ethicalabs/Research-Intent-Collab huggingface dataset:
Manipulative Design in The Digital Age: Trends and Impacts of Dark Patterns On Social Media Users

This study examines dark patterns in social media apps using bibliometric analysis and qualitative review of 32 Scopus-indexed articles (2015-2024). This study aims to identify research trends, key themes, and gaps related to manipulative interface designs that influence user behavior. The results of this study show that publication activity has increased since 2020, although citation impact remains low. Common dark patterns found include privacy zuckering, confirm shaming, and infinite scrolling-tactics that exploit cognitive biases and affect user decisions, privacy, and mental well-being. Children and adolescents are particularly vulnerable due to their limited ability to detect manipulation. Thematic mapping reveals dominant themes such as decision-making, privacy, and behavioral health, while areas such as digital well-being and user consent remain under-explored. Tools such as Bibliometrix and Biblioshiny were used to visualize trends, collaborations and keyword networks. This research contributes to understanding how dark patterns shape digital behavior and highlights the need for ethical design practices and regulatory frameworks in online environments.
Programmable low-threshold optical nonlinear activation functions for photonic neural networks

We experimentally demonstrate two types of programmable, low-threshold, optically controlled nonlinear activation functions, which are challenging to realize in photonic neural networks (PNNs). These devices rely on on-chip integrated Ge–Si photoelectric detectors and silicon electro-optical switches, and they generate rectified linear unit (ReLU) or sigmoid functions with arbitrary slopes without additional electrical processing. Both devices function at an extremely low threshold of 0.2 mW. The embedding of these nonlinear activation functions into convolutional neural networks facilitates the attainment of high inference accuracies of up to 95% when applied to Modified National Institute of Standards and Technology (MNIST) handwritten digit-classification tasks. The devices are suitable for low-power PNNs with an arbitrary number of propagation layers in photonic-computing chips.
A Custom Deep Learning Model With Explainable Artificial Intelligence for Interpretable Brain Tumor Classification

ABSTRACT                                        Brain tumors are critical neurological disorders affecting mankind. The Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans play an important role in diagnosing brain tumors, but need an expert interpretation. Although deep learning methods can automate tumor detection, their lack of interpretability and clinical trust remains a major limitation. To address this concern, this paper presents a BrainTumorClassificationNetwork‐Convolutional Neural Network (BTCNet‐CNN) model with several layers for detecting and classifying MRI scanned images into four categories: glioma, meningioma, no tumor, and pituitary. The model was trained and evaluated on a publicly available dataset comprising 5824 MRI images. Augmentation techniques such as horizontal flipping, random rotations, zooming, shifting, and shearing were implemented on the training set, increasing data diversity and enhancing BTCNet‐CNN's robustness to variations in tumor orientation, scale, and position. To evaluate the effectiveness of the proposed model, various popular pre‐trained networks such as ResNet50 and InceptionV3 are also implemented on the dataset with metrics such as accuracy, loss, precision, recall, F1 score, and AUROC. Among these three models, the BTCNet‐CNN has achieved superior performance with an accuracy of 99.31%, a loss of 0.118, and an average precision, recall, and F1 score of 99.25%, 99.25%, and 99.5%, respectively, on the unseen data. Statistical significance was evaluated using McNemar's test, confirming that BTCNet‐CNN's predictions are significantly better than those of ResNet50 and InceptionV3 (                     p                      &lt; 0.05). To enhance trust and interpretability of models' decisions, various Explainable AI (XAI) techniques, including Occlusion Sensitivity, LIME, Smooth Gradients, and Saliency Maps, were integrated. Finally, a Streamlit‐based web application was developed to facilitate real‐time prediction and visualization, ensuring practical applicability in clinical diagnosis.
MXene‐Nanoflakes‐Enabled All‐Optical Nonlinear Activation Function for On‐Chip Photonic Deep Neural Networks

