Christopher Clark

Christopher Clark
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Christopher Clark
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Computer Science - Computer Vision and Pattern Recognition (4)
 
Computer Science - Distributed; Parallel; and Cluster Computing (4)
 
Astrophysics of Galaxies (2)
 
Statistics - Applications (2)
 
Physics - Physics and Society (2)
 
Computer Science - Neural and Evolutionary Computing (1)
 
Quantitative Biology - Neurons and Cognition (1)
 
Computer Science - Learning (1)
 
Computer Science - Artificial Intelligence (1)
 
Computer Science - Sound (1)
 
Physics - Medical Physics (1)
 
Instrumentation and Methods for Astrophysics (1)

Publications Authored By Christopher Clark

2016Oct
Authors: Demitri Muna, Michael Alexander, Alice Allen, Richard Ashley, Daniel Asmus, Ruyman Azzollini, Michele Bannister, Rachael Beaton, Andrew Benson, G. Bruce Berriman, Maciej Bilicki, Peter Boyce, Joanna Bridge, Jan Cami, Eryn Cangi, Xian Chen, Nicholas Christiny, Christopher Clark, Michelle Collins, Johan Comparat, Neil Cook, Darren Croton, Isak Delberth Davids, Éric Depagne, John Donor, Leonardo A. dos Santos, Stephanie Douglas, Alan Du, Meredith Durbin, Dawn Erb, Daniel Faes, J. G. Fernández-Trincado, Anthony Foley, Sotiria Fotopoulou, Søren Frimann, Peter Frinchaboy, Rafael Garcia-Dias, Artur Gawryszczak, Elizabeth George, Sebastian Gonzalez, Karl Gordon, Nicholas Gorgone, Catherine Gosmeyer, Katie Grasha, Perry Greenfield, Rebekka Grellmann, James Guillochon, Mark Gurwell, Marcel Haas, Alex Hagen, Daryl Haggard, Tim Haines, Patrick Hall, Wojciech Hellwing, Edmund Christian Herenz, Samuel Hinton, Renee Hlozek, John Hoffman, Derek Holman, Benne Willem Holwerda, Anthony Horton, Cameron Hummels, Daniel Jacobs, Jens Juel Jensen, David Jones, Arna Karick, Luke Kelley, Matthew Kenworthy, Ben Kitchener, Dominik Klaes, Saul Kohn, Piotr Konorski, Coleman Krawczyk, Kyler Kuehn, Teet Kuutma, Michael T. Lam, Richard Lane, Jochen Liske, Diego Lopez-Camara, Katherine Mack, Sam Mangham, Qingqing Mao, David J. E. Marsh, Cecilia Mateu, Loïc Maurin, James McCormac, Ivelina Momcheva, Hektor Monteiro, Michael Mueller, Roberto Munoz, Rohan Naidu, Nicholas Nelson, Christian Nitschelm, Chris North, Juan Nunez-Iglesias, Sara Ogaz, Russell Owen, John Parejko, Vera Patrício, Joshua Pepper, Marshall Perrin, Timothy Pickering, Jennifer Piscionere, Richard Pogge, Radek Poleski, Alkistis Pourtsidou, Adrian M. Price-Whelan, Meredith L. Rawls, Shaun Read, Glen Rees, Hanno Rein, Thomas Rice, Signe Riemer-Sørensen, Naum Rusomarov, Sebastian F. Sanchez, Miguel Santander-García, Gal Sarid, William Schoenell, Aleks Scholz, Robert L. Schuhmann, William Schuster, Peter Scicluna, Marja Seidel, Lijing Shao, Pranav Sharma, Aleksandar Shulevski, David Shupe, Cristóbal Sifón, Brooke Simmons, Manodeep Sinha, Ian Skillen, Bjoern Soergel, Thomas Spriggs, Sundar Srinivasan, Abigail Stevens, Ole Streicher, Eric Suchyta, Joshua Tan, O. Grace Telford, Romain Thomas, Chiara Tonini, Grant Tremblay, Sarah Tuttle, Tanya Urrutia, Sam Vaughan, Miguel Verdugo, Alexander Wagner, Josh Walawender, Andrew Wetzel, Kyle Willett, Peter K. G. Williams, Guang Yang, Guangtun Zhu, Andrea Zonca

The Astropy Project (http://astropy.org) is, in its own words, "a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages." For five years this project has been managed, written, and operated as a grassroots, self-organized, almost entirely volunteer effort while the software is used by the majority of the astronomical community. Read More

Random Field Theory has been used in the fMRI literature to address the multiple comparisons problem. The method provides an analytical solution for the computation of precise p-values when its assumptions are met. When its assumptions are not met the thresholds generated by Random Field Theory can be more conservative than Bonferroni corrections, which are arguably too stringent for use in fMRI. Read More

