ethicalabs/Research-Intent-Collab huggingface dataset:
Topological skyrmion phases of matter

We introduce topological phases of matter defined by skyrmions in the ground state spin -- or pseudospin -- expectation value textures in the Brillouin zone, the chiral and helical topological skyrmion phases of matter. These phases are protected by a symmetry present in centrosymmetric superconductors. We consider a tight-binding model for spin-triplet superconductivity in transition metal oxides and find it realizes each of these topological skyrmion phases. The chiral phase is furthermore realized for a parameter set characterizing Sr$_2$RuO$_4$ with spin-triplet superconductivity. We also find two types of topological phase transitions by which the skyrmion number can change. The second type occurs without the closing of energy gaps in a system described by a quadratic Hamiltonian without breaking the protecting symmetries when atomic spin-orbit coupling is non-negligible and there is a suitable additional degree of freedom. This contradicts the ``flat band'' limit assumption important in use of entanglement spectrum and Wilson loops, and in construction of the ten-fold way classification scheme of topological phases of matter. We furthermore predict two kinds of bulk-boundary correspondence signatures -- one for measurements which execute a partial trace over degrees of freedom other than spin, which yields quantized transport signatures -- and a second resulting from skyrmions trapping defects with their own non-trivial topology that is discussed in a second work, which yields generalizations of unpaired Majorana zero-modes.
Diffractive Optical Neural Networks: Pathways Toward Intelligent and Scalable Photonic Computing

ABSTRACT                   Optical neural networks have witnessed remarkable progress in recent years, driven by the demand for parallel, high efficiency, and ultrafast information processing. Among various optical neural network architectures, diffractive optical neural networks (DONNs) have emerged as a particularly promising approach due to their scalability in the neuron number, ultrahigh throughput, and passive low‐power operation. Although free‐space implementations laid the foundation for DONNs, on‐chip implementations were later developed to overcome integration challenges and to enhance compatibility with photonic circuits, forming together the two primary platforms of DONNs. Across both free‐space and on‐chip implementations, increasing efforts have been devoted to reconfigurability and multidimensional multiplexing, enabling more flexible and sophisticated optical computing functions. Beyond their intrinsic advantages for imaging and sensing, DONNs have been increasingly adopted for broader neural computing tasks, extending toward large model‐level functionalities. This review provides a comprehensive overview of DONNs, covering their theoretical foundations and structural evolution from free‐space systems to integrated on‐chip and hybrid photonic configurations. It further summarizes recent progress in enhancing computational flexibility through multidimensional modulation and reconfigurable control strategies. Additionally, representative applications and emerging directions are discussed. Finally, the review identifies key challenges and outlines prospective pathways toward intelligent and scalable photonic computing systems.
A pipeline for data and knowledge extraction from material science literature to accelerate scientific discovery.

Scientific literature is growing at an exponential pace which makes it difficult for scientists to search through and effectively utilize the data contained in it. In this work, we developed methods and data sets needed to extract knowledge and material property data from a corpus of 2.4 million materials science articles. We uniquely identified extracted polymer materials by training supervised clustering models using parameterized cosine distances with hierarchical agglomerative clustering that achieve state-of-the-art results on a benchmark data set of polymer named entity clusters. In addition, we built sequence labeling models that can tag property information using an ontology specific to the materials domain. MaterialsBERT, a pre-trained encoder fine-tuned on the aforementioned corpus of materials science papers was used as the encoder for the sequence labeling model and outperforms the baselines tested for data sets in the materials domain. We developed two pipelines, one that combines sequence labeling outputs with heuristic rules, and another using prompts to a large language model, to extract material property records from our corpus of papers. The extracted data is made available to the public through the interface polymerscholar.org. A subset of the extracted data was used to train machine learning models to predict the power conversion efficiency of polymer solar cells, thus demonstrating an end-to-end pipeline that goes from literature-extracted data to data-driven insights. This work will reduce the time taken during the search as well as the discovery phase of experimental work, thus allowing researchers to move beyond an Edisonian trial-and-error approach. ; Ph.D.
Semantic Alignment for Multimodal Large Language Models

Research on Multi-modal Large Language Models (MLLMs) towards the multi-image cross-modal instruction has received increasing attention and made significant progress, particularly in scenarios involving closely resembling images (e.g., change captioning). Existing MLLMs typically follow a two-step process in their pipelines: first, extracting visual tokens independently for each input image, and then aligning these visual tokens from different images with the Large Language Model (LLM) in its textual feature space. However, the independent extraction of visual tokens for each image may result in different semantics being prioritized for different images in the first step, leading to a lack of preservation of linking information among images for subsequent LLM analysis. This issue becomes more serious in scenarios where significant variations exist among the images (e.g., visual storytelling). To address this challenge, we introduce Semantic Alignment for Multi-modal large language models (SAM). By involving the bidirectional semantic guidance between different images in the visual-token extraction process, SAM aims to enhance the preservation of linking information for coherent analysis and align the semantics of different images before feeding them into LLM. As the test bed, we propose a large-scale dataset named MmLINK consisting of 69K samples. Different from most existing datasets for MLLMs fine-tuning, our MmLINK dataset comprises multi-modal instructions with significantly diverse images. Extensive experiments on the group captioning task and the storytelling task prove the effectiveness of our SAM model, surpassing the state-of-the-art methods by a large margin (+37% for group captioning and +22% for storytelling on CIDEr score). Project page: https://mccartney01.github.io/SAM.
Andy Witkin: From Computer Vision to Computer Graphics

The article is a retrospective on Andy Witkin, who helped merge the fields of computer vision and computer graphics through his work at Schlumberger, Carnegie Mellon University, and Pixar.
ENERGY SECURITY, RESILIENCE AND CRITICAL INFRASTRUCTURE PROTECTION: SPANISH PUERTOLLANO REFINERY CRISIS

Critical Energy Infrastructures can suffer different impacts from accidents and natural disasters which concern the whole energy system and specific functional aspects of Energy Security as well. A negative energy event -i.e. a strategic refinery blockade due to an accidentcan provide useful experiences which demonstrate the connection among logistic efficiency, resilience and Energy Security. Spanish refinery Puertollano suffered an accident in year 2003 which stopped oil logistics in a significant part of the country. Military oil refined products logistics in Spain are managed by CLH oil products logistic company -operating in the framework of a Public-Private Partnership and civil-military cooperation modeland then this enterprise facilitated the implementation of a resilience measures aimed to guarantee the operation of oil products logistic chain in the affected area. Management of Puertollano refinery crisis in 2003 showed positive results for ensuring National Energy Security, market and business normal function, system stability and infrastructure re-adjustment. This crisis advanced criteria and lesson learned for Critical Energy Infrastructure Protection and business continuity planning.
Exploring the frontiers of synthetic biology: modelling, characterization, and applications of genetics circuits

L'aplicació de la enginyeria a la biologia requereix un conjunt de normes que en garanteixin la predictibilitat. Malgrat això, certs processos biològics dificulten la aplicació d’aquestes normes, afectant a la predictibilitat. A la primera part d’aquesta tesi doctoral hem abordat aquest problema focalitzant-nos en la càrrega genètica, una de les principals fonts d’impredictibilitat als circuits genètics a la biologia sintètica. Hem formulat un model matemàtic que descriu els efectes de la càrrega genètica sobre l’expressió genètica. Addicionalment, el model ha sigut validat experimentalment en diferents escenaris compostos per gens controlats per diferents mecanismes de regulació. La validació experimental als diferents escenaris coincideix amb els resultats predits pel model, proporcionant una base per a l’avenç de la biologia sintètica. A la segona part s’ha explorat l’ús de termòmetres de RNA per al control de l’expressió de recombinases, induint modificacions irreversibles al DNA. S’han caracteritzat les contribucions de la força del promotor, la temperatura, polsos temporals i la distribució espacial en l’eficiència de recombinació. Per acabar s’ha explorat la utilitat d’aquesta nova eina en tres possibles aplicacions: en la creació de patrons espacials en 2D, en el registre de gradients de temperatura sobre tires de paper, i en la creació de dispositius genètics on la lisis cel·lular és controlada per temperatura.
A Digital Patient Engagement and Monitoring System to Improve Quality, Safety, and Continuity of Care for Pediatric Sickle Cell Anemia: A Cross-Sectional Study of Caregiver Perceptions

