Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Model-Free Learning to Overcome Hardware Nonidealities in Analog-to-Information Converters

This paper considers compressed sensing (CS) in the context of RF spectrum sensing and presents an efficient approach for learning hardware nonidealities in an analog-to-information converter (A2IC). The proposed methodology is based on the learned iterative shrinkage-thresholding algorithm (LISTA), which enables co-optimization of the hardware and the reconstruction algorithm and leads to a model-free recovery approach that is optimally tuned for the unique computational constraints and hardware nonidealities present in the RF frontend. To achieve this, we devise a training protocol that employs a dataset and neural network of minimal sizes. We demonstrate the effectiveness of our methodology on simulated data from a model of a well-established CS A2IC in the presence of linear impairments and noise. The recovery process extrapolates from training on 1-sparse signals to recovering the support of signals whose sparsity runs up to the theoretical optimum for $\ell^{1}$-based algorithms across a range of typical operating SNRs.
Employing minimum distance classifier for emotion recognition analysis using EEG signals

Emotion recognition is a process of identifying human emotions using facial expressions, speech, physiological signals, etc. EEG signal has a low noise ratio, as they originate from brain and are not prone to social masking. We have selected the physiological EEG signal to recognise human emotions. The acquired EEG signal is processed, and various prominent features have been extracted on the basis of which the emotions are classified using the minimum distance classifier into four quadrants of valence and arousal. From the analysis, we have discussed how these different feature combinations and the different distances which are applied for emotion classification have been selected. From the analysis Manhattan distance is proved as the best for feature classification as it gives the accuracy of 97.5%.
A service evaluation of parent adherence with dysphagia management therapy guidelines: reports from family carers supporting children with complex needs in Greece

Abstract Purpose: Many children with complex needs exhibit eating, drinking, and/or swallowing disorders (dysphagia). These children often have associated learning needs, and require assistance from carers for daily tasks such as eating and drinking. The aim of this study was to identify which strategies to manage dysphagia were challenging for family carers, and reasons for any non-adherence. Method: In this service evaluation researchers observed carers during mealtimes, and investigated carer opinions of strategies used to minimise the risks of dysphagia. Eight children with complex needs aged 3.4–7.5 years and their primary family caregiver participated. Results: Adherence with speech and language pathologists’ dysphagia recommendations overall was over 50% in all but one case. For specific strategies, the highest adherence was observed for diet modifications of foods (89%), communication during the mealtime (83%), amount of food to present (81%), and the pacing of fluids and foods (81%). Lower levels of adherence were identified in relation to postural management (58%), environmental changes (58%), utensils (56%), and preparatory strategies (49%). Conclusions: Adherence with use of strategies to support mealtimes was over 50% in all but one case. Findings suggest that support is essential to promote safe mealtimes, reduce family carers’ stress and increase knowledge, confidence, and adherence in implementing dysphagia guidelines in the family home. Implications for rehabilitation Difficulties with eating, drinking and swallowing (dysphagia) can impact on the parent–child mealtime experience. Mealtime strategies as recommended by a speech-language pathologist can support children who have difficulties eating, drinking, and swallowing. Some strategies to support eating, drinking, and swallowing are easier for carers to adhere to than others.
Energy Efficiency in MIMO Interference Channels: Social Optimality and Max-Min Fairness

In this paper, we consider the energy efficiency optimization problem in MIMO multi-cell systems, where all users suffer from intercell interference. To solve this multi-objective optimization problem, we consider both socially optimal solutions and max-min fairness solutions. We propose two novel iterative algorithms that converge to socially optimal solutions and max-min fairness solutions. The proposed algorithms have the following advantages: 1) fast convergence as the structure of the original optimization problem is preserved as much as possible in the approximate problem solved in each iteration, and 2) efficient implementation as each approximate problem is natural for parallel computation and/or its solution has a closed-form expression. The advantages of the proposed algorithms are also illustrated numerically.
Method of Estimating Direction of Arrival of Sound Source for Monaural Hearing Based on Temporal Modulation Perception

Although humans are capable of using monaural and modulation cues for sound localization, it is not yet clear how they can use that information to estimate the direction of arrival (DOA) of a sound source in 3D space. Our previous study revealed that the head-related modulation transfer function (HR-MTF) contains significant trends and features, which can be used for DOA estimation. This paper proposes a method of estimating the DOA in a 3D space by using the monaural modulation spectrum (MMS), based on the concept of modulation transfer function (MTF) and auditory perception of temporal modulation. We carried out over 51, 840 simulations with several signal types and multiple subjects to simultaneously estimate the azimuth and the elevation of an incoming sound source. The root mean square error (RMSE) was derived to evaluate the accuracy of monaural DOA estimates. Our results indicated that the proposed method could adequately estimate the DOA in 3D space with an overall mean RMSE of 21.9 degrees.
Dual Memory Network Model for Biased Product Review Classification

