Traffic prediction serves as a cornerstone for systems and network services such as the Web of Vehicles (WoV), online navigation, and smart city applications. Despite the proliferation of model architectures in recent years, existing approaches often suffer from highly customized structures and weak transferability, making it difficult to cope with increasing task heterogeneity and modeling complexity. To address these challenges, we propose ST-LEGO, a modular assembly framework driven by large language models (LLMs) that supports flexible structural composition and automated code generation. ST-LEGO employs a multi-agent collaborative system comprising a Prompt Agent, Assemble Agent, and Code Agent, which are responsible for understanding task requirements, dynamically assembling structural modules, and automatically generating executable PyTorch code. By introducing a standardized module library and an intermediate structural description language (DSL), the framework enables controllable generation, reusable composition, and cross-task generalization of model architectures. Empirical results on multiple real-world traffic datasets demonstrate that models generated by ST-LEGO achieve superior accuracy, structural diversity, and convergence compared to a wide range of manually designed baselines. These results highlight the unique potential and scalability of LLMs as structural architects for traffic prediction, offering a new paradigm for integrating language models into web-interactive intelligent transportation systems.

• We propose a dynamic step-wise uncertainty estimation method for chemical problems. • We propose a collaborative framework for chemical problem solving. • Experiments across LLMs and datasets show ChemAU improves chemistry reasoning. Large language models (LLMs) have demonstrated remarkable reasoning capabilities and natural language understanding, leading to the widespread adoption across diverse applications. However, their effectiveness diminishes considerably when applied to chemistry-related problems, which involve specific terminology, chemical notation systems, and complex nomenclature conventions. These unique characteristics pose challenges for LLMs, which are primarily trained on general corpora with limited chemistry-specific data, resulting in inadequate chemical knowledge and hallucinations during reasoning. Existing methods remain insufficient to fully address these limitations. To bridge this gap, we propose ChemAU , a collaborative framework that integrates general and specialized LLMs for chemical reasoning. Our framework introduces a novel dynamic step-wise uncertainty estimation method tailored for the chemistry domain. This method precisely identifies chemical knowledge deficiencies in general LLM reasoning, after which the framework facilitates targeted knowledge supplementation via the chemistry-specific LLM. Experimental evaluations with widely-used LLMs across multiple chemical datasets demonstrate that ChemAU significantly enhances both reasoning accuracy and uncertainty estimation. Code is available at https://github.com/xinyi23/ChemAU .

Point cloud completion seeks to reconstruct a complete and high-fidelity point cloud from an incomplete and low-quality input. Current methods predominantly rely on Transformer architectures for feature extraction. However, these approaches face two major limitations, including the computational complexity associated with the attention mechanism and the potential loss of fine-grained details during pooling operations. These issues hinder their performance on large-scale and highly fragmented point clouds. To overcome these challenges, we propose 3DMambaComplete, a novel point cloud completion method based on the selective State Space Model (SSM), particularly leveraging the Mamba architecture. Unlike traditional Transformer-based methods, 3DMambaComplete utilizes Mamba’s linear-time complexity to efficiently extract global features with significantly reduced computational overhead. Furthermore, we introduce the concepts of discriminative nodes, referred to as hyperpoints, along with dynamic offsets, to improve reconstruction quality. Specifically, the HyperPoint Generation Module encodes the downsampled features of the point cloud using the Mamba Encoder, producing a set of hyperpoints that capture critical information. Subsequently, the HyperPoint Spread Module disperses these hyperpoints across various spatial locations employing dynamic offsets to mitigate aggregation. Finally, the Point Deformation Module implements a deformation technique to transform the 2D mesh into a detailed 3D structure, resulting in high-quality point cloud completions. Experiments on widely used benchmark datasets show that 3DMambaComplete outperforms existing point cloud completion techniques in both quantitative and qualitative evaluations.

Automated log analysis is crucial to ensure the high availability and reliability of complex systems. The advent of Large Language Models (LLMs) in Natural Language Processing (NLP) has ushered in a new era of language model-driven automated log analysis, garnering significant interest. Within this field, two primary paradigms based on language models for log analysis have become prominent. Small Language Models (SLMs) (such as BERT) follow the pre-train and fine-tune paradigm, focusing on the specific log analysis task through fine-tuning on supervised datasets. On the other hand, LLMs (such as ChatGPT) following the in-context learning paradigm, analyze logs by providing a few examples in prompt contexts without updating parameters. Despite their respective strengths, both models exhibit inherent limitations. By comparing SLMs and LLMs, we notice that SLMs are more cost-effective but less powerful, whereas LLMs with large parameters are highly powerful but expensive and inefficient. To tradeoff between the performance and inference costs of both models in automated log analysis, this article introduces an adaptive log analysis framework known as AdaptiveLog, which effectively reduces the costs associated with LLM while ensuring superior results. This framework collaborates an LLM and an SLM, strategically allocating the LLM to tackle complex logs while delegating simpler logs to the SLM. Specifically, to efficiently query the LLM, we propose an adaptive selection strategy based on the uncertainty estimation of the SLM, where the LLM is invoked only when the SLM is uncertain. In addition, to enhance the reasoning ability of the LLM in log analysis tasks, we propose a novel prompt strategy by retrieving similar error-prone cases as the reference, enabling the model to leverage past error experiences and learn solutions from these cases. We evaluate AdaptiveLog on different log analysis tasks, Extensive experiments demonstrate that AdaptiveLog achieves state-of-the-art results across different tasks, elevating the overall accuracy of log analysis while maintaining cost efficiency. Our source code and detailed experimental data are available at https://github.com/LeaperOvO/AdaptiveLog-review .

