Spiking neural networks for computer vision

State-of-the-art computer vision systems use frame-based cameras that sample the visual scene as a series of high-resolution images. These are then processed using convolutional neural networks using neurons with continuous outputs. Biological vision systems use a quite different approach, where the eyes (cameras) sample the visual scene continuously, often with a non-uniform resolution, and generate neural spike events in response to changes in the scene. The resulting spatio-temporal patterns of events are then processed through networks of spiking neurons. Such event-based processing offers advantages in terms of focusing constrained resources on the most salient features of the perceived scene, and those advantages should also accrue to engineered vision systems based upon similar principles. Event-based vision sensors, and event-based processing exemplified by the SpiNNaker (Spiking Neural Network Architecture) machine, can be used to model the biological vision pathway at various levels of detail. Here we use this approach to explore structural synaptic plasticity as a possible mechanism whereby biological vision systems may learn the statistics of their inputs without supervision, pointing the way to engineered vision systems with similar online learning capabilities

Bacterial retention in three soils of the Rolling Pampa, Argentina, under simulated rainfall

Fil: Behrends Kraemer, Filipe B. Universidad de Buenos Aires (UBA), Facultad de Agronomía, Cátedra de Manejo y Conservación de Suelos; Argentina.Fil: Chagas, Celio I. Universidad de Buenos Aires (UBA), Facultad de Agronomía, Cátedra de Manejo y Conservación de Suelos; Argentina.Fil: Irurtia, Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA); Argentina.Fil: Garibaldi, Lucas Alejandro. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. INIBIOMA-CONICET. Laboratorio Ecotono; Argentina. Universidad de Buenos Aires (UBA). Facultad de Agronomía; Argentina.Fil: Behrends Kraemer, Filipe B. Instituto Nacional de Tecnología Agropecuaria (INTA); Argentina.Fil: Garibaldi, Lucas Alejandro. Universidad de Buenos Aires (UBA). Facultad de Agronomía; Argentina.Bacterial retention by soils is a key factor in predicting bacterial transport through surface runoff into water bodies. The objective was to evaluate biological, soil and hydrologic factors that affect bacterial retention in three soil types of the Rolling Pampa, Argentina. Simulated rainfall was applied on field plots previously inoculated with Escherichia coli and simultaneously biological variables such as bacterial adsorption and distribution coefficient were measured at laboratory. Soil variables, particularly pH, exchangeable sodium percentage and organic carbon as well as biological variables proved to be important properties in the regulation of bacterial retention processes. There were no significant differences between the biological variables measured in soils and in the sediments. Most of the microorganisms in the runoff from all sites were either free of (in the < 2 μm sediment fraction) or associated with small soil particles (2 to 50 μm), therefore management practices, such as filter strips, should be regarded with caution when implemented

The UK Clinical Research Collaboration (UKCRC) Tissue Directory and Coordination Centre: the UK’s centre for facilitating the usage of human samples for medical research

The UKCRC Tissue Directory and Coordination Centre was established to improve access to and utilisation of UK human tissue samples for medical research. The key output of the Centre is the creation of the UK’s first pan-disease Tissue Directory (https://directory.biobankinguk.org/). Any researcher can search the Directory based on a series of simple key words including disease classification, age, sex, sample type, preservation details, quality indicators and datasets available. The Directory as of April 2017 contains 100 Bioresources. Researchers seeking fresh samples can also search for facilities that offer bespoke collection services. Future work of the Centre will be to explore greater standardisation of biobanking activities across the UK and to facilitate an inter-connected research infrastructure related to the use of human biosamples

Investigating the detection of adverse drug events in a UK general practice electronic health-care database

Data-mining techniques have frequently been developed for Spontaneous reporting databases. These techniques aim to find adverse drug events accurately and efficiently. Spontaneous reporting databases are prone to missing information,under reporting and incorrect entries. This often results in a detection lag or prevents the detection of some adverse drug events. These limitations do not occur in electronic healthcare databases. In this paper, existing methods developed for spontaneous reporting databases are implemented on both a spontaneous reporting database and a general practice electronic health-care database and compared. The results suggests that the application of existing methods to the general practice database may help find signals that have gone undetected when using the spontaneous reporting system database. In addition the general practice database provides far more supplementary information, that if incorporated in analysis could provide a wealth of information for identifying adverse events more accurately

Attributes for causal inference in electronic healthcare databases

Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria

Attributes for causal inference in electronic healthcare databases

Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria

There is no "Theory of Everything" inside E8

We analyze certain subgroups of real and complex forms of the Lie group E8, and deduce that any "Theory of Everything" obtained by embedding the gauge groups of gravity and the Standard Model into a real or complex form of E8 lacks certain representation-theoretic properties required by physical reality. The arguments themselves amount to representation theory of Lie algebras in the spirit of Dynkin's classic papers and are written for mathematicians.Comment: Final version, to appear in Communications in Mathematical Physics. The main difference, from the previous version, is the creation of a new section, containing a response to Lisi's objections to our wor