ethicalabs/Research-Intent-Collab huggingface dataset:
Whose Responsibility to Protect? The Duties of Humanitarian Intervention

The International Commission on Intervention and State Sovereignty???s report, The Responsibility to Protect, argues that when a state is unable or unwilling to uphold its citizens??? basic human rights, such as in cases of genocide, ethnic cleansing, and crimes against humanity, the international community has a responsibility to protect these citizens by undertaking humanitarian intervention. An essential issue, however, remains unresolved: which particular agent in the international community has the duty to intervene? In this article, I critically examine four ways of assigning this duty. Although I highlight the benefits of institutionalising the responsibility to protect, I argue that we should adopt, in the short term at least, a consequentialist solution: humanitarian intervention should be the responsibility of the intervener that will be the most effective.
Additional file 3 of The pattern of histone H3 epigenetic posttranslational modifications is regulated by the VRK1 chromatin kinase

Additional file 3. Fig. S3: Effect of VRK1 depletion on the epigenetic modifications of H3K27 in the presence or absence of serum in A549 lung adenocarcinoma cells.
Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment

Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss in older adults. While anti-vascular endothelial growth factor (anti-VEGF) therapy and novel treatments for geographic atrophy have improved management, timely diagnosis and personalized intervention remain a challenge. Artificial intelligence (AI), such as machine learning and deep learning models, shows promise in AMD diagnosis, classification, and treatment planning. This review summarizes AI's recent advancements, highlights its clinical utility, and addresses key limitations for wider real-world implementation in AMD. We conducted systematic search of PubMed from its conception up to August 1, 2024. Studies utilizing AI-based algorithms for AMD management were identified and categorized into early detection/classification and prediction of disease progression/treatment response. Data extraction focused on AI model performance, imaging modalities, and clinical applicability. Of 193 records screened, 47 studies were included, in which 19 studies focused on early detection/classification and 28 on prediction of disease progression/treatment response. AI models demonstrated high accuracy in AMD classification and progression prediction, including in real-world settings. Prediction models for treatment response, particularly anti-VEGF therapy, could provide recommendations on optimizing injection timelines. Recent studies have also begun tackling previous challenges, such as algorithmic biases, limited generalizability, and AI's "black-box" nature. AI-based models offer significant potential to transform AMD care through timely detection and personalized treatment; however, clinical integration depends on improving model interpretability and validating tools across diverse populations. As AI continues to evolve, ongoing research is needed to refine AI models and support their translation into evidence-based, real-world applicability in AMD.
Bayesian active learning of interatomic force field for molecular dynamics simulation of Pt/Ag(111)

Force field is a central requirement in molecular dynamics (MD) simulation for accurate description of the potential energy landscape and the time evolution of individual atomic motions. Most energy models are limited by a fundamental tradeoff between accuracy and speed. Although ab initio MD based on density functional theory (DFT) has high accuracy, its high computational cost prevents its use for large-scale and long-timescale simulations. Here, we use Bayesian active learning to construct a Gaussian process model of interatomic forces to describe Pt deposited on Ag(111). An accurate model is obtained within one day of wall time after selecting only 126 atomic environments based on two- and three-body interactions, providing mean absolute errors of 52 and 142 meV/Å for Ag and Pt, respectively. Our work highlights automated and minimalistic training of machine-learning force fields with high fidelity to DFT, which would enable large-scale and long-timescale simulations of alloy surfaces at first-principles accuracy.
The issuance of a Central Bank Digital Currency (CBDC): legal and economic challenges

Although the interconnection between technology, finance, and money has always been consistent, in the last decade the proliferation of digital technology has generated unprecedented transformative changes to the financial and monetary systems. Breakthroughs include the growth of FinTech payment solutions, the emergence of Bitcoin and altcoins, and the imminent launch of Facebook’s global stablecoin. These acted as a catalyst pushing several central banks to research the possibility of issuing a Central Bank Digital Currency (CBDC). This thesis aims to investigate the major legal and economic challenges of introducing CBDC within the EU and other selected jurisdictions. To achieve this goal, the legal basis for issuing such a currency is analyzed. Next, this research examined how CBDC would be treated by the monetary law as a new means of payment with the legal tender status. Moreover, the potential impacts on financial stability, monetary policy, and the banking sector are studied. While increasingly stronger motivations are driving many central banks towards the issuance of a CBDC, this thesis concludes that resolving such legal and economic challenges is the sine qua no for its launch. However, this may require amendments to the existing legal framework
Removing Terrorist Content Online

