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The current bottleneck in continuous sign language recognition (CSLR) research lies in the fact that most publicly available datasets are limited to laboratory environments or television program recordings, resulting in a single background environment with uniform lighting, which significantly deviates from the diversity and complexity found in real-life scenarios. To address this challenge, we have constructed a new, large-scale dataset for Chinese continuous sign language (CSL) based on complex environments, termed the complex environment - chinese sign language dataset (CE-CSL). This dataset encompasses 5,988 continuous CSL video clips collected from daily life scenes, featuring more than 70 different complex backgrounds to ensure representativeness and generalization capability. To tackle the impact of complex backgrounds on CSLR performance, we propose a time-frequency network (TFNet) model for continuous sign language recognition. This model extracts frame-level features and then utilizes both temporal and spectral information to separately derive sequence features before fusion, aiming to achieve efficient and accurate CSLR. Experimental results demonstrate that our approach achieves significant performance improvements on the CE-CSL, validating its effectiveness under complex background conditions. Additionally, our proposed method has also yielded highly competitive results when applied to three publicly available CSL datasets., Comment: 11 pages, 3 figures

Urban traffic safety is a pressing concern in modern transportation systems, especially in rapidly growing metropolitan areas where increased traffic congestion, complex road networks, and diverse driving behaviors exacerbate the risk of traffic incidents. Traditional traffic crash data analysis offers valuable insights but often overlooks a broader range of road safety risks. Near-crash events, which occur more frequently and signal potential collisions, provide a more comprehensive perspective on traffic safety. However, city-scale analysis of near-crash events remains limited due to the significant challenges in large-scale real-world data collection, processing, and analysis. This study utilizes one month of connected vehicle data, comprising billions of records, to detect and analyze near-crash events across the road network in the City of San Antonio, Texas. We propose an efficient framework integrating spatial-temporal buffering and heading algorithms to accurately identify and map near-crash events. A binary logistic regression model is employed to assess the influence of road geometry, traffic volume, and vehicle types on near-crash risks. Additionally, we examine spatial and temporal patterns, including variations by time of day, day of the week, and road category. The findings of this study show that the vehicles on more than half of road segments will be involved in at least one near-crash event. In addition, more than 50% near-crash events involved vehicles traveling at speeds over 57.98 mph, and many occurred at short distances between vehicles. The analysis also found that wider roadbeds and multiple lanes reduced near-crash risks, while single-unit trucks slightly increased the likelihood of near-crash events. Finally, the spatial-temporal analysis revealed that near-crash risks were most prominent during weekday peak hours, especially in downtown areas., Comment: 36 pages, 8 figures

The bottleneck associated with the key-value(KV) cache presents a significant challenge during the inference processes of large language models. While depth pruning accelerates inference, it requires extensive recovery training, which can take up to two weeks. On the other hand, width pruning retains much of the performance but offers slight speed gains. To tackle these challenges, we propose KVPruner to improve model efficiency while maintaining performance. Our method uses global perplexity-based analysis to determine the importance ratio for each block and provides multiple strategies to prune non-essential KV channels within blocks. Compared to the original model, KVPruner reduces runtime memory usage by 50% and boosts throughput by over 35%. Additionally, our method requires only two hours of LoRA fine-tuning on small datasets to recover most of the performance.

This paper presents a diffusion-based recommender system that incorporates classifier-free guidance. Most current recommender systems provide recommendations using conventional methods such as collaborative or content-based filtering. Diffusion is a new approach to generative AI that improves on previous generative AI approaches such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). We incorporate diffusion in a recommender system that mirrors the sequence users take when browsing and rating items. Although a few current recommender systems incorporate diffusion, they do not incorporate classifier-free guidance, a new innovation in diffusion models as a whole. In this paper, we present a diffusion recommender system that augments the underlying recommender system model for improved performance and also incorporates classifier-free guidance. Our findings show improvements over state-of-the-art recommender systems for most metrics for several recommendation tasks on a variety of datasets. In particular, our approach demonstrates the potential to provide better recommendations when data is sparse., Comment: 8 pages

