ethicalabs/Research-Intent-Collab huggingface dataset:
TimeSenCLIP: A Time Series Vision-Language Model for Remote Sensing

Vision-language models (VLMs) have shown significant promise in remote sensing applications, particularly for land-use and land-cover (LULC) mapping via zero-shot classification and retrieval. However, current approaches face several key challenges, such as the dependence on caption-based supervision, which is often not available or very limited in terms of the covered semantics, and the fact of being adapted from generic VLM architectures that are suitable for very high resolution images. Consequently, these models tend to prioritize spatial context over spectral and temporal information, limiting their effectiveness for medium-resolution remote sensing imagery. In this work, we present TimeSenCLIP, a lightweight VLM for remote sensing time series, using a cross-view temporal contrastive framework to align multispectral Sentinel-2 time series with geo-tagged ground-level imagery, without requiring textual annotations. Unlike prior VLMs, TimeSenCLIP emphasizes temporal and spectral signals over spatial context, investigating whether single-pixel time series contain sufficient information for solving a variety of tasks.
Systematic Review of Infrastructure as Code (IaC) and GitOps for Cloud Automation and Governance

This paper presents a systematic review of Infrastructure as Code (IaC) and GitOps, exploring their transformative roles in cloud automation and governance. IaC, an approach that defines and provisions infrastructure using code, automates cloud infrastructure management, offering significant improvements in consistency, scalability, and agility. GitOps, as an extension of IaC, leverages Git repositories as the single source of truth, enabling seamless continuous delivery and deployment (CI/CD) workflows. This review highlights the synergistic benefits of combining IaC and GitOps, including enhanced operational efficiency, faster scaling, and stronger governance. However, the review also identifies key challenges in their implementation, such as toolchain integration, complexity in large-scale environments, and ensuring governance and security across cloud platforms. Case studies and emerging trends, including AI-powered automation and multi-cloud strategies, are discussed to illustrate real-world applications and future opportunities. The paper concludes by identifying areas for future research, including the development of advanced automation tools, multi-cloud governance frameworks, and improved security compliance mechanisms.
Indigenous data protection in wastewater surveillance: balancing public health monitoring with privacy rights

Wastewater-based epidemiology (WBE) has revolutionized public health surveillance by enabling real-time monitoring of disease patterns across populations through analysis of community wastewater. This innovative approach provides precise geographical tracking of pathogen levels and disease spread by detecting viral RNA and bacterial DNA signatures. Beyond pathogen detection, wastewater analysis reveals comprehensive community health data, including human genomic information and biomarkers of prescription medication and substance use patterns. For Indigenous populations, whose communities often occupy distinct geographical areas, this detailed biological data collection raises significant privacy and ethical concerns, particularly given historical patterns of research exploitation. By examining international case studies, we analyze instances where Indigenous genomic data and traditional knowledge have been misused in psychiatric and neuroscience research contexts, highlighting violations of informed consent principles, data sovereignty rights, and reinforcement of harmful stereotypes. The current regulatory gap in wastewater surveillance ethics necessitates the development of specialized WBE protocols for Indigenous communities. These guidelines must balance public health benefits with stringent privacy protections through authentic community engagement and Indigenous data governance rights recognition. This framework supports both epidemiological research advancement and the protection of Indigenous communities’ autonomy in the age of genomic surveillance.
Development MEIS (Military Education Information System): HRIS (Human Resources Information System) Integration for Competence and Placement of Personnel Positions

MEIS (Military Education Information System) is an information system used in the field of education in military organizations, while HRIS (Human Resources Information System) is an information system in the HRD (Human Resources Department). The rapid development of technology has resulted in a lot of changes aimed at adjusting technology and meeting the needs of the organization, especially in the field of education. The research was conducted on previous research, by paying attention to how and when the development has been carried out. The research found that not too much research was carried out in the military sphere of education. Through several stages in the review of the literature that has been obtained, it is very helpful in a more structured selection. So, from the results of the literature research, it can be seen that there have been some technological updates that have been made, but some are still concepts and plans. The merger between MEIS and HRIS is an effort in optimizing the system so that problems in development related to budget and personnel limitations can be overcome. Problems in the budget and personnel are often found in developing countries that have limitations, especially in the issue of budget and personnel who are professional in their fields. The combination of two or more systems will be able to provide utilization of the system effectively and efficiently by adjusting to the needs of the organization, in this case, the military organization, which has a more complex organizational structure.
Constraint-Aware Neurosymbolic Uncertainty Quantification with Bayesian Deep Learning for Scientific Discovery

