Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Post-Retirement Work Always Prevent Depression?

Given the rising popularity of post-retirement work and its potential benefits for older adults’ mental health, this study examined older adults’ adaptation ability as a conditional factor for the impact of post-retirement work on depression. Quantitative data from 1433 working older adults and 1433 non-working older adults were analyzed using the PROCESS macro in SPSS to test a moderated regression model with adaptation ability as the moderator. Results showed that older adults with lower adaptation ability demonstrated significantly lower depression if they worked (vs. did not work). Older adults with higher adaptation ability exhibited significantly higher depression if they worked (vs. did not work). These findings were subsequently verified with a robustness check. Overall, post-retirement work did not prevent depression for the entire sample; working only alleviated depression among older adults with limited adaptation ability. Older adults with stronger adaptation ability could better maintain their mental health by staying retired. This study fills a knowledge gap regarding the relationship between post-retirement work and mental health. Implications for aging societies are also discussed.
Blockchain Interoperability

Blockchain is one of the most disrupting technologies in recent years and has been used in several domains, such as health, supply chain and finance. In addition, organisations started integrating their blockchains with other blockchains or other external software systems. However, this is challenging for blockchain systems as interoperability is not a native design feature. Several academic and industrial efforts were performed to enable blockchain interoperability in the last years, but they were proposed for specific domain scenarios. To the best of our knowledge, there is not a general-purpose interoperability solution that enables blockchain interoperability. This research project aims to define a general-purpose blockchain interoperability framework to enable blockchain interoperability. This framework will comprise methods, guidelines, specifications and software pieces that ease interoperability between blockchains. This paper presents this research project, describing its research questions, the adopted and projected research methodologies
Setting Hardware Root-of-Trust from Edge to Cloud, and How to Use it

For decades, Trusted Computing has tried to anchor trust in the hardware, and the existence of Trusted Platform Modules (TPM) in most modern design is evidence that this approach is now well understood. The default behavior of recent Operating Systems like Windows 11 is even to deny booting if this security feature is absent. But this approach is not sufficient in a modern world where one needs to trust remote platforms. To preserve confidence in security, one needs to limit the trusted computing base (TCB) of a system at a level where an assessment can make sense. Trusted Execution Architecture (TEA) is the result of a partnership with ProvenRun to implement a TCB in Atos servers in a consistent way, from Edge to High Performance Computing. This allows to envision security features based on some common Root-of-Trust known to different platforms, at different scales and levels of interaction.
Nonparametric Gaussian Process Covariances via Multidimensional Convolutions

A key challenge in the practical application of Gaussian processes (GPs) is selecting a proper co-variance function. The process convolutions construction of GPs allows some additional flexibility, but still requires choosing a proper smoothing kernel, which is non-trivial. Previous approaches have built covariance functions by using GP priors over the smoothing kernel, and by extension the covariance, as a way to bypass the need to specify it in advance. However, these models have been limited in several ways: they are restricted to single dimensional inputs, e.g. time; they only allow modelling of single outputs and they do not scale to large datasets since inference is not straightforward. In this paper, we introduce a nonparametric process convolution formulation for GPs that alleviates these weaknesses. We achieve this using a functional sampling approach based on Math-eron’s rule to perform fast sampling using interdo-main inducing variables. We test the performance of our model on benchmarks for single output, multi-output and large-scale GP regression, and find that our approach can provide improvements over standard GP models, particularly for larger datasets.
Crack Extension Life and Critical Crack Length Prediction Based on XGBoost

Damage tolerance design can ensure the structural safety of civil aircraft throughout its life cycle, which requires accurate analysis of crack extension life and crack extension length. This paper proposes an XGBoost-based crack extension life and crack extension length prediction method for civil aircraft structures. The method uses machine learning algorithms to train the structural state prediction model. The advantage of this method is that it can quickly determine the crack life and crack length without relying on the processing technology and engineering diagnosis experience of a large amount of collected data, which provides a more flexible method to determine the crack extension life and crack extension length under various influencing factors. By comparing a variety of machine learning algorithms, XGBoost model obtained the highest test scores, the experimental results show that the method achieves accurate prediction of crack extension life and critical crack length, which can be used for rapid engineering evaluation.
Nanoparticles from Microalgae and Their Biomedical Applications

