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In modern transportation systems, network‐wide traffic flow estimation is crucial for informed decision making, strategic infrastructure planning, and effective traffic management. While the limited availability of observed road‐segment traffic flow data presents a significant challenge, the emerging collection of Global Navigation Satellite System (GNSS) speed data across the entire network provides an alternative method for estimating the missing traffic flow information. To this end, this paper introduces a novel approach to estimating network‐wide road‐segment traffic flow. This approach takes advantage of the abundantly available GNSS speed data, coupled with only sparsely observed traffic flow samples. By integrating the principles of dynamic traffic assignment models with sparse recovery techniques, we formulate the problem of traffic flow estimation as a Least Absolute Shrinkage and Selection Operator (LASSO) optimization task. The efficacy and practical applicability of our proposed method are validated through evaluations using both hypothetical and real‐world case studies. The experimental findings exhibit a close alignment between the estimated and ground‐truth link flows across different time periods. Additionally, the method consistently produces low mean estimation errors for the majority of road segments, underlining the potential for our approach in effectively managing traffic flow estimation for large‐scale road networks, particularly in situations characterized by data scarcity. [ABSTRACT FROM AUTHOR]

The focus of this paper is to improve simulation-based inference through improved denoising of experimental data. Statistical inference of parameters governing a complex physical process from observed data is an important task for several scientific domains. In a likelihood-free inference setup, the inference engine used to make inference from the experimental data can be trained using simulated data. In many scenarios, experimental data is corrupted by noise during the data acquisition process, which is either unaccounted for in the simulator or whose strength might not match with the one set in the simulator. This deteriorates the accuracy of the inference. While advances in denoising can be leveraged to address this challenge, many denoising techniques ignore the fact that experimental data in many scientific domains tends to be irregular/graph-structured. This paper addresses these two challenges by developing a kernel-based learnable graph (KBLG) denoiser, which can be used to denoise experimental graph-structured data. In order to exhibit the efficacy of the developed denoiser in simulation-driven inference problems, this paper considers the problem of inference of the position of an interaction in an astroparticle detector. The available simulated data in this case is the snapshots of the luminous responses of the photomultiplier tube sensors used within the dark matter detection experiment. In experimental situations, these measurements are corrupted by noise generated by secondary optical and electronic processes. The proposed KBLG denoiser is used to denoise the multiple snapshots of the experimental measurements, which are then used for position reconstruction using a multilayer perceptron trained using noiseless simulated data. Numerical results exhibit that the proposed KBLG denoiser outperforms a graph-agnostic denoiser in terms of MLP-based position reconstruction performances for different levels of noise (noise variance). [ABSTRACT FROM AUTHOR]

Summary: This paper develops a new image encryption algorithm based on a novel two‐dimensional chaotic map and bit‐level dynamic scrambling. First, multiple one‐dimensional chaotic maps are coupled to construct a novel two dimensions Logistic‐Sine‐coupling chaos map (2D‐LSCCM). The performance analysis shows that the 2D‐LSCCM has more complex chaotic characteristics and wider chaotic range than many extant 2D chaos maps. Second, original image matrix combines with hash algorithm SHA‐256 to generate a hash value. The initial values of 2D‐LSCCM are generated based on the hash value. Third, the original image matrix is divided into multiple sub‐matrices by wavelet transform, followed by scrambling by an improved Knuth shuffle algorithm. Fourth, the scrambled multiple sub‐matrices are stitched into an image matrix of M×N×3$$ M\times N\times 3 $$ and converted into a binary matrix. The chaotic sequence generated by 2D‐LSCCM is introduced as a control sequence to control the bit‐level scrambling of pixel points, which realizes the bit‐level dynamic scrambling. Finally, the diffusion operation is performed by parameter par and chaotic sequence to obtain the final encrypted image. The algorithm security analysis and simulation examples demonstrate the effectiveness of the proposed encryption scheme. [ABSTRACT FROM AUTHOR]

This paper presents a proof-of-concept semi-supervised autoencoder for the energy reconstruction of scattering particle interactions inside dualphase time projection chambers (TPCs), such as XENONnT. This autoencoder model is trained on simulated XENONnT data and is able to simultaneously reconstruct photosensor array hit patterns and infer the number of electrons in the gas gap, which is proportional to the energy of ionization signals in the TPC. Development plans for this autoencoder model are discussed, including future work in developing a faster simulation technique for dual-phase TPCs.

