ethicalabs/Research-Intent-Collab huggingface dataset:
Optimal Transport for Time-Varying Multi-Agent Coverage Control

Coverage control algorithms have traditionally focused on static target densities, where agents are deployed to optimally cover a fixed spatial distribution. However, many applications involve time-varying densities, including environmental monitoring, surveillance, and adaptive sensor deployment. Although time-varying coverage strategies have been studied within Voronoi-based frameworks, recent works have reformulated static coverage control as a semi-discrete optimal transport problem. Extending this optimal transport perspective to time-varying scenarios has remained an open challenge. This paper presents a rigorous optimal transport formulation for time-varying coverage control, in which agents minimize the instantaneous Wasserstein distance to a continuously evolving target density. The proposed solution relies on a coupled system of differential equations governing agent positions and the dual variables that define Laguerre regions. In one-dimensional domains, the resulting system admits a closed-form analytical solution, offering both computational benefits and theoretical insight into the structure of optimal time-varying coverage. Numerical simulations demonstrate improved tracking performance compared to quasi-static and Voronoi-based methods, validating the proposed framework.
Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery

Abstract                   We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure–activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification.
Statistical Inference: The Big Picture

Statistics has moved beyond the frequentist-Bayesian controversies of the past. Where does this leave our ability to interpret results? I suggest that a philosophy compatible with statistical practice, labeled here statistical pragmatism, serves as a foundation for inference. Statistical pragmatism is inclusive and emphasizes the assumptions that connect statistical models with observed data. I argue that introductory courses often mischaracterize the process of statistical inference and I propose an alternative "big picture" depiction.
Trusted Source Alignment in Large Language Models

Large language models (LLMs) are trained on web-scale corpora that inevitably include contradictory factual information from sources of varying reliability. In this paper, we propose measuring an LLM property called trusted source alignment (TSA): the model's propensity to align with content produced by trusted publishers in the face of uncertainty or controversy. We present FactCheckQA, a TSA evaluation dataset based on a corpus of fact checking articles. We describe a simple protocol for evaluating TSA and offer a detailed analysis of design considerations including response extraction, claim contextualization, and bias in prompt formulation. Applying the protocol to PaLM-2, we find that as we scale up the model size, the model performance on FactCheckQA improves from near-random to up to 80% balanced accuracy in aligning with trusted sources.
Integration of LIME Explainable AI to Enhance Interpretability of Deep Learning Models in Box Palette Classification

In the food production system, manual box palette arrangement often encounters errors, such as incorrect stacking patterns, mismatched box quantities, and improper mixing of product variants. This issue occurs in a soy sauce production company with limited infrastructure, leading to the mixing of two product variants with the same box size in a single production line. This results in disruptions to the production flow and significant potential losses. This study proposes a solution using deep learning to detect box palette arrangement patterns. The Convolutional Neural Network (CNN) method is chosen because it has proven effective in image classification. Additionally, this study implements Explainable AI (XAI) to provide explanations related to classification results, increasing user confidence in the system. The Local Interpretable Model-agnostic Explanations (LIME) technique will be used to provide interpretations. This research produces the development of a deep learning model to classify box palette arrangements. Furthermore, the implementation of LIME in this study successfully provides interpretations of the model's predictions. The result is evident in the result where it shows that MobileNetV2 give an F1-Score of 100%, and LIME fidelity score of 0.2 and stability score of 0.2.
Teaching critical infrastructure protection and resilience using exercises and WebEOC: An examination of UAE undergraduate students’ after-action reports

