A Theoretical Analysis of NDCG Type Ranking Measures

A central problem in ranking is to design a ranking measure for evaluation of ranking functions. In this paper we study, from a theoretical perspective, the widely used Normalized Discounted Cumulative Gain (NDCG)-type ranking measures. Although there are extensive empirical studies of NDCG, little is known about its theoretical properties. We first show that, whatever the ranking function is, the standard NDCG which adopts a logarithmic discount, converges to 1 as the number of items to rank goes to infinity. On the first sight, this result is very surprising. It seems to imply that NDCG cannot differentiate good and bad ranking functions, contradicting to the empirical success of NDCG in many applications. In order to have a deeper understanding of ranking measures in general, we propose a notion referred to as consistent distinguishability. This notion captures the intuition that a ranking measure should have such a property: For every pair of substantially different ranking functions, the ranking measure can decide which one is better in a consistent manner on almost all datasets. We show that NDCG with logarithmic discount has consistent distinguishability although it converges to the same limit for all ranking functions. We next characterize the set of all feasible discount functions for NDCG according to the concept of consistent distinguishability. Specifically we show that whether NDCG has consistent distinguishability depends on how fast the discount decays, and 1/r is a critical point. We then turn to the cut-off version of NDCG, i.e., NDCG@k. We analyze the distinguishability of NDCG@k for various choices of k and the discount functions. Experimental results on real Web search datasets agree well with the theory.Comment: COLT 201

Sharp Multiple Instance Learning for DeepFake Video Detection

With the rapid development of facial manipulation techniques, face forgery has received considerable attention in multimedia and computer vision community due to security concerns. Existing methods are mostly designed for single-frame detection trained with precise image-level labels or for video-level prediction by only modeling the inter-frame inconsistency, leaving potential high risks for DeepFake attackers. In this paper, we introduce a new problem of partial face attack in DeepFake video, where only video-level labels are provided but not all the faces in the fake videos are manipulated. We address this problem by multiple instance learning framework, treating faces and input video as instances and bag respectively. A sharp MIL (S-MIL) is proposed which builds direct mapping from instance embeddings to bag prediction, rather than from instance embeddings to instance prediction and then to bag prediction in traditional MIL. Theoretical analysis proves that the gradient vanishing in traditional MIL is relieved in S-MIL. To generate instances that can accurately incorporate the partially manipulated faces, spatial-temporal encoded instance is designed to fully model the intra-frame and inter-frame inconsistency, which further helps to promote the detection performance. We also construct a new dataset FFPMS for partially attacked DeepFake video detection, which can benefit the evaluation of different methods at both frame and video levels. Experiments on FFPMS and the widely used DFDC dataset verify that S-MIL is superior to other counterparts for partially attacked DeepFake video detection. In addition, S-MIL can also be adapted to traditional DeepFake image detection tasks and achieve state-of-the-art performance on single-frame datasets.Comment: Accepted at ACM MM 2020. 11 pages, 8 figures, with appendi

Research on Community Competition and Adaptive Genetic Algorithm for Automatic Generation of Tang Poetry

As there are many researches about traditional Tang poetry, among which automatically generated Tang poetry has arouse great concern in recent years. This study presents a community-based competition and adaptive genetic algorithm for automatically generating Tang poetry. The improved algorithm with community-based competition that has been added aims to maintain the diversity of genes during evolution; meanwhile, the adaptation means that the probabilities of crossover and mutation are varied from the fitness values of the Tang poetry to prevent premature convergence and generate better poems more quickly. According to the analysis of experimental results, it has been found that the improved algorithm is superior to the conventional method

Complete genome sequence of biocontrol strain Bacillus velezensis YC89 and its biocontrol potential against sugarcane red rot

IntroductionSugarcane is one of the most important sugar crops worldwide, however, sugarcane production is seriously limited by sugarcane red rot, a soil-borne disease caused by Colletotrichum falcatum. Bacillus velezensis YC89 was isolated from sugarcane leaves and can significantly inhibited red rot disease caused by C. falcatum.MethodsIn this study, the genome of YC89 strain was sequenced, its genome structure and function were analyzed using various bioinformatics software, and its genome was compared with those of other homologous strains. In addition, the effectiveness of YC89 against sugarcane red rot and the evaluation of sugarcane plant growth promotion were also investigated by pot experiments.ResultsHere, we present the complete genome sequence of YC89, which consists of a 3.95 Mb circular chromosome with an average GC content of 46.62%. The phylogenetic tree indicated that YC89 is closely related to B. velezensis GS-1. Comparative genome analysis of YC89 with other published strains (B. velezensis FZB42, B. velezensis CC09, B. velezensis SQR9, B. velezensis GS-1, and B. amyloliquefaciens DSM7) revealed that the strains had a part common coding sequences (CDS) in whereas 42 coding were unique of strain YC89. Whole-genome sequencing revealed 547 carbohydrate-active enzymes and identified 12 gene clusters encoding secondary metabolites. Additionally, functional analysis of the genome revealed numerous gene/gene clusters involved in plant growth promotion, antibiotic resistance, and resistance inducer synthesis. In vitro pot tests indicated that YC89 strain controlled sugarcane red rot and promoted the growth of sugarcane plants. Additionally, it increased the activity of enzymes involved in plant defense, such as superoxide dismutase, peroxidase, polyphenol oxidase, chitinase, and β-1,3-glucanase.DiscussionThese findings will be helpful for further studies on the mechanisms of plant growth promotion and biocontrol by B. velezensis and provide an effective strategy for controlling red rot in sugarcane plants

Triphenylphosphine-Based Covalent Organic Frameworks and Heterogeneous Rh-P-COFs Catalysts

The synthesis of phosphine-based functional covalent organic frameworks (COFs) has attracted great attention recently. Herein, we present two examples of triphenylphosphine-based COFs (termed P-COFs) with well-defined crystalline structures, high specific surface areas, and good thermal stability. Furthermore, rhodium catalysts with these P-COFs as support material show high turnover frequency for the hydroformylation of olefins, as well as excellent recycling performance. This work not only extends the phosphine-based COF family, but also demonstrates their application in immobilizing homogeneous metal-based (e.g., Rh-phosphine) catalysts for application in heterogeneous catalysis