ethicalabs/Research-Intent-Collab huggingface dataset:
Structure-Sensitive Quantum Runtime (SSQR): An Adaptive Runtime Framework for Structure-Aware Error Mitigation in NISQ Quantum Computing

Structure-Sensitive Quantum Runtime (SSQR): An Adaptive Runtime Framework for Structure-Aware Error Mitigation in NISQ Quantum Computing proposes a structure-sensitive runtime architecture for adaptive execution in Noisy Intermediate-Scale Quantum (NISQ) computing. Rather than applying a fixed error mitigation strategy to every quantum circuit, the proposed framework introduces a pre-execution decision layer that analyzes circuit structure and estimates execution reliability before selecting an appropriate mitigation strategy. The paper defines the core concepts of Structure Profile, Structure Fingerprint, Structure Complexity Index (SCI), Runtime Risk, and Confidence Score, and integrates them into a layered runtime architecture consisting of Structure Analysis, Hardware Abstraction, Runtime Intelligence, Adaptive Optimization, and Knowledge layers. This work is intentionally presented as a position and methodology paper. It introduces the mathematical framework, architectural design, theoretical complexity analysis, related-work comparison, and a pre-registered experimental protocol, but does not claim experimental validation or performance improvements. Those questions are reserved for subsequent papers in the SSQR research series. The proposed framework is intended to provide a falsifiable foundation for future research on adaptive quantum runtime systems, structure-aware error mitigation, and experience-driven execution strategies across NISQ quantum computing platforms.
SOLIDIFICATION FURNACE FOR MICROGRAVITY EXPERIMENTS ON SOUNDING ROCKETS DOI 10.5028/jatm.2012.04021412

The Brazilian Microgravity Program is mainly based on experiments carried out on sounding rockets. Up to present days, three missions carrying a total of of experiments were made. In all flights, a fast solidi?cation furnace, capable of producing temperatures up to 900o C, was used to process metal and semiconductor alloys in microgravity environment. This paper has described the furnace and the solidification experiment made during the last parabolic flight, called Maracati Mission, which has occurred in December, 2010.
Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer

Ovarian cancer (OC) is a predominant cause of fatality amongst gynecological malignancies, frequently identified at its later stages owing to its asymptomatic characteristics and the absence of adequate screening techniques. Imaging techniques such as ultrasound (US), magnetic resonance imaging (MRI), and computed tomography (CT) are crucial for diagnosis, but traditional methods rely heavily on subjective evaluations by radiologists. AI and radiomics offer a data-driven approach to extract quantitative features from medical images, enabling more accurate and personalized diagnosis and prognosis. This review highlights the role of AI in improving the analysis of biomarkers like CA-125, HE4, and microRNAs, and discusses the potential of integrating multiomics data (genomics, transcriptomics, epigenomics, etc.) with imaging data to enhance predictive models. Radiomics, which involves extracting high-dimensional features from medical images, has shown promise in differentiating between benign and malignant tumors, predicting genetic mutations (e.g., BRCA), and assessing tumor heterogeneity. Artificial intelligence (AI) models, particularly deep learning (DL) algorithms, have demonstrated high accuracy in diagnosing OC and predicting patient outcomes, often outperforming traditional methods.
Dependence-robust confidence intervals for capture-recapture surveys

Capture-recapture (CRC) surveys are used to estimate the size of a population whose members cannot be enumerated directly. CRC surveys have been used to estimate the number of Covid-19 infections, people who use drugs, sex workers, conflict casualties, and trafficking victims. When $k$ capture samples are obtained, counts of unit captures in subsets of samples are represented naturally by a $2^k$ contingency table in which one element -- the number of individuals appearing in none of the samples -- remains unobserved. In the absence of additional assumptions, the population size is not identifiable (i.e. point-identified). Stringent assumptions about the dependence between samples are often used to achieve point-identification. However, real-world CRC surveys often use convenience samples in which the assumed dependence cannot be guaranteed, and population size estimates under these assumptions may lack empirical credibility. In this work, we apply the theory of partial identification to show that weak assumptions or qualitative knowledge about the nature of dependence between samples can be used to characterize a non-trivial confidence set for the true population size. We construct confidence sets under bounds on pairwise capture probabilities using two methods: test inversion bootstrap confidence intervals, and profile likelihood confidence intervals. Simulation results demonstrate well-calibrated confidence sets for each method. In an extensive real-world study, we apply the new methodology to the problem of using heterogeneous survey data to estimate the number of people who inject drugs in Brussels, Belgium.
Can crop simulation models be used to predict local to regional maize yields and total production in the U.S. Corn Belt?

