ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Deep Geodesic Learning for Segmentation and Anatomical Landmarking

In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space. The overall approach employs three inter-related steps. In the first step, we propose a deep neural network architecture with carefully designed regularization, and network hyper-parameters to perform image segmentation without the need for data augmentation and complex post-processing refinement. In the second step, we formulate the landmark localization problem directly on the geodesic space for sparsely-spaced anatomical landmarks. In the third step, we utilize a long short-term memory network to identify the closely-spaced landmarks, which is rather difficult to obtain using other standard networks. The proposed fully automated method showed superior efficacy compared to the state-of-the-art mandible segmentation and landmarking approaches in craniofacial anomalies and diseased states. We used a very challenging CBCT data set of 50 patients with a high-degree of craniomaxillofacial variability that is realistic in clinical practice. The qualitative visual inspection was conducted for distinct CBCT scans from 250 patients with high anatomical variability. We have also shown the state-of-the-art performance in an independent data set from the MICCAI Head-Neck Challenge (2015).
Physically plausible propeller noise prediction via recursive corrections leveraging prior knowledge and experimental data

For propeller-driven vessels, cavitation is the most dominant noise source producing both structure-borne and radiated noise impacting wildlife, passenger comfort, and underwater warfare. Physically plausible and accurate predictions of the underwater radiated noise at design stage, i.e., for previously untested geometries and operating conditions, are fundamental for designing silent and efficient propellers. State-of-the-art predictive models are based on physical, data-driven, and hybrid approaches. Physical models (PMs) meet the need for physically plausible predictions but are either too computationally demanding or not accurate enough at design stage. Data-driven models (DDMs) are computationally inexpensive ad accurate on average but sometimes produce physically implausible results. Hybrid models (HMs) combine PMs and DDMs trying to take advantage of their strengths while limiting their weaknesses but state-of-the-art hybridisation strategies do not actually blend them, failing to achieve the HMs full potential. In this work, for the first time, we propose a novel HM that recursively correct a state-of-the-art PM by means of a DDM which simultaneously exploits the prior physical knowledge in the definition of its feature set and the data coming from a vast experimental campaign at the Emerson Cavitation Tunnel on the Meridian standard propeller series behind different severities of the axial wake. Results in different extrapolating conditions, i.e., extrapolation with respect to propeller rotational speed, wakefield, and geometry, will support our proposal both in terms of accuracy and physical plausibility.
Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images

Human activities in the sea, such as intensive fishing and exploitation of offshore wind farms, may impact negatively on the marine mega fauna. As an attempt to control such impacts, surveying, and tracking of marine animals are often performed on the sites where those activities take place. Nowadays, thank to high resolution cameras and to the development of machine learning techniques, tracking of wild animals can be performed remotely and the analysis of the acquired images can be automatized using state-of-the-art object detection models. However, most state-of-the-art detection methods require lots of annotated data to provide satisfactory results. Since analyzing thousands of images acquired during a flight survey can be a cumbersome and time consuming task, we focus in this article on the weakly supervised detection of marine animals. We propose a modification of the patch distribution modeling method (PaDiM), which is currently one of the state-of-the-art approaches for anomaly detection and localization for visual industrial inspection. In order to show its effectiveness and suitability for marine animal detection, we conduct a comparative evaluation of the proposed method against the original version, as well as other state-of-the-art approaches on two high-resolution marine animal image datasets. On both tested datasets, the proposed method yielded better F1 and recall scores (75% recall/41% precision, and 57% recall/60% precision, respectively) when trained on images known to contain no object of interest. This shows a great potential of the proposed approach to speed up the marine animal discovery in new flight surveys. Additionally, such a method could be adopted for bounding box proposals to perform faster and cheaper annotation within a fully-supervised detection framework.
Short-Term Load Forecasting Using Recurrent Neural Networks With Input Attention Mechanism and Hidden Connection Mechanism

Short-term load forecasting is a critical task in the smart grid, which can be used to optimize power deployment and reduce power losses. Recurrent neural networks (RNNs) are the most popular deep learning models for short-term load forecasting. However, despite of achieving better forecasting accuracy than the traditional models, the performance of the existing RNN-based load forecasting approaches is still unsatisfactory for practical usage. Therefore, in this work, we have proposed input attention mechanism (IAM) and hidden connection mechanism (HCM) to greatly enhance the accuracy and efficiency of RNN-based load forecasting models. Specifically, we use IAM to assign the importance weights on input layers, which have better performances in both efficiency and accuracy than traditional attention mechanisms. To further enhance the models' efficiency, HCM is then applied to utilize residual connections to enhance the model's converging speed. We have applied both IAM and HCM on four state-of-the-art RNN implementations, and then conducted extensive experimental studies on two public datasets. Experimental results show that the proposed RNNs with IAM and HCM models achieve much better performances than the state-of-the-art baselines in both accuracy and efficiency. Ablation studies show that both IAM and HCM are essential to achieve such superior performances.
A Spectral Approach to Inter-Carrier Interference Mitigation in OFDM Systems

