ethicalabs/Research-Intent-Collab huggingface dataset:
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical image analysis, especially for digital pathology where the training data are rare. In this study, we evaluate the zero-shot segmentation performance of SAM model on representative segmentation tasks on whole slide imaging (WSI), including (1) tumor segmentation, (2) non-tumor tissue segmentation, (3) cell nuclei segmentation. Core Results: The results suggest that the zero-shot SAM model achieves remarkable segmentation performance for large connected objects. However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image. We also summarized the identified limitations for digital pathology: (1) image resolution, (2) multiple scales, (3) prompt selection, and (4) model fine-tuning. In the future, the few-shot fine-tuning with images from downstream pathological segmentation tasks might help the model to achieve better performance in dense object segmentation.
Impact of sustainable agricultural cultivation system policy on food security and welfare of rice farmers in south Sumatra province, Indonesia

Ensuring food security has become an issue for a critical country at various levels of economic development, but the agricultural sector plays a strategic role in improving food availability. Sustainable food production and environmental protection should be government policy. This research aimed to analyze the impact of the sustainable agricultural cultivation system (SACS) policy on food security and the welfare of rice farmers. The area of the research, Tanjung Lago District, of South Sumatra province, were selected purposively. The research method used was the survey method, and the sample withdrawal method was a sample random method with 30 farmers in 2021. The results showed that with the implementation of SACS, land productivity increased to 6.5 tons/ha, previously 4.5 tons/ha. Therefore, the farmers' income also increased, amounting to Rp1,647,464.00/ha. Food availability increased the impact of production, and the rice stock was steadier than before. Better food affordability due to distribution, stabilization of supply and price, stock management, and access to markets and information are raising the usability of food, including improving consumption patterns and food safety and quality. The welfare of farmers is feeling prosperous because of the SACS. So, SACS could improve food security and farmers' welfare
Simulations of Galaxy Formation and Large Scale Structure

Das Studium der Entstehung und Entwicklung von Galaxien ist eines der interessantesten Gebiete der Kosmologie. In dieser Arbeit verwenden wir die sogenannte Resimulationstechnik und berechnen ultrahochauflösende Computersimulationen der Strukturentstehung im Universum. Zunächst simulieren wir eine sorgfältig ausgewählte große Region mittlerer Dichte mit vier verschiedenen Auflösungen und untersuchen die Abhängigkeit der Eigenschaften der Halos aus dunkler Materie von der Materiedichte in ihrer lokalen Umgebung. Wir finden eine schwache Abhängigkeit der Spin- und Konzentrationsparameter. Die Form der Halos, ihre Entstehungszeitpunkte sowie der Zeitpunkt des letzten Verschmelzens mit einer etwa gleich großen Galaxie sind dagegen umgebungsunabhängig. In einem weiteren Schritt wenden wir ein semi-analytisches Modell der Galaxienentstehung auf die Simulationen an. Unsere Modellgalaxien zeigen starke Umgebungsabhängigkeiten. Im zweiten Teil dieser Arbeit vergleichen wir die beobachtete Struktur und Kinematik der bekannten Satellitengalaxien der Milchstraße mit den Eigenschaften simulierter Subhalos. Es war behauptet worden, diese beobachteten Eigenschaften würden nicht mit den simulierten übereinstimmen, was das kosmologische LambdaCDM Modell in Frage gestellt hat. Wir berechnen die Entstehung des Halos einer Galaxie von der Größe der Milchstraße mit bisher unerreichter Auflösung und untersuchen dieses sogenannte "Substruktur-Problem" erneut. Wir finden, daß die von unserer Simulation vorhergesagten Geschwindigkeitsdispersionen beobachteter Sternverteilungen exakt mit den beobachteten Dispersionen übereinstimmen. Dies ist ein Triumph für das LambdaCDM Modell. Andere Formen dunkler Materie, die vorgeschlagen wurden, um das Substruktur-Problem zu lösen, würden die gefundene übereinstimmung zerstören. Im dritten Teil dieser Arbeit untersuchen wir, ob die Gammastrahlung, die durch Selbst-Annihilation entstehen kann, falls die dunkle Materie im Universum aus schwach wechselwirkenden massiven Teilchen besteht, mit der nächsten Generation von Gammastrahlungsteleskopen beobachtet werden kann. Wir verwenden unsere hochauflösende Simulation des Galaxienhalos und finden, daß frühere Untersuchungen die Bedeutung der Gammastrahlung von Subhalos überschätzt haben. Wir schlagen eine neue Beobachtungsstrategie vor und zeigen, daß bei derzeit diskutierten Modellen für die Teilchen der dunklen Materie das galaktische Zentrum mit GLAST beobachtet werden kann.
deSHAME Croatia, Serbia, and Montenegro: Harmful contacts and Self-generated Child Sexual Abuse Material (SG-CSAM) among high school students

