ethicalabs/Research-Intent-Collab huggingface dataset:
MODELADO, SIMULACIÓN Y CONTROL DEL ROBOT PARA CIRUGÍA LAPAROSCÓPICA 'LAPBOT' MODELING, SIMULATION AND CONTROL OF SURGICAL LAPAROSCOPIC ROBOT 'LAPBOT'

Este artículo presenta el modelado matemático y estructural, la simulación por computador y el control por par calculado del robot para cirugía laparoscópica ‘LapBot’, que ha sido desarrollado en el Grupo de Investigación de Automática Industrial de la Universidad del Cauca, Colombia. Inicialmente se muestra un resumen de los principales robots utilizados como asistentes para cirugías de laparoscopia en el mundo, y de cómo tratan ellos el problema del paso por la incisión practicada en la cavidad abdominal. Con base en lo anterior se describen los requerimientos que deben cumplir los robots de este tipo y a partir de éstos se diseña el robot LapBot. Se muestra el modelo cinemático y dinámico del robot LapBot, así como el modelo de la restricción espacial que representa el punto de incisión abdominal. Se implementa una estrategia de control basada en el modelo del robot (control por par calculado). Diversas trayectorias en un plano y en un espacio de tres dimensiones son utilizadas para validar tanto el modelo como el controlador.This paper presents the mathematical and structural model, simulation and computed torque control of the LapBot robot, developed by the Group of Investigation of Industrial Automatics, of the University of Cauca, Colombia. First, a summary of the principal surgery assistant robots of the world is presented, and how they solve the problem of passing through the incision into the abdominal cavity. Based on this, the conditions that must be fulfilled by the robots of this type is exposed, and from these conditions the LapBot robot is designed. Its kinematics and dynamics model is shown, as well as the mathematical spatial restriction that incision represents. A control strategy based on the model (computed torque control) is implemented. Several trajectories defined in a plane and in a three dimensions space are used to validate the model and the control.
Finite-round quantum error correction on symmetric quantum sensors

The Heisenberg limit provides a quadratic improvement over the standard quantum limit, and is the maximum quantum advantage that quantum sensors could provide over classical methods. This limit remains elusive, however, because of the inevitable presence of noise decohering quantum sensors. Namely, if infinite rounds of quantum error correction corrects any part of a quantum sensor's signal, a no-go result purports that the standard quantum limit scaling can not be exceeded using Markovian quantum error correction. We side-step this no-go result and prove that in the limit of a large number of qubits, our quantum field sensing protocol has a precision that approaches the Heisenberg limit despite a linear rate of deletion errors. This is achieved by using an optimally determined, finite number of rounds of quantum error correction married with adaptive, non-Markovian signal recovery procedures. Our protocol is based on permutation invariant quantum error correction codes, which can be designed to admit correction for a tunable number of bit-flip, phase-flip, and deletion errors. We discuss near-term implementations using quantum control assisted by coupling the spins to a common bosonic mode.
Reference-State Error Mitigation: A Strategy for High Accuracy Quantum Computation of Chemistry

Main manuscript: 12 pages, 3 figures. Attached supporting information: 21 pages, 8 figures
DoubleGen: Debiased Generative Modeling of Counterfactuals

Generative models for counterfactual outcomes face two key sources of bias. Confounding bias arises when approaches fail to account for systematic differences between those who receive the intervention and those who do not. Misspecification bias arises when methods attempt to address confounding through estimation of an auxiliary model, but specify it incorrectly. We introduce DoubleGen, a doubly robust framework that modifies generative modeling training objectives to mitigate these biases. The new objectives rely on two auxiliaries -- a propensity and outcome model -- and successfully address confounding bias even if only one of them is correct. We provide finite-sample guarantees for this robustness property. We further establish conditions under which DoubleGen achieves oracle optimality -- matching the convergence rates standard approaches would enjoy if interventional data were available -- and minimax rate optimality. We illustrate DoubleGen with three examples: diffusion models, flow matching, and autoregressive language models.
Research on Architecture for Long-tailed Genre Computer Intelligent Classification with Music Information Retrieval and Deep Learning

