Optimal software-implemented Itoh--Tsujii inversion for GF(2m2^m)

Field inversion in GF($2^m$) dominates the cost of modern software implementations of certain elliptic curve cryptographic operations, such as point encoding/hashing into elliptic curves. Itoh--Tsujii inversion using a polynomial basis and precomputed table-based multi-squaring has been demonstrated to be highly effective for software implementations, but the performance and memory use depend critically on the choice of addition chain and multi-squaring tables, which in prior work have been determined only by suboptimal ad-hoc methods and manual selection. We thoroughly investigated the performance/memory tradeoff for table-based linear transforms used for efficient multi-squaring. Based upon the results of that investigation, we devised a comprehensive cost model for Itoh--Tsujii inversion and a corresponding optimization procedure that is empirically fast and provably finds globally-optimal solutions. We tested this method on 8 binary fields commonly used for elliptic curve cryptography; our method found lower-cost solutions than the ad-hoc methods used previously, and for the first time enables a principled exploration of the time/memory tradeoff of inversion implementations

Agenda for Translating Physical Activity, Nutrition, and Weight Management Interventions for Cancer Survivors into Clinical and Community Practice.

Evidence supporting physical activity, diet, and weight management for cancer survivors has grown, leading to the development of guidelines and interventions. The next step is to identify necessary practice and policy changes and to develop a research agenda to inform how interventions can be delivered to survivors most effectively and efficiently in health care settings and by community-based organizations. Here, an agenda is proposed for research, practice, and policy that incorporates recommendations for a range of programming options, a patient-centered, tailored screening and referral approach, and training needs for survivorship care providers and providers of exercise, nutrition, and weight management services. Research needs to focus on sustainability, dissemination, and implementation. Needed policy changes are presented, as well as opportunities to leverage current health care policies

Moving Through Cancer: Setting the Agenda to Make Exercise Standard in Oncology Practice

International evidence?based guidelines support the prescription of exercise for all individuals living with and beyond cancer.This article describes the agenda of the newly formed Moving Through Cancer initiative, which has a primary objective of making exercise standard practice in oncology by 2029

A connectome of the adult drosophila central brain

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions. Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain

Machine Learning and Optimization for Neural Circuit Reconstruction

Mapping neuroanatomy, in the pursuit of linking hypothesized computational models consistent with observed functions to the actual physical structures, has been a long-standing fundamental problem in neuroscience. One primary interest is in mapping the network structure of neural circuits by identifying the morphology of each neuron and the locations of synaptic connections between neurons, a field of study called connectomics. Currently, the most promising approach for obtaining such maps of neural circuit structure is volume electron microscopy of a stained and fixed block of tissue.While recent advances in volume electron microscopy make feasible the imaging of very large circuits at sufficient resolution to discern even the smallest neuronal processes, image analysis remains a key challenge limiting the rate of discovery. Existing fully-automated algorithms offer inadequate accuracy to replace human annotators, and semi-automated methods offer only limited speedup. Towards addressing this image analysis problem, we designed, implemented, and evaluated novel methods based on machine learning and optimization related to three different sub-problems:Detection of cell boundaries at the per-voxel level is a key analysis step, given that cell boundaries serve as the primary indication of cell morphology, We propose a highly-scalable, layered architecture for classification on 3-D volumes: unlike conventional dense deep learning approaches, this architecture relies on simple, parallelizable clustering algorithms and convex optimization to learn wide, sparse models. By exploiting rotational invariance of the data distribution and a highly-efficient distributed GPU implementation, we achieved performance comparable to or better than deep convolutional networks trained for weeks with only several hours of training, enabling much faster iteration on model design.Certain promising high-throughput microscopy techniques result in significant discontinuities between section images even after alignment, due to variations in imaging conditions and section thickness, among other artifacts. These artifacts impede truly 3-D analysis of these volumes. We propose an iterative coarse-to-fine procedure that optimizes the parameters of spatially vary linear transformations of the intensity data in order to minimize discontinuities along the section axis, subject to detail-preserving regularization. Testing showed this technique to yield significant quantitative improvement in image quality, and qualitatively corrected essentially all visible discontinuities without any noticeable loss of detail; it also significantly improved 3-D segmentation accuracy.To integrate higher-level prior information about shape, we introduce a new machine learning approach for image segmentation, based on a joint energy model over image features and novel local binary shape descriptors. These descriptors compactly represent rich shape information at multiple scales, including interactions between multiple objects. Our approach reflects the inherent combinatorial nature of dense image segmentation problems. We propose efficient algorithms for learning deep neural networks to model the joint energy, and for local optimization of this energy in the space of supervoxel agglomerations. This architecture yields state-of-the-art performance on several challenging electron microscopy datasets.These advances constitute critical progress towards fully-automated reconstruction of circuits of hundreds of thousands of neurons

Machine Learning and Optimization for Neural Circuit Reconstruction

Mapping neuroanatomy, in the pursuit of linking hypothesized computational models consistent with observed functions to the actual physical structures, has been a long-standing fundamental problem in neuroscience. One primary interest is in mapping the network structure of neural circuits by identifying the morphology of each neuron and the locations of synaptic connections between neurons, a field of study called connectomics. Currently, the most promising approach for obtaining such maps of neural circuit structure is volume electron microscopy of a stained and fixed block of tissue.While recent advances in volume electron microscopy make feasible the imaging of very large circuits at sufficient resolution to discern even the smallest neuronal processes, image analysis remains a key challenge limiting the rate of discovery. Existing fully-automated algorithms offer inadequate accuracy to replace human annotators, and semi-automated methods offer only limited speedup. Towards addressing this image analysis problem, we designed, implemented, and evaluated novel methods based on machine learning and optimization related to three different sub-problems:Detection of cell boundaries at the per-voxel level is a key analysis step, given that cell boundaries serve as the primary indication of cell morphology, We propose a highly-scalable, layered architecture for classification on 3-D volumes: unlike conventional dense deep learning approaches, this architecture relies on simple, parallelizable clustering algorithms and convex optimization to learn wide, sparse models. By exploiting rotational invariance of the data distribution and a highly-efficient distributed GPU implementation, we achieved performance comparable to or better than deep convolutional networks trained for weeks with only several hours of training, enabling much faster iteration on model design.Certain promising high-throughput microscopy techniques result in significant discontinuities between section images even after alignment, due to variations in imaging conditions and section thickness, among other artifacts. These artifacts impede truly 3-D analysis of these volumes. We propose an iterative coarse-to-fine procedure that optimizes the parameters of spatially vary linear transformations of the intensity data in order to minimize discontinuities along the section axis, subject to detail-preserving regularization. Testing showed this technique to yield significant quantitative improvement in image quality, and qualitatively corrected essentially all visible discontinuities without any noticeable loss of detail; it also significantly improved 3-D segmentation accuracy.To integrate higher-level prior information about shape, we introduce a new machine learning approach for image segmentation, based on a joint energy model over image features and novel local binary shape descriptors. These descriptors compactly represent rich shape information at multiple scales, including interactions between multiple objects. Our approach reflects the inherent combinatorial nature of dense image segmentation problems. We propose efficient algorithms for learning deep neural networks to model the joint energy, and for local optimization of this energy in the space of supervoxel agglomerations. This architecture yields state-of-the-art performance on several challenging electron microscopy datasets.These advances constitute critical progress towards fully-automated reconstruction of circuits of hundreds of thousands of neurons

Einfuehrung in die Softwareentwicklung mit C

SIGLETIB: RN 3147 (99) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman