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On September 30, 2012, Webby released his seventh mixtape, Bars On Me hosted by famed mixtape DJ, DJ Drama.[22] The mixtape included guest appearances from Bun B, D. Lector, Emilio Rojas, Jon Connor, Kid Ink, Method Man, Nikkiya and Prodigy, with notable production credits coming from Cardo, Sap, Don Cannon, Omen, Ski Beatz, and WillPower among others.[23] Its release was covered by MTV, who interviewed him as he described the title saying, "There's definitely a few meanings that you could use with it, but the main one being lyrical bars. That's what I'm known for, that's what I continue to do, and I think a lot of artists kinda need to step their bars up a little bit."[24] It featured all original material and received over 100,000 downloads on DatPiff in less than a week.[25][26]
Garrix performed at Ultra Music Festival 2014, where he debuted several new and unreleased tracks, including collaborations with American DJ Dillon Francis, Hardwell, and Dutch DJ Afrojack. In June, he collaborated with Dutch DJ Sander van Doorn and Canadian duo DVBBS on "Gold Skies". This was his first vocal track which featured the singer Aleesia.[21] He also released the track "Proxy" for free download. Most of these songs were featured on Garrix's debut EP, Gold Skies, which was released on 8 July 2014.[22]
Cognitive science has a longstanding interest in the ways that people acquire and use abstract vs. concrete words (e.g., truth vs. piano). One dominant theory holds that abstract and concrete words are subserved by two parallel semantic systems. We recently proposed an alternative account of abstract-concrete word representation premised upon a unitary, high dimensional semantic space wherein word meaning is nested. We hypothesize that a range of cognitive and perceptual dimensions (e.g., emotion, time, space, color, size, visual form) bound this space, forming a conceptual topography. Here we report a normative study where we examined the clustering properties of a sample of English words (N = 750) spanning a spectrum of concreteness in a continuous manner from highly abstract to highly concrete. Participants (N = 328) rated each target word on a range of 14 cognitive dimensions (e.g., color, emotion, valence, polarity, motion, space). The dimensions reduced to three factors: Endogenous factor, Exogenous factor, and Magnitude factor. Concepts were plotted in a unified, multimodal space with concrete and abstract concepts along a continuous continuum. We discuss theoretical implications and practical applications of this dataset. These word norms are freely available for download and use at -coglab.com/data/. PMID:29075224
Cognitive science has a longstanding interest in the ways that people acquire and use abstract vs. concrete words (e.g., truth vs. piano). One dominant theory holds that abstract and concrete words are subserved by two parallel semantic systems. We recently proposed an alternative account of abstract-concrete word representation premised upon a unitary, high dimensional semantic space wherein word meaning is nested. We hypothesize that a range of cognitive and perceptual dimensions (e.g., emotion, time, space, color, size, visual form) bound this space, forming a conceptual topography. Here we report a normative study where we examined the clustering properties of a sample of English words ( N = 750) spanning a spectrum of concreteness in a continuous manner from highly abstract to highly concrete. Participants ( N = 328) rated each target word on a range of 14 cognitive dimensions (e.g., color, emotion, valence, polarity, motion, space). The dimensions reduced to three factors: Endogenous factor, Exogenous factor, and Magnitude factor. Concepts were plotted in a unified, multimodal space with concrete and abstract concepts along a continuous continuum. We discuss theoretical implications and practical applications of this dataset. These word norms are freely available for download and use at -coglab.com/data/.
The function of a non-protein-coding RNA is often determined by its structure. Since experimental determination of RNA structure is time-consuming and expensive, its computational prediction is of great interest, and efficient solutions based on thermodynamic parameters are known. Frequently, however, the predicted minimum free energy structures are not the native ones, leading to the necessity of generating suboptimal solutions. While this can be accomplished by a number of programs, the user is often confronted with large outputs of similar structures, although he or she is interested in structures with more fundamental differences, or, in other words, with different abstract shapes. Here, we formalize the concept of abstract shapes and introduce their efficient computation. Each shape of an RNA molecule comprises a class of similar structures and has a representative structure of minimal free energy within the class. Shape analysis is implemented in the program RNAshapes. We applied RNAshapes to the prediction of optimal and suboptimal abstract shapes of several RNAs. For a given energy range, the number of shapes is considerably smaller than the number of structures, and in all cases, the native structures were among the top shape representatives. This demonstrates that the researcher can quickly focus on the structures of interest, without processing up to thousands of near-optimal solutions. We complement this study with a large-scale analysis of the growth behaviour of structure and shape spaces. RNAshapes is available for download and as an online version on the Bielefeld Bioinformatics Server. 2b1af7f3a8