ethicalabs/Research-Intent-Collab huggingface dataset:
Vision Language Model is NOT All You Need: Augmentation Strategies for Molecule Language Models

Recently, there has been a growing interest among researchers in understanding molecules and their textual descriptions through molecule language models (MoLM). However, despite some early promising developments, the advancement of MoLM still trails significantly behind that of vision language models (VLM). This is because unique challenges exist apart from VLM in the field of MoLM due to 1) a limited amount of molecule-text paired data and 2) missing expertise that occurred due to the specialized areas of focus among the experts. To this end, we propose AMOLE, which 1) augments molecule-text pairs with structural similarity preserving loss, and 2) transfers the expertise between the molecules. Specifically, AMOLE enriches molecule-text pairs by sharing descriptions among structurally similar molecules with a novel structural similarity preserving loss. Moreover, we propose an expertise reconstruction loss to transfer knowledge from molecules that have extensive expertise to those with less expertise. Extensive experiments on various downstream tasks demonstrate the superiority of AMOLE in comprehending molecules and their descriptions, highlighting its potential for application in real-world drug discovery. The source code for AMOLE is available at https://github.com/Namkyeong/AMOLE.
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification

Clinical notes are assigned ICD codes - sets of codes for diagnoses and procedures. In the recent years, predictive machine learning models have been built for automatic ICD coding. However, there is a lack of widely accepted benchmarks for automated ICD coding models based on large-scale public EHR data.   This paper proposes a public benchmark suite for ICD-10 coding using a large EHR dataset derived from MIMIC-IV, the most recent public EHR dataset. We implement and compare several popular methods for ICD coding prediction tasks to standardize data preprocessing and establish a comprehensive ICD coding benchmark dataset. This approach fosters reproducibility and model comparison, accelerating progress toward employing automated ICD coding in future studies. Furthermore, we create a new ICD-9 benchmark using MIMIC-IV data, providing more data points and a higher number of ICD codes than MIMIC-III. Our open-source code offers easy access to data processing steps, benchmark creation, and experiment replication for those with MIMIC-IV access, providing insights, guidance, and protocols to efficiently develop ICD coding models.
More engagement but less participation: China’s alternative approach to public communication of science and technology

This essay critically traces the development of public engagement with science in China in the past decade and relevant scholarly studies. While confirming the country’s tremendous progress in the field achieved either by official efforts or by social media empowerment, it argues that science communication advances have not realized the public engagement with science ideal of enabling the public to participate in constructive dialogue for policymaking. However, citing recent studies on the specifics of China’s science communication, ranging from scientists’ reliance on their organization, to the consequences of attitudinal polarization, this article appeals to an alternative research agenda to broaden our understanding of the dynamic science communication process in the world’s most populous nation.
The impact of workplace relationships on nurse-reported quality of care and patient safety in the North West Province

Aim and objectives: This study aims to investigate the impact of workplace relationships on nurse-reported quality of care and patient safety. Background: There is a direct link between workplace relationships between nurses and physicians, their managers and nurse colleagues, and improved patient outcomes. However, there is a dearth of literature on this in developing countries. Design: This study applied a cross-sectional survey design. Method: A multilevel sampling method was applied. Purposive sampling was applied to the selection of the province, health sector and hospitals (n=3). All-inclusive sampling was applied to in-patient units of the selected hospitals and nursing staff in those units (n=236). Data was collected in April 2021 using validated instruments. Findings: Nurse managers' ability, leadership, and support were not experienced as positively contributing to the practice environment and had most impact on quality of care and patient safety. Collegial nurse physician relationships were experienced as contributing positively to the practice environment, and had most impact on adverse events, namely medication errors, patient falls after admission and healthcare-associated infections. Increased exposure to COVID-19 patients resulted in more positive perceptions of nurses regarding collegial nurse-physician relationships. The most common perpetrators of workplace violence (WPV) were supervisors/managers, followed by nursing colleagues. On average participants experience more personal WPV than physical WPV. Personal WPV had more effect on quality of care and patient safety, and adverse events than physical WPV. Conclusions: Positive workplace relationships or collegiality, especially nurse-manager relationships, appear to have the most impact on nurse-perceived quality of care and patient safety, followed by nurse-physician relationships, and then workplace violence. Relevance to clinical practice: The focus of education interventions should be on developing leadership, and recruiting and retaining relationship-focused leaders, as leaders have the greatest impact on nurse-reported quality of care and patient safety.
A novel prognostic immunoscore based on The Cancer Genome Atlas to predict overall survival in colorectal cancer patients

