Reviewerly/exHarmony huggingface dataset:
FRPNet: An improved Faster-ResNet with PASPP for real-time semantic segmentation in the unstructured field scene

Authors:
raw_author_name: Biao Yang
raw_affiliation_string: School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213000, China
raw_author_name: Sen Yang
raw_affiliation_string: School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213000, China
raw_author_name: Peng Wang
raw_affiliation_string: School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213000, China
raw_author_name: Hai Wang
raw_affiliation_string: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
raw_author_name: Jiaming Jiang
raw_affiliation_string: School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213000, China
raw_author_name: Rongrong Ni
raw_affiliation_string: School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213000, China
raw_author_name: Changchun Yang
raw_affiliation_string: School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213000, China

Abstract: The agricultural environment has numerous unstructured scenes, like back roads, alameda, and farmland. Existing semantic segmentation approaches of structured roads cannot meet the real-time and accuracy requirements when encountering unstructured scenes, thus hindering the autonomous operation of the intelligent agents in these scenes. To address the gordian problems, FRPNet is proposed to conduct real-time unstructured semantic segmentation in the field scene. Specifically, the semantic contexts are accurately extracted by introducing customized residual connections into the lightweight FasterNet-based encoder. Afterward, a modified partial Atrous spatial pyramid pooling (PASPP) is proposed to extract multi-scale features from the high-level semantic embedding, which improves the recognition of irregular boundaries and confused classes. Finally, a decoder whose structure is symmetric with the encoder is proposed to segment unstructured scenes by decoding the multi-scale semantic embedding. Additionally, a niche-targeting loss function called Ohd-Loss is proposed to optimize FRPNet. It enhances the model's focus on small-sample classes and addresses the issues of imbalanced class distribution and loss of scene details. Quantitative evaluations show that MIoU of FRPNet reaches 55.10% and 53.17% in the RUGD and RELLIS test sets, respectively. Meanwhile, FLOPs and Params are reduced to 5.27G and 9.74 M, which indicates that FRPNet effectively improves the segmentation accuracy in unstructured field scenes while satisfying real-time requirements. Qualitative evaluations of the self-developed unmanned vehicle running on the back roads verify the generalization performance of FRPNet. In a nutshell, FRPNet endows autonomous agents to perceive the surrounding field scenes with low-cost RGB cameras in real-time, facilitating the subsequent decision-making process. The code will be released at https://github.com/beautifulgirl11/FRPNet.
Temporal relation extraction with contrastive prototypical sampling

Authors:
raw_author_name: Chenhan Yuan
raw_affiliation_string: National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, United Kingdom
raw_author_name: Qianqian Xie
raw_affiliation_string: National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, United Kingdom
raw_author_name: Sophia Ananiadou
raw_affiliation_string: Alan Turing Institute, London, United Kingdom; National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, United Kingdom

Abstract: Temporal relation extraction aims to infer the temporal order of either two events in the document. Because of the nature of events in real life, severe imbalanced temporal relation classes exist in the temporal relation extraction task. Even though various methods have been proposed to improve the overall performance, the accuracy of these methods on minority temporal classes is limited. In this work, we present a contrastive prototypical learning architecture to address this problem, which explicitly models the spatial similarity between instances in the embedding space so that instances from minority classes can be distinguished from the large classes. To make it compatible with current temporal relation extraction settings, we propose a novel sampling memory queue-based method so that the architecture can be applied to a limited batch size scenario. We further design a context encoding layer that incorporates both contextualized information and linguistic features such as tense information and dependency. Our extensive experiments on TimeBank-Dense, TDDiscourse, and MATRES datasets demonstrate that our model can significantly improve the performance of minority relation classes and, therefore increase the overall learning ability.
In situ three-dimensional reconstruction of laser powder bed fusion parts by light field camera

Authors:
raw_author_name: Xiuhua Li
raw_affiliation_string: School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei 430072, China
raw_author_name: Hui Li
raw_affiliation_string: School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei 430072, China

