ethicalabs/Research-Intent-Collab huggingface dataset:
Fast evaluation of far-field signals for time-domain wave propagation

Time-domain simulation of wave phenomena on a finite computational domain often requires a fictitious outer boundary. An important practical issue is the specification of appropriate boundary conditions on this boundary, often conditions of complete transparency. Attention to this issue has been paid elsewhere, and here we consider a different, although related, issue: far-field signal recovery. Namely, from smooth data recorded on the outer boundary we wish to recover the far-field signal which would reach arbitrarily large distances. These signals encode information about interior scatterers and often correspond to actual measurements. This article expresses far-field signal recovery in terms of time-domain convolutions, each between a solution multipole moment recorded at the boundary and a sum-of-exponentials kernel. Each exponential corresponds to a pole term in the Laplace transform of the kernel, a finite sum of simple poles. Greengard, Hagstrom, and Jiang have derived the large-$\ell$ (spherical-harmonic index) asymptotic expansion for the pole residues, and their analysis shows that, when expressed in terms of the exact sum-of-exponentials, large-$\ell$ signal recovery is plagued by cancellation errors. Nevertheless, through an alternative integral representation of the kernel and its subsequent approximation by a {\em smaller} number of exponential terms (kernel compression), we are able to alleviate these errors and achieve accurate signal recovery. We empirically examine scaling relations between the parameters which determine a compressed kernel, and perform numerical tests of signal teleportation from one radial value $r_1$ to another $r_2$, including the case $r_2=\infty$. We conclude with a brief discussion on application to other hyperbolic equations posed on non-flat geometries where waves undergo backscatter.
Facilitating bias in cost-effectiveness analysis: CHEERS 2022 and the creation of assumption-driven imaginary value claims in health technology assessment [version 1; peer review: 3 approved]

The current standards for health technology cost-effectiveness assessment rest on the creation of lifetime assumption-driven modeled simulations for imaginary pricing and consequent patient access recommendations. A recent BMJ paper reports a detailed assessment of 8,192 cost-effectiveness analyses, concluding that industry-sponsored modeled claims were more likely to publish incremental cost-effectiveness ratios (ICERs) below a USD 50,000 threshold than non-industry sponsored studies, supporting the claim that the product was cost-effective. This is unsurprising; indeed, the opposite can occur with a modeled claim deliberately resulting in ICER is excess of USD 50,000. This methodology is well entrenched with the recently published Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 guidance for creating imaginary cost-effectiveness modeled claims ensuring the opportunity for deliberately manipulated cost-effectiveness claims. This overlooks limitations imposed by fundamental measurement, rendering assumption-driven simulations redundant analytical exercises. Manipulation of ICERs and claims for cost-effectiveness are mathematically impossible; including cost-utility thresholds, because the preference or utilities supporting creation of quality-of-life years (QALYs) are ordinal scores. Nevertheless, with the promotion of CHEERS 2022, the belief in imaginary modeled value claims is both facilitated and reinforced. With CHEERS 2022, there is a concerted effort, largely in self-interest, to perpetuate the current belief system. This is a self-defeating strategy. Rather than admitting to the deficiencies of assumption-driven simulated imaginary claims, leaders are maintaining that health system decision makers can ignore standards of normal science and fundamental measurement in value claims for pharmaceutical products. This disregard of standards that are commonplace in the sciences and mainstream social sciences perpetuates the opportunity for self-serving modeled claims; where models are a marketing vehicle leading to sponsored systematic bias in formulary submissions. This supports the need for a NEW START paradigm for health technology assessment, focusing on evaluable single-attribute value claims, meeting the required standards for normal science and fundamental measurement.
Building public trust and confidence in secondary use of health data for healthcare improvement and research: a qualitative study pre-protocol [version 2; peer review: 2 approved]

Background  Secondary use of health data provides opportunities to drive improvements in healthcare provision, personalised medicine, comparative effectiveness research, health services innovation, and policy and practice. However, secondary data use requires compliance with relevant legislation, implementation of technical safeguards, ethical data management, and respect for data sharers. Existing evidence suggests widespread support for secondary use of health data among the public, which co-exists with concerns about privacy, confidentiality and misuse of data. Balancing the protection of individuals’ rights against the use of their health data for societal benefits is of vital importance, and trust underpins this process. The study protocol explores how to build public trust and confidence in the secondary use of health data through all key stakeholder groups in Ireland, towards developing a culture that promotes a safe and trustworthy use of data. Methods  This study will adopt a qualitative cross-sectional approach conducted in accordance with the Consolidated Criteria for Reporting Qualitative Research COREQ guidelines. Participants in the study will include academics and researchers; healthcare professionals, data protection, ethics and privacy experts and data controllers; pharmaceutical industry and patients and public. Purposive and convenience sampling techniques will be utilised to recruit the participants, and data will be collected utilizing focus groups that may be supplemented with semi-structured interviews. Data will be coded by themes using reflexive thematic analysis (TA) and collective intelligence (CI) will be convened post-analysis to explore the preliminary findings with the participants. Ethics and Dissemination  Ethical approval was obtained from the Royal College of Surgeons in Ireland Research Ethics Committee (REC202208013). Final data analysis and dissemination is expected by Q1 2024. Findings will be disseminated through peer-reviewed journal publications, presentations at relevant conferences, and other academic, public and policy channels. Lay summaries will be designed for Public and Patient Involvement (PPI) contributors and general public.
Approximation algorithms for optimization of real-valued general conjugate complex forms

