Miracle-dz/UniFAR-FTUs huggingface dataset:
A Graph-Structured Dataset for Wikipedia Research

Wikipedia is a rich and invaluable source of information. Its central place on the Web makes it a particularly interesting object of study for scientists. Researchers from different domains used various complex datasets related to Wikipedia to study language, social behavior, knowledge organization, and network theory. While being a scientific treasure, the large size of the dataset hinders pre-processing and may be a challenging obstacle for potential new studies. This issue is particularly acute in scientific domains where researchers may not be technically and data processing savvy. On one hand, the size of Wikipedia dumps is large. It makes the parsing and extraction of relevant information cumbersome. On the other hand, the API is straightforward to use but restricted to a relatively small number of requests. The middle ground is at the mesoscopic scale, when researchers need a subset of Wikipedia ranging from thousands to hundreds of thousands of pages but there exists no efficient solution at this scale. In this work, we propose an efficient data structure to make requests and access subnetworks of Wikipedia pages and categories. We provide convenient tools for accessing and filtering viewership statistics or “pagecounts” of Wikipedia web pages. The dataset organization leverages principles of graph databases that allows rapid and intuitive access to subgraphs of Wikipedia articles and categories. The dataset and deployment guidelines are available on the LTS2 website https://lts2.epfl. ch/Datasets/Wikipedia/.
Magnetoresistive Sensor Development Roadmap (Non-Recording Applications)

Magnetoresistive (MR) sensors have been identified as promising candidates for the development of high-performance magnetometers due to their high sensitivity, low cost, low power consumption, and small size. The rapid advance of MR sensor technology has opened up a variety of MR sensor applications. These applications are in different areas that require MR sensors with different properties. Future MR sensor development in each of these areas requires an overview and a strategic guide. An MR sensor roadmap (non-recording applications) was therefore developed and made public by the Technical Committee of the IEEE Magnetics Society with the aim to provide an research and development (R&D) guide for MR sensors intended to be used by industry, government, and academia. The roadmap was developed over a three-year period and coordinated by an international effort of 22 taskforce members from ten countries and 17 organizations, including universities, research institutes, and sensor companies. In this paper, the current status of MR sensors for non-recording applications was identified by analyzing the patent and publication statistics. As a result, timescales for MR sensor development were established and critical milestones for sensor parameters were extracted in order to gain insight into potential MR sensor applications (non-recording). Five application areas were identified, and five MR sensor roadmaps were established. These include biomedical applications, flexible electronics, position sensing and human–computer interactions, non-destructive evaluation and monitoring, and navigation and transportation. Each roadmap was analyzed using a logistic growth model, and new opportunities were predicted based on the extrapolated curve, forecast milestones, and professional judgment of the taskforce members. This paper provides a framework for MR sensor technology (non-recording applications) to be used for public and private R&D planning, in order to provide guidance into likely MR sensor applications, products, and services expected in the next 15 years and beyond.
Correlated Downlink-to-Uplink Interference Suppression for Full-Duplex C-RAN Systems

In full-duplex cloud radio access network systems, the uplink received signal of each remote radio head (RRH) contains downlink-to-uplink (D2U) interference signal which is a linear combination of the transmit signals of nearby RRHs including itself. Since the fronthaul links that connect RRHs to a baseband processing unit (BBU) have finite capacity, the RRHs forward quantized versions of the uplink received signals to the BBU. To improve the fidelity of the quantization, it is important to suppress the impact of the D2U interference signals prior to the quantization. This paper tackles the problem of jointly optimizing the D2U interference suppression and fronthaul quantization strategies with the goal of maximizing the sum rate of uplink user equipments. To tackle the formulated non-convex problem, we derive an iterative algorithm based on the successive convex approximation approach. To save the complexity, a simpler algorithm, which non-iteratively pre-fixes the D2U interference suppression matrices, is also proposed. Extensive numerical results confirm the advantages of the proposed schemes compared to conventional schemes.
Component Classification of References

