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Point cloud registration is the process of aligning and merging multiple point clouds into a same coordinate system. It has many applications in computer vision, robotics, 3D reconstruction, augmented reality, and autonomous driving. Despite its wide-ranging utility, traditional algorithms grapple with challenges such as substantial noise, outliers, limited overlap, motion distortions, and handling large scenes. These complexities impede the effective deployment of point cloud registration in practical real-world scenarios. This paper conducts a comprehensive inquiry into the domain of deep learning-based point cloud registration. It synthesizes recent advancements in algorithms about point cloud registration grounded in deep learning. The exposition delineates insights from four distinct perspectives: multi-layer perceptron, deep neural network, graph convolutional network, and attention mechanism. Furthermore, the paper systematically reviews prevalent datasets and evaluation metrics associated with deep learning-based point cloud registration. A discussion on the existing challenges in point cloud registration and a forward-looking exploration of potential research directions are included. [ABSTRACT FROM AUTHOR]

Background and Objectives Methods and Results Conclusions Current national or regional guidelines for the pathology reporting on invasive breast cancer differ in certain aspects, resulting in divergent reporting practice and a lack of comparability of data. Here we report on a new international dataset for the pathology reporting of resection specimens with invasive cancer of the breast. The dataset was produced under the auspices of the International Collaboration on Cancer Reporting (ICCR), a global alliance of major (inter‐)national pathology and cancer organizations.The established ICCR process for dataset development was followed. An international expert panel consisting of breast pathologists, a surgeon, and an oncologist prepared a draft set of core and noncore data items based on a critical review and discussion of current evidence. Commentary was provided for each data item to explain the rationale for selecting it as a core or noncore element, its clinical relevance, and to highlight potential areas of disagreement or lack of evidence, in which case a consensus position was formulated. Following international public consultation, the document was finalized and ratified, and the dataset, which includes a synoptic reporting guide, was published on the ICCR website.This first international dataset for invasive cancer of the breast is intended to promote high‐quality, standardized pathology reporting. Its widespread adoption will improve consistency of reporting, facilitate multidisciplinary communication, and enhance comparability of data, all of which will help to improve the management of invasive breast cancer patients. [ABSTRACT FROM AUTHOR]

Transparent objects are encountered frequently in our daily lives, yet recognizing them poses challenges for conventional vision sensors due to their unique material properties, not being well perceived from RGB or depth cameras. Overcoming this limitation, thermal infrared cameras have emerged as a solution, offering improved visibility and shape information for transparent objects. In this paper, we present TRansPose, the first large-scale multispectral dataset that combines stereo RGB-D, thermal infrared (TIR) images, and object poses to promote transparent object research. The dataset includes 99 transparent objects, encompassing 43 household items, 27 recyclable trashes, 29 chemical laboratory equivalents, and 12 non-transparent objects. It comprises a vast collection of 333,819 images and 4,000,056 annotations, providing instance-level segmentation masks, ground-truth poses, and completed depth information. The data was acquired using an FLIR A65 thermal infrared camera, two Intel RealSense L515 RGB-D cameras, and a Franka Emika Panda robot manipulator. Spanning 87 sequences, TRansPose covers various challenging real-life scenarios, including objects filled with water, diverse lighting conditions, heavy clutter, non-transparent or translucent containers, objects in plastic bags, and multi-stacked objects. Supplementary material can be accessed from the following link: https://sites.google.com/view/transpose-dataset. [ABSTRACT FROM AUTHOR]

The extensive spread of fake news on social networks is carried out by a diverse range of users, encompassing private individuals, newspapers, and organizations. With widely accessible image and video editing tools, malicious users can easily create manipulated media. They can then distribute this content through multiple fake profiles, aiming to maximize its social impact. To tackle this problem effectively, it is crucial to possess the ability to analyze shared media to identify the originators of fake news. To this end, multimedia forensics research has advanced tools that examine traces in media, revealing valuable insights into its origins. While combining these tools has proven to be highly efficient in creating profiles of image and video creators, it is important to note that most of these tools are not specifically designed to function effectively in the complex environment of content exchange on social networks. In this paper, we introduce the problem of establishing associations between images and their source profiles as a means to tackle the spread of disinformation on social platforms. To this end, we assembled SocialNews , an extensive image dataset comprising more than 12,000 images sourced from 21 user profiles across Facebook, Instagram, and Twitter, and we propose three increasingly realistic and challenging experimental scenarios. We present two simple yet effective techniques as benchmarks, one based on statistical analysis of Discrete Cosine Transform (DCT) coefficients and one employing a neural network model based on ResNet, and we compare their performance against the state of the art. Experimental results show that the proposed approaches exhibit superior performance in accurately classifying the originating user profiles. • We introduce SocialNews, a novel dataset for disinformation detection online. • Images were sourced primarily from social profiles of news agencies around the world. • The goal is to automatically identify the profile of the user that shared an image. • We introduce two benchmarks methods: a DCT-based one and a ResNet-based one. • Experiments show that the proposed benchmark methods outperform the state of the art. [ABSTRACT FROM AUTHOR]

First-person captioning is significant because it provides veracious descriptions of egocentric scenes in a unique perspective. Also, there is a need to caption the scene, a.k.a. life-logging, for patients, travellers, and emergency responders in an egocentric narrative. Ego-captioning is indeed non-trivial since (1) Ego-images can be noisy due to motion and angles; (2) Describing a scene in a first-person narrative involves drastically different semantics; (3) Empirical implications have to be made on top of visual appearance because the cameraperson is often outside the field of view. We note we humans make good sense out of casual footage thanks to our contextual awareness in judging when and where the event unfolds, and whom the cameraperson is interacting with. This inspires the infusion of such "contexts" for situation-aware captioning. We create EgoCap which contains 2.1K ego-images, over 10K ego-captions, and 6.3K contextual labels, to close the gap of lacking ego-captioning datasets. We propose EgoFormer , a dual-encoder transformer-based network which fuses both contextual and visual features. The context encoder is pre-trained on ImageNet before fine tuning with context classification tasks. Similar to visual attention, we exploit stacked multi-head attention layers in the captioning decoder to reinforce attention to the context features. The EgoFormer has realized state-of-the-art performance on EgoCap achieving a CIDEr score of 125.52. The EgoCap dataset and EgoFormer are publicly available at https://github.com/zdai257/EgoCap-EgoFormer. • First quantitative study of image captioning in an egocentric perspective. • Introduction of transformer-based context fusion architecture. • EgoCap, a sizable first-person image caption dataset, is released. [ABSTRACT FROM AUTHOR]

The agricultural industry is being transformed, thanks to recent innovations in computer vision and deep learning. However, the lack of specific datasets collected in natural agricultural environments is, arguably, the main bottleneck for novel discoveries and benchmarking. The present work provides a novel dataset, Magro, and a framework to expand data collection. We present the first version of the Magro Dataset V1.0, consisting of nine ROS bags (and the corresponding raw data) containing data collected in apple and pear crops. Data were gathered, repeating a fixed trajectory on different days under different illumination and weather conditions. To support the evaluation of loop closure algorithms, the trajectories are designed to have loop closures, revisiting some places from different viewpoints. We use a Clearpath's Jackal robot equipped with stereo cameras pointing to the front and left side, a 3D LIDAR, three inertial measurement units (IMU), and wheel encoders. Additionally, we provide calibrated RTK GPS data that can be used as ground truth. Our dataset is openly available, and it will be updated to have more data and variability. Finally, we tested two existing state-of-the-art algorithms for vision and point cloud-based localization and mapping on our novel dataset to validate the dataset's usability. [ABSTRACT FROM AUTHOR]

