ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Interpretability-Driven Sample Selection Using Self Supervised Learning for Disease Classification and Segmentation

In supervised learning for medical image analysis, sample selection methodologies are fundamental to attain optimum system performance promptly and with minimal expert interactions (e.g. label querying in an active learning setup). In this article we propose a novel sample selection methodology based on deep features leveraging information contained in interpretability saliency maps. In the absence of ground truth labels for informative samples, we use a novel self supervised learning based approach for training a classifier that learns to identify the most informative sample in a given batch of images. We demonstrate the benefits of the proposed approach, termed Interpretability-Driven Sample Selection (IDEAL), in an active learning setup aimed at lung disease classification and histopathology image segmentation. We analyze three different approaches to determine sample informativeness from interpretability saliency maps: (i) an observational model stemming from findings on previous uncertainty-based sample selection approaches, (ii) a radiomics-based model, and (iii) a novel data-driven self-supervised approach. We compare IDEAL to other baselines using the publicly available NIH chest X-ray dataset for lung disease classification, and a public histopathology segmentation dataset (GLaS), demonstrating the potential of using interpretability information for sample selection in active learning systems. Results show our proposed self supervised approach outperforms other approaches in selecting informative samples leading to state of the art performance with fewer samples.
Color Image Denoising via Cross-Channel Texture Transferring

Image denoising can reduce the perturbation inevitably generated during image signal acquisition and its subsequent processing. While the utilization of nonlocal properties can enhance the performance of the state-of-the-art denoising methods, a heavy computational burden is incurred especially for color images. Inspired by the high correlation in the texture information over color channels, for a reduction of the computational burden, this letter proposes denoising the luma channel first, and then, performing a patch-wise linear prediction to transfer the texture information of the denoised luma channel to the other two channels. The texture transferring is adapted to local characteristic (i.e., variance of the local patches) for a reduction of color smearing caused by large prediction error especially along edges. Experimental results confirm that the proposed method achieves performance improvement over the state-of-the-art color image denoising methods only at a slightly increased complexity of single-channel denoising.
Fixed-Point NLMS and IPNLMS VLSI Architectures for Accurate FECG and FHR Processing

Capturing signals without noise and interference while monitoring the maternal abdomen's fetal electrocardiogram (FECG) is a challenging task. This method can provide fetal monitoring for long hours, not harming the pregnant woman or the fetus. Such non-invasive FECG raw signal suffers from various interference sources as the bio-electric maternal potentials include her ECG component. Therefore, a critical step in the non-invasive FECG is to design the filtering of components derived from the maternal ECG. There is an increasing demand for portable devices to extract a pure FECG signal and to detect fetal heart rate (FHR) with precision. Dedicated CMOS architectures enable higher energy efficiency in portable devices. This paper proposes VLSI architectures dedicated to FECG extraction and FHR processing. Fixed-point architectures for the FECG detection exploring the NLMS (normalized least mean square), IPNLMS (improved proportional NLMS), and three different division VLSI CMOS architectures are designed herein. An architecture based on the Pan-Tompkins algorithm that processes the FECG for extracting the FHR, extending the functionally of the system, is also proposed. The results show that the NLMS and IPNLMS based architectures effectively detect the R-peaks of FECG with a detection accuracy of 92.86% and 93.75%, respectively. The synthesis results shows that our NLMS architecture proposal saves 13.3 % energy, due to a reduction of 279 clock cycles, compared to the state of the art. On the other hand, the IPNLMS algorithm results in +0.89% detection accuracy at the price of 42% additional energy consumption w.r.t NLMS.
A numerical investigation of the mechanics of intracranial aneurysms walls: Assessing the influence of tissue hyperelastic laws and heterogeneous properties on the stress and stretch fields

