ethicalabs/Research-Intent-Collab huggingface dataset:
Statistical Mechanical Theory of Cooperative Phenomena. II. Super-Effective-Field Theory with Applications to Exotic Phase Transitions

A new scheme to study exotic phase transitions is formulated by introducing the concept of a super-effective field. A general mechanism of phase transitions is elucidated and a general criterion of order parameters is proposed on the basis of the newly formulated super-effective-field theory. An alternative formulation based on a decoupled density matrix is also given. It is easily shown using these formulations that a quantum chiral order appears in the antiferromagnetic XY model on the triangular lattice. A super-effective-field theory of spin glasses is also presented.
Policy Gradient Method For Robust Reinforcement Learning

Accepted by ICML 2022
Patchwork sampling of stochastic differential equations

We propose a method to sample stationary properties of solutions of stochastic differential equations, which is accurate and efficient if there are rarely visited regions or rare transitions between distinct regions of the state space. The method is based on a complete, non-overlapping partition of the state space into patches on which the stochastic process is ergodic. On each of these patches we run simulations of the process strictly truncated to the corresponding patch, which allows effective simulations also in rarely visited regions. The correct weight for each patch is obtained by counting the attempted transitions between all different patches. The results are patchworked to cover the whole state space. We extend the concept of truncated Markov chains which is originally formulated for processes which obey detailed balance to processes not fulfilling detailed balance. The method is illustrated by three examples, describing the one-dimensional diffusion of an overdamped particle in a double-well potential, a system of many globally coupled overdamped particles in double-well potentials subject to additive Gaussian white noise, and the overdamped motion of a particle on the circle in a periodic potential subject to a deterministic drift and additive noise. In the appendix we explain how other well-known Markov chain Monte Carlo algorithms can be related to truncated Markov chains.
Federated Learning and Edge Computing for Latency Reduction in Smart Farming IoT Sensor Data Filtering

The integration of Federated Learning and Edge Computing has emerged as a promising approach to handle latency, security, and bandwidth limitations in IoT sensor networks for smart farming. Traditional cloud-based models are usually afflicted with high communication costs and privacy concerns, hence proven to be inefficient for real-time agricultural applications. FL enables decentralized machine learning, thus allowing models to be trained right on IoT devices without requiring any raw data centralization, hence preserving data privacy and reducing transmission overhead. On the other hand, Edge Computing enhances local processing of data for reduced latency, reduced dependency on cloud infrastructure for quicker decision-making. However, despite advantages in both, certain key challenges like heterogeneous data processing, communication overhead, resource constraints, and security vulnerabilities remain concerning. This paper reviews the related literature with regard to the existing FL, Edge Computing, and IoT data filtering techniques. It outlines the critical research gap on the scalability in large-scale farms, energy-efficient learning models, and secure FL. The study explores enhancing the privacy, efficiency, and real-world deployment issues of FL techniques within an agricultural IoT system. This research hence tries to bridge the gaps with respect to the optimization of FL in smart farming for reducing latency by filtering data in real time for decision-making with increased security concerning IoT-driven agricultural systems.
A Comprehensive Analysis of Agricultural Non-Point Source Pollution in China: Current Status, Risk Assessment and Management Strategies

With the expansion of the scope and diversity of agricultural non-point source (AGNPS) pollution, environmental pollution in water bodies and soils has intensified, seriously affecting the quality of life of urban and rural residents. This paper presents a comprehensive analysis of the current status of AGNPS pollution from five aspects: fertilizers, pesticides, agricultural films, livestock and crop residues. The study also includes a comprehensive risk assessment of the nitrogen and phosphorus pollution, microplastic pollution, and heavy metal contamination attributed to the current AGNPS pollution in China. The findings reveal widespread nitrogen and phosphorus pollution across provinces in China, a low risk of microplastic pollution, and acute contamination with heavy metals such as As, Cd and Hg, notably in Hubei, Gansu, Liaoning, Guizhou, and Hunan. Additionally, the study reveals that in Chinese provinces currently facing severe heavy metal pollution, heavy metal contamination constitutes a significant carcinogenic risk to adult health and a serious non-carcinogenic risk to child health. Consequently, guided by the ‘3R theory’ principles of source reduction, interception, and remediation, this paper suggests an array of expanded strategies for AGNPS pollution management. These strategies are proposed to aid in the sustainable advancement of AGNPS pollution control.
Exploring Hazard of Social Media Use on Adolescent Mental Health

