ethicalabs/Research-Intent-Collab huggingface dataset:
Neural Networks for Postprocessing Ensemble Weather Forecasts

AbstractEnsemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a flexible alternative based on neural networks that can incorporate nonlinear relationships between arbitrary predictor variables and forecast distribution parameters that are automatically learned in a data-driven way rather than requiring prespecified link functions. In a case study of 2-m temperature forecasts at surface stations in Germany, the neural network approach significantly outperforms benchmark postprocessing methods while being computationally more affordable. Key components to this improvement are the use of auxiliary predictor variables and station-specific information with the help of embeddings. Furthermore, the trained neural network can be used to gain insight into the importance of meteorological variables, thereby challenging the notion of neural networks as uninterpretable black boxes. Our approach can easily be extended to other statistical postprocessing and forecasting problems. We anticipate that recent advances in deep learning combined with the ever-increasing amounts of model and observation data will transform the postprocessing of numerical weather forecasts in the coming decade.
Investigation of critical failures using root cause analysis methods: Case study of ASH cement PLC

Like other modern day process industries, most cement manufacturing operations are continuously sorting after state-of-the-art failure identification and analysis approaches that can help avert the reoccurrence of failures, owing to the huge costs of downtime associated with critical plant assets such as the rotary kilns. Research-based investigation of the root causes of high impact failures of critical industrial assets have been dominated by the use of complex mathematical methods for analysing experimentally and numerically simulatedscenarios. While the academic contributions of such approaches is highly commendable, the potential of deploying them to the industry as well as their ability to simulate experiential learning is significantly lower than when “real life” industrial case studies are used. Through the application of a fully integratedcement plant located in Northern Nigeria as case study; this paper employs two popular risk analysis techniques (fault tree analysis and reliability block diagram) to detect the causal factors as well as their interrelations of a chronic rotary kiln refractory brick failure. Unlike the previous plant-based investigations whichcontinuously attributed the failure causes to refractory brick design/manufacturing, the current approach provides a detailed, almost macroscopic dimension of vulnerabilities in maintenance, operations and qualitypractices in the plant. Through a combination of theory and immense practical knowledge of the case study plant, the current investigation team also provided several vital and realistic recommendations that could eliminate or significantly reduce the possibility of kiln refractory brick failure in the plant. The cornerstoneof this paper is not to undermine the currently used Apollo method of root cause failure analysis in this cementplant, but rather to provide a complementary holistic approach to the investigation of critical failures.Therefore, the robustness of in-plant failure analysis can be enhanced through effective integration of theindividual approaches
Long-Period Comets and the Oort Cloud

Long-period (LP) comets, Halley-type (HT) comets, and even some comets of the Jupiter family, probably come from the Oort cloud, a huge reservoir of icy bodies that surrounds the solar system. Therefore, these comets become important probes to learn about the distant Oort cloud population. We review the fundamental dynamical properties of LP comets, and what is our current understanding of the dynamical mechanisms that bring these bodies from the distant Oort cloud region to the inner planetary region. Most new comets have original reciprocal semimajor axes in the range 2 × 10−5 < 1/aorig < 5 × 10−5 AU−1. Yet, this cannot be taken to represent the actual space distribution of Oort cloud comets, but only the region in the energy space in which external perturbers have the greatest efficiency in bringing comets to the inner planetary region. The flux of Oort cloud comets in the outer planetary region is found to be at least several tens times greater than the flux in the inner planetary region. The sharp decrease closer to the Sun is due to the powerful gravitational fields of Jupiter and Saturn that prevent most Oort cloud comets from reaching the Earth’s neighborhood (they act as a dynamical barrier). A small fraction of ∼10−2 Oort cloud comets become Halley type (orbital periods P < 200 yr), and some of them can reach short-period orbits with P <20 yr. We analyze whether we can distinguish the latter, very ‘old’ LP comets, from comets of the Jupier family coming from the Edgeworth–Kuiper belt.
The Interstellar Medium of Gamma‐Ray Burst Host Galaxies. I. Echelle Spectra of                     <i>Swift</i>                     GRB Afterglows

We present optical echelle spectra of four gamma-ray burst (GRB) afterglows (GRB 050730, GRB 050820, GRB 051111, and GRB 060418) discovered during the first 1.5 yr of operation of the Swift satellite and localized by either the Swift telescope or follow-up ground-based imaging. We analyze the spectra to derive accurate column density measurements for the transitions arising in the interstellar medium (ISM) of the GRB host galaxies. These measurements can be used to constrain the physical properties of the ISM, including the metallicity, dust-to-gas ratio, ionization state, and chemical abundances of the gas. We also present measurements of the strong Mg II systems in the GRB afterglow spectra. With the publication of this paper, we provide the first data release of echelle afterglow spectra by the GRAASP collaboration to the general community.
Policy Briefing::Wholesale Central Bank Digital Currency (CBDC) and UK Economic Security

