Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Endoscopic Visual Measurements

Robotic endoscopic systems offer a minimally invasive approach to the examination of internal body structures, and their application is rapidly extending to cover the increasing needs for accurate therapeutic interventions. In this context, it is essential for such systems to be able to perform measurements, such as measuring the distance traveled by a wireless capsule endoscope, so as to determine the location of a lesion in the gastrointestinal tract, or to measure the size of lesions for diagnostic purposes. In this paper, we investigate the feasibility of performing contactless measurements using a computer vision approach based on neural networks. The proposed system integrates a deep convolutional image registration approach and a multilayer feed-forward neural network into a novel architecture. The main advantage of this system, with respect to the state-of-the-art ones, is that it is more generic in the sense that it is 1) unconstrained by specific models, 2) more robust to nonrigid deformations, and 3) adaptable to most of the endoscopic systems and environment, while enabling measurements of enhanced accuracy. The performance of this system is evaluated under ex vivo conditions using a phantom experimental model and a robotically assisted test bench. The results obtained promise a wider applicability and impact in endoscopy in the era of big data.
Heart Sound Segmentation—An Event Detection Approach Using Deep Recurrent Neural Networks

<italic>Objective:</italic> In this paper, we accurately detect the state-sequence <italic>first heart sound (S1)–systole–second heart sound (S2)–diastole</italic>, i.e., the positions of S1 and S2, in heart sound recordings. We propose an event detection approach without explicitly incorporating <italic>a priori</italic> information of the state duration. This renders it also applicable to recordings with cardiac arrhythmia and extendable to the detection of extra heart sounds (third and fourth heart sound), heart murmurs, as well as other acoustic events. <italic>Methods:</italic> We use data from the 2016 PhysioNet/CinC Challenge, containing heart sound recordings and annotations of the heart sound states. From the recordings, we extract spectral and envelope features and investigate the performance of different deep recurrent neural network (DRNN) architectures to detect the state sequence. We use virtual adversarial training, dropout, and data augmentation for regularization. <italic>Results:</italic> We compare our results with the state-of-the-art method and achieve an average score for the four events of the state sequence of <inline-formula><tex-math notation="LaTeX">${\bf F}_{1}\approx 96$</tex-math></inline-formula>% on an independent test set. <italic>Conclusion:</italic> Our approach shows state-of-the-art performance carefully evaluated on the 2016 PhysioNet/CinC Challenge dataset. <italic>Significance:</italic> In this work, we introduce a new methodology for the segmentation of heart sounds, suggesting an event detection approach with DRNNs using spectral or envelope features.
Unobtrusive Activity Recognition of Elderly People Living Alone Using Anonymous Binary Sensors and DCNN

Elderly population (over the age of 60) is predicted to be 1.2 billion by 2025. Most of the elderly people would like to stay alone in their own house due to the high eldercare cost and privacy invasion. Unobtrusive activity recognition is the most preferred solution for monitoring daily activities of the elderly people living alone rather than the camera and wearable devices based systems. Thus, we propose an unobtrusive activity recognition classifier using deep convolutional neural network (DCNN) and anonymous binary sensors that are passive infrared motion sensors and door sensors. We employed Aruba annotated open data set that was acquired from a smart home where a voluntary single elderly woman was living inside for eight months. First, ten basic daily activities, namely, Eating, Bed_to_Toilet, Relax, Meal_Preparation, Sleeping, Work, Housekeeping, Wash_Dishes, Enter_Home, and Leave_Home are segmented with different sliding window sizes, and then converted into binary activity images. Next, the activity images are employed as the ground truth for the proposed DCNN model. The 10-fold cross-validation evaluation results indicated that our proposed DCNN model outperforms the existing models with F1-score of 0.79 and 0.951 for all ten activities and eight activities (excluding Leave_Home and Wash_Dishes), respectively.
Template-Based 3D Non-Rigid Shape Estimation from Monocular Image Sequences

This paper addresses the problem of reconstructing non-rigid 3D geometries from temporal image sequences captured with only a single camera under full perspective projection. Without the knowledge of a shape deformation model, this task is severly under-constrained, because multiple shape configurations can produce the same image projections. The challenge remains even if a template 3D model of the static, un-deformed state is available, because the depth along the line of sight is unkown. Often, this is handled by assuming an orthographic camera model. In contrast, we address a full perspective camera model. Also, our reconstruction is not limited to the model parts that are visible in the current image, but deformation is estimated for the entire template across the temporal sequence. In a first step, we compute a template of the geometry in un-deformed pose, assuming that the object was captured while being static. Next, the object starts to deform while being captured by a single camera, and the non-rigid shape is reconstructed sequentially by estimating the camera position and the deformations with respect to the template model. Our objective minimization function combines image data and temporal consistency information, and constrains the deformation space by a rotation-invariant volumetric graph Laplacian and as-rigid-as-possible constraints defined on the tesselation of the template model. The method is evaluated on synthetic and real data, including different object classes, thereby concentrating on the class of articulated deformations.
Data-Driven Distributed Optimal Consensus Control for Unknown Multiagent Systems With Input-Delay