In this paper, we propose a Musical Attention Network (MAN) architecture for long-tailed, imbalanced music genre classification which is often ignored and quite prevalent in Music Information Retrieval (MIR). Here, the challenge is to classify the genre of long-tailed music accurately. Inspired by the recent progress in NLP, the proposed model can take advantage of genre correlations to better identify informative segments. Comprehensive experimental results demonstrate our model brings significant improvement comparing with other music genre classification models on a large-scaled benchmark dataset.
Supplementary Table S5 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;Multivariate Analysis of Prognostic Factors Based on Tumor Tissue&lt;/p&gt;
Glacial-Lake-Bench: A Global Multi-Sensor Benchmark Dataset for Evaluating Deep Learning Models for Glacial Lake Mapping

Glacial lakes are among the most sensitive indicators of climate change, closely linked to natural hazards and are natural reservoirs of freshwater resources. Thus, automated mapping and monitoring of glacial lakes is imperative. However, most automated approaches either remain regional in scope or show limited performance under challenging conditions such as cloud cover, shadows, and spatially small or frozen lakes. The global scale analysis and comparative evaluation of deep learning models is primarily hindered by the lack of readily available datasets for training data. To address this gap, we present Glacial Lake-Bench (GLB), a multisource remote sensing dataset comprising Sentinel-2, Sentinel-1, and Copernicus DEM-derived terrain (11 channels in total). GLB consists of 19,115 image-label pairs (256x256x11) spanning all Randolph Glacier Inventory (RGI) regions except Antarctica, providing the first-ever global, multi-sensor dataset for glacial lake segmentation. In addition, we compiled Glacial Lake-Bench-Challenge (GLBC), a curated subset of 1,105 image-label pairs representing scenes with cloud cover, shadow, frozen lake surfaces, and small lakes to establish a community standard for evaluating model robustness under difficult conditions. Labels are derived from Zhang et al. (2024); we independently quantify label quality through stratified sampling of 50 image-label pairs from each RGI region, amounting to 900 chips in total. Our quality assessment reveals a mean Intersection over Union (mIoU) of 0.95, precision of 0.99, recall of 0.96, and per-region agreement that is consistent with the known difficulty of small, turbid, shadowed lakes in high-mountain terrain. To demonstrate that the dataset is usable, well-posed, and appropriately challenging, we provide reference baselines from two convolutional networks (U-Net, DeepLabv3+) and two Geo-Foundation Models (GFMs) (DOFA, Prithvi-EO-2.0), evaluated with a recommended leave-one-region-out (LORO) protocol that minimizes spatial autocorrelation, alongside a random split and the GLBC subset. Baseline mIoU reaches 0.80&ndash;0.85 on GLB and drops to 0.74&ndash;0.79 on GLBC, confirming that the challenge subset isolates genuinely difficult conditions. The GLB dataset is available at https://zenodo.org/records/17917359 (Kaushik, 2026)
Bayesian deep learning–based<scp><sup>1</sup>H‐MRS</scp>of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout

PurposeTo develop a Bayesian convolutional neural network (BCNN) with Monte Carlo dropout sampling for metabolite quantification with simultaneous uncertainty estimation in deep learning–based proton MRS of the brain.MethodsHuman brain spectra were simulated using basis spectra for 17 metabolites and macromolecules (N = 100 000) at 3.0 Tesla. In addition, actual in vivo spectra (N = 5) were modified by adjusting SNR and linewidth with increasing severity of spectral degradation (N = 50). A BCNN was trained on the simulated spectra to generate a noise‐free, line‐narrowed, macromolecule signal‐removed, metabolite‐only spectrum from a typical human brain spectrum. At inference, each input spectrum was Monte Carlo dropout sampled (50 times), and the resulting mean spectrum and variance spectrum were used for metabolite quantification and uncertainty estimation, respectively.ResultsUsing the simulated spectra, the mean absolute percent errors of the BCNN‐predicted metabolite content were &lt; 10% for Cr, Glu, Gln, mI, NAA, and Tau (&lt; 5% for Glu, NAA, and mI). For all metabolites, the correlations (r's) between the ground‐truth error and BCNN‐predicted uncertainty ranged 0.72–0.94 (0.83 ± 0.06;p &lt; 0.001). Using the modified in vivo spectra, the extent of variation in the estimated metabolite content against the increasing severity of spectral degradation tended to be smaller with BCNN than with linear combination of model spectra (LCModel). Overall, the variation in metabolite content tended to be more highly correlated with the uncertainty from BCNN than with the Cramér‐Rao lower‐bounds from LCModel (0.938 ± 0.019 vs. 0.881 ± 0.057 [p = 0.115]).ConclusionThe BCNN with Monte Carlo dropout sampling may be used in deep learning–based MRS for the estimation of uncertainty in the machine‐predicted metabolite content, which is important in the clinical application of deep learning–based MRS.
Article 22 Automated individual decision-making, including profiling

