Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking

Discovering entity mentions that are out of a Knowledge Base (KB) from texts plays a critical role in KB maintenance, but has not yet been fully explored. The current methods are mostly limited to the simple threshold-based approach and feature-based classification; the datasets for evaluation are relatively rare. In this work, we propose BLINKout, a new BERT-based Entity Linking (EL) method which can identify mentions that do not have a corresponding KB entity by matching them to a special NIL entity. To this end, we integrate novel techniques including NIL representation, NIL classification, and synonym enhancement. We also propose Ontology Pruning and Versioning strategies to construct out-of-KB mentions from normal, in-KB EL datasets. Results on four datasets of clinical notes and publications show that BLINKout outperforms existing methods to detect out-of-KB mentions for medical ontologies UMLS and SNOMED CT

Embeddable Advanced Sensors for Harsh Environment Sensing Applications

Research and development in advanced sensors with embedded monitoring capability have experienced significant growth in recent years, fueled by their broad applications in real-time measurement of a wide variety of physical, chemical, and biological quantities. Compared with conventional sensors with bulky assemblies, recent progress in 3D manufacturing technologies (e.g., ultrafast laser micromachining and additive manufacturing) has opened up a new avenue in one-step fabrication of assembly-freemicro devices in various materials as well as the development of compact, customized, and intricate smart structures/components. The merits of these advanced manufacturing techniques enable the integration of embeddable advanced sensors into smart structures and components for improved robustness, enriched functionality, enhanced intelligence, and unprecedented performance

Simultaneous Measurement of Temperature and Pressure with Cascaded Extrinsic Fabry-Perot Interferometer and Intrinsic Fabry-Perot Interferometer Sensors

This paper presents an approach for simultaneous measurement of temperature and pressure using miniaturized fiber inline sensors. The approach utilizes the cascaded optical fiber inline intrinsic Fabry-Perot interferometer and extrinsic Fabry-Perot interferometer as temperature and pressure sensing elements, respectively. A CO2 laser was used to create a loss between them to balance their reflection power levels. The multiplexed signals were demodulated using a Fast Fourier transform-based wavelength tracking method. Experimental results showed that the sensing system could measure temperature and pressure unambiguously in a pressure range of 0 to 6.895 x 105 Pa and a temperature range from 20°C to 700°C

A quantum-inspired classical algorithm for separable Non-negative Matrix Factorization

Non-negative Matrix Factorization (NMF) asks to decompose a (entry-wise) non-negative matrix into the product of two smaller-sized nonnegative matrices, which has been shown intractable in general. In order to overcome this issue, separability assumption is introduced which assumes all data points are in a conical hull. This assumption makes NMF tractable and is widely used in text analysis and image processing, but still impractical for huge-scale datasets. In this paper, inspired by recent development on dequantizing techniques, we propose a new classical algorithm for separable NMF problem. Our new algorithm runs in polynomial time in the rank and logarithmic in the size of input matrices, which achieves an exponential speedup in the low-rank setting