Abstract   Crop simulation models are used at the field scale to estimate crop yield potential, optimize current management, and benchmark input-use efficiency. At issue is the ability of crop models to predict local and regional actual yield and total production without need of site-year specific calibration of internal parameters associated with fundamental physiological processes. In this study, a well-validated maize simulation model was used to estimate yield potential for 45 locations across the U.S. Corn Belt, including both irrigated and rainfed environments, during four years (2011–2014) that encompassed diverse weather conditions. Simulations were based on measured weather data, dominant soil properties, and key management practices at each location (including sowing date, hybrid maturity, and plant density). The same set of internal model parameters were used across all site-years. Simulated yields were upscaled from locations to larger spatial domains (county, agricultural district, state, and region), following a bottom-up approach based on a climate zone scheme and distribution of maize harvested area. Simulated yields were compared against actual yields reported at each spatial level, both in absolute terms as well as deviations from long-term averages. Similar comparisons were performed for total maize production, estimated as the product of simulated yields and official statistics on maize harvested area in each year. At county-level, the relationship between simulated and actual yield was better described by a curvilinear model, with decreasing agreement at higher yields (>12 Mg ha−1). Comparison of actual and simulated yield anomalies, as estimated from the yearly yield deviations from the long-term actual and simulated average yield, indicated a linear relationship at county-level. In both cases (absolute yields and yield anomalies comparisons), the agreement increased with increasing spatial aggregation (from county to region). An approach based on long-term actual and simulated yields and year-specific simulated yield allowed estimation of actual yield with a high degree of accuracy at county level (RMSE ≤ 18%), even in years with highly favorable weather or severe drought. Estimates of total production, which are of greatest interest to buyers and sellers in the market, were also in close agreement with actual production (RMSE ≤ 22%). The approach proposed here to estimate yield and production can complement other approaches that rely on surveys, field crop cuttings, and empirical statistical methods and serve as basis for in-season yield and production forecasts.
Updated heliospheric modulation potential of cosmic rays and station-specific scaling factors for 1964-202

Galactic cosmic rays (GCR) are energetic particles originating from galactic or extra-galactic sources. When they arrive inside our heliosphere, they are modulated by the magnetic irregularities in the solar wind flow from the Sun, deflecting and slowing down the GCR particles. The level of this modulation varies according to solar activity, especially the 11-year solar cycle. The heliospheric modulation potential, denoted by &amp;#981;, describes the average energy loss of particle in MV and quantifies the level of modulation. It can be determined using ground-based neutron monitor (NM) measurements of GCRs by multiple stations. Here we use the most recent version of the NM yield function and a RMSE-minimization method to compute a new and more accurate version of the modulation potential &amp;#981; and station-specific scaling factors &amp;#954;, which can be used to scale the level of count rates to the theoretical NM count rate given by the model. The new version offers daily resolution of &amp;#981; and can be conveniently updated with new measurements, stations, or updates to datasets whenever they might occur. The scaling factors and their variation can be used to scale the data for physical analyses or to identify outliers, errors or physical phenomena which do not match with the model.
Advances in Automatic Speech Recognition: From Audio-Only To Audio-Visual Speech Recognition

Major developments in the field of finding more natural ways of interacting with computers have been taking place. The clear focus lies on making technology more approachable to people. The concept that computers can comprehend our various gestures by eyes, voices, touch and our different movements to interact is called the Natural User Interface (NUI). Today, many of these elements are available in mobile phones, PCs, and in other devices. Speech technologies particularly play a substantial role in this evolution. Significant advancement in automatic speech recognition (ASR) for well defined applications like dictation and medium vocabulary transaction processing assignments in comparatively controlled environments have been made. But, automatic speech recognition still has to reach a level needed for speech to become a completely pervasive user interface because even in clean acoustic surroundings, the state of ASR system performance falls behind human speech perception. Visual speech recognition, however, is a promising source of extra speech information and it has successfully exhibited to enhance noise robustness of automatic speech recognizers, thereby promising to expand their usability in the human computer interaction. In this paper, the main components of audio-visual speech recognition, i.e., the audio and the video components are discussed, along with the latest advancements made in this field. Further, the research goes beyond the recent advancements and discusses the future scope of audio video speech recognition and mentions some likely future developments, evaluating each on the basis of its performance. Graphs are plotted based on experiments to depict the performance improvements from audio- only ASR to audio-video ASR, along with its expected performance level in future.
Implementation of judicial independence in Uzbekistan and Kazakhstan in the rule of law context

Proposes this research is seeking to combine the theory and practice of judicial independence based in Western notions of the “Rule of Law”, modern developments of this doctrine in the documents and laws of both Uzbekistan and Kazakhstan.
Spatial wheat yield prediction using crop simulation model, GIS, remote sensing and ground observed data

