Statistics on continuous IBD data: Exact distribution evaluation for a pair of full(half)-sibs and a pair of a (great-) grandchild with a (great-) grandparent

BACKGROUND: Pairs of related individuals are widely used in linkage analysis. Most of the tests for linkage analysis are based on statistics associated with identity by descent (IBD) data. The current biotechnology provides data on very densely packed loci, and therefore, it may provide almost continuous IBD data for pairs of closely related individuals. Therefore, the distribution theory for statistics on continuous IBD data is of interest. In particular, distributional results which allow the evaluation of p-values for relevant tests are of importance. RESULTS: A technology is provided for numerical evaluation, with any given accuracy, of the cumulative probabilities of some statistics on continuous genome data for pairs of closely related individuals. In the case of a pair of full-sibs, the following statistics are considered: (i) the proportion of genome with 2 (at least 1) haplotypes shared identical-by-descent (IBD) on a chromosomal segment, (ii) the number of distinct pieces (subsegments) of a chromosomal segment, on each of which exactly 2 (at least 1) haplotypes are shared IBD. The natural counterparts of these statistics for the other relationships are also considered. Relevant Maple codes are provided for a rapid evaluation of the cumulative probabilities of such statistics. The genomic continuum model, with Haldane's model for the crossover process, is assumed. CONCLUSIONS: A technology, together with relevant software codes for its automated implementation, are provided for exact evaluation of the distributions of relevant statistics associated with continuous genome data on closely related individuals

Born-Infeld black holes coupled to a massive scalar field

Born-Infeld black holes in the Scalar-Tensor Theories of Gravity, in the case of massless scalar field, have been recently obtained. The aim of the current paper is to study the effect from the inclusion of a potential for the scalar field in the theory, through a combination of analytical techniques and numerical methods. The black holes coupled to a massive scalar field have richer causal structure in comparison to the massless scalar field case. In the latter case, the black holes may have a second, inner horizon. The presence of potential for the scalar field allows the existence of extremal black holes for certain values of the mass of the scalar field and the magnetic (electric) charge of the black hole. The linear stability against spherically symmetric perturbations is studied. Arguments in favor of the general stability of the solutions coming from the application of the "turning point" method are also presented.Comment: 26 pages, 16 figure

Quantitative Photo-acoustic Tomography with Partial Data

Photo-acoustic tomography is a newly developed hybrid imaging modality that combines a high-resolution modality with a high-contrast modality. We analyze the reconstruction of diffusion and absorption parameters in an elliptic equation and improve an earlier result of Bal and Uhlmann to the partial date case. We show that the reconstruction can be uniquely determined by the knowledge of 4 internal data based on well-chosen partial boundary conditions. Stability of this reconstruction is ensured if a convexity condition is satisfied. Similar stability result is obtained without this geometric constraint if 4n well-chosen partial boundary conditions are available, where $n$ is the spatial dimension. The set of well-chosen boundary measurements is characterized by some complex geometric optics (CGO) solutions vanishing on a part of the boundary.Comment: arXiv admin note: text overlap with arXiv:0910.250

Automated Exploration and Implementation of Distributed CNN Inference at the Edge

For model inference of Convolutional Neural Networks (CNNs), we nowadays witness a shift from the Cloud to the Edge. Unfortunately, deploying and inferring large, compute-and-memory-intensive CNNs on Internet-of-Things devices at the Edge is challenging as they typically have limited resources. One approach to address this challenge is to leverage all available resources across multiple edge devices to execute a large CNN byproperly partitioning it and running each CNN partition on a separate edge device. However, there currently does not exist a design and programming framework that takes a trained CNN model as input and subsequently allows for efficiently exploring and automatically implementing a range of different CNN partitions on multiple edge devices to facilitate distributed CNN inference. Therefore, in this paper, we propose a novel framework that automates the splitting of a CNN model into a set of sub-models as well as the code generation needed for the distributed and collaborative execution of these sub-models on multiple, possibly heterogeneous, edge devices, while supporting the exploitation of parallelism among and within the edge devices. In addition, since the number of different CNN mapping possibilities on multiple edge devices is vast, our framework also features a multi-stage and hierarchical Design Space Exploration methodology to efficiently search for (near-)optimal distributed CNN inference implementations. Our experimental results demonstrate that our work allows for rapidly finding and realizing distributed CNN inference implementations with reduced energy consumption and memory usage per edge device, and under certain conditions, with improved system throughput as well

