ethicalabs/Research-Intent-Collab huggingface dataset:
Supplemental Material: Landscape inversion episodes in SE China during the Mesozoic– early Cenozoic: Constrained by trace-element contents, Nd isotope geochemistry, and detrital zircon U-Pb geochronology of sedimentary basins

&lt;p&gt;Table S1: Information of samples from Yong’an basin. Table S2: Analysis results of trace elements from Yong’an basin. Table S3: Lithology and Nd isotopic compositions of samples from Yong’an basin. Table S4: Detrital zircon U-Pb geochronology of samples from Yong’an basin. Table S5: Element ratios of samples from Yong’an basin. Table S6: Calculation of denudation rates based on low-temperature thermochronology data.&lt;/p&gt;
Building Switching Hybrid Recommender System Using Machine Learning Classifiers and Collaborative Filtering

Recommender systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. To date a number of recommendation algorithms have been proposed, where collaborative filtering and content-based filtering are the two most famous and adopted recommendation techniques. Collaborative filtering recommender systems recommend items by identifying other users with similar taste and use their opinions for recommendation; whereas content-based recommender systems recommend items based on the content information of the items. Moreover, machine learning classifiers can be used for recommendation by training them on content information. These systems suffer from scalability, data sparsity, over specialization, and cold-start problems resulting in poor quality recommendations and reduced coverage. Hybrid recommender systems combine individual systems to avoid certain aforementioned limitations of these systems. In this paper, we proposed unique generalized switching hybrid recommendation algorithms that combine machine learning classifiers with the collaborative filtering recommender systems. Experimental results on two different data sets, show that the proposed algorithms are scalable and provide better performance—in terms of accuracy and coverage—than other algorithms while at the same time eliminate some recorded problems with the recommender systems.
From Myanmar to The Hague. A Feminist Perspective on the Search for Gender Justice by Rohingya Women before the International Criminal Court

Sexual and gender-based violence during conflict remains a widespread issue, with women and girls being particularly vulnerable. Since its establishment, the International Criminal Court has prioritised the prosecution of sexual violence through some successful ground-breaking judgments. However, it has also received harsh criticism due its failure to incorporate an intersectional perspective into its jurisprudence. While the Office of the Prosecutor has committed itself to incorporating intersectionality in its prosecutions in its 2014 Policy Paper, it has yet to deliver an intersectional analysis in one of its rulings. The recent authorisation from the Pre-Trial Chamber to proceed with an investigation for the alleged crimes perpetrated against the Rohingya community in Myanmar could provide such an occasion. While the ethnic minority is suffering from gross human rights violations, rape is being used systematically against Rohingya women. In light of these considerations, this thesis seeks to analyse how an intersectional approach could assist the International Criminal Court in analysing and addressing the sexual violence suffered by Rohingya women. To achieve this, an historical overview of the prosecution of sexual violence before international criminal tribunals was provided, together with the main criticisms regarding their track records. Secondly, the theory of intersectionality was presented and analysed in the context of anti-discrimination law, international human rights law and international criminal law. Finally, the theory of intersectionality was applied to the case study of Rohingya women in light of the possible claims of genocide and crimes against humanity they could bring before the court. On one hand, the analysis has demonstrated that an intersectional approach can indeed help the International Criminal Court put emphasis on the gravity of the violence suffered by the victims as well as the systematic and organised nature of such attacks. On the other hand, there is a risk that Rohingya women will be further stereotyped or that the violence suffered by those who do not fit within the victim model will be left unpunished.
Prospects for Deep-Learning-Based Mass Reconstruction of Ultra-High-Energy Cosmic Rays using Simulated Air-Shower Profiles

Knowledge of the mass composition of ultra-high-energy cosmic rays is crucial to understanding their origins; however, current approaches have limited event-by-event resolution. With fluorescence telescope measurements of the longitudinal shower profile, there are opportunities to improve this situation by applying Machine Learning (ML) to leverage more information beyond $X_{max}$ alone. To our knowledge, we present the first study of a deep-learning neural-network approach to predict a primary's mass ($\ln{A}$) directly from the longitudinal energy-deposit profile of simulated extensive air showers. We train and validate our model on simulated showers, generated with CONEX and EPOS-LHC, covering nuclei from $A = 1$ to 61, sampled uniformly in $\ln{A}$. After rescaling, our network achieves a maximum bias better than 0.4 in $\ln{A}$ with a resolution between 1.5 for protons and 1 for iron, corresponding to a proton-iron Merit Factor of 2.19 (AUC = 0.976). We benchmark this against simpler ML models trained on profile-shape parameters ($X_{ max}$, $E_{cal}$, $R$, and $L$) extracted from the same data. We find that even simple models can substantially exceed published benchmarks for combinations of these observables, demonstrating that ML methods applied even to standard profile-shape parameters can significantly improve available mass sensitivity. The CNN outperforms this strong baseline, and this performance is only mildly degraded when cross-predicting on simulations made with the Sibyll-2.3d hadronic interaction model, showing robustness against model choice. The network also maintains its performance across a wide range of noise conditions. An ablation study further demonstrates that the full profile contains composition-sensitive structure not captured by the GH parameterization, while the strong performance of the CNN suggests this information should be resolvable in real events.
Health insurance coverage, care accessibility and affordability for adult survivors of childhood cancer: a cross-sectional study of a nationally representative database

We describe national patterns of health insurance coverage and care accessibility and affordability in a national sample of adult childhood cancer survivors (CCS) compared to adults without cancer.Using data from the 2010-2014 National Health Interview Survey (NHIS), we selected a sample of all CCS age 21 to 65 years old and a 1:3 matched sample of controls without a history of cancer. We examined insurance coverage, care accessibility and affordability in CCS and controls. We tested for differences in the groups in bivariate analyses and multivariable logistic regression models.Of all respondents age 21-65 in the full NHIS sample, 443 (0.35 %) were CCS. Fewer CCS were insured (76.4 %) compared to controls (81.4 %, p = 0.067). Significantly more CCS reported delaying medical care (24.7 vs 13.0 %), needing but not getting medical care in the previous 12 months (20.0 vs 10.0 %), and having trouble paying medical bills (40.3 vs 19.7 %) compared to controls (p < 0.0001 for all). More CCS reported trouble with care affordability in the previous 12 months compared to controls on six categories of care and for a combined measure of affordability (p < 0.0001 for composite of all). Adjusted analyses demonstrated that these differences comparing CCS to controls remained significant.CCS report problems with health care accessibility and affordability. These analyses support the development of policies to assure that CCS have access to affordable services.Efforts to improve access to high-quality and affordable insurance for CCS may help reduce the gaps in getting medical care and problems with affordability. Health care providers should be aware that such problems exist and should discuss affordability and ability to obtain care with patients.
International trade restrictions and their impact on international relations: dispute settlement within the World Trade Organization

In a world where trade relations between states are of tremendous importance, it is inevitable to avoid conflict. For the purposes of minimizing the risk of differences coming between various jurisdictions, the World Trade Organization has established principles, rules and mechanisms which seek to create a uniform set of rules that Members are bound by. Nevertheless, even though Members have undertaken to comply with the rules outlined in several Agreements, they still tend to violate them due to reasons such as conflicting internal laws, national security issues or dissatisfaction with the policies of their trading partners. The dispute settlement procedure and the recommendations issued by the WTO are of a binding nature; however, it has been observed that states do not always comply, which further leads to the imposition of sanctions. The study suggests that the legal outcomes of cases brought to the Dispute Settlement Body of the WTO vary, as do their effects of international relations since issues related to trade mostly arise out of political differences. Nevertheless, over time, states tend to solve their disputes due to reasons of co-dependency and collateral damage.
The respective role of climate mitigation, sustainable land use and area-based conservation to curb future biodiversity loss

Increasing conservation efforts are required to avert biodiversity decline caused by climate and land use changes. In a recent study (Hari et al. 2024; preprint), we combined climate change scenarios (RCP2.6 and RCP6.0) and land use change projections to assess their impact on future species distribution for a large number of mammals, birds and amphibians. Future projections of land use change were derived from the Land Use Harmonization dataset v2 (LUH2), which does not make any explicit assumptions about the area under protection in these scenarios.Here, we extend the scope of our future biodiversity projections by adding an additional layer of different protected area (PA) scenarios. In the first conservation scenario, we fix the PAs based on the World Database on Protected Areas (WDPA), thereby assuming that PAs will remain the same in the future as it is today. In a second category of scenarios, we create land use scenarios compatible with the Global Biodiversity Framework&amp;#8217;s &amp;#8220;30 by 30&amp;#8221; target based on the spatially optimized dataset by Jung et al. (2021) combined with LUH2.We show that combining climate mitigation measures with sustainable land use is more beneficial for biodiversity than any PA scenario alone. However, PA expansion significantly reduces species loss, particularly in biodiversity hotspots. While any level of area-based conservation yields notable biodiversity benefits, the 30% PA target proves especially effective under high-emission scenarios, preventing up to 11.2% more land use-driven species loss in regions such as West, Central, East and South Africa compared to scenarios without PAs.
Structural and Dynamic Properties of the CAGQW Peptide in Water:  A Molecular Dynamics Simulation Study Using Different Force Fields

