Eight-input optical programmable logic array enabled by parallel spectrum modulation

Despite over 40 years' development of optical logic computing, the studies have been still struggling to support more than four operands, since the high parallelism of light has not been fully leveraged blocked by the optical nonlinearity and redundant input modulation in existing methods. Here, we propose a scalable multi-input optical programmable logic array (PLA) with minimal logical input, enabled by parallel spectrum modulation. By making full use of the wavelength resource, an eight-input PLA is experimentally demonstrated, and there are 2^256 possible combinations of generated logic gates. Various complex logic fuctions, such as 8-256 decoder, 4-bit comparator, adder and multiplier are experimentally demonstrated via leveraging the PLA. The scale of PLA can be further extended by fully using the dimensions of wavelength and space. As an example, a nine-input PLA is implemented to realize the two-dimensional optical cellular automaton for the first time and perform Conway's Game of Life to simulate the evolutionary process of cells. Our work significantly alleviates the challenge of extensibility of optical logic devices, opening up new avenues for future large-scale, high-speed and energy-efficient optical digital computing

Integrated transcriptomics, proteomics and metabolomics-based analysis uncover TAM2-associated glycolysis and pyruvate metabolic remodeling in pancreatic cancer

IntroductionTumor-associated macrophage 2 (TAM2) abundantly infiltrates pancreatic ductal adenocarcinoma (PAAD), and its interaction with malignant cells is involved in the regulation of tumor metabolism. In this study, we explored the metabolic heterogeneity involved in TAM2 by constructing TAM2-associated metabolic subtypes in PAAD.Materials and methodsPAAD samples were classified into molecular subtypes with different metabolic characteristics based on a multi-omics analysis strategy. 20 PAAD tissues and 10 normal pancreatic tissues were collected for proteomic and metabolomic analyses. RNA sequencing data from the TCGA-PAAD cohort were used for transcriptomic analyses. Immunohistochemistry was used to assess TAM2 infiltration in PAAD tissues.ResultsThe results of transcriptomics and immunohistochemistry showed that TAM2 infiltration levels were upregulated in PAAD and were associated with poor patient prognosis. The results of proteomics and metabolomics indicated that multiple metabolic processes were aberrantly regulated in PAAD and that this dysregulation was linked to the level of TAM2 infiltration. WGCNA confirmed pyruvate and glycolysis/gluconeogenesis as co-expressed metabolic pathways of TAM2 in PAAD. Based on transcriptomic data, we classified the PAAD samples into four TAM2-associated metabolic subtypes (quiescent, pyruvate, glycolysis/gluconeogenesis and mixed). Metabolic subtypes were each characterized in terms of clinical prognosis, tumor microenvironment, immune cell infiltration, chemotherapeutic drug sensitivity, and functional mechanisms.ConclusionOur study confirmed that the metabolic remodeling of pyruvate and glycolysis/gluconeogenesis in PAAD was closely related to TAM2. Molecular subtypes based on TAM2-associated metabolic pathways provided new insights into prognosis prediction and therapy for PAAD patients

Attention-Aware Sampling via Deep Reinforcement Learning for Action Recognition

Deep learning based methods have achieved remarkable progress in action recognition. Existing works mainly focus on designing novel deep architectures to achieve video representations learning for action recognition. Most methods treat sampled frames equally and average all the frame-level predictions at the testing stage. However, within a video, discriminative actions may occur sparsely in a few frames and most other frames are irrelevant to the ground truth and may even lead to a wrong prediction. As a result, we think that the strategy of selecting relevant frames would be a further important key to enhance the existing deep learning based action recognition. In this paper, we propose an attentionaware sampling method for action recognition, which aims to discard the irrelevant and misleading frames and preserve the most discriminative frames. We formulate the process of mining key frames from videos as a Markov decision process and train the attention agent through deep reinforcement learning without extra labels. The agent takes features and predictions from the baseline model as input and generates importance scores for all frames. Moreover, our approach is extensible, which can be applied to different existing deep learning based action recognition models. We achieve very competitive action recognition performance on two widely used action recognition datasets

Adaptive Kernel Density Estimation for Traffic Accidents Based on Improved Bandwidth Research on Black Spot Identification Model

