322,738 research outputs found
A study of set-sharing analysis via cliques
We study the problem of efficient, scalable set-sharing analysis of logic
programs. We use the idea of representing sharing information as a pair of
abstract substitutions, one of which is a worst-case sharing representation
called a clique set, which was previously proposed for the case of inferring
pair-sharing. We use the clique-set representation for (1) inferring actual
set-sharing information, and (2) analysis within a top-down framework. In
particular, we define the abstract functions required by standard top-down
analyses, both for sharing alone and also for the case of including freeness in
addition to sharing. Our experimental evaluation supports the conclusion that,
for inferring set-sharing, as it was the case for inferring pair-sharing,
precision losses are limited, while useful efficiency gains are obtained. At
the limit, the clique-set representation allowed analyzing some programs that
exceeded memory capacity using classical sharing representations.Comment: 15 pages, 0 figure
Evaluating openEHR for storing computable representations of electronic health record phenotyping algorithms
Electronic Health Records (EHR) are data generated during routine clinical
care. EHR offer researchers unprecedented phenotypic breadth and depth and have
the potential to accelerate the pace of precision medicine at scale. A main EHR
use-case is creating phenotyping algorithms to define disease status, onset and
severity. Currently, no common machine-readable standard exists for defining
phenotyping algorithms which often are stored in human-readable formats. As a
result, the translation of algorithms to implementation code is challenging and
sharing across the scientific community is problematic. In this paper, we
evaluate openEHR, a formal EHR data specification, for computable
representations of EHR phenotyping algorithms.Comment: 30th IEEE International Symposium on Computer-Based Medical Systems -
IEEE CBMS 201
Architecture for dual-mode quadruple precision floating point adder
This paper presents a configurable dual-mode architecture for floating point (F.P.) adder. The architecture (named as QPdDP) works in dual-mode which can operates either for quadruple precision or dual (two-parallel) double precision. The architecture follows the standard state-of-the-art flow for floating point adder. It is aimed for the computation of normal as well as sub-normal operands, along with the support for the exceptional case handling. The key sub-components in the architecture are re-designed & optimized for on-the-fly dual-mode processing, which enables efficient resource sharing for dual precision operands. The data-path is optimized for minimal multiplexing circuitry overhead. The presented dual- mode architecture provide SIMD support for double precision operands, along with high (quadruple) precision support. The proposed architecture is synthesized using UMC 90nm technology ASIC implementation. It is compared with the best available literature works, and have shown better design metrics in terms of area, period and area × period, along with more computational support.published_or_final_versio
Spot the difference: Comparing results of analyses from real patient data and synthetic derivatives
BACKGROUND: Synthetic data may provide a solution to researchers who wish to generate and share data in support of precision healthcare. Recent advances in data synthesis enable the creation and analysis of synthetic derivatives as if they were the original data; this process has significant advantages over data deidentification.
OBJECTIVES: To assess a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel) for its ability to produce data that can be used for research purposes while obviating privacy and confidentiality concerns.
METHODS: We explored three use cases and tested the robustness of synthetic data by comparing the results of analyses using synthetic derivatives to analyses using the original data using traditional statistics, machine learning approaches, and spatial representations of the data. We designed these use cases with the purpose of conducting analyses at the observation level (Use Case 1), patient cohorts (Use Case 2), and population-level data (Use Case 3).
RESULTS: For each use case, the results of the analyses were sufficiently statistically similar (
DISCUSSION AND CONCLUSION: This article presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in clinical research for faster insights and improved data sharing in support of precision healthcare
Enhanced sharing analysis techniques: a comprehensive evaluation
Sharing, an abstract domain developed by D. Jacobs and A. Langen for the analysis of logic
programs, derives useful aliasing information. It is well-known that a commonly used core
of techniques, such as the integration of Sharing with freeness and linearity information, can
significantly improve the precision of the analysis. However, a number of other proposals for
refined domain combinations have been circulating for years. One feature that is common
to these proposals is that they do not seem to have undergone a thorough experimental
evaluation even with respect to the expected precision gains.
In this paper we experimentally
evaluate: helping Sharing with the definitely ground variables found using Pos, the domain
of positive Boolean formulas; the incorporation of explicit structural information; a full
implementation of the reduced product of Sharing and Pos; the issue of reordering the
bindings in the computation of the abstract mgu; an original proposal for the addition of
a new mode recording the set of variables that are deemed to be ground or free; a refined
way of using linearity to improve the analysis; the recovery of hidden information in the
combination of Sharing with freeness information. Finally, we discuss the issue of whether
tracking compoundness allows the computation of more sharing information
Dynamic Geospatial Spectrum Modelling: Taxonomy, Options and Consequences
Much of the research in Dynamic Spectrum Access (DSA) has focused on opportunistic access in the temporal domain. While this has been quite useful in establishing the technical feasibility of DSA systems, it has missed large sections of the overall DSA problem space. In this paper, we argue that the spatio-temporal operating context of specific environments matters to the selection of the appropriate technology for learning context information. We identify twelve potential operating environments and compare four context awareness approaches (on-board sensing, databases, sensor networks, and cooperative sharing) for these environments. Since our point of view is overall system cost and efficiency, this analysis has utility for those regulators whose objectives are reducing system costs and enhancing system efficiency. We conclude that regulators should pay attention to the operating environment of DSA systems when determining which approaches to context learning to encourage
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