Objectives The objective of this research presentation is to examine the prevalence, characteristics, and implications of harmful online contacts and self-generated child sexual abuse material (SG-CSAM) among high school students in Croatia, Serbia, and Montenegro, with the aim of informing prevention strategies and response frameworks. Conducted under the deSHAME initiative coordinated by the Safer Internet Center Croatia, the research provides a detailed analysis of online risks facing adolescents in the digital era. Methods The study utilized a criterion-based stratified sample, collecting data from national samples of Croatia, Serbia and Montenegro, with a focus on students in 1st and 3rd grades. Data were collected through a comprehensive, standardized questionnaire, which underwent a rigorous double-blind translation process and pilot testing to ensure cultural and linguistic accuracy. The survey captured a wide range of data, including socio-demographic information, internet usage patterns, exposure to online sexual harassment, and experiences with self-generated sexual content. Ethical approval was secured from relevant authorities, and participants provided informed consent, with safeguards to minimize secondary traumatization. Responses were analyzed quantitatively to identify patterns, prevalence, and risk factors associated with SG-CSAM and harmful online interactions. Results The findings in Croatia reveal alarming statistics: nearly 40% of students reported receiving sexually explicit content from known or unknown individuals, and 10% admitted to sharing their own sexualized material. Among these, 56% shared under persuasion, while 22% reported coercion or threats, highlighting the critical risks of sextortion. Additionally, 41.2% of Croatian youth reported experiencing sexual harassment online, with similar trends observed in Serbia and Montenegro. Conclusions The presentation will discuss practical implications, including the necessity for sustained efforts in digital literacy education, the development of standardized response protocols in schools, and the promotion of interdisciplinary approaches to enhance the digital well-being and safety of children across the region. This research contributes to a growing body of evidence advocating for systemic and proactive measures against online sexual exploitation of minors. The study underscores the inadequacies in current prevention and response mechanisms, as more than half of the students chose to ignore incidents, and only a minority sought support from adults. These findings stress the urgent need for targeted education and intervention programs aimed at empowering students, equipping educators, and fostering cross-sectoral collaboration.
Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback

Reward models trained through Reinforcement Learning from AI Feedback (RLAIF) methods frequently suffer from limited generalizability, which hinders the alignment performance of policy models. This challenge stems from various issues, including distribution shift, preference label noise, and mismatch of overly challenging samples with model capacity. In this paper, we aim to enhance the generalizability of reward models through a data-centric approach, driven by the insight that these issues are inherently intertwined from a uniform perspective of data difficulty. Accordingly, we propose a novel framework, Curriculum-RLAIF, which constructs preference pairs with varying difficulty levels and then produces a specific curriculum for reward model training. Comprehensive experimental results suggest that reward models trained with Curriculum-RLAIF achieve improved generalizability, boosting the alignment performance of policy models by a significant margin without incurring additional inference costs compared to various existing non-curriculum baselines. Further analysis and comparison with alternative strategies highlight the superiority of Curriculum-RLAIF in simplicity, efficiency, and effectiveness.
Algorithm or Creative? A Three-Arm Experimental Design for Decomposing Algorithmic Bias in Platform A/B Tests

Online advertising platforms host hundreds of thousands of A/B tests, but the platform's delivery algorithm routes each creative to the audience it predicts will engage. Every two-arm test therefore conflates the creative's effect with the algorithm's targeting response, and adjusting for the realized audience is biased because audience is a post-treatment mediator. We propose a three-arm design that adds an arm exposing the algorithm to the treatment metadata while holding the user-facing creative identical to control, point-identifying the natural indirect (algorithmic) and direct (creative) effects without sequential ignorability. In a live Meta campaign with a women-targeted text fragment, the algorithmic channel raises female impression share by +2.07 ppt while the creative channel moves it by -0.68 ppt; roughly three-quarters of the absolute reallocation is algorithmic, and a conventional two-arm test understates the algorithmic channel by a factor of two. The design isolates the contribution of platform's algorithm to the outcome which is separable from creative content.
A scoping review of qualitative studies on sexual and reproductive health and rights in Uganda: Exploring factors at multiple levels

