Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasts of Electric Vehicle Energy Consumption Based on Characteristic Speed Profiles and Real-Time Traffic Data

Despite the increased interest in battery electric vehicles (BEV), limited range abilities unsettle customers, which is often related to range anxiety. A better understanding of energy consumption and the possibility to accurately predict the remaining battery energy along an upcoming route may help to reduce this stress perception by means of advanced in-vehicle information systems. Addressing the trend towards vehicles with on-board cloud communication and information systems, the present research focuses on electric powertrain consumption and speed profile forecasts. A meaningful prediction of a speed profile for a given route is a basic prerequisite for an accurate consumption forecast. This study proposes a methodology to derive such a speed profile from real-time traffic data obtained from HERE Technologies while considering individual driving style characteristics. Given the predicted speed profile, a detailed BEV consumption model which accounts for BEV specific energy management strategies and environmental factors is used to obtain a consumption forecast. Prediction uncertainties are analyzed and parameter sensitivities with respect to energy consumption are derived as a function of the route dependent mean vehicle speed. Within a field study with Mercedes Benz EQC experimental vehicles, covering thirty-two test cycles, it is shown that the proposed methodology can accurately predict energy consumption for long look-ahead horizons and significantly reduces the variance in prediction compared to a typical baseline strategy.
IsoKV: An Isolation Scheme for Key-Value Stores by Exploiting Internal Parallelism in SSD

Modern data centers aim to take advantage of high parallelism in storage devices for I/O intensive applications such as storage servers, cache systems, and key-value stores. Key-value stores are the most typical applications that should provide a highly reliable service with high-performance. To increase the I/O performance of key-value stores, many data centers have actively adopted next-generation storage devices such as Non-Volatile Memory Express (NVMe) based Solid State Devices (SSDs). NVMe SSDs and its protocol are characterized to provide a high degree of parallelism. However, they may not guarantee predictable performance while providing high performance and parallelism. For example, heavily mixed read and write requests can result in performance degradation of throughput and response time due to the interference between the requests and internal operations (e.g., Garbage Collection (GC)). To minimize the interference and provide higher performance, this paper presents IsoKV, an isolation scheme for key-value stores by exploiting internal parallelism in SSDs. IsoKV manages the level of parallelism of SSD directly by running application-driven flash management scheme. By storing data with different characteristics in each dedicated internal parallel units of SSD, IsoKV reduces interference between I/O requests. Also, IsoKV synchronizes the LSM-tree logic and data management in SSD to eliminate GC. We implement IsoKV on RocksDB and evaluate it using Open-Channel SSD. Our extensive experiments have shown that IsoKV improves overall throughput and response time on average 1.20× and 43% compared with the existing scheme, respectively.
Tumor-infiltrating Lymphocytes are Independent Favorable Prognostic Indicator in 17-year Disease-Free Survival in Lymph Node-Negative Triple-Negative Breast Cancer Patient

Aims: To estimate the prognostic value of tumor-infiltrating lymphocytes, among other variables, in triple-negative breast cancer patients with a 17-year disease-free survival. Study Design: A retrospective study of 79 patients was conducted to investigate treatment, and clinical, microscopic and immunohistochemical tumor characteristics. Place and Duration of Study: Pathology Division, National Cancer Institute (INCA), Rio de Janeiro, RJ, Brazil, between January 1992 and December 1996. Methodology: Histologically diagnosed 79 node-negative triple-negative breast cancer patients underwent partial or total mastectomy with axillary lymphadenectomy, with or without radiotherapy, chemotherapy and/or hormone therapy. Disease-free survival was estimate by the Kaplan-Meier method and log-rank test. Prognostic variables were obtained by Cox regression models. Results: The 17-year disease-free survival was 50.6%. Disease-free survival was worse in patients aged 51-82 years, who underwent neoadjuvant chemotherapy and had skin compromise, geographic necrosis, grade 3 tumors, had no tumor-infiltrating lymphocytes, had vascular/lymphatic invasion, CD44 + /CD24 -/low and elevated Ki-67. The risk of recurrence and/or metastasis, adjusted for the remaining variables of the final Cox model was 2.44 times higher for patients aged 51-82 years, 2.60 times higher for patients undergoing neoadjuvant chemotherapy, 3.97 times higher for grade 3 tumors and 0.34 times for patients with tumor-infiltrating lymphocytes. Conclusion: The risk of recurrence and/or metastasis, adjusted for the remaining variables of the model, was about 2.5 times higher for older patients undergoing neoadjuvant chemotherapy. In grade 3 tumor patients, the risk increased almost fourfold. Patients with tumor-infiltrating lymphocytes had a 66% lower risk, i.e, tumor-infiltrating lymphocytes were shown to be a protective factor.
An Anti-Eavesdropping Strategy for Precoding-Aided Spatial Modulation With Rough CSI of Eve

