58 results
A crinoid fauna and a new species of Pycnocrinus from the Martinsburg Formation (Upper Ordovician), lower Hudson Valley, New York
- James C. Brower, Carlton E. Brett, Howard R. Feldman
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- Journal:
- Journal of Paleontology , First View
- Published online by Cambridge University Press:
- 13 May 2024, pp. 1-18
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A new crinoid fauna has been discovered in the Upper Ordovician (Katian) Martinsburg Formation at a small shale quarry, locally known as the ‘Shale Bank,’ on the Shawangunk Ridge in Ulster County, NY. The assemblage, which is from a relatively low energy, offshore mud-bottom environment, includes four identified species, including a new species of glyptocrinid camerate, Pycnocrinus mohonkensis n. sp., described herein. Crinoid taxa in order of increasing branch density in the assemblage include (1) the dicyclic inadunate Merocrinus curtus with irregularly isotomous and heterotomous, non-pinnulate arms and a stout cylindrical column exceeding 700 mm; (2) the disparids Cincinnaticrinus varibrachialus, with heterotomous non pinnulate arms, and Ectenocrinus simplex, with extensively branched ramulate arms and meric columns of 460–500 mm; and (3) the camerate Pycnocrinus mohonkensis n. sp., with uniserial pinnulate arms and a somewhat shorter column. Some cylindrical stems with nodose and holomeric columnals are thought to belong to unknown camerate crinoids with pinnulate arms. Filtration theory is used to model food capture in the Martinsburg crinoids. Surprisingly, even densely pinnulate camerates were able to survive in this setting, suggesting that ambient currents attained velocities exceeding 25 cm/sec even in this offshore setting. Similar assemblages were widespread in eastern Laurentia during the Late Ordovician.
UUID: http://zoobank.org/23ca31e8-f572-4520-ba1d-891e3abb950d
Strategies to promote language inclusion at 17 CTSA hubs
- Linda Sprague Martinez, Cristina Araujo Brinkerhoff, Riana C. Howard, James A. Feldman, Erin Kobetz, J. Tommy White, Laurene Tumiel Berhalter, Alicia Bilheimer, Megan Hoffman, Carmen R. Isasi, Cynthia Killough, Julia Martinez, Johanna Chesley, Arshiya A. Baig, Capri Foy, Nadia Islam, Antonia Petruse, Carolina Rosales, Michele D. Kipke, Lourdes Baezconde-Garbanati, Tracy A. Battaglia, Rebecca Lobb
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- Journal:
- Journal of Clinical and Translational Science / Volume 8 / Issue 1 / 2024
- Published online by Cambridge University Press:
- 25 March 2024, e67
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The prioritization of English language in clinical research is a barrier to translational science. We explored promising practices to advance the inclusion of people who speak languages other than English in research conducted within and supported by NIH Clinical Translational Science Award (CTSA) hubs. Key informant interviews were conducted with representatives (n = 24) from CTSA hubs (n = 17). Purposive sampling was used to identify CTSA hubs focused on language inclusion. Hubs electing to participate were interviewed via Zoom. Thematic analysis was performed to analyze interview transcripts. We report on strategies employed by hubs to advance linguistic inclusion and influence institutional change that were identified. Strategies ranged from translations, development of culturally relevant materials and consultations to policies and procedural changes and workforce initiatives. An existing framework was adapted to conceptualize hub strategies. Language justice is paramount to bringing more effective treatments to all people more quickly. Inclusion will require institutional transformation and CTSA hubs are well positioned to catalyze change.
Perception, as you make it
- David W. Vinson, Drew H. Abney, Dima Amso, Anthony Chemero, James E. Cutting, Rick Dale, Jonathan B. Freeman, Laurie B. Feldman, Karl J. Friston, Shaun Gallagher, J. Scott Jordan, Liad Mudrik, Sasha Ondobaka, Daniel C. Richardson, Ladan Shams, Maggie Shiffrar, Michael J. Spivey
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- Journal:
- Behavioral and Brain Sciences / Volume 39 / 2016
- Published online by Cambridge University Press:
- 05 January 2017, e260
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The main question that Firestone & Scholl (F&S) pose is whether “what and how we see is functionally independent from what and how we think, know, desire, act, and so forth” (sect. 2, para. 1). We synthesize a collection of concerns from an interdisciplinary set of coauthors regarding F&S's assumptions and appeals to intuition, resulting in their treatment of visual perception as context-free.