Complex polynomial optimization has recently gained more and more attention in both theory and practice. In this paper, we study the optimization of a real-valued general conjugate complex form over various popular constraint sets including the m-th roots of complex unity, the complex unit circle, and the complex unit sphere. A real-valued general conjugate complex form is a homogenous polynomial function of complex variables as well as their conjugates, and always takes real values. General conjugate form optimization is a wide class of complex polynomial optimization models, which include many homogenous polynomial optimization in the real domain with either discrete or continuous variables, and Hermitian quadratic form optimization as well as its higher degree extensions. All the problems under consideration are NP-hard in general and we focus on polynomial-time approximation algorithms with worst-case performance ratios. These approximation ratios improve previous results when restricting our problems to some special classes of complex polynomial optimization, and improve or equate previous results when restricting our problems to some special classes of polynomial optimization in the real domain. These algorithms are based on tensor relaxation and random sampling. Our novel technical contributions are to establish the first set of probability lower bounds for random sampling over the m-th root of unity, the complex unit circle, and the complex unit sphere, and propose the first polarization formula linking general conjugate forms and complex multilinear forms.
Taming the wild in impartial combinatorial games

We introduce a misere quotient semigroup construction in impartial combinatorial game theory, and argue that it is the long-sought natural generalization of the normal-play Sprague-Grundy theory to misere play. Along the way, we illustrate how to use the theory to describe complete analyses of two wild taking and breaking games.
Reform of the welfare state or dreams about universal basic income?

What does the future hold for the welfare state? An assessment of its ongoing viability in the years to come, and challenge to the idea that it would – or should – be replaced in the foreseeable future by a universal basic income.
Dispute Settlement between Indonesia and Australia on A4 Copy Paper AntiDumping Case by World Trade Organization (W.T.O.)

The World Trade Organisation (WTO) has rendered the resolution of the A4 copy paperanti-dumping dispute between Indonesia and Australia a matter of worldwide significancein recent times. The dispute originated when Australia levied anti-dumping tariffs on A4copy paper imported from Indonesia. Consultations were conducted between Indonesia andAustralia, but, a consensus could not be achieved. Indonesia subsequently initiated legalproceedings before the World Trade Organisation (WTO), which ultimately rendered averdict in favour of Indonesia's claim against Australia for the imposition of an antidumping duty (BMAD) on A4 copy paper originating from Indonesia. An examination ofdispute settlement proceedings reveals that the World Trade Organisation (WTO) aims toguarantee that anti-dumping measures do not infringe against WTO regulations. Thisarticle will provide a detailed analysis of the World Trade Organisation (WTO) resolutionof the A4 copy paper anti-dumping conflict between Indonesia and Australia, and itsconsequences for international commerce.
An early health technology assessment of 3D anatomic models in pediatric congenital heart surgery: potential cost-effectiveness and decision uncertainty

Background: Three-dimensional anatomic models have been used for surgical planning and simulation in pediatric congenital heart surgery. This research is the first to evaluate the potential cost-effectiveness of 3D anatomic models with the intent to guide surgeons and decision makers on its use.Method: A decision tree and subsequent Markov model with a 15-year time horizon was constructed and analyzed for nine cardiovascular surgeries. Epidemiological, clinical, and economic data were derived from databases. Literature and experts were consulted to close data gaps. Scenario, one-way, threshold, and probabilistic sensitivity analysis captured methodological and parameter uncertainty.Results: Incremental costs of using anatomical models ranged from -366€ (95% credibility interval: -2595€; 1049€) in the Norwood operation to 1485€ (95% CI: 1206€; 1792€) in atrial septal defect repair. Incremental health-benefits ranged from negligible in atrial septal defect repair to 0.54 Quality Adjusted Life Years (95% CI: 0.06; 1.43) in truncus arteriosus repair. Variability in the results was mainly caused by a temporary postoperative quality-adjusted life years gain.Conclusion: For complex operations, the implementation of anatomic models is likely to be cost-effective on a 15 year time horizon. For the right indication, these models thus provide a clinical advantage at an acceptable cost.
I. AFRICAN COURT ON HUMAN AND PEOPLES' RIGHTS, <i>AFRICAN COMMISSION ON HUMAN AND PEOPLES' RIGHTS V GREAT SOCIALIST PEOPLE'S LIBYAN ARAB JAMAHIRIYA</i>, ORDER FOR PROVISIONAL MEASURES 25 MARCH 2011

