Miracle-dz/UniFAR-FTUs huggingface dataset:
A Research of Truck’s Quality Online Estimation Based on CAN Information

The quality of trucks has a huge change from empty to fully load; the change of quality highly affects truck’s driving [1]. This paper takes a truck as research object and structured the vehicle quality online estimation model considering LS and vehicle sensor technology; the study set the default value of the road gradient to 0 and this can not only satisfy better calculation accuracy, but also enhance the efficiency and instantaneity of the algorithm. We created a quality online estimation model with MATLAB/SIMULINK and a truck dynamics simulation model with TRUCKSIM; after the simulation analysis in different operating conditions, we verified the quality online estimation model [2]. It can be found that when the model is running in different gears, there will be large data fluctuations in the initial stage of simulation, and it will quickly converge to the allowable error range of the truth value in 2 minutes. The model can guarantee the 5 percent accuracy and good simulation results. Keywords—CAN; truck; TRUCKSIM; LS; quality online estimation; S-Function
Linking Clicks to Bricks: Spillover Benefits of Online Advertising

Businesses have widely used email ads to directly send promotional information to consumers. While email ads serve as a convenient channel that allows firms to target consumers online, are they effective in increasing offline revenues for firms that predominantly sell in brick-and-mortar stores? Is the effect of email ads, if any, heterogeneous across different consumer segments? If so, on which consumers is the effect highest? In this research, we address these questions using a unique high-dimensional observational dataset from one of the largest retailers in the US, which links each consumer’s online behaviors to the item-level purchase records in physical stores. We use a doubly robust estimator (DRE) that incorporates nonparametric machine learning methods and allows us to perform causal estimation on observational data. Using the DRE we find that receiving email ads can increase a consumer’s spending in physical stores by approximately $11.82. Additionally, we find that the increased offline sales result from increased purchase probability and a wider variety of products being purchased. Further, we use a data-driven approach to demonstrate that the effect of email ads is heterogeneous across different consumer segments. Interestingly, the effect is highest among consumers who have fewer interactions with the focal retailer recently (i.e., lower email opening frequency). Overall, our results suggest a reminder effect of email ads. Receiving email ads from the retailer can generate awareness and remind the consumer of the retailer’s offerings of various products and services, which gradually increase the consumer’s purchase probability in the retailer’s physical stores. These findings have direct implications for marketers to improve their digital marketing strategy design and for policy makers who are interested in evaluating the economic impact of prevalent email advertising.
Use of close-range photogrammetry and UAV in documentation of architecture monuments

The available photogrammetric solutions, combined with properly conducted surveying measurements enable to create fully applicable, three-dimensional models of architectural structures, which can be applied in, i.e. protection of cultural heritage or conservational documentation. Currently, we can see an increase in use of digital non-metric cameras in a photogrammetric inventory of sacred monuments. Properly obtained and analysed data allow to create a digital model of an object, which later might serve as a basis for a vector image used for architectural and construction purposes. The paper presents the procedure of creation of 3D models of sacred historic monuments of relatively small sizes with the use of terrestrial photogrammetry and UAV using the Agisoft PhotoScan Professional software.
Classification de variables et analyse multivariée de données mixtes issues d’une étude BCI

