240 research outputs found

    An adaptive fusion model for distributed detection systems with unequiprobable sources

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    In a traditional communication system, a single sensor such as a radar or a sonar is used to detect targets. Since the reliability of a single sensor is limited, distributed detection systems in which several sensors are employed simultaneously have received increasing attention in recent years. We consider a distributed detection system which consists of a number of independent local detectors and a fusion center. Chair and Varshney have derived an optimal decision rule for fusing decisions based on. the Baysian criterion. To implement such a rule, the probability of detection PD and the probability of false alarm PF for each local detector must be known. This thesis introduces an adaptive fusion model using the fusion result as a supervisor to estimate the PD and PF The fusion results are classified as reliable and unreliable . Reliable results will be used as a reference to update the weights in the fusion center. Unreliable results will be discarded. The thesis concludes with simulation results which conform to the analysis

    Origin of Weaker Fermi Level Pinning and Localized Interface States at Metal Silicide Schottky Barriers

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    The Schottky barriers of transition metal silicides on silicon are characterized by two anomalous features, a face dependence of Schottky barrier heights (SBHs) and a weaker than expected dependence of SBHs on work function or “weaker Fermi level pinning.” Density functional supercell calculations reported here find that these features arise from the occurrence of localized gap states at interfacial coordination defects, in addition to the usual metal-induced gap states (MIGSs), and these lead to pinning energies that increase sequentially across the Si gap from PtSi2 to YbSi2. The interfacial gap states vary in shape with face orientation and cause the unusual face-dependent SBHs. The localized interface defect states are a key missing addition to the MIGS model, which are needed to describe fully the interface bonding such as face orientation or coordination defects. This anomalous Fermi level pinning does not reduce gap state densities but could be used to better control SBHs by creating specific configurations with near band edge pinning energies, thus giving low contact resistances in highly scaled silicon devices or 2D semiconductors

    Understanding the Impact of Adversarial Robustness on Accuracy Disparity

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    While it has long been empirically observed that adversarial robustness may be at odds with standard accuracy and may have further disparate impacts on different classes, it remains an open question to what extent such observations hold and how the class imbalance plays a role within. In this paper, we attempt to understand this question of accuracy disparity by taking a closer look at linear classifiers under a Gaussian mixture model. We decompose the impact of adversarial robustness into two parts: an inherent effect that will degrade the standard accuracy on all classes due to the robustness constraint, and the other caused by the class imbalance ratio, which will increase the accuracy disparity compared to standard training. Furthermore, we also show that such effects extend beyond the Gaussian mixture model, by generalizing our data model to the general family of stable distributions. More specifically, we demonstrate that while the constraint of adversarial robustness consistently degrades the standard accuracy in the balanced class setting, the class imbalance ratio plays a fundamentally different role in accuracy disparity compared to the Gaussian case, due to the heavy tail of the stable distribution. We additionally perform experiments on both synthetic and real-world datasets to corroborate our theoretical findings. Our empirical results also suggest that the implications may extend to nonlinear models over real-world datasets. Our code is publicly available on GitHub at https://github.com/Accuracy-Disparity/AT-on-AD.Comment: Accepted at ICML 202

    The relationship between mindfulness and suboptimal health status: a chain/serial mediation model

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    BackgroundSuboptimal health status (SHS) represents a third state between health and disease and often progresses into chronic conditions, negatively impacting an individual’s well-being. Studies have shown that mindfulness has a beneficial effect on various SHS symptoms. This study aims to explore the influence of mindfulness on SHS and its underlying mechanisms, with a particular focus on examining the mediating roles of stress and social support.MethodsA total of 173 healthy Chinese college or graduate students, with an average age of 21.85 years, participated in this study. Measurements were taken using the Five Factor Mindfulness Questionnaire, the Sub-Health Measurement Scale, the Perceived Stress Scale, and a self-constructed scale that included demographic information. The PROCESS plugin for SPSS was used to assess mediating effects.ResultsSignificant correlations were found among SHS, social support, mindfulness, and perceived stress (|r| = 0.38–0.85, p < 0.01). Specifically, mindfulness showed a significant positive correlation with SHS and social support (r = 0.38–0.77), while perceived stress was significantly negatively correlated with mindfulness, social support, and SHS (|r| = 0.45–0.85). Perceived social support was positively associated with SHS (r = 0.65). Furthermore, social support and perceived stress partially mediated the influence of mindfulness on SHS. Additionally, a sequential mediation effect of perceived social support and stress in the relationship between mindfulness and SHS was supported.ConclusionThe cultivation of trait mindfulness may be advantageous for individuals’ sub-health. Perceived social support and perceived stress are important underlying mechanisms contributing to this effect

    Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation

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    Adversarial Robustness Distillation (ARD) is a promising task to solve the issue of limited adversarial robustness of small capacity models while optimizing the expensive computational costs of Adversarial Training (AT). Despite the good robust performance, the existing ARD methods are still impractical to deploy in natural high-security scenes due to these methods rely entirely on original or publicly available data with a similar distribution. In fact, these data are almost always private, specific, and distinctive for scenes that require high robustness. To tackle these issues, we propose a challenging but significant task called Data-Free Adversarial Robustness Distillation (DFARD), which aims to train small, easily deployable, robust models without relying on data. We demonstrate that the challenge lies in the lower upper bound of knowledge transfer information, making it crucial to mining and transferring knowledge more efficiently. Inspired by human education, we design a plug-and-play Interactive Temperature Adjustment (ITA) strategy to improve the efficiency of knowledge transfer and propose an Adaptive Generator Balance (AGB) module to retain more data information. Our method uses adaptive hyperparameters to avoid a large number of parameter tuning, which significantly outperforms the combination of existing techniques. Meanwhile, our method achieves stable and reliable performance on multiple benchmarks.Comment: Accepted by AAAI2

    Dietary inflammatory index, and depression and mortality risk associations in U.S. adults, with a special focus on cancer survivors

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    IntroductionA higher risk for depression and mortality is associated with the inflammatory potential of diet measured through the Dietary Inflammatory Index (DII). The roles of DII in the risk of depression and death in cancer survivors were unclear. We aimed to examine the association between energy-adjusted DII (E-DII) score and risk of depression, and mortality using data from the 2007–2018 National Health and Nutrition Examination Survey (NHANES), with a special focus on cancer survivors.MethodsThe 24-h dietary recall interview was used as a basis to calculate the E-DII score and the Patient Health Questionnaire-9 (PHQ-9) was used to measure the depressive outcomes. Logistic regression analyses were performed to determine the association between quartiles of E-DII score and depression. Cox proportional hazard regression and competing risk analyses were used to estimate the risks of quartiles of E-DII score or depression on mortality.ResultsA total of 27,447 participants were included; including 24,694 subjects without cancer and 2,753 cancer survivors. The E-DII score and depression were not distributed differently between the two groups. However, the E-DII scores were positively associated with within each group’s depression (all P trend < 0.001) and participants with higher E-DII scores had a higher risk of depression (subjects without cancer: ORQ4vsQ1: 2.17, 95% CI: 1.75–2.70; cancer survivors: ORQ4vsQ1: 1.78, 95% CI: 1.09–2.92). The median follow-up time were 87 person-months, a total of 1,701 (4.8%) and 570 (15.2%) all-cause deaths in subjects without cancer and cancer survivors were identified by the end of 2019. The highest E-DII scores quartile was associated with the highest risk of all-cause (HRQ4vsQ1: 1.90, 95% CI: 1.54–2.35) and cardiovascular disease (CVD) cause death (HRQ4vsQ1: 2.50, 95% CI: 1.69–2.3.7) in the subjects without cancer. Moreover, participants with depressive symptoms had higher all-cause mortality (HR: 1.29, 95% CI: 1.04–1.59). No significant correlation was found for E-DII scores or depression with all-cause, cancer-cause or CVD-cause mortality in cancer survivors.ConclusionOur findings demonstrate that E-DII score was positively associated with depression risk. A higher E-DII score or depressive symptom may increase the risks of all-cause and CVD-cause mortality only among general subjects
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