In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user profile and product information in a unified model which may not be able to learn salient features of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks. Then, the two representations are used jointly for sentiment prediction. The use of separate models aims to capture user profiles and product information more effectively. Compared to state-of-the-art unified prediction models, the evaluations on three benchmark datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are also deemed very significant measured by p-values.
Inverse Atmoshperic Scattering Modeling with Convolutional Neural Networks for Single Image Dehazing

Single image dehazing is an ill-posed problem. Most existing works use the atmospheric scattering model (ASM) [1] and some natural priors to dehazing. Recently, DehazeNet [2] was developed using deep learning approach achieves the state-of-the-art results on many test hazy images, which motivates us to adopt the deep learning approach in this study. After carefully research on the essential principle of dehazing using ASM, we develop a novel end-to-end convolutional neural network to efficiently implement the inverse ASM for single image dehazing, which is termed as IASM -Net. Specifically, the ASM is equivalently decomposed into three sub-model and the IASM-Net is designed to model these sub-model efficiently. Our study shows that IASM-Net is jointly optimized by minimizing the model error between the output and the clear ground truth, which gives better performance compared existing CNN-based transmission model approach [1], [2]. To facilitate this study, a sufficient aerial image set containing more than 6K aerial photos is built to train and test our IASM-Net. Experimental results validate the effectiveness and efficiency of our well designed IASM -Net.
Golfer Body Posture Monitoring Using e-Textile Technology

Being a beginner in any sport that is difficult to learn is what usually ticks people off from further learning the sport. Golf is no exception to this, and with that, the study conducted utilized E-textile technology, specifically wearable accelerometers, to develop a wearable device that would monitor a person's golf swings. A professional golf instructor validates the data obtained by the device. It is concluded that the device is able to monitor the body posture of the golf player wearing the device.
Habitual stone-tool-aided extractive foraging in white-faced capuchins, Cebus capucinus

Habitual reliance on tool use is a marked behavioral difference between wild robust (genus Sapajus) and gracile (genus Cebus) capuchin monkeys. Despite being well studied and having a rich repertoire of social and extractive foraging traditions, Cebus sp have rarely been observed engaging in tool use and have never been reported to use stone tools. In contrast, habitual tool use and stone-tool use by Sapajus is widespread. We discuss factors which might explain these differences in patterns of tool use between Cebus and Sapajus. We then report the first case of habitual stone-tool use in a gracile capuchin: a population of white-faced capuchins (Cebus capucinus imitator) in Coiba National Park, Panama who habitually rely on hammerstone and anvil tool use to access structurally protected food items in coastal areas including Terminalia catappa seeds, hermit crabs, marine snails, terrestrial crabs, and other items. This behavior has persisted on one island in Coiba National Park since at least 2004. From one year of camera trapping, we found that stone tool use is strongly male-biased. Of the 205 unique camera-trap-days where tool use was recorded, adult females were never observed to use stone-tools, although they were frequently recorded at the sites and engaged in scrounging behavior. Stone-tool use occurs year-round in this population, and over half of all identifiable individuals were observed participating. At the most active tool use site, 83.2% of days where capuchins were sighted corresponded with tool use. Capuchins inhabiting the Coiba archipelago are highly terrestrial, under decreased predation pressure and potentially experience resource limitation compared to mainland populations– three conditions considered important for the evolution of stone tool use. White-faced capuchin tool use in Coiba National Park thus offers unique opportunities to explore the ecological drivers and evolutionary underpinnings of stone tool use in a comparative within- and between-species context.
Description of Localized Josephson Plasma Waves: Legendre Functions vs WKB Approximation

Layered superconductors represent unusual materials that invoke the interest of many researchers. To describe electrodynamics in layered superconductors, well-known coupled sine-Gordon equations are widely used. The field components of the waves localized on layered superconductors in the presence of dc magnetic field can be found in WKB (quasiclassical) approximation. However, this approximation does not allow studying dispersion curves in important regions, namely near the light line and at the frequencies close to the Josephson plasma frequency. In this work, we show that the theoretical study of plasma waves localized on a layered superconductor can be performed in more accurate form using Legendre functions.
The role of contextual information during skilled anticipation

