Functional renormalization group study of an eight-band model for the iron arsenides

We investigate the superconducting pairing instabilities of eight-band models for the iron arsenides. Using a functional renormalization group treatment, we determine how the critical energy scale for superconductivity depends on the electronic band structure. Most importantly, if we vary the parameters from values corresponding to LaFeAsO to SmFeAsO, the pairing scale is strongly enhanced, in accordance with the experimental observation. We analyze the reasons for this trend and compare the results of the eight-band approach to those found using five-band models.Comment: 11 pages, 10 figure

Facility for fast neutron irradiation tests of electronics at the ISIS spallation neutron source

The VESUVIO beam line at the ISIS spallation neutron source was set up for neutron irradiation tests in the neutron energy range above 10 MeV. The neutron flux and energy spectrum were shown, in benchmark activation measurements, to provide a neutron spectrum similar to the ambient one at sea level, but with an enhancement in intensity of a factor of 107. Such conditions are suitable for accelerated testing of electronic components, as was demonstrated here by measurements of soft error rates in recent technology field programable gate arrays

Intraspecific genetic variation in complex assemblages from mitochondrial metagenomics: comparison with DNA barcodes

Metagenomic shotgun sequencing, using Illumina technology, and de novo genome assembly of mixed field-collected amples of invertebrates readily produce mitochondrial genome sequences, allowing rapid identification and quantification of species diversity. However, intraspecific genetic variability present in the specimen pools is lost during mitogenome assembly, which limits the utility of ‘mitochondrial metagenomics’ for studies of population diversity. 2. Using 10 natural communities (>2600 individuals) of leaf beetles (Chrysomelidae), DNA variation in the mitochondrial cox1-5’ ‘barcode’ was compared for Sanger sequenced individuals and Illumina shotgun sequenced specimen pools. 3. Generally, only a single mitochondrial contig was assembled per species, even in the presence of intraspecific variation. Ignoring ambiguity from the use of two different assemblers, the cox1 barcode regions from these assemblies were exact nucleotide matches of a Sanger sequenced barcode in 90.7% of cases, which dropped to 76.0% in assemblies from samples with large intra and interspecific variability. Nucleotide differences between barcodes from both data types were almost exclusively in synonymous 3rd codon position, although the number of affected sites was very low, and the greatest discrepancies were correlated with poor quality of Sanger sequences. 4. Unassembled shotgun reads were also used to score single nucleotide polymorphisms and to calculate intraspecific nucleotide diversity (pi) for all available populations at each site. These values correlated with Sanger sequenced cox1 variation but were significantly higher. 5. Overall, the assemblage-focused shotgun sequencing of pooled samples produced nucleotide variation data comparable to the well-established specimen-focused Sanger approach. The findings thus extend the application of mitochondrial metagenomics of complex biodiversity samples to the estimation of diversity below the species level

On landmark selection and sampling in high-dimensional data analysis

In recent years, the spectral analysis of appropriately defined kernel matrices has emerged as a principled way to extract the low-dimensional structure often prevalent in high-dimensional data. Here we provide an introduction to spectral methods for linear and nonlinear dimension reduction, emphasizing ways to overcome the computational limitations currently faced by practitioners with massive datasets. In particular, a data subsampling or landmark selection process is often employed to construct a kernel based on partial information, followed by an approximate spectral analysis termed the Nystrom extension. We provide a quantitative framework to analyse this procedure, and use it to demonstrate algorithmic performance bounds on a range of practical approaches designed to optimize the landmark selection process. We compare the practical implications of these bounds by way of real-world examples drawn from the field of computer vision, whereby low-dimensional manifold structure is shown to emerge from high-dimensional video data streams.Comment: 18 pages, 6 figures, submitted for publicatio

Vicarious Reinforcement in Rhesus Macaques (Macaca Mulatta)

