A multitude of interconnected risk eventsranging from regulatory changes to geopolitical tensions-can trigger ripple effects across firms.Identifying inter-firm risk relations is thus crucial for applications like portfolio management and investment strategy.Traditionally, such assessments rely on expert judgment and manual analysis, which are, however, subjective, labor-intensive, and difficult to scale.To address this, we propose a systematic method for extracting inter-firm risk relations using Form 10-K filings-authoritative, standardized financial documents-as our data source.Leveraging recent advances in natural language processing, our approach captures implicit and abstract risk connections through unsupervised fine-tuning based on chronological and lexical patterns in the filings.This enables the development of a domain-specific financial encoder with a deeper contextual understanding and introduces a quantitative risk relation score for transparency, interpretable analysis.Extensive experiments demonstrate that our method outperforms strong baselines across multiple evaluation settings.

Tags play a critical role in enhancing product discoverability, optimizing search results, and enriching recommendation systems on e-commerce platforms. Despite the recent advancements in large language models (LLMs), which have shown proficiency in processing and understanding textual information, their application in tag generation remains an under-explored yet complex challenge. To this end, we introduce a novel method for automatic product tagging using LLMs to create behavior-enhanced tags (BETags). Specifically, our approach begins by generating base tags using an LLM. These base tags are then refined into BETags by incorporating user behavior data. This method aligns the tags with users' actual browsing and purchasing behavior, enhancing the accuracy and relevance of tags to user preferences. By personalizing the base tags with user behavior data, BETags are able to capture deeper behavioral insights, which is essential for understanding nuanced user interests and preferences in e-commerce environments. Moreover, since BETags are generated offline, they do not impose real-time computational overhead and can be seamlessly integrated into downstream tasks commonly associated with recommendation systems and search optimization. Our evaluation of BETag across three datasets--- Amazon (Scientific), MovieLens-1M, and FreshFood---shows that our approach significantly outperforms both human-annotated tags and other automated methods. These results highlight BETag as a scalable and efficient solution for personalized automated tagging, advancing e-commerce platforms by creating more tailored and engaging user experiences.

In this paper, we address the challenge of discovering financial signals in narrative financial reports.As these documents are often lengthy and tend to blend routine information with new information, it is challenging for professionals to discern critical financial signals.To this end, we leverage the inherent nature of the year-to-year structure of reports to define a novel signal-highlighting task; more importantly, we propose a compare-andcontrast multistage pipeline that recognizes different relationships between the reports and locates relevant rationales for these relationships.We also create and publicly release a humanannotated dataset for our task.Our experiments on the dataset validate the effectiveness of our pipeline, and we provide detailed analyses and ablation studies to support our findings.

On a multimedia digital home network, an audio player may be separated from the video player and both of them are connected by wireless interface, like 802.11 series. Due to forwarding, rewinding, or packet loss, it may make the video player and the audio player asynchronous. Based on the original UPnP AV architecture, a synchronous mechanism is proposed in this paper. With exchanging both status of the video player and the audio player in UPnP messages, the synchronization is accomplished. A prototype was also implemented to verify its feasibility.