Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease causing patients to quickly lose motor neurons. The disease is characterized by a fast functional impairment and ventilatory decline, leading most pat...
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Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease causing patients to quickly lose motor neurons. The disease is characterized by a fast functional impairment and ventilatory decline, leading most patients to die from respiratory failure. To estimate when patients should get ventilatory support, it is helpful to adequately profile the disease progression. For this purpose, we use dynamic Bayesian networks (DBNs), a machine learning model, that graphically represents the conditional dependencies among variables. However, the standard DBN framework only includes dynamic (time-dependent) variables, while most ALS datasets have dynamic and static (time-independent) observations. Therefore, we propose the sdtDBN framework, which learns optimal DBNs with static and dynamic variables. Besides learning DBNs from data, with polynomial-time complexity in the number of variables, the proposed framework enables the user to insert prior knowledge and to make inference in the learned DBNs. We use sdtDBNs to study the progression of 1214 patients from a Portuguese ALS dataset. First, we predict the values of every functional indicator in the patients' consultations, achieving results competitive with state-of-the-art studies. Then, we determine the influence of each variable in patients' decline before and after getting ventilatory support. This insightful information can lead clinicians to pay particular attention to specific variables when evaluating the patients, thus improving prognosis. The case study with ALS shows that sdtDBNs are a promising predictive and descriptive tool, which can also be applied to assess the progression of other diseases, given time-dependent and time-independent clinical observations.
Reducing power consumption through high-level synthesis has attracted a growing interest from researchers due to its large potential for power reduction. In this work we study functional unit binding (or module assign...
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ISBN:
(纸本)0780387368
Reducing power consumption through high-level synthesis has attracted a growing interest from researchers due to its large potential for power reduction. In this work we study functional unit binding (or module assignment) given a scheduled data flow graph under a dual-Vdd framework. We assume that each functional unit can be driven by a low Vdd or a high Vdd dynamically during run time to save dynamic power. We develop a polynomial-time optimal algorithm for assigning low Vdd to as many operations as possible under the resource and time constraint, and in the same time minimizing total switching activity through functional unit binding. Our algorithm shows consistent improvement over a design flow that separates voltage assignment from functional unit binding. We also change the initial scheduling to examine power-latency tradeoff scenarios. Experimental results show that we can achieve a 21% power reduction when latency bound is tight. When latency is relaxed by 10 to 100%, the power reduction is 31 to 73% compared to the synthesis results for the case of single high Vdd without latency relaxation. We also show comparison data of energy consumption under the same experimental setting.
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