In multi-hop reasoning, multi-round retrieval-augmented generation (RAG) methods typically rely on LLM-generated content as the retrieval query. However, these approaches are inherently vulnerable to knowledge overshadowing—a phenomenon where critical information is overshadowed during generation. As a result, the LLM-generated content may be incomplete or inaccurate, leading to irrelevant retrieval and causing error accumulation during the iteration process. To address this challenge, we propose ActiShade, which detects and activates overshadowed knowledge to guide large language models(LLMs) in multi-hop reasoning. Specifically, ActiShade iteratively detects the overshadowed keyphrase in the given query, retrieves documents relevant to both the query and the overshadowed keyphrase, and generates a new query based on the retrieved documents to guide the next-round iteration. By supplementing the overshadowed knowledge during the formulation of next-round queries while minimizing the introduction of irrelevant noise, ActiShade reduces the error accumulation caused by knowledge overshadowing. Extensive experiments show that ActiShade outperforms existing methods across multiple datasets and LLMs.

Anomaly detection is valuable for real-world applications, such as industrial quality inspection. However, most approaches focus on detecting local structural anomalies while neglecting compositional anomalies incorporating logical constraints. In this paper, we introduce LogSAD, a novel multi-modal framework that requires no training for both Logical and Structural Anomaly Detection. First, we propose a match-of-thought architecture that employs advanced large multi-modal models (i.e. GPT-4V) to generate matching proposals, formulating interests and compositional rules of thought for anomaly detection. Second, we elaborate on multi-granularity anomaly detection, consisting of patch tokens, sets of interests, and composition matching with vision and language foundation models. Subsequently, we present a calibration module to align anomaly scores from different detectors, followed by integration strategies for the final decision. Consequently, our approach addresses both logical and structural anomaly detection within a unified framework and achieves state-of-the-art results without the need for training, even when compared to supervised approaches, highlighting its robustness and effectiveness. Code is available at https://github.com/zhang0jhon/LogSAD.

Point clouds have demonstrated extensive application prospects in various fields, including research related to the evaluation of textile pilling. We collect 3D point cloud data in the actual test environment of textiles, which has been organized and named the TextileNet dataset. To the best of our knowledge, it is the first publicly available 3D point cloud dataset in the field of textile pilling assessment. Based on the Non-parametric Network for 3D point cloud analysis (Point-NN), we construct a Few-parameter Network called Point-FN for experiments on the TextileNet dataset. Experimental results indicate that under conditions with a parameter count of only 0.5M and FLOPs of 1.7G, Point-FN achieves an Overall Accuracy (OA) of 91.1% and a Mean per-class Accuracy (MA) of 93.0%. Moreover, under the testing conditions of a single RTX 2080Ti GPU, Point-FN demonstrates an inference speed of 164 FPS. Testing results on other publicly available datasets also validate the competitive performance of Point-FN. The proposed TextileNet dataset will be publicly available.

This paper uses novel sensor data to study drivers' risky phone use behavior. The results challenge the conventional wisdom of moral hazard in insurance. We first identify handheld phone use behavior ("HPU") and quantify its causal impact on accident likelihood ("riskiness") using exhaustive fixed-effect models. We then find HPU to be risky but insensitive to both insurance coverage changes and weather shocks that increase its riskiness. This contradicts the prevailing theoretical prediction and empirical studies that have thus far relied on claims data alone. On the other hand, an experiment with a one-time text-message warning led to a persistent 15% HPU reduction. Drivers' inattention to risk thus limits moral hazard.

Image dehazing aims to restore the blurry image information caused by the ambiguities of unknown scene radiance and transmission. Instead of using paired images or depth information, we propose an Unsupervised Conditional Disentangle Network (UCDN) using unpaired dataset. Our approach enforces the constraint by introducing physical-based disentanglement. Unlike other unsupervised dehazing models, our approach adapts the multi-concentration of fog and outperforms on the dataset with different concentrations. Extensive experiments on synthesized dataset demonstrate that our approach can surpass state-of-the-arts. Meanwhile, through benchmarking on our collected natural hazy dataset, our approach can generate more perceptually appealing dehazing results.