Connectivity-based parcellation of the thalamus explains specific cognitive and behavioural symptoms in patients with bilateral thalamic infarct

A novel approach based on diffusion tractography was used here to characterise the cortico-thalamic connectivity in two patients, both presenting with an isolated bilateral infarct in the thalamus, but exhibiting partially different cognitive and behavioural profiles. Both patients (G.P. and R.F.) had a pervasive deficit in episodic memory, but only one of them (R.F.) suffered also from a dysexecutive syndrome. Both patients had an MRI scan at 3T, including a T1-weighted volume. Their lesions were manually segmented. T1-volumes were normalised to standard space, and the same transformations were applied to the lesion masks. Nineteen healthy controls underwent a diffusion-tensor imaging (DTI) scan. Their DTI data were normalised to standard space and averaged. An atlas of Brodmann areas was used to parcellate the prefrontal cortex. Probabilistic tractography was used to assess the probability of connection between each voxel of the thalamus and a set of prefrontal areas. The resulting map of corticothalamic connections was superimposed onto the patients' lesion masks, to assess whether the location of the thalamic lesions in R.F. (but not in G. P.) implied connections with prefrontal areas involved in dysexecutive syndromes. In G.P., the lesion fell within areas of the thalamus poorly connected with prefrontal areas, showing only a modest probability of connection with the anterior cingulate cortex (ACC). Conversely, R.F.'s lesion fell within thalamic areas extensively connected with the ACC bilaterally, with the right dorsolateral prefrontal cortex, and with the left supplementary motor area. Despite a similar, bilateral involvement of the thalamus, the use of connectivity-based segmentation clarified that R.F.'s lesions only were located within nuclei highly connected with the prefrontal cortical areas, thus explaining the patient's frontal syndrome. This study confirms that DTI tractography is a useful tool to examine in vivo the effect of focal lesions on interconnectivity brain patterns

ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks

Hash codes are efficient data representations for coping with the ever growing amounts of data. In this paper, we introduce a random forest semantic hashing scheme that embeds tiny convolutional neural networks (CNN) into shallow random forests, with near-optimal information-theoretic code aggregation among trees. We start with a simple hashing scheme, where random trees in a forest act as hashing functions by setting `1' for the visited tree leaf, and `0' for the rest. We show that traditional random forests fail to generate hashes that preserve the underlying similarity between the trees, rendering the random forests approach to hashing challenging. To address this, we propose to first randomly group arriving classes at each tree split node into two groups, obtaining a significantly simplified two-class classification problem, which can be handled using a light-weight CNN weak learner. Such random class grouping scheme enables code uniqueness by enforcing each class to share its code with different classes in different trees. A non-conventional low-rank loss is further adopted for the CNN weak learners to encourage code consistency by minimizing intra-class variations and maximizing inter-class distance for the two random class groups. Finally, we introduce an information-theoretic approach for aggregating codes of individual trees into a single hash code, producing a near-optimal unique hash for each class. The proposed approach significantly outperforms state-of-the-art hashing methods for image retrieval tasks on large-scale public datasets, while performing at the level of other state-of-the-art image classification techniques while utilizing a more compact and efficient scalable representation. This work proposes a principled and robust procedure to train and deploy in parallel an ensemble of light-weight CNNs, instead of simply going deeper.Comment: Accepted to ECCV 201

Coordination of Dynamic Software Components with JavaBIP

JavaBIP allows the coordination of software components by clearly separating the functional and coordination aspects of the system behavior. JavaBIP implements the principles of the BIP component framework rooted in rigorous operational semantics. Recent work both on BIP and JavaBIP allows the coordination of static components defined prior to system deployment, i.e., the architecture of the coordinated system is fixed in terms of its component instances. Nevertheless, modern systems, often make use of components that can register and deregister dynamically during system execution. In this paper, we present an extension of JavaBIP that can handle this type of dynamicity. We use first-order interaction logic to define synchronization constraints based on component types. Additionally, we use directed graphs with edge coloring to model dependencies among components that determine the validity of an online system. We present the software architecture of our implementation, provide and discuss performance evaluation results.Comment: Technical report that accompanies the paper accepted at the 14th International Conference on Formal Aspects of Component Softwar

