Partial point attack approaches focus on leveraging the fewest points to achieve the best attack efficiency for easy implementation in the physical domain. For the first time, this paper proposes that the partial point attack strategy should pay attention to not only the selection and disturbance of points, but also the penetration of current defense methods. By re-examining characteristics of previous partial point attack approaches leading to performance improvement, we discover two fundamental principles: first, the selection of attacked points should consider not only the favourable visual salience but also the proper position concentration, thus to acquire effective structural destruction on the basis of remaining imperceptible; second, the perturbation of target points should form meaningful structures rather than outliers. To achieve this, we first propose a novel distributed-concentrated point selection (DPS) strategy, which is easier to concentrate salient points containing rich local information in a few tiny regions. Additionally, to enhance the penetration efficacy and real-time performance of attack point clouds against defenses, we further design a perturbation network based on the multi-scale penetration loss (L_msp), which can generate adversarial samples with as few outliers as possible only through a single forward propagation. Experimental results demonstrate that the real-time distributed-concentrated penetration attack (RDPA) framework can achieve state-of-the-art (SOTA) success rates by perturbing only 3.5% of points, and have the best penetration for mainstream defense methods such as SRS and SOR.

The impairment of sperm maturation is one of the major pathogenic factors in male subfertility, a serious medical and social problem affecting millions of global couples. Regrettably, the existing research on sperm maturation is slow, limited, and fragmented, largely attributable to the lack of a global molecular view. To fill the data gap, we newly established a database, namely the Sperm Maturation Database (SperMD, http://bio-add.org/SperMD ). SperMD integrates heterogeneous multi-omics data (170 transcriptomes, 91 proteomes, and five human metabolomes) to illustrate the transcriptional, translational, and metabolic manifestations during the entire lifespan of sperm maturation. These data involve almost all crucial scenarios related to sperm maturation, including the tissue components of the epididymal microenvironment, cell constituents of tissues, different pathological states, and so on. To the best of our knowledge, SperMD could be one of the limited repositories that provide focused and comprehensive information on sperm maturation. Easy-to-use web services are also implemented to enhance the experience of data retrieval and molecular comparison between humans and mice. Furthermore, the manuscript illustrates an example application demonstrated to systematically characterize novel gene functions in sperm maturation. Nevertheless, SperMD undertakes the endeavor to integrate the islanding omics data, offering a panoramic molecular view of how the spermatozoa gain full reproductive abilities. It will serve as a valuable resource for the systematic exploration of sperm maturation and for prioritizing the biomarkers and targets for precise diagnosis and therapy of male subfertility.

Abstract Digital image noise may be introduced during acquisition, transmission, or processing and affects readability and image processing effectiveness. The accuracy of established image processing techniques, such as segmentation, recognition, and edge detection, is adversely impacted by noise. There exists an extensive body of work which focuses on circumventing such issues through digital image enhancement and noise reduction, but this work is limited by a number of constraints including the application of non-adaptive parameters, potential loss of edge detail information, and (with supervised approaches) a requirement for clean, labeled, training data. This paper, developed on the principle of Noise2Void, presents a new unsupervised learning approach incorporating a pseudo-siamese network. Our method enables image denoising without the need for clean images or paired noise images, instead requiring only noise images. Two independent branches of the network utilize different filling strategies, namely zero filling and adjacent pixel filling. Then, the network employs a loss function to improve the similarity of the results in the two branches. We also modify the Efficient Channel Attention module to extract more diverse features and improve performance on the basis of global average pooling. Experimental results show that compared with traditional methods, the pseudo-siamese network has a greater improvement on the ADNI dataset in terms of quantitative and qualitative evaluation. Our method therefore has practical utility in cases where clean images are difficult to obtain.

