Miracle-dz/UniFAR-FTUs huggingface dataset:
NMPC for Multicopter's Trajectory Tracking Using Modified Rodrigues Parameters

Unit quaternion has become a favourite singularity-free attitude representation in the attitude control problem of multicopters, enabling them to perform large and complex maneuvers. This paper studies the challenge of implementing Nonlinear Model Predictive Control (NMPC) of a multicopter's pose, using unit quaternion. Alternatively, it proposes representing attitude minimally with Modified Rodrigues Parameters (MRPs) and their shadow set. NMPC is formulated based on the dynamics of a multicopter, with the aim to regulate its position, velocity, error in attitude and error in angular velocity to their desired values. The proposed controller is implemented on-board a hexacopter and verified in simulations and real experiments. The resulting closed loop system is shown to track large set points and perform 360 degree flips without running into a singularity, while demonstrating similar controller performance and computational costs as compared to the one using unit quaternion. Finally, the trajectory tracking capability is demonstrated by implementing 8-shaped trajectory tracking.
The effect of myoelectric computer interface training on arm kinematics and function after stroke

Abnormal co-activation patterns of arm muscles is a substantial cause of impaired arm function after stroke. We designed a myoelectric computer interface (MCI) training paradigm to help stroke survivors reduce this abnormal coactivation. Here, we evaluated the effects of MCI training on function and arm kinematics in 32 chronic stroke survivors. We compared the effects of training duration and isometric vs. movement-based training conditions in 3 different groups. All groups reduced abnormal co-activation in targeted muscles, and showed reduced arm impairment after 6 weeks of training. They also showed improvements in arm kinematics as well as functional scores. Moreover, the gains persisted, though most were reduced, at one month after training stopped. These results suggest that MCI training holds promise to improve arm function after stroke.
Self-Tuning Active Tuned Mass Damper Utilizing Constrained Multi-Variable Sliding Mode Extremum-Seeking

In this paper, an electromechanical actuator is utilized for bidirectional power flow in a tuned mass damper (TMD). The actuator force is controlled to achieve desired mechanical damping and stiffness values for the whole system by controlling the current using a power electronic converter. The resulting TMD can autonomously change its damping and stiffness values to minimize a host structure's displacement under different excitation frequencies. A multi-variable sliding mode extremum-seeking algorithm is used to tune the TMD damping and stiffness levels to optimal values. Analytic and simulation results show that the active TMD exhibits better performance when compared to non-adaptive and robust TMDs.
Tele-monitoring and tele-rehabilitation of the shoulder muscular-skeletal diseases through wearable systems

In this work we present the development and preliminary testing of a wearable-technology-enabled platform for the remote rehabilitation of a large number of shoulder muscular-skeletal diseases. The presented system (Shoulphy) is conceived to lead and assess the patient, wearing a minimal set of inertial sensors, through personalized physical rehabilitation programs under the remote supervision of the physician/therapist. We have introduced a minimal inertial sensor set and an associated biomechanical reconstruction method based on a bi-articular model of the shoulder. We have tested the system in classical shoulder rehabilitation exercises and we have demonstrated that the system is able to discriminate between correct and compensatory movement strategies.
Measuring weight and location of individual bites using a sensor augmented smart plate

In this work, a novel plate system that can detect weight and location of individual bites during meals is presented. The system consists of a base station with sensors and a detachable off-the-shelf polymer plate with three com- partments. By combining data from multiple weight sensors, the weight of individual bites can be accurately measured and localized on the plate to determine the compartment from which they were taken. With prior knowledge of the weight of the food in each compartment at the start of the meal, the system can estimate the nutritional value of the consumed food. In a test conducted in a controlled home environment, the system was able to measure the weight of consumed food in each compartment with a maximum relative error of 1.4%. The goal of the system is to replace traditional monitoring tools and to automatically monitor the amount of consumption.
Using Tweets and Emojis to Build TEAD: an Arabic Dataset for Sentiment Analysis

