Miracle-dz/UniFAR-FTUs huggingface dataset:
Completely Uncoupled Algorithms for Network Utility Maximization

In this paper, we present two completely uncoupled algorithms for utility maximization. In the first part, we present an algorithm that can be applied for general non-concave utilities. We show that this algorithm induces a perturbed (by ${\epsilon }$ ) Markov chain, whose stochastically stable states are the set of actions that maximize the sum utility. In the second part, we present an approximate sub-gradient algorithm for concave utilities, which is considerably faster and requires lesser memory. We study the performance of the sub-gradient algorithm for decreasing and fixed step sizes. We show that, for decreasing step sizes, the Cesaro averages of the utilities converges to a neighborhood of the optimal sum utility. For constant step size, we show that the time average utility converges to a neighborhood of the optimal sum utility. Our main contribution is the expansion of the achievable rate region, which has not been considered in the previous paper on completely uncoupled algorithms for utility maximization. This expansion aids in allocating a fair share of resources to the nodes, which is important in applications like channel selection, user association, and power control.
Sex and Maturation Differences in Performance of Functional Jumping and Landing Deficits in Youth Athletes.

CONTEXT Understanding how neuromuscular and biomechanical deficits that are associated with knee injuries change as youth mature may improve injury prevention strategies in this population. OBJECTIVE To investigate sex and maturation differences in jump-landing technique performance in youths using a practical clinical tool. DESIGN Cross-sectional study. SETTING High Performance Center Laboratory. PARTICIPANTS A total of 165 youth athletes were included in this study. MAIN OUTCOME MEASURES The main outcome measures were each of the 10 items of the modified tuck jump assessment and the total score. These measures include (1) knee valgus at landing, (2) thighs do not reach parallel, (3) thighs not equal side to side, (4) foot placement not shoulder width apart, (5) foot placement not parallel, (6) foot contact timing not equal, (7) excessive landing contact noise, (8) pause between jumps, (9) technique declines prior to 10 seconds, and (10) does not land in same footprint. RESULTS Only knee valgus at landing had a significant sex × maturation interaction. The main effect of maturation was significant for items 2, 3, 6, 7, 9, and total score. Plyometric technique performance improved with increasing maturation. The main effect of sex was significant for items 1 and 9, with males performing better than females. CONCLUSIONS Female athletes demonstrate increased knee valgus at landing and fatigue relative to males during jump-landing performance. Overall, there was a trend of improved jump-landing performance with maturation.
Improving the Performance of Anthropometry Measurements in the Pediatric Intensive Care Unit

Introduction: Obtaining anthropometry measurements in critically ill children is challenging. Our objective was to improve the process of obtaining anthropometry measurements in the pediatric intensive care unit (PICU; even if previously obtained) using a dedicated PICU nutrition support team (NST). Methods: PICU staff were trained to perform anthropometry measurements through online education, skills training, and just-in-time bedside teaching by the PICU NST. Equipment was upgraded and standardized throughout the PICU along with implementation of preselected orders in the electronic medical record. Data were collected before and immediately after intervention and at monthly intervals from 12 to 36 months to test sustainability of practice change. PICU staff were surveyed on barriers to anthropometry measurements at 36 months after initial intervention. Results: Compared with baseline, the intervention resulted in more patients with orders for weight, stature, and head circumference (all P < 0.001) at PICU admission. Correspondingly, more patients had measurements of weight (P = 0.04), stature (P = 0.01), and head circumference (P = 0.009) at PICU admission. For long-stay patients (>7 days), compliance improved with measurements of serial weights (P = 0.002), stature (P < 0.001), and head circumference (P = 0.02). Between 12 and 36 months after the intervention, there was a noticeable trend to increases in weight measurements at PICU admission, and to a lesser extent, of stature and head circumference. Competing clinical priorities were a key barrier to anthropometry measurements. Conclusions: Performance of anthropometry measurements in the PICU can be improved by a dedicated PICU NST; however, sustaining these improvements is challenging due to competing clinical priorities.
On the integrability of Birkhoff billiards

In this survey, we provide a concise introduction to convex billiards and describe some recent results, obtained by the authors and collaborators, on the classification of integrable billiards, namely the so-called Birkhoff conjecture. This article is part of the theme issue ‘Finite dimensional integrable systems: new trends and methods’.
Single Channel Speech Separation with Constrained Utterance Level Permutation Invariant Training Using Grid LSTM