In September 2023, The University of Manchester hosted delegates from government, the private sector and academia to examine the potential ramifications of adopting a RLN on UK economic security. They found that a UK Wholesale CBDC (or more accurately UK participation in a Regulated Liability Network [RLN]) presents a viable and important opportunity to improve the competitiveness of the UK’s financial services industry at limited cost or risk to the UK taxpayer. As well as benefiting the financial services sector, there is also significant potential economic security benefit related to the more effective regulation of financial markets through the adoption of a UK RLN.
Non-local topological electromagnetic phases of matter

In 2+1D, topological electromagnetic phases are defined as atomic-scale media which host photonic monopoles in the bulk band structure and respect bosonic symmetries. Additionally, they support topologically protected spin-1 edge states, which are fundamentally different than spin-1/2 and pseudo-spin-1/2 edge states arising in fermionic and pseudo-fermionic systems. The striking feature of the edge state is that all electric and magnetic field components vanish at the boundary. This surprising open boundary solution of Maxwell's equations, dubbed the quantum gyroelectric effect [Phys. Rev. A 98, 023842 (2018)], only occurs in the presence of temporal as well as spatial dispersion (nonlocality) and is the supersymmetric partner of the topological Dirac edge state where the spinor wave function completely vanishes at the boundary. In this paper, we generalize these topological electromagnetic phases beyond the continuum approximation to the exact lattice field theory of a periodic atomic crystal. To accomplish this, we put forth the concept of microscopic (nonlocal) photonic band structure of solids, analogous to the traditional theory of electronic band structure. Our definition of topological invariants and topological phases uses optical Bloch modes and can be applied to naturally occurring crystalline materials. For the photon propagating within a crystal, our theory shows that besides the Chern invariant $\mathfrak{C}\in\mathbb{Z}$, there are also symmetry-protected topological (SPT) invariants $ν\in\mathbb{Z}_N$ which are related to the cyclic point group $C_N$ of the crystal $ν=\mathfrak{C}\mod N$. Due to the rotational symmetries of light $\mathcal{R}(2π)=+1$, these SPT phases are manifestly bosonic and behave very differently from their fermionic counterparts $\mathcal{R}(2π)=-1$ encountered in conventional condensed matter systems.
Courts for Africa: Considering the Co-Existence of the African Court on Human and Peoples' Rights and the African Court of Justice

Institutional strengthening has characterised the transformation of the OAU into the AU. This contribution focuses on two new judicial institutions in Africa, the African Court on Human and Peoples' Rights (ACHR) and the African Court of Justice (ACJ). The ACHR Protocol, adopted in 1998, and entering into force in 2004, mainly entrusts the ACHR with the application and interpretation of the African Charter on Human and Peoples' Rights (African Charter). The ACJ Protocol, adopted in 2003, not yet in force, establishes the ACJ as the AU's principal judicial organ. As the AU Constitutive Act embraces the promotion and protection of human rights as founding principle, the possibility of overlapping mandates and conflicting judgments between the two Courts arises. The authors try to balance the important role of the ACJ in realising human rights with the need for coherence, consistency and certainty with respect to African human rights law. Some solutions, such as institutional cooperation, preliminary referrals on crucial questions of human rights from the ACJ to the ACHR, the accession of the AU to the African Charter and harmonisation through Rules of Procedure, are suggested.
How Well Does LCA Model Land Use Impacts on Biodiversity?—A Comparison with Approaches from Ecology and Conservation

The modeling of land use impacts on biodiversity is considered a priority in life cycle assessment (LCA). Many diverging approaches have been proposed in an expanding literature on the topic. The UNEP/SETAC Life Cycle Initiative is engaged in building consensus on a shared modeling framework to highlight best-practice and guide model application by practitioners. In this paper, we evaluated the performance of 31 models from both the LCA and the ecology/conservation literature (20 from LCA, 11 from non-LCA fields) according to a set of criteria reflecting (i) model completeness, (ii) biodiversity representation, (iii) impact pathway coverage, (iv) scientific quality, and (v) stakeholder acceptance. We show that LCA models tend to perform worse than those from ecology and conservation (although not significantly), implying room for improvement. We identify seven best-practice recommendations that can be implemented immediately to improve LCA models based on existing approaches in the literature. We further propose building a "consensus model" through weighted averaging of existing information, to complement future development. While our research focuses on conceptual model design, further quantitative comparison of promising models in shared case studies is an essential prerequisite for future informed model choice.
Genetic Testing and the Future of Disability Insurance: Thinking about Discrimination in the Genetic Age

