An Open-source End-to-End Logic Optimization Framework for Large-scale Boolean Network with Reinforcement Learning
We propose an open-source end-to-end logic optimization framework for large-scale boolean network with reinforcement learning.
cs.AI↗
arXiv subjects
Zhen Li
Publications and source records attributed to Zhen Li.
Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding
3D Visual Grounding (3DVG) aims at localizing 3D object based on textual descriptions. Conventional supervised methods for 3DVG often necessitate extensive annotations and a predefined vocabulary, which can be restrictive. To address this issue, we propose a novel visual programming approach for zero-shot open-vocabulary 3DVG, leveraging the capabilities of large language models (LLMs). Our approach begins with a unique dialog-based method, engaging with LLMs to establish a foundational understanding of zero-shot 3DVG. Building on this, we design a visual program that consists of three types of modules, i.e., view-independent, view-dependent, and functional modules. These modules, specifically tailored for 3D scenarios, work collaboratively to perform complex reasoning and inference. Furthermore, we develop an innovative language-object correlation module to extend the scope of existing 3D object detectors into open-vocabulary scenarios. Extensive experiments demonstrate that our zero-shot approach can outperform some supervised baselines, marking a significant stride towards effective 3DVG.
cs.CV↗
Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning
The intricate process of bubble growth dynamics involves a broad spectrum of physical phenomena from microscale mechanics of bubble formation to macroscale interplay between bubbles and surrounding thermo-hydrodynamics. Traditional bubble dynamics models including atomistic approaches and continuum-based methods segment the bubble dynamics into distinct scale-specific models. In order to bridge the gap between microscale stochastic fluid models and continuum-based fluid models for bubble dynamics, we develop a composite neural operator model to unify the analysis of nonlinear bubble dynamics across microscale and macroscale regimes by integrating a many-body dissipative particle dynamics (mDPD) model with a continuum-based Rayleigh-Plesset (RP) model through a novel neural network architecture, which consists of a deep operator network for learning the mean behavior of bubble growth subject to pressure variations and a long short-term memory network for learning the statistical features of correlated fluctuations in microscale bubble dynamics. Training and testing data are generated by conducting mDPD and RP simulations for nonlinear bubble dynamics with initial bubble radii ranging from 0.1 to 1.5 micrometers. Results show that the trained composite neural operator model can accurately predict bubble dynamics across scales, with a 99% accuracy for the time evaluation of the bubble radius under varying external pressure while containing correct size-dependent stochastic fluctuations in microscale bubble growth dynamics. The composite neural operator is the first deep learning surrogate for multiscale bubble growth dynamics that can capture correct stochastic fluctuations in microscopic fluid phenomena, which sets a new direction for future research in multiscale fluid dynamics modeling.
physics.flu-dyn↗
Limit of the Maximum Random Permutation Set Entropy
The Random Permutation Set (RPS) is a new type of set proposed recently, which can be regarded as the generalization of evidence theory. To measure the uncertainty of RPS, the entropy of RPS and its corresponding maximum entropy have been proposed. Exploring the maximum entropy provides a possible way of understanding the physical meaning of RPS. In this paper, a new concept, the envelope of entropy function, is defined. In addition, the limit of the envelope of RPS entropy is derived and proved. Compared with the existing method, the computational complexity of the proposed method to calculate the envelope of RPS entropy decreases greatly. The result shows that when $N \to \infty$, the limit form of the envelope of the entropy of RPS converges to $e \times (N!)^2$, which is highly connected to the constant $e$ and factorial. Finally, numerical examples validate the efficiency and conciseness of the proposed envelope, which provides a new insight into the maximum entropy function.
cs.IT↗
Random Graph Set and Evidence Pattern Reasoning Model
Evidence theory is widely used in decision-making and reasoning systems. In previous research, Transferable Belief Model (TBM) is a commonly used evidential decision making model, but TBM is a non-preference model. In order to better fit the decision making goals, the Evidence Pattern Reasoning Model (EPRM) is proposed. By defining pattern operators and decision making operators, corresponding preferences can be set for different tasks. Random Permutation Set (RPS) expands order information for evidence theory. It is hard for RPS to characterize the complex relationship between samples such as cycling, paralleling relationships. Therefore, Random Graph Set (RGS) were proposed to model complex relationships and represent more event types. In order to illustrate the significance of RGS and EPRM, an experiment of aircraft velocity ranking was designed and 10,000 cases were simulated. The implementation of EPRM called Conflict Resolution Decision optimized 18.17\% of the cases compared to Mean Velocity Decision, effectively improving the aircraft velocity ranking. EPRM provides a unified solution for evidence-based decision making.
