ethicalabs/Research-Intent-Collab huggingface dataset:
In silico structure prediction of Maturase K Protein of Annona muricata from its amino acid sequence using AI guided 3D structure prediction tool -AlphaFold and identification of its functional regions using the ConSurf Server

Proteins are the essential biomolecules of life. They are the basic building blocks made of the same 20 amino acids. Understanding a protein’s structure plays a pivotal role in revealing its function in the genome of an organism. Studying the protein’s native conformation could pave the way to design novel drugs and help in bringing cure to some serious health ailments. Our current research work focuses on analyzing the structure of a plant protein called Maturase K of Annona muricata, a medicinally potent plant belonging to the family Annonaceae. This wonder plant and its plant extracts have taken center stage in the scientific and medical research due to its myriad medicinal properties present in its phytochemical compounds that help to cure or to control several types of infectious diseases including certain cancers, as in colon cancer. Recent advent of the Artificial Intelligence tools and its explosive foray into the field of Bioinformatics has revolutionized the field of structural biology, through its protein structure prediction tool called ‘AlphaFold’. This tool delivers highly accurate structure predictions of vast numbers of proteins, which otherwise would have been time consuming via experimental determination. The current study focuses on the use of one such AI based structure prediction tool called ‘AlphaFold’ to predict and elucidate the 3D structure of our query protein ‘Maturase K’ and analyze the protein's functional domains using the ‘ConSurf server’. Both the operations were performed solely based on its amino acids and the residue conservation in the secondary structural elements. The conserved and non-conserved amino acids during the course of evolution might further play a key role in establishing an evolutionary relationship and evolutionary divergence among its members and also helps to predict and understand the protein’s stability leaving a huge scope in finding the possible binding sites to design drugs to alleviate diseases including cancer and predict new protein functionalities
PharmacoForge: pharmacophore generation with diffusion models

Structure-based drug design (SBDD) is enhanced by machine learning (ML) to improve both virtual screening and                     de novo                     design. Despite advances in ML tools for both strategies, screening remains bounded by time and computational cost, while generative models frequently produce invalid and synthetically inaccessible molecules. Screening time can be improved with pharmacophore search, which quickly identifies ligands in a database that match a pharmacophore query. In this work, we introduce PharmacoForge, a diffusion model for generating 3D pharmacophores conditioned on a protein pocket. Generated pharmacophore queries identify ligands that are guaranteed to be valid, commercially available molecules. We evaluate PharmacoForge against automated pharmacophore generation methods using the LIT-PCBA benchmark and ligand generative models through a docking-based evaluation framework. We further assess pharmacophore quality through a retrospective screening of the DUD-E dataset. PharmacoForge surpasses other pharmacophore generation methods in the LIT-PCBA benchmark, and resulting ligands from pharmacophore queries performed similarly to                     de novo                     generated ligands when docking to DUD-E targets and had lower strain energies compared to                     de novo                     generated ligands.
The Role of Women in Conflict Resolution and Peace Building Mechanisms in Auchi

This paper addresses the vital and often undervalued role of women in all stages of conflict resolution and peace building. The study is grounded in the observation that, despite decades of evidence, formal peace processes frequently overlook women’s practical, community-based contributions, leading to fragile and unsustainable peace agreements. The main objective is to establish a forceful argument for the systematic and meaningful inclusion of women, moving beyond tokenism to genuine power-sharing. This research adopts the Feminist Institutionalism and the Critical Peace and Conflict Studies theories to examine how rigid, patriarchal institutions exclude women and how their exclusion negatively affects peace durability. The method adopted is the library research method, drawing exclusively from existing scholarly literature, official policy documents, and expert analyses to build a robust, evidence-based argument. Key arguments presented include: women's participation leads to more sustainable peace; local-level peace initiatives, led primarily by women, are critical for post-conflict stability; and international policy, while progressive in principle (e.g., UNSCR 1325), often fails in practical implementation. The paper concludes that “peace cannot be comprehensive unless it is truly inclusive”. Recommendations focus on mandatory quotas, gender-sensitive training for mediators, and direct funding for women's grassroots organizations.
Fast Model Editing at Scale

