Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus

In systems of multiple agents, identifying the cause of observed agent dynamics is challenging. Often, these agents operate in diverse, non-stationary environments, where models rely on hand-crafted environment-specific features to infer influential regions in the system's surroundings. To overcome the limitations of these inflexible models, we present GP-LAPLACE, a technique for locating sources and sinks from trajectories in time-varying fields. Using Gaussian processes, we jointly infer a spatio-temporal vector field, as well as canonical vector calculus operations on that field. Notably, we do this from only agent trajectories without requiring knowledge of the environment, and also obtain a metric for denoting the significance of inferred causal features in the environment by exploiting our probabilistic method. To evaluate our approach, we apply it to both synthetic and real-world GPS data, demonstrating the applicability of our technique in the presence of multiple agents, as well as its superiority over existing methods.Comment: KDD '18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Pages 1254-1262, 9 pages, 5 figures, conference submission, University of Oxford. arXiv admin note: text overlap with arXiv:1709.0235

Experimental demonstration of a graph state quantum error-correction code

Scalable quantum computing and communication requires the protection of quantum information from the detrimental effects of decoherence and noise. Previous work tackling this problem has relied on the original circuit model for quantum computing. However, recently a family of entangled resources known as graph states has emerged as a versatile alternative for protecting quantum information. Depending on the graph's structure, errors can be detected and corrected in an efficient way using measurement-based techniques. In this article we report an experimental demonstration of error correction using a graph state code. We have used an all-optical setup to encode quantum information into photons representing a four-qubit graph state. We are able to reliably detect errors and correct against qubit loss. The graph we have realized is setup independent, thus it could be employed in other physical settings. Our results show that graph state codes are a promising approach for achieving scalable quantum information processing

The plant disease resistance gene Asc-1 prevents disruption of sphingolipid metabolism during AAL-toxin-induced programmed cell death

The nectrotrophic fungus Alternaria alternata f.sp. lycopersici infects tomato plants of the genotype asc/asc by utilizing a host-selective toxin, AAL-toxin, that kills the host cells by inducing programmed cell death. Asc-1 is homologous to genes found in most eukaryotes from yeast to humans, suggesting a conserved function. A yeast strain with deletions in the homologous genes LAG1 and LAC1 was functionally complemented by Asc-1, indicating that Asc-1 functions in an analogous manner to the yeast homologues. Examination of the yeast sphingolipids, which are almost absent in the lag1Deltalac1Delta mutant, showed that Asc-1 was able to restore the synthesis of sphingolipids. We therefore examined the biosynthesis of sphingolipids in tomato by labeling leaf discs with L-[3-H-3] serine. In the absence of AAL-toxin, there was no detectable difference in sphingolipid labeling between leaf discs from Asc/Asc or asc/asc leaves. In the presence of pathologically significant concentrations of AAL-toxin however, asc/asc leaf discs showed severely reduced labeling of sphingolipids and increased label in dihydrosphingosine (DHS) and 3-ketodihydrosphingosine (3-KDHS). Leaf discs from Asc/Asc leaves responded to AAL-toxin treatment by incorporating label into different sphingolipid species. The effects of AAL-toxin on asc/asc leaflets could be partially blocked by the simultaneous application of AAL-toxin and myriocin. Leaf discs simultaneously treated with AAL-toxin and myriocin showed no incorporation of label into sphingolipids or long-chain bases as expected. These results indicate that the presence of Asc-1 is able to relieve an AAL-toxin-induced block on sphingolipid synthesis that would otherwise lead to programmed cell death

Survival of entanglement in thermal states

We present a general sufficiency condition for the presence of multipartite entanglement in thermal states stemming from the ground state entanglement. The condition is written in terms of the ground state entanglement and the partition function and it gives transition temperatures below which entanglement is guaranteed to survive. It is flexible and can be easily adapted to consider entanglement for different splittings, as well as be weakened to allow easier calculations by approximations. Examples where the condition is calculated are given. These examples allow us to characterize a minimum gapping behavior for the survival of entanglement in the thermodynamic limit. Further, the same technique can be used to find noise thresholds in the generation of useful resource states for one-way quantum computing.Comment: 6 pages, 2 figures. Changes made in line with publication recommendations. Motivation and concequences of result clarified, with the addition of one more example, which applies the result to give noise thresholds for measurement based quantum computing. New author added with new result

Incorporating spatial correlations into multispecies mean-field models

In biology, we frequently observe different species existing within the same environment. For example, there are many cell types in a tumour, or different animal species may occupy a given habitat. In modeling interactions between such species, we often make use of the mean-field approximation, whereby spatial correlations between the locations of individuals are neglected. Whilst this approximation holds in certain situations, this is not always the case, and care must be taken to ensure the mean-field approximation is only used in appropriate settings. In circumstances where the mean-field approximation is unsuitable, we need to include information on the spatial distributions of individuals, which is not a simple task. In this paper, we provide a method that overcomes many of the failures of the mean-field approximation for an on-lattice volume-excluding birth-death-movement process with multiple species. We explicitly take into account spatial information on the distribution of individuals by including partial differential equation descriptions of lattice site occupancy correlations. We demonstrate how to derive these equations for the multispecies case and show results specific to a two-species problem. We compare averaged discrete results to both the mean-field approximation and our improved method, which incorporates spatial correlations. We note that the mean-field approximation fails dramatically in some cases, predicting very different behavior from that seen upon averaging multiple realizations of the discrete system. In contrast, our improved method provides excellent agreement with the averaged discrete behavior in all cases, thus providing a more reliable modeling framework. Furthermore, our method is tractable as the resulting partial differential equations can be solved efficiently using standard numerical techniques