In this paper, the structural and dynamic properties of the peptide with the sequence CAGQW have been extensively investigated using molecular dynamics simulations. Several simulations for a total simulation time of > 1.5μs were performed using the GROMOS96 and OPLS-AA/L force fields. The effects of simulation protocols and force fields on the structural and dynamic properties of the peptide have been analyzed. Accounting for long range electrostatic interactions using the particle mesh Ewald method resulted in a tendency to slow the end-to-end contact formations. In contrast to the relatively modest effect of the simulation protocol, the force field had a larger influence on the properties of the peptide. Use of GROMOS96 resulted in a faster end-to-end contact formation kinetics than using OPLS-AA/L. The ratio of maximum lifetime values, obtained from GROMOS96 (16 ns) and OPLS-AA/L (24 ns) force fields (accounting for the difference between the experimental and calculated solvent viscosity) vs the experimental lifetime (73 ns) resulted in 4.6 and 3.0 times faster kinetics, respectively. This faster encounter kinetics can be related to the free energy profile versus end-to-end distance which indicates a stronger preference for small end-to-end distance states in the case of GROMOS96 compared to other force fields. Furthermore, the comparison with results obtained on the same peptide by Yeh and Hummer (J. Am. Chem. Soc. 2002, 124, 6563-6568) using AMBER94 and CHARMM22 force fields indicates the following order for the end-to-end contact formation kinetics: GROMOS96 > OPLS-AA/L > AMBER94 ≥ CHARMM22.
Emerging regenerative medicine for hemorrhagic stroke: An update on stem cell therapies

Hemorrhagic stroke (HS) is a disastrous occurrence with high mortality rates, however, there are no established treatments for this pathology. Given recent advances in stem cell therapies, concerted research efforts have elucidated the therapeutic potential of stem cells in treating HS. In 2020, our group reviewed the use of SCs in regulating the neuroinflammatory reactions which occur after HS.1 This review will build on that work, highlighting more recent advancements in the field of SC treatments for HS. Ultimately, with greater focus in this area of research, innovative SC therapies can be optimized in preclinical studies and soon be transferred to clinical applications for HS therapeutics.
SRFed: Mitigating Poisoning Attacks in Privacy-Preserving Federated Learning with Heterogeneous Data

Federated Learning (FL) enables collaborative model training without exposing clients' private data, and has been widely adopted in privacy-sensitive scenarios. However, FL faces two critical security threats: curious servers that may launch inference attacks to reconstruct clients' private data, and compromised clients that can launch poisoning attacks to disrupt model aggregation. Existing solutions mitigate these attacks by combining mainstream privacy-preserving techniques with defensive aggregation strategies. However, they either incur high computation and communication overhead or perform poorly under non-independent and identically distributed (Non-IID) data settings. To tackle these challenges, we propose SRFed, an efficient Byzantine-robust and privacy-preserving FL framework for Non-IID scenarios. First, we design a decentralized efficient functional encryption (DEFE) scheme to support efficient model encryption and non-interactive decryption. DEFE also eliminates third-party reliance and defends against server-side inference attacks. Second, we develop a privacy-preserving defensive model aggregation mechanism based on DEFE. This mechanism filters poisonous models under Non-IID data by layer-wise projection and clustering-based analysis. Theoretical analysis and extensive experiments show that SRFed outperforms state-of-the-art baselines in privacy protection, Byzantine robustness, and efficiency.
A Graph Neural Network Recommendation Model with Knowledge Graph and Attention Mechanism

Abstract         With the growth of e-commerce and social media platforms, recommendation systems have become crucial for businesses to increase user satisfaction and retention. Collaborative filtering has been the primary recommendation algorithm, but it faces challenges such as the cold start problem and data sparsity. Researchers have tried to introduce supplementary information to complement the data, and Graph Neural Networks (GNNs) have been useful in capturing the relevance of graphs in recommender systems. In addition, traditional GNNs only fuse the first-order neighborhood features of nodes and cannot capture the deeper structural relationships of nodes in the network. Therefore, when the data set is sparse and each node has only a very small number of neighbors, the recommendation quality of traditional GNN-based recommendation algorithms decreases significantly. In this paper, we propose a GNN recommendation model based on user-side information, which can effectively capture the potential interest of users through user relationship graph for recommendation, and fully exploit the large amount of association information among users. The traditional collaborative filtering idea is incorporated with the knowledge graph as the auxiliary information to fully utilize the potential information from both ends to improve the accuracy and diversity of the recommendation model. In addition, the interaction behaviors and opinions in the user-item graph are jointly captured. Different strengths of user-item interactions are considered, which indicate that various interactions contribute differently to user preferences (or item characteristics), and thus attention mechanisms are used to achieve this goal. The algorithm has been verified by public dataset, and it has better recommendation effect than several other classical recommendation algorithms.
Advancements in protein structure prediction: A comparative overview of AlphaFold and its derivatives