Abstract2D metal carbides and nitrides (MXene) are promising material platforms for on‐chip neural networks owing to their nonlinear saturable absorption effect. The localized surface plasmon resonances in metallic MXene nanoflakes may play an important role in enhancing the electromagnetic absorption; however, their contribution is not determined due to the lack of a precise understanding of its localized surface plasmon behavior. Here, a saturable absorber made of MXene thin film and a silicon waveguide with MXene flakes overlayer are developed to perform neuromorphic tasks. The proposed configurations are reconfigurable and can therefore be adjusted for various applications without the need to modify the physical structure of the proposed MXene‐based activator configurations via tuning the wavelength of operation. The capability and feasibility of the obtained results of machine‐learning applications are confirmed via handwritten digit classification task, with near 99% accuracy. These findings can guide the design of advanced ultrathin saturable absorption materials on a chip for a broad range of applications.
Towards Intersectional Properties: A Legal Materialist Take of Indigenous Peoples’ Land Rights in the Inter-American Court on Human Rights

Fil: Gebruers Hanya, Cecilia Lis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martín. Instituto de Altos Estudios Sociales; Argentina
Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking

Abstract                   AlphaFold can predict the structures of monomeric and multimeric proteins with high accuracy but has a limit on the number of chains and residues it can fold. Here we show that a combination of AlphaFold and all-atom symmetric docking simulations enables highly accurate prediction of the structure of complex symmetrical assemblies. We present a method to predict the structure of complexes with cubic – tetrahedral, octahedral and icosahedral – symmetry from sequence. Focusing on proteins where AlphaFold can make confident predictions on the subunit structure, 27 cubic systems were assembled with a median TM-score of 0.99 and a DockQ score of 0.72. 21 had TM-scores of above 0.9 and were categorized as acceptable- to high-quality according to DockQ. The resulting models are energetically optimized and can be used for detailed studies of intermolecular interactions in higher-order symmetrical assemblies. The results demonstrate how explicit treatment of structural symmetry can significantly expand the size and complexity of AlphaFold predictions.
Gender Equality in Europe; Developments with reference to Gender Pay Gap and Violence

The European Union has kept the agenda of gender equality at the front line of legislation and policy development. The research examined the policies and the efficacy of the European Union in endorsing gender equality and determines where the Union’s system has proved to be less effective. Despite the efforts by the European Union in addressing gender-related issues, a slow improvement has been indicated in achieving gender parity. Inequality persists in many domains of gender equality, including the gender pay gap and gender-based violence. Our research suggests that adequate attention in terms of relevance, effective implementation and funding must be given to all domains of gender equality.
Some consequences of thermodynamic feasibility for the multistability and injectivity in chemical reaction networks

Power law dynamics is used to describe the stability behavior in metabolic networks such as chemical reaction networks (CRN's). These systems allow multiple steady states within a single stoichiometric class. On the other side thermodynamic constraints such as loop-less fluxes represented by the Gorban theorem of alternatives applied to these networks reveal considerable restrictions to their dynamics by eliminating multistability of CRN's in general. Thermodynamic feasible CRN's are contained in the class of injective CRN's. We can give an alternative proof of the detailed balance with Brewer's Fixed Point Theorem. Furthermore we can derive by the loop-less principle the extended detailed balance. This paper establishes a link between recent research in CRN theory and thermodynamic basics. The result has also consequences for the picture of multiple steady states as assumed for cell differentiation and regulation. CRN's provide from their perspective not enough means to maintain multistability without regulation or external control.
Towards More Realistic Simulated Datasets for Benchmarking Deep Learning Models in Regulatory Genomics

Abstract                                    Deep neural networks and support vector machines have been shown to accurately predict genome-wide signals of regulatory activity from raw DNA sequences. These models are appealing in part because they can learn predictive DNA sequence features without prior assumptions. Several methods such as in-silico mutagenesis, GradCAM, DeepLIFT, Integrated Gradients and Gkm-Explain have been developed to reveal these learned features. However, the behavior of these methods on regulatory genomic data remains an area of active research. Although prior work has benchmarked these methods on simulated datasets with known ground-truth motifs, these simulations employed highly simplified regulatory logic that is not representative of the genome. In this work, we propose a novel pipeline for designing simulated data that comes closer to modeling the complexity of regulatory genomic DNA. We apply the pipeline to build simulated datasets based on publicly-available chromatin accessibility experiments and use these datasets to bench-mark different interpretation methods based on their ability to identify ground-truth motifs. We find that a GradCAM-based method, which was reported to perform well on a more simplified dataset, does not do well on this dataset (particularly when using an architecture with shorter convolutional kernels in the first layer), and we theoretically show that this is expected based on the nature of regulatory genomic data. We also show that Integrated Gradients sometimes performs worse than gradient-times-input, likely owing to its linear interpolation path. We additionally explore the impact of user-defined settings on the interpretation methods, such as the choice of “reference”/”baseline”, and identify recommended settings for genomics. Our analysis suggests several promising directions for future research on these model interpretation methods. Code and links to data are available at                   https://github.com/kundajelab/interpret-benchmark                   .
Deep learning for protein secondary structure prediction: Pre and post-AlphaFold

This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure prediction. Previous studies showed that deep neural networks had uplifted the accuracy of three-state secondary structure prediction to more than 80%. Favored deep learning methods, such as convolutional neural networks, recurrent neural networks, inception networks, and graph neural networks, have been implemented in protein secondary structure prediction. Methods adapted from natural language processing (NLP) and computer vision are also employed, including attention mechanism, ResNet, and U-shape networks. In the post-AlphaFold era, PSSP studies focus on different objectives, such as enhancing the quality of evolutionary information and exploiting protein language models as the PSSP input. The recent trend to utilize pre-trained language models as input features for secondary structure prediction provides a new direction for PSSP studies. Moreover, the state-of-the-art accuracy achieved by previous PSSP models is still below its theoretical limit. There are still rooms for improvement to be made in the field.
Resilience of the physicochemical properties of graphene-based materials for applications in harsh radiation environments

The development of radiation-tolerant materials capable of maintaining structural, electrical, and thermal stability in extreme, radiation-rich environments remains a critical challenge in materials science. In this work, the effects of 60 MeV 35Cl ion irradiation on highly oriented pyrolytic graphite (HOPG) and multilayer reduced graphene oxide (ML-rGO) were investigated. The samples were exposed to fluences of 5.11 x 10^9 and 1.3 x 10^10 ions/cm^2 and characterized by X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), and electrical transport measurements. The results show that the irradiation response is strongly influenced by the initial structural organization of the material. In HOPG, ion exposure leads to a progressive loss of crystalline order, evidenced by XRD peak broadening and an increase in the Raman ID/IG ratio, accompanied by a reduction in electrical transport performance. In contrast, ML-rGO exhibits distinct behavior at higher fluences, suggesting partial structural reorganization. The appearance of more defined graphitic features in XRD and Raman analyses, along with changes in surface morphology and electrical response, suggests the formation of more ordered sp2 domains. These findings indicate that irradiation effects vary with the initial degree of order, providing useful insights for selecting carbon-based materials for devices operating under severe radiation conditions.
Modulation of Galactic Cosmic Rays in the Inner Heliosphere, Comparing with PAMELA Measurements

