Big Data Analytics for Network Level Short-Term Travel Time Prediction with Hierarchical LSTM and Attention

The travel time data collected from widespread traffic monitoring sensors necessitate big data analytic tools for querying, visualization, and identifying meaningful traffic patterns. This paper utilizes a large-scale travel time dataset from Caltrans Performance Measurement System (PeMS) system that is an overflow for traditional data processing and modeling tools. To overcome the challenges of the massive amount of data, the big data analytic engines Apache Spark and Apache MXNet are applied for data wrangling and modeling. Seasonality and autocorrelation were performed to explore and visualize the trend of time-varying data. Inspired by the success of the hierarchical architecture for many Artificial Intelligent (AI) tasks, we consolidate the cell and hidden states passed from low-level to the high-level LSTM with an attention pooling similar to how the human perception system operates. The designed hierarchical LSTM model can consider the dependencies at different time scales to capture the spatial-temporal correlations of network-level travel time. Another self-attention module is then devised to connect LSTM extracted features to the fully connected layers, predicting travel time for all corridors instead of a single link/route. The comparison results show that the Hierarchical LSTM with Attention (HierLSTMat) model gives the best prediction results at 30-minute and 45-min horizons and can successfully forecast unusual congestion. The efficiency gained from big data analytic tools was evaluated by comparing them with popular data science and deep learning frameworks

Weighted Bayesian Gaussian Mixture Model for Roadside LiDAR Object Detection

Background modeling is widely used for intelligent surveillance systems to detect moving targets by subtracting the static background components. Most roadside LiDAR object detection methods filter out foreground points by comparing new data points to pre-trained background references based on descriptive statistics over many frames (e.g., voxel density, number of neighbors, maximum distance). However, these solutions are inefficient under heavy traffic, and parameter values are hard to transfer from one scenario to another. In early studies, the probabilistic background modeling methods widely used for the video-based system were considered unsuitable for roadside LiDAR surveillance systems due to the sparse and unstructured point cloud data. In this paper, the raw LiDAR data were transformed into a structured representation based on the elevation and azimuth value of each LiDAR point. With this high-order tensor representation, we break the barrier to allow efficient high-dimensional multivariate analysis for roadside LiDAR background modeling. The Bayesian Nonparametric (BNP) approach integrates the intensity value and 3D measurements to exploit the measurement data using 3D and intensity info entirely. The proposed method was compared against two state-of-the-art roadside LiDAR background models, computer vision benchmark, and deep learning baselines, evaluated at point, object, and path levels under heavy traffic and challenging weather. This multimodal Weighted Bayesian Gaussian Mixture Model (GMM) can handle dynamic backgrounds with noisy measurements and substantially enhances the infrastructure-based LiDAR object detection, whereby various 3D modeling for smart city applications could be created

A Graph-Based Collision Resolution Scheme for Asynchronous Unsourced Random Access

This paper investigates the multiple-input-multiple-output (MIMO) massive unsourced random access in an asynchronous orthogonal frequency division multiplexing (OFDM) system, with both timing and frequency offsets (TFO) and non-negligible user collisions. The proposed coding framework splits the data into two parts encoded by sparse regression code (SPARC) and low-density parity check (LDPC) code. Multistage orthogonal pilots are transmitted in the first part to reduce collision density. Unlike existing schemes requiring a quantization codebook with a large size for estimating TFO, we establish a \textit{graph-based channel reconstruction and collision resolution (GB-CR$^2$)} algorithm to iteratively reconstruct channels, resolve collisions, and compensate for TFO rotations on the formulated graph jointly among multiple stages. We further propose to leverage the geometric characteristics of signal constellations to correct TFO estimations. Exhaustive simulations demonstrate remarkable performance superiority in channel estimation and data recovery with substantial complexity reduction compared to state-of-the-art schemes.Comment: 6 pages, 6 figures, submitted to IEEE GLOBECOM 202

Spatial-Temporal Deep Embedding for Vehicle Trajectory Reconstruction from High-Angle Video

Spatial-temporal Map (STMap)-based methods have shown great potential to process high-angle videos for vehicle trajectory reconstruction, which can meet the needs of various data-driven modeling and imitation learning applications. In this paper, we developed Spatial-Temporal Deep Embedding (STDE) model that imposes parity constraints at both pixel and instance levels to generate instance-aware embeddings for vehicle stripe segmentation on STMap. At pixel level, each pixel was encoded with its 8-neighbor pixels at different ranges, and this encoding is subsequently used to guide a neural network to learn the embedding mechanism. At the instance level, a discriminative loss function is designed to pull pixels belonging to the same instance closer and separate the mean value of different instances far apart in the embedding space. The output of the spatial-temporal affinity is then optimized by the mutex-watershed algorithm to obtain final clustering results. Based on segmentation metrics, our model outperformed five other baselines that have been used for STMap processing and shows robustness under the influence of shadows, static noises, and overlapping. The designed model is applied to process all public NGSIM US-101 videos to generate complete vehicle trajectories, indicating a good scalability and adaptability. Last but not least, the strengths of the scanline method with STDE and future directions were discussed. Code, STMap dataset and video trajectory are made publicly available in the online repository. GitHub Link: shorturl.at/jklT0

Experimental and Stress-Strain Equation Investigation on Compressive Strength of Raw and Modified Soil in Loess Plateau