The use of artificial intelligence to remove so-called terrorist content from online platforms, is the object of this chapter. As a first step, the analysis identifies the complex and tangled relationship between different levels in regulating the issue. In doing so, the author notes that the United Nations’ role seem to be particularly unclear and major international initiatives – as the Global Internet Forum to Counter Terrorism – aimed at managing the challenge of terrorist content online are characterized by a lack of democratic architecture and even of transparency as regards its governance and activities. As a consequence, the removal of “dangerous” content is often left to voluntary, informal and unregulated partnerships between the public and the private sector. As a second point, the analysis remarks that regional organizations – first of all, the European Union – are taking steps to try to regulate these partnerships, but the outcome is characterized by several drawbacks, both in terms of human rights protection and institutional issues. Ultimately, the chapter assesses these challenges through the lenses of constitutional law and argues in favor of a more pronounced role of international and regional bodies, which avert excessive informality and privatization in such a sensitive field.
Smart Farming IoT sensor data filtering using Pattern Analysis and Edge computing to reduce latency

From prehistoric times to the present, agriculture has always played an important role because of how crucial it is to the continued existence of humans. Given the world’s expanding population, it is essential to increase agricultural output. The lack of available resources and severe weather has only made matters worse. By combining conventional farming practices with innovations like the Internet of Things (IoT), artificial intelligence (AI), and cloud computing, ”smart farming” allows farmers to increase their harvest quality and longevity while still coming close to their crops’ full yield potential. Because IoT devices generate so much data, it is not a good idea to transfer all redundant data from IoT sensors to the cloud and risk experiencing latency issues. Data filtering based on patterns can be done locally on the device or at the edge to conserve bandwidth. As a result, this study proposes that pattern recognition be used to eliminate redundancy before data enters the cloud rather than after it arrives. This will be demonstrated by developing a Farm Cloud Solution (FCS) method which focuses on removing the excess redundant data from the temperature and moisture readings gathered from four different farms within the vicinity. The FCS method involves pre-processing with repetition removal (RR), invalid data removal (IDR), and linear redundancy removal (LRR). A minimum 28.6 % improvement in the latency and more than 5700 bytes were saved from an average of 16136 bytes of data, roughly 35% of the data. The error is allowed within a reasonable 1% margin, allowing the farmer to forecast the upcoming harvest. Less data was sent to the cloud while retaining all vital information This FCS algorithm is a unique approach to two-tier architecture and could potentially prove to be the best option for smart farming technology.
ORDERING OF A THERMOTROPIC MESOGEN AT PHASE TRANSITION TEMPERATURE — A STATISTICAL APPROACH BASED ON QUANTUM MECHANICS

The molecular ordering of a thermotropic mesogen, 4-alkenyl bicyclohexylnitrile (ALKBCHN), has been carried out on the basis of quantum mechanics and intermolecular forces. The evaluation of atomic charges and dipole moment at each atomic centre has been done through the Complete Neglect Differential Overlap (CNDO/2) method. The configurational energy has been computed using the Rayleigh-Schrodinger perturbation method. The total interaction energy values obtained through these computations were used to calculate the probability of each configuration in a dielectric medium (i.e. non-interacting and non-mesogenic solvent, benzene) at phase transition temperature (364.7 K) using the Maxwell–Boltzmann formula. It has been observed that in dielectric medium the energies/probabilities are redistributed and there is a considerable rise in the probability of interactions, although the order of preference remains the same. An attempt has been made to develop a new and interesting model of mesogen in a dielectric medium. The present article offers a theoretical support to the experimental observations.
3D-printing for Aerospace : Fatigue Behaviour of Additively Manufactured Titanium

Additional funding agencies: Clean Sky 2 under the European Union’s Horizon 2020 research and innovation programme.
BYDV PREDICTOR: a simulation model to predict aphid arrival, epidemics of                     <i>Barley yellow dwarf virus</i>                     and yield losses in wheat crops in a Mediterranean‐type environment

BYDV PREDICTOR, a simulation model, was developed to forecast aphid outbreaks and                     Barley yellow dwarf virus                     (BYDV) epidemics in wheat crops in the grainbelt region of southwest Australia, which has a Mediterranean‐type climate. The model used daily rainfall and mean temperature to predict aphid (                     Rhopalosiphum padi                     ) buildup in each locality before the commencement of the cereal‐growing season in late autumn, and to forecast the timing of aphid immigration into crops. The introduction of BYDV by aphid immigrants, aphid buildup within the crop, spread of BYDV, and yield losses were predicted for different sowing dates. The model simulations were validated with 10 years’ field data from five different sites in the grainbelt, representing a wide range of scenarios. When first aphid arrival dates ranging from 1 June to 2 September were compared with predictions, 65% of the variation between sites and years was explained. Progress curves for the predicted percentage of plants infected with the serotype BYDV‐PAV closely resembled the starting point and shape of those recorded in 14 out of 18 scenarios. Sensitivity analysis confirmed that the combination of a high proportion of immigrants vectoring BYDV, early sowing of crops and early start to aphid arrival relative to sowing date led to the most BYDV spread and greatest yield loss. The model was incorporated into a decision support system used by farmers in targeting sprays against aphids to prevent virus spread in autumn and winter. BYDV PREDICTOR could serve as a template for modelling similar virus/aphid vector pathosystems in other regions of the world, especially those with Mediterranean‐type climates.
Interpreting the Effects of Environmental Variables on a Multistep Deep Learning Model for Algal Bloom Prediction Using Explainable Artificial Intelligence

