Adaptive Partitioning for Large-Scale Dynamic Graphs

Abstract—In the last years, large-scale graph processing has gained increasing attention, with most recent systems placing particular emphasis on latency. One possible technique to improve runtime performance in a distributed graph processing system is to reduce network communication. The most notable way to achieve this goal is to partition the graph by minimizing the num-ber of edges that connect vertices assigned to different machines, while keeping the load balanced. However, real-world graphs are highly dynamic, with vertices and edges being constantly added and removed. Carefully updating the partitioning of the graph to reflect these changes is necessary to avoid the introduction of an extensive number of cut edges, which would gradually worsen computation performance. In this paper we show that performance degradation in dynamic graph processing systems can be avoided by adapting continuously the graph partitions as the graph changes. We present a novel highly scalable adaptive partitioning strategy, and show a number of refinements that make it work under the constraints of a large-scale distributed system. The partitioning strategy is based on iterative vertex migrations, relying only on local information. We have implemented the technique in a graph processing system, and we show through three real-world scenarios how adapting graph partitioning reduces execution time by over 50 % when compared to commonly used hash-partitioning. I

A differential approach to shape from polarization

© 2017. The copyright of this document resides with its authors. State-of-the-art formulations of the Shape from Polarisation problem consist of several steps based on merging physical principles that prevent this problem being described by a single mathematical framework. In addition, specular and diffuse reflections need to be separately considered, making the three-dimensional shape reconstruction not easily applicable to heterogeneous scenes consisting of different materials. In this work we derive a unified specular/diffuse reflection parametrisation of the Shape from Polarisation problem based on a linear partial differential equation capable of recovering the level-set of the surface. The inherent ambiguity of the Shape from Polarization problem becomes evident through the impossibility of reconstructing the whole surface with this differential approach. To overcome this limitation, we consider shading information elegantly embedding this new formulation into a two-lights calibrated photometric stereo approach. Thus we derive an albedo independent and well-posed differential model based on a system of hyperbolic PDEs capable of reconstructing the shape with no ambiguity. We validate the geometrical properties of the new differential model for the Shape from Polarisation problem using synthetic and real data by computing the isocontours of the shape under observation. Lastly, we show the suitability of this new model to elegantly fit into a variational solver that is able to provide 3D shape reconstructions from synthetic and real data

A single-lobe photometric stereo approach for heterogeneous material

Shape from shading with multiple light sources is an active research area, and a diverse range of approaches have been proposed in recent decades. However, devising a robust reconstruction technique still remains a challenging goal, as the image acquisition process is highly nonlinear. Recent Photometric Stereo variants rely on simplifying assumptions in order to make the problem solvable: light propagation is still commonly assumed to be uniform, and the Bidirectional Reflectance Distribution Function is assumed to be diffuse, with limited interest for specular materials. In this work, we introduce a well-posed formulation based on partial differential equations (PDEs) for a unified reflectance function that can model both diffuse and specular reflections. We base our derivation on ratio of images, which makes the model independent from photometric invariants and yields a well-posed differential problem based on a system of quasi-linear PDEs with discontinuous coefficients. In addition, we directly solve a differential problem for the unknown depth, thus avoiding the intermediate step of approximating the normal field. A variational approach is presented ensuring robustness to noise and outliers (such as black shadows), and this is confirmed with a wide range of experiments on both synthetic and real data, where we compare favorably to the state of the art.Roberto Mecca is a Marie Curie fellow of the “Istituto Nazionale di Alta Matematica” (Italy) for a project shared with University of Cambridge, Department of Engineering and the Department of Mathematics, University of Bologna

xDGP: A Dynamic Graph Processing System with Adaptive Partitioning

13 pagesMany real-world systems, such as social networks, rely on mining efficiently large graphs, with hundreds of millions of vertices and edges. This volume of information requires partitioning the graph across multiple nodes in a distributed system. This has a deep effect on performance, as traversing edges cut between partitions incurs a significant performance penalty due to the cost of communication. Thus, several systems in the literature have attempted to improve computational performance by enhancing graph partitioning, but they do not support another characteristic of real-world graphs: graphs are inherently dynamic, their topology evolves continuously, and subsequently the optimum partitioning also changes over time. In this work, we present the first system that dynamically repartitions massive graphs to adapt to structural changes. The system optimises graph partitioning to prevent performance degradation without using data replication. The system adopts an iterative vertex migration algorithm that relies on local information only, making complex coordination unnecessary. We show how the improvement in graph partitioning reduces execution time by over 50%, while adapting the partitioning to a large number of changes to the graph in three real-world scenarios

Region and volume dependencies in spectral linewidth assessed by 1H 2D chemical shift imaging in the monkey brain at 7T

High magnetic fields increase the sensitivity and spectral dispersion in MR spectroscopy. In contrast, spectral peaks are broadened in vivo at higher field strength due to stronger susceptibility-induced effects. Strategies to minimize the spectral linewidth are therefore of critical importance. In the present study, 1H 2D chemical shift imaging (CSI) at short echo time was performed in the macaque monkey brain at 7 T. Dependencies of spectral linewidth on the CSI voxel size were determined by data reconstruction at different spatial resolution. An overall linewidth narrowing at increased spatial resolution is shown and regional differences are demonstrated

Sharp wave-ripple complexes in a reduced model of the hippocampal CA3-CA1 network of the macaque monkey