Introduction The management of chronic conditions like sickle cell anemia (SCA) requires high levels of patient and caregiver engagement to ensure adherence to treatment and prevent complications. Digital health interventions offer a promising avenue to support this engagement. This study evaluates the awareness, perceived usefulness, and readiness of caregivers to adopt a digital patient engagement and monitoring system for pediatric SCA management. Methods A cross-sectional survey was conducted using a structured Arabic-language questionnaire administered to 165 primary caregivers of children with SCA. The instrument included closed-ended Likert-scale items on current care challenges and digital readiness, alongside open-ended questions for qualitative insights. Quantitative data were analyzed descriptively with 95% confidence intervals, and qualitative responses underwent thematic analysis. Results Participants were predominantly parents (95.7%) and well educated (84.2% secondary or university graduates). Although 74.5% reported that clinic information was sufficient, adherence challenges remained: 44.2% sometimes and 11.5% frequently forgot appointments or medications, while 41.2% reported at least one missed event within six months. Perceptions toward digital tools were highly favourable: 86.1% (95% CI 80.2-90.5) endorsed their usefulness, 92.1% (95% CI 87.0-95.4) expressed readiness to use them, and 89.7% (95% CI 84.6-93.8) trusted digital platforms to manage health data. Nearly all respondents (97%) welcomed mobile notifications. Thematic analysis (n = 105 comments) identified four dominant needs: automated reminders (31%), emergency guidance (28%), improved communication (22%), and access to laboratory results (24%). Conclusion Caregivers of children with SCA demonstrate high readiness and a strong positive perception toward a digital engagement system. Such a system is well-positioned to address critical gaps in adherence and communication, thereby potentially improving the quality, safety, and continuity of care. The alignment between desired features and the system's design suggests high potential for successful implementation and impact.
Comprehensive molecular docking on the AlphaFold-predicted protein structure proteome: identifying target protein candidates for puberulic acid

Identifying the molecular targets of toxic compounds remains a major challenge in toxicology, particularly when adverse effects occur in off-target organs and the mechanism of action is unknown. To address this issue, a comprehensive computational pipeline was developed to perform high-throughput molecular docking across the entire AlphaFold2-predicted structural proteome of representative organisms such as human and mouse, followed by enrichment analysis to estimate biological processes potentially affected by ligand binding. The pipeline was first evaluated using six known drug-target pairs. In several cases, the known targets were ranked between the top 2 and 250 proteins (top 0.009-1.15%) among more than 21,000 proteins, and displayed docking poses consistent with experimentally observed binding conformations. However, performance was limited for certain targets, such as carbonic anhydrase II with acetazolamide, where the binding pocket was broad, leading to inaccurate docking results. The pipeline was subsequently applied to puberulic acid, a compound suspected of causing severe nephrotoxicity. Screening identified sodium/myo-inositol cotransporter 2 (SLC5A11) as a high-affinity target in both human and mouse, suggesting a mechanism involving disruption of renal osmoregulation. Although docking scores represent only theoretical binding estimates and do not directly imply physiological effects, their distribution was independent of protein length and AlphaFold2 confidence scores (pLDDT), supporting the methodological robustness. This in silico framework enables hypothesis-driven identification of potential target proteins for toxicants or therapeutics and offers a useful tool for predictive toxicology, particularly when experimental data are limited. The pipeline is available at: https://github.com/toxtoxcat/reAlldock.
Law No. 64 of February 6th, 2006 concerning ratification and execution of the agreement between the Italian Government and the United Nations for the implementation of the decisions of the International Criminal Tribunal for Rwanda, done in Rome on 17th M

Il contributo commenta la legge di ratifica ed esecuzione dellAccordo tra Italia e lONU per lesecuzione delle sentenze del Tribunale internazionale per il Ruanda stipulato nel 2004
Motion Sensitive Contrastive Learning for Self-supervised Video Representation

Accepted by ECCV2022, 17 pages
Artificial intelligence generates content: a new driving force for reshaping the film and television media industry

AI generated content, as an emerging force, is reshaping every aspect of film and television production, content creation, and dissemination. It can efficiently produce diverse scripts, dialogues, and scene designs, and accurately locate audience needs through data analysis, thereby promoting the personalization and customization of film and television works. This article aims to explore in depth the application and impact of AI generated content in the film and television media industry, in order to provide useful references and inspirations for the future development of the industry.
Many-hypercube codes: High-rate quantum error-correcting codes for high-performance fault-tolerant quantum computing

20 pages, 16 figures
Regulators and the Application of the Human Rights to Drinking Water and Sanitation in Latin America and the Caribbean

Drinking Water and Sanitation services regulators have great responsibility in the realization of the Human Rights to Water and Sanitation (DHAS). To what extent are DHAS being incorporated into the regulation of Drinking Water and Sanitation services in Latin America and the Caribbean? This publication seeks to identify trends in the region and good practices that help its full incorporation.
Climate Models and Their Projections of Future Changes