Our paper presents a distant supervision algorithm for automatically collecting and labeling ‘TEAD’, a dataset for Arabic Sentiment Analysis (SA), using emojis and sentiment lexicons. The data was gathered from Twitter during the period between the 1st of June and the 30th of November 2017. Although the idea of using emojis to collect and label training data for SA, is not novel, getting this approach to work for Arabic dialect was very challenging. We ended up with more than 6 million tweets labeled as Positive, Negative or Neutral. We present the algorithm used to deal with mixed-content tweets (Modern Standard Arabic MSA and Dialect Arabic DA). We also provide properties and statistics of the dataset along side experiments results. Our try outs covered a wide range of standard classifiers proved to be efficient for sentiment classification problem.
Modeling sensory-motor decisions in natural behavior

Although a standard reinforcement learning model can capture many aspects of reward-seeking behaviors, it may not be practical for modeling human natural behaviors because of the richness of dynamic environments and limitations in cognitive resources. We propose a modular reinforcement learning model that addresses these factors. Based on this model, a modular inverse reinforcement learning algorithm is developed to estimate both the rewards and discount factors from human behavioral data, which allows predictions of human navigation behaviors in virtual reality with high accuracy across different subjects and with different tasks. Complex human navigation trajectories in novel environments can be reproduced by an artificial agent that is based on the modular model. This model provides a strategy for estimating the subjective value of actions and how they influence sensory-motor decisions in natural behavior. Author summary It is generally agreed that human actions can be formalized within the framework of statistical decision theory, which specifies a cost function for actions choices, and that the intrinsic value of actions is controlled by the brain’s dopaminergic reward machinery. Given behavioral data, the underlying subjective reward value for an action can be estimated through a machine learning technique called inverse reinforcement learning. Hence it is an attractive method for studying human reward-seeking behaviors. Standard reinforcement learning methods were developed for artificial intelligence agents, and incur too much computation to be a viable model for real-time human decision making. We propose an approach called modular reinforcement learning that decomposes a complex task into independent decision modules. This model includes a frequently overlooked variable called the discount factor, which controls the degree of impulsiveness in seeking future reward. We develop an algorithm called modular inverse reinforcement learning that estimates both the reward and the discount factor. We show that modular reinforcement learning may be a useful model for natural navigation behaviors. The estimated rewards and discount factors explain human walking direction decisions in a virtual-reality environment, and can be used to train an artificial agent that can accurately reproduce human navigation trajectories.
Enhancing countries' fitness with recommender systems on the international trade network

Prediction is one of the major challenges in complex systems. The prediction methods have shown to be effective predictors of the evolution of networks. These methods can help policy makers to solve practical problems successfully and make better strategy for the future. In this work, we focus on exporting countries' data of the international trading network. A recommendation system is then used to identify the products corresponding to the production capacity of each individual country, but are somehow overlook by the country. Then, we simulate the evolution of the country's fitness if it would have followed the recommendations. The result of this work is the combination combine these two methods to provide insights to countries on how to enhance the diversification of their exported products in a scientific way and improve national competitiveness significantly, especially for developing countries.
Towards Energy-Efficient High-Throughput Photonic NoCs for 2.5D Integrated Systems: A Case for AWGRs