This paper is concerned with data-driven distributed optimal consensus control for unknown multiagent systems (MASs) with input delays. The input-delayed MAS model is first converted into a delay-free form using a model reduction method. By establishing an equivalent relationship on the predesigned performance indices of the two MASs, optimal consensus control of input-delayed MAS can be fully transformed to that of delay-free MAS. Based on the coupled Hamilton–Jacobi equations and Bellman’s optimality principle, optimal consensus control policies are derived for the transformed delay-free MAS. Then a policy iteration algorithm based on distributed asynchronous update mechanism is proposed to learn the coupled Hamilton–Jacobi–Bellman equations online. To perform the proposed data-driven adaptive dynamic programming algorithm, we adopt the measured data-based critic-actor neural networks to approximate the value functions and the control policies, respectively. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.
Beneficial Effect of Minimal Interference on Item Memory But Not on Source Memory in Alzheimer's Disease.

OBJECTIVES Research suggests beneficial effects of minimal interference on item memory in mild Alzheimer's disease (AD). We conducted a study to test whether these beneficial effects extend to source memory, i.e., the ability to remember the episodic context in which an information was previously acquired. METHOD Twenty-six mild AD participants and 28 controls manipulated six objects or watched the experimenter manipulating six other objects. After immediate item recall ("what were the items? ") and source recall ("by whom the items were previously manipulated? "), an interference or a minimal interference condition was administrated. In the interference condition, participants were assessed with neuropsychological tests for 10 min. In the minimal interference condition, they rested alone in a dark and quiet room for 10 minutes. Both interference and minimal interference conditions were followed by the delayed recall, on which participants were asked to remember the previously-presented objects and their source. RESULTS Higher item memory was observed following minimal interference than following interference in AD participants (p < .01) and controls (p < .01). Also, AD participants demonstrated higher item memory on immediate recall than following interference (p < .001) or minimal interference (p < .001); controls also demonstrated higher item memory on immediate recall than following interference (p < .001) or minimal interference (p < .001). Considering source memory, similar source memory was observed following interference and minimal interference in AD participants (p > .1) and controls (p > .1). Also, AD participants demonstrated higher source memory on immediate recall than following interference (p < .001) or minimal interference (p < .001); controls also demonstrated higher source memory on immediate recall than following interference (p < .001) or minimal interference (p < .001). CONCLUSION Failures of hippocampus-dependent associative or consolidation processes in AD may preclude benefits of minimal interference for source memory. Nevertheless, AD patients may show some capacity to retain simple material, should the material presentation be followed by short delays that are free of further stimuli.
LRR for Subspace Segmentation via Tractable Schatten- $p$  Norm Minimization and Factorization

Recently, nuclear norm-based low rank representation (LRR) methods have been popular in several applications, such as subspace segmentation. However, there exist two limitations: one is that nuclear norm as the relaxation of rank function will lead to the suboptimal solution since nuclear norm-based minimization subproblem tends to the over-relaxations of singular value elements and treats each of them equally; the other is that solving LRR problems may cause more time consumption due to involving singular value decomposition of the large scale matrix at each iteration. To overcome both disadvantages, this paper mainly considers two tractable variants of LRR: one is Schatten-<inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> norm minimization-based LRR (i.e., <inline-formula> <tex-math notation="LaTeX">${S}_{p}$ </tex-math></inline-formula>NM_LRR) and the other is Schatten-<inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> norm factorization-based LRR (i.e., <inline-formula> <tex-math notation="LaTeX">${S}_{p}$ </tex-math></inline-formula>NF_LRR) for <inline-formula> <tex-math notation="LaTeX">${p=1}$ </tex-math></inline-formula>, 2/3 and 1/2. By introducing two or more auxiliary variables in the constraints, the alternating direction method of multiplier (ADMM) with multiple updating variables can be devised to solve these variants of LRR. Furthermore, both computational complexity and convergence property are given to evaluate nonconvex multiblocks ADMM algorithms. Several experiments finally validate the efficacy and efficiency of our methods on both synthetic data and real world data.
Unified Low-Rank Matrix Estimate via Penalized Matrix Least Squares Approximation

