Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes by Xing Hu, Shiqiang Hu, Xiaoyu Zhang, Huanlong Zhang, Lingkun Luo

“…We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. …”
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Semantic-Assisted LIDAR Tightly Coupled SLAM for Dynamic Environments by Peng Liu, Yuxuan Bi, Jialin Shi, Tianyi Zhang, Caixia Wang

“…Experimental results from KITTI and M2DGR datasets demonstrate that our method exhibits generalization ability towards unknown data while effectively mitigating dynamic interference in real-world environments. …”
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Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity Recognition by Xin Guo, Young Kim, Xueli Ning, Se Dong Min

“…Combining these innovations, we conduct extensive experiments on three HAR benchmark datasets: KU-HAR, UniMiB SHAR, and USC-HAD. Experimental results demonstrate that our proposed enhancement scheme substantially elevates the performance of the Transformer model in HAR.…”
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Rural-Urban Disparity in Premature Cancer Mortality in Young People Aged 15–44 Years in China, 2004–2021 by Chunrong Chen, Xing Xing, Shaojie Li, Bo Qu, Chunyu Liu, He Zhu, He Zhu

“…ObjectiveThis study aims to examine and compare premature cancer mortality in young people aged 15–44 years old between rural and urban areas to inform early-onset cancer prevention.MethodsThe data were obtained from the China Death Surveillance Datasets from 2004 to 2021. The study sample consisted of cancer deaths of young people aged 15–44 years old. …”
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A distributed photovoltaic short‐term power forecasting model based on lightweight AI for edge computing in low‐voltage distribution network by Yuanliang Fan, Han Wu, Jianli Lin, Zewen Li, Lingfei Li, Xinghua Huang, Weiming Chen, Jian Zhao

“…By conducting validation on real‐world datasets, the results demonstrate that the model presented in this article possesses a smaller size and higher forecasting accuracy compared to other state‐of‐the‐art forecasting models.…”
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Tracking Regional Tissue Volume and Function Change in Lung Using Image Registration by Kunlin Cao, Gary E. Christensen, Kai Ding, Kaifang Du, Maghavan L. Raghavan, Ryan E. Amelon, Kimberly M. Baker, Eric A. Hoffman, Joseph M. Reinhardt

“…In this work we utilize image-matching procedures to correlate fluid redistribution and reabsorption to changes in regional lung function. Lung CT datasets from six human subjects were used in this study. …”
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Few-Shot Graph Anomaly Detection via Dual-Level Knowledge Distillation by Xuan Li, Dejie Cheng, Luheng Zhang, Chengfang Zhang, Ziliang Feng

“…Comprehensive experiments conducted on four datasets verify that DualKD remarkably outperforms the advanced baselines, highlighting its effectiveness in enhancing identification performance.…”
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Meaningful Multimodal Emotion Recognition Based on Capsule Graph Transformer Architecture by Hajar Filali, Chafik Boulealam, Khalid El Fazazy, Adnane Mohamed Mahraz, Hamid Tairi, Jamal Riffi

“…Our suggested approach for more accurate multimodal emotion recognition has been shown through extensive examinations, producing state-of-the-art results with accuracies of 69% and 56% on two public datasets, MELD and MOSEI, respectively.…”
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Detection to false data for smart grid by Jian Zheng, Shumiao Ren, Jingyue Zhang, Yu Kui, Jingyi Li, Qin Jiang, Shiyan Wang

“…Finally, experiments on simulated and real datasets indicate that the proposed method obtains 0.9738 detected accuracy, and the sensitivity to false data reaches 0.9387, defeating against the competitors, moreover, the proposed method does not exhibit exponential detection time as data volume augments. …”
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Kindlin-1 promotes gastric cancer cell motility through the Wnt/β-catenin signaling pathway by Jun-Ho Jang, Jiyoon Jung, Hyeon-Gu Kang, Woong Kim, Won-Jin Kim, Hana Lee, Ju Yeon Cho, Ran Hong, Jeong Won Kim, Joon-Yong Chung, Kyung-Hee Chun, Seok-Jun Kim

“…Kindlin-1 expression was analyzed in 359 gastric cancer tissue samples provided by Kangnam Sacred Heart Hospital and publicly available GSE datasets. Kindlin-1 showed significantly higher expression in gastric cancer tissues than that in normal tissues, and high Kindlin-1 expression was associated with poor prognosis. …”
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Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks by Thomas Nowotny, James P Turner, James C Knight

