Miracle-dz/UniFAR-FTUs huggingface dataset:
Lane Endpoint Detection and Position Accuracy Evaluation for Sensor Fusion-Based Vehicle Localization on Highways

Landmark-based vehicle localization is a key component of both autonomous driving and advanced driver assistance systems (ADAS). Previously used landmarks in highways such as lane markings lack information on longitudinal positions. To address this problem, lane endpoints can be used as landmarks. This paper proposes two essential components when using lane endpoints as landmarks: lane endpoint detection and its accuracy evaluation. First, it proposes a method to efficiently detect lane endpoints using a monocular forward-looking camera, which is the most widely installed perception sensor. Lane endpoints are detected with a small amount of computation based on the following steps: lane detection, lane endpoint candidate generation, and lane endpoint candidate verification. Second, it proposes a method to reliably measure the position accuracy of the lane endpoints detected from images taken while the camera is moving at high speed. A camera is installed with a mobile mapping system (MMS) in a vehicle, and the position accuracy of the lane endpoints detected by the camera is measured by comparing their positions with ground truths obtained by the MMS. In the experiment, the proposed methods were evaluated and compared with previous methods based on a dataset acquired while driving on 80 km of highway in both daytime and nighttime.
Regressing Controversy of Music Artists from Microblogs

Social media represents a valuable data source for researchers to analyze how people feel about a variety of topics, from politics to products to entertainment. This paper addresses the detection of controversies involving music artists, based on microblogs. In particular, we develop a new controversy detection dataset consisting of 53,441 tweets related to 95 music artists, and we devise and evaluate a comprehensive set of user-and content-based feature candidates to regress controversy. The evaluation results show a strong performance of the presented approach in the controversy detection task: F1 score of 0.811 in a classification task and RMSE of 0.688 in a regression task, using controversy scores in the range [1, 4]. In addition, the results obtained in applying the presented approach on a dataset from a different domain (CNN news controversy) demonstrate transferability of the developed feature set, with a significant improvement over prior approaches. A combination of the adopted Gradient Boosting based classifier and the developed feature set results in an F1 score of 0.775, which represents an improvement of 9.8% compared to the best prior result on this dataset.
Matrix stiffness mechanically conditions EMT and migratory behavior of oral squamous cell carcinoma

ABSTRACT Tumors are composed of heterogeneous phenotypes, each having different sensitivities to the microenvironment. One microenvironment characteristic – matrix stiffness – helps to regulate malignant transformation and invasion in mammary tumors, but its influence on oral squamous cell carcinoma (OSCC) is unclear. We observed that, on stiff matrices, a highly invasive OSCC cell line (SCC25) comprising a low E-cad to N-cad ratio (InvH/E:NL; SCC25) had increased migration velocity and decreased adhesion strength compared to a less invasive OSCC cell line (Cal27) with high E-cad to N-cad ratio (InvL/E:NH; Cal27). However, InvL/E:NH cells acquire a mesenchymal signature and begin to migrate faster when exposed to prolonged time on a stiff niche, suggesting that cells can be mechanically conditioned. Owing to increased focal adhesion assembly, InvL/E:NH cells migrated faster, which could be reduced when increasing integrin affinity with high divalent cation concentrations. Mirroring these data in human patients, we observed that collagen organization, an indicator of matrix stiffness, was increased with advanced disease and correlated with early recurrence. Consistent with epithelial tumors, our data suggest that OSCC cells are mechanically sensitive and that their contribution to tumor progression is mediated in part by this sensitivity. This article has an associated First Person interview with the first author of the paper. Highlighted Article: Matrix stiffness modulates OSCC cell migration similarly to that of other epithelial carcinomas but by using a different focal-adhesion-mediated mechanism.
Sparsity in Variational Autoencoders

Working in high-dimensional latent spaces, the internal encoding of data in Variational Autoencoders becomes naturally sparse. We discuss this known but controversial phenomenon sometimes refereed to as overpruning, to emphasize the under-use of the model capacity. In fact, it is an important form of self-regularization, with all the typical benefits associated with sparsity: it forces the model to focus on the really important features, highly reducing the risk of overfitting. Especially, it is a major methodological guide for the correct tuning of the model capacity, progressively augmenting it to attain sparsity, or conversely reducing the dimension of the network removing links to zeroed out neurons. The degree of sparsity crucially depends on the network architecture: for instance, convolutional networks typically show less sparsity, likely due to the tighter relation of features to different spatial regions of the input.
Brain-inspired automated visual object discovery and detection