Modelling the long-term carbon cycle, atmospheric CO2, and Earth surface temperature from late Neoproterozoic to present day

Over geological timescales, CO2 levels are determined by the operation of the long term carbon cycle, and it is generally thought that changes in atmospheric CO2 concentration have controlled variations in Earth's surface temperature over the Phanerozoic Eon. Here we compile independent estimates for global average surface temperature and atmospheric CO2 concentration, and compare these to the predictions of box models of the long term carbon cycle COPSE and GEOCARBSULF. We find a strong relationship between CO2 forcing and temperature from the proxy data, for times where data is available, and we find that current published models reproduce many aspects of CO2 change, but compare poorly to temperature estimates. Models are then modified in line with recent advances in understanding the tectonic controls on carbon cycle source and sink processes, with these changes constrained by modelling 87Sr/86Sr ratios. We estimate CO2 degassing rates from the lengths of subduction zones and rifts, add differential effects of erosion rates on the weathering of silicates and carbonates, and revise the relationship between global average temperature changes and the temperature change in key weathering zones. Under these modifications, models produce combined records of CO2 and temperature change that are reasonably in line with geological and geochemical proxies (e.g. central model predictions are within the proxy windows for >~75% of the time covered by data). However, whilst broad long-term changes are reconstructed, the models still do not adequately predict the timing of glacial periods. We show that the 87Sr/86Sr record is largely influenced by the weathering contributions of different lithologies, and is strongly controlled by erosion rates, rather than being a good indicator of overall silicate chemical weathering rates. We also confirm that a combination of increasing erosion rates and decreasing degassing rates over the Neogene can cause the observed cooling and Sr isotope changes without requiring an overall increase in silicate weathering rates. On the question of a source or sink dominated carbon cycle, we find that neither alone can adequately reconstruct the combination of CO2, temperature and strontium isotope dynamics over Phanerozoic time, necessitating a combination of changes to sources and sinks. Further progress in this field relies on >108 year dynamic spatial reconstructions of ancient tectonics, paleogeography and hydrology. Whilst this is a significant challenge, the latest reconstruction techniques, proxy records and modelling advances make this an achievable target

ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL VOC (per-category comparisons in Table 3, distribution of localization difficulty in Fig 16), a list of queries used for obtaining object detection images (Appendix C), and some additional reference

Preserving Madagascar's Natural Heritage: The Importance of Keeping the Island's Vertebrate Fossils in the Public Domain

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Biological diversification linked to environmental stabilization following the Sturtian Snowball glaciation

The body fossil and biomarker records hint at an increase in biotic complexity between the two Cryogenian Snowball Earth episodes (ca. 661 million to ≤650 million years ago). Oxygen and nutrient availability can promote biotic complexity, but nutrient (particularly phosphorus) and redox dynamics across this interval remain poorly understood. Here, we present high-resolution paleoredox and phosphorus phase association data from multiple globally distributed drill core records through the non-glacial interval. These data are first correlated regionally by litho- and chemostratigraphy, and then calibrated within a series of global chronostratigraphic frameworks. The combined data show that regional differences in postglacial redox stabilization were partly controlled by the intensity of phosphorus recycling from marine sediments. The apparent increase in biotic complexity followed a global transition to more stable and less reducing conditions in shallow to mid-depth marine environments and occurred within a tolerable climatic window during progressive cooling after post-Snowball super-greenhouse conditions

Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

The impact of emotional well-being on long-term recovery and survival in physical illness: a meta-analysis

This meta-analysis synthesized studies on emotional well-being as predictor of the prognosis of physical illness, while in addition evaluating the impact of putative moderators, namely constructs of well-being, health-related outcome, year of publication, follow-up time and methodological quality of the included studies. The search in reference lists and electronic databases (Medline and PsycInfo) identified 17 eligible studies examining the impact of general well-being, positive affect and life satisfaction on recovery and survival in physically ill patients. Meta-analytically combining these studies revealed a Likelihood Ratio of 1.14, indicating a small but significant effect. Higher levels of emotional well-being are beneficial for recovery and survival in physically ill patients. The findings show that emotional well-being predicts long-term prognosis of physical illness. This suggests that enhancement of emotional well-being may improve the prognosis of physical illness, which should be investigated by future research