Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Analysis of Food Intake and Meal Microstructure Based on Continuous Weight Measurements

The structure of the cumulative food intake (CFI) curve has been associated with obesity and eating disorders. Scales that record the weight loss of a plate from which a subject eats food are used for capturing this curve; however, their measurements are contaminated by additive noise and are distorted by certain types of artifacts. This paper presents an algorithm for automatically processing continuous in-meal weight measurements in order to extract the clean CFI curve and in-meal eating indicators, such as total food intake and food intake rate. The algorithm relies on the representation of the weight-time series by a string of symbols that correspond to events such as bites or food additions. A context-free grammar is next used to model a meal as a sequence of such events. The selection of the most likely parse tree is finally used to determine the predicted eating sequence. The algorithm is evaluated on a dataset of 113 meals collected using the Mandometer, a scale that continuously samples plate weight during eating. We evaluate the effectiveness for seven indicators and for bite-instance detection. We compare our approach with three state-of-the-art algorithms, and achieve the lowest error rates for most indicators (24 g for total meal weight). The proposed algorithm extracts the parameters of the CFI curve automatically, eliminating the need for manual data processing, and thus facilitating large-scale studies of eating behavior.
Theory and Practice of Coroutines with Snapshots

While event-driven programming is a widespread model for asynchronous computing, its inherent control flow fragmentation makes event-driven programs notoriously difficult to understand and maintain. Coroutines are a general control flow construct that can eliminate control flow fragmentation. However, coroutines are still missing in many popular languages. This gap is partly caused by the difficulties of supporting suspendable computations in the language runtime. We introduce first-class, type-safe, stackful coroutines with snapshots, which unify many variants of suspendable computing. Our design relies solely on the static metaprogramming support of the host language, without modifying the language implementation or the runtime. We also develop a formal model for type-safe, stackful and delimited coroutines, and we prove the respective safety properties. We show that the model is sufficiently general to express iterators, single-assignment variables, async-await, actors, event streams, backtracking, symmetric coroutines and continuations. Performance evaluations reveal that the proposed metaprogramming-based approach has a decent performance, with workload-dependent overheads of 1.03-2.11 x compared to equivalent manually written code, and improvements of up to 6 x compared to other approaches.
A Piecewise-Markovian Lyapunov Approach to Reliable Output Feedback Control for Fuzzy-Affine Systems With Time-Delays and Actuator Faults

This paper addresses the problem of delay-dependent robust and reliable <inline-formula> <tex-math notation="LaTeX">$\mathscr {H}_{\infty }$ </tex-math></inline-formula> static output feedback (SOF) control for a class of uncertain discrete-time Takagi–Sugeno fuzzy-affine (FA) systems with time-varying delay and actuator faults in a singular system framework. The Markov chain is employed to describe the actuator faults behaviors. In particular, by utilizing a system augmentation approach, the conventional closed-loop system is converted into a singular FA system. By constructing a piecewise-Markovian Lyapunov–Krasovskii functional, a new <inline-formula> <tex-math notation="LaTeX">$\mathscr {H}_{\infty }$ </tex-math></inline-formula> performance analysis criterion is then presented, where a novel summation inequality and S-procedure are succeedingly employed. Subsequently, thanks to the special structure of the singular system formulation, the piecewise-affine SOF controller design is proposed via a convex program. Lastly, illustrative examples are given to show the efficacy and less conservatism of the presented approach.
Deep Hyperspectral Image Sharpening

Hyperspectral image (HSI) sharpening, which aims at fusing an observable low spatial resolution (LR) HSI (LR-HSI) with a high spatial resolution (HR) multispectral image (HR-MSI) of the same scene to acquire an HR-HSI, has recently attracted much attention. Most of the recent HSI sharpening approaches are based on image priors modeling, which are usually sensitive to the parameters selection and time-consuming. This paper presents a deep HSI sharpening method (named DHSIS) for the fusion of an LR-HSI with an HR-MSI, which directly learns the image priors via deep convolutional neural network-based residual learning. The DHSIS method incorporates the learned deep priors into the LR-HSI and HR-MSI fusion framework. Specifically, we first initialize the HR-HSI from the fusion framework via solving a Sylvester equation. Then, we map the initialized HR-HSI to the reference HR-HSI via deep residual learning to learn the image priors. Finally, the learned image priors are returned to the fusion framework to reconstruct the final HR-HSI. Experimental results demonstrate the superiority of the DHSIS approach over existing state-of-the-art HSI sharpening approaches in terms of reconstruction accuracy and running time.
Bone marrow mesenchymal stem cells reduce ureteral stricture formation in a rat model via the paracrine effect of extracellular vesicles