Direct Preference Optimization (DPO) is a method for enhancing model performance by directly optimizing for the preferences or rankings of outcomes, instead of traditional loss functions. This approach has proven effective in aligning Large Language Models (LLMs) with human preferences. Despite its widespread use across various tasks, DPO has been criticized for its sensitivity to the effectiveness of Supervised Fine-Tuning (SFT) and its limitations in enabling models to learn human-preferred responses, leading to less satisfactory performance. To address these limitations, we propose Aligned Supervised Fine-Tuning (ASFT), an effective approach that better aligns LLMs with pair-wise datasets by optimizing absolute likelihood for each response, rather than using the Bradley-Terry model, and eliminates the need for a reference model. Through theoretical gradient analysis, we demonstrate that ASFT mitigates the issue where the DPO loss function decreases the probability of generating human-dispreferred data at a faster rate than it increases the probability of producing preferred data. Additionally, we compare ASFT to DPO and its latest variants, such as the single-step approach ORPO, using the latest instruction-tuned model Llama3, which has been fine-tuned on UltraFeedback and HH-RLHF. We evaluated performance on instruction-following benchmarks like MT-Bench and traditional text generation metrics such as BLEU-4 and ROUGE-L. Extensive experiments demonstrate that ASFT is an effective alignment approach, consistently outperforming existing methods.

Primordial Black Holes (PBHs) are capable of emitting extremely energetic particles independent of their interactions with the Standard Model. In this work, we investigate a particularly interesting scenario in which PBHs evaporating in the early universe may be responsible for some of the observed high-energy neutrinos above the TeV or PeV scale in the present universe. We compute the energy spectrum of neutrinos directly emitted by PBHs with a monochromatic mass function and estimate the wash-out point, which determines the maximum energy of the spectrum. We find that the spectrum generally extends to high energies following a power law of $E_{\nu}^{-3}$ until it reaches the wash-out point, which crucially depends on the PBH mass. For PBHs of $10^{13}$ grams, the spectrum can extend up to the PeV scale, though the flux is too low for detection. We also consider an indirect production mechanism involving dark particles that are emitted by PBHs and decay into neutrinos at a much later epoch. This mechanism allows lighter (such as those in the gram to kilogram range) PBHs to produce more energetic neutrino fluxes without being washed out by the thermal plasma in the early universe. In this scenario, we find that ultra-high-energy neutrinos around or above the EeV scale can be generated, with sufficiently high fluxes detectable by current and future high-energy neutrino observatories such as IceCube and GRAND., Comment: 23 pages, 4 figures

Under high perpendicular magnetic field and at low temperatures, graphene develops an insulating state at the charge neutrality point. This state, dubbed $\nu=0$, is due to the interplay between electronic interactions and the four-fold spin and valley degeneracies in the flat band formed by the $n=0$ Landau level. Determining the ground state of $\nu=0$, including its spin and valley polarization, has been a theoretical and experimental undertaking for almost two decades. Here, we present experiments probing the bulk thermal transport properties of monolayer graphene at $\nu=0$, which directly probe its ground state and collective excitations. We observe a vanishing bulk thermal transport, in contradiction with the expected ground state, predicted to have a finite thermal conductance even at very low temperature. Our result highlight the need for further investigations on the nature of $\nu=0$., Comment: Contains Supplementary Information

Emerging efforts in AI alignment seek to broaden participation in shaping model behavior by eliciting and integrating collective input into a policy for model finetuning. While pluralistic, these processes are often linear and do not allow participating stakeholders to confirm whether potential outcomes of their contributions are indeed consistent with their intentions. Design prototyping has long advocated for rapid iteration using tight feedback loops of ideation, experimentation, and evaluation to mitigate these issues. We thus propose policy prototyping for LLMs, a new process that draws inspiration from prototyping practices to enable stakeholders to collaboratively and interactively draft LLM policies. Through learnings from a real-world LLM policymaking initiative at an industrial AI lab, we motivate our approach and characterize policy prototyping with four guiding principles. Because policy prototyping emphasizes a contrasting set of priorities compared to previous approaches, we envision our approach to be a valuable addition to the methodological repertoire for pluralistic alignment.

