ethicalabs/Research-Intent-Collab huggingface dataset:
Dynamic-eDiTor: Training-Free Text-Driven 4D Scene Editing with Multimodal Diffusion Transformer

Recent progress in 4D representations, such as Dynamic NeRF and 4D Gaussian Splatting (4DGS), has enabled dynamic 4D scene reconstruction. However, text-driven 4D scene editing remains under-explored due to the challenge of ensuring both multi-view and temporal consistency across space and time during editing. Existing studies rely on 2D diffusion models that edit frames independently, often causing motion distortion, geometric drift, and incomplete editing. We introduce Dynamic-eDiTor, a training-free text-driven 4D editing framework leveraging Multimodal Diffusion Transformer (MM-DiT) and 4DGS. This mechanism consists of Spatio-Temporal Sub-Grid Attention (STGA) for locally consistent cross-view and temporal fusion, and Context Token Propagation (CTP) for global propagation via token inheritance and optical-flow-guided token replacement. Together, these components allow Dynamic-eDiTor to perform seamless, globally consistent multi-view video without additional training and directly optimize pre-trained source 4DGS. Extensive experiments on multi-view video dataset DyNeRF demonstrate that our method achieves superior editing fidelity and both multi-view and temporal consistency prior approaches. Project page for results and code: https://di-lee.github.io/dynamic-eDiTor/
Topological phases in condensed matter systems: A study of symmetries, quasiparticles and phase transitions

The research described in this thesis focuses on topological phases in condensed matter systems. It can be roughly divided into two parts. In the first part noninteracting systems are studied. The symmetry algebra of a charged spin-1/2 particle coupled to a non-Abelian magnetic field is determined, which explains the finite and infinite degeneracy of the energy. This system is a candidate for a continuum model of a three-dimensional topological insulator.Next, a two-dimensional version is considered on a sphere, where its spectrum is solved. The planar version of the sam model is probed by the insertion of a non-Abelian flux. Starting from a spin-polarized state, the adiabatic insertion of the flux results in a state with nontrivial spin-texture which is recognized as a quantum Hall skyrmion.The second part covers topological phases which stem from an underlying interacting model and that carry quasiparticles with fractional statistics. By applying a technique called topological symmetry breaking transitions between different phases can be induced. A careful treatment shows that different domains may appear in the broken phase separated by domain walls and it leads to a clear interpretation of confined particles.Moreover, phase transitions induced by multilayered condensates are considered. Non-Abelian phases as well as an entire hierarchy of Abelian fractional quantum Hall states are treated. A special focus is given to the study of the one-dimensional boundary between the two phases.
Novel kinetic consistent 3d mhd algorithm for high performance parallel computing systems

The impressive progress of the kinetic consistent schemes in the solution of the gas dynamics problems and the development of the effective parallel algorithms for the modern high performance parallel computing systems lead to the development of advanced methods for the solution of the magnetohydrodynamics problems for plasma physics. The novel feature of the method is the formulation of the complex Boltzmann-like distribution function of the kinetic method with the implementation of the electromagnetic interaction term. The numerical method is based on the explicit schemes, due to the logical simplicity and high efficiency of the algorithm and the easy adaptation to the modern high performance parallel computing systems.
Benchmarking the Discovery Engine

The Discovery Engine is a general purpose automated system for scientific discovery, which combines machine learning with state-of-the-art ML interpretability to enable rapid and robust scientific insight across diverse datasets. In this paper, we benchmark the Discovery Engine against five recent peer-reviewed scientific publications applying machine learning across medicine, materials science, social science, and environmental science. In each case, the Discovery Engine matches or exceeds prior predictive performance while also generating deeper, more actionable insights through rich interpretability artefacts. These results demonstrate its potential as a new standard for automated, interpretable scientific modelling that enables complex knowledge discovery from data.
A scenario-based approach to tackle trade-offs between biodiversity conservation and land use pressure in Central Italy