Scientific Artificial Intelligence (AI) applications require models that deliver trustworthy uncertainty estimates while respecting domain constraints. Existing uncertainty quantification methods lack mechanisms to incorporate symbolic scientific knowledge, while neurosymbolic approaches operate deterministically without principled uncertainty modeling. We introduce the Constraint-Aware Neurosymbolic Uncertainty Framework (CANUF), unifying Bayesian deep learning with differentiable symbolic reasoning. The architecture comprises three components: automated constraint extraction from scientific literature, probabilistic neural backbone with variational inference, and differentiable constraint satisfaction layer ensuring physical consistency. Experiments on Materials Project (140,000+ materials), QM9 molecular properties, and climate benchmarks show CANUF reduces Expected Calibration Error by 34.7% versus Bayesian neural networks while maintaining 99.2% constraint satisfaction. Ablations reveal constraint-guided recalibration contributes 18.3% performance gain, with constraint extraction achieving 91.4% precision. CANUF provides the first end-to-end differentiable pipeline simultaneously addressing uncertainty quantification, constraint satisfaction, and interpretable explanations for scientific predictions.
Development and evaluation of a physiologically based pharmacokinetic model to predict carvedilol-paroxetine metabolic drug–drug interaction in healthy adults and its extrapolation to virtual chronic heart failure patients for dose optimization

Purpose: The metabolic drug–drug interactions (mDDIs) are one of the most important challenges faced by the pharmaceutical industry during the drug development stage and are frequently associated with labeling restrictions and withdrawal of drugs. The capacity of physiologically based pharmacokinetic (PBPK) models to absorb and upgrade with the newly available information on drug and population-specific parameters, makes them a preferred choice over the conventional pharmacokinetic models for predicting mDDIs. Method: A PBPK model capable of predicting the stereo-selective disposition of carvedilol after administering paroxetine by incorporating mechanism (time) based inhibition of CYP2D6 and CYP3A4 was developed by using the population-based absorption, distribution, metabolism and elimination (ADME) simulator, Simcyp®. Results: The model predictions for both carvedilol enantiomers were in close agreement with the observed PK data, as the ratios for observed/predicted PK parameters were within the 2-fold error range. The developed PBPK model was successful in capturing an increase in exposures of R and S-carvedilol, due to the time-based inhibition of CYP2D6 enzyme caused by paroxetine. Conclusion: The developed model can be used for exploring complex clinical scenarios, where multiple drugs are given concurrently, particularly in diseased populations where no clinical trial data is available.
Using landscape graphs to assess the ecological relevance of biodiversity conservation plans for forest habitat networks

Conservation measures integrated into planning documents are designed for the protection and management of natural habitats. They are part of planning strategies for maintaining biodiversity and the functional connectivity of habitat networks. However, spatial planning strategies need to consider a wide range of socio-economic requirements in addition to biodiversity issues. This leads to a mismatch between the planning documents emerging from stakeholders’ discussions and the most ecologically relevant conservation strategies for habitat networks. Here, we use a generic spatial modelling approach based on landscape graphs to model forest habitat networks in four areas matching the boundaries of French inter-municipal planning instruments (SCoTs). This enables us to consistently compare our modelling outputs with the habitat networks defined in the corresponding planning documents, for a representative set of ecological profiles. Independently, we assess ecological connectivity using metrics focusing on several key components of habitat connectivity, i.e., habitat patch area, inter-patch dispersal, and network traversability. Our results reveal both similarities and differences among SCoTs in the way they map and conserve habitat networks for different species profiles. The SCoTs differed particularly in the importance accorded to each component of habitat connectivity, although the metric related to habitat area was overall well accounted for. Our connectivity modelling framework provides a testing ground to support decision-making when mapping spatial habitat networks for biodiversity conservation and land-use planning.
LibriVAD: A Scalable Open Dataset with Deep Learning Benchmarks for Voice Activity Detection

