Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous 3D Object Recognition and Pose Estimation Based on RGB-D Images

Object recognition and pose estimation are essential functions in applications of computer vision, and they also are fundamental modules in robotic vision systems. In recent years, RGB-D cameras become more and more popular, and the 3D object recognition technology has got more and more attention. In this paper, a novel design of simultaneous 3D object recognition and pose estimation algorithm is proposed based on RGB-D images. The proposed system converts the input RGB-D image to colored point cloud data and extracts features of the scene from the colored point cloud. Then, the existing color signature of histograms of orientations (CSHOT) description algorithm is employed to build descriptors of the detected features based on local texture and shape information. Given the extracted feature descriptors, a two-stage matching process is performed to find correspondences between the scene and a colored point cloud model of an object. Next, a Hough voting algorithm is used to filter out matching errors in the correspondence set and estimate the initial 3D pose of the object. Finally, the pose estimation stage employs RANdom SAmple Consensus (RANSAC) and hypothesis verification algorithms to refine the initial pose and filter out poor estimation results with error hypotheses. Experimental results show that the proposed system not only successfully recognizes the object in a complex scene but also accurately estimates the 3D pose information of the object with respect to the camera.
Effect of Change in Total Cholesterol Levels on Cardiovascular Disease Among Young Adults

Background Although high serum cholesterol in young adults is known to be a predictor for cardiovascular events, there is not enough evidence for the association of cholesterol level change with cardiovascular disease (CVD). This study aimed to evaluate whether the change in cholesterol is associated with incidence of CVD among young adults. Methods and Results We examined 2 682 045 young adults (aged 20–39 years) who had undergone 2 consecutive national health check‐ups provided by Korean National Health Insurance Service between 2002 and 2005. Cholesterol levels were classified into low (<180 mg/dL), middle (180–240 mg/dL) and high (≥240 mg/dL). CVD events were defined as ≥2 days hospitalization attributable to CVD for 10 years follow‐up. Increased cholesterol levels were significantly associated with elevated ischemic heart disease risk (adjusted hazard ration [aHR]=1.21; 95% confidence interval [CI]=1.03–1.42 in low‐high group and aHR=1.21; 95% CI=1.15–1.27 in middle‐high group) and cerebrovascular disease (CEVD) risk (aHR=1.24; 95% CI=1.05–1.47 in low‐high group and aHR=1.09; 95% CI=1.02–1.16 in middle‐high group). Decreased cholesterol levels were associated with reduced ischemic heart disease risk (aHR=0.91; 95% CI=0.88–0.95 in middle‐low group, aHR=0.65; 95% CI=0.56–0.75 in high‐low group and aHR=0.68; 95% CI=0.65–0.73 in high‐middle group). Furthermore, lower cerebrovascular disease risk (aHR=0.76; 95% CI=0.62–0.92) was observed in the high‐low group compared with patients with sustained high cholesterol. Conclusions The findings of our study indicate that increased cholesterol levels were associated with high CVD risk in young adults. Furthermore, young adults with decreased cholesterol levels had reduced risk for CVD.
Synchrotron Radiation X-ray Diffraction Techniques Applied to Insect Flight Muscle

X-ray fiber diffraction is a powerful tool used for investigating the molecular structure of muscle and its dynamics during contraction. This technique has been successfully applied not only to skeletal and cardiac muscles of vertebrates but also to insect flight muscle. Generally, insect flight muscle has a highly ordered structure and is often capable of high-frequency oscillations. The X-ray diffraction studies on muscle have been accelerated by the advent of 3rd-generation synchrotron radiation facilities, which can generate brilliant and highly oriented X-ray beams. This review focuses on some of the novel experiments done on insect flight muscle by using synchrotron radiation X-rays. These include diffraction recordings from single myofibrils within a flight muscle fiber by using X-ray microbeams and high-speed diffraction recordings from the flight muscle during the wing-beat of live insects. These experiments have provided information about the molecular structure and dynamic function of flight muscle in unprecedented detail. Future directions of X-ray diffraction studies on muscle are also discussed.
The constrained optimization extreme learning machine based on the hybrid loss function for regression

In training dataset, the extreme learning machines (ELMs) for data regression are sensitive to noise. This deficiency could be partially overcome by the Constrained optimization based ELM for Regression (C-ELM-R) and the low noise could be managed. However, in general, this is not the case in the real world. This paper addresses a significant note of the C-ELM-Rs poor generalization ability while dealing with the large noise. The Constrained optimization ELM for Regression based on Hybrid loss function (HC-ELM-R) is proposed in this paper with the specific end goal to deal with this problem. The 11 norm loss function and the 12 norm loss function are combined by the Hybrid loss for limiting the negative influence of large noise on the output weights estimation. The HCO-ELM-R's hybrid loss function can improve the tolerance to large noise, hence, it is less sensitive to large noise as compared to the C-ELM-R. To guarantee that the HC-ELM-R is easy to be solved, the proposed hybrid loss function is specially made smooth and differentiable. Finally, for the verification of the performance of the proposed HC-ELM-R the SinC standard testing function and the actual datasets from UCI machine learning repository were used. The results demonstrated that the robustness of the HC-ELM-R on datasets with large noise is higher as compared to the C-ELM-R and the weighted C-ELM-R.
Sliding window bag-of-visual-words for low computational power robotics scene matching

