Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Wiener-Based Reconstruction of Graph Signals

This paper proposes strategies for distributed Wiener-based reconstruction of graph signals from subsampled measurements. Given a stationary signal on a graph, we fit a distributed autoregressive moving average graph filter to a Wiener graph frequency response and propose two reconstruction strategies: i) reconstruction from a single temporal snapshot; ii) recursive signal reconstruction from a stream of noisy measurements. For both strategies, a mean square error analysis is performed to highlight the role played by the filter response and the sampled nodes, and to propose a graph sampling strategy. Our findings are validated with numerical results, which illustrate the potential of the proposed algorithms for distributed reconstruction of graph signals.
Empirical Studies on Phase Retrieval

In phase retrieval, the signal is recovered from magnitude data that can be collected in various ways, ranging from the Fourier transform to general linear transform. Through experimental observation, we address the questions: (1) which sampling method, given the number of measurements versus signal size, best recovers the underlying signal? and (2) for each sampling method which recovery algorithm maximizes recovery success? Our empirical results contribute to better characterizing the phase retrieval problem.
Effect of long chain fatty acids on triacylglycerol accumulation, fatty acid composition and related gene expression in primary cultured bovine satellite cells

Abstract This study was conducted to determine the effects of long chain fatty acids (LCFAs) on triacylglycerol (TAG) content, as well as on genes associated with lipid synthesis and fatty acid composition in bovine satellite cells. Both saturated (palmitic and stearic) and unsaturated (oleic and linoleic) fatty acids stimulated the TAG accumulation at a concentration of 100 µM and oleate increased it significantly more than stearate and palmitate. The results revealed that the lipid droplet formation was markedly stimulated by linoleate and oleate at 100 µM. Compared to control, the expressions of adipose triglyceride lipase, carnitine acyltransferase 1 and the fatty acid translocase 36 were upregulated by LCFAs. All the fatty acids also significantly increased diacylglycerol acyltransferase 2 than the untreated control (p < 0.05). The monounsaturated fatty acids significantly increased (p < 0.05) in response to oleate and linoleate compared to the control as did the polyunsaturated fatty acids (p < 0.05), in addition to stearate, linoleate and oleate. In contrast, saturated fatty acids were significantly decreased in the oleate and linoleate-treated groups. The study results contribute to our enhanced understanding of LCFAs’ regulatory roles on the bovine cell lipid metabolism.
Impact of Addition of Large Scale Renewable Generation in North West Territory of KSA

The electricity demand in the Saudi Arabia is expected to reach around 125 GW by 2032. The ever increasing electric demand coupled with the need to conserve the oil and natural gas resources has provided KSA an incentive to move towards the path of the renewable. The North West (NWOA) territory close to Gulf of Aqaba in Saudi Arabia has been identified as one region with good potential for renewable energy development. This region is characterized by excellent and steady wind speed profile and solar irradiation. However, one of the major hurdles that slow down the increased utilization of renewable is grid access. This paper examines several alternatives to connect approximately 10000 MW of NWOA/NEOA renewable generation to the load centers in Central Operating Area (COA). Alternatives span the use of 500kV, 725kV, and HVDC technologies is considered. Techno-economic evaluation of those alternatives is presented in this paper.
Selection of UE-based Virtual Small Cell Base Stations using Affinity Propagation Clustering

5G will require a number of Key Technological Components to meet its very ambitious goals, including Heterogeneous Networks a nd Small Cells. The Dense Deployme nt of Small Cell Base Stations (SBSs) will play a major role as they can be installed in a more targeted manner to relieve traffic in hot spot areas, increase coverage, and spectral efficiency. However, the current deployment of the SBS is static (examples include Femto, Pico, and Nano cells), normally deployed on demand around hotspots. In the scenario of unpredictable crowd movement, these static deployment of small cell can be inefficient with high CAPEX and OPEX. This paper focuses on the scenario where UE-based Virtual Small Cell Base Stations (UE-VBSs) can be dynamically selected among UEs to deal effectively with the non-stationary, non-uniform distribution of mobile traffic with respect to time and space domain. To implement this concept, we propose an efficient clustering technique (Affinity Propagation Clustering) to select the best set of UE-VBSs to supplement an overlay loaded SBS. Simulative evaluation highlights salient features of the technique, as well as its limitations with regard to scalability. Further, we discuss the modification of the algorithm according to our objec tive sc enario focusing on red ucing the message passing procedure to and from only a number of eligible UE-VBSs using the power received by a UE from the eligible UE-VBSs as a parameter. The algorithm is implemented and evaluated in MATLAB and, also validated using NS3 simulator.
Multi-View Transformation via Mutual-Encoding InfoGenerative Adversarial Networks