Africa has been struggling for years to establish a mechanism of human rights protection comparable to other international and regional mechanisms. Illiteracy and the low standards of economic development and social welfare, especially in rural areas, as well as the absence of financial resources were certainly not the best grounds to build on. Moreover, as Nmehielle notes, the creation of a human rights mechanism in Africa was equally hinged on other questions, more controversial ones, such as the existence of the concept of ‘law’ and ‘rights’ in pre-colonial Africa.1 In this respect, a Western-style mechanism of human rights protection would be naturally perceived with suspicion, as a form of foreign intervention.
Separate or joint? Estimation of multiple labels from crowdsourced annotations

Artificial intelligence techniques aimed at more naturally simulating human comprehension fit the paradigm of multi-label classification. Generally, an enormous amount of high-quality multi-label data is needed to form a multi-label classifier. The creation of such datasets is usually expensive and time-consuming. A lower cost way to obtain multi-label datasets for use with such comprehension simulation techniques is to use noisy crowdsourced annotations. We propose incorporating label dependency into the label-generation process to estimate the multiple true labels for each instance given crowdsourced multi-label annotations. Three statistical quality control models based on the work of Dawid and Skene are proposed. The label-dependent DS (D-DS) model simply incorporates dependency relationships among all labels. The label pairwise DS (P-DS) model groups labels into pairs to prevent interference from uncorrelated labels. The Bayesian network label-dependent DS (ND-DS) model compactly represents label dependency using conditional independence properties to overcome the data sparsity problem. Results of two experiments, "affect annotation for lines in story" and "intention annotation for tweets", show that (I) the ND-DS model most effectively handles the multi-label estimation problem with annotations provided by only about five workers per instance and that (2) the P-DS model is best if there are pairwise comparison relationships among the labels. To sum up, flexibly using label dependency to obtain multi-label datasets is a promising way to reduce the cost of data collection for future applications with minimal degradation in the quality of the results.
REMOTE SENSING APPLICATIONS IN AGRICULTURE AND SOIL SCIENCE PART II: REMOTE SOIL MONITORING: ORGANIZATION OF SOIL MONITORING BASED ON REMOTE SENSING IMAGERY

A paper addressing general issues of concern in organizing a system of soil monitoring based on remote sensing imagery covers such topics as general principles which must be observed, the selection of indices optimizing the acquisition of useful information, and the adoption of suitable methodologies. A subsequent section of the paper focuses on the types of information to be derived from soil monitoring in arid regions and areas susceptible to heightened soil erosion. Translated by Edward Torrey, Alexandria, VA 22308 from: G. V. Dobrovol'skiy and V L. Andronikov, eds., Aerokosmicheskiye metody v pochvovedenii i ikh ispol'zovaniye v sel'skom khozyaystve: sbornik nauchnykh trudov [Remote Sensing Methods in Soil Science and Their Utilization in Agriculture: A Collection of Scientific Works]. Moscow: Nauka, 1990, pp. 154–161.
Automatic flood detection from Sentinel-1 data using Deep learning: Demonstration of NASA-ETCI Benchmark datasets.

Floods are the most frequent, costliest natural disasters having devastating consequences on people, infrastructure, and the ecosystem. The accurate and rapid mapping of the flooded areas becomes more crucial when floods strike densely populated cities. During flood events near real-time satellite imagery has been proven to be an efficient management tool for disaster management authorities. However one of the challenges is accurate classification and segmentation of flooded water and permanent water. Binary segmentation using the threshold split-based method is commonly used in this regard, however, the generalization ability of this method is limited due to the effects of backscatter, geographical area, and time of image collection. Recent advancements in deep learning algorithms for image segmentation has demonstrated the excellent potential of Convolutional Neural Networks(CNN) for improving flood detection, although there have been limited studies in this domain due to the lack of large scale labeled flood event dataset. In this project, we present a U-net based deep learning approach by leveraging publicly available Sentinel-1 dataset provided jointly by NASA Interagency Implementation and Advanced Concepts Team and IEEE GRSS Earth Science Informatics Technical Committee. Dataset is composed of 66,810 tiles of 256×256 pixels, distributed respectively across the training, validation and test sets and cover flood events from Nebraska, North Alabama, Bangladesh, Red River North and Florence. Specifically we proposed an Unet architecture based convolutional neural network (CNN) with a backbone of EfficientNetb7, trained against the dataset. We then evaluated the performance of the model with multiple training, testing and validation. Two evaluation methods - Intersection over Union (IOU) and F-Score are adopted to evaluate the model performance. During testing, the model achieved the meanIOU score of 75.06% and F-Score of 74.98%. We hope to further improve the performance of the network by performing hyper-parameter tuning and to develop a model which can be used for near-real-time flood mapping.
STRATEGIES TO IMPROVE THE SUSTAINABILITY IN PROMOTING TRANSPARENCY, ACCOUNTABILITY AND ANTI-CORRUPTION: AN IMAGINARY DIALOGUE

