Active learning from noisy tagged images

Learning successful image classification models requires large quantities of labelled examples that are generally hard to obtain. On the other hand, the web provides an abundance of loosely labelled images, i.e. tagged in websites such as Flickr. Although these images are cheap and massively available, their tags are typically noisy and unreliable. In an attempt to use such images for training a classifier, we propose a simple probabilistic model to learn a latent semantic space from which deep vector representations of the images and tags are generated. This latent space is subsequently used in an active learning framework based on adaptive submodular optimisation that selects informative images to be labelled. Afterwards, we update the classifier according to the importance of each labelled image to best capture the information they provide. Through this simple approach, we are able to train a classifier that performs well using a fraction of the effort that is typically required for image labelling and classifier training.M. Ehsan Abbasnejad, Anthony Dick, Qinfeng Shi, Anton van den Henge

Tragedy revisited

“Freedom in a commons brings ruin to all.” So argued ecologist Garrett Hardin in “The Tragedy of the Commons” in the 13 December 1968 issue of Science (1). Hardin questioned society's ability to manage shared resources and avoid an environmentally and socially calamitous free-for-all. In the 50 years since, the essay has influenced discussions ranging from climate change (see page 1217) to evolution, from infectious disease to the internet, and has reached far beyond academic literature—but not without criticism. Considerable work, notably by Nobelist Elinor Ostrom (2), has challenged Hardin, particularly his emphasis on property rights and government regulatory leviathans as solutions. Instead, research has documented contexts, cases, and principles that reflect the ability of groups to collectively govern common resources. To mark this anniversary and celebrate the richness of research and practice around commons and cooperation, Science invited experts to share some contemporary views on such tragedies and how to avert them. —Brad Wibl

A Model System for Dimensional Competition in Nanostructures: A Quantum Wire on a Surface

The retarded Green’s function (E−H + iε)−1is given for a dimensionally hybrid Hamiltonian which interpolates between one and two dimensions. This is used as a model for dimensional competition in propagation effects in the presence of one-dimensional subsystems on a surface. The presence of a quantum wire generates additional exponential terms in the Green’s function. The result shows how the location of the one-dimensional subsystem affects propagation of particles

Non-Minimal Warm Inflation and Perturbations on the Warped DGP Brane with Modified Induced Gravity

We construct a warm inflation model with inflaton field non-minimally coupled to induced gravity on a warped DGP brane. We incorporate possible modification of the induced gravity on the brane in the spirit of $f(R)$-gravity. We study cosmological perturbations in this setup. In the case of two field inflation such as warm inflation, usually entropy perturbations are generated. While it is expected that in the case of one field inflation these perturbations to be removed, we show that even in the absence of the radiation field, entropy perturbations are generated in our setup due to non-minimal coupling and modification of the induced gravity.Comment: 29 pages, 7 figures, Accepted by Gen. Rel Gravi

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Performance of the MICE diagnostic system

Muon beams of low emittance provide the basis for the intense, well-characterised neutrino beams of a neutrino factory and for multi-TeV lepton-antilepton collisions at a muon collider. The international Muon Ionization Cooling Experiment (MICE) has demonstrated the principle of ionization cooling, the technique by which it is proposed to reduce the phase-space volume occupied by the muon beam at such facilities. This paper documents the performance of the detectors used in MICE to measure the muon-beam parameters, and the physical properties of the liquid hydrogen energy absorber during running