Miracle-dz/UniFAR-FTUs huggingface dataset:
The Ubiquitous Model for Dynamic Diffusion of Information Technology

The recent information technology products with capability of performing multiple functions owing to the digital convergence, e.g., smart phones with fast Internet connection, provide ubiquitous access to the Internet for consumers. To model diffusion processes of such products and services requires a comprehensive modeling framework. We address major issues in the framework which include the consumer heterogeneity of diffusion modeling and market segmentation determined by the adoption process, the need for more generalized model than the successive generation substation theory, the distinction of the adoption processes based on the sequence of adopting different technologies, and the network externalities. The comprehensive diffusion model based on the Bass Model was proposed, and the hierarchical Bayesian model was formulated for estimation. We conjecture that a segment could be defined by the process of how consumers adopt the IT service, and we develop the diffusion models for each of the segments. The proposed model thus consists of those segment specific models, and is capable of identifying the market potential for each segment. The empirical analysis using data on Internet subscriptions via PC and digital cellular phone in the U.S. and Japan shows that the proposed model can capture the interactive effect between two diffusion processes, and provides an excellent fit to the data. The hypotheses developed in the theoretical framework are supported, and we discuss theoretical contributions and managerial implications.
Speckle suppression and texture preservation in optical coherence tomography images using variational image decomposition

Filtering off speckle noise while preserving fine details for optical coherence tomography(OCT) is particularly challenging. In this paper, an efficient method based on variational image decomposition(VID) is proposed to suppress speckle from OCT retinal images. A new BL-G-BM3D model based on VID is proposed to decompose one OCT retinal image into the background part, the texture part and noise. Each part is described by suitable function space separately and processed individually. The proposed model is able to preserve structural information while sufficiently suppressing speckle noise. We test the proposed method on two raw OCT retinal images with low contrast and high noise level, and compare with four other related and widely used filtering methods in terms of both quantitative evaluation and visual quality.The experimental results have demonstrated the validity of the proposed method.
Multi-Focus Image Fusion Based on Improved CNN

In order to avoid the limitations of artificial feature extraction, the CNN model is adopted to extract image features by big data-driven adaptive learning, which improves the accuracy of the features. For avoiding the loss of spatial information, an improved CNN model based on up-sampling is proposed, which consists of six layers of superimposed small convolution. The multi-layer design not only expands the receptive field, but also reduces the number of training parameters, and improves the running speed. The fusion method based on improved CNN model is proposed for multi-focus images. The improved CNN model divides the input image into focus region and non-focus region, and form the decision map. According to the decision map optimized by GFF, the focus regions are intergraded by pixel-by-pixel weighted fusion strategy to obtain fusion image. Experimental results show that the fusion results of the proposed method are clear in detail, complete in structure, no distortion in contrast, and no artifacts in the picture. It effectively avoids grayscale discontinuity, artifacts and other problems, and is better than classical methods we selected.
Multi-scale Deep Curve Estimation for Low-light Image Enhancement

Due to the limitation of the device, pictures taken in low-light environment usually consist of unpleasant deterioration, such as low contrast and color distortion. In this paper, we propose a Multi-scale Deep Curve Estimation network (MSDCE) for low-light image enhancement, which formulates the single low-light image enhancement task as a pixel-wise curve estimation by paired learning. To impose more priors of low-light regions, we propose an inverse illuminance map as part of the Curve Estimation network input. The curve estimation network backbone is composed of multi-scale modules which learns information from multi-scale feature streams and ensures the information exchange across different scales. Compared with several state-of-the-art methods, our method is significantly better. From the perspective of visual evaluation, our MSDCE can effectively improve the contrast and illumination of the image, and ensure the color fidelity of the image. CCS CONCEPTS • Computing methodologies • Artificial intelligence • Computer vision • Computer vision problems • Reconstruction
A Comprehensive Survey on Imputation of Missing Data in Internet of Things

The Internet of Things (IoT) is enabled by the latest developments in smart sensors, communication technologies, and Internet protocols with broad applications. Collecting data from IoT and generating information from these data become tedious tasks in real-life applications when missing data are encountered in datasets. It is of critical importance to deal with the missing data timely for intelligent decision-making. Hence, this survey attempts to provide a structured and comprehensive overview of the research on the imputation of incomplete data in IoT. The article starts by providing an overview of incomplete data based on the architecture of IoT. Then, it discusses the various strategies to handle the missing data, the assumptions used, the computing platform, and the issues related to them. The article also explores the application of imputation in the area of IoT. We encourage researchers and data analysts to use known imputation techniques and discuss various issues and challenges. Finally, potential future directions regarding the method are suggested. We believe this survey will provide a better understanding of the research of incomplete data and serve as a guide for future research.
Establishment of Speaker Recognition Corpus for Intelligent Attendance System

