Miracle-dz/UniFAR-FTUs huggingface dataset:
Driving Lane Change Intent Recognition Based on Vehicle Exterior Environment Variables

Because driver lane change operation is not only related to the dynamic parameters of the vehicle, but also is closely related to the complex and changing external environment, driver lane change intent recognition accuracy is relatively low. This paper proposes the decision factor through the driver lane change decision-making analysis, which reflects the influence of the external environment on driver lane changing operation. Furthermore, a hidden Markov-based driver lane-changing intent recognition model is established, based on the dynamic parameters of the vehicle and the decision factors of the external environment as the observation variables, the driver lane change intent as a hidden variable, which is experimentally verified using real vehicle data from the NGSIM database. The experimental results show that the recognition accuracy of driver's lane change intention based on decision-making of vehicle external environment is higher than the recognition accuracy of drivers' lane change intent using only the vehicle's own dynamic parameters.
Enhancement of electrolaryngeal speech using Frequency Auditory Masking and GMM based voice conversion

Laryngectomees lose their voice box after surgery and adapt various methods to restore their voice, one of them being Electrolaryngeal speech. The Electrolarynx suffers from producing natural speech by generating mechanical form of speech with suppressed unvoiced features, device and environment noise. This paper tends to remove the echo noise, device noise and environmental noise thereby enhancing the Electrolaryngeal speech to be more intelligible by spectral mapping using Gaussian Mixture Model (GMM) and auditory masking. The low frequency noise is masked by the pre-emphasised speech signal by determining the absolute threshold of masking. The spectral mapping technique using GMM based voice conversion in association with the source-filter model improves the voice quality and prosody. The objective and subjective evaluation measures, depict the significant enhancement of electrolaryngeal speech compared to previous enhancement methods which removed only low frequency noise and failed to include voice quality.
Co-Seismic and Postseismic Fault Models of the 2018 Mw 6.4 Hualien Earthquake Occurred in the Junction of Collision and Subduction Boundaries Offshore Eastern Taiwan

The ascending and descending InSAR deformations derived from ALOS-2 and Sentinel-1 satellite SAR images and GPS displacements are used to estimate the fault model of the 2018 Mw 6.4 Hualien earthquake. The sinistral strike-slip fault dipping to the west with a high dip angle of 89.4° and a rake angle of 201.7° is considered as the seismogenic fault of this event. This seismogenic fault also triggered the ruptures of the Milun fault, which dips to the east with a dip angle of ~72°, and an unknown west-dipping fault with a dip angle of 85.2°. Two predicted faulting models indicate that the InSAR deformation fields include more postseismic slip than those of the GPS data. The north segment of the Milun fault and west-dipping fault have been triggered by the rupture of the seismogenic fault, but the postseismic slip occurred only in the south segment of the Milun fault. The InSAR-derived co-seismic and postseismic faulting model suggests that the significant slip concentrates at depths of 2.4–15.0 km of the main fault, 0.0–14.0 km of the Milun fault. Only minor slip is detected on the west-dipping fault. The maximum fault slip of ca. 2.1 m is located at the depth of ca. 2.4 km under the Meilun Tableland. The Coulomb failure stress (CFS) change calculated by the co-seismic and postseismic faulting model shows that there is a significant CFS increase in the east of the south segment of the Milun fault, but few of the aftershocks occur in this area, which indicates a high risk of future seismic hazard.
Assembly of Cellular Microstructures into Lobule-Like 3D Microtissues Based on Microrobotic Manipulation* Research supported by the Beijing Natural Science Foundation under Grant 4164099and the National Natural Science Foundation of China under grants 61603044and 61520106011.

Fabrication of cellular microtissues in vitro that reproduce physiology of human liver has shown great potential and demand in clinical and biomedical research. However, creating tissue-engineered constructs in vitro with both organ-like geometry and biological functions presents unique challenge attribute to complex structure and function of human liver. Here, we have developed photopattern and microrobotic manipulation for fabrication of cellular microtissue in vitro as a substitution of liver lobule as well as liver functions. Poly (ethylene) glycol diacrylate (PEGDA) hydrogel containing hepatocytes are introduced into microfluidic channel to produce cell encapsulated 2D microstructures with lobule-like pattern by photocrosslinking. Microrobotic manipulation is developed for 3D assembly of 2D microstructures into 3D microtissues based on liquid force. To form an integration with lobule-like structure, randomly oriented 2D microstructures are aligned into regular shape by self-alignment process based on hydrophilic-hydrophobic interactions. After the 2D fabrication and 3D assembly process, cells can proliferate and spread in PEGDA hydrogel. During long-term culture, cells perform high viability in both 2D microstructures and 3D microtissues. Albumin secretion of hepatocytes encapsulating in microtissues maintain during the culture period. It indicates that hepatocytes can keep high viability and some liver functions in these microtissues which providing a potential demonstration for biomedical research in the future.
A Discrete Artificial Bee Colony for Distributed Permutation Flowshop Scheduling Problem with Total Flow Time Minimization

