ethicalabs/Research-Intent-Collab huggingface dataset:
High-Performance Radix-2, 3 and 5 Parallel 1-D Complex FFT Algorithms for Distributed-Memory Parallel Computers

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An Efficient Missing Data Prediction Technique using Recursive Reliability-Based Imputation for Collaborative Filtering Recommender System

Collaborative filtering recommender system is utilized as a significant method to suggest products to users depending on their preferences. It is quite complicated when the user preference and rating data is sparse. Missing value occurs when there are no stored values for the specified dataset. Typical missing data are of three categories such as (i) Missing completely at random, (ii) Missing at random, and (iii) Missing not at random. The missing values in the dataset affect the accuracy and cause deprived prediction outcomes. In order to alleviate this issue, the data imputation method is exploited. Imputation is the process of reinstating the missing value with a substitute to preserve the data in a dataset. It involves multiple approaches to evaluate the missing value. In this paper, we reviewed the progression of various imputation techniques and their limitations. Furthermore, we endeavored k-recursive reliabilitybased imputation (k-RRI) to resolve the boundaries faced in existing approaches. Experimental results evince that the studied methodology appreciably improves the prediction accuracy of the recommendation system.
Plasma Growth Hormone Pulses Induce Male-biased Pulsatile Chromatin Opening and Epigenetic Regulation in Adult Mouse Liver

Abstract                 Sex-differences in plasma growth hormone (GH) profiles, pulsatile in males and persistent in females, regulate sex differences in hepatic STAT5 activation linked to sex differences in gene expression and liver disease susceptibility, but little is understood about the fundamental underlying, GH pattern-dependent regulatory mechanisms. Here, DNase hypersensitivity site (DHS) analysis of liver chromatin accessibility in a cohort of 18 individual male mice established that the endogenous male rhythm of plasma GH pulse-stimulated liver STAT5 activation induces dynamic, repeated cycles of chromatin opening and closing at several thousand liver DHS and comprises a novel mechanism conferring male bias to liver chromatin accessibility. Strikingly, a single physiological replacement dose of GH given to hypophysectomized male mice restored, within 30 min, liver STAT5 activity and chromatin accessibility at 83% of the pituitary hormone-dependent dynamic male-biased DHS. Sex-dependent transcription factor binding patterns and chromatin state analysis identified key genomic and epigenetic features distinguishing this dynamic, STAT5-driven mechanism of male-biased chromatin opening from a second GH-dependent mechanism operative at static male-biased DHS, which are constitutively open in male liver. Dynamic but not static male-biased DHS adopt a bivalent-like epigenetic state in female liver, as do static female-biased DHS in male liver, albeit using distinct repressive histone marks in each sex, namely, H3K27me3 at female-biased DHS in male liver, and H3K9me3 at male-biased DHS in female liver. Moreover, sex-biased H3K36me3 marks are uniquely enriched at static sex-biased DHS, which may serve to keep these sex-dependent hepatocyte enhancers free of H3K27me3 repressive marks and thus constitutively open. Pulsatile chromatin opening stimulated by endogenous, physiological hormone pulses is thus one of two distinct GH-determined mechanisms for establishing widespread sex differences in hepatic chromatin accessibility and epigenetic regulation, both closely linked to sex-biased gene transcription and the sexual dimorphism of liver function.
Fog Computing Distributing Data and Intelligence for Resiliency and Scale Necessary for IoT

The Internet of Everything (IoE) is more than a $19 trillion opportunity over 10 years. Fifty billions of devices will be connected to various networks in 2020. This is bringing new technical challenges in all domains and specifically in the data processing. Distributed intelligence is one of the key technological answers. We call it "fog computing." Fog can provide intelligent connection of people, processes, data, and things in hierarchical Internet of Things networks. By supplementing the cloud and providing intermediate layers of computation, networking, and storage, fog nodes can optimize IoE deployments---greatly enhancing latency, bandwidth, reliability, security, and overall IoE network performance. The article will analyze the architecture and main design choices of this technology.
CF4CF: Recommending Collaborative Filtering algorithms using Collaborative Filtering

Automatic solutions which enable the selection of the best algorithms for a new problem are commonly found in the literature. One research area which has recently received considerable efforts is Collaborative Filtering. Existing work includes several approaches using Metalearning, which relate the characteristics of datasets with the performance of the algorithms. This work explores an alternative approach to tackle this problem. Since, in essence, both are recommendation problems, this work uses Collaborative Filtering algorithms to select Collaborative Filtering algorithms. Our approach integrates subsampling landmarkers, which are a data characterization approach commonly used in Metalearning, with a standard Collaborative Filtering method. The experimental results show that CF4CF competes with standard Metalearning strategies in the problem of Collaborative Filtering algorithm selection.
Influencing the United Nations Security Council - the role of representative legitimacy : A qualitative comparison of elected members' influence in decisions made on the Syrian Conflict