This chapter describes global climate models and their output. The current approaches for analyzing their simulations, characterizing the range of likely future outcomes, and making projections relevant for impact analysis are described, specifically referring to the latest assessment report of the Intergovernmental Panel on Climate Change. We provide a summary of future projections of average temperature and precipitation changes at continental scales, together with a broad brush picture of the likely changes in indices of extremes, characterizing both temperature and precipitation events. An analysis of changes in growing season length is also presented as an example of climate model output analysis directly relevant to studies of climate change impacts on food security.
Explaining How Deep Neural Networks Forget by Deep Visualization

Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life. Taking the advantages of interpretable machine learning (interpretable ML), this paper proposes a novel tool called Catastrophic Forgetting Dissector (or CFD) to explain catastrophic forgetting in continual learning settings. We also introduce a new method called Critical Freezing based on the observations of our tool. Experiments on ResNet articulate how catastrophic forgetting happens, particularly showing which components of this famous network are forgetting. Our new continual learning algorithm defeats various recent techniques by a significant margin, proving the capability of the investigation. Critical freezing not only attacks catastrophic forgetting but also exposes explainability.
Assessment of Projected Climate Change Impact on Green Gram Productivity through DSSAT Model under South Gujarat Environmental Condition

An investigation on climate change impact on green gram was conducted at Agronomy farm, NAU, Navsari, Gujarat during the rabi seasons of 2021-22. The calibrated and validated DSSAT CROPGRO simulation model was used to determine the productivity of green grams under various climate change scenarios. In this experiment, the effects of CO2 concentration, solar radiation and maximum and minimum temperatures on green gram productivity were examined individually and in combination. According to the sensitivity of the DSSAT CROPGRO model, under the conditions of projected climate change, the best case scenario for its favourable impact on green gram yield was a rise in CO2 concentration (600 ppm), along with solar radiation up to a certain level (1 unit), which may nullified the adverse effect of increases in temperature on green gram productivity.
Noise Crystallization and Liquid Noise: Zero-shot Video Generation using Image Diffusion Models

Although powerful for image generation, consistent and controllable video is a longstanding problem for diffusion models. Video models require extensive training and computational resources, leading to high costs and large environmental impacts. Moreover, video models currently offer limited control of the output motion. This paper introduces a novel approach to video generation by augmenting image diffusion models to create sequential animation frames while maintaining fine detail. These techniques can be applied to existing image models without training any video parameters (zero-shot) by altering the input noise in a latent diffusion model. Two complementary methods are presented. Noise crystallization ensures consistency but is limited to large movements due to reduced latent embedding sizes. Liquid noise trades consistency for greater flexibility without resolution limitations. The core concepts also allow other applications such as relighting, seamless upscaling, and improved video style transfer. Furthermore, an exploration of the VAE embedding used for latent diffusion models is performed, resulting in interesting theoretical insights such as a method for human-interpretable latent spaces.
Circuit compilation and error mitigation for near-term quantum computing

Compilation de circuits et atténuation des erreurs pour l'informatique quantique à court terme                 L'informatique quantique promet de résoudre des problèmes classiquement insolvables. Il a connu une croissance rapide ces dernières années et plusieurs plateformes quantiques basées sur différentes technologies ont été proposées, soit par le milieu universitaire, soit par l'industrie. Des algorithmes quantiques ont également été développés pour surpasser en théorie leurs homologues classiques. Malgré les progrès, le matériel quantique d'aujourd'hui est toujours sensible au bruit et difficile à contrôler, ce qui empêche la démonstration de l'avantage quantique sur les ordinateurs quantiques développés à court terme. Il existe un écart entre les ressources nécessaires aux algorithmes quantiques et les implémentations matérielles bruitées. Cette thèse vise à contribuer à combler cet écart à l'aide de la co-conception matériel-logiciel, en se concentrant sur la compilation de circuits et l'atténuation des erreurs. En particulier, cette thèse étudie (1) Le problème de cartographie des qubits pour compiler un circuit sur le matériel quantique développé à court terme, en tenant compte de la contrainte matérielle. (2) Le mécanisme de programmation multiple pour améliorer l'utilisation du matériel quantique développé à court terme. (3) La comparaison multi-plateforme pour évaluer la technique de multi-programmation sur différentes plateformes quantiques. (4) Le découplage dynamique pour réduire les erreurs de décohérence et le phénomène de crosstalk. La thèse est conclue en discutant des futures directions de recherche pour permettre des applications quantiques sur du matériel quantique développé à court terme.