Abstract: The assessment of output quality in the laser powder bed fusion process entails the analysis of the three-dimensional morphology of the manufactured parts, which plays a crucial role in quality control to prevent metallurgical defects. This study proposes a novel approach of three-dimensional reconstruction for laser powder bed fusion parts using a light field camera. Initially, a consistency method for high-precision camera calibration was explored to determine the virtual camera pixel size and coefficient of effective focal length of light field camera. Subsequently, the model based light field epipolar-plane image Unet is designed to obtain disparity information without any data augmentation techniques. By leveraging both binocular and light field optical paths, a robust mapping relationship of the disparity, depth, and visually coherent three-dimensional contour was established. Finally, the three-dimensional contours of laser powder bed fusion parts were reconstructed by the established mapping with a single exposure of the light field camera. The proposed method enables three-dimensional contour visualization of laser powder bed fusion parts from multi-view perspectives, which facilitates in situ monitoring of the spattering distance, three-dimensional spatial points and lines of the printed layers and contours of printed parts. With a calibration error of 50 μm and resolution of 180 μm for the light field camera (Lytro illum camera), the maximum relative and absolute errors between reconstructed three-dimensional spatial points were 4.33 % and 260 μm respectively when compared with theoretical dimension, and 0.5 % and 210 μm respectively when compared with manufactured dimension. The maximum root-mean-square error of reconstructed three-dimensional contour surfaces was found to be 1.55 mm. This study represents the first application of a light field camera for in situ three-dimensional reconstruction of laser powder bed fusion parts, which notably can be obtained from only a single round of calibration and exposure, enabling seamless monitoring of their surface morphology and facilitating online quality control. These advancements have vast potential for enhancing high-end additive manufacturing.
The world in a grain of sand: Condensing the string vacuum degeneracy

Authors:
raw_author_name: Yang-Hui He
raw_affiliation_string: Department of Mathematics, City, University of London, London, EC1V0HB, UK; London Institute, Royal Institution of GB, 21 Albemarle St, London, W1S 4BS, UK; Merton College, University of Oxford, OX1 4JD, UK; School of Physics, NanKai University, Tianjin, 300071, PR China

Abstract: The proliferation of vacuum solutions to string theory highly deter the search for the Standard Model. We propose a novel approach to this problem by finding an efficient measure of similarity of vacua. Using one million concrete examples, the paradigm of few-shot machine-learning represents them as points in Euclidean three-space with similar points clustered together. We thereby compress the search space for desired physics to within one percent of the original. Our analysis provides an explicit method for machine-learning the landscape and finding 'typicality', even with minuscule data.
Lightweight Whole-Body Human Pose Estimation With Two-Stage Refinement Training Strategy

Authors:
raw_author_name: Zhewei Zhang
raw_affiliation_string: Department of Electronic Engineering, Tsinghua University, Beijing, China
raw_author_name: Junyu Shen
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Shengjin Wang
raw_affiliation_string: Department of Electronic Engineering, Tsinghua University, Beijing, China

Abstract: Human whole-body pose estimation is a challenging task since the model needs to learn more keypoints than the body-only case. To meet the needs of real-time performance while maintaining accuracy is also a hard issue in whole-body pose estimation due to the learning capability of lightweight networks. In order to solve the above problems to a large extent, we propose a light whole-body pose estimation method with an optimized training strategy. The model is designed based on bottom-up architecture as a base network followed by a refinement network. We propose a two-stage training process, which learns rough features in the first stage and then improves estimation precision in the second stage. An online data augmentation procedure is proposed in the second stage to improve refinement performance. We also introduce a separate learning refinement structure that fine-tunes for body, foot, and hand part independently. Experimental results show that our method improves over 8%–10% average precision compared with other lightweight state-of-the-art approaches in the whole-body pose estimation task, with nearly a quarter (25%) size of model parameters saved.
A combined deep CNN with a chimp optimization approach for lung cancer diagnosis

Authors:
raw_author_name: M. Chinnadurai
raw_affiliation_string: Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India

Abstract: The most common type of disease that is normal among women is lung cancer. It is one of the main reasons among women, despite great efforts to prevent it through trackers. An automatic disease detection system helps doctors identify and provide accurate results, thus minimizing the mortality rate. Computer Aided Diagnosis (CAD) has minimal human intervention and produces more accurate results than humans. It will be a difficult and lengthy task that depends on the experience of the pathologists. Deep learning methods have been shown to give better results when correlated with ML and extract the best highlights from images. The main objective of this article is to propose a deep learning technique in combination with a convolution neural network (CNN) with a chimpanzee optimization algorithm to diagnose lung cancer. Here, CNN is used for feature extraction and used for extracted feature detection. Experimental results show that the proposed system achieves 100% accuracy, 99% sensitivity, 99% recall, and 98% F1 score compared to other traditional models. As the system achieved correct results, it can help doctors easily investigate lung cancer.
Blending of a novel all sky imager model with persistence and a satellite based model for high-resolution irradiance nowcasting