This thesis investigates the topic of elected members of the United Nations Security Council by addressing the research question under what conditions is an elected member of the United Nations Security Council likely to influence decisions in maintaining international peace and security? In investigating the explanatory power of the theoretical argument ‘representative legitimacy’, which suggests that elected members making their proposed actions legitimate by anchoring these with the broader UN membership are more likely to face less opposition and thus increase the likelihood for influence, it tests the hypothesis an elected member anchoring its proposed actions with the broader UN membership is more likely to influence the UNSC decisions. The study is conducted through a structured, focused comparison of three elected members, New Zealand 2015-2016, Japan 2016-2017, and Sweden 2017-2018, and their influence in decisions made on the Syrian conflict. Chosen on a most similar case-design with dependent variable variation, and analyzed through resolutions, meeting records and statements, the empirics give partial support for the hypothesis. The results suggest that representative legitimacy can be a condition under which an elected member is likely to influence the Council, however, one case suggests that representative legitimacy is not necessary.
Evaluation of air quality in Sunway City, Selangor, Malaysia from a mobile monitoring campaign using air pollution micro-sensors

Due to the increase of the human population and the rapid industrial growth in the past few decades, air quality monitoring is essential to assess the pollutant levels of an area. However, monitoring air quality in a high-density area like Sunway City, Selangor, Malaysia is challenging due to the limitation of the local monitoring network. To establish a comprehensive data for air pollution in Sunway City, a mobile monitoring campaign was employed around the city area with a duration of approximately 6 months, from September 2018 to March 2019. Measurements of air pollutants such as carbon dioxide (CO2) and nitrogen dioxide (NO2) were performed by using mobile air pollution sensors facilitated with a GPS device. In order to acquire a more in-depth understanding on traffic-related air pollution, the measurement period was divided into two different time blocks, which were morning hours (8 a.m.-12 p.m.) and afternoon hours (3 p.m.-7 p.m.). The data set was analysed by splitting Sunway City into different zones and routes to differentiate the conditions of each region. Meteorological variables such as ambient temperature, relative humidity, and wind speed were studied in line with the pollutant concentrations. The air quality in Sunway City was then compared with various air quality standards such as Malaysian Air Quality Standards and World Health Organisation (WHO) guidelines to understand the risk of exposure to air pollution by the residence in Sunway City.
Integrating a Real-time Surgical Robot Dynamic Simulator with 3D Slicer

Background Medical robotics, particularly image-  guided robotic systems, have revolutionized the surgical  field by improving precision and accuracy. 3D Slicer(1),  an open-source platform, has become a crucial tool in  this field as it allows for visualization, processing, and  registration of 2D and 3D medical imaging data, making  it an essential component in current research in robotic  intervention(2) (3). However, there is a missing compo-  nent in 3D Slicer - a native physics engine for simulating  the interaction of a robot with the anatomy. AMBF(4),  an open-source software, was designed to address this  issue by simulating the kinematics, dynamics, and in-  teraction of complex surgical robots. By integrating 3D  Slicer and AMBF using Robot Operating System (ROS),  we can empower researchers to utilize both the extensive  capabilities of 3D Slicer for visualization, processing, and  registration of medical imaging data, and the physics-  based constraint of AMBF for simulating the interac-  tion of a robot with the anatomy. By combining these  two platforms, researchers will have a comprehensive tool  to study and develop projects in medical robotics, ulti-  mately contributing to the advancement of the field.
Landslide Early Warning Systems: Requirements and Solutions for Disaster Risk Reduction—India

AbstractGlobally the prevalence of landslides has increased, impacting more than 4.8 million people between 1998 and 2017 and reported more than 18,000 casualties [UNDP]. The scenario has worsened dramatically, and it has become imperative to develop early warning systems to save human life. This demands the need for systems that could identify the potential of imminent landslides and disseminate the information related to landslide initiation in real-time. This would provide the opportunity to save lives. However, globally the research on reliable end-to-end systems for early warning of landslides is still in its nascent stage. Therefore, this paper explores in detail the requirements for developing systems for real-time monitoring, detection, and early warning of landslides. An integrated solution for building the real-time landslide monitoring and early warning system to provide community-scale disaster resilience is also proposed. This solution integrates multiple modules such as a heterogeneous sensor system, data storage and management, event detection framework, alert dissemination, and emergency communication system to address issues such as capturing dynamic variability, managing multi-scale voluminous datasets, extracting key triggering information regarding the onset of possible landslide, multilevel alert dissemination, and robust emergency communication among the stakeholders respectively. The paper also presents two case studies of real-time landslide early warning systems deployed in North-eastern Himalayas and Western Ghats of India. These case studies demonstrate the approaches utilized for risk assessment, risk analysis, risk evaluation, risk visualization, risk control, risk communication, and risk governance. The results from the deployed system in the case study areas demonstrate the capability of the IoT system to gather Spatio-temporal triggers for multiple types of landslides, detection and decision of specific scenarios, and the impact of real-time data on mitigating the imminent disaster.
Modeling noise and error correction for Majorana-based quantum computing