With the rapid development of information technology, student attendance has changed from paper attendance to machine attendance, such as taking photos, scanning QR codes, positioning, etc. These attendance needs to turn on the camera to take photos, which is slightly inefficient, or turn on the positioning service. However, many people think that turning on the positioning service will infringe on personal privacy. Therefore, we need to consider a more efficient Attendance method that does not infringe on personal privacy. Voice, as a signal that can quickly obtain and contain a variety of information, can be used for class students' attendance. Speaker recognition corpus is the basis of speech speaker recognition research. Diversified, large-scale and high-quality speaker recognition corpus plays an important role in improving the performance of speaker recognition system. At present, although there are many standardized corpora, there are few corpora for student attendance scenes. Therefore, this topic studies the speaker's speech feature parameters, and selects the appropriate Chinese phrases to establish the speaker's corpus.
Modulating Effects of Landscape Characteristics on Responses to Warming Differ Among Butterfly Species

Understanding and predicting biodiversity responses to climate change are vital to inform conservation strategies, but this is not straightforward as climate change responses depend on the landscape context and differ among species. Here, we quantified changes in the distribution and abundance of 30 butterfly species in the Netherlands in relation to climate change and in landscapes that vary in the amount and connectivity of (semi-)natural vegetation (SNV). We obtained yearly counts of well-monitored butterfly species from 327 time series over 27 years (1992–2018). We used these counts to build mixed effect hurdle models to relate species’ occurrence and abundance to temperature and the amount and connectivity of SNV around the sites. For 55% of the butterfly species, an increased amount or connectivity of SNV corresponded with stronger increases or reduced decreases in occurrence in response to warming, indicating that SNV may facilitate range expansion or mitigate extirpations due to climate change. However, for the occurrence of the other species we found no or a negative interaction between warming and SNV. Further, we did not find indications of a mitigating effect of SNV on abundance responses to warming. Our results thus suggest that increasing the amount and connectivity of SNV does not offer a “one-size-fits-all” solution, highlighting the need for additional measures if butterfly diversity is to be conserved.
Comparative Risks of Fracture Among Direct Oral Anticoagulants and Warfarin: A Systematic Review and Network Meta-Analysis

Importance Previous studies have shown the effectiveness and safety of direct oral anticoagulants (DOACs), including lower fracture risks, compared to warfarin. However, direct or indirect comparisons between different DOACs are scarce in the literature. Objective This study aims to compare fracture risks among different DOACs and warfarin, including apixaban, rivaroxaban, dabigatran, and edoxaban, in patients with non-valvular atrial fibrillation (NVAF) or venous thromboembolism (VTE). Methods We searched PubMed/MEDLINE, Embase, Cochrane CENTRAL, and Web of Science for randomized controlled trials and cohort studies comparing the fracture risks among patients who used warfarin or DOACs, up to March 2021. Two authors extracted data and appraised the risk of bias of included studies. The primary outcome was fracture risk. We performed pairwise meta-analyses to compare differences between medications and network meta-analyses using frequentist random-effects models to compare through indirect evidence. We used surface under the cumulative ranking curve (SUCRA) and mean ranks to determine the probability of a DOAC ranking best in terms of fracture risk. Results Thirty-one studies were included in the final analysis. Twenty-four randomized controlled trials and seven cohort studies with 455,343 patients were included in the systematic review and network meta-analysis. Compared to warfarin, the risk of any fractures was lowest with apixaban [relative risk (RR) = 0.59; 95% confidence interval (CI): 0.48–0.73], followed by rivaroxaban (RR: 0.72; 95% CI: 0.60–0.86), edoxaban (RR: 0.88; 95% CI: 0.62–1.23), and dabigatran (RR = 0.90; 95% CI: 0.75–1.07). No substantial inconsistency between direct and indirect evidence was detected for all outcomes. Conclusions All DOACs were safer than warfarin concerning the risk of fracture; however, apixaban had the lowest relative risk of fracture within the class of DOACs. Further head-to-head prospective studies should confirm the comparative safety profiles of DOACs regarding fractures.
The Intracellularly Acting Effector Foa3 Suppresses Defense Responses When Infiltrated Into the Apoplast