Abstract                Article 3(2)(b) (Monitoring of data subjects’ behaviour); Article 4(4) (Definition of ‘profiling’); Article 5(1)(a) (Fair and transparent processing) (see also recitals 39 and 60); Article 5(2) (Accountability); Article 6 (Legal grounds for processing of personal data); Article 8 (Conditions applicable to children’s consent in relation to information society services); Article 12 (see too recital 58); Article 13(2)(f) (Information on the existence of automated decision-making); Article 14(2)(g) (Information on the existence of automated decision-making); Article 15(1)(h) (Right of access regarding automated decision-making); Article 21 (Right to object) (see also recital 70); Article 23 (Restrictions); Article 35(3)(a) (Data protection impact assessment) (see too recital 84); Article 47(2)(e) (Binding corporate rules); Article 70(1)(f) (EDPB guidelines on automated decisions based on profiling).
FLOOD EARLY WARNING SYSTEMS (FEWS) IN ENHANCING DISASTER RISK REDUCTION AND COMMUNITY RESILIENCE: A SYSTEMATIC REVIEW

The increasing frequency and severity of flood events necessitate robust Flood Early Warning Systems (FEWS) to mitigate disaster impacts and enhance community resilience. This systematic review aims to synthesise recent advances and practices in FEWS and their effectiveness in fostering resilient communities. This study addresses challenges of integrating technological innovations and community engagement to improve flood preparedness and response. Methodologically, a comprehensive scholarly literature search was conducted using Scopus and Web of Science databases, covering publications from 2021 onwards. The flow of the study is based on the PRISMA framework. The analysis was based on a total of (n=238) articles were screened, with (n=32) studies meeting the inclusion criteria. The study yielded results indicating that integrating technological innovations, community engagement, local knowledge, policy intervention, and investment are imperative in improving community resilience through Flood EWS. The economic analyses indicated that such systems are cost-effective and have strong community backing. Notably, the identification and classification of digital tools in disaster education, local knowledge in context, the role of community engagement, and the technological systems, as well as the place and importance of policy and cost intervention, all emerged as important themes. In this regard, the study makes a call for further research towards the development of integrated, adaptable, and community-based FEWS frameworks that cater to the needs and conditions of specific places. By so doing, this research provides a comprehensive review that supplies critical information for practitioners, policymakers, and researchers working advancement and implementation of flood EWS to enhance resilient communities in the wake of increasing flood risk.
Pool Boiling with Non-condensable Gas in Microgravity: Results of a Sounding Rocket Experiment

Pool boiling experiments in microgravity have been performed in the Sounding Rocket Maser 11. A heated plate of 1cm2 was located at the bottom of a small cylindrical tank partly filled with a refrigerant Novec HFE7000 pressurized with Nitrogen. Experiments were performed at different reservoir pressures and wall heat fluxes. The wall heat flux and wall temperature were simultaneously measured during the experiment and the behavior of the bubbles on the heater was filmed with a video camera through the transparent wall of the reservoir. The presence of Nitrogen dissolved inside the liquid led to a strong Marangoni convection around the bubble. The effect of Marangoni convection and evaporation on the wall heat transfer is analyzed in function of the relative values of the wall temperature and saturation temperature.
SUPPLY CHAIN DISPUTE RESOLUTION THROUGH ALTERNATIVE DISPUTE RESOLUTION (ADR) MECHANISM (VIZ, ARBITRATION, MEDIATION, CONCILIATION, AND NEGOTIATION) IN INDIA.

