Miracle-dz/UniFAR-FTUs huggingface dataset:
Covid-19 related stress and coping strategies among adults with chronic disease in Southwest Ethiopia.

Abstract Background: Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus which are a large family of viruses that are common in people and many different species of animals which can affect people physically and psychologically. The older people and those with underlying medical problems are more likely to develop serious illness and death. Objective the aim of this study was to determine Covid-19 related stress and coping strategies among adults with chronic disease in Bench-Sheko,West Omo and Keffa Zones, southwest Ethiopia Methods Institutional based cross-sectional study was applied among 613 adults with chronic disease. A simple sampling technique was applied. Correlational analysis was used to determine the relationship between the COVID-19 related stress score and coping strategy types. To measure the strength of association between dependent variables and independent variables and Pearson coefficients (r) with 95% Confidence interval (CI) were calculated. Finally, the variable, which shows statistical significance (p-value < 0.05 cut point) were used to quantify the associations among variables. Results: Around 613 participants of 96% response rate were participated. About 68.4% were moderately stressed; low stress was 17.8% and severe stress was 13.9 %. Covid-19 related perceived stress score were positively associated with coping strategies types of like sell-distraction, active coping, denial, emotional support, behavioral disengagement, venting, and use of instrument, positive reframing, self-blaming, planning, humoring and religion. The most preferable types of coping strategies were religious, instrumental and active coping strategies and while the least used were substance used. Conclusion: Significant numbers of participants were suffered from severe perceived stress due to covid-19 outbreak in this study area. Both adaptive and maladaptive Coping strategy types were significantly associated with stress. Substance use and self-blaming were the types coping strategies which were not associated with perceived stress.
Estimation of LC50 Values by Assay of Lactate Dehydrogenase and DNA Redistribution in Human Lymphocyte Cultures

The acute toxicities of selected drugs were assayed biochemically in vitro by estimation of LC50 dose in human lymphocyte cultures. Two methods were compared for estimation of LC50 values, the redistribution of DNA and lactate dehydrogenase after treatment with various concentrations of the compound. Ten of the chemicals used had previously been tested in HeLa cells using morphological changes as the endpoint. The LC50 values found in this study were a little lower than the IC50 values for HeLa cells. The enzyme assay gave lower LC50 values than the DNA assay, but the differences were not significant. LC50 values were also found for two drugs tested in several cell lines with redistribution of DNA and proteins. Again, no significant differences were apparent.
A Novel Framework for Improving Pulse-Coupled Neural Networks With Fuzzy Connectedness for Medical Image Segmentation

A pulse-coupled neural network (PCNN) is a promising image segmentation approach that requires no training. However, it is challenging to successfully apply a PCNN to medical image segmentation due to common but difficult scenarios such as irregular object shapes, blurred boundaries, and intensity inhomogeneity. To improve this situation, a novel framework incorporating fuzzy connectedness (FC) is proposed. First, a comparative study of the traditional PCNN models is carried out to analyze the framework and firing mechanism. Then, the characteristic matrix of fuzzy connectedness (CMFC) is presented for the first time. The CMFC can provide more intensity information and spatial relationships at the pixel level, which is helpful for producing a more reasonable firing mechanism in the PCNN models. Third, by integrating the CMFC into the PCNN framework models, a construction scheme of FC-PCNN models is designed. To illustrate this concept, a general solution that can be applied to different PCNN models is developed. Next, the segmentation performances of the proposed FC-PCNN models are evaluated by comparison with the traditional PCNN models, the traditional segmentation methods, and deep learning methods. The test images include synthetic and real medical images from the Internet and three public medical image datasets. The quantitative and visual comparative analysis demonstrates that the proposed FC-PCNN models outperform the traditional PCNN models and the traditional segmentation methods and achieve competitive performance to the deep learning methods. In addition, the proposed FC-PCNN models have favorable capability to eliminate inference from surrounding artifacts.
Distribution of Landscape Architecture Based on 3D Images and Virtual Reality Rationality Study

In order to ensure the rationality of landscape design, it is of great practical significance to simulate the planning effect. Aiming at the problems of traditional garden landscape distribution simulation technology, this paper designs a new garden landscape distribution rationality simulation technology based on three-dimensional image and virtual reality technology. Firstly, a rational analysis method for landscape garden distribution based on three-dimensional images is proposed. Based on the correlation between different coordinate systems under a specific angle of view, a camera pose matrix is constructed, and SURF feature point detection is performed on the landscape garden image and the above feature points are clustered and matched. Secondly, the three-dimensional image reconstruction of the landscape garden image is realized based on the registered image feature points. Based on the three-dimensional image reconstruction results and virtual reality technology, the three-dimensional image analysis of the rationality of landscape distribution is completed. Finally, the case analysis and performance test results illustrate the superiority of the realized landscape garden distribution rationality technology. This method can provide scientific reference and basis for the modern landscape gardening industry to adopt three-dimensional images and other technologies.
VSA-CGAN: An Intelligent Generation Model for Deep Learning Sample Database Construction