Music is omnipresent among human cultures and moves us both physically and emotionally. The perception of emotions in music is influenced by both psychophysical and cultural factors. Chinese traditional instrumental music differs significantly from Western music in cultural origin and music elements. However, previous studies on music emotion perception are based almost exclusively on Western music. Therefore, the construction of a dataset of Chinese traditional instrumental music is important for exploring the perception of music emotions in the context of Chinese culture. The present dataset included 273 10-second naturalistic music excerpts. We provided rating data for each excerpt on ten variables: familiarity, dimensional emotions (valence and arousal), and discrete emotions (anger, gentleness, happiness, peacefulness, sadness, solemnness, and transcendence). The excerpts were rated by a total of 168 participants on a seven-point Likert scale for the ten variables. Three labels for the excerpts were obtained: familiarity, discrete emotion, and cluster. Our dataset demonstrates good reliability, and we believe it could contribute to cross-cultural studies on emotional responses to music. [ABSTRACT FROM AUTHOR]

Powdery mildew, dead arm and vineyard downy mildew diseases are frequently seen in the vineyards in the Gediz River Basin, West Anatolia of Turkey. These diseases can be detected early using artificial intelligence (AI)–based systems that can contribute to crop yields and also reduce the labour of the farmer and the amount of pesticides used. This article presents a dataset – namely, Hermos – for use in such AI-based systems. Hermos contains four classes of grape leaf images: leaves with powdery mildew, leaves with dead arm, leaves with downy mildew and healthy leaves. We have currently 492 images and 13,913 labels in the dataset. We have published Hermos in the Linked Open Data (LOD) cloud in order to make it easier for consumers to access, process and manipulate the data. [ABSTRACT FROM AUTHOR]

Test smells are considered bad practices that can reduce the test code quality, thus harming software testing goals and maintenance activities. Prior studies have investigated the diffusion of test smells and their impact on test code maintainability. However, we cannot directly compare the outcomes of the studies as most of them use customized datasets. In response, we introduced the TSSM (Test Smells and Structural Metrics) dataset, containing test smells detected using the JNose Test tool and structural metrics (test code and production code) calculated with the CK metrics tool of 13,703 open‐source Java systems from GitHub. In addition, we perform an empirical study to investigate the relationship between test smells and structural metrics of test code and the relationship between test smells on a large‐scale dataset. We split the projects into three clusters to analyze the distribution of test smells, the co‐occurrences among test smells, and the correlation of test smells and structural metrics of test code. The ratio of smelly test classes with a specific test smell is similar among the clusters, but we could observe a significant difference in the number of test smells among them. The test smells Sleepy Test, Mystery Guest, and Resource Optimism rarely occur in the three clusters, and the last two are strongly correlated, indicating that those test smells are more severe than others. Our results point out that most test smells have a moderate correlation with high complexity, large size, and coupling of the test code, indicating that they can also negatively affect its quality. To support further studies, we made our dataset publicly available. [ABSTRACT FROM AUTHOR]

Cross-view multi-object tracking aims to link objects between frames and camera views with substantial overlaps. Although cross-view multi-object tracking has received increased attention in recent years, existing datasets still have several issues, including (1) missing real-world scenarios, (2) lacking diverse scenes, (3) containing a limited number of tracks, (4) comprising only static cameras, and (5) lacking standard benchmarks, which hinder the investigation and comparison of cross-view tracking methods. To solve the aforementioned issues, we introduce DIVOTrack: a new cross-view multi-object tracking dataset for DIVerse Open scenes with dense tracking pedestrians in realistic and non-experimental environments. Our DIVOTrack has fifteen distinct scenarios and 953 cross-view tracks, surpassing all cross-view multi-object tracking datasets currently available. Furthermore, we provide a novel baseline cross-view tracking method with a unified joint detection and cross-view tracking framework named CrossMOT, which learns object detection, single-view association, and cross-view matching with an all-in-one embedding model. Finally, we present a summary of current methodologies and a set of standard benchmarks with our DIVOTrack to provide a fair comparison and conduct a comprehensive analysis of current approaches and our proposed CrossMOT. The dataset and code are available at https://github.com/shengyuhao/DIVOTrack. [ABSTRACT FROM AUTHOR]

This study introduces QMNN, a quantum mesh neural network model incorporating a Quantum Neural Network Expectation (QNNE) circuit, and evaluates its performance on MNIST and CIFAR-10 datasets. QMNN employs innovative quantum mesh architecture with increased random layers, while the QNNE circuit, inspired by Circuit 15, integrates extra Hadamard gates for enhanced feature extraction. Implemented using PennyLane libraries, the model demonstrates superior accuracy in training and testing on both datasets compared to classical neural networks, emphasizing its effectiveness in image classification. While classic methods like CNNs may achieve higher accuracy, the significance lies in QMNN's ability, along with the QNNE circuit, to improve accuracy without introducing additional optimization parameters. The observed quantum advantage suggests that strong entanglement between qubits may enhance pixel correlations, highlighting the potential of quantum-inspired models in advancing image diagnostics. Conclusively, this study provides a stepping stone for further research into the synergy between quantum computing and neural networks, showcasing the promising role of QMNN in augmenting image classification accuracy. The results indicate notable improvements, with MNIST testing accuracy reaching 92.80% and CIFAR-10 testing accuracy achieving 36.90%, demonstrating the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

The aim of image inpainting is to fill in damaged areas according to certain rules based on information about the adjacent positions of missing areas and the overall structure of the image, a technique that plays a key role in various tasks in computer vision. With the rapid development of deep learning, researchers have combined it with image inpainting and achieved excellent performance. To gain insight into the techniques involved, this paper summarizes the latest research advances in the field of image inpainting. Firstly, existing classical image inpainting methods are reviewed, and traditional image inpainting methods and their advantages and disadvantages are introduced. Secondly, three classical network models are outlined, and the image inpainting methods are classified into single-stage, multi-stage and a priori condition-guided approaches according to different network types and model structures. Representative algorithms among them are selected and their important technical improvement ideas are analyzed and summarized. Then, the common datasets commonly used in image inpainting tasks and the evaluation metrics used to evaluate inpainting results are introduced. The paper presents a comprehensive summary of the various algorithms in terms of network models and inpainting methods, and selects representative algorithms for quantitative and qualitative comparative analysis. Finally, the future development trends and research directions have prospected, and the current problems of image inpainting are summarized. [ABSTRACT FROM AUTHOR]

