Analysis of distribution time of multiple files in a P2P network

Peer-to-Peer (P2P) file sharing attracts much attention due to its scalability and robustness. One important metric in measuring the performance of a P2P file sharing system is the amount of time required for all peers to get the files. We refer this time as the file distribution time. Researchers have proposed protocols to minimize the file distribution time under different situation. However, most works are based on the single-file scenario. On the other hand, there are studies showing that in a file sharing application, users may download multiple files at the same time. In this paper, we analyze the minimum time needed to distribute multiple files. We develop an explicit expression for the minimum amount of time needed to distribute multiple files in a heterogeneous P2P fluid model. Unlike the single-file scenario, we demonstrate that the theoretical lower bound in multi-file is not always achievable. With a comprehensive consideration of all the configurations, we elaborate how to partition the bandwidth capacities of both seeds and leechers for a particular file such that the finish time is optimal.     

Approximations and reducts with covering generalized rough sets

The covering generalized rough sets are an improvement of traditional rough set model to deal with more complex practical problems which the traditional one cannot handle. It is well known that any generalization of traditional rough set theory should first have practical applied background and two important theoretical issues must be addressed. The first one is to present reasonable definitions of set approximations, and the second one is to develop reasonable algorithms for attributes reduct. The existing covering generalized rough sets, however, mainly pay attention to constructing approximation operators. The ideas of constructing lower approximations are similar but the ideas of constructing upper approximations are different and they all seem to be unreasonable. Furthermore, less effort has been put on the discussion of the applied background and the attributes reduct of covering generalized rough sets. In this paper we concentrate our discussion on the above two issues. We first discuss the applied background of covering generalized rough sets by proposing three kinds of datasets which the traditional rough sets cannot handle and improve the definition of upper approximation for covering generalized rough sets to make it more reasonable than the existing ones. Then we study the attributes reduct with covering generalized rough sets and present an algorithm by using discernibility matrix to compute all the attributes reducts with covering generalized rough sets. With these discussions we can set up a basic foundation of the covering generalized rough set theory and broaden its applications.     

Avatar mobility in user-created networked virtual worlds: measurements, analysis, and implications

We collected mobility traces of avatars spanning multiple regions in Second Life, a popular user-created virtual world. We analyzed the traces to characterize the dynamics of the avatars’ mobility and behavior, both temporally and spatially. We discuss the implications of our findings on the design of peer-to-peer architecture, interest management, mobility modeling of avatars, server load balancing and zone partitioning, caching, and prefetching for user-created virtual worlds.     

Maximum Penalized Likelihood Kernel Regression for Fast Adaptation

This paper proposes a nonlinear generalization of the popular maximum-likelihood linear regression (MLLR) adaptation algorithm using kernel methods. The proposed method, called maximum penalized likelihood kernel regression adaptation (MPLKR), applies kernel regression with appropriate regularization to determine the affine model transform in a kernel-induced high-dimensional feature space. Although this is not the first attempt of applying kernel methods to conventional linear adaptation algorithms, unlike most of other kernelized adaptation methods such as kernel eigenvoice or kernel eigen-MLLR, MPLKR has the advantage that it is a convex optimization and its solution is always guaranteed to be globally optimal. In fact, the adapted Gaussian means can be obtained analytically by simply solving a system of linear equations. From the Bayesian perspective, MPLKR can also be considered as the kernel version of maximum a posteriori linear regression (MAPLR) adaptation. Supervised and unsupervised speaker adaptation using MPLKR were evaluated on the Resource Management and Wall Street Journal 5K tasks, respectively, achieving a word error rate reduction of 23.6% and 15.5% respectively over the speaker-independently model.     

Transverse offset loss between two identical noncircular single-mode waveguides

We derive an approximation for the transverse offset loss based on the Laplacian mode-field half-width (MFHW) and far-field second moment for two arbitrary identical noncircular single-mode waveguides. The relation is reduced to the known result for circular-symmetric, single-mode waveguides. Numerical results for symmetrical rectangular waveguides show that the transverse offset loss calculated from the Laplacian MFHW is more accurate than that from the elliptical Gaussian MFHW for small transverse offsets. To our knowledge, this is the first calculation of the errors in the approximate transverse offset loss relations derived from the Laplacian and the elliptical Gaussian MFHW for noncircular optical waveguides.     

