption. To address these limitations, we propose a novel methodology that combines the generative capabilities of LLMs with the fast and accurate retrieval capabilities of vector databases. This advanced retrieval system can efficiently handle both tabular and non-tabular data, understand natural language user queries, and retrieve relevant information without fine-tuning. The developed model, Generative Text Retrieval (GTR), is adaptable to both unstructured and structured data with minor refinement. GTR was evaluated on both manually annotated and public datasets, achieving over 90% accuracy and delivering truthful outputs in 87% of cases. Our model achieved state-of-the-art performance with a Rouge-L F1 score of 0.98 on the MSMARCO dataset. The refined model, Generative Tabular Text Retrieval (GTR-T), demonstrated its efficiency in large database querying, achieving an Execution Accuracy (EX) of 0.82 and an Exact-Set-Match (EM) accuracy of 0.60 on the Spider dataset, using an open-source LLM. These efforts leverage Generative AI and In-Context Learning to enhance human-text interaction and make advanced AI capabilities more accessible. By integrating robust retrieval systems with powerful LLMs, our approach aims to democratize access to sophisticated AI tools, improving the efficiency, accuracy, and scalability of AI-driven information retrieval and database querying.

The evolution of industry has enabled the integration of physical and digital systems, facilitating the collection of extensive data on manufacturing processes. This integration provides a reliable solution for improving process quality and managing equipment health. However, data collected from real manufacturing processes often exhibit challenging properties, such as severe class imbalance, high rates of missing values, and noisy features, which hinder effective machine learning implementation. In this study, a rare class prediction approach is developed for in situ data collected from a smart semiconductor manufacturing process. The primary objective is to build a model that addresses issues of noise and class imbalance, enhancing class separation. The developed approach demonstrated promising results compared to existing literature, which would allow the prediction of new observations that could give insights into future maintenance plans and production quality. The model was evaluated using various performance metrics, with ROC curves showing an AUC of 0.95, a precision of 0.66, and a recall of 0.96

In the rapidly evolving field of healthcare and beyond, the integration of generative AI in Electronic Health Records (EHRs) represents a pivotal advancement, addressing a critical gap in current information extraction techniques. This paper introduces GAMedX, a Named Entity Recognition (NER) approach utilizing Large Language Models (LLMs) to efficiently extract entities from medical narratives and unstructured text generated throughout various phases of the patient hospital visit. By addressing the significant challenge of processing unstructured medical text, GAMedX leverages the capabilities of generative AI and LLMs for improved data extraction. Employing a unified approach, the methodology integrates open-source LLMs for NER, utilizing chained prompts and Pydantic schemas for structured output to navigate the complexities of specialized medical jargon. The findings reveal significant ROUGE F1 score on one of the evaluation datasets with an accuracy of 98\%. This innovation enhances entity extraction, offering a scalable, cost-effective solution for automated forms filling from unstructured data. As a result, GAMedX streamlines the processing of unstructured narratives, and sets a new standard in NER applications, contributing significantly to theoretical and practical advancements beyond the medical technology sphere.

We have investigated the $\rho^{0}$ meson photoproduction in ultraperipheral isobaric collisions between $_{44}^{96}\textrm{Ru}+_{44}^{96}\textrm{Ru}$ and $_{40}^{96}\textrm{Zr}+_{40}^{96}\textrm{Zr}$ at $\sqrt{s_{NN}}=200$ GeV, employing the dipole model with the equivalent photon approximation. By implementing the Woods-Saxon distribution to represent the nuclear mass density, which is derived from density functional theory with an inclusion of nuclear deformation effects, we have calculated the transverse momentum $q_{T}$ spectra in isobaric collisions. We observe the characteristic dip behavior in these spectra, indicative of diffraction phenomena in high-energy physics. We notice that the deformation effects cause a nearly linear increase with $q_{T}^{2}$ for $q_{T}^{2}\lesssim0.015$ $\textrm{GeV}^{2}$, aligning with experimental observations. We offer a simple explanation for the observed behavior in these spectra by introducing the effective width of the nuclei in the thickness function. We also extend our discussion on the $\rho^{0}$ meson photoproduction with the targets $^{63}\textrm{Cu}$,$^{197}\textrm{Au}$, and $^{238}\textrm{U}$., Comment: 9 pages, 5 figures

