ethicalabs/Research-Intent-Collab huggingface dataset:
Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data

Abstract                Galaxy morphology classification plays a crucial role in understanding the structure and evolution of the Universe. With galaxy observation data growing exponentially, machine learning has become a core technology for this classification task. However, traditional machine learning methods predominantly rely on supervised learning frameworks, and their dependence on large amounts of labeled samples limits practical applications. To address this challenge, we propose an innovative hybrid semisupervised model, Wasserstein GAN for galaxy classification (GC-SWGAN), designed to tackle galaxy morphology classification under conditions of limited labeled data. This model integrates semisupervised generative adversarial networks (GANs) with Wasserstein GAN with gradient penalty, establishing a multitask learning framework. Within this framework, the discriminator and classifier are designed independently while sharing part of the architecture. By collaborating with the generator, the model significantly enhances both classification performance and sample generation capabilities, while also improving convergence and stability during training. Experimental results demonstrate that, on the Galaxy10 DECaLS data set, GC-SWGAN achieves comparable or even superior classification accuracy (exceeding 75%) using only one-fifth of the labeled samples typically required by conventional fully supervised methods. Under identical labeled conditions, the model displays excellent generalization performance, attaining approximately 84% classification accuracy. Notably, in extreme scenarios where only 10% of the data is labeled, GC-SWGAN still achieves high classification accuracy (over 68%), fully demonstrating its stability and effectiveness in low-labeled data environments. Furthermore, galaxy images generated by GC-SWGAN are visually similar to real samples. Our approach provides a new solution to the challenge of needing to manually label large amounts of data in astronomy.
Preparation, structure study and electrochemistry of layered H2V3O8 materials: High capacity lithium-ion battery cathode

Abstract   The present study explores H2V3O8 as high capacity cathode material for lithium-ion batteries (LIB's). Despite having high discharge capacity, H2V3O8 material suffers from poor electrochemical stability for prolonged cycle life. Ultra-long H2V3O8 nanobelts with ordered crystallographic patterns are synthesized via a hydrothermal process to mitigate this problem. The growth of the crystal is facile along [001] direction, and the most common surface is (001) as suggested by Wulff construction study. Electrochemical performance of H2V3O8 cathode is tested against Li/Li+ at various current rates. At 50 mA g−1current rate, it delivers a discharge capacity of 308 mAh g−1, whereas, at 3000 mA g−1, an initial discharge capacity of 144 mAh g−1 is observed and stabilized at 100 mAh g−1 till 500 cycles. Further, the density functional theory (DFT) based simulations study of both the pristine and lithiated phase of H2V3O8 cathode materials is undertaken. DFT study reveals the presence of hydrogen as hydroxyl unit in the framework of the host. In correlation, the magnetic property of vanadium atoms is examined in detail with through partial density of states (PDOS) calculation during three stage lithiation processes and evaluating various potential steps involved in lithium insertion.
The influence of transformational leadership behaviors on organizational conflict management

Conflict is central to work team high performance because conflict is considered a natural part of the team environment that makes team decision-making effective. Additionally, research indicates that the responsibility for managing conflict within an organization falls directly on the shoulders of the team leader. The ability of the team leader to manage conflict so that team members feel to express their concerns and opinions is critical to achieve integration of all team members. Several studies suggest that the appropriate use of conflict management styles by the team leader is key to constructive conflict management and high team performance.This study examined the relationship among transformational leadership and conflict management styles. This was accomplished by investigating the strength of this relationship in organizations operating in tourism sector. The findings show that leader?s transformational leadership behaviors have an influence on conflict management style. The hypotheses show that transformational leadership influences conflict management style and integrating and compromising conflict management styles are more successful in conflict resolution.
Privacy-preserving machine learning for healthcare: open challenges and future perspectives

Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the entire ML pipeline, from model training to inference. In this paper, we conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service, and perform a comprehensive review of existing trends, identify challenges, and discuss opportunities for future research directions. The aim of this review is to guide the development of private and efficient ML models in healthcare, with the prospects of translating research efforts into real-world settings.
Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