In this paper, we propose a new method for intercarrier interference (ICI) mitigation in orthogonal frequency-division multiplexing (OFDM) systems. The proposed approach views the signal reconstruction problem at the receiver end as an integer least squares (ILS) problem, and uses a recently developed spectral approach called sequential probabilistic ILS (SPILS) to solve it. The proposed approach outperforms other state-of-the-art approaches while having the same computational complexity. In addition, we present a novel extension to the SPILS scheme that allows the generation of soft decisions (for communication systems which use soft-decision decoding). The use of soft-decision decoding (naturally) brings significant improvement in the detection reliability, and we show that the proposed method again outperforms other state-of-the-art approaches. To better address the tradeoff between performance and complexity, we first suggest a novel method to reduce the number of matrix inversions required and hence, to reduce the implementation complexity without any degradation in performance. We also introduce a novel low complexity scheme termed Quick SPILS (QSPILS) in which we lose a little in detection reliability, but significantly reduce the implementation complexity.
Video Deraining Using the Visual Properties of Rain Streaks

In computer vision applications, the visibility of the video content is crucial to perform analysis for better accuracy. The visibility can be affected by several atmospheric interferences in challenging weather - one such interference is the appearance of rain streaks. Recently, rain streak removal has achieved plenty of interest among researchers, as it has some exciting applications such as autonomous cars, intelligent traffic monitoring systems, multimedia, etc. In this paper, we propose a novel and simple method of rain streak removal by combining three novel extracted visual features focusing on the temporal appearance, wide shape and relative location of the rain streak. We called it the TAWL (Temporal Appearance, Width, and Location) method. The proposed TAWL method adaptively uses features from different resolutions and frame rates. Moreover, it progressively processes features from the upcoming frames so that it can remove rain in real-time. Experiments have been conducted using video sequences with both real rain and synthetic rain to compare the performance of the proposed method against the relevant state-of-the-art methods. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods by removing more rain streaks while keeping other moving regions.
Direct localisation of molecules in tissue sections of growing antler tips using MALDI imaging

The astonishing growth rate of deer antlers offers a valuable model for the discovery of novel factors and regulatory systems controlling rapid tissue growth. Numerous molecules have been identified in growing antlers using a variety of techniques. However, little is known about the spatial distribution of these molecules in situ. A technique that has the potential to help in this regard is direct proteomic analysis of tissue sections by matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS). The present study applied this technique to spatially map molecules in antler tissue sections. Two protonated molecular ions were selected: m/z 6679 and m/z 6200 corresponding to VEGF and thymosin beta-10, respectively. Superimposition of the respective ion images on to histologically stained samples showed distinct spatial distribution across the antler tissue sections which were consistent with the previous reports using in situ hybridization. Two other molecular ions specifically m/z 8100 and m/z 11,800 were also selected, corresponding to reported masses of urocortin precursor and thioredoxin, respectively. As the spatial distribution of these proteins is not specifically known, MALDI-IMS was used as a potential technique to obtain information on their distribution on antler tips. The presence of all these molecules in deer antlers were further confirmed using LC-MS/MS data. The present study also demonstrated that MALDI-IMS could be further used to image antler sections with an extended ion mass range of up to m/z 45,000, thus potentially increasing the ability to discover the distribution of a larger set of molecules that may play an important role in antler growth. We have thus demonstrated that MALDI-IMS is a promising technique for generating molecular maps with high spatial resolution which can aid in evaluating the function of novel molecules during antler growth.
Cost standard set program: moving forward to standardization of cost assessment based on clinical condition

This communication piece is reporting the launching of the International Cost Standard set program, aiming to introduce standardized frameworks to measure costs for specific clinical conditions worldwide. A scientific committee including 16 international healthcare cost assessment experts from several countries, and International Consortium for Health Outcomes Measurement was formed to introduce the program. The committee got together in Lisbon for a first scientific meeting, followed by an international conference where time-driven activity-based costing applied studies were shared with the community. The cost standard set program start to offer instruments for people to measure with real-world data, the financial impact of having access to health technologies, improving the ability to evaluate inequity. Those advances might represent a paradigm shift in our ability to generate cost information on an individual level.
Consistent 3D human body segmentation based on combinatorial descriptor in spectral domain

