Miracle-dz/UniFAR-FTUs huggingface dataset:
Highway Traffic Modeling and Decision Making for Autonomous Vehicle Using Reinforcement Learning

This paper studies the decision making problem of autonomous vehicles in traffic. We model the interaction between an autonomous vehicle and the environment as a stochastic Markov decision process (MDP) and consider the driving style of an experienced driver as the target to be learned. The road geometry is taken into consideration in the MDP model in order to incorporate more diverse driving styles. By designing the reward function of the MDP, the desired, driving behavior of the autonomous vehicle is obtained using reinforcement learning. Simulated results demonstrate the desired driving behaviors of an autonomous vehicle.
A model-based approach to investigate the effect of elevated interstitial fluid pressure on strain elastography

Finite element (FE) modeling provides a useful tool to understand the mechanical behavior of complex tissues, such as cancers, in a variety of testing conditions. Although a number of numerical and analytical models for cancerous tumors are retrievable in the literature, none of these models is capable of completely describing the behavior of a cancer embedded in a normal tissue in the conditions typical for an ultrasound elastography experiment. In this paper, we first design and implement a realistic FE model of the mechanical behavior of a cancer embedded in a normal tissue under ultrasound elastography testing conditions. In addition to the commonly used tissue mechanical properties, for the cancer, elevated interstitial fluid pressure (IFP) is incorporated in the model. IFP is a parameter of great clinical significance, but it is not typically considered in elastographic models of tumors. The developed model is then used to thoroughly study the effect of IFP on the axial, lateral and volumetric strains inside the tumor. The results of this study demonstrate that the presence of the IFP affects both the temporal and spatial distributions of the axial, lateral, volumetric strains and related elastographic parameters. Thus, these results lead to two important considerations: (1) that a correct interpretation of experimental elastographic data need a clear understanding of the effect of the IFP on the obtained elastograms and (2) that this IFP-dependent alteration of the elastographic parameters may provide an opportunity to non-invasively gain localized information about this clinically relevant parameter.
Core-Shell Interface-Oriented Synthesis of Bowl-Structured Hollow Silica Nanospheres Using Self-Assembled ABC Triblock Copolymeric Micelles.

Hollow porous silica nanospheres (HSNs) are emerging classes of cutting-edge nanostructured materials. They have elicited much interest as carriers of active molecule delivery due to their amorphous chemical structure, nontoxic nature, and biocompatibility. Structural development with hierarchical morphology is mostly required to obtain the desired performance. In this context, large through-holes or pore openings on shells are desired so that the postsynthesis loading of active-molecule onto HSNs via a simple immersion method can be facilitated. This study reports the synthesis of HSNs with large through-holes or pore openings on shells, which are subsequently termed bowl-structured hollow porous silica nanospheres (BHSNs). The synthesis of BHSNs was mediated by the core-shell interfaces of the core-shell corona-structured micelles obtained from a commercially available ABC triblock copolymer (polystyrene- b-poly(2-vinylpyridine)- b-poly(ethylene oxide) (PS-P2VP-PEO)). In this synthesis process, polymer@SiO2 composite structure was formed because of the deposition of silica (SiO2) on the micelles' core. The P2VP block played a significant role in the hydrolysis and condensation of the silica precursor, i.e., tetraethylorthosilicate (TEOS) and then maintaining the shell's growth. The PS core of the micelles built the void spaces. Transmission electron microscopy (TEM) images revealed a spherical hollow structure with an average particle size of 41.87 ± 3.28 nm. The average diameter of void spaces was 21.71 ± 1.22 nm, and the shell thickness was 10.17 ± 1.68 nm. According to the TEM image analysis, the average large pore was determined to be 15.95 nm. Scanning electron microscopy (SEM) images further confirmed the presence of large single pores or openings in shells. These were formed as a result of the accumulated ethanol on the PS core acting to prevent the growth of silica.
HigB Reciprocally Controls Biofilm Formation and the Expression of Type III Secretion System Genes through Influencing the Intracellular c-di-GMP Level in Pseudomonas aeruginosa