In order to solve the problem of model accuracy reduction caused by the difficulty of obtaining specific training samples or the insufficient number of samples in the application of existing object detection and recognition model based on deep learning, this article proposes a conditional generative adversarial network model (VSA-CGAN), which integrates the self-attention mechanism of visual perception to optimize the inference of object attention feature maps, so as to learn the global information of the image and the detailed features of the object. It is designed to add conditional features in the generator and the discriminator, associate the specific dimensions of the data with the semantic features, and explicitly indicate the model to generate the corresponding object signature category information, so as to generate the feature representation of the image which is more suitable for the distribution of the original data. The model in this article has completed numerical experiments on several general standard data sets, and compared with several mainstream generative adversarial network models in image data augmentation performance. The experimental results show that the generation model in this article has excellent object simulation ability and strong application prospects.
DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks

Bio-inspired spiking neural networks (SNNs), operating with asynchronous binary signals (or spikes) distributed over time, can potentially lead to greater computational efficiency on event-driven hardware. The state-of-the-art SNNs suffer from high inference latency, resulting from inefficient input encoding, and sub-optimal settings of the neuron parameters (firing threshold, and membrane leak). We propose DIET-SNN, a low latency deep spiking network that is trained with gradient descent to optimize the membrane leak and the firing threshold along with other network parameters (weights). The membrane leak and threshold for each layer of the SNN are optimized with end-to-end backpropagation to achieve competitive accuracy at reduced latency. The analog pixel values of an image are directly applied to the input layer of DIET-SNN without the need to convert to spike-train. The information is converted into spikes in the first convolutional layer where leaky-integrate-and-fire (LIF) neurons integrate the weighted inputs and generate an output spike when the membrane potential crosses the trained firing threshold. The trained membrane leak controls the flow of input information and attenuates irrelevant inputs to increase the activation sparsity in the convolutional and linear layers of the network. The reduced latency combined with high activation sparsity provides large improvements in computational efficiency. We evaluate DIET-SNN on image classification tasks from CIFAR and ImageNet datasets on VGG and ResNet architectures. We achieve top-1 accuracy of 66.52% with 25 timesteps (inference latency) on the ImageNet dataset with 3.1X less compute energy than an equivalent standard ANN. Additionally, DIET-SNN performs 5-100X faster inference compared to other state-of-the-art SNN models.
Concurrent Fixed-Size Allocation and Free in Constant Time

Our goal is to efficiently solve the dynamic memory allocation problem in a concurrent setting where processes run asynchronously. On $p$ processes, we can support allocation and free for fixed-sized blocks with $O(1)$ worst-case time per operation, $\Theta(p^2)$ additive space overhead, and using only single-word read, write, and CAS. While many algorithms rely on having constant-time fixed-size allocate and free, we present the first implementation of these two operations that is constant time with reasonable space overhead.
A New Information-Theoretic Method for Advertisement Conversion Rate Prediction for Large-Scale Sparse Data Based on Deep Learning

With the development of online advertising technology, the accurate targeted advertising based on user preferences is obviously more suitable both for the market and users. The amount of conversion can be properly increased by predicting the user’s purchasing intention based on the advertising Conversion Rate (CVR). According to the high-dimensional and sparse characteristics of the historical behavior sequences, this paper proposes a LSLM_LSTM model, which is for the advertising CVR prediction based on large-scale sparse data. This model aims at minimizing the loss, utilizing the Adaptive Moment Estimation (Adam) optimization algorithm to mine the nonlinear patterns hidden in the data automatically. Through the experimental comparison with a variety of typical CVR prediction models, it is found that the proposed LSLM_LSTM model can utilize the time series characteristics of user behavior sequences more effectively, as well as mine the potential relationship hidden in the features, which brings higher accuracy and trains faster compared to those with consideration of only low or high order features.
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the Loop