Abstract Event camera is a new vision sensor that produces independent asynchronous responses to each pixel's change of illumination intensity. The unique principle of event camera has many advantages over traditional cameras, such as low latency, high temporal resolution, and high dynamic range (HDR). These advantages make event camera ideal for dealing with high speed, HDR visual tasks, especially in automatic driving scenes. In this study, we propose an image generation network named Event‐based attention generative adversarial networks (EAGAN), which simultaneously deals with optical flow and depth estimation of monocular event camera data. In addition to the innovative network architecture and loss function suitable for depth estimation, we are also the first to process incomplete training data to obtain more dense and uniform prediction results. Experiments on the multi‐vehicle stereo event camera dataset show that our EAGAN is competitive on the depth estimation task and achieves the state‐of‐the‐art effect in the optical flow estimation task.

Smart healthcare has undergone new opportunities and challenges with the arrival of the Industry 4.0 era. The intelligent imaging diagnosis system is a staple part of smart healthcare, helping doctors make clinical decisions. Nevertheless, intelligent diagnosis analysis is still confronted with the issue that it is challenging to extract effective features from the limited and high‐dimensional data, particularly in resting‐state data of amnesic mild cognitive impairment (aMCI). Furthermore, the intelligent imaging diagnosis system for aMCI is conductive to make timely predicting groups that may convert to Alzheimer’s disease (AD). To improve the system’s detection performance and reduce its data redundancy, we first develop an adaptive structure feature generation strategy (ASFGS) based on the Laplacian matrix and sparse autoencoder to obtain the structural features of brain functional network (BFN). Concurrently, we present a multiscale local feature detection strategy (MLFDS) to overcome the low utilization of local features of BFN. And finally, multiscale features, including structural features and multiscale local features, are fused by concatenation method to further improve the detection performance of aMCI system. Support vector machine based on radial basis function (RBF‐SVM) for small data learning is adopted to evaluate the effectiveness of the proposed features. Besides, we employ leave‐one‐out cross‐validation strategy to avoid the overfitting problem of classifier training process. The experiment results elucidate that the accuracy (ACC) and the area under the curve (AUC) in this work provide 86.57% and 86.36%, respectively, which outperforms the traditional methods and offers new insights for accuracy requirements of the aMCI system.

In recent years, convolutional neural networks (CNNs) have been at the centre of the advances and progress of advanced driver assistance systems and autonomous driving. This paper presents a point‐wise pyramid attention network, namely, PPANet, which employs an encoder‐decoder approach for semantic segmentation. Specifically, the encoder adopts a novel squeeze nonbottleneck module as a base module to extract feature representations, where squeeze and expansion are utilized to obtain high segmentation accuracy. An upsampling module is designed to work as a decoder; its purpose is to recover the lost pixel‐wise representations from the encoding part. The middle part consists of two parts point‐wise pyramid attention (PPA) module and an attention‐like module connected in parallel. The PPA module is proposed to utilize contextual information effectively. Furthermore, we developed a combined loss function from dice loss and binary cross‐entropy to improve accuracy and get faster training convergence in KITTI road segmentation. The paper conducted the training and testing experiments on KITTI road segmentation and Camvid datasets, and the evaluation results show that the proposed method proved its effectiveness in road semantic segmentation.

A multitude of similar or even identical objects are positioned closely in dense scenes, which brings about difficulties in object-detecting and object-counting. Since the poor performance of Faster R-CNN, recent works prefer to detect dense objects with the utilization of multi-layer feature maps. Nevertheless, they require complex post-processing to minimize overlap between adjacent bounding boxes, which reduce their detection speed. However, we find that such a multi-layer prediction is not necessary. It is observed that there exists a waste of ground-truth boxes during sampling, causing the lack of positive samples and the final failure of Faster R-CNN training. Motivated by this observation we propose a multiple-step sampling method for anchor sampling. Our method reduces the waste of ground-truth boxes in three steps according to different rules. Besides, we balance the positive and negative samples, and samples at different quality. Our method improves base detector (Faster R-CNN), the detection tests on SKU-110K and CARPK benchmarks indicate that our approach offers a good trade-off between accuracy and speed.