ABSTRACT                There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ∼2800 galaxies with visual classification from GZ1, we reach an accuracy of ∼0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ∼2.5 per cent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).
‘We get the most information from the sources we trust least’: residents' perceptions of risk communication on industrial contamination

This article provides insight into associations between risk communication, trust and risk perception through empirical research examining residents' trust in sources of risk communication about contaminants from industrial sites in New South Wales (NSW), Australia. The research addresses a gap in empirical research within Australia, and responds to a broader call for further research to improve the understanding and use of risk communication practices. In 2009, a survey of 800 randomly sampled residents living near two industrial source sites were gathered, and focus groups held with 80 of the surveyed residents. The research found that community groups and local councils were the sources that residents trusted most for information about contaminant risk. However, they actually received most risk communication from the sources they trusted least – namely the media, the NSW government and industry. This has implications for the efficacy of risk communication, given that focus group findings suggest that t...
Techniques for In Situ TEM Characterization and Electrochemistry of Nanoscale Battery Materials

Abstract not Available.
Thirty Years of Studies on Transparency, Accountability, and Corruption in the Public Sector: The State of the Art and Opportunities for Future Research

This study was carried out from 1984 to 2013 in the context of governance, with a view to analyzing the discussions on transparency, accountability, and corruption in the public sector in professio...
Recognition and Organization of Speech and Audio

An overview of the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.
Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search

Abstract                                    AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10-30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score ≥0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook                   https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb                   .
Biodiversity conservation, traditional agriculture and ecotourism: Land cover/land use change projections for a natural protected area in the northeastern Yucatan Peninsula, Mexico

Abstract   In addition to preserving ecosystems and biodiversity, natural protected areas (NPAs) in Mexico are homelands for people, largely indigenous, who traditionally base their resource management on a multiple use strategy. We analyzed land use and land cover changes in the Otoch Ma’ax Yetel Kooh NPA in the northeastern Yucatan Peninsula, Mexico, where Yucatec Maya recently incorporated ecotourism to their set of economic activities. We evaluated changes in land use using vegetation maps from 1999 to 2003 and predicted vegetation cover in 2011 by developing a cellular automata and Markovian chains model. We observed slight increases in the area covered by medium stages of secondary succession, while new milpa plots appeared in areas of all succession stages. We used three scenarios to predict land cover in 2011: (a) milpa agriculture implemented at the same rate; (b) milpa agriculture decreases due to the growing demand of ecotourism; and (c) milpa agriculture disappears due to parceling of communally owned land. All scenarios predict slight increases in the area covered by secondary succession at the expense of milpas or younger stages of succession, with no major differences between the three predictive scenarios. Our results provide guidelines for managing the NPA, suggesting that biodiversity conservation, traditional agriculture and ecotourism are compatible activities.
3D printing : Potential analysis and implementation of an Additive Manufacturing system to reduce production equipment costs

För att validera både produkter och processer i fordonsindustrin använder man särskild produktionsutrustning som t.ex. jiggar för att kontrollera passningar mellan olika komponenter, monteringsfixturer och andra gripdon för att underlätta montering och skydd för att undvika skador eller skrapmärken på ytor och komponenter som redan monterats. Audi Bryssel outsourcar idag 94 % av sin tillverkningsutrustning till externa verktygsleverantörer. Denna process är dyr och innebär långa leveranstider. Syftet med rapporten är att undersöka om det är ekonomiskt försvarbart att investera i Adderande tillverkning (AT) för att effektivisera den befintliga leveranskedjan och därigenom reducera kostnader och förkorta leveranstider. Om så är fallet bör ett AT system införas, vilket kräver en budget på € 200,000. Först genomfördes en förstudie med kartläggning av den AT-teknologin. Förstudien inkluderade en omfattande litteraturstudie, informations-, kunskaps- och erfarenhetsutbyte med andra tillverkningsenheter inom Audi som redan har erfarenhet av AT och besök hos leverantörer och tjänsteleverantörer av AT. Utifrån denna förstudie gjordes en djupare analys av lämplig AT metod utifrån tre praktiska exempel för att evaluera potentialen med tekniken. Dessa exempel är ett kontrollpanelsskydd, kombiinstrumentets skyddshuva och en fästanordning till motorhuvens gångjärn. Sista steget i analyskedjan är en breakeven analys för att visa om en investering i additiva tillverkningsmetoder är ekonomiskt lönsam. Rapporten visar att Selektiv Lasersintring är en särskilt lämplig metod att använda för att framställa produktionsutrustning som t.ex. skydd för komponenter. Däremot är det inte ekonomiskt eller produktionstekniskt försvarbart för Audi Bryssel att förbinda sig till endast en additiv tillverkningsmetod. Breakeven analysen visar att det är en ekonomisk lönsam investering om man utvecklar den tekniska utrustningen inhouse och outsourcar 3D-printningen. Att etablera AT-kunskap inom Audi Bryssel êr den åtgärd som ger högst förädlingsvärde.
Building a Generative Adversarial Network for Image Synthesis