Abstract                Colorectal cancer (CRC) is highly prevalent worldwide. The relationship between the infiltration of immunocytes in CRC and clinical outcome has been investigated in recent years. The present study aims to construct a new prognostic signature using an immunocyte panel. Our novel prognostic immunoscore included 13 types of immunocytes, which were identified by least absolute shrinkage and selection operator (LASSO)-Cox regression. The time-dependent receiver operating characteristic (ROC) curve and Kaplan–Meier survival estimates were applied to evaluate the prognostic ability. Compared with the signature based on a single immune marker (i.e., CD8 mRNA expression and CD8+ expressing T cells), the novel prognostic immunoscore possessed better specificity and sensitivity of prognosis (area under the curves (AUCs) are 0.852, 0.856, and 0.774 for 1-, 2-, and 3-year survival times, respectively). Significant differences were identified between the high and low immunoscore groups in overall survival and disease-free survival in training and validation cohorts. Combining the immunoscore with clinical information may provide a more accurate prognosis for CRC. The immunoscore can identify patients with poor outcomes in the high Tumor Mutational Burden (TMB) group, who may benefit the most from immunotherapy. The immunoscore was also closely related to two immune checkpoints (i.e., PD-L1 and PD-1, r = 0.3087 and r = 0.3341, respectively). Collectively, our study demonstrates that the novel prognostic immunoscore reported here may be useful in distinguishing different prognoses and may improve the clinical management of patients with CRC.
The Role of Social Comparison in the Relationship Between Social Media Use and Adolescent Mental Health

Against the backdrop of the growing prevalence of social media, adolescents are confronted with psychological distress such as anxiety, depression, and diminished self-esteem in the course of their engagement with social media. This paper explores the role of social comparison in the association between social media use and adolescent mental health from the perspectives of social psychology and media psychology. Drawing on the Uses and Gratifications Theory (UGT), Cultivation Theory, and Social Comparison Theory (SCT), this paper investigates the impact of social comparison on the motivational, cognitive, and emotional dimensions of adolescents. Social comparison, as discussed in this paper, can potentially facilitate self-improvement through an incentive effect; however, it may also exacerbate psychological distress under the influence of idealized content and algorithmic reinforcement. This paper provides theoretical implications for comprehending the psychological impacts of social media and for the development of adolescent media literacy.
Research data management in health and biomedical citizen science: practices and prospects

AbstractBackgroundPublic engagement in health and biomedical research is being influenced by the paradigm of citizen science. However, conventional health and biomedical research relies on sophisticated research data management tools and methods. Considering these, what contribution can citizen science make in this field of research? How can it follow research protocols and produce reliable results?ObjectiveThe aim of this article is to analyze research data management practices in existing biomedical citizen science studies, so as to provide insights for members of the public and of the research community considering this approach to research.MethodsA scoping review was conducted on this topic to determine data management characteristics of health and bio medical citizen science research. From this review and related web searching, we chose five online platforms and a specific research project associated with each, to understand their research data management approaches and enablers.ResultsHealth and biomedical citizen science platforms and projects are diverse in terms of types of work with data and data management activities that in themselves may have scientific merit. However, consistent approaches in the use of research data management models or practices seem lacking, or at least are not prevalent in the review.ConclusionsThere is potential for important data collection and analysis activities to be opaque or irreproducible in health and biomedical citizen science initiatives without the implementation of a research data management model that is transparent and accessible to team members and to external audiences. This situation might be improved with participatory development of standards that can be applied to diverse projects and platforms, across the research data life cycle.
Interoperability Between Central Bank Digital Currency Systems and Fast Payment Systems

Central banks around the world are actively researching and investigating the benefits, challenges, and design options of wholesale and retail central bank digital currencies (CBDCs). Since CBDCs are one of the most critical components of a national payment system (NPS), it is important that their interoperability with other payment systems is one of the key considerations in the design process. The ITS Technology and Innovation (ITSI) team, in collaboration with the World Banks’s Finance Competitiveness and Innovation (FCI) Global Practice, has conducted technology design experiments on two specific scenarios regarding CBDC system interoperability with fast payment systems (FPS). In the first scenario, the experiment investigated the option of settling FPS obligations in a wholesale CBDC system, including the option to reserve funds to guarantee the settlement of FPS net obligations. In the second scenario, the team investigated the interoperability between users within the FPS and retail CBDC users, including the transfer of funds among both types of users, using common services such as address resolution services. This experiment illustrated how CBDC systems can interoperate with retail payment systems through an interlinking bridge that was used to route messages and application programming interface (API) calls among different systems. The programmability features of distributed ledge technology (DLT) were used to link the settlement in CBDC to the transfer of funds in the FPS. The technical applicability for this type of interoperability was demonstrated through the experiments, with the caveat that these experiments do not take into account complexities that may be involved with live systems.
Orbital Graph Convolutional Neural Network for Material Property Prediction

