ethicalabs/Research-Intent-Collab huggingface dataset:
The Effect of Situational Leadership Behavior Organizational Culture and Human Resources Management Strategy on Education and Training Institution Productivity (Survey on Educational and Vocational Training Institutions in West Java Province)

The aim of this research is to analyze: situational leadership behavior, organizational culture and productivity of vocational training institutes in west Java Province. The correlation between situational leadership behavior and organizational culture at vocational training institutes, the effect of situational leadership behavior and organizational culture toward productivity of vocational training institutes in west Java Province. This research uses organizational behavior and human resources management approach. The type of the research is descriptive and verificative, while the method used both descriptive and explanatory survey. Investigation type is casualty and time horizon in cross sectional. The sample size used is proportionate sampling by taking sample 115 vocational training institute of spread over 19 locations totally, all its population counted 719 vocational training institutes in west Java Province. The data analyzed by descriptive analytic and path analysis. The result of research shows, 1) situational leadership behavior and organizational culture in generalities, rather high score and the productivity at vocational training institutes to society, cooperation with company or industry in training program development, and placement of training graduate assessed by rather low, 2) there is correlation which significant between situational leadership behavior and organizational at vocational training institutes in west Java Province, 3) situational leadership behavior, organizational culture has significant effect simultaneously and partially productivity of vocational training institutes in west Java Province.
Promotion and Protection of Human Rights in the context of Corporate Social Respnsibility by hotels in Zanzibar. : A Minor Field Study

This minor field study is an explorative analysis of the promotion/protection of human rights in the context of corporate social responsibility by two hotels in Zanzibar. The aim of the thesis is to analyze the motivations of the CSR practices of two hotels in Zanzibar and if the CSR practices can be used to promote/protect stakeholders human rights. The theory used to support the analysis is the stakeholder theory to demonstrate the responsibilities corporations have towards their stakeholders. The primary data is collected through interviews with one manager from each hotel to examine the motivations for the CSR, and with one stakeholder related to each hotel to examine the effects in terms of human rights. The findings indicate that the CSR practices are mostly motivated through altruistic/philanthropic reasons within the social/environmental category and that the three human rights examined have successfully been promoted and protected using the hotels CSR practices.
Access to health insurance coverage among sub-Saharan African migrants living in France: Results of the ANRS-PARCOURS study

Migrants' access to care depends on their health insurance coverage in the host country. We aimed to evaluate in France the dynamic and the determinants of health insurance coverage acquisition among sub-Saharan migrants.In the PARCOURS life-event retrospective survey conducted in 2012-2013 in health-care facilities in the Paris region, data on health insurance coverage (HIC) each year since arrival in France has been collected among three groups of sub-Saharan migrants recruited in primary care centres (N = 763), centres for HIV care (N = 923) and for chronic hepatitis B care (N = 778). Year to year, the determinants of the acquisition and lapse of HIC were analysed with mixed-effects logistic regression models.In the year of arrival, 63.4% of women and 55.3% of men obtained HIC. But three years after arrival, still 14% of women and 19% of men had not obtained HIC. HIC acquisition was accelerated in case of HIV or hepatitis B infection, for migrants arrived after 2000, and for women in case of pregnancy and when they were studying. Conversely, it was slowed down in case of lack of a residency permit and lack of financial resources for men. In addition, women and men without residency permits were more likely to have lost HIC when they had one.In France, the health insurance system aiming at protecting all, including undocumented migrants, leads to a prompt access to HIC for migrants from sub-Saharan Africa. Nevertheless, this access may be impaired by administrative and social insecurities.
The Selection of Diffusion Models for Influence Optimization in Social Networks

Given a network G = (V,E) and opinion data collected from independent diffusion processes, we might want to 1) learn the network’s influence function and 2) use this knowledge of influence to, under a constraint k, select the k nodes that will influence the maximal number of nodes in V. The process of learning and maximizing influence in a social network requires, as a presupposition, the underlying model class to be learned. However, though much attention has been paid to learning model parameters and their corresponding influence functions, and to maximizing influence in a graph given an influence function, the process of selecting the underlying model class has so far been neglected. I advance two main arguments regarding the process of learning and maximizing influence in social networks. First, I argue that the Linear Threshold model can be expected to outperform the Voter model at both predicting and maximizing influence, and empirically may outperform the Independent Cascade model as well. To do so, I examine the relationships between the Voter model, the Independent Cascade model, and the Linear Threshold model, and in particular the theoretical relationship between the Voter model and the Linear Threshold model. I argue that learning and maximizing influence under the Voter model are subsets of the tasks of learning and maximizing influence under the Linear Threshold model. This conclusion is also weakly supported by data collected from Twitter. Second, I argue that the task of maximizing influence is a distinct process from predicting influence, and models that perform well at one task do not necessarily perform well at the other. This possibly complicates the application of traditional model selection techniques to the problem of model selection for influence maximization.
Decomposing weather forecasting into advection and convection with neural networks