Over the years, microalgae have been a source of useful compounds mainly used as food and dietary supplements. Recently, microalgae have been used as a source of metabolites that can participate in the synthesis of several nanoparticles through inexpensive and environmentally friendly routes alternative to chemical synthesis. Notably, the occurrence of global health threats focused attention on the microalgae application in the medicinal field. In this review, we report the influence of secondary metabolites from marine and freshwater microalgae and cyanobacteria on the synthesis of nanoparticles that were applied as therapeutics. In addition, the use of isolated compounds on the surface of nanoparticles to combat diseases has also been addressed. Although studies have proven the beneficial effect of high-value bioproducts on microalgae and their potential in medicine, there is still room for understanding their exact role in the human body and translating lab-based research into clinical trials.
Future Database Engine Development: You Will Only Need One Programming Language

Database systems must make good use of the hardware for high performance. This is usually done by implementing their core components (storage engine, optimizer and query execution engine) in a low-level programming language (PL) such as C/C++ that can directly "talk to the hardware. " But these PLs traditionally lacked high-level abstractions, lowering DBMS developer productivity. Some systems [18, 16, 10] then mix different PLs to balance productivity and performance. For example, Presto [16] and Spark [18] originally used Java but are now replacing their query engines with new ones [12, 1] built in C++ for higher performance. However, doing so brings such non-trivial challenges as interacting with different PL runtimes [1].
R OBUST L p -N ORM L INEAR D ISCRIMINANT A NALYSIS WITH P ROXY M ATRIX O PTIMIZATION

Linear Discriminant Analysis (LDA) is an established supervised dimensionality reduction method that is traditionally based on the L 2 -norm. However, the standard L 2 -norm LDA is susceptible to outliers in the data that often contribute to a drop in accuracy. Using the L 1 or fractional p -norms makes LDA more robust to outliers, but it is a harder problem to solve due to the nature of the corresponding objective functions. In this paper, we leverage the orthogonal constraint of the Grassmann manifold to iteratively obtain the optimal projection matrix for the data in a lower dimensional space. Instead of optimizing the matrix directly on the manifold, we use the proxy matrix optimization (PMO) method, utilizing an auxiliary matrix in ambient space that is retracted to the closest location on the manifold along the loss minimizing geodesic. The L p -LDA-PMO learning is based on backpropagation, which allows easy integration in a neural network and flexibility to change the value of the p -norm. Our experiments on synthetic and real data show that using fractional p -norms for LDA leads to an improvement in accuracy compared to the traditional L 2 -based LDA.
Appendix for “SE(2)-Equivariant Pushing Dynamics Models for Tabletop Object Manipulation”

In model-free pushing manipulation methods, a policy that directly maps a visual observation to a sequence of pushing actions is learned without learning a pushing dynamics model. They typically require to design a task-specific reward function and train the policy in an end-to-end manner with a large amount of data. Researchers have attempted to solve diverse tasks with these methods such as grasping [1, 2, 3], singulation [4], object rearrangement [5, 6], object sorting [7, 8], and invisible target object finding [9, 10]. Model-free methods are known to have very good convergence performance, but they require a lot of data. Also, when a new task is given, the agent must be trained from scratch. On the other hand, in model-based approaches, the learned model is reusable given a new task, and much less additional data is needed.
Intraoperative Cochlear Nerve Monitoring in Cochlear Implantation after Vestibular Schwannoma Resection

Background: The use of a cochlear implant (CI) for hearing rehabilitation after vestibular schwannoma (VS) resection is widely spreading. The procedure is usually performed simultaneously to tumor resection with a translabyrinthine approach. To ensure the best device function, assessing the integrity of the cochlear nerve is of primary importance. Methods: A narrative review of the literature on the present topic was carried out up to June 2022. Finally, nine studies were considered. Results: Electrically evoked auditory brainstem responses (eABR) is the most widely used method of intraoperative monitoring of cochlear nerve (CN) during VS resection, although its limits are known. It can be assessed through the CI electrode array or through an intracochlear test electrode (ITE). Variations of the graph are evaluated during the surgical procedure, in particular the wave V amplitude and latency. As tumor dissection progresses, the parameters may change, informing of the CN status, and the surgical procedure may be modulated. Conclusion: An eABR positive result seems to be reliably correlated with a good CI outcome in those cases in which a clear wave V is recorded before and after tumor removal. On the contrary, in those cases in which the eABR is lost or altered during the surgical procedure, the positioning of a CI is still debatable.
Effect of Milk Protein and Whey Permeate in Large-Quantity Lipid-Based Nutrient Supplement on Early Child Development among Children with Stunting: A Randomized 2 × 2 Factorial Trial in Uganda

