Miracle-dz/UniFAR-FTUs huggingface dataset:
L-RBF: A Customer Churn Prediction Model Based on Lasso + RBF

With the development of market economic, customer churn prediction play a critical role in the company management. How-ever, customer information is complex, it contains multi-dimensional features. What's worse, the number of customer churn very small among the whole consumers, and the features of customer are dynamically changing, which is challenging for traditional statistical methods. Fortunately, as the rise of edge computing, more computing related to data-intensive applications will be de-centralized to edge smart terminals. Moreover, edge computing focuses on real-time, short-cycle analysis, which is useful for dealing with dynamic changes problems in customer features. Therefore, to address these issues about customer messages, this paper proposes a simple and effective model to predictive customer churn with a higher accuracy, named L-RBF. The basic idea is to utilize Lasso Regression algorithm (Lasso) for optimizing Radial Basis Function Neural Network (RBF). At first, placing customer features on edge terminals, and Lasso is utilized to extract the correlated features of customer churn. Then, according to the correlated features and lasso regression equation, we can automatically set the topology and parameter information of RBF. Finally, experimental results indicate that L-RBF has a higher recall rate and stronger prediction classification ability compared with the previous work, included Logistic Regression (Log-R), RBF and Boosting.
Negative Capacitance Field-Effect Transistor Based on a Two-Dimensional Ferroelectric

Negative capacitance field effect transistors (NCFETs) based on ferroelectric materials have been the focus of intensive research activities because of their relatively small sub-threshold swing. This work proposes and presents a comprehensive study of a NCFET based on few-layer $\alpha$-In2 Se3 as the ferroelectric in order to reduce the sub-threshold swing through voltage amplification effect. By employing first principles electronic structure calculations, the Landau constants of mono and few-layer $\alpha$-In2 Se3 are extracted which were utilized for analyzing the characteristics of a NCFET with a monolayer MoS2 as the channel material. Sub-threshold swings in the range of $\sim 27 -59$ mV/dec were achieved for few-layer $\alpha$-In2 Se3 that can be further improved by increasing the thickness of the ferroelectric layer and by using a thinner or high-$\kappa$ insulate layer.
Stiffness-Mass Decoupled Honeycomb-like Disk Resonator Gyroscope

We report, for the first time, the overall performance of a novel stiffness-mass decoupled honeycomb-like disk resonator gyroscope (HDRG). Compared with the former HDRG, this new design significantly increases the quality factor and effective mass of the resonator through hanging lumped masses on certain rings and arcs. The quality factor of the optimized resonator is 171.6k, more than twice the amount of the former design 81.1k. The bias instability of the gyroscope reaches 0.11°/h at room temperature without any compensation, more than eighty times smaller than the former design, exhibiting significant performance improvement. Moreover, ranging from −40°C to 60°C, the temperature coefficient of scale factor is 0.188%/°C, and the maximum variations of zero offset and bias instability over temperature are 40°/h and 0.12°/h respectively, showing good robustness to environment disturbance.
Food Image Classification with Deep Features

In this paper, deep feature extraction, feature concatenation and support vector machine (SVM) classifier are used for efficient classification of food images. Classification of foods according to their images becomes a popular research task for various reasons such as food image retrieval and image based self-dietary assessment. For deep feature extraction, pre-trained AlexNet and VGGl6 models are considered. The features of size 4096 are extracted from fc6 and fc7 layers and concatenated with various combinations to determine best deep feature sequence for food image classification. The concatenated features are then classified with SVM. Three publicly available datasets namely FOOD-5K, FOOD-11 and FOOD-101 are used in evaluation of the proposed method and the accuracy metric is considered for performance evaluation. The experimental results show an accuracy of 99.00% for FOOD-5K dataset and 88.08% and 62.44% for FOOD-11 and FOOD-101 datasets, respectively. We further carried out experiments with fine-tuning of a pre-trained CNN model on FOOD-101 dataset and obtained 79.86% accuracy score. The obtained results are also compared with some other methods and it is seen that our performance is better than the other methods on FOOD-11 and FOOD-101 datasets.
Realization of Multiband Microwave Metamaterials Fabricated via Low-Cost Inkjet Printing

We present design and computational analysis of multiband metamaterial structures that are fabricated via low-cost inkjet printing. In particular, three-dimensional arrangements of split-ring resonators with different sizes are considered to achieve bandstop characteristics at multiple frequencies. A fast and accurate solver is employed to analyze alternative arrangements, while considering different parameters to obtain strong resonances at desired frequencies. The obtained designs are fabricated in a low-cost inkjet-printing setup, which is based on using conventional printers that are modified and loaded with silver-based inks. Initial results shown in this paper demonstrate the feasibility of low-cost multiband metamaterials.
Event-based Hybrid Impedance and Admittance Control

