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We study the investment incentives created by truthful mechanisms that allocate resources using approximation algorithms. Some approximation algorithms guarantee nearly 100% of the optimal welfare in the allocation problem but guarantee nothing when accounting for investment incentives. An algorithm's allocative and investment guarantees coincide if and only if its confirming negative externalities are sufficiently small. We introduce fast approximation algorithms for the knapsack problem that have no confirming negative externalities and guarantees close to 100% for both allocation and investment. [ABSTRACT FROM AUTHOR]

Two methods are presented for finding the top‐k items in data streams using Field Programmable Gate Arrays (FPGAs). These methods deploy two variants of a novel accelerator architecture capable of extracting an approximate list of the topmost frequently occurring items in a single pass over the input stream without the need for random access. The first variant of the accelerator implements the well‐known Probabilistic sampling algorithm by mapping its main processing stages to a hardware architecture consisting of two custom systolic arrays. The proposed architecture retains all the properties of this algorithm, which works even if the stream size is unknown at run time. The architecture shows better scalability compared to other architectures that are based on other stream algorithms. In addition, experimental results on both synthetic and real datasets, when implementing the accelerator on an Intel Arria 10 GX 1150 FPGA device, showed very good accuracy and significant throughput gains compared to the existing software and hardware‐accelerated solutions. The second variant of the accelerator is specifically tailored for applications requiring higher accuracy, provided that the size of the stream is known at run time. This variant takes advantage of the embedded memory resources in an FPGA to implement a sketch‐based filter that precedes the main systolic array in the accelerator's pipeline. This filter enhances the accuracy of the accelerator by pre‐processing the stream to remove much of the insignificant items, allowing the accelerator to process a significantly smaller filtered stream. [ABSTRACT FROM AUTHOR]

Antenna arrays have gained significant interest in millimetre‐wave communication systems as an enabling technology to achieve higher capacity and mitigate the high propagation loss. Such arrays with a large bandwidth need to be efficiently calibrated to maximise their performance. An antenna array calibration method based on a stochastic approximation algorithm and simultaneous perturbation has been developed and the procedures to implement it in both frequency and time domains have been presented. The approaches to define objective functions and establish gradient approximations to fulfill a successful convergence for acquiring calibration coefficients in both domains have been explored. In the time domain implementation, only a fraction of the measurement time was required to calibrate an antenna array of ultrawide bandwidth compared with other methods using a perturbation technique. The effectiveness of the proposed method has been validated via numerical experiments in both domains. [ABSTRACT FROM AUTHOR]

This study proposes an adaptive compressed sensing (CS) method for video recovery based on spatial-temporal difference saliency in compressive domain. Firstly, the theoretical framework of realtime adaptive CS for video restoration is established, which includes two components: on-chip and off-chip. Secondly, the computation model of compressive domain spatial-temporal difference-based saliency detection is built, which merely depends on the intra-frame and inter-frame similarities of compressive measurements. Thirdly, binary-group approximation algorithm of subrate allocation is presented to achieve a quasi-optimal solution. Finally, simulation experiments are designed to evaluate the performance of the proposed method, and it is indicated by the experimental results that the proposed method is suitable for realtime implementation and holds higher peak signal to noise ratio compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A novel abnormity detection method is presented which combines the low-rank structured sparse representation and reduced dictionary learning. The multi-scale three-dimensional gradient is used as low-level feature by encoding the spatiotemporal information. A group of reduced sparse dictionaries is learnt by low-rank approximation based on the structured sparsity property of the video sequence. The contribution of this study is three-fold: (i) the normal feature clusters can be represented effectively by the reduced dictionaries which are learnt based on the low-rank nature of the data; (ii) the size of dictionary is determined adaptively by the sparse learning method according to the scene, which makes the representation more compact and efficient; and (iii) the proposed abnormity detection method is of low time complexity and real-time detection can be obtained. The authors have evaluated the proposed method against the state-of-the-art methods on the public datasets and very promising results have been achieved. [ABSTRACT FROM AUTHOR]