Contributors
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- By Mitchell Aboulafia, Frederick Adams, Marilyn McCord Adams, Robert M. Adams, Laird Addis, James W. Allard, David Allison, William P. Alston, Karl Ameriks, C. Anthony Anderson, David Leech Anderson, Lanier Anderson, Roger Ariew, David Armstrong, Denis G. Arnold, E. J. Ashworth, Margaret Atherton, Robin Attfield, Bruce Aune, Edward Wilson Averill, Jody Azzouni, Kent Bach, Andrew Bailey, Lynne Rudder Baker, Thomas R. Baldwin, Jon Barwise, George Bealer, William Bechtel, Lawrence C. Becker, Mark A. Bedau, Ernst Behler, José A. Benardete, Ermanno Bencivenga, Jan Berg, Michael Bergmann, Robert L. Bernasconi, Sven Bernecker, Bernard Berofsky, Rod Bertolet, Charles J. Beyer, Christian Beyer, Joseph Bien, Joseph Bien, Peg Birmingham, Ivan Boh, James Bohman, Daniel Bonevac, Laurence BonJour, William J. Bouwsma, Raymond D. Bradley, Myles Brand, Richard B. Brandt, Michael E. Bratman, Stephen E. Braude, Daniel Breazeale, Angela Breitenbach, Jason Bridges, David O. Brink, Gordon G. Brittan, Justin Broackes, Dan W. Brock, Aaron Bronfman, Jeffrey E. Brower, Bartosz Brozek, Anthony Brueckner, Jeffrey Bub, Lara Buchak, Otavio Bueno, Ann E. Bumpus, Robert W. Burch, John Burgess, Arthur W. Burks, Panayot Butchvarov, Robert E. Butts, Marina Bykova, Patrick Byrne, David Carr, Noël Carroll, Edward S. Casey, Victor Caston, Victor Caston, Albert Casullo, Robert L. Causey, Alan K. L. Chan, Ruth Chang, Deen K. Chatterjee, Andrew Chignell, Roderick M. Chisholm, Kelly J. Clark, E. J. Coffman, Robin Collins, Brian P. Copenhaver, John Corcoran, John Cottingham, Roger Crisp, Frederick J. Crosson, Antonio S. Cua, Phillip D. Cummins, Martin Curd, Adam Cureton, Andrew Cutrofello, Stephen Darwall, Paul Sheldon Davies, Wayne A. Davis, Timothy Joseph Day, Claudio de Almeida, Mario De Caro, Mario De Caro, John Deigh, C. F. Delaney, Daniel C. Dennett, Michael R. DePaul, Michael Detlefsen, Daniel Trent Devereux, Philip E. Devine, John M. Dillon, Martin C. Dillon, Robert DiSalle, Mary Domski, Alan Donagan, Paul Draper, Fred Dretske, Mircea Dumitru, Wilhelm Dupré, Gerald Dworkin, John Earman, Ellery Eells, Catherine Z. Elgin, Berent Enç, Ronald P. Endicott, Edward Erwin, John Etchemendy, C. Stephen Evans, Susan L. Feagin, Solomon Feferman, Richard Feldman, Arthur Fine, Maurice A. Finocchiaro, William FitzPatrick, Richard E. Flathman, Gvozden Flego, Richard Foley, Graeme Forbes, Rainer Forst, Malcolm R. Forster, Daniel Fouke, Patrick Francken, Samuel Freeman, Elizabeth Fricker, Miranda Fricker, Michael Friedman, Michael Fuerstein, Richard A. Fumerton, Alan Gabbey, Pieranna Garavaso, Daniel Garber, Jorge L. A. Garcia, Robert K. Garcia, Don Garrett, Philip Gasper, Gerald Gaus, Berys Gaut, Bernard Gert, Roger F. Gibson, Cody Gilmore, Carl Ginet, Alan H. Goldman, Alvin I. Goldman, Alfonso Gömez-Lobo, Lenn E. Goodman, Robert M. Gordon, Stefan Gosepath, Jorge J. E. Gracia, Daniel W. Graham, George A. Graham, Peter J. Graham, Richard E. Grandy, I. Grattan-Guinness, John Greco, Philip T. Grier, Nicholas Griffin, Nicholas Griffin, David A. Griffiths, Paul J. Griffiths, Stephen R. Grimm, Charles L. Griswold, Charles B. Guignon, Pete A. Y. Gunter, Dimitri Gutas, Gary Gutting, Paul Guyer, Kwame Gyekye, Oscar A. Haac, Raul Hakli, Raul Hakli, Michael Hallett, Edward C. Halper, Jean Hampton, R. James Hankinson, K. R. Hanley, Russell Hardin, Robert M. Harnish, William Harper, David Harrah, Kevin Hart, Ali Hasan, William Hasker, John Haugeland, Roger Hausheer, William Heald, Peter Heath, Richard Heck, John F. Heil, Vincent F. Hendricks, Stephen Hetherington, Francis Heylighen, Kathleen Marie Higgins, Risto Hilpinen, Harold T. Hodes, Joshua Hoffman, Alan Holland, Robert L. Holmes, Richard Holton, Brad W. Hooker, Terence E. Horgan, Tamara Horowitz, Paul Horwich, Vittorio Hösle, Paul Hoβfeld, Daniel Howard-Snyder, Frances Howard-Snyder, Anne Hudson, Deal W. Hudson, Carl A. Huffman, David L. Hull, Patricia Huntington, Thomas Hurka, Paul Hurley, Rosalind Hursthouse, Guillermo Hurtado, Ronald E. Hustwit, Sarah Hutton, Jonathan Jenkins Ichikawa, Harry A. Ide, David Ingram, Philip J. Ivanhoe, Alfred L. Ivry, Frank Jackson, Dale Jacquette, Joseph Jedwab, Richard Jeffrey, David