With no effective therapy to prevent or treat ureteral stricture (US), a multifactorial fibrotic disease after iatrogenic injury of the ureter, the need for new therapies is urgent. Mesenchymal stem cells (MSCs) have been widely studied for treating tissue defects and excessive fibrosis, and recent studies established that one of the main therapeutic vectors of MSCs is comprised in their secretome and represented by extracellular vesicles (EVs). Thus, we have determined to explore the specific role of MSCs‐derived EVs (MSC‐EVs) treatment in a pre‐clinical model of US. The results firstly showed that either a bolus dose of MSCs or a bolus dose of MSC‐EVs (administration via renal‐arterial) significantly ameliorated ureteral fibrosis and recuperated ureter morphological development in a US rat model. We confirmed our observations through MSCs or MSC‐EVs treatment alleviated hydronephrosis, less renal dysfunction and blunted transforming growth factor‐β1 induced fibration. Due to MSC‐EVs are the equivalent dose of MSCs, and similar curative effects of transplantation of MSCs and MSC‐EVs were observed, we speculated the curative effect of MSCs in treating US might on account of the release of EVs through paracrine mechanisms. Our study demonstrated an innovative strategy to counteract ureteral stricture formation in a rat model of US.
A Multithreshold Iterative DBIM-Based Algorithm for the Imaging of Heterogeneous Breast Tissues

Objective: This paper proposes a novel microwave imaging (MWI) multifrequency technique, which combines compressive sensing (CS) with the well-known distorted Born iterative method. CS strategies are emerging as a promising tool in MWI applications, which can improve reconstruction quality and/or reduce the number of data samples. Methods: The proposed approach is based on iterative shrinkage thresholding algorithm (ISTA), which has been modified to include an automatic and adaptive selection of multithreshold values. Results: This adaptive multithreshold ISTA implementation is applied in reconstruction of two-dimensional (2-D) numerical heterogeneous breast phantoms, where it outperforms the standard thresholding implementation. We show that our approach is also successful in 3-D simulations of a realistic imaging experiment, despite the mismatch between the data and our algorithm's forward model. Conclusion: These results suggest that the proposed algorithm is a promising tool for medical MWI applications. Significance: Important novelties of this approach are the use of multiple thresholds to recover the different unknowns in the Debye model as well as the adaptive selection of these thresholds. Moreover, we have shown that employing modified hard constraints inside the linear step of the inversion procedure can enhance reconstruction quality.
Three-Dimensional Feature-Enhanced Network for Automatic Femur Segmentation

Automatic femur segmentation from computed tomography volume is a crucial but challenging task for computer-aided diagnosis in orthopedic surgeries. The main obstacles are weak bone boundaries, narrowness of joint space, variations in femur density and shape, as well as diverse leg postures. In this paper, we presented a novel 3-D feature-enhanced network to address these challenges. The novelty of our approach lies in two feature enhancement modules, including the edge detection task and the multi-scale features fusion. First, the edge detection task was embedded into femur segmentation from computed tomography volume to solve the problems of narrow joint space and weak femur boundary. Crucially, a task-specific edge detector was used to optimize the performance of femur segmentation in an end-to-end trainable system. Second, the multi-scale features fusion provided both local and global contexts to handle the problems of large variations in leg postures as well as femur shape and density. The results demonstrated that accurate 3-D femur segmentation with a high Dice similarity coefficient of 96.88% was achieved using the developed method, and the segmentation of computed tomography volume took 0.93 s on an average.
Open-Ended Long-form Video Question Answering via Adaptive Hierarchical Reinforced Networks

Open-ended long-form video question answering is challenging problem in visual information retrieval, which automatically generates the natural language answer from the referenced long-form video content according to the question. However, the existing video question answering works mainly focus on the short-form video question answering, due to the lack of modeling the semantic representation of long-form video contents. In this paper, we consider the problem of long-form video question answering from the viewpoint of adaptive hierarchical reinforced encoder-decoder network learning. We propose the adaptive hierarchical encoder network to learn the joint representation of the long-form video contents according to the question with adaptive video segmentation. we then develop the reinforced decoder network to generate the natural language answer for open-ended video question answering. We construct a large-scale long-form video question answering dataset. The extensive experiments show the effectiveness of our method.
Bioinformatic Workflow Extraction from Scientific Texts based on Word Sense Disambiguation