“…We implemented Eventprop in the GPU-enhanced neural networks framework (GeNN) and used it for training recurrent SNNs on the Spiking Heidelberg Digits (SHD) and Spiking Speech Commands (SSC) datasets. We found that learning depended strongly on the loss function and extended Eventprop to a wider class of loss functions to enable effective training. …”
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Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks by Linyan Li, Yu Sun, Fuyuan Hu, Tao Zhou, Xuefeng Xi, Jinchang Ren

“…The experimental results show that the inception scores of the proposed model on the CUB and Oxford-102 datasets have reached 4.48 and 4.16, improved by 2.75% and 6.42% compared to Attentional Generative Adversarial Networks (AttenGAN). …”
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Integrative multi-omics analysis reveals the translational landscape of the plant-parasitic nematode Meloidogyne incognita by Zhaolu Zhu, Dexin Bo, Chuanshuai Xie, Dadong Dai, Donghai Peng, Ming Sun, Jinshui Zheng

“…Collectively, our study provides comprehensive datasets and explores the genome-wide translational landscape of M. incognita, shedding light on the contributions of translational regulation during parasitism.…”
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How data validity and granularity affect jobs accessibility: A case study from the Czech Republic by Franke Daniel, Peltan Tomáš, Novotný Vojtìch

“…Using a selected case study area from the Czech Republic, the aim of this paper is to explore the sensitivity of the job-related accessibility calculation to different data on the land-use component, in order to explore and possibly overcome these problems with validity. Two different datasets were compared: valid jobs data based on available population and commuting data from the Census of the Czech Statistical Office with very coarse aggregation, and land-use data from the Urban Atlas with low validity of what is measured but very fine spatial aggregation. …”
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A deep learning ICDNET architecture for efficient classification of histopathological cancer cells using Gaussian noise images by Hui Zong, Wenlong An, Xin Chen, Zhanhui Yang, Heng Zhou, Xiangchao Liu, Jianchu Lin, Chuanyue Zong

“…To evaluate the performance of the ICDNET network model, experiments were conducted on four major public datasets with the addition of Gaussian noise. The experimental results demonstrate excellent accuracy and the ability to handle limited training samples, highlighting the potential of the ICDNET model to assist pathologists in pathological diagnosis.…”
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Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Adrianna Piszcz

“…Advanced deep learning algorithms play a key role in optimizing energy usage in smart cities, leveraging massive datasets to increase efficiency and sustainability. …”
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Identifying Key Biomarkers and Immune Infiltration in Female Patients with Ischemic Stroke Based on Weighted Gene Co-Expression Network Analysis by Haipeng Xu, Kelin He, Rong Hu, YanZhi Ge, Xinyun Li, Fengjia Ni, Bei Que, Yi Chen, Ruijie Ma

“…Herein, we used weighted gene co-expression network analysis (WGCNA) to determine the key modules and core genes in female IS patients using the GSE22255, GSE37587, and GSE16561 datasets from the GEO database. Subsequently, we performed functional enrichment analysis and built a protein-protein interaction (PPI) network. …”
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Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting by Hyun-Jung Bae, Jong-Seong Park, Ji-hyeok Choi, Hyuk-Yoon Kwon

“…In this study, we investigate short-term load forecasting (STLF) for large-scale electricity usage datasets. We propose a new prediction model for STLF that combines data clustering and dimensionality reduction schemes to handle large-scale electricity usage data effectively. …”
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The Eastern Mediterranean Sea mean sea level decadal slowdown: the effects of the water budget by Federica Borile, Nadia Pinardi, Nadia Pinardi, Vladyslav Lyubartsev, Mahmud Hasan Ghani, Antonio Navarra, Antonio Navarra, Jacopo Alessandri, Emanuela Clementi, Giovanni Coppini, Lorenzo Mentaschi, Giorgia Verri, Vladimir Santos da Costa, Enrico Scoccimarro, Francesco Misurale, Antonio Novellino, Paolo Oddo

“…This paper analyses the decadal variability of the Mean Sea Level (MSL) trend for the Mediterranean Sea and three subregions using a combination of satellite altimetry, tide gauges and reanalyses datasets for the past 30 years (1993–2022). These estimates indicate a decadal variability of the MSL across the analysed period, and a trend slowdown in the 2013–2022 decade compared to previous periods. …”
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Enhancing V2X Security Through Combined Rule-Based and DL-Based Local Misbehavior Detection in Roadside Units by Seungyoung Park, Duksoo Kim, Seokwoo Lee

“…However, due to the fixed locations of RSUs, they often receive only truncated sequences of BSMs from passing vehicles, which necessitates LMBD on these incomplete datasets. To mitigate the performance degradation of DL-based anomaly detection in truncated sequences, we integrated a rule-based method performed for single or two consecutively received BSMs. …”
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