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The microbial biosynthesis of proteins, primary metabolites, and chemicals is gaining extraordinary momentum and is presently viewed as an advancing approach in the industrial research sector. Increased threats to the environment and the possibility of declining petroleum assets have switched the spotlight to microbial cell factories (MCFs). Aside from possessing various advantages over chemical synthesis, such as less toxicity, cheaper methodologies, and an environmentally benign nature, microbes can be cultivated in fermenters, resulting in an effective bioprocessing approach in terms of industrial relevance. As the overwhelming majority of biodiversity is microbial, this review first highlights the microbial biodiversity of industrially vital microorganisms. Then, the paper delineates the production pathways for generating valuable bioproducts via microbial workhorses. Many host cells synthesize bio-compounds as a part of their natural mechanism; however, several techniques have also been developed to attain the desired end product from non-native microbes with selected properties. The microbial biosynthetic pathways can be categorized as native-existing pathways, heterologous pathways, and artificial de novo pathways. Systems metabolic engineering, which integrates metabolic engineering with evolutionary engineering, synthetic biology, and systems biology, has further revolutionized the field of engineering robust phenotypes. The employment of these strategies improves the performance of the strain, eventually achieving high titer and productivity rates of bio-chemicals. Modern trends and tools for exploiting native pathways and designing non-native-created pathways are also briefly discussed in this paper. Finally, the review discusses the use of microbial workhorses for producing a myriad of materials and chemicals, including carboxylic acids, amino acids, plant natural products (PNPs), carotenoids, flavors, and fragrances, unveiling the efficacy of utilizing microbial species to generate sustainable bio-based products. [ABSTRACT FROM AUTHOR]

Recognizing actions from still images has been popularly studied recently. In this paper, we model an action class as a flexible number of spatial configurations of body parts by proposing a new spatial sum–product network (SPN). First, we discover a set of parts in image collections via unsupervised learning. Then, our new spatial SPN is applied to model the spatial relationship and also the high-order correlations of parts. To learn robust networks, we further develop a hierarchical spatial SPN method, which models pairwise spatial relationship between parts inside subimages and models the correlation of subimages via extra layers of SPN. Our method is shown to be effective on two benchmark data sets. [ABSTRACT FROM PUBLISHER]

With the rapid development of electronic power systems, DC-DC converters are widely used, including the Four-Switch Buck–Boost (FSBB) converter, which has a unique advantage in scenarios with a wide range of voltage inputs. To improve the response speed and anti-interference capability of the FSBB converter, this paper proposes an adaptive global fast integral terminal sliding mode control method. Through an in-depth analysis of the fundamental characteristics of the FSBB converter, this study employed a global fast integral terminal sliding mode controller combined with an adaptive algorithm to significantly improve the dynamic performance and robustness of the FSBB converter. The simulation and experimental results show that the proposed method could track the reference voltage quickly and accurately in different modes, exhibiting excellent system robustness compared to conventional sliding mode control, terminal sliding mode control, and PID control methods. [ABSTRACT FROM AUTHOR]

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications. [ABSTRACT FROM AUTHOR]

The digital revolution places great emphasis on digital media watermarking due to the increased vulnerability of multimedia content to unauthorized alterations. Recently, in the digital boom in the technology of hiding data, research has been tending to perform watermarking with numerous architectures of deep learning, which has explored a variety of problems since its inception. Several watermarking approaches based on deep learning have been proposed, and they have proven their efficiency compared to traditional methods. This paper summarizes recent developments in conventional and deep learning image and video watermarking techniques. It shows that although there are many conventional techniques focused on video watermarking, there are yet to be any deep learning models focusing on this area; however, for image watermarking, different deep learning-based techniques where efficiency in invisibility and robustness depends on the used network architecture are observed. This study has been concluded by discussing possible research directions in deep learning-based video watermarking. [ABSTRACT FROM AUTHOR]

When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. To rigorously evaluate the robustness and generalizability of current approaches, we propose a new benchmark, including two corruption datasets SemanticKITTI-C and SemanticPOSS-C, which feature 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic segmentation models, especially spanning different input representations (e.g., point clouds, voxels, projected images, and etc.), network architectures and training schemes. Through this study, we obtain two insights: (1) We find out that the input representation plays a crucial role in robustness. Specifically, under specific corruptions, different representations perform variously. (2) Although state-of-the-art methods on LiDAR semantic segmentation achieve promising results on clean data, they are less robust when dealing with noisy data. Finally, based on the above observations, we design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications. It is promising that our benchmark, comprehensive analysis, and observations can boost future research in robust LiDAR semantic segmentation for safety-critical applications. [ABSTRACT FROM AUTHOR]

The robustness of sampling scheme ensures that the exact samples are not crucial to recover the signal under considered and some errors can be tolerated. This paper addresses that two kinds of nonuniform average sampling schemes are robust for finitely generated shift-invariant signals in mixed Lebesgue spaces. The results show that the mixed shift-invariant signals can be reconstructed stably from two classes of nonuniform average samples, respectively, when the generators and sampling functionals are disturbed slightly. [ABSTRACT FROM AUTHOR]

Independent component analysis (ICA), as a statistical and computational approach, has been successfully applied to digital signal processing. Performance analysis for the ICA approach is perceived as a challenging task to work on. This contribution concerns the complex-valued FastICA algorithm in the range of ICA over the complex number domain. The focus is on the robust and equivariant behavior analysis of the complex-valued FastICA estimator. Although the complex-valued FastICA algorithm as well as its derivatives have been widely used methods for approaching the complex blind signal separation problem, rigorous mathematical treatments of the robust measurement and equivariance for the complex-valued FastICA estimator are still missing. This paper strictly analyzes the robustness against outliers and separation performance depending on the global system. We begin with defining the influence function (IF) of complex-valued FastICA functional and followed by deriving its closed-form expression. Then, we prove that the complex-valued FastICA algorithm based on the optimizing cost function is linear-equivariant, depending only on the source signals. [ABSTRACT FROM AUTHOR]