Majorana-based quantum computing seeks to use the non-local nature of Majorana zero modes to store and manipulate quantum information in a topologically protected way. While noise is anticipated to be significantly suppressed in such systems, finite temperature and system size result in residual errors. In this work, we connect the underlying physical error processes in Majorana-based systems to the noise models used in a fault tolerance analysis. Standard qubit-based noise models built from Pauli operators do not capture leading order noise processes arising from quasiparticle poisoning events, thus it is not obviousa priorithat such noise models can be usefully applied to a Majorana-based system. We develop stochastic Majorana noise models that are generalizations of the standard qubit-based models and connect the error probabilities defining these models to parameters of the physical system. Using these models, we compute pseudo-thresholds for thed=5Bacon-Shor subsystem code. Our results emphasize the importance of correlated errors induced in multi-qubit measurements. Moreover, we find that for sufficiently fast quasiparticle relaxation the errors are well described by Pauli operators. This work bridges the divide between physical errors in Majorana-based quantum computing architectures and the significance of these errors in a quantum error correcting code.
Quantum error-corrected computation of molecular energies

We present the first demonstration of an end-to-end pipeline with quantum error correction (QEC) for a quantum computation of the electronic structure of molecular systems. We calculate the ground-state energy of molecular hydrogen, using quantum phase estimation (QPE) on qubits encoded with the $[[7, TRUE,3]]$ color code on Quantinuum H2-2. We obtain improvements in computational fidelity by (1) introducing several partially fault-tolerant (FT) techniques for the Clifford+$R_{Z}$ (arbitrary-angle single-qubit rotation) gate set, and (2) integrating Steane QEC gadgets for real-time error correction. In particular, the latter enhances the QPE circuits' performance despite the complexity of the extra QEC circuitry. The encoded circuits contain up to 1585 (546) fixed and 7202 (1702) conditional physical two-qubit gates (mid-circuit measurements), and $\sim$3900 ($\sim$760) total operations are applied on average. The energy $E$ is experimentally estimated to within $E - E_{\mathrm{FCI}} = 0.001(13)$ hartree, where $E_{\mathrm{FCI}}$ denotes the exact ground state energy within the given basis set. Additionally, we conduct numerical simulations with tunable noise parameters to identify the dominant sources of noise. We find that orienting the QEC protocols towards higher memory noise protection is the most promising avenue to improve our experimental results.
Chapter 10 One Medicine?: Advocating (Inter)disciplinarity at the Interfaces of Animal Health, Human Health, and the Environment

This chapter discusses the recent emergence of advocacy for 'One Health' (OH): the idea that greater interdisciplinarity across the domains of human and animal health research, clinical practice and policy is essential for addressing contemporary problems such as zoonotic disease, food safety, cancer and drug development. Over the past decade, the language of OH has been taken up by increasingly prominent actors in global health and biomedicine, including funders, international agencies and pharmaceutical companies; however, there has been a long history of veterinary led advocacy for similar ideas since the late 19th century. This longer history raises an immediate question: given that ideas of collaboration and convergence between human and veterinary medicine have been being advanced for such a long time, why has OH come to the fore at this particular point in time? This chapter analyses the emergence and growth of OH, following the key actors, events, disciplines, and agendas that have contributed to its increasing popularity, while tracing its origins in the histories of animal health, global development, and infectious disease. Using bibliometrics of key OH terms in academic journals, alongside qualitative analysis of academic, policy, and online documents, this chapter shows that while OH has been adopted by institutions across human and animal health, it is predominantly used by scientists publishing in veterinary science journals. This raises questions about the extent to which OH is interdisciplinary, to which actors and in which contexts: to what extent is it a 'top-down' or 'bottom-up; version of interdisciplinarity? The implications of these findings in the broader context of agenda-building across the life and environmental sciences of the early 21st century are then discussed.
Ecological Marine Units as a Framework for Collaborative Data Science and Knowledge Discovery

