Ultra narrow AuPd and Al wires

In this letter we discuss a novel and versatile template technique aimed to the fabrication of sub-10 nm wide wires. Using this technique, we have successfully measured AuPd wires, 12 nm wide and as long as 20 $\mu$m. Even materials that form a strong superficial oxide, and thus not suited to be used in combination with other techniques, can be successfully employed. In particular we have measured Al wires, with lateral width smaller or comparable to 10 nm, and length exceeding 10 $\mu$m.Comment: 4 pages, 4 figures. Pubblished in APL 86, 172501 (2005). Added erratum and revised Fig.

An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning

Recently, bi-level optimization (BLO) has taken center stage in some very exciting developments in the area of signal processing (SP) and machine learning (ML). Roughly speaking, BLO is a classical optimization problem that involves two levels of hierarchy (i.e., upper and lower levels), wherein obtaining the solution to the upper-level problem requires solving the lower-level one. BLO has become popular largely because it is powerful in modeling problems in SP and ML, among others, that involve optimizing nested objective functions. Prominent applications of BLO range from resource allocation for wireless systems to adversarial machine learning. In this work, we focus on a class of tractable BLO problems that often appear in SP and ML applications. We provide an overview of some basic concepts of this class of BLO problems, such as their optimality conditions, standard algorithms (including their optimization principles and practical implementations), as well as how they can be leveraged to obtain state-of-the-art results for a number of key SP and ML applications. Further, we discuss some recent advances in BLO theory, its implications for applications, and point out some limitations of the state-of-the-art that require significant future research efforts. Overall, we hope that this article can serve to accelerate the adoption of BLO as a generic tool to model, analyze, and innovate on a wide array of emerging SP and ML applications

Advancing Model Pruning via Bi-level Optimization

The deployment constraints in practical applications necessitate the pruning of large-scale deep learning models, i.e., promoting their weight sparsity. As illustrated by the Lottery Ticket Hypothesis (LTH), pruning also has the potential of improving their generalization ability. At the core of LTH, iterative magnitude pruning (IMP) is the predominant pruning method to successfully find 'winning tickets'. Yet, the computation cost of IMP grows prohibitively as the targeted pruning ratio increases. To reduce the computation overhead, various efficient 'one-shot' pruning methods have been developed, but these schemes are usually unable to find winning tickets as good as IMP. This raises the question of how to close the gap between pruning accuracy and pruning efficiency? To tackle it, we pursue the algorithmic advancement of model pruning. Specifically, we formulate the pruning problem from a fresh and novel viewpoint, bi-level optimization (BLO). We show that the BLO interpretation provides a technically-grounded optimization base for an efficient implementation of the pruning-retraining learning paradigm used in IMP. We also show that the proposed bi-level optimization-oriented pruning method (termed BiP) is a special class of BLO problems with a bi-linear problem structure. By leveraging such bi-linearity, we theoretically show that BiP can be solved as easily as first-order optimization, thus inheriting the computation efficiency. Through extensive experiments on both structured and unstructured pruning with 5 model architectures and 4 data sets, we demonstrate that BiP can find better winning tickets than IMP in most cases, and is computationally as efficient as the one-shot pruning schemes, demonstrating 2-7 times speedup over IMP for the same level of model accuracy and sparsity.Comment: Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022

Research on user recruitment algorithms based on user trajectory prediction with sparse mobile crowd sensing

Sparse mobile crowd sensing saves perception cost by recruiting a small number of users to perceive data from a small number of sub-regions, and then inferring data from the remaining sub-regions. The data collected by different people on their respective trajectories have different values, and we can select participants who can collect high-value data based on their trajectory predictions. In this paper, we study two aspects of user trajectory prediction and user recruitment. First, we propose an STGCN-GRU user trajectory prediction algorithm, which uses the STGCN algorithm to extract features related to temporal and spatial information from the trajectory map, and then inputs the feature sequences into GRU for trajectory prediction, and this algorithm improves the accuracy of user trajectory prediction. Second, an ADQN (action DQN) user recruitment algorithm is proposed.The ADQN algorithm improves the objective function in DQN on the idea of reinforcement learning. The action with the maximum input value is found from the Q network, and then the output value of the objective function of the corresponding action Q network is found. This reduces the overestimation problem that occurs in Q networks and improves the accuracy of user recruitment. The experimental results show that the evaluation metrics FDE and ADE of the STGCN-GRU algorithm proposed in this paper are better than other representative algorithms. And the experiments on two real datasets verify the effectiveness of the ADQN user selection algorithm, which can effectively improve the accuracy of data inference under budget constraints

Intelligent crowd sensing pickpocketing group identification using remote sensing data for secure smart cities

As a public infrastructure service, remote sensing data provided by smart cities will go deep into the safety field and realize the comprehensive improvement of urban management and services. However, it is challenging to detect criminal individuals with abnormal features from massive sensing data and identify groups composed of criminal individuals with similar behavioral characteristics. To address this issue, we study two research aspects: pickpocketing individual detection and pickpocketing group identification. First, we propose an IForest-FD pickpocketing individual detection algorithm. The IForest algorithm filters the abnormal individuals of each feature extracted from ticketing and geographic information data. Through the filtered results, the factorization machines (FM) and deep neural network (DNN) (FD) algorithm learns the combination relationship between low-order and high-order features to improve the accuracy of identifying pickpockets composed of factorization machines and deep neural networks. Second, we propose a community relationship strength (CRS)-Louvain pickpocketing group identification algorithm. Based on crowdsensing, we measure the similarity of temporal, spatial, social and identity features among pickpocketing individuals. We then use the weighted combination similarity as an edge weight to construct the pickpocketing association graph. Furthermore, the CRS-Louvain algorithm improves the modularity of the Louvain algorithm to overcome the limitation that small-scale communities cannot be identified. The experimental results indicate that the IForest-FD algorithm has better detection results in Precision, Recall and F1score than similar algorithms. In addition, the normalized mutual information results of the group division effect obtained by the CRS-Louvain pickpocketing group identification algorithm are better than those of other representative methods

Research on rainy day traffic sign recognition algorithm based on PMRNet

The recognition of traffic signs is of great significance to intelligent driving and traffic systems. Most current traffic sign recognition algorithms do not consider the impact of rainy weather. The rain marks will obscure the recognition target in the image, which will lead to the performance degradation of the algorithm, a problem that has yet to be solved. In order to improve the accuracy of traffic sign recognition in rainy weather, we propose a rainy traffic sign recognition algorithm. The algorithm in this paper includes two modules. First, we propose an image deraining algorithm based on the Progressive multi-scale residual network (PMRNet), which uses a multi-scale residual structure to extract features of different scales, so as to improve the utilization rate of the algorithm for information, combined with the Convolutional long-short term memory (ConvLSTM) network to enhance the algorithm's ability to extract rain mark features. Second, we use the CoT-YOLOv5 algorithm to recognize traffic signs on the recovered images. In this paper, in order to improve the performance of YOLOv5 (You-Only-Look-Once, YOLO), the 3 × 3 convolution in the feature extraction module is replaced by the Contextual Transformer (CoT) module to make up for the lack of global modeling capability of Convolutional Neural Network (CNN), thus improving the recognition accuracy. The experimental results show that the deraining algorithm based on PMRNet can effectively remove rain marks, and the evaluation indicators Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are better than the other representative algorithms. The mean Average Precision (mAP) of the CoT-YOLOv5 algorithm on the TT100k datasets reaches 92.1%, which is 5% higher than the original YOLOv5

Detection and differentiation of Borrelia burgdorferi sensu lato in ticks collected from sheep and cattle in China

<p>Abstract</p> <p>Background</p> <p>Lyme disease caused by <it>Borrelia burgdorferi </it>sensu lato complex is an important endemic zoonosis whose distribution is closely related to the main ixodid tick vectors. In China, isolated cases of Lyme disease infection of humans have been reported in 29 provinces. Ticks, especially ixodid ticks are abundant and a wide arrange of <it>Borrelia </it>natural reservoirs are present. In this study, we developed a reverse line blot (RLB) to identify <it>Borrelia </it>spp. in ticks collected from sheep and cattle in 7 Provinces covering the main extensive livestock regions in China.</p> <p>Results</p> <p>Four species-specific RLB oligonucleotide probes were deduced from the spacer region between the 5S-23S rRNA gene, along with an oligonucleotide probe which was common to all. The species specific probes were shown to discriminate between four genomic groups of <it>B. burgdorferi </it>sensu lato i.e. <it>B. burgdorferi </it>sensu stricto, <it>B. garinii, B. afzelii</it>, and <it>B. valaisiana</it>, and to bind only to their respective target sequences, with no cross reaction to non target DNA. Furthermore, the RLB could detect between 0.1 pg and 1 pg of <it>Borrelia </it>DNA.</p> <p>A total of 723 tick samples (<it>Haemaphysalis, Boophilus, Rhipicephalus </it>and <it>Dermacentor</it>) from sheep and cattle were examined with RLB, and a subset of 667 corresponding samples were examined with PCR as a comparison. The overall infection rate detected with RLB was higher than that of the PCR test.</p> <p>The infection rate of <it>B. burgdoreri </it>sensu stricto was 40% in south areas; while the <it>B. garinii infection rate </it>was 40% in north areas. The highest detection rates of <it>B. afzelii </it>and <it>B. valaisiana </it>were 28% and 22%, respectively. Mixed infections were also found in 7% of the ticks analyzed, mainly in the North. The proportion of <it>B. garinii </it>genotype in ticks was overall highest at 34% in the whole investigation area.</p> <p>Conclusion</p> <p>In this study, the RLB assay was used to detect <it>B. burgdorferi </it>sensu lato in ticks collected from sheep and cattle in China. The results showed that <it>B. burdorferi senso stricto </it>and <it>B. afzelii </it>were mainly distributed in the South; while <it>B. garinii </it>and <it>B. valaisiana </it>were dominant in the North. <it>Borrelia </it>spirochaetes were detected in <it>Rhipicephalus </it>spp for the first time. It is suggested that the <it>Rhipicephalus </it>spps might play a role in transmitting <it>Borrelia </it>spirochaetes.</p