Robust Voice Activity Detection (VAD) remains a challenging task, especially under noisy, diverse, and unseen acoustic conditions. Beyond algorithmic development, a key limitation in advancing VAD research is the lack of large-scale, systematically controlled, and publicly available datasets. To address this, we introduce LibriVAD - a scalable open-source dataset derived from LibriSpeech and augmented with diverse real-world and synthetic noise sources. LibriVAD enables systematic control over speech-to-noise ratio, silence-to-speech ratio (SSR), and noise diversity, and is released in three sizes (15 GB, 150 GB, and 1.5 TB) with two variants (LibriVAD-NonConcat and LibriVAD-Concat) to support different experimental setups. We benchmark multiple feature-model combinations, including waveform, Mel-Frequency Cepstral Coefficients (MFCC), and Gammatone filter bank cepstral coefficients, and introduce the Vision Transformer (ViT) architecture for VAD. Our experiments show that ViT with MFCC features consistently outperforms established VAD models such as boosted deep neural network and convolutional long short-term memory deep neural network across seen, unseen, and out-of-distribution (OOD) conditions, including evaluation on the real-world VOiCES dataset. We further analyze the impact of dataset size and SSR on model generalization, experimentally showing that scaling up dataset size and balancing SSR noticeably and consistently enhance VAD performance under OOD conditions. All datasets, trained models, and code are publicly released to foster reproducibility and accelerate progress in VAD research.
Understanding the potential and pitfalls of digital phenotypes to measure population mental health and wellbeing

Abstract   Background  Digital phenotypes such as social media data are increasingly being used to infer supposed trends in population mental health and wellbeing. These methods are attractive for their potential scale and timeliness. However, many methodological aspects need investigation to establish if these approaches could be applied successfully. We aim to explore these challenges by linking digital phenotypes to high-quality epidemiological data in a birth cohort study.    Methods  We retrospectively linked social media data from Twitter for 661 consenting participants in the Avon Longitudinal Study of Parents and Children, a multigenerational population cohort study. Participants volunteered their Twitter usernames, after a series of focus groups with 14 cohort participants about the acceptability of social media data linkage. Twitter data were used to test the effectiveness of inferring mental health and wellbeing from a range of sentiment analysis algorithms in combination with behavioural and temporal features. These methods were evaluated with participant-level survey data collected during the COVID-19 pandemic (April 9, 2020, to July 3, 2020), including with the Warwick Edinburgh Mental Wellbeing Scale, the General Anxiety Disorder-7 scale, and the Mood and Feelings Questionnaire. Ethical approval for the study was obtained from the Avon Longitudinal Study of Parents and Children Ethics and Law Committee, and informed consent was obtained from participants.    Findings  Twitter provides a specific Application Programming Interface for academic research, granting free access to the full history archive and thus enabling the collection of data from cohorts. The focus groups determined that data linkage with Twitter and subsequent sharing is acceptable when data are anonymised. However, publicly available data such as Twitter data can be easily identifiable, and we are therefore exploring methods that would preserve privacy more effectively for data sharing. Our initial results show that temporal features have a key influence on the sensitivity and specificity of a model for the inference of wellbeing, anxiety, and depression when used in combination with textual and behavioural features. This influence varies depending on the specific outcome measure. Ongoing analyses will establish whether extrapolating successful models to alternative population samples is possible.    Interpretation  Early results suggest that different mental health and wellbeing outcomes require individual models with uniquely tuned features. Cohort studies are a rich source of training data, but attention must be given to adequate anonymisation practices.    Funding  This work was supported by the Health Foundation, the Alan Turing Institute, a Philip Leverhulme Prize to CMAH, and the Medical Research Council.
Parallel Guided Local Search and Some Preliminary Experimental Results for Continuous Optimization

This paper proposes a Parallel Guided Local Search (PGLS) framework for continuous optimization. In PGLS, several guided local search (GLS) procedures (agents) are run for solving the optimization problem. The agents exchange information for speeding up the search. For example, the information exchanged could be knowledge about the landscape obtained by the agents. The proposed algorithm is applied to continuous optimization problems. The preliminary experimental results show that the algorithm is very promising .
Strength of Constitutional Court Decisions in Judicial Review of the 1945 Constitution in Indonesia

This research discusses the strength of the Constitutional Court's decision regarding judicial review in Indonesia. The Constitutional Court is an institution of judicial power tasked with exercising the authority of judicial review born from the 3rd Amendment to the 1945 Constitution. In this study, the philosophical and normative foundations that form the basis for the strength of the Constitutional Court's decision to conduct judicial review will be discussed. The method used is normative juridical, using secondary materials such as laws, decisions of the Constitutional Court, and related legal literature. This study's results confirm that the Constitutional Court's decision to conduct a judicial review has a powerful dimension of power. This power can be viewed from several angles, both philosophical and normative. In the philosophical aspect, the strength of the Constitutional Court's decision lies in the principles of popular sovereignty and constitutional supremacy. While in the normative aspect, the strength of the Constitutional Court's decision is based on the provisions of laws and regulations governing the authority and procedures of the Constitutional Court in conducting judicial reviews.
Benchmarking Vision Language Models on German Factual Data

