14 research outputs found

    Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory

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    Split learning is a privacy-preserving distributed learning paradigm in which an ML model (e.g., a neural network) is split into two parts (i.e., an encoder and a decoder). The encoder shares so-called latent representation, rather than raw data, for model training. In mobile-edge computing, network functions (such as traffic forecasting) can be trained via split learning where an encoder resides in a user equipment (UE) and a decoder resides in the edge network. Based on the data processing inequality and the information bottleneck (IB) theory, we present a new framework and training mechanism to enable a dynamic balancing of the transmission resource consumption with the informativeness of the shared latent representations, which directly impacts the predictive performance. The proposed training mechanism offers an encoder-decoder neural network architecture featuring multiple modes of complexity-relevance tradeoffs, enabling tunable performance. The adaptability can accommodate varying real-time network conditions and application requirements, potentially reducing operational expenditure and enhancing network agility. As a proof of concept, we apply the training mechanism to a millimeter-wave (mmWave)-enabled throughput prediction problem. We also offer new insights and highlight some challenges related to recurrent neural networks from the perspective of the IB theory. Interestingly, we find a compression phenomenon across the temporal domain of the sequential model, in addition to the compression phase that occurs with the number of training epochs.Comment: Accepted to Proc. IEEE Globecom 202

    Immunogenicity and smoking-cessation outcomes for a novel nicotine immunotherapeutic.

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    NicVAX, a nicotine vaccine (3'AmNic-rEPA), has been clinically evaluated to determine whether higher antibody (Ab) concentrations are associated with higher smoking abstinence rates and whether dosages and frequency of administration are associated with increased Ab response. This randomized, double-blinded, placebo-controlled multicenter clinical trial (N = 301 smokers) tested the results of 200- and 400-µg doses administered four or five times over a period of 6 months, as compared with placebo. 3'AmNic-rEPA recipients with the highest serum antinicotine Ab response (top 30% by area under the curve (AUC)) were significantly more likely than the placebo recipients (24.6% vs. 12.0%, P = 0.024, odds ratio (OR) = 2.69, 95% confidence interval (CI), 1.14-6.37) to attain 8 weeks of continuous abstinence from weeks 19 through 26. The five-injection, 400-µg dose regimen elicited the greatest Ab response and resulted in significantly higher abstinence rates than placebo. This study demonstrates, as proof of concept, that 3'AmNic-rEPA elicits Abs to nicotine and is associated with higher continuous abstinence rates (CAR). Its further development as a treatment for nicotine dependence is therefore justified
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