Abstract         Background  Previous studies have cautioned about the negative impact of social media use on the mental health of adolescents and emerging adults. In 2021, the National Institutes of Health Malaysia emphasized the urgent need to address digital-related mental health issues, which pose a significant gap in our understanding. Nevertheless, there has been a lack of research on the effects of social media addiction on the mental well-being of local adolescents. This study aims to examine the effect of social media use on mental health issues among adolescents in Kelantan, Malaysia. Methods  A total of 384 respondents voluntarily participated in this cross-sectional study involving ten districts in Kelantan using the snowball sampling method. Results  Analysed data finds social media addiction correlates significantly with depression, suicide ideation and self-esteem. Multiple regression analysis indicated that suicide ideation was predicted by social media addiction and depression but not self-esteem. Furthermore, social media moderates the relationship between depression and suicidal ideation. Conclusion  Controlling social media use may prevent addiction and promote greater mental health among adolescents.
Protein 3D Structure Identification by AlphaFold: a Physics-Based Prediction or Recognition Using Huge Databases?

The great success of AlphaFold programs poses the questions: (i) What is the main reason for this success? (ii) What AlphaFolds does: physics-based prediction of the spatial structure of a protein from its amino acid sequence or recognition of this structure from similarity of the target sequence to some parts of sequences with already known spatial structures? The answers given here are: (i) the main reason for the AlphaFold’s success is the usage of huge databases which already cover virtually all protein superfamilies existing in Nature; (ii) using these databases, multiple sequence alignments, and coevolutionary information – like correlations of amino acid residues of the contacting chain regions – AlphaFold recognizes a spatial structure by similarity of the target sequence (or its parts) to related sequence(s) with already known spatial structures. We emphasize that this does not diminish the merit and utility of AlphaFold but only explains the basis of its success.
Object detection with computer vision Algorithm for car detection

Στην συγκεκριμένη πτυχιακή κατασκευάσαμε ένα ανίχνευτη αυτοκινήτων που σε προειδοποιεί όταν φτάσεις κοντά σε ένα άλλο αυτοκίνητο. Αυτό το πετυχαίνουμε σκανάροντας εικόνες και βίντεο και τοποθετώντας ένα τετράγωνο κουτί γύρο από το αυτοκίνητο. Το τετράγωνο έχει συγκεκριμένα χρώματα που σου λένε ποσό πολύ απέχεις από το μπροστινό αυτοκίνητο. Η έρευνας μας βασίστηκε στο να τεστάρουμε κάποιους αλγόριθμους βασισμένους στα νευρωνικά δίκτυα . Επίσης ανακαλύψαμε ότι συγκεκριμένα στα αυτοκίνητα το να χρησιμοποιούμε γκρι φίλτρο οι αλγόριθμοι έχουν μεγαλύτερη ακρίβεια.
A Thai case study: One Health approach for participatory research design to strengthen community responsibility in waste and waste water management and collaboration at Animal/Human/Environment interface

In the framework of ComAcross EuropeAid project, a case study is implemented in Ayutthaya province, Thailand in order to address Health impacts of waste and water resources management at provincial and municipality levels in Thailand, according to the community needs as it has been revealed through bottom-up participatory approach. The study involves local authorities, communities and provincial authorities as well as academic staff. Capacities are built in participatory epidemiology, health risk mapping and participatory modelling in order to identify Environmental Health issues in link with livelihoods (notably agriculture and livestock farming) and to map the institutional local context and coordination gaps. We aim at improving the coordination between actors and sectors (Agriculture/Health/Environment) and to provide scientific evidences regarding health risk linked to waste and water management. Agricultural waste water and landfill contamination to the natural water bodies will be studied as well as social responsibility of each stakeholder and coordination gaps between activity sectors to overcome waste and waste water management problems for communities and at provincial level. One Health approach can lead to communities' empowerment to find solutions to their own development challenges. (Texte intégral)
DEVELOPMENT AND VALIDATION OF A NEW DISEASE SPECIFIC QUALITY OF LIFE MEASURE FOR ATRIAL FIBRILLATION DERIVED FROM PATIENT PERSPECTIVES: THE ATRIAL FIBRILLATION PATIENT-REPORTED OUTCOME MEASURES (AF-PROMS) QUESTIONNAIRE