The problem of multi-view transformation is associated with transforming available source views of a given object into unknown target views. To solve this problem, a Mutual-Encoding InfoGenerative Adversarial Networks (MEIGANs)-based algorithm is proposed in this paper. A mutual-encoding representation learning network is proposed to obtain multi-view representations, i.e., it guarantees through encoders different views of the same object are mapped to the common representation, which carries enough information with respect to the object itself. An InfoGenerative Adversarial Networks-based transformation network is proposed to transform multi-views of the given object, which carries the representation information in the generative models and discriminative models, guaranteeing the synthetic transformed view matches the source view. The advantages of the MEIGAN are that it bypasses direct mappings among different views, and can solve the problem of missing views in training data and the problem of mapping between transformed views and source views. Finally, experiments on incomplete data to complete data restoration tasks on MNIST, CelebA, and multi-view angle transformation tasks on 3-D rendered chairs and multi-view clothing show the proposed algorithm yields satisfactory transformation results.
Self-Calibrating Nonlinear Reconstruction Algorithms for Variable Density Sampling and Parallel Reception MRI

Compressed Sensing has allowed a significant reduction of acquisition times in MRI, especially in the high resolution (e.g., 400 μm) context. However, in this setting CS must be combined with parallel reception as multichannel coil acquisitions maintain high input signal-to-noise ratio (SNR). To get rid of usual parallel imaging limitations (output SNR loss), non-Cartesian trajectories provide a gain in sampling efficiency in the CS context. In this paper, we propose a self-calibrating MRI reconstruction framework that handles variable density sampling. Low resolution sensitivity maps are estimated from the low frequency k-space content using an original and fast method while MR images are reconstructed using a nonlinear iterative algorithm, which promotes sparsity in the wavelet domain. As regards the optimization task, we compare three first-order proximal gradient methods: Forward Backward (FB), Fast Iterative Soft Thresolding Algorithm (FISTA) and Proximal Optimized Gradient Method (POGM) and evaluate their respective convergence speed. Comparison with state-of-the-art (i.e., ℓl-ESPIRiT) suggests that our self-calibrating POGM-based algorithm outperforms current approaches both in terms of image quality and computing time on several acquired data collected at 7 Tesla and we will focus more specifically on prospective non-Cartesian 8-fold accelerated in vivo Human brain data.
Correction of Mobile Positioning and Direction via CNNs Based on Street View Images

We present an application of convolutional neural networks (CNNs) in street view images recognition to help users correct positioning errors and find the right direction under GPS positioning. We create a CNN model for each GPS coordinate point, and the datasets for each model are street view images of eight directions of each point and image samples of the surrounding neighboring and other points. The output labels of each model are eight directions and others. Therefore, we could use a CNN model to rough identify whether an image belongs to the assigned coordinate point and to know which possible direction it is facing. In addition, in order to improve the efficiency of matching possible points, we propose a spiral search neighbors approach. Finally, we use speeded up robust features (SURF) for more precise positioning.
Effects of Latency Jitter on Simulator Sickness in a Search Task

Low latency is a fundamental requirement for Virtual Reality (VR) systems to reduce the potential risks of cybersickness and to increase effectiveness, efficiency and user experience. In contrast to the effects of uniform latency degradation, the influence of latency jitter on user experience in VR is not well researched, although today's consumer VR systems are vulnerable in this respect. In this work we report on the impact of latency jitter on cybersickness in HMD-based VR environments. Test subjects are given a search task in Virtual Reality, provoking both head rotation and translation. One group experienced artificially added latency jitter in the tracking data of their head-mounted display. The introduced jitter pattern was a replication of a real-world latency behavior extracted and analyzed from an existing example VR-system. The effects of the introduced latency jitter were measured based on self-reports simulator sickness questionnaire (SSQ) and by taking physiological measurements. We found a significant increase in self-reported simulator sickness. We therefore argue that measure and control of latency based on average values taken at a few time intervals is not enough to assure a required timeliness behavior but that latency jitter needs to be considered when designing experiences for Virtual Reality.
Comparative Polarity Analysis on Amazon Product Reviews Using Existing Machine Learning Algorithms

Opinion mining has emerged as an active domain among the research fraternity because an enormous amount of heterogeneous user data is continuously increasing every day via www, viz., e-commerce websites, social networks, discussion forums, blogs etc. Intentions are expressed in a different way with different vocabulary, short forms, and jargon making the data massive and disorganized. It has turned out an exciting new trend in social media with a scope of practical applications like analyzing marketing strategy, political analysis, social media analysis, financial analysis to take an effective decision based on user's feedback. With that being said, there are many unsolvable issues and challenges still existing that keeps the field more dynamic. As we know, it is very difficult to understand human language and more complex to program the machine to analyze the user's context. The proposed work aims at evaluating the sentiments of Amazon product reviews by using programming languages with the help of existing natural language processing APIs. Our aim is to categorize the sentiments of a Meta dataset, parse and protrude the comparative accuracy levels. Senti - A new algorithm is proposed to analyze the sentiments accurately which outperforms the existing APIs.
Lucid Virtual Dreaming: Antecedents and Consequents of Virtual Lucidity During Virtual Threat