Silicon Photonics (SiPs) can overcome the energy and bandwidth limitations of electrical interconnects in networks-on-chip (NoCs) and enable efficient global all-to-all connectivity–the ideal from a performance perspective. Unfortunately, state-of-the-art SiP switching fabrics impose power overheads for thermo-optical control of microring resonators (MRs), excessive crosstalk, or challenging physical layout. The Arrayed Waveguide Grating Router (AWGR) is a SiP device that provides layout-efficient and scalable all-to-all connectivity through wavelength-routing on a passive and compact platform. Recent technological advances now enable AWGR integration with significantly reduced footprint (<1mm2), crosstalk (<−38dB), and loss (<2dB), making AWGRs emerge as a major enabler for energy-efficient all-to-all connectivity in NoCs. This paper, for the first time, populates the design space of AWGR-based NoC topologies and compares AWGRs to state-of-the-art SiP fabrics and aggressive electrical baselines. Our results make a compelling case for AWGRs in interposer-based systems with processor disintegration, which have high bandwidth demands, large on-chip distances, and less stringent area constraints. AWGR-based NoCs can offer an average of 1.67× power savings, 1.23× speed-up, and 1.5× lower energy-delay-product (EDP) on PARSEC3.0/SPLASH-2× workloads compared to electrical baselines, and fewer waveguides, wavelengths, or MRs than alternative SiP crossbar fabrics.
Discovery of Causal Paths in Cardiorespiratory Parameters: A Time-Independent Approach in Elite Athletes

Training of elite athletes requires regular physiological and medical monitoring to plan the schedule, intensity and volume of training, and subsequent recovery. In sports medicine, ECG-based analyses are well-established. However, they rarely consider the correspondence of respiratory and cardiac activity. Given such mutual influence, we hypothesize that athlete monitoring might be developed with causal inference and that detailed, time-related techniques should be preceded by a more general, time-independent approach that considers the whole group of participants and parameters describing whole signals. The aim of this study was to discover general causal paths among cardiac and respiratory variables in elite athletes in two body positions (supine and standing), at rest. ECG and impedance pneumography signals were obtained from 100 elite athletes. The mean heart rate, the root-mean-square difference of successive RR intervals (RMSSD), its natural logarithm (lnRMSSD), the mean respiratory rate (RR), the breathing activity coefficients, and the resulting breathing regularity (BR) were estimated. Several causal discovery frameworks were applied, comprising Generalized Correlations (GC), Causal Additive Modeling (CAM), Fast Greedy Equivalence Search (FGES), Greedy Fast Causal Inference (GFCI), and two score-based Bayesian network learning algorithms: Hill-Climbing (HC) and Tabu Search. The discovery of cardiorespiratory paths appears ambiguous. The main, still mild, rules best supported by data are: for supine - tidal volume causes heart activity variation, which causes average heart activity, which causes respiratory timing; and for standing - normalized respiratory activity variation causes average heart activity. The presented approach allows data-driven and time-independent analysis of elite athletes as a particular population, without considering prior knowledge. However, the results seem to be consistent with the medical background. Causality inference is an interesting mathematical approach to the analysis of biological responses, which are complex. One can use it to profile athletes and plan appropriate training. In the next step, we plan to expand the study using time-related causality analyses.
Improved Sparse Adaptive Algorithms for Accurate Non-contact Heartbeat Detection Using Time-Window-Variation Technique

Recently, a sparse adaptive algorithm termed zero-attracting sign least-mean-square (ZA-SLMS), has been clarified to be able to reconstruct robustly heartbeat spectrum by Doppler radar signal. However, since the strengths of noise evidently differ under different body motions, the sparse heartbeat spectra cannot be always acquired accurately by the constant regularization parameter (REPA) that balances the gradient correction and the sparse penalty, applying in the ZA-SLMS algorithm. In this paper, an improved ZA-SLMS algorithm is proposed by introducing adaptive REPA (AREPA), where the proportion of sparse penalty is adjusted based on the standard deviation of radar data. Moreover, to enhance the stability of heartbeat detection, a time-window-variation (TWV) technique is further introduced in the improved ZA-SLMS algorithm, considering the fact that the position of spectral peak associated with the heart rate (HR) is stable when the length of time window changes within a short period. Experimental results measured against five subjects validated that our proposal reliably improves the error of HR estimation than the standard ZA-SLMS algorithm.
Learning to Embed Semantic Correspondence for Natural Language Understanding