In this paper, we introduce a new method, based on a sliding window geometrical extension to Bag-of-Visual-Words (called swBOVW) intended for application to low computational power robots. Benchmarked against RANSAC as a geometric validator to BOVW, three implementations of this technique are presented to improve either the performance or the computational cost. The three implementations are: as a replacement to RANSAC as a geometric validator; as a supplement to RANSAC; and as a replacement to traditional BOVW when the number of images in the database can be reduced. Seeking to utilise some of the geometric information ignored by traditional BOVW, this technique is developed from the use of sub-regions in Spatial Pyramids, and applied to the matching of whole images. This technique is applied in the context of humanoid robotic soccer to the problem of field end symmetry, and provides geometric validation along the horizontal axis of images. When applied, the technique has been able to either halve the cases of unresolved image queries, or halve the computational cost required to achieve comparable results to the benchmark.
Mandibular anterior crowding: normal or pathological?

ABSTRACT The teeth become very close to each other when they are crowded, but their structures remain individualized and, in this situation, the role of the epithelial rests of Malassez is fundamental to release the EGF. The concept of tensegrity is fundamental to understand the responses of tissues submitted to forces in body movements, including teeth and their stability in this process. The factors of tooth position stability in the arch - or dental tensegrity - should be considered when one plans and perform an orthodontic treatment. The direct causes of the mandibular anterior crowding are decisive to decide about the correct retainer indication: Should they be applied and indicated throughout life? Should they really be permanently used for lifetime? These aspects of the mandibular anterior crowding and their implication at the orthodontic practice will be discussed here to induct reflections and insights for new researches, as well as advances in knowledge and technology on this subject.
A broadband dual-polarized antenna with coupling-resonators for base-station applications

A broadband dual-polarized antenna is proposed, which consists of two orthogonal dipoles coupled four L-shaped resonators. The proposed antenna have two resonant modes in the working band, the lower mode is come from the 1/2 dipole itself, while the upper one is attributed to coupling between two orthogonal dipoles. Moreover, the upper mode can be further controlled by coupling four L-shaped resonators to realize more broadband performance for LTE base-station applications. The antenna achieves a impedance bandwidth of 45.5% for SWR<1.5 at both ports, a isolation more than 26dB between ports, and a stable Half-Power Bandwidth (HPBW) of 65.5 °± 2.9 ° over 1.7–2.7GHz band. A good agreement is obtained between the simulation and the measurement results.
FPGA Investigation on Error-Floor Performance of a Concatenated Staircase and Hamming Code for 400G-ZR Forward Error Correction

With a record 200Gbps 50-piece-FPGA implementation, we investigate performance of a concatenated-staircase-Hamming code proposed for OIF-400GZR. FPGA emulations reveal an error flare at 10−10 BER lowering the predicted BER threshold from 1.25e-2 to 1.21e-2.
Internal model control of a paper machine headbox time-delay system with uncertain parameters in discrete-time

A paper machine headbox time-delay system is concerned in this study which is controlled by an Internal Model Control (IMC) in the discrete-time case. We note that the chosen system contain a parametric uncertainties. However, the IMC is applied to the considered system to ensure the desired performances adjacent to the complexities of the system and to illustrate the effectiveness of this approach in the face of external perturbations and parametric uncertainties. In the one hand, the Kharitinov theorem is applied as a strategy to verify stability conditions. In the other hand, the Linear Matrix Inequalities (LMI) method is applied to the headbox system to ensure the corrector's stability conditions with the presence of uncertain parameters.
Stochastic geometry based pricing for infrastructure sharing in IoT networks

In this paper, we propose a stochastic geometry based pricing for infrastructure sharing in Internet of Things (IoT) networks. We consider a game consisting of a Network Operator (NO) as the seller and an IoT Device Owner (DO) as the buyer in which the seller owns an infrastructure that can address the communication needs of the DO. Using the proposed scheme, we show that DO and NO can reach a win-win deal in which a reasonable cost is imposed to DO in exchange of providing an acceptable coverage by the NO. In particular, we show that the DO can achieve a coverage probability of interest at a lower cost compared to the case in which the proposed pricing model is absent. The proposed idea provides a transparent pricing model between NOs and DOs and paves the road for IoT applications to become more widespread.
A removing redundancy Restricted Boltzmann Machine