Numerical simulations have been extensively used in the past two decades for the study of intracranial aneurysms (IAs), a dangerous disease that occurs in the arteries that reach the brain and affect overall 3.2% of a population without comorbidity with up to 60% mortality rate, in case of rupture. The majority of those studies, though, assumed a rigid-wall model to simulate the blood flow. However, to also study the mechanics of IAs walls, it is important to assume a fluid-solid interaction (FSI) modeling. Progress towards more reliable FSI simulations is limited because FSI techniques pose severe numerical difficulties, but also due to scarce data on the mechanical behavior and material constants of IA tissue. Additionally, works that have investigated the impact of different wall modeling choices for patient-specific IAs geometries are a few and often with limited conclusions. Thus our present study investigated the effect of different modeling approaches to simulate the motion of an IA. We used three hyperelastic laws - the Yeoh law, the three-parameter Mooney-Rivlin law, and a Fung-like law with a single parameter - and two different ways of modeling the wall thickness and tissue mechanical properties - one assumed that both were uniform while the other accounted for the heterogeneity of the wall by using a "hemodynamics-driven" approach in which both thickness and material constants varied spatially with the cardiac-cycle-averaged hemodynamics. Pulsatile numerical simulations, with patient-specific vascular geometries harboring IAs, were carried out using the one-way fluid-solid interaction solution strategy implemented in solids4foam, an extension of OpenFOAM®, in which the blood flow is solved and applied as the driving force of the wall motion. We found that different wall morphology models yield smaller absolute differences in the mechanical response than different hyperelastic laws. Furthermore, the stretch levels of IAs walls were more sensitive to the hyperelastic and material constants than the stress. These findings could be used to guide modeling decisions on IA simulations, since the computational behavior of each law was different, for example, with the Yeoh law being the fastest to converge.
Differences in Insulin Sensitivity, Secretion, and the Metabolic Clearance Rate of Glucose in Newly Diagnosed Type 2 Diabetes Mellitus Patients: The Influences of Body Mass Index and Fatty Liver

Background: Obesity and nonalcoholic fatty liver disease are strongly associated with type 2 diabetes mellitus (T2DM), affecting insulin sensitivity and β-cell function. They interact, exacerbating the development of hyperinsulinemia to T2DM. Methods: Through oral glucose tolerance and insulin secretion tests, the relationships between insulin sensitivity and secretion, glucose clearance, body mass index (BMI), and fatty liver were studied in newly diagnosed T2DM patients. The homeostasis model assessment of insulin resistance (HOMA-IR), homeostasis model assessment of β-cell function (HOMA-β), insulin sensitivity index (ISI), and metabolic clearance rate (MCR) of glucose were calculated to analyze insulin sensitivity and β-cell function. Results: There were no differences in HOMA-IR, HOMA-β, first-phase insulin secretion (1st PH), second-phase insulin secretion (2nd PH), ISI, or MCR between lean fatty liver and lean nonfatty liver patients. Both overweight/obesity (ow/ob) and patients with fatty liver increased HOMA-IR, and decreased ISI and MCR. In the ow/ob subgroup, patients with fatty liver had severe insulin resistance but greater HOMA-β, 1st PH, and 2nd PH than individuals with nonfatty liver. The difference in MCR between fatty liver and nonfatty liver groups was not significant. Conclusion: BMI and hepatic steatosis are independent determinants of increased insulin resistance and decreased MCR. However, it is steatosis, not BMI, related to the increase in insulin secretion.
GPU Sparse Ray-Traced Segmentation

This paper introduces a real-time region growing segmentation algorithm, designed for graphics processing units (GPUs), which labels only a fraction of the input elements. Instead of searching locally around each element for strong similarity, like state-of-the-art segmentation and pre-segmentation methods do, the proposed algorithm searches both locally and remotely, using a unique ray tracing-based search strategy, which quickly covers the segmentation search space. The presented algorithm fully exploits the parallelism of the GPUs, sparsely segmenting high-resolution images (4K) in real-time on low range laptops and other mobile devices, approximately 5x times faster than the state-of-the-art simple linear iterative clustering (SLIC). While this paper demonstrates the results with images, the algorithm is trivially modifiable to work with input sets of any dimension. In contrast to the state-of-the-art real-time GPU methods, this algorithm doesn't require additional merging steps, as pre-segmentation methods do, and it produces complete segmentation. Additionally, post-segmentation optional stages for complete labeling and region merging on the GPU are also provided, although they are not always necessary.
Restoring our senses, restoring the Earth. Fostering imaginative capacities through the arts for envisioning climate transformations

Humanity has never lived in a world of global average temperature above two degrees of current levels. Moving towards such High-End Climate Change (HECC) futures presents fundamental challenges to current governance structures and involves the need to confront high uncertainties, non-linear dynamics and multiple irreversibilities in global social-ecological systems. In order to face HECC, imaginative practices able to support multiple ways of learning about and experiencing the future are necessary. In this article we analysed a set of arts-based activities conducted within the five-year EU-funded project IMPRESSIONS aimed at identifying transformative strategies to high-end climate change. The exploratory artistic activities were carried out alongside a science-led participatory integrated assessment process with stakeholders from the Iberian Peninsula. Our arts-based approach combined a range of performative, visual and reflexive practices with the ambition to reach out to more-than-rational but also practical elements of HECC futures. Our study suggests that the arts-based approach helped to bring out new ways of seeing, feeling and interpreting the world which may support the development of individual and collective sensibilities needed to address HECC.
Role of Defects on the Particle Size-Capacitance Relationship of Zn-Co Mixed Metal Oxide Supported on Heteroatom-Doped Graphenes as Supercapacitors

