A Proof-Producing Compiler for Blockchain Applications

Cairo is a programming language for running decentralized applications (dapps) at scale. Programs written in the Cairo language are compiled to machine code for the Cairo CPU architecture, and cryptographic protocols are used to verify the results of the execution traces efficiently on blockchain. We explain how we have extended the Cairo compiler with tooling that enables users to prove, in the Lean 3 proof assistant, that compiled code satisfies high-level functional specifications. We demonstrate the success of our approach by verifying primitives for computations with an elliptic curve over a large finite field, as well as their use in the validation of cryptographic signatures

Progression Analysis and Stage Discovery in Continuous Physiological Processes Using Image Computing

We propose an image computing-based method for quantitative analysis of continuous physiological processes that can be sensed by medical imaging and demonstrate its application to the analysis of morphological alterations of the bone structure, which correlate with the progression of osteoarthritis (OA). The purpose of the analysis is to quantitatively estimate OA progression in a fashion that can assist in understanding the pathophysiology of the disease. Ultimately, the texture analysis will be able to provide an alternative OA scoring method, which can potentially reflect the progression of the disease in a more direct fashion compared to the existing clinically utilized classification schemes based on radiology. This method can be useful not just for studying the nature of OA, but also for developing and testing the effect of drugs and treatments. While in this paper we demonstrate the application of the method to osteoarthritis, its generality makes it suitable for the analysis of other progressive clinical conditions that can be diagnosed and prognosed by using medical imaging

Cairo – a Turing-complete STARK-friendly CPU architecture

Proof systems allow one party to prove to another party that a certain statement is true. Most existing practical proof systems require that the statement will be represented in terms of polynomial equations over a finite field. This makes the process of representing a statement that one wishes to prove or verify rather complicated, as this process requires a new set of equations for each statement. Various approaches to deal with this problem have been proposed. We present Cairo, a practically-efficient Turing-complete STARK-friendly CPU architecture. We describe a single set of polynomial equations for the statement that the execution of a program on this architecture is valid. Given a statement one wishes to prove, Cairo allows writing a program that describes that statement, instead of writing a set of polynomial equations

Wndchrm – an open source utility for biological image analysis

<p>Abstract</p> <p>Background</p> <p>Biological imaging is an emerging field, covering a wide range of applications in biological and clinical research. However, while machinery for automated experimenting and data acquisition has been developing rapidly in the past years, automated image analysis often introduces a bottleneck in high content screening.</p> <p>Methods</p> <p><it>Wndchrm </it>is an open source utility for biological image analysis. The software works by first extracting image content descriptors from the raw image, image transforms, and compound image transforms. Then, the most informative features are selected, and the feature vector of each image is used for classification and similarity measurement.</p> <p>Results</p> <p><it>Wndchrm </it>has been tested using several publicly available biological datasets, and provided results which are favorably comparable to the performance of task-specific algorithms developed for these datasets. The simple user interface allows researchers who are not knowledgeable in computer vision methods and have no background in computer programming to apply image analysis to their data.</p> <p>Conclusion</p> <p>We suggest that <it>wndchrm </it>can be effectively used for a wide range of biological image analysis tasks. Using <it>wndchrm </it>can allow scientists to perform automated biological image analysis while avoiding the costly challenge of implementing computer vision and pattern recognition algorithms.</p

Gene Expression in the Rodent Brain is Associated with Its Regional Connectivity

The putative link between gene expression of brain regions and their neural connectivity patterns is a fundamental question in neuroscience. Here this question is addressed in the first large scale study of a prototypical mammalian rodent brain, using a combination of rat brain regional connectivity data with gene expression of the mouse brain. Remarkably, even though this study uses data from two different rodent species (due to the data limitations), we still find that the connectivity of the majority of brain regions is highly predictable from their gene expression levels–the outgoing (incoming) connectivity is successfully predicted for 73% (56%) of brain regions, with an overall fairly marked accuracy level of 0.79 (0.83). Many genes are found to play a part in predicting both the incoming and outgoing connectivity (241 out of the 500 top selected genes, p-value<1e-5). Reassuringly, the genes previously known from the literature to be involved in axon guidance do carry significant information about regional brain connectivity. Surveying the genes known to be associated with the pathogenesis of several brain disorders, we find that those associated with schizophrenia, autism and attention deficit disorder are the most highly enriched in the connectivity-related genes identified here. Finally, we find that the profile of functional annotation groups that are associated with regional connectivity in the rodent is significantly correlated with the annotation profile of genes previously found to determine neural connectivity in C. elegans (Pearson correlation of 0.24, p<1e-6 for the outgoing connections and 0.27, p<1e-5 for the incoming). Overall, the association between connectivity and gene expression in a specific extant rodent species' brain is likely to be even stronger than found here, given the limitations of current data