Here we report the first empirical findings on virtual lucidity (VL), a new construct similar to lucidity during dreaming, but regarding awareness that one is having a virtual experience. VL concerns the depth and breadth of this awareness, as well as the extent it affords regulatory monitoring and control. To study VL, we adapted a measure from lucid dreaming research to assess whether more VL predicted lower fear, but not less enjoyment, during a virtual reality (VR) threat scenario of walking, and being asked to step off, a wooden plank seemingly high above a city. We examined predictors of VL and related outcomes across a community sample and lucid dream trainees at a meditation retreat center. In line with hypotheses, higher VL predicted less fear, more enjoyment, and greater likelihood of stepping off the plank. Moreover, a number of dispositional factors predicted greater VL and lower fear. Lucid dream retreatants, engaged in a contemplative practice called illusory form yoga, experienced more VL and less fear compared to nonretreatants, with marginally higher likelihood of stepping off the plank. Finally, VL mediated all significant relations between predictors and outcomes. Results held controlling for presence or fear of heights. We discuss the potential validity and utility of VL, its relation to presence, and examples of how it may inform the development and application of VR and related technologies.
A Hybrid Approach to Cutting Tool Remaining Useful Life Prediction Based on the Wiener Process

Accurate remaining useful life prediction is meaningful for cutting tool usability evaluation. Over the years, experience-based models, data-driven models, and physics-based models have been used individually to predict cutting tool remaining useful lives. In order to improve prediction performances, different prognostics models can be combined to leverage their advantages. In this paper, a hybrid cutting tool remaining useful life prediction approach is proposed by combining a data-driven model and a physics-based model. By using force, vibration and acoustic emission signals, the data-driven model monitors cutting tool wear conditions based on empirical mode decomposition and back propagation neural network. On the basis of the Wiener process, the physics-based model builds a cutting tool condition degradation model to predict cutting tool remaining useful lives. Experimental study verifies the approach's effectiveness, accuracy, and robustness. Then, cutting tool remaining useful lives can be predicted more accurately during the machining process.
Research on road semantic segmentation based on hybrid auto-encoder

Road detection is an important part of the environment perception technology of unmanned vehicles. Using computer vision technology to achieve the semantic segmentation of environmental scenes is one of the key technology to ensure the safe driving of unmanned vehicles. In this paper, a hybrid autoencoder semantic segmentation model combining sparse autoencoder and denoising autoencoder is proposed. The model uses sparse autoencoder to conduct sparse semantic encoding and use denoising autoencoder to conduct robust semantic encoding. In this way, the features learned by the model are more conducive for semantic segmentation. Through the deep rich structure semantic segmentation model, an optimized selection of image semantics can be achieved, and further improve the semantic segmentation performance. Experiments show that this model is simpler, with less training time, and better comprehensive segmentation performance.
Visual Perception of Real World Depth Map Resolution for Mixed Reality Rendering

Compositing virtual objects into photographs with known real world geometry is a common task in mixed reality (MR) applications. This geometry enables rendering of global illumination effects, such as mutual lighting, shadowing, and occlusions between the background photograph and virtual objects. Obtaining high fidelity geometric representations of the real world can be a costly procedure, and is often approximated with depth data. However, it is not clear how much fidelity the depth data should have in order to maintain high visual quality in MR rendering. in this paper, we investigate the relationship between real world depth fidelity and visual quality in MR rendering. We do this by conducting a series of user experiments that measure how seamlessly virtual objects are blended with the background under varying depth resolutions. We independently evaluate the noticeability of multiple composition artifacts that occur with approximate depth. Perceptual thresholds in depth resolution are then obtained for each artifact. The findings can be used to inform trade-off decisions for optimising depth acquisition pipelines in MR applications.
An Improved Structures of Modified CUK Converter using VLSI module (MCCVLSI) for High-Voltage Renewable Energy Applications

In this paper, an improved structures of modified CUK converter (MCCVLSI) is presented using Voltage Lift Switched Inductor (VLSI) module. The proposed converter provides a negative high DC voltage which is a doable solution for renewable energy applications. A three new modified CUK converter structures are derived with the help of VLSI module as; MCCVLSI-XLL, MCCVLSI-L YL and MCCVLSI-LLZ. The complete mathematical analysis is provided to calculated voltage conversion ratio. Moreover, the proposed structures are compared with each others in terms of voltage conversion ratio and number of components. The working mode of MCCVLSI-XLL is explained in details. The qualities of the presented structures are reviewed in details. The simulation results are provided and the obtained results show a good agreement with theoretical analysis.
Bone mineral density of human ear ossicles: An assessment of structure in relation to function