Alan Johnson, Edward Johnson, Mark D. Jordan, Richard Joyce, Hwa Yol Jung, Robert Hillary Kane, Tomis Kapitan, Jacquelyn Ann K. Kegley, James A. Keller, Ralph Kennedy, Sergei Khoruzhii, Jaegwon Kim, Yersu Kim, Nathan L. King, Patricia Kitcher, Peter D. Klein, E. D. Klemke, Virginia Klenk, George L. Kline, Christian Klotz, Simo Knuuttila, Joseph J. Kockelmans, Konstantin Kolenda, Sebastian Tomasz Kołodziejczyk, Isaac Kramnick, Richard Kraut, Fred Kroon, Manfred Kuehn, Steven T. Kuhn, Henry E. Kyburg, John Lachs, Jennifer Lackey, Stephen E. Lahey, Andrea Lavazza, Thomas H. Leahey, Joo Heung Lee, Keith Lehrer, Dorothy Leland, Noah M. Lemos, Ernest LePore, Sarah-Jane Leslie, Isaac Levi, Andrew Levine, Alan E. Lewis, Daniel E. Little, Shu-hsien Liu, Shu-hsien Liu, Alan K. L. Chan, Brian Loar, Lawrence B. Lombard, John Longeway, Dominic McIver Lopes, Michael J. Loux, E. J. Lowe, Steven Luper, Eugene C. Luschei, William G. Lycan, David Lyons, David Macarthur, Danielle Macbeth, Scott MacDonald, Jacob L. Mackey, Louis H. Mackey, Penelope Mackie, Edward H. Madden, Penelope Maddy, G. B. Madison, Bernd Magnus, Pekka Mäkelä, Rudolf A. Makkreel, David Manley, William E. Mann (W.E.M.), Vladimir Marchenkov, Peter Markie, Jean-Pierre Marquis, Ausonio Marras, Mike W. Martin, A. P. Martinich, William L. McBride, David McCabe, Storrs McCall, Hugh J. McCann, Robert N. McCauley, John J. McDermott, Sarah McGrath, Ralph McInerny, Daniel J. McKaughan, Thomas McKay, Michael McKinsey, Brian P. McLaughlin, Ernan McMullin, Anthonie Meijers, Jack W. Meiland, William Jason Melanson, Alfred R. Mele, Joseph R. Mendola, Christopher Menzel, Michael J. Meyer, Christian B. Miller, David W. Miller, Peter Millican, Robert N. Minor, Phillip Mitsis, James A. Montmarquet, Michael S. Moore, Tim Moore, Benjamin Morison, Donald R. Morrison, Stephen J. Morse, Paul K. Moser, Alexander P. D. Mourelatos, Ian Mueller, James Bernard Murphy, Mark C. Murphy, Steven Nadler, Jan Narveson, Alan Nelson, Jerome Neu, Samuel Newlands, Kai Nielsen, Ilkka Niiniluoto, Carlos G. Noreña, Calvin G. Normore, David Fate Norton, Nikolaj Nottelmann, Donald Nute, David S. Oderberg, Steve Odin, Michael O’Rourke, Willard G. Oxtoby, Heinz Paetzold, George S. Pappas, Anthony J. Parel, Lydia Patton, R. P. Peerenboom, Francis Jeffry Pelletier, Adriaan T. Peperzak, Derk Pereboom, Jaroslav Peregrin, Glen Pettigrove, Philip Pettit, Edmund L. Pincoffs, Andrew Pinsent, Robert B. Pippin, Alvin Plantinga, Louis P. Pojman, Richard H. Popkin, John F. Post, Carl J. Posy, William J. Prior, Richard Purtill, Michael Quante, Philip L. Quinn, Philip L. Quinn, Elizabeth S. Radcliffe, Diana Raffman, Gerard Raulet, Stephen L. Read, Andrews Reath, Andrew Reisner, Nicholas Rescher, Henry S. Richardson, Robert C. Richardson, Thomas Ricketts, Wayne D. Riggs, Mark Roberts, Robert C. Roberts, Luke Robinson, Alexander Rosenberg, Gary Rosenkranz, Bernice Glatzer Rosenthal, Adina L. Roskies, William L. Rowe, T. M. Rudavsky, Michael Ruse, Bruce Russell, Lilly-Marlene Russow, Dan Ryder, R. M. Sainsbury, Joseph Salerno, Nathan Salmon, Wesley C. Salmon, Constantine Sandis, David H. Sanford, Marco Santambrogio, David Sapire, Ruth A. Saunders, Geoffrey Sayre-McCord, Charles Sayward, James P. Scanlan, Richard Schacht, Tamar Schapiro, Frederick F. Schmitt, Jerome B. Schneewind, Calvin O. Schrag, Alan D. Schrift, George F. Schumm, Jean-Loup Seban, David N. Sedley, Kenneth Seeskin, Krister Segerberg, Charlene Haddock Seigfried, Dennis M. Senchuk, James F. Sennett, William Lad Sessions, Stewart Shapiro, Tommie Shelby, Donald W. Sherburne, Christopher Shields, Roger A. Shiner, Sydney Shoemaker, Robert K. Shope, Kwong-loi Shun, Wilfried Sieg, A. John Simmons, Robert L. Simon, Marcus G. Singer, Georgette Sinkler, Walter Sinnott-Armstrong, Matti T. Sintonen, Lawrence Sklar, Brian Skyrms, Robert C. Sleigh, Michael Anthony Slote, Hans Sluga, Barry Smith, Michael Smith, Robin Smith, Robert Sokolowski, Robert C. Solomon, Marta Soniewicka, Philip Soper, Ernest Sosa, Nicholas Southwood, Paul Vincent Spade, T. L. S. Sprigge, Eric O. Springsted, George J. Stack, Rebecca Stangl, Jason Stanley, Florian Steinberger, Sören Stenlund, Christopher Stephens, James P. Sterba, Josef Stern, Matthias