ethicalabs/Research-Intent-Collab huggingface dataset:
Multi‐wavelength diffractive optical neural network integrated with 2D photonic crystals for joint optical classification

Abstract                   Optical neural networks (ONNs) have demonstrated unique advantages in overcoming the limitations of traditional electronic computing through their inherent physical properties, including high parallelism, ultra‐wide bandwidth, and low power consumption. As a crucial implementation of ONNs, on‐chip diffractive optical neural network (DONN) offers an effective solution for achieving highly integrated and energy‐efficient machine learning tasks. Notably, wavelength, as a fundamental degree of freedom in optical field manipulation, exhibits multidimensional multiplexing capabilities that can significantly enhance computational parallelism. However, existing DONNs predominantly operate under single‐wavelength mechanisms, limiting the computational throughput. Here, we propose a multi‐wavelength visual classification architecture termed PhC‐DONN, which integrates two‐dimensional photonic crystal (PhC) components with diffractive computing units. The architecture comprises three functional modules: (1) a PhC convolutional layer that enables multi‐wavelength feature extraction; (2) a three‐stage diffraction layer performing parallel modulation of optical fields; and (3) a PhC nonlinear activation layer implementing wavelength nonlinear computation. The results demonstrate that the PhC‐DONN achieves classification accuracies of 99.09 % on the MNIST dataset, 66.41 % on the CIFAR‐10 dataset, and 92.25 % on KTH human action recognition. By introducing a wavelength‐parallel classification mechanism, the architecture accomplishes multi‐channel inference during a single light propagation pass, resulting in a 32‐fold enhancement in computational throughput compared to conventional DONNs while improving classification accuracy. This work not only establishes a novel optical classification paradigm for multi‐wavelength optical neural network, but also provides a viable pathway towards constructing large‐scale photonic intelligence parallel processors.
A Comprehensive Review of Stem Cell Therapy: From Hematopoietic Transplantation to the Horizon of Regenerative Medicine

Stem cell therapy is a transformative approach in modern medicine, utilizing the unique properties of stem cells—self-renewal and multilineage differentiation—to treat a diverse spectrum of debilitating diseases. This paper, through a literature review method, systematically explores the established clinical applications, recent advances, and future challenges of stem cell therapies. The review focuses on two principal arms: Hematopoietic Stem Cell Transplantation (HSCT), a clinical cornerstone for treating hematological malignancies and blood disorders, and the rapidly expanding field of regenerative medicine utilizing Mesenchymal Stem Cells (MSCs) and Induced Pluripotent Stem Cells (iPSCs). Key findings reveal significant progress in refining HSCT protocols, including reduced-intensity conditioning and enhanced graft-versus-host disease (GVHD) prevention using advanced protocols and cellular agents like MSCs. Furthermore, technological innovations such as CRISPR-based gene editing and engineered cellular therapies, alongside emerging technologies like 3D bioprinting and organoids, are poised to revolutionize the treatment of genetic disorders and solid tumors. The paper concludes that while significant hurdles remain regarding cellular heterogeneity and tumorigenicity, continued research into precision medicine and bioengineering will pave the way for safer, more efficient, and personalized regenerative therapies.
Supplementary Table S3 from Blood-Based Genomic Alteration Signature for Predicting Progression-Free Survival in &lt;i&gt;De Novo&lt;/i&gt; Metastatic Hormone-Sensitive Prostate Cancer: A Real-World Study

&lt;p&gt;Clinical Data Correlated with Genomic Alterations&lt;/p&gt;
Trends of planet formation through chemical analysis of protoplanetary disks