Material representations that are compatible with machine learning models play a key role in developing models that exhibit high accuracy for property prediction. Atomic orbital interactions are one of the important factors that govern the properties of crystalline materials, from which the local chemical environments of atoms is inferred. Therefore, to develop robust machine learningmodels for material properties prediction, it is imperative to include features representing such chemical attributes. Here, we propose the Orbital Graph Convolutional Neural Network (OGCNN), a crystal graph convolutional neural network framework that includes atomic orbital interaction features that learns material properties in a robust way. In addition, we embedded an encoder-decoder network into the OGCNN enabling it to learn important features among basic atomic (elemental features), orbital-orbital interactions, and topological features. We examined the performance of this model on a broad range of crystalline material data to predict different properties. We benchmarked the performance of the OGCNN model with that of: 1) the crystal graph convolutional neural network (CGCNN), 2) other state-of-the-art descriptors for material representations including Many-body Tensor Representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), and 3) other conventional regression machine learning algorithms where different crystal featurization methods have been used. We find that OGCNN significantly outperforms them. The OGCNN model with high predictive accuracy can be used to discover new materials among the immense phase and compound spaces of materials
Regelation: why does ice melt under pressure?

Unlike other unusual materials whose bonds contract under compression, the O:H nonbond undergoes contraction and the H-O bond elongation towards O:H and H-O length symmetry in water and ice. The energy drop of the H-O bond dictates the melting point Tm depression of ice. Once the pressure is relieved, the O:H-O bond fully recovers its initial state, resulting in Regelation.
Sustainable Agricultural Chemistry and Food Security in Andhra Pradesh and India

Agriculture remains central to the economy of India, with Andhra Pradesh representing a diverse agro-ecological landscape facing critical challenges such as soil degradation, inefficient input use, and climate variability. This study examines the role of sustainable agricultural chemistry in enhancing food security by improving productivity and environmental sustainability. Adopting a mixed-methods approach, primary data were collected from 400 farmers across major districts, supported by secondary data and expert interviews. The findings reveal that sustainable practices, including slow-release fertilizers, bio-based soil amendments, and integrated pest management, significantly improve crop yields (by 12–18%) and reduce pest damage. Moreover, households adopting these practices demonstrated higher food availability and stability, indicating a positive correlation between sustainable chemistry and food security outcomes. Despite these benefits, adoption remains constrained by limited awareness, high initial costs, and inadequate policy support. The study highlights the need for targeted subsidies, strengthened extension services, and localized innovations to promote sustainable inputs. It concludes that integrating environmentally sound chemical practices with traditional farming systems can enhance agricultural resilience and contribute to long-term food security in India.
Privacy Concerns when Modeling Users in Collaborative Filtering Recommender Systems

This chapter investigates ways to deal with privacy rules when modeling preferences of users in recommender systems based on collaborative filtering. It argues that it is possible to find a good compromise between quality of predictions and protection of personal data. Thus, it proposes a methodology that fulfills with strictest privacy laws for both centralized and distributed architectures. The authors hope that their attempts to provide an unified vision of privacy rules through the related works and a generic privacy-enhancing procedure will help researchers and practitioners to better take into account the ethical and juridical constraints as regards privacy protection when designing information systems.
On the Propagation Velocity of Cosmic Ray Modulation Wave in the Outer Heliosphere

Abstract   The expected propagation velocity of cosmic ray modulation effects in the outer heliosphere is calculated analytically and numerically. In case of a harmonic or symmetric wave of cosmic ray modulation, the propagation velocity of the modulation has been found to be UM = 2u, where u is solar wind velocity (with good agreement with Dorman's earlier work of 1967). In case of an asymmetric wave of cosmic ray modulation, its phase velocity of propagation is UM ≠ 2u. The UM value has been found for different shapes of the cosmic ray modulation wave. The calculation results are compared with Voyager-1,2 and Pioneer-10, 11 measurement data. A good agreement has been obtained.
raaSAFT: A framework enabling coarse-grained molecular dynamics simulations based on the SAFT-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si16.gif" display="inline" overflow="scroll"><mml:mi>γ</mml:mi></mml:math> Mie force field

