For a graph G, a biclique edge partition SBP(G) is a collection of bicliques (complete bipartite subgraphs) Bi such that each edge of G is contained in exactly one Bi. The Minimum Biclique Edge Partition Problem (MBEPP) asks for SBP(G) with the minimum size. In this paper, we show that for arbitrary small ε>0, (6053/6052-ε)-approximation of MBEPP is NP-hard.

In and we have presented a simulation-based algorithm for optimizing the average reward in a parameterized continuous-time, finite-state semi-Markov Decision Process (SMDP). We approximated the gradient of the average reward. Then, a simulation-based algorithm was proposed to estimate the approximate gradient of the average reward (called GSMDP), using only a single sample path of the underlying Markov chain. GSMDP was proved to converge with probability 1. In this paper, we give bounds on the approximation and estimation errors for GSMDP algorithm. The approximation error of that approximation is the size of the difference between the true gradient and the approximate gradient. The estimation error, the size of the difference between the output of the algorithm and its asymptotic output, arises because the algorithm sees only a finite data sequence.

This paper deals with the maximum-weight 2-path packing problem (M2PP), which is the problem of computing a set of vertex-disjoint paths of length 2 in a given edge-weighted complete graph so that the total weight of edges in the paths is maximized. Previously, Hassin and Rubinstein gave a randomized cubic-time approximation algorithm for M2PP which achieves an expected ratio of - ε ≈ 0.5223 - ε for any constant ε > 0. We refine their algorithm and derandomize it to obtain a deterministic cubic-time approximation algorithm for the problem which achieves a better ratio (namely, 0.5265 - ε for any constant ε>0).

The coupled mode equation describing the propagation of light in a disordered waveguide system composed of randomly different cores in size is analytically solved by the perturbation method and the average amplitude of light is derived. In the summation of a perturbation series only successive scatterings from different cores are taken into account. The result obtained shows that the average amplitude behaves as if in an ordered waveguide system composed of identical cores at short distance and decreases exponentially with increasing distance at large distance. The result is compared with the result obtained by the coherent potential approximation and the both results are in good agreement with each other. The results are also compared with the results obtained by numerically solving the coupled mode equation.

A CMOS current-mode nth-switchable-root circuit composed of a compact logarithm circuit, a divide-by-n circuit, and a compact exponential circuit is proposed. The n can be selected from 5 values by three switches. Simulation results indicate that the compact nth-switchable-root circuit has a wide input-current range for relative errors less than 3%, low power dissipations below 630 µW, and high bandwidth over 330 MHz.

This paper studies reflection and transmission of a TE plane wave from a two-dimensional random slab with statistically anisotropic fluctuation by means of the stochastic functional approach. By starting with a representation of the random wavefield presented in the previous paper [IEICE Trans. Electron., vol.E92-C, no.1, pp.77-84, Jan. 2009], a solution algorithm of the multiple renormalized mass operator is newly shown even for anisotropic fluctuation. The multiple renormalized mass operator, the first-order incoherent scattering cross section and the optical theorem are numerically calculated and illustrated in figures. The relation between statistical properties and anisotropic fluctuation is discussed.

This paper proposes a method for handling out-of-vocabulary (OOV) words that cannot be translated using conventional phrase-based statistical machine translation (SMT) systems. For a given OOV word, lexical approximation techniques are utilized to identify spelling and inflectional word variants that occur in the training data. All OOV words in the source sentence are then replaced with appropriate word variants found in the training corpus, thus reducing the number of OOV words in the input. Moreover, in order to increase the coverage of such word translations, the SMT translation model is extended by adding new phrase translations for all source language words that do not have a single-word entry in the original phrase-table but only appear in the context of larger phrases. The effectiveness of the proposed methods is investigated for the translation of Hindi to English, Chinese, and Japanese.