cs.AI↗
Hierarchical Query Classification in E-commerce Search
E-commerce platforms typically store and structure product information and search data in a hierarchy. Efficiently categorizing user search queries into a similar hierarchical structure is paramount in enhancing user experience on e-commerce platforms as well as news curation and academic research. The significance of this task is amplified when dealing with sensitive query categorization or critical information dissemination, where inaccuracies can lead to considerable negative impacts. The inherent complexity of hierarchical query classification is compounded by two primary challenges: (1) the pronounced class imbalance that skews towards dominant categories, and (2) the inherent brevity and ambiguity of search queries that hinder accurate classification. To address these challenges, we introduce a novel framework that leverages hierarchical information through (i) enhanced representation learning that utilizes the contrastive loss to discern fine-grained instance relationships within the hierarchy, called ''instance hierarchy'', and (ii) a nuanced hierarchical classification loss that attends to the intrinsic label taxonomy, named ''label hierarchy''. Additionally, based on our observation that certain unlabeled queries share typographical similarities with labeled queries, we propose a neighborhood-aware sampling technique to intelligently select these unlabeled queries to boost the classification performance. Extensive experiments demonstrate that our proposed method is better than state-of-the-art (SOTA) on the proprietary Amazon dataset, and comparable to SOTA on the public datasets of Web of Science and RCV1-V2. These results underscore the efficacy of our proposed solution, and pave the path toward the next generation of hierarchy-aware query classification systems.
cs.IR↗
A phenomenological model for dark matter phase space distribution
Understanding the nature of dark matter is among the top priorities of modern physics. However, due to its inertness, detecting and studying it directly in terrestrial experiments is extremely challenging. Numerical N-body simulations currently represent the best approach for studying the particle properties and phase space distribution, assuming the collisionless nature of dark matter. These simulations also address the lack of a satisfactory theory for predicting the universal properties of dark matter halos, including the density profile and velocity distribution. In this work, we propose a new phenomenological model for the dark matter phase space distribution. This model aims to provide an NFW-like density profile, velocity magnitude distribution, and velocity component distributions that align closely with simulation data. Our model is relevant both for theoretical modeling of dark matter distributions, as well as for underground detector experiments that rely on the dark matter velocity distribution for experimental analysis.
astro-ph.GA↗
Non-abelian extensions and Wells exact sequences of Lie-Yamaguti algebras
The goal of the present paper is to investigate non-abelian extensions of Lie-Yamaguti algebras and explore extensibility of a pair of automorphisms about a non-abelian extension of Lie-Yamaguti algebras. First, we study non-abelian extensions of Lie-Yamaguti algebras and classify the non-abelian extensions in terms of non-abelian cohomology groups. Next, we characterize the non-abelian extensions in terms of Maurer-Cartan elements. Moreover, we discuss the equivalent conditions of the extensibility of a pair of automorphisms about a non-abelian extension of Lie-Yamaguti algebras, and derive the fundamental sequences of Wells in the context of Lie-Yamaguti algebras. Finally, we discuss the previous results in the case of abelian extensions of Lie-Yamaguti algebras.
math.RA↗
Photonic Spin-Orbit Coupling Induced by Deep-Subwavelength Structured Light
We demonstrate both theoretically and experimentally beam-dependent photonic spin-orbit coupling in a two-wave mixing process described by an equivalent of the Pauli equation in quantum mechanics. The considered structured light in the system is comprising a superposition of two orthogonal spin-orbit-coupled states defined as spin up and spin down equivalents. The spin-orbit coupling is manifested by prominent pseudo spin precession as well as spin-transport-induced orbital angular momentum generation in a photonic crystal film of wavelength thickness. The coupling effect is significantly enhanced by using a deep-subwavelength carrier envelope, different from previous studies which depend on materials. The beam-dependent coupling effect can find intriguing applications; for instance, it is used in precisely measuring variation of light with spatial resolution up to 15 nm.