Task-oriented collaborative dialogues have become an indispensable form of communication in our daily work and learning, in which participants exchange ideas and share information to advance goals. It is crucial to automatically analyze participants' contributions and understand these dialogues relative to individuals with limited attention spans. In this paper, seven Discourse Role (DR) labels are designed to describe discourse's different roles in collaborative dialogues for goal achievement. We collected about 11K discourses from a publicly available dialogue corpus and annotated them with DR tags to construct a dataset named MRDR (Meeting Recorder Discourse Role). In addition, this paper proposes a novel hierarchical model, STTAHM (Speaker Turn and Topic-Aware Hierarchical Model), for Discourse Role classification. The model is equipped to perceive speaker turn and dialogue topic and can effectively capture the discourse's local and global semantic information. Experimental results show that our proposed method is effective on the constructed dataset, and the accuracy of Discourse Role classification reaches 86.99%. [ABSTRACT FROM AUTHOR]

In [1] Karanwal et al. introduced the novel color descriptor in Face Recognition (FR) called as Fused Local Color Pattern (FLCP). In FLCP, the RGB color format is utilized for extracting features. From R, G and B channels, the MRELBP-NI, 6 × 6 MB-LBP and RD-LBP are imposed for feature extraction and then all are integrated to form the FLCP size. FLCP beats the accuracy of various methods. The one major shortcoming observed in [1] is that the basic format RGB is used for extracting features. Literature suggests that other hybrid formats achieves better recognition rates than RGB. Motivated from literature, the proposed work uses the hybrid color space format RCrQ for feature extraction. In this format R channel is taken from RGB, Cr channel is taken from YCbCr and Q channel is taken from YIQ. On R channel, MRELBP-NI is imposed for extracting features, On Cr channel 6 × 6 MB-LBP is imposed and on Q channel RD-LBP is imposed for extracting features. Then all channel features are joined to build the robust and discriminant feature called as Robust And Discriminant Local Color Pattern (RADLCP). Compression and matching is assisted from PCA and SVMs. For evaluating results GT face dataset is used. Results proves the potency of RADLCP in contrast to gray scale based implemented descriptors. RADLCP also beats the results of FLCP. Several literature techniques are also outclassed by RADLCP. For evaluating all the results MATLAB R2021a is used. [ABSTRACT FROM AUTHOR]

Over the last few years, a plethora of papers presenting machine-learning-based approaches for intrusion detection have been published. However, the majority of those papers do not compare their results with a proper baseline of a signature-based intrusion detection system, thus violating good machine learning practices. In order to evaluate the pros and cons of the machine-learning-based approach, we replicated a research study that uses a deep neural network model for intrusion detection. The results of our replicated research study expose several systematic problems with the used datasets and evaluation methods. In our experiments, a signature-based intrusion detection system with a minimal setup was able to outperform the tested model even under small traffic changes. Testing the replicated neural network on a new dataset recorded in the same environment with the same attacks using the same tools showed that the accuracy of the neural network dropped to 54%. Furthermore, the often-claimed advantage of being able to detect zero-day attacks could not be seen in our experiments. [ABSTRACT FROM AUTHOR]

The Post-Rebel Electoral Parties (PREP) dataset offers an important new tool to study the transformation of rebel groups into political parties. It provides longitudinal data on the electoral participation and performance in national elections of political parties formed by armed opposition groups after civil war. Post-rebel electoral parties sit at the center of overlapping research agendas that address how best to build durable peace, and how to build resilient political systems and capable states after devastating conflict. A better understanding of how and why these parties participate and perform in elections over time is critical to any assessment of liberal peacebuilding. These parties – their strategic choices, organizational development, and impact on the political systems in which they participate – are also relevant to the broader study of comparative democratization, political parties, and party systems. Our dataset follows these parties forward through up to three decades of participation in postwar electoral politics. The current version of the data consists of 78 distinct parties derived from 56 conflict actors. The data cover 322 legislative election years and 216 executive elections in 39 countries over 30 years (1990–2021). This article describes the data and articulates the need for and motivation behind the dataset. We then illustrate the relevance of the data by testing the impact of rebel participation on the long-term peace. [ABSTRACT FROM AUTHOR]

Colorectal cancer is one of the most lethal cancers because of late diagnosis and challenges in the selection of therapy options. The histopathological diagnosis of colon adenocarcinoma is hindered by poor reproducibility and a lack of standard examination protocols required for appropriate treatment decisions. In the current study, using state-of-the-art approaches on benchmark datasets, we analyzed different architectures and ensembling strategies to develop the most efficient network combinations to improve binary and ternary classification. We propose an innovative two-stage pipeline approach to diagnose colon adenocarcinoma grading from histological images in a similar manner to a pathologist. The glandular regions were first segmented by a transformer architecture with subsequent classification using a convolutional neural network (CNN) ensemble, which markedly improved the learning efficiency and shortened the learning time. Moreover, we prepared and published a dataset for clinical validation of the developed artificial neural network, which suggested the discovery of novel histological phenotypic alterations in adenocarcinoma sections that could have prognostic value. Therefore, AI could markedly improve the reproducibility, efficiency, and accuracy of colon cancer diagnosis, which are required for precision medicine to personalize the treatment of cancer patients. [ABSTRACT FROM AUTHOR]

Purpose: Meningiomas are the most common primary brain tumors in adults with management varying widely based on World Health Organization (WHO) grade. However, there are limited datasets available for researchers to develop and validate radiomic models. The purpose of our manuscript is to report on the first dataset of meningiomas in The Cancer Imaging Archive (TCIA). Acquisition and validation methods: The dataset consists of pre‐operative MRIs from 96 patients with meningiomas who underwent resection from 2010–2019 and include axial T1post and T2‐FLAIR sequences—55 grade 1 and 41 grade 2. Meningioma grade was confirmed based on the 2016 WHO Bluebook classification guideline by two neuropathologists and one neuropathology fellow. The hyperintense T1post tumor and hyperintense T2‐FLAIR regions were manually contoured on both sequences and resampled to an isotropic resolution of 1 × 1 × 1 mm3. The entire dataset was reviewed by a certified medical physicist. Data format and usage notes: The data was imported into TCIA for storage and can be accessed at https://doi.org/10.7937/0TKV‐1A36. The total size of the dataset is 8.8GB, with 47 519 individual Digital Imaging and Communications in Medicine (DICOM) files consisting of 384 image series, and 192 structures. Potential applications: Grade 1 and 2 meningiomas have different treatment paradigms and are often treated based on radiologic diagnosis alone. Therefore, predicting grade prior to treatment is essential in clinical decision‐making. This dataset will allow researchers to create models to auto‐differentiate grade 1 and 2 meningiomas as well as evaluate for other pathologic features including mitotic index, brain invasion, and atypical features. Limitations of this study are the small sample size and inclusion of only two MRI sequences. However, there are no meningioma datasets on TCIA and limited datasets elsewhere although meningiomas are the most common intracranial tumor in adults. [ABSTRACT FROM AUTHOR]

The availability of datasets scraped from the European Union's websites has greatly advanced the systematic analysis of European integration. But despite their enormous value, European Union databases contain almost no information about policymaking during Europe's first two decades, and for later periods, they suffer from far more inconsistencies and errors than has been previously recognised. This article draws upon extensive archival research and manual coding to identify and correct several of these limitations. I present a new dataset (EUPROPS) containing information on proposals for European Union policy from 1958 to 2021 and their outcomes. To illustrate the value of the dataset, I present some surprising initial findings about patterns of policymaking across this 60-year period and identify avenues for future research. [ABSTRACT FROM AUTHOR]