References for books and articles require specific components. Identification of various components of the references is an important task in publishing industry. It also can facilitate the automatic categorization of the articles. This work is an attempt to represent the components of references using a list of parameters using various visual and syntactic features. The components are then classified using multi layer perceptron (MLP). The effectiveness of the proposed classifier is demonstrated on journal articles and is found to produce satisfactory result. It can be applied for other references as well. Moreover, other classification tools such as Bayes’ classifier or support vector machine (SVM) can be used in this regard.
Comparative Study on Performance of IGZO Transistors With Sputtered and Atomic Layer Deposited Channel Layer

The structural, chemical, and electrical properties of amorphous indium gallium zinc oxide (a-IGZO) films by magnetron sputtering and atomic layer deposition (ALD) were investigated where both a-IGZO films had a comparable cation composition. The ALD-derived a-IGZO film exhibited the higher atomic packing density, the effective suppression of trap-like oxygen vacancy defect (V<sub>O</sub>), and the enhancement in the hybridization of the sp orbital of In, Ga, and Zn cations compared to those of the sputtered a-IGZO film. Hence, a significant improvement in terms of the field-effect mobility was observed for the thin-film transistors with an In<sub>0.50</sub>Ga<sub>0.34</sub>Zn<sub>0.16</sub>O channel by ALD (36.6 cm<inline-formula> <tex-math notation="LaTeX">$^{\textsf {2}}/\text{V}\cdot \text{s}$ </tex-math></inline-formula>) compared to that of the sputtered In<sub>0.48</sub>Ga<sub>0.38</sub>Zn<sub>0.14</sub>O transistor (20.1 cm<inline-formula> <tex-math notation="LaTeX">$^{\textsf {2}}/\text{V}\cdot \text{s}$ </tex-math></inline-formula>); the <inline-formula> <tex-math notation="LaTeX">${I}_{\text {ON/OFF}}$ </tex-math></inline-formula> ratios for both were ~10<sup>7</sup>. Simultaneously, the gate bias stress stability and photobias stress stability were also improved for the IGZO transistors with an ALD-derived channel, which can be explained by its reduced trap-like V<sub>O</sub> density.
Will Carbon Nanotube Memory Replace DRAM?

In this paper, we discuss an exciting memory technology made from carbon nanotubes. Carbon nanotubes provide a predictable resistive element that can be used to fabricate very dense and very fast-switching memory cells. Nantero NRAM employs electrostatic forces to connect and disconnect these nanotubes in a memory design notably impervious to external effects including heat, shock and vibration, magnetism, and radiation. NRAM maintains its state permanently and may be rewritten arbitrarily many times without degrading. Not only NRAM is well positioned to replace DRAM in existing applications, but also its combination of high speed, persistence, density, and low power enables a slew of exciting new applications. Production of NRAM devices is on track for near-term commercialization through Nantero licensees.
Caching on the Move: A User Interest-Driven Caching Strategy for D2D Content Sharing

Device-to-device (D2D) content sharing helps to accommodate the exponentially surge in mobile data traffic. However, how to cache in mobile users is crucial to ensure the above advantages. There are three issues that have not been fully considered in the previous works. First, the ignorance of mobility cannot depict the random connectivity of mobile users in D2D content sharing. Second, the lack of the diverse and complete information on user interest leads to unsatisfied demands of users. Third, caching a complete content will be wasteful due to the limited cache capacity, which is also not likely to be obtained in one connection. Regarding the three issues, we construct not only a user mobility model, which helps to provide the contact opportunity for the users sharing contents, but also a user interest prediction model, which combines the social proximity and the dynamic content popularity. Moreover, we exploit the maximum distance separable code to encode the contents into smaller partitions. Accordingly, we formulate a mobility-aware and user interest-driven caching problem as a 0–1 multiple knapsack problem. Due to its NP-hard property, we prove that this problem falls into the category of monotone submodular function over one matroid and multiple knapsack constraints. Then, we develop a corresponding algorithm based on a greedy approach, which approximates the optimum within a constant factor in polynomial time. Numerical results demonstrate the performance and the effectiveness of our proposed algorithm.
A State Assignment Method for Extended Burst-Mode gC Finite State Machines Based on Genetic Algorithm