In this paper, an anti-eavesdropping strategy is proposed for secure precoding-aided spatial modulation networks, under the assumption that the rough channel state information of eavesdropper can be obtained at the transmitter. Traditionally, artificial noise (AN) can be always projected into the null-space of the legitimate channel, however it may lead to some security loss since this strategy dispenses with a holistic consideration for secure transmissions. To reduce the computational complexity of our optimization problem, we derive a closed-form expression that is a loose bound of the approximate rate over the illegitimate channel. Then a concave maximization problem is formulated for optimizing the covariance matrix of AN. Simulation results show that our proposed low-complexity scheme performs closely to the method which directly maximizes the approximate secrecy rate expression, and harvests significant secrecy rate gains compared with the traditional null-space projection benchmark.
Optimal Relay Selection for Secure NOMA Systems Under Untrusted Users

The key idea of non-orthogonal multiple access (NOMA) is to serve multiple users in the same frequency band to improve the spectral efficiency. Whereas a security flaw emerges in the presence of untrusted users or low security clearance users who can access the messages of trusted users due to the spectrum sharing and may work as eavesdroppers. To achieve reliable and secure communications of NOMA systems under untrusted users, we in this paper propose two relay selection (RS) schemes termed decode-and-forward (DF) and amplify-and-forward (AF) protocols based optimal relay selection (i.e., DORS and AORS) schemes, respectively. To explicitly reveal the benefits of the DORS and AORS schemes, we derive the exact and asymptotic closed-form expressions of the secrecy outage probability (SOP) and positive secrecy capacity probability (PSCP) achieved by the two schemes, and analyze the optimality of the two schemes. Analysis and simulation results indicate that both the DORS and AORS schemes significantly outperform the benchmark schemes, in addition to achieving the same SOP and desirable PSCP at high SNR.
3D heterogeneous integration of high performance high-K metal gate GaN NMOS and Si PMOS transistors on 300mm high-resistivity Si substrate for energy-efficient and compact power delivery, RF (5G and beyond) and SoC applications