This paper focuses on the ethics of AI and the principles behind it. Prior to the rules governing AI, it is necessary to understand the ethical issues related to the development, implementation and responsible use of AI systems. These ethical foundations are necessary to protect the rights and dignity of individuals, ensure fairness in data access and processing, minimise biases and risks associated with algorithms, promote transferability in automated decisions, and promote trustworthiness in technology, human benefit and social welfare. It is also proposed to study the ethics of AI from an all-encompassing point of view that integrates both applied ethics and metaethics of AI. [ABSTRACT FROM AUTHOR]

The algorithm proposed in this paper aims at solving the problem of star map matching in high-limiting-magnitude astronomical images, which is inspired by geometric voting star identification techniques. It is a two-step star map matching algorithm relying only on angular features, and adopts a reasonable matching strategy to overcome the problem of poor real-time performance of the geometric voting algorithm when the number of stars is large. The algorithm focuses on application scenarios where there are a large number of dense stars (limiting magnitude greater than 13, average number of stars per square degree greater than 185) in the image, which is different from the sparse star identification problem of the star tracker, which is more challenging for the robustness and real-time performance of the algorithm. The proposed algorithm can be adapted to application scenarios such as unreliable brightness information, centroid positioning error, visual axis pointing deviation, and a large number of false stars, with high accuracy, robustness, and good real-time performance. [ABSTRACT FROM AUTHOR]

Rank regression method has been widely pursued for robust inference in statistical models. Unfortunately, there does not exist related literature for the function-on-scalar model, which is the focus of this paper. We study the robust estimation based on rank regression and B-spline approximations for the function-on-scalar model and further establish the theoretical properties of the proposed method under regularity conditions. Extensive simulation studies and two real data applications are given to illustrate the merits of the proposed approach. Numerical results show that the proposed method is competitive with existing robust estimation procedures. [ABSTRACT FROM AUTHOR]

Distributed Energies Resources (DERs) can be controlled for supporting the voltage regulation at nodes of an Active Distribution Network (ADN) where they are connected. However, since the ADN is a Multi-Input Multi-Output (MIMO) system with coupled dynamics, the controller of a DER mutually interacts with all other controllers through the distribution lines. These interactions lead to operating conflicts which may drive the ADN to work close to its voltage stability boundaries. To achieve a stable voltage regulation without new investment in the existing ADNs, the present paper proposes a straightforward decentralized design of the multi-loop controllers based on the property of integral controllability. The main feature of the method is that the design problem can be expressed by a single parameter designed both for reducing the effects of the undesired coupling and for increasing the degree of robust stability in the presence of parameter uncertainty in the matrix plant. Simulation studies are developed to illustrate the design result and the performance achieved under different operating conditions. The performance is also compared with the one obtained by another method in terms of the integral absolute error. [ABSTRACT FROM AUTHOR]

Image watermarking often involves the use of handheld devices under non-structured conditions for authentication purposes, particularly in the print-cam process where smartphone cameras are used to capture watermarked printed images. However, these images frequently suffer from perspective distortions, making them unsuitable for automated information detection. To address this issue, Cam-Unet, an end-to-end neural network architecture, is presented to predict the mapping from distorted images to rectified ones, specifically tailored for print-cam challenges applied to ID images. Given the limited availability of large-scale real datasets containing ground truth distortions, we created an extensive synthetic dataset by subjecting undistorted images to print-cam attacks. The proposed network is trained on this dataset, using various data augmentation techniques to improve its generalization capabilities. Accordingly, this paper presents an image watermarking system for the print-cam process. The approach combines Fourier transform-based watermarking with Cam-Unet as perspective distortion correction. Results show that the proposed method outperforms existing watermarking approaches typically employed to counter print-cam attacks and achieves an optimal balance between efficiency and cost-effectiveness. [ABSTRACT FROM AUTHOR]

Many properties of the structure and dynamics of complex networks derive from the characteristics of the spectrum of the associated Laplacian matrix, specifically from the set of its eigenvalues. In this paper, we show that there exist graphs for which the ratio between the length of the spectrum (that is, the difference between the largest and smallest eigenvalues of the Laplacian matrix) and its spread (the difference between the second smallest eigenvalue and the smallest one) is equal to the golden ratio. We call such graphs Golden Laplacian Graphs (GLG). In this paper, we first find all such graphs with a number of nodes n ≤ 10 . We then prove several graph-theoretic and algebraic properties that characterize these graphs. These graphs prove to be extremely robust, as they have large vertex and edge connectivity along with a large isoperimetric constant. Finally, we study the synchronization properties of GLGs, showing that they are among the top synchronizable graphs of the same size. Therefore, GLGs represent very good candidates for engineering and communication networks. [ABSTRACT FROM AUTHOR]

This paper aims to craft adversarial queries for image retrieval, which uses image features for similarity measurement. Many commonly used methods are developed in the context of image classification. However, these methods, which attack prediction probabilities, only exert an indirect influence on the image features and are thus found less effective when being applied to the retrieval problem. In designing an attack method specifically for image retrieval, we introduce opposite-direction feature attack (ODFA), a white-box attack approach that directly attacks query image features to generate adversarial queries. As the name implies, the main idea underpinning ODFA is to impel the original image feature to the opposite direction, similar to a U-turn. This simple idea is experimentally evaluated on five retrieval datasets. We show that the adversarial queries generated by ODFA cause true matches no longer to be seen at the top ranks, and the attack success rate is consistently higher than classifier attack methods. In addition, our method of creating adversarial queries can be extended for multi-scale query inputs and is generalizable to other retrieval models without foreknowing their weights, i.e., the black-box setting. [ABSTRACT FROM AUTHOR]

Representation‐based classification (RC) is an effective gauge of data similarity between a single instance and the whole dataset, which extends traditional individual‐wise distance metrics using representation coefficients. These coefficients show remarkable discrimination nature via various regularisation terms, but the interference from potentially uncorrelated objects involved in this single‐to‐global relation can degrade the effectiveness of the coefficients. In order to filter out those unproductive, or even counter‐productive, information from the decision making processes, this paper proposes a local representation‐based classification (LRC) algorithm to improve the classification accuracy or the RC approach. LRC uses a single‐to‐local relation induced by the local representation‐based neighbourhood (LRN) of each object, rather than the single‐to‐global relationship used by RC. Thanks to LRN, a compact and relevant dataset can be formed by selecting the most relevant data instances in the original dataset, to render a robust representation of a query. LRC was applied to multiple publicly available datasets, and the experimental results demonstrate the superiority of the proposed LRC algorithm as evidenced by the higher classification accuracy and more noise‐tolerant capability in reference to alternative RC approaches. Moreover, the sampling ability of LRN is also verified via a comparative study. [ABSTRACT FROM AUTHOR]

When there is a mismatch in electrical power produced and the demanded electrical loading in an interconnected electrical power system, it results in deviations in frequencies in the constituent areas and in tie-line power as well. This issue is addressed by automatic generation control which when coupled with intelligent controller leads to fast recovery of the deviations both in tie-line power and frequencies from the assigned ranges associated with each of these areas. This paper is an attempt to utilize fuzzy logic-based fractional-order tilt-integral-derivative (Fuzzy FOTID) controller with gains/parameter optimized using the recent Coati Optimization Algorithm. Comparative studies are undertaken as a part of this work to exhibit the Fuzzy FOTID controller's supremacy over other popular controllers like proportional-integral-derivative, TID, and FOTID. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of maximum power point tracking of photovoltaic (PV) systems in the presence of model uncertainty as well as varying load and atmospheric conditions using techniques based on a Takagi–Sugeno fuzzy model. The proposed approach relies on the linear matrix inequality tool, Lambert W function, and the Newton–Raphson method. First, adopting a quadratic Lyapunov function, an active observer-based fuzzy non-parallel distributed compensation (non-PDC) controller is designed for asymptotic tracking of the desired reference input. Next, to subdue the impact of uncertainty on the PV system, the closed-loop nominal system is regarded as a reference model, and then the main control law is developed using an online lumped uncertainty estimator and keeping the nominal control law within the staple controller. This control law does not require that the bounds on uncertainties be known. The reference voltage is determined by a novel maximum power point-seeking algorithm that is organized based on the one-diode model of PV panel, Lambert W function, and Newton–Raphson method. Finally, simulations are performed for three scenarios to point out the merits and effectiveness of the proposed methodology in the presence of system uncertainties, environmental changes, and load variations. [ABSTRACT FROM AUTHOR]

Active Distribution Networks are Multi-Input Multi-Output (MIMO) systems with coupled dynamics, which cause interactions among the control loops of Distributed Energy Resources (DERs). This undesired effect leads to performance degradation of voltage control. To mitigate the effects of this unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction's sources. In detail, each DER is equipped with a double-loop PI to control the active and reactive power output of the voltage source converter, which connects the DER to the network's node. Furthermore, to guarantee ancillary services, the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter's pulse are designed according to a procedure with two sequential steps based on the Internal Model Control (IMC) technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in the presence of model parameter uncertainty in the MIMO plant, modeling various operating conditions of the ADN, including a step connection of large loads, renewable energy source variations, and changes in the substation transformer ratio. [ABSTRACT FROM AUTHOR]

The introduction of concepts of Game Theory and Ordinary Differential Equations into Biology gave birth to the field of Evolutionary Stable Strategies, with applications in Biology, Genetics, Politics, Economics and others. In special, the model composed by two players having two pure strategies each results in a planar polynomial vector field with an invariant octothorpe. In this paper we provide sufficient and necessary conditions for structural stability in this model. [ABSTRACT FROM AUTHOR]

The renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS's robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS's robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power. [ABSTRACT FROM AUTHOR]

Least Squares Support Vector Regression (LSSVR) which is a least squares version of the Support Vector Regression (SVR) is defined with a regularized squared loss without epsilon-insensitiveness. LSSVR is formulated in the dual space as a linear equality constrained quadratic minimization which can be transformed into solution of a linear algebraic equation system. As a consequence of this system where the number of Lagrange multipliers is half that of classical SVR, LSSVR has much less time consumption compared to the classical SVR. Despite this computationally attractive feature, it lacks the sparsity characteristic of SVR due to epsilon-insensitiveness. In LSSVR, every (training) input data is treated as a support vector, yielding extremely poor generalization performance. To overcome these drawbacks, the epsilon-insensitive LSSVR with epsilon-insensitivity at quadratic loss, in which sparsity is directly controlled by the epsilon parameter, is derived in this paper. Since the quadratic loss is sensitive to outliers, its weighted version (epsilon insensitive WLSSVR) has also been developed. Finally, the performances of epsilon-insensitive LSSVR and epsilon-insensitive WLSSVR are quantitatively compared in detail with those commonly used in the literature, pruning-based LSSVR and weighted pruning-based LSSVR. Experimental results on simulated and 8 different real-life data show that epsilon-insensitive LSSVR and epsilon-insensitive WLSSVR are superior in terms of computation time, generalization ability, and sparsity. [ABSTRACT FROM AUTHOR]