Authors:
raw_author_name: Wiebke Herzberg
raw_affiliation_string: Fraunhofer Institute for Solar Energy Systems ISE, Freiburg, Germany
raw_author_name: Anna Dittmann
raw_affiliation_string: Fraunhofer Institute for Solar Energy Systems ISE, Freiburg, Germany
raw_author_name: Elke Lorenz
raw_affiliation_string: Fraunhofer Institute for Solar Energy Systems ISE, Freiburg, Germany

Abstract: High shares of variable energy sources such as photovoltaics (PV) make balancing network load and generation increasingly challenging. Electricity grids with high PV-penetration benefit from the consideration of intra-minute and intra-hour variabilities via nowcasts (shortest-term forecasts). In this study we present a novel all-sky imager (ASI) nowcasting system which is benchmarked against an established ASI method, a satellite nowcasting system and persistence. Nowcasts of our novel ASI method, satellite method and persistence are subsequently combined to a hybrid model with improved accuracy. ASI systems exploit sky images from fisheye cameras to analyze sky conditions, predict upcoming cloud situations and derive irradiance nowcasts from these. Our ASI method uses a novel machine-learning (ML) based approach that makes direct use of pixel values and other image features. Measurements from a spatially distributed irradiance measuring network of eight stations within a radius of 10km around the camera position are used to train our ASI and our hybrid model and validate our nowcasting results. In the evaluation we put a special focus on irradiance variability conditions. The high share of stable cloud situations within our dataset (∼68 %) results in a relatively good performance of persistence nowcasts. In higher variability situations persistence is quickly outperformed by all other nowcasting methods. Our ASI method clearly outperforms the reference ASI method at all lead times (LTs). It moreover exhibits a significantly lower root mean square error (RMSE) than the satellite-based method up to LT≤11min ahead and a lower mean absolute error (MAE) throughout the entire interval. The hybrid model brings about an RMSE improvement score of 5-13% over the respective optimal individual method.
DVF:Multi-agent Q-learning with difference value factorization

Authors:
raw_author_name: Anqi Huang
raw_affiliation_string: Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu Province, China
raw_author_name: Yongli Wang
raw_affiliation_string: Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu Province, China
raw_author_name: Jianghui Sang
raw_affiliation_string: Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu Province, China
raw_author_name: Xiaoli Wang
raw_affiliation_string: Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu Province, China

Abstract: In cooperative multi-agent games, agents are required to learn effective cooperative behaviors within complex action spaces. An effective approach is to utilize the Individual-Global-Max (IGM) principle to decompose the global reward signal into individual contributions from each agent. However, existing methods confront two significant challenges: ensuring the accuracy of the value function decomposition process and guaranteeing monotonic improvement in joint policies. These challenges become exacerbated in scenarios involving non-monotonic matrices. To address these challenges, we introduce a novel and flexible value factorization method called Difference Value Factorization (DVF). The key idea of our method is to transform the IGM principle into a new form DVF-IGM, which addresses non-monotonic constraints by ensuring the consistency of the IGM process between the joint difference value and the complex non-linear sum of independent difference values. A centralized evaluator is employed to estimate global Q-values, which not only enhances expressiveness but also constructs difference values for updating individual value functions. We demonstrate that DVF-IGM is an equivalent transformation of IGM and that DVF has the monotonic improvement property. Empirically, our method has been shown to maintain and recover the optimal policy in non-monotonic matrix games and achieve state-of-the-art performance in cooperative tasks within the StarCraft Multi-Agent Challenge (SMAC).
Quartzite complexities: Non-destructive analysis of bifacial points from Västerbotten, Sweden

Authors:
raw_author_name: Mattias Sjölander
raw_affiliation_string: Environmental Archaeology Lab, Department of Historical, Philosophical and Religious Studies, Umeå University, Umeå, Sweden
raw_author_name: Johan Linderholm
raw_affiliation_string: Environmental Archaeology Lab, Department of Historical, Philosophical and Religious Studies, Umeå University, Umeå, Sweden
raw_author_name: Paul Geladi
raw_affiliation_string: Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences, Umeå, Sweden
raw_author_name: Philip I. Buckland
raw_affiliation_string: Environmental Archaeology Lab, Department of Historical, Philosophical and Religious Studies, Umeå University, Umeå, Sweden