Abstract                                   Background                   Atrial Fibrillation (AF) is the most common cardiac arrhythmia, significantly impacting patients’ quality of life (QoL) by affecting their physical, psychological, and social well-being. Existing tools lack a comprehensive, AF-specific approach to health-related QoL (HRQoL) that involves patients in the development process from the outset.                                                   Purpose                   This study aimed to develop and validate a novel, AF-specific patient-reported outcome measure (PROM) designed to assess HRQoL in patients with AF.                                                   Methods                   A prospective, observational study was conducted at two centres in the UK, with 895 participants enrolled. Extensive patient and public involvement (PPI), conducted through focus groups and qualitative interviews, played a key role in guiding item generation and domain selection to ensure content validity, as outlined in Fig. 1.                                                   Results                   Tool development involved focus groups for item generation, followed by expert review and qualitative interviews for content and face validity, resulting in a draft questionnaire with 28 items. Statistical item reduction was applied during the pilot (n = 265) and field validation (n = 550). Exploratory factor analysis identified 5 optimal factors explaining 65.15% of the variance, with excellent sampling adequacy (KMO = 0.923) and significant Bartlett’s Test of Sphericity (P &amp;lt; 0.001), confirming data suitability (Fig. 2). Clinical review refined this into a 3-factor model: Physical Symptoms [F1], Social Wellbeing and Activities of Daily Living [F2], and Psychological/Treatment Concerns [F3] (Table 1). Confirmatory factor analysis demonstrated excellent internal consistency (Cronbach’s alpha F1 = 0.89, F2 = 0.92, F3 = 0.9) and strong test–retest reliability (Inter class correlation &amp;gt;0.97). The tool was highly responsive to clinical changes, especially post-catheter ablation, with moderate to large effect sizes (Cohen’s d ≥ 1.0 for overall score) (Fig. 3). Known-group validity was supported by significant score differences (P &amp;lt; 0.01) between healthy volunteers and AF patients across all domains (Table 2).                                                   Conclusion                   AF-PROMs is the first AF-specific QoL tool developed with the largest participant cohort to date and patient collaboration from the outset. Its strengths include extensive PPI, high reliability, and sensitivity to clinical interventions, making it a valuable and practical tool for improving patient-centred outcomes in both clinical practice and research.
Sequence Stratigraphic Framework, Trace Element and Stable Isotope Geochemistry of the Late Ordovician Red River Formation, Williston Basin, North Dakota

Sequence Stratigraphic Framework, Trace Element and Stable Isotope Geochemistry of the Late Ordovician Red River Formation, Williston Basin, North Dakota
Optimization Study of Dual-Path Logistics Model for Interstellar Material Transport Integrating Space Elevators and Chemical Rockets

Addressing the extreme logistical challenge of transporting 100 million tons of materials across planets for the lunar colony construction mission launching in 2050, this paper establishes a dual-path logistics modeling framework integrating path deconstruction, parameter correction, and multi-objective optimization. The study categorizes transport routes into traditional rocket-based and space elevator-based modes. By introducing a geocorrection factor, it precisely quantifies the impact of different latitude launch sites on rocket payload capacity. Additionally, it derives the lunar soft-landing payload rate for space elevators using the Tsiolkovsky equation. At the optimization decision level, a nonlinear programming model balancing time and cost was established. Utilizing a heuristic algorithm based on a greedy strategy and grid search, the study compared the performance differences among pure rocket, pure elevator, and hybrid transport scenarios. To address real-world random uncertainties, disturbance terms such as equipment failures and maintenance costs were incorporated. The system's robustness was evaluated by constructing a set of near-optimal solutions. Simulation results confirm that the hybrid transport strategy optimally resolves the conflict between rockets' high initial costs and space elevators' limited early-stage capacity, achieving global benefit maximization under specific parameter constraints. This research provides a scientific quantitative basis and decision template for logistics planning in large-scale interstellar colonization.
Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged Photonic-Electrical Silicon Transceiver for High-speed Optical Interconnects