Supercapacitors are considered among the most promising electrical energy storage devices, there being a need to achieve the highest possible energy storage density. Herein small mixed Zn-Co metal oxide nanoparticles are grown on doped graphene (O-, N- and, B-doped graphenes). The electrochemical properties of the resulting mixed Zn-Co metal oxide nanoparticles (4 nm) grown on B-doped graphene exhibit an outstanding specific capacitance of 2568 F g-1 at 2 A g-1 , ranking this B-doped graphene composite among the best performing electrodes. The energy storage capacity is also remarkable even at large current densities (i.e., 640 F g-1 at 40 A g-1 ). In contrast, larger nanoparticles are obtained using N- and O-doped graphenes as support, the resulting materials exhibiting lower performance. Besides energy storage, the Zn-Co oxide on B-doped graphene shows notable electrochemical performance and stability obtaining a maximum energy density of 77.6 W h Kg-1 at 850 W Kg-1 , a power density of 8500 W Kg-1 at 28.3 W h Kg-1 , and a capacitance retention higher than 85% after 5000 cycles. The smaller nanoparticle size and improved electrochemical performance on B-doped graphene-based devices are attributed to the higher defect density and nature of the dopant element on graphene.
Analysis of Spatio-Temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks

Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate (equal error rate) of 0.7 percent an improvement ratio of 371 percent compared to previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of client's footstep data and to provide insights of the feature learning process.
Shape autotuning activation function

The choice of activation function is essential for building state-of-the-art neural networks. At present, the most widely-used activation function with effectiveness is ReLU. However, ReLU suffers from the weakness including non-zero mean, negative missing, and unbounded output, thus it has potential disadvantages in the optimization process. In this paper, we propose a novel activation function, namely ?Shape Autotuning Activation Function? (SAAF), to overcome these three challenges simultaneously. The SAAF inherits merits of smooth activation functions (such as Sigmoid and Tanh) and piecewise activation functions (such as ReLU and its variants), and avoids their deficiencies. Specifically, the SAAF adaptively adjusts a pair of independent trainable parameters to capture negative information and provide a near-zero mean output, resulting in better generalization performance and faster learning speed. At the same time, it provides bounded outputs to ensure a more stable distribution of output during network training. We evaluated SAAF on deep networks applied to a variety of tasks, including image classification, machine translation, and generative modeling. Comprehensive comparison study shows that the proposed SAAF is superior to state-of-the-art activation functions.
Modeling multiple dependent variables in meta-analysis of single-case experimental design using multilevel modeling

Although meta-analyses of single-case experimental design (SCED) often include multiple types of dependent variables (DVs), multiple DVs are rarely considered within models in the analysis. Baek et al. (Journal of Experimental Education, 90(4), 934-961, 2022) identified several statistical issues that arise when researchers fail to model multiple DVs in meta-analyses of SCED data. However, the degree to which non-modeling of multiple DVs impacts the results of the meta-analysis of SCED has not been fully examined. In this simulation study, we have systematically investigated the impact of non-modeling of multiple DVs when analyzing meta SCED data using multilevel modeling. The result demonstrates that modeling multiple DVs has advantages over the non-modeling option for meta-analysis of SCED. Modeling multiple DVs enables the determination of precise effects from different DVs in addition to the unbiased and accurate average effect and accurate estimates and inferences for the error variances at the study level as well as the observation level. The current study also reveals potential factors (i.e., the number of DVs, degree of heterogeneity in the level-1 error variances and autocorrelation, and presence of the moderator effect) that impact the precision and accuracy of the variance parameters.
Use of the CuFe2O4/biochar composite to remove methylene blue, methyl orange and tartrazine dyes from wastewater using photo-Fenton process

In this study, CuFe2O4 ferrite was supported on biochar produced from malt biomass residues as a photocatalyst for degradation of methylene blue (MB), methyl orange (MO), and tartrazine (TZ) dyes. XRD, FT-IR, and FE-SEM were used to characterize the crystallinity and morphology of the samples. The characterization showed that the ferrite was uniformly supported on the surface of the biochar, confirming the formation of the composite. Degradation tests showed that CuFe2O4 degraded approximately 50, 47, and 62% of MB, MO, and TZ dyes, respectively, after 60 min of reaction. On the other hand, the CuFe2O4/biochar composite showed a significant increase in dye degradation, ~ 100%, for all three dyes. This increase in degradation efficiency may be due to less agglomeration of supported particles and due to decreased recombination of electron/hole pairs. Thus, results showed that the photocatalyst composite produced in this study is an effective alternative for removing dyes from wastewater.
[Diagnostics and treatment of clinically relevant paraneoplastic syndromes]