Abstract: Northern Fennoscandia is a geologically complex region affected by both glacial and postglacial processes. Quartzite was a key material type utilized by hunter-gatherers in Northern Sweden around the period 4 000 – 2 000 BP, and is thus critical to the understanding of raw material procurement and material flow within the region. However, there is a severe lack of methodological development in the characterization of these materials, and provenance of locally available geological material is complex and fraught with uncertainty. 126 quartz/quartzite points and preforms were sampled from 47 archaeological sites along the upper Ångerman river valley in Västerbotten, Sweden. The material has been analysed non-destructively using three separate portable spectroscopic instrumentations (Near-infrared, Raman, X-Ray Fluorescence). Evaluation of the spectra and exploratory data analysis using Principal Component Analysis demonstrates detectable differences in the material that likely stem from diagenetic/paragenetic origin. The presence of graphite, muscovite and biotite could likewise provide information on the material's metamorphic grade. In addition to reaffirming the potential of field-based screening instrumentation, these results will benefit future surveys of geological sources in the region. They also indicate potential for the construction of a predictive model that could classify the quartzite based on its chemical characteristic. Such a model would prove useful in future spatial analysis and testing of models of raw material management.
Context-based modeling for accurate logo detection in complex environments

Authors:
raw_author_name: Zhixiang Jia
raw_affiliation_string: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, Shandong Province, China
raw_author_name: Sujuan Hou
raw_affiliation_string: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, Shandong Province, China
raw_author_name: Peng Li
raw_affiliation_string: School of Mathematics and Statistics, Shandong Normal University, Jinan 250014, Shandong Province, China

Abstract: Logo detection involves the tasks of locating and classifying logo objects in images and videos, and has been widely applied in the real world. However, most existing approaches rely on general object detection strategies that do not fully utilize the unique characteristics of logos. This can lead to sub-optimal performance in complex environments, especially when logos are small or have varying sizes and shapes. We observe that logos belonging to the same category often share similar context information, such as background dependency of the logo. This motivates us to incorporate contextual information to improve logo detection. Our proposed method, Context-based Modeling Enhancement Network (CME-Net), aims to enhance the distinctive region feature of logos using contextual information. We achieve this by modeling both the logo and its background region to extract their contextual information. This contextual information serves as a guide for enhancing the saliency of the distinctive regions within the logo image. To further improve the accuracy of detection, we have implemented a scale feature balance strategy. This strategy solves the problem of losing scale information caused by enhancement, ensuring that all scales are appropriately considered. Additionally, noise generated during the enhancement process is also effectively suppressed. By effectively leveraging contextual information, our method successfully tackles the challenge of accurately locating logo objects. Our extensive experiments on four public benchmark datasets demonstrate that CME-Net improves the accuracy of logo detection in complex environments.
CKTN: Commonsense knowledge transfer network for human activity understanding

Authors:
raw_author_name: Quanle Liu
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun 130012, PR China
raw_author_name: Xiangjiu Che
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun 130012, PR China
raw_author_name: Mengxue Zhao
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun 130012, PR China
raw_author_name: Huan Xu
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun 130012, PR China
raw_author_name: Yaxin Li
raw_affiliation_string: College of Software, Jilin University, Changchun 130012, PR China

Abstract: In most human activity understanding research, various deep learning networks are well designed to fit the character distribution of different human activities in images. After being fed with enough training data, the networks can perceive related patterns passively. Such abilities originate from the stimulation of training data, but these networks lack the ability to perform subjective judgment. When lacking sufficient training data, networks have difficulty perceiving valuable information, which leads to poor performance. However, human beings can infer human activities in complex situations through subjective judgment with their knowledge. On this point, we propose the commonsense knowledge transfer network (CKTN), which is empowered with human cognitive mechanisms and is able to understand human activities by using prior commonsense knowledge. More specifically, we study the structural combination rules of human bodies and explore the co-occurrence relationship between different body parts and objects. Then, we construct the part action cooccurrence probability matrix and human skeleton knowledge matrix, which work as prior knowledge, guiding our network to absorb and reason what the human activities in images are. Compared with traditional deep learning methods, our network realizes the unification of both subjective cognition and passive perception abilities. Experiments show the effectiveness of this method.