An optimal approach for low-power migraine prediction models in the state-of-the-art wireless monitoring devices

Host immunogenetic factors can affect late complications of urogenital infections with Chlamydia trachomatis. These findings are creating new avenues for updating existing risk prediction models for C. trachomatis-associated tubal factor infertility (TFI). Research into host factors and its utilization may therefore have future implications for diagnosing C. trachomatis-induced infertility. We outline the epidemiological situation regarding C. trachomatis and TFI in high-income countries. Thereupon, we review the main characteristics of the population undergoing fertility work-up and identify screening and diagnostic strategies for TFI currently in place. The Netherlands is an exemplary model for the state of the art in high-income countries. Within the framework of existing clinical approaches, we propose a scenario for the translation of relevant genome-based information into triage of infertile women, with the objective of implementing genetic profiling in the routine investigation of TFI. Furthermore, we describe the state of the art in relevant gene- and single nucleotide polymorphism (SNP) based clinical prediction models and place our perspectives in the context of these applications. We conclude that the introduction of a genetic test of proven validity into the assessment of TFI should help reduce patient burden from invasive and costly examinations by achieving a more precise risk stratification.

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THIN FLEXIBLE POLYMER-BASED ENERGY SYSTEMS FOR LOW-POWER WIRELESS MONITORING DEVICES

Proceedings of the International Conference on Biomedical Electronics and Devices ◽

10.5220/0003793802390244 ◽

2012 ◽

Keyword(s):

Low Power ◽

Energy Systems ◽

Wireless Monitoring ◽

Flexible Polymer ◽

Monitoring Devices

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What is the state of the art in commercial EDA tools for low power?

Proceedings of 1996 International Symposium on Low Power Electronics and Design ◽

10.1109/lpe.1996.547503 ◽

2002 ◽

Cited By ~ 4

Author(s):

O. Coudert ◽

R. Haddad ◽

K. Keutzer

Keyword(s):

Low Power ◽

State Of The Art ◽

The State ◽

Eda Tools

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Convolver Design and Convolve-Accumulate Unit Design for Low-Power Edge Computing

Sensors ◽

10.3390/s21155081 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5081

Author(s):

Hsu-Yu Kao ◽

Xin-Jia Chen ◽

Shih-Hsu Huang

Keyword(s):

Power Consumption ◽

Low Power ◽

State Of The Art ◽

Clock Cycle ◽

The State ◽

Edge Computing ◽

Convolution Operation ◽

Product Matrix ◽

Unit Design ◽

Overall Performance

Convolution operations have a significant influence on the overall performance of a convolutional neural network, especially in edge-computing hardware design. In this paper, we propose a low-power signed convolver hardware architecture that is well suited for low-power edge computing. The basic idea of the proposed convolver design is to combine all multipliers’ final additions and their corresponding adder tree to form a partial product matrix (PPM) and then to use the reduction tree algorithm to reduce this PPM. As a result, compared with the state-of-the-art approach, our convolver design not only saves a lot of carry propagation adders but also saves one clock cycle per convolution operation. Moreover, the proposed convolver design can be adapted for different dataflows (including input stationary dataflow, weight stationary dataflow, and output stationary dataflow). According to dataflows, two types of convolve-accumulate units are proposed to perform the accumulation of convolution results. The results show that, compared with the state-of-the-art approach, the proposed convolver design can save 15.6% power consumption. Furthermore, compared with the state-of-the-art approach, on average, the proposed convolve-accumulate units can reduce 15.7% power consumption.

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A Multi-Scale Approach for Graph Link Prediction

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5731 ◽

2020 ◽

Vol 34 (04) ◽

pp. 3308-3315 ◽

Cited By ~ 1

Author(s):

Lei Cai ◽

Shuiwang Ji

Keyword(s):

Neural Networks ◽

Link Prediction ◽

Prediction Models ◽

State Of The Art ◽

The State ◽

Graph Structure ◽

Scale Invariant ◽

Multi Scale ◽

Heuristic Learning ◽

Graph Neural Networks

Deep models can be made scale-invariant when trained with multi-scale information. Images can be easily made multi-scale, given their grid-like structures. Extending this to generic graphs poses major challenges. For example, in link prediction tasks, inputs are represented as graphs consisting of nodes and edges. Currently, the state-of-the-art model for link prediction uses supervised heuristic learning, which learns graph structure features centered on two target nodes. It then learns graph neural networks to predict the existence of links based on graph structure features. Thus, the performance of link prediction models highly depends on graph structure features. In this work, we propose a novel node aggregation method that can transform the enclosing subgraph into different scales and preserve the relationship between two target nodes for link prediction. A theory for analyzing the information loss during the re-scaling procedure is also provided. Graphs in different scales can provide scale-invariant information, which enables graph neural networks to learn invariant features and improve link prediction performance. Our experimental results on 14 datasets from different areas demonstrate that our proposed method outperforms the state-of-the-art methods by employing multi-scale graphs without additional parameters.

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Practical Picture Processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051700 ◽

1974 ◽

Vol 32 ◽

pp. 338-339

Author(s):

T. A. Welton

Keyword(s):

Radiation Damage ◽

Coherence Length ◽

Spatial Information ◽

State Of The Art ◽

Coherent Radiation ◽

The State ◽

Energy Spread ◽

Electron Micrograph ◽

Picture Processing ◽

Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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