Reviewerly/exHarmony huggingface dataset:
Research on New Media Automatic Question Answering and Learning Robot for Specific Scene

In this paper, the new media automatic question and answer and learning robot for specific scene is studied. Firstly, an automatic question answering system is proposed. Then, the core automatic question answering module is designed in detail, and a semi-generative and semiretrieval automatic question answering model is proposed. Finally, different automatic question answering models and automatic question answering systems are tested and verified. The results show that the proposed model obtains a total of 1900 correct feedback answers, with an accuracy rate of 95%. At the same time, the response time of the system to the related speech of new media is only 0.98s, and the accuracy of the answer to the well-expressed speech is up to 98%. It can be concluded that the automatic question answering model in the system is feasible and stable, and is suitable for new media automatic question answering and learning.
Research on Entity Alignment Method in Civil Aviation Equipment Domain Based on Translation Embedding

Because of the characteristics of rich semantic information, complex attribute information and simple relational information in the inter-domain entity alignment dataset, the BERT model, attention mechanism and TransH model are used to embed semantics description, attribute and relation respectively, and the final entity is obtained by combining multiple embedding methods. Finds aligned entities by comparing the cosine distance between entity vectors. In this paper, the model is first applied to the domain-specific entity alignment dataset constructed in this study, achieving good performance results, which demonstrates the effectiveness of the model in the civil aviation equipment domain entity alignment task. Then, a comparative experiment is conducted with the current superior translation embedding-based entity alignment model on the domain-specific dataset. Compared with the translation embedding-based entity alignment model currently applied to large-scale datasets, this model has strong advantages. Finally, entity alignment experiments are conducted separately using the semantic information embedding, attribute embedding, and relation embedding of the model, which fully verifies the effectiveness of each part of the model. Compared with the current better entity alignment model based on translation embedding, the model has a specific improvement in the Hist@1 and Hist@10 on the alignment data set between the fields of civil aviation equipment.
The Economic Value of Dual-Token Blockchains

It is standard for blockchain platforms to issue native tokens, or crytpocurrencies, that users must own to operate within the platform. Some blockchains, however, decided to issue two tokens, establishing a dual system, with one token typically for governance and the other for implementing functions on the blockchain, such as executing transactions or smart contracts. Therefore, the two tokens are used for different activities. Typically, owning the governance tokens gives the right to receive the other token for free, as a reward for participating in the blockchain decision-making and voting processes. However, both tokens can also be traded on some exchange nodes, which means that platform functions could be implemented even without owning governance tokens. In this paper, we discuss some economic fundamentals of dual-token blockchain platforms—in particular, how to establish their economic value and the market relative attractiveness of the two tokens. We do so by introducing some simple numerical indicators, based on prices, and traded circulating monetary quantities. Such indicators, which are meant to reflect the platform’s view on the tokens’ market desirability, could be computed in real time and used to support the platform’s policy making.
A Dependently Typed Language with Dynamic Equality

Dependent type systems are powerful tools for preventing bugs in programs. Unlike other formal methods, dependent type systems can reuse the methodology and syntax familiar to functional programmers to construct formal proofs. However, usability issues, like confusing error messages, often arise from the conservative equalities required by such type theories. We address this issue by designing a dependently typed language where equality checking is delayed until runtime. What were once static errors can now be presented to programmers as warnings. When runtime failures occur, the blame system provides clear error messages indicating the exact static assumption violated during execution. We present several examples in this system, introduce novel correctness properties, and prove them for a fragment of the language. Our full system handles dependent indexed data and pattern matching, which are difficult for dependent gradual typing systems to manage. Finally, we have implemented a prototype of the language.
LASSO and Elastic Net Tend to Over-Select Features

Machine learning methods have been a standard approach to select features that are associated with an outcome and to build a prediction model when the number of candidate features is large. LASSO is one of the most popular approaches to this end. The LASSO approach selects features with large regression estimates, rather than based on statistical significance, that are associated with the outcome by imposing an L1-norm penalty to overcome the high dimensionality of the candidate features. As a result, LASSO may select insignificant features while possibly missing significant ones. Furthermore, from our experience, LASSO has been found to select too many features. By selecting features that are not associated with the outcome, we may have to spend more cost to collect and manage them in the future use of a fitted prediction model. Using the combination of L1- and L2-norm penalties, elastic net (EN) tends to select even more features than LASSO. The overly selected features that are not associated with the outcome act like white noise, so that the fitted prediction model may lose prediction accuracy. In this paper, we propose to use standard regression methods, without any penalizing approach, combined with a stepwise variable selection procedure to overcome these issues. Unlike LASSO and EN, this method selects features based on statistical significance. Through extensive simulations, we show that this maximum likelihood estimation-based method selects a very small number of features while maintaining a high prediction power, whereas LASSO and EN make a large number of false selections to result in loss of prediction accuracy. Contrary to LASSO and EN, the regression methods combined with a stepwise variable selection method is a standard statistical method, so that any biostatistician can use it to analyze high-dimensional data, even without advanced bioinformatics knowledge.
Improving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion Detection