Abstract   Resilience for the United Arab Emirates (UAE) was part of His Highness Sheikh Zayed the founder's vision for the UAE when it was formed in 1971. Consequently, national, and local (Emirate) governments adopt established standards and regulations and work with institutions that integrate robust training, testing, and exercises, to enhance their response capabilities in protecting Critical Infrastructure and Key Resources (CIKR). Rabdan Academy (RA) is an academic institution established in 2015 to train the next generation of youth in safety, security, defense, emergency, and crisis management (SSDEC) disciplines. Most RA students are employed and sponsored by local and national level agencies that contribute to Critical Infrastructure Protection and Resilience (CIPR) efforts in the UAE.  This paper aims at providing the reader with a brief overview of UAE's CIPR policies and efforts, followed by an examination of how RA faculty have integrated the Homeland Security Exercise and Evaluation Program (HSEEP) methods and CIPR concepts into various undergraduate level courses to enhance practical learning. Specifically, it details the creation and implementation of a simulated operational exercise using WebEOC a crisis management software (CMS). It demonstrates the advantages and limitations for student learning through role-playing in a virtual emergency operations center (EOC) exercise.  Sixty-five after action reports (AARs) submitted by students are examined and highlight that most of the students felt the scenario(s), the injects, and the WebEOC platform, added realism and provided an authentic learning environment, communicating between players was easy, and this was a good preparation for their careers. Limitations included information communications technology (ICT) issues, and a sense of isolation from peers due to COVID-19 enforced distance learning. The article concludes with future directions for research and the implications of using such tools for teaching CIPR, business continuity management, emergency management, and homeland security management. Suggestions are made to create communities of practice with the help of the alumni network, to practice innovative technologies and exercise and evaluation skills.
Productive Crop Field Detection: A New Dataset and Deep-Learning Benchmark Results

Preprint of the paper https://doi.org/10.1109/lgrs.2023.3296064 published in IEEE Geoscience and Remote Sensing Letters
Mapping best practices on transparency, integrity, accountability and anti-corruption: Case studies from selected parliaments

This analysis explores examples of best practices from selected parliaments in the areas of transparency, integrity, accountability and anti-corruption, and reflects on the effectiveness of the analysed approaches and their possible applicability for the European Parliament (EP). Findings confirm the need for the establishment of an independent European Union (EU) ethics body, granted investigative and enforcement powers with full transparency of enquiries, decisions and/or proposals as a pre-condition for its effectiveness and regaining citizens’ trust in the work of EU institutions. Stricter rules should be applied for Members of the European Parliament (MEPs) and EP staff regarding post-employment lobbying activities, along with more comprehensive and consistent disclosure of data on meetings with lobbyists, including foreign (third country) entities. In addition, mandatory training for all MEPs on integrity, transparency, accountability and anti-corruption standards should be introduced. Finally, more effective mechanisms of citizens and civil society engagement in the EP work should be introduced as a way of further strengthening the EP’s accountability and improving its responsiveness to citizens' concerns.
A Path Integral Approach to the Theory of Heliospheric Cosmic‐Ray Modulation

This paper introduces the path integral method, which has been widely used in quantum mechanics and statistical mechanics, into the field of cosmic-ray modulation theory to solve the Fokker-Planck equation for cosmic-ray transport. The path integral approach recognizes that the motion of cosmic rays is a Markov stochastic process. The derivation of the path integral yields a Lagrangian, L, consisting of parameters characterizing particle diffusion, drift, convection, adiabatic energy change, and Fermi acceleration. When its action functional integral ∫Ldt is minimized, it yields an Euler-Lagrange equation that describes the most probable trajectory of charged particles randomly walking in heliospheric magnetic fields. The most probable trajectory is equivalent to the classical trajectory of particles in quantum mechanics. A general solution to the cosmic-ray modulation equation with an initial boundary value problem is also formulated in this paper. The path integral has been applied to an example case of steady-state, one-dimensional, spherically symmetric modulation with a boundary at 100 AU. The modulated cosmic-ray spectra obtained with the path integral method agree very well with those from other methods, even though a simple semiclassical approximation is used in the evaluation of the path integral in this calculation. In addition to being able to calculate the modulated spectrum, the path integral method reveals new information about the average behavior of individual particles during their transit through the heliosphere, such as the particle trajectories, energy-loss behavior, and source-particle distribution, all of which are normally not available through simply solving the Fokker-Planck equation. It is expected that more complex modulation problems can also be dealt with by this method, since with the path integral approach, the mathematical problem of cosmic-ray modulation can be treated as a problem of quantum mechanics, for which many mathematical tools have been developed in the past five decades.
Learning, sampling and inference with stochastic differential equations.