Operational weather forecasting models have advanced for decades on both the explicit numerical solvers and the empirical physical parameterization schemes. However, the involved high computational costs and uncertainties in these existing schemes are requiring potential improvements through alternative machine learning methods. Previous works use a unified model to learn the dynamics and physics of the atmospheric model. Contrarily, we propose a simple yet effective machine learning model that learns the horizontal movement in the dynamical core and vertical movement in the physical parameterization separately. By replacing the advection with a graph attention network and the convection with a multi-layer perceptron, our model provides a new and efficient perspective to simulate the transition of variables in atmospheric models. We also assess the model's performance over a 5-day iterative forecasting. Under the same input variables and training methods, our model outperforms existing data-driven methods with a significantly-reduced number of parameters with a resolution of 5.625 deg. Overall, this work aims to contribute to the ongoing efforts that leverage machine learning techniques for improving both the accuracy and efficiency of global weather forecasting.
Integrated Seismic Program (ISP): A New Python GUI-Based Software for Earthquake Seismology and Seismic Signal Processing

Abstract                Integrated Seismic Program (ISP) is a graphical user interface designed to facilitate and provide a user-friendly framework for performing diverse common and advanced tasks in seismological research. ISP is composed of five main modules for earthquake location, time–frequency analysis and advanced signal processing, implementation of array techniques to estimate the slowness vector, seismic moment tensor inversion, and receiver function computation and analysis. In addition, several support tools are available, allowing the user to create an event database, download data from International Federation of Digital Seismograph Networks services, inspect the background noise, and compute synthetic seismograms. ISP is written in Python3, supported by several open-source and/or publicly available tools. Its modular design allows for new features to be added in a collaborative development environment.
Near-Term Detectability of Terrestrial Extrasolar Planets: TEP Network Observations of CM Draconis

Results from a photometric search for extrasolar planetary transits across the eclipsing binary CM Dra are presented. The TEP (Transits of Extrasolar Planets) network has observed this star since 1994, and a lightcurve with 617 hours of coverage has been obtained. The data give a complete phase coverage of the CM Dra system at each of the 3 years of observations, with a noise of less than 5 mmag. New epoch and period values for CM Dra are derived, and a low flare rate of 0.025/hr has been confirmed. The absence of periodic variations in eclipse minimum times excludes the presence of very massive planets with periods of less than a few years. The lightcurve was visually scanned for the presence of unusual events which may be indicative of transits of extrasolar planets with 'massive earth' sizes. Six suspicious events were found which are being followed up for future transits, by planets with sizes between 1.5 and 2.5 RE (Earth Radii). However, none of these events has amplitudes compatible with planets larger than 2.5 RE. Coplanar planets larger than 2.5 RE and with orbital periods of less than 60 days can therefore be ruled out with a confidence of about 80%. Planets smaller than 1.5 RE cannot be detected in the data without a sub-noise detection algorithm. A preliminary signal detection analysis shows that there is a 50% detection confidence for 2 RE planets with a period from 10 to 30 days with the current data. This data-set demonstrates that it is possible to detect terrestrial sized planets with ground based photometry, and that strong constraints on the sizes of planets orbiting in the plane of the CM Dra system can be set.
Aspects of the modulation of cosmic rays in the outer heliosphere

Masters
Distinct distances on a sphere

We study the Erdos distance conjecture on the unit sphere in three dimensions using Fourier analytic methods.
Spatial data fusion adjusting for preferential sampling using integrated nested Laplace approximation and stochastic partial differential equation

Abstract                Spatially misaligned data can be fused by using a Bayesian melding model that assumes that underlying all observations there is a spatially continuous Gaussian random field. This model can be employed, for instance, to forecast air pollution levels through the integration of point data from monitoring stations and areal data derived from satellite imagery. However, if the data present preferential sampling, that is, if the observed point locations are not independent of the underlying spatial process, the inference obtained from models that ignore such a dependence structure may not be valid. In this paper, we present a Bayesian spatial model for the fusion of point and areal data that takes into account preferential sampling. Fast Bayesian inference is performed using the integrated nested Laplace approximation and the stochastic partial differential equation approaches. The performance of the model is assessed using simulated data in a range of scenarios and sampling strategies that can appear in real settings. The model is also applied to predict air pollution in the USA.
Dispute Settlement in the World Trade Organization