We present LT3SD, a novel latent diffusion model for large-scale 3D scene generation. Recent advances in diffusion models have shown impressive results in 3D object generation, but are limited in spatial extent and quality when extended to 3D scenes. To generate complex and diverse 3D scene structures, we introduce a latent tree representation to effectively encode both lower-frequency geometry and higher-frequency detail in a coarse-to-fine hierarchy. We can then learn a generative diffusion process in this latent 3D scene space, modeling the latent components of a scene at each resolution level. To synthesize large-scale scenes with varying sizes, we train our diffusion model on scene patches and synthesize arbitrary-sized output 3D scenes through shared diffusion generation across multiple scene patches. Through extensive experiments, we demonstrate the efficacy and benefits of LT3SD for large-scale, high-quality unconditional 3D scene generation and for probabilistic completion for partial scene observations., Comment: Project page: https://quan-meng.github.io/projects/lt3sd/ Video: https://youtu.be/AJ5sG9VyjGA

By sharing complementary perceptual information, multi-agent collaborative perception fosters a deeper understanding of the environment. Recent studies on collaborative perception mostly utilize CNNs or Transformers to learn feature representation and fusion in the spatial dimension, which struggle to handle long-range spatial-temporal features under limited computing and communication resources. Holistically modeling the dependencies over extensive spatial areas and extended temporal frames is crucial to enhancing feature quality. To this end, we propose a resource efficient cross-agent spatial-temporal collaborative state space model (SSM), named CollaMamba. Initially, we construct a foundational backbone network based on spatial SSM. This backbone adeptly captures positional causal dependencies from both single-agent and cross-agent views, yielding compact and comprehensive intermediate features while maintaining linear complexity. Furthermore, we devise a history-aware feature boosting module based on temporal SSM, extracting contextual cues from extended historical frames to refine vague features while preserving low overhead. Extensive experiments across several datasets demonstrate that CollaMamba outperforms state-of-the-art methods, achieving higher model accuracy while reducing computational and communication overhead by up to 71.9% and 1/64, respectively. This work pioneers the exploration of the Mamba's potential in collaborative perception. The source code will be made available., Comment: Submitted to AAAI 2025

Time-varying effects have unveiled new possibilities for manipulating electromagnetic waves through the temporal dimension. In this study, we experimentally explore these effects in the nonlinear optical process of terahertz (THz) generation using optically pumped indium tin oxide (ITO) films. The ultrafast carrier dynamics in the ITO film endow the second-order nonlinear susceptibility (\c{hi}(2)) with sub-picosecond temporal evolution, establishing a temporal boundary for the generated THz waves. We observe significant amplitude and frequency modulations in the THz generation at various transients, attributed to the time-varying complex amplitude of the \c{hi}(2). Moreover, we also observed polarization modulations when further exploiting the tensor properties of \c{hi}(2). This work advances the exploration of time-varying effects into the nonlinear regime through frequency down-conversion, effectively transferring the strong time-varying material response from the near-infrared (NIR) band to the THz band. These findings open up new opportunities for realizing time-varying phenomena that specifically require single-cycle modulation.

Local vortex-structured auroral spiral and a large-scale transpolar arc (TPA) were contemporaneously observed by the Polar ultraviolet imager (UVI), when a substorm almost recovered. The TPA grew along the dawnside auroral oval from the nightside to the dayside (oval-aligned TPA), and a chain of multiple auroral spots and spiral were located azimuthally near the poleward edge of the nightside auroral oval. Contemporaneous appearances of the TPA and the auroral spiral can be seen after the spiral appeared alone. Polar also detected the oval-aligned TPA and another dawnside TPA with the nightside end distorted toward the premidnight sector (J-shaped TPA) before and after the spiral's formation, respectively. To examine these associated magnetotail structures, we performed global magnetohydrodynamic (MHD) simulations, based on two different types of code, BAT-S-RUS and improved REPPU, and examined how the field-aligned current (FAC) profiles varied in association with changes of the auroral form to TPA and/or auroral spiral. Global MHD simulations with the two different types of code can reproduce the TPAs and the associated FAC structures in the magnetotail. The auroral spiral and its nightside FAC profile, however, were not formed in both simulations, suggesting that its formation process cannot be treated within an MHD framework but is closely related to some kinetic process. When the J-shaped TPA and the auroral spiral contemporaneously appeared, the two MHD simulations could not reproduce the TPA, spiral and their associated magnetotail FAC structures, also advocating that a kinetic effect related to the spiral formation might prevent the TPA occurrence., Comment: Manuscript + Figures and Table: 35 pages Supporting Information: 3 pages