In the framework of a one-boson-exchange model, we carry out a comprehensive investigation of the $\Xi_cN/\Lambda_c\Sigma/\Xi_c^{\prime}N/\Sigma_c\Lambda/\Xi_c^*N/\Sigma_c^*\Lambda/\Sigma_c\Sigma/\Sigma_c^*\Sigma$ interactions. We consider the $S$-$D$ wave mixing effects and the coupled channel effects to derive the relevant effective potentials. Our results can predict several possible charm-strange deuteron-like $\Xi_c^{(',*)}N$ hexaquarks, the $\Xi_c^{\prime}N$ molecules with $I(I^P)=0(0^+)$, $0(1^+)$, $1(1^+)$, the $\Xi_c^*N$ molecules with $0(1^+)$, $0(2^+)$, $1(2^+)$, and the coupled $\Xi_cN/\Xi_c^{\prime}N/\Xi_c^*N/\Sigma_c\Sigma/\Sigma_c^*\Sigma$ molecule with $0(1^+)$. We expect the experiments to search for our predictions of the $\Xi_c^{(\prime,*)}N$ bound states., Comment: 10 pages, accepted by the prd

The negatively charged boron-vacancy center ($\mathrm{V}_{\mathrm{B}}^-$) in hexagonal boron nitride (hBN) has recently emerged as a highly promising quantum sensor. Compared to the nitrogen-vacancy (NV) center in diamond, the change with temperature of the spin transition energy of $\mathrm{V}_{\mathrm{B}}^-$ is more than an order of magnitude larger, making it a potential nanoscale thermometer with superior sensitivity. However, the underlying mechanism of the observed large temperature dependence remains an open question. In this work, using isotopically purified $\mathrm{h}{}^{10}\mathrm{B}{}^{15}\mathrm{N}$, we systematically characterize the zero-field splitting, hyperfine interaction, and spin relaxation time of $\mathrm{V}_{\mathrm{B}}^-$ from 10 to 350$~$K. We carry out first-principle calculations of the $\mathrm{V}_{\mathrm{B}}^-$ spin-phonon interaction and show that a second-order effect from finite-temperature phonon excitations is responsible for the observed changes in experiments. By fitting our experimental results to a physically motivated model, we extract the dominant phonon mode which agrees well with our simulations. Finally, we investigate the dynamic nuclear spin polarization process at cryogenic temperatures. Our results provide key insights in $\mathrm{V}_{\mathrm{B}}^-$ centers and their utilization as nanoscale thermometers and phonon sensors., Comment: 7 pages, 4 figures and 1 table in main. 9 pages and 5 figures in supplementary

We present a theoretical study of the formation of self-trapped excitons (STEs) and the associated broadband emission in metal-halide perovskites Cs$_4$SnBr$_6$ and Cs$_2$AgInCl$_6$, using time-dependent density functional theory (TDDFT) with the dielectric-dependent hybrid (DDH) functional. Our approach allows for an accurate description of the excitonic effect and geometry relaxation in the electronic excited states and yields optical gap, STE emission energy, and emission spectra in reasonable agreement with experiments. We point out the significance of considering geometry relaxations in the electronic excited state by showing that the exciton-phonon coupling computed in the ground-state atomic geometry is insufficient to describe the physical properties of STEs. Overall, we find that TDDFT with the DDH hybrid functional is a suitable approach for the study of the formation of STEs in perovskite and provides insights for designing metal-halide perovskites with tailored emission properties.

We present an efficient particle-particle random phase approximation (ppRPA) approach that predicts accurate excitation energies of point defects, including the nitrogen-vacancy (NV$^-$) and the silicon-vacancy (SiV$^0$) centers in diamond and the divacancy center (VV$^0$) in 4H silicon carbide, with errors within 0.2 eV compared with experimental values. Starting from the ($N+2$)-electron ground state calculated with the density functional theory (DFT), the ppRPA excitation energies of the $N$-electron system are calculated as the differences between the two-electron removal energies of the ($N+2$)-electron system. We demonstrate that the ppRPA excitation energies converge rapidly with a few hundred of canonical active-space orbitals. We also show that active-space ppRPA has weak DFT starting-point dependence and is significantly cheaper than the corresponding ground-state DFT calculation. This work establishes ppRPA as an accurate and low-cost tool for investigating excited-state properties of point defects and opens up new opportunities for applications of ppRPA to periodic bulk materials.

Inorganic semiconductor materials are integral to various modern technologies, yet their brittleness and limited deformability/processability pose a significant challenge in the development of flexible, wearable, and miniaturized electronics. The recent discovery of room-temperature plasticity in a few inorganic semiconductors offers a promising pathway to address this challenge, but the deformation mechanisms of these materials remain unclear. Here, we investigate the deformation of InSe, a two-dimensional (2D) van der Waals (vdW) semiconductor with substantial plasticity. By developing a machine-learned deep potential, we perform atomistic simulations that capture the deformation features of hexagonal InSe upon out-of-plane compression. Surprisingly, we discover that InSe plastifies through a so-far unrecognized martensitic phase transformation; that is, the layered hexagonal structure is converted to a tetragonal lattice with specific orientation relationship. This observation is corroborated by high-resolution experimental observations and theory. It suggests a change of paradigm, where the design of new plastically-deformable inorganic semiconductors should focus on compositions and structures that favor phase transformations rather than traditional dislocation slip.

