Using Weak Supervision and Data Augmentation in Question Answering

The onset of the COVID-19 pandemic accentuated the need for access to biomedical literature to answer timely and disease-specific questions. During the early days of the pandemic, one of the biggest challenges we faced was the lack of peer-reviewed biomedical articles on COVID-19 that could be used to train machine learning models for question answering (QA). In this paper, we explore the roles weak supervision and data augmentation play in training deep neural network QA models. First, we investigate whether labels generated automatically from the structured abstracts of scholarly papers using an information retrieval algorithm, BM25, provide a weak supervision signal to train an extractive QA model. We also curate new QA pairs using information retrieval techniques, guided by the clinicaltrials.gov schema and the structured abstracts of articles, in the absence of annotated data from biomedical domain experts. Furthermore, we explore augmenting the training data of a deep neural network model with linguistic features from external sources such as lexical databases to account for variations in word morphology and meaning. To better utilize our training data, we apply curriculum learning to domain adaptation, fine-tuning our QA model in stages based on characteristics of the QA pairs. We evaluate our methods in the context of QA models at the core of a system to answer questions about COVID-19

Use Of A Surface-emitting Micro-laser Array For Optical Computing

Surface emitting microlaser arrays have many features that make them highly desirable for use in optical computing. In this talk, we will describe various applications of the novel device for optical computing, with emphasis on ,neural network implementations

Use Of A Surface-emitting Micro-laser Array For Optical Computing

Surface emitting microlaser arrays have many features that make them highly desirable for use in optical computing. In this talk, we will describe various applications of the novel device for optical computing, with emphasis on ,neural network implementations

Multiwavelength, Multilevel Optical Storage Using Dielectric Mirrors

Optical storage systems store information at densities higher than other technologies and are less expensive per byte. Optical disk storage has been touted as a replacement for magnetic disks, but suffers from longer access times and lower data rates. The lower data rate of optical disks is partially due to lower disk rotation rates, but mainly a result of reading optical disks individually, rather than in parallel like magnetic disks. Reading several optical disks in parallel is possible but may complicate the removability of the disks. In this letter, we describe a wavelength-selective, multilayer disk based on dielectric mirrors that has potential to achieve a high degree of integration and parallelism. © 1994 IEE

Diffraction efficiency and signal-to-noise ratio of multiplexed volume phase holograms recorded in a photographic emulsion

The problems related to noise that arise during recording and reconstruction of holograms used in optical data storage or in massive optical interconnection systems are quite similar and can be analyzed in order to improve the quality of the images that these optical systems provide. In this paper, we will analyze noise in cases in which several coherent object waves are simultaneously stored in a phase recording material in a way that allows us to obtain information about the relationship that exists between the recording material and the number of waves that are being stored. The material used in this study is Agfa Gevaert 8E75 HD holographic film processed with a rehalogenating—type bleach bath without a fixation step. Additionally, we show experimentally that it is possible to holographically store more than 400 waves at the same time (in a coherent fashion) using the same storage geometry, with a signal-to-noise ratio larger than 20 and an average diffraction efficiency of 15%.Part of this work was supported by the Direcció General d'Ensenyaments Universitaris i Investigació of the Generalitat Valenciana, Spain (Project GV-1165/93) and the Comisión Interministerial de Ciencia y Tecnología, Spain (Project MAT93-0369)