Toxin-antitoxin (TA) systems play important roles in bacteria persister formation. Increasing evidence demonstrate the roles of TA systems in regulating virulence factors in pathogenic bacteria. The toxin HigB in Pseudomonas aeruginosa contributes to persister formation and regulates the expression of multiple virulence factors and biofilm formation. However, the regulatory mechanism remains elusive. In this study, we explored the HigB mediated regulatory pathways. We demonstrate that HigB decreases the intracellular level of c-di-GMP, which is responsible for the increased expression of the type III secretion system (T3SS) genes and repression of biofilm formation. By analyzing the expression levels of the known c-di-GMP metabolism genes, we find that three c-di-GMP hydrolysis genes are up regulated by HigB, namely PA2133, PA2200 and PA3825. Deletion of the three genes individually or simultaneously diminishes the HigB mediated regulation on the expression of T3SS genes and biofilm formation. Therefore, our results reveal novel functions of HigB in P. aeruginosa.
Embedding Fuzzy K-Means with Nonnegative Spectral Clustering via Incorporating Side Information

As one of the most widely used clustering techniques, the fuzzy K-Means (also called FKM or FCM) assigns every data point to each cluster with a certain degree of membership. However, conventional FKM approach relies on the square data fitting term which is not robust to data outliers and ignores the prior information, which leads to unsatisfactory clustering results. In this paper, we present a novel and robust fuzzy K-Means clustering algorithm, namely Embedding Fuzzy K-Means with Nonnegative Spectral Clustering via Incorporating Side Information. The proposed method combines fuzzy K-Means with nonnegative spectral clustering into a unified model, and further takes the advantage of the prior knowledge of data pairs such that the quality of similarity graph is enhanced and the clustering performance is effectively improved. Besides, the ℓ2,1-norm loss function is adopted in the objective function, which achieves better robustness to outliers. Last, experimental results on benchmark datasets verify the effectiveness and superiority of the proposed clustering method.
Gaussian Message Passing for Overloaded Massive MIMO-NOMA

This paper considers a low-complexity Gaussian message passing (GMP) Multi-User Detection (MUD) scheme for a coded massive multiple-input multiple-output (MIMO) system with non-orthogonal multiple access (massive MIMO-NOMA), in which a base station with <inline-formula> <tex-math notation="LaTeX">$N_{s}$ </tex-math></inline-formula> antennas serves <inline-formula> <tex-math notation="LaTeX">$N_{u}$ </tex-math></inline-formula> sources simultaneously in the same frequency. Both <inline-formula> <tex-math notation="LaTeX">$N_{u}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$N_{s}$ </tex-math></inline-formula> are large numbers, and we consider the overloaded cases with <inline-formula> <tex-math notation="LaTeX">$N_{u}>N_{s}$ </tex-math></inline-formula>. The GMP for MIMO-NOMA is a message passing algorithm operating on a fully-connected loopy factor graph, which is well understood to fail to converge due to the correlation problem. The GMP is attractive as its complexity order is only linearly dependent on the number of users, compared to the cubic complexity order of linear minimum mean square error (LMMSE) MUD. In this paper, we utilize the large-scale property of the system to simplify the convergence analysis of the GMP under the overloaded condition. We prove that the <italic>variances</italic> of the GMP definitely converge to the mean square error (MSE) of the LMMSE multi-user detection. Second, the <italic>means</italic> of the traditional GMP will fail to converge when <inline-formula> <tex-math notation="LaTeX">$N_{u}/N_{s}< (\sqrt {2}-1)^{-2}\approx 5.83$ </tex-math></inline-formula>. Therefore, we propose and derive a new convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the LMMSE multi-user detection performance for any <inline-formula> <tex-math notation="LaTeX">$N_{u}/N_{s}>1$ </tex-math></inline-formula>, and show that it has a faster convergence speed than the traditional GMP with the same complexity. Finally, the numerical results are provided to verify the validity and accuracy of the theoretical results presented.
Tumor and endothelial cells collaborate via transcellular receptor complexes