Stochastic differential equations (SDEs) constitute a formidable tool for modeling the dynamics of continuous-time stochastic processes, and offer a natural framework for the probabilistic modeling of high-dimensional data. Consequently, they have garnered increasing attention in generative machine learning. Despite their promise, the applications of SDEs in machine learning have been limited due to the lack of scalable learning approaches that can train flexible neural networks to approximate stochastic processes, and the difficulty of conducting tractable inference and sampling caused by inefficient SDE solvers. In this dissertation, I outline my efforts to develop novel computational models capable of efficient and scalable learning, sampling, and inference from SDEs. Specifically, I introduce several approaches to learning SDEs for probabilistic modeling, including fitting non-linear forward and backward SDEs with neural networks and learning with limited data. Next, I present a novel deep model designed to learn SDE dynamics while satisfying given constraints on the marginal probability of the SDE. Furthermore, I develop an efficient algorithm for drawing samples from high-dimensional SDEs, which proves effective in generating diverse and high-fidelity data, such as realistic images and videos. ; Ph.D.
The Role of Pickup Ions in the Interaction of the Solar Wind with the Local Interstellar Medium. I. Importance of Kinetic Processes at the Heliospheric Termination Shock

Abstract                The role of pickup ions (PUIs) in the solar wind interaction with the local interstellar medium is investigated with 3D, multifluid simulations. The flow of the mixture of all charged particles is described by the ideal MHD equations, with the source terms responsible for charge exchange between ions and neutral atoms. The thermodynamically distinct populations of neutrals are governed by individual sets of gas dynamics Euler equations. PUIs are treated as a separate, comoving fluid. Because the anisotropic behavior of PUIs at the heliospheric termination shocks is not described by the standard conservation laws (a.k.a. the Rankine–Hugoniot relations), we derived boundary conditions for them, which are obtained from the dedicated kinetic simulations of collisionless shocks. It is demonstrated that this approach to treating PUIs makes the computation results more consistent with observational data. In particular, the PUI pressure in the inner heliosheath (IHS) becomes higher by ∼40%–50% in the new model, as compared with the solutions where no special boundary conditions are applied. Hotter PUIs eventually lead to charge-exchange-driven cooling of the IHS plasma, which reduces the IHS width by ∼15% (∼8–10 au) in the upwind direction, and even more in the other directions. The density of secondary neutral atoms born in the IHS decreases by ∼30%, while their temperature increases by ∼60%. Simulation results are validated with New Horizons data at distances between 11 and 47 au.
Implementation of a wireless sensor network for agricultural monitoring for Internet of Things (IoT)

The agricultural sector is the first industry and the most impacted by the climate changes. The delicate environment that must manage some type of crops require d the constant monitoring and maintenance of the greenhouse. The Internet of Things (IoT) gives a new alternative for real time environmental monitoring of variables such as temperature, humidity and solar irradiation that can contribute for the health y growth of the crop , and also impact for the plagu es and sickness presence . The objective of this thesis is built a Wireless S ensor N etwork using radiofrequency devices and environmental sensor s . The limitation s of this master thesis are: the location of the sensor node , the external conditions that wi ll not impact the network, the simulation, test and pilots that are deployed in a controlled space . The wireless sensor network proposed employ s the Zolertia Motes using IEEE802.15.4 standard . This device allows low power consumption , as the nodes must be located in places where it may handle several week s without change depending on the autonomy of the ir batteries cell . The network protocol manage d works over l ow consumption , as same as the transmitted and received packets of data . T he standard used on this project is the 6LowPAN . T he network co nfigured works over the stack protocol IPv6 so that all the devices handled UDP and manage this internet package. The Raspberry Pi 3 B will work as border router between the sensor nodes and the exterior consider ed as Internet using the IPv4 standard internet router protocol. The framework used for the network implementation is ContikiOS installed on th e gateway and tested using one mote located in the la b . The data manage d in this experiment has low data rate as this measurement do not require a permanent monitoring and high speed . T he atmospherically changes are not variant enough to be observe d constantly . The sample rate will be 1 package each 10 minutes. This project aim s to develop a full network implementa tion since the mote until the dashboard .
Crystal Structure and Electrochemistry of Na<sub>2-X</sub>Li<sub>x</sub>FePO<sub>4</sub>F (0≤x≤1) New Cathode Materials for Na- and Li-Ion Batteries