Low-rank matrix estimation arises in a number of statistical and machine learning tasks. In particular, the coefficient matrix is considered to have a low-rank structure in multivariate linear regression and multivariate quantile regression. In this paper, we propose a method called penalized matrix least squares approximation (PMLSA) toward a unified yet simple low-rank matrix estimate. Specifically, PMLSA can transform many different types of low-rank matrix estimation problems into their asymptotically equivalent least-squares forms, which can be efficiently solved by a popular matrix fast iterative shrinkage-thresholding algorithm. Furthermore, we derive analytic degrees of freedom for PMLSA, with which a Bayesian information criterion (BIC)-type criterion is developed to select the tuning parameters. The estimated rank based on the BIC-type criterion is verified to be asymptotically consistent with the true rank under mild conditions. Extensive experimental studies are performed to confirm our assertion.
A German version of the Three‐Dimensional Work Fatigue Inventory (3D‐WFI): Factor structure, internal consistency, and correlates

Work fatigue represents an essential construct for understanding employee health and safety. In this study, we developed and explored the psychometric properties of a German version of the Three-Dimensional Work Fatigue Inventory and examined a set of hypothesized correlates of work fatigue. Data came from a sample of 439 German workers. Consistent with the original measure, confirmatory factor analyses supported a three-factor solution (physical, mental, and emotional work fatigue), and estimates of internal consistency reliability exceeded . 90 for each dimension of work fatigue. The measure demonstrated a meaningful pattern of associations with predictors (time pressure, job control, psychological detachment, relaxation, and trait negative and positive affect) and an important outcome (work engagement). The German Three-Dimensional Work Fatigue Inventory will encourage research on an essential construct for employee health and safety using broader populations of workers.
Security Research on Wireless Networked Control Systems Subject to Jamming Attacks

In this paper, a countermeasure for wireless networked control systems suffering from jamming attacks is studied by a variable sampling approach. A Stackelberg game framework is utilized to analyze interactions between a smart jammer and a legitimate user. The variable sampling approach is exploited to deal with data packets dropout between a sensor and a controller. Moreover, a resilient variable sampling controller is designed by a delta operator method. Besides, stability conditions are provided for systems with proposed controller. Finally, a numerical simulation is provided to validate the effectiveness of the proposed method.
MANAGEMENT OF ENDOCRINE DISEASE: Critical review of the evidence underlying management of glucocorticoid-induced hyperglycaemia.

Glucocorticoids are frequently prescribed to patients with a wide range of inflammatory and autoimmune diseases. The semi-synthetic glucocorticoid prednisolone is most commonly prescribed and in two main patterns. Prednisolone is prescribed short-term at medium-high doses to treat an acute inflammatory illness or long-term at lower doses to attenuate chronic inflammatory disease progression. In hospitalized patients with acute prednisolone-induced hyperglycaemia there is a distinct circadian pattern of glucose elevation, which occurs predominantly in the afternoon and evening. As a morning dose of isophane insulin has a pharmacokinetic pattern that matches this pattern of glucose elevation, treatment comprising a basal dose of morning isophane insulin in combination with short-acting insulin boluses is generally recommended. However, evidence is lacking that isophane-based basal bolus insulin is more efficacious than other insulin regimens. In outpatients, low dose prednisolone causes a small increase in post glucose-load glucose concentration but no change in overall glycaemic control as measured by glycosylated haemoglobin. If treatment is indicated, metformin has been shown to be effective and may attenuate other adverse effects of long-term prednisolone therapy. Further studies are necessary in order to identify factors underlying the variability in response to insulin therapy and clinical benefits of treatment in hospitalized patients with prednisolone-induced hyperglycaemia. In outpatients prescribed low dose prednisolone, the cardiovascular risk associated with postprandial hyperglycaemia and efficacy of hypoglycaemic therapies should be evaluated in future randomized clinical trials.
An Uncontrolled Manifold Analysis of Arm Joint Variability in Virtual Planar Position and Orientation Telemanipulation

<italic>Objective:</italic> In teleoperated robot-assisted tasks, the user interacts with manipulators to finely control remote tools. Manipulation of robotic devices, characterized by specific kinematic and dynamic proprieties, is a complex task for the human sensorimotor system due to the inherent biomechanical and neuronal redundancies that characterize the human arm and its control. We investigate how master devices with different kinematics structures and how different task constraints influence users capabilities in exploiting arm redundancy. <italic>Methods:</italic> A virtual teleoperation workbench was designed and the arm kinematics of seven users was acquired during the execution of two planar virtual tasks, involving either the control of position only or position-orientation of a tool. Using the uncontrolled manifold analysis of arm joint variability, we estimated the logarithmic ratio between the task irrelevant and the task relevant manifolds (<inline-formula><tex-math notation="LaTeX">${\bf R}_{{\bf v}}$</tex-math></inline-formula>). <italic>Results:</italic> The <inline-formula><tex-math notation="LaTeX">${\bf R}_{{\bf v}}$</tex-math></inline-formula> values obtained in the position-orientation task were higher than in the position only task, while no differences were found between the master devices. A modulation of <inline-formula><tex-math notation="LaTeX">${\bf R}_{{\bf v}}$</tex-math></inline-formula> was found through the execution of the position task and a positive correlation was found between task performance and redundancy exploitation. <italic>Conclusion:</italic> Users exploited additional portions of arm redundancy when dealing with the tool orientation. The <inline-formula><tex-math notation="LaTeX">${\bf R}_{{\bf v}}$</tex-math></inline-formula> modulation seems influenced by the task constraints and by the users possibility of reconfiguring the arm position. Significance: This paper advances the general understanding of the exploitation of arm redundancy in complex tasks, and can improve the development of future robotic devices.
Dominant pollinators drive non-random community assembly and shared flower colour patterns in daisy communities