Many cellular signaling pathways are initiated by cell‐surface ligand‐sensing complexes that incorporate not just one but multiple receptors. Most studies focus on receptors coexpressed on a single cell (cis interactions), but complexes containing receptors on adjacent cells (trans interactions) are also possible. Recent work by Morin et al published in this journal provides critical evidence for such trans interactions between Neuropilin‐1 (NRP1) expressed on human tumor cells and vascular endothelial growth factor receptor 2 (VEGFR2) expressed on adjacent endothelial cells, with the ligand VEGFA binding and bridging the two receptors. They show that the formation of these complexes is correlated with reduced tumor proliferation and increased patient survival. They also observe trans NRP1‐VEGFA‐VEGFR2 repressing angiogenesis and cis NRP1‐VEGFA‐VEGFR2 increasing angiogenesis in selected cancers. The distinct molecular signature of each tumor and each patient will determine which type of complexes dominate and will influence prognosis and treatment. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
A Systematic Mapping Study on API Documentation Generation Approaches

Background: Application Programming Interfaces (APIs) are key to software reuse. Software developers can link functionality and behaviour found in other software with their own software by taking an API into use. However, figuring out how an API works is usually demanding, and may require that the developers spend a notable amount of time familiarizing themselves with the API. Good API documentation is of key importance to simplify this task. Objective: To present a comprehensive, unbiased overview of the state-of-the-art on tools and approaches for API documentation generation. Method: A systematic mapping study on published tools and approaches that can be used for generating API documentation, or for assisting in the API documentation process. Results: 36 studies on API documentation generation tools and approaches analyzed and categorized in a variety of ways. Among other things, the paper presents an overview of what kind of tools have been developed, what kind of documentation they generate, and what sources the documentation approaches require. Conclusion: Out of the identified approaches, many contribute to API documentation in the areas of natural language documentation and code examples and templates. Many of the approaches contribute to ease API users' understanding and learning of the API, but also to the maintenance and generation of API documentation. Most of the approaches are automatic, simplifying the API documentation generation notably, under the assumption that relevant sources for the generation are available. Most of the API documentation approaches are evaluated either by exercise of the approach followed by analysis of the results, or by empirical evaluation methods.
A Novel Method for Classifying Driver Mental Workload Under Naturalistic Conditions With Information From Near-Infrared Spectroscopy

Driver cognitive distraction is a critical factor in road safety, and its evaluation, especially under real conditions, presents challenges to researchers and engineers. In this study, we considered mental workload from a secondary task as a potential source of cognitive distraction and aimed to estimate the increased cognitive load on the driver with a four-channel near-infrared spectroscopy (NIRS) device by introducing a machine-learning method for hemodynamic data. To produce added cognitive workload in a driver beyond just driving, two levels of an auditory presentation n-back task were used. A total of 60 experimental data sets from the NIRS device during two driving tasks were obtained and analyzed by machine-learning algorithms. We used two techniques to prevent overfitting of the classification models: (1) k-fold cross-validation and principal-component analysis, and (2) retaining 25% of the data (testing data) for testing of the model after classification. Six types of classifier were trained and tested: decision tree, discriminant analysis, logistic regression, the support vector machine, the nearest neighbor classifier, and the ensemble classifier. Cognitive workload levels were well classified from the NIRS data in the cases of subject-dependent classification (the accuracy of classification increased from 81.30 to 95.40%, and the accuracy of prediction of the testing data was 82.18 to 96.08%), subject 26 independent classification (the accuracy of classification increased from 84.90 to 89.50%, and the accuracy of prediction of the testing data increased from 84.08 to 89.91%), and channel-independent classification (classification 82.90%, prediction 82.74%). NIRS data in conjunction with an artificial intelligence method can therefore be used to classify mental workload as a source of potential cognitive distraction in real time under naturalistic conditions; this information may be utilized in driver assistance systems to prevent road accidents.
Will Artificial Intelligence Replace the Human Echocardiographer?: Clinical Considerations