We present a data-derived, ecosystem mapping approach for the global ocean as commissioned by the Group on Earth Observations (GEO) and as a contribution to the Marine Biodiversity Observation Network (MBON). These ecological marine units (EMUs) are comprised of a global point mesh framework, created from over 52 million points from NOAA’s World Ocean Atlas with a spatial resolution of 1 by 1 degree (∼27 x 27 km at the equator) at 44 varying depths and a temporal resolution that is currently decadal. Each point carries attributes of chemical and physical oceanographic structure (temperature, salinity, dissolved oxygen, nitrate, silicate, phosphate) as likely drivers of many marine ecosystem responses. We used a k-means statistical clustering algorithm to identify physically distinct, relatively homogenous, volumetric regions within the water column (the EMUs). Backwards stepwise discriminant analysis determined if all of six variables contributed significantly to the clustering, and a pseudo F-statistic gave us an optimum number of clusters worldwide at 37. A major intent of the EMUs is to support marine biodiversity conservation assessments, economic valuation studies of marine ecosystem goods and services, and studies of ocean acidification and other impacts. As such, they represent a rich geospatial accounting framework for these types of studies, as well as for scientific research on species distributions. To further benefit the community and facilitate collaborate knowledge building, data products are shared openly and interoperably via www.esri.com/ecological-marine-units. This includes provision of 3D point mesh and EMU clusters at the surface, bottom, and within the water column in varying formats via download, web services or web apps, as well as generic algorithms and GIS workflows that scale from global to regional and local. Work is in progress to delineate EMUs at finer spatial and temporal resolutions and to include ocean currents and various biodiversity observations. A major aim is for the ocean science community members to move the research forward with higher-resolution data from their own field studies or areas of interest, with the original EMU project team assisting with GIS implementation (especially via a new online discussion forum), and hosting of additional data products as needed.
One Health or Three? Transmission modelling of                   <i>Klebsiella</i>                   isolates reveals ecological barriers to transmission between humans, animals and the environment

Abstract                                    The                   Klebsiella                   group is highly diverse both genetically and ecologically, being commonly recovered from humans, livestock, plants, soil, water, and wild animals. Many species are opportunistic pathogens, and can harbour diverse classes of antimicrobial resistance (AMR) genes.                   K. pneumoniae                   is responsible for a high public-health burden, due in part to the rapid spread of health-care associated clones that are non-susceptible to carbapenems.                   Klebsiella                   thus represents a highly pertinent taxon for assessing the risk to public health posed by animal and environmental reservoirs. Here we report an analysis of 6548 samples and 3,482 genome sequences representing 15                   Klebsiella                   species sampled over a 15-month period from a wide range of clinical, community, animal and environmental settings in and around the city of Pavia, in the northern Italian region of Lombardy. Despite carbapenem-resistant clones circulating at a high frequency in the hospitals, we find no genotypic or phenotypic evidence for non-susceptibility to carbapenems outside of the clinical environment. The non-random distribution of species and strains across sources point to ecological barriers that are likely to limit AMR transmission. Although we find evidence for occasional transmission between settings, hierarchical modelling and intervention analysis suggests that direct transmission from the multiple non-human (animal and environmental) sources included in our sample accounts for less than 1% of hospital disease, with the vast majority of clinical cases originating from other humans.
VitiBench Dataset Suite: Accessible Datasets for Teaching, Benchmarking and Prototyping Viticulture-Related Deep Learning

The VitiBench Dataset Suite is a collection of datasets designed to support computer vision applications in viticulture. It comprises two real-world grapevine leaf image datasets and one synthetic vineyard segmentation dataset. The first leaf dataset contains 472 RGB images representing six Vitis vinifera L. cultivars acquired between May and July 2017 at the University of Trás-os-Montes e Alto Douro experimental vineyards, Portugal, under controlled white-background conditions. The second dataset contains 815 RGB images from twelve grapevine cultivars acquired between July and September 2019 under natural vineyard conditions and varying illumination scenarios. The third dataset was generated using a procedural modelling framework, which automatically produces rural-like virtual environments. Experimental tests on the dataset suite confirm its adequacy to be reused in contexts such as educational activities, benchmarking studies, algorithm validation, and rapid prototyping of classification and semantic segmentation methods for digital ampelography, precision viticulture, and agricultural artificial intelligence.
Deconstruction of Sustainable Development Discourses: Avoiding Skepticism Pitfalls with a postmodern perspective

Sustainable development has been a concept, a movement, and a commitment, one of a kind. In its short lifetime, it has been subject to many changes and improvements. Many countries have joined under the umbrella of sustainable development and its goals, but the definition of sustainable development has remained ambiguous. In this paper, the ambiguity of this definition with a postmodern look has been studied to deconstruct it with the hope of finding the roots of skepticism about this concept. Here it has been argued that instead of ignoring the skepticism, we must acknowledge its causes and try to eliminate them. Nevertheless, the result of deconstruction is not a definitive answer it shows us the path to do this by making sure that any invention we hope might come under this vision was compatible with current rules and procedures at the same time. (The article is now accepted for publication in the journal: World Review of Science, Technology and Sustainable Development. Before publication, due to journal policy, the prior-to-peer-review manuscripts can not be publicly available.)