Steup, M. A. Stewart, Leopold Stubenberg, Edith Dudley Sulla, Frederick Suppe, Jere Paul Surber, David George Sussman, Sigrún Svavarsdóttir, Zeno G. Swijtink, Richard Swinburne, Charles C. Taliaferro, Robert B. Talisse, John Tasioulas, Paul Teller, Larry S. Temkin, Mark Textor, H. S. Thayer, Peter Thielke, Alan Thomas, Amie L. Thomasson, Katherine Thomson-Jones, Joshua C. Thurow, Vzalerie Tiberius, Terrence N. Tice, Paul Tidman, Mark C. Timmons, William Tolhurst, James E. Tomberlin, Rosemarie Tong, Lawrence Torcello, Kelly Trogdon, J. D. Trout, Robert E. Tully, Raimo Tuomela, John Turri, Martin M. Tweedale, Thomas Uebel, Jennifer Uleman, James Van Cleve, Harry van der Linden, Peter van Inwagen, Bryan W. Van Norden, René van Woudenberg, Donald Phillip Verene, Samantha Vice, Thomas Vinci, Donald Wayne Viney, Barbara Von Eckardt, Peter B. M. Vranas, Steven J. Wagner, William J. Wainwright, Paul E. Walker, Robert E. Wall, Craig Walton, Douglas Walton, Eric Watkins, Richard A. Watson, Michael V. Wedin, Rudolph H. Weingartner, Paul Weirich, Paul J. Weithman, Carl Wellman, Howard Wettstein, Samuel C. Wheeler, Stephen A. White, Jennifer Whiting, Edward R. Wierenga, Michael Williams, Fred Wilson, W. Kent Wilson, Kenneth P. Winkler, John F. Wippel, Jan Woleński, Allan B. Wolter, Nicholas P. Wolterstorff, Rega Wood, W. Jay Wood, Paul Woodruff, Alison Wylie, Gideon Yaffe, Takashi Yagisawa, Yutaka Yamamoto, Keith E. Yandell, Xiaomei Yang, Dean Zimmerman, Günter Zoller, Catherine Zuckert, Michael Zuckert, Jack A. Zupko (J.A.Z.)
- Edited by Robert Audi, University of Notre Dame, Indiana
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- The Cambridge Dictionary of Philosophy
- Published online:
- 05 August 2015
- Print publication:
- 27 April 2015, pp ix-xxx
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- By Eric Adler, Anoushka Afonso, Dean B. Andropoulos, Adel Bassily-Marcus, Yaakov Beilin, Elliott Bennett-Guerrero, Howard H. Bernstein, Marc J. Bloom, David Bronheim, Albert T. Cheung, Samuel DeMaria, Deborah Dubensky, James B. Eisenkraft, Jonathan Elmer, Liza J. Enriquez, Jonathan Epstein, Jeffrey M. Feldman, Gregory W. Fischer, Brigid Flynn, Jennifer A. Frontera, Richard S. Gist, Glenn P. Gravlee, Christina L. Jeng, Ronald A. Kahn, Jenny Kam, Mukul Kapoor, Jung Kim, Roopa Kohli-Seth, Aaron F. Kopman, Tuula S. O. Kurki, Andrew B. Leibowitz, Matthew Levin, Adam I. Levine, Michael S. Lewis, Justin Lipper, Martin London, Michael L. McGarvey, Alexander J. C. Mittnacht, Timothy Mooney, Diana Mungall, Yasuharu Okuda, Peter J. Papadakos, Jayashree Raikhelkar, Lakshmi V. Ramanathan, David L. Reich, Meg A. Rosenblatt, Corey Scurlock, Tamas Seres, Linda Shore-Lesserson, Marc E. Stone, Daniel M. Thys, Judit Tolnai, David Wax, Nathaen Weitzel
- David L. Reich, Mount Sinai School of Medicine, New York
- Edited by Ronald A. Kahn, Mount Sinai School of Medicine, New York, Alexander J. C. Mittnacht, Mount Sinai School of Medicine, New York, Andrew B. Leibowitz, Mount Sinai School of Medicine, New York, Marc E. Stone, Mount Sinai School of Medicine, New York, James B. Eisenkraft, Mount Sinai School of Medicine, New York
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- Monitoring in Anesthesia and Perioperative Care
- Published online:
- 05 July 2011
- Print publication:
- 08 August 2011, pp vii-ix
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- By Phillip L. Ackerman, Soon Ang, Susan M. Barnett, G. David Batty, Anna S. Beninger, Jillian Brass, Meghan M. Burke, Nancy Cantor, Priyanka B. Carr, David R. Caruso, Stephen J. Ceci, Lillia Cherkasskiy, Joanna Christodoulou, Andrew R. A. Conway, Christine E. Daley, Janet E. Davidson, Jim Davies, Katie Davis, Ian J. Deary, Colin G. DeYoung, Ron Dumont, Carol S. Dweck, Linn Van Dyne, Pascale M. J. Engel de Abreu, Joseph F. Fagan, David Henry Feldman, Kurt W. Fischer, Marisa H. Fisher, James R. Flynn, Liane Gabora, Howard Gardner, Glenn Geher, Sarah J. Getz, Judith Glück, Ashok K. Goel, Megan M. Griffin, Elena L. Grigorenko, Richard J. Haier, Diane F. Halpern, Christopher Hertzog, Robert M. Hodapp, Earl Hunt, Alan S. Kaufman, James C. Kaufman, Scott Barry Kaufman, Iris A. Kemp, John F. Kihlstrom, Joni M. Lakin, Christina S. Lee, David F. Lohman, N. J. Mackintosh, Brooke Macnamara, Samuel D. Mandelman, John D. Mayer, Richard E. Mayer, Martha J. Morelock, Ted Nettelbeck, Raymond S. Nickerson, Weihua Niu, Anthony J. Onwuegbuzie, Jonathan A. Plucker, Sally M. Reis, Joseph S. Renzulli, Heiner Rindermann, L. Todd Rose, Anne Russon, Peter Salovey, Scott Seider, Ellen L. Short, Keith E. Stanovich, Ursula M. Staudinger, Robert J. Sternberg, Carli A. Straight, Lisa A. Suzuki, Mei Ling Tan, Maggie E. Toplak, Susana Urbina, Richard K. Wagner, Richard F. West, Wendy M. Williams, John O. Willis, Thomas R. Zentall