In dynamic, temporally constrained tasks, individuals often need to anticipate what will happen next prior to information becoming available within the environment. In such situations, the availability of contextual information can facilitate anticipation, often in conjunction with postural information. While many researchers have identified the specific sources of postural information facilitating anticipation, few have investigated the specific sources of contextual information employed. In two experiments, we presented skilled and less-skilled tennis players with animations of rallies from real matches that omitted access to postural information from the opponent, constraining participants to anticipate based on contextual information alone. In Experiment 1, participants anticipated the outcome of an opponent’s shot under three conditions in which the sequence length (i.e., number of shots in a rally) preceding the same occluded shot was varied. Participants anticipated shot direction more accurately when the preceding shot sequence was presented than not. In Experiment 2, we presented animations that depicted the ball, the players, or both, in either dynamic or still form. Those conditions in which only the ball was depicted yielded the lowest response accuracy scores. It appears that information from the player and ball motion is required to provide the context under which skilled performers can consciously pick up and utilise information to anticipate more accurately than their less-skilled counterparts.
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Convolutional Neural Network

Nowadays, deep learning can be employed to a wide ranges of fields including medicine, engineering, etc. In deep learning, Convolutional Neural Network (CNN) is extensively used in the pattern and sequence recognition, video analysis, natural language processing, spam detection, topic categorization, regression analysis, speech recognition, image classification, object detection, segmentation, face recognition, robotics, and control. The benefits associated with its near human level accuracies in large applications lead to the growing acceptance of CNN in recent years. The primary contribution of this paper is to analyze the impact of the pattern of the hidden layers of a CNN over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the Modified National Institute of Standards and Technology (MNIST) dataset. Also, is to observe the variations of accuracies of the network for various numbers of hidden layers and epochs and to make comparison and contrast among them. The system is trained utilizing stochastic gradient and backpropagation algorithm and tested with feedforward algorithm.
The Exchange of Knowledge Using Cloud Robotics

To enable robots to perform human-level tasks flexibly in varying conditions, we need a mechanism that allows them to exchange knowledge between themselves for crowd-sourcing the knowledge gap problem. One approach to achieve this is to equip a cloud application with a range of encyclopedic knowledge (i.e. ontologies) and execution logs of different robots performing the same tasks in different environments. In this paper, we show how knowledge exchange between robots can be done using OPENEASE as the cloud application. We equipped OPENEASE with ontologies about the kitchen domain, execution logs of three robots operating in two different kitchens, and semantic descriptions of both environments. By addressing two different use cases, we show that two PR2 robots and one Fetch robot can successfully adapt each other's plan parameters and sub symbolic data to the experiments that they are conducting.
Back-Stepping Sliding Mode Control of Quadrotor under Dynamic Uncertainties

this paper addresses the development of high order Sliding Mode Control (SMC) of a Quadrotor. This development has been done using Back-Stepping Sliding Mode Control (BSMC). By considering external disturbances and dynamic uncertainties in parameters, this approach guaranties the convergence of system states to their desired values. Based on Lyapunov stability theory, main achievement of this investigation is demonstrated for close loop dynamic stabilization of Quadrotor against sudden external disturbance in states and control inputs and also overcoming to parameters uncertainties. Performance impact of this strategy is illustrated on attitude and altitude control of a Quadrotor.
Performance Indicator for Benchmarking Force-Controlled Robots

Robot-based sensitive assembly is a recent and growing trend in robotics. Force-controlled robots are expected to interact with an unknown environment using solely force feedback information. In general, the exact contact is difficult to predict due to various impact factors, such as the dynamics of the interaction, work-piece stiffness and geometry, the robot's configuration, and the efficiency of the control algorithm. Currently, there is no general indicator for evaluating the performance of a force-controlled robot. This work presents a concept of such a performance indicator. In order to test the proposed concept for comparison, an experimental setup is presented that simulates a contour-following task under force control. This setup is used to test two robots with different force-controllers and control principles, namely direct force and impedance control. The results indicate good applicability of the proposed performance indicator to benchmark force-controlled robots, and this is extensively discussed.
A Collision Rate-Based Backoff Algorithm for Contention-Based Wireless Networks

