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The assertion that there are a limited set of generalizable good educational practices (Chickering & Gamson, 1987) with a common model of preferred active student engagement in learning (Kuh, 2001) is appealing to those responsible for simply stated institutional outcomes and to the faculty who teach in fields that espouse the same practices and outcomes (Braxton, 1998). After all, if they are wrong and educational experience and good educational practices differ in important, substantive, and replicable ways by area of academic major, then assessment, accountability, administration, and admissions become more complicated and less amenable to central "oversight" and uniform standards. This paper reports that there are indeed important differences in student experience and engagement by academic discipline, that disciplinary patterns of student experience cluster, and that academic performance by students in these clusters is differentially predicted by standard admissions measures and student engagement factors. Recognizing the differences and identifying the predictors will lead to better admission practices. A listing of Principal Components and Items is appended. (Contains 1 footnote, 1 figure, and 3 tables.) [This study is a Student Experience in the Research University (SERU) Project and Consortium Research Paper.]

We all develop patterns based on childhood experiences that follow us throughout our lives. It's so crucial for our personal evolution to recognize these patterns so that we can tweak them when we find they're affecting our lives and our relationships. But how do we do this? Welcome to Lean In. A podcast that explores [...]

We all develop patterns based on childhood experiences that follow us throughout our lives. It's so crucial for our personal evolution to recognize these patterns so that we can tweak them when we find they're affecting our lives and our relationships. But how do we do this? Welcome to Lean In. A podcast that explores [...]

The concept of chemical bonding can ultimately be seen as a rationalization of the recurring structural patterns observed in molecules and solids. Chemical intuition is nothing but the ability to recognize and predict such patterns, and how they transform into one another. Here we discuss how to use a computer to identify atomic patterns automatically, so as to provide an algorithmic definition of a bond based solely on structural information. We concentrate in particular on hydrogen bonding -- a central concept to our understanding of the physical chemistry of water, biological systems and many technologically important materials. Since the hydrogen bond is a somewhat fuzzy entity that covers a broad range of energies and distances, many different criteria have been proposed and used over the years, based either on sophisticate electronic structure calculations followed by an energy decomposition analysis, or on somewhat arbitrary choices of a range of structural parameters that is deemed to correspond to a hydrogen-bonded configuration. We introduce here a definition that is univocal, unbiased, and adaptive, based on our machine-learning analysis of an atomistic simulation. The strategy we propose could be easily adapted to similar scenarios, where one has to recognize or classify structural patterns in a material or chemical compound.

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A method of recognizing or classifying patterns is based on an artificial immune system (AIS), which includes an algorithm and a computational model of nonlinear dynamics inspired by the behavior of a biological immune system. The method has been proposed as the theoretical basis of the computational portion of a star-tracking system aboard a spacecraft. In that system, a newly acquired star image would be treated as an antigen that would be matched by an appropriate antibody (an entry in a star catalog). The method would enable rapid convergence, would afford robustness in the face of noise in the star sensors, would enable recognition of star images acquired in any sensor or spacecraft orientation, and would not make an excessive demand on the computational resources of a typical spacecraft. Going beyond the star-tracking application, the AIS-based pattern-recognition method is potentially applicable to pattern- recognition and -classification processes for diverse purposes -- for example, reconnaissance, detecting intruders, and mining data.

A method of recognizing or classifying patterns is based on an artificial immune system (AIS), which includes an algorithm and a computational model of nonlinear dynamics inspired by the behavior of a biological immune system. The method has been proposed as the theoretical basis of the computational portion of a star-tracking system aboard a spacecraft. In that system, a newly acquired star image would be treated as an antigen that would be matched by an appropriate antibody (an entry in a star catalog). The method would enable rapid convergence, would afford robustness in the face of noise in the star sensors, would enable recognition of star images acquired in any sensor or spacecraft orientation, and would not make an excessive demand on the computational resources of a typical spacecraft. Going beyond the star-tracking application, the AIS-based pattern-recognition method is potentially applicable to pattern- recognition and -classification processes for diverse purposes -- for example, reconnaissance, detecting intruders, and mining data.

Recorded at the Kanuga Summer Dream Conference 2009. Presented by the Haden Institute . Distributed by SeedWork.

This paper describes a technique for obstacle detection, based on the expansion of the image-plane projection of a textured object, as its distance from the sensor decreases. Information is conveyed by vectors whose components represent first-order temporal and spatial derivatives of the image intensity, which are related to the time to collision through the local divergence. Such vectors may be characterized as patterns corresponding to 'safe' or 'dangerous' situations. We show that essential information is conveyed by single-bit vector components, representing the signs of the relevant derivatives. We use two recently developed, high capacity classifiers, employing neural learning techniques, to recognize the imminence of collision from such patterns.