We used the Five-hundred-meter Aperture Spherical radio Telescope (FAST) to search for the molecular emissions in the L-band between 1.0 and 1.5 GHz toward four comets, C/2020 F3 (NEOWISE), C/2020 R4 (ATLAS), C/2021 A1 (Leonard), and 67P/Churyumov-Gerasimenko during or after their perihelion passages. Thousands of molecular transition lines fall in this low-frequency range, many attributed to complex organic or prebiotic molecules. We conducted a blind search for the possible molecular lines in this frequency range in those comets and could not identify clear signals of molecular emissions in the data. Although several molecules have been detected at high frequencies of great than 100 GHz in comets, our results confirm that it is challenging to detect molecular transitions in the L-band frequency ranges. The non-detection of L-band molecular lines in the cometary environment could rule out the possibility of unusually strong lines, which could be caused by the masers or non-LTE effects. Although the line strengths are predicted to be weak, for FAST, using the ultra-wide bandwidth receiver and improving the radio frequency interference environments would enhance the detectability of those molecular transitions at low frequencies in the future., Comment: 14 pages, 6 figures, 6 tables, accepted for publication in RAA

As the body of academic literature continues to grow, researchers face increasing difficulties in effectively searching for relevant resources. Existing databases and search engines often fall short of providing a comprehensive and contextually relevant collection of academic literature. To address this issue, we propose a novel framework that leverages Natural Language Processing (NLP) techniques. This framework automates the retrieval, summarization, and clustering of academic literature within a specific research domain. To demonstrate the effectiveness of our approach, we introduce CyLit, an NLP-powered repository specifically designed for the cyber risk literature. CyLit empowers researchers by providing access to context-specific resources and enabling the tracking of trends in the dynamic and rapidly evolving field of cyber risk. Through the automatic processing of large volumes of data, our NLP-powered solution significantly enhances the efficiency and specificity of academic literature searches. We compare the literature categorization results of CyLit to those presented in survey papers or generated by ChatGPT, highlighting the distinctive insights this tool provides into cyber risk research literature. Using NLP techniques, we aim to revolutionize the way researchers discover, analyze, and utilize academic resources, ultimately fostering advancements in various domains of knowledge.

This study introduces PI-AstroDeconv, a physics-informed semi-supervised learning method specifically designed for removing beam effects in astronomical telescope observation systems. The method utilizes an encoder-decoder network architecture and combines the telescope's point spread function or beam as prior information, while integrating fast Fourier transform accelerated convolution techniques into the deep learning network. This enables effective removal of beam effects from astronomical observation images. PI-AstroDeconv can handle multiple PSFs or beams, tolerate imprecise measurements to some extent, and significantly improve the efficiency and accuracy of image deconvolution. Therefore, this algorithm is particularly suitable for astronomical data processing that does not rely on annotated data. To validate the reliability of the algorithm, we used the SKA Science Data Challenge 3a datasets and compared it with the CLEAN deconvolution method at the 2-D matter power spectrum level. The results demonstrate that our algorithm not only restores details and reduces blurriness in celestial images at the pixel level but also more accurately recovers the true neutral hydrogen power spectrum at the matter power spectrum level., Comment: 15 pages, 7 figures

There have been many studies on analyzing self-supervised speech Transformers, in particular, with layer-wise analysis. It is, however, desirable to have an approach that can pinpoint exactly a subset of neurons that is responsible for a particular property of speech, being amenable to model pruning and model editing. In this work, we identify a set of property neurons in the feedforward layers of Transformers to study how speech-related properties, such as phones, gender, and pitch, are stored. When removing neurons of a particular property (a simple form of model editing), the respective downstream performance significantly degrades, showing the importance of the property neurons. We apply this approach to pruning the feedforward layers in Transformers, where most of the model parameters are. We show that protecting property neurons during pruning is significantly more effective than norm-based pruning. The code for identifying property neurons is available at https://github.com/nervjack2/PropertyNeurons., Comment: Accepted by SLT 2024