The paper is an imaginary dialogue. The dialogue is between two professionals: SDGist and Auditor. To bring up their imagination, postmodernism is used to explore the dialogue. Both are trying to discuss the current issues on SDGs. The objective of the paper is to explicate strategies to improve SDGs in promoting transparency, accountability and anti-corruption, which is in particular to promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels, especially on the Goal 16.4 to 16.6.The result of the dialogue, there are 9 strategies to improve the SDGs in promoting transparency, accountability and anti-corruption. They are the ICSP on SDGs Implementation: Socializing the Sustainability Reporting, implementing of e-government, promoting more investment, fitting policies nationally and internationally relevant to the goal 16.4 to 16.6, supporting inclusive growth and well being, ensuring the 5Psâ€™ sustainability, promoting partnerships, strengthening data availability and capacity, and facilitating follow up and review.
Deep-learning classifications of galaxy morphologies in large JWST surveys

Investigations of AGN host galaxies including their merger history and morphology have been conducted up to z ~ 2. However, new JWST surveys such as COSMOS-Web and CEERS permit investigation of the properties of AGN host galaxy populations in rest-frame optical between 0.5 < z < 4; this spans the entire cosmic noon period where both SMBH growth and galaxy star formation rates peak. We use the deep-learning classifier ‘Zoobot’ to determine galaxy morphologies in the aforementioned surveys. This is conducted via a transfer learning method that inherits prior training of Zoobot on ground-based imagery. Predictions of the JWST morphologies are then applied to the large multi-wavelength imaging set of COSMOS-Web, after being fine-tuned using Galaxy Zoo volunteer classifications of CEERS images. The predictive capabilities of Zoobot will be discussed, as well as how well it compares to human volunteer classifications, and potential use cases for the fine-tuned model.
Memetic firefly algorithm for combinatorial optimization

Firefly algorithms belong to modern meta-heuristic algorithms inspired by nature that can be successfully applied to continuous optimization problems. In this paper, we have been applied the firefly algorithm, hybridized with local search heuristic, to combinatorial optimization problems, where we use graph 3-coloring problems as test benchmarks. The results of the proposed memetic firefly algorithm (MFFA) were compared with the results of the Hybrid Evolutionary Algorithm (HEA), Tabucol, and the evolutionary algorithm with SAW method (EA-SAW) by coloring the suite of medium-scaled random graphs (graphs with 500 vertices) generated using the Culberson random graph generator. The results of firefly algorithm were very promising and showed a potential that this algorithm could successfully be applied in near future to the other combinatorial optimization problems as well.
Graph-Free Knowledge Distillation for Graph Neural Networks

Knowledge distillation (KD) transfers knowledge from a teacher network to a student by enforcing the student to mimic the outputs of the pretrained teacher on training data. However, data samples are not always accessible in many cases due to large data sizes, privacy, or confidentiality. Many efforts have been made on addressing this problem for convolutional neural networks (CNNs) whose inputs lie in a grid domain within a continuous space such as images and videos, but largely overlook graph neural networks (GNNs) that handle non-grid data with different topology structures within a discrete space. The inherent differences between their inputs make these CNN-based approaches not applicable to GNNs. In this paper, we propose to our best knowledge the first dedicated approach to distilling knowledge from a GNN without graph data. The proposed graph-free KD (GFKD) learns graph topology structures for knowledge transfer by modeling them with multinomial distribution. We then introduce a gradient estimator to optimize this framework. Essentially, the gradients w.r.t. graph structures are obtained by only using GNN forward-propagation without back-propagation, which means that GFKD is compatible with modern GNN libraries such as DGL and Geometric. Moreover, we provide the strategies for handling different types of prior knowledge in the graph data or the GNNs. Extensive experiments demonstrate that GFKD achieves the state-of-the-art performance for distilling knowledge from GNNs without training data.
Benchmarking deep learning models on large healthcare datasets

Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4) publicly available dataset, which includes all patients admitted to an ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the benchmarking tasks. Our results show that deep learning models consistently outperform all the other approaches especially when the 'raw' clinical time series data is used as input features to the models.