Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Analysis of Muscle Coordination at Different Force Levels during Grip and Pinch with Multiplex Recurrence Network*

Muscle synergistic contraction to produce force has been recognized as an important neurophysiological mechanism in neuromuscular system. Despite a range of approaches, such as nonnegative matrix factorization or principal component analysis that have been widely used, limitations still exist in analysis of dynamic coordination of multiple muscles. In addition, it is still less studied about the potential difference of muscle dynamic coordination at different force levels during grip and pinch within the same framework. With this aim, this study analyzed the dynamic coordination of multiple upper-limb muscles at low, medium and high force levels during pinch and grip with multiplex recurrence network (MRN). Twenty-four healthy subjects participated in the experiment. Subjects were instructed to grip an apparatus to match the target force as stably as they could for 10 s. Surface electromyographic (sEMG) signals were recorded from 8 upper-limb muscles and analyzed by the MRN. The interlayer mutual information (I) and the average edge overlap (ω) of MRNs were calculated to quantify muscle correlation and muscle synchronization, respectively. Results showed that I and ω values of extrinsic muscles’ MRNs during grip were significantly higher than that during grip at medium and high force. Furthermore, the I and ω values of extrinsic muscle networks during grip increased with augmented force levels. No significant changes were found for the intrinsic muscles with force output levels. These findings indicate that the muscles coordination patterns between grip and pinch were different and higher co-contraction of extrinsic muscles is favorable to synergistic force production. With the force output increased, muscles’ coordination was augmented in extrinsic muscles, but no change in intrinsic muscles because of independent and complicated control of fingers. This study provides an analytical tool for dynamic muscles coordination and provides insights into the mechanisms of synergistic control of muscle contractions for force production.Clinical Relevance—This study provides a novel analytical tool for muscle coordination during force production, which may facilitate the evaluation of neuromuscular function or serve as indicators for neuromuscular disorders.
Light and scanning electron microscopic studies on subcommissural organ of the pig

The subcommissural organ (SCO) of pig located on the ventral aspect of posterior commissure was constituted by an ependyma and a hypendyma. The pseudostratified columnar ciliated ependyma of SCO comprised 8–11 rows of round, oval to elongated type-I and type-II nuclei. Less basophilic type-I and darkly stained type-II nuclei were categorised on the basis of distribution of nuclear chromatin and staining affinity. The hypendyma contained hypendymal cells, micro- and macro-glial cells, blood capillaries and nerve fibres. The neurosecretory substance in the form of small, pink structures was mainly localised towards the supranuclear portion of the ependyma, free ventricular surface and lumen of blood capillaries. Scanning electron microscopy revealed ciliated and non-ciliated apical spherical protrusions along with tufts of cilia. A few apical spherical protrusions possessed small microvilli like structure.
Identity Recognition by Walking Outdoors Using Multimodal Sensor Insoles

Recently, gait attracts attention as a practical biometric for devices that naturally possess walking pattern sensing. In the present study, we explored the feasibility of using a multimodal smart insole for identity recognition. We used sensor insoles designed and implemented by us to collect kinetic and kinematic data from 59 participants that walked outdoors. Then, we evaluated the performance of four neural network architectures, which are a baseline convolutional neural network (CNN), a CNN with a multi-stage feature extractor, a CNN with an extreme learning machine classifier using sensor-level fusion and CNN with extreme learning machine classifier using feature-level fusion. The networks were trained with segmented insole data using 0%, 50%, and 70% segmentation overlap, respectively. For 70% segmentation overlap and both-side data, we obtained mean accuracies of 72.8% ±0.038, 80.9% ±0.036, 80.1% ±0.021 and 93.3% ±0.009, for the four networks, respectively. The results suggest that multimodal sensor-enabled footwear could serve biometric purposes in the next generation of body sensor networks.
A simplified method for online extraction of skin conductance features: A pilot study on an immersive virtual-reality-based motor task

There is a growing body of literature that recognizes the importance of Skin Conductance (SC) for assessing changes in emotional states, such as engagement to learning tasks, and its importance to estimate possible drawbacks affecting overall performance. To date, most of the commonly used methods for SC signal analysis, i.e. detecting its phasic and tonic components and thus extracting informative features, are either too simple and unreliable or too complex and thus inaccessible and inflexible, as well as unable to perform online analyses. The current work proposes a simplified but clear and effective algorithm based on a Machine State to search for expected behaviors in the well-defined morphology of the signal. Eleven (11) features were correctly extracted from 79 healthy subjects during an experimental setup for immersive virtual rehabilitation (balance study case). The method was also successfully applied as a tool to identify significant changes in the subjective psychophysiological response to different experimental conditions. These results point toward a potential role in virtual rehabilitation applications by getting real-time feedback in human-in-the-loop approaches.
An Attention-Based Deep Learning Method for Schizophrenia Patients Classification Using DNA Methylation Data*

