New York University

About: New York University is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 72380 authors who have published 165545 publications receiving 8334030 citations. The organization is also known as: NYU & University of the City of New York.

Consequences of Automatic Evaluation: Immediate Behavioral Predispositions to Approach or Avoid the Stimulus:

Abstract: Research on automatic attitude activation has documented a pervasive tendency to nonconsciously classify most if not all incoming stimuli as either good or bad. Two experiments tested a functional explanation for this effect. The authors hypothesized that automatic evaluation results directly in behavioral predispositions toward the stimulus, such that positive evaluations produce immediate approach tendencies, and negative evaluations produce immediate avoidance tendencies. Participants responded to attitude object stimuli either by pushing or by pulling a lever. Consistent with the hypothesis, participants were faster to respond to negatively valenced stimuli when pushing the lever away (avoid) than when pulling it toward them (approach) but were faster to respond to positive stimuli by pulling than by pushing the lever. This pattern held even when evaluation of the stimuli was irrelevant to the participants’ conscious task. The automatic classification of stimuli as either good or bad appears to have d...

Context-aware recommender systems

Abstract: The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research.

Emotion and Cognition: Insights from Studies of the Human Amygdala

Abstract: Traditional approaches to the study of cognition emphasize an information-processing view that has generally excluded emotion. In contrast, the recent emergence of cognitive neuroscience as an inspiration for understanding human cognition has highlighted its interaction with emotion. This review explores insights into the relations between emotion and cognition that have resulted from studies of the human amygdala. Five topics are explored: emotional learning, emotion and memory, emotion's influence on attention and perception, processing emotion in social stimuli, and changing emotional responses. Investigations into the neural systems underlying human behavior demonstrate that the mechanisms of emotion and cognition are intertwined from early perception to reasoning. These findings suggest that the classic division between the study of emotion and cognition may be unrealistic and that an understanding of human cognition requires the consideration of emotion.

Invariant Scattering Convolution Networks

Abstract: A wavelet scattering network computes a translation invariant image representation which is stable to deformations and preserves high-frequency information for classification. It cascades wavelet transform convolutions with nonlinear modulus and averaging operators. The first network layer outputs SIFT-type descriptors, whereas the next layers provide complementary invariant information that improves classification. The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having the same Fourier power spectrum. State-of-the-art classification results are obtained for handwritten digits and texture discrimination, with a Gaussian kernel SVM and a generative PCA classifier.

Interfirm Collaboration Networks: The Impact of Large-Scale Network Structure on Firm Innovation

Abstract: The structure of alliance networks influences their potential for knowledge creation. Dense local clustering provides information transmission capacity in the network by fostering communication and cooperation. Nonredundant connections contract the distance between firms and give the network greater reach by tapping a wider range of knowledge resources. We propose that firms embedded in alliance networks that exhibit both high clustering and high reach (short average path lengths to a wide range of firms) will have greater innovative output than firms in networks that do not exhibit these characteristics. We find support for this proposition in a longitudinal study of the patent performance of 1,106 firms in 11 industry-level alliance networks.