Fossil evidence for spin alignment of SDSS galaxies in filaments

We search for and find fossil evidence that the distribution of the spin axes of galaxies in cosmic web filaments relative to their host filaments are not randomly distributed. This would indicate that the action of large scale tidal torques effected the alignments of galaxies located in cosmic filaments. To this end, we constructed a catalogue of clean filaments containing edge-on galaxies. We started by applying the Multiscale Morphology Filter (MMF) technique to the galaxies in a redshift-distortion corrected version of the Sloan Digital Sky Survey DR5. From that sample we extracted those 426 filaments that contained edge-on galaxies (b/a < 0.2). These filaments were then visually classified relative to a variety of quality criteria. Statistical analysis using "feature measures" indicates that the distribution of orientations of these edge-on galaxies relative to their parent filament deviate significantly from what would be expected on the basis of a random distribution of orientations. The interpretation of this result may not be immediately apparent, but it is easy to identify a population of 14 objects whose spin axes are aligned perpendicular to the spine of the parent filament (\cos \theta < 0.2). The candidate objects are found in relatively less dense filaments. This might be expected since galaxies in such locations suffer less interaction with surrounding galaxies, and consequently better preserve their tidally induced orientations relative to the parent filament. The technique of searching for fossil evidence of alignment yields relatively few candidate objects, but it does not suffer from the dilution effects inherent in correlation analysis of large samples.Comment: 20 pages, 19 figures, slightly revised and upgraded version, accepted for publication by MNRAS. For high-res version see http://www.astro.rug.nl/~weygaert/SpinAlignJones.rev.pd

Comparing two appraisal models of interest

"Interest is an emotion associated with curiosity, exploration, and knowledge-seeking (Fredrickson, 1998; Izard, 1977; Silvia, 2005a, 2005b, 2006; Tomkins, 1962). The first researchers to propose an appraisal structure of interest were Smith and Ellsworth (1985). An alternative appraisal structure of interest was proposed by Silvia (2005a, 2005b). Experiment 1 tested these competing models. Participants viewed copies of calming and disturbing classical and contemporary paintings, rated each picture for appraisals, and reported their experienced interest, pleasantness/enjoyment, and disturbingness. Experiment 2 aimed to replicate the appraisal structures for the emotion of interest and measured viewing time. Results showed (1) interest and pleasantness were unrelated; (2) novelty-complexity positively predicted interest; (3) disturbing pictures were highly interesting; (4) and viewing time positively predicted interest."--Abstract from author supplied metadata

Novel Rule Base Development from IED-Resident Big Data for Protective Relay Analysis Expert System

Many Expert Systems for intelligent electronic device (IED) performance analyses such as those for protective relays have been developed to ascertain operations, maximize availability, and subsequently minimize misoperation risks. However, manual handling of overwhelming volume of relay resident big data and heavy dependence on the protection experts’ contrasting knowledge and inundating relay manuals have hindered the maintenance of the Expert Systems. Thus, the objective of this chapter is to study the design of an Expert System called Protective Relay Analysis System (PRAY), which is imbedded with a rule base construction module. This module is to provide the facility of intelligently maintaining the knowledge base of PRAY through the prior discovery of relay operations (association) rules from a novel integrated data mining approach of Rough-Set-Genetic-Algorithm-based rule discovery and Rule Quality Measure. The developed PRAY runs its relay analysis by, first, validating whether a protective relay under test operates correctly as expected by way of comparison between hypothesized and actual relay behavior. In the case of relay maloperations or misoperations, it diagnoses presented symptoms by identifying their causes. This study illustrates how, with the prior hybrid-data-mining-based knowledge base maintenance of an Expert System, regular and rigorous analyses of protective relay performances carried out by power utility entities can be conveniently achieved

Boredom, Information-Seeking and Exploration

Any adaptive organism faces the choice between taking actions with known benefits (exploitation), and sampling new actions to check for other, more valuable opportunities available (exploration). The latter involves information- seeking, a drive so fundamental to learning and long-term reward that it can reasonably be considered, through evolution or development, to have acquired its own value, independent of immediate reward. Similarly, behaviors that fail to yield information may have come to be associated with aversive experiences such as boredom, demotivation, and task disengagement. In accord with these suppositions, we propose that boredom reflects an adaptive signal for managing the exploration-exploitation tradeoff, in the service of optimizing information acquisition and long-term reward. We tested participants in three experiments, manipulating the information content in their immediate task environment, and showed that increased perceptions of boredom arise in environments in which there is little useful information, and that higher boredom correlates with higher exploration. These findings are the first step toward a model formalizing the relationship between exploration, exploitation and boredom