**Background:** Uganda is burdened by high unintended and teen pregnancies, high sexually transmitted infections, and harm caused by unsafe abortion. **Objectives:** Explore factors influencing sexual and reproductive health and rights (SRHR) in Uganda by synthesizing evidence from qualitative studies using a scoping review.  **Eligibility criteria:** Original qualitative peer-reviewed research studies published between 2002 and 2023 in any language exploring factors influencing SRHR in Uganda. **Sources of evidence:** Eight databases searched using qualitative/mixed methods search filters and no language limits. **Charting methods:** Information extracted included author, article title, publication year, study aims, participant description, data collection type, sample size, main findings, factors at the individual, interpersonal, community, and policy levels, implications for SRHR in Uganda, and study limitations. Quality of the selected articles was assessed using the Critical Appraisal Skills Programme tool.**Results:** One hundred and seventy-three studies met inclusion criteria. At the individual level, knowledge and attitudes towards SRHR, risky sexual behavior, and access to maternal SRHR services were identified as critical factors influencing health outcomes. Interpersonal factors included communication with sexual partners and relationships with family, school, and community members. Healthcare organization factors included adolescent access to education, SRHR services, and HIV prevention. Cultural and social factors included gendered norms and male involvement in SRHR. Policy-level factors included the importance of aligning policy and practice.**Conclusions:** Multiple factors at individual, interpersonal, community, healthcare, cultural and policy levels were found to influence SRHR in Uganda. The findings suggest that interventions targeting multiple levels of the socio-ecological system may be necessary to improve SRHR outcomes. This review highlights the need for a holistic approach that considers the broader socio-ecological context. Reducing identified gaps in the literature, particularly between policy and practice related to SRHR, is urgently needed in Uganda. We hope this review will inform the development of policies and interventions to improve SRHR outcomes.**Keywords:** Reproductive health; sexual and reproductive health and rights (SRHR); qualitative research; scoping review; Uganda
Test Paper 2

We propose another method.
Modified guided-discovery methods in physics laboratories: Pre-service teachers’ conceptual and procedural knowledge, views of nature of science, and motivation

AbstractAlthough laboratory work is considered a significant factor in helping students develop their science process skills and cultivate alternative knowledge construction, there are still gaps in the selection, integration, and implementation of the basic elements in physics laboratories. In this study, three modified guided discovery and conventional methods in integration with different forms of laboratories, the explicit approach of process skills, and the implicit approach to the nature of science were implemented. This study aimed to explore pre-service teachers’ views towards nature of science (NOS), pedagogical and forms of laboratory orientation, practice of science process skills (PS), and motivation. The groups were also compared in terms of the dependent and selected covariate variables. This study employed a tandem design Phase III with a quasi-experimental approach. Purposive and random sampling were used to select study participants. It was found that pre-service physics teachers had naïve views towards NOS and PS before and after the intervention. However, after the intervention, there were changes in pedagogy and forms of laboratory orientation, PS practice, and motivation. The groups were also significantly different in conceptual and procedural knowledge and motivation, but similar in views of NOS. In addition, the groups were not significant for covariate variables. Generally, this study indicates that modified guided-discovery methods in physics laboratories have made differences in procedural and conceptual knowledge, motivation, and practicing process skills. It was suggested that physics teachers and physics laboratory curricula employ a variety of learning models and integrate basic components to assist and improve students’ learning.
Autonomous Agricultural Robotics for Crop Harvesting and Weed Detection Using AI

The integration of autonomous robotics in agriculture, particularly for crop harvesting and weed detection, is revolutionizing modern farming practices. This chapter explores the critical role of Artificial Intelligence (AI), machine learning, and advanced sensor technologies in enabling autonomous systems to optimize agricultural processes with unprecedented precision. By focusing on key applications such as precision herbicide application, real-time crop harvesting, and environmental adaptation, this work highlights the transformative potential of autonomous agricultural robots in enhancing productivity, sustainability, and operational efficiency. Through advanced navigation systems, including GPS and geospatial technologies, robots can navigate complex terrains and adjust to varying weather conditions, ensuring seamless operation across diverse farming environments. The precision in weed detection and herbicide application offers a sustainable approach to pest control, significantly reducing the environmental impact of conventional farming practices. Furthermore, by improving the accuracy, timing, and speed of harvesting operations, autonomous robots contribute to higher crop yields while minimizing waste. Despite the significant advancements, challenges remain in scaling these technologies for widespread adoption, particularly concerning cost, infrastructure, and operational reliability in real-world conditions. This chapter provides a comprehensive overview of the current state of autonomous agricultural robotics, highlighting emerging trends, technological advancements, and future directions for research and development in this rapidly evolving field.
Adaptive Strength of Armenian Barley (&lt;i&gt;Hordeum vulgare&lt;/i&gt; L.) Genotype to Salinity Stress: Implications for Sustainable Agriculture and Food Security within the SDGs