As we enter the new century, humanity wields increasing power to understand, alter, and control the world in which we live. The mysteries of our genetic code provide remarkable new insights into our unique human characteristics. Rapid developments in information technology provide instant access to limitless data. The information age has taken hold, and the genetic revolution is in full swing. With apologies to Aldous Huxley, we stand at the precipice of a brave new world.It has been just 50 years since James Watson and Francis Crick's groundbreaking discovery of the double helix. Since then, profound developments in the science of genetics have been staggering. More staggering still are the potential benefits, the boundless horizons, the promised and unimagined applications of their work, and the work of the many scientists involved in the sequencing of the human genome. There can be no doubt that a firm and unwavering commitment to the betterment of humankind has fueled this tireless effort.
Hydrodynamic Model Ensembles for Climate Change Projections in Estuarine Regions

In the current context of climate change, understanding the effects of the changing conditions on estuaries is of utmost importance to protect populations and ecosystems. Given the diversity of impacts depending on the region, there is a need for local and dedicated studies to understand and mitigate the risks. Numerical models can provide forecasts of extreme floods and sea-level rise (SLR). However, they can present inaccuracies. In this work, the ensemble technique was applied to improve the numerical modeling forecasting for estuaries by considering scenarios of extreme river flow discharges (EFDs) and SLR scenarios for 2050 and 2100. The simulations were performed for two different estuarine regions in northern Portugal, and the superensemble was constructed with the results of two different numerical models. The results differed per estuary, highlighting the importance of a local approach. For the Douro estuary dynamics, the results showed that for the EFD, the effects of the SLR were not noticeable, indicating that, in this estuary, the river component was more important than the maritime component. In contrast, the Minho estuary dynamics were found to be affected by the SLR along the whole estuarine region, indicating a maritime influence and a worsening of the flood conditions for future scenarios.
Multi-annotator Deep Learning: A Probabilistic Framework for Classification

Solving complex classification tasks using deep neural networks typically requires large amounts of annotated data. However, corresponding class labels are noisy when provided by error-prone annotators, e.g., crowdworkers. Training standard deep neural networks leads to subpar performances in such multi-annotator supervised learning settings. We address this issue by presenting a probabilistic training framework named multi-annotator deep learning (MaDL). A downstream ground truth and an annotator performance model are jointly trained in an end-to-end learning approach. The ground truth model learns to predict instances' true class labels, while the annotator performance model infers probabilistic estimates of annotators' performances. A modular network architecture enables us to make varying assumptions regarding annotators' performances, e.g., an optional class or instance dependency. Further, we learn annotator embeddings to estimate annotators' densities within a latent space as proxies of their potentially correlated annotations. Together with a weighted loss function, we improve the learning from correlated annotation patterns. In a comprehensive evaluation, we examine three research questions about multi-annotator supervised learning. Our findings show MaDL's state-of-the-art performance and robustness against many correlated, spamming annotators.
Antimicrobial Stewardship and Resistance Patterns in Ganjavian Hospital, Dezful, Southwest of Iran: A Two-Year Analysis

Background: Antimicrobial stewardship studies are essential to prevent microbial resistance, ensure proper antimicrobial use, and reduce treatment costs. This study aimed to examine the prescription trends of eight costly antimicrobial agents, including carbapenem (imipenem/meropenem), voriconazole, vancomycin, liposomal amphotericin B, colistin, linezolid, teicoplanin, and caspofungin, at Ganjavian hospital in Dezful. Methods: This cross-sectional study collected prescription forms for the eight targeted antimicrobials from March 2018 to March 2020. The recorded data included patient information, microbiological findings, infection sites, and details of antimicrobial use. Results: The analysis of 200 patients revealed that the most common infection sites were the bloodstream (41%), respiratory system (24.5%), and unidentified sources (13%). The majority of patients were admitted to internal wards (29.5%), general intensive care units (25.5%), and neonatal intensive care units (13%). The predominant bacterial isolates were Escherichia coli (19.5%), Acinetobacter baumannii (14.5%), Pseudomonas aeruginosa (12.5%), and Staphylococcus aureus (11.5%). Multidrug-resistant (MDR) bacteria caused 53% of infections, and 33.5% were caused by bacteria resistant to all tested antimicrobials. Conclusion: MDR bacteria pose a significant challenge, underscoring the critical need for nosocomial infection control, antimicrobial stewardship, and continuous monitoring of antimicrobial resistance patterns in this medical center.
DeepCFL: Deep Contextual Features Learning from a Single Image