Following the rapid development of wireless networks, IEEE 802.11 technology has become the most widely used wireless network access technology. The contention channel of the distributed coordination function (DCF) is the default mechanism, which is used to determine the waiting time required before sending data. Once any collision has happened, the contention window expands to prevent future collisions, whereas when data has been successfully transmitted, the contention window shrinks to reduce backoff time. Determination of the increase or decrease of contention windows has become a vital to the transmission performance of networks. Through experimentation with different numbers of stations and different contention window sizes, this study found a collision rate range with superior efficiency, and thus proposed a collision rate-based backoff algorithm (CRBA). The proposed algorithm adjusts the contention window size according to collision rate, achieving enhanced efficiency and a reduced collision rate. According to the analysis of experimental results, the CRBA outperformed other relevant channel contention mechanisms (BEB, EIED, LILD, and ELBA) in terms of throughput, collision rate, and channel utilization rate.
Least Square Reinforcement Learning for Solving Inverted Pendulum Problem

Inverted pendulum is one of the classic control problem that could be solved by reinforcement learning approach. Most of the previous work consider the problem in discrete state space with only few exceptions assume continuous state domain. In this paper, we consider the problem of cart-pole balancing in the continuous state space setup with constrained track length. We adopted a least square temporal difference reinforcement learning algorithm for learning the controller. A new reward function is then proposed to better reflect the nature of the task. In addition, we also studied various factors which play important roles in the success of the learning. The empirical studies validate the effectiveness of our method.
A Monocular SLAM System Leveraging Structural Regularity in Manhattan World

The structural features in Manhattan world encode useful geometric information of parallelism, orthogonality and/or coplanarity in the scene. By fully exploiting these structural features, we propose a novel monocular SLAM system which provides accurate estimation of camera poses and 3D map. The foremost contribution of the proposed system is a structural feature-based optimization module which contains three novel optimization strategies. First, a rotation optimization strategy using the parallelism and orthogonality of 3D lines is presented. We propose a global binding method to compute an accurate estimation of the absolute rotation of the camera. Then we propose an approach for calculating the relative rotation to further refine the absolute rotation. Second, a translation optimization strategy leveraging coplanarity is proposed. Coplanar features are effectively identified, and we leverage them by a unified model handling both points and lines to calculate the relative translation, and then the optimal absolute translation. Third, a 3D line optimization strategy utilizing parallelism, orthogonality and coplanarity simultaneously is proposed to obtain an accurate 3D map consisting of structural line segments with low computational complexity. Experiments in man-made environments have demonstrated that the proposed system outperforms existing state-of-the-art monocular SLAM systems in terms of accuracy and robustness.
Feature-Based Transfer Learning for Robotic Push Manipulation

This paper presents a data-efficient approach to learning transferable forward models for robotic push manipulation. Our approach extends our previous work on contact-based predictors by leveraging information on the pushed object's local surface features. We test the hypothesis that, by conditioning predictions on local surface features, we can achieve generalisation across objects of different shapes. In doing so, we do not require a CAD model of the object but rather rely on a point cloud object model (PCOM). Our approach involves learning motion models that are specific to contact models. Contact models encode the contacts seen during training time and allow generating similar contacts at prediction time. Predicting on familiar ground reduces the motion models' sample complexity while using local contact information for prediction increases their transferability. In extensive experiments in simulation, our approach is capable of transfer learning for various test objects, outperforming a baseline predictor. We support those results with a proof of concept on a real robot.
Zika virus found in brain tissue of a multiple sclerosis patient undergoing an acute disseminated encephalomyelitis-like episode

Background: A range of different neurological manifestations has been reported in fetuses and adults after Zika virus (ZIKV) infection. Objective: We describe a detection of the ZIKV in the brain tissue from a multiple sclerosis (MS) patient with acute disseminated encephalomyelitis (ADEM)-like event in Rio de Janeiro, Brazil. Methods: Biological samples collected during the hospitalization were tested by serology and molecular diagnostic for various infectious agents. Histopathological analysis was performed using the anti-flavivirus group 4G2 monoclonal antibody, anti-ZIKV non-structural 1 (NS1) monoclonal antibody, and anti-CD4, CD8, and CD11b antibodies. Results: Anti-ZIKV IgM and IgG antibodies were positive in the serum and urine. A brain biopsy showed ZIKV protein in brain cells and T CD8 infiltration in brain tissue. Conclusion: Our data describe the coexistence of a recent central nervous system (CNS) ZIKV infection accompanied by a severe ADEM-like syndrome outcome in a patient with clinical history of MS. A de novo immune response concomitant with ZIKV infection might be involved in the mechanism of the ADEM-like syndrome and response to immunotherapy. The present report reinforces the importance of providing the differential diagnosis of acute episodes of MS exacerbation in an environment prone to ZIKV expression.