Miracle-dz/UniFAR-FTUs huggingface dataset:
Health education impact on knowledge and management of arboviral diseases in Kenya: Evidence from randomised control trials

ABSTRACT Communities’ knowledge and management strategies are crucial for mitigating and controlling the threat of existing and emerging diseases. In this study, we conducted randomised control trials (RCT) to examine the impact of health education on households’ knowledge and management of three Arboviral Diseases (ADs); Rift Valley fever, Chikungunya fever, and Dengue fever in Kenya. The study was based on a sample of 629 households drawn from the three of Kenya’s AD hotspot counties; Baringo, Kwale, and Kilifi. Employing a difference-in-difference method, our findings indicate that health education intervention significantly improved households’ understanding of ADs transmission modes, causes, and prevention strategies. However, this intervention did not sufficiently influence households’ disease management behaviour. We recommend the implementation of community engagement and outreach initiatives which have the potential to drive behavioural changes at the household level, thus enhancing the management and control of ADs in Kenya.
Comparative research on microclimate between forest gaps and non-gaps of Smith fir forests in the Sejila Mountains,southeastern Tibet

Through the microclimate observation of gaps and non-gaps( under-canopy and open spaces) of Abies georgei forest in the Sejila Mountain,southeastern Tibet,we monitored daytime total radiation,air temperature,relative humidity,surface temperature and 5- 15 cm layer soil temperature,and analyzed the similarities and differences of microclimate between forest gaps and non-gaps in summer time. The results showed that,the annual change of total solar radiation to fir gaps presented bimodal curve and the peak values appeared in May and August. The diurnal and annual variation ranges of total solar radiation in forest gaps were less than that of open spaces,and meanwhile,the annual total solar radiation of gaps was 1 173. 28 MJ / m2,which was significantly lower than that of the open spaces( P 0. 01). The changing curves of air temperature in gaps and non-gaps during summer daytime were of inverted S,and the diurnal variation range of air temperature in gaps was small,ranging between open spaces and undercanopy,and it was closed to that in under-canopy and significantly lower than that in the open spaces.The relative humidity of gaps was higher than that of open spaces and under-canopy,meanwhile the variation range of it was small. The diurnal variation trend of surface temperature in forest gaps and nongap was basically consistent with the change of air temperature. The temperatures of land surface and soil layers in open spaces were significantly higher than that in gaps and under-canopy. The soil temperature decreased with the increase of soil depth,and there was no obvious diurnal variation under 10 cm layer.Due to the influence of solar radiation to the surface temperature,the diurnal variation amplitude of it was much higher than that of the soil temperature.
MULTIPLICACIÓN CLONAL DE PINOS A TRAVÉS DEL USO DE ESTACAS: UNA ALTERNATIVA PARA MANTENER GANANCIAS GENÉTICAS FORESTALES

In countries with a high development in clonal forestry are used cuttings for the multiplication of forest species with aims of reforestation and commercial plantations. It is applied in Forest Genetic Improvement, since the genetic traits are transmitted to the descendants. Nevertheless, the capacity of rooting of cuttings decrease with the maturity, which represents an obstacle. In the Instituto de Genetica Forestal, cuttings of Pinus jaliscana it has been rooted with results of 90%, using material juvenile of hedges, which offers an alternative for the cloning of pines.
Implications of a 14 200 year contiguous fire record for understanding human—climate relationships at Goochs Swamp, New South Wales, Australia

This paper interprets macroscopic charcoal (>250 μm), humification and loss-on-ignition over the last ~14200 cal. BP from Goochs Swamp, located to the west of Sydney in the Greater Blue Mountains World Heritage Area. This study aimed to investigate relationships between humans, climate and fire through time, primarily by comparison of these palaeoenvironmental indices with archaeological evidence from the region. Climatic forcing can explain all periods of change in the history of fire at Goochs Swamp: fire activity was variable during the Lateglacial—Holocene transition, low during the relatively stable climate of the early Holocene, and high but variable after the onset of modern El Niño from the mid Holocene. Although the dominant control on fire in this environment during the Holocene appears to be climate, fluctuations in the late Holocene may reflect anthropogenic fire or human responses to climate change. The archaeological record of the Blue Mountains and other parts of the Sydney Basin illustrates that Aboriginal people altered subsistence, resource and land-use patterns in the late Holocene. We propose that these cultural measures were adopted to overcome new risks as the frequency of ENSO events increased, and the natural fire regime and resource reliability changed. These strategies perhaps included a more systematic use of fire. The most parsimonious interpretation of the evidence for changes in fire activity at Goochs Swamp in the light of nearby archaeological evidence is that Aboriginal people used fire within a changing climatic framework.
Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis

Thorax disease analysis in large-scale, multicentre, and multi-scanner settings is often limited by strict privacy policies. Federated learning (FL) offers a potential solution, while traditional parameter-based FL can be limited by issues such as high communication costs, data leakage, and heterogeneity. Distillation-based FL can improve efficiency, but it relies on a proxy dataset, which is often impractical in clinical practice. To address these challenges, we introduce a data-free distillation-based FL approach FedKDF. In FedKDF, the server employs a lightweight generator to aggregate knowledge from different clients without requiring access to their private data or a proxy dataset. FedKDF combines the predictors from clients into a single, unified predictor, which is further optimized using the learned knowledge in the lightweight generator. Our empirical experiments demonstrate that FedKDF offers a robust solution for efficient, privacy-preserving federated thorax disease analysis.Clinical relevance: This study offers a privacy-preserving method for multi-centre thorax disease analysis, improving diagnostics without compromising patient data.
Self-Supervised Multi-Modality Learning for Multi-Label Skin Lesion Classification

The clinical diagnosis of skin lesion involves the analysis of dermoscopic and clinical modalities. Dermoscopic images provide a detailed view of the surface structures whereas clinical images offer a complementary macroscopic information. The visual diagnosis of melanoma is also based on seven-point checklist which involves identifying different visual attributes. Recently, supervised learning approaches such as convolutional neural networks (CNNs) have shown great performances using both dermoscopic and clinical modalities (Multi-modality). The seven different visual attributes in the checklist are also used to further improve the the diagnosis. The performances of these approaches, however, are still reliant on the availability of large-scaled labeled data. The acquisition of annotated dataset is an expensive and time-consuming task, more so with annotating multi-attributes. To overcome this limitation, we propose a self-supervised learning (SSL) algorithm for multi-modality skin lesion classification. Our algorithm enables the multi-modality learning by maximizing the similarities between paired dermoscopic and clinical images from different views. In addition, we generate surrogate pseudo-multi-labels that represent seven attributes via clustering analysis. We also propose a label-relation-aware module to refine each pseudo-label embedding and capture the interrelationships between pseudo-multi-labels. We validated the effectiveness of our algorithm using well-benchmarked seven-point skin lesion dataset. Our results show that our algorithm achieved better performances than other state-of-the-art SSL counterparts.
Correlation Aware Sparsified Mean Estimation Using Random Projection

We study the problem of communication-efficient distributed vector mean estimation, a commonly used subroutine in distributed optimization and Federated Learning (FL). Rand-$k$ sparsification is a commonly used technique to reduce communication cost, where each client sends $k<d$ of its coordinates to the server. However, Rand-$k$ is agnostic to any correlations, that might exist between clients in practical scenarios. The recently proposed Rand-$k$-Spatial estimator leverages the cross-client correlation information at the server to improve Rand-$k$'s performance. Yet, the performance of Rand-$k$-Spatial is suboptimal. We propose the Rand-Proj-Spatial estimator with a more flexible encoding-decoding procedure, which generalizes the encoding of Rand-$k$ by projecting the client vectors to a random $k$-dimensional subspace. We utilize Subsampled Randomized Hadamard Transform (SRHT) as the projection matrix and show that Rand-Proj-Spatial with SRHT outperforms Rand-$k$-Spatial, using the correlation information more efficiently. Furthermore, we propose an approach to incorporate varying degrees of correlation and suggest a practical variant of Rand-Proj-Spatial when the correlation information is not available to the server. Experiments on real-world distributed optimization tasks showcase the superior performance of Rand-Proj-Spatial compared to Rand-$k$-Spatial and other more sophisticated sparsification techniques.
Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation

As location-based services (LBS) have grown in popularity, the collection of human mobility data has become increasingly extensive to build machine learning (ML) models offering enhanced convenience to LBS users. However, the convenience comes with the risk of privacy leakage since this type of data might contain sensitive information related to user identities, such as home/work locations. Prior work focuses on protecting mobility data privacy during transmission or prior to release, lacking the privacy risk evaluation of mobility data-based ML models. To better understand and quantify the privacy leakage in mobility data-based ML models, we design a privacy attack suite containing data extraction and membership inference attacks tailored for point-of-interest (POI) recommendation models, one of the most widely used mobility data-based ML models. These attacks in our attack suite assume different adversary knowledge and aim to extract different types of sensitive information from mobility data, providing a holistic privacy risk assessment for POI recommendation models. Our experimental evaluation using two real-world mobility datasets demonstrates that current POI recommendation models are vulnerable to our attacks. We also present unique findings to understand what types of mobility data are more susceptible to privacy attacks. Finally, we evaluate defenses against these attacks and highlight future directions and challenges.
AI (r)evolution - where are we heading? Thoughts about the future of music and sound technologies in the era of deep learning