ABSTRACT                                        In this study, a sequence‐to‐sequence (Seq2Seq) deep learning model was developed to predict the chlorophyll‐                     a                     concentration, which serves as a quantitative indicator of algal blooms, and its prediction performance was evaluated at eight different time steps from                     t                      + 1 to                     t                      + 28 days to analyze how varying prediction intervals affect model performance. The results demonstrated that the one‐step‐ahead prediction model performed best, achieving a Nash–Sutcliffe efficiency (NSE) of 0.908, whereas performance decreased with increasing prediction time step, with the NSE declining to 0.255 for 28‐day‐ahead predictions. This study also applied Shapley additive explanations analysis, a representative explainable artificial intelligence (XAI) method for quantitatively interpreting machine learning results, to further investigate the effects of environmental variables on model performance across these prediction intervals. The analysis revealed that flow rate tended to exhibit greater importance in short‐term predictions, whereas sunshine duration became more influential in longer term predictions. Overall, this study quantitatively interprets machine learning model results using XAI, contributing to the enhanced practical applicability of machine learning models in the field.
A new heuristics for the event ordering in binary decision diagram applied in fault tree analysis

Fault tree is a common approach in probabilistic risk assessment of complex engineering systems. Since their introduction, binary decision diagrams proved to be a valuable tool for complete quantification of hard fault tree models. As is known, the size of the binary decision diagram representation is mainly determined by the quality of the selected fault tree event ordering scheme. Finding the optimal event ordering for binary decision diagram representation is a computationally intractable problem, for which reason heuristic approaches are applied to find reasonable good ordering schemes. The existing method for finding optimal ordering schemes related to special types of fan-in 2 read-once formulas is employed in our research to develop a new heuristic for fault tree. Various fault tree simplification methods are used for the sake of reducing fault tree model discrepancy from fan-in 2 read-once formulas. The reduced fault tree is traversed in a depth-first manner, as for every gate, the best ordering scheme is chosen from selected sets of input permutations. The quality of the final event ordering scheme is compared to orderings produced with depth-first left most heuristic on a set of fault tree models addressed in the literature as well as on a set of our hard models. Our method proves to be a useful heuristic for finding good static event ordering, and it compares favourably to the known heuristic based on a depth-first left most assignment approach.
Axions and Cosmic Magnetic Fields

We argue that if axions are the dark matter, their coupling to electromagnetism results in exponential growth of a helical magnetic field when the axion field first rolls down its potential. After an inverse cascade, the relevant length scales to day are of order 10-100 kpc, of astrophysical interest. Our mechanism for allowing the field to grow relies on a nuance of MHD. Faraday's Law says that an electric field is needed to create a magnetic field. Previous authors relied on conventional Ohm's law to calculate E, but the resistivity is negligible and therefore they assume E is as well. We use a modified Ohm's Law that includes the effects of self-induction in limiting the current driven by a given E, which allows a magnetic field to grow.
SMALL ANGULAR SCALE MEASUREMENTS OF THE COSMIC MICROWAVE BACKGROUND TEMPERATURE POWER SPECTRUM FROM QUaD

We present measurements of the cosmic microwave background (CMB) radiation temperature anisotropy in the multipole range 2000 &lt;ℓ &lt;3000 from the QUaD telescope's second and third observing seasons. After masking the brightest point sources our results are consistent with the primary ΛCDM expectation alone. We estimate the contribution of residual (un-masked) radio point sources using a model calibrated to our own bright source observations, and a full simulation of the source finding and masking procedure. Including this contribution slightly improves the χ2. We also fit a standard Sunyaev-Zel'dovich (SZ) template to the bandpowers and see no strong evidence of an SZ contribution, which is as expected for σ8 0.8. © 2009. The American Astronomical Society.
Fourth order Schrödinger equation with mixed dispersion on certain Cartan-Hadamard manifolds