While large pre-trained models have enabled impressive results on a variety of downstream tasks, the largest existing models still make errors, and even accurate predictions may become outdated over time. Because detecting all such failures at training time is impossible, enabling both developers and end users of such models to correct inaccurate outputs while leaving the model otherwise intact is desirable. However, the distributed, black-box nature of the representations learned by large neural networks makes producing such targeted edits difficult. If presented with only a single problematic input and new desired output, fine-tuning approaches tend to overfit; other editing algorithms are either computationally infeasible or simply ineffective when applied to very large models. To enable easy post-hoc editing at scale, we propose Model Editor Networks using Gradient Decomposition (MEND), a collection of small auxiliary editing networks that use a single desired input-output pair to make fast, local edits to a pre-trained model's behavior. MEND learns to transform the gradient obtained by standard fine-tuning, using a low-rank decomposition of the gradient to make the parameterization of this transformation tractable. MEND can be trained on a single GPU in less than a day even for 10 billion+ parameter models; once trained MEND enables rapid application of new edits to the pre-trained model. Our experiments with T5, GPT, BERT, and BART models show that MEND is the only approach to model editing that effectively edits the behavior of models with more than 10 billion parameters. Code and data available at https://sites.google.com/view/mend-editing.
Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI

Accepted for the Pluralistic Alignment workshop at NeurIPS 2024
AFISTA: Accelerated FISTA for sparse signal recovery and compressive sensing

This paper presents a new fast iterative shrinkage-thresholding algorithm, termed AFISTA. The essential idea is to improve the convergence rate of FISTA using a new continuation strategy leading to a less number of iterations compared to FISTA. The convergence theorem of the AFISTA is proposed. In order to further accelerate the AFISTA method, it is equipped with the Barzilai-Borwein (BB) method. Also, for applications with orthogonal sensing matrix A, we proposed a specialized version of the AFISTA method. AFISTA is tailored for solving the basis pursuit problem which can be applied successfully on a variety of problems arising in signal and image processing issues such as sparse signal recovery, signal and image denoising, image restoration, and compressive sensing. To show the efficiency of the method, we compare our results with generalizations of linearized Bregman and fixed - point continuation (FPC) methods in sparse signal recovery applications, with split Bregman method in compressive sensing for sparse MRI and with Gradient projection for sparse reconstruction (GPSR) method in image deconvolution. Numerical results demonstrate that AFISTA overcomes all of the compared methods in convergence rate and some of them in both convergence rate and quality of reconstructed results.
Distilling Large Vision-Language Model with Out-of-Distribution Generalizability

Large vision-language models have achieved outstanding performance, but their size and computational requirements make their deployment on resource-constrained devices and time-sensitive tasks impractical. Model distillation, the process of creating smaller, faster models that maintain the performance of larger models, is a promising direction towards the solution. This paper investigates the distillation of visual representations in large teacher vision-language models into lightweight student models using a small- or mid-scale dataset. Notably, this study focuses on open-vocabulary out-of-distribution (OOD) generalization, a challenging problem that has been overlooked in previous model distillation literature. We propose two principles from vision and language modality perspectives to enhance student's OOD generalization: (1) by better imitating teacher's visual representation space, and carefully promoting better coherence in vision-language alignment with the teacher; (2) by enriching the teacher's language representations with informative and finegrained semantic attributes to effectively distinguish between different labels. We propose several metrics and conduct extensive experiments to investigate their techniques. The results demonstrate significant improvements in zero-shot and few-shot student performance on open-vocabulary out-of-distribution classification, highlighting the effectiveness of our proposed approaches. Poster: https://xuanlinli17.github.io/pdfs/iccv23_large_vlm_distillation_poster.pdf Code: https://github.com/xuanlinli17/large_vlm_distillation_ood
Cosmic microwave background power spectrum estimation with the destriping technique

16 pages, 23 figures, submitted to MNRAS
Learning from experience: privacy and the secondary use of data in health research

Health services research must continually address the question: Under what conditions may data not collected specifically for research, such as primary medical data, be re-used for research without compromising the privacy of the data-subjects?                   For secondary use of data in research there are basically three options. Option A: Use personal data with consent or other assent from the data-subjects. To make this both fairer and more practical, in many circumstances broader construals of consent, or permission or approval, need to be explored and instituted. Option B: Anonymise the data, then use them. For many studies, this is the most practical and desirable option. The craft of anonymisation, including reversible anonymisation, or key-coding, needs to be developed and more fully supported under law. Option C: Use personal data without explicit consent, under a public interest mandate. Whether and how the data should be anonymised will depend on the situation.                   Public health mandates and protections deserve to be clarified, strengthened and extended for a variety of surveillance, registration, clinical audit, health services research and other types of investigation.                   Safeguards are an integral part of the research promise to the public, offer crucial reassurance and should be emphasised. For health services research, databases are core resources, and their stewardship must be cultivated.
Present State and Problems of Work Environment Control in the Workplaces using Hazardous Materials based on the Occupational Safety and Health Act in Japan

