Fast Byzantine Leader Election in Dynamic Networks

We study the fundamental Byzantine leader election problem in dynamic networks where the topology can change from round to round and nodes can also experience heavy {\em churn} (i.e., nodes can join and leave the network continuously over time). We assume the full information model where the Byzantine nodes have complete knowledge about the entire state of the network at every round (including random choices made by all the nodes), have unbounded computational power and can deviate arbitrarily from the protocol. The churn is controlled by an adversary that has complete knowledge and control over which nodes join and leave and at what times and also may rewire the topology in every round and has unlimited computational power, but is oblivious to the random choices made by the algorithm. Our main contribution is an $O(\log^3 n)$ round algorithm that achieves Byzantine leader election under the presence of up to $O({n}^{1/2 - \epsilon})$ Byzantine nodes (for a small constant $\epsilon > 0$) and a churn of up to \\$O(\sqrt{n}/\poly\log(n))$ nodes per round (where $n$ is the stable network size).The algorithm elects a leader with probability at least $1-n^{-\Omega(1)}$ and guarantees that it is an honest node with probability at least $1-n^{-\Omega(1)}$; assuming the algorithm succeeds, the leader's identity will be known to a $1-o(1)$ fraction of the honest nodes. Our algorithm is fully-distributed, lightweight, and is simple to implement. It is also scalable, as it runs in polylogarithmic (in $n$) time and requires nodes to send and receive messages of only polylogarithmic size per round.To the best of our knowledge, our algorithm is the first scalable solution for Byzantine leader election in a dynamic network with a high rate of churn; our protocol can also be used to solve Byzantine agreement in a straightforward way.We also show how to implement an (almost-everywhere) public coin with constant bias in a dynamic network with Byzantine nodes and provide a mechanism for enabling honest nodes to store information reliably in the network, which might be of independent interest.

Acute Cocoa Supplementation Increases Postprandial HDL Cholesterol and Insulin in Obese Adults with Type 2 Diabetes after Consumption of a High-Fat Breakfast

BACKGROUND: Dietary cocoa is an important source of flavonoids and is associated with favorable cardiovascular disease effects, such as improvements in vascular function and lipid profiles, in nondiabetic adults. Type 2 diabetes (T2D) is associated with adverse effects on postprandial serum glucose, lipids, inflammation, and vascular function.

OBJECTIVE: We examined the hypothesis that cocoa reduces metabolic stress in obese T2D adults after a high-fat fast-food-style meal.

METHODS: Adults with T2D [n = 18; age (means ± SEs): 56 ± 3 y; BMI (in kg/m(2)): 35.3 ± 2.0; 14 women; 4 men) were randomly assigned to receive cocoa beverage (960 mg total polyphenols; 480 mg flavanols) or flavanol-free placebo (110 mg total polyphenols; <0.1 mg flavanols) with a high-fat fast-food-style breakfast [766 kcal, 50 g fat (59% energy)] in a crossover trial. After an overnight fast (10-12 h), participants consumed the breakfast with cocoa or placebo, and blood sample collection [glucose, insulin, lipids, and high-sensitivity C-reactive protein (hsCRP)] and vascular measurements were conducted at 0.5, 1, 2, 4, and 6 h postprandially on each study day. Insulin resistance was evaluated by homeostasis model assessment.

RESULTS: Over the 6-h study, and specifically at 1 and 4 h, cocoa increased HDL cholesterol vs. placebo (overall Δ: 1.5 ± 0.8 mg/dL; P ≤ 0.01) but had no effect on total and LDL cholesterol, triglycerides, glucose, and hsCRP. Cocoa increased serum insulin concentrations overall (Δ: 5.2 ± 3.2 mU/L; P < 0.05) and specifically at 4 h but had no overall effects on insulin resistance (except at 4 h, P < 0.05), systolic or diastolic blood pressure, or small artery elasticity. However, large artery elasticity was overall lower after cocoa vs. placebo (Δ: -1.6 ± 0.7 mL/mm Hg; P < 0.05), with the difference significant only at 2 h.

CONCLUSION: Acute cocoa supplementation showed no clear overall benefit in T2D patients after a high-fat fast-food-style meal challenge. Although HDL cholesterol and insulin remained higher throughout the 6-h postprandial period, an overall decrease in large artery elasticity was found after cocoa consumption. This trial was registered at clinicaltrials.gov as NCT01886989.

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.

Young Stellar Object VARiability (YSOVAR): Long Timescale Variations in the Mid-infrared

The YSOVAR (Young Stellar Object VARiability) Spitzer Space Telescope observing program obtained the first extensive mid-infrared (3.6 and 4.5 μm) time series photometry of the Orion Nebula Cluster plus smaller footprints in 11 other star-forming cores (AFGL 490, NGC 1333, Mon R2, GGD 12-15, NGC 2264, L1688, Serpens Main, Serpens South, IRAS 20050+2720, IC 1396A, and Ceph C). There are ~29,000 unique objects with light curves in either or both IRAC channels in the YSOVAR data set. We present the data collection and reduction for the Spitzer and ancillary data, and define the "standard sample" on which we calculate statistics, consisting of fast cadence data, with epochs roughly twice per day for ~40 days. We also define a "standard sample of members" consisting of all the IR-selected members and X-ray-selected members. We characterize the standard sample in terms of other properties, such as spectral energy distribution shape. We use three mechanisms to identify variables in the fast cadence data—the Stetson index, a χ2 fit to a flat light curve, and significant periodicity. We also identified variables on the longest timescales possible of six to seven years by comparing measurements taken early in the Spitzer mission with the mean from our YSOVAR campaign. The fraction of members in each cluster that are variable on these longest timescales is a function of the ratio of Class I/total members in each cluster, such that clusters with a higher fraction of Class I objects also have a higher fraction of long-term variables. For objects with a YSOVAR-determined period and a [3.6]-[8] color, we find that a star with a longer period is more likely than those with shorter periods to have an IR excess. We do not find any evidence for variability that causes [3.6]-[4.5] excesses to appear or vanish within our data set; out of members and field objects combined, at most 0.02% may have transient IR excesses.