We study the fourth order Schrödinger equation with mixed dispersion on an $N$-dimensional Cartan-Hadamard manifold. At first, we focus on the case of the hyperbolic space. Using the fact that there exists a Fourier transform on this space, we prove the existence of a global solution to our equation as well as scattering for small initial data. Next, we obtain weighted Strichartz estimates for radial solutions on a large class of rotationally symmetric manifolds by adapting the method of Banica and Duyckaerts (Dyn. Partial Differ. Equ., 07). Finally, we give a blow-up result for a rotationally symmetric manifold relying on a localized virial argument.
The Politics of International Peace and Security: Introducing a New Dataset on the Creation of United Nations Security Council Subsidiary Bodies

Abstract                This paper introduces new data on the creation of subsidiary bodies (SBs) by members of the United Nations Security Council (UNSC) between 1972 and 2020. Delegation to SBs is one of the principal means through which the UNSC acts, and these bodies are designed to carry out crucial functions such as peacekeeping, implementing sanctions, and investigating crises. Yet, no research has systematically evaluated their creation, design, and use. Our dataset includes a typology of all proposed and created SBs as well as information about their purpose and design. After introducing the data, we empirically analyze the determinants of SB creation. Multivariate regression demonstrates that SBs are more likely to be created when the preferences of the permanent members are aligned. Moreover, stronger bodies are more likely to be created during periods of high preference alignment, while middle- and lower-strength bodies are less influenced by member alignment. These results provide unique evidence demonstrating how politics affects the choice of when and how the UNSC responds to global problems. Our data and analysis paint a picture of a more proactive UNSC than is commonly portrayed in the literature, and these data will enable scholars to further analyze UNSC action.
AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data Generation

Remote sensing image object detection (RSIOD) aims to identify and locate specific objects within satellite or aerial imagery. However, there is a scarcity of labeled data in current RSIOD datasets, which significantly limits the performance of current detection algorithms. Although existing techniques, e.g., data augmentation and semi-supervised learning, can mitigate this scarcity issue to some extent, they are heavily dependent on high-quality labeled data and perform worse in rare object classes. To address this issue, this paper proposes a layout-controllable diffusion generative model (i.e. AeroGen) tailored for RSIOD. To our knowledge, AeroGen is the first model to simultaneously support horizontal and rotated bounding box condition generation, thus enabling the generation of high-quality synthetic images that meet specific layout and object category requirements. Additionally, we propose an end-to-end data augmentation framework that integrates a diversity-conditioned generator and a filtering mechanism to enhance both the diversity and quality of generated data. Experimental results demonstrate that the synthetic data produced by our method are of high quality and diversity. Furthermore, the synthetic RSIOD data can significantly improve the detection performance of existing RSIOD models, i.e., the mAP metrics on DIOR, DIOR-R, and HRSC datasets are improved by 3.7%, 4.3%, and 2.43%, respectively. The code is available at https://github.com/Sonettoo/AeroGen.
Harnessing Digital Phenotyping to Advance University Student Mental Health (Brightline): Study Protocol for a Prospective Observational Study

ABSTRACT                                    Introduction                   Mental health issues such as depression and anxiety are highly and disproportionally prevalent among university students. Beyond the academic rigor, stressors imposed by a new environment result in them being vulnerable to the onset and manifestation of mental health symptomatology. Leveraging smartphones and wearables for digital phenotyping capabilities is an innovative approach for monitoring and intervening in the mental health conditions of university students. This provides a unique opportunity to collect and identify digital and behavioral biomarkers, subsequently enabling the development of predictive models to identify university students at risk.                                                     Methods and analysis                   This study — Brightline— will employ an observational study design over a 6-month period, recruiting 500 students from a major public university in Singapore. Passive data collection will occur continuously throughout the monitoring period through a wearable device (Fitbit Charge 6) and smartphone sensors via the Brightline app, which utilizes a digital phenotyping data collection platform. Active data collection will consist of self-report questionnaires to be completed at the beginning of the study and follow-up assessments at one, three, and six months after. The passive and active data collected will be analyzed to identify the digital biomarkers associated with depression, anxiety, stress, loneliness, and affect among university students. Predictive models of these mental health issues will also be developed.                                                     Ethics and dissemination                   This study was approved by the Nanyang Technological University Institutional Review Board (IRB-2023-894). Findings from this study will be published in peer-reviewed journals and presented at academic conferences.                                                     Trial registration                   NCT06770075.                                                     Strengths and limitations of this study                                                               Brightline is a large-scale longitudinal study that aims to examine the mental health of university students over time using smartphones and wearables.                                                                 This study has significant clinical implications, offering insights into the early warning signs and trajectories of university student’s mental health.                                                                 These findings may inform the development of personalized and preventative interventions for integration into university mental health services.                                                                 Challenges in adhering to active and passive data collection may lead to missing data and participant dropout, potentially affecting the development of predictive models.