- Edited by Robert J. Sternberg, Oklahoma State University, Scott Barry Kaufman, New York University
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- The Cambridge Handbook of Intelligence
- Published online:
- 05 June 2012
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- 30 May 2011, pp xi-xiv
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Poverty and spatial dimensions of non-timber forest extraction
- ALEJANDRO LÓPEZ-FELDMAN, JAMES E. WILEN
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- Journal:
- Environment and Development Economics / Volume 13 / Issue 5 / October 2008
- Published online by Cambridge University Press:
- 01 October 2008, pp. 621-642
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Conservationists promote non-timber forest products (NTFP) to simultaneously alleviate poverty and conserve ecosystems. Unfortunately, little is known about how such products actually contribute to poverty alleviation, or how various complementary policies such as green marketing campaigns or cooperative management might impact resource health and users' welfare. This paper develops a simple NTFP extraction model that focuses on spatial and labor market dimensions of use in both managed and unmanaged settings. The model contrasts patterns of spatial use, resource health, and income generation under open access and community-managed institutions. We then test the conceptual model by investigating the case of xate production in the rainforest of Chiapas, Mexico, using survey work conducted over two separate periods. The empirical investigation reveals spatial patterns and labor market outcomes predicted by the model. We find NTFP use is mainly conducted by households with low opportunity costs of time and fewer income generation opportunities.
Potential for misclassification of mild cognitive impairment: A study of memory scores on the Wechsler Memory Scale-III in healthy older adults
- BRIAN L. BROOKS, GRANT L. IVERSON, JAMES A. HOLDNACK, HOWARD H. FELDMAN
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- Journal:
- Journal of the International Neuropsychological Society / Volume 14 / Issue 3 / May 2008
- Published online by Cambridge University Press:
- 17 April 2008, pp. 463-478
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The psychometric criterion of mild cognitive impairment (MCI) generally involves having an unusually low score on memory testing (i.e., −1.5 SDs). However, healthy older adults can obtain low scores, particularly when multiple memory measures are administered. In turn, there is a substantial risk of psychometrically misclassifying MCI in healthy older adults. This study examined the base rates of low memory scores in older adults (55–87 years; n = 550) from the Wechsler Memory Scale–Third Edition (WMS-III; Wechsler, 1997b) standardization sample. The WMS-III consists of four co-normed episodic memory tests (i.e., Logical Memory, Faces, Verbal Paired Associates, and Family Pictures) that yield eight age- and demographically-adjusted standard scores (Auditory Recognition and Working Memory tests not included). When the eight age-adjusted scores were examined simultaneously, 26% of older adults had one or more scores at or below the 5th percentile (i.e., −1.5 SDs). On the eight demographically- adjusted scores, 39% had at least one score at or below the 5th percentile. There was an inverse relationship between intellectual abilities and prevalence of low memory scores, particularly with the age-adjusted WMS-III scores. Understanding the base rates of low scores can reduce the overinterpretation of low memory scores and minimize false-positive misclassification.
