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This paper studies the problem of learning the conditional distribution of a high-dimensional output given an input, where the output and input may belong to two different domains, e.g., the output is a photo image and the input is a sketch image. We solve this problem by cooperative training of a fast thinking initializer and slow thinking solver. The initializer generates the output directly by a non-linear transformation of the input as well as a noise vector that accounts for latent variability in the output. The slow thinking solver learns an objective function in the form of a conditional energy function, so that the output can be generated by optimizing the objective function, or more rigorously by sampling from the conditional energy-based model. We propose to learn the two models jointly, where the fast thinking initializer serves to initialize the sampling of the slow thinking solver, and the solver refines the initial output by an iterative algorithm. The solver learns from the difference between the refined output and the observed output, while the initializer learns from how the solver refines its initial output. We demonstrate the effectiveness of the proposed method on various conditional learning tasks, e.g., class-to-image generation, image-to-image translation, and image recovery. The advantage of our method over GAN-based methods is that our method is equipped with a slow thinking process that refines the solution guided by a learned objective function. [ABSTRACT FROM AUTHOR]

Distributed primal-dual methods have been widely used for solving large-scale constrained optimization problems. The majority of existing results focus on the problems with decoupled constraints. Some recent works have studied the problems subject to separable globally coupled constraints. This paper considers the distributed optimization problems with globally coupled constraints over networks without requiring the separability of the globally coupled constraints. This is made possible by the local estimates of the constraint violations. For solving such a problem, we propose a primal-dual algorithm in the augmented Lagrangian framework, combining the average consensus technique. We first establish a non-ergodic convergence rate of $\mathcal {O}(1/k)$ in terms of the objective residual for solving a distributed constrained convex optimization problem, where $k$ is the iteration counter. Specifically, the global objective function is the aggregate of the local convex and possibly non-smooth costs, and the coupled constraint is the sum of the local linear equality constraints. The numerical results illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

$k$ k -means method using Lloyd heuristic is a traditional clustering method which has played a key role in multiple downstream tasks of machine learning because of its simplicity. However, Lloyd heuristic always finds a bad local minimum, i.e., the bad local minimum makes objective function value not small enough, which limits the performance of $k$ k -means. In this paper, we use coordinate descent (CD) method to solve the problem. First, we show that the $k$ k -means minimization problem can be reformulated as a trace maximization problem, then a simple and efficient coordinate descent scheme is proposed to solve the maximization problem. Two interesting findings through theory are that Lloyd cannot decrease the objective function value of $k$ k -means produced by our CD further, and our proposed method CD to solve $k$ k -means problem can avoid produce empty clusters. In addition, according to the computational complexity analysis, it is verified CD has the same time complexity with original $k$ k -means method. Extensive experiments including statistical hypothesis testing, on several real-world datasets with varying number of clusters, varying number of samples and varying number of dimensions show that CD performs better compared to Lloyd, i.e., lower objective value, better local minimum and fewer iterations. And CD is more robust to initialization than Lloyd whether the initialization strategy is random or initialization of $k$ k -means++. [ABSTRACT FROM AUTHOR]

As well known, low rank representation method (LRR) has obtained promising performance for subspace clustering, and many LRR variants have been developed, which mainly solve the three problems existing in LRR: 1) Problem of mean calculation; 2) Problem of deviating from the real low rank solution; 3) Problem of high computation cost on the large-scale data. In this paper, we first propose a universal LRR method referring to the first two problems. More specifically, we introduce the ability of removing the optimal mean automatically into LRR and extend it to a Schatten $p$ -norm minimization problem. Then, referring to the third problem, we reformulate the universal LRR version as an equivalent fast optimization version by removing the null space of data. More importantly, the effective theory proof is proposed to guarantee that the fast optimization method can dramatically improve the algorithmic computation efficiency on the large-scale data but without any loss of information. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Weighted multi-view clustering (MVC) aims to combine the complementary information of multi-view data (such as image data with different types of features) in a weighted manner to obtain a consistent clustering result. However, when the cluster-wise weights across views are vastly different, most existing weighted MVC methods may fail to fully utilize the complementary information, because they are based on view-wise weight learning and can not learn the fine-grained cluster-wise weights. Additionally, extra parameters are needed for most of them to control the weight distribution sparsity or smoothness, which are hard to tune without prior knowledge. To address these issues, in this paper we propose a novel and effective Cluster-weighted mUlti-view infoRmation bottlEneck (CURE) clustering algorithm, which can automatically learn the cluster-wise weights to discover the discriminative clusters across multiple views and thus can enhance the clustering performance by properly exploiting the cluster-level complementary information. To learn the cluster-wise weights, we design a new weight learning scheme by exploring the relation between the mutual information of the joint distribution of a specific cluster (containing a group of data samples) and the weight of this cluster. Finally, a novel draw-and-merge method is presented to solve the optimization problem. Experimental results on various multi-view datasets show the superiority and effectiveness of our cluster-wise weighted CURE over several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Although the logically-centralized perspective is offered in Software-Defined Networking (SDN), the data plane is still distributed. Update commands sent by the centralized controller are executed asynchronously and independently in each switch. The timed SDN enables synchronous and coordinated update operations as each update command can be triggered by a pre-defined timestamp. Prior work on timed update mainly focuses on producing a congestion-free update sequence, whereas finding a congestion-free timed update sequence may prolong the update time. Even worse, such an update order may not exist. In this paper, we propose Chronus+, a novel timed update system that utilizes the switch buffer to shorten the update time while minimizing the switch buffer during updates. First, we formulate the Minimum Switch Buffer Problem (MSBP) as an integer linear programming and show its hardness. Then, we propose a set of efficient algorithms to solve the problem in polynomial time. Extensive evaluations in Mininet and large-scale simulations show that Chronus+ can reduce the update time by at least 17% and the switch buffer size by at least 27% compared with state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