An older population with an increased prevalence of cardiovascular disease and an aging workforce are engendering a state of healthcare crisis in cardiology.1 Most cardiologists now face an unprecedented time crunch as they rush through their appointments to perform and interpret more and more procedures. The need to multitask creates exhaustion leading to burnout and frequent reporting errors.2 The recent interest in using artificial intelligence techniques, such as machine learning, may offer a solution to reduce physician workload including repetitive and tedious tasks involved in diagnosing and analyzing patient data and imaging. To this end, the study by Zhang and colleagues3 in this issue of Circulation adds to the growing enthusiasm for developing a machine learning algorithm that automates several facets of echocardiography measurement and interpretation. Zhang and colleagues used a deep learning model that has enjoyed spectacular success in addressing computer vision problems including image classification, face recognition, robot navigation, and driverless cars to name a few. Although traditional machine learning workflow includes an initial stage of feature engineering and selection from the data for classification, deep learning methods attempt to learn the important features directly from the raw image data (with minimal preprocessing). Zhang and colleagues applied an algorithm that has triumphed in image recognition tasks and reported a 96% accuracy for distinguishing between broad echocardiographic view classes (eg, parasternal long axis from short axis, or an apical view) and an 84% accuracy overall (including partially obscured views). These results are consistent with a recent study that applied deep learning with convolutional neural networks for view classification of echocardiograms.4 However, Zhang et al notably used a deeper architecture with more layers (18 versus 11), considered a larger number of echocardiography view classes (23 versus 15 views), and applied their technique to a larger data set (14 035 versus 267 echocardiographic studies). The authors also reported an overall metric of accuracy of image segmentation ranging from 72% to 90% for image segmentation. Although deep learning has been explored previously for segmenting the left ventricle,5 the work by Zhang et al was much more extensive with additional cardiac segments beyond the left ventricle and a larger data set, involving millions of images from 14 035 studies. Moreover, the authors succeeded in going a step beyond simple classification and segmentation by providing an algorithm for automated quantification of cardiac structure and function. The comparison with manually recorded measurements, however, showed wide limits of agreements emphasizing the potential real-world variability of echocardiography measurements. Independent verification from a core laboratory or the use of a gold standard like cardiac magnetic resonance was © 2018 American Heart Association, Inc.
Atorvastatin upregulates apolipoprotein M expression via attenuating LXRα expression in hyperlipidemic apoE-deficient mice

Apolipoprotein M (apoM) is a recently identified human apolipoprotein that is associated with the formation of high-density lipoprotein (HDL). Studies have demonstrated that statins may affect the expression of apoM; however, the regulatory effects of statins on apoM are controversial. Furthermore, the underlying mechanisms by which statins regulate apoM remain unclear. In the present study, in vivo and in vitro models were used to investigate whether the anti-atherosclerotic effects of statins are associated with its apoM-regulating effects and the underlying mechanism. Hyperlipidemia was induced by in apolipoprotein E-deficient mice by providing a high-fat diet. Atorvastatin was administered to hyperlipidemic mice and HepG2 cells to investigate its effect on apoM expression. The liver X receptor α (LXRα) agonist T0901317 was also administered together with atorvastatin to hyperlipidemic mice and HepG2 cells. The results revealed that atorvastatin increased apoM expression, which was accompanied with decreased expression of LXRα in the liver of hyperlipidemic apolipoprotein E-deficient mice and HepG2 cells. Additionally, apoM upregulation was inhibited following treatment with T0901317. In summary, atorvastatin exhibited anti-atherosclerotic effects by upregulating apoM expression in hyperlipidemic mice, which may be mediated by the inhibition of LXRα.
Altered thermoregulation as a driver of host behaviour in glochidia-parasitised fish