Abstract   Land Use and Land Cover Changes (LULCC) are recognized among the main drivers affecting biodiversity and ecosystem services. Especially in areas with high biophysical and socioeconomic heterogeneity, the need to find optimal planning solutions to combine human and natural systems still remains an open issue. This study aims to investigate how different planning strategies affecting the spatial arrangement of LULCC can produce different impacts in terms of ecosystem conditions in the Lazio region, Central Italy. Starting from the same LULCC magnitude observed in the past through an inventory approach, three different future scenarios to 2030 were depicted by means of the InVEST tool Scenario Generator: the “Business as Usual” (BaU) and, alternatively, one avoiding changes within the “Natura 2000″ sites (N2K) and another within the regional most “Degraded Municipalities” (DM). The ecological impacts of these scenarios were then assessed using the InVEST Habitat Quality model, adopting Habitat Quality (HQ) as a proxy for biodiversity. In order to characterize LULCC impacts at multiple scales, the assessment was carried out both at the regional level and within distinct ecological units. Independently from the spatial arrangement of projected LULCC, HQ decreased under all three scenarios. Nonetheless, HQ values varied among scenarios, highlighting a strict relationship between the spatial arrangement and the ecological impact of LULCC. Compared to BaU, alternative scenarios, as well as their combination into a “Best Scenario”, reduced negative impacts on HQ. These results highlighted the weak sustainability of pursuing with past urban planning strategies, while allowed to foster innovative planning approaches to mitigate habitats loss and degradation. The proposed methodology was effective to localize the conservation priorities as well as ameliorating the reliability of planning strategies based on their ecological performance. Furthermore, it supports the resolution of planning conflicts between contrasting demands (e.g., urban expansion vs biodiversity conservation), thus enhancing simultaneous benefits for both nature and people.
High-sensitivity measurements of the cosmic microwave background power spectrum with the extended Very Small Array

© 2004 RAS, MNRAS. Accepted 2004 July 7. Received 2004 June 30; in original form 2004 February 23. First of all we thank the anonymous referee for detailed comments that have significantly improved the style of the paper and presentation of the results. CD is grateful to Tim Pearson for reading the text carefully and making useful comments on the paper. We thank the staff of Jodrell Bank Observatory, Mullard Radio Astronomy Observatory and the Teide Observatory for assistance in the day-to-day operation of the VSA. We are very grateful to PPARC for the funding and support for the VSA project and to the Instituto de Astrofísica de Canarias (IAC) for supporting and maintaining the VSA in Tenerife. Partial financial support was provided by Spanish Ministry of Science and Technology project AYA2001-1657. JAR-M acknowledges the hospitality of the IAC during several visits. CD acknowledges PPARC for funding a post-doctoral research associate position for part of this work. KL, NR, AS and RS are funded by PPARC studentships. YAH is supported by the Space Research Institute of KACST. AS acknowledges the support of St. Johns College, Cambridge. CO thanks the African Institute for Mathematical Sciences for their hospitality.
Mitigating bias in deep learning: training unbiased models on biased data for the morphological classification of galaxies

ABSTRACT                Galaxy morphologies and their relation with physical properties have been a relevant subject of study in the past. Most galaxy morphology catalogues have been labelled by human annotators or by machine learning models trained on human-labelled data. Human-generated labels have been shown to contain biases in terms of the observational properties of the data, such as image resolution. These biases are independent of the annotators, that is, are present even in catalogues labelled by experts. In this work, we demonstrate that training deep learning models on biased galaxy data produces biased models, meaning that the biases in the training data are transferred to the predictions of the new models. We also propose a method to train deep learning models that considers this inherent labelling bias, to obtain a de-biased model even when training on biased data. We show that models trained using our deep de-biasing method are capable of reducing the bias of human-labelled data sets.
A Survey on Cross-Domain Sequential Recommendation

Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain). In this survey, we first define the CDSR problem using a four-dimensional tensor and then analyze its multi-type input representations under multidirectional dimensionality reductions. Following that, we provide a systematic overview from both macro and micro views. From a macro view, we abstract the multi-level fusion structures of various models across domains and discuss their bridges for fusion. From a micro view, focusing on the existing models, we first discuss the basic technologies and then explain the auxiliary learning technologies. Finally, we exhibit the available public datasets and the representative experimental results as well as provide some insights into future directions for research in CDSR.
Robotic dismembered pyeloplasty surgical simulation using a 3D-printed silicone-based model: development, face validation and crowdsourced learning outcomes assessment