Significance The Internet contains millions of videos and images of objects, captured contextually, just as they would appear in their natural environments. Such access to large-scale data has injected an urgency to an age-old query: Can one automatically browse these data, as a human would, to construct brain-like and computationally tractable models of the visual world? If successful, many of the scalability and robustness limitations of existing computer vision methodologies, which primarily rely on supervised frameworks requiring manually labeled exemplars, will be removed. As a step toward solving this problem, we leverage widely accepted findings in the fields of cognitive psychology and neurophysiology to develop an unsupervised framework for automated isolation of objects and their efficient detection and localization in new environments. Despite significant recent progress, machine vision systems lag considerably behind their biological counterparts in performance, scalability, and robustness. A distinctive hallmark of the brain is its ability to automatically discover and model objects, at multiscale resolutions, from repeated exposures to unlabeled contextual data and then to be able to robustly detect the learned objects under various nonideal circumstances, such as partial occlusion and different view angles. Replication of such capabilities in a machine would require three key ingredients: (i) access to large-scale perceptual data of the kind that humans experience, (ii) flexible representations of objects, and (iii) an efficient unsupervised learning algorithm. The Internet fortunately provides unprecedented access to vast amounts of visual data. This paper leverages the availability of such data to develop a scalable framework for unsupervised learning of object prototypes—brain-inspired flexible, scale, and shift invariant representations of deformable objects (e.g., humans, motorcycles, cars, airplanes) comprised of parts, their different configurations and views, and their spatial relationships. Computationally, the object prototypes are represented as geometric associative networks using probabilistic constructs such as Markov random fields. We apply our framework to various datasets and show that our approach is computationally scalable and can construct accurate and operational part-aware object models much more efficiently than in much of the recent computer vision literature. We also present efficient algorithms for detection and localization in new scenes of objects and their partial views.
Generalized Extreme Value Trimmed Filter for Random Impulse Noise Suppression in Color Images

Noise suppression is the first prior task in a machine vision and human perception. In this paper, a novel method to remove high volumes of impulse noise is proposed. Our filter has designed based on the concept of a frequency data distribution that is not always symmetric. The filter employs a Generalized Extreme Value (GEV) distribution for fitting pixel values in each sliding window. From GEV approximation, the impulsive noise pixels were trimmed out when their values were more than the minima and maxima threshold values. From the experimental results, the proposed algorithm provided good performance for removing high impulse noise and outperformed when compared with the state-of-the-art methods.
Fault Diagnosis and Minimum Rational Entropy Fault Tolerant Control of Stochastic Distribution Collaborative Systems

In this paper, a fault-tolerant control scheme is presented for a class of stochastic distribution collaborative control systems, which are composed of three subsystems connected in series to complete the control target. The radial basis function neural network is used to approximate the output probability density function of the third subsystem, which is also the output of the entire system. When fault occurs in the first subsystem, an adaptive diagnostic observer is designed to estimate the value of fault. However, the first subsystem does not have the ability of self-recovery, minimum rational entropy controllers are designed in the latter subsystems to compensate the influence of the fault and minimize the entropy of the system output. A numerical simulation is given to verify the effectiveness of the proposed scheme.
International Conference on Electrical Power Drive Systems ( ICEPDS ) Positioning Control System Design Considering Mechatronic Module Torque Loop Response Time

The study is devoted to the position control system synthesis for electromechanic modules with a specified dynamics and taking into account the motor torque loop time response. Mathematical models in the state equations form, characteristic polynomials, and transfer matrices for systems with position regulators are obtained. It makes possible to ensure the system dynamics corresponding to the Bessel polynomials with an acceptable bandwidth value. Synthesis and analysis of perfomances are carried out using the MathCAD's symbolic mathematics. The principles of regulators fixed adjustment providing the position control system robustness in presence of the mechanics inertia variation are developed.
An Excitation System of Synchronous Generators Based on WAMS and Energy Function for Improvement of Transient Stability

In this paper, we propose a novel excitation system for improvement of the transient stability. The proposed method controls the excitation voltage of synchronous generators (SGs) to promote decrease in an energy function after a disturbance. The abovementioned control action can be achieved by using wide area information obtained by a wide area measurement system (WAMS). Numerical simulations are carried out for a modified IEEE RTS-24 system with 23 generators. The numerical examples show the effectiveness of the proposed method for improvement of the first swing stability by comparing the proposed excitation system with a conventional automatic voltage regulator under three-line-to-ground faults. Impact of the communication latency in the WAMS-based method on the stabilizing effect is also investigated by setting various length of the latency in the numerical simulation.
A Comparitive Analysis of MPPT Algorithms for Solar Photovoltaic Systems to Improve the Tracking Accuracy

This paper presents a comparative analysis on two MPPT algorithms employed in Solar Photovoltaic System. The MPPT algorithm helps to obtain maximum power output from SPV Panels under wide varying climatic conditions. The most popular MPPT algorithms used in SPV systems are Incremental Conductance Algorithm (ICA) and P&O Algorithm (POA). This paper provides comparative analysis of ICA and P&O algorithm based on the parameters such as tracking accuracy, speed of convergence and tracking time. The validation of results of comparison between ICA and POA is also analyzed by using MATLAB Simulink Software. The results obtained shows that ICA is a most suitable MPPT technique for increasing the efficiency of operation of SPV systems.
Research on R&D Innovation Efficiency of Regional High-Tech Industry Considering Spatial Effect

The stochastic frontier model is used to measure the R&D innovation efficiency of high-tech industry in 30 provinces in China during 2009–2015 and analyze the influence of spatial effects on the R&D innovation efficiency. The study found that the R&D innovation efficiency of regional high-tech industries have a big difference and a significant spatial correlation; innovation outputs show spatial effect which has significant positive impact on innovation efficiency; there is significant U type relationship between firm size and R&D innovation efficiency; government financial support has a significant negative impact on R&D innovation efficiency, government tax support has a significant positive effect on R&D innovation efficiency. Regional science and technology level and regional economic level have no significant effect on the R&D innovation efficiency.
Overview of Fiber Optic Surface Plasmon Resonance Biosensors for Medical Applications

Fiber Optic Surface Plasmon Resonance (FO-SPR) biosensors are practical miniature sensors that provide real-time monitoring and label-free detection of the concentration of a desired analyte for remote in situ use. These sensors utilize Surface Plasmon Waves (SPWs) excited at a Metal-Dielectric Interface (MDI). In our paper, we explain the background theory of SPWs, operation principle, conventional system design and performance parameters of FO-SPR sensors.
Impact of Adaptive Consistency on Distributed SDN Applications: An Empirical Study

Scalability of the control plane in a software-defined network (SDN) is enabled by means of decentralization of the decision-making logic, i.e., by replication of controller functions to physically or virtually dislocated controller replicas. Replication of a centralized controller state also enables the protection against controller failures by means of primary and backup replicas responsible for managing the underlying SDN data plane devices. In this paper, we investigate the effect of the deployed consistency model on scalability and correctness metrics of the SDN control plane. In particular, we compare the strong and eventual consistency, and make a case for a novel adaptive consistency approach. The existing controller platforms rely on either strong or eventual consistency mechanisms in their state distribution. We show how an adaptive consistency model offers the scalability benefits in terms of the total request-handling throughput and response time, in contrast to the strong consistency model. We also outline how the adaptive consistency approach can provide for correctness semantics that are unachievable with the eventual consistency paradigm in practice. The adaptability of our approach provides a balanced and tunable tradeoff of scalability and correctness for the SDN application implemented on top of the adaptive framework. To validate our assumptions, we evaluate and compare the different approaches in an emulated testbed with an example of a load balancer controller application. The experimental setup comprises up to five extended OpenDaylight controller instances and two network topologies from the area of service provider and data center networks.
Evaluation of Photovoltaic Systems for Reactive Power Compensation in Low Voltage Power Systems

The four-quadrant operation ability of photovoltaic (PV) inverters makes them promising candidates for reactive power compensation in low voltage systems. In this paper, utilization of PV inverters instead of conventional reactive power compensation units is evaluated. The use of PV inverters for reactive power compensation as well as active power supplying is investigated considering a real life system. The considered system suffers from low capacitive power factor due to the connected online UPS system. The paper firstly analyzes utilization of low voltage PV systems for reactive power compensation purpose technically, and then presents a detailed economic study in terms of short and long-term costs. The costs are evaluated considering reactive demand charge to the customer.
Obsessive–compulsive symptoms in schizophrenia: a review

Abstract Objective To review the available literature on obsessive–compulsive symptom (OCS)/obsessive–compulsive disorder (OCD) in patients with schizophrenia. Methodology Electronic searches were carried out to locate studies reporting various aspects of OCS/OCD in patients with schizophrenia. Results Available evidence suggests that prevalence of OCS/OCD in patients with schizophrenia is much higher than prevalence in general population and it is seen in all the stages of schizophrenia, starting from at risk mental state to chronic/stabilisation/deficit phases. Symptom profile of OCS/OCD in schizophrenia is similar to that seen in patients with OCD only. Presence of OCS/OCD is associated with higher severity of symptoms of schizophrenia and more negative outcome. At present there is very limited data on the efficacy/effectiveness of various pharmacological measures and psychological interventions, for management of OCS/OCD in patients with schizophrenia. There is some evidence pointing towards beneficial effect of certain antipsychotics, antidepressants and cognitive behaviour therapy. Management of OCS/OCD in patients with schizophrenia involves proper assessment. If the OCS/OCD is related to use of particular antipsychotic use, initial attempt must be made to reduce the dose of antipsychotics, however, if this is not effective, than addition of a selective serotonin reuptake inhibitor (SSRIs) must be considered. If the OCS/OCD is not related to the use of antipsychotic medication, than depending up on the severity of psychotic symptoms, addition of SSRIs must be considered. Conclusion This review suggests that OCS/OCD is highly prevalent among patients with schizophrenia and there is limited good quality evidence to make any specific recommendations for management.
Encoding Local Contexts of Sentences with Convolutions on pq-Gram Representations of Dependency Trees