Generative Adversarial Networks (GANs) have emerged as a powerful class of generative models, capable of synthesizing realistic images by leveraging adversarial training. It explores the process of building a Generative Adversarial Network for image synthesis, delving into the underlying architecture, training methodology, and potential applications. Generative Adversarial  Networks typically run unsupervised and use a cooperative zero- sum game framework to learn, where one person's gain equals  another person's loss. The proposed Generative Adversarial Network architecture consists of a generator network that learns to create images from random noise and a discriminator network trained to distinguish between real and generated images. Through an adversarial training process, these networks iteratively refine their capabilities, resulting in a generator that produces increasingly realistic pictures and a discriminator with enhanced discriminative abilities. Generative Adversarial Networks are an effective tool for producing realistic, high-quality outputs in a variety of fields,  including text and image generation, because of this back-and- forth competition, which results in the creation of increasingly  convincing and indistinguishable synthetic data.
Consolidated Proceedings of the Second ECSCI Workshop on Critical Infrastructure Protection and Resilience

Modern critical infrastructures (or “critical entities” as now defined in the new EU-CER Directive) are becoming increasingly complex, turning into distributed, large-scale cyber-physical systems. Cyberphysical attacks are increasing in number, scope, and sophistication, making it difficult to predict their total impact. Thus, addressing cyber security and physical security separately is no longer effective, but more integrated approaches, that consider both physical security risks and cyber-security risks, along with their interrelationships, interactions and cascading effects, are needed to face the challenge of combined cyber-physical attacks. Addressing them successfully, need coordinated and integrated responses, which must be disseminated and exploited further to the EU funded projects’ frameworks or individual research studies’ reports, through raising awareness initiatives, such as the 2nd ECSCI Workshop on CIP. This workshop presented the different approaches on integrated (i.e., cyber and physical) security in several different industrial sectors, such as finance, healthcare, energy, air transport, communications, industrial plants, gas, and water. The peculiarities of critical infrastructure protection in each one of these sectors have been discussed and addressed by the different projects of the ECSCI cluster that presented their outcomes, discussing the technical, ethical and societal aspects and the underlying technologies. Specifically, novel techniques have been presented for integrated security modelling, IoT security, artificial intelligence for securing critical infrastructures, resilience of critical infrastructures, ethical and legal aspects of cybersecurity, combating hybrid threats to critical infrastructure, cyber and physical threats detection, increased automation for detection, prevention and mitigation measure, information and knowledge sharing, standards and regulations for the protection of critical infrastructures, common platforms for cascading effects on the different critical infrastructures, combined safety and security solutions, cyber security awareness, and the landscape of advanced combined cyber and physical threats. The workshop included three opening remarks, three keynote speeches, twenty-one project presentations, two roundtable and panel discussions, twenty-one thematic presentations, and closing remarks. The audience included scientists and experts in the field of critical infrastructure protection, CISOs, CIOs, CERTs, CSIRTs, CSOs, cyber and physical security experts representing different sectors and policy makers for critical infrastructure protection.
Citizen science in environmental health research: A comparison with conventional approaches and creation of a guidance tool issued from the LILAS initiative

