Miracle-dz/UniFAR-FTUs huggingface dataset:
Sedimentology and Petrography of Selected North Sumatra Pre- Tertiary Formations: Anticipating New Petroleum Systems in Western Indonesia

This research is focused on studying the depositional system and petrographic characteristics of petroleum system that form in North Sumatera, Indonesia to reveal the diagenesis, metamorphism, alteration indicators related to reservoir and source rock potential. Field work was integrated with paleontology analysis and comprehensive petrographic study of selected thin sections. Paleozoic and Mesozoic Stratigraphy of North Sumatra can be divided into Tapanuli Group (Alas Fm, Kluet Fm, Bohorok Fm) and Peusangan Group (Pangururan Bryozoa Bed, Batumilmil Fm, Kaloi Fm, Kualu Fm). Pre-Tertiary Rocks were variously deposited in deep marine (ie: Sibaganding Limestone) with Radiolarian Limestone; shallow marine (ie: Batumilmil Fm, Kaloi Fm) with Limestone and dolomitic limestone; moraine glacier and till (ie: Bohorok Pebbly Mudstone); and shallow water (ie: Kualu Mudstone, Pangururan Bryozoa Bed). Batumilmil Fm can be divided into Dolomitic red limestone, Light-grey Limestone, Dark-grey Limestone, and Carbonaceous Claystone. Reservoir potential was formed by farctures, disslutions and dolomitization (ie: Kaloi and Batumilmil Dolomitic Limestone). Further more, source rock potential was found in Batumilmil and Kualu Mudstone.
A Description of Grip and Pinch Strength in Children Aged 6 to 11 Years Using the Martin Vigorimeter

The vigorimeter is potentially a useful instrument for measuring grip and pinch strength in children. However, no normative or comparative data are supplied by the manufacturer, and only two previous studies using it to test children's hand strength could be found in the literature. The purpose of this study was to collect preliminary hand strength data in children aged 6 to 11 years. Spherical grip, tip pinch and tripod pinch strength were measured in 134 children. Graphs displaying the results suggest that, on average, strength increases with age, boys are stronger than girls, and the right hand is often (but not always) stronger than the left. While far from producing norms, this study did demonstrate that the vigorimeter has utility as an instrument for measuring grip and pinch strength in children, and provided some preliminary baseline data to assist clinicians in judging the results of hand strength tests. Le vigorimètre peut être un instrument utile pour mesurer la force de préhension grossière et la force de préhension fine chez les enfants. Cependant, le fabricant ne fournit pas de données normatives ou comparatives et on ne trouve dans la littérature que deux études dans lesquelles l'instrument a été utilisé pour évaluer la force de prehension des enfants. Le but de cette étude était de recueillir des données préliminaires sur la force de préhension des enfants âgés de 6 à 11 ans. La force de la prise palmaire sphérique, des prises digitales et des prises tridigitales a été mesurée chez 134 enfants. Les résultats présentés dans les graphiques suggèrent qu'en général la force augmente avec l'âge, les garçons sont plus forts que les filles et la main droite est souvent (mais ce n'est pas toujours le cas) plus forte que la gauche. Loin de produire des normes, cette étude a toutefois démontré que le vigorimètre est un instrument utile pour mesurer la force de préhension grossière et la force de préhension fine chez les enfants. L'étude fournit également quelques données de base préliminaires qui aideront les cliniciens à apprécier les résultats des tests sur la force de préhension.
Exploring Communication Patterns Among Hospice Nurses and Family Caregivers: A Content Analysis of In-home Speech Interactions

Interactions among hospice care providers and caregivers are a promising area of exploration, as we seek to better understand the context of hospice care and how to support those engaged in this complex relationship. This study examines patterns within naturally occurring speech between nurses and family caregivers during hospice visits. Based on previous work, we propose that nurse communication can be more or less facilitative or directive in style and this in turn shapes nurse-family caregiver interactions. We analyzed nurse-caregiver speech interactions recorded during seven hospice encounters. Findings show significant differences in the speech patterns associated with facilitative or directive styles. Directive styles tended to be denser and nurse oriented and offered less opportunity for caregiver self-expression, while facilitative styles were more caregiver oriented and more vivid and responsive and made more space for caregiver expression. There were also significant differences in the amount of cognitive, emotional, or task-oriented speech and how these terms shaped the nature and focus of the interactions. This study provides empirical support for best communication practices and may help us better understand how specific communication patterns facilitate positive outcomes by supporting caregivers’ own needs for interaction and self-expression.
Properties of the Tangle for Uniform Random and Random Walk Tip Selection