BACKGROUND: Premature newborns have a higher risk of abnormal visual development and visual impairment. OBJECTIVE: To develop a computational methodology to help assess functional vision in premature infants by tracking iris distances. METHODS: This experimental study was carried out with children up to two years old. A pattern of image capture with the visual stimulus was proposed to evaluate visual functions of vertical and horizontal visual tracking, visual field, vestibulo-ocular reflex, and fixation. The participants' visual responses were filmed to compose a dataset and develop a detection algorithm using the OpenCV library allied with FaceMesh for the detection and selection of the face, detection of specific facial points and tracking of the iris positions is done. A feasibility study was also conducted from the videos processed by the software. RESULTS: Forty-one children of different ages and diagnoses participated in the experimental study, forming a robust dataset. The software resulted in the tracking of iris positions during visual function evaluation stimuli. Furthermore, in the feasibility study, 8 children participated, divided into Pre-term and Term groups. There was no statistical difference in any visual variable analyzed in the comparison between groups. CONCLUSION: The computational methodology developed was able to track the distances traveled by the iris, and thus can be used to help assess visual function in children. [ABSTRACT FROM AUTHOR]

The IoMT (Internet of Medical Things) has allowed for uninterrupted, critical patient observation, improved diagnosis precision, and efficient therapy. However, despite the usefulness of such medical things (devices), they also raise a lot of confidentiality and security issues since they provide potential entry points for hackers to exploit. Therefore, there is a pressing need for a technique for detecting network intrusions that combines precision, flexibility, and consistency. Addressing diverse information sources is challenging for finding negligible intrusions in sophisticated network systems, a core problem for current Intrusion Detection Systems (IDS). In this research, we propose a deep learning-based method for efficient network IDS in cases when data is unevenly distributed. Therefore, to address the poor identification rate of intrusions, we present a unique CGAN-CNN (Conditional Generative Adversarial Network-Convolutional Neural Network) IDS approach that oversamples from the unbalanced information based on the CGAN paradigm to overcome the functional deterioration induced by such unbalanced data, especially during intrusion detection. In addition, the sub-networks' critic and generator each get additional constraints as part of the CGAN's standard operating procedure, which helps to reduce the amount of leeway in the convergence process and speeds up the impact of convergence. To validate the effectiveness of the suggested model, we conducted an investigation using the most contemporary publicly available datasets, namely NIDS (Network Intrusion Detection System), and the CICDDoS2019 (Canadian Institute for Cybersecurity Distributed Denial of Service 2019) dataset from the Canadian Institution for Cybersecurity, and for healthcare-oriented image datasets Kaggle, respectively. The experimental findings validated the superiority of the CGAN-CNN approach described in this research. Notified as more trustworthy indications, F1-score and precision performed at 97.88%, and 97.15%, respectively. [ABSTRACT FROM AUTHOR]

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-event observation solely or bi-temporal (pre- and post-event) observations across diverse land cover types. We demonstrate that optical satellite data yield higher separability than C-Band and L-Band Synthetic Aperture Radar (SAR), which exhibit considerable overlap in burned and unburned sample distribution, as evidenced by SAR-based boxplots. With U-Net, we further explore how different input channels influence the detection accuracy. Our findings reveal that deep neural networks enhance SAR's performance in mapping burned areas. Notably, C-Band SAR shows a higher dependency on pre-event data than L-Band SAR for effective detection. A comparative analysis of U-Net and its variants indicates that U-Net works best with single-sensor data, while the late fusion architecture marginally surpasses others in the fusion of optical and SAR data. Accuracy across sensors is highest in closed forests, with sequentially lower performance in open forests, shrubs, and grasslands. Future work will extend the data from both spatial and temporal dimensions to encompass varied vegetation types and climate zones, furthering our understanding of multi-source and multi-frequency satellite remote sensing capabilities in wildfire detection and monitoring. [ABSTRACT FROM AUTHOR]

This article introduces a new dataset on how international organizations (IOs) justify their authority. For a long time, IOs were believed to derive legitimacy from member-state consent and technocratic problem-solving capacities. Over recent decades, the growing politicization of IOs, political polarization within Western democracies, and power shifts in the international system have spurred IOs' efforts to justify their right to rule, using a variety of legitimation practices. While research on the theory and practice of IO legitimation has grown considerably over the past decade, much of this work builds on case studies of prominent global and regional IOs. As a result, we lack data suitable for systematic comparative analyses across time, IOs, and world regions. The Legitimation Strategies of Regional Organizations (LegRO) dataset aims to narrow this gap, providing data on the standards, intensity, and modes of legitimation for twenty eight regional IOs from 1980 to 2019. These variables inform theoretical and policy-relevant research on contemporary global governance by providing the first systematic overview of IOs' legitimation practices. [ABSTRACT FROM AUTHOR]

The Primary Care Audit, Teaching and Research Open Network (Patron) is an Australian database of de-identified electronic medical records from primary care patients. It includes over 3.5 million patients from 129 primary care practices in Victoria and Queensland. The database contains a wide range of de-identified data, including reason for visit, clinical observations, diagnosis, medications, and referrals. The data can be used by government, educational institutions, researchers, and industry with approved applications. The database aims to inform research, policy questions, and medical training. However, it acknowledges limitations, such as the concentration of data in Victoria and the underrepresentation of First Nations Australians. Access to the data requires approval from the Data Governance Committee and an ethics committee. [Extracted from the article]

The real-time monitoring of wheat phenology variations among different varieties and their adaptive responses to environmental conditions is essential for advancing breeding efforts and improving cultivation management. Many remote sensing efforts have been made to relieve the challenges of key phenophase detection. However, existing solutions are not accurate enough to discriminate adjacent phenophases with subtle organ changes, and they are not real-time, such as the vegetation index curve-based methods relying on entire growth stage data after the experiment was finished. Furthermore, it is key to improving the efficiency, scalability, and availability of phenological studies. This study proposes a two-stage deep learning framework called PhenoNet for the accurate, efficient, and real-time classification of key wheat phenophases. PhenoNet comprises a lightweight encoder module (PhenoViT) and a long short-term memory (LSTM) module. The performance of PhenoNet was assessed using a well-labeled, multi-variety, and large-volume dataset (WheatPheno). The results show that PhenoNet achieved an overall accuracy (OA) of 0.945, kappa coefficients (Kappa) of 0.928, and F1-score (F1) of 0.941. Additionally, the network parameters (Params), number of operations measured by multiply-adds (MAdds), and graphics processing unit memory required for classification (Memory) were 0.889 million (M), 0.093 Giga times (G), and 8.0 Megabytes (MB), respectively. PhenoNet outperformed eleven state-of-the-art deep learning networks, achieving an average improvement of 3.7% in OA, 5.1% in Kappa, and 4.1% in F1, while reducing average Params, MAdds, and Memory by 78.4%, 85.0%, and 75.1%, respectively. The feature visualization and ablation analysis explained that PhenoNet mainly benefited from using time-series information and lightweight modules. Furthermore, PhenoNet can be effectively transferred across years, achieving a high OA of 0.981 using a two-stage transfer learning strategy. Furthermore, an extensible web platform that integrates WheatPheno and PhenoNet and ensures that the work done in this study is accessible, interoperable, and reusable has been developed (https://phenonet.org/). [ABSTRACT FROM AUTHOR]