Paraneoplastic syndromes (PS) are rare disorders with often complex clinical manifestations that occur in association with a tumor without being triggered by direct tumor invasion or compression. They arise from tumor secretions of hormones, peptides or cytokines or from immune cross-reactivity between malignant and healthy tissue. They are categorized into endocrine, neurological, dermatological, rheumatological, and hematological PS. The PS most commonly occurs in small cell lung carcinoma but also in association with other respiratory tract tumors, gynecological tumors, and hematological malignancies. The PS can precede a tumor diagnosis, therefore timely diagnosis can improve the prognosis of a malignant disease. The diagnostics are based on the clinical presentation as well as diagnostic methods depending on the underlying pathogenesis. The most important treatment approach involves the best possible treatment of the tumor and a targeted treatment is only sometimes possible. This review focuses on the clinically most frequently encountered PS.
Environmental management in Ramsar designated wetland areas in Vietnam: studies from U Minh Thuong and Tram Chim national parks (Mekong Delta)

This study investigated the possibility of using remotely sensed data and field surveys for understanding the environmental management practices in two Ramsar sites - U Minh Thong and Tram Chim national parks - in the Vietnamese Mekong Delta. Enhanced agriculture, infrastructure development, changes in hydrological regime, forest fires, and natural resources exploitation are the key variables that caused the depletion of these two wetland areas. Land cover, particularly vegetation coverage, has been changed considerably during the post-war period and agriculture has been intensified in the surrounding areas of U Minh Thuong and Tram Chim wetlands. The current water management strategies in U Minh Thuong and Tram Chim were designated to ensure proper water circulation during the dry and wet seasons in a way helpful to agriculture in the buffer zones and to prevent forest fires during the dry season. It is found that the water management strategies to prevent forest fires in both the parks resulted in the accumulation of toxic agrochemicals within the park during the wet season. Both U Minh Thuong and Tram Chim wetlands are invaded by alien plant species which is threatening the natural biodiversity of the area. Proper monitoring and control of invasive species is necessary for protecting the natural biodiversity of these wetland ecosystems. Proper law enforcement and an interactive and inclusive wetland management should be practiced in order to conserve these valuable wetland ecosystems.
Autonomous near-field communication (NFC) sensors for long-term preventive care of fine art objects

We present the design and pilot trial of near-field communication (NFC) sensors for the long-term preventive care of fine art objects. This work was undertaken to address the unmet need for a permanent and unified object specific sensory and digital data labelling system for fine art objects that does not require large-scale wireless infrastructure. The sensor-tags are demonstrated in a six-month pilot study to evaluate the temperature and humidity buffering performance of a microclimate enclosure (MCE) constructed to protect a late C16th panel painting. The framed painting fitted with NFC sensor-tags was hung in a busy, and environmentally uncontrolled, Cambridge college dining hall to mimic a typically large, semipublic exhibition space. The resulting visibility of climatic conditions and the detailed analytics made possible from the data sets harvested from the NFC sensor-tags have provided invaluable information to the painting conservators. This study provides the first steps in understanding how object specific electronic sensor-tags will play a critical role in improving the display management, storage and long-term preventive care of fine art objects. (C) 2018 Elsevier B.V. All rights reserved.
Multi-view hypergraph neural networks for student academic performance prediction

Academic performance prediction is a fundamental and hot issue in educational data mining (EDM). Recently, researchers have proposed a series of effective machine learning (ML) based classification strategies to predict students' academic performance. However, prior arts are typically concerned about individual models but neglect the association among students, which might considerably have an effect on the integrity of the academic performance-related representations. Meanwhile, students' multi-viewing behavior contains complex relations among students. Therefore, we propose a Multi-View Hypergraph Neural Network (MVHGNN) for predicting students' academic performance. MVHGNN uses hypergraphs to construct high-order relations among students. The semantic information implied by multiple behaviors is consolidated through meta-paths. Further, a Cascade Attention Transformer (CAT) module is introduced to mine the weight of different behaviors by the self-attention mechanism. Our method is evaluated on real campus student behavioral datasets. The experimental results demonstrate that our method outperforms the state-of-the-art ones.