Our solar system’s formation, as well as the formation of exoplanetary systems, may be able to be better explained by studying young stars with disks  within young (1-5 Myr) star forming regions, such as Lupus, Rho Ophiucus, and Chameleon 1. Data of 13CO, C18O, C2H, and N2H+ were taken using the Atacama Large Millimeter Array (ALMA) at radio wavelengths, where the detection statistics among the twenty disks for each region was analyzed  in relationship with various factors including the disk radial size, the effective temperature of their star, as well as the spatial distribution within a star forming region. Within this, extended disks were more likely to have detections of molecular emission than disks that solely had stars with effective temperatures above K7, while disks that were both extended and had stars hotter than K7 had the most positively identified species. Subregions in Lupus, Rho Ophiuchus, and Chameleon 1 with a higher disk density also generally had more chemical detections attributed to those disks. Separate trends in chemical detections appeared for Lupus and Chameleon 1, and Rho Ophiucus respectively. Lupus and Chameleon one both had appreciable amounts of N2H+ and C2H, suggesting that both CO freeze out had occurred and that there were other hydrocarbon particles, where this abundance of particles suggest that the disks of both of these regions are past the initial stages of planetary formation. Rho Ophiucus, however, lacks N2H+ data, and almost every single disk has 13CO and C18O present within them, with little C2H, suggesting that the disks of this region are still in initial stages of planetary formation.
Development of an innovative therapy combining stem cells and an implantable medical device for regenerative medicine

Développement d'un médicament de thérapie innovante combinant des cellules souches et un dispositif médical implantable pour la médecine régénérative                 Ces dernières années ont vu émerger des avancées marquantes en médecine régénérative et en ingénierie tissulaire qui ont transformé le domaine de la santé. Ces techniques novatrices ciblent les défis inhérents à la réparation des tissus humains, notamment le cartilage et l'os, en exploitant des conceptions combinatoires de dispositifs médicaux implantables et de médicaments de thérapie innovante. Malgré ces progrès, des obstacles majeurs persistent. Notre équipe développe des solutions thérapeutiques avancées qui intègrent des dispositifs médicaux implantables combinés à des cellules souches mésenchymateuses, en ciblant des applications orthopédiques. Trois avancés clés prennent forme : i) la mise au point de pansements thérapeutiques polymériques via des techniques d'electrospinning pour régénérer l'os et le cartilage dans les stades avancés de l'arthrose, ii) l’optimisation d'un implant novateur pour la prévention de l'arthrose précoce, et iii) le développement d’une approche thérapeutique utilisant des substituts osseux hybrides et des nano-complexes therapeutiques pour surmonter les défis de la vascularisation dans les cas de défauts osseux de taille critique. Enfin, notre collaboration avec notre « spin-off » Lamina Therapeutics reflète notre engagement à concrétiser les découvertes effectuées en laboratoire par des applications cliniques efficaces. En conclusion, nos avancées en médecine régénérative mettent en lumière des perspectives prometteuses de percées cliniques, tout en ouvrant une nouvelle voie de progrès dans la prise en charge des patients.
NAVIGATING THE LEGAL LABYRINTH: CLOUD SERVICE PROVIDER ACCOUNTABILITY FOR THIRD-PARTY TRADE SECRET MISAPPROPRIATION IN THE ERA OF CONTRACTUAL SHIELDS AND REGULATORY FRAGMENTATION

Cloud computing has fundamentally transformed information storage and enterprise operations, yet this technological shift has exposed critical vulnerabilities in trade secret protection. Corporate data once confined to internal networks now spans global cloud infrastructures, making Cloud Service Providers (CSPs) essential intermediaries in safeguarding proprietary knowledge. The central research problem examined in this study is whether, and to what extent, CSPs can be held legally accountable when third-party actors misappropriate trade secrets by exploiting security gaps, contractual disclaimers, and fragmented regulatory standards. The primary objective of this research is to critically analyze the liability regime applicable to CSPs in cases of third-party trade secret misappropriation, identifying doctrinal and practical gaps within statutory frameworks, judicial reasoning, and contractual protections. The study employs a comparative doctrinal legal research methodology, examining statutory provisions, case law, and authoritative secondary literature across three key jurisdictions: India, the United States, and the European Union. The research reveals that India's reliance on contractual shields and fragmented statutory mechanisms fails to delineate clear CSP responsibilities, particularly where breaches stem from multi-tenant misconfigurations or supply-chain vulnerabilities. In contrast, the United States provides consolidated remedies through the Defend Trade Secrets Act, while the European Union operates a layered framework integrating trade secret law with mandatory cybersecurity duties under GDPR Article 32 and the NIS2 Directive. Across all jurisdictions, evidentiary challenges in establishing "reasonable security," causation, and attribution significantly complicate accountability determinations. The study concludes that targeted reforms are essential to address these deficiencies. Key recommendations include enacting a model Indian trade secrets statute incorporating explicit cloud-security benchmarks, implementing legally mandated contractual clauses ensuring audit rights and incident reporting, establishing calibrated safe harbors for CSPs tied to demonstrable security controls, and developing international frameworks to facilitate cross-border evidence recovery. These measures aim to balance innovation with robust trade secret protection in an era where cloud architecture has become indispensable to economic life. Keywords: Cloud computing, Cloud Service Providers, Contractual liability, Cybersecurity, Intermediary liability, Regulatory fragmentation, Trade secrets
Universality, Phase Transitions and Statistical Mechanics

This survey will describe some mathematical results and conjectures related to phase transitions and statistical mechanics. In particular, we will focus on the principle of universality which asserts that singularities associated with second order phase transitions are universal. This means that they do not depend on the details of the model. Hence, although the temperature at which a phase transition occurs is typically model dependent, the macroscopic or long distance behavior at the transition is believed to depend on only a few general features such as the dimension of space and the symmetry of the model. Universality explains why relatively simple mathematical models can give quantitatively accurate information about transitions for a wide class of physical and mathematical systems. After a brief digression about random walks and self-avoiding walks we shall focus on the Ising model for interacting spins. The phase transition of the Ising model is believed to describe liquid-gas transitions, coupled chains of quantum anharmonic oscillators, certain quantum field theories and models of probabilistic cellular automata. The last section is devoted to a review of some problems in random Schrodinger operators and GOE and GUE matrix ensembles. The universality of eigenvalue correlations is explored.
The Methanol Multibeam Survey

A purpose built 7-beam methanol receiver, installed on the Parkes Radio Telescope, was used to survey the Galactic plane for newly forming high mass stars, pinpointed by strong methanol maser emission at 6.7 GHz. The Methanol Multibeam (MMB) survey observed over 60% of the Galactic plane, detecting close to 1000 sources. The MMB survey provides a huge resource for studies of high-mass star formation, an important stage in the evolution of the interstellar medium. The MMB survey is also a valuable resource for investigations into the structure and dynamics of our Galaxy: with narrow velocity ranges of emission (typically only a few km/s) and velocities closely correlated with the systemic velocity of their surrounding molecular clouds, 6.7-GHz methanol masers provide estimates of the spiral arm velocities and their structure. I will discuss the techniques and properties of the MMB survey, before outlining recent results, which include the identification of regions of enhanced star formation believed to be indicative of the origins of the spiral arms and the interaction of the Galactic bar with the 3-kpc arms. I will also discuss the various follow-up programmes including a study of magnetic fields through associated hydroxyl masers.
THE DISCRIMINATION AGAINST THE UNTOUCHABLE CASTE IN ASIAN INDIAN SOCIETY AS REFLECTED IN ARUNDHATI ROY’S THE GOD OF SMALL THINGS

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Threshold Effects in Parameter Estimation as Phase Transitions in Statistical Mechanics

24 pages, 3 figures, submitted for publication
Web-Based ERP System Prototype for Enhanced Inventory and Supply Chain Management in the Logistics Industry

Technological advancements have driven companies to adopt innovative solutions to improve efficiency and streamline operations. PT Kesuma Express, a logistics company in Indonesia, faced inefficiencies and frequent errors due to its reliance on manual processes such as transaction recording, document management, and income reporting. This research developed a web-based Enterprise Resource Planning (ERP) system prototype to address these challenges, focusing on Inventory and Supply Chain Management (SCM) modules. The prototype uses the Rapid Application Development (RAD) model and the Laravel framework and includes key features such as transaction recording, customer data management, and financial reporting. The inventory module enhances truck data management, monitors truck availability, and alerts users about data discrepancies, ensuring accuracy and timely actions. The SCM module supports the delivery process by providing real-time delivery status tracking that is accessible to both warehouse staff and administrators, enabling transparency and efficiency in goods distribution. The system replaced PT Kesuma Express manual processes, significantly improving operational efficiency and reducing errors. By integrating inventory and SCM capabilities, the ERP system has streamlined operations, enhanced data accuracy, and improved customer service quality. This research demonstrates the transformative potential of ERP systems for logistics companies aiming to modernize their operations.
Doubly Robust Alignment for Large Language Models

Accepted to NeurIPS 2025
Application Research of Artificial Intelligence Generated Content in Programming Courses

Against the background of digital transformation of education and the demand for teaching innovation in programming courses, this paper analyzes the state of Artificial Intelligence Generated Content (AIGC) technology application, and elaborates in-depth on how AIGC empowers the teaching of programming courses. By introducing the new technologies, methods, and programs brought by AIGC, such as intelligent instructional design assistance, course learning assistant applications, and the outlook of new digital teaching materials, this paper demonstrates the great potential of AIGC in teaching. These innovations are expected to promote personalization and differentiation of teaching and learning, enhance the quality and effectiveness of teaching and learning, and provide forward-looking insights and directions for further deepening education reform. In addition, this paper explores the challenges of implementing these AIGC-enabling strategies, which provide useful references for practitioners and are valuable for promoting the development of programming education.
Transfer Learning with Pre-trained Conditional Generative Models

Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods always assume at least one of (i) source and target task label spaces overlap, (ii) source datasets are available, and (iii) target network architectures are consistent with source ones. However, holding these assumptions is difficult in practical settings because the target task rarely has the same labels as the source task, the source dataset access is restricted due to storage costs and privacy, and the target architecture is often specialized to each task. To transfer source knowledge without these assumptions, we propose a transfer learning method that uses deep generative models and is composed of the following two stages: pseudo pre-training (PP) and pseudo semi-supervised learning (P-SSL). PP trains a target architecture with an artificial dataset synthesized by using conditional source generative models. P-SSL applies SSL algorithms to labeled target data and unlabeled pseudo samples, which are generated by cascading the source classifier and generative models to condition them with target samples. Our experimental results indicate that our method can outperform the baselines of scratch training and knowledge distillation.
Science Communication and Public Engagement

This book offers a historical description of science communication and addresses the gaps in the literature with the correspondent counterproposals to address these issues to promote audiences' engagement with science. Denisse Vásquez-Guevara offers practical guidelines to develop and implement effective science communication. Contributors present several communication theories, research and participatory intervention methodologies, data collection, and facilitation tools contrasted with scientific evidence and case studies to illustrate how to carry out engaging initiatives that unite researchers and people around collaboration and interest in scientific research. Scholars of science communication, participatory-action research, and decolonial methodologies will find this book of particular interest.
Kasteran* — Entity-Component-System Architecture — Compiler, Programming Languages, Type Theory, Sovereign AI, and Post-Cloud Architecture (Kasteran)

The Entity-Component-System (ECS) architecture is a design pattern that separates data (components) from behavior (systems) while treating entities as lightweight identifiers. ECS has become the dominant architecture for game engines, physics simulation, and real-time interactive applications due to its cache-friendly data layout, composability, and suitability for parallel execution. This document surveys ECS design principles, compares production ECS frameworks (Unity DOTS, Bevy, Flecs), and examines Kasteran*'s native ECS support through its linear type system and data-oriented design capabilities.    Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores compiler, programming languages in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.
Harnessing Small‐Data Machine Learning for Transformative Mental Health Forecasting: Towards Precision Psychiatry With Personalised Digital Phenotyping