In Japan, working environment measurement is prescribed in the designated workplaces using hazardous materials. Measurements should be carried out periodically and countermeasures are performed depending on the results. By introducing such a system, working environments have remarkably improved. However, in the designated workplaces, measurements should be continued even in work environments found safe. On the other hand, measurement need not be obliged for non-designated workplaces even if hazardous materials are utilized.In the United States of America and many European countries, work environment management and work management are carried out by measuring personal exposure concentrations. In Japan, the Ministry of Health, Labour and Welfare is now discussing the introduction of personal exposure monitoring. However, many problems exist to prevent the simple introduction of American and European methods. This paper describes the brief history, present state and problems of work environment control in Japan, comparing with the systems of American and European countries.
Neural Network Load Forecasting with Weather Ensemble Predictions

In recent years, a large amount of literature has evolved on the use of artificial neural networks (ANNs) for electric load forecasting. ANNs are particularly appealing because of their ability to model an unspecified nonlinear relationship between load and weather variables. Weather forecasts are a key input when the ANN is used for forecasting. This paper investigates the use of weather ensemble predictions in the application of ANNs to load forecasting for lead times from one to ten days ahead. A weather ensemble prediction consists of multiple scenarios for a weather variable. We use these scenarios to produce multiple scenarios for load. The results show that the average of the load scenarios is a more accurate load forecast than that produced using traditional weather forecasts. We use the load scenarios to estimate the uncertainty in the ANN load forecast. This compares favorably with estimates based solely on historical load forecast errors.
Effect of Aluminium Doping and Annealing Temperature on the Structure and Electrochemistry of High-Capacity xLi<sub>2</sub>MnO<sub>3</sub>•(1-x)LiMO<sub>2</sub> (M = Mn, Ni, Co, Al; x = 0.5) Cathode Material for Lithium-Ion Battery

Abstract not Available.
Efficiently computing logical noise in quantum error-correcting codes

16 pages + references, 3 figures
Practical Guidelines for the Selection and Evaluation of Natural Language Processing Techniques in Requirements Engineering

Natural Language Processing (NLP) is now a cornerstone of requirements automation. One compelling factor behind the growing adoption of NLP in Requirements Engineering (RE) is the prevalent use of natural language (NL) for specifying requirements in industry. NLP techniques are commonly used for automatically classifying requirements, extracting important information, e.g., domain models and glossary terms, and performing quality assurance tasks, such as ambiguity handling and completeness checking. With so many different NLP solution strategies available and the possibility of applying machine learning alongside, it can be challenging to choose the right strategy for a specific RE task and to evaluate the resulting solution in an empirically rigorous manner. In this chapter, we present guidelines for the selection of NLP techniques as well as for their evaluation in the context of RE. In particular, we discuss how to choose among different strategies such as traditional NLP, feature-based machine learning, and language-model-based methods. Our ultimate hope for this chapter is to serve as a stepping stone, assisting newcomers to NLP4RE in quickly initiating themselves into the NLP technologies most pertinent to the RE field.
Central Bank Digital Currency

Payment and settlement systems and services have become vital components of the economic life of contemporary societies. Over the last decades, they have gone through several waves of reforms that have changed the landscape of the national payments system (NPS) in virtually every country in the world. Recently, the issuance of central bank digital currency (CBDC) has become a highly debated financial-sector topic worldwide. The international financial community is studying aspects of CBDC, from design to implementation, and implications for the financial system, including on policy, regulation, and oversight and the economy more broadly. This guidance note is intended to be used primarily by central banks and relevant public authorities considering the issuance of CBDC and by those institutions and individuals supporting them in this process.
THE ETHNIC MINORITY RIGHT PROTECTION AND ITS POLITICAL REPRESENTATION IN ETHIOPIA

Scholarly interest in Ethiopia’s ethnic federalism and political representation of minorities has frequently been on ethno-territorial organization, constitutional design, power-sharing arrangements, but the rights of minority under majority and their decision making power are often left out from analyses. In the current politics of Ethiopia Minorities are not supported well, but they are also sometimes bothered and ignored. These unequal policies have resulted in discrimination of different rights in society. The Constitution of Ethiopia off course provided equality before the law for all citizens regardless of their religion, race, language or gender. At the same time, it declared the value of cultural diversity. However minorities did not have a chance to win in an election since the system of election in Ethiopia is a simple majority, when there is no one particular group which holds numerical dominance, simple majoritarianism can work against the rights of minorities unless they are specifically safeguarded. In addition even though the constitution of the 1995 FDRE provides article in support of minorities representation but it fails to apply practically.
Utilizing Wearable Sensors to Investigate the Impact of Everyday Activities on Heart Rate