L'objectif de ce travail est de traiter des donnees complexes issues de la technique des Brain Computer Interfaces (BCI) au moyen de methodes statistiques multivariees (approche PCAmix et classification de variables) afin de mieux comprendre et interpreter les relations qui existent entre elles. Cet article presente ainsi la classification de variables qui a pour but de reunir des variables fortement liees entre elles. L'approche proposee fonctionne avec des donnees mixtes, c'est a dire des donnees contenant des variables numeriques et des variables categorielles. Deux algorithmes de classification de variables sont decrits : un de classification hierarchique et un autre de partitionnement de type k-means. Une rapide description de la methode PCAmix (qui permet de faire de l'analyse en composantes principales pour des donnees mixtes) est fournie, vu que le calcul des variables synthetiques resumant les classes de variables obtenues est fonde sur cette methode multivariee. Enfin, les approches PCAmix et ClustOfVar (implementees dans les packages R ClustOfVar et PCAmixdata) sont mises en oeuvre sur les donnees reelles issues de l'etude BCI. Des recommandations, reposant non seulement sur des criteres de performances, d'efficience mais aussi de satisfaction, ont pu etre faites concernant le choix d'interface dans l'usage des claviers virtuels, notamment pour des personnes avec des problemes moteurs tels que la maladie de Charcot. ABSTRACT. The aim of this work is to analyze complex data from a Brain Computer Interfaces (BCI) study using multivariate statistical methods (PCAmix approach and clustering of variables) to better understand and interpret their relationships. This article presents clustering of variables which aim is to lump together strongly related variables. The proposed approach works on a mixed data set, i.e. on a data set which contains numerical variables and categorical variables. Two algorithms of clustering of variables are described : a hierarchical clustering and a k-means type clustering. A brief description of PCAmix method (that is a principal component analysis for mixed data) is provided, since the calculus of the synthetic variables summarizing the obtained clusters of variables is based on this multivariate method. Finally, the PCAmix and ClustOfVar approaches (implemented in the R packages ClustOfVar and PCAmixdata) are illustrated on a real dataset from a BCI (brain computer interface) study. Recommendations, based not only on performance, efficiency, but also on satisfaction criteria, could be made concerning the choice of interface in the use of virtual keyboards, especially for people with motor disorder such as Charcot's disease. MOTS-CLES. Classification de variables, donnees mixtes, analyse en composantes principales, packages R, brain computer interface, analyse multivariee des donnees, visualisation des donnees. KEYWORDS. clustering of variables, mixed data, principal component analysis for mixed data, R packages, brain computer interface, multivariate data analysis, data visualization.
Reproductive tendency of clonal plants in various habitats.

This paper summed up the reproductive strategies and the ecological adaptability of clonal plants in six different habitats such as forest understory,steppe,desert,farmlands and water area,and discussed the trade-off relations or relative importance of sexual vs.asexual reproduction which regularly change with water status,light and temperature.Such regularity was clarified as:the harsher the environment is,the more asexual reproduction would contribute to the regeneration and survival of the populations.In addition,we compared the ecological and functional properties of sexual vs.asexual reproduction modes,reviewed recent researches on the trade-off relations between the two reproductive modes,and mentioned some factors influencing such tradeoff relations.Finally,we made a prospect about future research of reproductive strategy of clonal plants especially in terms of approaches.
Relationship between Cytochrome P450 2C19 Gene Polymorphism and Primary Hepatocellular Carcinoma

Background:Cytochrome P450 (CYP) 2C19 polymorphism is associated with variable ability to metabolize mephenytoin, known as the extensive metabolizer and poor metabolizer. Mutant allele is the molecular biology basis of CYP2C19 gene polymorphism. Aims:To appraise the genotypes of CYP2C19 alleles in patients with primary hepatocellular carcinoma (HCC) and to clarify the relationship between CYP2C19 gene polymorphism and primary HCC. Methods:Forty-eight primary HCC patients and 88 healthy controls were enrolled in this study. Primers were designed and mutants CYP2C19m1 and CYP2C19m2 were tested by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The gene polymorphism of these two groups was analyzed and compared. Results:CYP2C19 poor metabolizers (m1/m1 and m1/m2 genotypes) were found in 12 cases (25.0%) of HCC group whereas only in 10 (11.4%) of the healthy controls, the difference was statistically significant (P0.05, OR=3116, 95% CI:1140～7113). Conclusions:CYP2C19 poor metabolizer has some correlation with primary HCC, and this may increase the susceptibility to HCC.
Traffic Sign Classification using Hybrid HOG-SURF Features and Convolutional Neural Networks