Pressure-sensitive smart textiles are widely applied in the fields of healthcare, sports monitoring, and intelligent homes. The integration of devices embedded with pressure sensing arrays is expected to enable comprehensive scene coverage and multi-device integration. However, the implementation of identity recognition, a fundamental function in this context, relies on extensive device-specific datasets due to variations in pressure distribution across different devices. To address this challenge, we propose a novel user identification method based on contrastive learning. We design two parallel branches to facilitate user identification on both new and existing devices respectively, employing supervised contrastive learning in the feature space to promote domain unification. When encountering new devices, extensive data collection efforts are not required; instead, user identification can be achieved using limited data consisting of only a few simple postures. Through experimentation with two 8-subject pressure datasets (BedPressure and ChrPressure), our proposed method demonstrates the capability to achieve user identification across 12 sitting scenarios using only a dataset containing 2 postures. Our average recognition accuracy reaches 79.05%, representing an improvement of 2.62% over the best baseline model.

Controlling the state of molecules on surfaces is crucial for the advancement of molecular electronics. While reversible control of bistable molecule switches has been demonstrated, achieving controllable multi-state switching at the molecular scale remains a formidable challenge. In this study, we introduce a simple and effective strategy for the on-demand control of a multi-level molecular switch by creating a molecular dimer. We explore the transition behavior of a pyrrolidine dimer system on the Cu(100) surface using low-temperature scanning tunneling microscopy. By fine-tuning the voltage and tip-molecule distance, we can selectively determine the dominant state of the dimer system. Both intermolecular and tip-molecule interactions play a role in modifying the transition pathways, enabling precise multi-state control.

Purpose: To develop a simultaneous multislice (SMS) first-pass perfusion technique that can achieve whole heart coverage with high multi-band factors, while avoiding the issue of slice leakage. Methods: The proposed Simultaneous Multislice Imaging via Linear phase modulated Extended field of view (SMILE) treats the SMS acquisition and reconstruction within an extended field of view framework, allowing arbitrarily under-sampling of phase encoding lines of the extended k-space matrix and enabling the direct application of 2D parallel imaging reconstruction techniques. We presented a theoretical framework that offers insights into the performance of SMILE. We performed retrospective comparison on 28 subjects and prospective perfusion experiments on 49 patients undergoing routine clinical CMR studies with SMILE at multiband (MB) factors of 3-5, with a total acceleration factor ($R$) of 8 and 10 respectively, and compared SMILE to conventional SMS techniques using standard FOV 2D CAIPI acquisition and standard 2D slice separation techniques including split-slice GRAPPA and ROCK-SPIRiT. Results: Retrospective studies demonstrated 5.2 to 8.0 dB improvement in signal to error ratio (SER) of SMILE over CAIPI perfusion. Prospective studies showed good image quality with grades of 4.5 $\pm$ 0.5 for MB=3, $R$=8 and 3.6 $\pm$ 0.8 for MB=5, $R$=10. (5-point Likert Scale) Conclusion: The theoretical derivation and experimental results validate the SMILE's improved performance at high acceleration and MB factors as compared to the existing 2D CAIPI SMS acquisition and reconstruction techniques for first-pass myocardial perfusion imaging., Comment: 15 pages, 12 figures

Scene coordinate regression (SCR) methods have emerged as a promising area of research due to their potential for accurate visual localization. However, many existing SCR approaches train on samples from all image regions, including dynamic objects and texture-less areas. Utilizing these areas for optimization during training can potentially hamper the overall performance and efficiency of the model. In this study, we first perform an in-depth analysis to validate the adverse impacts of these areas. Drawing inspiration from our analysis, we then introduce an error-guided feature selection (EGFS) mechanism, in tandem with the use of the Segment Anything Model (SAM). This mechanism seeds low reprojection areas as prompts and expands them into error-guided masks, and then utilizes these masks to sample points and filter out problematic areas in an iterative manner. The experiments demonstrate that our method outperforms existing SCR approaches that do not rely on 3D information on the Cambridge Landmarks and Indoor6 datasets., Comment: ECCV2024