Drs. Brooks, Iverson, and Feldman have no known, perceived, or actual conflict of interest with this research. Dr. Holdnack is the Senior Research Director with The Psychological Corporation. (JINS, 2008, 14, 463–478.)
V - Clustering
- Ronen Feldman, Bar-Ilan University, Israel, James Sanger, ABS Ventures, Boston, Massachusetts
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- The Text Mining Handbook
- Published online:
- 08 August 2009
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- 11 December 2006, pp 82-93
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Summary
Clustering is an unsupervised process through which objects are classified into groups called clusters. In categorization problems, as described in Chapter IV, we are provided with a collection of preclassified training examples, and the task of the system is to learn the descriptions of classes in order to be able to classify a new unlabeled object. In the case of clustering, the problem is to group the given unlabeled collection into meaningful clusters without any prior information. Any labels associated with objects are obtained solely from the data.
Clustering is useful in a wide range of data analysis fields, including data mining, document retrieval, image segmentation, and pattern classification. In many such problems, little prior information is available about the data, and the decision-maker must make as few assumptions about the data as possible. It is for those cases the clustering methodology is especially appropriate.
Clustering techniques are described in this chapter in the context of textual data analysis. Section V.1 discusses the various applications of clustering in text analysis domains. Sections V.2 and V.3 address the general clustering problem and present several clustering algorithms. Finally Section V.4 demonstrates how the clustering algorithms can be adapted to text analysis.
CLUSTERING TASKS IN TEXT ANALYSIS
One application of clustering is the analysis and navigation of big text collections such as Web pages. The basic assumption, called the cluster hypothesis, states that relevant documents tend to be more similar to each other than to nonrelevant ones.
II - Core Text Mining Operations
- Ronen Feldman, Bar-Ilan University, Israel, James Sanger, ABS Ventures, Boston, Massachusetts
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- The Text Mining Handbook
- Published online:
- 08 August 2009
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- 11 December 2006, pp 19-56
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Summary
Core mining operations in text mining systems center on the algorithms that underlie the creation of queries for discovering patterns in document collections. This chapter describes most of the more common – and a few useful but less common – forms of these algorithms. Pattern-discovery algorithms are discussed primarily from a high-level definitional perspective. In addition, we examine the incorporation of background knowledge into text mining query operations. Finally, we briefly treat the topic of text mining query languages.
CORE TEXT MINING OPERATIONS
Core text mining operations consist of various mechanisms for discovering patterns of concept occurrence within a given document collection or subset of a document collection. The three most common types of patterns encountered in text mining are distributions (and proportions), frequent and near frequent sets, and associations.
Typically, when they offer the capability of discovering more than one type of pattern, text mining systems afford users the ability to toggle between displays of the different types of patterns for a given concept or set of concepts. This allows the richest possible exploratory access to the underlying document collection data through a browser.
Distributions
This section defines and discusses some of text mining's most commonly used distributions. We illustrate this in the context of a hypothetical text mining system that has a document collection W composed of documents containing news wire stories about world affairs that have all been preprocessed with concept labels.
III - Text Mining Preprocessing Techniques
- Ronen Feldman, Bar-Ilan University, Israel, James Sanger, ABS Ventures, Boston, Massachusetts
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- The Text Mining Handbook
- Published online:
- 08 August 2009
- Print publication:
- 11 December 2006, pp 57-63
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Summary
Effective text mining operations are predicated on sophisticated data preprocessing methodologies. In fact, text mining is arguably so dependent on the various preprocessing techniques that infer or extract structured representations from raw unstructured data sources, or do both, that one might even say text mining is to a degree defined by these elaborate preparatory techniques. Certainly, very different preprocessing techniques are required to prepare raw unstructured data for text mining than those traditionally encountered in knowledge discovery operations aimed at preparing structured data sources for classic data mining operations.
A large variety of text mining preprocessing techniques exist. All in some way attempt to structure documents – and, by extension, document collections. Quite commonly, different preprocessing techniques are used in tandem to create structured document representations from raw textual data. As a result, some typical combinations of techniques have evolved in preparing unstructured data for text mining.
Two clear ways of categorizing the totality of preparatory document structuring techniques are according to their task and according to the algorithms and formal frameworks that they use.
Task-oriented preprocessing approaches envision the process of creating a structured document representation in terms of tasks and subtasks and usually involve some sort of preparatory goal or problem that needs to be solved such as extracting titles and authors from a PDF document. Other preprocessing approaches rely on techniques that derive from formal methods for analyzing complex phenomena that can be also applied to natural language texts.
XII - Text Mining Applications
- Ronen Feldman, Bar-Ilan University, Israel, James Sanger, ABS Ventures, Boston, Massachusetts
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- The Text Mining Handbook
- Published online:
- 08 August 2009
- Print publication:
- 11 December 2006, pp 273-314
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Summary
Many text mining systems introduced in the late 1990s were developed by computer scientists as part of academic “pure research” projects aimed at exploring the capabilities and performance of the various technical components making up these systems. Most current text mining systems, however – whether developed by academic researchers, commercial software developers, or in-house corporate programmers – are built to focus on specialized applications that answer questions peculiar to a given problem space or industry need. Obviously, such specialized text mining systems are especially well suited to solving problems in academic or commercial activities in which large volumes of textual data must be analyzed in making decisions.