The distributed permutation flowshop scheduling problem (DPFSP) has important applications in modern industries. This paper presents a discrete artificial bee colony (DABC) algorithm for solving the DPFSP with total flow time criterion. We use the job permutation based representation to represent the candidate solutions. The insertion operator is adapted to generate neighboring solutions in the search process. We re-design the search phases including employ bees, onlooker bees, and scout bees according to the problem-specific knowledge. A comprehensive computational campaign based on the 720 benchmark instances demonstrates the effectiveness of the proposed DABC algorithm for solving the DPFSP with the total flowtime criterion.
Longitudinal Anti-skid Control Of Electric Vehicle Based On Kalman Filter And ADRC

In order to realize the longitudinal anti-skid control of electric vehicle under the condition of low adhesion road, Kalman filter method is used to estimate longitudinal speed of electric vehicle which is a necessary information for vehicle longitudinal anti-skid control. Based on the LuGre tire model, the longitudinal dynamic model of the vehicle is established. In addition, Active Disturbance Rejection Controller(ADRC) is designed to realize the traction control of the electric vehicle for its good performance to suppress the influence of parameter uncertainty and unknown disturbance. Assuming that the pavement conditions are known, the maximum torque allowed for the motor output are estimated. In order to prevent excessive driving force resulting in vehicle slip and energy loss, the output torque is limited by dynamic saturation nonlinear control. Finally, the simulation results show that Kalman filter can estimate the longitudinal velocity accurately, and under the action of active disturbance rejection control and dynamic saturation nonlinear control, the safety and stability of electric vehicle longitudinal driving are improved and the loss of drive energy is reduced. What's more, the comparisons of simulation results of ADRC and PID controller show that ADRC performs better in suppressing the influence of parameter uncertainty and unknown disturbance.
Novel Recommender Systems Using Personalized Sentiment Mining

Websites like Netflix, Amazon, Yelp have lot of reviews and ratings. Ratings are of usually on a scale of 1-5 points or stars. Reviews are free-form text consisting of a few sentences. Text sentiment analysis classification has occupied a pivotal role in sentiment analysis research as it offers important opinion mining options. Using these reviews and ratings of a person, we can recommend him new products, movies, restaurants. Usually Recommender systems match user patterns by finding similar users and recommendations are developed. We solve the problem of taking into account user’s personal sentiments and judgments making the recommendations more directed and useful to him. In this paper we provide an example using movies from the MovieLens dataset. Making recommendations using sentiment tags along with usual recommendations proves to be a novel and more intuitive way from a user’s likability of the movie and preferences standpoint. We analyze various methods like Unigrams, Bigrams, Support vector machines, Bernoulli Naive Bayes, Random Forests on popular datasets like Yelp, MovieLens to get the best method for sentiment generation. Then we introduce a novel recommendation systems combining the Alternate Least Square (ALS) method and sentiment generation recommending movies to users which proved to give lesser RMSE than traditional models on the MovieLens Dataset.
Quantifying the legacy of snowmelt timing on soil greenhouse gas emissions in a seasonally dry montane forest

The release of water during snowmelt orchestrates a variety of important belowground biogeochemical processes in seasonally snow‐covered ecosystems, including the production and consumption of greenhouse gases (GHGs) by soil microorganisms. Snowmelt timing is advancing rapidly in these ecosystems, but there is still a need to isolate the effects of earlier snowmelt on soil GHG fluxes. For an improved mechanistic understanding of the biogeochemical effects of snowmelt timing during the snow‐free period, we manipulated a high‐elevation forest that typically receives over two meters of snowfall but little summer precipitation to influence legacy effects of snowmelt timing. We altered snowmelt rates for two years using black sand to accelerate snowmelt and white fabric to postpone snowmelt, thus creating a two‐ to three‐week disparity in snowmelt timing. Soil microclimate and fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) were monitored weekly to monthly during the snow‐free period. Microbial abundances were estimated by potential assays near the end of each snow‐free period. Although earlier snowmelt caused soil drying, we found no statistically significant effects (p < 0.05) of altered snowmelt timing on fluxes of CO2 or N2O, or soil microbial abundances. Soil CH4 fluxes, however, did respond to snowmelt timing, with 18% lower rates of CH4 uptake in the earlier snowmelt treatment, but only after a dry winter. Cumulative CO2 emission and CH4 uptake were 43% and 88% greater, respectively, after the dry winter. We conclude that soil GHG fluxes can be surprisingly resistant to hydrological changes associated with earlier snowmelt, likely because of persistent moisture and microbial activities in deeper mineral soils. As a result, a drier California in the future may cause seasonally snow‐covered soils in the Sierra Nevada to emit more GHGs, not less.
Semi-Online Multiple Object Tracking Using Graphical Tracklet Association