While learning embedding models has yielded fruitful results in several NLP subfields, most notably Word2Vec, embedding correspondence has relatively not been well explored especially in the context of natural language understanding (NLU), a task that typically extracts structured semantic knowledge from a text. A NLU embedding model can facilitate analyzing and understanding relationships between unstructured texts and their corresponding structured semantic knowledge, essential for both researchers and practitioners of NLU. Toward this end, we propose a framework that learns to embed semantic correspondence between text and its extracted semantic knowledge, called semantic frame. One key contributed technique is semantic frame reconstruction used to derive a one-to-one mapping between embedded vectors and their corresponding semantic frames. Embedding into semantically meaningful vectors and computing their distances in vector space provides a simple, but effective way to measure semantic similarities. With the proposed framework, we demonstrate three key areas where the embedding model can be effective: visualization, semantic search and re-ranking.
Bagging and deep learning in optimal individualized treatment rules

An ENsemble Deep Learning Optimal Treatment (EndLot) approach is proposed for personalized medicine problems. The statistical framework of the proposed method is based on the outcome weighted learning (OWL) framework which transforms the optimal decision rule problem into a weighted classification problem. We further employ an ensemble of deep neural networks (DNNs) to learn the optimal decision rule. Utilizing the flexibility of DNNs and the stability of bootstrap aggregation, the proposed method achieves a considerable improvement over existing methods. An R package “ITRlearn” is developed to implement the proposed method. Numerical performance is demonstrated via simulation studies and a real data analysis of the Cancer Cell Line Encyclopedia data.
Rapid Prototyping Method for 3D Printed Biomaterial Constructs with Vascular Structures

This paper presents a fabrication method for rapid prototyping of 3D biomaterial constructs with vascular structures. The method relies on poloxamer fugitive ink, which is over casted with a custom-made alginate based model extracellular matrix (ECM). The presented method is simple to implement and compatible with standard cell culture workflows used in biomedical research and pharmaceutical development. We present the material preparation, gelation properties and printing methods in detail. First experiments demonstrate the suitability of the ECM constructs for 3D tissue culture.
What About the Role of Virtual Reality in Parkinson Disease’s Cognitive Rehabilitation? Preliminary Findings From a Randomized Clinical Trial

Background: The purpose of this study is to evaluate the effects of a virtual reality training with BTS Nirvana (BTS-N) system in the cognitive and behavioral recovery in patients with Parkinson disease (PD). The BTS-N is a semi-immersive therapy system used for motor and cognitive rehabilitation of patients with neurological diseases, by creating virtual scenarios with which the patient may interact. Methods: We enrolled 20 patients with PD undergoing neurorehabilitation. All the patients were randomized into 2 groups: experimental group performing semi-immersive virtual reality training with BTS-N and control group undergoing traditional cognitive training. Each participant was evaluated before and immediately after the end of the training. Each cognitive training consisted of 3 sessions a week, each lasting 60 minutes, for 8 weeks, for a total of 24 sessions in each group. Results: Experimental group showed a greater improvement in cognitive functioning, with regard to executive and visuospatial abilities, as compared with the control group. Conclusion: Our findings suggest that rehabilitation through a new virtual reality instrument could be a valuable tool in improving cognitive and behavioral outcomes of patients with PD.
Deep Learning for Medication Assessment of Individuals with Parkinson’s Disease Using Wearable Sensors

Motor fluctuations between “OFF” state (with no benefit from medication) and “ ON” state (with optimum benefit from medication) are a major focus of clinical managements in individuals with mid-stage and advance Parkinson’s disease (PD). In this work, an automated algorithm based on Long Short-Term Memory (LSTM) as a deep learning method is developed to identify motor fluctuations in individuals with PD using wearable sensors during a variety of daily living activities. This network was evaluated on two datasets i.e., Dataset 1 and Dataset 2) that included recordings of 19 individuals with PD using subject-based leave-one-out cross-validation. The designed LSTM network yielded promising results using only one ankle sensor with an average classification rate of 73% and 77% for Dataset 1 and Dataset 2, respectively.
Effects of instructed meditation augmented by computer-rendered artificial virtual environment on heart rate variability