In this paper, we present our sentence encoding approach that uses local contexts constructed from pq-gram representations of a sentence’s dependency tree. The context localization scope can be adjusted through parameters p, the dependency depth, and q, the dependency width, which allows controlling context-sensitivity. We show competitive results of using our sentence encoding approach for sentence-pair modeling tasks.
Woodlice of Belgium: an annotated checklist and bibliography (Isopoda, Oniscidea)

Abstract Woodlice are key organisms for nutrient cycling in many terrestrial ecosystems; however, knowledge on this invertebrate group is limited as for other soil fauna taxa. Here, we present an annotated checklist of the woodlice of Belgium, a small but densely populated country in Western Europe. We reviewed all 142 publications on Belgian woodlice, the oldest dating back to 1831 and re-identified all doubtful specimens from the Royal Belgian Institute of Natural Sciences (RBINS) collection. These data is complemented with observations from extensive field surveys dating from March 2014 until December 2017. We report 36 species of woodlice with free-living populations for Belgium. Nine species can be added compared to the latest checklist published in 2000 being Hyloniscusriparius (C. Koch, 1838), Miktoniscuspatiencei Vandel, 1946, Trichoniscoidessarsi Patience, 1908, Haplophthalmusmontivagus Verhoeff, 1941, Porcelliomonticola Lereboullet, 1853, Metatrichoniscoidesleydigii (Weber, 1880), Trichoniscusalemannicus Verhoeff, 1917, Elumacaelata (Miers, 1877) and Philosciaaffinis Verhoeff, 1908. Two species are deleted from the checklist (Ligidiumgermanicum Verhoeff, 1901 and Armadillidiumdepressum Brandt, 1833) because records are doubtful and no material has been preserved. Additionally the data of the field surveys is used to determine a species status of occurrence in Belgium. For each species, a short overview of their first records is provided and their confirmation as part of the Belgian fauna, their current status, as well as a complete bibliography of the species in Belgium.
Road Surface Classification Using a Deep Ensemble Network with Sensor Feature Selection

Deep learning is a fast-growing field of research, in particular, for autonomous application. In this study, a deep learning network based on various sensor data is proposed for identifying the roads where the vehicle is driving. Long-Short Term Memory (LSTM) unit and ensemble learning are utilized for network design and a feature selection technique is applied such that unnecessary sensor data could be excluded without a loss of performance. Real vehicle experiments were carried out for the learning and verification of the proposed deep learning structure. The classification performance was verified through four different test roads. The proposed network shows the classification accuracy of 94.6% in the test data.
An RFID Sensor for Early Expiry Detection of Packaged Foods

An RFID sensor with the application in the food industry is proposed and investigated. The sensor can be used to evaluate the expiry date of foods. It consists of an RFID tag (the IC and the antenna) and a conductive polymer. The operational mechanism of the proposed sensor is based on the conductivity change of a polymer when it is exposed to the spoiled food. The results show increment of the read range due to decreases of the polymer conductivity at the sensing region influenced by exposure to the rotten food. Consequently, the reader installed at a certain distance can read/detect the RFID signal of a close-to-spoiled food.
Predicting Stances in Twitter Conversations for Detecting Veracity of Rumors: A Neural Approach

Detecting rumors is a crucial task requiring significant time and manual effort in forms of investigative journalism. In social media such as Twitter, unverified information can get disseminated rapidly making early detection of potentially false rumors critical. We observe that the early reactions of people towards an emerging claim can be predictive of its veracity. We propose a novel neural network architecture using the stances of people engaging in a conversation on Twitter about a rumor for detecting its veracity. Our proposed solution comprises two key steps. We first detect the stance of each individual tweet, by considering the textual content of the tweet, its timestamp, as well as the sequential conversation structure leading up to the target tweet. Then we use the predicted stances of all tweets in a conversation tree to determine the veracity of the original rumor. We evaluate our model on the SemEval2017 rumor detection dataset and demonstrate that our solution outperforms the state-of-the-art approaches for both stance prediction and rumor veracity prediction tasks.