Understanding the salinity stress processes is crucial for crop development and sustainable agriculture. Salinity stress has the highest level of adverse impact on crop growth and development, agricultural yield, and food security when compared with other abiotic stressors. Therefore, it is important to protect, investigate, and document traditional growing plants that can reduce salt damage and increase salt tolerance in newly grown crops for food security and sustainable agriculture. This research aimed to examine how the genotype of the cereal crop barley (Hordeum vulgare L.) responded to salinity stress. The seeds of the local genotype of barley (Alashkert) have been grown for 3 months in the greenhouse of YSU (Republic of Armenia), in perlite pots, while applying various concentrations of NaCl (0, 100, 200, 300, 400, and 500 mM). In response to increased salt stress, barley's stem length and diameter, root, stem, leaf biomass and water content, chlorophyll content, and gas exchange all reduced. However, the data also demonstrate that the barley cultivar absorbs more Na+ ions and increases the Na+/K+ ratio within its leaves, shoots, and roots under higher saline conditions. Our findings suggest that the "Alashkert" local barley genotype has potential as a crop for use in saline agriculture in Armenia and other countries. Therefore, our in-depth primary analysis of barley genotype at physiological, morphological, and biochemical levels gives a comprehensive insight into the potential to improve agricultural practices for crop improvements and management programs.
Towards Unified Vision-Language Models with Incomplete Multi-Modal Inputs

Video-Language Models (VLMs) have demonstrated impressive multi-modal reasoning capabilities across diverse computer vision applications. However, these VLMs are task-specific and assume that both video and language inputs are complete. However, real-world VLM applications might face challenges due to deactivated sensors (e.g., cameras are unavailable due to data privacy), yielding modality-incomplete data and leading to inconsistency between training and testing data.  While straightforward incomplete input can boast training generalization-ability and lead to training failure, its potential risks to VLMs regarding safety and trustworthiness have been largely neglected.  To this end, we make the first attempt to propose a unified incomplete video-language model to process the incomplete multi-modal inputs. Extensive experimental results show that our  method can serve as a plug-and-play module for previous works to improve their performance in various multi-modal tasks.
Training Sound Event Detection with Soft Labels from Crowdsourced Annotations

ICASSP 2023
Critical infrastructure protection by advanced modelling, simulation and optimization for cascading failure mitigation and resilience

Protection des Infrastructures Essentielles par Advanced Modélisation, simulation et optimisation pour l’atténuation et résilience de défaillance en cascade                 Sans cesse croissante complexité et l'interdépendance des infrastructures critiques modernes, avec des environs de risque plus en plus complexes, posent des défis uniques pour leur exploitation sûre, fiable et efficace. L'objectif de la présente thèse est sur la modélisation, la simulation et l'optimisation des infrastructures critiques (par exemple, les réseaux de transmission de puissance) à l'égard de leur vulnérabilité et la résilience aux défaillances en cascade. Cette étude aborde le problème en modélisant infrastructures critiques à un niveau fondamental, en se concentrant sur la topologie du réseau et des modèles de flux physiques dans les infrastructures critiques. Un cadre de modélisation hiérarchique est introduit pour la gestion de la complexité du système. Au sein de ces cadres de modélisation, les techniques d'optimisation avancées (par exemple, non-dominée de tri binaire évolution différentielle (NSBDE) algorithme) sont utilisés pour maximiser à la fois la robustesse et la résilience (capacité de récupération) des infrastructures critiques contre les défaillances en cascade. Plus précisément, le premier problème est pris à partir d'un point de vue de la conception du système holistique, c'est-à-dire certaines propriétés du système, tels que ses capacités de topologie et de liaison, sont redessiné de manière optimale afin d'améliorer la capacité de résister à des défaillances systémiques de système. Les deux modèles de défaillance en cascade topologiques et physiques sont appliquées et leurs résultats correspondants sont comparés. En ce qui concerne le deuxième problème, un nouveau cadre est proposé pour la sélection optimale des mesures appropriées de récupération afin de maximiser la capacité du réseau d’infrastructure critique de récupération à partir d'un événement perturbateur. Un algorithme d'optimisation de calcul pas cher heuristique est proposé pour la solution du problème, en intégrant des concepts fondamentaux de flux de réseau et le calendrier du projet. Exemples d'analyse sont effectués en se référant à plusieurs systèmes de CI réalistes.
Effectiveness of Mediation and Arbitration as Alternative Dispute Resolution Methods in Mexico

Purpose: To aim of the study was to analyze effectiveness of mediation and arbitration as alternative dispute resolution methods  Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.  Findings: Mediation and arbitration offer efficient, cost-effective alternatives to litigation. Mediation involves facilitated negotiation sessions with a neutral mediator, fostering open communication and tailored solutions. Arbitration entails submitting disputes to a neutral arbitrator for a binding decision. Both methods ensure confidentiality, expertise, and enforceability, making them highly effective in resolving disputes outside of court.  Unique Contribution to Theory, Practice and Policy: Theory of social exchange, justice theory &amp; game theory may be used to anchor future studies on effectiveness of mediation and arbitration as alternative dispute resolution methods. Practitioners in the field of alternative dispute resolution should prioritize enhancing their skills and competencies to effectively facilitate mediation and arbitration processes. Policymakers should prioritize the promotion of mediation and arbitration as viable alternatives to traditional litigation within legal frameworks and regulatory environments.