Functionality Discovery and Prediction of Physical Objects

Functionality is a fundamental attribute of an object which indicates the capability to be used to perform specific actions. It is critical to empower robots the functionality knowledge in discovering appropriate objects for a task e.g. cut cake using knife. Existing research works have focused on understanding object functionality through human-object-interaction from extensively annotated image or video data and are hard to scale up. In this paper, we (1) mine object-functionality knowledge through pattern-based and model-based methods from text, (2) introduce a novel task on physical object-functionality prediction, which consumes an image and an action query to predict whether the object in the image can perform the action, and (3) propose a method to leverage the mined functionality knowledge for the new task. Our experimental results show the effectiveness of our methods

Functionality Discovery and Prediction of Physical Objects

Functionality is a fundamental attribute of an object which indicates the capability to be used to perform specific actions. It is critical to empower robots the functionality knowledge in discovering appropriate objects for a task e.g. cut cake using knife. Existing research works have focused on understanding object functionality through human-object-interaction from extensively annotated image or video data and are hard to scale up. In this paper, we (1) mine object-functionality knowledge through pattern-based and model-based methods from text, (2) introduce a novel task on physical object-functionality prediction, which consumes an image and an action query to predict whether the object in the image can perform the action, and (3) propose a method to leverage the mined functionality knowledge for the new task. Our experimental results show the effectiveness of our methods.</jats:p

Secure genotype imputation using homomorphic encryption

Genotype imputation estimates missing genotypes from the haplotype or genotype reference panel in individual genetic sequences, which boosts the potential of genome-wide association and is essential in genetic data analysis. However, the genetic sequences involve people’s privacy, confirming an individual’s identification and even disease information. This work proposes a secure genotype imputation model, which uses a linear regression model and the homomorphic encryption scheme over ciphertext to impute missing genotypes. The inference model is trained with float plaintext parameters, which are round into integers to avoid high complexity homomorphic evaluation on float number operations without bootstrapping operations. Even though the rounding parameters in the inference model are not the same as those in the trained model, We find that it will no effect on the outcome of the homomorphic prediction. Thus, a high-efficiency genotype imputation inference model over the ciphertext is obtained while keeping the high-security level. The simulation results indicate that the accuracy of the secure inference model is almost the same as the original model trained on float parameters. The secure inference model’s accuracy is 98.6% for a single genotype

Microsoft Concept Graph: Mining Semantic Concepts for Short Text Understanding

Knowlege is important for text-related applications. In this paper, we introduce Microsoft Concept Graph, a knowledge graph engine that provides concept tagging APIs to facilitate the understanding of human languages. Microsoft Concept Graph is built upon Probase, a universal probabilistic taxonomy consisting of instances and concepts mined from the Web. We start by introducing the construction of the knowledge graph through iterative semantic extraction and taxonomy construction procedures, which extract 2.7 million concepts from 1.68 billion Web pages. We then use conceptualization models to represent text in the concept space to empower text-related applications, such as topic search, query recommendation, Web table understanding and Ads relevance. Since the release in 2016, Microsoft Concept Graph has received more than 100,000 pageviews, 2 million API calls and 3,000 registered downloads from 50,000 visitors over 64 countries. </jats:p

Satellite Observations Reveal Declining Diatom Concentrations in the Three Gorges Reservoir: The Impacts of Dam Construction and Local Climate

An effective satellite observation system is developed to retrieve the diatom concentration in freshwater ecosystems that could be utilized for understanding aquatic biogeochemical cycles. Although the singular value decomposition-based retrieval model can reflect the complicated diatom dynamics, the spatial distribution and temporal trend in diatom concentration on a large scale, as well as its driving mechanism, remain prevalently elusive. Based on the Google Earth Engine platform, this study uses Sentinel-2 MultiSpectral Instrument imagery to track the comprehensive diatom dynamics in a large reservoir, i.e., the Three Gorges Reservoir, in China during the years 2019–2023. The results indicate that a synchronous diatom distribution is found between the upstream and downstream artificial lakes along the primary tributary in the Three Gorges Reservoir, and the causal relationships between the declining diatom trend and hydrological/meteorological drivers on the monthly and yearly scales are highlighted. Moreover, the Sentinel-derived diatom concentration can be used to ascertain whether the dominant algae are harmful during bloom periods and aid in distinguishing algal blooms from ship oil spills. This study is a significant step forward in tracking the diatom dynamics in a large-scale freshwater ecosystem involving complex coupling drivers