With the growing demand for real-world 3-D understanding, learning effective representations of 3-D data has become increasingly important for tasks such as shape classification, model retrieval, scene reconstruction, and point cloud completion. Although previous work has explored self-supervised learning within individual modalities (e.g., point clouds or images), the potential of multi-modal supervision remains largely underexplored due to the lack of aligned and scalable training signals. In this work, we present DR-Point, a tri-modal pre-training framework that jointly learns from RGB images, depth maps, and 3-D point clouds to build a unified embedding space across modalities. By enforcing cross-modal consistency among RGB-depth-point triplets, DR-Point achieves effective 2-D-3-D feature alignment without manual annotations. A differentiable rendering module further enhances geometric fidelity by synthesizing depth cues and refining structural details in reconstructed point clouds. Extensive experiments on benchmarks demonstrate that DR-Point consistently outperforms state-of-the-art self-supervised methods on 3-D classification, segmentation, and completion. These results highlight the advantages of multi-modal pre-training for unified 3-D understanding and its potential to benefit a wide range of vision and graphics applications.

Variation of Arctic sea ice has significant impacts on polar ecosystems, transporting routes, coastal communities, and global climate. Tracing the change of sea ice at a finer scale is paramount for both operational applications and scientific studies. Recent pan-Arctic sea ice forecasting methods that leverage advances in artificial intelligence have made promising progress over numerical models. However, forecasting sea ice at higher resolutions is still under-explored. To bridge the gap, we propose a two-module cooperative deep learning framework, IceDiff, to forecast sea ice concentration at finer scales. IceDiff first leverages a vision transformer to generate coarse yet superior forecasting results over previous methods at a regular 25 km grid. This high-quality sea ice forecasting can be utilized as reliable guidance for the next module. Subsequently, an unconditional diffusion model pre-trained on low-resolution sea ice concentration maps is utilized for sampling down-scaled sea ice forecasting via a zero-shot guided sampling strategy and a patch-based method. For the first time, IceDiff demonstrates sea ice forecasting with a 6.25 km resolution. IceDiff extends the boundary of existing sea ice forecasting models and more importantly, its capability to generate high-resolution sea ice concentration data is vital for pragmatic usages and research. Code is available at https://github.com/EtronTech/IceDiff.

Automated log analysis plays a vital role in software operations, with deep learning methods demonstrating effectiveness for analyzing logs from individual systems. However, existing methods face limitations in efficiency, adaptability, and knowledge preservation in system-incremental log analysis. Continual learning offers a solution by expanding the model’s ability to analyze logs from the increasing number of systems. For evaluating these methods in system-incremental log analysis, we introduce LogSI, a novel benchmark with four essential abilities for system-incremental log analysis. We perform a comprehensive evaluation of various baselines on LogSI, examining their robustness against different system permutations. Additionally, we conduct an in-depth study on the factors that influence their robustness. The datasets and source code of this paper can be found in https://github.com/nonauthor/LogSIbenchmark.

System-incremental log analysis, involves the ongoing training of a model using logs from diverse systems to enable effective resolution of log analysis tasks across an expanding array of systems. Existing continual learning methods, which are based on prompt tuning, have shown challenges in insufficient knowledge transfer and increasing catastrophic forgetting. To tackle these challenges, we present LogHPT, a novel continual learning method based on a hierarchical prompt tuning frame-work specifically tailored for system-incremental log analysis. LogHPT incorporates four types of prompt meticulously crafted to capture log knowledge across various granularities, thereby enhancing knowledge transfer. Subsequently, we employ a key-value mechanism to discern the most suitable prompts for the input logs. Additionally, we use general prompt learning based on knowledge distillation to mitigate catastrophic forgetting. To evaluate the performance of LogHPT, we conduct comprehensive experiments focusing on two fundamental subtasks: log parsing and log anomaly detection. The results show that LogHPT achieves state-of-the-art (SOTA) performance. The source code and datasets for this paper are accessible at the following link: https://github.com/nonauthor/LogHPT.