This paper presents a study on modelling user free walk mobility in virtual reality (VR) art exhibition. The main objective is to investigate and model users’ mobility sequences during interactions with artwork in VR. We employ a range of machine learning (ML) techniques to define scenes of interest in VR, capturing user mobility patterns. Our approach utilises a long short-term memory (LSTM) model to effectively model and predict users’ future movements in VR environments, particularly in scenarios where clear walking paths and directions are not provided to participants. The DL model demonstrates high accuracy in predicting user movements, enabling a better understanding of audience interactions with the artwork. It opens avenues for developing new VR applications, such as community-based navigation, virtual art guides, and enhanced virtual audience engagement. The results highlight the potential for improved user engagement and effective navigation within virtual environments. [ABSTRACT FROM AUTHOR]

The literature on the Arab world is full of accounts on how authoritarian regimes manipulate the secular-Islamist cleavage to remain in power. This literature, however, has two gaps. First, there is no comparative data on the regional patterns of inclusion and exclusion that characterize regime-opposition relations in the Arab world. Second, studies usually do not account for the different shades of secular and Islamist groups. This research note bridges both gaps by introducing an original dataset: the Arab Opposition Power Relations dataset. The dataset codes power relations between four secular and Islamist groups in 13 Arab countries between 2005 and 2016 to a total of 624 group observations. Descriptive analysis of the dataset illustrates the changing patterns of inclusion and exclusion within and across countries both before and after the 2011 Arab uprisings. In so doing, the dataset opens new research frontiers for the vibrant literature on the resilience of authoritarianism in the Arab world. [ABSTRACT FROM AUTHOR]

Introduction: Computed tomography (CT) was a widely used diagnostic technique for COVID-19 during the pandemic. High-Resolution Computed Tomography (HRCT), is a type of computed tomography that enhances image resolution through the utilization of advanced methods. Due to privacy concerns, publicly available COVID-19 CT image datasets are incredibly tough to come by, leading to it being challenging to research and create AI-powered COVID-19 diagnostic algorithms based on CT images. Data description: To address this issue, we created HRCTCov19, a new COVID-19 high-resolution chest CT scan image collection that includes not only COVID-19 cases of Ground Glass Opacity (GGO), Crazy Paving, and Air Space Consolidation but also CT images of cases with negative COVID-19. The HRCTCov19 dataset, which includes slice-level and patient-level labeling, has the potential to assist in COVID-19 research, in particular for diagnosis and a distinction using AI algorithms, machine learning, and deep learning methods. This dataset, which can be accessed through the web at http://databiox.com, includes 181,106 chest HRCT images from 395 patients labeled as GGO, Crazy Paving, Air Space Consolidation, and Negative. [ABSTRACT FROM AUTHOR]

Extant scholarship has until now relied on informal-theoretic, case study, and interpretative methods to assess patterns of norm development in cyberspace. Ideally, these accounts would be complemented with more systematic cross-national and longitudinal empirical evidence. To address this gap, this article introduces the International Cyber Expression Dataset. The dataset includes a corpus of more than 34,000 official expressions of view by states and their authorized representatives regarding the international politics of cyberspace. The article describes the sources of these data and demonstrates the dataset's usefulness, with an Online appendix containing an exploratory analysis of norm convergence. Future research can leverage the dataset to empirically test questions of theory and policy. For example, the dataset can be used to study how foundational theories of norm diffusion apply to cyberspace. It can also be paired with existing cyber conflict datasets to study the conditions under which state practice influences cyber discourse, and vice versa. [ABSTRACT FROM AUTHOR]

This study introduces the Significant Incidents Against Americans Abroad (SIAAA) Dataset, the foremost systematic data compilation capturing antagonistic actions directed toward American nationals, entities, and interests across the globe from 1987 to 2015. Primarily derived from the US Department of State Bureau of Diplomatic Security reports, the SIAAA assembles 5,272 events targeting American citizens and interests, encompassing both violent and non-violent actions across military, diplomatic, business, and civilian dimensions. In this article, we examine the general trend of incidents and introduce key variables including target typology, geographic dispersion, type of the incident, and perpetrator ideology. As an illustrative example of its potential research application, we focus on NATO's aerial bombing of Yugoslavia, highlighting how the SIAAA can be employed effectively. We conclude with propositions for future research, underscoring the dataset's potential for both academic and policy analysis. Este trabajo presenta el conjunto de datos con relación a incidentes significativos contra estadounidenses en el extranjero (SIAAA, por sus siglas en inglés), el cual es la compilación sistemática de datos más importante que recoge acciones de carácter antagónico dirigidas a ciudadanos, entidades e intereses estadounidenses en todo el mundo desde 1987 hasta 2015. Los datos recogidos en el SIAAA provienen, principalmente, de los informes de la Oficina de Seguridad Diplomática del Departamento de Estado de EE. UU. El SIAAA reúne 5272 eventos dirigidos a ciudadanos e intereses estadounidenses, que abarcan acciones, tanto violentas como no violentas, en dimensiones militares, diplomáticas, comerciales y civiles. En este artículo, examinamos la tendencia general de estos incidentes e introducimos variables clave como la tipología del objetivo, la dispersión geográfica, el tipo de incidente y la ideología del perpetrador. A modo de ejemplo ilustrativo de la aplicación potencial del SIAAA para la investigación, nos centramos en el bombardeo aéreo que llevó a cabo la OTAN sobre Yugoslavia, destacando cómo se puede emplear el SIAAA de manera efectiva. Concluimos este artículo realizando propuestas para futuras investigaciones y subrayando el potencial de este conjunto de datos tanto para el análisis académico como para el análisis de las políticas. Cette étude présente l'ensemble de données Significant Incidents Against Americans Abroad (SIAAA, ou Incidents importants subis par des Américains à l'étranger), une compilation de données systématique de premier plan qui représente les actions antagonistes à l'encontre de ressortissants, entités et intérêts américains dans le monde entre 1987 et 2015. Principalement dérivé des rapports du Service de sécurité diplomatique du département d'État des États-Unis, le SIAAA regroupe 5 272 événements ciblant des citoyens et intérêts américains : des actes violents et pacifiques dans les domaines militaire, diplomatique, commercial et civil. Dans cet article, nous examinons la tendance générale des incidents et présentons des variables clés, dont la typologie de la cible, la dispersion géographique, le type d'incident et l'idéologie de l'auteur. À titre d'exemple d'illustration de son application potentielle en recherche, nous nous focalisons sur les bombardements aériens de l'OTAN en Yougoslavie pour montrer un emploi efficace du SIAAA. Nous concluons sur des propositions de futurs travaux de recherche qui soulignent le potentiel de l'ensemble de données pour l'analyse académique et politique. [ABSTRACT FROM AUTHOR]