Plant pathogens employ secreted proteins, among which are effectors, to manipulate and colonize their hosts. A large fraction of effectors is translocated into host cells, where they can suppress defense signaling. Bacterial pathogens directly inject effectors into host cells via the type three secretion system, but it is little understood how eukaryotic pathogens, such as fungi, accomplish this critical process and how their secreted effectors enter host cells. The root-infecting fungus Fusarium oxysporum (Fo) secrets numerous effectors into the extracellular space. Some of these, such as Foa3, function inside the plant cell to suppress host defenses. Here, we show that Foa3 suppresses pattern-triggered defense responses to the same extent when it is produced in planta irrespective of whether the protein carries the PR1 secretory signal peptide or not. When a GFP-tagged Foa3 was targeted for secretion it localized, among other locations, to mobile subcellular structures of unknown identity. Furthermore, like the well-known cell penetrating peptide Arginine 9, Foa3 was found to deliver an orthotospovirus avirulence protein-derived peptide into the cytosol, resulting in the activation of the matching resistance protein. Finally, we show that infiltrating Foa3 into the apoplast results in strong suppression of the pattern-triggered immune responses, potentially indicating its uptake by the host cells in absence of a pathogen.
Functional Interactions Between the Parafascicular Thalamic Nucleus and Motor Cortex Are Altered in Hemiparkinsonian Rat

Parkinson’s disease (PD) is characterized by aberrant discharge patterns and exaggerated oscillatory activity within basal ganglia-thalamocortical circuits. We have previously observed substantial alterations in spike and local field potential (LFP) activities recorded in the thalamic parafascicular nucleus (PF) and motor cortex (M1), respectively, of hemiparkinsonian rats during rest or catching movements. This study explored whether the mutual effects of the PF and M1 depended on the amplitude and phase relationship in their identified neuron spikes or group rhythmic activities. Microwire electrode arrays were paired and implanted in the PF and M1 of rats with unilateral dopaminergic cell lesions. The results showed that the identified PF neurons exhibited aberrant cell type-selective firing rates and preferential and excessive phase-locked firing to cortical LFP oscillations mainly at 12–35 Hz (beta frequencies), consistent with the observation of identified M1 neurons with ongoing PF LFP oscillations. Experimental evidence also showed a decrease in phase-locking at 0.7–12 Hz and 35–70 Hz in the PF and M1 circuits in the hemiparkinsonian rats. Furthermore, anatomical evidence was provided for the existence of afferent and efferent bidirectional reciprocal connectivity pathways between the PF and M1 using an anterograde and retrograde neuroanatomical tracing virus. Collectively, our results suggested that multiple alterations may be present in regional anatomical and functional modes with which the PF and M1 interact, and that parkinsonism-associated changes in PF integrate M1 activity in a manner that varies with frequency, behavioral state, and integrity of the dopaminergic system.
PROBABILISTIC MODEL OF LASER RANGE FINDER FOR THREE DIMENSIONAL GRID CELL IN CLOSE RANGE ENVIRONMENT

The probabilistic model of a laser scanner presents an important aspect for simultaneous localization and map-building (SLAM). However, the characteristic of the beam of the laser range finder under extreme incident angles approaching 90 0 has not been thoroughly investigated. This research paper reports the characteristic of the density of the range value coming from a laser range finder under close range circumstances where the laser is imposed with a high incident angle. The laser was placed in a controlled environment consisting of walls at a close range and 1000 iteration of scans was collected. The assumption of normal density of the metrical data collapses when the beam traverses across sharp edges in this environment. The data collected also shows multimodal density at instances where the range has discontinuity. The standard deviation of the laser range finder is reported to average at 10.54 mm, with 0.96 of accuracy. This significance suggests that under extreme incident angles, a laser range finder reading behaves differently compared to normal distribution. The use of this information is crucial for SLAM activity in enclosed environments such as inside piping grid or other cluttered environments. KEYWORDS: Hokuyo UTM-30LX; kernel density estimation; probabilistic model
Analysis of Age-Dependent Transcriptomic Changes in Response to Intracerebral Hemorrhage in Mice

Age is a well-known risk factor that is independently associated with poor outcomes after intracerebral hemorrhage (ICH). However, the interrelationship between age and poor outcomes after ICH is not well defined. In this study, we aimed to investigate this relationship based on collagenase-induced ICH mice models. After being assessed neurological deficit 24 h after ICH, mice were euthanized and brain perihematomal tissues were used for RNA-sequencing (RNA-seq). And then the functions of differentially expressed genes (DEGs) identified by RNA-seq were analyzed using Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Ingenuity Pathway Analysis (IPA) and protein-protein interaction (PPI) analysis. In addition, we performed real-time quantitative polymerase chain reaction (RT-qPCR) for validation of candidate DEGs. In the behavioral tests, aged mice presented significantly worse neurological function than young mice and greater weight loss than aged sham controls 24 h after ICH. In DEGs analysis, ICH affected the expression of more genes in young mice (2,337 DEGs) compared with aged mice (2,005 DEGs). We found aged mice exhibited increased brain inflammatory responses compared with young animals and ICH induced significant activation of the interferon-β (IFN-β) and IFN signaling pathways exclusively in aged mice. Moreover, further analysis demonstrated that ICH resulted in the activation of cytosolic DNA-sensing pathway with the production of downstream molecule type I IFN, and the response to type I IFN was more significant in aged mice than in young mice. In agreement with the results of RNA-seq, RT-qPCR indicated that the expression of candidate genes of cyclic GMP-AMP synthase (cGAS), Z-DNA-binding protein 1 (ZBP1), and IFN-β was significantly altered in aged mice after ICH. Taken together, our study indicated that compared to young animals, aged mice exhibit increased vulnerability to ICH and that the differences in transcriptional response patterns to ICH between young and aged mice. We believe that these findings will facilitate our understanding of ICH pathology and help to translate the results of preclinical studies into a clinical setting.
High-Pressure Microfluidics for Ultra-Fast Microbial Phenotyping