Stunting affects 22% children globally, putting them at risk of adverse outcomes including delayed development. We investigated the effect of milk protein (MP) vs. soy and whey permeate (WP) vs. maltodextrin in large-quantity, lipid-based nutrient supplement (LNS), and LNS itself vs. no supplementation, on child development and head circumference among stunted children aged 1–5 years. We conducted a randomized, double-blind, community-based 2 × 2 factorial trial in Uganda (ISRCTN1309319). We randomized 600 children to one of four LNS formulations (~535 kcal/d), with or without MP (n = 299 vs. n = 301) or WP (n = 301 vs. n = 299), for 12 weeks or to no supplementation (n = 150). Child development was assessed using the Malawi Development Assessment Tool. Data were analyzed using linear mixed-effects models. Children had a median [interquartile range] age of 30 [23; 41] months and mean ± standard deviation height-for-age z-score of −3.02 ± 0.74. There were no interactions between MP and WP for any of the outcomes. There was no effect of either MP or WP on any developmental domain. Although LNS itself had no impact on development, it resulted in 0.07 (95%CI: 0.004; 0.14) cm higher head circumference. Neither dairy in LNS, nor LNS in itself, had an effect on development among already stunted children.
M6A RNA methylation modification and tumor immune microenvironment in lung adenocarcinoma

Lung adenocarcinoma is one of the deadliest tumors. Studies have shown that N6-methyladenosine RNA methylation regulators, as a dynamic chemical modification, affect the occurrence and development of lung adenocarcinoma. To investigate the relationship between mutations and expression levels of m6A regulators in lung adenocarcinoma, we investigated the mutations and expression levels of 38 m6A regulators. We found that mutations in m6A regulatory factors did not affect the changes in expression levels, and 19 differentially expressed genes were identified. All tumor samples were classified into two subtypes based on the expression levels of 19 differentially expressed m6A-regulated genes. Survival analysis showed significant differences in survival between the two subtypes. To explore the relationship between immune cell infiltration and survival in both subtypes, we calculated the infiltration of 23 immune cells in both subtypes, and we found that the subtype with high immune cell infiltration had better survival. We found that subtypes with low tumor purity and high stromal and immune scores had better survival. The m6A-related immune genes were identified by taking the intersection of differentially expressed genes and immune genes in the two isoforms and calculating the Pearson correlation coefficients between the intersecting immune genes and the differentially expressed m6A-regulated genes. Finally, a prognostic model associated with m6A and associated with immunity was developed using prognostic genes screened from m6A-associated immune genes. The predictive power of the model was evaluated and our model was able to achieve good prediction.
Anisotropic Diffusion Model Combined with Local Entropy

Perona-Malik (P-M) model is a classical anisotropic diffusion denoising model, but it is not able to preserve the important details effectively such as the texture of the image. To address this problem, an improved P-M model based on local entropy is proposed. The diffusion coefficient of the model not only depends on the image gradient, but also depends on the local region information described by local entropy. The experimental results show that the proposed model removes noises effectively, preserves the boundaries better, and maintains important details of the image well.
Should one minimize the Bellman residual or maximize the mean value

This paper aims at theoretically and empirically comparing two standard optimization criterion for Reinforcement Learning: i) maximization of the mean value (predominant approach in policy search algorithms) and ii) minimization of the Bellman residual (mainly used in approximate dynamic programming). For doing so, we introduce a new policy search algorithm based on the minimization of the residual $\|T_* v_\pi - v_\pi\|_{1,\nu}$. We prove that it enjoys a performance bound that is better than the sole known bound for maximizing the mean value and that matches the best known bounds in approximate dynamic programming. We also conduct experiments on randomly generated generic Markov decision processes to compare both approaches empirically. It turns out that the Bellman residual is a good optimization proxy only if there is a good match between the sampling distribution and the discounted state occupancy distribution induced by the optimal policy, while maximizing the mean value is rather insensitive to this issue.
Method and Device for Effectively Utilizing OpenCV to Detect PCB Board Size

: In the process of PCB board production, it is necessary to accurately measure the contour dimensions of the PCB board and the dimensions of various types of wires, holes, or slots on the PCB board to avoid a large number of defective products flowing into the subsequent production process. In existing technology, the measurement of the dimensions of wires, holes, or slots on the PCB board mostly relies on manual work, which has the problems of low measurement efficiency and high measurement error rate, Unable to meet the efficient and high-quality production needs of PCB boards. This article proposes a method and device for effectively utilizing OpenCv to detect PCB board size. This device is based on an optical image measurement system, combined with the template matching method in OpenCv, and can automatically complete forming inspection and size measurement by scanning multiple PCB boards by one time.This device can complete measurement evaluation, report generation, and SPC data analysis for multiple PCB boards, achieving high automation and significantly improving measurement accuracy system.
EmbedR-Net: Using CNN to Embed Mark With Recovery Through Deep Convolutional GAN for Secure eHealth Systems