Online multiple object tracking (MOT) is highly challenging when multiple objects have similar appearance or under long occlusion. In this letter, we propose a semi-online MOT method using online discriminative appearance learning and tracklet association with a sliding window. We connect similar detections of neighboring frames in a temporal window, and improve the performance of appearance feature by online discriminative appearance learning. Then, tracklet association is performed by minimizing a subgraph decomposition cost. Occlusions and missing detections are recovered after tracklet stitching. Our method has been tested on two public datasets. Experimental results have demonstrated the significant performance improvement of our method. Specifically, the proposed method is improved by 8.31% and 12.38% in terms of Multiple Object Tracking Accuracy and Multiple Object Tracking Precision, respectively, as compared to the baseline.
Novel Analytical Formulation for Shielding Effectiveness Calculation of Lossy Enclosures Containing Elliptical Apertures

This paper presents a new analytical formula to evaluate the shielding effectiveness (SE) of a lossy rectangular enclosure with elliptical and/or circular apertures excited by an external plane wave. On the basis of circuit model theory and Bethe theory of small holes, we extended Yin's model to account for apertures of elliptical shapes. The hole is then treated as a transmission line with an intrinsic impedance depending on its polarizability. The model takes easily into account higher order resonant modes. For validation, a commercial software is employed and enclosures with different size and apertures are studied. The proposed model shows accurate results in good agreement with the full wave simulation ones.
Spectral Clustering by Joint Spectral Embedding and Spectral Rotation

Spectral clustering is an important clustering method widely used for pattern recognition and image segmentation. Classical spectral clustering algorithms consist of two separate stages: 1) solving a relaxed continuous optimization problem to obtain a real matrix followed by 2) applying ${K}$ -means or spectral rotation to round the real matrix (i.e., continuous clustering result) into a binary matrix called the cluster indicator matrix. Such a separate scheme is not guaranteed to achieve jointly optimal result because of the loss of useful information. To obtain a better clustering result, in this paper, we propose a joint model to simultaneously compute the optimal real matrix and binary matrix. The existing joint model adopts an orthonormal real matrix to approximate the orthogonal but nonorthonormal cluster indicator matrix. It is noted that only in a very special case (i.e., all clusters have the same number of samples), the cluster indicator matrix is an orthonormal matrix multiplied by a real number. The error of approximating a nonorthonormal matrix is inevitably large. To overcome the drawback, we propose replacing the nonorthonormal cluster indicator matrix with a scaled cluster indicator matrix which is an orthonormal matrix. Our method is capable of obtaining better performance because it is easy to minimize the difference between two orthonormal matrices. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method (called JSESR).
A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.
Dilutional coagulopathy in pediatric scoliosis surgery: A single center report

Children undergoing posterior spinal fusion experience high blood loss often necessitating transfusion. An appropriately activated coagulation system provides hemostasis during surgery, but pathologic dysregulation can cause progressive bleeding and increased transfusions. Despite receiving antifibrinolytics for clot stabilization, many patients still require transfusions.
Synchronization of Chaotic Systems Having Triangular Structure via Observer

Observer-based synchronization of chaotic systems is investigated in this paper. The considered chaotic systems have triangular structure. A robust observer, which can guarantee globally convergence, is designed. Then, a parameterized feedback controller which uses the states of the observer is given. The proposed method can guarantee the convergence of the synchronization errors. Finally, simulation studies are carried out to show the effectiveness of the proposed method.
Deep Semantic Segmentation Neural Networks of Railway Scene

It is crucial that the situation of railway scene needs to be periodically monitored to ensure railway safety. Usually, the camera is placed in the driver's cab on a moving vehicle to generate a large number of high-resolution images of railway scene. The traditional image processing inspection methods can only inspect railway scene of the local and simple background. They are feasible sometimes, but not universal and efficient. In this paper, a novel and practical approach is proposed to visual railway scene detection with Deep Semantic Segmentation Convolutional Neural Networks(DSSCNN). It is helpful for precisely locating and detailedly describe important parts of the railway scene in the entire image instead of separately detecting like traditional methods. In addition, three particular blocks named Conv Block, Dilated Conv Block, and Sum Block in DSSCNN are designed to extract more context information from railway images and enhance the segmentation performance. It shows that DSSCNN trained end-to-end and encoder-decoder network structure for semantic segmentation of railway scene are more superior than UNet and Fully Convolutional Networks(FCN) on the same datasets. At the same time, it is also verified that it is feasible and efficient to describe, visual and locate the key parts of the railway scene by DSSCNN.
Yoga as a Complementary Therapy for Smoking Cessation: Results from BreathEasy ,a Randomized Clinical trial.