The growing number of applications for distributed ledger technologies is driving both industry and academia to solve the limitations of blockchain, particularly its scalability issues. Recent distributed ledger technologies have replaced the blockchain linear structure with a more flexible directed acyclic graph in an attempt to accommodate a higher throughput. Despite the fast-growing diffusion of directed acyclic graph based distributed ledger technologies, researchers lack a basic understanding of their behavior. In this paper we analyze the Tangle, a directed acyclic graph that is used (with certain modifications) in various protocols such as IOTA, Byteball, Avalanche or SPECTRE. Our contribution is threefold. First, we run simulations in a continuous-time model to examine tip count stability and cumulative weight evolution while varying the rate of incoming transactions. In particular we confirm analytical predictions on the number of tips with uniform random tip selection strategy. Second, we show how different tip selection algorithms affect the growth of the Tangle. Moreover, we explain these differences by analyzing the spread of exit probabilities of random walks. Our findings confirm analytically derived predictions and provide novel insights on the different phases of growth of cumulative weight as well as on the average time difference for a transaction to receive its first approval when using distinct tip selection algorithms. Lastly, we analyze simulation overhead and performance as a function of Tangle size and compare results for different tip selection algorithms.
Study on the Frequency-Modulated Continuous-Wave LiDAR Mutual Interference

We present mutual interference problems in the frequency-modulated continuous-wave (FMCW) LiDAR. There are two types of the mutual interference, the transient interference, and the in-band interference. We show that the former case induces degradation of signal-to-noise ratio (SNR) in measurement, and the latter case can introduce ghost images and/or objects in 3-D mapping.
Towards Non-Invasive Recognition of Developers' Flow States with Computer Interaction Traces

Flow is a holistic description of people's optimal experiences during creative activities that can be characterized as being totally concentrated on, and actively involved in the task, enjoying the process of creation, and achieving a balance between one's skill and the task's challenge. Understanding software developers' flow states has attracted an increasing attention in both research and practice because of the strong link between being in flow and achieving good performance. In this paper, we study the problem of tracking and recognizing developers' flow states by tracing their computer interactions including activities of using the keyboard, mouse, IDE functions, and switching application windows. Compared to the traditional approaches that rely on self-reports or wearable sensors, a major advantage of the proposed approach is being non-invasive for not requiring any additional efforts from the developers after the training phase is completed, which is important because the developers' flow states can easily be interrupted by external interferences. Based on the captured interaction traces, we represent the developers' activities with extensive features, and propose to address the flow state recognition problem using machine learning technologies. And a hierarchical recognition model is built following the multi-dimensional construct of the flow concept, which is interpretable and effective. We develop a prototype system and conduct a 17-day field study in a medium-sized IT company in China to collect real-world data. The results show that our approach is effective by achieving the highest recognition accuracy of 92.6%, and efficient for performing real-time recognition.
Successive Relative Transfer Function Identification Using Blind Oblique Projection

Distortionless speech extraction in a reverberant environment can be achieved by applying a beamforming algorithm, provided that the relative transfer functions (RTFs) of the sources and the covariance matrix of the noise are known. In this paper, the challenge of RTF identification in a multi-speaker scenario is addressed. We propose a successive RTF identification (SRI) technique, based on the sole assumption that sources do not become simultaneously active. That is, we address the challenge of estimating the RTF of a specific speech source while assuming that the RTFs of all other active sources in the environment were previously estimated in an earlier stage. The RTF of interest is identified by applying the blind oblique projection (BOP)-SRI technique. When a new speech source is identified, the BOP algorithm is applied. BOP results in a null steering toward the RTF of interest, by means of applying an oblique projection to the microphone measurements. We prove that by artificially increasing the rank of the range of the projection matrix, the RTF of interest can be identified. An experimental study is carried out to evaluate the performance of the BOP-SRI algorithm in various signal to noise ratio (SNR) and signal to interference ratio (SIR) conditions and to demonstrate its effectiveness in speech extraction tasks.
PI-controller Fuzzy Tuner Based on Transient Quality Estimation to Control DC Drive Speed

The quality improvement of a DC drive speed control is the scope of this research. Classical PI-controllers with constant parameters does not provide high quality of transients for all DC drive functioning modes due to the motor nonlinearity and parameters nonstationarity. In this regard, there is a need to develop control systems, which take this fact into account and, to some extent, compensate it. The existing methods to develop DC drive adaptive control system are analyzed. It is concluded that the most promising way to solve this problem is to apply fuzzy logic to implement a PI-controller parameters adjuster. A comparison of the existing fuzzy tuners is made, and their shortcomings are found. A new fuzzy tuner is proposed and implemented taking into consideration the transients quality requirements. A model of a DC drive of NI-ELVIS II test bench is developed to conduct experiments. Full-scale experiments are also performed using this test bench. The results show that the proposed tuner, which uses an overshoot and a transient time estimation as input variables, is able to reduce the overshoot value by 15% comparing to the classical speed PI-controller and by averagely 10% in comparison with the exiting tuners under the conditions of the DC drive parameters nonstationarity, while the transient time remains the same.
Analysis of High-Temperature Carrier Transport Mechanisms for High Al-Content Al0.6Ga0.4N MSM Photodetectors