We study the dynamics of a small solid particle arising from the dewetting of a thin film on a curved substrate driven by capillarity, where mass transport is controlled by surface diffusion. We consider the case when the size of the deposited particle is much smaller than the local radius of curvature of the substrate surface. The application of the Onsager variational principle leads to a reduced-order model for the dynamic behaviour of particles on arbitrarily curved substrates. We demonstrate that particles move toward region of the substrate surface with lower mean curvature with a determined velocity. In particular, the velocity is proportional to the substrate curvature gradient and inversely proportional to the size of the particle, with a coefficient that depends on material properties that include the surface energy, surface diffusivity, density, and Young's (wetting) angle. The reduced model is validated by comparing with numerical results for the full, sharp-interface model in both two and three dimensions., Comment: 12 pages, 8 figures

Scaling up robot learning requires large and diverse datasets, and how to efficiently reuse collected data and transfer policies to new embodiments remains an open question. Emerging research such as the Open-X Embodiment (OXE) project has shown promise in leveraging skills by combining datasets including different robots. However, imbalances in the distribution of robot types and camera angles in many datasets make policies prone to overfit. To mitigate this issue, we propose RoVi-Aug, which leverages state-of-the-art image-to-image generative models to augment robot data by synthesizing demonstrations with different robots and camera views. Through extensive physical experiments, we show that, by training on robot- and viewpoint-augmented data, RoVi-Aug can zero-shot deploy on an unseen robot with significantly different camera angles. Compared to test-time adaptation algorithms such as Mirage, RoVi-Aug requires no extra processing at test time, does not assume known camera angles, and allows policy fine-tuning. Moreover, by co-training on both the original and augmented robot datasets, RoVi-Aug can learn multi-robot and multi-task policies, enabling more efficient transfer between robots and skills and improving success rates by up to 30%. Project website: https://rovi-aug.github.io., Comment: CoRL 2024 (Oral). Project website: https://rovi-aug.github.io

Existing tools for laypeople to create personal classifiers often assume a motivated user working uninterrupted in a single, lengthy session. However, users tend to engage with social media casually, with many short sessions on an ongoing, daily basis. To make creating personal classifiers for content curation easier for such users, tools should support rapid initialization and iterative refinement. In this work, we compare three strategies -- (1) example labeling, (2) rule writing, and (3) large language model (LLM) prompting -- for end users to build personal content classifiers. From an experiment with 37 non-programmers tasked with creating personalized comment moderation filters, we found that with LLM prompting, participants reached 95\% of peak performance in 5 minutes, beating other strategies due to higher recall, but all strategies struggled with iterative refinement. Despite LLM prompting's better performance, participants preferred different strategies in different contexts and, even when prompting, provided examples or wrote rule-like prompts, suggesting hybrid approaches.

On-demand control of molecular actions is a crucial step toward the realization of single-molecule functional devices. Such a control can be achieved by manipulating interactions between individual molecules and their nanoscale environment. In this study, we induce and manipulate the conformational transition of a single molecular adsorbate by exciting its vibrations with tunneling electrons using scanning tunneling microscopy. Several transition pathways between two structural states of a pyrrolidine molecule on a Cu(100) surface have been identified as being driven by different molecular vibrations. Density functional theory simulations further determine the nuclear motions of these vibrational modes. The introduction of tip-induced van der Waals forces and intermolecular interactions allows for precise manipulation of the molecule-environment interaction, which shifts the vibrational energies and alters the transition probability through different channels between the two structural states. This work reveals how molecular conformational transitions can be modulated by external force fields in a tunable nano-cavity, highlighting the potential to deliberately engineer molecule-environment interactions for specific molecular functions.

Despite significant advancements in monocular depth estimation for static images, estimating video depth in the open world remains challenging, since open-world videos are extremely diverse in content, motion, camera movement, and length. We present DepthCrafter, an innovative method for generating temporally consistent long depth sequences with intricate details for open-world videos, without requiring any supplementary information such as camera poses or optical flow. DepthCrafter achieves generalization ability to open-world videos by training a video-to-depth model from a pre-trained image-to-video diffusion model, through our meticulously designed three-stage training strategy with the compiled paired video-depth datasets. Our training approach enables the model to generate depth sequences with variable lengths at one time, up to 110 frames, and harvest both precise depth details and rich content diversity from realistic and synthetic datasets. We also propose an inference strategy that processes extremely long videos through segment-wise estimation and seamless stitching. Comprehensive evaluations on multiple datasets reveal that DepthCrafter achieves state-of-the-art performance in open-world video depth estimation under zero-shot settings. Furthermore, DepthCrafter facilitates various downstream applications, including depth-based visual effects and conditional video generation., Comment: Project webpage: https://depthcrafter.github.io