In this paper, the classification problem of schizophrenia patients from healthy controls is considered, whose goal is to explore the relationship between DNA characteristics and schizophrenia. However, the DNA methylation data has the properties of small samples in high dimension and non-Gaussian distribution which makes it hard to do classification with DNA methylation data. Hence a classification method based on deep learning is designed. We propose a feature selection method based on attention mechanism which embeds a weight gated layer in the network structure to get a task-related sparse representation of the DNA methylation data. The performance of proposed method outperforms existing feature selection methods. On a real-world data set, the classification with proposed method achieves a high accuracy.
Surgical tool segmentation and localization using spatio-temporal deep network

Laparoscopic cholecystectomy surgery is a minimally invasive surgery to remove the gallbladder, where surgical instruments are inserted through small incisions in the abdomen with the help of a laparoscope. Identification of tool presence and precise segmentation of tools from the video is very important in understanding the quality of the surgery and training budding surgeons. Precise segmentation of tools is required to track the tools during real-time surgeries. In this paper, a new pixel-wise instance segmentation algorithm is proposed, which segments and localizes the surgical tool using spatio-temporal deep network. The performance of the proposed has been compared with the state-of-the-art image-based instance segmentation method using the Cholec80 dataset. It is also compared with methods in the literature using frame-level presence detection and spatial detection with good results.
Automated identification of metamorphic test scenarios for an ocean-modeling application

Metamorphic testing seeks to validate software in the absence of test oracles. Our application domain is ocean modeling, where test oracles often do not exist, but where symmetries of the simulated physical systems are known. In this short paper we present work in progress for automated generation of metamorphic test scenarios using machine learning.Metamorphic testing may be expressed as f(g(X))=h(f(X)) with f being the application under test, with input data X, and with the metamorphic relation (g, h). Automatically generated metamorphic relations can be used for constructing regression tests, and for comparing different versions of the same software application.Here, we restrict to h being the identity map. Then, the task of constructing tests means finding different g which we tackle using machine learning algorithms. These algorithms typically minimize a cost function. As one possible g is already known to be the identity map, for finding a second possible g, we construct the cost function to minimize for g being a metamorphic relation and to penalize for g being the identity map. After identifying the first metamorphic relation, the procedure is repeated with a cost function rewarding g that are orthogonal to previously found metamorphic relations.For experimental evaluation, two implementations of an oceanmodeling application will be subjected to the proposed method with the objective of presenting the use of metamorphic relations to test the implementations of the applications.
Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading

Using medical images recorded in clinical practice has the potential to be a game-changer in the application of machine learning for medical decision support. Thousands of medical images are produced in daily clinical activity. The diagnosis of medical doctors on these images represents a source of knowledge to train machine learning algorithms for scientific research or computer-aided diagnosis. However, the requirement of manual data annotations and the heterogeneity of images and annotations make it difficult to develop algorithms that are effective on images from different centers or sources (scanner manufacturers, protocols, etc.). The objective of this article is to explore the opportunities and the limits of highly heterogeneous biomedical data, since many medical data sets are small and entail a challenge for machine learning techniques. Particularly, we focus on a small data set targeting meningioma grading. Meningioma grading is crucial for patient treatment and prognosis. It is normally performed by histological examination but recent articles showed that it is possible to do it also on magnetic resonance images (MRI), so non-invasive. Our data set consists of 174 T1-weighted MRI images of patients with meningioma, divided into 126 benign and 48 atypical/anaplastic cases, acquired using 26 different MRI scanners and 125 acquisition protocols, which shows the enormous variability in the data set. The performed preprocessing steps include tumor segmentation, spatial image normalization and data augmentation based on color and affine transformations. The preprocessed cases are passed to a carefully trained 2-D convolutional neural network. Accuracy above 74% was obtained, with the high-grade tumor recall above 74%. The results are encouraging considering the limited size and high heterogeneity of the data set. The proposed methodology can be useful for other problems involving classification of small and highly heterogeneous data sets.
PA-GAN: A Patch-Attention Based Aggregation Network for Face Recognition in Surveillance

