Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths

Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding space. This mapping is learned on training data of seen classes and is expected to have transfer ability to unseen classes. In this paper, we tackle this problem by exploiting the intrinsic relationship between the semantic space manifold and the transfer ability of visual-semantic mapping. We formalize their connection and cast zero-shot recognition as a joint optimization problem. Motivated by this, we propose a novel framework for zero-shot recognition, which contains dual visual-semantic mapping paths. Our analysis shows this framework can not only apply prior semantic knowledge to infer underlying semantic manifold in the image feature space, but also generate optimized semantic embedding space, which can enhance the transfer ability of the visual-semantic mapping to unseen classes. The proposed method is evaluated for zero-shot recognition on four benchmark datasets, achieving outstanding results.Comment: Accepted as a full paper in IEEE Computer Vision and Pattern Recognition (CVPR) 201

Crystal structure of E. coli arginyl-tRNA synthetase and ligand binding studies revealed key residues in arginine recognition

The arginyl-tRNA synthetase (ArgRS) catalyzes the esterification reaction between L-arginine and its cognate tRNA(Arg). Previously reported structures of ArgRS shed considerable light on the tRNA recognition mechanism, while the aspect of amino acid binding in ArgRS remains largely unexplored. Here we report the first crystal structure of E. coli ArgRS (eArgRS) complexed with L-arginine, and a series of mutational studies using isothermal titration calorimetry (ITC). Combined with previously reported work on ArgRS, our results elucidated the structural and functional roles of a series of important residues in the active site, which furthered our understanding of this unique enzyme. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s13238-013-0012-1) contains supplementary material, which is available to authorized users

Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets

Time series modeling has attracted extensive research efforts; however, achieving both reliable efficiency and interpretability from a unified model still remains a challenging problem. Among the literature, shapelets offer interpretable and explanatory insights in the classification tasks, while most existing works ignore the differing representative power at different time slices, as well as (more importantly) the evolution pattern of shapelets. In this paper, we propose to extract time-aware shapelets by designing a two-level timing factor. Moreover, we define and construct the shapelet evolution graph, which captures how shapelets evolve over time and can be incorporated into the time series embeddings by graph embedding algorithms. To validate whether the representations obtained in this way can be applied effectively in various scenarios, we conduct experiments based on three public time series datasets, and two real-world datasets from different domains. Experimental results clearly show the improvements achieved by our approach compared with 17 state-of-the-art baselines.Comment: An extended version with 11 pages including appendix; Accepted by AAAI'202

De-fine: Decomposing and Refining Visual Programs with Auto-Feedback

Visual programming, a modular and generalizable paradigm, integrates different modules and Python operators to solve various vision-language tasks. Unlike end-to-end models that need task-specific data, it advances in performing visual processing and reasoning in an unsupervised manner. Current visual programming methods generate programs in a single pass for each task where the ability to evaluate and optimize based on feedback, unfortunately, is lacking, which consequentially limits their effectiveness for complex, multi-step problems. Drawing inspiration from benders decomposition, we introduce De-fine, a general framework that automatically decomposes complex tasks into simpler subtasks and refines programs through auto-feedback. This model-agnostic approach can improve logical reasoning performance by integrating the strengths of multiple models. Our experiments across various visual tasks show that De-fine creates more accurate and robust programs, setting new benchmarks in the field

Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models

We do not pursue a novel method in this paper, but aim to study if a modern text-to-image diffusion model can tailor any task-adaptive image classifier across domains and categories. Existing domain adaptive image classification works exploit both source and target data for domain alignment so as to transfer the knowledge learned from the labeled source data to the unlabeled target data. However, as the development of the text-to-image diffusion model, we wonder if the high-fidelity synthetic data from the text-to-image generator can serve as a surrogate of the source data in real world. In this way, we do not need to collect and annotate the source data for each domain adaptation task in a one-for-one manner. Instead, we utilize only one off-the-shelf text-to-image model to synthesize images with category labels derived from the corresponding text prompts, and then leverage the surrogate data as a bridge to transfer the knowledge embedded in the task-agnostic text-to-image generator to the task-oriented image classifier via domain adaptation. Such a one-for-all adaptation paradigm allows us to adapt anything in the world using only one text-to-image generator as well as the corresponding unlabeled target data. Extensive experiments validate the feasibility of the proposed idea, which even surpasses the state-of-the-art domain adaptation works using the source data collected and annotated in real world.Comment: 11 pages, 6 figure

Future of Networked Information Society: A Deeply Interconnected “Primitive Society”

Human society is evolving toward the future network information society. In this paper, we identify the interconnected level as the key factor driving the evolution of human society and incorporate it into our proposed evolutionary model of social formation. We show the entire process of social formation evolution at the interconnected level through theoretical analysis and simulation. Our result is consistent with what human beings have gone through. By contrast, the result presents the following four characteristics of the future network information society: the personalization of goods or services, the downsizing of enterprises or organizations, the decentralization of production or life, and the sharing of production or living tools. We regard the future network information society as a deeply interconnected “primitive society”

Herb-Drug Interaction: Effects of Relinqing® Granule on the Pharmacokinetics of Ciprofloxacin, Sulfamethoxazole, and Trimethoprim in Rats

Relinqing granule (RLQ) is the best-selling Chinese patent drug for treatment of urinary system diseases. In this study, the effects of RLQ on the pharmacokinetics of ciprofloxacin, sulfamethoxazole, and trimethoprim in SD rats were investigated. Rats were randomly divided into control group 1, control group 2, RLQ group 1, and RLQ group 2. RLQ group 1 and RLQ group 2 were treated orally with RLQ for 7 days, and rats were treated with the same volume of water in control group 1 and control group 2. Then, RLQ group 1 and control group 1 were given intragastrically ciprofloxacin on day 8, while RLQ group 2 and control group 2 were given intragastrically sulfamethoxazole and trimethoprim on day 8. Blood samples were collected and determined. There was no significant influence of pharmacokinetic parameters of trimethoprim on two groups. But some pharmacokinetic parameters of ciprofloxacin and sulfamethoxazole in RLQ pretreated rats were evidently altered (P < 0.05), which indicated that absorption of ciprofloxacin and sulfamethoxazole in RLQ pretreated rats was significantly affected. It indicated the coadministration of RLQ would have an influence on the efficacy of ciprofloxacin and sulfamethoxazole, and the doses of ciprofloxacin tablet and compound sulfamethoxazole tablet need adjustment