This paper proposes a new algorithm for state assignment of Extended Burst-Mode Asynchronous Finite State Machines (XBM_AFSM). The proposal is based on genetic algorithm and it introduces a novel style of state assignment. It improves the results and it overcomes the previous methods found in literature once it addresses the "state minimization", the "critical race free coding" and "coverage" as a single problem. Furthermore, it is able to detect the conflicts in XBM specification and to insert the minimum number of state variables in the XBM specification in order to eliminate those conflicts. A dedicated computational tool called SAGAAs_gC implemented the algorithm and it was tested in a set of 39 XBM benchmarks. When it is compared to 3D tool, our method achieved an average reduction of 21.4%, 16.5% and 12.12% in amount of state variables, number of literals and transistors, respectively. Results show that the method and dedicated computational tool SAGAAs_gC achieved good and reliable results, showing a high potential of practical implementation in actual circuit designs.
Analysis of Performance Metrics with Mesenchymal Stem Cell Classification and Optimization Algorithms

Stem cells are biological cells found in all multicellular organisms, that can divide (through mitosis) and differentiate into diverse specialized cell types and can self-renew to produce more stem cells. Optimization algorithms have been proved to be good solutions for many practical applications. They were mainly inspired by natural evolutions. However, they are still faced to some problems such as trapping in local minimums, having low speed of convergence, and also having high order of complexity for implementation. Mesenchymal Stems or stromal Cells (MSCs) are part of maintaining as well as repairing tissues. The functions are mainly examined in bone marrow-derived MSC. In the current study, segmentations are performed through usage of Graph-based image segmentations. Feature extraction is performed through Wavelet while Feature Selection is performed through Stem Cell Optimization techniques. Naïve Bayes as well as Support Vector Machines are utilized for classifiers. Results show that the Stem Cell Optimization has better classification accuracy than Information Gain (IG) and Genetic Algorithm (GA).
Design and Simulation of Small Satellite Power System in Simulink/Matlab for Preliminary Performance Estimation

This paper presents the design and simulation of Power Subsystem of small satellites using Simulink/Matlab. It includes the models of solar cell array, DC/DC converters, battery charge/ discharge controller, power control distribution unit and load. The results of solar cell model are compared with the IV characteristics of Spectrolab GaAs solar cells datasheet and satisfactory match is obtained between the two. These simulations include the different modes of operation of the power system in an orbit cycle. The complete model is simulated for 100 minutes of sun-synchronous orbit including eclipse period of 35 minutes. This model lends itself not only for easy analytical manipulations, but it is also useful for determining which types of solar cells, batteries and loads can be successfully combined.
Mapping and Placement in NoC-based Reconfigurable Systems Using an Adaptive Tabu Search Algorithm

Mapping and Placement still are big challenges in Networks-on-Chip (NoCs) design, due to the scalability, although several heuristics have been proposed to solve them. These problems belong to the class of Quadratic Assignment Problems (QAP). For NoC-based dynamically reconfigurable systems (NoC-DRSs), both mapping and placement problems present an additional complexity level due the reconfigurable layers/scenarios, being treated only by Genetic Algorithm meta-heuristics; however, several researches have described Tabu Search meta-heuristics as the best QAP solvers. This paper presents a formalization for the mapping and placement on 2D-Mesh FPGA NoC-DRSs, and provides as solver, a novel approach of adaptive Tabu Search, named Nav-adaTS. Results with a series of benchmarks are presented and compared to a basic adaptive Tabu Search and to the genetic algorithm implementation.
Output Voltage Regulation For dc–dc Buck Converters: a Passivity–Based PI Design