We aim to investigate advancing the state of the art of detection, classification and localization (DCL) in the field of bioacoustics. The two primary goals are to develop transferable technologies for detection and classification in: (1) the area of advanced algorithms, such as deep learning and other methods; and (2) advanced systems, capable of real-time and archival and processing. This project will focus on long-term, continuous datasets to provide automatic recognition, minimizing human time to annotate the signals. Read More

Overarching goals for this work aim to advance the state of the art for detection, classification and localization (DCL) in the field of bioacoustics. This goal is primarily achieved by building a generic framework for detection-classification (DC) using a fast, efficient and scalable architecture, demonstrating the capabilities of this system using on a variety of low-frequency mid-frequency cetacean sounds. Two primary goals are to develop transferable technologies for detection and classification in, one: the area of advanced algorithms, such as deep learning and other methods; and two: advanced systems, capable of real-time and archival processing. Read More

This work presents a new toolkit for describing the acoustic properties of the ocean environment before, during and after a sound event caused by an underwater seismic air-gun. The toolkit uses existing sound measures, but uniquely applies these to capture the early time period (actual pulse) and late time period (reverberation and multiple arrivals). In total, 183 features are produced for each air-gun sound. Read More

While the animal bioacoustics community at large is collecting huge amounts of acoustic data at an unprecedented pace, processing these data is problematic. Currently in bioacoustics, there is no effective way to achieve high performance computing using commericial off the shelf (COTS) or government off the shelf (GOTS) tools. Although several advances have been made in the open source and commercial software community, these offerings either support specific applications that do not integrate well with data formats in bioacoustics or they are too general. Read More

Goals of this research phase is to investigate advanced detection and classification pardims useful for data-mining passive large passive acoustic archives. Technical objectives are to develop and refine a High Performance Computing, Acoustic Data Accelerator (HPC-ADA) along with MATLAB based software based on time series acoustic signal Detection cLassification using Machine learning Algorithms, called DeLMA. Data scientists and biologists integrate to use the HPC-ADA and DeLMA technologies to explore data using newly developed techniques aimed at inspection of data extracted at large spatial and temporal scales. Read More

We use the published photometry and spectroscopy of 22 galaxies in the Herschel Reference Survey to determine that the value of the dust mass absorption coefficient $\kappa_{d}$ at a wavelength of 500 $\mu m$ is $\kappa_{500} = 0.051^{+0.070}_{-0. Read More

Previous research indicates that race, ethnicity, and gender influence legislative behavior in important ways. The bulk of this research, however, focuses on the way these characteristics shape an individual legislator's behavior, making it less clear how they account for relationships between legislators. We study the cosponsorship process in order to understand the race and gender based dynamics underlying the relational component of representation. Read More

This paper presents a new software model designed for distributed sonic signal detection runtime using machine learning algorithms called DeLMA. A new algorithm--Acoustic Data-mining Accelerator (ADA)--is also presented. ADA is a robust yet scalable solution for efficiently processing big sound archives using distributing computing technologies. Read More

We present the properties of the first 250 $\mu$m blind sample of nearby galaxies (15 < D < 46 Mpc) containing 42 objects from the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS). Herschel's sensitivity probes the faint end of the dust luminosity function for the first time, spanning a range of stellar mass (7.4 < log$_{10}$ M$_{\star}$ < 11. Read More

Mastering the game of Go has remained a long standing challenge to the field of AI. Modern computer Go systems rely on processing millions of possible future positions to play well, but intuitively a stronger and more 'humanlike' way to play the game would be to rely on pattern recognition abilities rather then brute force computation. Following this sentiment, we train deep convolutional neural networks to play Go by training them to predict the moves made by expert Go players. Read More

A fundamental goal of systems neuroscience is to probe the dynamics of neural activity that drive behavior. Here we present an instrument to simultaneously manipulate neural activity via Channelrhodopsin, monitor neural response via GCaMP3, and observe behavior in freely moving C. elegans. Read More

In this paper, we propose a method to improve sound classification performance by combining signal features, derived from the time-frequency spectrogram, with human perception. The method presented herein exploits an artificial neural network (ANN) and learns the signal features based on the human perception knowledge. The proposed method is applied to a large acoustic dataset containing 24 months of nearly continuous recordings. Read More

In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) up-calls in the presence of high levels of ambient and interfering noise. We apply a continuous region algorithm on the spectrogram to extract the regions of interest, and then use grid masking techniques to generate a small feature set that is then used in an artificial neural network classifier to identify the NARW up-calls. It is shown that the proposed technique is effective in detecting and capturing even very faint up-calls, in the presence of ambient and interfering noises. Read More

The following work outlines an approach for automatic detection and recognition of periodic pulse train signals using a multi-stage process based on spectrogram edge detection, energy projection and classification. The method has been implemented to automatically detect and recognize pulse train songs of minke whales. While the long term goal of this work is to properly identify and detect minke songs from large multi-year datasets, this effort was developed using sounds off the coast of Massachusetts, in the Stellwagen Bank National Marine Sanctuary. Read More