Face recognition in unconstraint surveillance is a complicated problem on account of motion blur, expression variations and low resolution. Recent works have demonstrated that patch-attention is strictly more powerful than convolution in recognition models. In this study, we investigate the task of unconstraint surveillance face recognition. First, a Patch-Attention Generative Adversarial Network (PA-GAN) model is devised to aggregate some robust features on behalf of a set of raw surveillance frames, which not only increases the recognition accuracy but also reduces the computational costs of face matching. Second, an improved center loss function combined with abundant unlabeled surveillance faces is utilized to accurately classify the known identities. With the proposed method, the discriminativeness of the face representations is largely enhanced. Finally, the proposed method is verified in two widely used datasets, IJB-A dataset and QMUL-SurvFace dataset to demonstrate the effectiveness. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed design can achieve competitive accuracy on both the verification and identification protocols.
Effect of Anion Composition on the Bias Stress Stability in Zn-O-N Thin-Film Transistors

In this letter, the effects of negative and positive bias stability with respect to the anion composition of Zn–O–N thin-film transistors were researched. In the positive bias stress tests, the threshold voltage shift was high for ZnON devices with high nitrogen composition. In contrast to this, the negative bias stress test results have smaller <inline-formula> <tex-math notation="LaTeX">$\Delta ~\text{V}_{th}$ </tex-math></inline-formula> values when nitrogen composition in ZnON was low. This different degradation behavior of ZnON for thin-film transistors was analyzed by using the subgap density of states which were obtained from monochromatic photonic capacitance-voltage measurement.
Fuzzy-Import Hashing: A Malware Analysis Approach

Malware has remained a consistent threat since its emergence, growing into a plethora of types and in large numbers. In recent years, numerous new malware variants have enabled the identification of new attack surfaces and vectors, and have become a major challenge to security experts, driving the enhancement and development of new malware analysis techniques to contain the contagion. One of the preliminary steps of malware analysis is to remove the abundance of counterfeit malware samples from the large collection of suspicious samples. This process assists in the management of man and machine resources effectively in the analysis of both unknown and likely malware samples. Hashing techniques are one of the fastest and efficient techniques for performing this preliminary analysis such as fuzzy hashing and import hashing. However, both hashing methods have their limitations and they may not be effective on their own, instead the combination of two distinctive methods may assist in improving the detection accuracy and overall performance of the analysis. This paper proposes a Fuzzy-Import hashing technique which is the combination of fuzzy hashing and import hashing to improve the detection accuracy and overall performance of malware analysis. This proposed Fuzzy-Import hashing offers several benefits which are demonstrated through the experimentation performed on the collected malware samples and compared against stand-alone techniques of fuzzy hashing and import hashing.
Comparison of Three Normalization Methods in Monitoring Analgesic Depth with Photoplethysmographic Diastolic Interval

The purpose of the present study was to investigate the ability of three different normalization methods, namely root mean square (RMS) value, mean value, and maximum which referred to pulse beat interval (PBI), based on photoplethysmographic diastolic interval (DI) in response to laryngeal mask airway (LMA) insertion under various remifentanil concentrations during general anesthesia. Sixty patients were randomly allocated to one of the four groups to receive a possible remifentanil effect-compartment target concentration (Ceremi) of 0, 1, 3, or 5 ng/ml, and an effect-compartment target controlled infusion of propofol to maintain the state entropy (SE) at 40~60. Three normalized measures DIRMS, DIMean, and DIPBI were compared with the DI values without normalization. Before LMA insertion, only DI showed a considerable correlation with remifentanil concentrations. DIRMS and DIMean performed better than DI in discriminating ‘insufficient’ concentrations (0 and 1 ng/ml) from ‘sufficient’ concentrations (3 and 5 ng/ml). DIRMS was superior to all other variables in grading analgesic depth after nociceptive event occurred with PK value of 0.836. These results demonstrate that the normalization using RMS value, compared to using mean value and maximum, seems to provide a more effective approach for signal pre-processing.
Data-Driven Representations for Testing Independence: A Connection with Mutual Information Estimation

From the design of a data-driven partition, this paper addresses the problem of testing independence between two multidimensional random variables from i.i. d. samples. The empirical log-likelihood statistics is adopted with the objective of approximating the sufficient statistics of a test against independence that knows the two distributions (the oracle test). It is shown that approximating the sufficient statistics of the oracle test (asymptotically) offers a connection with the problem of estimating mutual information. Applying these ideas in the context of a data-dependent tree-structured partition (TSP), we derive concrete sufficient conditions on the parameters of the TSP scheme to obtain a strongly consistent test of independence distribution-free over the family of joint probabilities equipped with densities.
Proof-Carrying Approximate Circuits