Fisher–von Mises–Langevin distributions are widely used for modeling directional data. In this paper, the problem of testing homogeneity of mean directions of several Fisher–von Mises–Langevin populations is considered when the concentration parameters are unknown and heterogeneous. First, an adaptive test based on the likelihood ratio statistic is proposed. Critical points are evaluated using a parametric bootstrap. Second, a heuristic test statistic is considered based on pairwise group differences. A nonparametric bootstrap procedure is adapted for evaluating critical points. Finally, a permutation test is also proposed. An extensive simulation study is performed to compare the size and power values of these tests with those proposed earlier. It is observed that both parametric and nonparametric bootstrap based tests achieve size values quite close to the nominal size. Asymptotic tests and permutation tests have size values higher than the nominal size. Bootstrap tests are seen to have very good power performance. The robustness of tests is also studied by considering contamination in Fisher–von Mises–Langevin distributions. R packages are developed for the actual implementation of all tests. A real data set has been considered for illustrations. [ABSTRACT FROM AUTHOR]

Cancer is one of the leading causes of deaths worldwide. Survival analysis and prediction of cancer patients is of great significance for their precision medicine. The robustness and interpretability of the survival prediction models are important, where robustness tells whether a model has learned the knowledge, and interpretability means if a model can show human what it has learned. In this paper, we propose a robust and interpretable model SurvConvMixer, which uses pathways customized gene expression images and ConvMixer for cancer short-term, mid-term and long-term overall survival prediction. With ConvMixer, the representation of each pathway can be learned respectively. We show the robustness of our model by testing the trained model on absolutely untrained external datasets. The interpretability of SurvConvMixer depends on gradient-weighted class activation mapping (Grad-Cam), by which we can obtain the pathway-level activation heat map. Then wilcoxon rank-sum tests are conducted to obtain the statistically significant pathways, thereby revealing which pathways the model focuses on more. SurvConvMixer achieves remarkable performance on the short-term, mid-term and long-term overall survival of lung adenocarcinoma, lung squamous cell carcinoma and skin cutaneous melanoma, and the external validation tests show that SurvConvMixer can generalize to external datasets so that it is robust. Finally, we investigate the activation maps generated by Grad-Cam, after wilcoxon rank-sum test and Kaplan–Meier estimation, we find that some survival-related pathways play important role in SurvConvMixer. [ABSTRACT FROM AUTHOR]

The continuous evolution of artificial intelligence and cyber–physical systems has presented promising opportunities for optimizing traffic signal control in densely populated urban areas, with the aim of alleviating traffic congestion. One area that has garnered significant interest from both researchers and practitioners is the application of deep reinforcement learning (DRL) in traffic signal control. However, DRL-based algorithms often suffer from instability due to the dynamic nature of traffic flows. Discrepancies between the environments used for training and those encountered during deployment often lead to operational failures. Moreover, conventional DRL-based traffic signal control algorithms tend to reveal vulnerabilities when faced with unforeseen events, such as sensor failure. These challenges highlight the need for innovative solutions to enhance the robustness and adaptability of such systems. To address these pertinent issues, this paper introduces StageLight, a novel two-stage multiscale learning approach, which involves learning optimal timings on a coarse time scale in stage 1, while finetuning them on a finer time scale in stage 2. Our experimental results demonstrate StageLight's remarkable capability to generalize across diverse traffic conditions and its robustness to various sensor-failure scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a new approach for the solution of the electromagnetic interference impact problem in closed-loop control systems with proportional-integral-derivative (PID) controllers is proposed. The approach is based on the application of a combined controller that consists of three controllers: PID, PI and I, when only one of them is operating at a time, and which one is operating determines the control error value. The proposed combined controller allows for achieving better resistance of the control system to the noise signals produced by electromagnetic interference compared to PID and PID with low-pass filters in derivative term controllers without deteriorating the dynamic performance of the control system. The operation of the controller has been analysed using simulation and experiments with plants, the dynamics of which are described by first-order plus dead-time transfer functions. [ABSTRACT FROM AUTHOR]

Msplit estimation is a modern estimation method that has found various applications in processing geodetic data. Its basic variants were not meant to be robust against outliers; however, the practical applications showed that the method could be used in such a context. Therefore, there is a need to describe the robustness of different Msplit estimation variants. The paper uses the global breakdown point in an extended interval (GBdP- e) but also introduces the tolerance for growing errors of observations (TGE) to perform such an examination. It presents such measures obtained for the absolute Msplit estimation and robust Msplit estimation variants, which have not been shown before. The results prove that the absolute Msplit estimation predominates the squared Msplit estimation in such a context. Furthermore, the robust variants are much less sensitive to outliers than both basic variants mentioned. TGE not only describes how the method tolerates outliers but could also be applied to assume the most appropriate values of the steering parameters, which seems essential. The paper shows the theoretical relationship between basic Msplit estimation variants and respective M -estimation methods. It is a basis for introducing and deriving GBdP- e and also TGE for M -estimation. The paper shows that both measures are equivalent in the case of M -estimation. TGE could provide information about that estimation type's sensitivity to growing errors of observations (also robustness to outliers) that is unavailable by applying other measures, including classical breakdown points, influence functions, rejection points, or mean success rate. TGE presents the robustness potential of the M -estimation variants in a rather vivid and straightforward way, even for methods not classified as robust against outliers, e.g., the least-squares estimation. [ABSTRACT FROM AUTHOR]

