GREEN + IDMaps: A practical soulution for ensuring fairness in a biased internet

GREEN is a proactive queue-management (PQM) algorithm that removes TCP's bias against connections with longer round-trip times, while maintaining high link utilization and low packet-loss. GREEN applies knowledge of the steady-state behavior of TCP connections to proactively drop packets, thus preventing congestion from ever occurring. As a result, GREEN ensures much higher fairness between flows than other active queue management schemes like Flow Random Early Drop (FRED) and Stochastic Fair Blue (SFB), which suffer in topologies where a large number of flows have widely varying round-trip times. GREEN'S performance relies on its ability to gauge a flow's round-trip time (RTT). In previous work, we presented results for an ideal GREEN router which has accurate RTT information for a flow. In this paper, we present a practical solution based on IDMaps, an Internet distance-estimation service, and compare its performance to an ideal GREEN router. We show that a solution based on IDMaps is practical and maintains high fairness and link utilization, and low packet-loss rates

Nicotine-induced survival signaling in lung cancer cells is dependent on their p53 status while its down-regulation by curcumin is independent

<p>Abstract</p> <p>Background</p> <p>Lung cancer is the most lethal cancer and almost 90% of lung cancer is due to cigarette smoking. Even though nicotine, one of the major ingredients of cigarette smoke and the causative agent for addiction, is not a carcinogen by itself, several investigators have shown that nicotine can induce cell proliferation and angiogenesis. We observed that the proliferative index of nicotine is different in the lung cancer cell lines H1299 (p53-/-) and A549 (p53+/+) which indicates that the mode of up-regulation of survival signals by nicotine might be different in cells with and without p53.</p> <p>Results</p> <p>While low concentrations of nicotine induced activation of NF-κB, Akt, Bcl2, MAPKs, AP1 and IAPs in H1299, it failed to induce NF-κB in A549, and compared to H1299, almost 100 times higher concentration of nicotine was required to induce all other survival signals in A549. Transfection of WT-p53 and DN-p53 in H1299 and A549 respectively, reversed the mode of activation of survival signals. Curcumin down-regulated all the survival signals induced by nicotine in both the cells, irrespective of their p53 status. The hypothesis was confirmed when lower concentrations of nicotine induced NF-κB in two more lung cancer cells, Hop-92 and NCI-H522 with mutant p53 status. Silencing of p53 in A549 using siRNA made the cells susceptible to nicotine-induced NF-κB nuclear translocation as in A549 DN-p53 cells.</p> <p>Conclusions</p> <p>The present study reveals a detrimental role of nicotine especially in lung cancer patients with impaired p53 status and identifies curcumin as a potential chemopreventive.</p

Deep Learning with Abstention: Algorithms for Robust Training and Predictive Uncertainty

Thesis (Ph.D.)--University of Washington, 2020Machine learning using deep neural networks -- also called ``Deep Learning'' -- has been at the center of practically every major advance in artificial intelligence over the last several years, revolutionizing the fields of computer vision, speech recognition and machine translation. Spurred by these successes, there is now tremendous interest in using deep learning-based systems in all domains where computers can be used to make predictions after being trained on large quantities of data. In this thesis, we tackle two important practical challenges that arise when using deep neural networks (DNNs) for classification. \textit{First, we study the problem of how to robustly train deep models when the training data itself is unreliable.} This is a common occurrence in real-world deep learning where large quantities of data can be easily collected, but due to the enormous size of the datasets required for deep learning, perfectly labeling them is usually infeasible; when such label noise is significant, the performance of the resulting classifier can be severely affected. To tackle this, we devise a novel algorithmic framework for training deep models using an \textit{abstention} based approach. We show how such a ``deep abstaining classifier'' can improve robustness to different types of label noise, and unlike other existing approaches, also {\em learns features} that are indicative of unreliable labels. State-of-the-art performance is achieved for noise-robust learning on a number of standard benchmarks; further gains on noise-robustness are then shown by combining abstention with techniques from classical control and semi-supervised learning. \textit{The second challenge we consider is improving the predictive uncertainty of DNNs}. In many applications, and especially in high-risk ones, it is critical to have a reliable measure of confidence in the model's prediction. DNNs, however, often exhibit pathological \textit{overconfidence}, rendering standard methods of confidence-based thresholding ineffective. We first take a closer look at the roots of overconfidence in DNNs, discovering that introducing uncertainty into the training labels leads to significantly reduced overconfidence and improved probability estimates associated with predicted outcomes; using this observation, we show how threshold-based abstention can be made to work again. Then we consider the problem of open set detection -- where the classifier is presented with an instance from an unknown category -- and demonstrate how our abstention framework can be a very reliable open set detector. The contributions of this thesis are thus two-fold, and in two parts -- part one on robust training, and part two on predictive uncertainty -- but unified by a common theme: abstention. Our work demonstrates that such an approach is highly effective both while training and deploying DNNs for classification, and hence, a useful addition to a real-world deep learning pipeline