Miracle-dz/UniFAR-FTUs huggingface dataset:
Periodic motion planning and control for double rotary pendulum via virtual holonomic constraints

Periodic motion planning for an under-actuated system is rather difficult due to differential dynamic constraints imposed by passive dynamics, and it becomes more difficult for a system with higher underactuation degree, that is with a higher difference between the number of degrees of freedom and the number of independent control inputs. However, from another point of view, these constraints also mean some relation between state variables and could be used in the motion planning. We consider a double rotary pendulum, which has an underactuation degree 2. A novel periodic motion planning is presented based on an optimization search. A necessary condition for existence of the whole periodic trajectory is given because of the higher underactuation degree of the system. Moreover this condition is given to make virtual holonomic constraint ( VHC ) based control design feasible. Therefore, an initial guess for the optimization of planning a feasible periodic motion is based on this necessary condition. Then, VHCs are used for the system transformation and transverse linearization is used to design a static state feedback controller with periodic matrix function gain. The controller gain is found through another optimization procedure. The effectiveness of initial guess and performance of the closed-loop system are illustrated through numerical simulations.
Embedding Temporally Consistent Depth Recovery for Real-time Dense Mapping in Visual-inertial Odometry

Dense mapping is always the desire of simultaneous localization and mapping (SLAM), especially for the applications that require fast and dense scene information. Visual-inertial odometry (VIO) is a light-weight and effective solution to fast self-localization. However, VIO-based SLAM systems have difficulty in providing dense mapping results due to the spatial sparsity and temporal instability of the VIO depth estimations. Although there have been great efforts on real-time mapping and depth recovery from sparse measurements, the existing solutions for VIO-based SLAM still fail to preserve sufficient geometry details in their results. In this paper, we propose to embed depth recovery into VIO-based SLAM for real-time dense mapping. In the proposed method, we present a subspace-based stabilization scheme to maintain the temporal consistency and design a hierarchical pipeline for edge-preserving depth interpolation to reduce the computational burden. Numerous experiments demonstrate that our method can achieve an accuracy improvement of up to 49.1 cm compared to state-of-the-art learning-based methods for depth recovery and reconstruct sufficient geometric details in dense mapping when only 0.07% depth samples are available. Since a simple CPU implementation of our method already runs at 10–20 fps, we believe our method is very favorable for practical SLAM systems with critical computational requirements.
A High-Resolution 220-GHz Ultra-Wideband Fully Integrated ISAR Imaging System

In this paper, an ultra-wideband fully integrated imaging radar at sub-terahertz (sub-THz) frequencies is presented, which demonstrates a fine lateral resolution without using any focal lens/mirror. We have achieved a lateral resolution of 2 mm for an object at 23-cm distance as well as a range resolution of 2.7 mm. To achieve the decent range resolution, in a frequency modulation continuous wave radar configuration, a state-of-the-art chirp bandwidth (BW) of 62.4 GHz at a center frequency of 221.1 GHz is generated and efficiently radiated. We have presented a design technique for the optimal design of the passive embedding around the core transistor to maximize the tuning BW of the voltage controlled oscillator. At the receiver side, to maximize the intermediate frequency level, a subharmonic mixer is utilized, which is designed for the lowest conversion loss. Finally, to obtain the fine lateral resolution, we have implemented near-field beamforming algorithm based on the inverse synthetic aperture radar (ISAR) systems. The synthesized beamwidth is less than 0.5°; hence, high-resolution images are reconstructed. The system is fabricated in a 55-nm BiCMOS process. To the best of our knowledge, this is the first imaging radar at THz/sub-THz frequencies, which utilizes ISAR to achieve a high lateral resolution while the radar system is fully integrated.
A Novel Relative Camera Motion Estimation Algorithm with Applications to Visual Odometry

In this paper, we propose a novel method to estimate the relative camera motions of three consecutive images. Given a set of point correspondences in three views, the proposed method determines the fundamental matrix representing the geometrical relationship between the first two views by using the eight-point algorithm. Then, by minimizing the proposed cost function with the fundamental matrix, the relative camera motions over three views are precisely estimated. The experimental results show that the proposed method outperforms the conventional two-view and three-view geometry-based method in terms of the accuracy.
Emotional Bodily Expressions for Culturally Competent Robots through Long Term Human-Robot Interaction