This paper introduces a method for automatic workflow extraction from texts using Process-Oriented Case-Based Reasoning (POCBR). While the current workflow management systems implement mostly different complicated graphical tasks based on advanced distributed solutions (e.g., cloud computing and grid computation), workflow knowledge acquisition from texts using case-based reasoning represents more expressive and semantic case representations. We propose in this context, an ontology-based workflow extraction framework to acquire processual knowledge from texts. Our methodology extends the classic NLP techniques to extract and disambiguate complex tasks and relations in texts. Using a graph-based representation of workflows and a domain ontology, our extraction process uses a context-aware approach to recognize workflow components in texts: data and control flows. We applied our framework in a technical domain in bioinformatics: i.e., phylogenetic analyses. An evaluation based on workflow semantic similarities in a gold standard proves that our approach provides promising results in the process extraction domain. Both data and implementation of our framework are available in: http://labo.bioinfo. uqam.ca/tgowler.
Reliable and enhanced cooperative cross-layer medium access control scheme for vehicular communication

In an unreliable cluster-based, broadcast vehicular network setting, the authors investigate the transmission reliability and throughput performance of random network coding (RNC) as a function of the percentage of packet generation rate and transmit power-to-noise ratio. In this study, a novel scheme called reliable and efficient cooperative cross-layer medium access control (RECMAC) is proposed. The proposed scheme consists of a source vehicle broadcasting packets to a set of receivers (i.e. one-to-many) over independent broadcast erasure channels. The source vehicle performs RNC on N packets and broadcasts the encoded message to a set of receivers. In each hop, several vehicles form a cluster and cooperatively transmit the encoded or re-encoded packet. The combination of the RNC, cluster based, and cooperative communications enables RECMAC to optimally minimise data redundancy, which means less overhead, and improve reliability as opposed to coding-based solutions. Theoretical analyses and simulation results show that under the same conditions RECMAC scheme can achieve improved performance in terms of transmission reliability and throughput.
The processing of task-irrelevant emotion and colour in the Approach-Avoidance Task

ABSTRACT When processing information about human faces, we have to integrate different sources of information like skin colour and emotional expression. In 3 experiments, we investigated how these features are processed in a top-down manner when task instructions determine the relevance of features, and in a bottom-up manner when the stimulus features themselves determine process priority. In Experiment 1, participants learned to respond with approach-avoidance movements to faces that presented both emotion and colour features (e.g. happy faces printed in greyscale). For each participant, only one of these two features was task-relevant while the other one could be ignored. In contrast to our predictions, we found better learning of task-irrelevant colour when emotion was task-relevant than vice versa. Experiment 2 showed that the learning of task-irrelevant emotional information was improved in general when participants’ awareness was increased by adding NoGo-trials. Experiment 3 replicated these results for faces and emotional words. We conclude that during the processing of faces, both bottom-up and top-down processes are involved, such that task instructions and feature characteristics play a role. Ecologically significant features like emotions are not necessarily processed with high priority. The findings are discussed in the light of theories of attention and cognitive biases.
UUV’s Hierarchical DE-Based Motion Planning in a Semi Dynamic Underwater Wireless Sensor Network

This paper describes a reflexive multilayered mission planner with a mounted energy efficient local path planner for unmanned underwater vehicle’s (UUV) navigation throughout complex subsea volume in a time variant semi-dynamic operation network. The UUV routing protocol in underwater wireless sensor network is generalized with a homogeneous dynamic knapsack-traveler salesman problem emerging with an adaptive path planning mechanism to address UUV’s long-duration missions on dynamically changing subsea volume. The framework includes a base layer of global path planning, an inner layer of local path planning and an environmental sublayer. Such a multilayer integrated structure facilitates the framework to adopt any algorithm with real-time performance. The evolutionary technique known as differential evolution (DE) algorithm is employed by both base and inner layers to examine the performance of the framework in efficient mission timing and its resilience against the environmental disturbances. Relying on reactive nature of the framework and fast computational performance of the DE algorithm, the simulations show promising results and this new framework guarantees a safe and efficient deployment in a turbulent uncertain marine environment passing through a proper sequence of stations considering various constraint in a complex environment.
A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy

High-frequency oscillations (HFOs) are spontaneous magnetoencephalography (MEG) patterns that have been acknowledged as a putative biomarker to identify epileptic foci. Correct detection of HFOs in the MEG signals is crucial for the accurate and timely clinical evaluation. Since the visual examination of HFOs is time-consuming, error-prone, and with poor inter-reviewer reliability, an automatic HFOs detector is highly desirable in clinical practice. However, the existing approaches for HFOs detection may not be applicable for MEG signals with noisy background activity. Therefore, we employ the stacked sparse autoencoder (SSAE) and propose an SSAE-based MEG HFOs (SMO) detector to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first attempt to conduct HFOs detection in MEG using deep learning methods. After configuration optimization, our proposed SMO detector is outperformed other classic peer models by achieving 89.9% in accuracy, 88.2% in sensitivity, and 91.6% in specificity. Furthermore, we have tested the performance consistency of our model using various validation schemes. The distribution of performance metrics demonstrates that our model can achieve steady performance.
Can patients afford the cost of treatment for multidrug-resistant tuberculosis in Ethiopia?