Abstract                We develop a numerical model to study the time-dependent modulation of galactic cosmic rays in the inner heliosphere. In the model, a time-delayed modified Parker heliospheric magnetic field (HMF) and a new diffusion coefficient model, NLGCE-F, from Qin &amp; Zhang, are adopted. In addition, the latitudinal dependence of magnetic turbulence magnitude is assumed to be                                                                   from the observations of Ulysses, and the radial dependence is assumed to be                                                                  , where we choose an expression of S as a function of the heliospheric current sheet tilt angle. We show that the analytical expression used to describe the spatial variation of HMF turbulence magnitude agrees well with the Ulysses, Voyager 1, and Voyager 2 observations. By numerically calculating the modulation code, we get the proton energy spectra as a function of time during the recent solar minimum, it is shown that the modulation results are consistent with the Payload for Antimatter-Matter Exploration and Light-nuclei Astrophysics measurements.
Survey of critical failure events in on-chip interconnect by fault tree analysis

Abstract                   In this paper, a framework based on reliability physics is proposed for adopting fault tree analysis (FTA) to the on-chip interconnect system of a semiconductor. By integrating expert knowledge and experience regarding the possibilities of failure on basic events, critical issues of on-chip interconnect reliability will be evaluated by FTA. In particular, FTA is used to identify the minimal cut sets with high risk priority. Critical events affecting the on-chip interconnect reliability are identified and discussed from the viewpoint of long-term reliability assessment. The moisture impact is evaluated as an external event.
Microbial innovations in agriculture: interdisciplinary approaches to leveraging microbes for food sustainability and security

Ensuring food security and promoting sustainability are huge global challenges that humanity must address. The field of microbiology presents promising solutions by leveraging the immense diversity and capabilities of microorganisms. These microscopic life forms play vital roles throughout the food production cycle, from enhancing soil fertility and boosting plant growth to controlling pests and diseases, facilitating food processing, and managing waste. Exploiting beneficial microbes can increase crop yields, improve nutrient bioavailability, and reduce reliance on synthetic agrochemicals, thereby contributing to environmental sustainability. Moreover, microbial biotechnologies enable the development of innovative food products, enhance food safety measures, and prolong shelf life, consequently minimizing food waste. Significantly, microbial-based solutions can be tailored to local contexts, fostering inclusive and equitable food systems accessible to diverse communities. However, fully realizing the potential of microbiology in food systems requires a multidisciplinary approach, integrating advancements in microbial ecology, genomics, metabolomics, bio preservation, and biotechnology. This abstract explores the potential of microbiology to contribute significantly to food security and sustainability, emphasizing the importance of interdisciplinary research and the integration of microbiological innovations into agricultural practices and food systems.
The role of nonpermanent members on the United Nations Security Council: The case of Sweden 2017

This thesis investigates the role nonpermanent members have on the United Nations Security Council (UNSC). This as the contemporary scholarly debate on the UNSC tends to exclude the nonpermanent members in the study of the only international institution that can bind all UN members under its decisions. Building on the slim literature existing on why states seek the nonpermanent seats, this thesis uses the case of Sweden in order to investigate whether Sweden has met its objectives during its first year on the council. This case also contributes to a broader question of the potential influence nonpermanent members can have on the council. The results of this thesis are in line with the research it is building on, inherited from a liberal strand of international relations, that Sweden has influenced outcomes in line with its objectives and that nonpermanent members can influence the council in terms of resolutions, making them relevant to study in research concerning the UNSC. It also provides additional knowledge to the existing research it is building on by expanding the scope in how nonpermanent members can influence the council, which can be used in future studies.
Evidence for Radio Detection of Extensive Air Showers Induced by Ultra High Energy Cosmic Rays

4 pages, 4 figures
sj-docx-1-jre-10.1177_1556264621992214 – Supplemental material for Citizens, Research Ethics Committee Members and Researchers’ Attitude Toward Information and Consent for the Secondary Use of Health Data: Implications for Research Within Learning Health Systems

Supplemental material, sj-pdf-1-eeg-10.1177_1556264621992214 for Citizens, Research Ethics Committee Members and Researchers’ Attitude Toward Information and Consent for the Secondary Use of Health Data: Implications for Research Within Learning Health Systems by Annabelle Cumyn, Roxanne Dault, Adrien Barton, Anne-Marie Cloutier and Jean-François Ethier in Journal of Empirical Research on Human Research Ethics
Validity of root cause analysis in investigating adverse events in psychiatry