Liquid-based cytology (LBC) plays a crucial role in the effective early detection of cervical cancer, contributing to substantially decreasing mortality rates. However, the visual examination of microscopic slides is a challenging, time-consuming, and ambiguous task. Shortages of specialized staff and equipment are increasing the interest in developing artificial intelligence (AI)-powered portable solutions to support screening programs. This paper presents a novel approach based on a RetinaNet model with a ResNet50 backbone to detect the nuclei of cervical lesions on mobile-acquired microscopic images of cytology samples, stratifying the lesions according to The Bethesda System (TBS) guidelines. This work was supported by a new dataset of images from LBC samples digitalized with a portable smartphone-based microscope, encompassing nucleus annotations of 31,698 normal squamous cells and 1395 lesions. Several experiments were conducted to optimize the model’s detection performance, namely hyperparameter tuning, transfer learning, detected class adjustments, and per-class score threshold optimization. The proposed nucleus-based methodology improved the best baseline reported in the literature for detecting cervical lesions on microscopic images exclusively acquired with mobile devices coupled to the µSmartScope prototype, with per-class average precision, recall, and F1 scores up to 17.6%, 22.9%, and 16.0%, respectively. Performance improvements were obtained by transferring knowledge from networks pre-trained on a smaller dataset closer to the target application domain, as well as including normal squamous nuclei as a class detected by the model. Per-class tuning of the score threshold also allowed us to obtain a model more suitable to support screening procedures, achieving F1 score improvements in most TBS classes. While further improvements are still required to use the proposed approach in a clinical context, this work reinforces the potential of using AI-powered mobile-based solutions to support cervical cancer screening. Such solutions can significantly impact screening programs worldwide, particularly in areas with limited access and restricted healthcare resources.
Radar-Camera Fusion Network for Depth Estimation in Structured Driving Scenes

Depth estimation is an important part of the perception system in autonomous driving. Current studies often reconstruct dense depth maps from RGB images and sparse depth maps obtained from other sensors. However, existing methods often pay insufficient attention to latent semantic information. Considering the highly structured characteristics of driving scenes, we propose a dual-branch network to predict dense depth maps by fusing radar and RGB images. The driving scene is divided into three parts in the proposed architecture, each predicting a depth map, which is finally merged into one by implementing the fusion strategy in order to make full use of the potential semantic information in the driving scene. In addition, a variant L1 loss function is applied in the training phase, directing the network to focus more on those areas of interest when driving. Our proposed method is evaluated on the nuScenes dataset. Experiments demonstrate its effectiveness in comparison with previous state of the art methods.
MCMNET: Multi-Scale Context Modeling Network for Temporal Action Detection

Temporal action detection is a very important and challenging task in the field of video understanding, especially for datasets with significant differences in action duration. The temporal relationships between the action instances contained in these datasets are very complex. For such videos, it is necessary to capture information with a richer temporal distribution as much as possible. In this paper, we propose a dual-stream model that can model contextual information at multiple temporal scales. First, the input video is divided into two resolution streams, followed by a Multi-Resolution Context Aggregation module to capture multi-scale temporal information. Additionally, an Information Enhancement module is added after the high-resolution input stream to model both long-range and short-range contexts. Finally, the outputs of the two modules are merged to obtain features with rich temporal information for action localization and classification. We conducted experiments on three datasets to evaluate the proposed approach. On ActivityNet-v1.3, an average mAP (mean Average Precision) of 32.83% was obtained. On Charades, the best performance was obtained, with an average mAP of 27.3%. On TSU (Toyota Smarthome Untrimmed), an average mAP of 33.1% was achieved.
Vehicle Detection in Multisource Remote Sensing Images Based on Edge-Preserving Super-Resolution Reconstruction

