Owing to the capability for reliable and all-weather long-range sensing, the fusion of LiDAR and Radar has been widely applied to autonomous vehicles for robust perception. In practical operation, well manually calibrated extrinsic parameters, which are crucial for the fusion of multi-modal sensors, may drift due to the vibration. To address this issue, we present a novel targetless calibration approach, termed LiRaCo, for the extrinsic 6DoF calibration of LiDAR and Radar sensors. Although both types of sensors can obtain geometric information, bridging the geometric correspondences between multi-modal data without any clues of explicit artificial markers is nontrivial, mainly due to the low vertical resolution of scanning Radar. To achieve the targetless calibration, LiRaCo leverages a spatial occupancy consistency between LiDAR point clouds and Radar scans in a common cylindrical representation, considering the increasing data sparsity with distance for both sensors. Specifically, LiRaCo expands the valid Radar scanned pixels into 3D occupancy grids to constrain LiDAR point clouds based on spatial consistency. Consequently, a cost function involving extrinsic calibration parameters is formulated based on the spatial overlap of 3D grids and LiDAR points. Extrinsic parameters are finally estimated by optimizing the cost function. Comprehensive quantitative and qualitative experiments on two real outdoor datasets with different LiDAR sensors demonstrate the feasibility and accuracy of the proposed method. The source code will be publicly available.

Using large language models (LLMs) integration platforms without transparency about which LLM is being invoked can lead to potential security risks. Specifically, attackers may exploit this black-box scenario to deploy malicious models and embed viruses in the code provided to users. In this context, it is increasingly urgent for users to clearly identify the LLM they are interacting with, in order to avoid unknowingly becoming victims of malicious models. However, existing studies primarily focus on mixed classification of human and machine-generated text, with limited attention to classifying texts generated solely by different models. Current research also faces dual bottlenecks: poor quality of LLM-generated text (LLMGT) datasets and limited coverage of detectable LLMs, resulting in poor detection performance for various LLMGT in black-box scenarios. We propose the first LLMGT fingerprint detection model, \textbf{FDLLM}, based on Qwen2.5-7B and fine-tuned using LoRA to address these challenges. FDLLM can more efficiently handle detection tasks across multilingual and multi-domain scenarios. Furthermore, we constructed a dataset named \textbf{FD-Datasets}, consisting of 90,000 samples that span multiple languages and domains, covering 20 different LLMs. Experimental results demonstrate that FDLLM achieves a macro F1 score 16.7\% higher than the best baseline method, LM-D.

Conversational recommender systems (CRS) enable users to articulate their preferences and provide feedback through natural language. With the advent of large language models (LLMs), the potential to enhance user engagement with CRS and augment the recommendation process with LLM-generated content has received increasing attention. However, the efficacy of LLM-powered CRS is contingent upon the use of prompts, and the subjective perception of recommendation quality can differ across various recommendation domains. Therefore, we have developed a ChatGPT-based CRS to investigate the impact of these two factors, prompt guidance (PG) and recommendation domain (RD), on the overall user experience of the system. We conducted an online empirical study (N = 100) by employing a mixed-method approach that utilized a between-subjects design for the variable of PG (with vs. without) and a within-subjects design for RD (book recommendations vs. job recommendations). The findings reveal that PG can substantially enhance the system's explainability, adaptability, perceived ease of use, and transparency. Moreover, users are inclined to perceive a greater sense of novelty and demonstrate a higher propensity to engage with and try recommended items in the context of book recommendations as opposed to job recommendations. Furthermore, the influence of PG on certain user experience metrics and interactive behaviors appears to be modulated by the recommendation domain, as evidenced by the interaction effects between the two examined factors. This work contributes to the user-centered evaluation of ChatGPT-based CRS by investigating two prominent factors and offers practical design guidance.

A major challenge for building statistical models in the big data era is that the available data volume may exceed the computational capability. A common approach to solve this problem is to employ a subsampled dataset that can be handled by the available computational resources. In this paper, we propose a general subsampling scheme for large-scale multi-class logistic regression, and examine the variance of the resulting estimator. We show that asymptotically, the proposed method always achieves a smaller variance than that of the uniform random sampling. Moreover, when the classes are conditional imbalanced, significant improvement over uniform sampling can be achieved. Empirical performance of the proposed method is compared to other methods on both simulated and real-world datasets, and these results match and confirm our theoretical analysis.