Previous research has supported the use of virtual reality (VR) to decrease stress, anxiety, perceptions of pain, and increase positive affect. However, the effect of VR on blood pressure (BP) and autonomic function in healthy populations have not been explored. This study quantifies the effect of instructed meditation augmented by a virtual environment (VE) on BP and heart rate variability (HRV) during rest and following physical (isometric handgrip) or mental (serial sevens subtraction) stress. Sixteen healthy participants underwent all conditions, and those that responded to the stress tests were included in the analysis of stress recovery. Results showed that under resting conditions, VE had no significant effect on BP or HRV when compared to seated rest and the VE video on a 2D screen. Following serial sevens, VE maintained the increased low frequency (LF) power of HRV $( 66 \pm 4$ normalized units (n.u.)) compared to seated rest $( 55 \pm 5\mathrm {n}$.u., $\mathrm {p}=0.0060)$; VE maintained the decreased high frequency (HF) power of HRV $( 34 \pm 4\mathrm {n}$.u.) compared to seated rest $( 44 \pm 5\mathrm {n}$.u., $\mathrm {p}=0.014)$; and VE maintained the increased LF/HF ratio $( 2.4 \pm 0.5)$ compared to seated rest $( 1.6 \pm 0.3$, $\mathrm {p}=0.012)$. Hence, after mental stress, VE sustains the increased sympathetic drive and reduced parasympathetic drive. VE may act as a stimulatory driver for autonomic activity and BP. Further studies are required to investigate the effect of different types of VE on BP and autonomic function.
The Unbearable Lightness of Being Retired

RÉSUMÉ Dans cette étude qualitative, j’ai utilisé une approche fondée sur la gérontologie narrative pour examiner les perceptions liées à la retraite dans un groupe de 26 hommes et femmes qui ont pris leur retraite après un mandat de chef de la direction. Le guide d’entrevue a pris en compte la perspective du parcours de vie. Trois thèmes clés ont émergé : 1) l’importance de la productivité et du réseautage, alors que les participants gravissaient les échelons de l’entreprise ; 2) le sentiment d’avoir une date « meilleur avant » et d’avoir cédé aux pressions de la société en faveur de la retraite ; et 3) l’impression troublante de se sentir insignifiant à la retraite tout en ayant le désir de poursuivre l’épanouissement personnel en continuant à travailler contre rémunération. Après une carrière marquée par des réalisations pionnières, les participants ont éprouvé un sentiment insoutenable de légèreté lors de l’abandon des responsabilités professionnelles. Ces résultats mettent en lumière l’importance de la perspective de parcours de vie pour examiner la retraite à la fois comme une expérience personnelle et un phénomène social. Les résultats contribuent également à la compréhension théorique du vieillissement productif en illustrant comment les idées préconçues sur la productivité contrastent avec les aspirations associées à une retraite remplie de loisirs, d’une manière qui pourrait réduire les contributions à l’emploi de certaines personnes. ABSTRACT In this qualitative study, I followed an approach to examine perceptions about retirement, using an interview guide informed by the life course perspective, among 26 men and women who had retired from positions as chief executive officers. Three key themes emerged: (1) the importance of productivity and networking as participants rose up the corporate ladder; (2) the sense of having a “best before” date and experiencing societal pressures to retire; and (3) struggles with feeling insignificant in retirement while desiring personal fulfillment through continued engagement in paid work. These findings shed light on the value of using a life course perspective to examine retirement as both a personal experience and as a social phenomenon. Findings also contribute to theoretical understandings of productive aging by illustrating how preconceptions about productivity contrast with ideations of a leisure-filled retirement in ways that can foreshorten the employment contributions of some individuals.