Advances in sensor technologies have provided the opportunity to perform continuous and unobtrusive capturing of physiological signals. One particular application that has benefitted from this technology is the remote monitoring and management of cardiovascular conditions. In this paper, details of an investigation considering the impact of everyday activities on heart rate are presented. ECG and accelerometer signals collected from wearable wireless sensors have been utilized to investigate the underlying relationships between physiological and activity-related profile information. The impact of activities on heart rate has been captured through analysis of the patterns of heart rate using the CUSUM algorithm. Subsequently, results have shown that a change in the pattern of heart rate is detected shortly after an activity commences. Further extensions of the research are also proposed, including integration of a range of ECG features and intelligent data analysis techniques, thereby facilitating the future development of context aware health monitoring mechanisms.
Decolonizing Indigenous Law: Self-Determination and Vulnerability in the Mapuche Case

In the present essay, the author—and Mapuche, at the same time—critically analyzes the construction of the Mapuche people as a “vulnerable human group” under the International Human Rights Law and then, according to decolonial option, proposes a hypothesis: if the indigenous people are vulnerable, by definition, to claim the right to self-determination, in the Mapuche case, it is an oxymoron.
Sustainable development and the fisheries sector and aquaculture in Gabon : a study of the sustainable management of fishery resources and their ecosystem in the provinces of the Estuary and Ogooué Maritime

À l'échelle mondiale, la pêche maritime est caractérisée par une surexploitation des ressources disponibles dans les différentes pêcheries ayant des impacts très importants sur l'écosystème marin et côtier. Cette surpêche est facilitée par le progrès technique et par une demande de consommation en constante augmentation, notamment dans les pays développés. Pour faire face à cette évolution, des politiques de gestion durable de ces ressources halieutiques sont progressivement mises en place, allant des approches conventionnelles (gestion des stocks exploités) à l'approche écosystémique des pêches qui prône la réconciliation de l'exploitation durable et de la conservation de l'écosystème. Notre thèse aborde les conditions de développement d'une telle gestion durable de la pêche dans le cas précis des provinces de l'Estuaire et de l'Ogooué Maritime au Gabon. Le diagnostic établi met en évidence une exploitation désordonnée des ressources halieutiques et une détérioration générale de l'état des écosystèmes côtiers, nécessitant des mesures d'ajustement de la pression de pêche. Cependant, beaucoup de lacunes restent à combler (politique d'exploitation durable, formation des acteurs, évaluation des ressources et de l'écosystème, etc.) pour une meilleure compréhension et un suivi plus rigoureux des mutations qui affectent la durabilité des pêcheries et la vulnérabilité globale des écosystèmes côtiers. Dans ces deux provinces du Gabon, nous montrons que l'application de l'approche écosystémique des pêches, prenant la forme d'une gouvernance concertée dans le secteur des pêches et de l'aquaculture, apparaît comme une nécessité pour donner à ce secteur une place de choix dans ce pays.
The Australian moratorium on genetics and life insurance: evaluating policy compared to Parliamentary recommendations regarding genetic discrimination

Objectives and importance of study:                     Genetic discrimination is a health policy issue of international concern to clinicians, patients, researchers, and policy makers, and threatens the success of genomic medicine. In Australia, genetic discrimination in life insurance is legal and leads to public health harms, including deterring at-risk individuals from clinically indicated testing. In 2018, a Parliamentary Joint Committee recommended an urgent ban on the use of predictive genetic test results in life insurance underwriting in Australia, to be implemented in a form similar to the UK Code on genetic testing and life insurance. In 2019, the insurance industry, through the Financial Services Council (FSC), introduced a self-regulated moratorium that applies until 2024, but only to life insurance policies up to certain financial limits. The FSC moratorium will be reviewed in late 2022, but has no government oversight.                                                           Study type:                     Policy implementation evaluation                                                           Methods:                     We used policy evaluation methods to 1) summarise the key recommendations of the 2018 Parliamentary Committee that are directed towards practical aspects of policy development and content; and 2) assess the level of disparity between the implemented moratorium and the recommendations of the Committee.                                                           Results:                     There is a substantial disparity between the Australian moratorium and the Parliamentary Committee recommendations across key areas, including addressing self-regulation, co-development of policy, protection of tests taken during its term, and similarity with the UK Code. The FSC moratorium offers less protection to consumers than the UK Code on a number of measures, including the level of financial coverage, the involvement of government, certainty provided to individuals who have genetic testing, and the treatment of research results.                                                           Conclusions:                     The FSC moratorium is a step forward for Australia, but falls short of the Parliamentary recommendations. Further regulation by the Australian Government may be required to achieve the aims of the Parliamentary recommendations and ensure the intended level of consumer protection.