As one of the most important models of deep learning, Restricted Boltzmann Machine(RMB) has been extensively studied and concerned in recent years. However, because RBM model has high redundancy, it greatly affects its convergence speed and training time. In this paper, a Removing Redundancy Restricted Boltzmann Machine is proposed to improve the original RBM from the viewpoint of eliminating redundant hidden units. The structure of the Restricted Boltzmann Machine is optimized by removing redundancy, which greatly reduces the number of hidden units, improves the training efficiency of the RBM and shortens the training time and improves the convergence rate of the model. Experimental results show, the improved method can eliminate a large number of redundant hidden elements without affecting the Reconstruction Error, which is very important for optimizing the structure of the restricted Boltzmann machine and accelerating the learning speed.
ADMM for joint data and off-grid NBI recovery in OFDM systems

Joint data and off-grid narrow-band interference (NBI) recovery is investigated in orthogonal-frequency-division-multiplexing (OFDM) systems using compressive-sensing (CS) framework. The joint recovery problem is formulated as a convex optimization problem of three weighted norms. A reduced computational complexity and scalable algorithm is proposed to solve the recovery problem based on the alternating-direction-method-of-multipliers (ADMM). Simulation results, show that the average-run-time of solving the joint recovery problem using the proposed ADMM algorithm is much less than the average-run-time of the interior point method.
Process mining application for discovering student learning paths

Personalized learning has been gaining attention in the research area of online learning systems to enhance learning process efficiency. Several studies have been successfully used educational data mining to discover insight knowledge to improve personalized learning. However, educational data mining is a data-centric analysis, the visual presentation of the learning process from the start to the end is still absent as well as the learning process variants. The objective of this study is to enhance personalized learning by extracting learning process models from student groups who have different learning backgrounds. We proposed a method to analyze event logs from real-world educational data which has a high degree of dynamic and complexity. The process mining is the basis for the purposed method. The interquartile range method is applied to eliminate outliers from event logs. And heuristics miner is used for extracting the learning process models. The result of the proposed method provides visual presentations of actual learning process models and variants from different groups of students.
Energy-efficient wake-up radio protocol using optimal sensor-selection for IoT

Energy conservation in sensor nodes of an IoT framework is a challenging problem. Wake-up radio, a new technology being developed by IEEE, describes a broad solution to this challenge. In this paper, we address the issue of optimal sensor nodes selection to maximize the energy-efficiency of a WSN for IoT applications. A wake-up protocol is then applied on the optimally selected-sensor nodes. The node selection is formulated as an optimization problem with spatio-temporal correlation constraints thus minimizing redundant data transfer. Subsequently, a MLE method is used to solve the problem. Additional novelty of this work is the development of a wake-up protocol over hexagonal grids when a mobile sink is used for data aggregation leading to improved energy-efficiency. The impact of the proposed method on decision errors in IoT applications is also discussed. Extensive experiments are performed to analyse the energy-efficiency, estimation error, modelling accuracy, network lifetime of WSN in the context of IoT applications. Results indicate an improvement in energy-efficiency when compared to conventional methods.
Development of a Vietnamese speech recognition system for Viettel call center

In this paper, we first present our effort to collect a 85.8 hour corpus for Vietnamese telephone conversational speech from our Viettel call center. After that, various techniques such as time delay deep neural network (TDNN) with sequence training, data augmentation are applied to build the speech recognition system. Our final system achieves a low word error rate at 17.44% for this challenging corpus. To the best of our knowledge, it is the first attempt to build Vietnamese corpus and speech recognition system for the customer service domain.
Outlier detection algorithm based on SOM neural network for spatial series dataset

Outlier detection is an important branch of data mining which has been applied in different fields. Facing the multidimensional spatial series dataset containing both isolated and assembled outliers, many existing methods become unsatisfactory or even inapplicable. In this paper, we propose an outlier detection algorithm based on SOM (Self-Organizing Maps) neural network for the spatial series dataset. Firstly, we introduce the principle of the clustering algorithm based on SOM neural network. Secondly, the outlier detection strategy is designed according to the topological distribution of neurons. Finally, in order to verify the effectiveness and reliability of the proposed algorithm, several experiments are performed in this paper. The simulation illustrates that the proposed algorithm based on SOM neural network is very effective and reliable for spatial series dataset.
Genistein and AG1024 synergistically increase the radiosensitivity of prostate cancer cells