As one of the most commonly used clustering methods, the fuzzy C-means (FCM) clustering strategy extends the notion of hard clustering to associate each pattern with every cluster using a membership function. Although a lot of efforts have been made by the clustering community, it is still unclear how to evaluate the fuzziness of different versions of FCM. To fill this theoretical blank, by observing a family of objective functions, a definition of fuzzy degree is provided to quantify the fuzziness of different versions of FCM according to their dedicated objective functions. Then, a general fuzzy C-means (GFCM) clustering algorithm is proposed to solve the clustering problem of using FCM under different distance metrics and fuzzy degrees. From the perspective of fuzzy attribute, the using of this fuzzy degree can be used to reveal the essential difference among a group of FCM-based clustering algorithms, and the proposed GFCM achieves the aim of using objective functions to control the fuzziness of clustering results. Additionally, some properties and relations of these FCM-based clustering algorithms under different fuzzy degrees are discussed and the convergence and stability of the proposed GFCM are proved. Finally, extensive experiments have been performed to demonstrate that by comparison with the effect caused by distance metric, the choices of fuzzy degree have a more significant effect and improvement on the performances of a FCM-based clustering algorithm. [ABSTRACT FROM AUTHOR]

Low-light photography conditions degrade image quality. This study proposes a novel Retinex-based low-light enhancement method to correctly decompose an input image into reflectance and illumination. Subsequently, we can improve the viewing experience by adjusting the illumination using intensity and contrast enhancement. Because image decomposition is a highly ill-posed problem, constraints must be properly imposed on the optimization framework. To meet the criteria of ideal Retinex decomposition, we design a nonconvex $L_{p}$ norm and apply shrinkage mapping to the illumination layer. In addition, edge-preserving filters are introduced using the plug-and-play technique to improve illumination. Pixel-wise weights based on variance and image gradients are adopted to suppress noise and preserve details in the reflectance layer. We choose the alternating direction method of multipliers (ADMM) to solve the problem efficiently. Experimental results on several challenging low-light datasets show that our proposed method can more effectively enhance image brightness as compared with state-of-the-art methods. In addition to subjective observations, the proposed method also achieved competitive performance in objective image quality assessments. [ABSTRACT FROM AUTHOR]

This article deals with the low-sidelobe shaped-beam pattern synthesis (SBPS) problem using two nonuniformly spaced subarray configurations, including amplitude and amplitude–phase subarray configurations. An optimization model is proposed for the SBPS problem to minimize the peak sidelobe level by jointly optimizing the subarray configuration and element excitations/positions. A typical advantage of the proposed model is to reduce the system control points while maintaining the benign array performance. However, the formulated problem is a mixed-integer nonconvex one, which makes it challenging. Besides, this problem cannot be directly solved due to the existence of coupling optimization variables. Thus, a set of auxiliary variables is introduced to move the coupling optimization variables into equality constraints. These equality constraints are converted into quadratic penalty forms and are added to the objective function. Then, an efficient iterative algorithm is proposed using the penalty-based optimization method, which solves the original problem by iteratively solving a series of subproblems based on the inexact block coordinate descent technique. Numerical examples with different beam patterns are performed to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A heterogeneous information network (HIN) is one whose nodes model objects of different types and whose links model objects’ relationships. To enrich its information, objects in an HIN are typically associated with additional attributes. We call such an HIN an Attributed HIN or AHIN. We study the problem of clustering objects in an AHIN, taking into account objects’ similarities with respect to both object attribute values and their structural connectedness in the network. We show how supervision signal, expressed in the form of a must-link set and a cannot-link set, can be leveraged to improve clustering results. We put forward the SCHAIN algorithm to solve the clustering problem, and two highly efficient variants, SCHAIN-PI and SCHAIN-IRAM, which employ the power iteration based method and the implicitly restarted Arnoldi method respectively to compute eigenvectors of a matrix. We conduct extensive experiments comparing SCHAIN-based algorithms with other state-of-the-art clustering algorithms. Our results show that SCHAIN-IRAM outperforms other competitors in terms of clustering effectiveness and is highly efficient. [ABSTRACT FROM AUTHOR]