Human ear ossicles are essential for normal sound conduction from the external environment to the inner ear. These bones are subjected to high biomechanical loads due to the sustained vibrations which occur with reception of sound. It is expected that the bones would undergo a significant amount of remodeling and change in bone mineral density during the lifespan of an individual. Therefore, the aim of the study was to evaluate the possible changes in bone mineral density of regions of the ossicular chain during postnatal life. Forty four left and 36 right sets of human ear ossicles, ranging from a sub‐adult age group to a mature adult age group, were sourced from the School of Anatomical Sciences, University of Witwatersrand. The ear ossicles were scanned using a micro‐focus CT X‐ray. A three‐dimensional reconstruction of each ossicle was created from the CT scan. Bone mineral density was then determined at specific sites on the ossicles. There was no statistically significant variation found in the bone mineral density in relation to the age of the specimens. However, the handle of the malleus, the incudo‐stapedial joint and the insertion site for the tendon of stapedius had lower bone mineral densities when compared to adjacent articulation and nonattachment sites on the ossicular chain. This is possibly due to biomechanical stress in response to sound conduction rather than ageing. Lower bone mineral density may be indicative of regions that experience the highest biomechanical force, thus, resulting in increased remodeling. Clin. Anat. 31:1158–1166, 2018. © 2018 Wiley Periodicals, Inc.
Domain Wall Motion-Based Dual-Threshold Activation Unit for Low-Power Classification of Non-Linearly Separable Functions

Recently, a great deal of scientific endeavour has been devoted to developing spin-based neuromorphic platforms owing to the ultra-low-power benefits offered by spin devices and the inherent correspondence between spintronic phenomena and the desired neuronal, synaptic behavior. While domain wall motion-based threshold activation unit has previously been demonstrated for neuromorphic circuits, it remains well known that neurons with threshold activation cannot completely learn nonlinearly separable functions. This paper addresses this fundamental limitation by proposing a novel domain wall motion-based dual-threshold activation unit with additional nonlinearity in its function. Furthermore, a new learning algorithm is formulated for a neuron with this activation function. We perform 100 trials of tenfold training and testing of our neural networks on real-world datasets taken from the UCI machine learning repository. On an average, the proposed algorithm achieves <inline-formula><tex-math notation="LaTeX">$\text{1.04}\times - \text{6.54}\times$</tex-math></inline-formula> lower misclassification rate (MCR) than the traditional perceptron learning algorithm. In a circuit-level simulation, the neural networks with the proposed activation unit are observed to outperform the perceptron networks by as much as <inline-formula><tex-math notation="LaTeX">$\text{2.98}\times$</tex-math></inline-formula> MCR. The energy consumption of a neuron having the proposed domain wall motion-based activation unit averages to <inline-formula><tex-math notation="LaTeX">$\text{35 fJ}$</tex-math></inline-formula> approximately.
Fully Unsupervised Optimization of CNN Features Towards Content Based Image Retrieval

In this paper, we propose an effective retraining method for optimizing the Convolutional Neural Network (CNN) features towards Content Based Image Retrieval (CBIR). To accomplish this goal, we utilize a pretrained CNN model, and we adapt it to a lightweight fully convolutional model which allows for producing compact image representations, reducing the storage requirements. Subsequently, we obtain the feature representations of the last convolutional layer using the max pooling operation, and we retrain the weights of the convolutional layers in a fully unsupervised fashion, aiming to produce more efficient compact image descriptors which improve the retrieval performance both in terms of time and precision. The experimental validation on three publicly available image retrieval datasets indicates the effectiveness of the proposed method in learning more efficient representations for the retrieval task, accomplishing significantly enhanced performance in all the used datasets.
MOOC: A Mobility Control Based Clustering Scheme for Area Coverage in FANETs

Area coverage is one of the most common and critical applications for Flying Ad hoc Networks (FANETs), which typically involves a host of small or mini Unmanned Aerial Vehicles (UAVs). The increasingly large scale of FANETs brings challenges of networking and management, for which clustering scheme is an effective technique. However, there is a conflict between area coverage and network connectivity which is required for clustering and data transmission among UAVs. In this paper, we propose a MObility cOntrol based Clustering (MOOC) scheme for area coverage in FANETs. In MOOC, a mobility control strategy is designed for different types of nodes based on virtual forces, considering both reducing overlapped coverage and maintaining connections to improve clustering performances. The coverage and clustering performances of MOOC are analyzed and compared with other approaches through extensive simulations. The results show that MOOC outperforms the compared approaches by at least 37% and 235% respectively in terms of coverage efficiency while maintaining a high network connectivity. Meanwhile, MOOC also outperforms the other clustering algorithms by at least 503% on average in terms of cluster head duration.