Consistent 3D human body segmentation plays a vital role in many human-oriented applications. Recently research used supervised methods to achieve state-of-the-art performance. However, requiring massive labelled data and tedious training is a costly process. Unsupervised methods do not need labelling and training but struggle to achieve consistent segmentation for a non-rigid deformable mesh. Moreover, the segmentation style is also fixed. In the paper, we aim to achieve high-performance, consistent 3D human mesh segmentation avoiding fully-labelled data and time-consuming training. Specifically, this paper designs a Laplacian operator by incorporating mesh saliency, in which a face-level filter is proposed to improve the detection of concave vertices. Accordingly, we construct a combinatorial descriptor by explicitly employing the global and local attributes derived from the spectrum of the proposed saliency Laplacian operator to achieve consistent segmentation in the spectral domain. An automatic determination mechanism is adopted to determine the number of segments. Extensive experimental results demonstrate that the presented method is effective and efficient for many 3D meshes, especially for the human body shape. The segmentation results are comparable to other state-of-the-art performances without requiring time-consuming labelling and training on large-scale datasets.
Acute Mitral Valve Regurgitation Presenting With Right Upper Lobe Opacification

There is literature describing unilateral or focal pulmonary edema due to mitral regurgitation. The proposed mechanism is a regurgitant jet propelling blood towards the orifice of a particular pulmonary vein within the left atrium, which selectively pressurizes that vein. The increased hydrostatic pressure is transmitted to the pulmonary capillaries that drain into that vein, causing focal consolidation. A 62-year-old female presented with acute hypoxic respiratory failure. Her dyspnea started suddenly and she was unresponsive when she arrived at the emergency department via emergency medical services. Her initial oxygen saturation was 23% and she was immediately intubated. Sequential chest radiographs demonstrated dense consolidation in the right upper lung field and then opacification of the right hemithorax. These asymmetric lung findings were suspicious for infectious etiology but she was afebrile with no respiratory secretions and had normal inflammatory markers. Echocardiography showed a ruptured anterior papillary muscle causing a flail mitral valve leaflet with severe mitral regurgitation. The patient developed cardiogenic shock; she had an intra-aortic balloon pump placed for afterload reduction and was taken to the operating room for an emergency mitral valve replacement. Her clinical status rapidly improved and she made a full recovery. As in this case, acute mitral regurgitation can present with sudden life-threatening respiratory failure and cardiogenic shock so prompt diagnosis is critical. This is often misdiagnosed as pneumonia or other respiratory illnesses. Awareness, early diagnosis, and treatment of this entity could provide significant morbidity and mortality benefits for patients.
Fast, robust and efficient 2D pattern recognition for re-assembling fragmented images

We discuss the realization of a fast, robust and accurate pattern matching algorithm for comparison of digital images implemented by discrete Circular Harmonic expansions based on sampling theory. The algorithm and its performance for reassembling fragmented digital images are described in detail and illustrated by examples and data from the experimentation on an art fresco real problem. Because of the huge database of patterns and the large-scale dimension, the results of the experimentation are relevant to describe the power of discrimination and the efficiency of such method. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Same as it ever was? The Aurignacian of the Swabian Jura and the origins of Palaeolithic art

The Aurignacian of the Swabian Jura constitutes a key region for the understanding of the behaviour of the first populations of modern humans in Europe. The region has yielded works of figurative art and musical instruments that are among the oldest in the world. The objects are evidence for the existence of a new type of society distinct from those known in previous phases of human prehistory. This article highlights the innovations intrinsic to the beginning of the Upper Palaeolithic and contests the idea of a gradual evolution, which erodes the clear distinction between the Middle Palaeolithic and the Upper Palaeolithic at some point in the transition. (C) 2017 Published by Elsevier Ltd.
State of the Art in Sensor Technologies for Sewer Inspection