Ureteropelvic junction obstruction (UPJO) is an uncommonly encountered pathology, posing a challenge for resident training. We describe the development and face validation of a robotic pyeloplasty simulation using a 3D-printed silicone-based model of UPJO for surgical training, in combination with crowdsourced scoring to objectively assess performance and learning outcomes. The organs were created using 3D modeling software and printed using a silicone-based material by Lazarus 3D, LLC. They were secured in a laparoscopic box trainer and the robotic system was docked. Eight residents and three faculty each performed two robotic-assisted right dismembered pyeloplasties on separate occaisions. Face validity was evaluated on a 5-point Likert scale. Crowd-Sourced Assessment of Technical Skills (C-SATS Inc.) scored surgical performance using the Global Evaluative Assessment of Robotic Skills (GEARS) criteria, based on video review of each simulation. All participants completed the simulation twice with fully patent anastomoses. Average time to complete the first and second trials was 44.4 min and 43.2 min, respectively. The average GEARS score was 17.1 and 17.6 for the first and second trials respectively. Participants improved on average in all 5 GEARS categories, with significant improvement in depth perception (p = 0.006). The model received mean scores (out of 5) of 4.36 for aesthetics, 4.18 for overall feel, 3.55 for realism, 4.72 for usability, and 4.72 for suturability. Residents had a significant increase in confidence between initial and final surveys on a 5-point Likert Scale: 1.63 vs. 2.38 (p = 0.03). Using 3D-printed silicone-based models, participants completed robotic-assisted dismembered pyeloplasties for training and skill acquisition. We demonstrated face validity of the simulation, which was also found to improve participant speed and significantly improve resident confidence. Crowdsourced assessment demonstrated significant improvement in depth perception.
A statistical mechanical analysis of the effect of long-chain alcohols and high pressure upon the phase transition temperature of lipid bilayer membranes

Long-chain n-alcohols decrease the main phase-transition temperature of lipid vesicle membranes at low concentrations but increase it at high concentrations. The nonlinear phenomenon is unrelated to the interdigitation and is analyzed by assuming that alcohols form solid solutions with solid as well as liquid phases. The biphasic response originates from the balance of the free energy difference of alcohols in the liquid and solid membranes (delta gA) and the alcohol-lipid interaction free energy difference (delta u) between the two phases. When delta gA less than 0 and delta u greater than 0, or delta gA less than delta u less than 0, the transition temperature decreases monotonously according to the increase in the alcohol concentration. When delta gA greater than 0 and delta u less than 0, or delta gA greater than delta u greater than 0, it increases monotonously. Biphasic response occurs with a minimum temperature when delta u greater than delta gA greater than 0, and with a maximum temperature when delta u less than delta gA less than 0. When the alcohol carbon-chain length becomes closer to the lipid carbon-chain length, delta u is equalized by delta gA, and the temperature minimum of the main transition is shifted to extremely low alcohol concentrations. Hence, long-chain alcohols predominantly elevate the main transition temperature and lose their anesthetic potency. High pressure decreased both delta gA and delta u. Presumably, high pressure improves the packing efficiency of liquid membranes and decreases the difference between the solid and liquid membrane properties.
Towards Aligned and Efficient Large Language Models

Large language models (LLMs) have rapidly transformed the landscape of AI, demonstrating remarkable capabilities across reasoning, communication, and problem-solving. Yet, realizing their full potential requires addressing two critical challenges. First, their behavior must be steered and refined after training to ensure reliability, safety, and alignment with human values and intentions. Second, their large scale comes with substantial costs in training and deployment, necessitating research into more efficient methods. My research centers on advancing both of these fronts—making LLMs both aligned and efficient. On one side, I investigate post-training techniques that allow models to better reflect human preferences, demonstrate strong reasoning capabilities, and mitigate hallucination. On the other side, I study methods for improving data efficiency in training and inference efficiency in deployment. Together, these thrusts highlight a broader vision of enabling LLMs that are not only powerful, but also trustworthy and accessible at scale.
Evaluating Prediction Accuracy for Collaborative Filtering Algorithms in Recommender Systems

Recommender systems are a relatively new technology that is commonly used by e-commerce websites and streaming services among others, to predict user opinion about products. This report studies two specific recommender algorithms, namely FunkSVD, a matrix factorization algorithm and Item-based collaborative filtering, which utilizes item similarity. This study aims to compare the prediction accuracy of the algorithms when ran on a small and a large dataset. By performing cross-validation on the algorithms, this paper seeks to obtain data that supposedly may clarify ambiguities regarding the accuracy of the algorithms. The tests yielded results which indicated that the FunkSVD algorithm may be more accurate than the Item-based collaborative filtering algorithm, but further research is required to come to a concrete conclusion.
Practice Note: Why We Started Talking About Menstruation—Looking Back (and Looking Forward) with the UN Special Rapporteur on the Human Rights to Water and Sanitation

Abstract In this conversation, Catarina de Albuquerque, former UN Special Rapporteur on the human rights to water and sanitation, and her former advisor, Virginia Roaf, discuss how menstrual health and menstruation have become critical to understanding the contribution that the water, sanitation and hygiene (WASH) sector can make to ensuring gender equality. They look back at country missions and the many conversations with women and girls that led to a closer examination of how stigma around menstruation limits access to education, work, and a life in dignity. WASH provides a strong entry point for addressing taboos relating to menstruation, but the authors identify that one must get past this often technical understanding to address deeply entrenched gender stereotypes.
Improved Measurement of the Angular Power Spectrum of Temperature Anisotropy in the Cosmic Microwave Background from Two New Analyses of BOOMERANG Observations