DeepDrummer is a drum loop generation tool that uses active learning to learn the preferences (or current artistic intentions) of a human user from a small number of interactions. The principal goal of this tool is to enable an efficient exploration of new musical ideas. We train a deep neural network classifier on audio data and show how it can be used as the core component of a system that generates drum loops based on few prior beliefs as to how these loops should be structured. We aim to build a system that can converge to meaningful results even with a limited number of interactions with the user. This property enables our method to be used from a cold start situation (no pre-existing dataset), or starting from a collection of audio samples provided by the user. In a proof of concept study with 25 participants, we empirically demonstrate that DeepDrummer is able to converge towards the preference of our subjects after a small number of interactions.
Impedance Control of Hydraulic Series Elastic Actuator with a Model-Based Control Design

Traditional mechanical actuators are designed with a high stiffness, which increases the system bandwidth. The operation of stiff actuator in uncertain environments is a challenging task due to physical interactions with the environment. Series elastic actuators (SEAs) have become the prominent method for decreasing stiffness between power output shafts and the environment in electric torque-controlled light arm applications. Compared to lightweight arms, the hydraulic actuated SEAs (HSEAs) can provide a much higher power-to-weight ratio. However, the control design for an HSEA is a challenging task due to the high non-linear dynamics of hydraulic systems. In this study, a novel subsystem-dynamics-based controller for an HSEA is designed using the virtual decomposition control (VDC) approach as a framework. The designed controller is incorporated as an inner-loop controller for previously designed a novel impedance controller. The one degrees-of-freedom (DOF) experimental setup is used to verify the control performance of the proposed controller.
GHSCN: A Graph Neural Network-Based API Popularity Prediction Method in Service Ecosystem

With the rapid development of technologies in the field of service computing, and increasing of complex business requirements, more and more large-scale service ecosystem emerges. Thus, many researches of service ecosystem focus on issues related to optimization such as service recommendation and load balancing, so the API popularity prediction problem studied in this paper, which is basis for this service ecosystem optimization, becomes a research hotspot in this field. However, many existing researches are predicting the popularity of APIs based on API functions, QoS, history usage patterns and social relationships, which are difficult to obtain and cannot reflect the overall structure of the underlying service ecosystem. Therefore, we propose an innovative API popularity prediction method in service ecosystem based on Graph Neural Network (GNN). Concretely, a Global-Service Ecosystem Network (GSEN) model is proposed firstly, for modeling a given service ecosystem to a network that can depict the complex structure of service ecosystem and the functions, QoS, history usage patterns and social relationships of APIs. Then, a Graph Heterogeneous Spatiotemporal Convolutional Network (GHSCN) model is proposed to predict the popularity of APIs based on GSEN, and for getting better prediction accuracy, four different Heterogeneous Spatiotemporal Convolutional Kernels are proposed to extract the features of different elements which have different mechanisms to affect the popularity of target API. Finally, extensive experiments based on the data crawled from ProgrammableWeb.com show that our method achieves a superior performance in API popularity prediction, and the importance of the introduction of our model to service ecosystems.
Carbide Formation in Refractory Mo15Nb20Re15Ta30W20 Alloy under a Combined High-Pressure and High-Temperature Condition

In this work, the formation of carbide with the concertation of carbon at 0.1 at. % in refractory high-entropy alloy (RHEA) Mo15Nb20Re15Ta30W20 was studied under both ambient and high-pressure high-temperature conditions. The x-ray diffraction of dilute carbon (C)-doped RHEA under ambient pressure showed that the phases and lattice constant of RHEA were not influenced by the addition of 0.1 at. % C. In contrast, C-doped RHEA showed unexpected phase formation and transformation under combined high-pressure and high-temperature conditions by resistively employing the heated diamond anvil cell (DAC) technique. The new FCC_L12 phase appeared at 6 GPa and 809 °C and preserved the ambient temperature and pressure. High-pressure and high-temperature promoted the formation of carbides Ta3C and Nb3C, which are stable and may further improve the mechanical performance of the dilute C-doped alloy Mo15Nb20Re15Ta30W20.
A3D Printed Modular Soft Gripper for Conformal Grasping

