Actes du Congrès Collèges célébrations 92

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Pathologies of the large-N limit for RP^{N-1}, CP^{N-1}, QP^{N-1} and mixed isovector/isotensor sigma-models

We compute the phase diagram in the N\to\infty limit for lattice RP^{N-1}, CP^{N-1} and QP^{N-1} sigma-models with the quartic action, and more generally for mixed isovector/isotensor models. We show that the N=\infty limit exhibits phase transitions that are forbidden for any finite N. We clarify the origin of these pathologies by examining the exact solution of the one-dimensional model: we find that there are complex zeros of the partition function that tend to the real axis as N\to\infty. We conjecture the correct phase diagram for finite N as a function of the spatial dimension d. Along the way, we prove some new correlation inequalities for a class of N-component sigma-models, and we obtain some new results concerning the complex zeros of confluent hypergeometric functions.Comment: LaTeX, 88 pages, 33 figure

Parametric study of EEG sensitivity to phase noise during face processing

<b>Background: </b> The present paper examines the visual processing speed of complex objects, here faces, by mapping the relationship between object physical properties and single-trial brain responses. Measuring visual processing speed is challenging because uncontrolled physical differences that co-vary with object categories might affect brain measurements, thus biasing our speed estimates. Recently, we demonstrated that early event-related potential (ERP) differences between faces and objects are preserved even when images differ only in phase information, and amplitude spectra are equated across image categories. Here, we use a parametric design to study how early ERP to faces are shaped by phase information. Subjects performed a two-alternative force choice discrimination between two faces (Experiment 1) or textures (two control experiments). All stimuli had the same amplitude spectrum and were presented at 11 phase noise levels, varying from 0% to 100% in 10% increments, using a linear phase interpolation technique. Single-trial ERP data from each subject were analysed using a multiple linear regression model. <b>Results: </b> Our results show that sensitivity to phase noise in faces emerges progressively in a short time window between the P1 and the N170 ERP visual components. The sensitivity to phase noise starts at about 120–130 ms after stimulus onset and continues for another 25–40 ms. This result was robust both within and across subjects. A control experiment using pink noise textures, which had the same second-order statistics as the faces used in Experiment 1, demonstrated that the sensitivity to phase noise observed for faces cannot be explained by the presence of global image structure alone. A second control experiment used wavelet textures that were matched to the face stimuli in terms of second- and higher-order image statistics. Results from this experiment suggest that higher-order statistics of faces are necessary but not sufficient to obtain the sensitivity to phase noise function observed in response to faces. <b>Conclusion: </b> Our results constitute the first quantitative assessment of the time course of phase information processing by the human visual brain. We interpret our results in a framework that focuses on image statistics and single-trial analyses

The impact of the North Atlantic Oscillation on the uptake and accumulation of anthropogenic CO2 by North Atlantic Ocean mode waters

Author Posting. © American Geophysical Union, 2011. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 25 (2011): GB3022, doi:10.1029/2010GB003892.The North Atlantic Ocean accounts for about 25% of the global oceanic anthropogenic carbon sink. This basin experiences significant interannual variability primarily driven by the North Atlantic Oscillation (NAO). A suite of biogeochemical model simulations is used to analyze the impact of interannual variability on the uptake and storage of contemporary and anthropogenic carbon (Canthro) in the North Atlantic Ocean. Greater winter mixing during positive NAO years results in increased mode water formation and subsequent increases in subtropical and subpolar Canthro inventories. Our analysis suggests that changes in mode water Canthro inventories are primarily due to changes in water mass volumes driven by variations in water mass transformation rates rather than local air-sea CO2 exchange. This suggests that a significant portion of anthropogenic carbon found in the ocean interior may be derived from surface waters advected into water formation regions rather than from local gas exchange. Therefore, changes in climate modes, such as the NAO, may alter the residence time of anthropogenic carbon in the ocean by altering the rate of water mass transformation. In addition, interannual variability in Canthro storage increases the difficulty of Canthro detection and attribution through hydrographic observations, which are limited by sparse sampling of subsurface waters in time and space.We would like to acknowledge funding from the NOAA Climate Program under the Office of Climate Observations and Global Carbon Cycle Program (NOAA‐NA07OAR4310098), NSF (OCE‐0623034), NCAR, the WHOI Ocean Climate Institute, a National Defense Science and Engineering Graduate Fellowship and an Environmental Protection Agency STAR graduate fellowship. NCAR is sponsored by the National Science Foundation

The Sorcerer II Global Ocean Sampling Expedition: Northwest Atlantic through Eastern Tropical Pacific

