526 research outputs found

    SEVEN: Deep Semi-supervised Verification Networks

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    Verification determines whether two samples belong to the same class or not, and has important applications such as face and fingerprint verification, where thousands or millions of categories are present but each category has scarce labeled examples, presenting two major challenges for existing deep learning models. We propose a deep semi-supervised model named SEmi-supervised VErification Network (SEVEN) to address these challenges. The model consists of two complementary components. The generative component addresses the lack of supervision within each category by learning general salient structures from a large amount of data across categories. The discriminative component exploits the learned general features to mitigate the lack of supervision within categories, and also directs the generative component to find more informative structures of the whole data manifold. The two components are tied together in SEVEN to allow an end-to-end training of the two components. Extensive experiments on four verification tasks demonstrate that SEVEN significantly outperforms other state-of-the-art deep semi-supervised techniques when labeled data are in short supply. Furthermore, SEVEN is competitive with fully supervised baselines trained with a larger amount of labeled data. It indicates the importance of the generative component in SEVEN.Comment: 7 pages, 2 figures, accepted to the 2017 International Joint Conference on Artificial Intelligence (IJCAI-17

    Monitoring the suitability of the fit of a lower-limb prosthetic socket using artificial neural network in commonly encountered walking conditions

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    Prosthetic sockets are still routinely designed without the aid of quantitative measurement, relying instead on the experience and skill of clinicians. Sockets remain the most common cause for complaint regarding the suitability of a prosthesis, and poor pressure distribution is implicated in many forms of unacceptable care outcomes. Monitoring pressure distribution has been effectively restricted to laboratory settings, and only limited work has examined conditions other than flat walking. In this work, a transtibial amputee completed static and dynamic tasks on flat ground, on slopes and with changes to prosthetic materials and alignment. This was achieved using a set of wireless measurement nodes and custom LabView and MATLAB code, using external strain measurements and a neural network to understand the internal pressure distribution. Future work will focus on modifying the software to be more user-friendly for a clinical operator, and in simplifying the required hardware. Although the system in its current form facilitated the desired measurements effectively, it required engineering support to function accurately. Improving the reliability and stability of the system will be necessary before routine use is possible

    Applying Ensemble Neural Networks to an Inverse Problem Solution to Prosthetic Socket Pressure Measurement

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    Ensemble neural networks are commonly used as a method to boost performance of artificial intelligence applications. By collating the response of multiple networks with differences in composition or training and hence a range of estimation error, an overall improvement in the appraisal of new problem data can be made. In this work, artificial neural networks are used as an inverse-problem solver to calculate the internal distribution of pressures on a lower limb prosthetic socket using information on the deformation of the external surface of the device. Investigation into the impact of noise injection was studied by changing the maximum noise alteration parameter and the differences in network composition by altering the variance around this maximum noise value. Results indicate that use of ensembles of networks provides a meaningful improvement in overall performance. RMS error expressed as a percentage of the total applied load was 3.86% for the best performing ensemble, compared to 5.32% for the mean performance of the networks making up that ensemble. Although noise injection resulted in an improvement in typical network estimates of load distribution, ensembles performed better with low noise and low variance between network training patterns. These results mean that ensembles have been implemented in the research tool under developmen

    Client-Level Coverage of Needle and Syringe Program and High-Risk Injection Behaviors: A Case Study of People Who Inject Drugs in Kermanshah, Iran

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    Background: Needle-syringe programs (NSP) have been running in Iran since 2002. However, the coverage of such program among the NSP clients at the individual level was not studies yet. This study aimed to determine the client coverage of NSP and its correlation with high injection-related risk behaviors. Methods: A cross-sectional survey was conducted in Kermanshah province, Iran, in 2014. 230 people who inject drugs (PWID) recruited from two drop-in centers (DICs) from April to September 2014, participated in a face-to-face interview to provide information related individual coverage of NSP, demographic characteristics, and injecting behaviors 30 days prior to the interview. Findings: Overall, the average of syringe coverage was 158% [95% confidence interval (CI) = 65.7-205.5], while 56% (95% CI = 40-97) have individual converge less than 100%. Needle/syringe sharing was significantly higher among individual with low NSP coverage [adjusted odds ratio (AOR) = 2.6, 95% CI = 1.3-6.2]. About 85% participants with coverage of less than 100% reported reuse of syringe within the last 30 days (AOR = 3.2, 95% CI = 1.4-7.7). Conclusion: PWID are different regarding their NSP individual-level converges. There are certain clusters of PWID, who do not receive sufficient number of syringes. Given that insufficient individual syringe coverage level is highly associated with injection risk behaviors, reasons for such low converge need to be assessed and addressed carefully

    The effect of on-site and outreach-based needle and syringe programs in people who inject drugs in Kermanshah, Iran

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    Background: Needle and syringe programs (NSPs) are widely used to reduce harms associated with drug injecting. This study assessed the effect of facility-based (on-site services at drop-in centre) and outreach models of NSP on injection risk behaviours. Methods: Self-reported data from 455 people who injected drugs (PWID) during 2014 in Kermanshah, Iran, were examined to measure demographic characteristics and risk behaviors. Self-reported and program data were also assessed to identify their main source of injection equipment. Participants were divided into three sub-groups: facility-based NSP users, outreach NSP users and non-users (comparison group). Coarsened exact matching was used to make the three groups statistically equivalent based on age, place of residence, education and income, and groups were compared regarding the proportion of borrowing or lending of syringes/cookers, reusing syringes and recent HIV testing. Results: Overall, 76% of participants reported any NSP service use during the two months prior to interview. Only 23% (95%CI: 17-27) reported outreach NSP as their main source of syringes. Using facility-based NSP significantly decreased recent syringe borrowing (OR: 0.27, 95%CI: 0.10-0.70), recent syringe reuse (OR: 0.38, 95%CI: 0.23-0.68) and increased recent HIV testing (OR: 2.60, 95%CI: 1.48-4.56). Similar effects were observed among outreach NSP users; in addition, the outreach NSP model significantly reduced the chance of lending syringes (OR: 0.31, 95%CI: 0.15-0.60), compared to facility-based NSP (OR: 1.25, 95%CI: 0.74-2.17). Conclusion: These findings suggest that the outreach NSP model is as effective as facility-based NSP in reducing injection risk behaviours and increasing the rate of HIV testing. Outreach NSP was even more effective than facility-based in reducing the lending of syringes to others. Scaling up outreach NSP is an effective intervention to further reduce transmission of HIV via needle sharing
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