At present, the total length of accident blackspot accounts for 0.25% of the total length of the road network, while the total number of accidents that occurred at accident black spots accounts for 25% of the total number of accidents on the road network. This paper describes a traffic accident black spot recognition model based on the adaptive kernel density estimation method combined with the road risk index. Using the traffic accident data of national and provincial trunk lines in Shanghai and ArcGIS software, the recognition results of black spots were compared with the recognition results of the accident frequency method and the kernel density estimation method, and the clustering degree of recognition results of adaptive kernel density estimation method were analyzed. The results show that: the accident prediction accuracy index values of the accident frequency method, kernel density estimation method, and traffic accident black spot recognition model were 14.39, 16.36, and 18.25, respectively, and the lengths of the traffic accident black spot sections were 184.68, 162.45, and 145.57, respectively, which means that the accident black spot section determined by the accident black spot recognition model was the shortest and the number of traffic accidents identified was the largest. Considering the safety improvement budget of 20% of the road length, the adaptive kernel density estimation method could identify about 69% of the traffic accidents, which was 1.13 times and 1.27 times that of the kernel density estimation method and the accident frequency method, respectively

Literature review of driving risk identification research based on bibliometric analysis

In order to understand the current research status and development direction of driving risk identification at home and abroad, relevant literatures in the field of driving risk identification from the China National Knowledge Infra-structure (CNKI) and Web of Science (WOS) in recent 12 years (2011–2022) were selected as research samples, and literature metrology tools VOSviewer and Citespace were used for visual analysis. The situation was analyzed from the aspects of chronological distribution, national cooperation network, distribution of domestic institutions, journal performance and keywords overview, literature coupling clustering and research hotspots. The results show that the number of published papers fluctuates year by year, and China, the United States and Germany have the largest number of published papers. The United States is at the center of international cooperation. The CNKI shows that universities in China such as Chang'an University and Chongqing Jiaotong University have published a large number of documents. According to the statistics of WOS, Accident Analysis & Prevention is the most widely published journal in the world. The average level of the journal is high and the quality of articles is better. Combining the research contents of CNKI and WOS, the main research directions can be clustered into five cluster themes by using the coupling function in VOSviewer, including driving risk assessment considering driver factors, the influence of driving environment on driving risk, driving risk assessment considering multi-source characteristic data, multi-aspect research on driving risk and risk identification of non-traditional vehicles in specific scenarios. Human-machine co-driving, artificial intelligence, intelligent driving, risk identification and natural driving are the current research hotspots and the future research trends

Numerical Investigation of Background Noise in a Circulating Water Tunnel

The presence of excessive background noise in hydrodynamic noise experiments conducted in circulating water tunnels can significantly impact the accuracy and reliability of experimental test results. To address this issue, it is crucial to evaluate and optimize the background noise during the design stage. In this research, acoustic field model and fluid–solid coupling numerical calculation model of circulating water tunnels are established. Utilizing the finite element method, we analyze the flow noise and flow-excited noise resulting from wall pressure pulses in the circulating water tunnel. Furthermore, we conduct a noise contribution analysis and explore strategies for structural vibration noise control. The results demonstrate that both flow noise and flow-excited noise decrease with increasing frequency, with flow-excited noise being the primary component of the tunnel’s background noise. The presence of resonant peaks significantly contributes to the elevated flow-excited noise levels. Moreover, enhancing structural stiffness and damping proves less effective in suppressing low-frequency peaks. Additionally, employing sound measurement pods suspended from the side of the test section for noise measurement exhibits a high error rate at low frequencies. This research provides insights into optimizing background noise in water tunnels, thereby informing future enhancements in tunnel design

Adaptive Kernel Density Estimation for Traffic Accidents Based on Improved Bandwidth Research on Black Spot Identification Model

At present, the total length of accident blackspot accounts for 0.25% of the total length of the road network, while the total number of accidents that occurred at accident black spots accounts for 25% of the total number of accidents on the road network. This paper describes a traffic accident black spot recognition model based on the adaptive kernel density estimation method combined with the road risk index. Using the traffic accident data of national and provincial trunk lines in Shanghai and ArcGIS software, the recognition results of black spots were compared with the recognition results of the accident frequency method and the kernel density estimation method, and the clustering degree of recognition results of adaptive kernel density estimation method were analyzed. The results show that: the accident prediction accuracy index values of the accident frequency method, kernel density estimation method, and traffic accident black spot recognition model were 14.39, 16.36, and 18.25, respectively, and the lengths of the traffic accident black spot sections were 184.68, 162.45, and 145.57, respectively, which means that the accident black spot section determined by the accident black spot recognition model was the shortest and the number of traffic accidents identified was the largest. Considering the safety improvement budget of 20% of the road length, the adaptive kernel density estimation method could identify about 69% of the traffic accidents, which was 1.13 times and 1.27 times that of the kernel density estimation method and the accident frequency method, respectively