The world's oceans contain a complex mixture of micro-organisms that are for the most part, uncharacterized both genetically and biochemically. We report here a metagenomic study of the marine planktonic microbiota in which surface (mostly marine) water samples were analyzed as part of the Sorcerer II Global Ocean Sampling expedition. These samples, collected across a several-thousand km transect from the North Atlantic through the Panama Canal and ending in the South Pacific yielded an extensive dataset consisting of 7.7 million sequencing reads (6.3 billion bp). Though a few major microbial clades dominate the planktonic marine niche, the dataset contains great diversity with 85% of the assembled sequence and 57% of the unassembled data being unique at a 98% sequence identity cutoff. Using the metadata associated with each sample and sequencing library, we developed new comparative genomic and assembly methods. One comparative genomic method, termed “fragment recruitment,” addressed questions of genome structure, evolution, and taxonomic or phylogenetic diversity, as well as the biochemical diversity of genes and gene families. A second method, termed “extreme assembly,” made possible the assembly and reconstruction of large segments of abundant but clearly nonclonal organisms. Within all abundant populations analyzed, we found extensive intra-ribotype diversity in several forms: (1) extensive sequence variation within orthologous regions throughout a given genome; despite coverage of individual ribotypes approaching 500-fold, most individual sequencing reads are unique; (2) numerous changes in gene content some with direct adaptive implications; and (3) hypervariable genomic islands that are too variable to assemble. The intra-ribotype diversity is organized into genetically isolated populations that have overlapping but independent distributions, implying distinct environmental preference. We present novel methods for measuring the genomic similarity between metagenomic samples and show how they may be grouped into several community types. Specific functional adaptations can be identified both within individual ribotypes and across the entire community, including proteorhodopsin spectral tuning and the presence or absence of the phosphate-binding gene PstS

Assessing EEG neuroimaging with machine learning

Neuroimaging techniques can give novel insights into the nature of human cognition. We do not wish only to label patterns of activity as potentially associated with a cognitive process, but also to probe this in detail, so as to better examine how it may inform mechanistic theories of cognition. A possible approach towards this goal is to extend EEG 'brain-computer interface' (BCI) tools - where motor movement intent is classified from brain activity - to also investigate visual cognition experiments. We hypothesised that, building on BCI techniques, information from visual object tasks could be classified from EEG data. This could allow novel experimental designs to probe visual information processing in the brain. This can be tested and falsified by application of machine learning algorithms to EEG data from a visual experiment, and quantified by scoring the accuracy at which trials can be correctly classified. Further, we hypothesise that ICA can be used for source-separation of EEG data to produce putative activity patterns associated with visual process mechanisms. Detailed profiling of these ICA sources could be informative to the nature of visual cognition in a way that is not accessible through other means. While ICA has been used previously in removing 'noise' from EEG data, profiling the relation of common ICA sources to cognitive processing appears less well explored. This can be tested and falsified by using ICA sources as training data for the machine learning, and quantified by scoring the accuracy at which trials can be correctly classified using this data, while also comparing this with the equivalent EEG data. We find that machine learning techniques can classify the presence or absence of visual stimuli at 85% accuracy (0.65 AUC) using a single optimised channel of EEG data, and this improves to 87% (0.7 AUC) using data from an equivalent single ICA source. We identify data from this ICA source at time period around 75-125 ms post-stimuli presentation as greatly more informative in decoding the trial label. The most informative ICA source is located in the central occipital region and typically has prominent 10-12Hz synchrony and a -5 μV ERP dip at around 100ms. This appears to be the best predictor of trial identity in our experiment. With these findings, we then explore further experimental designs to investigate ongoing visual attention and perception, attempting online classification of vision using these techniques and IC sources. We discuss how these relate to standard EEG landmarks such as the N170 and P300, and compare their use. With this thesis, we explore this methodology of quantifying EEG neuroimaging data with machine learning separation and classification and discuss how this can be used to investigate visual cognition. We hope the greater information from EEG analyses with predictive power of each ICA source quantified by machine learning separation and classification and discuss how this can be used to investigate visual cognition. We hope the greater information from EEG analyses with predictive power of each ICA source quantified by machine learning might give insight and constraints for macro level models of visual cognition

The Endurance of Uncertainty: Antisociality and Ontological Anarchy in British Psychiatry, 1950-2010

Research into the biological markers of pathology has long been a feature of British psychiatry. Such somatic indicators and associated features of mental disorder often intertwine with discourse on psychological and behavioral correlates and causes of mental ill-health. Disorders of sociality – particularly psychopathy and antisocial personality disorder – are important instances where the search for markers of pathology has a long history; research in this area has played an important role in shaping how mental health professionals understand the conditions. Here, I characterize the multiplicity of psychiatric praxis that has sought to define the mark of antisociality as a form of “ontological anarchy.” I regard this as an essential feature of the search for biological and other markers of an unstable referent, positing that uncertainties endure – in part – precisely because of attempts to build consensus regarding the ontology of antisociality through biomedical means. Such an account is suggestive of the co-production of biomarkers, mental disorder, and psychiatric institutions