We have demonstrated industry’s first 300mm 3D heterogeneous integration of high performance, low-leakage high-K dielectric metal gate enhancement-mode (e-mode) GaN NMOS and Si PMOS transistors on 300mm high-resistivity (HR) Si(111) substrate, enabled by 300mm GaN MOCVD epitaxy and 300mm 3D layer transfer. The fabricated (bottom device layer) high-K dielectric e-mode GaN NMOS transistors, integrated on a 300mm HR Si(111) substrate, show excellent electrical characteristics and figure-of-merits (FOM) for realizing energy-efficient, compact voltage regulators and RF front-end components such as power amplifiers, low-noise amplifiers and RF switches, with (i) IOFF as low as 100pA/μm (VD=5V, VG=0V), (ii) high ID,max=1.5mA/μm; (iii) RON as low as 610Ω-μm, significantly better than industry-standard Si transistors at equivalent drain breakdown (BVD), (iv) excellent RF performance: fT=190GHz, fMAX=300GHz, PAE=56% at mmwave frequency (f=28GHz), and PAE=70% at sub-7GHz (f=5GHz), significantly better than industry-standard GaAs and Si RF transistors, (v) excellent RF switch FOM, RonCoff=110fs, and (vi) low noise figure, NFmin=1.36dB (f=28GHz), 0.4dB (f=5GHz) and 0.27dB (f=1.8GHz), all at SoC-compatible voltages. The fabricated (top device layer) LG=65nm and 130nm Si PMOS transistors, which are monolithically integrated on top of the bottom GaN NMOS transistors by 300mm 3D layer transfer, show respectively, high drive current of 0.85mA/μm, and low Ioff of 150pA/μm at VD=-1.2V. Such a monolithic 3D integration of GaN NMOS and Si PMOS enables full integration of energy-efficient, truly compact power delivery and RF solutions with CMOS digital signal processing, logic computation and control, memory functions and analog circuitries for next generation power delivery, RF (5G and beyond) and SoC applications.
A Shallow Convolutional Neural Network for Classifying MI-EEG

Deep neural network is a hotspot in the field of Machine Learning, which can realize deep hierarchical representation of input data. In this paper, a simplified Shallow Convolutional Neural Network (SCNN) is used to classify Motor Imagery Electroencephalogram (MI-EEG). The network uses a single feature layer to mine the intrinsic features of EEG sequences, and uses the training time and accuracy to evaluate the classification effect. Different from other methods, CNN integrates feature extraction and predicted classification results to optimize the whole process, which can automatically extract features from original data. Experiments on the BCI Competition IV Dataset 2b competition dataset and the BCI Competition II Dataset III competition dataset show that the overall performance under SCNN is the best.
An Experimental Investigation of Enhanced SM-OFDM Over Indoor Rician Multipath Channels

In this letter, we investigate enhanced spatial modulation (ESM) aided orthogonal frequency division multiplexing (OFDM) systems. ESM extends the conventional SM by introducing a series of signal constellations. Existing research shows that ESM displays performance superiority over SM in flat-fading channels. For frequency-selective fading channels, we design a practical implementation of an ESM&SM aided OFDM hybrid system to characterize the bit error rate (BER) performance of ESM and conventional SM with an experimental wireless testbed. Form the experimental results, it is found that ESM achieves better BER performance than SM on frequency-selective channels, which are consistent with the Monte Carlo simulation results.
Immersion in Virtual Reality Video Games for Improving Physical Performance Measures: A Review

Virtual reality (VR) active video gaming, also called ”exergaming” has become an emerging trend in entertainment but also fitness, education and health sectors. It requires bodily movements to play and function as a form of physical activity (PA). Since this type of gaming is becoming more and more popular and accessible and the trends suggest that it may become a mainstream media, the scientific community is discussing on its usefulness. There are number of claims about its ability to improve health via an increase in PA but researchers also argue that it is still a form of time spent in front of monitor which comes with many negative sides. The aim of this paper is to do an overview of the scientific research domain and outline the strengths, weaknesses and opportunities of immersive VR gaming to promote PA. The available evidence indicates that active video games can replace sedentary time spent in front of a computer or TV with physical activity and make a measurable contribution to the improvement of health. However whether it is a sustainable way to motivate gamers in PA is questionable.
Classification Technique for Heart Disease Prediction in Data Mining

Currently, a lot of methods have been implemented for prediction analysis. In the proposed study, clustering and classification of the input information for heart disease forecasting is executed with the help of k-means clustering algorithm and SVM (support vector machine) classification model on the basis of prediction analysis methods. The back propagation algorithm along with k-means clustering algorithm is applied for the clustering of information. These algorithms support to enhance the precision of prediction analysis. A heart disease data suite obtained from the UCI repository is used for judging the performance of proposed algorithm. This data suite comprises total 76 features. But, whole tests require a subset of 14 features. Particularly, Cleveland database particularly is utilized by the machine learning researchers throughout the tests. A comparison between proposed study and earlier method (using arithmetic mean) is performed in terms of certain parameters such as accuracy, error recognition rate and execution time.
Collecting Influencers: A Comparative Study of Online Network Crawlers