We describe here raaSAFT, a Python code that enables the setup and running of coarse-grained molecular dynamics simulations in a systematic and efficient manner. The code is built on top of the popular HOOMD-blue code, and as such harnesses the computational power of GPUs. The methodology makes use of the SAFT-γ Mie force field, so the resulting coarse grained pair potentials are both closely linked to and consistent with the macroscopic thermodynamic properties of the simulated fluid. In raaSAFT both homonuclear and heteronuclear models are implemented for a wide range of compounds spanning from linear alkanes, to more complicated fluids such as water and alcohols, all the way up to nonionic surfactants and models of asphaltenes and resins. Adding new compounds as well as new features is made straightforward by the modularity of the code. To demonstrate the ease-of-use of raaSAFT, we give a detailed walkthrough of how to simulate liquid–liquid equilibrium of a hydrocarbon with water. We describe in detail how both homonuclear and heteronuclear compounds are implemented. To demonstrate the performance and versatility of raaSAFT, we simulate a large polymer-solvent mixture with 300 polystyrene molecules dissolved in 42 700 molecules of heptane, reproducing the experimentally observed temperature-dependent solubility of polystyrene. For this case we obtain a speedup of more than three orders of magnitude as compared to atomistically-detailed simulations.
Detecting Sexually Explicit Content in the Context of the Child Sexual Abuse Materials (CSAM): End-to-End Classifiers and Region-Based Networks

Accepted at ECML PKDD 2023 - SoGOOD Workshop
An Historical Disquisition Concerning The Knowledge which the Ancients had of India; And The Progress of Trade with that Country prior to the Discovery of the Passage to it by the Cape Of Good Hope : With an Appendix, Containing Observations on the Civil Policy - the Laws and Judical Proceedings - the Arts - the Sciences - and Religious Institutions, of the Indians / By William Robertson, D.D.F.R.S. Ed. Principal Of the University, and Historiographer To His Majesty For Scotland

Vorlageform der Veröffentlichungsangabe: Basil: Printed and sold By J. J. Tourneisen. MDCCXCII.
Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions

The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of the AI revolution. Nevertheless, these LLMs exhibit a notable limitation, as they are primarily adept at processing textual information. To address this constraint, researchers have endeavored to integrate visual capabilities with LLMs, resulting in the emergence of Vision-Language Models (VLMs). These advanced models are instrumental in tackling more intricate tasks such as image captioning and visual question answering. In our comprehensive survey paper, we delve into the key advancements within the realm of VLMs. Our classification organizes VLMs into three distinct categories: models dedicated to vision-language understanding, models that process multimodal inputs to generate unimodal (textual) outputs and models that both accept and produce multimodal inputs and outputs.This classification is based on their respective capabilities and functionalities in processing and generating various modalities of data.We meticulously dissect each model, offering an extensive analysis of its foundational architecture, training data sources, as well as its strengths and limitations wherever possible, providing readers with a comprehensive understanding of its essential components. We also analyzed the performance of VLMs in various benchmark datasets. By doing so, we aim to offer a nuanced understanding of the diverse landscape of VLMs. Additionally, we underscore potential avenues for future research in this dynamic domain, anticipating further breakthroughs and advancements.
Learning to generate molecules : using the diffusion model to drug discovery

We begin by discussing the context of drug development and generative models. We then set out the objectives and three research questions relating to the effectiveness of diffusion models and the constraints of real-life applications. Next, the organization of the research project and its sources of bias, schedule and budget. We then take a look at the state of the art of diffusion models, to define them, describe how they work and the processes that make them up. We review their different architectures: Denoising Diffusion Probabilistic Models (DDPMs), Noise Conditioned Score Networks (NCSNs) and Stochastic Differential Equations (SDEs). The conditional mode is tested in an initial experiment using a DDPM and images. We then discuss molecular design, de novo design and problem statements such as unconstrained, property-constrained and structure-constrained molecular generation. We review different molecular representations such as SMILES, SELFIES, molecular fingerprints and graphical notations. We then discuss the different generative models used for molecular generation, such as RNNs, VAEs, GANs and diffusion models. We look at molecular databases and frameworks for evaluating generative models. We describe our thinking process, the development of our DiffGenMol application and its functionalities, and the metrics and visualizations implemented. This application is then used to carry out two experiments. The first experiment aims to find out which molecular representation allows DDPM to be the most efficient for unconstrained molecular generation. The aim of the second experiment is to find out which molecular representation allows DDPM to be most effective in generating property-constrained molecules. For each experiment, we describe the objectives, the method used, the evolution of the metrics during model training, the results and their interpretation. We then discuss the approach taken in this work, the difficulties encountered and the choices made. We then answer the three research questions with the help of the results obtained and our various readings. Finally, we conclude with the most relevant results, possible improvements and a personal impression of how this research work has gone. Keywords: generative models, diffusion models, DDPM, conditional generation, molecular generation, molecular representations, SMILES, SELFIES.