Development Toward a Ground-Based Interferometric Phased Array for Radio Detection of High Energy Neutrinos

The in-ice radio interferometric phased array technique for detection of high energy neutrinos looks for Askaryan emission from neutrinos interacting in large volumes of glacial ice, and is being developed as a way to achieve a low energy threshold and a large effective volume at high energies. The technique is based on coherently summing the impulsive Askaryan signal from multiple antennas, which increases the signal-to-noise ratio for weak signals. We report here on measurements and a simulation of thermal noise correlations between nearby antennas, beamforming of impulsive signals, and a measurement of the expected improvement in trigger efficiency through the phased array technique. We also discuss the noise environment observed with an analog phased array at Summit Station, Greenland, a possible site for an interferometric phased array for radio detection of high energy neutrinos.Comment: 13 Pages, 14 Figure

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

A Short Review of Ethical Challenges in Clinical Natural Language Processing

Clinical NLP has an immense potential in contributing to how clinical practice will be revolutionized by the advent of large scale processing of clinical records. However, this potential has remained largely untapped due to slow progress primarily caused by strict data access policies for researchers. In this paper, we discuss the concern for privacy and the measures it entails. We also suggest sources of less sensitive data. Finally, we draw attention to biases that can compromise the validity of empirical research and lead to socially harmful applications.Comment: First Workshop on Ethics in Natural Language Processing (EACL'17