One-vs-One classification for deep neural networks

For performing multi-class classification, deep neural networks almost always employ a One-vs-All (OvA) classification scheme with as many output units as there are classes in a dataset. The problem of this approach is that each output unit requires a complex decision boundary to separate examples from one class from all other examples. In this paper, we propose a novel One-vs-One (OvO) classification scheme for deep neural networks that trains each output unit to distinguish between a specific pair of classes. This method increases the number of output units compared to the One-vs-All classification scheme but makes learning correct decision boundaries much easier. In addition to changing the neural network architecture, we changed the loss function, created a code matrix to transform the one-hot encoding to a new label encoding, and changed the method for classifying examples. To analyze the advantages of the proposed method, we compared the One-vs-One and One-vs-All classification methods on three plant recognition datasets (including a novel dataset that we created) and a dataset with images of different monkey species using two deep architectures. The two deep convolutional neural network (CNN) architectures, Inception-V3 and ResNet-50, are trained from scratch or pre-trained weights. The results show that the One-vs-One classification method outperforms the One-vs-All method on all four datasets when training the CNNs from scratch. However, when using the two classification schemes for fine-tuning pre-trained CNNs, the One-vs-All method leads to the best performances, which is presumably because the CNNs had been pre-trained using the One-vs-All scheme

Protocol of the Healthy Brain Study: An accessible resource for understanding the human brain and how it dynamically and individually operates in its bio-social context

The endeavor to understand the human brain has seen more progress in the last few decades than in the previous two millennia. Still, our understanding of how the human brain relates to behavior in the real world and how this link is modulated by biological, social, and environmental factors is limited. To address this, we designed the Healthy Brain Study (HBS), an interdisciplinary, longitudinal, cohort study based on multidimensional, dynamic assessments in both the laboratory and the real world. Here, we describe the rationale and design of the currently ongoing HBS. The HBS is examining a population-based sample of 1,000 healthy participants (age 30-39) who are thoroughly studied across an entire year. Data are collected through cognitive, affective, behavioral, and physiological testing, neuroimaging, bio-sampling, questionnaires, ecological momentary assessment, and real-world assessments using wearable devices. These data will become an accessible resource for the scientific community enabling the next step in understanding the human brain and how it dynamically and individually operates in its bio-social context. An access procedure to the collected data and bio-samples is in place and published on https://www.healthybrainstudy.nl/en/data-and-methods. https://www.trialregister.nl/trial/795

Protocol of the Healthy Brain Study: An accessible resource for understanding the human brain and how it dynamically and individually operates in its bio-social context

The endeavor to understand the human brain has seen more progress in the last few decades than in the previous two millennia. Still, our understanding of how the human brain relates to behavior in the real world and how this link is modulated by biological, social, and environmental factors is limited. To address this, we designed the Healthy Brain Study (HBS), an interdisciplinary, longitudinal, cohort study based on multidimensional, dynamic assessments in both the laboratory and the real world. Here, we describe the rationale and design of the currently ongoing HBS. The HBS is examining a population-based sample of 1,000 healthy participants (age 30-39) who are thoroughly studied across an entire year. Data are collected through cognitive, affective, behavioral, and physiological testing, neuroimaging, bio-sampling, questionnaires, ecological momentary assessment, and real-world assessments using wearable devices. These data will become an accessible resource for the scientific community enabling the next step in understanding the human brain and how it dynamically and individually operates in its bio-social context. An access procedure to the collected data and bio-samples is in place and published on https://www.healthybrainstudy.nl/en/data-and-methods/access. Trail registration: https://www.trialregister.nl/trial/7955

Discovery prospects of dwarf spheroidal galaxies for indirect dark matter searches

We study the prospects for the Large Synoptic Survey Telescope (LSST) to find new dwarf spheroidal galaxies in the Milky Way. Adopting models of Milky-Way halo substructure and phenomenological prescriptions connecting subhalos and satellite galaxies, we obtain surface brightness distributions of V-band magnitude that lead us to predict that LSST will discover tens to hundreds of dwarf spheroidal galaxies above its sensitivity. The soon-to-be-discovered dwarfs will be interesting targets for indirect searches of dark matter annihilation yields. We forecast the distribution function of gamma-ray emission from dark matter annihilation in these objects, and discuss the detectability of these signals at both Fermi Large Area Telescope (LAT) and Cherenkov Telescope Array (CTA). By combining information from the predicted dwarf galaxies, we obtain an expected sensitivity to the annihilation cross section σ v of 10-26 cm3 s-1 (for dark matter particles of mass 10 GeV with Fermi-LAT) and 5× 10-24 cm3 s-1 (for dark matter particles of mass 500 GeV with CTA). We find that the current uncertainties in the mass measurement of the Milky-Way halo are relatively minor compared with the Poisson errors associated to drawing the most promising dwarfs from the underlying flux distribution

Discovery prospects of dwarf spheroidal galaxies for indirect dark matter searches

We study the prospects for the Large Synoptic Survey Telescope (LSST) to find new dwarf spheroidal galaxies in the Milky Way. Adopting models of Milky-Way halo substructure and phenomenological prescriptions connecting subhalos and satellite galaxies, we obtain surface brightness distributions of V-band magnitude that lead us to predict that LSST will discover tens to hundreds of dwarf spheroidal galaxies above its sensitivity. The soon-to-be-discovered dwarfs will be interesting targets for indirect searches of dark matter annihilation yields. We forecast the distribution function of gamma-ray emission from dark matter annihilation in these objects, and discuss the detectability of these signals at both Fermi Large Area Telescope (LAT) and Cherenkov Telescope Array (CTA). By combining information from the predicted dwarf galaxies, we obtain an expected sensitivity to the annihilation cross section σ v of 10-26 cm3 s-1 (for dark matter particles of mass 10 GeV with Fermi-LAT) and 5× 10-24 cm3 s-1 (for dark matter particles of mass 500 GeV with CTA). We find that the current uncertainties in the mass measurement of the Milky-Way halo are relatively minor compared with the Poisson errors associated to drawing the most promising dwarfs from the underlying flux distribution

Cortical profiles of numerous psychiatric disorders and normal development share a common pattern

The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of psychiatric disorders. The pubertal timing of the expression of PC1-related genes implicates disrupted neurodevelopment in the pathogenesis of the spectrum of psychiatric diseases emerging during adolescence