The point clouds acquired by a vehicle-borne mobile laser scanning (MLS) system have shown great potential for many applications such as intelligent transportation systems, road infrastructure inventories, and high-definition (HD) maps to support the advanced driver-assistance systems (ADAS) and autonomous vehicles (AVs). This paper presents a novel two-step approach to automated detection and reconstruction of three-dimensional (3D) highway curves from MLS point clouds. However, when dealing with noisy, unstructured, dense point clouds, we often face some challenges, most notably in handling of the outliers introduced during road marking detection and in recognition of curve types during 3D curve reconstruction. Our approach is formed by two main algorithms: a detector based on intensity variance and a robust model fitting estimator. The experimental results obtained using both a virtual scan dataset and a real MLS dataset demonstrated that our approach is very promising in handling of the outliers and reconstruction of 3D road curves. Specifically, a relative accuracy of 0.6% has been achieved in estimation of circle radii based on the virtual scan dataset. A comparative study also showed that our road marking detection approach is more effective and more stable than state-of-the-art approaches.

This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e.g., laser scanned point clouds). We formulate multi-model fitting problem as a sequential decision making process. We then use a deep reinforcement learning algorithm to learn the optimal decisions towards the best fitting result. In this paper, we have compared our method against the state-of-the-art on simulated data. The results demonstrated that our approach significantly reduced the number of fitting iterations.

The internet of Things (IoT) and Wireless Sensor Networks (WSNs) benefit smart home implementation. This paper introduces an inner sensor alarm system, which can send alarm messages as well as evidence material to outer media. Our system provides a checking function for real time exceptional events, while sending the alarm massage to the user. This application has three gradations: Client Sensor, Data Collector and Data Center. Client Sensor creates data, Data Collector collects and analyzes data and Data Center stores the alarm data materials. We build the network topology, communication model, data synchronous model and data storage mode to ensure the efficiency of the operation. Data transportation is considered carefully, so only the alarm data can be sent to the Data Center. Alarm messages are checked by user finally. This man checking procedure reduces the alarm false positive rate, and saves the common resource observably.

How interacting neurons give rise to meaningful behavior is an ultimate challenge in neuroscience. Synaptic connections lead to interacting neural ensemble activities. As one of the spike time coding scheme, neuronal interactions have been studied intensively. Several algorithms based on neuron pair-wise analysis have been proposed to estimate and study the interaction strength between neurons. Cross correlation, mutual information, and Granger causality are some of the examples. However, these methods are mathematical measures that can not distinguish if there is a functionally direct connection between a neuron pair. The network likelihood model on the other hand takes into account interconnectivity among a neural ensemble. It not only renders the interconnection strength between neurons but also accounts for physical connectivity between two neurons. Using this modeling approach to estimating neuronal connection, the current practice utilizes the maximum likelihood estimation, which is computationally expensive. In this study, we propose a new estimation algorithm for the spike firing probability model using a perceptron bank. This new model not only is computationally efficient, we were also able to interpret neural data from rat's motor cortices in relation to rat's learning decision and control behavior. Specifically the proposed perceptron bank was created based on simultaneous multi-channel chronic recordings from rats motor cortical areas while rats learned to perform a cue directed paddle press task. Our results show that significant changes (p = 0.1%) in functional neural synaptic efficacies from excitatory to inhibitory took place while rats learned to perform the decision and control task. This may indicate that neural plasticity and neural adaptation represented in temporal firing patterns is underlying the behavioral learning process.

For plate detection, we found that when the plate is disturbed by complex upright borderlines, location can be inaccurate or even missed. On the basis of this, a new vehicle license plate location algorithm based on salient feature is introduced. It makes use of the texture feature, geometric characteristics and color information. By raw location and precise location, it can quickly locate the license plate position accurately and distinguishes the color type. Our experiment results show that this algorithm has a fast, efficient performance of locating vehicle license plate under complex background.