Simple Summary: Poultry meat is an affordable, lean source of animal protein. Where does it come from? Broilers (meat chickens) or turkeys raised on farms stem from core breeding families, which are crossed to generate commercial hybrids. It takes around four years for the birds from the core families to the birds on the farms. Over time, poultry breeding has developed from primarily looking at production (e.g., live weight and egg production) to balanced, holistic breeding, including a wide range of attributes (e.g., gait, leg health, robustness and cardiovascular health). All birds in the core breeding families are carefully measured for over forty items covering bird health and welfare, robustness, environmental impact, reproduction and production. Modern poultry breeding aims for the holistic improvement of all the traits included in a broad and balanced breeding goal. New and improved selection techniques and analytical tools are continuously developed to allow increases in the accuracy of selection's long-term progress. There are different broiler and turkey genotypes for different market requirements, and commercial portfolios will continue evolving with future markets and customer preferences. This paper provides a comprehensive overview of the history of commercial poultry breeding, from domestication to the development of science and commercial breeding structures. The development of breeding goals over time, from mainly focusing on production to broad goals, including bird welfare and health, robustness, environmental impact, biological efficiency and reproduction, is detailed. The paper outlines current breeding goals, including traits (e.g., on foot and leg health, contact dermatitis, gait, cardiovascular health, robustness and livability), recording techniques, their genetic basis and how trait these antagonisms, for example, between welfare and production, are managed. Novel areas like genomic selection and gut health research and their current and potential impact on breeding are highlighted. The environmental impact differences of various genotypes are explained. A future outlook shows that balanced, holistic breeding will continue to enable affordable lean animal protein to feed the world, with a focus on the welfare of the birds and a diversity of choice for the various preferences and cultures across the world. [ABSTRACT FROM AUTHOR]

This paper presents a high imperceptible and robust audio watermarking algorithm by optimizing the scaling parameter as per the imperceptible requirements with minimum bit error rate. The spread spectrum-based audio watermarking is used in this paper, where the scaling parameter is optimized to achieve trade-off between imperceptibility and robustness of watermarked audio. Primarily, the effect of scaling parameter on the perceptual quality of watermarked audio is investigated and the total error introduced in the host audio due to embedding the watermark is computed. The scaling parameter is optimized for maximizing the robustness by considering objective difference grade (ODG) score (for music signals), perceptual evaluation of speech quality (PESQ) score (for speech signals) as a constraint to meet the imperceptibility requirements. To solve this proposed optimization problem, two search algorithms are developed. The embedding is performed in low-pass framelet transform coefficients through SVD with the optimized scaling parameter. The experimental results show that the proposed algorithm achieves good imperceptibility with an average ODG score of −0.32 and PESQ score of 3.86 for music and speech signals, respectively, under various payload conditions. The proposed algorithm shows better robustness to the common signal processing attacks such as noise addition, filtering, resampling, MP3 compression, amplitude scaling, cropping, and requantization. [ABSTRACT FROM AUTHOR]

Portable document format (PDF) files are widely used in file transmission, exchange, and circulation because of their platform independence, small size, good browsing quality, and the ability to place hyperlinks. However, their security issues are also more thorny. It is common to distribute printed PDF files to different groups and individuals after printing. However, most PDF watermarking algorithms currently cannot resist print–scan attacks, making it difficult to apply them in leak tracing of both paper and scanned versions of PDF documents. To tackle this issue, we propose an invisible digital watermarking technology based on modifying the edge pixels of text strokes to hide information in PDFs, which achieves high robustness to print–scan attacks. Moreover, it cannot be detected by human perception systems. This method focuses on the representation of text by embedding watermarks by changing the features of the text to ensure that changes in these features can be reflected in the scanned PDF after printing. We first segment each text line into two sub-blocks, then select the row of pixels with the most black pixels, and flip the edge pixels closest to this row. This method requires the participation of original PDF documents in detection. The experimental results show that all peak signal-to-noise ratio (PSNR) values of our proposed method exceed 32 dB, which indicates satisfactory invisibility. Meanwhile, this method can extract the hidden information with 100% accuracy under the JPEG compression attack, and has high robustness against noise attacks and print–scan attacks. In the case of no attacks, the watermark can be recovered without any loss. In terms of practical applications, our method can be applied in the practical leak tracing of official paper documents after distribution. [ABSTRACT FROM AUTHOR]

In this paper, we present the envisioned style and scope of the new topic "Explanation Paradigms Leveraging Analytic Intuition" (ExPLAIn) with the International Journal on Software Tools for Technology Transfer (STTT). Intention behind this new topic is to (1) explicitly address all aspects and issues that arise when trying to, if possible, reveal and then confirm hidden properties of black-box systems, or (2) to enforce vital properties by embedding them into appropriate system contexts. Machine-learned systems, such as Deep Neural Networks, are particularly challenging black-box systems, and there is a wealth of formal methods for analysis and verification waiting to be adapted and applied. The selection of papers of this first Special Section of ExPLAIn, most of which were co-authored by editorial board members, is an illustrative example of the style and scope envisioned: In addition to methodological papers on verification, explanation, and their scalability, case studies, tool papers, literature reviews, and position papers are also welcome. [ABSTRACT FROM AUTHOR]

Background: Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance. Methods: The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast. Results: The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners. Conclusions: The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development – Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible. [ABSTRACT FROM AUTHOR]

Achieving higher accuracy and robustness stands as the central objective in the navigation field. In complex urban environments, the integrity of GNSS faces huge challenges and the performance of integrated navigation systems can be significantly affected. As the proportion of faulty measurements rises, it can result in both missed alarms and false positives. In this paper, a robust method based on factor graph is proposed to improve the performance of integrated navigation systems. We propose a detection method based on multi-conditional analysis to determine whether GNSS is anomalous or not. Moreover, the optimal weight of GNSS measurement is estimated under anomalous conditions to mitigate the impact of GNSS outliers. The proposed method is evaluated through real-world road tests, and the results show the positioning accuracy of the proposed method is improved by more than 60% and the missed alarm rate is reduced by 80% compared with the traditional algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we explore the mechanisms of central pattern generators (CPGs), circuits that can generate rhythmic patterns of motor activity without external input. We study the half-center oscillator, a simple form of CPG circuit consisting of neurons connected by reciprocally inhibitory synapses. We examine the role of asymmetric coupling factors in shaping rhythm activity and how different network topologies contribute to network efficiency. We have discovered that neurons with lower synaptic strength are more susceptible to noise that affects rhythm changes. Our research highlights the importance of asymmetric coupling factors, noise, and other synaptic parameters in shaping the broad regimes of CPG rhythm. Finally, we compare three topology types' regular regimes and provide insights on how to locate the rhythm activity. [ABSTRACT FROM AUTHOR]

