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

Scale Invariant Privacy Preserving Video via Wavelet Decomposition

Video surveillance has become ubiquitous in the modern world. Mobile devices, surveillance cameras, and IoT devices, all can record video that can violate our privacy. One proposed solution for this is privacy-preserving video, which removes identifying information from the video as it is produced. Several algorithms for this have been proposed, but all of them suffer from scale issues: in order to sufficiently anonymize near-camera objects, distant objects become unidentifiable. In this paper, we propose a scale-invariant method, based on wavelet decomposition

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

ZS-SRT: An Efficient Zero-Shot Super-Resolution Training Method for Neural Radiance Fields

Neural Radiance Fields (NeRF) have achieved great success in the task of synthesizing novel views that preserve the same resolution as the training views. However, it is challenging for NeRF to synthesize high-quality high-resolution novel views with low-resolution training data. To solve this problem, we propose a zero-shot super-resolution training framework for NeRF. This framework aims to guide the NeRF model to synthesize high-resolution novel views via single-scene internal learning rather than requiring any external high-resolution training data. Our approach consists of two stages. First, we learn a scene-specific degradation mapping by performing internal learning on a pretrained low-resolution coarse NeRF. Second, we optimize a super-resolution fine NeRF by conducting inverse rendering with our mapping function so as to backpropagate the gradients from low-resolution 2D space into the super-resolution 3D sampling space. Then, we further introduce a temporal ensemble strategy in the inference phase to compensate for the scene estimation errors. Our method is featured on two points: (1) it does not consume high-resolution views or additional scene data to train super-resolution NeRF; (2) it can speed up the training process by adopting a coarse-to-fine strategy. By conducting extensive experiments on public datasets, we have qualitatively and quantitatively demonstrated the effectiveness of our method

Toward Computational Fact-Checking ∗

Our news are saturated with claims of “facts ” made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim “cherry-picking”? This paper proposes a framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate practical fact-checking tasks—reverse-engineering (often intentionally) vague claims, and countering questionable claims—as computational problems. Along with the modeling framework, we develop an algorithmic framework that enables efficient instantiations of “meta ” algorithms by supplying appropriate algorithmic building blocks. We present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.

Surface metallization of solid lubricants and its effect on the mechanical properties of Fe-based bit matrix

Self-lubricating impregnated diamond bit can provide a new technical solution to the lunar drilling problem, but the poor wettability between the solid lubricants and the bit matrix can lead to degradation of physical and mechanical properties of the bit matrix. The influence of properties of the solid lubricant MOS2, WS2 and CAF2 on the electroless plating was studied, and the influence of the solid lubricant coating on the indentation hardness and bending strength of the bit matrix was investigated. The results show that the surface nickel plating of the above three solid lubricants can be achieved by the chemical plating method, but there are some differences in their plating appearances. Under the same volume concentration of condition, MoS2 and WS2 surface metallization can improve the mechanical properties of self-lubricating impregnated diamond bit matrix, but the effect of CaF2 is insignificant