A series of the mixed sodium-lithium iron fluorophosphates Na2-xLixFePO4F with 0≤x≤1 has been synthesized by mechanochemically assisted solid-state route. Their crystal and local structure, and electrochemical properties were investigated by X-ray and neutron powder diffraction, 57Fe Mössbauer spectroscopy and electrochemical measurements with GITT. Three single-phase compounds Na2FePO4F, Na1.5Li0.5FePO4F and NaLiFePO4F were obtained. The former crystallize with space group Pbcn, while the latter - with Pnma, differing by Na+/Li+ distribution. All as-prepared Na2-xLixFePO4F samples are electrochemically active in Li cell pointing to the occurrence of a gradual Na+/Li+ ion exchange. Ex situ XRD and EDX study indicates that it results in the formation of lithium rich phases: NaLiFePO4F (S.G. Pbcn) for Na2FePO4F and Na0.3Li1.7FePO4F (S.G. Pnma) for Na1.5Li0.5FePO4F. Charge-discharge curves exhibit sloping profiles with an average potential of 3.3-3.4 V vs. Li/Li+. The average value of DLi determined by GITT is 10-15 and 10-16 cm2 s-1 for NaLiFePO4F and Na0.3Li1.7FePO4F, respectively.
Impacts of Sexual and Reproductive Health and Rights Misinformation in Digital Spaces on Human Rights Protection and Promotion: Scoping Review (Preprint)

BACKGROUND                   Sexual and reproductive health and rights (SRHR) are foundational to both individual autonomy and global well-being. Misinformation in this domain poses serious risks by undermining evidence-based decision-making, weakening systems of accountability, and perpetuating social injustices.                                                     OBJECTIVE                   This scoping review aimed to map and synthesize evidence on the forms, spread, and impacts of misinformation related to SRHR in digital spaces, with a particular focus on implications for the protection and promotion of human rights.                                                     METHODS                   We conducted a scoping review of scientific papers and gray literature. It was guided by the JBI (Joanna Briggs Institute) population, exposure, and outcomes framework. The extracted information was documented following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Thematic analysis was carried out and mapped against human rights standards: (1) equality and nondiscrimination; (2) Availability, Accessibility, Acceptability, and Quality; (3) informed decision-making; (4) privacy and confidentiality; (5) participation and inclusion; and (6) accountability.                                                     RESULTS                   Of the 254 eligible studies and documents, 133 focused on the information ecosystem, 37 on the individual, 32 on service delivery and health system, 31 on law and policy, and 21 on community levels. SRHR misinformation impacts individuals’ informed SRHR decisions by shaping their beliefs, attitudes, and health-seeking behaviors. It reinforces harmful and discriminatory social norms at community levels and the exclusion of marginalized voices. SRHR misinformation impacts health systems by shaping provider knowledge and practice, disrupting service delivery, and creating barriers to equitable care. It may function as a legal and policy tool to erode SRHR protections. The design of online platforms, digital marketing strategies, and content moderation policies enables misinformation to spread widely while restricting credible SRHR content.                                                     CONCLUSIONS                   SRHR misinformation in digital spaces is a systemic issue that undermines human rights across multiple levels, highlighting the urgent need for integrated, rights-based approaches to research, policy, and intervention.                                                     CLINICALTRIAL
Regional climate model data used within the SWURVE project &ndash; 1: projected changes in seasonal patterns and estimation of PET

Climate data for studies within the SWURVE (Sustainable Water: Uncertainty, Risk and Vulnerability in Europe) project, assessing the risk posed by future climatic change to various hydrological and hydraulic systems were obtained from the regional climate model HadRM3H, developed at the Hadley Centre of the UK Met Office. This paper gives some background to HadRM3H; it also presents anomaly maps of the projected future changes in European temperature, rainfall and potential evapotranspiration (PET, estimated using a variant of the Penman formula). The future simulations of temperature and rainfall, following the SRES A2 emissions scenario, suggest that most of Europe will experience warming in all seasons, with heavier precipitation in winter in much of western Europe (except for central and northern parts of the Scandinavian mountains) and drier summers in most parts of western and central Europe (except for the north-west and the eastern part of the Baltic Sea). Particularly large temperature anomalies (>6°C) are projected for north-east Europe in winter and for southern Europe, Asia Minor and parts of Russia in summer. The projected PET displayed very large increases in summer for a region extending from southern France to Russia. The unrealistically large values could be the result of an enhanced hydrological cycle in HadRM3H, affecting several of the input parameters to the PET calculation. To avoid problems with hydrological modelling schemes, PET was re-calculated, using empirical relationships derived from observational values of temperature and PET.