In this paper, we propose a new PAPR reduction by using the hybrid of partial transmit sequences (PTS) and cascade adaptive peak power reduction (CAPPR) methods with side information (SI) technique coded by genetic algorithm (GA). These methods are used in an Orthogonal Frequency Division Multiplexing (OFDM) system. The OFDM employs orthogonal sub-carriers for data modulation. These sub-carriers unexpectedly present a large peak to average power ratio (PAPR) in some cases. A proposed reduction method realizes both the advantages of PTS and CAPPR at the same time. In order to obtain the optimum condition on PTS for PAPR reduction, a quite large calculation cost is demanded and thus it is impossible to obtain the optimum PTS in a short time. In the proposed method, by using the pseudo-optimum condition based on a GA coded SI technique, the total calculation cost becomes drastically reduced. In simulation results, the proposed method shows the improvement on PAPR and also reveals the high performance on bit error rate (BER) of an OFDM system.

In this paper, we analyze the stability of XCP (eXplicit Control Protocol) in a network with heterogeneous XCP flows (i.e., XCP flows with different propagation delays). Specifically, we model a network with heterogeneous XCP flows using fluid-flow approximation. We then derive the conditions that XCP control parameters should satisfy for stable XCP operation. Furthermore, through several numerical examples and simulation results, we quantitatively investigate effect of system parameters and XCP control parameters on stability of the XCP protocol. Our findings include: (1) when XCP flows are heterogeneous, XCP operates more stably than the case when XCP flows are homogeneous, (2) conversely, when variation in propagation delays of XCP flows is large, operation of XCP becomes unstable, and (3) the output link bandwidth of an XCP router is independent of stability of the XCP protocol.

Frequent Itemsets(FI) mining is a popular and important first step in analyzing datasets across a broad range of applications. There are two main problems with the traditional approach for finding frequent itemsets. Firstly, it may often derive an undesirably huge set of frequent itemsets and association rules. Secondly, it is vulnerable to noise. There are two approaches which have been proposed to address these problems individually. The first problem is addressed by the approach Frequent Closed Itemsets(FCI), FCI removes all the redundant information from the result and makes sure there is no information loss. The second problem is addressed by the approach Approximate Frequent Itemsets(AFI), AFI could identify and fix the noises in the datasets. Each of these two concepts has its own limitations, however, the authors find that if FCI and AFI are put together, they could help each other to overcome the limitations and amplify the advantages. The new integrated approach is termed Noise-tolerant Frequent Closed Itemset(NFCI). The results of the experiments demonstrate the advantages of the new approach: (1) It is noise tolerant. (2) The number of itemsets generated would be dramatically reduced with almost no information loss except for the noise and the infrequent patterns. (3) Hence, it is both time and space efficient. (4) No redundant information is in the result.

The IEEE 802.16e standard adopts a power-saving mode (PSM) with a truncated binary exponent (TBE) algorithm for determining sleep intervals. Although the TBE algorithm allows more flexibility in determining sleep intervals, it does not consider the network delay of a response packet. In this letter, we suggest PSM that is based on the conditional probability density function (c-pdf) for the response packet's arrival time at the base station (BS). The proposed algorithm determines sleep interval placement so that the response packet may arrive at the BS during each sleep interval with the same conditional probability. The results show that the proposed algorithm outperforms the TBE algorithm with respect to the packet-buffering delay in the BS and the energy consumption of a mobile station (MS).

In this paper, we propose a new approximate algorithm for the model predictive control (MPC) problem with a time-varying reference of hybrid systems. The proposed algorithm consists of an offline computation and an online computation. In the offline computation, candidates of mode sequences are derived. In the online computation, after the mode sequence is uniquely decided among candidates, the finite-time optimal control problem, i.e., the quadratic programming problem, is solved. So by applying the proposed algorithm, the computational amount of the online computation is decreased. First, the MPC problem with a time-varying reference is formulated. Next, the proposed algorithm is explained, and the accuracy of the obtained approximate solution is discussed. Finally, the effectiveness of the proposed method is shown by a numerical example.