Hierarchical Design Space Exploration for Distributed CNN Inference at the Edge

Convolutional Neural Network (CNN) models for modern applications are becoming increasingly deep and complex. Thus, the number of different CNN mapping possibilities when deploying a CNN model on multiple edge devices is vast. Design Space Exploration (DSE) methods are therefore essential to find a set of optimal CNN mappings subject to one or more design requirements. In this paper, we present an efficient DSE methodology to find (near-)optimal CNN mappings for distributed inference at the edge. To deal with the vast design space of different CNN mappings, we accelerate the searching process by proposing and utilizing a multi-stage hierarchical DSE approach together with a tailored Genetic Algorithm as the underlying search engine

Case Study on Limits and Consequences of Agricultural Technologies in the North-East Region of the People's Republic of Bulgaria

The Food and Agriculture Program at IIASA focuses its research activities on understanding the nature and dimension of the world's food situation and problems, on exploring possible alternative policies which could improve the present situation in the short and long term, and on investigating the consequences of such policies at various levels -- global, national and regional -- and in various time horizons. One part of the research activities focussed on investigations of alternative paths of technology transformation in agriculture with respect to resource limitations and environmental consequences in the long term. The general approach and methodology developed for this investigation is being applied in several case studies on the regional level. The reason for the studies is not only to validate the general methodology but also to develop an applicable tool for detailed investigations for a particular region which could then be applied on a number of similar regions. Furthermore, some specific aspects are being addressed in all these case studies which has been initiated within the IIASA's Food and Agriculture Program. This will allow the behavior of various systems to be compared, according to the selected aspects, and analyzed (in different social, economic and natural resource conditions) according to the selected aspects. One of the case studies is being carried out for the north-east region of Bulgaria. This paper describes the first phase of the study, the problem identification, the formulation of goals, and the basic methodological framework

Scenario Based Run-time Switching for Adaptive CNN-based Applications at the Edge

Convolutional Neural Networks (CNNs) are biologically inspired computational models that are at the heart of many modern computer vision and natural language processing applications. Some of the CNN-based applications are executed on mobile and embedded devices. Execution of CNNs on such devices places numerous demands on the CNNs, such as high accuracy, high throughput, low memory cost, and low energy consumption. These requirements are very difficult to satisfy at the same time, so CNN execution at the edge typically involves trade-offs (e.g., high CNN throughput is achieved at the cost of decreased CNN accuracy). In existing methodologies, such trade-offs are either chosen once and remain unchanged during a CNN-based application execution, or are adapted to the properties of the CNN input data. However, the application needs can also be significantly affected by the changes in the application environment, such as a change of the battery level in the edge device. Thus, CNN-based applications need a mechanism that allows to dynamically adapt their characteristics to the changes in the application environment at run-time. Therefore, in this article, we propose a scenario-based run-time switching (SBRS) methodology, that implements such a mechanism

Path ORAM: An Extremely Simple Oblivious RAM Protocol

We present Path ORAM, an extremely simple Oblivious RAM protocol with a small amount of client storage. Partly due to its simplicity, Path ORAM is the most practical ORAM scheme known to date with small client storage. We formally prove that Path ORAM has a O(log N) bandwidth cost for blocks of size B = Omega(log^2 N) bits. For such block sizes, Path ORAM is asymptotically better than the best known ORAM schemes with small client storage. Due to its practicality, Path ORAM has been adopted in the design of secure processors since its proposal