Online network crawling tasks require a lot of efforts for the researchers to collect the data. One of them is identification of important nodes, which has many applications starting from viral marketing to the prevention of disease spread. Various crawling algorithms has been suggested but their efficiency is not studied well. In this paper we compared six known crawlers on the task of collecting the fraction of the most influential nodes of graph. We analyzed crawlers behavior for four measures of node influence: node degree, k-coreness, betweenness centrality, and eccentricity. The experiments confirmed that greedy methods perform the best in many settings, but the cases exist when they are very inefficient.
Low Torque Ripple Synchronous Reluctance Machine with Stator Cage Winding

A low torque ripple is of major importance in electrical machines to avoid unwanted secondary effects such as vibration, acoustic noise and wear of mechanical components. This work investigates synchronous reluctance machines using a stator cage winding and the impact of its number of stator bars on the torque ripple. Firstly, the electromagnetic properties of a stator cage winding are discussed. Secondly, the output torque is calculated analytically based on principle of air-gap shear stress. Suitable combinations of number of stator bars and pole pairs are derived to reduce the torque ripple without additional sensitivity analysis or numerical optimization procedures. Finally, finite element analysis for different operating modes, numbers of stator bars and rotor geometries validate the derived results.
Predicting the Probability of Student's Academic Abilities and Progress with EMIR and Data from Current and Graduated Students

In 2016, Kobe Tokiwa University constructed an office for institutional research (IR) promotion. The purpose of this office is to propose, manage, arrange, and collect information on students at the university not only as a general management strategy, but also to support enrollment management. Our database currently contains 3,495 points of data (i.e., headcounts), each containing 1,246 items of numerical value. Last year, we reported on an analysis that focused on the "student dropout" phenomenon by using these data from both current graduate and dropout students. This year, we formulated a research question that is centered on predicting the probability of students' progress and academic abilities through Enrollment Management / Institutional Research (EMIR). We obtained results with these data by processing them through a machine learning technique using random forest, which yielded a correction rate of about 90%.
Comparison of Different Optimal Placement Models of FACTS Devices in Power System Networks on a Limited Budget

The problem of transmission network congestion or overloading poses a serious challenge to most power utilities around the world. One solution to this problem is the use of FACTS devices. This paper presents an empirical comparison of different optimal placement models of FACTS devices in a power system network with the objective to maximize the system loadability (SL). The three configuration are considered in this comparison. In the first case only TCPST is considered, while in the second case only TCSC is considered and in the third one a combination of several devices is considered. The results show that the first and the third configuration performs better economically and in terms of system losses.
Bayesian Matching Pursuit-Based Channel Estimation for Millimeter Wave Communication

Hybrid precoding is considered as a solution to reduce the high power consumption caused by devices operating at radio frequency (RF) in millimeter wave (mmWave) communication. For hybrid precoding, the channel state information (CSI) is critical but hard to obtain because of the analog precoding at RF and the large number of antennas. mmWave channel has been proved to be sparse by real-world experiments. Compressive sensing (CS) methods can be applied to the channel estimation to decrease complexity. However, there is a distinct performance gap between the estimation of the existing CS methods with or without given sparsity pattern (SP). In this letter, a new method based on Bayesian matching pursuit(BMP) idea is proposed to improve sparse channel estimation performance. We make appropriate assumptions according to the characteristics of mmWave channel. We select a set of candidate SPs with high posterior probabilities to estimate CSI. Numerical simulation shows that our proposed method has significantly improved channel estimation performance with acceptable complexity compared to existing methods including orthogonal matching pursuit, sparse Bayesian learning and Bayesian compressive sensing.
Composition Optimization and Device Understanding of Si-Ge-As-Te Ovonic Threshold Switch Selector with Excellent Endurance