SummaryRoot cause analysis (RCA), imported from high-reliability industries into health two decades ago, is the mandated methodology to investigate adverse events in most health systems. In this analysis, we argue that the validity of RCA in health and in psychiatry must be established, given the impact of these investigations on mental health policy and practice.
An Efficient Technique to Determine the Power Spectrum from Cosmic Microwave Background Sky Maps

35 pages, 8 figures, LaTex, aaspp4. Accepted by ApJ. Minor changes to match accepted version
PROSPECTS FOR INTERNATIONAL SETTLEMENTS IN CENTRAL BANK DIGITAL CURRENCIES ON A PLATFORM BASIS IN FOREIGN COUNTRIES AND RUSSIA

The article investigates the main problems, state and development of the system of modern cross-border settlements in the global economy; shows the classification of multilateral platforms of central bank digital currencies through the prism of currency agreements used in cross-border payments; considers models of interoperability of central bank digital currencies and options for access of payment service providers; analyzes international projects of both wholesale and retail multicurrency systems of central bank digital currencies; proposes the author’s version of the Eurasian project of a multicurrency system of central banks, which can increase the efficiency of cross-border payments of the Russian Federation and countries that are its main trading partners.
Recommender system for IT security scanning service : Collaborative filtering in an error report scenario

Rekommendationssystem är idag en självklar del av manga användargränssnitt. Exempel på dessa som många av oss interagerar med dagligen är system som föreslår nästa ord när vi skriver, nästa produkt när vi handlar online eller nästa media när vi använder streaming-tjänster. En vanlig teknik för att producera rekommendationer är Collaborativer Filtering, vilken använder Nearest Neighbor-algoritmer för att rekommendera så att en användares historik (beskriven som en vektor) förflyttas närmre de närmaste grannarna om rekommendationen följs. I denna uppsats redovisas en anpassning av ett Collaborative Filtering-rekommendationssystem för användning i samband med skanning efter it-säkerhetsrisker, med målet att producera rekommendationer rörande vilken säkerhetsrisk som bör prioriteras för åtgärd. Ett sådant error report scenario (riskrapport-scenario) för med sig vissa skillnader jämfört med ett e-handel/streaming-scenario som gör det nödvändigt att anpassa de typiska Collaboritve Filtering-systemet innan det är applicerbart. Det här arbetet utförs i samarbete med företaget Detectify, som tillhandahåller en produkt med vilken användare kan upptäcka säkerhetsrisker i deras internet-kopplade mjukvara (exempelvis hemsidor och web-applikationer). Historiska prioriteringar rörande åtgärdande av säkerhetsrisker måste beräknas ut tidigare användares rapporter om hunna risker, alltså från brusiga vektor-värda signaler. En användare kan inte heller fritt välja att öka sin konsumption av någon produkt i ett sortiment, utan istället måste en rekommendation röra vilket objekt i en användares befintliga innehav (de funna riskerna i deras senaste rapport) som användaren bör försöka minska antalet av. I den här uppsatsen presenteras ett Collaborative Filtering-rekommendationssystem anpassat till detta scenario. Algoritmen motiveras med en extensiv litteraturstudie av relevant litteratur och utvärderas med syntetisk data vilket möjliggör undersökning av hur olika nivåer av brus och gleshet (sparsity) inverkar på rekommendationerna. Resultaten som presenteras tillhandahåller referensnivåer för under vilken grad av brus och gleshet algoritmen kan förväntas prestera väl. Sammanfattningsvis utvecklas, utvärderas och presenteras en modifikation av Cillaborative Filtering-rekommendationssystem som möjliggör dessa användade i ett scenario som ej beskrivs i den genomgångna litteraturen.