Differentiable Neural Computers with Memory Demon

A Differentiable Neural Computer (DNC) is a neural network with an external memory which allows for iterative content modification via read, write and delete operations. We show that information theoretic properties of the memory contents play an important role in the performance of such architectures. We introduce a novel concept of memory demon to DNC architectures which modifies the memory contents implicitly via additive input encoding. The goal of the memory demon is to maximize the expected sum of mutual information of the consecutive external memory contents.Comment: NeurIPS 2022 Workshop On Memory in Artificial and Real Intelligenc

The effect of negative feedback on motivation : a meta-analytic investigation

textAlthough the most prominent view in psychological theory has been that negative feedback should generally have a detrimental impact on motivation, competing perspectives and caveats on this prominent view have suggested that negative feedback may sometimes have neutral or even positive effects on motivation. A meta-analysis of 79 studies examined the effect of negative feedback on motivation and related outcomes with both child and adult samples. Results indicated that negative feedback compared to positive feedback decreased intrinsic motivation and perceived competence. This effect is much smaller when compared to neutral or no feedback. Moderator tests revealed that the effect of negative feedback seems to be less demotivating when a) the feedback statement includes instructional details to improve, b) compared to objective versus normative standards, and c) the task is interesting. Implications for future research and applications to real-world settings are discussed.Educational Psycholog

The Bee\u27s Knees or Spines of a Spider: What Makes an \u27Insect\u27 Interesting?

Insects and their kin (bugs) are among the most detested and despised creatures on earth. Irrational fears of these mostly harmless organisms often restrict and prevent opportunities for outdoor recreation and leisure. Alternatively, Shipley and Bixler (2016) theorize that direct and positive experiences with bugs during middle childhood may result in fascination with insects leading to comfort in wildland settings. The objective of this research was to examine and identify the novel and unfamiliar bug types that people are more likely to find interesting and visually attend to when spontaneously presented with their images. This research examined these questions through four integrated exploratory studies. The first study (n = 216) found that a majority of adults are unfamiliar with a majority of bugs, despite the abundance of many common but ˜unfamiliar\u27 bugs. The second (n = 15) and third (n = 308) study examined participant\u27s first impressions of unfamiliar bugs. The second study consisted of in-depth interviews, while the third study had participants report their perceptions of bugs across multiple emotional dimensions. Together, both studies suggest there are many unfamiliar bugs that are perceptually novel and perceived as interesting when encountered. The fourth study (n = 48) collected metrics of visual attention using eye-tracking by measuring visual fixations while participants viewed different bugs identified through previous studies as either being interesting or disinteresting. The findings of the fourth study suggest that interesting bugs can capture more visual attention than uninteresting bugs. Results from all four studies provide a heuristic for interpretive naturalists, magazine editors, marketers, public relation advisors, filmmakers, and any other visual communication professional that can be used in the choice of images of unfamiliar images of insects and other small invertebrates to evoke situational interest and motivate subsequent behavior

AnICA: Analyzing Inconsistencies in Microarchitectural Code Analyzers

Microarchitectural code analyzers, i.e., tools that estimate the throughput of machine code basic blocks, are important utensils in the tool belt of performance engineers. Recent tools like llvm-mca, uiCA, and Ithemal use a variety of techniques and different models for their throughput predictions. When put to the test, it is common to see these state-of-the-art tools give very different results. These inconsistencies are either errors, or they point to different and rarely documented assumptions made by the tool designers. In this paper, we present AnICA, a tool taking inspiration from differential testing and abstract interpretation to systematically analyze inconsistencies among these code analyzers. Our evaluation shows that AnICA can summarize thousands of inconsistencies in a few dozen descriptions that directly lead to high-level insights into the different behavior of the tools. In several case studies, we further demonstrate how AnICA automatically finds and characterizes known and unknown bugs in llvm-mca, as well as a quirk in AMD's Zen microarchitectures.Comment: To appear in Proceedings of the ACM on Programming Languages (PACMPL), Vol. 6, No. OOPSLA