In this article, we propose a dynamic Boolean network‐type mathematical representation of networked critical infrastructures under stress. Our formulation is obtained as a threshold‐based approximation of a discrete‐time switched system meant to describe failure and recovery phases that may span considerably different time periods. The formalism can incorporate mitigating factors, resource constraints, and allocation strategies affecting the response of the system and the unfolding of restoration processes. As such, the tool may inform both contingency planning and consequence assessment. We also discuss an illustrative case study concerning the recovery process of selected infrastructures of the Thessaloniki Port in an earthquake scenario. Simulations are performed for assigned parameter distributions associated with the different operational modes. Finally, a performance loss indicator is computed to assess the overall service perturbation affecting the system. For the needs of this analysis, fragility curves have been selected from the literature, whereas restoration curves, recovery priorities, classification of components, and other modeling variables have been defined in collaboration with the local Port Authority. [ABSTRACT FROM AUTHOR]

Purpose: This work characterizes a novel exponential 4DCT reconstruction algorithm (EXPO), in phantom and patient, to determine its impact on image quality as compared to the standard cosine‐squared weighted 4DCT reconstruction. Methods: A motion platform translated objects in the superior–inferior (S‐I) direction at varied breathing rates (8–20 bpm) and couch pitches (0.06–0.1) to evaluate interplay between parameters. Ten‐phase 4DCTs were acquired and data were reconstructed with cosine squared and EXPO weighting. To quantify the magnitude of image blur, objects were translated in the anterior–posterior (A‐P) and S‐I directions for full‐width half maximum (FWHM) analysis between both 4DCT algorithms and a static case. 4DCT sinogram data for 10 patients were retrospectively reconstructed using both weighting factors. Image subtractions elucidated intensity and boundary differences. Subjective image quality grading (presence of image artifacts, noise, spatial resolution (i.e., lung/liver boundary sharpness), and overall image quality) was conducted yielding 200 evaluations. Results: After taking static object size into account, the FWHM of EXPO reconstructions in the A‐P direction was 3.3 ± 1.7 mm (range: 0–4.9) as compared to cosine squared 9.8 ± 4.0 mm (range: 2.6–14.4). The FWHM of objects translated in the S‐I direction reconstructed with EXPO agreed better with the static FWHM than the cosine‐squared reconstructions. Slower breathing periods, faster couch pitches, and intermediate 4DCT phases had the largest reductions of blurring with EXPO. 18 of 60 comparisons of artifacts were improved with EXPO reconstruction, whereas no appreciable changes were observed in image quality scores. In 18 of 20 cases, EXPO provided sharper images although the reduced projections also increased baseline noise. Conclusion: Exponential weighted 4DCT offers potential for reducing image blur (i.e., improving image sharpness) in 4DCT with a tendency to reduce artifacts. Future work will involve evaluating the impact on treatment planning including delineation ability and dose calculation. [ABSTRACT FROM AUTHOR]

Variable weight (VW) codes for spectral amplitude coding-optical code division multiple access system are useful in multimedia applications. VW code algorithm is proposed which is based on enhanced and modified double weight codes. The proposed algorithm can be used for any combination of weights >2. The cross-correlation among all users is at most 1. This code construction algorithm ensures higher power at receiver for higher-weight users. Lower-weight users receive less power compared with higher-weight users. The difference in received power translates as varying performance, and are useful for multimedia applications. The performance of VW code is analysed using balanced detection (BD) exclusive-OR operation and direct detection with exclusive-OR operation. VW code gives comparable or better performance than previously reported code. It is observed that supportable number of users for higher weight, at bit error rate (BER) of 10-9 (data) are 28, 33 and 35 for random diagonal (RD) code, Khazani-Syed (KS) code and VW code, respectively, using BD. Supportable number of users for lower weight, at BER of 10-4 (voice) are 23, 31 and 37 for RD code, KS code and VW code, respectively, using BD. [ABSTRACT FROM AUTHOR]

Abstract: This research has 6 fundamental aims: (i) to present a modified version of Taylor's interpolation, one that is more effective and faster than the original; (ii) outline the capability of artificial neural networks (ANNs) to perform an optimal functional approximation of the digital elevation model reconstruction from a satellite map, using a small and independent sample of Global Positioning System observations; (iii) demonstrate experimentally how ANNs outperform the traditional and most used algorithm for the height interpolation (Taylor's interpolation); (iv) introduce a new ANN, the Conic Net, able to outperform the results of the classic and more known multilayer perceptron; (v) determine that Conic Nets, even when using Taylor's modified interpolation as input features, are able to optimally approximate the heights with one order of magnitude more than the original satellite map; and (vi) make evident the possibility to interpolate the DEM heights through an ANN, which learns a data set of known points. [ABSTRACT FROM AUTHOR]

Among all image representation and classification methods, sparse representation has proven to be an extremely powerful tool. However, a limited number of training samples are an unavoidable problem for sparse representation methods. Many efforts have been devoted to improve the performance of sparse representation methods. In this study, the authors proposed a novel framework to improve the classification accuracy of sparse representation methods. They first introduced the concept of the approximations of all training samples (i.e., virtual training samples). The advantage of this is that the application of virtual training samples can allow noise in original training samples to be partially reduced. Then they proposed an efficient and competent objective function to disclose more discriminant information between different classes, which is very significant for obtaining a better classification result. The devised sparse representation method employs both the original and virtual training samples to improve the classification accuracy since the two kinds of training samples makes sample information to be fully exploited in a good way, also satisfactory robustness to be obtained. The experimental results on the JAFFE, ORL, Columbia Object Image Library (COIL-100) AR and CMU PIE databases show that the proposed method outperforms the state-of-art image classification methods. [ABSTRACT FROM AUTHOR]

Construction laborer assignment is the assignment of laborers in a team to the tasks for a daily work in a construction project. This study proposes a mathematical programming approach to examine the optimal construction laborer assignment problem with the objectives of productivity and occupational health and safety while considering equity between laborers. Several linearization techniques are presented to transform the mathematical programming model into a mixed-integer linear program, which can be solved by off-the-shelf mixed-integer linear solvers. The proposed model is applied to extensive numerical experiments and the results show that the mathematical programming approach outperforms a conventional heuristic by over 10% in terms of job completion time. This implies considerable overhead, equipment, and manpower savings for the construction industry. [ABSTRACT FROM AUTHOR]

Pade-Chebyshev approximation of Clenshaw-Lord type with method of moments is proposed for wide-band analysis of an arbitrary-shaped perfect electric conductor structure. Moreover, various Chebyshev polynomials, such as first, third, and fourth kinds, are used to express the singular points in the graph to display the expected result for scattering characteristics. The proposed algorithm has a wide radius of convergence due to the use of Chebyshev polynomials and improves calculation speed because Clenshaw-Lord-type Pade approximation requires fewer sampling points than Maehly-type Pade approximation in the derivation of the rational function. The results of the proposed method agree very well with the exact solution and reveal its possibility of obtaining more accurate solution than the asymptotic waveform evaluation technique. [ABSTRACT FROM AUTHOR]

Probabilistic approaches to rough sets are still an important issue in rough set theory. Although many studies have been written on this topic, they focus on approximating a crisp concept in the universe of discourse, with less effort on approximating a fuzzy concept in the universe of discourse. This article investigates the rough approximation of a fuzzy concept on a probabilistic approximation space over two universes. We first present the definition of a lower and upper approximation of a fuzzy set with respect to a probabilistic approximation space over two universes by defining the conditional probability of a fuzzy event. That is, we define the rough fuzzy set on a probabilistic approximation space over two universes. We then define the fuzzy probabilistic approximation over two universes by introducing a probability measure to the approximation space over two universes. Then, we establish the fuzzy rough set model on the probabilistic approximation space over two universes. Meanwhile, we study some properties of both rough fuzzy sets and fuzzy rough sets on the probabilistic approximation space over two universes. Also, we compare the proposed model with the existing models to show the superiority of the model given in this paper. Furthermore, we apply the fuzzy rough set on the probabilistic approximation over two universes to emergency decision-making in unconventional emergency management. We establish an approach to online emergency decision-making by using the fuzzy rough set model on the probabilistic approximation over two universes. Finally, we apply our approach to a numerical example of emergency decision-making in order to illustrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This article examines the application of a path-based algorithm to the static and fixed demand asymmetric traffic assignment problem. The algorithm is of the simplicial decomposition type and it solves the equilibration or master problem step by means of five existing projection methods for variational inequality problems to evaluate their performance on real traffic networks. The projection methods evaluated are: (1) a cost approximation-based method for minimizing the Fukushima's gap function, (2) the modified descent method of Zhu and Marcotte (), (3) the double projection method of Khobotov () and three of its recently developed variants (Nadezhkina and Takahashi, ; Wang et al., ; and He et al., 2012); (4) the method of Solodov and Svaiter (); and (5) the method of Solodov and Tseng (). These projection methods do not require evaluation of the Jacobians of the path cost functions. The source for asymmetries are link costs with interactions, as in the case of priority ruled junctions. The path-based algorithm has been computationally tested using the previous projection methods on three medium to large networks under different levels of congestion and the computational results are presented and discussed. Comparisons are also made with the basic projection algorithm for the fixed demand asymmetric traffic assignment problem. Despite the lack of monotonicity properties of the test problems, the only method that failed to converge under heavy congestion levels was the basic projection algorithm. The fastest convergence was obtained in all cases solving the master problem step using the method of He et al. (2012), which is a variant of Khobotov's method. [ABSTRACT FROM AUTHOR]

Time delay generates exponential transcendental terms in characteristic equations. Subsequently, the applied methods are narrow to assess the stability map of delayed fractional-order systems with vanishing fractions. In this case, it is convenient to approximate these equations with its integer-order counterparts to design controllers or investigate features of the systems. However, delay can cause enormous differences between characteristic of these two equations. This study offers a method to survey analytically the stability of fractional-order time delayed linear time invariant systems with infinitesimal fractions. In this method, equations are transferred to an explicit expression which enables one to analyse stability of retarded, neutral and even integer-order systems simultaneously. [ABSTRACT FROM AUTHOR]

The adaptive cross approximation (ACA) enhanced multilevel characteristic basis function method (MLCBFM) is studied in this article. It is well known that the impedance submatrices containing the reaction terms of low-level basis functions such as Rao-Wilton-Glisson basis functions have low-rank property. However, the authors find that the high-level impedance matrices between nonoverlapping high-level blocks are also low rank and can be compressed efficiently. This property is used to efficiently calculate the high-level reduced matrix of the MLCBFM through the ACA algorithm in this study. Furthermore an efficient scheme for the high-level characteristic basis functions (CBFs) generation is given, which reduces the redundant operations in the high-level CBFs generation step without loss of accuracy. Numerical results are given to demonstrate the accuracy and efficiency of the presented method. [ABSTRACT FROM AUTHOR]

This paper examines the problem of testing and confidence set construction for one-dimensional functions of the coefficients in autoregressive (AR( p)) models with potentially persistent time series. The primary example concerns inference on impulse responses. A new asymptotic framework is suggested and some new theoretical properties of known procedures are demonstrated. I show that the likelihood ratio (LR) and LR± statistics for a linear hypothesis in an AR( p) can be uniformly approximated by a weighted average of local-to-unity and normal distributions. The corresponding weights depend on the weight placed on the largest root in the null hypothesis. The suggested approximation is uniform over the set of all linear hypotheses. The same family of distributions approximates the LR and LR± statistics for tests about impulse responses, and the approximation is uniform over the horizon of the impulse response. I establish the size properties of tests about impulse responses proposed by Inoue and Kilian (2002) and Gospodinov (2004), and theoretically explain some of the empirical findings of Pesavento and Rossi (2007). An adaptation of the grid bootstrap for impulse response functions is suggested and its properties are examined. [ABSTRACT FROM AUTHOR]

In massive multiple‐input–multiple‐output (MIMO) systems, the downlink zero‐forcing (ZF) precoding method needs to compute the pseudoinverse of large matrices, which introduces high computational complexity, especially when the number of antennas is large or infinite. Although, based on Neumann series expansion, several approximation algorithms could reduce the complexity, calculating the optimisation coefficients would cost large amounts of resources and produce large delay. A novel method, also based on Neumann series expansion, is proposed. In this method, the multiplication of full matrices is transformed into multiplication of diagonal matrices by hollow matrices, effectively reducing computational complexity. Meanwhile, consideration of a simple expression of optimisation coefficients so as to avoid large delay is very useful in high‐mobility scenarios. Analysis and simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop an integrated approach for solving multicriteria group decision‐making problems with multigranular unbalanced hesitant fuzzy linguistic term sets (HFLTSs). Firstly, a signed distance‐based transformation function is proposed to unify multigranular unbalanced hesitant fuzzy linguistic (HFL) assessments. Secondly, a mathematical programming model based on the maximum consensus is constructed to allocate decision‐makers (DMs)' weights objectively. Thirdly, a new signed distance‐based preference score function is defined to aggregate HFL assessments and determine the weak ranking of alternatives, and a novel preference, indifference, and incomparability test framework is constructed to identify the subtle relations among alternatives. On these bases, a signed distance‐based ORESTE (Organísation, rangement et Synthèse de données relarionnelles, in French) method, in which knowledge regarding criterion values and weights are expressed as multigranular unbalanced HFLTSs, is developed to obtain the ranking of alternatives. Finally, an illustrative example, followed by sensitivity and comparative analyses, is presented to verify the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Bayesian networks (BNs) have been widely used in causal analysis because they can express the statistical relationship between significant variables. To gain superior causal analysis results, numerous studies have emphasized the importance of combining a knowledge‐based approach and a data‐based approach. However, combining these two approaches is a difficult task because it can reduce the effectiveness of the BN structure learning. Further, the learning schemes of BNs for computational efficiency can cause an inadequate causal analysis. To address these problems, we propose a knowledge‐driven BN structure calibration algorithm for rich causal semantics. We first present an algorithm that can efficiently identify the subnetworks that can be altered to satisfy the learning condition of the BNs. We then reflect experts' knowledge to reduce erroneous causalities from the learned network. Experiments on various simulation and benchmark data sets were conducted to examine the properties of the proposed method and to compare its performance with an existing method. Further, an experimental study with real data from semiconductor fabrication plants demonstrated that the proposed method provided superior performance in improving structural accuracy. [ABSTRACT FROM AUTHOR]

A novel energy-efficient 8 bit 2.78 kS/s capacitance-to-digital converter (CDC) interface for capacitive pressure sensors is presented. A new direct-capacitance-comparison technique (DCCT) is proposed and employed together with a successive approximation register (SAR) algorithm to resolve the sensor capacitance by, directly, comparing it with an on-chip binary-weighted capacitive digital-to-analogue converter (DAC) array. An offset DAC array compensates for the sensor rest and parasitic capacitances, accordingly, the interface gives a dynamically zoomed digital-output code that corresponds only to the sensor capacitance change. The full-scale input capacitance of the CDC is easily adjustable to interface to a wide range of sensors. The proposed 8 bit SAR-based CDC is designed and simulated using 0.18 μm standard CMOS technology. The CDC exhibits a capacitance sensing range from 4 to 6 pF, provides a resolution and linearity of 7.26 and 8.2 bit, respectively. It achieves a figure-of-merit of 1.8 pJ/step at 1.4 V supply and 36 μs conversion time. Compared with the state-of-the- art implementations with similar performance. [ABSTRACT FROM AUTHOR]

Abstract: Correlation clustering possibly represents the most intuitive form of clustering construction. It gives solutions that can be approximated while automatically selecting the number of clusters. This approach handles scenarios where the focus is on relationships between the objects instead of on actual representations of the objects. The suitability of this method extends to the structured objects, for which feature vectors are not easy to obtain. Given the increasing scale of data these days, correlation clustering has become a powerful addition to the fields of data mining and agnostic learning. Correlation clustering considers a weighted graph G=(V,E), where the edge weight indicates whether two nodes are similar (positive edge weight) or different (negative edge weight). The task is to find a clustering that either maximizes agreements or minimizes disagreements. Unlike other clustering algorithms, this does not require choosing the number of clusters (k) in advance. The objective to minimize the sum of weights of the cut edges is independent of the number of clusters. Methodologies, such as approximations and linear programming formulations, have been used to approach this problem. This paper focuses on the problem of correlation clustering and lists the solutions proposed by various researchers. These solutions approach the problem using different computational techniques. Correlation clustering‐based applications such as entity de‐duplication, signed social networks, and problem of aggregating multiples have also been discussed. [ABSTRACT FROM AUTHOR]

An iterative implementation of eigenspace-based beamforming is introduced. An approximate expression of signal subspace projection is first derived, and then the eigenspace-based beamformer is innovatively implemented with a two-stage beamforming scheme. The proposed method is capable of avoiding the source number estimation or subspace projection operation, which are indispensable for existing solutions. Hence, it can be fast and conveniently applied in programming. The effectiveness of the proposed technique is demonstrated by simulations [ABSTRACT FROM AUTHOR]

A foreground digital self-calibration technique that improves capacitor matching of a digital-to-analogue converter (DAC) employed in successive approximation register (SAR) analogue-to-digital converters (ADCs) is presented. By exploiting an antisymmetric behaviour of capacitor mismatch errors in SAR ADCs and the proposed DAC switching scheme, the calibration technique significantly reduces capacitor mismatch errors without resorting to extensive computation or dedicated circuits. [ABSTRACT FROM AUTHOR]

A controlled accuracy approximation scheme of the sigmoid function for artificial neuron implementation based on Taylor's theorem and the Lagrange form of the error is proposed. The main advantages of the proposed solution are two: it provides a systematic way to guarantee the required accuracy and it reuses the circuitry of the linear part of the neuron to compute the sigmoid function. The sigmoid derivative is also available for artificial neural networks with online learning capabilities. [ABSTRACT FROM AUTHOR]

Localisation error due to sensitivity to position ambiguity, with respect to some neighbouring nodes, is a major problem in wireless localisation. To solve this problem, the tangent linear approximation (TLA) for the nonlinear localisation model is derived, contributing to incorporating the effect of the neighbours' position ambiguity into belief propagation (BP) localisation. Theoretical analysis and numerical results indicate that the TLA-BP algorithm can acquire higher accuracy and convergence probability compared with other typical methods. [ABSTRACT FROM AUTHOR]

Image registration is an essential and important process in seal identification. Rigid image registration in seal identification is known to be more suitable than elastic registration. The registration process is quite sensitive to outliers in matched feature point pairs. A novel method to take the matching outliers as data corrupted by 'sample-specific' error which can be modelled by a column sparse matrix is proposed. An optimisation problem is developed to describe this model. By solving the optimisation problem, corruption can be eliminated and the transformation model can be recovered simultaneously. An efficient algorithm called column sparse registration is given via the augmented Lagrange multiplier method. Experiments on real-world seal registration data demonstrate that the proposed method is robust to outliers among matched pairs and outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]