Artificial Intelligence (AI) technologies such as deep learning are evolving very quickly bringing many changes to our everyday lives. To explore the future impact and potential of AI in the field of music and sound technologies a doctoral day was held between Queen Mary University of London (QMUL, UK) and Sciences et Technologies de la Musique et du Son (STMS, France). Prompt questions about current trends in AI and music were generated by academics from QMUL and STMS. Students from the two institutions then debated these questions. This report presents a summary of the student debates on the topics of: Data, Impact, and the Environment; Responsible Innovation and Creative Practice; Creativity and Bias; and From Tools to the Singularity. The students represent the future generation of AI and music researchers. The academics represent the incumbent establishment. The student debates reported here capture visions, dreams, concerns, uncertainties, and contentious issues for the future of AI and music as the establishment is rightfully challenged by the next generation.
Prompt-Engineering and Transformer-based Question Generation and Evaluation

Question generation has numerous applications in the educational context. Question generation can prove helpful for students when reviewing content and testing themselves. Furthermore, a question generation model can aid teachers by lessening the burden of creating assessments and other practice material. This paper aims to find the best method to generate questions from textual data through a transformer model and prompt engineering. In this research, we finetuned a pretrained distilBERT model on the SQuAD question answering dataset to generate questions. In addition to training a transformer model, prompt engineering was applied to generate questions effectively using the LLaMA model. The generated questions were compared against the baseline questions in the SQuAD dataset to evaluate the effectiveness of four different prompts. All four prompts demonstrated over 60% similarity on average. Of the prompt-generated questions, 30% achieved a high similarity score greater than 70%.
A transfer learning approach with convolutional neural network for Face Mask Detection

Due to the epidemic of the coronavirus (Covid-19) and its rapid spread around the world, the world has faced an enormous crisis. To prevent the spread of the coronavirus, the World Health Organization (WHO) has introduced the use of masks and keeping social distance as the best preventive method. So, developing an automatic monitoring system for detecting facemasks in some crowded places is essential. To do this, we propose a mask recognition system based on transfer learning and Inception v3 architecture. In the proposed method, two datasets are used simultaneously for training including the Simulated Mask Face Dataset (SMFD) and MaskedFace-Net (MFN) This paper tries to increase the accuracy of the proposed system by optimally setting hyper-parameters and accurately designing the fully connected layers. The main advantage of the proposed method is that in addition to masked and unmasked faces, it can also detect cases of incorrect use of mask. Therefore, the proposed method classifies the input face images into three categories. Experimental results show the high accuracy and efficiency of the proposed method; so, this method has achieved an accuracy of 99.47% and 99.33% in training and test data respectively
Variance-based sensitivity of Bayesian inverse problems to the prior distribution

The formulation of Bayesian inverse problems involves choosing prior distributions; choices that seem equally reasonable may lead to significantly different conclusions. We develop a computational approach to better understand the impact of the hyperparameters defining the prior on the posterior statistics of the quantities of interest. Our approach relies on global sensitivity analysis (GSA) of Bayesian inverse problems with respect to the hyperparameters defining the prior. This, however, is a challenging problem--a naive double loop sampling approach would require running a prohibitive number of Markov chain Monte Carlo (MCMC) sampling procedures. The present work takes a foundational step in making such a sensitivity analysis practical through (i) a judicious combination of efficient surrogate models and (ii) a tailored importance sampling method. In particular, we can perform accurate GSA of posterior prediction statistics with respect to prior hyperparameters without having to repeat MCMC runs. We demonstrate the effectiveness of the approach on a simple Bayesian linear inverse problem and a nonlinear inverse problem governed by an epidemiological model.
Mask Propagation for Efficient Video Semantic Segmentation

Video Semantic Segmentation (VSS) involves assigning a semantic label to each pixel in a video sequence. Prior work in this field has demonstrated promising results by extending image semantic segmentation models to exploit temporal relationships across video frames; however, these approaches often incur significant computational costs. In this paper, we propose an efficient mask propagation framework for VSS, called MPVSS. Our approach first employs a strong query-based image segmentor on sparse key frames to generate accurate binary masks and class predictions. We then design a flow estimation module utilizing the learned queries to generate a set of segment-aware flow maps, each associated with a mask prediction from the key frame. Finally, the mask-flow pairs are warped to serve as the mask predictions for the non-key frames. By reusing predictions from key frames, we circumvent the need to process a large volume of video frames individually with resource-intensive segmentors, alleviating temporal redundancy and significantly reducing computational costs. Extensive experiments on VSPW and Cityscapes demonstrate that our mask propagation framework achieves SOTA accuracy and efficiency trade-offs. For instance, our best model with Swin-L backbone outperforms the SOTA MRCFA using MiT-B5 by 4.0% mIoU, requiring only 26% FLOPs on the VSPW dataset. Moreover, our framework reduces up to 4x FLOPs compared to the per-frame Mask2Former baseline with only up to 2% mIoU degradation on the Cityscapes validation set. Code is available at https://github.com/ziplab/MPVSS.
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation

We introduce EV3, a novel meta-optimization framework designed to efficiently train scalable machine learning models through an intuitive explore-assess-adapt protocol. In each iteration of EV3, we explore various model parameter updates, assess them using pertinent evaluation methods, and then adapt the model based on the optimal updates and previous progress history. EV3 offers substantial flexibility without imposing stringent constraints like differentiability on the key objectives relevant to the tasks of interest, allowing for exploratory updates with intentionally-biased gradients and through a diversity of losses and optimizers. Additionally, the assessment phase provides reliable safety controls to ensure robust generalization, and can dynamically prioritize tasks in scenarios with multiple objectives. With inspiration drawn from evolutionary algorithms, meta-learning, and neural architecture search, we investigate an application of EV3 to knowledge distillation. Our experimental results illustrate EV3's capability to safely explore the modeling landscape, while hinting at its potential applicability across numerous domains due to its inherent flexibility and adaptability. Finally, we provide a JAX implementation of EV3, along with source code for experiments, available at: https://github.com/google-research/google-research/tree/master/ev3.
New U–Pb geochronologic and palaeomagnetic constraints on the late Palaeoproterozoic Hartley magmatic event: evidence for a potential large igneous province in the Kaapvaal Craton during Kalahari assembly, South Africa

Abstract The volcanic Hartley Formation (part of the Olifantshoek Supergroup, which is dominated by red bed successions) in South Africa recorded depositional and tectonic conditions along the western Kaapvaal Craton during the late Palaeoproterozoic. It formed in association with red bed deposition elsewhere in the cratonic hinterland and along the craton’s northern margin. However, the exact correlation of the Olifantshoek Supergroup with these other red-bed successions is hindered by poor geochronological constraints. Herein, we refine the age and palaeopole of the Hartley Formation, and provide geochronological constraints for large-scale 1.93–1.91 Ga bimodal magmatism on the Kaapvaal Craton (herein named the Hartley large igneous province). We present new age constraints for the mafic and felsic phases of this event at 1923 ± 6 Ma and 1920 ± 4 Ma, respectively, which includes the first reported age dating of the Tsineng Dyke Swarm that has been linked to Hartley volcanism. A mean 1.93–1.91 Ga palaeomagnetic pole for the Hartley large igneous province at 22.7°N, 328.6°E with A95 = 11.7° represents a significant improvement on a previously published virtual geomagnetic pole. This improved pole is used to refine the late Palaeoproterozoic apparent polar wander path of the Kaapvaal Craton. This can assist in correlation of red-bed successions in southern Africa.
The diminishing state of shared reality on US television news

The potential for a large, diverse population to coexist peacefully is thought to depend on the existence of a ``shared reality:'' a public sphere in which participants are exposed to similar facts about similar topics. A generation ago, broadcast television news was widely considered to serve this function; however, since the rise of cable news in the 1990s, critics and scholars have worried that the corresponding fragmentation and segregation of audiences along partisan lines has caused this shared reality to be lost. Here we examine this concern using a unique combination of data sets tracking the production (since 2012) and consumption (since 2016) of television news content on the three largest cable and broadcast networks respectively. With regard to production, we find strong evidence for the ``loss of shared reality hypothesis:'' while broadcast continues to cover similar topics with similar language, cable news networks have become increasingly distinct, both from broadcast news and each other, diverging both in terms of content and language. With regard to consumption, we find more mixed evidence: while broadcast news has indeed declined in popularity, it remains the dominant source of news for roughly 50\% more Americans than does cable; moreover, its decline, while somewhat attributable to cable, appears driven more by a shift away from news consumption altogether than a growth in cable consumption. We conclude that shared reality on US television news is indeed diminishing, but is more robust than previously thought and is declining for somewhat different reasons.
Topological, or Non-topological? A Deep Learning Based Prediction

Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by incorporating persistent homology and graph neural network which offers an accuracy of 91.4% and an F1 score of 88.5% in classifying topological vs. non-topological materials, outperforming the other state-of-the-art classifier models. The incorporation of the graph neural network encodes the underlying relation between the atoms into the model based on their own crystalline structures and thus proved to be an effective method to represent and process non-euclidean data like molecules with a relatively shallow network. The persistent homology pipeline in the suggested neural network is capable of integrating the atom-specific topological information into the deep learning model, increasing robustness, and gain in performance. It is believed that the presented work will be an efficacious tool for predicting the topological class and therefore enable the high-throughput search for novel materials in this field.