Malicious attacks can cause significant damage to the structure and functionality of complex networks. Previous research has pointed out that the ability of networks to withstand malicious attacks becomes weaker when networks are coupled. However, traditional research on improving the robustness of networks has focused on individual low-order or higher-order networks, lacking studies on coupled networks with higher-order and low-order networks. This paper proposes a method for optimizing the robustness of coupled networks with higher-order and low-order based on a simulated annealing algorithm to address this issue. Without altering the network's degree distribution, the method rewires the edges, taking the robustness of low-order and higher-order networks as joint optimization objectives. Making minimal changes to the network, the method effectively enhances the robustness of coupled networks. Experiments were conducted on Erdős–Rényi random networks (ER), scale-free networks (BA), and small-world networks (SW). Finally, validation was performed on various real networks. The results indicate that this method can effectively enhance the robustness of coupled networks with higher-order and low-order. [ABSTRACT FROM AUTHOR]

Benefiting from large-scale pretrained vision language models (VLMs), the performance of visual question answering (VQA) has approached human oracles. However, finetuning such models on limited data often suffers from overfitting and poor generalization issues, leading to a lack of model robustness. In this paper, we aim to improve input robustness from an information bottleneck perspective when adapting pretrained VLMs to the downstream VQA task. Input robustness refers to the ability of models to defend against visual and linguistic input variations, as well as shortcut learning involved in inputs. Generally, the representations obtained by pretrained VLMs inevitably contain irrelevant and redundant information for a specific downstream task, resulting in statistically spurious correlations and insensitivity to input variations. To encourage representations to converge to a minimal sufficient statistic in multimodal learning, we propose Correlation Information Bottleneck (CIB), which seeks a tradeoff between compression and redundancy in representations by minimizing the mutual information (MI) between inputs and representations while maximizing the MI between outputs and representations. Moreover, we derive a tight theoretical upper bound for the mutual information between multimodal inputs and representations, incorporating different internal correlations that guide models to learn more robust representations and facilitate modality alignment. Extensive experiments consistently demonstrate the effectiveness and superiority of the proposed CIB in terms of input robustness and accuracy. [ABSTRACT FROM AUTHOR]

The robust and sparse portfolio selection problem is one of the most-popular and -frequently studied problems in the optimization and financial literature. By considering the uncertainty of the parameters, the goal is to construct a sparse portfolio with low volatility and decent returns, subject to other investment constraints. In this paper, we propose a new portfolio selection model, which considers the perturbation in the asset return matrix and the parameter uncertainty in the expected asset return. We define three types of stationary points of the penalty problem: the Karush–Kuhn–Tucker point, the strong Karush–Kuhn–Tucker point, and the partial minimizer. We analyze the relationship between these stationary points and the local/global minimizer of the penalty model under mild conditions. We design a penalty alternating-direction method to obtain the solutions. Compared with several existing portfolio models on seven real-world datasets, extensive numerical experiments demonstrate the robustness and effectiveness of our model in generating lower volatility. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new filtering algorithm, the variational Bayesian-based adaptive maximum correntropy generalized high-degree cubature Kalman filter, which is designed to improve filtering accuracy under conditions of unknown measurement noise covariance and measurement outliers. Considering that the generalized high-degree cubature rule can solve the high dimensional nonlinear problem well, based on the generalized high-degree cubature Kalman filter, the variational Bayesian method is utilized to approximate the measurement noise covariance, and the maximum correntropy criterion is used to reduce the influence of the measurement outliers on the state estimation. Additionally, we introduce a resistance factor to correct measurement values and optimize the kernel bandwidth for different types of noise. Simulation experiments on target tracking and integrated navigation demonstrate that our proposed algorithm effectively suppresses unknown time-varying noise and non-Gaussian mutation noise, outperforming existing filtering algorithms in terms of estimation accuracy, robustness, and adaptability. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have emerged as a prominent model in medical image segmentation, achieving remarkable advancements in clinical practice. Despite the promising results reported in the literature, the effectiveness of DNNs necessitates substantial quantities of high-quality annotated training data. During experiments, we observe a significant decline in the performance of DNNs on the test set when there exists disruption in the labels of the training dataset, revealing inherent limitations in the robustness of DNNs. In this paper, we find that the neural memory ordinary differential equation (nmODE), a recently proposed model based on ordinary differential equations (ODEs), not only addresses the robustness limitation but also enhances performance when trained by the clean training dataset. However, it is acknowledged that the ODE-based model tends to be less computationally efficient compared to the conventional discrete models due to the multiple function evaluations required by the ODE solver. Recognizing the efficiency limitation of the ODE-based model, we propose a novel approach called the nmODE-based knowledge distillation (nmODE-KD). The proposed method aims to transfer knowledge from the continuous nmODE to a discrete layer, simultaneously enhancing the model's robustness and efficiency. The core concept of nmODE-KD revolves around enforcing the discrete layer to mimic the continuous nmODE by minimizing the KL divergence between them. Experimental results on 18 organs-at-risk segmentation tasks demonstrate that nmODE-KD exhibits improved robustness compared to ODE-based models while also mitigating the efficiency limitation. [ABSTRACT FROM AUTHOR]