Any experienced lawyer knows that cases are most often won or lost on procedural grounds; yet procedural issues are often considered too technical for proper treatment in legal literature. In this extensively revised 2004 edition of Palmeter and Mavroidis' authoritative book on WTO dispute settlement, the authors discuss all WTO dispute settlement provisions and their interpretation in WTO jurisprudence. All the decisions of panels and the Appellate Body are discussed, from the inception of the WTO in 1995 until the end of May 2003. Although the book contains considerable technical expertise, it is at the same time written for accessibility to a wide readership. This volume - an essential tool for practitioners, diplomats and government lawyers - is a comprehensive study of compulsory third party adjudication in international law.
Development of a time-series shotgun metagenomics database for monitoring microbial communities at the Pacific coast of Japan

AbstractAlthough numerous metagenome, amplicon sequencing-based studies have been conducted to date to characterize marine microbial communities, relatively few have employed full metagenome shotgun sequencing to obtain a broader picture of the functional features of these marine microbial communities. Moreover, most of these studies only performed sporadic sampling, which is insufficient to understand an ecosystem comprehensively. In this study, we regularly conducted seawater sampling along the northeastern Pacific coast of Japan between March 2012 and May 2016. We collected 213 seawater samples and prepared size-based fractions to generate 454 subsets of samples for shotgun metagenome sequencing and analysis. We also determined the sequences of 16S rRNA (n = 111) and 18S rRNA (n = 47) gene amplicons from smaller sample subsets. We thereafter developed the Ocean Monitoring Database for time-series metagenomic data (http://marine-meta.healthscience.sci.waseda.ac.jp/omd/), which provides a three-dimensional bird’s-eye view of the data. This database includes results of digital DNA chip analysis, a novel method for estimating ocean characteristics such as water temperature from metagenomic data. Furthermore, we developed a novel classification method that includes more information about viruses than that acquired using BLAST. We further report the discovery of a large number of previously overlooked (TAG)n repeat sequences in the genomes of marine microbes. We predict that the availability of this time-series database will lead to major discoveries in marine microbiome research.
MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning

Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmic proposals toward systematic and empirical exploration across models, datasets, and hardware constraints. We introduce MerLin, an open-source framework designed as a discovery engine for photonic and hybrid quantum machine learning. MerLin integrates optimized strong simulation of linear optical circuits into standard PyTorch and scikit learn workflows, enabling end-to-end differentiable training of quantum layers.   MerLin is designed around systematic benchmarking and reproducibility. As an initial contribution, we reproduce eighteen state-of-the-art photonic and hybrid QML works spanning kernel methods, reservoir computing, convolutional and recurrent architectures, generative models, and modern training paradigms. These reproductions are released as reusable, modular experiments that can be directly extended and adapted, establishing a shared experimental baseline consistent with empirical benchmarking methodologies widely adopted in modern artificial intelligence.   By embedding photonic quantum models within established machine learning ecosystems, MerLin allows practitioners to leverage existing tooling for ablation studies, cross-modality comparisons, and hybrid classical-quantum workflows. The framework already implements hardware-aware features, allowing tests on available quantum hardware while enabling exploration beyond its current capabilities, positioning MerLin as a forward-looking co-design tool linking algorithms, benchmarks, and hardware.
Aligning large language models with radiologists by reinforcement learning from AI feedback for chest CT reports

Large language models (LLMs) often struggle to fully capture the nuanced preferences and clinical judgement of radiologists in medical report summarization even when fine-tuned on massive medical reports. This could lead to the generated radiology reports lacking the professionalism and sufficient quality required in critical diagnostic decision-making.To investigate the alignment of LLMs with radiologists by reinforcement learning from AI feedback (RLAIF) for high-quality, clinically significant impression summarization in Chest CT reports.From the data set of our hospital, 94,844 chest CT reports (median age, 56 years [IQR, 46-69 years]; 29,350 male and 34,036 female patients) were included in this retrospective study. We created a comparative data set of chest CT reports annotated by radiologists, in which the impressions of the reports generated by fine-tuned LLMs and radiologists were further reviewed and ranked by Tongyi Qianwen (Qwen) and a radiologist. Using the comparative data set, we performed further fine-tuning by reinforcement learning to align LLMs with radiologists. The performance of the aligned LLMs was evaluated on a test set in terms of metrics including ROUGE, BLEU, Precision, Recall and F1 score.The results showed that an average agreement rate of the ranking between the radiologist and Qwen is up to 77.9 %. The aligned LLM achieved consistent performance improvement over its unaligned counterpart by 2.56 %, 1.77 % and 1.13 % in terms of Precision, Recall and F1 score, respectively (P < 0.001).In this study, the results demonstrated the effect of the explicit alignment of LLMs with radiologists using RLAIF in generating impressions of chest CT reports. Additionally, the study indicated the possibility that AI can serve as an alternative to humans in providing feedback for aligning LLMs with radiologists.This study confirms the benefit of reinforcement learning from AI feedback for impression summarization, paving the way for the application of AI feedback in clinical CT report summarization. It will serve as the basis for the future development of multi-model large language models in the field of radiology.
Galaxy morphological classification in deep-wide surveys via unsupervised machine learning