As a special kind of soil is widely distributed in Loess Plateau of northwest China, it is difficult to use for growing crops and has poor structural property. According to local arid climate, the best utilization of the soil is as earthen construction material and it has been used for thousands of years. To research and improve the mechanical properties, the study investigates the response of soil with cement, lime, sand, and straw as admixtures to compressive loading. The influence on compressive strength and ductility of additives in different proportions is compared and analysed. The experimental data is also used for the formulation of dimensionless and generalized models describing the raw soil and modified soil’s full stress-strain response. The models can be applied to soil and modified soil in Loess Plateau with variable strength and deformation characteristics and therefore may be exploited for earthen construction design and nonlinear structural analyses

Explanation-Guided Backdoor Attacks on Model-Agnostic RF Fingerprinting

Despite the proven capabilities of deep neural networks (DNNs) for radio frequency (RF) fingerprinting, their security vulnerabilities have been largely overlooked. Unlike the extensively studied image domain, few works have explored the threat of backdoor attacks on RF signals. In this paper, we analyze the susceptibility of DNN-based RF fingerprinting to backdoor attacks, focusing on a more practical scenario where attackers lack access to control model gradients and training processes. We propose leveraging explainable machine learning techniques and autoencoders to guide the selection of positions and values, enabling the creation of effective backdoor triggers in a model-agnostic manner. To comprehensively evaluate our backdoor attack, we employ four diverse datasets with two protocols (Wi-Fi and LoRa) across various DNN architectures. Given that RF signals are often transformed into the frequency or time-frequency domains, this study also assesses attack efficacy in the time-frequency domain. Furthermore, we experiment with potential defenses, demonstrating the difficulty of fully safeguarding against our attacks

Overexpression of Peptide-Encoding OsCEP6.1 Results in Pleiotropic Effects on Growth in Rice (O. sativa)

Plant peptide hormone plays an important role in regulating plant developmental programs via cell-to-cell communication in a non-cell autonomous manner. To characterize the biological relevance of C-TERMINALLY ENCODED PEPTIDE (CEP) genes in rice, we performed a genome-wide search against public databases using bioinformatics approach and identified six additional CEP members. Expression analysis revealed a spatial-temporal pattern of OsCEP6.1 gene in different tissues and at different developmental stages of panicle. Interestingly, the expression level of the OsCEP6.1 was also significantly up-regulated by exogenous cytokinin. Application of a chemically synthesized 15-amino-acid OsCEP6.1 peptide showed that OsCEP6.1 had a negative role in regulating root and seedling growth, which was further confirmed by transgenic lines. Furthermore, the constitutive expression of OsCEP6.1 was sufficient to lead to panicle architecture and grain size variations. Scanning electron microscopy analysis revealed that the phenotypic variation of OsCEP6.1 overexpression lines resulted from decreased cell size but not reduced cell number. Moreover, starch accumulation was not significantly affected. Taken together, these data collectively suggest that the OsCEP6.1 peptide might be involved in regulating the development of panicles and grains in rice

Mental health status and its associated factors among female nurses in the normalization of COVID-19 epidemic prevention and control in China

ObjectiveTo investigate mental health status and its associated factors among female nurses in the normalization of COVID-19 epidemic prevention and control in China.MethodsRandom cluster sampling was applied to recruit 740 female nurses in China. The respondents completed the survey with mobile devices. Demographic questionnaire, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Insomnia Severity Index, and The Impact of Event Scale-Revised were used to assess demographic Information, anxiety, depression, insomnia and PTSD symptoms, respectively. The associated factors of mental health status were identified by binary logistic regression analysis.ResultsThe prevalence of anxiety and depression was 7.9 and 17.8%, respectively. Insomnia was an associated factor of anxiety (OR = 6.237, 95%CI = 6.055–23.761, P < 0.001) and depression (OR = 9.651, 95%CI = 5.699–22.370, P < 0.001), while PTSD was an associated factor of anxiety (OR = 11.995, 95%CI = 2.946–13.205, P < 0.001) and depression (OR = 11.291, 95%CI = 6.056–15.380, P < 0.001), Being married was a protective factor of depression (OR = 0.811, 95%CI = 1.309–6.039, P < 0.01).ConclusionFemale nurses showed problems in mental health. Insomnia, PTSD and marital status were associated with mental health. The hospital management should pay more attention to the unmarried groups, and strive to improve the sleep quality of female nurses and reduce their stress caused by traumatic events

The performance of large-pitch AC-LGAD with different N+ dose

AC-Coupled LGAD (AC-LGAD) is a new 4D detector developed based on the Low Gain Avalanche Diode (LGAD) technology, which can accurately measure the time and spatial information of particles. Institute of High Energy Physics (IHEP) designed a large-size AC-LGAD with a pitch of 2000 {\mu}m and AC pad of 1000 {\mu}m, and explored the effect of N+ layer dose on the spatial resolution and time resolution. The spatial resolution varied from 32.7 {\mu}m to 15.1 {\mu}m depending on N+ dose. The time resolution does not change significantly at different N+ doses, which is about 15-17 ps. AC-LGAD with a low N+ dose has a large attenuation factor and better spatial resolution. Large signal attenuation factor and low noise level are beneficial to improve the spatial resolution of the AC-LGAD sensor