In this work we explore the composition space of the Ovonic Threshold Switch (OTS) selector device based on Si-Ge-As-Te material system. Physical Vapor Deposition (PVD) co-sputtering capabilities enabled the tuning of the As/Te ratio, Ge and Si content to increase the crystallization temperature TX>450°C. Guided by ab initio calculations, we optimize the composition and achieve stable endurance characteristics of more than 1011 cycles on 65nm devices fabricated on 300mm wafers. Finally, we propose a switching model that can predict the statistical distribution of the threshold voltage (VTH), explaining VTH drift and time-dependent instabilities.
Efficient Reconstruction Architecture of Compressed Sensing and Integrated Source-Channel Decoder Based on Reed Solomon Code

A novel high-efficient reconstruction architecture of compressed sensing and an area-efficient integrated source-channel decoder based on Reed-Solomon (RS) code are proposed in this letter. The proposed reconstruction architecture only includes a parameter calculation (PC) block and a target locator and evaluator solution (TLES) block, resulting in 30 times higher throughput rate achieved and 85% area saved compared with previously reported reconstruction architecture based on orthogonal marching pursuit (OMP) algorithm. Moreover, the proposed integrated source-channel decoder can reduce 15.3% and 28.5% area respectively for the sampling rate of 25% and 12.5%.
Exploring Performance Bounds of Visual Place Recognition Using Extended Precision

Recent advances in image description and matching allowed significant improvements in Visual Place Recognition (VPR). The wide variety of methods proposed so far and the increase of the interest in the field have rendered the problem of evaluating VPR methods an important task. As part of the localization process, VPR is a critical stage for many robotic applications and it is expected to perform reliably in any location of the operating environment. To design more reliable and effective localization systems this letter presents a generic evaluation framework based on the new Extended Precision performance metric for VPR. The proposed framework allows assessment of the upper and lower bounds of VPR performance and finds statistically significant performance differences between VPR methods. The proposed evaluation method is used to assess several state-of-the-art techniques with a variety of imaging conditions that an autonomous navigation system commonly encounters on long term runs. The results provide new insights into the behaviour of different VPR methods under varying conditions and help to decide which technique is more appropriate to the nature of the venture or the task assigned to an autonomous robot.
Relationship Between Wellbeing and Academic Achievement Among University Students

This study aimed to explore students' academic achievement from the aspect of their level of happiness. The study is a correlational research to investigate these two important factors of youth life satisfaction. The total number of 180 university students completed the demographic questionnaire and Oxford Happiness Questionnaire. Academic achievement was defined as the mean of two recent CGPA of students. Analysis of data, using descriptive data and Pearson Correlation showed that there is a significant positive relationship between happiness and academic achievement in participants. This findings can help professionals to design related strategies to enhance students' happiness and academic achievements.
Ge oxide scavenging and gate stack nitridation for strained Si0.7Ge0.3 pFinFETs enabling 35% higher mobility than Si

We demonstrate multiple ways to reduce the D<inf>IT</inf> of Si-cap-free low-Ge-content (25-30%) SiGe gate stack. The D<inf>IT</inf> is reduced by i) Ge oxide scavenging via Ge condensation or by the work function metal (WFM), ii) nitridation of gate dielectrics and iii) optimized high-pressure anneal (HPA). A moderate nitridation of the interface layer (IL) is beneficial in EOT and D<inf>IT</inf> reduction, while nitridation of the HfO<inf>2</inf> dramatically reduces D<inf>IT</inf> by negating an ALD TiN-induced D<inf>IT</inf> increase. The optimized gate stack is evaluated in 8-nm-wide strained Si<inf>0 7</inf>Ge<inf>0 3</inf> pFinFETs integrated on 300 mm Si wafers, for which a 35% improvement in high-field mobility is demonstrated as compared to Si pFinFET counterparts.