physics.optics↗
Three-dimensional velocity fields of the solar filament eruptions detected by CHASE
The eruption of solar filaments, also known as prominences appearing off-limb, is a common phenomenon in the solar atmosphere. It ejects massive plasma and high-energy particles into interplanetary space, disturbing the solar-terrestrial environment. It is vital to obtain the three-dimensional velocity fields of erupting filaments for space-weather predictions. We derive the three-dimensional kinematics of an off-limb prominence and an on-disk filament, respectively, using the full-disk spectral and imaging data detected by the Chinese H$α$ Solar Explorer (CHASE). It is found that both the prominence and the filament experience a fast semicircle-shaped expansion at first. The prominence keeps propagating outward with an increasing velocity until escaping successfully, whereas the south leg of the prominence finally moves back to the Sun in a swirling manner. For the filament, the internal plasma falls back to the Sun associated with an anticlockwise rotation in the late ejection, matching the failed eruption without a coronal mass ejection. During the eruptions, both the prominence and the filament show material splitting along the line-of-sight direction, revealed by the bimodal H$α$ spectral profiles. For the prominence, the splitting begins at the top and gradually spreads to almost the whole prominence with a fast blue-shift component and a slow red-shift component. The material splitting in the filament is more fragmental. As shown by the present results, the CHASE full-disk spectroscopic observations make it possible to systematically study the three-dimensional kinematics of solar filament eruptions.
astro-ph.SR↗
Cohomologies, non-abelian extensions and Wells sequences of lambda-weighted Rota-Baxter Lie coalgebras
In this paper, we investigate cohomologies and non-abelian extensions of lambda-weighted Rota-Baxter Lie coalgebras. First, we consider Lie comodules and cohomologies of lambda-weighted Rota-Baxter Lie coalgebras. Next, we study non-abelian extensions of lambda-weighted Rota-Baxter Lie coalgebras and classify the non-abelian extensions in terms of non-abelian cohomology group. Furthermore, we explore extensibility of a pair of automorphisms about a non-abelian extension of lambda-weighted Rota-Baxter Lie coalgebras, and derive the fundamental sequences of Wells in the context of lambda-weighted Rota-Baxter Lie coalgebras. Finally, we discuss the previous results in the case of abelian extensions of lambda-weighted Rota-Baxter Lie coalgebras.
math.RA↗
GauU-Scene: A Scene Reconstruction Benchmark on Large Scale 3D Reconstruction Dataset Using Gaussian Splatting
We introduce a novel large-scale scene reconstruction benchmark using the newly developed 3D representation approach, Gaussian Splatting, on our expansive U-Scene dataset. U-Scene encompasses over one and a half square kilometres, featuring a comprehensive RGB dataset coupled with LiDAR ground truth. For data acquisition, we employed the Matrix 300 drone equipped with the high-accuracy Zenmuse L1 LiDAR, enabling precise rooftop data collection. This dataset, offers a unique blend of urban and academic environments for advanced spatial analysis convers more than 1.5 km$^2$. Our evaluation of U-Scene with Gaussian Splatting includes a detailed analysis across various novel viewpoints. We also juxtapose these results with those derived from our accurate point cloud dataset, highlighting significant differences that underscore the importance of combine multi-modal information
cs.CV↗
Converging Many-Body Perturbation Theory for Ab Initio Nuclear Structure: II. Brillouin-Wigner Perturbation Series for Open-Shell Nuclei
Brillouin-Wigner (BW) perturbation theory is developed for both ground and excited states of open-shell nuclei. We show that with optimal partitioning of the many-body Hamiltonian proposed earlier by the authors [Z. Li and N. Smirnova, arXiv:2306.13629], one can redefine the BW perturbation series for a given state of the effective Hamiltonian in a small P-space to be converging under the condition that the energy of this state is below the lowest eigenvalue of the Hamiltonian matrix block belonging to the complement of the P-space, characterized by the same good quantum numbers as the state under consideration. Specifically, the BW perturbative calculations for the lowest $J^π$ states are always converging due to the variational principle. This property does hold for both soft and hard internucleon interactions in the harmonic oscillator basis. To illustrate this method and check the convergence behavior, we present numerical studies of low-energy spectra of $^{5,6,7}$Li using the Daejeon16 and bare N3LO potentials.