This paper presents a global tracking passivity–based proportional–integral (PI) control for output voltage regulation of a dc–dc Buck converter. The proposed controller is based on passivity formulation since dc–dc Buck converter has a passive structure in open–loop. Additionally, the controller takes advantage of the PI actions to design a control law that guarantees asymptotically stability in the Lyapunov’s sense under closed–loop operation. The proposed controller does not depend on the parameters, which makes it a robust controller. The robustness of the proposed controller is checked by comparing its dynamical performance in front of a conventional PID controller. All simulation results were fulfilled via MATLAB software.
Sinusoidal Parameter Estimation From Signed Measurements Via Majorization–Minimization Based RELAX

We consider the problem of sinusoidal parameter estimation using signed observations obtained via one-bit sampling with fixed as well as time-varying thresholds. In a previous paper, a relaxation-based algorithm, referred to as 1bRELAX, has been proposed to iteratively maximize the likelihood function. However, the exhaustive search procedure used in each iteration of 1bRELAX is time-consuming. In this paper, we present a majorization–minimization (MM) based 1bRELAX algorithm, referred to as 1bMMRELAX, to enhance the computational efficiency of 1bRELAX. Using the MM technique, 1bMMRELAX maximizes the likelihood function iteratively using simple fast Fourier transform operations instead of the more computationally intensive search used by 1bRELAX. Both simulated and experimental results are presented to show that 1bMMRELAX can significantly reduce the computational cost of 1bRELAX while maintaining its excellent estimation accuracy.
Reconstructing of Networks With Binary-State Dynamics via Generalized Statistical Inference

Complex networks hosting binary-state dynamics can represent many phenomena in real world systems. Therefore, some approaches were proposed to reconstruct the structures of networks with binary-state dynamics. However, they often hold two assumptions: 1) require a priori knowledge about which state is the active state, or one state is imposed as the active state; and 2) only one side of transition probability is utilized for network reconstruction. Many binary-state dynamics, such as cooperative/defective state in evolutionary game, agree/disagree of two competing opinions, it is hard to define which state is the active state, what’s more, both sides of the transition probability depend on the states of neighbors. For this situation, if we only consider one side of transition probability, the reconstruction accuracy is greatly discounted because many data are not effectively used. By abandoning the two assumptions, we here develop a generalized statistical inference approach by exploiting the expectation-maximization algorithm to reconstruct networks. Our approach requires less information regarding the dynamics, indicating more potential applications. More importantly, our approach sufficiently mines the given data, the results on empirical and synthetic networks demonstrate the high-reconstruction accuracy. In addition, the method is parameter free and robust to the stochastic fluctuations.
Fault Diagnosis of Power Transformers With Membership Degree

Power transformers are important equipment for power systems, and a dissolved gas analysis (DGA) is widely used to detect incipient faults in oil-pregnant transformers. The conventional methods are prone to misinterpreting the gas data near the boundaries and the correct rate is low. Though a high correct rate is reported with intelligent methods as artificial neural network, support vector machine, and so on, these methods are usually too complicated to be implemented practically on a wide range. Based on clustering techniques, this paper proposes a new method for fault diagnosis of transformers with the DGA. A reference fault set is provided, and the fault diagnosis is implemented by calculating the membership of the DGA data to the reference fault set. Test with credible DGA dataset (201 field cases) shows that the correct rate of the new method is 89%, while the David triangle method is 79% and the IEC ratio method is 59%, which demonstrate the superiority of the proposed method to the conventional ones. The new method is simple and highly accurate, indicating a good application prospect in engineering practice.
A Generative Model For Electron Paths

Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using `arrow-pushing' diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction data. Instead of predicting product molecules directly from reactant molecules in one shot, learning a model of electron movement has the benefits of (a) being easy for chemists to interpret, (b) incorporating constraints of chemistry, such as balanced atom counts before and after the reaction, and (c) naturally encoding the sparsity of chemical reactions, which usually involve changes in only a small number of atoms in the reactants.We design a method to extract approximate reaction paths from any dataset of atom-mapped reaction SMILES strings. Our model achieves excellent performance on an important subset of the USPTO reaction dataset, comparing favorably to the strongest baselines. Furthermore, we show that our model recovers a basic knowledge of chemistry without being explicitly trained to do so.
A New Velocity Meter Based on Hall Effect Sensors for UAV Indoor Navigation

This paper presents a new approach to enhance the indoor navigation of unmanned aerial vehicles (UAVs). During indoor flight missions, UAVs rely on the integration of inertial navigation systems (INS) and aiding sensors measurements to estimate the UAV’s navigation states. These aiding measurements may be position, velocity, or attitudes. For indoor environments, the presence of absolute positioning systems, such as Wi-Fi, as an aiding sensor is not always guaranteed. Therefore, alternative aiding sources are required to mitigate the drift exhibited in INS and enhance the navigation accuracy. Different sensors have been utilized to aid INS. These aiding sensors should be carefully chosen, as they should not exceed the cost, weight, size, and power consumption limitations of the UAV. This paper presents a new utilization of Hall effect sensors to act as a flying air odometer (Air-Odo) for quadcopter UAV and to aid the INS with velocity update. Testing results confirmed the capabilities of the new approach to aid the navigation solution through real experiments, with velocity update applied to an extended Kalman filter. The new approach enhanced the navigation solution by 98% when compared with the stand-alone INS solution.
Hand-Gesture Recognition Using Two-Antenna Doppler Radar With Deep Convolutional Neural Networks

Low-cost consumer radar integrated circuits combined with recent advances in machine learning have opened up a range of new possibilities in smart sensing. In this paper, we use a miniature radar sensor to capture Doppler signatures of 14 different hand gestures and train a deep convolutional neural network (DCNN) to classify these captured gestures. We utilize two receiving antennas of a continuous-wave Doppler radar capable of producing the in-phase and quadrature components of the beat signals. We map these two beat signals into three input channels of a DCNN as two spectrograms and an angle of arrival matrix. The classification results of the proposed architecture show a gesture classification accuracy exceeding 95% and a very low confusion between different gestures. This is almost 10% improvement over the single-channel Doppler methods reported in the literature.
Radio Resource Allocation and Retransmission Schemes for URLLC Over 5G Networks

Low latency targets for ultra-reliable low latency communications (URLLC) may be conflicting with their stringent reliability requirements due to the need for re-transmissions. We explore, in this paper, the different resource allocation schemes for transmissions and re-transmissions depending on the requirements of the underlying service and on the traffic characteristics, focusing on the Industrial Internet of Things. We, namely, consider schemes with individual reservation versus a pool of contention-based reserved resources. We provide novel resource allocation schemes for initial transmissions and re-transmissions and derive corresponding analytical models for loss rates. We then show how to set the system parameters that allow meeting the URLLC requirements with low resource consumption.
Noise Filtering and Linearization of Single-Ended Sampled-Data Circuits

The performance of analog integrated circuits is often limited by the noise generated in its components. Several circuit techniques exist for mitigating the effects of the low-frequency noise. In this paper, a novel approach is described, which can also reduce even-order distortion, another major limitation of analog circuits. It may also allow the use of single-ended circuits in applications where usually differential structures are needed. In most cases, the resulting single-ended circuit will be much simpler than the fully or pseudo-differential one. It does not require common-mode feedback circuitry, nor single-ended-to-differential and differential-to-single-ended conversions at the input and output ports. The amplifiers may be single-ended, using, e.g., inverter-based circuits, and the use of baluns can be avoided. The number of pins and pads of the chip can be reduced. In addition, the single-ended opamps of the circuit may require less power than differential ones. These factors and the omission of the common-mode feedback circuits as well as the single-ended/differential converters will usually allow significant power savings.