Generating emotional bodily expressions for culturally competent robots has been gaining increased attention to enhance the engagement and empathy between robots and humans in a multi-culture society. In this paper, we propose an incremental learning model for selecting the user's representative or habitual emotional behaviors which place emphasis on individual users' cultural traits identified through long term interaction. Furthermore, a transformation model is proposed to convert the obtained emotional behaviors into a specific robot's motion space. To validate the proposed approach, the models were evaluated by two example scenarios of interaction. The experimental results confirmed that the proposed approach endows a social robot with the capability to learn emotional behaviors from individual users, and to generate its emotional bodily expressions. It was also verified that the imitated robot motions are rated emotionally acceptable by the demonstrator and recognizable by the subjects from the same cultural background with the demonstrator.
A Hybrid Expert System for Selection and Maintenance of Robotic Milling Conditions

A concept of a hybrid expert system for robot milling conditions control presented. Based on KUKA KR300 operating data. Milling conditions, measurements data, negative factors involved are considered. Expert estimations of actual milling episodes are used as criterion of successful milling. All measurable milling quality parameters are examined as possible feedback for milling conditions control. The choice is made in favor of using only real-time parameters available. Development of hybrid expert system includes method for expert estimations formalizing, system structure design, signal processing. An example of data processing and distribution within expert system given to further application in robotic milling control. The introduction of a hybrid expert control system into the robotic milling make it possible to implement two important functions for the manufacturing system. First, accumulate knowledge about the manufacturing process in form of measured data, simple integral and qualitative expert estimations – all in a solid and compact data format. Secondly, generate control signals for selection and maintenance of milling conditions using a rule's logic output system.
Multi-timescale Feature-extraction Architecture of Deep Neural Networks for Acoustic Model Training from Raw Speech Signal

This paper describes a new architecture of deep neural networks (DNNs) for acoustic models. Training DNNs from raw speech signals will provide 1) novel features of signals, 2) normalization-free processing such as utterance-wise mean subtraction, and 3) low-latency speech recognition for robot audition. Exploiting the longer context of raw speech signals seems useful in improving recognition accuracy. However, naive use of longer contexts results in the loss of short-term patterns; thus, recognition accuracy degrades. We propose a multi-timescale feature-extraction architecture of DNNs with blocks of different time scales, which enable capturing long- and short-term patterns of speech signals. Each block consists of complex-valued networks that correspond to Fourier and filterbank transformations for analysis. Experiments showed that the proposed multi-timescale architecture reduced the word error rate by about 3% compared with those only with the longterm context. Analysis of the extracted features revealed that our architecture efficiently captured the slow and fast changes of speech features.
Metabolic Syndrome and Development of Diabetes Mellitus: Predictive Modeling Based on Machine Learning Techniques

The objective of this inductive research was to investigate: 1) the relationship between diabetes mellitus and individual risk factors of metabolic syndrome (MetS), in a non-conservative setting; 2) the prediction of future onset of diabetes using relevant risk factors of MetS; and 3) to investigate the relative performance of machine learning methods when data sampling techniques are used to generate balanced training sets. The dataset used in this research contains 667 907 records for a period ranging from 2003 to 2013. Quantifying the contribution of individual risk factors of MetS in the development of diabetes in a non-conservative setting logistic regression analysis was performed. Our analyses contradict the view that diabetes is commonly associated with low levels of high-density lipoprotein (HDL). Instead, our results demonstrate that the increased levels of HDL are positively correlated with diabetes onset, particularly in women. We also proposed J48 decision tree and Naïve Bayes methods for prediction of future onset of diabetes using relevant risk factors obtained from logistic regression analysis, over balanced and unbalanced datasets. The results demonstrated the supremacy of Naïve Bayes with K-medoids under-sampling technique as compared to random under-sampling, oversampling, and no sampling. It is achieved on average 79% receiver operating characteristic performance with the increased true positive rate. The results of this paper suggest further research to clarify the pathophysiological significance of HDL and pathways in the development of diabetes.
HTTP/2-Based Streaming Solutions for Tiled Omnidirectional Videos