SETTING Ethiopia has a high prevalence of tuberculosis (TB) and is one of the countries with the highest burden of multidrug-resistant TB (MDR-TB). OBJECTIVE To understand the costs that patients incur in obtaining diagnosis and treatment for MDR-TB. DESIGN In March 2013, interviews were conducted with 169 MDR-TB patients at three hospitals in Ethiopia to identify the cost to patients and the impact on employment and family income. RESULTS The average MDR-TB patient incurred a total cost of US$1378, which represented 25 months of a mid-treatment household income of US$54. The impact on the patient's employment and on overall patient and family income was generally catastrophic: 74% of all respondents reported losing their jobs, 66% of patients lost household income, and household income was reduced by 38%. To help cover the costs, 38% of patients sold some type of property, while 7% leased out property and 41% took out loans, any of which could jeopardize their future financial situation even further. CONCLUSION Despite services being officially free of charge, most patients incurred catastrophic costs and suffered significant income loss as a result of obtaining diagnosis and treatment for MDR-TB.
A New Unsupervised Approach for Segmenting and Counting Cells in High-Throughput Microscopy Image Sets

New technological advances in automated microscopy have given rise to large volumes of data, which have made human-based analysis infeasible, heightening the need for automatic systems for high-throughput microscopy applications. In particular, in the field of fluorescence microscopy, automatic tools for image analysis are making an essential contribution in order to increase the statistical power of the cell analysis process. The development of these automatic systems is a difficult task due to both the diversification of the staining patterns and the local variability of the images. In this paper, we present an unsupervised approach for automatic cell segmentation and counting, namely CSC, in high-throughput microscopy images. The segmentation is performed by dividing the whole image into square patches that undergo a gray level clustering followed by an adaptive thresholding. Subsequently, the cell labeling is obtained by detecting the centers of the cells, using both distance transform and curvature analysis, and by applying a region growing process. The advantages of CSC are manifold. The foreground detection process works on gray levels rather than on individual pixels, so it proves to be very efficient. Moreover, the combination of distance transform and curvature analysis makes the counting process very robust to clustered cells. A further strength of the CSC method is the limited number of parameters that must be tuned. Indeed, two different versions of the method have been considered, CSC-7 and CSC-3, depending on the number of parameters to be tuned. The CSC method has been tested on several publicly available image datasets of real and synthetic images. Results in terms of standard metrics and spatially aware measures show that CSC outperforms the current state-of-the-art techniques.
Application of cmOCT and continuous wavelet transform analysis to the assessment of skin microcirculation dynamics

Abstract. Correlation mapping optical coherence tomography (cmOCT) is a powerful technique for the imaging of skin microvessels structure, based on the discrimination of the static and dynamic regions of the tissue. Although the suitability of cmOCT to visualize the microcirculation has been proved in humans and animal models, less evidence has been provided about its application to examine functional dynamics. Therefore, the goal of this research was validating the cmOCT method for the investigation into microvascular function and vasomotion. A spectral domain optical coherence tomography (SD-OCT) device was employed to image 90 sequential three-dimensional (3-D) OCT volumes from the forearm of 12 volunteers during a 25-min postocclusive reactive hyperemia (PORH) test. The volumes were processed using cmOCT to generate blood flow maps at selected cutaneous depths. The maps clearly trace flow variations during the PORH response for both capillaries and arterioles/venules microvascular layers. Continuous blood flow signals were reconstructed from cmOCT maps to study vasomotion by applying wavelet transform spectral analysis, which revealed fluctuations of flow during PORH, reflecting the regulation of microvascular tone mediated by endothelial cells and sympathetic nerves. The results clearly demonstrate that cmOCT allows the generation of functional information that may be used for diagnostic applications.
Reshaping Movement Distributions With Limit-Push Robotic Training

High-cost situations need to be avoided. However, occasionally, cost may only be learned by experience. Here, we tested whether an artificially induced unstable and invisible high-cost region, a “limit-push” force field, might reshape people’s motion distributions. Healthy and neurologically impaired (chronic stroke) populations attempted 600 interceptions of a projectile while holding a robot handle that could render forces to the hand. The “limit-push,” in the middle of the study, pushed the hand outward unless the hand stayed within a box-shaped region. Both healthy and some stroke survivors adapted through selection of safer actions, avoiding the high-cost regions (outside the box); they stayed more inside and even kept a greater distance from the box’s boundaries. This was supported by other measures that showed subjects distributed their hand movements within the box more uniformly. These effects lasted a very short time after returning to the no-force condition. Although most robotic teaching approaches focus on shifting the mean, this limit-push treatment demonstrates how both mean and variance might be reshaped in motor training and neurorehabilitation.