Generally, encountering conflicts/disputes in Supply Chain (value chain/network) Management (SCM) are unavoidable/inevitable owing to the contractual complexities and empirical eventualities (including incidental contingencies) and their efficient resolution through Alternative Dispute Resolution (ADR) mechanism (viz, Arbitration, Mediation, Conciliation, and Negotiation) in India is ubiquitous across all the Industries/Sectors. The alternative dispute resolution (ADR) methods and mechanism (viz, Arbitration, Mediation, Conciliation, and Negotiation) are always preferred to conventional Court-Litigation in business world owing to the impregnated propitious proposition with the ADR techniques. However, arbitration and mediation are the most preferred ADR techniques for supply chain dispute resolution not only in India but also in the World. In India, the Arbitration and Conciliation Act, 1996 (including amendments in 2015, 2019, and 2021) has been enacted to entrust necessary governing framework for dispute resolution through Arbitration and Conciliation mechanism or techniques of ADR across the business landscape including Supply Chain.&#x0D; This study primarily endeavours to elucidate the fundamental framework of ADR methods and mechanism efficacy vis-à-vis the empirical framework of Supply Chain (value chain/network) dispute resolution in India.&#x0D;
Rancang Bangun Data Warehouse Untuk Pembuatan Laporan dan Analisis pada Data Kunjungan Pasien Rawat Jalan Rumah Sakit Universitas Airlangga Berbasis Online Analytical Processing (OLAP)

Abstrak— Rumah Sakit Universitas Airlangga (RSUA) merupakan sarana pelayanan kesehatan yang dikelola di bawah naungan Universitas Airlangga. Seiring berjalannya proses bisnis, jumlah pasien RSUA yang semakin bertambah menyebabkan data kunjungan pasien rawat jalan yang harus dikelola oleh bagian rekam medis semakin banyak. Data tersebut dikelola untuk digunakan dalam pembuatan laporan. Informasi dalam laporan dihasilkan melalui perhitungan secara manual atau menggunakan formula Microsoft Excel menjadi kendala dalam pembuatan laporan selain adanya kebutuhan laporan dengan format beragam dan analisis multidimensional. Data warehouse berbasis Online Analytical Processing (OLAP) dapat diterapkan untuk menangani masalah tersebut. Tujuan penelitian ini adalah merancang dan membangun data warehouse berbasis OLAP agar dapat digunakan oleh bagian rekam medis RSUA dalam pembuatan laporan. Data warehouse dibangun melalui tujuh tahap yaitu analisis, desain, proses ETL (Extraction, Transformation, and Loading), penerapan OLAP, uji coba, eksplorasi untuk hasil laporan dan analisis, serta evalusi. Perancangan data warehouse menggunakan Nine Step Methodology dengan pemodelan berupa fact constellation schema. Hasil implementasi data warehouse adalah aplikasi OLAP yang dapat digunakan untuk membantu kinerja bagian rekam medis RSUA dalam pembuatan laporan, baik berupa tabel pivot maupun grafik. Penilaian pengguna terhadap sistem data warehouse menunjukkan kategori baik dengan hasil penilaian sebesar 73.61 persen. Kata Kunci— Data Warehouse, Rawat Jalan, ETL, Nine Step Methodology, OLAPAbstract— Airlangga University Hospital is a health care facilities managed by the auspices of Airlangga University. Increasing number of patients in RSUA caused more outpatients’ visits data must be managed by the medical record unit. The data was used to report making. The information in the reports generated through manual calculation or used function of Microsoft Excel became a problem of report making in addition to their reporting needs with diverse formats and multidimensional analysis. Data warehouse based on Online Analytical Processing (OLAP) could implemented to solved the problem. The goal of this research were to designing and implementing the data warehouse based on OLAP so it could be used by medical record unit to making report. Data warehouse was implemented in seven process : analysis, design, ETL (Extraction, Transformation, and Loading), implementing OLAP, trial, explore the report and analysis, and evaluation. Design of data warehouse were using Nine Step Methodology and fact constellation schema model.The outcome of this research was an OLAP application that can used to help the task of RSUA medical record unit to making report using pivot table or chart. User ratings against the data warehouse system showed good category with the results of 73.61 percent in assessment. Keywords— Data Warehouse, Outpatient, ETL, Nine Step Methodology, OLAP
Application of Semantic Web Technology to Establish Knowledge Management and Discovery in the Life Sciences.