ABSTRACT                   Advances in digital phenotyping have opened the door to continuous, individualised monitoring of mental health, but realising the full potential of these data demands machine learning models that can operate effectively in ‘small‐data’ regimes—where per‐user data are sparse, irregular and noisy. This article explores the feasibility, challenges and opportunities of small‐data machine learning approaches for forecasting individual‐level mental health trajectories. We examine the limitations of traditional clinical tools and population‐level models and argue that fine‐grained time‐series forecasting, powered by models such as tabular prior‐data fitted networks (TabPFN), Gaussian processes, Kalman filters and meta‐learning strategies, offers a path towards personalised, proactive psychiatry. Emphasis is placed on key clinical requirements: real‐time adaptation, uncertainty quantification, feature‐level interpretability and respect for interindividual variability. We discuss implementation barriers including data quality, model transparency and ethical considerations and propose practical pathways for deployment—such as integrated biosensor platforms and just‐in‐time adaptive interventions (JITAIs). We highlight the emerging convergence of small‐data ML, mobile sensing and clinical insight as a transformative force in mental healthcare. With interdisciplinary collaboration and prospective validation, these technologies have the potential to shift psychiatry from reactive symptom management to anticipatory, personalised intervention.
The impact of model size on catastrophic forgetting in Online Continual Learning

This study investigates the impact of model size on Online Continual Learning performance, with a focus on catastrophic forgetting. Employing ResNet architectures of varying sizes, the research examines how network depth and width affect model performance in class-incremental learning using the SplitCIFAR-10 dataset. Key findings reveal that larger models do not guarantee better Continual Learning performance; in fact, they often struggle more in adapting to new tasks, particularly in online settings. These results challenge the notion that larger models inherently mitigate catastrophic forgetting, highlighting the nuanced relationship between model size and Continual Learning efficacy. This study contributes to a deeper understanding of model scalability and its practical implications in Continual Learning scenarios.
Long-period comet flux in the planetary region: Dynamical evolution from the Oort cloud

This study analyzes the evolution of 2 × 105 orbits with initial parameters corresponding to the orbits of comets of the Oort cloud under the action of planetary, galactic, and stellar perturbations over 2 × 109 years. The dynamical evolution of comets of the outer (orbital semimajor axes a > 104 AU) and inner (5 × 103   104 AU in the interval 15 AU   25 AU) subsequently have a greater number of returns to the region q   104 AU (∼ 5.0 × 10−14 yr−1) is one order of magnitude lower than the rate of passage of comets from the outer Oort cloud (∼ 9.1 × 10−13 yr−1).
Order–disorder phase transition in an argon–krypton solid solution on the basis of the quasichemical approximation of statistical-mechanical theory

A new statistical-mechanical approach to the description of ordering in binary alloys is developed on the basis of Rott’s statistical method of conditional distributions. In a quasichemical approximation of the theory with account for at least five coordination spheres we predict an order–disorder phase transition in an argon-krypton solid solution, which can be observed experimentally.
Exploring Unsupervised Learning Paradigms for  Object Detection: A Survey

Unsupervised visual representation learning, also known as self-supervised learning, seeks to derive features without relying on manual annotations. Unsupervised learning algorithms for object detection are receiving increasing research attention as an alternative to resource-intensive supervised learning approaches requiring extensive manual annotations. This survey provides a comprehensive review of recent unsupervised and self-supervised learning paradigms proposed for object detection in images and videos. The key unsupervised learning approaches examined include contrastive learning, region proposal methods, clustering, adversarial training, and fusion of imperfect saliency models. The surveyed techniques incorporate innovative pretext tasks, multi-view comparisons, student-teacher frameworks, and noise modelling to enable unsupervised feature learning for detection. Evaluations demonstrate state-of-the-art performance on benchmark datasets surpassing supervised models in some cases. The survey provides an in-depth analysis of relative merits, limitations, and promising future directions to advance unsupervised object detection.