Accelerating Monte Carlo simulations with an NVIDIA® graphics processor

Modern graphics cards, commonly used in desktop computers, have evolved beyond a simple interface between processor and display to incorporate sophisticated calculation engines that can be applied to general purpose computing. The Monte Carlo algorithm for modelling photon transport in turbid media has been implemented on an NVIDIA® 8800gt graphics card using the CUDA toolkit. The Monte Carlo method relies on following the trajectory of millions of photons through the sample, often taking hours or days to complete. The graphics-processor implementation, processing roughly 110 million scattering events per second, was found to run more than 70 times faster than a similar, single-threaded implementation on a 2.67 GHz desktop computer

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit

The effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study

High-level brain function such as memory, classification or reasoning can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. The functional performance of neural networks is often critically dependent on the level of correlations in the neural activity. In finite networks, correlations are typically inevitable due to shared presynaptic input. Recent theoretical studies have shown that inhibitory feedback, abundant in biological neural networks, can actively suppress these shared-input correlations and thereby enable neurons to fire nearly independently. For networks of spiking neurons, the decorrelating effect of inhibitory feedback has so far been explicitly demonstrated only for homogeneous networks of neurons with linear sub-threshold dynamics. Theory, however, suggests that the effect is a general phenomenon, present in any system with sufficient inhibitory feedback, irrespective of the details of the network structure or the neuronal and synaptic properties. Here, we investigate the effect of network heterogeneity on correlations in sparse, random networks of inhibitory neurons with non-linear, conductance-based synapses. Emulations of these networks on the analog neuromorphic hardware system Spikey allow us to test the efficiency of decorrelation by inhibitory feedback in the presence of hardware-specific heterogeneities. The configurability of the hardware substrate enables us to modulate the extent of heterogeneity in a systematic manner. We selectively study the effects of shared input and recurrent connections on correlations in membrane potentials and spike trains. Our results confirm ...Comment: 20 pages, 10 figures, supplement

Deterministic networks for probabilistic computing

Neural-network models of high-level brain functions such as memory recall and reasoning often rely on the presence of stochasticity. The majority of these models assumes that each neuron in the functional network is equipped with its own private source of randomness, often in the form of uncorrelated external noise. However, both in vivo and in silico, the number of noise sources is limited due to space and bandwidth constraints. Hence, neurons in large networks usually need to share noise sources. Here, we show that the resulting shared-noise correlations can significantly impair the performance of stochastic network models. We demonstrate that this problem can be overcome by using deterministic recurrent neural networks as sources of uncorrelated noise, exploiting the decorrelating effect of inhibitory feedback. Consequently, even a single recurrent network of a few hundred neurons can serve as a natural noise source for large ensembles of functional networks, each comprising thousands of units. We successfully apply the proposed framework to a diverse set of binary-unit networks with different dimensionalities and entropies, as well as to a network reproducing handwritten digits with distinct predefined frequencies. Finally, we show that the same design transfers to functional networks of spiking neurons.Comment: 22 pages, 11 figure

Do capuchin monkeys (Sapajus apella) use exploration to form intuitions about physical properties?

We are grateful to the Royal Zoological Society of Scotland (RZSS) and the University of St Andrews for core financial support to the RZSS Edinburgh Zoo’s Living Links Research Facility where this project was carried out.Humans’ flexible innovation relies on our capacity to accurately predict objects’ behaviour. These predictions may originate from a “physics-engine” in the brain which simulates our environment. To explore the evolutionary origins of intuitive physics, we investigate whether capuchin monkeys’ object exploration supports learning. Two capuchin groups experienced exploration sessions involving multiple copies of two objects, one object was easily opened (functional), the other was not (non-functional). We used two within-subject conditions (enrichment-then-test, and test-only) with two object sets per group. Monkeys then underwent individual test sessions where the objects contained rewards, and they choose one to attempt to open. The monkeys spontaneously explored, performing actions which yielded functional information. At test, both groups chose functional objects above chance. While high performance of the test-only group precluded us from establishing learning during exploration, this study reveals the promise of harnessing primates’ natural exploratory tendencies to understand how they see the world.Publisher PDFPeer reviewe

Edge detection in multispectral images using the n-dimensional self-organizing map

We propose a new method for performing edge detection in multi-spectral images based on the self-organizing map (SOM) concept. Previously, 1-dimensional or 2-dimensional SOMs were trained to provide a linear mapping of high-dimensional multispectral vectors. Then, edge detection was applied on that mapping. However, the 1-dimensional SOM may not converge on a suitable global order for images with rich content. Likewise, the 2-dimensional SOM intro-duces false edges due to linearization artifacts. Our method feeds the edge detector without linearization. Instead, it exploits directly the distances of SOM neurons. This avoids the aforementioned draw-backs and is more general, as a SOM of arbitrary dimensionality can be used. We show that our method achieves significantly bet-ter edge detection results than previous work on a high-resolution multispectral image database. Index Terms — Multispectral imaging, Image edge detection, Self organizing feature maps, Machine Visio

Convergent evolution of pregnancy-specific glycoproteins in human and horse

Pregnancy-specific glycoproteins (PSGs) are members of the carcinoembryonic antigen cell adhesion molecule (CEACAM) family that are secreted by trophoblast cells. PSGs may modulate immune, angiogenic and platelet responses during pregnancy. Until now, PSGs are only found in species that have a highly invasive (hemochorial) placentation including humans, mice and rats. Surprisingly, analyzing the CEACAM gene family of the horse, which has a non-invasive epitheliochorial placenta, with the exception of the transient endometrial cups, we identified equine CEACAM family members that seem to be related to PSGs of rodents and primates. We identified seven genes that encode secreted PSG-like CEACAMs. Phylogenetic analyses indicate that they evolved independently from an equine CEACAM1-like ancestor rather than from a common PSG-like ancestor with rodents and primates. Significantly, expression of PSG-like genes (CEACAM44, CEACAM48, CEACAM49 and CEACAM55) was found in non-invasive as well as invasive trophoblast cells such as purified chorionic girdle cells and endometrial cup cells. Chorionic girdle cells are highly invasive trophoblast cells that invade the endometrium of the mare where they form endometrial cups and are in close contact with maternal immune cells. Therefore, the microenvironment of invasive equine trophoblast cells has striking similarities to the microenvironment of trophoblast cells in hemochorial placentas, suggesting that equine PSG-like CEACAMs and rodent and primate PSGs have undergone convergent evolution. This is supported by our finding that equine PSG-like CEACAM49 exhibits similar activity to certain rodent and human PSGs in a functional assay of platelet–fibrinogen binding. Our results have implications for understanding the evolution of PSGs and their functions in maternal–fetal interactions