The detection of burnt areas from satellite imagery is one of the most straightforward and useful applications of satellite remote sensing. In general, the approach relies on a change detection analysis applied on pre- and post-event images. This change detection analysis usually is carried out by comparing the values of specific spectral indices such as: NBR (normalised burn ratio), BAI (burn area index), MIRBI (mid-infrared burn index). However, some potential sources of error arise, particularly when near-real-time automated approaches are adopted. An automated approach is mandatory when the burnt area monitoring should operate systematically on a given area of large size (country). Potential sources of errors include but are not limited to clouds on the pre- or post-event images, clouds or topographic shadows, agricultural practices, image pixel size, level of damage, etc. Some authors have already noted differences between global databases of burnt areas based on satellite images. Sources of errors could be related to the spatial resolution of the images used, the land-cover mask adopted to avoid false alarms, and the quality of the cloud and shadow masks. This paper aims to compare different burnt areas datasets (EFFIS, ESACCI, Copernicus, FIRMS, etc.) with the objective to analyse their differences. The comparison is restricted to the Italian territory. Furthermore, the paper aims to identify the degree of approximation of these satellite-based datasets by relying on ground survey data as ground truth. To do so, ground survey data provided by CUFA (Comando Unità Forestali, Ambientali e Agroalimentari Carabinieri) and CFVA (Corpo Forestale e Vigilanza Ambientale Sardegna) were used. The results confirm the existence of significant differences between the datasets. The subsequent comparison with the ground surveys, which was conducted while also taking into account their own approximations, allowed us to identify the accuracy of the satellite-based datasets. [ABSTRACT FROM AUTHOR]

Named entity recognition (NER) is a key component of many natural language processing (NLP) applications. The majority of advanced research, however, has not been widely applied to low-resource languages represented by Malay due to the data-hungry problem. In this paper, we present a system for building a Malay NER dataset (MS-NER) of 20,146 sentences through labelled datasets of homologous languages and iterative optimisation. Additionally, we propose a Multi-Task framework, namely MTBR, to integrate boundary information more effectively for NER. Specifically, boundary detection is treated as an auxiliary task and an enhanced Bidirectional Revision module with a gated ignoring mechanism is proposed to undertake conditional label transfer. This can reduce error propagation by the auxiliary task. We conduct extensive experiments on Malay, Indonesian, and English. Experimental results show that MTBR could achieve competitive performance and tends to outperform multiple baselines. The constructed dataset and model would be made available to the public as a new, reliable benchmark for Malay NER. [ABSTRACT FROM AUTHOR]

The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification. This paper analyses VGG-16, InceptionResNetV2, DenseNet121, Inception V3, and MobileNetV2 models to classify real-life waste when trained on pristine and unadulterated materials, versus samples collected at a landfill site. When training on DiversionNet, the unadulterated material dataset with labels required for landfill modelling, classification accuracy was limited to 49.69% in the real environment. Using real-world samples in the newly formed RealWaste dataset showed that practical applications for deep learning in waste classification are possible, with Inception V3 reaching 89.19% classification accuracy on the full spectrum of labels required for accurate modelling. [ABSTRACT FROM AUTHOR]

The prediction of molecular properties is a crucial task in the field of drug discovery. Computational methods that can accurately predict molecular properties can significantly accelerate the drug discovery process and reduce the cost of drug discovery. In recent years, iterative updates in computing hardware and the rise of deep learning have created a new and effective path for molecular property prediction. Deep learning methods can leverage the vast amount of data accumulated over the years in drug discovery and do not require complex feature engineering. In this review, we summarize molecular representations and commonly used datasets in molecular property prediction models and present advanced deep learning methods for molecular property prediction, including state‐of‐the‐art deep learning networks such as graph neural networks and Transformer‐based models, as well as state‐of‐the‐art deep learning strategies such as 3D pre‐train, contrastive learning, multi‐task learning, transfer learning, and meta‐learning. We also point out some critical issues such as lack of datasets, low information utilization, and lack of specificity for diseases. [ABSTRACT FROM AUTHOR]

Room layout estimation aims to predict the location and range of layout planes of interior spaces. Previous works treat each layout plane as an independent individual without considering the ordinal relation between walls, resulting the loss of the wall planes and the lack of integrity. This paper proposes a novel two‐branch neural networks model to estimate 3D layouts of cuboid and non‐cuboid room types. The model embeds the ordinal relation between layout planes into the layout segmentation branch through an proposed ordinal classification loss function, and outputs both pixel‐level layout segmentation maps and layout plane parameter maps. Then, the instance‐level plane parameters of each layout plane are determined by using an instance‐aware pooling layer. Finally, the sharpness of layout edges of the 2D layout semantic segmentation map is optimized by using an improved depth map intersection algorithm. Furthermore, we annotate a large‐scale 3D room layout estimation dataset, InteriorNet‐Layout, to obtain a steady model. Experiments on synthesized real‐world datasets show that the proposed method achieves faster calculation while maintaining high accuracy. Code is available at https://github.com/Hui‐Yao/3D‐ordinal‐layout‐estimation. [ABSTRACT FROM AUTHOR]

Aims: Thymic epithelial tumours (TET), including thymomas and thymic carcinomas and thymic neuroendocrine neoplasms, are malignant neoplasms that can be associated with morbidity and mortality. Recently, an updated version of the World Health Organization (WHO) Classification of Thoracic Tumours 5th Edition, 2021 has been released, which included various changes to the classification of these neoplasms. In addition, in 2017 the Union for International Cancer Control (UICC) / American Joint Committee on Cancer (AJCC) published the 8th Edition Staging Manual which, for the first time, includes a TNM staging that is applicable to thymomas, thymic carcinomas, and thymic neuroendocrine neoplasms. Methods and Results: To standardize reporting of resected TET and thymic neuroendocrine neoplasms the accrediting bodies updated their reporting protocols. The International Collaboration on Cancer Reporting (ICCR), which represents a collaboration between various National Associations of Pathology, updated its 2017 histopathology reporting guide on TET and thymic neuroendocrine neoplasms accordingly. This report will highlight important changes in the reporting of TET and thymic neuroendocrine neoplasms based on the 2021 WHO, emphasize the 2017 TNM staging, and also comment on the rigour and various uncertainties for the pathologist when trying to follow that staging. Conclusion: The ICCR dataset provides a comprehensive, standardized template for reporting of resected TET and thymic neuroendocrine neoplasms. [ABSTRACT FROM AUTHOR]

Lateral movement (LM) is a principal, increasingly common, tactic in the arsenal of advanced persistent threat (APT) groups and other less or more powerful threat actors. It concerns techniques that enable a cyberattacker, after establishing a foothold, to maintain ongoing access and penetrate further into a network in quest of prized booty. This is done by moving through the infiltrated network and gaining elevated privileges using an assortment of tools. Concentrating on the MS Windows platform, this work provides the first to our knowledge holistic methodology supported by an abundance of experimental results towards the detection of LM via supervised machine learning (ML) techniques. We specifically detail feature selection, data preprocessing, and feature importance processes, and elaborate on the configuration of the ML models used. A plethora of ML techniques are assessed, including 10 base estimators, one ensemble meta-estimator, and five deep learning models. Vis-à-vis the relevant literature, and by considering a highly unbalanced dataset and a multiclass classification problem, we report superior scores in terms of the F1 and AUC metrics, 99.41% and 99.84%, respectively. Last but not least, as a side contribution, we offer a publicly available, open-source tool, which can convert Windows system monitor logs to turnkey datasets, ready to be fed into ML models. [ABSTRACT FROM AUTHOR]

