Incremental Discovery of Prominent Situational Facts

We study the novel problem of finding new, prominent situational facts, which are emerging statements about objects that stand out within certain contexts. Many such facts are newsworthy---e.g., an athlete's outstanding performance in a game, or a viral video's impressive popularity. Effective and efficient identification of these facts assists journalists in reporting, one of the main goals of computational journalism. Technically, we consider an ever-growing table of objects with dimension and measure attributes. A situational fact is a "contextual" skyline tuple that stands out against historical tuples in a context, specified by a conjunctive constraint involving dimension attributes, when a set of measure attributes are compared. New tuples are constantly added to the table, reflecting events happening in the real world. Our goal is to discover constraint-measure pairs that qualify a new tuple as a contextual skyline tuple, and discover them quickly before the event becomes yesterday's news. A brute-force approach requires exhaustive comparison with every tuple, under every constraint, and in every measure subspace. We design algorithms in response to these challenges using three corresponding ideas---tuple reduction, constraint pruning, and sharing computation across measure subspaces. We also adopt a simple prominence measure to rank the discovered facts when they are numerous. Experiments over two real datasets validate the effectiveness and efficiency of our techniques

Structured querying of annotation-rich web text with shallow semantics

Abstract Information discovery on the Web has so far been dominated by keyword-based document search. However, recent years have witnessed arising needs from Web users to search for named entities, e.g., finding all Silicon Valley companies. With existing Web search engines, users have to digest returned Web pages by themselves to find the answers. Entity search has been introduced as a solution to this problem. However, existing entity search systems are limited in their capability to address complex information needs that involve multiple entities and their interrelationships. In this report, we introduce a novel entity-centric structured querying mechanism called Shallow Semantic Query (SSQ) to overcome this limitation. We cover two key technical issues with regard to SSQ, ranking and query processing. Comprehensive experiments show that (1) our ranking model beats state-of-the-art entity ranking methods; (2) the proposed query processing algorithm based on our new Entity-Centric Index is more efficient than a baseline extended from existing entity search systems

FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image

Precise Tooth Cone Beam Computed Tomography (CBCT) image segmentation is crucial for orthodontic treatment planning. In this paper, we propose FDNet, a Feature Decoupled Segmentation Network, to excel in the face of the variable dental conditions encountered in CBCT scans, such as complex artifacts and indistinct tooth boundaries. The Low-Frequency Wavelet Transform (LF-Wavelet) is employed to enrich the semantic content by emphasizing the global structural integrity of the teeth, while the SAM encoder is leveraged to refine the boundary delineation, thus improving the contrast between adjacent dental structures. By integrating these dual aspects, FDNet adeptly addresses the semantic gap, providing a detailed and accurate segmentation. The framework's effectiveness is validated through rigorous benchmarks, achieving the top Dice and IoU scores of 85.28% and 75.23%, respectively. This innovative decoupling of semantic and boundary features capitalizes on the unique strengths of each element to significantly elevate the quality of segmentation performance.Comment: This work has been submitted to the IEEE ISBI 2024 for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl