Distributed Private Online Learning for Social Big Data Computing over Data Center Networks

With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massive online social data are obtained. In order to exploit knowledge from copious amounts of data obtained and predict social behavior of users, we urge to realize data mining in social networks. Almost all online websites use cloud services to effectively process the large scale of social data, which are gathered from distributed data centers. These data are so large-scale, high-dimension and widely distributed that we propose a distributed sparse online algorithm to handle them. Additionally, privacy-protection is an important point in social networks. We should not compromise the privacy of individuals in networks, while these social data are being learned for data mining. Thus we also consider the privacy problem in this article. Our simulations shows that the appropriate sparsity of data would enhance the performance of our algorithm and the privacy-preserving method does not significantly hurt the performance of the proposed algorithm.Comment: ICC201

Phosphorus and nitrogen adsorption capacities of biochars derived from feedstocks at different pyrolysis temperatures

This study investigates the P and NO3− adsorption capacities of different biochars made from plant waste including rice straw (RSB), Phragmites communis (PCB), sawdust (SDB), and egg shell (ESB) exposed to a range of pyrolysis temperatures (300, 500 and 700 °C). Results indicate that the effect of pyrolysis temperature on the physiochemical properties of biochar varied with feedstock material. Biochars derived from plant waste had limited adsorption or even released P and NO3−, but adsorption of P capacity could be improved by adjusting pyrolysis temperature. The maximum adsorption of P on RSB700, PCB300, and SDB300, produced at pyrolysis temperature of 700, 300 and 300 °C, was 5.41, 7.75 and 3.86 mg g−1, respectively. ESB can absorb both P and NO3−, and its adsorption capacity increased with an increase in pyrolysis temperature. The maximum NO3− and P adsorption for ESB700 was 1.43 and 6.08 mg g−1, respectively. The less negative charge and higher surface area of ESB enabled higher NO3− and P adsorption capacity. The P adsorption process on RSB, PCB, SDB and ESB, and the NO3− adsorption process on ESB were endothermic reactions. However, the NO3− adsorption process on RSB, PCB and SDB was exothermic. The study demonstrates that the use of egg shell biochar may be an effective way to remove, through adsorption, P and NO3− from wastewater

Loss Gradient Gaussian Width based Generalization and Optimization Guarantees

Generalization and optimization guarantees on the population loss in machine learning often rely on uniform convergence based analysis, typically based on the Rademacher complexity of the predictors. The rich representation power of modern models has led to concerns about this approach. In this paper, we present generalization and optimization guarantees in terms of the complexity of the gradients, as measured by the Loss Gradient Gaussian Width (LGGW). First, we introduce generalization guarantees directly in terms of the LGGW under a flexible gradient domination condition, which we demonstrate to hold empirically for deep models. Second, we show that sample reuse in finite sum (stochastic) optimization does not make the empirical gradient deviate from the population gradient as long as the LGGW is small. Third, focusing on deep networks, we present results showing how to bound their LGGW under mild assumptions. In particular, we show that their LGGW can be bounded (a) by the $L_2$-norm of the loss Hessian eigenvalues, which has been empirically shown to be $\tilde{O}(1)$ for commonly used deep models; and (b) in terms of the Gaussian width of the featurizer, i.e., the output of the last-but-one layer. To our knowledge, our generalization and optimization guarantees in terms of LGGW are the first results of its kind, avoid the pitfalls of predictor Rademacher complexity based analysis, and hold considerable promise towards quantitatively tight bounds for deep models

Pyrimido[4,5‐ d ]pyrimidin‐4(1 H )‐one Derivatives as Selective Inhibitors of EGFR Threonine 790 to Methionine 790 (T790M) Mutants

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99681/1/8387_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99681/2/anie_201302313_sm_miscellaneous_information.pd

Pyrimido[4,5‐ d ]pyrimidin‐4(1 H )‐one Derivatives as Selective Inhibitors of EGFR Threonine 790 to Methionine 790 (T790M) Mutants

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99673/1/8545_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99673/2/ange_201302313_sm_miscellaneous_information.pd

Stability Based Generalization Bounds for Exponential Family Langevin Dynamics

Recent years have seen advances in generalization bounds for noisy stochastic algorithms, especially stochastic gradient Langevin dynamics (SGLD) based on stability (Mou et al., 2018; Li et al., 2020) and information theoretic approaches (Xu and Raginsky, 2017; Negrea et al., 2019; Steinke and Zakynthinou, 2020). In this paper, we unify and substantially generalize stability based generalization bounds and make three technical contributions. First, we bound the generalization error in terms of expected (not uniform) stability which arguably leads to quantitatively sharper bounds. Second, as our main contribution, we introduce Exponential Family Langevin Dynamics (EFLD), a substantial generalization of SGLD, which includes noisy versions of Sign-SGD and quantized SGD as special cases. We establish data-dependent expected stability based generalization bounds for any EFLD algorithm with a O(1/n) sample dependence and dependence on gradient discrepancy rather than the norm of gradients, yielding significantly sharper bounds. Third, we establish optimization guarantees for special cases of EFLD. Further, empirical results on benchmarks illustrate that our bounds are non-vacuous, quantitatively sharper than existing bounds, and behave correctly under noisy labels

Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks

Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks. These preliminary models often rely on off-the-shelf learning models to predict from historical statistics while disregarding the physics governing the generating behaviors of these flows. This paper instead introduces Flow Neural Network (FlowNN) to improve the feature representation with learned physical bias. This is implemented by an induction layer, working upon the embedding layer, to impose the physics connected data correlations, and a self-supervised learning strategy with stop-gradient to make the learned physics universal. For the short-timescale network prediction tasks, FlowNN achieves 17% - 71% of loss decrease than the state-of-the-art baselines on both synthetic and real-world networking datasets, which shows the strength of this new approach. Code will be made available.Comment: re-organize the presentatio