This paper presents a kinetic model for the coupled evolution of radiation, electrons, and ions in a radiation plasma system. The model is solved using two methods. The gas-kinetic scheme (GKS) for electron and ion hydrodynamics and the unified gas-kinetic scheme (UGKS) for non-equilibrium radiative transfer. The UGKS accurately captures multiscale photon transport from free streaming to diffusion across varying fluid opacities. This approach enables the scheme to model equilibrium plasma with non-equilibrium radiation transport. The model is validated through several test cases, including radiative transfer in kinetic and diffusion regimes, Marshak wave, Radiative shock, 3T double lax shock tube problem, two-dimensional Sedov blast wave, and two-dimensional tophat based problem. These tests demonstrate the current scheme's capability to handle diverse radiation plasma scenarios.

Expanding the receptive field in a deep learning model for large-scale 3D point cloud segmentation is an effective technique for capturing rich contextual information, which consequently enhances the network's ability to learn meaningful features. However, this often leads to increased computational complexity and risk of overfitting, challenging the efficiency and effectiveness of the learning paradigm. To address these limitations, we propose the Local Split Attention Pooling (LSAP) mechanism to effectively expand the receptive field through a series of local split operations, thus facilitating the acquisition of broader contextual knowledge. Concurrently, it optimizes the computational workload associated with attention-pooling layers to ensure a more streamlined processing workflow. Based on LSAP, a Parallel Aggregation Enhancement (PAE) module is introduced to enable parallel processing of data using both 2D and 3D neighboring information to further enhance contextual representations within the network. In light of the aforementioned designs, we put forth a novel framework, designated as LSNet, for large-scale point cloud semantic segmentation. Extensive evaluations demonstrated the efficacy of seamlessly integrating the proposed PAE module into existing frameworks, yielding significant improvements in mean intersection over union (mIoU) metrics, with a notable increase of up to 11%. Furthermore, LSNet demonstrated superior performance compared to state-of-the-art semantic segmentation networks on three benchmark datasets, including S3DIS, Toronto3D, and SensatUrban. It is noteworthy that our method achieved a substantial speedup of approximately 38.8% compared to those employing similar-sized receptive fields, which serves to highlight both its computational efficiency and practical utility in real-world large-scale scenes.

We present a robust refinement method for estimating oriented normals from unstructured point clouds. In contrast to previous approaches that either suffer from high computational complexity or fail to achieve desirable accuracy, our novel framework incorporates sign orientation and data augmentation in the feature space to refine the initial oriented normals, striking a balance between efficiency and accuracy. To address the issue of noise-caused direction inconsistency existing in previous approaches, we introduce a new metric called the Chamfer Normal Distance, which faithfully minimizes the estimation error by correcting the annotated normal with the closest point found on the potentially clean point cloud. This metric not only tackles the challenge but also aids in network training and significantly enhances network robustness against noise. Moreover, we propose an innovative dual-parallel architecture that integrates Multi-scale Local Feature Aggregation and Hierarchical Geometric Information Fusion, which enables the network to capture intricate geometric details more effectively and notably reduces ambiguity in scale selection. Extensive experiments demonstrate the superiority and versatility of our method in both unoriented and oriented normal estimation tasks across synthetic and real-world datasets among indoor and outdoor scenarios. The code is available at https://github.com/YingruiWoo/OCMG-Net.git., Comment: 18 pages, 16 figures