Background and objective: Understanding how polyprotic acids behave is critical in various fields, including chemistry, biology, and medicine. Elucidation of the behavior of polyprotic acids through utilization of alpha functions, which provide insightful graphical representations of species distribution with varying pH levels, has been considered. The current paper offers an in-depth exploration of alpha functions and their significance in portraying the proportions of ionized and non-ionized species, a crucial aspect in predicting acid behavior, buffering capacity, and identifying optimal pH conditions. Materials and methods: The Microsoft Excel and its Solver tool to explore polyprotic acid equilibria have been utilized. Synthetic datasets were generated using Excel spreadsheets, while Solver facilitated non-linear curve fitting for parameter optimization. Results and conclusion: A novel contribution of this paper lies in introducing Excel-based tools that facilitate the generation of alpha function plots. These tools simplify the process of visualizing species distribution and pave the way for more advanced analyses. Additionally, the paper showcases the utilization of Excel Solver for robust nonlinear curve fitting of dissociation constants. This advanced computational technique enhances the accuracy of dissociation constant determination, ensuring more reliable predictions. An intriguing aspect of the proposed method is its ability to exhibit robustness even when the total acid concentration is unknown. This characteristic sets the approach apart, overcoming a common challenge in acid-base studies and analytical chemistry. The ability to accurately determine dissociation constants in such circumstances further underscores the practicality and relevance of the method. The implications of this research extend beyond the realm of theoretical understanding. The practical applications of the findings are particularly noteworthy, with a significant impact on pharmaceutical industries. The insights gained from alpha function analysis can be instrumental in drug formulation processes and in optimizing the solubility of pharmaceutical compounds. These applications can potentially enhance drug efficacy, patient experience, and overall healthcare outcomes. [ABSTRACT FROM AUTHOR]

In the backdrop of the ongoing reforms within the electricity market and the escalating integration of renewable energy sources, power service providers encounter substantial trading risks stemming from the inherent uncertainties surrounding market prices and load demands. This paper endeavors to address these challenges by proposing a credibility theory-based information gap decision theory (CTbIGDT) to improve robustness of electricity trading under uncertainties. To begin, we establish credibility theory as a foundational risk assessment methodology for uncertain price and load, incorporating both necessity and randomness measures. Subsequently, we advance the concept by developing the CTbIGDT optimization model, grounded in the consideration of expected costs, with the primary aim of fortifying the robustness of electricity trading practices. The ensuing model is then transformed into an equivalent form and solved using established standard optimization techniques. To validate the efficacy and robustness of our proposed methodology, a case study is conducted utilizing a modified IEEE 33-node distribution network system. The results of this study serve to underscore the viability and potency of the CTbIGDT model in enhancing the effectiveness of electricity trading strategies in an uncertain environment. [ABSTRACT FROM AUTHOR]

In this paper, we continue the research cycle on the properties of convolutional neural network-based image recognition systems and ways to improve noise immunity and robustness. Currently, a popular research area related to artificial neural networks is adversarial attacks. The adversarial attacks on the image are not highly perceptible to the human eye, and they also drastically reduce the neural network's accuracy. Image perception by a machine is highly dependent on the propagation of high frequency distortions throughout the network. At the same time, a human efficiently ignores high-frequency distortions, perceiving the shape of objects as a whole. We propose a technique to reduce the influence of high-frequency noise on the CNNs. We show that low-pass image filtering can improve the image recognition accuracy in the presence of high-frequency distortions in particular, caused by adversarial attacks. This technique is resource efficient and easy to implement. The proposed technique makes it possible to measure up the logic of an artificial neural network to that of a human, for whom high-frequency distortions are not decisive in object recognition. [ABSTRACT FROM AUTHOR]

Analyzing the robustness of networks against random failures or malicious attacks is a critical research issue in network science, as it contributes to enhancing the robustness of beneficial networks and effectively dismantling harmful ones. Most studies commonly neglect the impact of the attack success rate (ASR) and assume that attacks on the network will always be successful. However, in real-world scenarios, attacks may not always succeed. This paper proposes a novel robustness measure called Robustness-ASR (RASR), which utilizes mathematical expectations to assess network robustness when considering the ASR of each node. To efficiently compute the RASR for large-scale networks, a parallel algorithm named PRQMC is presented, which leverages randomized quasi-Monte Carlo integration to approximate the RASR with a faster convergence rate. Additionally, a new attack strategy named HBnnsAGP is introduced to better assess the lower bound of network RASR. Finally, the experimental results on six representative real-world complex networks demonstrate the effectiveness of the proposed methods compared with the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Star image registration is the most important step in the application of astronomical image differencing, stacking, and mosaicking, which requires high robustness, accuracy, and real-time capability on the part of the algorithm. At present, there are no high-performance registration algorithms available in this field. In the present paper, we propose a star image registration algorithm that relies only on radial module features (RMF) and rotation angle features (RAF) while providing excellent robustness, high accuracy, and good real-time performance. The test results on a large amount of simulated and real data show that the comprehensive performance of the proposed algorithm is significantly better than the four classical baseline algorithms as judged by the presence of rotation, insufficient overlapping area, false stars, position deviation, magnitude deviation, and complex sky background, making it a more ideal star image registration algorithm than current alternatives. [ABSTRACT FROM AUTHOR]

The problem of algorithmic bias in machine learning has recently gained a lot of attention due to its potentially strong impact on our societies. In much the same manner, algorithmic biases can alter industrial and safety-critical machine learning applications, where high-dimensional inputs are used. This issue has, however, been mostly left out of the spotlight in the machine learning literature. Contrary to societal applications, where a set of potentially sensitive variables, such as gender or race, can be defined by common sense or by regulations to draw attention to potential risks, the sensitive variables are often unsuspected in industrial and safety-critical applications. In addition, these unsuspected sensitive variables may be indirectly represented as a latent feature of the input data. For instance, the predictions of an image classifier may be altered by reconstruction artefacts in a small subset of the training images. This raises serious and well-founded concerns about the commercial deployment of AI-based solutions, especially in a context where new regulations address bias issues in AI. The purpose of our paper is, then, to first give a large overview of recent advances in robust machine learning. Then, we propose a new procedure to detect and to treat such unknown biases. As far as we know, no equivalent procedure has been proposed in the literature so far. The procedure is also generic enough to be used in a wide variety of industrial contexts. Its relevance is demonstrated on a set of satellite images used to train a classifier. In this illustration, our technique detects that a subset of the training images has reconstruction faults, leading to systematic prediction errors that would have been unsuspected using conventional cross-validation techniques. [ABSTRACT FROM AUTHOR]

