Miracle-dz/UniFAR-FTUs huggingface dataset:
Seawater Carbonate Chemistry Distributions Across the Eastern South Pacific Ocean Sampled as Part of the GEOTRACES Project and Changes in Marine Carbonate Chemistry Over the Past 20 Years

The US GEOTRACES Eastern Pacific Zonal Transect in 2013 provided an opportunity to complement studies of the sources, sinks, and internal cycling of trace elements and isotopes (TEIs) with observations of seawater carbon dioxide (CO2)–carbonate chemistry. Across the Peru-Tahiti section, very large horizontal gradients in surface/mixed layer dissolved inorganic carbon (DIC) and total alkalinity (TA) from the nutrient-rich, low-oxygen coastal upwelling region adjacent to Peru to the oligotrophic central Pacific. Near the coast of Peru, upwelling of CO2 rich waters from the oxygen deficient zone (ODZ) impinged at the surface with very high partial pressures of CO2 (pCO2; >800-1,200 µatm), and low pH (7.55 to 7.8). These waters were also undersaturated with respect to aragonite, a common calcium carbonate (CaCO3) mineral. These chemical conditions are not conducive to pelagic and shelf calcification, with shelf calcareous sediments vulnerable to CaCO3 dissolution, and to the future impacts of ocean acidification. A comparison to earlier data collected from 1991 to 1994 suggests that surface dissolved inorganic carbon (DIC) and pCO2 have increased by as much as 3% and 20%, respectively, while pH and saturation state for aragonite (Waragonite) have decreased by as much as 0.063 and 0.54, respectively. In intermediate waters (~200-500 m), dissolved oxygen has decreased (loss of up to -43 µmoles kg-1) and nitrate increased (gain of up to 5 µmoles kg-1) over the past twenty years and this likely reflects the westward expansion of the ODZ across the central Eastern South Pacific Ocean. Over the same period, DIC and pCO2 increased by as much as +45 µmoles kg-1 and +145 µatm, respectively, while pH and Waragonite decreased by -0.091 and -0.45, respectively. Such rapid change in pH and CO2–carbonate chemistry over the past twenty years has implications for changing the thermodynamics and solubility of intermediate water TEIs, but also for the marine ecosystem of the upper waters, especially for the vertically migrating community present in the eastern South Pacific.
Estimating Prevalence of Hepatitis C Virus Infection in the United States, 2013‐2016

Hepatitis C virus (HCV) infection is the most commonly reported bloodborne infection in the United States, causing substantial morbidity and mortality and costing billions of dollars annually. To update the estimated HCV prevalence among all adults aged ≥18 years in the United States, we analyzed 2013‐2016 data from the National Health and Nutrition Examination Survey (NHANES) to estimate the prevalence of HCV in the noninstitutionalized civilian population and used a combination of literature reviews and population size estimation approaches to estimate the HCV prevalence and population sizes for four additional populations: incarcerated people, unsheltered homeless people, active‐duty military personnel, and nursing home residents. We estimated that during 2013‐2016 1.7% (95% confidence interval [CI], 1.4‐2.0%) of all adults in the United States, approximately 4.1 (3.4‐4.9) million persons, were HCV antibody‐positive (indicating past or current infection) and that 1.0% (95% CI, 0.8‐1.1%) of all adults, approximately 2.4 (2.0‐2.8) million persons, were HCV RNA–positive (indicating current infection). This includes 3.7 million noninstitutionalized civilian adults in the United States with HCV antibodies and 2.1 million with HCV RNA and an estimated 0.38 million HCV antibody‐positive persons and 0.25 million HCV RNA–positive persons not part of the 2013‐2016 NHANES sampling frame. Conclusion: Over 2 million people in the United States had current HCV infection during 2013‐2016; compared to past estimates based on similar methodology, HCV antibody prevalence may have increased, while RNA prevalence may have decreased, likely reflecting the combination of the opioid crisis, curative treatment for HCV infection, and mortality among the HCV‐infected population; efforts on multiple fronts are needed to combat the evolving HCV epidemic, including increasing capacity for and access to HCV testing, linkage to care, and cure.
Improved smoothed analysis of multiobjective optimization

We present several new results about smoothed analysis of multiobjective optimization problems. Motivated by the discrepancy between worst-case analysis and practical experience, this line of research has gained a lot of attention in the last decade. We consider problems in which d linear and one arbitrary objective function are to be optimized over a set S⊆{0,1}n of feasible solutions. We improve the previously best known bound for the smoothed number of Pareto-optimal solutions to O(n2dφd), where φ denotes the perturbation parameter. Additionally, we show that for any constant c the c-th moment of the smoothed number of Pareto-optimal solutions is bounded by O((n2dφd)c). This improves the previously best known bounds significantly. Furthermore, we address the criticism that the perturbations in smoothed analysis destroy the zero-structure of problems by showing that the smoothed number of Pareto-optimal solutions remains polynomially bounded even for zero-preserving perturbations. This broadens the class of problems captured by smoothed analysis and it has consequences for non-linear objective functions. One corollary of our result is that the smoothed number of Pareto-optimal solutions is polynomially bounded for polynomial objective functions.
Findings of the WMT 2018 Shared Task on Quality Estimation