We explore implementing a multilevel deep neural network to enhance the performance of a 4-channel photonic-electrical hybrid-packaged silicon transceiver. Stable transmission and reception of 150 Gbps/λ PAM4 signals are achieved, which shows the potential for beyond-400G optical interconnects.
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning

Since the resurgence of deep learning, vision-language models (VLMs) enhanced by large language models (LLMs) have grown exponentially in popularity. However, while LLMs can utilize extensive background knowledge and task information with in-context learning, most VLMs still struggle with understanding complex multi-modal prompts with multiple images, making VLMs less effective in downstream vision-language tasks. In this paper, we address the limitation above by 1) introducing vision-language Model with Multi-Modal In-Context Learning(MMICL), a new approach to allow the VLM to deal with multi-modal inputs efficiently; 2) proposing a novel context scheme to augment the in-context learning ability of the VLM; 3) constructing the Multi-modal In-Context Learning (MIC) dataset, designed to enhance the VLM's ability to understand complex multi-modal prompts. Our experiments confirm that MMICL achieves new state-of-the-art zero-shot performance on a wide range of general vision-language tasks, especially for complex benchmarks, including MME and MMBench. Our analysis demonstrates that MMICL effectively tackles the challenge of complex multi-modal prompt understanding and emerges the impressive ICL ability. Furthermore, we observe that MMICL successfully alleviates language bias in VLMs, a common issue for VLMs that often leads to hallucination when faced with extensive textual context. Our code, dataset, dataset tool, and model are available at https://github.com/PKUnlp-icler/MIC
The Global Fund: why anti-corruption, transparency and accountability matter

Abstract                 Background                 The creation of the Global Fund to Fight AIDS, Tuberculosis and Malaria, also known as the Global Fund, was prompted by the lack of a timely and effective global response, and the need for financing to fight against three devastating diseases: HIV/AIDS, tuberculosis, and malaria. During the formation of the Global Fund, necessary anti-corruption, transparency, and accountability (ACTA) structures were not put in place to prevent fraud and corruption in its grants, which resulted in the misuse of funds by grant recipients and an eventual loss of donor confidence in 2011. The Global Fund has instituted various ACTA mechanisms to address this misuse of funding and the subsequent loss of donor confidence, and this paper seeks to understand these implementations and their impacts over the past decade, in an effort to probe ACTA more deeply.                                Results                 By restructuring the governing committees in 2011, and the Audit and Finance; Ethics and Governance; and Strategy Committees in 2016, the Global Fund has delineated committee mandates and strengthened the Board’s oversight of operations. Additionally, the Global Fund has adopted a rigorous risk management framework which it has worked into all aspects of its functioning. An Ethics and Integrity Framework was adopted in 2014 and an Ethics Office was established in 2016, resulting in increased conflict of interest disclosures and greater considerations of ethics within the organization. The Global Fund’s Office of the Inspector General (OIG) has effectively performed internal and external audits and investigations on fraud and corruption, highlighted potential risks for mitigation, and has implemented ACTA initiatives, such as the I Speak Out Now! campaign to encourage whistleblowing and educate on fraud and corruption.                                Conclusions                 From 2011 onwards, the Global Fund has developed a number of ACTA mechanisms which, in particular, resulted in reduced grant-related risks and procurement fraud as demonstrated by the decreased classification from high to moderate in 2017, and the reduction of investigations in 2018 respectively. However, it is crucial that the Global Fund continues to evaluate the effectiveness of these mechanisms; monitor for potential perverse impacts; and make necessary changes, when and where they are needed.
Networking Knowledge Creation             <b>Reinventing Discovery</b>             The New Era of Networked Science             <b>               <i>by Michael Nielsen</i>             </b>             Princeton University Press, Princeton, NJ, 2012. 272 pp. $24.95, £16.95. ISBN 9780691148908.