The goal of security is to protect digital assets, devices and services from being disrupted, exploited or stolen by unauthorised users. It is also about having reliable media information available at the right time. However, the media distortion will pose many potential risks in eHealth systems. In this paper, a new data hiding method called EmbedR-Net based on Convolutional Neural Network (CNN) and Deep Convolutional Generative Adversarial Network (DCGAN) is proposed, which can prevent the copyright violation of the medical images. First, a CNN based embedder network is designed for imperceptibly hiding medical images as marks in the carrier image. Second, we compute the diagonal value of the marked image for hidden mark recovery. Last, the DCGAN network is designed to robustly recover the hidden mark using the diagonal value of the marked image. Compared to existing methods, experiments on five different datasets have shown that the proposed EmbedR-Net obtains superior performance.
Drowsiness and tiredness detection system by observing the visible properties of human eyes

In this paper, we present a robust system for identifying and predicting physical conditions like sleeplessness, tiredness, and potential unwellness in construction worker by observing the visible properties and movement pattern of their eyes. Site managers are responsible for taking care of the physical condition of a worker in construction site, which cause a lot of trouble and is time consuming. Our system aims to automate this entire process. We rely on the fact that the above-mentioned physical conditions directly affect the visible properties of human eyes like color, and eye movement. Our approach collects individual eye data over time to determine a normal point for the said individual and tries to find the deviation from the normal point to identify any abnormalities which might be a direct result of sleeplessness or tiredness. For this purpose, we propose an original algorithm. Then we have successfully built and deployed a system that identifies workers physical condition reading the properties of their eyes.
An improved mobile vehicle detection method based on Gaussian mixture model

To overcome the defects of the Gaussian mixture model(GMM) in widely used vehicle detection method,an improved algorithm for moving vehicle detection based on Gaussian mixture model(GMM) is proposed.For the defect of long-term "ghosts" in Gaussian mixture model,a new update method of weights and variances is employed to accelerate the elimination of "ghosts",and the performance of vehicle detection can be improved.Additionally,in the existing GMM,all of the pixels are modeled by fixed number of distributions,so main memories are wasted.To save memories,a self-adapting method is adopted.For the pixels whose distribution numbers are not up to maximum,the approach of adaptive change for distribution numbers is used to effectively decrease the total number of distributions and save memory space.Experimental results show that the improved GMM method provides superior performance in the elimination of "ghosts" and computing speed for vehicle detection.
Several nonparametric and semiparametric approaches to linear mixed model regression

Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric (P) models, correctness of the assumed model is critical for the validity of the ensuing inference. An incorrectly specified P means model may be improved by using a local, or nonparametric (NP), model. Two local models are proposed by a pointwise weighting of the marginal and conditional variance–covariance matrices. However, NP models tend to fit to irregularities in the data and may provide fits with high variance. Model robust regression techniques estimate mean response as a convex combination of a P and a NP model fit to the data. It is a semiparametric method by which incomplete or incorrectly specified P models can be improved by adding an appropriate amount of the NP fit. We compare the approximate integrated mean square error of the P, NP, and mixed model robust methods via a simulation study and apply these methods to two real data sets: the monthly wind speed data from countries in Ireland and the engine speed data.
An lpGBT subsystem for environmental monitoring of experiments

In this paper, the Low Power Giga Bit Transceiver (lpGBT) built-in system for environmental monitoring of the LHC experiments is presented. Eight external analogue inputs and eight internal voltages are multiplexed into an instrumentation amplifier with selectable gain, whose output is digitised by a 10-bit SAR ADC. A programmable current source can be enabled for each external input to implement resistance measurements. Internal channels are used to monitor power supplies and the output of the temperature sensor. The environmental monitoring system includes a precise 1 V reference voltage source and a 10-bit voltage DAC. All blocks were designed and fabricated in 65 nm CMOS technology, fully characterised, and the pre- and post-irradiation measurement results are presented in this work.
Denoising Approach of High Density Point Clouds Based on Mixed Classification of Curvature Features

In order to resolve the existing problems in algorithms for smoothing and denoising complex point clouds,an effective and feature-preserving 3D point-cloud smoothing method based on the mixed curvature feature classification was presented.It combined local plane projection and bilateral denoising method.The local curvature feature of each point was evaluated by principal component analysis.According to the features,the point clouds were divided into plane,sub-featured and rich-featured classes and linearly mixed type.With the different neighborhood types,the combination of the plane projection smoothing method for plane class and the variable parameter bilateral filtering method for sub-featured and rich-featured classes were applied.The algorithm presented has been applied to process point-cloud data sets obtained by a high resolution 3D human body laser scanner.The experimental results show that the proposed method can effectively remove noise and preserve geometric features while can significantly simplify the normal direction adjustment process.