Similar to LLMs, the development of vision language models is mainly driven by English datasets and models trained in English and Chinese language, whereas support for other languages, even those considered high-resource languages such as German, remains significantly weaker. In this work we present an analysis of open-weight VLMs on factual knowledge in the German and English language. We disentangle the image-related aspects from the textual ones by analyzing accu-racy with jury-as-a-judge in both prompt languages and images from German and international contexts. We found that for celebrities and sights, VLMs struggle because they are lacking visual cognition of German image contents. For animals and plants, the tested models can often correctly identify the image contents ac-cording to the scientific name or English common name but fail in German lan-guage. Cars and supermarket products were identified equally well in English and German images across both prompt languages.
The Proceedings of Joint 28th ICFA Advanced Beam Dynamics and Advanced Nova Accelerator Workshops on Quantum Aspects of Beam Physics

The Joint 28th ICFA (International Committee for Future Accelerators) Advanced Beam Dynamics and Advanced and Novel Accelerators Workshop on ''QUANTUM ASPECTS OF BEAM PHYSICS and Other Critical Issues of Beams in Physics and Astrophysics'', was held on January 7-11, 2003, in Hiroshima, Japan. This was the third in the QABP workshop series. The first QABP workshop was launched in January 1998, in Monterey, California, and the second was held in October 2000, in Capri, Italy. Over the past five years, this workshop series has passed its torch around the world, from the U.S. to Europe, and this time to Japan in Asia. Following the footsteps of the first two workshops, this one in Hiroshima was again a tremendous success. The frontier of beam research points to increasingly higher energy, greater brightness and lower emittance beams with ever-increasing particle species. These demands have triggered a rapidly growing number of beam phenomena that involve quantum effects. With the significant advancement of laser and accelerator technologies, there is also a growing interest in using high energy, high intensity particle and photon beams for laboratory astrophysics investigations, as well as the application of beam physics expertise to astrophysics studies. It has therefore become amore » tradition that this workshop series attracted a broad spectrum of experts from beam physics, astrophysics, cosmology, particle physics, condensed matter physics, nuclear physics, atomic physics, and laser science, to explore a common frontier where their individual expertise and interests overlapped.« less
Hospice Palliative Care: A Model for Quality End-of-Life Care

Abstract                Hospice is a program of care provided across a variety of settings, based on the understanding that dying is part of the normal life cycle. As people experience this last phase of life, hospice provides comprehensive palliative medical and supportive services, compassion, and care with the goals of comfort and quality of life closure. A hospice supports the patient through the dying process and the family through the experience of caregiving, the patient’s illness, dying, and their own bereavement. Understanding that the last phase of life is as individual as each person who experiences it, a hospice advocates so that people may live the remainder of their lives with dignity and die in a manner that is meaningful to them.
Combining health technology assessment and health technology management to deliver cost-effective prescribing and cost containment – the Irish experience

Health technology assessment (HTA) is a widely used process to systematically evaluate the properties, effects, and impacts of health technology, with the main purpose of informing policymaking in healthcare. However, to ensure optimal use of scarce healthcare budgets, there is a need to look beyond HTA - to post-reimbursement health technology management (HTM).This review aims to describe the significant impact HTA, in combination with HTM, has made to the cost-effective prescribing and cost-containment in Ireland. This review will cover a number of specific examples, such as; specific reimbursement criteria for sacubitril/valsartan (Entresto®) and evolocumab (Repatha®), the introduction of a reimbursement application system for lidocaine 5% medicated plaster (Versatis®), and the introduction of 'preferred' biosimilars for patent-expired biologic medicines.Healthcare payers are increasingly recognizing the benefits of HTM particularly when used in conjunction with HTA. This review highlights several examples whereby significant cost-savings can be made through HTM of not only new drugs but also drugs which are already available on the market.
Quantum Computing and Error Mitigation with Deep Learning for Frenkel Excitons

Quantum computers, currently in the noisy intermediate-scale quantum (NISQ) era, have started to provide scientists with a novel tool to explore quantum physics and chemistry. While several electronic systems have been extensively studied, Frenkel excitons, as prototypical optical excitations, remain among the less-explored applications. Here, we first use variational quantum deflation to calculate the eigenstates of the Frenkel Hamiltonian and evaluate the observables based on the oscillator strength for each eigenstate. Furthermore, using NISQ quantum computers requires performing error mitigation techniques alongside simulations. To deal with noisy qubits, we developed a deep-learning-based framework combined with a post-selection technique to learn the noise pattern and mitigate the error. Our mitigation methods work well and outperform the conventional post-selection and remain valid on real hardware.