Traffic signs play an important role in safety of drivers and regulation of traffic. Traffic sign classification is thus an important problem to solve for the advent of autonomous vehicles. There have been several works that focus on traffic sign classification using various machine learning techniques. While works involving the use of convolutional neural networks with RGB images have shown remarkable results, they require a large amount of training time, and some of these models occupy a huge chunk of memory. Earlier works like HOG-SVM make use of local feature descriptors for classification problem but at the expense of reduced performance. This paper explores the use of hybrid features by combining HOG features and SURF with CNN classifier for traffic sign classification. We propose a unique branching based CNN classifier which achieves an accuracy of 98.48% on GTSRB test set using just 1.5M trainable parameters.
Interfaces and Stability in Halide Perovskite Solar Cells

In this paper, I give a brief summary over the current development of interface engineering in perovskite solar cells (PSCs) and the corresponding impact on device performance and stability. Principal loss mechanisms in the PSC can be attributed to imperfect interfaces. Aside from the key electronic properties at these interfaces, such as energy level alignment and band offsets, the device functionality is governed by the complex chemistry between the halide perovskite absorber and the adjacent functional layers.
Correction: Membrane microdomains and cytoskeleton organization shape and regulate the IL-7 receptor signalosome in human CD4 T-cells.

DOI 10.1074/jbc.AAC119. 008288 Correction: Membrane microdomains and cytoskeleton organization shape and regulate the IL-7 receptor signalosome in human CD4 T-cells. Blanche Tamarit, Florence Bugault, Anne-Hélène Pillet, Vincent Lavergne, Pascal Bochet, Nathalie Garin, Ulf Schwarz, Jacques Thèze, and Thierry Rose In Fig. 5b, an incorrect image was used for Jak3. Fig. 6j did not indicate the borders between different sections of the same gel. Incorrect traces were used in Fig. S4. These errors have now been corrected and do not affect the results or conclusions of this work.
Eight autopsy cases of melanoma brain metastases showing angiotropism and pericytic mimicry. Implications for extravascular migratory metastasis

Metastatic tumor spread is a complex multistep process. Due to the blood‐brain barrier, metastasis to the central nervous system is restrictive with a distinct predilection for certain tumor types. In melanoma patients, brain metastasis is a common endpoint with the majority showing evidence of widespread disease at autopsy. In a previous murine melanoma model, we have shown that melanoma cells migrate along preexisting vessels into the brain, showing angiotropism/vascular co‐option and pericytic mimicry.
A federated EHR network data completeness tracking system

Abstract Objective The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network. Materials and Methods The CTX applies a systems-based approach to design workflow and technology for assessing completeness across distributed electronic health record data repositories participating in a queryable, federated network. The CTX invokes 2 positive feedback loops that utilize open source tools (DQe-c and Vue) to integrate technology and human actors in a system geared for increasing capacity and taking action. A pilot implementation of the system involved 6 ARCH partner sites between January 2017 and May 2018. Results The ARCH CTX has enabled the network to monitor and, if needed, adjust its data management processes to maintain complete datasets for secondary use. The system allows the network and its partner sites to profile data completeness both at the network and partner site levels. Interactive visualizations presenting the current state of completeness in the context of the entire network as well as changes in completeness across time were valued among the CTX user base. Discussion Distributed clinical data networks are complex systems. Top-down approaches that solely rely on technology to report data completeness may be necessary but not sufficient for improving completeness (and quality) of data in large-scale clinical data networks. Improving and maintaining complete (high-quality) data in such complex environments entails sociotechnical systems that exploit technology and empower human actors to engage in the process of high-quality data curating. Conclusions The CTX has increased the network’s capacity to rapidly identify data completeness issues and empowered ARCH partner sites to get involved in improving the completeness of respective data in their repositories.
A High Full Well Capacity CMOS Image Sensor for Space Applications