Extracellular chronic recordings have been used as important evidence in neuroscientific studies to unveil the fundamental neural network mechanisms in the brain. Spike detection is the first step in the analysis of recorded neural waveforms to decipher useful information and provide useful signals for brain-machine interface applications. The process of spike detection is to extract action potentials from the recordings, which are often compounded with noise from different sources. This study proposes a new detection algorithm that leverages a technique from wavelet-based image edge detection. It utilizes the correlation between wavelet coefficients at different sampling scales to create a robust spike detector. The algorithm has one tuning parameter, which potentially reduces the subjectivity of detection results. Both artificial benchmark data sets and real neural recordings are used to evaluate the detection performance of the proposed algorithm. Compared with other detection algorithms, the proposed method has a comparable or better detection performance. In this letter, we also demonstrate its potential for real-time implementation.

Neural spike detection is the very first step in the analysis of recorded neural waveforms for brain machine interface applications and for neuroscientific studies. Spike detection accuracy and algorithm robustness is an important consideration in developing detection algorithms. For real neural recording data without respective ground truth, the evaluation of detection performance is a challenge. In the present paper we evaluate the detections by inspecting the detected spike waveforms for their compliance with neural spike electrophysiological properties. After classifying similar waveforms into one cluster, those qualified detections are determined to be spikes with high confidence. This new spike detection evaluation method is based on using the waveform phase information for cluster analysis. By including clustering as an integral step in the detection algorithm, we can refine detection results and improve detection performance. The new algorithm is easy to implement and is effective as demonstrated using both artificial and real neural waveforms.

This paper proposes a comprehensive new neural spike detection and sorting system. As a critical first step to all neuroscientific studies of the nervous system using chronically implanted electrodes in the brain areas of interest, high performance neural spike detection and sorting from the massive amount of continuously recorded neural data is a challenging task, especially in real time applications such as brain machine interface. Many existing spike detection and sorting systems use simple thresholding as the first step to admit a large number of possible spikes for further sorting using various clustering algorithms. Significant efforts have gone into developing sophisticated sorting algorithms, many of which are time consuming in applications. In this paper, we develop a new system that is based on a reliable detection algorithm using multiple correlations of wavelet coefficients, which is robust and can be implemented in real time. Because of the advanced detection step, the system becomes less demanding on the performance of the sorting or clustering algorithms. This has simplified the overall system, and made real time interface and other real time applications possible. We tested the newly proposed system extensively, compared with several popular systems including commercial packages. While most thresholding based detection systems usually create a large number of false alarms, test results show that our proposed system on both artificial and real neural data have produced few false alarms but with high detection rates.

In distributed collaborative design environments, product models and virtual studios should keep consistency to create sense of sharing the world and collaboration among physically distributed users. Model consistency is usually achieved by maintaining dynamic shared states. To make those events in physical locations virtually "seen" by the other participants, networks communications are indispensable. Because of limited network bandwidths and CPU resources in distributed and collaborative design environments, to decrease communication traffic is a critical issue. In this paper, we present a "near-sight" mode, which resorts to a method of using AOI, area of interest, to reduce efficiently communications to maintain dynamic shared states.

When building the virtual world for distributed collaborative design, the senses of sharing the world and cooperation between physically distributed hosts and users are usually achieved by a maintained dynamic shared state. So that those events that happen in another physical location can be virtually "seen" by other participants, communication through a network is indispensable. Because of the limited network bandwidth and limited CPU resources reserved for communication in a virtual world simulation, reducing communication traffic is a critical issue. We present an efficient model, the improved "lazy model", to reduce communication for maintaining the dynamic shared state. We also propose an approximate method using AOI, area of interest, to enhance the implementation of this model.

Fast and accurate collision detection is critical for the performance of the simulation of a virtual space where a great number of objects are presented and groups of people are working collaboratively. Most previous algorithms consume much memory resource or CPU resource. We present a new algorithm that can greatly reduce the number of pairs needed to check for collision detection in a distributed large-scale virtual space for collaborative design. Based on the idea of object oriented modeling, it makes good use of the concept of the virtual grid which will consume no unnecessary memory resource and CPU resource, and at the same time, fully utilize the distributed resource to boost up the speed of collision detection.