360° video streaming is coming up against two major technical challenges: network resource consumption and Quality of Experience (QoE). Dynamically adapting the content delivery process to the user behavior is a promising approach to ensure both important network resource savings and satisfying experiences. In this paper, we propose to leverage HTTP Adaptive Streaming (HAS), tiled-based 360° video encoding and the HTTP/2 protocol to implement this dynamic content delivery process. The 360° video stream is spatially encoded into tiles and temporally divided into segments. The client executes two viewport predictions for each segment, one before and one during its delivery. Upon every prediction, it decides on a priority and a quality level for each tile of the video segment; tiles overlapping with the predicted viewport get higher priorities and quality levels. Then it exploits the priority and stream termination features of the HTTP/2 protocol to enforce its decisions. We compare our proposed solution with four alternative schemes on a set of 360° video streaming sessions corresponding to various types of videos, user behaviors and network conditions. Our solution provides better performances: a higher quality on the viewport pixels, a lower ratio of unreceived viewport pixels in bandwidth-constrained networks, and a reduction of the bandwidth consumption, up to 12% compared to the alternative schemes exploiting 2 viewport predictions per video segment.
Vehicle and Pedestrian Recognition Using Multilayer Lidar based on Support Vector Machine

Moving-object tracking (estimating position and velocity of moving objects) is a key technology for autonomous driving systems and driving assistance systems in mobile robotics and vehicle automation domains. To predict and avoid collisions, the tracking system has to recognize objects as accurately as possible. This paper presents a method for recognizing vehicles (cars and bicyclists) and pedestrians using multilayer lidar (3D lidar). Lidar data are clustered, and eight-dimensional features are extracted from each of clustered lidar data, such as distance from the lidar, velocity, object size, number of lidar-measurement points, and distribution of reflection intensities. A multiclass support vector machine is applied to classify cars, bicyclists, and pedestrians from these features. Experiments using “The Stanford Track Collection” data set allow us to compare the proposed method with a method based on the random forest algorithm and a conventional 26-dimensional feature-based method. The comparison shows that the proposed method improves recognition accuracy and processing time over the other methods. Therefore, the proposed method can work well under low computational environments.
Optimal Implementation of Simulink Models on Multicore Architectures with Partitioned Fixed Priority Scheduling

Model-based design using the Simulink modeling formalism and associated toolchain has gained popularity in the development of real-time embedded systems. However, the current research on software synthesis for Simulink models has a critical gap for providing a deterministic, semantics-preserving implementation on multicore architectures with partitioned fixed-priority scheduling. In this paper, we consider a semantics-preservation mechanism that combines (1) the RT blocks from Simulink, and (2) task offset assignment to separate the time windows to access shared buffers by communicating tasks. We study the software synthesis problem that optimizes control performance by judiciously assigning task offsets, task priorities, and task communication mechanisms. We develop a problem-specific exact algorithm that uses an abstraction layer to hide the complexity of timing analysis. Experimental results show that it may run a few orders of magnitude faster than a direct formulation in integer linear programming.
Severe Eosinophilia in Myelodysplastic Syndrome With a Defined and Rare Cytogenetic Abnormality

Myelodysplastic syndromes (MDS) are a heterogeneous group clonal disorders of hematopoietic stem cells (HSC) characterized by ineffective hematopoiesis that lead to variable grades of impaired blood cell production. Chromosomal aberrations are often detected in MDS patients and thus cytogenetic analysis is useful for the diagnosis of these disorders. Common recurring chromosomal defects, such as the −5/5q- and −7/7q- are relatively well characterized cytogenetic abnormalities in MDS, however, the biological significance of uncommon cytogenetic alterations is unknown. We report here, two cases of peripheral blood and bone marrow hypereosinophilia in patients with MDS harboring the unbalanced translocation der(1;7)(q10;p10), a poorly characterized cytogenetic abnormality that is found in certain myeloid malignancies, including MDS. The patients reported here presented hypereosinophilia that was refractory to steroids and cytotoxic therapy, leading to severe target tissue damage that ultimately resulted in fatal end-organ failure. Potential roles of the der(1;7)(q10;p10) aberrations in the pathogenesis of aggressive eosinophilia and disease prognosis are discussed here.
EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies

A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.
Zolpidem Activation of Alpha 1-Containing GABAA Receptors Selectively Inhibits High Frequency Action Potential Firing of Cortical Neurons