Background and Aims As most plants rely on pollination for persistence in communities, pollination interactions should be important determinants of plant community assembly. Here, trait and phylogenetic null modelling approaches were combined with pollinator interaction networks to elucidate the processes structuring flower colour assembly patterns in Asteraceae communities in Namaqualand, South Africa. Methods Plant species were assigned to flower colour pattern categories (CPCs) that incorporate the complexity of the bulls-eye colour pattern, using pollinator vision models. Null models were used to assess whether daisy communities exhibit clustering (driven by filtering, facilitation or convergence) or overdispersion (driven by competitive exclusion or character displacement) of CPCs. Next, flower visitor networks were constructed for communities with non-random CPC assembly to confirm the functional role of pollinators in determining floral trait assembly. Key Results Plant species are unevenly distributed across CPCs, the majority of which are not phylogenetically conserved, suggesting that certain CPCs have a selective advantage. Clustering of CPCs in communities is more frequent than overdispersion, and this does not reflect non-random phylogenetic assembly. In most communities at least one CPC is overrepresented relative to null assemblages. Interaction networks show that each community has a single dominant pollinator that strongly interacts with the overrepresented CPC, suggesting a role for pollinator preferences in driving clustered assembly of CPCs within daisy communities. Conclusion This novel approach, which demonstrates non-random assembly of complex flower colour patterns and corroborates their functional association with particular pollinators, provides strong evidence that pollinators influence plant community assembly. Results suggest that in some community contexts the benefits of pollinator sharing outweigh the costs of heterospecific pollen transfer, generating clustered assembly. They also challenge the perception of generalized pollination in daisies, suggesting instead that complex daisy colour patterns represent a pollination syndrome trait linked to specific fly pollinators.
Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets

Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and reduce the risk of disability or even death. Understanding the location and size of infarcts plays a critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions are laborious and time-consuming. In this paper, we propose a novel automatic method to segment acute ischemic stroke from diffusion weighted images (DWIs) using deep 3-D convolutional neural networks (CNNs). Our method can efficiently utilize 3-D contextual information and automatically learn very discriminative features in an end-to-end and data-driven way. To relieve the difficulty of training very deep 3-D CNN, we equip our network with dense connectivity to enable the unimpeded propagation of information and gradients throughout the network. We train our model with Dice objective function to combat the severe class imbalance problem in data. A DWI data set containing 242 subjects (90 for training, 62 for validation, and 90 for testing) with various types of acute ischemic stroke was constructed to evaluate our method. Our model achieved high performance on various metrics (Dice similarity coefficient: 79.13%, lesionwise precision: 92.67%, and lesionwise F1 score: 89.25%), outperforming the other state-of-the-art CNN methods by a large margin. We also evaluated the model on ISLES2015-SSIS data set and achieved very competitive performance, which further demonstrated its generalization capacity. The proposed method is fast and accurate, demonstrating a good potential in clinical routines.
Temperature Compensation of Elasto-Magneto-Electric (EME) Sensors in Cable Force Monitoring Using BP Neural Network

Techniques based on the elasto-magnetic (EM) effect have been receiving increasing attention for their significant advantages in cable stress/force monitoring of in-service structures. Variations in ambient temperature affect the magnetic behaviors of steel components, causing errors in the sensor and measurement system results. Therefore, temperature compensation is essential. In this paper, the effect of temperature on the force monitoring of steel cables using smart elasto-magneto-electric (EME) sensors was investigated experimentally. A back propagation (BP) neural network method is proposed to obtain a direct readout of the applied force in the engineering environment, involving less computational complexity. On the basis of the data measured in the experiment, an improved BP neural network model was established. The test result shows that, over a temperature range of approximately −10 °C to 60 °C, the maximum relative error in the force measurement is within ±0.9%. A polynomial fitting method was also implemented for comparison. It is concluded that the method based on a BP neural network can be more reliable, effective and robust, and can be extended to temperature compensation of other similar sensors.