We report the most complete analysis to date of observations of the cosmic microwave background (CMB) obtained during the 1998 flight of BOOMERANG. We use two quite different methods to determine the angular power spectrum of the CMB in 20 bands centered at l=50-1000, applying them to ~50% more data than has previously been analyzed. The power spectra produced by the two methods are in good agreement with each other and constitute the most sensitive measurements to date over the range 300<l<1000. The increased precision of the power spectrum yields more precise determinations of several cosmological parameters than previous analyses of BOOMERANG data. The results continue to support an inflationary paradigm for the origin of the universe, being well fitted by a ~13.5 Gyr old, flat universe composed of approximately 5% baryonic matter, 30% cold dark matter, and 65% dark energy, with a spectral index of initial density perturbations ns~1.
Toward sustainable adaptation to future climate change: insights from vulnerability and resilience approaches analyzing agrarian system of Iran

There is no certainty that adaptation to climate change is sustainable, and new approaches to assess current climate change adaptation trajectories are sorely needed. In this paper, we review the farmer-focused approaches (typical of vulnerability approaches) and agro-ecosystem-focused approaches (typical of resilience approaches). We propose that a combination of the two may be a better way to conceptualize sustainable adaptation to future climate change within an agro-ecological system. To test our hypothesis, we use the case study of Iran, a land that has shown both tremendous resilience and vulnerability in its agro-ecological system. We explore the changes that have occurred in the Iranian farming system and their implications for farmers’ resilience to climate change through an integrated lens combining vulnerability approaches and resilience approaches. During the previous five decades, we describe how Iranian peasants have become small farmers, the land tenure system has changed from a traditional landlord-sharecropping system to family farms, and the quantity and quality of the agro-ecological resources have changed considerably. Our integrative approach provides important insights for both research and policy. We show that combining the two approaches can have far-reaching implications for farmers’ adaptation to future climate change knowledge, policy, and practice since one approach aims to decrease farmers vulnerability and the other approach aims to build resilient agro-ecosystems.
Impact of Depression on Anxiety, Well-being, and Suicidality in Mexican Adolescent and Young Adult Students From Mexico City

Background: Depression, anxiety, well-being, and suicidality are highly associated during adolescence and greatly predict mental health outcomes during adulthood. This study explored relationships between these variables among students from Mexico City.Methods: This representative cross-sectional study was carried out in education centers in Mexico City during the 2019-2020 academic year. Using a smartphone app, we implemented validated questionnaires for depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder-7), well-being (World Health Organization 5 Well-Being Index), and risk of suicide (Columbia-Suicide Severity Rating Scale). Partial least squares structural equation modeling was performed for the entire sample and after stratifying by gender.Results: Out of 3,042 students, 1,686 were females; mean age of the sample was 17.3 years. Compared to males, females had higher levels of anxiety, depressive symptoms, and suicidal ideation and lower levels of self-perceived well-being. Structural equation models indicated that depression was the main predictor of the rest of the outcomes in the overall sample. The role of anxiety was heterogeneous across genders and not clearly correlated to suicidal behavior or well-being.Conclusions: Large-scale mental health screening using an online tool proved feasible, with high response rates. Depression was the most important factor influencing anxiety, suicidal behavior, and well-being in Mexican high school students. The roles of depression and anxiety were heterogeneous across genders.Trial Registration: ClinicalTrials.gov Identifier: NCT04067076.
Correlation of Object Detection Performance with Visual Saliency and Depth Estimation

As object detection techniques continue to evolve, understanding their relationships with complementary visual tasks becomes crucial for optimising model architectures and computational resources. This paper investigates the correlations between object detection accuracy and two fundamental visual tasks: depth prediction and visual saliency prediction. Through comprehensive experiments using state-of-the-art models (DeepGaze IIE, Depth Anything, DPT-Large, and Itti's model) on COCO and Pascal VOC datasets, we find that visual saliency shows consistently stronger correlations with object detection accuracy (mA$ρ$ up to 0.459 on Pascal VOC) compared to depth prediction (mA$ρ$ up to 0.283). Our analysis reveals significant variations in these correlations across object categories, with larger objects showing correlation values up to three times higher than smaller objects. These findings suggest incorporating visual saliency features into object detection architectures could be more beneficial than depth information, particularly for specific object categories. The observed category-specific variations also provide insights for targeted feature engineering and dataset design improvements, potentially leading to more efficient and accurate object detection systems.