Public interest for citizen science (CS) in environmental health is growing. The goals of environmental health research projects are diverse, as are the methods used to reach these goals. Opportunities for greater implication of the civil society and related challenges differ at each step of such projects. These methodological aspects need to be widely shared and understood by all stakeholders. The LILAS initiative (acronym for "application of citizen science approaches such as LIving LAbS to research on environmental exposures and chronic risks") aimed to 1) favor a mutual understanding of the main issues and research methods in environmental health, of their stakes for different actors, but also of the requirements, strengths and limitations of these methods and to 2) identify expected benefits and points of attention related to stronger degrees of participation as part of environmental health research projects.The LILAS initiative gathered institutional researchers, academics and civil society representatives interested in environmental exposures. Five meetings allowed to collectively identify different types of environmental health research studies and reflect about the benefits, limitations, and methodological issues related to the introduction of growing citizen participation as part of such studies. An analytic table matrix summarizing these aspects was co-created and filled by participants, as a tool devoted to help stakeholders with the definition of future CS research projects in environmental health.For different fields of research (e.g.: studies for assessment of environmental exposures, interventions on these exposures, quantitative risk assessment, epidemiological studies), the matrix lists expected benefits for various stakeholders, the fundamental principles of research methods and related practical constraints, but also advantages and limitations related to the use of CS or conventional research approaches.The LILAS initiative allowed to develop a tool which provides consolidated grounds for the co-creation of research projects on environmental exposures involving CS.
On the use of a spectroscopic force field in molecular dynamics simulations

Pour reproduire les spectres vibrationnels, il est indispensable d'inclure des termes croises dans la fonction energie potentielle, ce qui permet d'augmenter sensiblement la confiance dans les resultats des simulations de Dynamique Moleculaire. L'introduction du terme Urey-Bradley et la resolution de la redondance induite par la nouvelle coordonnee interne conduit a l'introduction rationnelle des termes d'interaction entre coordonnees d'elongation et de deformation angulaire. L'influence du champ de forces «spectroscopique « ainsi obtenu sur les simulations de Dynamique Moleculaire d'un modele de biomembrane est examinee.
Linking the interplay of resilience, vulnerability, and adaptation to long-term changes in metropolitan spaces for climate-related disaster risk management

Accelerated urbanization and development in disaster-prone areas have prompted urban authorities to adopt adaptation strategies to improve resilience and vulnerability to climate change and related disasters. Therefore, assessing resilience, vulnerability and their evolution over time becomes crucial in comprehending the dynamics of interactions between resilience, vulnerability, adaptive capacity, and urban spatial changes. Using an empirical study in Taipei Metropolis, Taiwan, we developed a Framework for Metropolitan Resilience-Vulnerability-Adaptation Assessments. This framework integrated spatial statistics with multicriteria decision-making analyses to assess overall resilience and capacities to tackle climate-related disaster risks, as well as their dynamics between the years 2001 and 2016. Our approach further employed resilience and vulnerability change models to explore the connections between adaptive capacities and improvements in resilience and vulnerability over time. Results indicate that low-resilience and high-vulnerability areas are significantly clustered and continue to deteriorate, particularly in fast-growing peri-urban and old-downtown communities. Moreover, comparing the effects of adaptive factors shows that increasing investments in transportation networks, public lands, and infrastructure does not lead to the improvements in resilience and vulnerability in the long-term. This implies that conflicts and trade-offs may exist between certain adaptation options and resilience building efforts. Numerous disaster-proof, public facilities and emergency responses may simply provide short-term benefits and potentially lead to maladaptive outcomes. They create a way to encourage large-scale land development and urban space changes, thereby locking in adaptation pathways focused on short-term resilience improvement, while strengthening the vulnerability loops in the long-term. Our findings provide metropolitan governors and stakeholders valuable insights into formulating more effective adaptive policies that reconcile resilience and vulnerability. They also broaden the scope for urban land use policy-making and metropolitan governance, providing opportunities to mitigate climate-related disaster risks more effectively.
The rate of cosmic ray showers at large zenith angles: a step towards the detection of ultra-high energy neutrinos by the Pierre Auger Observatory

22 pages, 12 figures, to appear in Astroparticle Physics