Pioneer Factors Compete for Epigenetic Factors in Switching Stem Cell Fates

During development, progenitor cells can activate one cell fate while simultaneously silencing another, a process that is tightly regulated in adult tissues. However, this process is often derailed in diseases such as cancers, leading to uncontrolled growth and malignancy. At the crossroads of fate-switching, pioneer factors are a class of transcription factors equipped to bind cognate motifs in closed chromatin. Once they access the closed chromatin, pioneer factors can either act as transcriptional activators or repressors of cell fates by recruiting co-activators or co-repressors. Nevertheless, whether and how a single factor can simultaneously silence the old fate while activating the new cell fate remains largely unknown

Efficient HDR Reconstruction from Real-World Raw Images

High dynamic range (HDR) imaging is still a significant yet challenging problem due to the limited dynamic range of generic image sensors. Most existing learning-based HDR reconstruction methods take a set of bracketed-exposure sRGB images to extend the dynamic range, and thus are computational- and memory-inefficient by requiring the Image Signal Processor (ISP) to produce multiple sRGB images from the raw ones. In this paper, we propose to broaden the dynamic range from the raw inputs and perform only one ISP processing for the reconstructed HDR raw image. Our key insights are threefold: (1) we design a new computational raw HDR data formation pipeline and construct the first real-world raw HDR dataset, RealRaw-HDR; (2) we develop a lightweight-efficient HDR model, RepUNet, using the structural re-parameterization technique; (3) we propose a plug-and-play motion alignment loss to mitigate motion misalignment between short- and long-exposure images. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in both visual quality and quantitative metrics

Demand Prediction by Incorporating Internet-of-Things Data: A Case of Automobile Repair and Maintenance Service

While anecdotal evidence highlights the value of Internet-of-Things (IoT) data for business operations, rigorous empirical validation is still limited. The key challenge lies in integrating IoT analytics into business evaluation. To address the issues, we focus on the automotive industry and study the value of telematics data, an important IoT application in this domain, in terms of predicting maintenance, repair, and operations (MRO) service demands. Our approach involves building a prediction system with users’ driving behavior, MRO service records, and environmental data (weather and traffic). We show a substantial improvement in prediction performance upon incorporating user behavior information derived from IoT data. Specifically, we find that hard acceleration, hard braking, and speeding rank the third, fifth, and sixth, respectively, in terms of their contribution to the MRO prediction. Our results shed light on the design of product-service systems (PSS), an emerging trend to integrate product offerings with service offerings

Event-triggered communication for passivity and synchronisation of multi-weighted coupled neural networks with and without parameter uncertainties

A multi-weighted coupled neural networks (MWCNNs) model with event-triggered communication is studied here. On the one hand, the passivity of the presented network model is studied by utilising Lyapunov stability theory and some inequality techniques, and a synchronisation criterion based on the obtained output-strict passivity condition of MWCNNs with eventtriggered communication is derived. On the other hand, some robust passivity and robust synchronisation criteria based on output-strict passivity of the proposed network with uncertain parameters are presented. At last, two numerical examples are provided to testify the effectiveness of the output-strict passivity and robust synchronisation results

Towards Mitigating Spurious Correlations in the Wild: A Benchmark and a more Realistic Dataset

Deep neural networks often exploit non-predictive features that are spuriously correlated with class labels, leading to poor performance on groups of examples without such features. Despite the growing body of recent works on remedying spurious correlations, the lack of a standardized benchmark hinders reproducible evaluation and comparison of the proposed solutions. To address this, we present SpuCo, a python package with modular implementations of state-of-the-art solutions enabling easy and reproducible evaluation of current methods. Using SpuCo, we demonstrate the limitations of existing datasets and evaluation schemes in validating the learning of predictive features over spurious ones. To overcome these limitations, we propose two new vision datasets: (1) SpuCoMNIST, a synthetic dataset that enables simulating the effect of real world data properties e.g. difficulty of learning spurious feature, as well as noise in the labels and features; (2) SpuCoAnimals, a large-scale dataset curated from ImageNet that captures spurious correlations in the wild much more closely than existing datasets. These contributions highlight the shortcomings of current methods and provide a direction for future research in tackling spurious correlations. SpuCo, containing the benchmark and datasets, can be found at https://github.com/BigML-CS-UCLA/SpuCo, with detailed documentation available at https://spuco.readthedocs.io/en/latest/.Comment: Package: https://github.com/BigML-CS-UCLA/SpuC

Realization of a three-dimensional photonic topological insulator

Confining photons in a finite volume is in high demand in modern photonic devices. This motivated decades ago the invention of photonic crystals, featured with a photonic bandgap forbidding light propagation in all directions. Recently, inspired by the discoveries of topological insulators (TIs), the confinement of photons with topological protection has been demonstrated in two-dimensional (2D) photonic structures known as photonic TIs, with promising applications in topological lasers and robust optical delay lines. However, a fully three-dimensional (3D) topological photonic bandgap has never before been achieved. Here, we experimentally demonstrate a 3D photonic TI with an extremely wide (> 25% bandwidth) 3D topological bandgap. The sample consists of split-ring resonators (SRRs) with strong magneto-electric coupling and behaves as a 'weak TI', or a stack of 2D quantum spin Hall insulators. Using direct field measurements, we map out both the gapped bulk bandstructure and the Dirac-like dispersion of the photonic surface states, and demonstrate robust photonic propagation along a non-planar surface. Our work extends the family of 3D TIs from fermions to bosons and paves the way for applications in topological photonic cavities, circuits, and lasers in 3D geometries

Enjoying the golden years: social participation and life satisfaction among Chinese older adults

IntroductionOlder adults commonly face the risk of social isolation, which poses a significant threat to their quality of life. This study explores the association between social participation and life satisfaction among older Chinese adults.MethodsData were sourced from the 2018 China Health and Retirement Longitudinal Study. Regression analysis and mediation analysis were employed to examine the relationship between social participation and life satisfaction, with a focus on the roles of loneliness and self-rated health.ResultsThe results indicate that social participation is significantly positively associated with older adults' life satisfaction. Furthermore, the positive association is more pronounced with increased diversity in social activities. Mediation analysis reveals that reductions in feelings of loneliness and improvements in health levels mediate the relationship between social participation and life satisfaction. Further analysis showed that social participation had a greater positive association among rural older adults and those lacking family companionship.DiscussionThis study provides evidence for enhancing life satisfaction among older adults and highlights the importance of diversity in social participation