Approximate circuits (AxCs) tradeoff computational accuracy against improvements in hardware area, delay, or energy consumption. IP core vendors who wish to create such circuits need to convince consumers of the resulting approximation quality. As a solution, we propose proof-carrying AxCs. The vendor creates an approximate IP core together with a certificate that proves the approximation quality. The proof certificate is bundled with the approximate IP core and sent off to the consumer. The consumer can formally verify the approximation quality of the IP core at a fraction of the typical computational cost for formal verification. In this brief, we first make the case for proof-carrying AxCs and then demonstrate the feasibility of the approach by a set of synthesis experiments using an exemplary approximation framework.
An Efficient Method Based on Region-adjacent Embedding for Text Classification of Chinese Electronic Medical Records

In the field of natural language processing (NLP), word-embedding-based models have been widely applied in many tasks with great success, which are believed to make significant promotion to the development of text classification. We propose the region-adjacent embedding (RAE) to construct an effective model in this paper. RAE makes use of the context weight unit (CWU) combining adjacent words from different region to capture shalow-level context information and adds a self-attention unit (SAU) to learn deep-level semantic understandings. Our RAE model has two characteristics. First, RAE utilizes a lightweight network to regionalize the embeddings. Second, we pay attention to regionalization of embeddings without neglecting the connection with local embeddings. Based on this, we can connect the proposed RAE model acting as a bridge to the traditional word embeddings and downstream neural networks which are capable of deeper feature extraction. In this paper, we introduce RAE to the classification task on Chinese electronic medical records. The experiments show that structures with our method perform better than the plain structures themselves.
An Approach for Asbestos-related Pleural Plaque Detection

Asbestos is a toxic ore widely used in construction and commercial products. Asbestos tends to dissolve into fibers and after years inhaling them, these fibers calcify and form plaques on the pleura. Despite being benign, pleural plaques may indicate an immunologic deficiency or dysfunctional lung areas. We propose a pipeline for asbestos-related pleural plaque detection in CT images of the human thorax based on the following operations: lung segmentation, 3D patch selection along the pleura, a convolutional neural network (CNN) for feature extraction, and classification by support vector machines (SVM). Due to the scarcity of publicly available and annotated datasets of pleural plaques, the proposed CNN relies on architecture learning with random weights obtained by a PCA-based approach instead of using traditional filter learning by backpropagation. Experiments show that the proposed CNN can outperform its counterparts based on backpropagation for small training sets.
Predicting the Performance of Tunnel Boring Machines using Big Operational Data

Accurate prediction of the performance of tunnel boring machines (TBMs) is important to safe and efficient tunneling. Traditional prediction method of TBM performance is restricted by the lack of sufficient rock mass data and the prediction accuracy is low. A comprehensive management and preprocessing framework of TBM big operational data is proposed in this paper, after which a new prediction method of TBM performance is proposed. The effectiveness of the proposed method is tested on 4 different tunnels. Prediction results show that the big operational data based method can predict TBM performance with high accuracy, and in most cases, the random forest algorithm generates better prediction results than the long-short term memory (LSTM) neural network. The data imbalance phenomenon leads to the emergence of big prediction errors on certain operational segments, highlighting the necessity of adopting new sampling methods to create a more balanced dataset, and incremental learning methods to update the prediction model timely.
Safety Assessment of a Wearable Low-Intensity Pulsed Ultrasound Device for Relieving Mental Illness Symptoms

Depression is expected to be one of the significant global medical burdens. Ultrasound therapy, with much-encouraging evidence, has been demonstrated to have a beneficial effect on alleviating mental illness symptoms (neuropsychiatric conditions). However, the study of estimating the risk of using transcranial therapeutic ultrasound has barely been investigated. In this experiment, we develop a wearable head-mounted LIPUS device and assessed the possible tissue damage when applying the LIPUS stimulation on the brain. Our computer simulation and in-vitro experiment results demonstrate that the low-intensity ultrasound (LIPUS) device can safely deliver small doses of low-intensity pulsed ultrasound through the skull into the brain without significant thermal injury. The preliminary results indicate that this modality has the potential for the transcranial treatment of neurological diseases in safety.
Autonomous Driving of Truck-Trailer Mobile Robots with Linear-Fuzzy Control for Trajectory Following

The modeling and autonomous control of truck-trailer mobile robots for trajectory following are addressed. The robot kinematical model is analyzed and used for designing a positioning control system based on linear controllers integrated in a fuzzy-logic approach. The design takes into account both positioning performance and jack-knife avoidance. The results of robot positioning control are extended to trajectory following for which a novel strategy is proposed applicable to general shape desired trajectories. The effectiveness of the proposed methods are verified for linear, circular and sinusoidal trajectories where the mobile robot converges to the desired trajectories, avoiding jack-knife positions and with bounded values of input steering angle.