nucl-th↗
On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities
Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.
cs.CR↗
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI. Models such as SAM, DALL-E2, and GPT-4 showcase their adaptability by extracting intricate patterns and performing effectively across diverse tasks, thereby serving as potent building blocks for a wide range of AI applications. Autonomous driving, a vibrant front in AI applications, remains challenged by the lack of dedicated vision foundation models (VFMs). The scarcity of comprehensive training data, the need for multi-sensor integration, and the diverse task-specific architectures pose significant obstacles to the development of VFMs in this field. This paper delves into the critical challenge of forging VFMs tailored specifically for autonomous driving, while also outlining future directions. Through a systematic analysis of over 250 papers, we dissect essential techniques for VFM development, including data preparation, pre-training strategies, and downstream task adaptation. Moreover, we explore key advancements such as NeRF, diffusion models, 3D Gaussian Splatting, and world models, presenting a comprehensive roadmap for future research. To empower researchers, we have built and maintained https://github.com/zhanghm1995/Forge_VFM4AD, an open-access repository constantly updated with the latest advancements in forging VFMs for autonomous driving.
cs.CV↗
Data-driven Topology Optimization of Channel Flow Problems
Typical topology optimization methods require complex iterative calculations, which cannot meet the requirements of fast computing applications. The neural network is studied to reduce the time of computing the optimization result, however, the data-driven method for fluid topology optimization is less of discussion. This paper intends to introduce a neural network architecture that avoids time-consuming iterative processes and has a strong generalization ability for topology optimization for Stokes flow. Different neural network methods including Convolution Neural Networks (CNN), conditional Generative Adversarial Networks (cGAN), and Denoising Diffusion Implicit Models (DDIM) which have been already successfully used in solid structure optimization problems are mutated and examined for fluid topology optimization cases. The presented neural network method is tested on the channel flow topology optimization problems for Stokes flow. The results have shown that our presented method has high pixel accuracy and gains a 663 times decrease on average in execution time compared with the conventional method.
physics.comp-ph↗
ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp Detection
Accurate polyp detection is critical for early colorectal cancer diagnosis. Although remarkable progress has been achieved in recent years, the complex colon environment and concealed polyps with unclear boundaries still pose severe challenges in this area. Existing methods either involve computationally expensive context aggregation or lack prior modeling of polyps, resulting in poor performance in challenging cases. In this paper, we propose the Enhanced CenterNet with Contrastive Learning (ECC-PolypDet), a two-stage training \& end-to-end inference framework that leverages images and bounding box annotations to train a general model and fine-tune it based on the inference score to obtain a final robust model. Specifically, we conduct Box-assisted Contrastive Learning (BCL) during training to minimize the intra-class difference and maximize the inter-class difference between foreground polyps and backgrounds, enabling our model to capture concealed polyps. Moreover, to enhance the recognition of small polyps, we design the Semantic Flow-guided Feature Pyramid Network (SFFPN) to aggregate multi-scale features and the Heatmap Propagation (HP) module to boost the model's attention on polyp targets. In the fine-tuning stage, we introduce the IoU-guided Sample Re-weighting (ISR) mechanism to prioritize hard samples by adaptively adjusting the loss weight for each sample during fine-tuning. Extensive experiments on six large-scale colonoscopy datasets demonstrate the superiority of our model compared with previous state-of-the-art detectors.
cs.CV↗
Benchmarking the Robustness of LiDAR Semantic Segmentation Models
When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. To rigorously evaluate the robustness and generalizability of current approaches, we propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic segmentation models, especially spanning different input representations (e.g., point clouds, voxels, projected images, and etc.), network architectures and training schemes. Through this study, we obtain two insights: 1) We find out that the input representation plays a crucial role in robustness. Specifically, under specific corruptions, different representations perform variously. 2) Although state-of-the-art methods on LiDAR semantic segmentation achieve promising results on clean data, they are less robust when dealing with noisy data. Finally, based on the above observations, we design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications. It is promising that our benchmark, comprehensive analysis, and observations can boost future research in robust LiDAR semantic segmentation for safety-critical applications.
cs.CV↗