Three areas of analytical inquiry have proven particularly fertile ground for text mining applications. In various areas of corporate finance, bankers, analysts, and consultants have begun leveraging text mining capabilities to sift through vast amounts of textual data with the aims of creating usable forms of business intelligence, noting trends, identifying correlations, and researching references to specific transactions, corporate entities, or persons. In patent research, specialists across industry verticals at some of the world's largest companies and professional services firms apply text mining approaches to investigating patent development strategies and finding ways to exploit existing corporate patent assets better. In life sciences, researchers are exploring enormous collections of biomedical research reports to identify complex patterns of interactivities between proteins.
This chapter discusses prototypical text mining solutions adapted for use in each of these three problem spaces.
IX - Presentation-Layer Considerations for Browsing and Query Refinement
- Ronen Feldman, Bar-Ilan University, Israel, James Sanger, ABS Ventures, Boston, Massachusetts
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- The Text Mining Handbook
- Published online:
- 08 August 2009
- Print publication:
- 11 December 2006, pp 177-188
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Summary
Human-centered knowledge discovery places great emphasis on the presentation layer of systems used for data mining. All text mining systems built around a human-centric knowledge discovery paradigm must offer a user robust browsing capabilities as well as abilities to display dense and difficult-to-format patterns of textual data in ways that foster interactive exploration.
A robust text mining system should offer a user control over the shaping of queries by making search parameterization available through both high-level, easy-to-use GUI-based controls and direct, low-level, and relatively unrestricted query language access. Moreover, text mining systems need to offer a user administrative tools to create, modify, and maintain concept hierarchies, concept clusters, and entity profile information.
Text mining systems also rely, to an extraordinary degree, on advanced visualization tools. More on the full gamut of visualization approaches – from the relatively mundane to the highly exotic – relevant for text mining can be found in Chapter X.
BROWSING
Browsing is a term open to broad interpretation. With respect to text mining systems, however, it usually refers to the general front-end framework through which an enduser searches, queries, displays, and interacts with embedded or middle-tier knowledge-discovery algorithms.
The software that implements this framework is called a browser. Beyond their ability to allow a user to (a) manipulate the various knowledge discovery algorithms they may operate and (b) explore the resulting patterns, most browsers also generally support functionality to link to some portion of the full text of documents underlying the patterns that these knowledge discovery algorithms may return.
XI - Link Analysis
- Ronen Feldman, Bar-Ilan University, Israel, James Sanger, ABS Ventures, Boston, Massachusetts
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Based on the outcome of the preprocessing stage, we can establish links between entities either by using co-occurrence information (within some lexical unit such as a document, paragraph, or sentence) or by using the semantic relationships between the entities as extracted by the information extraction module (such as family relations, employment relationship, mutual service in the army, etc.). This chapter describes the link analysis techniques that can be applied to results of the preprocessing stage (information extraction, term extraction, and text categorization).
A social network is a set of entities (e.g., people, companies, organizations, universities, countries) and a set of relationships between them (e.g., family relationships, various types of communication, business transactions, social interactions, hierarchy relationships, and shared memberships of people in organizations). Visualizing a social network as a graph enables the viewer to see patterns that were not evident before.
We begin with preliminaries from graph theory used throughout the chapter. We next describe the running example of the 9/11 hijacker's network followed by a brief description of graph layout algorithms. After the concepts of paths and cycles in graphs are presented, the chapter proceeds with a discussion of the notion of centrality and the various ways of computing it. Various algorithms for partitioning and clustering nodes inside the network are then presented followed by a brief description of finding specific patterns in networks. The chapter concludes with a presentation of three low-cost software packages for performing link analysis.
X - Visualization Approaches
- Ronen Feldman, Bar-Ilan University, Israel, James Sanger, ABS Ventures, Boston, Massachusetts
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INTRODUCTION
Human-centric text mining emphasizes the centrality of user interactivity to the knowledge discovery process. As a consequence, text mining systems need to provide users with a range of tools for interacting with data. For a wide array of tasks, these tools often rely on very simple graphical approaches such as pick lists, drop-down boxes, and radio boxes that have become typical in many generic software applications to support query construction and the basic browsing of potentially interesting patterns.
In large document collections, however, problems of pattern and feature overabundance have led the designers of text mining systems to move toward the creation of more sophisticated visualization approaches to facilitate user interactivity. Indeed, in document collections of even relatively modest size, tens of thousands of identified concepts and thousands of interesting associations can make browsing with simple visual mechanisms such as pick lists all but unworkable. More sophisticated visualization approaches incorporate graphical tools that rely on advances in many different areas of computer and behavioral science research to promote easier and more intensive and iterative exploration of patterns in textual data.
Many of the more mundane activities that allow a user of a text mining system to engage in rudimentary data exploration are supported by a graphic user interface that serves as the type of basic viewer or browser discussed in Chapter VII. A typical basic browsing interface can be seen in Figure X.1.