Introduction: Regarding deep learning networks in medical sciences for improving diagnosis and treatment purposes and the existence of minimal resources for them, we decided to provide a set of magnetic resonance images of the cardiac and hepatic organs. Database description: The dataset included 124 patients (67 women and 57 men) with thalassemia (THM), the age range of (5–52) years. Patients were divided into two groups: with follow-up (1–5 times) at time intervals of about (5–6) months and without follow-up. T2* and, R2* values, the results of the Cardiac and Hepatic overload report (normal, mild, moderate, severe), and laboratory tests including Ferritin, Bilirubin (D, and T), AST, ALT, and ALP levels were provided as an Excel file. Also, the details of the patients' Echocardiogram data have been made available. This dataset CHMMOTv1) has been published in Mendeley Dataverse and also is accessible through the web at: http://databiox.com. [ABSTRACT FROM AUTHOR]

This research note highlights the underrepresentation of younger people in parliaments across the globe, which has been a persistent issue for decades. We introduce a novel open-access dataset containing information on mean and median age as well as the share of MPs in specific age brackets in party parliamentary groups in lower houses or single chambers of national parliaments. The dataset covers 163 cases from 30 countries and features variables that present these aforementioned shares in relation to the respective group's presence in the voting-age population (Age Representation Index). Furthermore, the dataset includes the share of women within the party parliamentary groups as well as identifiers that enable matching the dataset with indicators from the Political Party Database (PPDB), the V-Party dataset, PartyFacts and the Manifesto Project Database. Our main finding underlines the importance of research focusing on party level factors influencing youth representation as there is not only variation between countries, but also within them on the party-level. Additionally, we show that most party parliamentary groups in which women are underrepresented also have low relative shares of young people, which is in line with theoretical arguments on party behavior. [ABSTRACT FROM AUTHOR]

Fire detection is a critical safety issue due to the major and irreversible consequences of fire, from economic prejudices to loss of life. It is therefore of utmost importance to design reliable, automated systems that can issue early alarms. The objective of this review is to present the state of the art in the area of fire detection, prevention and propagation modeling with machine learning algorithms. In order to understand how an artificial intelligence application penetrates an area of fire detection, a quantitative scientometric analysis was first performed. A literature search process was conducted on the SCOPUS database using terms and Boolean expressions related to fire detection techniques and machine learning areas. A number of 2332 documents were returned upon the bibliometric analysis. Fourteen datasets used in the training of deep learning models were examined, discussing critically the quality parameters, such as the dataset volume, class imbalance, and sample diversity. A separate discussion was dedicated to identifying issues that require further research in order to provide further insights, and faster and more accurate models.. The literature survey identified the main issues the current research should address: class imbalance in datasets, misclassification, and datasets currently used in model training. Recent advances in deep learning models such as transfer learning and (vision) transformers were discussed. [ABSTRACT FROM AUTHOR]

Recent studies have identified G protein‐coupled receptor 40 (GPR40) as a promising target for treating type 2 diabetes mellitus, and GPR40 agonists have several superior effects over other hypoglycemic drugs, including cardiovascular protection and suppression of glucagon levels. In this study, we constructed an up‐to‐date GPR40 ligand dataset for training models and performed a systematic optimization of the ensemble model, resulting in a powerful ensemble model (ROC AUC: 0.9496) for distinguishing GPR40 agonists and non‐agonists. The ensemble model is divided into three layers, and the optimization process is carried out in each layer. We believe that these results will prove helpful for both the development of GPR40 agonists and ensemble models. All the data and models are available on GitHub. (https://github.com/Jiamin‐Yang/ensemble_model) [ABSTRACT FROM AUTHOR]

Physical rehabilitation plays a crucial role in restoring motor function following injuries or surgeries. However, the challenge of overcrowded waiting lists often hampers doctors' ability to monitor patients' recovery progress in person. Deep Learning methods offer a solution by enabling doctors to optimize their time with each patient and distinguish between those requiring specific attention and those making positive progress. Doctors use the flexion angle of limbs as a cue to assess a patient's mobility level during rehabilitation. From a Computer Vision perspective, this task can be framed as automatically estimating the pose of the target body limbs in an image. The objectives of this study can be summarized as follows: (i) evaluating and comparing multiple pose estimation methods; (ii) analyzing how the subject's position and camera viewpoint impact the estimation; and (iii) determining whether 3D estimation methods are necessary or if 2D estimation suffices for this purpose. To conduct this technical study, and due to the limited availability of public datasets related to physical rehabilitation exercises, we introduced a new dataset featuring 27 individuals performing eight diverse physical rehabilitation exercises focusing on various limbs and body positions. Each exercise was recorded using five RGB cameras capturing different viewpoints of the person. An infrared tracking system named OptiTrack was utilized to establish the ground truth positions of the joints in the limbs under study. The results, supported by statistical tests, show that not all state-of-the-art pose estimators perform equally in the presented situations (e.g., patient lying on the stretcher vs. standing). Statistical differences exist between camera viewpoints, with the frontal view being the most convenient. Additionally, the study concludes that 2D pose estimators are adequate for estimating joint angles given the selected camera viewpoints. [ABSTRACT FROM AUTHOR]

Stable isotopes in precipitation can effectively reveal the process of atmospheric water circulation, serving as an effective tool for hydrological and water resources research, climate change, and ecosystem studies. The scarcity of stable isotope data in precipitation has hindered comprehension of regional hydrology, climate, and ecology due to discontinuities on a temporal scale and unevenness on a spatial scale. To this end, we collated stable hydrogen and oxygen isotope data in precipitation from 1930 stations in Eurasia from 1961 to 2022, totalling 51,752 data records. Stable isotopes in precipitation across various regions of Eurasia, as a whole, decrease with increasing latitude and distance from the coast. In the summer, stable isotopes in precipitation are relatively enriched, while in winter, they are relatively depleted. In recent decades, the stable isotope values of Eurasian precipitation show an overall trend of increasing variation with the advancement of years, which is associated with global warming. Geographical location, underlying surface conditions, seasons, and atmospheric circulation are all factors that determine the characteristics of stable isotopes in precipitation. The dataset of stable isotopes in Eurasian precipitation provides a powerful tool for understanding changes in regional atmospheric water circulation and assists in conducting hydrological, meteorological, and ecological studies in related regions. [ABSTRACT FROM AUTHOR]