ABSTRACT                Galaxy morphology is a fundamental quantity, which is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmology (e.g. as a prior for photometric-redshift measurements and as contextual data for transient light-curve classifications). While a rich literature exists on morphological-classification techniques, the unprecedented data volumes, coupled, in some cases, with the short cadences of forthcoming ‘Big-Data’ surveys (e.g. from the LSST), present novel challenges for this field. Large data volumes make such data sets intractable for visual inspection (even via massively distributed platforms like Galaxy Zoo), while short cadences make it difficult to employ techniques like supervised machine learning, since it may be impractical to repeatedly produce training sets on short time-scales. Unsupervised machine learning, which does not require training sets, is ideally suited to the morphological analysis of new and forthcoming surveys. Here, we employ an algorithm that performs clustering of graph representations, in order to group image patches with similar visual properties and objects constructed from those patches, like galaxies. We implement the algorithm on the Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey, to autonomously reduce the galaxy population to a small number (160) of ‘morphological clusters’, populated by galaxies with similar morphologies, which are then benchmarked using visual inspection. The morphological classifications (which we release publicly) exhibit a high level of purity, and reproduce known trends in key galaxy properties as a function of morphological type at z &amp;lt; 1 (e.g. stellar-mass functions, rest-frame colours, and the position of galaxies on the star-formation main sequence). Our study demonstrates the power of unsupervised machine learning in performing accurate morphological analysis, which will become indispensable in this new era of deep-wide surveys.
Regulatory Challenges of AI-Driven Credit Scoring in Indonesian Banking: Between Algorithmic Bias and Consumer Protection

Artificial Intelligence (AI) based credit scoring systems have rapidly transformed lending practices in Indonesian banking. AI enables automated assessment using non-traditional variables, including digital footprints, e-commerce behavior, and mobile device metadata. While this innovation enhances efficiency and financial inclusion, it also creates significant legal and ethical challenges. Existing regulations, Bank Indonesia regulations, OJK regulations on risk management, and the Personal Data Protection Law have not yet provided a comprehensive and specific framework for algorithmic transparency, fairness, and liability in automated decision-making. This article examines the regulatory gaps in Indonesian laws related to AI-driven credit scoring, evaluates the risks of algorithmic bias, and formulates a normative model for consumer protection. Using a normative juridical method, this paper compares Indonesia’s regulatory landscape with global frameworks such as the EU Artificial Intelligence Act, OECD AI Principles, and U.S. fair lending rules. The study finds that the Indonesian financial regulatory regime lacks clear provisions on explainability, auditability, and accountability for AI decisions. It concludes that regulatory reform is required to mitigate discrimination risks and strengthen legal certainty for consumers and financial institutions.
Analyzing Images of Blood Cells with Quantum Machine Learning Methods: Equilibrium Propagation and Variational Quantum Circuits to Detect Acute Myeloid Leukemia

This paper presents a feasibility study demonstrating that quantum machine learning (QML) algorithms achieve competitive performance on real-world medical imaging despite operating under severe constraints. We evaluate Equilibrium Propagation (EP), an energy-based learning method that does not use backpropagation (incompatible with quantum systems due to state-collapsing measurements) and Variational Quantum Circuits (VQCs) for automated detection of Acute Myeloid Leukemia (AML) from blood cell microscopy images using binary classification (2 classes: AML vs. Healthy).  Key Result: Using limited subsets (50-250 samples per class) of the AML-Cytomorphology dataset (18,365 expert-annotated images), quantum methods achieve performance only 12-15% below classical CNNs despite reduced image resolution (64x64 pixels), engineered features (20D), and classical simulation via Qiskit. EP reaches 86.4% accuracy (only 12% below CNN) without backpropagation, while the 4-qubit VQC attains 83.0% accuracy with consistent data efficiency: VQC maintains stable 83% performance with only 50 samples per class, whereas CNN requires 250 samples (5x more data) to reach 98%. These results establish reproducible baselines for QML in healthcare, validating NISQ-era feasibility.