A study was conducted with a broad objective of developing and demonstrating a methodology for crop growth monitoring and yield forecasting which can provide periodical crop growth assessment with spatial information. The procedure was developed to generate grid-weather, link the point based simulation model WOFOST (World Food Studies) to spatial inputs like crop, soil and weather and predict wheat yield at grid and administrative scale. Two approaches were adopted to predict wheat yield; a) the regression approach, in which simulated potential yields were regressed with final estimated yields by Directorate of Economics and Statistics (DES) for each of the six major wheat growing states and b) forcing approach in which LAI for each grid (25km x 25km) derived from remote sensing was forced into the simulation model to divert the simulation output and final grain yield into right direction. The deviations between the estimated state yield and reported yield were more in case of the forcing (0.7 – 25.4 %) as compared to regression approach (0.5 – 9.2 %). However, the spatial variability at grid level was explained more in case of forcing approach. Results indicated that regression approach is suitable for in season yield forecasting at state level and forcing approach is better for spatial crop condition assessment and crop growth monitoring.
The trilogy on the role of the International Criminal Court from non-positivist perspectives. Part II: Analytical perspectives that denounce modernity and its subjectivities: approaches from disciplinary analytical frameworks coming from Philosophy, Theology, International Law and International Relations. Special Volume for the XV Anniversary of the IberoAmerican Institute of The Hague for Peace, Human Rights and International Justice

La trilogía sobre el papel de la Corte Penal Internacional (CPI) desde perspectivas no positivistas, presentada con motivo del XV aniversario del Instituto Iberoamericano de La Haya para la Paz, los Derechos Humanos y la Justicia Internacional, propone una mirada interdisciplinaria innovadora sobre la CPI. Bajo la dirección académica de Héctor Olasolo, Mario-Iván Urueña-Sánchez y J.R. Blaise MacLean, el proyecto integra enfoques filosóficos y teológicos con los del derecho internacional y las relaciones internacionales. El texto señala que la doctrina tradicional, centrada en el positivismo jurídico, ha sido insuficiente para comprender plenamente las condiciones que permitieron la creación y consolidación de la CPI. Por ello, se destaca la necesidad de recurrir a otras disciplinas que ayuden a interpretar mejor su funcionamiento, especialmente en lo relacionado con el tratamiento de atrocidades masivas en las sociedades. La trilogía se estructura en dos ejes principales de análisis: los estudios críticos y los enfoques histórico-contextuales subalternos, que atraviesan las cuatro disciplinas involucradas en la investigación.
Collaborative Competitive filtering II: Optimal Recommendation and Collaborative Games

Recommender systems have emerged as a new weapon to help online firms to realize many of their strategic goals (e.g., to improve sales, revenue, customer experience etc.). However, many existing techniques commonly approach these goals by seeking to recover preference (e.g., estimating ratings) in a matrix completion framework. This paper aims to bridge this significant gap between the clearly-defined strategic objectives and the not-so-well-justified proxy.   We show it is advantageous to think of a recommender system as an analogy to a monopoly economic market with the system as the sole seller, users as the buyers and items as the goods. This new perspective motivates a game-theoretic formulation for recommendation that enables us to identify the optimal recommendation policy by explicit optimizing certain strategic goals. In this paper, we revisit and extend our prior work, the Collaborative-Competitive Filtering preference model, towards a game-theoretic framework. The proposed framework consists of two components. First, a conditional preference model that characterizes how a user would respond to a recommendation action; Second, knowing in advance how the user would respond, how a recommender system should act (i.e., recommend) strategically to maximize its goals. We show how objectives such as click-through rate, sales revenue and consumption diversity can be optimized explicitly in this framework. Experiments are conducted on a commercial recommender system and demonstrate promising results.
Empowering Bodies, Empowering Lives: Fight For Sexual And Reproductive Health Rights

Sexual and Reproductive Health (SRH) of a woman has been an arena which has been largely ignored, belittled and has not found attention till few decades ago. It is only with the feminist movements that these concerns started being voiced up since early 20th century. Women being denied the right to have control over her own body is tantamount to denial of fundamental human rights. Since historical times, it is the male dominant patriarchal society which has been deciding the rules of the game with respect to sexuality and women’s reproductive health and women have got used to this socio-cultural framework, hence quietly accepted it as a norm. National and international campaigns demanding women’s right to SRH have brought the agenda to the centerstage. The research highlights the issues involved in the fight for SRH, the initiatives taken as well as the limitations and challenges in achieving these rights.
A Novel Dataset and Deep Learning Benchmark for Classical Music Form Recognition and Analysis