Recently, there is a vast interest in developing image feature learning methods that are independent of the training data, such as deep image prior, InGAN, SinGAN, and DCIL. These methods are unsupervised and are used to perform low-level vision tasks such as image restoration, image editing, and image synthesis. In this work, we proposed a new training data-independent framework, called Deep Contextual Features Learning (DeepCFL), to perform image synthesis and image restoration based on the semantics of the input image. The contextual features are simply the high dimensional vectors representing the semantics of the given image. DeepCFL is a single image GAN framework that learns the distribution of the context vectors from the input image. We show the performance of contextual learning in various challenging scenarios: outpainting, inpainting, and restoration of randomly removed pixels. DeepCFL is applicable when the input source image and the generated target image are not aligned. We illustrate image synthesis using DeepCFL for the task of image resizing.
Generative Modeling with Diffusion

17 pages with 6 figures
A Leak Localization Algorithm in Water Distribution Networks Using Probabilistic Leak Representation and Optimal Transport Distance

Leaks in water distribution networks are estimated to account for up to 30% of the total distributed water: the increasing demand, and the skyrocketing energy cost have made leak localization and adoption even more important to water utilities. Each leak scenario is run on a simulation model to compute the resulting values of pressure and flows over the whole network. The recorded values are seen as the signature of one leak scenario. The key distinguishing element in the present paper is the representation of a leak signature as a discrete probability distribution. In this representation the similarity between leaks can be captured by a distance between their associated probability distributions. This maps the problem of leak detection from the Euclidean physical space into a space whose elements are histograms, structured by a distance between histograms, namely the Wasserstein distance. This choice also matches the physics of the system: indeed, the equations modelling the generation of flow and pressure data are non-linear. Non-linear data structure is better represented by the Wasserstein distance than by the Euclidean distance. The signatures obtained through the simulation of a large set of leak scenarios are non-linearly clustered according in the Wasserstein space using Wasserstein barycenters as centroids. As a new set of sensor measurements arrives, the related signature is associated to the cluster with the closest barycenter. The location of the simulated leaks belonging to that cluster are the possible locations of the observed leak. This new theoretical and computational framework allows a richer representation of pressure and flow data embedding both the modelling and the computational modules in the Wasserstein space whose elements are the histograms endowed with the Wasserstein distance. The computational experiments on benchmark and real-world networks confirm the feasibility of the proposed approach.
The Role of Non-Governmental Actors in the Dispute Settlement Process in the World Trade Organization

Non-governmental international organizations, whose primary function and goal is to serve the public interest, have managed to gain access to international forums to achieve this important objective after overcoming numerous ups and downs. This access has taken the form of a legal institution known as "amicus curiae," where an amicus curiae is a natural or legal person who is not a party to the dispute but submits written or oral statements regarding the facts and rules governing the dispute at their own initiative or at the court's request. A review of judicial practice shows that the major players in this field are international non-governmental organizations, although their participation in the dispute resolution process has faced many obstacles and challenges. This research aims to explain the role of international non-governmental organizations and examine the consequences of their presence, including significant impacts such as combating impunity, achieving judicial transparency, democratizing international law, and ensuring fair trials.
Transición del manejo de plagas convencional hacia el agroecológico mediante la transferencia de técnicas de control integrado de plagas en tomate bajo cubierta en Concordia - Provincia de Entre Ríos, Argentina = The transition from chemical pest control to biological pest management through the transfer of integrated pest management techniques in greenhouse tomatoes in Concordia - Province of Entre Ríos, Argentina

EEA Concordia
Advancing DevOps Automation: A Framework for Efficient CI/CD Pipeline Orchestration

Fast and dependable delivery of high-quality applications demand CI/CD pipelines because software development requirements have accelerated. An extensive methodology in this article enhances DevOps automation through improved orchestration of CI/CD pipelines. This study discusses the principal barriers pipeline management faces through issues that affect scalability and also throws light on integration challenges between multiple tools and disparate systems. Gridworks enables automation of workflows by using IaC together with containerization and event-driven triggers and observability practices which eliminates the need for human manual involvement. Real-world environments experience positive outcomes through the framework which both improves deployment speed and reduces failures and builds connections among team members. Organizations obtain superior software delivery growth potential through their ability to keep business flexibility when they combine automation strategies with DevOps practices.