Here, we present a novel methodology based on high-pressure microfluidics to rapidly perform temperature-based phenotyping of microbial strains from deep-sea environments. The main advantage concerns the multiple on-chip temperature conditions that can be achieved in a single experiment at pressures representative of the deep-sea, overcoming the conventional limitations of large-scale batch metal reactors to conduct fast screening investigations. We monitored the growth of the model strain Thermococcus barophilus over 40 temperature and pressure conditions, without any decompression, in only 1 week, whereas it takes weeks or months with conventional approaches. The results are later compared with data from the literature. An additional example is also shown for a hydrogenotrophic methanogen strain (Methanothermococcus thermolithotrophicus), demonstrating the robustness of the methodology. These microfluidic tools can be used in laboratories to accelerate characterizations of new isolated species, changing the widely accepted paradigm that high-pressure microbiology experiments are time-consuming.
Incidence and Predictors of Calf Morbidity and Mortality From Birth to 6-Months of Age in Dairy Farms of Northwestern Ethiopia

The Ethiopian government has initiatives for expanding the commercial and smallholder market-oriented urban and peri-urban dairy production systems to meet the demands for dairy products. However, there have been only limited on-farm studies on the health performance of commercial dairy breeds. The aim of this longitudinal study was to quantify the incidence and identify predictors of calf morbidity and mortality from birth to 6 months of age in urban and peri-urban dairy farms of Northwest Ethiopia. A total of 439 calves aged below 6 months from 174 dairy farms were included in the study. We collected data on 35 potential risk factors to determine their effect on calf morbidity and mortality in the area. Kaplan–Meier survival analysis was used to summarize survival probability. The Cox proportional hazard regression model with shared frailty to account for unmeasured herd-specific heterogeneity was also used to identify and quantify factors associated with time to morbidity and mortality. Among 439 calves enrolled for 6 months of follow-up period, a total of 141 morbidities and 54 mortality events were recorded. This gives an overall morbidity and mortality incidence rates of 64 per 100-calf 6-months at risk (risk rate of 47.3%) and 19 per 100-calf 6-months at risk (risk rate of 17.9%), respectively. Diarrhea was the most frequent calf health problem with a risk rate of 25.2%. It was the cause of death for 33.3% of all the 54 calf deaths. Next to diarrhea, pneumonia ranked second with risk rate of 8.6% and was responsible for death of 12.9% of all the 54 calf deaths. Among 35 potential risk factors, calf age, vigor status at birth, calf breed, colostrum ingestion, and herd size were significant (p < 0.05) predictors of calf morbidity and mortality. The Cox-shared frailty model revealed that the herd frailty component had no significant effect on hazard estimates of the covariates of all-cause morbidity and mortality. This implies that the dairy herds participated in the study were homogeneous in the distribution of unmeasured random effects. In conclusion, the magnitude of calf morbidity and mortality was higher and above economically tolerable level in this study. This could impede the success of Ethiopia's dairy development initiative in general, and the livelihood of smallholder dairy producers in particular. Therefore, educating farmers aimed at mitigating the identified risk factors can reduce calf morbidity and mortality in the study areas.
Reducing Submodule Capacitance for Modular Multilevel Converter-Based Medium-Voltage Wind Power Converter

This study presents a strategy to reduce the capacitance of the submodule (SM) capacitor of a modular multilevel converter-based medium-voltage wind power converter. The design of the SM capacitor of the modular multilevel converter (MMC) should consider the actual operating conditions, especially the influence of wind speed, because wind speed will affect the voltage ripple and voltage amplitude of the SM capacitor. In the traditional method, the capacitance design of the SM capacitor will be based on relatively high wind speed and leave a certain safety margin. However, in most cases, the system operates at the highest-frequency wind speed (HFWS). For the MMC-based wind energy conversion system, a constant capacitor voltage ripple (CCVR) control method is proposed. Using this method, the SM capacitor voltage ripple can be significantly reduced by injecting the second harmonic component of circulating current. In this way, the SM capacitor with smaller capacitance can be used. Finally, the effectiveness of the proposed method is validated in RT-Lab Platform.