In this work, a 3D printed modular soft gripper with highly conformal soft fingers was developed. A soft auxetic structure with compliant ribs is 3D printed simultaneously with each soft pneumatic finger for conformal grasping. The fingers of the soft gripper were printed monolithically, without requiring support material and postprocessing, using a low-cost and open-source fused deposition modeling (FDM) 3D printer that employs a commercially available thermoplastic polyurethane (TPU). The soft fingers of the gripper were optimized using finite element modeling (FEM). The FE simulations accurately predicted the performance of the fingers in terms of deformation and blocked force. Also, FEM was used to predict the behavior of the auxetic structure and its compliant ribs to prove that it highly decreases the contact pressure by increasing the contact area between the soft fingers and the grasped objects. The contact pressure can be decreased by up to 8 times with the implementation of the auxetic structure. Also, the configuration of the highly conformal gripper can be easily modulated by changing the number of fingers attached to its base to tailor it for specific manipulation tasks. A wide variety of objects with different weights, shapes, sizes, textures and stiffnesses can be grasped using the soft modular gripper.
Multi-scale Entropy Evaluates the Proarrhythmic Condition of Persistent Atrial Fibrillation Patients Predicting Early Failure of Electrical Cardioversion

Atrial fibrillation (AF) is nowadays the most common cardiac arrhythmia, being associated with an increase in cardiovascular mortality and morbidity. When AF lasts for more than seven days, it is classified as persistent AF and external interventions are required for its termination. A well-established alternative for that purpose is electrical cardioversion (ECV). While ECV is able to initially restore sinus rhythm (SR) in more than 90% of patients, rates of AF recurrence as high as 20–30% have been found after only a few weeks of follow-up. Hence, new methods for evaluating the proarrhythmic condition of a patient before the intervention can serve as efficient predictors about the high risk of early failure of ECV, thus facilitating optimal management of AF patients. Among the wide variety of predictors that have been proposed to date, those based on estimating organization of the fibrillatory (f-) waves from the surface electrocardiogram (ECG) have reported very promising results. However, the existing methods are based on traditional entropy measures, which only assess a single time scale and often are unable to fully characterize the dynamics generated by highly complex systems, such as the heart during AF. The present work then explores whether a multi-scale entropy (MSE) analysis of the f-waves may provide early prediction of AF recurrence after ECV. In addition to the common MSE, two improved versions have also been analyzed, composite MSE (CMSE) and refined MSE (RMSE). When analyzing 70 patients under ECV, of which 31 maintained SR and 39 relapsed to AF after a four week follow-up, the three methods provided similar performance. However, RMSE reported a slightly better discriminant ability of 86%, thus improving the other multi-scale-based outcomes by 3–9% and other previously proposed predictors of ECV by 15–30%. This outcome suggests that investigation of dynamics at large time scales yields novel insights about the underlying complex processes generating f-waves, which could provide individual proarrhythmic condition estimation, thus improving preoperative predictions of ECV early failure.
Application of Three-Dimensional Laser Scanning in the Protection of Multi-Dynasty Ceramic Fragments

The number of fragments and the variety of primitive cultural relics unearthed in archaeology, especially the mixed fragments of several dynasties unearthed in Qinglong town, Shanghai, pose a great challenge to the manual splicing. The traditional manual comparison method is easy to cause the second damage to the cultural relics. In this paper, the edge feature is extracted based on removing the noise of point cloud, a bilateral filtering point cloud denoising algorithm based on salient features is proposed. By changing the step size and field of view, the Improved Artificial Fish Swarm Algorithm is used to get the matching strategy, and the point cloud is used to reconstruct 3D model by the Dual Quaternion Transformation method. The pairing of fragments and virtual reconstruction can effectively avoid the secondary damage of cultural relic fragments. It provides a feasible artificial intelligence solution for the protection and restoration of similar archaeological excavations.
Cold-Start Promotional Sales Forecasting Through Gradient Boosted-Based Contrastive Explanations

Multiple Machine Learning solutions in Industry exist where interpretability is required. In retail, this is especially important when dealing with cold-start forecasting of promotional sales. In the planning phase of these promotions, retailers produce sales predictions that are scrutinised by both forecasting experts and managers. In this paper, we combine the predictive benefits of Gradient Boosted Decision Trees regressors and the interpretability of contrastive explanations. These are implicitly generated by the manner we shape data. Our method presents the cold-start forecasts in relation to the observed promotional sales of other products, which we call neighbours. They are selected based on a measure of closeness to the predicted promotion, which is derived from the variable importance calculated during the training of the regressors. With this information, the expert reviewer adjusts the cold-start prediction by simply varying the contribution of the neighbours. To validate our results we test our method on a surrogate model, as well as on real-market data. The results on the surrogate model demonstrate that our method is able to accurately identify the features that contribute to sales and then select the closest neighbours to produce a contrastive explanation. The results on real-market data also show that the proposed method performs at a similar level to widespread methods such as conventional CatBoost, NGBoost or AutoGluon, and has the advantage of providing interpretability.