Fine-grained traffic management and prediction are fundamental to key applications such as autonomous driving, lane change guidance, and traffic signal control. However, obtaining lane-level traffic data has become a critical bottleneck for data-driven models due to limitations in the types and number of sensors and issues with the accuracy of tracking algorithms. To address this, we propose the Fine-grained Road Traffic Inference (FRTI) task, which aims to generate more detailed lane-level traffic information using limited road data, providing a more energy-efficient and cost-effective solution for precise traffic management. This task is abstracted as the first scene of the spatio-temporal graph node generation problem. We designed a two-stage framework-RoadDiff-to solve the FRTI task. This framework leverages the Road-Lane Correlation Autoencoder-Decoder and the Lane Diffusion Module to fully utilize the limited spatio-temporal dependencies and distribution relationships of road data to accurately infer fine-grained lane traffic states. Based on existing research, we designed several baseline models with the potential to solve the FRTI task and conducted extensive experiments on six datasets representing different road conditions to validate the effectiveness of the RoadDiff model in addressing the FRTI task. The relevant datasets and code are available at https://github.com/ShuhaoLii/RoadDiff.

In partial-to-complete point cloud completion, it is imperative that enabling every patch in the output point cloud faithfully represents the corresponding patch in partial input, ensuring similarity in terms of geometric content. To achieve this objective, we propose a straightforward method dubbed PPCL that aims to maximize the mutual information between two point patches from the encoder and decoder by leveraging a contrastive learning framework. Contrastive learning facilitates the mapping of two similar point patches to corresponding points in a learned feature space. Notably, we explore multi-layer point patches contrastive learning (MPPCL) instead of operating on the whole point cloud. The negatives are exploited within the input point cloud itself rather than the rest of the datasets. To fully leverage the local geometries present in the partial inputs and enhance the quality of point patches in the encoder, we introduce Multi-level Feature Learning (MFL) and Hierarchical Feature Fusion (HFF) modules. These modules are also able to facilitate the learning of various levels of features. Moreover, Spatial-Channel Transformer Point Up-sampling (SCT) is devised to guide the decoder to construct a complete and fine-grained point cloud by leveraging enhanced point patches from our point patches contrastive learning. Extensive experiments demonstrate that our PPCL can achieve better quantitive and qualitative performance over off-the-shelf methods across various datasets.

Logs play a critical role in providing essential information for system monitoring and troubleshooting. Recently, with the success of pre-trained language models (PLMs) and large language models (LLMs) in natural language processing (NLP), smaller PLMs (such as BERT) and LLMs (like GPT-4) have become the current mainstream approaches for log analysis. Despite the remarkable capabilities of LLMs, their higher cost and inefficient inference present significant challenges in leveraging the full potential of LLMs to analyze logs. In contrast, smaller PLMs can be fine-tuned for specific tasks even with limited computational resources, making them more practical. However, these smaller PLMs face challenges in understanding logs comprehensively due to their limited expert knowledge. To address the lack of expert knowledge and enhance log understanding for smaller PLMs, this paper introduces a novel and practical knowledge enhancement framework, called LUK, which acquires expert knowledge from LLMs automatically and then enhances the smaller PLM for log analysis with the expert knowledge. LUK can take full advantage of both types of models. Specifically, we design a multi-expert collaboration framework based on LLMs with different roles to acquire expert knowledge. In addition, we propose two novel pre-training tasks to enhance the log pre-training with expert knowledge. LUK achieves state-of-the-art results on different log analysis tasks, and extensive experiments demonstrate that expert knowledge from LLMs can be utilized more effectively to understand logs. Our source code and detailed experimental data are available athttps://github.com/LeaperOvO/LUK.

Learning unsigned distance fields (UDF) directly from raw point clouds as the implicit representation for surface reconstruction is a promising learning-based method for reconstructing open surfaces and supervision-free attributes. In most UDF methods, Chamfer Distance (CD), the commonly used metric in 3D domains, is reckoned as the preferable loss function for training neural networks that predict UDFs. However, CD intrinsically suffers from deficiencies like the insensitivity to point density distribution and the inclination to be diverged by outliers, which may severely hamper the reconstruction performance. In this regard, we propose DM-UDF, a method that learns density-regulated and multi-view consistent UDFs by revising CD loss with the dynamic three-phase loss function. Specifically, we adopt a carefully designed CD derivative called Density-aware Chamfer Distance (DCD) for detecting different density distributions to alleviate the distribution imbalance problem in the reconstructed surfaces. Further, to generate surfaces with fine-grained local details, a differentiable rendering view loss is also introduced into the hybrid design of our loss function, measuring the fidelity of projected images under different camera poses to maintain multi-view consistency. We conducted surface reconstruction tasks on both synthetic and real scan datasets and experimental results show that DM-UDF achieves state-of-the-art performance. Code is available at dm-udf.