This paper presents a high full well capacity (FWC) CMOS image sensor (CIS) for space applications. The proposed pixel design effectively increases the FWC without inducing overflow of photo-generated charge in a limited pixel area. An MOS capacitor is integrated in a pixel and accumulated charges in a photodiode are transferred to the in-pixel capacitor multiple times depending on the maximum incident light intensity. In addition, the modulation transfer function (MTF) and radiation damage effect on the pixel, which are especially important for space applications, are studied and analyzed through fabrication of the CIS. The CIS was fabricated using a 0.11 μm 1-poly 4-metal CIS process to demonstrate the proposed techniques and pixel design. A measured FWC of 103,448 electrons and MTF improvement of 300% are achieved with 6.5 μm pixel pitch.
A Review of Functional and Structural Neurobiology of the Action Observation Network in Autism Spectrum Disorder and Developmental Coordination Disorder

Recent research has reported motor impairment similarities between children with developmental coordination disorder (DCD) and a subgroup of individuals with autism spectrum disorder (ASD). However, there is a debate as to whether DCD is a co-occurring diagnosis in individuals with ASD and motor impairments (ASDd), or if motor impairments in ASD are distinct from DCD. However, the etiology of motor impairments is not well understood in either disorder. Clarifying comorbidities in ASD is important to determine different etiopathological phenotyping clusters in ASD and to understand the variety of genetic and environmental factors that contribute to the disorder. Furthermore, this distinction has important therapeutic relevance. Here we explore the current neuroimaging findings in ASD and DCD and discusses possible neural mechanisms that underlie similarities and differences between the disorders.
Showing Different Images Simultaneously by using Chromatic Temporal Response in Human Vision

In this paper, we propose a novel method for showing different images to multiple observers simultaneously by using the difference in their chromatic and temporal retinal response. The chromatic and temporal response characteristics of human retina have individuality, and thus each observer observes slightly different image, even if the same image is presented to these observers. In this paper, we formalize the chromatic and temporal relationship between the input stimulus and the response in human vision, and propose a method for showing arbitrary different images to individual observers simultaneously by using the difference in chromatic temporal response characteristics. We also show a method for obtaining chromatic and temporal response of human vision. Experimental results from a special camera which reproduces the impulse response of human retina show that our proposed method can represent different and arbitrary images to multiple observers.
Genome-wide Association Studies and Function Genome-wide association study identiﬁes the PLAG1-OXR1 region on BTA14 for carcass meat yield in cattle

Genome-wide association study identiﬁes the PLAG1-OXR1 region on BTA14 for carcass meat yield in cattle. Physiol 51: 137–144, 2019. doi:10.1152/physiolgenomics.00112. 2018. —Car-cass meat yield is an important carcass trait that contributes to the production efﬁciency and economic beneﬁts in beef cattle. It is therefore critical to identify quantitative trait loci associated with carcass traits to enable selection. Our previous studies have identiﬁed several causal variants within the pleomorphic adenoma gene 1 ( PLAG1 ) and coiled-coil-helix-coiled-coil-helix domain-containing 7 ( CHCHD7 ) genes on BTA14 for carcass traits in Chinese Simmental. In the current study, we carried out a genome-wide association study for carcass meat yield in 472 Wagyu cattle with Bovine HD SNP array. Our results showed that 27 single nucleotide polymorphisms (SNPs) were identiﬁed for tenderloin weight (TDW), striploin weight (SPW), chuck roll weight (CRW), bicep weight (BPW), knuckle weight (KCW), and ﬂank steak weight (FSW) in Wagyu cattle. Of these SNPs, 10 distinct SNPs were detected within the oxidation resistance 1 ( OXR1 ), fatty acid binding protein 5 ( FABP5 ), TNF receptor superfamily member 11b ( TNFRSF11B ), and zinc ﬁnger CCCH-type containing 3 ( ZC3H3 ) genes on BTA14. Notably, three signiﬁcant SNPs, BovineHD1400016738, BovineHD1400016743, and BovineHD1400016665 within OXR1 , were shown strong linkage disequilibrium (r 2 (cid:2) 0.8) and signiﬁcantly associated with CRW ( P (cid:3) 1.37 (cid:4) 10 (cid:5) 8 ~ 1.94 (cid:4) 10 (cid:5) 8 ). Moreover, Ingenuity Pathway Analysis showed that OXR1 , FABP5 , and CAP1A genes were involved in a single network and FABP5 may regulate the expression of OXR1 gene via node gene, peroxisome proliferator-activated receptor gamma ( PPARG ). Overall, this study suggests that OXR1 and FABP5 are candidate genes affecting carcass traits in Wagyu and the PLAG1- OXR1 region on BTA14 as a putative susceptibility locus for carcass meat yield for both Chinese Simmental and Wagyu.
Semi-Supervised Self-Training Method Based on an Optimum-Path Forest