Introduction There is evidence that yoga may be helpful as an aid for smoking cessation. Yoga has been shown to reduce stress and negative mood and may aid weight control, all of which have proven to be barriers to quitting smoking. This study is the first rigorous, randomized clinical trial of yoga as a complementary therapy for smokers attempting to quit. Methods Adult smokers (n=227; 55% women) were randomized to an 8-week program of cognitive-behavioral smoking cessation and either twice-weekly Iyengar Yoga, or general Wellness classes (control). Assessments included cotinine verified 7-day point prevalence abstinence (7PPA) at week 8, 3-month and 6-month follow-ups. Results At baseline participants' mean age was 46.2 years (SD=12.0), smoking rate 17.3 cigarettes/day (SD=7.6). Longitudinally adjusted models of abstinence outcomes demonstrated significant group effects favoring Yoga. Yoga participants had 37% greater odds of achieving abstinence than Wellness participants at EOT. Lower baseline smoking rates (<=10 cigarettes/day) were also associated with higher likelihood of quitting if given Yoga vs. Wellness (OR=2.43, 95% CI: 1.09-6.30) at EOT. A significant dose effect was observed for yoga (OR=1.12, 95% CI: 1.09-1.26), but not Wellness, such that each yoga class attended increased quitting odds at EOT by 12%. Latent Class Modeling revealed a 4-class model of distinct quitting patterns among participants. Conclusions Yoga appears to increase the odds of successful smoking abstinence, particularly among light smokers. Additional work is needed to identify predictors of quitting patterns and inform adjustments to therapy needed to achieve cessation and prevent relapse.
Reducing the Effect of Spurious Phase Variations in Neural Oscillatory Signals

The phase-reset model of oscillatory EEG activity has received a lot of attention in the last decades for decoding different cognitive processes. Based on this model, the ERPs are assumed to be generated as a result of phase reorganization in ongoing EEG. Alignment of the phase of neuronal activities can be observed within or between different assemblies of neurons across the brain. Phase synchronization has been used to explore and understand perception, attentional binding and considering it in the domain of neuronal correlates of consciousness. The importance of the topic and its vast exploration in different domains of the neuroscience presses the need for appropriate tools and methods for measuring the level of phase synchronization of neuronal activities. Measuring the level of instantaneous phase (IP) synchronization has been used extensively in numerous studies of ERPs as well as oscillatory activity for a better understanding of the underlying cognitive binding with regard to different set of stimulations such as auditory and visual. However, the reliability of results can be challenged as a result of noise artifact in IP. Phase distortion due to environmental noise artifacts as well as different pre-processing steps on signals can lead to generation of artificial phase jumps. One of such effects presented recently is the effect of low envelope on the IP of signal. It has been shown that as the instantaneous envelope of the analytic signal approaches zero, the variations in the phase increase, effectively leading to abrupt transitions in the phase. These abrupt transitions can distort the phase synchronization results as they are not related to any neurophysiological effect. These transitions are called spurious phase variation. In this study, we present a model to remove generated artificial phase variations due to the effect of low envelope. The proposed method is based on a simplified form of a Kalman smoother, that is able to model the IP behavior in narrow-bandpassed oscillatory signals. In this work we first explain the details of the proposed Kalman smoother for modeling the dynamics of the phase variations in narrow-bandpassed signals and then evaluate it on a set of synthetic signals. Finally, we apply the model on ongoing-EEG signals to assess the removal of spurious phase variations.
Adaptive Control for Disturbance Attenuation of Flexible Spacecraft

This paper addresses the problem of attitude stabilization for a flexible spacecraft in the presence of system model parameters unknown and external disturbances by means of adaptive backstepping methods. The Modified Rodrigues Parame-ters(MRPs) vector is adopted to describe the attitude variables. With an adaptive tuning law, the estimates of unknown parameters of spacecraft model is used to design the controller. Lyapunov analysis shows that the proposed controller can guarantee the asymptotical convergence of attitude vector and velocity variables. The robust performance of closed-loop system is evaluated by L2 gain to achieve L2 norm level of the disturbance attenuation. Additionally, the vibration of flexible appendixes can be suppressed by the controller. Numerical simulations demonstrate the effectiveness of the proposed adaptive backstepping control strategy.