Kernel methods such as the support vector machine are one of the most successful algorithms in modern machine learning. Their advantage is that linear algorithms are extended to non-linear scenarios in a straightforward way by the use of the kernel trick. However, naive use of kernel methods is computationally expensive since the computational complexity typically scales cubically with respect to the number of training samples. In this article, we review recent advances in the kernel methods, with emphasis on scalability for massive problems.

A two-quadrant CMOS current divider using a two-variable second-order Taylor series approximation is proposed. The approximation divider is realized with a compact circuit. The simulation results indicate that the compact divider has with sufficient accuracy, small distortion, and high bandwidth for only 1.8 V supply voltage.

This paper considers a two-echelon repair model where several series systems comprising multiple items are operated in each base. We propose a basic model and two modified models. For two models, approximation methods are developed to derive the system availability. The difference between the basic model and the first modified model is whether the normal items in failed series systems are available as spare or not. The second modified model relaxes the assumptions of the first modified model to reflect more realistic situation. We perform numerical analysis for the models to compare their system availabilities and verify the accuracy of the approximation methods.

In this letter, we consider a problem of global stabilization of a class of approximately feedback linearized systems. We propose a new nonlinear control approach which includes a nonlinear controller and a Lyapunov-based design method. Our new nonlinear control approach broadens the class of systems under consideration over the existing results.

Biologically-inspired approaches are one of the most promising approaches to realize highly-adaptive distributed systems. Biological systems inherently have self-* properties, such as self-stabilization, self-adaptation, self-configuration, self-optimization and self-healing. Thus, the application of biological systems into distributed systems has attracted a lot of attention recently. In this paper, we present one successful result of bio-inspired approach: we propose distributed algorithms for resource replication inspired by the single species population model. Resource replication is a crucial technique for improving system performance of distributed applications with shared resources. In systems using resource replication, generally, a larger number of replicas lead to shorter time to reach a replica of a requested resource but consume more storage of the hosts. Therefore, it is indispensable to adjust the number of replicas appropriately for the resource sharing application. This paper considers the problem for controlling the densities of replicas adaptively in dynamic networks and proposes two bio-inspired distributed algorithms for the problem. In the first algorithm, we try to control the replica density for a single resource. However, in a system where multiple resources coexist, the algorithm needs high network cost and the exact knowledge at each node about all resources in the network. In the second algorithm, the densities of all resources are controlled by the single algorithm without high network cost and the exact knowledge about all resources. This paper shows by simulations that these two algorithms realize self-adaptation of the replica density in dynamic networks.

In this paper, the problem of density estimation and clustering in sensor networks is considered. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. This paper develops a distributed variational Bayesian algorithm (DVBA) to estimate the parameters of this model. This algorithm produces an estimate of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, DVBA can be viewed as a distributed processing approach for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the proposed DVBA is then investigated. Finally, to verify the performance of DVBA, we perform several simulations of sensor networks. Simulation results are very promising.

With proliferation of smart handsets capable of mobile Internet, the severity of malware attacks targeting such handsets is rapidly increasing, thereby requiring effective countermeasure for them. However, existing signature-based solutions are not suitable for resource-poor handsets due to the excessive run-time overhead of matching against ever-increasing malware pattern database as well as the limitation of detecting well-known malware only. To overcome these drawbacks, we present a bio-inspired approach to discriminate malware (non-self) from normal programs (self) by replicating the processes of biological immune system. Our proposed approach achieves superior performance in terms of detecting 83.7% of new malware or their variants and scalable storage requirement that grows very slowly with inclusion of new malware, making it attractive for use with mobile handsets.

In this article, we introduce a new concept for the future information environment, called an "ambient information environment (AmIE)." We first explain it, especially emphasizing the difference from the existing ubiquitous information environment (UbIE), which is an interaction between users and environments. Then, we focus on an ambient networking environment (AmNE) which supports the AmIE as a networking infrastructure. Our approach of a biologically inspired framework is next described in order to demonstrate why such an approach is necessary in the AmIE. Finally, we show some example for building the AmNE.