As an essential technology for intelligent transportation management and traffic risk prevention and control, vehicle detection plays a significant role in the comprehensive evaluation of the intelligent transportation system. However, limited by the small size of vehicles in satellite remote sensing images and lack of sufficient texture features, its detection performance is far from satisfactory. In view of the unclear edge structure of small objects in the super-resolution (SR) reconstruction process, deep convolutional neural networks are no longer effective in extracting small-scale feature information. Therefore, a vehicle detection network based on remote sensing images (VDNET-RSI) is constructed in this article. The VDNET-RSI contains a two-stage convolutional neural network for vehicle detection. In the first stage, a partial convolution-based padding adopts the improved Local Implicit Image Function (LIIF) to reconstruct high-resolution remote sensing images. Then, the network associated with the results from the first stage is used in the second stage for vehicle detection. In the second stage, the super-resolution module, detection heads module and convolutional block attention module adopt the increased object detection framework to improve the performance of small object detection in large-scale remote sensing images. The publicly available DIOR dataset is selected as the experimental dataset to compare the performance of VDNET-RSI with that of the state-of-the-art models in vehicle detection based on satellite remote sensing images. The experimental results demonstrated that the overall precision of VDNET-RSI reached 62.9%, about 6.3%, 38.6%, 39.8% higher than that of YOLOv5, Faster-RCNN and FCOS, respectively. The conclusions of this paper can provide a theoretical basis and key technical support for the development of intelligent transportation.
A Second-Order Method for Removing Mixed Noise from Remote Sensing Images

Remote sensing image denoising is of great significance for the subsequent use and research of images. Gaussian noise and salt-and-pepper noise are prevalent noises in images. Contemporary denoising algorithms often exhibit limitations when addressing such mixed noise scenarios, manifesting in suboptimal denoising outcomes and the potential blurring of image edges subsequent to the denoising process. To address the above problems, a second-order removal method for mixed noise in remote sensing images was proposed. In the first stage of the method, dilated convolution was introduced into the DnCNN (denoising convolutional neural network) network framework to increase the receptive field of the network, so that more feature information could be extracted from remote sensing images. Meanwhile, a DropoutLayer was introduced after the deep convolution layer to build the noise reduction model to prevent the network from overfitting and to simplify the training difficulty, and then the model was used to perform the preliminary noise reduction on the images. To further improve the image quality of the preliminary denoising results, effectively remove the salt-and-pepper noise in the mixed noise, and preserve more image edge details and texture features, the proposed method employed a second stage on the basis of adaptive median filtering. In this second stage, the median value in the original filter window median was replaced by the nearest neighbor pixel weighted median, so that the preliminary noise reduction result was subjected to secondary processing, and the final denoising result of the mixed noise of the remote sensing image was obtained. In order to verify the feasibility and effectiveness of the algorithm, the remote sensing image denoising experiments and denoised image edge detection experiments were carried out in this paper. When the experimental results are analyzed through subjective visual assessment, images denoised using the proposed method exhibit clearer and more natural details, and they effectively retain edge and texture features. In terms of objective evaluation, the performance of different denoising algorithms is compared using metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR), and mean structural similarity index (MSSIM). The experimental outcomes indicate that the proposed method for denoising mixed noise in remote sensing images outperforms traditional denoising techniques, achieving a clearer image restoration effect.
Graph-theoretic characterization of unextendible product bases

Unextendible product bases (UPBs) play a key role in the study of quantum entanglement and nonlocality. Here we provide an equivalent characterization of UPBs in graph-theoretic terms. Different from previous graph-theoretic investigations of UPBs, which focused mostly on the orthogonality relations between different product states, our characterization includes a graph-theoretic reformulation of the unextendibility condition. Building on this characterization, we develop a constructive method for building UPBs in low dimensions and shed light on the open question of whether there exist genuinely unextendible product bases (GUPBs), that is, multipartite product bases that are unextendible with respect to every possible bipartition. We derive a lower bound on the size of any candidate GUPB, significantly improving over the state of the art. Moreover, we show that every minimal GUPB saturating our bound must be associated to regular graphs and discuss a possible path towards the construction of a minimal GUPB in a tripartite system of minimal local dimension. Finally, we apply our characterization to the problem of distinguishing UPB states under local operations and classical communication, deriving a necessary condition for reliable discrimination in the asymptotic limit.
Introduction to special section: The role of geochemical workflows in understanding resource plays