Sharp wave-ripple complexes observed in the hippocampal CA1 local field potential (LFP) are thought to play a major role in memory reactivation, transfer and consolidation. SPW-Rs are known to result from a complex interplay between local and upstream hippocampal ensembles. However, the key mechanisms that underlie these events remain partly unknown. In this work, we introduce a reduced, but realistic multi-compartmental model of the macaque monkey´s hippocampal CA3-CA1 network. The model consists of two semi-linear layers, each consisting of two-compartmental pyramidal neurons and one-compartmental perisomatic-targeting basket cells. Connections in the network were modeled as AMPA synapses, based on physiological and anatomical data. Notably, while auto-association fibers were prevalent in CA3, CA1 connectivity -inspired by recent findings- implemented a "feedback and reciprocal inhibition", dominated by recurrent inhibition and pyramidal cells-interneurons synapses. SPW-R episodes emerge spontaneously in the CA1 subfield LFP (which is assumed proportional to transmembrane currents across all compartments and medium resistivity): Episodes of short-lived high-frequency oscillations (ripples, 80-180 Hz) on top of a massive dendritic depolarization (< 20 Hz) with visual and quantitative characteristics observed experimentally [1]. Concomitantly, the CA3 subfield LFP presents episodes of quasi-synchronous neuronal bursting in the form of gamma episodes (25-75 Hz). The model reveals a lower bound for the minimal network that may generate SPW-R activity, and predicts a large number of features of in vivo hippocampal recordings in macaque monkeys [1]. Spike-LFP coherence analysis in CA1 displays reliable synchrony of spiking activity in the ripple LFP frequency band, suggesting that modeled SPW-R episodes reflect a genuine network oscillatory regime. Interestingly, interneuronal firing shows coherence increases concomitant with the beginning and the end of the SPW-R event, together with increases over gamma frequencies. The model suggests that activity of both pyramidal neurons and interneurons is critical for the local genesis and dynamics of physiological SPW-R activity. Unlike other models, we found that it is interneuronal silence, not interneuronal firing that triggers these fast oscillatory events, in line with the fact that unbalanced excitability of selected pyramidal cells marks the beginning of single network episodes. Interneuronal silence quickly increases population firing of pyramidal cells. The interneuronal population activity increases with some latency due to the unbalanced excitatory drive, becoming pivotal to pyramidal cell activity, and further pacing pyramidal cells due to interneuronal fast kinetic properties. Our modeled data suggests that this effect is possibly mediated by a silencing-and-rebound-excitation mechanism, maintaining the frequency of the field oscillation bounded to the ripple range. The reduced model suggests a simple mechanism for the occurrence of SPW-Rs, in light of recent experimental evidence. We provide new insights into the dynamics of the hippocampal CA3-CA1 network during ripples, and the relation between neuronal circuits' activity at meso- and microscopic scales. Finally, our model exhibits characteristic cell type-specific activity that might be critical for the emergence of physiological SPW-R activity and therefore, for the formation of hippocampus-dependent memory representations

Semi-calibrated Near Field Photometric Stereo

3D reconstruction from shading information through Photometric Stereo is considered a very challenging problem in Computer Vision. Although this technique can potentially provide highly detailed shape recovery, its accuracy is critically dependent on a numerous set of factors among them the reliability of the light sources in emitting a constant amount of light. In this work, we propose a novel variational approach to solve the so called semi-calibrated near field Photometric Stereo problem, where the positions but not the brightness of the light sources are known. Additionally, we take into account realistic modeling features such as perspective viewing geometry and heterogeneous scene composition, containing both diffuse and specular objects. Furthermore, we also relax the point light source assumption that usually constraints the near field formulation by explicitly calculating the light attenuation maps. Synthetic experiments are performed for quantitative evaluation for a wide range of cases whilst real experiments provide comparisons, qualitatively outperforming the state of the art.EPSRC; Roberto Mecca is a Marie Curie Fellow of the Istituto Nazionale di Alta Matematica, Ital

Ready ... Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals

Near-field photometric stereo in ambient light

Shape recovery from shading information has recently regained importance due to the improvement towards making the Photometric Stereo technique more reliable in terms of appearance of reflective objects. However, although more advanced models have been lately proposed, 3D scanners based on this technology do not provide reliable reconstructions as long as the considered irradiance equation neglects any additive bias. Depending on the context, such bias assumes different physical meanings. For example, in murky water it is known as saturated backscattered effect or for acquisition in pure air medium it is known as ambient light. Although the theoretical part covers both cases, this work mostly focuses on the pure air acquisition case. Indeed, we present a new approach based on ratios of differences of images where an exhaustive set of physical features are tackled while dealing with Photometric Stereo acquisition with considerable importance for the ambient light. To the best of our knowledge, this is the first attempt to recover the shape from Photometric Stereo considering simultaneously perspective viewing geometry, non-linear light propagation, both specular and diffuse reflectance plus the additive bias of the ambient light. Proof of concept is provided by showing experimental results on synthetic and real data.Roberto Mecca was supported through a Marie Curie fellowship of the "Istituto Nazionale di Alta Matematica”, Italy

Willpower and Conscious Percept: Volitional Switching in Binocular Rivalry

When dissimilar images are presented to the left and right eyes, awareness switches spontaneously between the two images, such that one of the images is suppressed from awareness while the other is perceptually dominant. For over 170 years, it has been accepted that even though the periods of dominance are subject to attentional processes, we have no inherent control over perceptual switching. Here, we revisit this issue in response to evidence that top-down attention can target perceptually suppressed ‘vision for action’ representations in the dorsal stream. We investigated volitional control over rivalry between apparent motion (AM), drifting (DM) and stationary (ST) grating pairs. Observers demonstrated a remarkable ability to generate intentional switches in the AM and D conditions, but not in the ST condition. Corresponding switches in the pursuit direction of optokinetic nystagmus verified this finding objectively. We showed it is unlikely that intentional perceptual switches were triggered by saccadic eye movements, because their frequency was reduced substantially in the volitional condition and did not change around the time of perceptual switches. Hence, we propose that synergy between dorsal and ventral stream representations provides the missing link in establishing volitional control over rivalrous conscious percepts