Utterance level permutation invariant training (uPIT) technique is a state-of-the-art deep learning architecture for speaker independent multi-talker separation. uPIT solves the label ambiguity problem by minimizing the mean square error (MSE) over all permutations between outputs and targets. However, uPIT may be sub-optimal at segmental level because the optimization is not calculated over the individual frames. In this paper, we propose a constrained uPIT (cuPIT) to solve this problem by computing a weighted MSE loss using dynamic information (i.e., delta and acceleration). The weighted loss ensures the temporal continuity of output frames with the same speaker. Inspired by the heuristics (i.e., vocal tract continuity) in computational auditory scene analysis, we then extend the model by adding a Grid LSTM layer, that we name it as cuPIT-Grid LSTM, to automatically learn both temporal and spectral patterns over the input magnitude spectrum simultaneously. The experimental results show 9.6% and 8.5% relative improvements on WSJ0-2mix dataset under both closed and open conditions comparing with the uPIT baseline.
Stabilization of the Reaction Wheel Pendulum via a Third Order Discontinuous Integral Sliding Mode Algorithm

In this work, the stabilization of the Reaction Wheel Pendulum using a Third Order Discontinuous Integral Sliding Mode as the control is done. The use of the discontinuous function in the integral action generates a continuous control signal and minimize the chattering effect. The states reach the origin even in presence of Lipschitz uncertain-ties/disturbances. The stability analysis is presented using a Lyapunov approach and homogeneity properties, ensuring a local finite-time convergence of the states to the origin. Simulations and experiments were made to check the performance of the presented algorithm.
Comparison of Handcrafted Features and Deep Learning in Classification of Medical X-ray Images

The rapid growth and spread of radiographic equipment in medical centres have resulted in a corresponding increase in the number of medical X-ray images produced. Therefore, more efficient and effective image classification techniques are required. Three different techniques for automatic classification of medical X-ray images were compared. A bag-of-visual-words model and a Convolutional Neural Network (CNN) were used to extract features from the images. The two groups of extracted feature vectors were each used to train a linear support vector machine classifier. Third, a fine-tuned CNN was used for end-to-end classification. A pre-trained CNN was used to overcome dataset limitations. The three techniques were evaluated on the ImageCLEF 2007 medical database. The database provides medical X-ray images in 116 categories. The experimental results showed that fine-tuned CNN outperforms the other two techniques by achieving per class classification accuracy above 80% in 60 classes compared to 24 and 26 classes for bag-of-visual-words and CNN extracted features respectively. However, certain classes remain difficult to classify accurately such as classes in the same sub-body region due to inter-class similarity.
Full-Duplex or Half-Duplex D2D Mode? Closed Form Expression of the Optimal Power Allocation

In this paper, we investigate and derive a closed-form expression for the power allocation scheme of full duplex (FD) device to device (D2D) communications underlaying wireless cellular network. In this scenario, we consider the FD-D2D pair sharing the uplink resources of cellular users. We first derive a closed-form expression for the ergodic rate of the D2D link. Then we formulate the optimization problem which aims to maximize the D2D link rate while fulfilling the minimum QoS requirement of the cellular user. We further derive a closed-form expression for the optimal power allocation strategy for both D2D and cellular users. The simulation results show the accuracy of the derived power allocation scheme and provide important insights on the separation distance between the D2D users and the interfering cellular user. In addition, the results provide important conditions to switch between FD and half duplex (HD) D2D modes.
Color Image Coding Based on Block Truncation Coding using Quadtree Segmentation

In this paper, we proposed a bit-rate adjustable color image compression technique based on block truncation coding. To exploit the similarity among the neighboring pixels, the quadtree segmentation technique is used to divide the color image into variable-sized blocks based on their block activities. Different rules are used to encode the image blocks of different sizes. Experimental results reveal that good image qualities of the reconstructed color images can be achieved by the proposed technique while keeping low bit rates.
Image Recognition Based on Separable Lattice Hmms Using a Deep Neural Network for Output Probability Distributions

This paper proposes an image recognition method based on separable lattice hidden Markov models (SLHMMs) using a deep neural network (DNN) for output probability distributions. The geometric variations of the object to be recognized, e.g., size and location, are essential in image recognition. SLHMMs, which have been proposed to reduce the effect of geometric variations, can perform elastic matching both horizontally and vertically. Gaussian distributions are typical for modeling the output distribution of SLHMMs. However, these distributions may not be sufficient to represent patterns of image regions. Our method integrates SLHMMs and a DNN and can be used to model an image effectively by explicit modeling of the generative process based on SLHMMs and advanced feature classification based on a DNN. image recognition experiments showed that the proposed method improves recognition performance.
The regulation of cilium assembly and disassembly in development and disease