This article is concerned with the trajectory optimization problem of a drilling process, which is of significance to drilling efficiency and safety. Due to the difference between an actual trajectory and a planned trajectory, this problem is a multiobjective optimization problem (MOP) with parameter uncertainties. To solve the problem, this study devises a new approach named nondominated sorting genetic Algorithm II using outlier removal (OR-NSGA-II). First, the optimization problem with parameter uncertainties is formulated, including two objective functions: 1) a trajectory length and 2) an expected value of drill-string torque. Then, an outlier-removal mechanism is devised in the sorting process of NSGA-II to reduce the negative effects of parameter uncertainties. Next, the crowding distance calculation in NSGA-II is improved to ensure population diversity. Comparison of simulation results show that our method is effective on the MOP with parameter uncertainties. [ABSTRACT FROM AUTHOR]

Target detection in high-frequency surface-wave radar (HFSWR) systems is a challenging task due to various sources of interference. In this article, we propose a robust constant false alarm rate (CFAR) detector to solve the problem of target detection for the HFSWR by exploiting the prior information on the detection environment. Clutter amplitudes in the HFSWR are commonly assumed to follow the Weibull distribution. Additionally, distribution parameters of the clutter have nonlocal self-similarity, and targets have sparsity as well as a certain geometric structure. We design an objective function based on the Bayesian framework, which consists of a data fidelity term and some regularization terms. Specifically, we use the nonlocal low-rank (NLR) regularization to represent the prior information regarding distribution parameters of clutter, and we adopt the combination of the sparsity and Laplacian regularization to represent the prior information regarding targets. The proposed objective function inherits advantages of the NLR, sparsity, and Laplacian regularization, thereby improving the performance of the proposed detector in the nonhomogeneous clutter. By optimizing the proposed objective function, we obtain estimates of clutter statistics for every cell under test and perform the CFAR detection. Experiments on both simulated data and experimental HFSWR data demonstrate that the proposed detector has a good false alarm regulation property and detection performance in the nonhomogeneous clutter. [ABSTRACT FROM AUTHOR]

We study the problem of visually monitoring a set of points on a 2.5D terrain using an unmanned aerial vehicle (UAV) with a downward-facing camera. The goal is to find a tour of minimum length for the UAV offline to visually inspect all points of interest. Varying terrain and limited field of view of the camera restrict the visibility of the UAV and can create obstacles in the flight path, making the problem challenging. The problem is NP-hard and generalizes the traveling salesperson problem (TSP). We present several algorithms to solve this problem. Our main theoretical contribution is a constant-factor approximation algorithm (assuming fixed parameters for the terrain). We also present a practical algorithm that uses the solution to a generalized TSP (GTSP) subinstance. We benchmark the GTSP-based algorithm using a branch-and-cut integer linear programming formulation and find that the proposed algorithm scales to much larger instances and is computationally fast. We also show proof-of-concept using field deployment of a UAV to visually monitor points of interest in the environment. [ABSTRACT FROM AUTHOR]

Electron beam inspection (EBI) with high resolution is a promising technique to improve the defect inspection on the surface of patterned wafer. However, high resolution usually means long inspection time, which results in the low throughput and limitation of EBI applied in practice. This study aims to optimize the inspection time of EBI by reducing the total number of inspection regions without loss of the accuracy. We first refine this defect inspection optimization problem as a partial congruent square cover problem. Then, we propose two novel mixed-integer linear programming models for this problem. To deal with the large-scale problems, an approximation algorithm is developed to obtain the high-quality solutions. This approximation algorithm efficiently utilizes the linear programming (LP) rounding technique and greedy strategy based on the proposed model. Compared with the existing algorithms in the literature, numerical results show the superiority of the proposed model and algorithm. Note to Practitioners—Defect inspection is a key process in wafer fabrication for identifying and inspecting the patterning defects generated during the complicated fabrication processes. Electron beam inspection (EBI) takes place of optical inspection gradually as the design rules keep shrinking and the circuits are more susceptible to nanoscale killer defects. Low throughput is the main drawback of EBI and the improvements on throughput have far-reaching significance on the high volume manufacturing of semiconductor products. This study aims to reduce the number of inspection regions to improve the total inspection time in the EBI process. Considering that the inspection time for each inspection region is constant, less inspection regions means less total inspection time. However, the positions of inspection regions are arbitrary across the continuous planar space, putting pressure on modeling and solving the problem. A preprocessing algorithm is designed to discover a limited number of candidate positions of inspection regions without loss of optimality, which greatly simplifies the problem. For dealing with large-scale instances, an approximation algorithm combining linear programming (LP)-rounding technique and greedy strategy is designed to get near optimal solutions. Since the number of inspection regions is one key factor determining the total inspection time, the proposed optimization methods have a potential to be applicable to enhance the efficiency of advanced EBI platforms, such as ASML HMI eP series. [ABSTRACT FROM AUTHOR]