Radiosensitivity of prostate cancer (PCa) cells promotes the curative treatment for PCa. The present study was designed to investigate the synergistic effect of genistein and AG1024 on the radiosensitivity of PCa cells. The optimal X-irradiation dose (4 Gy) and genistein concentration (30 µM) were selected by using the CCK-8 assay. Before X-irradiation (4 Gy), PC3 and DU145 cells were treated with genistein (30 µM), AG1024 (10 µM) and their combination. All treatments significantly reduced cell proliferation and enhanced cell apoptosis. Using flow cytometric analysis, we found that genistein arrested the cell cycle at S phase and AG1024 arrested the cell cycle at G2/M phase. Genistein treatment suppressed the homologous recombination (HRR) and the non-homologous end joining (NHEJ) pathways by inhibiting the expression of Rad51 and Ku70, and AG1024 treatment only inhibited the NHEJ pathway via the inactivation of Ku70 as detected by western blot analysis. Moreover, the combination treatment with genistein and AG1024 more effectively radiosensitized PCa cells than single treatments by suppressing cell proliferation, enhancing cell apoptosis and inactivating the HRR and NHEJ pathways. In vivo experiments demonstrated that animals receiving the combination treatment with genistein and AG1024 displayed obviously decreased tumor volume compared with animals treated with single treatment with either genistein or AG1024. We conclude that the combination of genistein (30 µM) and AG1024 (10 µM) exhibited a synergistic effect on the radiosensitivity of PCa cells by suppressing the HRR and NHEJ pathways.
Improving stereo vision based SLAM by integrating inertial measurements for person indoor navigation

Precise indoor localization is of great importance. For instance the efficiency and safety of first responders gets improved significantly. Stereo SLAM algorithms achieve remarkable results, nevertheless there are limits to what a single sensor type system can achieve in case of over-or underexposed images, highly dynamic camera movements and homogeneous environments. This paper proposes a straight forward yet very efficient method of integrating Inertial Measurement Unit (IMU) data to overcome those problems. The camera movement between consecutive frames can be seen as an optimization problem. Whereas existing algorithms obtain the initial guess of the camera by assuming constant velocity, this work exploits IMU data to predict the movement. Evaluation of the proposed localization system is conducted with a person carried sensor setup on typical indoor scenarios. It is demonstrated, that the accuracy of the initial guess can be enhanced considerably. Improved initial camera poses result in faster convergence and higher success rate of finding the optimum, which results in better overall localization quality. Scenarios, which could not be handled by the stand-alone version of the stereo SLAM are now feasible thanks to IMU data integration.
Example of control vector construction for a local economic system

In spite of the processes of globalization proceeding nowadays, local structures are of the great importance and must be in the focus of scientific research. Local economic system control has to be based on the second order cybernetics concepts, and be realized in different ways (using different control types). Implementation of agent-based model also might yield significant results. This paper proposes a meaningful example of these ideas combination. In the research, there has been made an attempt to classify practical realizations of different control efforts in a local economic system. Several simulation results also are under discussion.
A real-time gesture prediction system using neural networks and multimodal fusion based on data glove

Unlike static gesture recognition, a novel real-time gesture prediction system in this study can judge the intention of hand motion and predict the exact final gesture before the end of hand movement. Flex sensors are used to measure comprehensive motion data of data glove, which are positioned based on the biological muscle distribution characteristics of the hand. Position, velocity and acceleration information are extracted from raw data of data glove, while the adjacent finger-coupling features are also obtained by processing the position and velocity information. After data processing such as windowing and filtering, accuracy and effectiveness experiments are conducted to obtain the ideal features based on multimodal fusion. A combination of neural network and multiclass support vector machine (SVM) algorithms are used as prediction model. Neural network experiments are designed in which prediction time and accuracy are used as the optimization index to select the combination structure of the prediction model.
Monolayer Transition Metal Dichalcogenides as Light Sources

Reducing the dimensions of materials is one of the key approaches to discovering novel optical phenomena. The recent emergence of 2D transition metal dichalcogenides (TMDCs) has provided a promising platform for exploring new optoelectronic device applications, with their tunable electronic properties, structural controllability, and unique spin valley–coupled systems. This progress report provides an overview of recent advances in TMDC‐based light‐emitting devices discussed from several aspects in terms of device concepts, material designs, device fabrication, and their diverse functionalities. First, the advantages of TMDCs used in light‐emitting devices and their possible functionalities are presented. Second, conventional approaches for fabricating TMDC light‐emitting devices are emphasized, followed by introducing a newly established, versatile method for generating light emission in TMDCs. Third, current growing technologies for heterostructure fabrication, in which distinct TMDCs are vertically stacked or laterally stitched, are explained as a possible means for designing high‐performance light‐emitting devices. Finally, utilizing the topological features of TMDCs, the challenges for controlling circularly polarized light emission and its device applications are discussed from both theoretical and experimental points of view.