Logs as semi-structured text are rich in semantic information, making their comprehensive understanding crucial for automated log analysis. With the recent success of pre-trained language models in natural language processing, many studies have leveraged these models to understand logs. Despite their successes, existing pre-trained language models still suffer from three weaknesses. Firstly, these models fail to understand domain-specific terminology, especially abbreviations. Secondly, these models struggle to adequately capture the complete log context information. Thirdly, these models have difficulty in obtaining universal representations of different styles of the same logs. To address these challenges, we introduce KnowLog, a knowledge-enhanced pre-trained language model for log understanding. Specifically, to solve the previous two challenges, we exploit abbreviations and natural language descriptions of logs from public documentation as local and global knowledge, respectively, and leverage this knowledge by designing novel pre-training tasks for enhancing the model. To solve the last challenge, we design a contrastive learning-based pre-training task to obtain universal representations. We evaluate KnowLog by fine-tuning it on six different log understanding tasks. Extensive experiments demonstrate that KnowLog significantly enhances log understanding and achieves state-of-the-art results compared to existing pre-trained language models without knowledge enhancement. Moreover, we conduct additional experiments in transfer learning and low-resource scenarios, showcasing the substantial advantages of KnowLog. Our source code and detailed experimental data are available at https://github.com/LeaperOvO/KnowLog.

Estimating the complete 3D point cloud from a partial input is a key challenge in 3D vision. Existing point cloud completion networks overlook the long-range, hierarchical features and object details of the incomplete point cloud. To this end, we propose Hierarchical Feature Fusion Network (HFF-Net) for precise and detailed point cloud completion. To succeed at this task, HFF-Net estimates the missing Point Agents (PAs) by designing a topology-aware transformer-based encoder-decoder network with Multi-level Feature Learning (MFL), which hierarchically exploits the various regional and detailed information. Further, to make better utilization of the hierar-chical information captured from MFL, we devise the Hier-archical Features Fusion (HFF) module to convert them into cross-regional features. Besides, the predicted PAs is utilized by a multi-resolution output module to recover the missing point cloud in a coarse-to-fine manner. Experiments indi-cate that HFF-Net performs favorably against state-of-the-art (SOTA) approaches on both the new-proposed and existing datasets.

Existing point cloud completion methods mainly utilize the global shape representation to recover the missing regions of the 3D shape from the partial point cloud. However, these methods learn the global shape representations with continuous features against the inherently discrete nature of point cloud, hardly resulting in a high-quality structure for points. To address this challenge, we concentrate on discrete representations, which are potentially a more natural fit for the modalities of the point cloud. Therefore, we propose to employ Vector Quantization (VQ) Auto-Encoder and Dual-channel Transformer for point cloud completion (VQ-DcTr). The VQ-DcTr is apt to use discrete global features and exploit them in a well-structured generation process. Specifically, the vector quantization auto-encoder is integrated to learn a discrete latent representation along with inductive biases inherent in the transformer-based auto-encoder. By using the decoded seeds from the auto-encoder, the dual-channel transformer leverages point-wise and channel-wise attention to learn the splitting patterns in the previous Dual-channel Transformer Points Splitting (DCTPS) layer to perform the points splitting in the current DCTPS layer. In this way, we can obtain the locally compact and structured point cloud by capturing the structure characteristic of 3D shape in local patches. Extensive experiments on all standard benchmarks demonstrate that VQ-DcTr outperforms the state-of-the-art point cloud completion methods through qualitative and quantitative analysis.

Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications of 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, view-based, convolution-based, graph-based, generative model-based, transformer-based approaches, etc. And this survey summarizes the comparisons among these methods to provoke further research insights. Besides, this review sums up the commonly used datasets and illustrates the applications of point cloud completion. Eventually, we also discussed possible research trends in this promptly expanding field.

Network traffic anomaly detection is an important research content in the field of network and security management. By analyzing network traffic, the health of the network environment can be intuitively evaluated. In particular, analyzing network traffic provides practical and effective guidance for identification and classification of anomaly. This paper proposes a network traffic anomaly detection method based on wavelet analysis for pcap files contain two different delay injections. The wavelet analysis can effectively extract information from the signal and is suitable for the detection of anomaly. Firstly, wavelet analysis is used to extract the waveform features, and then the support vector machine is used for classification. In particular, packet lengths in the pcap files is parsed out to form a sequence of packet lengths in chronological order. Then followed by the wavelet analysis based packet length sequence feature extraction and feature selection methods, the resulting eigenvectors are used as input features to support vector machine for training the classifier. Thus to differentiate the two types of anomaly in the mixed traffic with both normal and abnormal traffic. The qualitative and quantitative experimental results show that our approach achieves good classification results.