Describing and Understanding Neighborhood Characteristics through Online Social Media

Geotagged data can be used to describe regions in the world and discover local themes. However, not all data produced within a region is necessarily specifically descriptive of that area. To surface the content that is characteristic for a region, we present the geographical hierarchy model (GHM), a probabilistic model based on the assumption that data observed in a region is a random mixture of content that pertains to different levels of a hierarchy. We apply the GHM to a dataset of 8 million Flickr photos in order to discriminate between content (i.e., tags) that specifically characterizes a region (e.g., neighborhood) and content that characterizes surrounding areas or more general themes. Knowledge of the discriminative and non-discriminative terms used throughout the hierarchy enables us to quantify the uniqueness of a given region and to compare similar but distant regions. Our evaluation demonstrates that our model improves upon traditional Naive Bayes classification by 47% and hierarchical TF-IDF by 27%. We further highlight the differences and commonalities with human reasoning about what is locally characteristic for a neighborhood, distilled from ten interviews and a survey that covered themes such as time, events, and prior regional knowledgeComment: Accepted in WWW 2015, 2015, Florence, Ital

Stimulus-invariant processing and spectrotemporal reverse correlation in primary auditory cortex

The spectrotemporal receptive field (STRF) provides a versatile and integrated, spectral and temporal, functional characterization of single cells in primary auditory cortex (AI). In this paper, we explore the origin of, and relationship between, different ways of measuring and analyzing an STRF. We demonstrate that STRFs measured using a spectrotemporally diverse array of broadband stimuli -- such as dynamic ripples, spectrotemporally white noise, and temporally orthogonal ripple combinations (TORCs) -- are very similar, confirming earlier findings that the STRF is a robust linear descriptor of the cell. We also present a new deterministic analysis framework that employs the Fourier series to describe the spectrotemporal modulations contained in the stimuli and responses. Additional insights into the STRF measurements, including the nature and interpretation of measurement errors, is presented using the Fourier transform, coupled to singular-value decomposition (SVD), and variability analyses including bootstrap. The results promote the utility of the STRF as a core functional descriptor of neurons in AI.Comment: 42 pages, 8 Figures; to appear in Journal of Computational Neuroscienc

Higher-Order Uncoupled Dynamics Do Not Lead to Nash Equilibrium \unicode{x2014} Except When They Do

The framework of multi-agent learning explores the dynamics of how individual agent strategies evolve in response to the evolving strategies of other agents. Of particular interest is whether or not agent strategies converge to well known solution concepts such as Nash Equilibrium (NE). Most ``fixed order'' learning dynamics restrict an agent's underlying state to be its own strategy. In ``higher order'' learning, agent dynamics can include auxiliary states that can capture phenomena such as path dependencies. We introduce higher-order gradient play dynamics that resemble projected gradient ascent with auxiliary states. The dynamics are ``payoff based'' in that each agent's dynamics depend on its own evolving payoff. While these payoffs depend on the strategies of other agents in a game setting, agent dynamics do not depend explicitly on the nature of the game or the strategies of other agents. In this sense, dynamics are ``uncoupled'' since an agent's dynamics do not depend explicitly on the utility functions of other agents. We first show that for any specific game with an isolated completely mixed-strategy NE, there exist higher-order gradient play dynamics that lead (locally) to that NE, both for the specific game and nearby games with perturbed utility functions. Conversely, we show that for any higher-order gradient play dynamics, there exists a game with a unique isolated completely mixed-strategy NE for which the dynamics do not lead to NE. These results build on prior work that showed that uncoupled fixed-order learning cannot lead to NE in certain instances, whereas higher-order variants can. Finally, we consider the mixed-strategy equilibrium associated with coordination games. While higher-order gradient play can converge to such equilibria, we show such dynamics must be inherently internally unstable

Impact of Al-Ahdeb Oil Field on the Surrounding Environment Using Remote Sensing and GIS Techniques, Wasit Governorate, Iraq

Remote sensing and Geographical Information System (GIS) are used in this study to detect environmental changes in the vicinity of Al-Ahdeb oil field, southwest of Kut City, Wasit Governorate. Different image indices, such as Normalized Differences Vegetation Index (NDVI), Normalized Differences Soil Index (NDSoI), and Normalized Differences Salinity Index (NDSI) are used. Two LANDSAT images with acquired data of September 2007 and 2016 are used to detect the environmental changes and to detect the effect of Al-Ahdeb oil field before and after construction and industrial operation. The results of change detection show there is a high decrease in the vegetation cover during the year 2016 compared with 2007, where the area of vegetation cover has decreased from (165.85 Km2) in 2007 to (119.62 Km2) in 2016. The change detection results from NDSI show that the saline soil in (2016) is higher than those in (2007). The NDSI derived from Landsat TM (2007) image confirms that there is significant increase of salinity in the study area, where the calculated area of the salinity in 2007 is (5.65 Km2) while in 2016 it is (21.951 Km2). Change detection, using NDSoI, show that the land in the study area is going toward desertification and soil degradation. The decrease in the vegetation cover, which in turn led to soil erosion in addition to water shortage and the pollution by the waste of the oil field, could be the main reasons of the desertification in the area. Keywords: Environmental change detection; Image Indices; NDVI; NDSI; NDSoI; Al Ahdeb oil field. DOI: 10.7176/JEES/12-8-03 Publication date:August 31st 202