This review provides a comprehensive analysis of AlphaFold (AF) and its derivatives (AF2 and AF3) in protein structure prediction. These tools have revolutionized structural biology with their highly accurate predictions, driving progress in protein modeling, drug discovery, and the study of protein dynamics. Its exceptional accuracy has redefined our understanding of protein folding, which enables groundbreaking advancements in protein design, disease research and discusses future integration with experimental techniques. In addition, their achievement features, architectures, important case studies, and noteworthy effects in the field of biology and medicine were evaluated. In consideration of the fact that AF2 is a relatively recent innovation, it has already been taken into account in many studies that highlight its applications in many ways. Moreover, the limitations of AF2 that directed to the introduction of AF3 are also reported, which is a great improvement as it provides precise predictions of the structures and interactions of proteins, DNA, RNA, and ligands, thereby aiding in the understanding of the molecular level. Addressing current challenges and forecasting future developments, this work underscores the lasting significance of AF in reshaping the scientific landscape of protein research.
VEGA: Towards an End-to-End Configurable AutoML Pipeline

Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models. However, designing an integrated AutoML system faces four great challenges of configurability, scalability, integrability, and platform diversity. In this work, we present VEGA, an efficient and comprehensive AutoML framework that is compatible and optimized for multiple hardware platforms. a) The VEGA pipeline integrates various modules of AutoML, including Neural Architecture Search (NAS), Hyperparameter Optimization (HPO), Auto Data Augmentation, Model Compression, and Fully Train. b) To support a variety of search algorithms and tasks, we design a novel fine-grained search space and its description language to enable easy adaptation to different search algorithms and tasks. c) We abstract the common components of deep learning frameworks into a unified interface. VEGA can be executed with multiple back-ends and hardwares. Extensive benchmark experiments on multiple tasks demonstrate that VEGA can improve the existing AutoML algorithms and discover new high-performance models against SOTA methods, e.g. the searched DNet model zoo for Ascend 10x faster than EfficientNet-B5 and 9.2x faster than RegNetX-32GF on ImageNet. VEGA is open-sourced at https://github.com/huawei-noah/vega.
Measurement of the Cosmic Microwave Background Polarization Lensing Power Spectrum from Two Years of POLARBEAR Data

Abstract                We present a measurement of the gravitational lensing deflection power spectrum reconstructed with two seasons of cosmic microwave background polarization data from the Polarbear experiment. Observations were taken at 150 GHz from 2012 to 2014 and surveyed three patches of sky totaling 30 square degrees. We test the consistency of the lensing spectrum with a cold dark matter cosmology and reject the no-lensing hypothesis at a confidence of 10.9σ, including statistical and systematic uncertainties. We observe a value of A                   L = 1.33 ± 0.32 (statistical) ±0.02 (systematic) ±0.07 (foreground) using all polarization lensing estimators, which corresponds to a 24% accurate measurement of the lensing amplitude. Compared to the analysis of the first-year data, we have improved the breadth of both the suite of null tests and the error terms included in the estimation of systematic contamination.
Modifying CAR-T cells with anti-checkpoints in cancer immunotherapy: A focus on anti PD-1/PD-L1 antibodies

Chimeric antigen receptor-modified T (CAR-T) are genetically engineered cells to express tumor-specific antigens revolutionizing the treatment of hematologic malignancies. The hostile tumor microenvironment (TME) remains a challenge for CAR-T cell therapy in solid tumors. As a solution, combinational therapy with immune checkpoint inhibitors (ICIs) is shown to improve the safety and efficacy of CAR-T cell therapy. To avoid side effects related to the application of ICIs in combinational therapy, engineering CARs to express tumor-specific antigens may help improvement of clinical outcomes. Those CARs expressing single chain variable fragments (scFvs) or nanobodies against immune checkpoint stimulatory or inhibitory molecules, such as the programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) signaling axis are being extensively studied in various clinical trials. In this review, we discuss the significance of anti-PD-(L)1 scFv-expressing CAR-T cells in the treatment of human cancers, describing current challenges and potential strategies to overcome such predicaments in the area of cancer immunotherapy.
Additional file 1 of Beyond coverage: a qualitative study exploring the perceived impact of Gabon’s health insurance plan on access to and quality of prenatal care

Additional file 1. Women Interview guide
Parallel implementations of cellular automata algorithms on the AGILA high performance computing system

Four basic cellular automata (CA) algorithms are implemented on a Beowulf cluster with 8 processors using MPI and C. The CA algorithms are, namely, (1) Game of Life, (2) Greenburg-Hasting, (3) Cyclic Space, and (4) Hodgepodge Machine. The mathematical and computational aspects of 1D and 2D CA are also considered.