Introduction: High frequency neuronal activity in the cerebral cortex can be induced by noxious stimulation during surgery, brain injury or poisoning. In this scenario, it is essential to block cortical hyperactivity to protect the brain against damage, e.g., by using drugs that act as positive allosteric modulators at GABAA receptors. Yet, cortical neurons express multiple, functionally distinct GABAA receptor subtypes. Currently there is a lack of knowledge which GABAA receptor subtypes would be a good pharmacological target to reduce extensive cortical activity. Methods: Spontaneous action potential activity was monitored by performing extracellular recordings from organotypic neocortical slice cultures of wild type and GABAAR-α1(H101R) mutant mice. Phases of high neuronal activity were characterized using peri-event time histograms. Drug effects on within-up state firing rates were quantified via Hedges’ g. Results: We quantified the effects of zolpidem, a positive modulator of GABAA receptors harboring α1-subunits, and the experimental benzodiazepine SH-053-2′F-S-CH3, which preferably acts at α2/3/5- but spares α1-subunits. Both agents decreased spontaneous action potential activity but altered the firing patterns in different ways. Zolpidem reduced action potential firing during highly active network states. This action was abolished by flumazenil, suggesting that it was mediated by benzodiazepine-sensitive GABAA receptors. SH-053-2′F-S-CH3 also attenuated neuronal activity, but unlike zolpidem, failed to reduce high frequency firing. To confirm that zolpidem actions were indeed mediated via α1-dependent actions, it was evaluated in slices from wild type and α(H101R) knock-in mice. Inhibition of high frequency action potential firing was observed in slices from wild type but not mutant mice. Conclusion: Our results suggest that during episodes of scarce and high neuronal activity action potential firing of cortical neurons is controlled by different GABAA receptor subtypes. Exaggerated firing of cortical neurons is reduced by positive modulation of α1-, but not α2/3/5-subunit containing GABAA receptors.
Functional Characterization of a Magnesium Transporter of Root Endophytic Fungus Piriformospora indica

Magnesium (Mg) is a crucial macronutrient required for the regular growth of plants. Here we report the identification, isolation and functional characterization of Mg-transporter PiMgT1 in root endophytic fungus Piriformospora indica. We also report the role of P. indica in the improvement of the Mg nutrition of the plant particularly under Mg deficiency condition. Protein BLAST (BLASTp) for conserved domains analysis showed that PiMgT1 belong to CorA like protein family of bacteria. We have also observed the presence of conserved ‘GMN’ signature sequence which suggests that PiMgT1 belongs to Mg transporter family. Phylogenetic analysis revealed that PiMgT1 clustered among fungal CorA family members nearer to basidiomycetes. Functionality of PiMgT1 was confirmed by complementation of a yeast magnesium transporter mutant CM66. We have observed that PiMgT1 restored the growth of mutant and showed comparable growth with that of WT. We found statistically significant (p < 0.05) two fold increase in the total intracellular Mg content of mutant complemented with PiMgT1 as compared to the mutant. These observations suggest that PiMgT1 is actively involved in Mg uptake by the fungus and may be helping in the nutritional status of the host plant.
Metabolic Profiling of Pleurotus tuoliensis During Mycelium Physiological Maturation and Exploration on a Potential Indicator of Mycelial Maturation

Pleurotus tuoliensis is a valuable and rare edible fungus with extremely high nutritional and medicinal value. However, the relative immaturity of P. tuoliensis cultivation technology leads to fluctuating yields and quality. The main difficulty in P. tuoliensis cultivation is estimate of mycelial maturity. There is currently no measurable indicator that clearly characterizes the physiological maturation of mycelia. The aim of this study was to identify potential indicators of physiological maturation for P. tuoliensis mycelia by using metabolomics approach. A metabolite profiling strategy involving gas chromatography-mass spectrometry (GC/MS) was used to analyze changes to extracellular metabolites in mycelia collected at mycelium physiological maturation period (MPMP) day 0, MPMP day 35 at 17°C and MPMP day 35 at 29°C. 72 differential metabolites (37.8% up-regulated and 62.2% down-regulated) were identified based on the selected criteria [variable important in projection (VIP) greater than 1.0 and p < 0.01]. Metabolic pathways enrichment analysis showed that these metabolites are involved in glycolysis, organic acid metabolism, amino acid metabolism, tricarboxylic acid cycle (TCA), sugar metabolism, nicotinate and nicotinamide metabolism, and oxidative phosphorylation. In addition, the pyrimidine synthesis pathway was significantly activated during mycelium physiological maturation of P. tuoliensis. The abundance of N-carbamoyl-L-aspartate (CA-asp), a component of this pathway, was significantly increased at MPMP day 35, which motivated us to explore its potential as an indicator for physiological maturation of mycelia. The content of CA-asp in mycelia changed in a consistent manner during physiological maturation. The feasibility of using CA-asp as an indicator for mycelial maturation requires further investigation.