Kernel Belief Propagation

We propose a nonparametric generalization of belief propagation, Kernel Belief Propagation (KBP), for pairwise Markov random fields. Messages are represented as functions in a reproducing kernel Hilbert space (RKHS), and message updates are simple linear operations in the RKHS. KBP makes none of the assumptions commonly required in classical BP algorithms: the variables need not arise from a finite domain or a Gaussian distribution, nor must their relations take any particular parametric form. Rather, the relations between variables are represented implicitly, and are learned nonparametrically from training data. KBP has the advantage that it may be used on any domain where kernels are defined (Rd, strings, groups), even where explicit parametric models are not known, or closed form expressions for the BP updates do not exist. The computational cost of message updates in KBP is polynomial in the training data size. We also propose a constant time approximate message update procedure by representing messages using a small number of basis functions. In experiments, we apply KBP to image denoising, depth prediction from still images, and protein configuration prediction: KBP is faster than competing classical and nonparametric approaches (by orders of magnitude, in some cases), while providing significantly more accurate results

CT and MRI fusion for postimplant prostate brachytherapy evaluation

Postoperative evaluation of prostate brachytherapy is typically performed using CT, which does not have sufficient soft tissue contrast for accurate anatomy delineation. MR-CT fusion enables more accurate localization of both anatomy and implanted radioactive seeds, and hence, improves the accuracy of postoperative dosimetry. We propose a method for automatic registration of MR and CT images without a need for manual initialization. Our registration method employs a point-to-volume registration scheme during which localized seeds in the CT images, produced by commercial treatment planning systems as part of the standard of care, are rigidly registered to preprocessed MRI images. We tested our algorithm on ten patient data sets and achieved an overall registration error of 1.6 ± 0.8 mm with a running time of less than 20s. With high registration accuracy and computational speed, and no need for manual intervention, our method has the potential to be employed in clinical applications

The 225-year precipitation variability inferred from tree-ring records in Shanxi Province, the North China, and its teleconnection with Indian summer monsoon

Understanding the interactions between the East Asian summer monsoon and Indian summer monsoon is a challenging task because of the insufficient proxy records. In this study, we reconstructed a 225-year precipitation record by combining ring widths of Pinus tabulaeformis and stable oxygen isotope ratios of Larix principis-rupprechtii using a multi-proxy dendroclimatology approach in the North China. The reconstructed record explained 51.9% of the variance in the observed precipitation during 1955–2003. The precipitation series could indicate the intensity of the East Asian summer monsoon. A spatial field analysis indicated that the series was strongly correlated with the reconstructed records of the surrounding area and a large part of the Indian subcontinent. The reconstructed records were significantly and positively correlated with All Indian Precipitation records (r = 0.32, n = 132, p < 0.001) and with a proxy of the Indian summer monsoon. These findings suggest that a persistent teleconnection exists between the reconstructed record and the Indian summer monsoon records from the past 225 years. The observed interannual synchronisation potentially resulted from the transport of partial water vapour from the Indian summer monsoon area to NC; however, this synchronisation could not be attributed to the El Nino-South Oscillation (ENSO). When considering an interdecadal time scale, the synchronisation with the North Atlantic Oscillation (NAO) has varied since 1779, implying that the NAO may serve as an additional atmospheric pattern that affects this teleconnectio

A Riemann solver at a junction compatible with a homogenization limit

We consider a junction regulated by a traffic lights, with n incoming roads and only one outgoing road. On each road the Phase Transition traffic model, proposed in [6], describes the evolution of car traffic. Such model is an extension of the classic Lighthill-Whitham-Richards one, obtained by assuming that different drivers may have different maximal speed. By sending to infinity the number of cycles of the traffic lights, we obtain a justification of the Riemann solver introduced in [9] and in particular of the rule for determining the maximal speed in the outgoing road.Comment: 19 page

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US

Support indigenous food system biocultural diversity

The Comment by Carol Zavaleta-Cortijo and colleagues1 was timely in emphasising the challenges faced by Indigenous peoples due to the combined effects of climate change, COVID-19, and longstanding inequities. Although pressure on Indigenous livelihoods is nothing new, current effects are extreme, both in terms of deaths due to the virus and disruptions to lifeways, including Indigenous food systems. Both the importance and also the vulnerability of Indigenous food systems, and therefore the obligation to “ensure that current decisions and development trajectories do not further jeopardise the resilience of Indigenous food systems, which have integral roles in the response of Indigenous populations to current and future pandemics and climatic changes”,1 should be highlighted in all pertinent policy and development arenas, including the Convention on Biological Diversity's Post-2020 Global Biodiversity Framework and upcoming UN Food Systems Summit, among many others. Our experiences in the Andean, Himalayan, and other mountainous regions offer the insight that Indigenous food system biocultural diversity provides the foundations for resilience. This diversity encompasses not only the many crop and livestock species, and their varieties and breeds, but also the wild organisms supporting and interacting with Indigenous agriculture and food. Traditional knowledge systems around this diversity provide health and sustainability solutions that are unique to place, but whose benefits are urgently needed globally.2, 3 The ongoing loss of this biocultural diversity and associated knowledge is a global tragedy. We draw hope from two ongoing movements regarding Indigenous food system biocultural diversity. First, Indigenous communities have organised around nurturing this diversity by sharing experiences and visions on food, health, climate adaptation, conservation, and livelihood generation with others facing similar threats around the world, through networks such as the International Network of Mountain Indigenous Peoples. Second, Indigenous communities are finding ways to engage on their own terms with national and international organisations and institutions about shared interests, on the basis of respect for Indigenous food system diversity and knowledge systems.4 In the aftermath of COVID-19 and ongoing efforts to adapt to and mitigate climate change, we heartily agree with Zavaleta-Cortijo and colleagues that food systems are essential to health and resilience in Indigenous communities. Moreover, as these biocultural processes embody the longest ongoing human experiences with the provision of food under environmental stresses, shocks, and extremes, we suggest that the world has much to learn from Indigenous food systems. Now, more than ever, what is needed is respect for diversity and for the knowledge systems that have both nurtured it and survived because of it

An Extended Polyanion Activation Surface in Insulin Degrading Enzyme

Insulin degrading enzyme (IDE) is believed to be the major enzyme that metabolizes insulin and has been implicated in the degradation of a number of other bioactive peptides, including amyloid beta peptide (Aβ), glucagon, amylin, and atrial natriuretic peptide. IDE is activated toward some substrates by both peptides and polyanions/anions, possibly representing an important control mechanism and a potential therapeutic target. A binding site for the polyanion ATP has previously been defined crystallographically, but mutagenesis studies suggest that other polyanion binding modes likely exist on the same extended surface that forms one wall of the substrate-binding chamber. Here we use a computational approach to define three potential ATP binding sites and mutagenesis and kinetic studies to confirm the relevance of these sites. Mutations were made at four positively charged residues (Arg 429, Arg 431, Arg 847, Lys 898) within the polyanion-binding region, converting them to polar or hydrophobic residues. We find that mutations in all three ATP binding sites strongly decrease the degree of activation by ATP and can lower basal activity and cooperativity. Computational analysis suggests conformational changes that result from polyanion binding as well as from mutating residues involved in polyanion binding. These findings indicate the presence of multiple polyanion binding modes and suggest the anion-binding surface plays an important conformational role in controlling IDE activity