MXene Functionalized,Highly Breathable and Sensitive Pressure Sensors with Multi-Layered Porous Structure

Breathable, flexible, and highly sensitive pressure sensors have drawn increasing attention due to their potential in wearable electronics for body-motion monitoring, human-machine interfaces, etc. However, current pressure sensors are usually assembled with polymer substrates or encapsulation layers, thus causing discomfort during wearing (i.e., low air/vapor permeability, mechanical mismatch) and restricting their applications. A breathable and flexible pressure sensor is reported with nonwoven fabrics as both the electrode (printed with MXene interdigitated electrode) and sensing (coated with MXene/silver nanowires) layers via a scalable screen-printing approach. Benefiting from the multi-layered porous structure, the sensor demonstrates good air permeability with high sensitivity (770.86–1434.89 kPa⁻¹), a wide sensing range (0–100 kPa), fast response/recovery time (70/81 ms), and low detection limit (≈1 Pa). Particularly, this sensor can detect full-scale human motion (i.e., small-scale pulse beating and large-scale walking/running) with high sensitivity, excellent cycling stability, and puncture resistance. Additionally, the sensing layer of the pressure sensor also displays superior sensitivity to humidity changes, which is verified by successfully monitoring human breathing and spoken words while wearing a sensor-embedded mask. Given the outstanding features, this breathable sensor shows promise in the wearable electronic field for body health monitoring, sports activity detection, and disease diagnosis.     

The Electrochemical Response of Single Crystalline Copper Nanowires to Atmospheric Air and Aqueous Solution

In this paper, single crystalline copper nanowires (CuNWs) have been electrochemically grown through anodic aluminum oxide template. The environmental stability of the as‐obtained CuNWs in both 40% relative humidity (RH) atmosphere and 0.1 m NaOH aqueous solution has been subsequently studied. In 40% RH atmosphere, a uniform compact Cu₂O layer is formed as a function of exposure time following the logarithmic law and epitaxially covers the CuNW surfaces. It is also found that the oxide layers on CuNWs are sequentially grown when subjected to the cyclic voltammetry measurement in 0.1 m NaOH solution. An epitaxially homogeneous Cu₂O layer is initially formed over the surface of the CuNW substrates by solid‐state reaction (SSR). Subsequently, the conversion of Cu₂O into epitaxial CuO based on the SSR takes place with the increase of applied potential. This CuO layer is partially dissolved in the solution forming Cu(OH)₂, which then redeposited on the CuNW surfaces (i.e., dissolution‐redeposition (DR) process) giving rise to a mixed polycrystalline CuO/Cu(OH)₂ layer. The further increase of applied potential allows the complete oxidation of Cu₂O into CuO to form a dual‐layer structure (i.e., CuO inner layer and Cu(OH)₂ outer layer) with random orientations through an enhanced DR process.     

Effect of Mn substitution on the oxidation/adsorption abilities of iron(III) oxyhydroxides

In this study, divalent manganese ions [Mn(II)] were substituted a part of divalent iron ions [Fe(II)] present in Fe oxyhydroxides to prepare novel composites (Mn@Feox). The composites were prepared by (1) simultaneous hydrolysis of Fe(II) and Mn(II), and (2) rapid oxidation with H₂O₂. The resulting Mn@Feox prepared with different molar ratios of Fe and Mn was characterized and evaluated for their abilities to adsorb arsenic species [As(III) and As(V)] in aqueous solution. X-ray diffraction and field emission transmission electron microscope analyses revealed Mn@Feox has a δ-(Fe_1?x, Mn_x)OOH-like structure with their mineralogical properties resembling those of feroxyhyte (δ-FeOOH). The increase in Mn substitution in Mn@Feox enhanced the oxidative ability to oxidize As(III) to As(V), but it decreased the adsorption capacity for both arsenic species. The optimal Mn/Fe molar ratio that could endow oxidation and magnetic capabilities to the composite without significantly compromising As adsorption capability was determined to be 0.1 (0.1Mn@Feox). The adsorption of As(III) on 0.1Mn@Feox was weakly influenced by pH change while As(V) adsorption showed high dependence on pH, achieving nearly complete removal at pH?<?5.7 but gradual decrease at pH?>?5.7. The adsorption kinetics and isotherms of As(III) and As(V) showed good conformity to pseudo-second-order kinetics model and Freundlich model, respectively.