Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection

Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, coreference, relation extraction, and question answering. Supervised learning from labeled hypernym sources, such as WordNet, limits the coverage of these models, which can be addressed by learning hypernyms from unlabeled text. Existing unsupervised methods either do not scale to large vocabularies or yield unacceptably poor accuracy. This paper introduces distributional inclusion vector embedding (DIVE), a simple-to-implement unsupervised method of hypernym discovery via per-word non-negative vector embeddings which preserve the inclusion property of word contexts in a low-dimensional and interpretable space. In experimental evaluations more comprehensive than any previous literature of which we are aware-evaluating on 11 datasets using multiple existing as well as newly proposed scoring functions-we find that our method provides up to double the precision of previous unsupervised embeddings, and the highest average performance, using a much more compact word representation, and yielding many new state-of-the-art results.Comment: NAACL 201

Unforgettable Ultimatums? Expectation Violations Promote Enhanced Social Memory Following Economic Bargaining

Recent work in the field of neuroeconomics has examined how people make decisions in interactive settings. However, less is currently known about how these social decisions influence subsequent memory for these interactions. We investigated this question by using functional magnetic resonance imaging to scan participants as they viewed photographs of people they had either recently played an Ultimatum Game with in the role of Responder, or that they had never seen before. Based on previous work that has investigated “cheater detection”, we were interested in whether participants demonstrated a relative enhanced memory for partners that made either fair or unfair proposals. We found no evidence, either behaviorally or neurally, supporting enhanced memory based on the amount of money offered by the Proposer. However, we did find that participants’ initial expectations about the offers they would experience in the game influenced their memory. Participants demonstrated relatively enhanced subjective memory for partners that made proposals that were contradictory to their initial expectations. In addition, we observed two distinct brain systems that were associated with partners that either offered more or less than the participants’ expectations. Viewing pictures of partners that exceeded initial expectations was associated with the bilateral anterior insula, anterior cingulate cortex/premotor area, striatum, and bilateral posterior hippocampi, while viewing partners that offered less than initial expectations was associated with bilateral temporal-parietal junction, right STS, bilateral posterior insula, and precuneus. These results suggest that memory for social interaction may not be guided by a specific cheater detection system, but rather a more general expectation violation system

Descriptive Mineralogy of Pugh Quarry, Northwestern Ohio: Sphalerite

Author Institution: Department of Geology, Miami UniversityThe Devonian rocks at Pugh Quarry have three distinct types of sphalerite (banded massive, spheroidal, and tiny euhedral). Occurrence of the banded massive sphalerite is restricted to the mineral zone, predominantly as blebs in marcasite. The color of banded sphalerite ranged from nearly colorless to various hues of yellow. The replacement of banded sphalerite by marcasite was observed. The spheroidal sphalerite occurred in association with marcasite of euhedral habit. The spherules were small, the largest no greater than 1 mm across. Where present in great profusion, the sphalerite spherules merged together forming botryoidal surfaces. The euhedral sphalerite occurred in the voids of the sponge-like and stromatolitic dolostone below the mineral zone, and in a layer of soft laminated mud associated with the dolostone. The euhedral sphalerite was predominantly red-brown, and no crystals larger than 1 mm in maximum dimension were observed

Descriptive Mineralogy of Pugh Quarry, Northwestern Ohio: Barite and Celestite

Author Institution: Department of Geology, Miami UniversityTwo sulfate minerals, barite and celestite, were identified in the Devonian rocks at Pugh Quarry. The barite occurs commonly as rosette-like clusters with wide variety of colors and crystal habits. Unusual features include paired crystals and hollow crystals. The former consist of two barite crystals growing parallel with each other on the dolostone matrix, and the latter only of a thin wall of barite commonly marked by cross striations. Celestite crystals have two different colors. The grayblue and blue crystals are prismatic to blocky and occur as well-formed individuals, whereas the colorless and white crystals are tabular to bladed and occur as distinct individuals and as compact clusters

Descriptive Mineralogy of Pugh Quarry, Northwestern Ohio: Calcite, Dolomite and Fluorite

Author Institution: Department of Geology, Miami UniversityCalcite is by far the most abundant mineral in the Devonian rocks at Pugh Quarry. The large crystals, from 5 mm to 10-15 cm long, are predominantly brown with wide variations in hue and intensity. The small crystals, smaller than 5 mm, range from colorless to gray-yellow to pale yellow. Both large and small crystals consist almost entirely of scalenohedral forms (dog-tooth spar habit). The most common and most easily recognized type of twinning in the large crystals is on the (0001) plane. Both dolomite and fluorite are minor minerals at Pugh Quarry, although fiuorite is found in all parts of the Quarry. Dolomite crystals occur as secondary encrusting material and are most common in vugs

Multivariate brain prediction of heart rate and skin conductance responses to social threat

Psychosocial stressors induce autonomic nervous system (ANS) responses in multiple body systems that are linked to health risks. Much work has focused on the common effects of stress, but ANS responses in different body systems are dissociable and may result from distinct patterns of cortical–subcortical interactions. Here, we used machine learning to develop multivariate patterns of fMRI activity predictive of heart rate (HR) and skin conductance level (SCL) responses during social threat in humans (N = 18). Overall, brain patterns predicted both HR and SCL in cross-validated analyses successfully (r(HR) = 0.54, r(SCL) = 0.58, both p < 0.0001). These patterns partly reflected central stress mechanisms common to both responses because each pattern predicted the other signal to some degree (r(HR→SCL) = 0.21 and r(SCL→HR) = 0.22, both p < 0.01), but they were largely physiological response specific. Both patterns included positive predictive weights in dorsal anterior cingulate and cerebellum and negative weights in ventromedial PFC and local pattern similarity analyses within these regions suggested that they encode common central stress mechanisms. However, the predictive maps and searchlight analysis suggested that the patterns predictive of HR and SCL were substantially different across most of the brain, including significant differences in ventromedial PFC, insula, lateral PFC, pre-SMA, and dmPFC. Overall, the results indicate that specific patterns of cerebral activity track threat-induced autonomic responses in specific body systems. Physiological measures of threat are not interchangeable, but rather reflect specific interactions among brain systems. SIGNIFICANCE STATEMENT We show that threat-induced increases in heart rate and skin conductance share some common representations in the brain, located mainly in the vmPFC, temporal and parahippocampal cortices, thalamus, and brainstem. However, despite these similarities, the brain patterns that predict these two autonomic responses are largely distinct. This evidence for largely output-measure-specific regulation of autonomic responses argues against a common system hypothesis and provides evidence that different autonomic measures reflect distinct, measurable patterns of cortical–subcortical interactions

Differential effects on natural killer cell production by membrane‐bound cytokine stimulations

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Py-Feat: Python Facial Expression Analysis Toolbox

Studying facial expressions is a notoriously difficult endeavor. Recent advances in the field of affective computing have yielded impressive progress in automatically detecting facial expressions from pictures and videos. However, much of this work has yet to be widely disseminated in social science domains such as psychology. Current state of the art models require considerable domain expertise that is not traditionally incorporated into social science training programs. Furthermore, there is a notable absence of user-friendly and open-source software that provides a comprehensive set of tools and functions that support facial expression research. In this paper, we introduce Py-Feat, an open-source Python toolbox that provides support for detecting, preprocessing, analyzing, and visualizing facial expression data. Py-Feat makes it easy for domain experts to disseminate and benchmark computer vision models and also for end users to quickly process, analyze, and visualize face expression data. We hope this platform will facilitate increased use of facial expression data in human behavior research.Comment: 25 pages, 3 figures, 5 table