We report the results of the WMT18 shared task on Quality Estimation, i.e. the task of predicting the quality of the output of machine translation systems at various granularity levels: word, phrase, sentence and document. This year we include four language pairs, three text domains, and translations produced by both statistical and neural machine translation systems. Participating teams from ten institutions submitted a variety of systems to different task variants and language pairs.
Screening of Some Drugs of Abuse in Outpatient Clinics in Al-Azhar University Hospitals

Background: Substance, which use among the youth worldwide is a major problem that has elicited concern from different individuals and groups. In Egypt, drug dependence is considered one of the serious problems that worry both the people and government; however, epidemiological data on drug dependence are still few. Drug addiction is one of the serious problems that worry the Egyptian government, as it deals with young people within the age of work and productivity. It may lead to many problems such as bad social adaptation, decreasing productivity at work or dismissing from job. Aim of the work: The study aims to give an information about the degree of prevalence of a social problem like drugs of abuse in ages more than 18 years in the Egyptian society from May 2016 to April 2017. Methods: This study was a cross sectional study which done on random urine sample of 500 persons in age of more than 18 years coming to outpatient clinics of Al-Azhar university hospitals namely El-Hussein and Bab Elshaaria university hospitals after taking full history for each one (patient sheet) with informed written consent. Results: This study found that prevalence of drugs abuse among all studied sample, negative were 215 (43.0%), and positive were 285 (57.0%). Regarding prevalence of types of drugs abuse among all studied sample; negative was 215 (43.0%), THC 105 (21.0%), THC & Opiate 15 (3.0%), THC & tramadol were 40 (8.0%), THC, tramadol & opiate 20 (4.0%), tramadol 55 (11.0%) and tramadol & opiate were 50 (10.0%). Percent of males was significantly higher among the studied groups positive than negative (100.0%, 95.3% respectively, p =0.000). Mean value of age was significantly lower among the studied groups positive than negative (37.96, 43.88 respectively, p <0.000). Conclusion: prevalence of drugs abuse among all studied sample, negative was 215 (43.0%), and positive were 285 (57.0%).
Urine Levels of Matrix Metalloproteinases and Tissue Inhibitor of Metalloproteinases in Children with Type 1 Diabetes Mellitus

Objective: Histopathological changes in the kidney in type 1 diabetes mellitus (T1DM) begin before detection of microalbuminuria. Therefore, there is interest in finding a better biomarker for the early detection of diabetic kidney injury. The aim of this present study was to determine whether urinary indicators of fibrosis are detectable early in the development of T1DM in children and if they may predict progressive renal injury. Methods: Urinary matrix metalloproteinase 2 and 9 (MMP2 and MMP9), tissue inhibitor of metalloproteinase 1 and 2 (TIMP1 and TIMP2) and transforming growth factor-β1 (TGF-β1) were assessed in 33 patients with T1DM with normal renal functions and in 24 healthy controls. Microalbuminuria was not present in the patient group with the exception of three patients. The results were adjusted to urine creatinine (Cr) and the differences between patients and controls were evaluated. These measurements were repeated after one year and the results were compared with the first year results. Results: Urine MMP2/Cr, MMP9/Cr, TIMP1/Cr, TIMP2/Cr, TGF-β1/Cr were not different between the patient and control groups (p>0.05). There were also no significant differences between the first and second year results for these biomarkers (p>0.05). None of these parameters were correlated with hemoglobin A1c, body mass index and duration of T1DM. Interestingly, all parameters were negatively correlated to age of onset of T1DM (p<0.05). Conclusion: Our findings suggest that urinary biomarkers of fibrosis do not show an increase in diabetic children without microalbuminuria. The results also indicate that the risk of early fibrosis may increase as age of onset of T1DM decreases.
A CMOS RF-Powered Tag with Sensing and Localization Capabilities

This paper presents a battery-less CMOS integrated RF tag with sensing and localization capabilities. In order to allow tag addressing, communication, and localization by external readers, the IC performs a back-scattering modulation of a sequence of received UWB pulses according to specific spreading codes. For this purpose a UWB RF switch is designed and connected to the UWB antenna. The adoption of backscattering is integral to achieve ultra-low power wireless communication. The use of quasi-orthogonal spreading codes allows communication and localization with multiple tags at the same time. The IC includes a low-power sensing interface suitable for internal temperature sensing or external capacitive sensors. The power required for operation is scavenged from an UHF signal and converted by means of an internal RF rectifier and of a dynamically reconfigurable charge pump circuit. An on-chip micropower oscillator clocks a digital control circuit. The circuit is implemented in a $0.18 \mu \mathrm{m}$ CMOS process. The architecture of the IC and preliminary test results are disclosed.
Improving Power Efficiency Through Fine-Grain Performance Monitoring in HPC Clusters