Automated computational analysis schemes for Western classical music analysis based on form and hierarchical structure have not received much attention in the literature so far. One reason, of course, is the paucity of labeled datasets — which, if available, could be used to train machine learning approaches. Dataset curation cannot be crowdsourced; one needs trained musicians to devote sizable effort to carry out such annotations. Further, such an analysis is not simple for beginners; obtaining labeled data that can capture the nuances of a musician's reasoning acquired over years of practice is fraught with challenges. To this end, we provide a system for computational analysis of classical music, both for machine learning and music researchers. First, we introduce a labeled dataset containing 200 classical music pieces annotated by form and phrases. Then, by leveraging this dataset, we show that deep learning-based methods can be used to learn Form Classification as well as Phrase Analysis and Classification, for which few (if any) results have been reported yet. Taken together, we provide the community with a unique dataset as well as a toolkit needed to analyze classical music structure, which can be used or extended to drive applications in both commercial and educational settings.
Metagenomic Analysis of Gut Microbiome Across Developmental Stage of Asian Corn Borer (Ostrinia furnacalis)

Ostrinia furnacalis is one of the most important agricultural pests in Asia. Previous studies utilizing 16S rRNA sequencing have established a foundational understanding of the taxonomic composition of its gut microbiota; however, the dynamic functional transitions across the host’s entire life cycle remain poorly understood. In this study, we used metagenomic sequencing to systematically characterize the gut microbiome across six groups representing different life stages and sexes of O. furnacalis: first-instar, third-instar, and fifth-instar larvae, pupae, and adults (both males and females). Microbial richness and evenness vary significantly across six groups representing different life stages and sexes. Species richness is highest in the first-instar larvae (L1D2), while evenness is relatively high in both first- and third-instar larvae (L1D2 and L3D2). Additionally, no sex-based differences were observed in either indicator during the adult stage. Enterococcus mundtii is the primary species driving community succession and rapidly achieves dominance after the third-instar stage. Co-occurrence network analysis revealed that the first-instar larval network exhibits the highest complexity, with positive correlations accounting for 96.6% of all edges. Conversely, the fifth-instar larvae exhibits the greatest proportion of negative correlation edges at 29.13%, while the pupal stage network is the most dispersive, indicating microbial reorganization during metamorphosis. Functional annotation reveals that carbohydrate and amino acid metabolism pathways are significantly enriched during the larval stage. In contrast, the pupal stage is characterized by enrichment in environmental information processing and a notable increase in polysaccharide lyases (PLs). This shift indicates that the microbiota transitioned from degrading plant polysaccharides to foraging host-derived glycans. The number of resistance genes in the first-instar larvae is significantly higher than that in all other groups representing different life stages and sexes. Collectively, this study systematically reveals the dynamic succession patterns of the gut microbiome throughout the life cycle of O. furnacalis and provides a theoretical foundation for the development of microbiome-based pest management strategies.
Exploration for deep geothermal reservoirs in Luxembourg and the surroundings - perspectives of geothermal energy use

The aim of this paper is to combine different types of information necessary for a first rather qualitative assessment of deep geothermal reservoirs in the region of Luxembourg. Within the geological framework, the study area encompasses Luxembourg and the surrounding areas of Belgium, Germany, and France. On the one hand, the focus is laid on low-enthalpy hydrothermal reservoirs in Mesozoic aquifers in the Trier-Luxembourg Embayment. On the other hand, petrothermal reservoirs in the Devonian basement of the Ardennes and Eifel regions are considered for exploitation by Enhanced/Engineered Geothermal Systems (EGS). For geothermal exploration and exploitation purposes, geological, thermal, hydrogeological and structural data are necessary. Among the Mesozoic aquifers, the Buntsandstein aquifer characterized by temperatures of up to 50°C is a suitable hydrothermal reservoir that could be exploited by means of heat pumps or provide direct heat for various applications. The most promising area is the zone of the SE-Luxembourg Graben. The aquifer is the warmest underneath the upper Alzette valley and the limestone plateau in Lorraine, where the Buntsandstein aquifer lies below a thick Mesozoic cover. At the base of an inferred Rotliegend graben in the same area, temperatures of up to 75°C are expected. However, geological and hydraulic conditions are uncertain. In the Lower Devonian basement, thick sandstone-/quartzite-rich formations with temperatures >90°C are expected at depths >3.5 km and likely offer the possibility of direct heat use. The setting of the Sudeifel (South Eifel) region, including the Mullerthal region near Echternach, as a tectonically active zone may offer the possibility of deep hydrothermal reservoirs in the fractured Lower Devonian basement. Based on recent data on the structure of the Trier-Luxembourg Basin, the new concept presents the Mullerthal-Sudeifel Depression as a Cenozoic tectonic structure that is still mobile and relevant for geothermal exploration. Beyond direct use of geothermal heat, the expected modest temperatures at 5 km depth (about 120°C) and increased permeability by EGS in the quartzite-rich Lochkovian could prospectively enable combined geothermal heat production and power generation in Luxembourg and the western realm of the Eifel region.