What happens to others profoundly influences our own behavior. Such other-regarding outcomes can drive observational learning, as well as motivate cooperation, charity, empathy, and even spite. Vicarious reinforcement may serve as one of the critical mechanisms mediating the influence of other-regarding outcomes on behavior and decision-making in groups. Here we show that rhesus macaques spontaneously derive vicarious reinforcement from observing rewards given to another monkey, and that this reinforcement can motivate them to subsequently deliver or withhold rewards from the other animal. We exploited Pavlovian and instrumental conditioning to associate rewards to self (M1) and/or rewards to another monkey (M2) with visual cues. M1s made more errors in the instrumental trials when cues predicted reward to M2 compared to when cues predicted reward to M1, but made even more errors when cues predicted reward to no one. In subsequent preference tests between pairs of conditioned cues, M1s preferred cues paired with reward to M2 over cues paired with reward to no one. By contrast, M1s preferred cues paired with reward to self over cues paired with reward to both monkeys simultaneously. Rates of attention to M2 strongly predicted the strength and valence of vicarious reinforcement. These patterns of behavior, which were absent in non-social control trials, are consistent with vicarious reinforcement based upon sensitivity to observed, or counterfactual, outcomes with respect to another individual. Vicarious reward may play a critical role in shaping cooperation and competition, as well as motivating observational learning and group coordination in rhesus macaques, much as it does in humans. We propose that vicarious reinforcement signals mediate these behaviors via homologous neural circuits involved in reinforcement learning and decision-making

Metagenome skimming of insect specimen pools: potential for comparative genomics

Metagenomic analyses are challenging in metazoans, but high-copy number and repeat regions can be assembled from lowcoverage sequencing by “genome skimming,” which is applied here as a new way of characterizing metagenomes obtained in an ecological or taxonomic context. Illumina shotgun sequencing on two pools of Coleoptera (beetles) of approximately 200 species each were assembled into tens of thousands of scaffolds. Repeated low-coverage sequencing recovered similar scaffold sets consistently, although approximately 70% of scaffolds could not be identified against existing genome databases. Identifiable scaffolds included mitochondrial DNA, conserved sequences with hits to expressed sequence tag and protein databases, and knownrepeatelementsof high and low complexity, includingnumerous copies ofrRNAandhistone genes.Assemblies of histones captured a diversity of gene order and primary sequence in Coleoptera. Scaffolds with similarity to multiple sites in available coleopteran genome sequences for Dendroctonus and Tribolium revealed high specificity of scaffolds to either of these genomes, in particular for high-copy number repeats. Numerous “clusters” of scaffolds mapped to the same genomic site revealed intraand/or intergenomic variation within a metagenome pool. In addition to effect of taxonomic composition of the metagenomes, the number of mapped scaffolds also revealed structural differences between the two reference genomes, although the significance of this striking finding remains unclear. Finally, apparently exogenous sequences were recovered, including potential food plants, fungal pathogens, and bacterial symbionts. The “metagenome skimming” approach is useful for capturing the genomic diversity of poorly studied, species-rich lineages and opens new prospects in environmental genomic

Wind-Tunnel Investigation of Wings with Ordinary Ailerons and Full-Span External-Airfoil Flaps

Report presents an investigation carried out in the NACA 7- by 10-foot wind tunnel of an NACA 23012 airfoil equipped, first, with a full-span NACA 23012 external-airfoil flap having a chord 0.20 of the main airfoil chord and with a full-span aileron with a chord 0.12 of the main airfoil chord on the trailing edge of the main airfoil and equipped second, with a 0.30-chord full-span NACA 23012 external-airfoil flap and a 0.13-chord full-span aileron. The results are arranged in three groups, the first two of which deal with the airfoil characteristics of the two airfoil-flap combinations and with the internal-control characteristics of the airfoil-flap-aileron combinations. The third group of tests deals with several means for balancing ailerons mounted on a special large-chord NACA 23012 external-airfoil flap. The tests included an ordinary aileron, a curtained-nose balance, a frise balance, and a tab

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches