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Accurate and effective localization of reliability-critical gates (RCGs) is one of the important prerequisites for low-cost circuit fault tolerance in the early stages of circuit design. This article introduces an accurate and effective approach for localizing RCGs in combinational logic blocks through a benchmarking technique. In the proposed approach, uniform non-Bernoulli sequences are used to produce a set of input vectors for driving circuits. A full-period linear congruential algorithm is employed to generate a sequence that provides the sampled order for the RCGs to be analyzed. This ensures that each gate in the circuit is treated as fairly as possible. To accelerate the localization process, an input-vector-based pruning technique combined with a counting method is also introduced to identify the specified number of RCGs. Then, the criticality of gate reliability for each RCG is measured through benchmarking. A clustering algorithm carries out the convergence checking for the proposed approach. The performance of the proposed approach was evaluated in terms of accuracy, stability, and time-space overhead by various simulations on 74-series circuits and ISCAS-85 benchmark circuits. The results show that its accuracy is close to that of the Monte Carlo model and its stability is better than that of other approximate methods. Moreover, compared with approximate methods, the time overhead of our approach is advantageous in the presence of similar memory overheads. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) has become an attractive solution to enhance the computing and storage capacity of mobile devices by leveraging available resources on edge nodes. In MEC, the arrivals of tasks are highly dynamic and are hard to predict precisely. It is of great importance yet very challenging to assign the tasks to edge nodes with guaranteed system performance. In this article, we aim to optimize the revenue earned by each edge node by optimally offloading tasks to the edge nodes. We formulate the revenue-driven online task offloading (ROTO) problem, which is proved to be NP-hard. We first relax ROTO to a linear fractional programming problem, for which we propose the Level Balanced Allocation (LBA) algorithm. We then show the performance guarantee of LBA through rigorous theoretical analysis, and present the LB-Rounding algorithm for ROTO using the primal-dual technique. The algorithm achieves an approximation ratio of $2(1+\xi)\ln (d+1)$ 2 (1 + ξ) ln (d + 1) with a considerable probability, where $d$ d is the maximum number of process slots of an edge node and $\xi$ ξ is a small constant. The performance of the proposed algorithm is validated through both trace-driven simulations and testbed experiments. Results show that our proposed scheme is more efficient compared to baseline algorithms. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) technology provisions unprecedented opportunities to evolve the interconnection among human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated Mobile Edge Computing (MEC) with remote clouds is a promising platform to enable delay-sensitive service provisioning for IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices. Thus, computation-intensive and sensing data from IoT devices can be offloaded to the MEC network immediately for processing, and the service response latency can be significantly reduced. In this paper, we first formulate two novel optimization problems for delay-sensitive IoT applications, i.e., the total utility maximization problems under both static and dynamic offloading task request settings, with the aim to maximize the accumulative user satisfaction on the use of the services provided by the MEC, and show the NP-hardness of the defined problems. We then devise efficient approximation and online algorithms with provable performance guarantees for the problems in a special case where the bandwidth capacity constraint is negligible. We also develop efficient heuristic algorithms for the problems with the bandwidth capacity constraint. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising in reducing service delays and enhancing user satisfaction, and the proposed algorithms outperform their counterparts by at least 10.8 percent. [ABSTRACT FROM AUTHOR]

In this paper, MUSE, an algorithm enabled by backscattering tag-to-tag network (BTTN) is presented to accomplish simultaneous 2-D localization of large-scale (10 m × 10 m) massive (20 $\sim$ ∼ 50) passive UHF RFIDs. In BTTNs, the most intractable problem is the high-frequency loss of range measurements. In a particular case of 30 tags to be located with maximum communication range being 3 m, the rate is nearly up to 85.75 percent. In the proposed framework, we utilize relevant knowledge in the theory of graphs to obtain underlying subsets in which tags can communicate with each other and then assemble them stage by stage to achieve overall localization. Theoretical analysis shows that multistage assembly imparts extraordinary characteristics to MUSE: Assembling rectifies fragment maps given some condition, and in later stages prevents errors flowing down into the next stage. Experimental analysis shows that the condition is easy to satisfy. Furthermore, an analytical expression for the Cramér-Rao lower bound is also derived as a benchmark to evaluate the localization performance. Extensive simulations demonstrate that MUSE outperforms existing algorithms for simultaneous localization. [ABSTRACT FROM AUTHOR]

Currently, a nonconvex economic dispatch problem is one of the research focuses in the field of smart grid (SG). A variety of algorithms are developed to solve it. However, these algorithms are prone to suffering from high computation cost and slow convergence rate, which creates an inevitable gap between theoretical analysis and practical real-time operations. In this article, we aim at providing an ensemble deep-learning-based approach to tackle such a challenging issue. First, a novel ensemble method is presented to explore the ground truth of nonconvex economic dispatch problems. Second, considering the time-varying total load demand, cost coefficients, and dispatchability of all generation units in a practical SG system as the features, a new deep neural network structure is proposed to learn the complex mapping from instant features to an optimal nonconvex economic dispatch solution. If such a mapping is well approximated by the designed deep neural network, no significant effort is required to solve a new economic dispatch problem, and the solution is obtained on the scale of milliseconds. Third, analyzing that a single deep neural network may be weak to a small part of the mapping space of the nonconvex economic dispatch problem, we further present an ensemble of multiple parallel deep neural networks trained sequentially with a simplified Adaboost.R2 algorithm. Finally, case studies reveal that the proposed approach achieves orders of magnitude speedup in computational time while guaranteeing similar or better performance on minimizing the overall generation cost compared to the state-of-the-art nonconvex economic dispatch algorithms. [ABSTRACT FROM AUTHOR]

We consider the two-dimensional multi-target detection (MTD) problem of estimating a target image from a noisy measurement that contains multiple copies of the image, each randomly rotated and translated. The MTD model serves as a mathematical abstraction of the structure reconstruction problem in single-particle cryo-electron microscopy, the chief motivation of this study. We focus on high noise regimes, where accurate detection of image occurrences within a measurement is impossible. To estimate the image, we develop an expectation-maximization framework that aims to maximize an approximation of the likelihood function. We demonstrate image recovery in highly noisy environments, and show that our framework outperforms the previously studied autocorrelation analysis in a wide range of parameters. [ABSTRACT FROM AUTHOR]

The Gomory-Hu tree is a popular optimization algorithm that enables to efficiently find a min-cut (or equivalently, max-flow) for every pair of nodes in a graph. However, graphs cannot capture broadcasting and interference in wireless: over wireless networks we need to resort to the information-theoretical cut-set to bound the max-flow. Leveraging the submodularity of mutual information, we show that the Gomory-Hu algorithm can be used to efficiently find information-theoretic rate characterizations such as the capacity or an approximation to the capacity, over a number of network scenarios including wireless Gaussian networks and deterministic relay networks. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) is a promising computing paradigm that incorporates computing capability into a radio access network (RAN) nearby user equipment (UE) to improve the quality of experience. However, MEC also encounters many challenges inherent in RANs, such as mobility management. In this letter, we develop a novel computation offloading scheme utilizing non-orthogonal multiple access (NOMA) and dual connectivity (DC) and focus on jointly optimizing task segmentation and power allocation to minimize the total energy consumption. Specifically, the joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem that is non-deterministic polynomial hard (NP-hard). To solve this problem, we propose a new heuristic algorithm to obtain a sub-optimal solution. We then propose an online learning algorithm based on a twin delayed deep deterministic policy gradient (TD3) to meet user mobility requirements. The numerical results show that the proposed scheme outperforms other schemes, and the TD3-based algorithm has comparable energy consumption performance and dramatically reduces the execution time compared with the heuristic algorithm. [ABSTRACT FROM AUTHOR]

In real cases, missing values tend to contain meaningful information that should be acquired or should be analyzed before the incomplete dataset is used for machine learning tasks. In this work, two algorithms named jointly fuzzy C-Means and vaguely quantified nearest neighbor (VQNN) imputation (JFCM-VQNNI) and jointly fuzzy C-Means and fitted VQNN imputation (JFCM-FVQNNI) have been proposed by considering clustering conception and sufficient extraction of uncertain information. In the proposed JFCM-VQNNI and JFCM-FVQNNI algorithm, the missing value is regarded as a decision feature, and then, the prediction is generated for the objects that contain at least one missing value. Specially, as for JFCM-VQNNI algorithm, indistinguishable matrixes, tolerance relations, and fuzzy membership relations are adopted to identify the potential closest filled values based on corresponding similar objects and related clusters. On the basis of JFCM-VQNNI algorithm, JFCM-FVQNNI algorithm synthetic analyzes the fuzzy membership of the dependent features for instances with each cluster. In order to fill the missing values more accurately, JFCM-FVQNNI algorithm performs fuzzy decision membership adjustment in each object with respect to the related clusters by considering highly relevant decision attributes. The experiments have been carried out on five datasets. Based on the analysis of root-mean-square error, mean absolute error, comparison of imputation values with actual values, and classification accuracy results analysis, we can draw the conclusion that the proposed JFCM-FVQNNI and JFCM-VQNNI algorithms yields sufficient and reasonable imputation performance results by comparing with fuzzy C-Means parameter-based imputation algorithm and fuzzy C-Means rough parameter-based imputation algorithm. [ABSTRACT FROM AUTHOR]

In the massive grant-free orthogonal frequency division multiple access (OFDMA), the timing and frequency offsets between users impose new challenges on joint active user detection (AUD) and channel estimation (CE) for the subsequent data recovery. In the asynchronous OFDMA, the timing and frequency offset effects can be modeled as the phase-shifting on the pilot matrix. As such, by constructing the measurement matrix with timing and frequency offsets, the joint estimation problem can be formulated as a multiple measurement vector (MMV) recovery problem with structured sparsity. However, such structured sparsity cannot be tackled by the existing compressed sensing (CS) techniques. To address this issue, we develop an efficient structured generalized approximate message passing (S-GAMP) algorithm, which includes the parallel AMP-MMV algorithm as a particular case. To deal with the high dimensionality of the measurement matrix, we propose the dynamic S-GAMP algorithm with a dynamic measurement matrix to reduce the computational complexity. Simulation results confirm the superiority of the proposed algorithms in grant-free OFDMA with both timing and frequency offsets. [ABSTRACT FROM AUTHOR]

False data injection attack on the state estimation algorithms have already showcased its detrimental effects for modern grid operation. This letter promotes a novel attack vector formulation policy for the linear state estimation algorithm by exploring the low-rank subspace of the mapping matrix. An extensive analysis on the IEEE 14 bus test bench promotes the aforementioned propositions. [ABSTRACT FROM AUTHOR]

Sensor placement methods for field estimation based on Gaussian processes are proposed. Generally, sensor placement methods determine the appropriate placement positions by selecting them from predefined candidate positions. Many criteria for the selection have been proposed, with which the quality of the placements is evaluated with regard to the field at the candidate positions. This means that these sensor placement methods seek to find the positions that can estimate the field at the candidate positions accurately. In practical situations, however, the candidate sensor placement region can be different from the target region for field estimation. In this paper, to make sensor placement methods applicable to this situation, we propose two sensor placement methods based on the mean squared error and on conditional entropy that can be applied to cases in which the sensor placement region is arbitrarily restricted. After formulating the sensor placement problems, two approximate algorithms are derived: the greedy algorithm and the convex-relaxation-based algorithm. The application of the proposed methods to sound field estimation is also illustrated, and their effectiveness was confirmed through numerical experiments. [ABSTRACT FROM AUTHOR]

This article presents a novel Bayesian approach for hyperspectral image unmixing. The observed pixels are modeled by a linear combination of material signatures weighted by their corresponding abundances. A spike-and-slab abundance prior is adopted to promote sparse mixtures and an Ising prior model is used to capture spatial correlation of the mixture support across pixels. We approximate the posterior distribution of the abundances using the expectation-propagation (EP) method. We show that it can significantly reduce the computational complexity of the unmixing stage and meanwhile provide uncertainty measures, compared to expensive Monte Carlo strategies traditionally considered for uncertainty quantification. Moreover, many variational parameters within each EP factor can be updated in a parallel manner, which enables mapping of efficient algorithmic architectures based on graphics processing units (GPUs). Under the same approximate Bayesian framework, we then extend the proposed algorithm to semi-supervised unmixing, whereby the abundances are viewed as latent variables and the expectation-maximization (EM) algorithm is used to refine the endmember matrix. Experimental results on synthetic data and real hyperspectral data illustrate the benefits of the proposed framework over state-of-art linear unmixing methods. [ABSTRACT FROM AUTHOR]

This paper presents density evolution (DE) techniques based on reciprocal channel approximation (RCA) for general bit-interleaved coded modulation (BICM) systems. The original RCA-based DE (RCA-DE) technique is implemented under the assumption of binary-input additive white Gaussian noise channels (BI-AWGNCs). In BICM systems, however, $M$ -ary modulation schemes are generally used, and they can be regarded as to be a parallel transmission of $\log _{2} M$ bits. Thus, $\log _{2} M$ separate bit-level channels, which are not Gaussian equivalent, need to be considered. In order to extend the conventional RCA-DE technique to BICM systems, we first establish a model of protograph BICM ensembles. Two methods are developed based on the bit error rate (BER) and the BICM capacity in order to find a corresponding BI-AWGNC that models each of the bit-level channels. Using these methods, we implement the protograph RCA-DE technique for BICM systems and show that it achieves an accurate estimation. As a practical application of the proposed RCA-DE method, we design bit interleavers in order to achieve better BICM performance in the 3GPP New Radio (NR) LDPC coding system. Numerical results show that performance gain of up to 0.3 dB is consistently achieved over a wide range of parameter values. [ABSTRACT FROM AUTHOR]

The maximum clique problem (MCP) has various applications to reveal the structure and function of graphs. Graphs are constantly updated in the real life. However, no algorithm is specifically designed for dynamic graph. Although MCP in dynamic graphs can be solved by simply invoking a state-of-the-art static approach, such as PMC, when the graph is updated, such an approach of simply re-calculating from scratch is inefficient. The key issue with MCP algorithm is to find a large clique, namely a seed, as fast as possible. Thus, search space can be pruned based on the seed. Size of the seed greedily found by PMC cannot be guaranteed, as it fluctuates considerably. Moreover, the time required to find a seed under PMC is up to $O(| E| \cdot \Delta (G))$ O (| E | · Δ (G)) , where $\Delta (G)$ Δ (G) is the highest degree in G. In this article, we intend to find a sizable seed by updating the previous maximum clique with the incident vertices of the inserted/deleted edge. Size of the seed now is guaranteed to be no less than $\omega (G^{\prime})\; - \;1$ ω (G ') - 1 , where $\omega (G^{\prime})$ ω (G ') is the size of the maximum clique on the updated graph. Moreover, the seed can be found in a time complexity of $O(\Delta (G)^{2})$ O (Δ (G) 2) . Two other crucial issues related to the MCP in dynamic graphs are refreshing rate and refreshing overhead. After a tight upper bound is imposed on $\omega (G^{\prime})$ ω (G ') , the necessity of refreshing is evaluated by comparing the seed with its largest challenger, then unnecessary refreshing is wiped out effectively. The size of the largest challenger is judiciously estimated using a lazy growth strategy. Subsequently, the search space in refreshing is confined on a much smaller subgraph using a local refreshing strategy. Extensive experiments indicate that the proposed approach outperforms the baseline algorithm by approximately one order of magnitude. [ABSTRACT FROM AUTHOR]

The $K$ K -means algorithm is one of the most popular clustering methods. However, it is a well-known fact that its performance, in terms of quality of the obtained solution and computational load, highly depends upon its initialization phase. For this reason, different initialization techniques have been developed throughout the years to enable its fast convergence to competitive solutions. In this sense, it is common practice to re-start the $K$ K -means algorithm several times via one of these techniques and keep the solution with the lowest error. Unfortunately, such a choice is still likely to be a poor approximation of the optimal set of centroids. In this article, we introduce a cheap Split-Merge step that can be used to re-start the $K$ K -means algorithm after reaching a fixed point. Under some settings, one can show that this approach reduces the error of the given fixed point without requiring any further iteration of the $K$ K -means algorithm. Moreover, experimental results show that this strategy is able to generate approximations with an associated error that is hard to reach for different multi-start methods, such as multi-start Forgy $K$ K -means, $K$ K -means++ and Hartigan $K$ K -means, while also computing a lower amount of distances than the previous algorithms. [ABSTRACT FROM AUTHOR]

Many successful methods have been proposed for learning low-dimensional representations on large-scale networks, while almost all existing methods are designed in inseparable processes, learning embeddings for entire networks even when only a small proportion of nodes are of interest. This leads to great inconvenience, especially on large-scale or dynamic networks, where these methods become almost impossible to implement. In this paper, we formalize the problem of separated matrix factorization, based on which we elaborate a novel objective function that preserves both local and global information. We compare our SMF framework with approximate SVD algorithms and demonstrate SMF can capture more information when factorizing a given matrix. We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes. By implementing separability, our algorithm reduces the redundant efforts to embed irrelevant nodes, yielding scalability to large networks. To further incorporate complex information into SepNE, we discuss several methods that can be used to leverage high-order proximities in large networks. We demonstrate the effectiveness of SepNE on several real-world networks with different scales and subjects. With comparable accuracy, our approach significantly outperforms state-of-the-art baselines in running times on large networks. [ABSTRACT FROM AUTHOR]

Dual Coordinate Descent (DCD) is one of the most popular optimization methods. The parallelization of DCD is difficult, as DCD is sequential in nature. As such, simultaneously running multiple DCD threads on batches of data elements causes result inaccuracy and slow convergence, due to the concurrent updates of multiple coordinates. Some parallelization methods adopt separable approximate functions that depend on the degree of parallelism. Such dependencies result in both poor scalability and slow-convergence. To address these challenges, in this paper we present a new parallel primal-dual algorithm for DCD, called PPD. In PPD, the block data distribution is utilized to obtain a new approximate function that is independent from the parallelism. Moreover, PPD is designed with a novel primal-dual acceleration scheme to approach the optimal solution closely and quickly. We demonstrate the advantages of PPD in terms of scalability and efficiency through experiments. [ABSTRACT FROM AUTHOR]

Spectral clustering is becoming more and more popular because it has good performance in discovering clusters with varying characteristics. However, it suffers from high computational cost, unstable clustering results and noises. This work presents a novel approximate spectral clustering based on dense cores and density peaks, called DCDP-ASC. It first finds a reduced data set by introducing the concept of dense cores; then defines a new distance based on the common neighborhood of dense cores and calculates geodesic distances between dense cores according to the new defined distance; after that constructs a decision graph with a parameter-free local density and geodesic distance for obtaining initial centers; finally calculates the similarity between dense cores with their new defined geodesic distance, employs normalized spectral clustering method to divide dense cores, and expands the result on dense cores to the whole data set by assigning each point to its representative. The results on some challenging data sets and the comparison of our algorithm with some other excellent methods demonstrate that the proposed method DCDP-ASC is more advantageous in identifying complex structured clusters containing a lot of noises. [ABSTRACT FROM AUTHOR]

Area coverage and network lifetime are two contradictory issues to the architecture development of a wireless sensor network (WSN). A satisfactory balance could be achieved by deploying abundant sensor nodes randomly and dividing them into $k$ exclusive cover sets. Toward self-organized partition with higher efficiency, we address the problem from the perspective of networked potential games and propose a memorial mixed-response algorithm (MMRA), which is implemented in a distributed and synchronous manner. Being viewed as a game player, each sensor node first updates its memory using a temporary action, which is generated by following a mixed response rule. After this, the coordination evolves into the next iteration by each player randomly drawing an action from its memory with equal probabilities. We prove that our algorithm converges with probability 1 to a convention of Nash equilibria, with the worst approximation ratio strictly larger than 0.5. Moreover, it is also found that a tradeoff between solution efficiency and computation time could be achieved via the adjustment of the amount of randomness introduced via the memory length $m$ as well as the probability $p_{m}$ , where better partition results are more likely to be generated using a larger $m$ and smaller $p_{m}$. Comparisons with existing distributed methods demonstrate the superiority of our method in terms of solution refinement as well as convergence speed. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and backscatter communication are two emerging technologies for low-power communication. In this paper, we consider a NOMA backscatter system, where signals from two backscatter devices are multiplexed on a frequency resource block using NOMA in each time slot. Our objective is to maximize the average energy efficiency by optimizing backscatter device pairing, reflection coefficients of backscatter devices, and the transmit power of the reader. We formulate the average energy efficiency maximization problem subject to the minimum circuit power and the minimum data rate requirements of the backscatter devices, and the transmit power constraint of the reader. The formulated problem is nonconvex. To obtain a suboptimal solution for this problem, we use alternating optimization technique and decompose the problem into two subproblems. The subproblems are solved by using fractional programming, Dinkelbach’s algorithm, and successive convex approximation method. Simulation results show that our proposed algorithm converges quickly to a suboptimal solution. Our proposed algorithm outperforms several baseline algorithms, including the genetic algorithm, fixed device pairing scheme, conventional device pairing scheme, maximum transmit power allocation scheme, and random reflection coefficient selection scheme, in terms of the average energy efficiency. The optimality gap of our proposed algorithm is investigated by comparing with the optimal scheme, in which the optimal device pairing is obtained based on exhaustive search. [ABSTRACT FROM AUTHOR]

We propose a robust normal estimation method for both point clouds and meshes using a low rank matrix approximation algorithm. First, we compute a local isotropic structure for each point and find its similar, non-local structures that we organize into a matrix. We then show that a low rank matrix approximation algorithm can robustly estimate normals for both point clouds and meshes. Furthermore, we provide a new filtering method for point cloud data to smooth the position data to fit the estimated normals. We show the applications of our method to point cloud filtering, point set upsampling, surface reconstruction, mesh denoising, and geometric texture removal. Our experiments show that our method generally achieves better results than existing methods. [ABSTRACT FROM AUTHOR]

This article presents a data-driven method for computing reachable sets where active learning (AL) is used to reduce the computational burden. Set-based methods used to estimate reachable sets typically do not scale well with the state-space dimension, or rely heavily on the existence of a model. If such a model is not available, it is simple to generate state trajectory data by numerically simulating black-box oracles of systems (whose dynamics are unknown) from sampled initial conditions. Using these data samples, the estimation of reachable sets can be posed as a classification problem, wherein AL can intelligently select samples that are most informative and least similar to previously labeled samples. By exploiting submodularity, the actively learned samples can be selected efficiently, with bounded suboptimality. Our proposed framework is illustrated by estimating the domains of attractions of model predictive controllers (MPCs) and reinforcement learners. We also consider a scenario where there are two oracles that differ with respect to evaluation costs and labeling accuracy. We propose a framework to reduce the dependency of the expensive oracle in labeling samples using disagreement-based AL (DBAL). The potential of the DBAL algorithm is demonstrated on a solver selection problem for real-time MPC. [ABSTRACT FROM AUTHOR]

This article presents an event-sampled integral reinforcement learning algorithm for partially unknown nonlinear systems using a novel dynamic event-triggering strategy. This is a novel attempt to introduce the dynamic triggering into the adaptive learning process. The core of this algorithm is the policy iteration technique, which is implemented by two neural networks. A critic network is periodically tuned using the integral reinforcement signal, and an actor network adopts the event-based communication to update the control policy only at triggering instants. For overcoming the deficiency of static triggering, a dynamic triggering rule is proposed to determine the occurrence of events, in which an internal dynamic variable characterized by a first-order filter is defined. Theoretical results indicate that the impulsive system driven by events is asymptotically stable, the network weight is convergent, and the Zeno behavior is successfully avoided. Finally, three examples are provided to demonstrate that the proposed dynamic triggering algorithm can reduce samples and transmissions even more, with guaranteed learning performance. [ABSTRACT FROM AUTHOR]

In this paper we study the deployment of multiple unmanned aerial vehicles (UAVs) to form a temporal UAV network for the provisioning of emergent communications to affected people in a disaster zone, where each UAV is equipped with a lightweight base station device and thus can act as an aerial base station for users. Unlike most existing studies that assumed that a UAV can serve all users in its communication range, we observe that both computation and communication capabilities of a single lightweight UAV are very limited, due to various constraints on its size, weight, and power supply. Thus, a single UAV can only provide communication services to a limited number of users. We study a novel problem of deploying $K$ UAVs in the top of a disaster area such that the sum of the data rates of users served by the UAVs is maximized, subject to that (i) the number of users served by each UAV is no greater than its service capacity; and (ii) the communication network induced by the $K$ UAVs is connected. We then propose a $\frac {1-1/e}{\lfloor \sqrt {K} \rfloor }$ -approximation algorithm for the problem, improving the current best result of the problem by five times (the best approximation ratio so far is $\frac {1-1/e}{5(\sqrt {K} +1)}$), where $e$ is the base of the natural logarithm. We finally evaluate the algorithm performance via simulation experiments. Experimental results show that the proposed algorithm is very promising. Especially, the solution delivered by the proposed algorithm is up to 12% better than those by existing algorithms. [ABSTRACT FROM AUTHOR]

Achieving high performance in many multi-server systems (e.g., web hosting center, cloud) requires finding a good assignment of worker threads to servers and also effectively allocating each server’s resources to its assigned threads. The assignment and allocation components of this problem have been studied extensively but largely separately in the literature. In this paper, we introduce the assign and allocate (AA) problem, which seeks to simultaneously find an assignment and allocation that maximizes the total utility of the threads. Assigning and allocating the threads together can result in substantially better overall utility than performing the steps separately, as is traditionally done. We model each thread by a utility function giving its performance as a function of its assigned resources. We first prove that the AA problem is NP-hard. We then present a $2 (\sqrt {2}-1) > 0.828$ factor approximation algorithm for concave utility functions, which runs in $O(mn^{2} + n (\log mC)^{2})$ time for $n$ threads and $m$ servers with $C$ amount of resources each. We also give a faster algorithm with the same approximation ratio and $O(n (\log mC)^{2})$ time complexity. We then extend the problem to two more general settings. First, we consider threads with nonconcave utility functions, and give a 1/2 factor approximation algorithm. Next, we give an algorithm for threads using multiple types of resources, and show the algorithm achieves good empirical performance. We conduct extensive experiments to test the performance of our algorithms on threads with both synthetic and realistic utility functions, and find that they achieve over 92% of the optimal utility on average. We also compare our algorithms with a number of practical heuristics, and find that our algorithms achieve up to 9 times higher total utility. [ABSTRACT FROM AUTHOR]

In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding a data collection tour for each UAV. To ensure the ‘freshness’ of the collected data, the total time spent in the tour of each UAV that consists of the UAV flying time and data collection time must be no greater than a given delay $B$ , e.g., 20 minutes. In this paper, we consider a problem of deploying the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour of any UAV is no greater than $B$. Specifically, we study two variants of the problem: one is that a UAV needs to fly to the location of each IoT device to collect its data; the other is that a UAV is able to collect the data of an IoT device if the Euclidean distance between them is no greater than the wireless transmission range of the IoT device. For the first variant of the problem, we propose a novel 4-approximation algorithm, which improves the best approximation ratio $4\frac {4}{7}$ for it so far. For the second variant, we devise the very first constant factor approximation algorithm. We also evaluate the performance of the proposed algorithms via extensive experiment simulations. Experimental results show that the numbers of UAVs deployed by the proposed algorithms are from 11% to 19% less than those by existing algorithms on average. [ABSTRACT FROM AUTHOR]

Motion planning in unstructured and low-speed environments is a fundamental and difficult task for all mobile robotics. If we view motion planning as an optimization problem, the non-convex collision avoidance constraints and the nonlinear vehicle dynamic constraints make motion planning challenging and time-consuming. In this paper, we propose a Successive Linearization in Feasible Set (SLiFS) algorithm to address these two difficulties. SLiFS consists of two steps. The first step is to iteratively construct convex feasible sets around the current trajectory to approximate non-convex collision avoidance constraints. The second step is to successively linearize the nonlinear dynamic constraints along the current trajectory and further penalize them into the objective function to avoid infeasible linearized constraints, so that the current trajectory can be reshaped within the obtained convex feasible sets by iteratively solving the linearized optimization problem. The main innovation of SLiFS algorithm is that we consider the $L_{1}$ norm type penalty function in the second step. We find that the sparsity of the $L_{1}$ norm might help to satisfy the robotic dynamic constraints by numerical experiments. Numerical testing results show that our proposed SLiFS algorithm has a high success rate to find feasible trajectories and costs much less time than the classical interior-point method. [ABSTRACT FROM AUTHOR]

The skyline query is one of the most important supporting technologies for the location-based query services in the road network. Usually, when a user queries the skyline points in the road network, the query area is a user-centered circle or rectangle area, without considering the impact of the current movement speed and direction of the user on the formation of the query area. In this context, a speed and direction aware skyline query method is proposed, which can provide the skyline query area for the users by considering their moving speed and direction. Since the efficiency to directly obtain points of interest from speed and direction aware query area is not high, a Voronoi based speed and direction query area generation algorithm is proposed to approximate the query area, so as to improve the obtaining efficiency of points of interest in the area. The experiments on road networks and points of interest data of Beijing show the performance of the proposed method in terms of query efficiency and quality. [ABSTRACT FROM AUTHOR]

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed choices can be a very challenging and time-consuming task. This paper seeks to help modellers by leveraging the Bayesian framework and the concept of automatic relevance determination (ARD), in order to automatically determine an optimal utility function specification from an exponentially large set of possible specifications in a purely data-driven manner. Based on recent advances in approximate Bayesian inference, a doubly stochastic variational inference is developed, which allows the proposed MNL-ARD model to scale to very large and high-dimensional datasets. Using semi-artificial choice data, the proposed approach is shown to be able to accurately recover the true utility function specifications that govern the observed choices. Moreover, when applied to real choice data, MNL-ARD is able discover high quality specifications that can outperform previous ones from the literature according to multiple criteria, thereby demonstrating its practical applicability. [ABSTRACT FROM AUTHOR]

Source localization has been a crucial fundamental service in wireless sensor networks (WSNs). Existing algorithms assume a regular region or controlled deployment. In practice, however, irregular network topologies often occurs, which greatly downgrade the localization performance. In this paper, we propose a new distributed localization approach based on Anchor Segmentation and Projection for Irregular networks (ASPI). The new framework is composed of three phases: anchor segmentation boarder construction, convex hull identification and projection-based localization. An anchor based network approximate convex segmentation method is proposed to reduce the consumption of network resources in the first two phases and an improved giftwrapping based convex hull identification method is provided to reduce the complexity. In the localization phase, we formulate the localization as a convex feasibility problem to avoid the multimodality in Maximum likelihood techniques and an alternative procedure is provided for inconsistent situation in the projection-based scheme. Experiments are conducted and the results demonstrate that our algorithm outperforms other existing solutions in irregular-shaped networks in higher accuracy with low complexity. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) is an important aspect of satellite communications. Different with the existing algorithms that typically use the first-order Taylor lower bound approximation to convert non-convex EE maximization (EEM) problems into convex ones, in this letter a two-step quadratic transformation method is presented. In the first step, the fractional form of the achievable rate over the total power consumption is converted into a non-fractional form based on quadratic transformation. In the second step, the fractional form of the signal power over the interference-and-noise power is further converted into a non-fractional form, still based on quadratic transformation. After the two-step quadratic transformation, the original EEM problem is converted into an equivalent convex one. Then an alternating optimization algorithm is presented to solve it by iteratively performing two stages until a stop condition is satisfied. Simulation results show that the presented algorithm can fast converge and its performance is better than that of the sequential convex approximation algorithm and the multibeam interference mitigation algorithm. [ABSTRACT FROM AUTHOR]

In order to simplify the node-wise scheduling algorithm of the alternating direction method of multipliers (NS-ADMM) decoding, approximate line segment projection algorithm (LSA) is adopted to perform the projection in the node-wise scheduling algorithm. Experimental results show that using approximate line segment projection instead of precise projection in node-wise scheduling algorithm will yield a poor frame error rate (FER) performance. To solve this problem, dynamic scheduling strategy that selects the message-passing schedule according to the convergence status of the check node is proposed. Simulation results demonstrate that the modified node-wise scheduling algorithm based on line segment projection outperforms existing decoding algorithms in terms of FER performance and convergence speed, while it has good error correction performance with a lower computational complexity. [ABSTRACT FROM AUTHOR]

Data representation techniques have made a substantial contribution to advancing data processing and machine learning (ML). Improving predictive power was the focus of previous representation techniques, which unfortunately perform rather poorly on the interpretability in terms of extracting underlying insights of the data. Recently, the Kolmogorov model (KM) was studied, which is an interpretable and predictable representation approach to learning the underlying probabilistic structure of a set of random variables. The existing KM learning algorithms using semi-definite relaxation with randomization (SDRwR) or discrete monotonic optimization (DMO) have, however, limited utility to big data applications because they do not scale well computationally.In this paper, we propose a computationally scalable KM learning algorithm, based on the regularized dual optimization combined with enhanced gradient descent (GD) method. To make our method more scalable to large-dimensional problems, we propose two acceleration schemes, namely, the eigenvalue decomposition (EVD) elimination strategy and an approximate EVD algorithm. Furthermore, a thresholding technique by exploiting the error bound analysis and leveraging the normalized Minkowski $\ell _1$ -norm, is provided for the selection of the number of iterations of the approximate EVD algorithm. When applied to big data applications, it is demonstrated that the proposed method can achieve compatible training/prediction performance with significantly reduced computational complexity; roughly two orders of magnitude improvement in terms of the time overhead, compared to the existing KM learning algorithms. Furthermore, it is shown that the accuracy of logical relation mining for interpretability by using the proposed KM learning algorithm exceeds 80%. [ABSTRACT FROM AUTHOR]

Motivated by the recent success of reinforcement learning algorithms, this paper studies a class of biased stochastic approximation (SA) procedures under a mild “ergodicity-like” assumption on the random noise sequence. Building on a multistep Lyapunov function that looks ahead to several future updates to accommodate the stochastic perturbations (thus gaining control over the bias), we prove a general result on the convergence of the SA iterates, and use it to derive non-asymptotic bounds on the mean-square error in the case of constant stepsizes. This novel viewpoint renders finite-time analysis of biased SA algorithms under a family of stochastic perturbations possible. For direct comparison with prior work, we demonstrate these bounds by applying them to TD-learning with linear function approximation, under the Markov chain observation model. The resultant finite-time error bound for TD-learning is the first of its kind, in the sense that it holds i) for the unmodified versions (i.e., without any modification to the updates) using even nonlinear approximators; as well as for Markov chains ii) under sublinear mixing conditions and iii) starting from any initial distribution, at least one of which has to be violated for existing results to be applicable. [ABSTRACT FROM AUTHOR]

Addressing non-convexity plays a fundamental role in solving the optimal electricity-gas flow models. In this paper, an improved spatial branch-and-bound algorithm is proposed to solve the non-convex problem, which is formulated as a mixed-integer bilinear programming, for its exact solution. The core of the algorithm is to divide the non-convex model into convex and small sub-models by branching on specific continuous variables, so that the non-convex problem can be equivalent to a rooted tree for exploration. The exactness of the algorithm is guaranteed by the same criterion as the classical branch-and-bound algorithm. To alleviate the computational burden, a novel two-stage spatial branching strategy is developed to improve the effectiveness and efficiency of the branching operations. The performance of the proposed algorithm is verified on two integrated electricity-gas systems with different sizes. Numerical results demonstrate that our method achieves a balance among feasibility, optimality, and efficiency. The comparison with another 6 convexification-based methods, 3 state-of-the-art non-convex optimization solvers, and 2 spatial branch-and-bound algorithms with classical branching rules further shows the superiority of our algorithm. [ABSTRACT FROM AUTHOR]

Learning to improve AUC performance for imbalanced data is an important machine learning research problem. Most methods of AUC maximization assume that the model function is linear in the original feature space. However, this assumption is not suitable for nonlinear separable problems. Although there have been some nonlinear methods of AUC maximization, scaling up nonlinear AUC maximization is still an open question. To address this challenging problem, in this paper, we propose a novel large-scale nonlinear AUC maximization method (named as TSAM) based on the triply stochastic gradient descents. Specifically, we first use the random Fourier feature to approximate the kernel function. After that, we use the triply stochastic gradients w.r.t. the pairwise loss and random feature to iteratively update the solution. Finally, we prove that TSAM converges to the optimal solution with the rate of $ \mathcal {O}(1/t)$ O (1 / t) after $t$ t iterations. Experimental results on a variety of benchmark datasets not only confirm the scalability of TSAM, but also show a significant reduction of computational time compared with existing batch learning algorithms, while retaining the similar generalization performance. [ABSTRACT FROM AUTHOR]

A current sensor based global maximum power point tracking (GMPPT) algorithm of photovoltaic system is proposed in this article to increase the tracking speed during partial shading condition. For the proposed algorithm, the tracking range of duty cycle is reduced by calculating both the upper limit and lower limit of duty cycle and skipping the duty cycle section without global maximum power point. Compared with maximum power trapezium (MPT) algorithm and the combination algorithm of MPT and search-skip-judge, the proposed algorithm reduces the tracking range of duty cycle during both tracking directions and only needs one current sensor to realize control. As a result, the proposed algorithm shows advantages of fast tracking speed, small energy loss, and low hardware cost. Followed by theoretical analysis, the simulation and experimental evaluation validate the claimed advantages of the proposed GMPPT algorithm. [ABSTRACT FROM AUTHOR]

Feature reduction is an important aspect of Big Data analytics on today’s ever-larger datasets. Rough sets are a classical method widely applied in attribute reduction. Most rough set algorithms use the priori domain knowledge of a dataset to process continuous attributes through using a membership function. Neighborhood rough sets (NRS) replace the membership function with the concept of neighborhoods, allowing NRS to handle scenarios where no a priori knowledge is available. However, the neighborhood radius of each object in NRS is fixed, and the optimization of the radius depends on grid searching. This diminishes both the efficiency and effectiveness, leading to a time complexity of not lower than $O(N^2)$ O (N 2) . To resolve these limitations, granular ball neighborhood rough sets (GBNRS), a novel NRS method with time complexity $O(N)$ O (N) , is proposed. GBNRS adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility in comparison to standard NRS methods. GBNRS is compared with the current state-of-the-art NRS method, FARNeMF, and find that GBNRS obtains both higher performance and higher classification accuracy on public benchmark datasets. All code has been released in the open source GBNRS library at http://www.cquptshuyinxia.com/GBNRS.html. [ABSTRACT FROM AUTHOR]

Stego-images are often contaminated by interchannel noise or active noise attack when communicating on the Web. And it is challenging to restore embedded image from corrupted stego-image. This paper studies a kNN-bit approximation algorithm to remove noises in embedded image. The proposed algorithm distinguishes reliable bits from extracted bits, and estimates pixel values by keeping reliable bits unchanged and correcting unreliable bits. Specifically, the 8th (highest) unreliable bit of a pixel can be approximated with its nearest neighbor pixels. And then, if an unreliable bit locates at any one of the $5^{th}\sim 7^{th}$ 5 t h ∼ 7 t h bits of a pixel, it is adjusted with two nearest neighbors of the pixel, where the pixel is in-between these two nearest neighbors. Finally, for other unreliable bits, each one is approximated by the maximum and minimum possible values of nearest neighbors of its pixel. We conduct experiments for illustrating the efficiency, and demonstrate that the proposed algorithm can recover the embedded images with good visual quality from corrupted stego-images. [ABSTRACT FROM AUTHOR]

Besides the traditional cartographic data sources, spatial information can also be derived from location-based sources. However, even though different location-based sources refer to the same physical world, each one has only partial coverage of the spatial entities, describe them with different attributes, and sometimes provide contradicting information. Hence, we introduce the spatial entity linkage problem, which finds which pairs of spatial entities belong to the same physical spatial entity. Our proposed solution (QuadSky) starts with a time-efficient spatial blocking technique (QuadFlex), compares pairwise the spatial entities in the same block, ranks the pairs using Pareto optimality with the SkyRank algorithm, and finally, classifies the pairs with our novel SkyEx-* family of algorithms that yield 0.85 precision and 0.85 recall for a manually labeled dataset of 1,500 pairs and 0.87 precision and 0.6 recall for a semi-manually labeled dataset of 777,452 pairs. Moreover, we provide a theoretical guarantee and formalize the SkyEx-FES algorithm that explores only 27 percent of the skylines without any loss in F-measure. Furthermore, our fully unsupervised algorithm SkyEx-D approximates the optimal result with an F-measure loss of just 0.01. Finally, QuadSky provides the best trade-off between precision and recall, and the best F-measure compared to the existing baselines and clustering techniques, and approximates the results of supervised learning solutions. [ABSTRACT FROM AUTHOR]

We propose two modified versions of the classical gradient ascent method to compute the capacity of finite-state channels with Markovian inputs. For the case that the channel mutual information rate is strongly concave in a parameter taking values in a compact convex subset of some Euclidean space, our first algorithm proves to achieve polynomial accuracy in polynomial time and, moreover, for some special families of finite-state channels our algorithm can achieve exponential accuracy in polynomial time under some technical conditions. For the case that the channel mutual information rate may not be strongly concave, our second algorithm proves to be at least locally convergent. [ABSTRACT FROM AUTHOR]

In the Internet of Vehicles (IoVs), vehicles generate and disseminate information, which makes the related vehicular services realized. However, the IoVs is an untrusted environment. Vehicles cannot evaluate the credibility of the received information, which makes it a challenge to implement data sharing in IoVs. Blockchain, constantly directed main attention, are considered as a feasible solution to address the challenge, due to its advantages of decentralization, unforgeability, and collective maintenance. The consensus mechanism of blockchain requires the miners in the system with strong computing power for mining, while the computing power of nodes in IoVs is limited, which restricts the application of blockchain in IoVs. In fact, the application of blockchain in IoVs can be implemented by employing edge computing. The key entity of edge computing is the edge servers(ESs). Roadside nodes (RSUs) can be deployed as ESs of edge computing in IoVs. In this article, we study the ESs deployment scheme for covering more vehicle nodes in IoVs, and propose a randomized algorithm to calculate approximation solutions. Finally, we simulated the performance of the proposed scheme and compared it with other deployment schemes. [ABSTRACT FROM AUTHOR]

Metric transition systems (MTSs) are proposed for quantitative verification of reactive systems. There are already a number of papers on quantitatively analyzing behaviors of systems based on MTSs. In this article, we make further progress along this research line by lifting safety properties, which assert that nothing “bad” happens during execution of systems, to MTSs. First, we introduce a distance threshold $\alpha \ \text{taken from [0,1],}$ which is used to analyze to what extent a system satisfies its specification. Then, we present a quantitative extension of safety properties, called $\alpha$ -safety properties. Furthermore, we give an alternative characterization of $\alpha$ -safety properties by means of their closure. In addition, an algorithm for verifying whether a system satisfies a subclass of $\alpha$ -safety properties is developed, assuming that the method to convert a regular $\alpha$ -safety property to an equivalent metric finite automaton has been given. Finally, we present an example to illustrate our approaches. [ABSTRACT FROM AUTHOR]

In addition to the word-level weight pruning, which excludes the 0-value weights from the neural network inference computation, it is recently demonstrated that the bit-level weight pruning, which excludes the 0-bits in the weight value representation regardless of whether the weight values are zero or not, is very effective to further accelerate the neural network computation without accuracy loss. This work overcomes the inherent limitation of the bit-level weight pruning, that is, the maximal computation speedup is bounded by the total number of nonzero bits of the weights and the bound is invariably considered “uncontrollable” (i.e., constant) for the neural network to be pruned. Precisely, this work, based on the signed-digit encoding 1) proposes a transformation technique which converts the two’s complement representation of every weight into a set of signed-digit representations of the minimal number of essential (i.e., nonzero) bits; 2) formulates the problem of selecting signed-digit representations of weights that maximize the parallelism of bit-level multiplication on the weights into a objective shortest path problem to achieve a maximal digit-index by digit-index (i.e., columnwise) compression for the weights and solves it efficiently using an approximation algorithm; 3) proposes a supporting novel acceleration architecture (DWP) with no additional inclusion of nontrivial hardware; and 4) proposes a variant of DWP to support bit-level parallel multiplication with the capability of predicting a tight worst-case latency of the parallel processing. Through experiments on several representative models using the ImageNet dataset, it is shown that our proposed approach is able to reduce the number of essential bits by 69% on AlexNet, 74% on VGG-16, and 68% on ResNet-152, by which our accelerator is able to reduce the inference computation time by up to $3.57\times $ over the conventional bit-level weight pruning. [ABSTRACT FROM AUTHOR]

Graph spectral sparsification aims to find a ultrasparse subgraph which can preserve the spectral properties of the original graph. The subgraph can be leveraged to construct a preconditioner to speed up the solution of the original graph’s Laplacian matrix. In this work, we propose feGRASS, a fast and effective graph spectral sparsification approach for the problem of large-scale power grid analysis and other problems with similar graphs. The proposed approach is based on two novel concepts: 1) effective edge weight and 2) spectral edge similarity. The former takes advantage of node degrees and breadth-first-search (BFS) distances, which leads to a scalable algorithm for generating low-stretch spanning trees (LSSTs). Then, the latter concept is leveraged during the recovery of spectrally critical off-tree edges to produce spectrally similar subgraphs. Compared with the most recent competitor , the proposed approach is much faster for producing high-quality spectral sparsifiers. Extensive experimental results have been demonstrated to illustrate the superior efficiency of a preconditioned conjugate gradient (PCG) algorithm based on the proposed approach, for solving large power grid problems and many other real-world graph Laplacians. For instance, a power grid matrix with 60 million unknowns and 260 million nonzeros can be solved (at a 1E-3 accuracy level) within 196 s and 12 PCG iterations, on a single CPU core. [ABSTRACT FROM AUTHOR]

Emerging virtual reality (VR) applications require high data rate transmission and low end-to-end latency, which has become one of the main challenges for future wireless networks. Unmanned aerial vehicle (UAV) mounted base stations and computing facilities can be used to provide better wireless connectivity and computing services to edge VR users to meet their computing needs and reduce the end-to-end latency. We propose a novel UAV assisted mobile edge computing (MEC) network to enable high-quality mobile 360-degree video VR applications by leveraging UAVs to provide the required communication and computing needs. Then, we formulate the joint UAV placement, MEC and radio resource allocation, and 360-degree video content layer assignment (UAV-MV) problem, which aims to select the allocation of computing and communications resources and the location of the UAVs such that the delivered quality of experience (QoE) is maximized across the mobile VR users, given various system constraints. We show that the problem is NP-hard, and decompose it into three lower-complexity subproblems that we solve sequentially. We design an approximation algorithm with performance guarantees that solves the UAV-MV problem based on the solutions to the three subproblems. Our simulation results show that the average QoE enabled by the proposed algorithm is 15% and 90% greater relative to two competitive reference methods. [ABSTRACT FROM AUTHOR]

In this paper, two widely used spectrum sensing algorithms, sphericity test and energy detection are considered. The statistics of sphericity test and energy detector with estimated noise power are weighted and fused to propose a mixture detector. The proposed mixture detector based on the fusion test statistics can simultaneously utilize the power information and correlation feature of the received signals. Specifically, the theoretical optimal weight is derived by maximizing the deflection coefficient of the mixture detector. Besides, the analytical expressions for the decision threshold, false alarm probability and detection probability of this algorithm are derived by moment-matching Beta approximation. Numerical examples are provided to illustrate the validity of theoretical analysis. And simulations show that the proposed mixture detector with optimal weight can improve the spectrum sensing performance compared with existing algorithms. [ABSTRACT FROM AUTHOR]

Multi-carrier faster-than-Nyquist (MFTN) signaling constitutes a promising spectrally efficient non-orthogonal physical layer waveform. In this correspondence, we propose a pair of low-complexity generalized approximate message passing (GAMP)-based frequency-domain equalization (FDE) algorithms for MFTN systems operating in multipath channels. To mitigate the ill-condition of the resultant equivalent channel matrix, we construct block circulant interference matrices by inserting a few cyclic postfixes, followed by truncating the duration of the inherent two-dimensional interferences. Based on the decomposition of the block circulant matrices, we develop a novel frequency-domain received signal model using the two-dimensional fast Fourier transform for mitigating the colored noise imposed by the non-orthogonal matched filter. Moreover, we derive a GAMP-based FDE algorithm and its refined version, where the latter relies on approximations for circumventing the emergence of the ill-conditioned matrices. Our simulation results demonstrate that, for a fixed spectral efficiency, MFTN signaling can significantly improve the bit error rate (BER) performance by jointly optimizing the time- and frequency-domain packing factors. Compared to its Nyquist-signaling counterpart, our proposed MFTN systems employing the refined GAMP equalizer can achieve about 39% higher transmission rates at a negligible BER performance degradation. [ABSTRACT FROM AUTHOR]

Duplication with Comparison (DWC) is an effective software-level solution to improve the reliability of computing devices. However, it introduces performance and energy consumption overheads that could be unsuitable for high-performance computing or real-time safety-critical applications. In this article, we present Reduced-Precision Duplication with Comparison (RP-DWC) as a means to lower the overhead of DWC by executing the redundant copy in reduced precision. RP-DWC is particularly suitable for modern mixed-precision architectures, such as NVIDIA GPUs, that feature dedicated functional units for computing with programmable accuracy. We discuss the benefits and challenges associated with RP-DWC and show that the intrinsic difference between the mixed-precision copies allows for detecting most, but not all, errors. However, as the undetected faults are the ones that fall into the difference between precisions, they are the ones that produce a much smaller impact on the application output and, thus, might be tolerated. We investigate RP-DWC impact into fault detection, performance, and energy consumption on Volta GPUs. Through fault injection and beam experiment, using three microbenchmarks and four real applications, we show that RP-DWC achieves an excellent coverage (up to 86 percent) with minimal overheads (as low as 0.1 percent time and 24 percent energy consumption overhead). [ABSTRACT FROM AUTHOR]