Next No AccessInterpretationJust-Accepted ArticlesIntroduction to special section: Role of geochemical workflows in understanding resource playsAuthors: Craig D. BarrieCatherine DonohueHumberto CarvajalShawn WrightAlan YuCaroline BurkeOlivia WoodruffEric MichaelCraig D. Barrie Applied Petroleum Technology UK, Tan-y-Graig Unit 8, Parc Caer Seion Conwy, UK. E-mail: .Search for more papers by this authorEmail the author at [email protected], Catherine Donohue GeoMark Research, Ltd., 9748 Whithorn Drive, Houston, Texas, USA. E-mail: .Search for more papers by this authorEmail the author at [email protected], Humberto Carvajal Core Laboratories, 6316 Windfern Rd., Houston, TX, USA. E-mail: .Search for more papers by this authorEmail the author at [email protected], Shawn Wright Hess, 1501 McKinney Street. Houston, TX, USA. E-mail: .Search for more papers by this authorEmail the author at [email protected], Alan Yu Chevron, 1400 Smith St, Houston, TX, USA. E-mail: .Search for more papers by this authorEmail the author at [email protected], Caroline Burke Bain Geophysical Services, 12202 PEBBLE MDWS, Houston, TX, USA. E-mail: .Search for more papers by this authorEmail the author at [email protected], Olivia Woodruff Kimmeridge, 1401 Lawrence Street, 17th Floor, Denver, CO, USA E-mail: .Search for more papers by this authorEmail the author at [email protected], and Eric Michael APT USA, 2101 CityWest Blvd, Suite 100 Houston, TX, USA. E-mail: .Search for more papers by this authorEmail the author at [email protected]https://doi.org/10.1190/int-2023-0830-spseintro.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked InReddit FiguresReferencesRelatedDetails Just-Accepted ArticlesPages: 1-55ISSN (print):2324-8858 ISSN (online):2324-8866 publication data© 2023 Society of Exploration Geophysicists and American Association of Petroleum GeologistsPublisher:Society of Exploration GeophysicistsAmerican Association of Petroleum Geologists HistoryPublished Online: 31 Aug 2023 CITATION INFORMATION Craig D.Barrie, CatherineDonohue, HumbertoCarvajal, ShawnWright, AlanYu, CarolineBurke, OliviaWoodruff, and EricMichael, (), "Introduction to special section: Role of geochemical workflows in understanding resource plays," Interpretation 0: 1-2. https://doi.org/10.1190/int-2023-0830-spseintro.1 Plain-Language Summary PDF Download Metrics Loading ...
Vehicle detection in diverse traffic using an ensemble convolutional neural backbone via feature concatenation

ABSTRACTNowadays, deploying an intelligent vehicle detection system (IVDS) in diverse traffic is a work priority. It provides real-time traffic information with vehicle counts and types of vehicles. IVDS deployment in diverse traffic is challenging because different vehicle classes occlude each other on the road. In recent years, convolutional neural network (CNN) based deep learning (DL) methods have attained incredible progress in implementing IVDS. However, most CNN-based DL methods do not include diverse traffic conditions in Asian countries. Also, due to existing feature extraction backbones, they cannot accurately detect multi-scale vehicles. This work proposes an advanced visual computing deep learning (AVCDL) method with a vast labeled vehicle dataset to detect vehicles in diverse traffic. It includes an ensemble backbone and an improved multi-stage vehicle detection head (MSVDH). An ensemble CNN backbone extracts the vehicle features and combines them on a single channel via a feature concatenation. The final detection is carried out by an improved MSVDH that classifies the target vehicles. The proposed method is examined, tested, and evaluated using traffic statistics. It is contrasted with current cutting-edge vehicle detection techniques. It achieves 86.32% mean average precision (mAP) on self-collected diverse traffic labeled dataset (DTLD) and 86.17% mAP on KITTI. Moreover, the real-time performance is validated with NVIDIA Jetson Tx2 and Nano boards. It achieves 15 frames per second (FPS) on Jetson Tx2 and 7 FPS on Jetson Nano.KEYWORDS: Convolutional neural networkdeep learningdiverse trafficfeature concatenationvehicle detection AcknowledgmentsThis work is supported by “Impacting Research, Innovation and Technology” (IM-PRINT) scheme under the “Intelligent Surveillance Data Retriever (ISDR) for Smart City Applications”, project Grant No: 7794/2016. It is funded by the “Ministry of Education” (MoE) and the “Ministry of Housing and Urban Affairs” (MoUD)Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Ministry of Education, India [7794/2016]; Ministry of Housing and Urban Affairs [7794/2016].
Segmentation of cardiac tissues and organs for CCTA images based on a deep learning model

Accurate segmentation of cardiac tissues and organs based on cardiac computerized tomography angiography (CCTA) images has played an important role in biophysical modeling and medical diagnosis. The existing research on segmentation of cardiac tissues generally rely on limited public data, which may lead to unsatisfactory performance. In this paper, we first present a unique dataset of three-dimensional (3D) CCTA images collected from multiple centers to remedy this shortcoming. We further propose to efficiently create labels by solving the Laplace’s equation with given boundary conditions. The generated images and labels are confirmed by cardiologists. A deep learning algorithm, based on 3D-Unet model trained with a combined loss function, is proposed to simultaneously segment aorta, left ventricle, left atrium, left atrial appendage and myocardium from the CCTA images. Experimental evaluations show that the model trained with a proposed combined loss function can improve the segmentation accuracy and robustness. By efficiently producing a patient-specific geometry for simulation, we believe that this learning-based approach could provide an avenue to combine with biophysical modeling for the study of hemodynamics in cardiac tissues.