An AlGaN-based solar blind ultraviolet (UV) metal–semiconductor–metal (MSM) photodetector (PD) with a high Al-content of 0.6 has been successfully fabricated. The device exhibits a cutoff wavelength of 255 nm corresponding to the sharp cutoff transmission spectrum of Al<sub>0.6</sub>Ga<sub>0.4</sub>N. In addition, dislocations in the Al<sub>0.6</sub>Ga<sub>0.4</sub>N epi-layer has been analyzed by high-resolution transmission electron microscope (TEM). The Al<sub>0.6</sub>Ga<sub>0.4</sub>N-based solar blind PD exhibits a responsivity of 0.51 A/W at 10 V with a high breakdown voltage of 470 V. Suggesting its potential applications for high-temperature solar blind UV detection, the <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula> characteristics have been comprehensively investigated to explore its high-temperature carrier transport mechanisms. It is convincingly demonstrated that the bias leakage current across the device is dominated by thermionic-field emission transport at low bias voltages and Poole–Frenkel emission at high bias voltages from room temperature up to 425 K.
Channel-specific Frequency-limited Model Reduction

The frequency-limited model reduction techniques aim to construct a model of significantly lower order which approximates the original higher order model accurately within some specified frequency interval. In this paper, we propose a frequency-limited model reduction technique for multi-input and multi-output systems wherein a different desired frequency region can be specified for each input-output channel. The reduced order model thus obtained ensures superior accuracy within the frequency regions specified for each input-output channel. A numerical example is presented to demonstrate the applicability and the efficacy of the proposed algorithm.
An Efficient Hardware Implementation of Reinforcement Learning: The Q-Learning Algorithm

In this paper we propose an efficient hardware architecture that implements the Q-Learning algorithm, suitable for real-time applications. Its main features are low-power, high throughput and limited hardware resources. We also propose a technique based on approximated multipliers to reduce the hardware complexity of the algorithm. We implemented the design on a Xilinx Zynq Ultrascale+ MPSoC ZCU106 Evaluation Kit. The implementation results are evaluated in terms of hardware resources, throughput and power consumption. The architecture is compared to the state of the art of Q-Learning hardware accelerators presented in the literature obtaining better results in speed, power and hardware resources. Experiments using different sizes for the Q-Matrix and different wordlengths for the fixed point arithmetic are presented. With a Q-Matrix of size $8\times4$ (8 bit data) we achieved a throughput of 222 MSPS (Mega Samples Per Second) and a dynamic power consumption of 37 mW, while with a Q-Matrix of size $256\times16$ (32 bit data) we achieved a throughput of 93 MSPS and a power consumption 611 mW. Due to the small amount of hardware resources required by the accelerator, our system is suitable for multi-agent IoT applications. Moreover, the architecture can be used to implement the SARSA (State-Action-Reward-State-Action) Reinforcement Learning algorithm with minor modifications.
A 4H-SiC UV Phototransistor With Excellent Optical Gain Based on Controlled Potential Barrier

In this article, we report the experimental results of a visible-light-blind 4H-polytype silicon carbide phototransistor able to detect ultraviolet (UV) radiations for wavelengths lower than 380 nm with a significative improvement in the optical gain compared with the state-of-the-art of 4H-SiC UV phototransistors. From the electro-optical measurements, the device shows a dark current of 0.62 pA, an ON-/OFF-current ratio of seven orders of magnitude up to bias voltage of −0.5 V, and an excellent optical gain of $1.14\cdot {10}^{{{5}}}$ at 300 nm, whereas it is only $2.6\cdot {10}^{-{{3}}}$ at 400 nm demonstrating a good rejection of visible radiations. Besides having high optical gain, the phototransistor is also more sensitive than the conventional 4H-SiC UV detectors for wavelengths with low penetration depths, because its structure is designed to have the electric field up to the radiated surface where the photogenerated electron–hole pairs are efficiently swept up before recombination occurs. The operating principle of the detector is also investigated, and we experimentally proved that differently from the conventional 4H-SiC bipolar junction transistor phototransistor, it is based on the change in the potential barrier height, which controls the current flow, due to the variation in the Fermi levels when the electron–hole pairs are photogenerated. A comparison with the state-of-the-art of 4H-SiC UV phototransistors is reported.
Bipartite Consensus for Second Order Multi-Agent Systems With Exogenous Disturbance via Pinning Control