ABSTRACT The primary cilium is an antenna-like organelle assembled on most types of quiescent and differentiated mammalian cells. This immotile structure is essential for interpreting extracellular signals that regulate growth, development and homeostasis. As such, ciliary defects produce a spectrum of human diseases, termed ciliopathies, and deregulation of this important organelle also plays key roles during tumor formation and progression. Recent studies have begun to clarify the key mechanisms that regulate ciliary assembly and disassembly in both normal and tumor cells, highlighting new possibilities for therapeutic intervention. Here, we review these exciting new findings, discussing the molecular factors involved in cilium formation and removal, the intrinsic and extrinsic control of cilium assembly and disassembly, and the relevance of these processes to mammalian cell growth and disease. Summary: This Review discusses the molecular factors involved in cilium formation and removal, the relevance of these processes to development and how recent findings are highlighting new possibilities for therapeutic intervention in disease.
A Storm in an IoT Cup: The Emergence of Cyber-Physical Social Machines

The concept of ‘social machines’ is increasingly being used to characterise various socio-cognitive spaces on the Web. Social machines are human collectives using networked digital technology which initiate real-world processes and activities including human communication, interactions and knowledge creation. As such, they continuously emerge and fade on the Web. The relationship between humans and machines is made more complex by the adoption of Internet of Things (IoT) sensors and devices. The scale, automation, continuous sensing, and actuation capabilities of these devices add an extra dimension to the relationship between humans and machines making it difficult to understand their evolution at either the systemic or the conceptual level. This article describes these new socio-technical systems, which we term Cyber-Physical Social Machines, through different exemplars, and considers the associated challenges of security and privacy.
AFZ converter: a new DC-DC topology applied to photovoltaic panels

Distributed Maximum Power Point Tracking (DMPPT) architectures are one of the most promising solutions to overcome the drawbacks associated to mismatching phenomena in photovoltaic (PV) applications. In this kind of architectures, a DC-DC module integrated converter manages each PV panel to harvest the maximum available power from the Sun. High efficiency, low cost and the capability of both voltage step-up and step-down are the most desired characteristics for the converters to be applied in this field. A new DC-DC converter is proposed in this paper, the Autotransformer Forward converter with type-Zeta resonant demagnetization (AFZ), as a good candidate to be applied in DMPPT architectures. The AFZ converter principle operation as well as its static characteristics are introduced. A 225W prototype has been built, according to the specifications obtained from a 100kW grid-connected PV system, to validate the proper operating of the AFZ converter, achieving efficiencies up to 94.82%.
Analysis of Content-Aware Image Compression with VGG16

Content-aware compression based on the use of saliency maps aims to improve the interpretability of an image by encoding the more relevant image regions with a higher quality than the rest of the image. This paper revisits two convolutional neural network (CNN) models based on VGG16, multi-structure region of interest (MS-ROI) and class activation map (CAM), which enable the localization of salient image regions. While the MS-ROI model allows for the localization of multiple salient image regions, the CAM model, on the other hand, tends to localize only the most relevant class. We use the contextual information provided by the obtained saliency maps to guide the compression. By encoding more important image regions at a higher bitrate and less important ones at a lower bitrate, different qualities of compression for the regions of interest and the background are obtained, while also achieving smooth transitions from salient to non-salient regions. The performance of both models is evaluated on images from the MIT Saliency Benchmark dataset and the General-100 dataset, and the results of the compression are compared to the standard JPEG compression at different quality factors. Experimental results show that for the files of approximately same size, the compression methods based on the two CNN models outperform the standard JPEG compression. When comparing the compression based on the MS-ROI model to the compression based on the CAM model, the former is characterized by a higher PSNR and a better visual quality of the obtained images.
Learning by Demonstration and Adaptation of Finishing Operations Using Virtual Mechanism Approach

In this paper we propose a new approach for efficient programming of grinding and polishing operation. In the proposed system, the initial policy is performed by a skilled operator and recorded with a passive digitizer. The demonstrated policy comprises both position and force data. The optimal robot execution of the task is provided by applying a virtual mechanism approach, which models the polishing/grinding tool as a serial kinematic chain. By joining the robot and the virtual mechanism in an augmented system, additional degrees of freedom are obtained and redundancy resolution can be applied to optimize the demonstrated motion. Another benefit of the proposed approach is that the same policy can be transferred to different combination of robots and grinding/polishing tools without any modification of the captured motion. The proposed approach requires known contact point between the treated object and the polishing/grinding tool. We propose a novel approach for accurate estimation of this point using data obtained from the force-torque sensor. Finally, the demonstrated path is refined to compensate for inaccurate calibration and different dynamics of a robot and the human demonstrator using iterative learning controller. The proposed method was verified in a real industrial environment.
Composite Learning Finite-Time Control With Application to Quadrotors