In this paper, we calculate the semileptonic decay $D\to K_0^\ast(1430)\ell\nu_{\ell}$ with $\ell=(e,\mu)$ induced by $c\to s\ell\nu_{\ell}$ transition. For the key component, $D\to K_0^\ast(1430)$ transition form factors (TFFs) $f_{\pm}(q^2)$ are calculated within the framework of QCD light-cone sum rule. Then, we consider two scenarios for $K_0^\ast(1430)$-meson twist-2 distribution amplitude. For the scenario 1 (S1), we take the truncated form based on Gegenbauer polynomial series. Meanwhile, we also consider the scenario 2 (S2) constructed by light-cone harmonic oscillator model, where the model parameters are fixed by the $K_0^\ast(1430)$-meson twist-2 distribution amplitude $10$th-order $\xi$-moments calculated by using the background field theory. For the TFFs at large recoil point, we have $f_+^{\rm (S1)}(0)=0.597^{+0.122}_{-0.121}$ and $f_-^{\rm (S1)}(0)=-0.136^{+0.023}_{-0.035}$, $f_+^{\rm (S2)}(0)= 0.663^{+0.135}_{-0.134}$ and $f_-^{\rm (S2)}(0)=-0.202^{+0.026}_{-0.046}$. After extrapolating TFFs to the whole physical $q^2$-region, we calculate the branching fractions of $D^0\to K_0^{\ast +}(1430)\ell^-\bar\nu_\ell$ and $D^+\to K_0^{\ast 0}(1430)\ell^+\nu_\ell$ which at $10^{-4}$-order level for the S1 and S2 cases. Meanwhile, we predict the CKM matrix $|V_{cs}|^{\rm (S1)}=0.997^{+0.258}_{-0.172}, |V_{cs}|^{\rm (S2)}=0.903^{+0.233}_{-0.155}$, and leptonic flavour universality $\mathcal{R}^{\rm (S1)}_{K_0^*}=0.768^{+0.560}_{-0.368}, \mathcal{R}_{K_0^*}^{\rm (S2)}=0.764^{+0.555}_{-0.365}$. Finally, we discuss the angular observables of forward-backward asymmetries, lepton polarization asymmetries and $q^2$-differential flat terms for this decay., Comment: 10 pages, 5 figures, comments welcome

In the fifth-generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems, downlink beamforming relies on the acquisition of downlink channel state information (CSI). Codebook based limited feedback schemes have been proposed and widely used in practice to recover the downlink CSI with low communication overhead. In such schemes, the performance of downlink beamforming is determined by the codebook design and the codebook indicator feedback. However, limited by the quantization quality of the codebook, directly utilizing the codeword indicated by the feedback as the beamforming vector cannot achieve high performance. Therefore, other feedback values, such as channel qualification indicator (CQI), should be considered to enhance beamforming. In this paper, we present the relation between CQI and the optimal beamforming vectors, based on which an empirical Bayes based intelligent tuning-free algorithm is devised to learn the optimal beamforming vector and the associated regularization parameter. The proposed algorithm can handle different communication scenarios of MIMO systems, including single stream and multiple streams data transmission scenarios. Numerical results have shown the excellent performance of the proposed algorithm in terms of both beamforming vector acquisition and regularization parameter learning.

The isomorphism problem for digraphs is a fundamental problem in graph theory. This problem for Cayley digraphs has been extensively investigated over the last half a century. In this paper, we consider this problem for $m$-Cayley digraphs which are generalization of Cayley digraphs. Let $m$ be a positive integer. A digraph admitting a group $G$ of automorphisms acting semiregularly on the vertices with exactly $m$ orbits is called an $m$-Cayley digraph of $G$. In particular, $1$-Cayley digraph is just the Cayley digraph. We first characterize the normalizer of $G$ in the full automorphism group of an $m$-Cayley digraph of a finite group $G$. This generalizes a similar result for Cayley digraph achieved by Godsil in 1981. Then we use this to study the isomorphisms of $m$-Cayley digraphs. The CI-property of a Cayley digraph (CI stands for `Cayley isomorphism') and the DCI-groups (whose Cayley digraphs are all CI-digraphs) are two key topics in the study of isomorphisms of Cayley digraphs. We generalize these concepts into $m$-Cayley digraphs by defining $m$CI- and $m$PCI-digraphs, and correspondingly, $m$DCI- and $m$PDCI-groups. Analogues to Babai's criterion for CI-digraphs are given for $m$CI- and $m$PCI-digraphs, respectively. With these we then classify finite $m$DCI-groups for each $m\geq 2$, and finite $m$PDCI-groups for each $m\geq 4$. Similar results are also obtained for $m$-Cayley graphs. Note that 1DCI-groups are just DCI-groups, and the classification of finite DCI-groups is a long-standing open problem that has been worked on a lot., Comment: 29