In recent years, pre-trained large language models (LLMs) have achieved tremendous success in the field of Natural Language Processing (NLP). Prior studies have primarily focused on general and generic domains, with relatively less research on specialized LLMs in the medical field. The specialization and high accuracy requirements for diagnosis in the medical field, as well as the challenges in collecting large-scale data, have constrained the application and development of LLMs in medical scenarios. In the field of ophthalmology, clinical diagnosis mainly relies on doctors' interpretation of reports and making diagnostic decisions. In order to take advantage of LLMs to provide decision support for doctors, we collected three modalities of ophthalmic report data and fine-tuned the LLaMA2 model, successfully constructing an LLM termed the "Ophtha-LLaMA2" specifically tailored for ophthalmic disease diagnosis. Inference test results show that even with a smaller fine-tuning dataset, Ophtha-LLaMA2 performs significantly better in ophthalmic diagnosis compared to other LLMs. It demonstrates that the Ophtha-LLaMA2 exhibits satisfying accuracy and efficiency in ophthalmic disease diagnosis, making it a valuable tool for ophthalmologists to provide improved diagnostic support for patients. This research provides a useful reference for the application of LLMs in the field of ophthalmology, while showcasing the immense potential and prospects in this domain.

Electronic defects in semiconductors form the basis for many emerging quantum technologies. Understanding defect spin and charge dynamics in solid state platforms is crucial to developing these building blocks, but many defect centers are difficult to access at the single-particle level due to the lack of sensitive readout techniques. A method for probing optically inactive spin defects would reveal semiconductor physics at the atomic scale and advance the study of new quantum systems. We exploit the intrinsic correlation between the charge and spin states of defect centers to measure defect charge populations and dynamics through the steady-state spin population, read-out at the single-defect level with a nearby optically active qubit. We directly measure ionization and charge relaxation of single dark defects in diamond, effects we do not have access to with traditional coherence-based quantum sensing. These spin resonance-based methods generalize to other solid state defect systems in relevant materials., Comment: 8 pages, 4 figures

Despite the great success of neural visual generative models in recent years, integrating them with strong symbolic reasoning systems remains a challenging task. There are two levels of symbol grounding problems among the core challenges: the first is symbol assignment, i.e. mapping latent factors of neural visual generators to semantic-meaningful symbolic factors from the reasoning systems by learning from limited labeled data. The second is rule learning, i.e. learning new rules that govern the generative process to enhance the symbolic reasoning systems. To deal with these two problems, we propose a neurosymbolic learning approach, Abductive visual Generation (AbdGen), for integrating logic programming systems with neural visual generative models based on the abductive learning framework. To achieve reliable and efficient symbol grounding, the quantized abduction method is introduced for generating abduction proposals by the nearest-neighbor lookup within semantic codebooks. To achieve precise rule learning, the contrastive meta-abduction method is proposed to eliminate wrong rules with positive cases and avoid less informative rules with negative cases simultaneously. Experimental results show that compared to the baseline approaches, AbdGen requires significantly less labeled data for symbol assignment. Furthermore, AbdGen can effectively learn underlying logical generative rules from data, which is out of the capability of existing approaches. The code is released at this link: https://github.com/candytalking/AbdGen.

We present a formulation of spin-conserving and spin-flip, hybrid time-dependent density functional theory (TDDFT), including the calculation of analytical forces, which allows for efficient calculations of excited state properties of solid-state systems with hundreds to thousands of atoms. We discuss an implementation on both GPU and CPU based architectures, along with several acceleration techniques. We then apply our formulation to the study of several point defects in semiconductors and insulators, specifically the negatively charged nitrogen-vacancy and neutral silicon-vacancy centers in diamond, the neutral divacancy center in 4H silicon carbide, and the neutral oxygen-vacancy center in magnesium oxide. Our results highlight the importance of taking into account structural relaxations in excited states, in order to interpret and predict optical absorption and emission mechanisms in spin-defects.

Recognizing target objects using an event-based camera draws more and more attention in recent years. Existing works usually represent the event streams into point-cloud, voxel, image, etc, and learn the feature representations using various deep neural networks. Their final results may be limited by the following factors: monotonous modal expressions and the design of the network structure. To address the aforementioned challenges, this paper proposes a novel dual-stream framework for event representation, extraction, and fusion. This framework simultaneously models two common representations: event images and event voxels. By utilizing Transformer and Structured Graph Neural Network (GNN) architectures, spatial information and three-dimensional stereo information can be learned separately. Additionally, a bottleneck Transformer is introduced to facilitate the fusion of the dual-stream information. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art performance on two widely used event-based classification datasets. The source code of this work is available at: \url{https://github.com/Event-AHU/EFV_event_classification}, Comment: Accepted by PRCV-2023