This paper addresses two composite learning controller designs of quadrotor dynamics with unknown dynamics and time-varying disturbances using the terminal sliding mode. For unknown system dynamics, the single-hidden-layer feedforward network is employed for approximation which provides the information for the disturbance observer. Based on composite learning using neural approximation and disturbance estimation, the terminal sliding mode control (TSMC) is synthesized to obtain the finite-time convergence performance. To overcome the singularity problem, nonsingular TSMC is proposed. The closed-loop system stability under the two proposed controllers is presented via Lyapunov approach and the system trajectory will converge to the region caused by approximation error and disturbance estimation error. Simulation results demonstrate that the composite learning can efficiently estimate the system uncertainty and the tracking performance under the proposed controllers can be enhanced.
Malaysian Carica papaya L. var. Eksotika: Current Research Strategies Fronting Challenges

Carica papaya L. or commonly known as papaya, is a major tropical crop consumed worldwide either as a vegetable or fresh fruit or processed products. In Malaysia, papaya was initially planted as a smallholder crop throughout the country. Eventually after 15 years of breeding and selection, a new variety, named C. papaya L. var. Eksotika, was released by the Malaysian Agricultural Research and Development Institute (MARDI) in 1987. This event changed the outlook of papaya planting from a smallholder crop to a plantation crop. Despite the blooming papaya business, the industry faced various disease issues that jeopardize its future. The most devastating was the papaya dieback disease, which affected approximately 800 hectares of plantation, destroyed approximately 1 million trees nationwide with total losses estimated at US$ 58 million. Even though Eksotika is a favored commercial variety with good eating and aesthetic quality fruit, its potential for more lucrative distant markets is tarnished with its short-shelf life fruits. Several strategies had been reported to address the challenges faced by Eksotika specifically against the dieback disease and the fruit’s short shelf-life. This review focuses on C. papaya L. var. Eksotika particularly on the strategies to address the challenges faced in order to sustain the economic value of this crop plant, which had contributed significantly to the Malaysian economy.
Efficient Circulant Matrix Construction and Implementation for Compressed Sensing

The design of measurement matrices is an important part in compressed sensing (CS). Random matrices superior to incoherence are considered to be optimal measurement matrices to achieve successful recovery. However, they are deficient in memory cost. Structure matrices like circulant matrices are preferred for low-memory cost. Nevertheless, their recovery performance is greatly damaged because of element coherence. In this paper, a new method called different-spaced selection & different-spaced flipping (DSS & DSF) is proposed to modify structure matrices. Based on circulant matrices, regular extraction and symbol flipping imposed on columns of measurement matrices can increase randomness to a large scale. As a result, not only near optimal recovery but also much less memory cost can be achieved. Compared with Gaussian random matrices, the memory cost can be reduced to 4% when measurement matrices based on circulant matrices are in 128 × 512 dimensions. An efficient hardware design and VLSI implementation are also presented at the end of this paper.
A Battery-Less BLE IoT Motion Detector Supplied by 2.45-GHz Wireless Power Transfer

Wireless power transfer (WPT) is a possible way to achieve cheap, rapid and large-scale deployment of battery-less IoT smart sensors. However, the key challenge in designing WPT-supplied smart sensors is to ensure that the harvested power is higher than the consumed power in presence of strong WPT path loss. This paper presents the design and optimization of a smart sensor supplied by 2.45-GHz RF power and performing infrared-based motion detection and Bluetooth Low Energy (BLE) communication. The full system (RF energy harvester, power management, sensor transducer and interface, control, data processing and wireless transmission) is implemented using low-power off-the-shelf components. In the sensing sub-system, low average power is achieved using efficient duty-cycling of the analog front-end. In the harvesting sub-system, architecture choice and design of the matching network and rectifier circuit allow to optimize the total power harvesting efficiency (PHE). The implemented smart sensor can operate reliably with an incident RF power as low as −10.9 dBm. The sensing sub-system exhibits an average power of 4.3 μW and a maximum number of transmitted event packets of 60 packets per hour.
2D SLAM Correction Prediction in Large Scale Urban Environments

Simultaneous Localization And Mapping (SLAM) is one of the major bricks needed to build truly autonomous mobile robots. The probabilistic formulation of SLAM is based on two models: the motion model and the observation model. In practice, these models, together with the SLAM map representation, do not model perfectly the robot's real dynamics, the sensor measurement errors and the environment. Consequently, systematic errors affect SLAM estimations. In this paper, we propose two approaches to predict corrections to be applied to SLAM estimations. Both are based on the Ensemble Multilayer Perceptron model. The first approach uses successive estimated poses to predict the errors, with no assumptions on the underlying SLAM process or sensor used. The second method is specific to 2D likelihood SLAM approaches, thus, the likelihood distributions are used to predict the corrections, making this second approach independent of the sensor used. We also build a hybrid correction module based on successive estimated poses and the likelihood distributions. The validity of both approaches is evaluated through two experiments using different evaluation metrics and sensor configurations.