It is well known that the microbiome data are ridden with outliers and have heavy distribution tails, but the impact of outliers and heavy-tailedness has yet to be examined systematically. This paper investigates the impact of outliers and heavy-tailedness on differential abundance analysis (DAA) using the linear models for the differential abundance analysis (LinDA) method and proposes effective strategies to mitigate their influence. The presence of outliers and heavy-tailedness can significantly decrease the power of LinDA. We investigate various techniques to address outliers and heavy-tailedness, including generalizing LinDA into a more flexible framework that allows for the use of robust regression and winsorizing the data before applying LinDA. Our extensive numerical experiments and real-data analyses demonstrate that robust Huber regression has overall the best performance in addressing outliers and heavy-tailedness. [ABSTRACT FROM AUTHOR]

Background: The World Health Organization (WHO)'s excess mortality estimates presented in May 2022 stirred controversy, due in part to the high estimate provided for Germany, which was later attributed to the spline model used. This paper aims to reproduce the problem using synthetic datasets, thus allowing the investigation of its sensitivity to parameters, both of the mortality curve and of the used method, thereby shedding light on the conditions that gave rise to this error and identifying possible remedies. Methods: A negative binomial model was used accounting for long-term change, seasonality, flu seasons, and heat waves. Simulated mortality curves from this model were then analysed using simple methods (mean, linear trend), the WHO method, and the method of Acosta and Irizarry. Results: The performance of the WHO's method with its original parametrization was indeed very poor, however it can be profoundly improved by a better choice of parameters. The Acosta–Irizarry method outperformed the WHO method despite being also based on splines, but it was also dependent on its parameters. Linear extrapolation could produce very good results, but was highly dependent on the choice of the starting year, while the average was the worst in almost all cases. Conclusions: Splines are not inherently unsuitable for predicting baseline mortality, but caution should be taken. In particular, the results suggest that the key issue is that the splines should not be too flexible to avoid overfitting. Even after having investigated a limited number of scenarios, the results suggest that there is not a single method that outperforms the others in all situations. As the WHO method on the German data illustrates, whatever method is chosen, it remains important to visualize the data, the fit, and the predictions before trusting any result. It will be interesting to see whether further research including other scenarios will come to similar conclusions. [ABSTRACT FROM AUTHOR]

Deploying Internet of Things (IoT) on low-power lossy wireless sensor/actuator networks (LLN) in harsh industrial environments presents challenges such as dynamic link qualities due to noise, signal attenuations and spurious interferences. However, the critical demand for industrial applications is reliability of data delivery on low-cost low-power sensor/actuator devices. To address these issues, this paper proposes a fully autonomous scheduling approach, called Auto-Sched, which ensures reliability of data delivery for both downlink and uplink traffic scheduling and enhances network robustness against node/link failures. To ensure reliability, Auto-Sched assigns retransmission time slots based on the reliability constraints of the communication link. To avoid collision issues, Auto-Sched creates an upward pipeline-like communication schedule for uplink end-to-end data delivery, and a downward pipeline-like communication schedule for downlink scheduling. For enhancing network robustness, we propose a simple algorithm for real-time autonomous schedule reconstruction, when node or link failures occur, with minimal influence on communication overhead. Performance evaluations quantified the performance of our proposed approaches under a variety of scenarios comparing them with existing approaches. [ABSTRACT FROM AUTHOR]

An urban public traffic network is a typical high-order complex network. There are multiple types of transportation in an urban public traffic network, and each type has different impacts on urban transportation. Robustness analyses of urban public traffic networks contribute to the safe maintenance and operation of urban traffic systems. In this paper, a new cascading failure model for urban public traffic networks is constructed based on a multi-subnet composite complex network model. In order to better simulate the actual traffic flow in the composite network, the concept of traffic function is proposed in the model. Considering the different effects of various relationships on nodes in the composite network, the traditional cascading failure model has been improved and a deliberate attack strategy and a random attack strategy have been adopted to study the robustness of the composite network. In the experiment, the urban bus–subway composite network in Qingdao, China, was used as an example for simulation. The experimental results showed that under two attack strategies, the network robustness did not increase with the increase in capacity, and the proportion of multiple relationships had a significant impact on the network robustness. [ABSTRACT FROM AUTHOR]

Wideband beamformers have been widely studied in wireless communication, remote sensing and so on. Generally speaking, to improve the spatial filtering ability of beamformers, there usually needs more sensors, which implies increased computational complexity and hardware costs. Besides that, wideband beamformers are known to be exceedingly sensitive to sensor mismatches in practice. Nevertheless, there is still a gap in research on the design of robust sparse wideband beamformers. In this paper, a two-step design of this topic is proposed. Firstly, a robust design based on the worst-case performance optimization (WCPO) using circular-model (CM) sensor mismatches is reformulated to address shortcomings of constraint sensitivity. Secondly, inspired by the joint sparse technology in compressive sensing theory, we focus on the sparse design of wideband beamformer. The constraints for the response characteristics and robustness are set from first step, and an iterative algorithm based on reweighted ℓ 2 , 1 optimization is adopted to achieve maximum sparsity of the sensor array. The mainly advantages of the work are that the proposed design exhibits accordant performance in terms of response and robustness, but few sensors compared with the counterpart with uniform array. Moreover, we surprisingly find that the optimized sparse array is also applicable to other design based on WCPO criterion. Simulation results are provided to verify the superior of the proposed methods compared to the existing counterparts. [ABSTRACT FROM AUTHOR]