VIII - Preprocessing Applications Using Probabilistic and Hybrid Approaches
- Ronen Feldman, Bar-Ilan University, Israel, James Sanger, ABS Ventures, Boston, Massachusetts
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The related fields of NLP, IE, text categorization, and probabilistic modeling have developed increasingly rapidly in the last few years. New approaches are tried constantly and new systems are reported numbering thousands a year. The fields largely remain experimental science – a new approach or improvement is conceived and a system is built, tested, and reported. However, comparatively little work is done in analyzing the results and in comparing systems and approaches with each other. Usually, it is the task of the authors of a particular system to compare it with other known approaches, and this presents difficulties – both psychological and methodological.
One reason for the dearth of analytical work, excluding the general lack of sound theoretical foundations, is that the comparison experiments require software, which is usually either impossible or very costly to obtain. Moreover, the software requires integration, adjustment, and possibly training for any new use, which is also extremely costly in terms of time and human labor.
Therefore, our description of the different possible solutions to the problems described in the first section is incomplete by necessity. There are just too many reported systems, and there is often no good reason to choose one approach against the other. Consequently, we have tried to describe in depth only a small number of systems. We have chosen as broad a selection as possible, encompassing many different approaches. And, of course, the results produced by the systems are state of the art or sufficiently close to it.
Contents
- Ronen Feldman, Bar-Ilan University, Israel, James Sanger, ABS Ventures, Boston, Massachusetts
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Appendix A - DIAL: A Dedicated Information Extraction Language for Text Mining
- Ronen Feldman, Bar-Ilan University, Israel, James Sanger, ABS Ventures, Boston, Massachusetts
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WHAT IS THE DIAL LANGUAGE?
This appendix provides an example of a dedicated information extraction language called DIAL (declarative information analysis language). The purpose of the appendix is to show the general structure of the language and offer some code examples that will demonstrate how it can be used to extract concepts and relationships; hence, we will not cover all aspects and details of the language.
The DIAL language is a dedicated information extraction language enabling the user to define concepts whose instances are found in a text body by the DIAL engine. A DIAL concept is a logical entity, which can represent a noun (such as a person, place, or institution), an event (such as a business merger between two companies or the election of a president), or any other entity for which a text pattern can be defined. Instances of concepts are found when the DIAL engine succeeds in matching a concept pattern to part of the text it is processing. Concepts may have attributes, which are properties belonging to the concept whose values are found in the text of the concept instance. For instance, a “Date” concept might have numeric day, month, and year attributes and a string attribute for the day of the week.
A DIAL concept declaration defines the concept's name, attributes, and optionally some additional code common to all instances of the concept.
I - Introduction to Text Mining
- Ronen Feldman, Bar-Ilan University, Israel, James Sanger, ABS Ventures, Boston, Massachusetts
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DEFINING TEXT MINING
Text mining can be broadly defined as a knowledge-intensive process in which a user interacts with a document collection over time by using a suite of analysis tools. In a manner analogous to data mining, text mining seeks to extract useful information from data sources through the identification and exploration of interesting patterns. In the case of text mining, however, the data sources are document collections, and interesting patterns are found not among formalized database records but in the unstructured textual data in the documents in these collections.
Certainly, text mining derives much of its inspiration and direction from seminal research on data mining. Therefore, it is not surprising to find that text mining and data mining systems evince many high-level architectural similarities. For instance, both types of systems rely on preprocessing routines, pattern-discovery algorithms, and presentation-layer elements such as visualization tools to enhance the browsing of answer sets. Further, text mining adopts many of the specific types of patterns in its core knowledge discovery operations that were first introduced and vetted in data mining research.
Because data mining assumes that data have already been stored in a structured format, much of its preprocessing focus falls on two critical tasks: Scrubbing and normalizing data and creating extensive numbers of table joins. In contrast, for text mining systems, preprocessing operations center on the identification and extraction of representative features for natural language documents.
VII - Probabilistic Models for Information Extraction
- Ronen Feldman, Bar-Ilan University, Israel, James Sanger, ABS Ventures, Boston, Massachusetts
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Several common themes frequently recur in many tasks related to processing and analyzing complex phenomena, including natural language texts. Among these themes are classification schemes, clustering, probabilistic models, and rule-based systems.
This section describes some of these techniques generally, and the next section applies them to the tasks described in Chapter VI.
Research has demonstrated that it is extremely fruitful to model the behavior of complex systems as some form of a random process. Probabilistic models often show better accuracy and robustness against the noise than categorical models. The ultimate reason for this is not quite clear and is an excellent subject for a philosophical debate.
Nevertheless, several probabilistic models have turned out to be especially useful for the different tasks in extracting meaning from natural language texts. Most prominent among these probabilistic approaches are hidden Markov models (HMMs), stochastic context-free grammars (SCFG), and maximal entropy (ME).
HIDDEN MARKOV MODELS
An HMM is a finite-state automaton with stochastic state transitions and symbol emissions (Rabiner 1990). The automaton models a probabilistic generative process. In this process, a sequence of symbols is produced by starting in an initial state, emitting a symbol selected by the state, making a transition to a new state, emitting a symbol selected by the state, and repeating this transition–emission cycle until a designated final state is reached.