This paper describes a technique for obstacle detection, based on the expansion of the image-plane projection of a textured object, as its distance from the sensor decreases. Information is conveyed by vectors whose components represent first-order temporal and spatial derivatives of the image intensity, which are related to the time to collision through the local divergence. Such vectors may be characterized as patterns corresponding to 'safe' or 'dangerous' situations. We show that essential information is conveyed by single-bit vector components, representing the signs of the relevant derivatives. We use two recently developed, high capacity classifiers, employing neural learning techniques, to recognize the imminence of collision from such patterns.

This paper describes a technique for obstacle detection, based on the expansion of the image-plane projection of a textured object, as its distance from the sensor decreases. Information is conveyed by vectors whose components represent first-order temporal and spatial derivatives of the image intensity, which are related to the time to collision through the local divergence. Such vectors may be characterized as patterns corresponding to 'safe' or 'dangerous' situations. We show that essential information is conveyed by single-bit vector components, representing the signs of the relevant derivatives. We use two recently developed, high capacity classifiers, employing neural learning techniques, to recognize the imminence of collision from such patterns.

Let H be a connected bipartite graph with n nodes and m edges. We give an O(n(m+n)) time algorithm to decide whether H is an interval bigraph. The best known algorithm has time complexity O(nm^6(m + n) \log n) and it was developed in 1997 [15]. Our approach is based on an ordering characterization of interval bigraphs introduced by Hell and Huang [11]. We transform the problem of finding the desired ordering to choosing strong components of a pair-digraph without creating conflicts. We make use of the structure of the pair-digraph as well as decomposition of bigraph H based on the special components of the pair-digraph. This way we make explicit what the difficult cases are and gain efficiency by isolating such situations. We believe our method can be used to find a desired ordering for other classes of graphs and digraphs having ordering characterization.

Let H be a connected bipartite graph with n nodes and m edges. We give an O(n(m+n)) time algorithm to decide whether H is an interval bigraph. The best known algorithm has time complexity O(nm^6(m + n) \log n) and it was developed in 1997 [15]. Our approach is based on an ordering characterization of interval bigraphs introduced by Hell and Huang [11]. We transform the problem of finding the desired ordering to choosing strong components of a pair-digraph without creating conflicts. We make use of the structure of the pair-digraph as well as decomposition of bigraph H based on the special components of the pair-digraph. This way we make explicit what the difficult cases are and gain efficiency by isolating such situations. We believe our method can be used to find a desired ordering for other classes of graphs and digraphs having ordering characterization.

Let H be a connected bipartite graph with n nodes and m edges. We give an O(n(m+n)) time algorithm to decide whether H is an interval bigraph. The best known algorithm has time complexity O(nm^6(m + n) \log n) and it was developed in 1997 [15]. Our approach is based on an ordering characterization of interval bigraphs introduced by Hell and Huang [11]. We transform the problem of finding the desired ordering to choosing strong components of a pair-digraph without creating conflicts. We make use of the structure of the pair-digraph as well as decomposition of bigraph H based on the special components of the pair-digraph. This way we make explicit what the difficult cases are and gain efficiency by isolating such situations. We believe our method can be used to find a desired ordering for other classes of graphs and digraphs having ordering characterization.

Let H be a connected bipartite graph with n nodes and m edges. We give an O(n(m+n)) time algorithm to decide whether H is an interval bigraph. The best known algorithm has time complexity O(nm^6(m + n) \log n) and it was developed in 1997 [15]. Our approach is based on an ordering characterization of interval bigraphs introduced by Hell and Huang [11]. We transform the problem of finding the desired ordering to choosing strong components of a pair-digraph without creating conflicts. We make use of the structure of the pair-digraph as well as decomposition of bigraph H based on the special components of the pair-digraph. This way we make explicit what the difficult cases are and gain efficiency by isolating such situations. We believe our method can be used to find a desired ordering for other classes of graphs and digraphs having ordering characterization.

Let H be a connected bipartite graph with n nodes and m edges. We give an O(n(m+n)) time algorithm to decide whether H is an interval bigraph. The best known algorithm has time complexity O(nm^6(m + n) \log n) and it was developed in 1997 [15]. Our approach is based on an ordering characterization of interval bigraphs introduced by Hell and Huang [11]. We transform the problem of finding the desired ordering to choosing strong components of a pair-digraph without creating conflicts. We make use of the structure of the pair-digraph as well as decomposition of bigraph H based on the special components of the pair-digraph. This way we make explicit what the difficult cases are and gain efficiency by isolating such situations. We believe our method can be used to find a desired ordering for other classes of graphs and digraphs having ordering characterization.