In addition to describing how the Internet has changed the practice of science, Nielsen suggests how it can lead to further innovations.
Rethinking Publicness of Care amid the Kindergarten Corruption Scandal : Beyond the Transparency Issue of Accounting

이 글은 최근 유치원 회계비리사태에서 제기된 정책 쟁점들을 공공성 강화라는 관점에서 분석하고, 회계투명성을 넘어서 돌봄 공공성 확충이라는 관점에서 필요한 정책 방향들을 논의하였다. 공공성은 공과 사, 정부와 이익집단의 이원적 대립으로 모두 포착될 수 없는 다차원적 개념으로 재정의할 수 있으며, 이 글에서는 소유권(설립주체), 재정, 통제의 세 가지 차원을 중심으로 사립유치원의 공공성 좌표를 진단하였다. 사립유치원 비리사태는 유치원시장에서 개인사업자가 지배적인 상황에서 비용지원에만 주력하고 관리감독 등의 통제적 공공성을 포기한 정부정책의 결과이다. 현재 사립유치원 공공성 강화방안의 핵심인 에듀파인 도입은 필수적이지만, 통제적 공공성의 확보가 자동적으로 고품질의 서비스를 적절하고 충분하게 보장하는 것은 아니다. 또 대안으로 주장되는 국공립유치원 40% 달성은 사립지배적인 생태계를 변화시키는 효과가 있겠지만 돌봄을 부차적인 기능으로 수행하고 있는 현재 국공립유치원의 관행이 변화되지 않는다면 돌봄공공성의 관점에서는 만족스러운 결과를 낳지 못할 것이다. 젠더 관점의 연구들은 돌봄이 사회정책 및 복지국가 재편의 중심 가치가 되어야 하며, 적절하고 충분한 수준의 돌봄의 사회화와 돌봄노동자의 처우와 노동권을 보장함으로써 돌봄을 저평가하는 사회적 인식과 관행이 해소되어야 한다고 주장해왔다. 이 글은 소유권, 재정, 통제 차원에서의 공공성 강화뿐 아니라 유치원(유아교육)서비스의 정체성에서 돌봄이 더 강화되고 사립유치원 교사의 근로조건과 처우를 개선하는 등 “돌봄적” 차원의 공공성이 강화될 필요가 있음을 주장하였다.
The Effect of Human Resource Information Systems (HRIS) and Artificial Intelligence on Defense Industry Performance

The purpose of this research is to analyze the effect of the Human Resource Information System (HRIS) on the performance of the defence industry and the influence of artificial intelligence on the performance of the defence industry during the disruption era. This study uses a quantitative research approach. The samples processed were 160 defence industry employees. The primary data collection technique is done by distributing questionnaires to respondents. The questionnaire uses a Likert scale as a measure of data. Data analysis was carried out through structural equation modelling with SmartPLS 3.0 software. The dependent variable is the employee performance variable, and the independent variable is the HRIS and job satisfaction variables. The results of data analysis in this study and the significance value obtained prove that HRIS is significantly related to employee performance. The more accurate, precise and complete the HRIS application, the higher the employee's performance in completing their work assignments and improving the process of the defence industry in making human resource decisions through systematic HRIS procedures, which include data collection, data storage, data maintenance, data retrieval, and data validation. Its strategic plan influences the progress of the defence industry in utilizing human resources quantitatively, and understanding environmental conditions can provide an overview of the future of the defence industry. Artificial Intelligence (AI) is significantly related to employee performance.
Climate Change Litigation and Enforcement in South African Environmental Law Context,

Climate change litigation has become a significant aspect of environmental law enforcement in South Africa, complemented by stringent legislative frameworks aimed at mitigating its impacts. A comprehensive review of judicial decisions related to climate change cases within South Africa's legal system was conducted. The analysis included a qualitative assessment of case outcomes and legal arguments presented by litigants. The findings indicate that while there has been an increase in climate change-related litigation, the proportion of successful cases remains relatively low, suggesting potential gaps in enforcement mechanisms. Despite notable efforts, current environmental law enforcement lacks robustness to address evolving climate change challenges, particularly in terms of proactive prevention and comprehensive adaptation measures. Enhanced training for legal practitioners on climate change impacts and mitigation strategies is recommended. Additionally, a more integrated approach between environmental laws and climate policy frameworks is suggested to improve the effectiveness of judicial decisions. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Development of the Korean-specific radiation-induced cancer risk model for level 3 probabilistic safety assessment (PSA)

ABSTRACTAs a follow-up measure to improve safety after the Fukushima nuclear disaster in 2011, a level 3 probabilistic safety assessment (PSA) was enacted as a notice in Korea. In this study, as a ...