Semi-supervised self-training method can train an effective classifier by exploiting labeled and unlabeled samples. Recently, a self-training method based on density peaks of data (STDP) is proposed. However, it still suffers from some shortcomings to be addressed. For example, STDP is affected by cut-off distance <inline-formula> <tex-math notation="LaTeX">$d_{c}$ </tex-math></inline-formula>. As a result, it is tricky for STDP to select an optimal parameter <inline-formula> <tex-math notation="LaTeX">$d_{c}$ </tex-math></inline-formula> on each data set. Furthermore, STDP has a poor performance on data sets with some variations in density because of cut-off distance <inline-formula> <tex-math notation="LaTeX">$d_{c}$ </tex-math></inline-formula>. In order to solve these problems, we present a new self-training method which connects unlabeled and labeled samples as vertexes of an optimum path forest to discover the underlying structure of feature space. Furthermore, the underlying structure of the feature space is used to guide the self-training method to train a classifier. Compared with STDP, our algorithm is free of parameters and can work better on data sets with some variations in density. Moreover, we are surprised to find that our algorithm also has some advantages in dealing with overlapping data sets. The experimental results on real data sets clearly demonstrate that our algorithm has better performance than some previous works in improving the performance of base classifiers of k-nearest neighbor, support vector machine and cart.
Segmentation of Liver Lesions Without Contrast Agents With Radiomics-Guided Densely UNet-Nested GAN

Segmentation of liver lesions on non-contrast magnetic resonance imaging (MRI) is critical for patient management and treatment planning. In clinical treatment, the imaging process suffers from high-risk, expensive, and time-consuming due to using contrast agents (CA). Furthermore, manual segmentation has the disadvantages of tedious, low-reproducibility, and high misdiagnosis rate. Although some deep-learning based works have attempted for liver lesions segmentation, they are all limited to the use of contrast-enhanced MRI. To avoid the limitations comes from CA, we proposed a Radiomics-guided Densely-UNet-Nested Generative Adversarial Networks (Radiomics-guided DUN-GAN) for automatic segmentation of liver lesions on non-contrast MRI. Radiomics-guided DUN-GAN includes a DUN segmentor and a Radiomics-guided discriminator. It uses radiomics feature of the multi-phase contrast image as prior knowledge to guide the extraction of key implicit contrast radiomics (ICR) features in non-contrast images, thus achieving the direct lesions segmentation without CA for the first time. In the DUN segmentor, an innovative nested structure of Densely-UNet-connection reliably completes the segmentation. The nested structure extracts global features, semantic features, and ICR features by reasonably sharing features and maximizing information flow. Those features are fused with a new direction strategy of multi-integration features to improve the segmentation ability. In the innovative Radiomics-guided discriminator, the radiomics feature combined with the semantic feature enhances the discrimination of Radiomics-guided discriminator. Moreover, it guides the segmentor for multiple feature extraction via using the adversarial mechanism. Radiomics-guided DUN-GAN learns the mapping relationship between images, extracting the key ICR in the non-contrast image, and finally completing the accurate segmentation. Radiomics-guided DUN-GAN obtained the Dice Similarity Coefficient results of 93.47± 0.83% for the segmentation of lesions in non-contrast images from 250 clinical subjects. The results verify the Radiomics-guided DUN-GAN is accurate and robust, and it has the possibility of becoming a safe, inexpensive, and time-saving medical assistant tool in clinical diagnosis.