ABSTRACT Parasites alter their host behaviour and vice versa as a result of mutual adaptations in the evolutionary arms race. One of these adaptations involves changes in host thermoregulation, which has the potential to harm the parasite and thereby act as a defence mechanism. We used a model of the brown trout (Salmo trutta) experimentally parasitised with glochidia ectoparasitic larvae from the endangered freshwater pearl mussel (Margaritifera margaritifera) to reveal whether parasitisation alters fish behavioural thermoregulation. A study using radiotelemetry temperature sensors was performed during almost one year of the M. margaritifera parasitic stage. Glochidia-infested S. trutta altered their thermoregulation through active searching for habitats with different thermal regimes. The general preference for temperatures in infested fish varied and was either above or below the temperature preferred by uninfested individuals. Infested fish also preferred different temperatures across localities, whereas uninfested fish maintained their thermal preference no matter which stream they inhabited. Glochidia further induced the expression of a behavioural syndrome among S. trutta personality traits, suggesting that it might increase the probability that the fish host would occur in the glochidia temperature optimum. Our findings present the first evidence that thermoregulation plays a fundamental role in the relationship of affiliated mussels and their fish hosts. Incorporating thermoregulation as a factor in the study of this relationship can help to interpret results from previous behavioural studies, as well as to optimise management measures related to endangered mussels. Summary: Thermoregulation plays a fundamental, up to now neglected, role in the relationship of endangered affiliated mussels and their fish hosts.
Traffic-Sign Spotting in the Wild via Deep Features

This paper focuses on traffic sign spotting (TSS which automatically recognizes not only the conventional traffic signs but also information, facility and service signs, and traffic lights. TSS is divided into two sequential tasks: detecting traffic sign candidate regions in an image and recognizing the traffic signs in the regions. It is a very challenging task. We make the following contributions: 1) we create a traffic sign collection from the driverless car. The traffic signs are shot under the natural environment which covers large variation in illuminance and weather conditions. It not only contains the common traffic signs but also contains the information, facility and service signs which are called signposts, as well as traffic lights. 2) we proposed a systematic solution. We construct an Inception convolutional neural network. We use Faster-RCNN for traffic sign detection and make it suitable to detect small targets. 3) We adopt three schemes for the common traffic signs, the signposts and the traffic lights, respectively. The experimental results demonstrate the effectiveness and efficiency of our methods. Our methods won the first place in the traffic sign recognition task of Intelligent Vehicle Future Challenge 2017, China.
Clinical Outcomes and Neuroimaging Profiles in Nondisabled Patients With Anticoagulant-Related Intracerebral Hemorrhage

Background and Purpose— The aim of this study was to prospectively validate our prior findings of smaller hematoma volume and lesser neurological deficit in nonvitamin K oral anticoagulant (NOAC) compared with Vitamin K antagonist (VKA)-related intracerebral hemorrhage (ICH). Methods— Prospective 12-month observational study in 15 tertiary stroke centers in the United States, Europe, and Asia. Consecutive patients with premorbid modified Rankin Scale score of <2 with acute nontraumatic anticoagulant-related ICH divided into 2 groups according to the type of anticoagulant: NOAC versus VKA. We recorded baseline ICH volume, significant hematoma expansion (absolute [12.5 mL] or relative [>33%] increase), neurological severity measured by National Institutes of Health Stroke Scale score, 90-day mortality, and functional status (modified Rankin Scale score). Results— Our cohort comprised 196 patients, 62 NOAC related (mean age, 75.0±11.4 years; 54.8% men) and 134 VKA related (mean age, 72.3±10.5; 73.1% men). There were no differences in vascular comorbidities, antiplatelet, and statin use; NOAC-related ICH patients had lower median baseline hematoma volume (13.8 [2.5–37.6] versus 19.5 [6.6–52.0] mL; P=0.026) and were less likely to have severe neurological deficits (National Institutes of Health Stroke Scale score of >10 points) on admission (37% versus 55.3%, P=0.025). VKA-ICH were more likely to have significant hematoma expansion (37.4% versus 17%, P=0.008). NOAC pretreatment was independently associated with smaller baseline hematoma volume (standardized linear regression coefficient:−0.415 [95% CI, −0.780 to −0.051]) resulting in lower likelihood of severe neurological deficit (odds ratio, 0.44; 95% CI, 0.22−0.85) in multivariable-adjusted models. Conclusions— Patients with NOAC-related ICH have smaller baseline hematoma volumes and lower odds of severe neurological deficit compared with VKA-related ICH. These findings are important for practicing clinicians making anticoagulation choices.