This paper focuses on the leader-following bipartite consensus problem for second order multi-agent systems (MASs) subject to the disturbance generated from exosystem. Different from some related works on this topic, the upper bound of disturbance is not required and the disturbance observer is proposed to estimate the exogenous disturbance. To guarantee the bipartite consensus of nonlinear MASs, both pinning control and disturbance observer strategy are employed. With the help of linear matrix inequality and Lyapunov stability theory, it is demonstrated that leader-following bipartite consensus for nonlinear MASs can be realized if a fraction of the agents are controlled under some sufficient conditions. The effectiveness of the developed approach is verified via simulations.
Remaining Useful Life Estimation for Predictive Maintenance Using Feature Engineering

Recently, machine learning techniques have been used to produce increasingly effective solutions to predict the remaining useful life (RUL) of assets accurately. This paper investigates the effect of different feature engineering approaches to the accuracy of RUL prediction. In this study, six different feature selection methods and many different regression algorithms were applied to choose the most accurate final model for prediction. Applied feature selection algorithms are Chi Squared, Spearman Correlation, Mutual Information, Fisher Score, Pearson Correlation and Count Based. Machine learning algorithms used in this work are Linear Regression, Bayesian Linear Regression, Poisson Regression, Neural Network Regression, Boosted Decision Tree Regression and Decision Forest Regression. In addition, two different feature engineering approaches were also tested on the benchmark dataset by transforming its feature space, with the goal of improving predictive modelling performance. Each combination of these methods were applied and totally 72 different models were constructed and compared with each other to evaluate their performances in terms of five different metrics, including mean absolute error, root mean squared error, relative absolute error, relative squared error and coefficient of determination.
Study of Coupler Performance for Multimode Polymer Waveguides

The optical interconnects have attracted great attention in recent years due to the significant increase of data demand in electronic devices. Optical waveguides can provide greater bandwidth and lower power consumption as compared to conventional copper links. However, the design of coupling schemes letting the signals drop-in/out between two waveguides are still quite challenged to achieve. In this paper, we carried a detail study of waveguide coupling tolerance and loss performance under different conditions. In addition, the performance of traditional Y-splitter and recently new proposed U-splitter are also investigated. Their coupling ratio as a function of contact length are given in this paper. The latter U-splitter provides a simpler mounting scheme and adjustable coupling ratio between two optical waveguides without damaging the waveguide core.
Fast Point Cloud Registration using Semantic Segmentation

Deep learning has recently delivered relatively high quality semantic segmentation of visual and point-cloud data. This paper is primarily concerned with the use of such semantic segmentation for point cloud registration. In particular, we are motivated by the need to speed up, for large scale data sets, algorithms for registration that guarantee optimality (in terms of maximising consensus). That semantic information can help prune bad hypotheses for point matches is rather obvious, and we demonstrate one such relatively simple approach by modifying a recent optimal registration algorithm [6] to take advantage of semantic information. However, we also make another contribution in proposing a novel variation of deep learning approaches to point cloud registration. Again, our motivation is handling large data sets and in this case we are able to provide an algorithm that achieves on par with state-of-the-art performance on the semantic segmentation task. In short, we have shown how to speed up both the generation of the semantic information, and how to use that semantic information to speed up point cloud registration, in the context of large scale point cloud data-sets.
Single View 3D Point Cloud Reconstruction using Novel View Synthesis and Self-Supervised Depth Estimation

Capturing large amounts of accurate and diverse 3D data for training is often time consuming and expensive, either requiring many hours of artist time to model each object, or to scan from real world objects using depth sensors or structure from motion techniques. To address this problem, we present a method for reconstructing 3D textured point clouds from single input images without any 3D ground truth training data. We recast the problem of 3D point cloud estimation as that of performing two separate processes, a novel view synthesis and a depth/shape estimation from the novel view images. To train our models we leverage the recent advances in deep generative modelling and self-supervised learning. We show that our method outperforms recent supervised methods, and achieves state of the art results when compared with another recently proposed unsupervised method. Furthermore, we show that our method is capable of recovering textural information which is often missing from many previous approaches that rely on supervision.
Achievable Rate Analysis on the Uplink of Massive MIMO with Superimposed Pilots and Arbitrary-Bit ADCs

multiple-input multiple-output (MIMO) has been proved to be a vital technology to achieve significant improvement in both data rate and link reliability. In this paper, a quantized massive MIMO system with superimposed pilots (SP) scheme and arbitrary-bit analog-to-digital converters (ADCs) is considered. By using the linear minimum mean-square-error (LMMSE) channel estimation and maximum ratio combining (MRC) detection, we derive a tight approximate expression of the uplink achievable rate considering the removal of estimated pilot. Based on the analytical expression, optimal pilot power factor which maximizes the achievable sum rate is deduced. Results show that the systems with higher-resolution ADCs or larger number of base station (BS) antennas need to allocate more power to pilots. Furthermore, numerical results show that SP outperforms time-multiplexed pilots (TP) for 1 bit quantization, while for systems with higher-resolution ADCs, SP scheme is suitable for the case where the number of BS antennas is relatively small.