This paper reviews the state of the art in sensors and automated inspection devices for enhanced sewer inspection. Efficiency, safety, environmental, and legislative concerns have made inspection and assessment of communal sewers a central issue to water and sewerage companies. Nowadays, the standard sewer inspection system is based on a wheeled platform on which a closed circuit television (CCTV) camera is mounted. One of the disadvantages of camera inspection systems is that they can only detect a small proportion of all possible damage in a sewer. The inspection outcome of such systems relies not only on the quality of the acquired images, but also on the off-line recognition and classification conducted by human operators. In consequence, CCTV-based platforms are frequently not effective. Infrared, microwave, optical, and ultrasonic-based sensors have been proposed to complement the existing CCTV-based approach and to improve inspection results. New inspection devices employing multiple sensors and being capable of carrying out remote sewer inspection tasks are under research.
Color Pixel Reconstruction for a Monolithic RGB-Z CMOS Imager

In this paper, we introduce the challenges, intrinsic and extrinsic, of color and depth sensors integration in the same matrix for a monolithic RBG-Z CMOS imager system. Due to the fact that the technology to conceive this type of circuit is still under development, the challenge that we address is the extrinsic one. It is a consequence of the heterogeneity of the matrix, where information is missing compared to what can be provided by separate RGB and Z systems. For that a first evaluation is done taking into account how the RGB-Z patterns could impact the demosaicing step. The evaluated pattern are in function of the different sizes between color and depth pixels. For the missing color reconstruction we have evaluated the state of the art algorithms, adapted to the missing information, and we propose an original adaptive algorithm using a new operator called semi-gradient (SG). To fill the lack of a mature technology for which real images are missing for this type of CMOS imager, a test environment was created and then used with three different databases, Kodak, McMaster, HDR+burst. The results show improvements on edges, corners, and narrow lines reconstruction, and a reduction of color and structural artefacts compared to the state-of-the-art reconstruction algorithms.
Study on Distributed Consistent Cooperative Control of Multi-ART in Automated Container Terminals

In order to address the congestion problem of vehicles in quay of automated container terminal with parallel layout and side-loading operations, the study regards the terminal operation system as a multi-agent system (MAS). Then, A dynamic cooperative speed regulation strategy for a group intelligence-oriented multi-ART (Artificial Intelligence Robot of Transportation) is proposed to realize the smart and distributed traffic control of container terminals. Via the real-time data interchange and message passing with ART group, the individual ART implements the collaborative speed regulation with the combination of the dynamic speed regulation strategy. Through the above-mentioned method, the sequence of ARTs arriving at the quay can be adjusted, which can decrease the waiting time of ARTs at the quay. Considering the consistency problem of individual and group agent among the dynamic speed regulation of ARTs, a novel distributed consensus protocol is established to assure that the individual status of ART will tend to converge basically during the collaborative speed regulation process, and the convergence of system can be guaranteed. Based on the case of automated container terminal of Tianjin Port, the paper establishes a multi-agent based simulation model, and simulates the decision-making process, and validates the effectiveness of mentioned model and strategy. The results demonstrate that the strategy can decrease the waiting time of ARTs under uncertainty environment of terminal and improve the operation efficiency of system.
Association between Economic Growth, Mortality, and Healthcare Spending in 31 High-Income Countries

This study aims to investigate the association between gross domestic product (GDP), mortality rate (MR) and current healthcare expenditure (CHE) in 31 high-income countries. We used panel data from 2000 to 2017 collected from WHO and OECD databases. The association between CHE, GDP and MR was investigated through a random-effects model. To control for reverse causality, we adopted a test of Granger causality. The model shows that the MR has a statistically significant and negative effect on CHE and that an increase in GDP is associated with an increase of CHE (p < 0.001). The Granger causality analysis shows that all the variables exhibit a bidirectional causality. We found a two-way relationship between GDP and CHE. Our analysis highlights the economic multiplier effect of CHE. In the debate on the optimal allocation of resources, this evidence should be taken into due consideration.
High voltage outdoor insulator surface condition evaluation using aerial insulator images

High voltage insulator detection and monitoring via drone-based aerial images is a cost-effective alternative in extreme winter conditions and complex terrains. The authors examine different surface conditions of the outdoor electrical insulator that generally occur under winter condition using image processing techniques and state-of-the-art classification methods. Two different types of classification approaches are compared: one method is based on neural networks (e.g. CNN, InceptionV3, MobileNet, VGG16, and ResNet50) and the other method is based on traditional machine learning classifiers (e.g. Bayes Net, Decision Tree, Lazy, Rules, and Meta classifiers). They are evaluated to discriminate the images of insulator surface exposed to freezing, wet, and snowing conditions. The results indicate that traditional machine learning methods with proper selection of features can show high classification accuracy. The classification of the insulator surfaces will assist in determining the insulator conditions, and take preventive measures for its protection.