Nowadays, power and energy consumption are of paramount importance. Further, reaching the Exascale target will not be possible in the short term without major breakthroughs in software and hardware technologies to meet power consumption constraints. In this context, this papers discusses the design and implementation of a system-wide tool to monitor, analyze and control power/energy consumption in HPC clusters. We developed a lightweight tool that relies on a fine-grain sampling of two CPU performance metrics: instructions throughput (IPC) and last level cache bandwidth. Thanks to the information provided by these metrics about hardware resources' activity, and using DVFS to control power/performance, we show that it is possible to achieve up to 16% energy savings at the cost of less than 3% performance degradation on real HPC applications.
Initial Study on the Architecture of Field Observation in 5G Era

Interoperability, power supply, and system maintenance are three important topics for field observation. Technologies associated with the Internet of Things (IOT) and 5G have great potential for field observation system improvement. In this manuscript, we discuss the architecture of field observation in the 5G era. Gateway, syntax and semantic interoperability are important aspects for system performance improvement. We developed a low-power data collector with sensing, communication, and power supply components, and we introduce our initial study on a real-world environment observation system implementation and experiment.
A Smart Post-Rectification Algorithm Based on an ANN Considering Reflectivity and Distance for Indoor Scenario Reconstruction

Indoor scene reconstruction is important for robot positioning and navigation in scenario reconstruction, especially in constructing a semantic map. In previous research, RGB-D cameras have been utilized to obtain a semantic map. However, because of indoor objects and depth sensors, the accuracy and precision of the depth values could be improved, which is a key factor in reconstructing indoor scenarios. Moreover, there is a relationship between reflectivity and depth accuracy. Therefore, to obtain depth information that is better than that obtained in our previous research, we present a smart post-rectification algorithm based on an artificial neural network (ANN). The algorithm improves the accuracy and precision of depth values by simultaneously considering reflectivity, distances, and different mechanisms of measuring depth. First, we analyze the RGB-D cameras’ characteristics, including the pinhole camera model, lens distortions, and the types of error factors due to the types of RGB-D cameras used. Then, this paper proposes a smart post-rectification algorithm for depth images based on an ANN considering the depth error caused by reflectivity, the distance-related depth error, and different mechanisms for measuring depth. Finally, we perform experiments to evaluate the accuracy and precision of the proposed post-rectification approach by using different types of depth sensors. To evaluate the performance of our proposed algorithm, the proposed approach is applied to RGB-D SLAM, which is tested in different indoor environments. The experimental results show that applying our post-rectification algorithm to indoor scenario reconstruction can result in more accurate and more detailed 3-D reconstruction of objects than other state-of-the-art methods, highlighting the robustness and efficiency of our proposed algorithm.
Safe Spaces and Safe Places

Transgender individuals in the United States face significant threats to interpersonal safety; however, there has as yet been relatively little research in the HCI and CSCW communities to document transgender individuals' experiences of technology-mediated safety and harm. In this study, we interviewed 12 transgender and non-binary individuals to understand how they find, create, and navigate safe spaces using technology. Managing safety was a universal concern for our transgender participants, and they experienced complex manifestations of harm through technology. We found that harmful experiences for trans users could arise as targeted or incidental affronts, as sourced from outsiders or insiders, and as directed against individuals or entire communities. Notably, some violations implicated technology design, while others tapped broader social dynamics. Reading our findings through the notions of 'space" and 'place," we unpack challenges and opportunities for building safer futures with transfolk, other vulnerable users, and their allies.
Towards parallel migrating birds framework for feature subset problem

Migrating birds optimization is of the most promising population based techniques applied to many real world applications in particular the feature subset problem. The sequential version of feature selection based on migrating birds optimization achieves good results but consumes a high computational time. To tackle this problem, we propose a parallel population feature selection approach based on migrating birds optimization (MBO) adapted to big data systems. This approach follows the paradigm of MapReduce and we describe different phases to implement it using Hadoop platform.
Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques

Abstract Background: In this study, a deep convolutional neural network (CNN)-based automatic segmentation technique was applied to multiple organs at risk (OARs) depicted in computed tomography (CT) images of lung cancer patients, and the results were compared with those generated through atlas-based automatic segmentation. Materials and methods: An encoder-decoder U-Net neural network was produced. The trained deep CNN performed the automatic segmentation of CT images for 36 cases of lung cancer. The Dice similarity coefficient (DSC), the mean surface distance (MSD) and the 95% Hausdorff distance (95% HD) were calculated, with manual segmentation results used as the standard, and were compared with the results obtained through atlas-based segmentation. Results: For the heart, lungs and liver, both the deep CNN-based and atlas-based techniques performed satisfactorily (average values: 0.87 < DSC < 0.95, 1.8 mm < MSD < 3.8 mm, 7.9 mm < 95% HD <11 mm). For the spinal cord and the oesophagus, the two methods had statistically significant differences. For the atlas-based technique, the average values were 0.54 < DSC < 0.71, 2.6 mm < MSD < 3.1 mm and 9.4 mm < 95% HD <12 mm. For the deep CNN-based technique, the average values were 0.71 < DSC < 0.79, 1.2 mm < MSD <2.2 mm and 4.0 mm < 95% HD < 7.9 mm. Conclusion: Our results showed that automatic segmentation based on a deep convolutional neural network enabled us to complete automatic segmentation tasks rapidly. Deep convolutional neural networks can be satisfactorily adapted to segment OARs during radiation treatment planning for lung cancer patients.
The role of trust in health information from medical authorities in accepting the HPV vaccine among African American parents

ABSTRACT This research examines how and why trust in health information from medical authorities (i.e., doctors or health care professionals and government health agencies) predicts acceptance of the HPV vaccine for one’s child among African American parents. A survey of African American parents recruited from community venues revealed that low trust in health information from government health agencies was associated with less favorable attitudes and intentions toward vaccinating their child against HPV. Trust in health information from a doctor or health care professional did not predict vaccine acceptance. Mediation analyses indicated that the relationship between trust in health information from government health agencies and vaccine acceptance was partially mediated by perceived vaccine efficacy. Implications of the findings on communicating to the African American community about the HPV vaccine are discussed.
EyeRing: an eye on a finger

Finger-worn devices are a greatly underutilized form of interaction with the surrounding world. By putting a camera on a finger we show that many visual analysis applications, for visually impaired people as well as the sighted, prove seamless and easy. We present EyeRing, a ring mounted camera, to enable applications such as identifying currency and navigating, as well as helping sighted people to tour an unknown city or intuitively translate signage. The ring apparatus is autonomous, however our system also includes a mobile phone or computation device to which it connects wirelessly, and an earpiece for information retrieval. Finally, we will discuss how different finger worn sensors may be extended and applied to other domains.
DAGON: Technology binding and local optimization by DAG matching

Technology binding is the process of mapping a technology independent description of a circuit into a particular technology. This paper outlines a formalism of this problem and offers a solution to the problem in terms of matching patterns, describing technology specific cells and optimizations, against a technology independent circuit represented as a directed acyclic graph. This solution is implemented in DAGON. DAGON rests on a firm algorithmic foundation, and is able to guarantee locally optimal matches against a set of over three thousand patterns. DAGON is an integral part of a synthesis system that has been found to provide industrial quality solutions to real circuit design problems.
Compressed Sensing Based Seizure Detection for an Ultra Low Power Multi-core Architecture

Extracting information from brain signals in advanced Brain Machine Interfaces (BMI) often requires computationally demanding processing. The complexity of the algorithms traditionally employed to process multi-channel neural data, such as Principal Component Analysis (PCA), dramatically increases while scaling-up the number of channels and requires more power-hungry computational platforms. This could hinder the development of low-cost and low-power interfaces which can be used in wearable or implantable real-time systems. This work proposes a new algorithm for the detection of epileptic seizure based on compressively sensed EEG information, and its optimization on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. With respect to traditional algorithms based on PCA, the proposed approach reduces the computational complexity by 4.4x in ARM Cortex M4-based MCU. Implementing this algorithm on Mr.Wolf platform allows to detect a seizure with 1 ms of latency after acquiring the EEG data for 1 s, within an energy budget of 18.4 µJ. A comparison with the same algorithm on a commercial MCU shows an improvement of 6.9x in performance and up to 18.4x in terms of energy efficiency.
K-SVD with a real L0 optimization: application to image denoising

This paper deals with sparse coding for dictionary learning in sparse representations. Because sparse coding involves an (cid:96) 0 - norm, most, if not all, existing solutions only provide an approximate solution. Instead, in this paper, a real (cid:96) 0 optimization is considered for the sparse coding problem providing a global optimal solution. The proposed method reformulates the optimization problem as a Mixed-Integer Quadratic Program (MIQP), allowing then to obtain the global optimal solu-tion by using an off-the-shelf solver. Because computing time is the main disadvantage of this approach, two techniques are proposed to improve its computational speed. One is to add suitable constraints and the other to use an appropriate initialization. The results obtained on an image denoising task demonstrate the feasibility of the MIQP approach for processing well-known benchmark images while achieving good performance compared with the most advanced methods.