Fulfilling Multiple Intent Queries Using Compound Responses

Information-seeking queries to a virtual assistant can often cover multiple facets of a given topic. For example, a query of the form “what is the latest on covid-19” can reasonably be answered with the latest epidemiological statistics, medical information of disease symptoms, news relating to disease spread, etc. No single interpretation of such a query is likely to meet all of the user\u27s original information-seeking intent. This disclosure describes techniques for answering queries that can include multiple reasonable answers derived from different corpora. The techniques incorporate information from the different corpora and presents it in a manner that obviates the need for a user to understand finer distinctions between the corpora. Effectively, the techniques provide a compound response that is stitched together from available facts, related news stories, and other sources of information to cover the user’s information needs

Use of graphene as protection film in biological environments

Corrosion of metal in biomedical devices could cause serious health problems to patients. Currently ceramics coating materials used in metal implants can reduce corrosion to some extent with limitations. Here we proposed graphene as a biocompatible protective film for metal potentially for biomedical application. We confirmed graphene effectively inhibits Cu surface from corrosion in different biological aqueous environments. Results from cell viability tests suggested that graphene greatly eliminates the toxicity of Cu by inhibiting corrosion and reducing the concentration of Cu(2+) ions produced. We demonstrated that additional thiol derivatives assembled on graphene coated Cu surface can prominently enhance durability of sole graphene protection limited by the defects in graphene film. We also demonstrated that graphene coating reduced the immune response to metal in a clinical setting for the first time through the lymphocyte transformation test. Finally, an animal experiment showed the effective protection of graphene to Cu under in vivo condition. Our results open up the potential for using graphene coating to protect metal surface in biomedical application

High-Performance Atomically-Thin Room-Temperature NO2 Sensor.

The development of room-temperature sensing devices for detecting small concentrations of molecular species is imperative for a wide range of low-power sensor applications. We demonstrate a room-temperature, highly sensitive, selective, stable, and reversible chemical sensor based on a monolayer of the transition-metal dichalcogenide Re0.5Nb0.5S2. The sensing device exhibits a thickness-dependent carrier type, and upon exposure to NO2 molecules, its electrical resistance considerably increases or decreases depending on the layer number. The sensor is selective to NO2 with only minimal response to other gases such as NH3, CH2O, and CO2. In the presence of humidity, not only are the sensing properties not deteriorated but also the monolayer sensor shows complete reversibility with fast recovery at room temperature. We present a theoretical analysis of the sensing platform and identify the atomically sensitive transduction mechanism

Use of graphene as protection film in biological environments

Corrosion of metal in biomedical devices could cause serious health problems to patients. Currently ceramics coating materials used in metal implants can reduce corrosion to some extent with limitations. Here we proposed graphene as a biocompatible protective film for metal potentially for biomedical application. We confirmed graphene effectively inhibits Cu surface from corrosion in different biological aqueous environments. Results from cell viability tests suggested that graphene greatly eliminates the toxicity of Cu by inhibiting corrosion and reducing the concentration of Cu2+ ions produced. We demonstrated that additional thiol derivatives assembled on graphene coated Cu surface can prominently enhance durability of sole graphene protection limited by the defects in graphene film. We also demonstrated that graphene coating reduced the immune response to metal in a clinical setting for the first time through the lymphocyte transformation test. Finally, an animal experiment showed the effective protection of graphene to Cu under in vivo condition. Our results open up the potential for using graphene coating to protect metal surface in biomedical application

Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation

As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class activation mapping and randomized input sampling have gained great popularity. However, the attribution methods based on these techniques provide lower resolution and blurry explanation maps that limit their explanation power. To circumvent this issue, visualization based on various layers is sought. In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation. We also propose a layer selection strategy that applies to the whole family of CNN-based models, based on which our extraction framework is applied to visualize the last layers of each convolutional block of the model. Moreover, we perform an empirical analysis of the efficacy of derived lower-level information to enhance the represented attributions. Comprehensive experiments conducted on shallow and deep models trained on natural and industrial datasets, using both ground-truth and model-truth based evaluation metrics validate our proposed algorithm by meeting or outperforming the state-of-the-art methods in terms of explanation ability and visual quality, demonstrating that our method shows stability regardless of the size of objects or instances to be explained.Comment: 9 pages, 9 figures, Accepted at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21

GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning

Despite the remarkable progress in the development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained on a particular task, based on specific data formats available in a set of medical records, tend to not generalize well to other tasks or databases in which the data fields may differ. To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), which is applicable to any EHR with minimal preprocessing for multiple prediction tasks. GenHPF resolves heterogeneity in medical codes and schemas by converting EHRs into a hierarchical textual representation while incorporating as many features as possible. To evaluate the efficacy of GenHPF, we conduct multi-task learning experiments with single-source and multi-source settings, on three publicly available EHR datasets with different schemas for 12 clinically meaningful prediction tasks. Our framework significantly outperforms baseline models that utilize domain knowledge in multi-source learning, improving average AUROC by 1.2%P in pooled learning and 2.6%P in transfer learning while also showing comparable results when trained on a single EHR dataset. Furthermore, we demonstrate that self-supervised pretraining using multi-source datasets is effective when combined with GenHPF, resulting in a 0.6%P AUROC improvement compared to models without pretraining. By eliminating the need for preprocessing and feature engineering, we believe that this work offers a solid framework for multi-task and multi-source learning that can be leveraged to speed up the scaling and usage of predictive algorithms in healthcare.Comment: Accepted by IEEE Journal of Biomedical and Health Informatic

Homogeneous bilayer graphene film based flexible transparent conductor

Graphene is considered a promising candidate to replace conventional transparent conductors due to its low opacity, high carrier mobility and flexible structure. Multi-layer graphene or stacked single layer graphenes have been investigated in the past but both have their drawbacks. The uniformity of multi-layer graphene is still questionable, and single layer graphene stacks require many transfer processes to achieve sufficiently low sheet resistance. In this work, bilayer graphene film grown with low pressure chemical vapor deposition was used as a transparent conductor for the first time. The technique was demonstrated to be highly efficient in fabricating a conductive and uniform transparent conductor compared to multi-layer or single layer graphene. Four transfers of bilayer graphene yielded a transparent conducting film with a sheet resistance of 180 {\Omega}_{\square} at a transmittance of 83%. In addition, bilayer graphene films transferred onto plastic substrate showed remarkable robustness against bending, with sheet resistance change less than 15% at 2.14% strain, a 20-fold improvement over commercial indium oxide films.Comment: Published in Nanoscale, Nov. 2011 : http://www.rsc.org/nanoscal