The last three decades has seen the successful development of many high-throughput technologies that have revolutionised and transformed biological research. The application of these technologies has generated large quantities of data allowing new approaches to analyze and integrate these data, which now constitute the field of Systems Biology. Systems Biology aims to enable a holistic understanding of a biological system by mapping interactions between all the biochemical components within the system. This requires integration of interdisciplinary data and knowledge to comprehensively explore the various biological processes of a system. Ontologies in biology (bio-ontologies) and the Semantic Web are playing an increasingly important role in the integration of data and knowledge by offering an explicit, unambiguous and rich representation mechanism. This increased influence led to the proposal of the Semantic Systems Biology paradigm to complement the techniques currently used in Systems Biology. Semantic Systems Biology provides a semantic description of the knowledge about the biological systems on the whole facilitating data integration, knowledge management, reasoning and querying. However, this approach is still a typical product of technology push, offering potential users access to the new technology. This doctoral thesis presents the work performed to bring Semantic Systems Biology closer to biological domain experts. The work covers a variety of aspects of Semantic Systems Biology: The Gene eXpression Knowledge Base is a resource that captures knowledge on gene expression. The knowledge base exploits the power of seamless data integration offered by the semantic web technologies to build large networks of varied datasets, capable of answering complex biological questions. The knowledge base is the result of the active collaboration with the Gastrin Systems Biology group here at the Norwegian University of Science and Technology. This resource was customised by the integration of additional data sets on users’ request. Additionally, the utility of the knowledge base is demonstrated by the conversion of biological questions into computable queries. The joint analysis of the query results has helped in filling knowledge gaps in the biological system of study. Biologists often use different bioinformatics tools to conduct complex biological analysis. However, using these tools frequently poses a steep learning curve for the life science researchers. Therefore, the thesis describes ONTO-ToolKit, a plug-in that allows biologists to exploit bio-ontology based analysis as part of biological workflows in Galaxy. ONTO-ToolKit allows users to perform ontology-based analysis to improve the depth of their overall analysis Visualisation plays a key role in aiding users understand and grasp the knowledge represented in bio-ontologies. To this end, OLSVis, a web application was developed to make ontology browsing intuitive and flexible. Finally, the steps needed to further advance the Semantic Systems Biology approach has been discussed.
Enhancing Privacy in Real-Time Video Streams: Techniques, Challenges, and Benchmark datasets Powered by Deep Learning

The exponential growth of video surveillance, live streaming platforms, and AI-driven analytics has created unprecedented threats to visual privacy. Traditional de-identification methods (pixelation, blurring) fail to balance privacy protection with contextual utility in dynamic environments. This systematic review of 30+ peer-reviewed studies uses a taxonomical framework to classify machine learning-based privacy preservation techniques into three domains: intervention methods (sensor saturation, broadcasting commands), obfuscation strategies (encryption, morphing, adaptive blurring), and secure processing pipelines.  We test convolutional neural networks (CNNs), YOLO-based object detection systems, and hybrid approaches including GAN-driven synthetic data substitution using public datasets (MARS, DukeMTMC, Market-1501). CNN-YOLO hybrid architectures provide 30+ FPS real-time performance with 92-98% detection accuracy, while GAN-based anonymization preserves visual usefulness better than traditional approaches.  Dataset scalability, illumination variability handling (accuracy drops 15-23% in low-light settings), occlusion resilience, and adversarial attack vulnerability remain key shortcomings.  Although promising, lightweight encryption approaches for edge devices cost 12-18% processing speed and lack defined privacy-utility trade-off measures. Implications: This work unifies computer vision, cryptography, and privacy engineering into a single taxonomy, showing that context-aware frameworks need multi-level security designs to manage varied threat scenarios.  Our findings help practitioners choose strategies depending on deployment restrictions (computational resources, latency, privacy regulations), yet 67% of reviewed methods lack real-world validation outside controlled datasets.This review uniquely synthesizes intervention, obfuscation, and secure processing research to provide uniform standards, context-adaptive privacy frameworks, and adversarially-robust de-identification systems.  Five key research directions—federated learning for distributed privacy, attention-mechanism-enhanced detection under occlusion, and explainable AI for privacy-utility optimization—will shape the next generation of ethical, scalable visual privacy solutions in pervasive video analytics.