Improvements in analytical technique and data processing lead to a large deposit of detrital zircon U–Pb and Hf isotopic data in published journal articles, thesis and reports. With the support of Deep‐time Digital Earth Big Science Program, OneSediment Project conducted a team‐working effort to assemble the detrital zircon U–Pb and Hf isotopic data for mainland China and adjacent regions. Entity‐relationship diagram was constructed for detrital zircon data to determine what information need to be collected. Researchers and students from 13 institutions were organized together to fulfil this task and generated 13 detrital zircon datasets. The assembled datasets totally include 6635 samples and 560,596 analyses. The samples were checked in terms of lithology and stratigraphy according to the latest stratigraphic framework. Collected detrital zircon U–Pb and Hf isotopic data were recalculated for the best U–Pb age and εHf(t) values. They can be used to track crustal growth, tectonic evolution, provenance analysis and palaeogeographical change in regional and, when combined with other dataset, global scales. [ABSTRACT FROM AUTHOR]

Robust object grasping in cluttered scenes is vital to all robotic prehensile manipulation. In this paper, we present the GraspNet-1Billion benchmark that contains rich real-world captured cluttered scenarios and abundant annotations. This benchmark aims at solving two critical problems for the cluttered scenes parallel-finger grasping: the insufficient real-world training data and the lacking of evaluation benchmark. We first contribute a large-scale grasp pose detection dataset. Two different depth cameras based on structured-light and time-of-flight technologies are adopted. Our dataset contains 97,280 RGB-D images with over one billion grasp poses. In total, 190 cluttered scenes are collected, among which 100 are training set and 90 are for testing. Meanwhile, we build an evaluation system that is general and user-friendly. It directly reports a predicted grasp pose's quality by analytic computation, which is able to evaluate any kind of grasp representation without exhaustively labeling the ground-truth. We further divide the test set into three difficulties to better evaluate algorithms' generalization ability. Our dataset, accessing API and evaluation code, are publicly available at www.graspnet.net. [ABSTRACT FROM AUTHOR]

The article focuses on leveraging pre-trained language models like BERT to enhance the summarization of tweet streams, introducing an extractive model that adapts the summary size automatically. It also presents a novel methodology for constructing tweet stream summarization datasets with minimal human effort and releases a new dataset called TES 2012-2016, potentially advancing research in incremental summarization.

Multimodal image matching is a core basis for information fusion, change detection, and image-based navigation. However, multimodal images may simultaneously suffer from severe nonlinear radiation distortion (NRD) and complex geometric differences, which pose great challenges to existing methods. Although deep learning-based methods had shown potential in image matching, they mainly focus on same-source images or single types of multimodal images such as optical-synthetic aperture radar (SAR). One of the main obstacles is the lack of public data for different types of multimodal images. In this paper, we make two major contributions to the community of multimodal image matching: First, we collect six typical types of images, including optical-optical, optical-infrared, optical-SAR, optical-depth, optical-map, and nighttime, to construct a multimodal image dataset with a total of 1200 pairs. This dataset has good diversity in image categories, feature classes, resolutions, geometric variations, etc. Second, we propose a scale and rotation invariant feature transform (SRIF) method, which achieves good matching performance without relying on data characteristics. This is one of the advantages of our SRIF over deep learning methods. SRIF obtains the scales of FAST keypoints by projecting them into a simple pyramid scale space, which is based on the study that methods with/without scale space have similar performance under small scale change factors. This strategy largely reduces the complexity compared to traditional Gaussian scale space. SRIF also proposes a local intensity binary transform (LIBT) for SIFT-like feature description, which can largely enhance the structure information inside multimodal images. Extensive experiments on these 1200 image pairs show that our SRIF outperforms current state-of-the-arts by a large margin, including RIFT, CoFSM, LNIFT, and MS-HLMO. Both the created dataset and the code of SRIF will be publicly available in https://github.com/LJY-RS/SRIF. [ABSTRACT FROM AUTHOR]

Objective: To describe the pharmacoepidemiology and costs associated with medications dispensed during pregnancy. Design: Pharmacoepidemiological study and cost analysis. Setting: Queensland, Australia. Population: All women who gave birth in Queensland between January 2013 and June 2018. Methods: We used a whole‐of‐population linked administrative dataset, Maternity1000, to describe medications approved for public subsidy that were dispensed to 255 408 pregnant women. We describe the volume of medications dispensed and their associated costs from a Government and patient perspective. Main outcome measures: Prevalence of medication use; proportion of total dispensings; total medication costs in AUD 2020/21 ($1AUD = $0.67USD/£0.55GBP in December 2022). Results: During pregnancy, 61% (95% CI 60.96–61.29%) of women were dispensed at least one medication approved for public subsidy. The mean number of items dispensed per pregnancy increased from 2.14 (95% CI 2.11–2.17) in 2013 to 2.47 (95% CI 2.44–2.51) in 2017; an increase of 15%. Furthermore, mean Government cost per dispensing increased by 41% from $21.60 (95% CI $20.99–$22.20) in 2013 to $30.44 (95% CI $29.38–$31.49) in 2017. These factors influenced the 53% increase in total Government expenditure observed for medication use during pregnancy between 2013 and 2017 ($2,834,227 versus $4,324,377); a disproportionate rise compared with the 17% rise in women's total out‐of‐pocket expenses observed over the same timeframe ($1,880,961 versus $2,204,415). Conclusions: Prevalence of medication use in pregnancy is rising and is associated with disproportionate and rapidly escalating cost implications for the Government. [ABSTRACT FROM AUTHOR]

Objectives: An essential aspect of network traffic classification is application identification. This involves capturing and analyzing the traffic patterns of applications. There are a few publicly available datasets that specifically capture streaming data from network-based applications. Therefore, our objective is to generate an up-to-date dataset with a focus on audio streaming data. This dataset can be a valuable resource for identifying audio streaming applications in the field of network traffic classification. Data description: The dataset contains network traffic captured during audio streaming communications on five trending applications: Google Meet, Skype, Telegram, WhatsApp, and SoundCloud. It includes 500 files in PCAP format captured by Wireshark and PCAPdroid tools during voice calls and online music playback. The concurrent utilization of these tools facilitates the avoidance of capturing background traffic. [ABSTRACT FROM AUTHOR]

In this paper, we present the USTC FLICAR Dataset, which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for heavy-duty autonomous aerial work robots. In recent years, numerous public datasets have played significant roles in the advancement of autonomous cars and unmanned aerial vehicles (UAVs). However, these two platforms differ from aerial work robots: UAVs are limited in their payload capacity, while cars are restricted to two-dimensional movements. To fill this gap, we create the "Giraffe" mapping robot based on a bucket truck, which is equipped with a variety of well-calibrated and synchronized sensors: four 3D LiDARs, two stereo cameras, two monocular cameras, Inertial Measurement Units (IMUs), and a GNSS/INS system. A laser tracker is used to record the millimeter-level ground truth positions. We also make its ground twin, the "Okapi" mapping robot, to gather data for comparison. The proposed dataset extends the typical autonomous driving sensing suite to aerial scenes, demonstrating the potential of combining autonomous driving perception systems with bucket trucks to create a versatile autonomous aerial working platform. Moreover, based on the Segment Anything Model (SAM), we produce the Semantic FLICAR dataset, which provides fine-grained semantic segmentation annotations for multimodal continuous data in both temporal and spatial dimensions. The dataset is available for download at: https://ustc-flicar.github.io/. [ABSTRACT FROM AUTHOR]