The Big Science projects common of multi-institute particle-physics collaborations generates unique needs for member management, including paper authorship tracking, shift assignments, subscription to mailing lists and access to 3rd party applications such as Github and Slack. For smaller collaborations under 200 people, often no facility for centralized member management is available and these needs are usually manually handled by long-term members despite the management becoming untenable as collaborations grow. To automate many of these tasks for the expanding XENON collaboration, we developed the XENONnT User Management Website, a web application that stores and updates data related to the collaboration members through the use of Node.js and MongoDB. We found that web frameworks are so mature and approachable such that a student can develop a good system to meet the unique needs of the collaboration. The application allows for the scheduling of shifts for members to coordinate between institutes. User manipulation of 3rd party applications are implemented using REST API integration. The XENONnT User Management Website is open source and is a show case of quick implementation of utility application using the web framework, which demonstrated the utility of web-based approaches for solving specific problems to aid the logistics of running Big Science collaborations.

Image recognition technology with faster training speed and higher recognition accuracy has always been the focus and frontier of intelligent technology research. Sorting image fast recognition is of great significance to improve logistics efficiency in unmanned warehouse and other occasions. The simulation of sorting image fast recognition based on deep learning is studied. A convolution neural network is designed. For the specific environment of logistics warehouse and the specified objects to be identified, the sorting image is not very clear because of the closed environment and illumination conditions of warehouse. Firstly, the dual tree complex wavelet transform is used to denoise the sorting image. Then, on the basis of AlexNet neural network, the convolution layer of convolution neural network is dealt with. ReLU layer and pooling layer parameters are redefined to speed up the learning speed of the neural network. Then, according to the new image classification task, the last three layers of the neural network are defined, which are full connection layer, Softmax layer and classification output layer, to adapt to the new image recognition. The proposed fast sorting image recognition technology based on depth learning has higher training speed and recognition accuracy in the face of more complex sorting image recognition.

The carotid web is commonly found in the carotid bulb or the beginning of the internal carotid artery. It presents as a thin layer of proliferative intimal tissue originating from the arterial wall and extending into the vessel lumen. A large body of research has proven that the carotid web is a risk factor for ischemic stroke. This review summarizes the current research status of the carotid web and focuses on its imaging presentation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Liang, Qin, Xie, Niu, Luo, Chen, Chen, Zhang and Wang.)

This work proposes a domain-informed neural network architecture for experimental particle physics, using particle interaction localization with the time-projection chamber (TPC) technology for dark matter research as an example application. A key feature of the signals generated within the TPC is that they allow localization of particle interactions through a process called reconstruction (i.e., inverse-problem regression). While multilayer perceptrons (MLPs) have emerged as a leading contender for reconstruction in TPCs, such a black-box approach does not reflect prior knowledge of the underlying scientific processes. This paper looks anew at neural network-based interaction localization and encodes prior detector knowledge, in terms of both signal characteristics and detector geometry, into the feature encoding and the output layers of a multilayer (deep) neural network. The resulting neural network, termed Domain-informed Neural Network (DiNN), limits the receptive fields of the neurons in the initial feature encoding layers in order to account for the spatially localized nature of the signals produced within the TPC. This aspect of the DiNN, which has similarities with the emerging area of graph neural networks in that the neurons in the initial layers only connect to a handful of neurons in their succeeding layer, significantly reduces the number of parameters in the network in comparison to an MLP. In addition, in order to account for the detector geometry, the output layers of the network are modified using two geometric transformations to ensure the DiNN produces localizations within the interior of the detector. The end result is a neural network architecture that has 60% fewer parameters than an MLP, but that still achieves similar localization performance and provides a path to future architectural developments with improved performance because of their ability to encode additional domain knowledge into the architecture., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liang, Higuera, Peters, Roy, Bajwa, Shatkay and Tunnell.)