Altitude variation of ion composition in the midlatitude trough region - Evidence for upward plasma flow

Altitude effect on ion concentration in midlatitude trough and plasmaspher

Comparison of thermal features associated with 2 phacoemulsification machines

Journal ArticlePURPOSE: To determine the thermal characteristics of the Legacy Advantec and Sovereign WhiteStar phacoemulsification machines during different clinically relevant scenarios. SETTING: In vitro study. METHODS: In water, temperature was recorded continuously on the sleeve in an artificial chamber, and the increase in temperature over baseline after 60 seconds of ultrasound was determined. This was done for continuous ultrasound, 50 ms on and 50 ms off (pulse), 6 ms on and 12 ms off (WhiteStar; Sovereign only) with aspiration blocked and not blocked, and with 100 g and 200 g weights suspended from the sleeve. RESULTS: Comparing temperature increase per 20% machine power increments, Sovereign ran hotter than Legacy Advantec for continuous ultrasound (2.31x) and pulse (2.23x). Blocking aspiration increased temperature over the unblocked state. Pulsing decreased temperature by 51% (Legacy Advantec, pulse), 52% (Sovereign, pulse), and 64% (WhiteStar). Weights had much more effect on the Legacy Advantec: 3.5 times more going from baseline to 100 g weights and 3.2 times more going from 100 to 200 g weights. For all these comparisons, the P value was less than 0.0001. CONCLUSIONS: The machines behaved fundamentally differently, with the Legacy Advantec controlling stroke length and Sovereign controlling a fixed power at any setting. Therefore, workload had a much bigger impact on Legacy Advantec thermal characteristics. Pulsing decreased heat produced directly related to the duty cycle. The most dangerous incision burn scenario is with continuous ultrasound, aspiration blocked, and a heavy workload

Business process reengineering : a study in theory and practice

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1994.Includes bibliographical references (leaves 108-109).by John S. Fleischli and J. Brinton Davis.M.S

Pragmatic Markers in a Diachronic Perspective

Proceedings of the Twenty-First Annual Meeting of the Berkeley Linguistics Society: General Session and Parasession on Historical Issues in Sociolinguistics/Social Issues in Historical Linguistics (1995

Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising Autoencoders

Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable to adversarial attacks. In the context of maMIMO power allocation, adversarial attacks refer to the injection of subtle perturbations into the deep learning model's input, during inference (i.e., the adversarial perturbation is injected into inputs during deployment after the model has been trained) that are specifically crafted to force the trained regression model to output an infeasible power allocation solution. In this work, we develop an autoencoder-based mitigation technique, which allows deep learning-based power allocation models to operate in the presence of adversaries without requiring retraining. Specifically, we develop a denoising autoencoder (DAE), which learns a mapping between potentially perturbed data and its corresponding unperturbed input. We test our defense across multiple attacks and in multiple threat models and demonstrate its ability to (i) mitigate the effects of adversarial attacks on power allocation networks using two common precoding schemes, (ii) outperform previously proposed benchmarks for mitigating regression-based adversarial attacks on maMIMO networks, (iii) retain accurate performance in the absence of an attack, and (iv) operate with low computational overhead.Comment: This work is currently under review for publicatio

A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning

Augmenting federated learning (FL) with direct device-to-device (D2D) communications can help improve convergence speed and reduce model bias through rapid local information exchange. However, data privacy concerns, device trust issues, and unreliable wireless channels each pose challenges to determining an effective yet resource efficient D2D structure. In this paper, we develop a decentralized reinforcement learning (RL) methodology for D2D graph discovery that promotes communication of non-sensitive yet impactful data-points over trusted yet reliable links. Each device functions as an RL agent, training a policy to predict the impact of incoming links. Local (device-level) and global rewards are coupled through message passing within and between device clusters. Numerical experiments confirm the advantages offered by our method in terms of convergence speed and straggler resilience across several datasets and FL schemes