This paper proposes an event-based hybrid impedance and admittance control method for robot manipulator, which improves the contact compliance between the robot and human operator. The novel method makes full use of the advantages of the classical impedance control and the classical admittance control by introducing a switching strategy. The simulation and experiment results on the Baxter robot show the effectiveness of our design.
A Design Pattern for Systems Composed from Intelligent Mechatronic Modules with Wireless Communication

This paper presents a software design pattern for systems composed from distributed intelligent mechatronic modules which communicate with each other via wireless interface. The design pattern is formed by integrating the reliable communication mechanism called channel, which comes from a system-level programming language SystemJ. The paper demonstrates how the design pattern can be applied in an industrial automation example.
First Demonstration of VUV Ramsey-Comb Excitation Based on HHG; Towards 1S-2S He+ Excitation

It is nearly 10 years since a measurement in muonic hydrogen by the CREMA collaboration resulted in a surprisingly small proton charge radius [1]. Since then, an increasing number of high-precision atomic physics experiments have been started (and performed) in order to test bound-state quantum electrodynamics (QED) and contribute to solving the so-called proton radius puzzle (PRP). It is noteworthy that due to the close to 100% correlation between the proton charge radius and the Rydberg constant, the PRP can as well be understood as a Rydberg puzzle. Remarkably, the latest results from laser spectroscopy measurements in atomic hydrogen [2, 3] disagree with each other and do so far not allow further conclusions.
Low temperature point contact spectroscopy and transport measurements on filled skutterudite compounds

Several members of the RPt<inf>4</inf>Ge<inf>12</inf> skutterudites class (R = Sr, Ba, Th, La, Pr) show at low temperature the phenomenon of superconductivity, with R = Pr exhibiting the highest transition temperature of about 8 K. PrPt<inf>4</inf>Ge<inf>12</inf> shows as well a wide variety of strongly correlated electron behavior, including metal-insulator transitions, spin fluctuations, and various magnetically ordered states. In this work, we have applied the point contact spectroscopy to investigate the superconducting order parameter of the PrPt<inf>4</inf>Ge<inf>12</inf> compound. Superconductivity in PrPt<inf>4</inf>Ge<inf>12</inf> is progressively suppressed by partially substituting the Pr ions with Ce ions. For this reason, electrical resistance measurements as a function of the temperature (R-T), in different applied magnetic fields have also been performed and analysed on Pr<inf>1-x</inf>Ce<inf>x</inf>Pt<inf>4</inf>Ge<inf>12</inf> samples for x = 0, 0.07, 0.10.
Emotion Recognition Based On CNN

Emotion is a state that comprehensively represents human feeling, thought, behavior and it exists everywhere in daily life. Emotion recognition is an important interdisciplinary research topic in the fields of neuroscience, psychology, cognitive science, computer science and artificial intelligence. Neural network is a statistical learning model inspired by biological neural networks. This paper attempts to use the EEG signal from the DEAP data set to classify the emotion of the subjects, this data set represents the emotional classification research. Then the principal component analysis is used to reduce the dimension of the preprocessed EEG data, so the main emotional EEG features are obtained. Then the accuracy of the classification of the training samples and the test samples is tested by the CNN algorithm, and the other classification methods are compared to obtain the nerves. The network can be used as a robust classifier for brain signals even better than traditional learning techniques.
Combining Kriging and Controlled Stratification to Identify Extreme Levels of Electromagnetic Interference

EMC risk analysis requires various configurations of coupling paths described by important sets of unknown or uncertain parameters. More specifically, values at risk corresponding to extreme values of relevant fields, currents or voltages are often the most important information with regard to a possible EMC risk. Therefore, we aim at estimating extreme quantiles of the relevant field, current or voltage. Controlled stratification accelerates the standard Empirical estimation convergence to sample output extreme values, thus reducing the required number of calls to cost-expensive full-wave simulations. However, controlled stratification requires a simple (i.e. fast calculation time) model with sufficient correlation to the initial model. The main idea in this communication is to use a surrogate model as a simple model. Kriging was previously identified as a surrogate model with relevant properties. In this paper, we investigate the performance of combined kriging and control stratification. We show that this combination outperforms the stand-alone kriging surrogate model for estimating extreme quantiles. On the contrary, the latter performs better to identify less extreme quantiles.
Integral Active Finite-time Disturbance Rejection Control for Attitude Tracking of Quad-rotor

For the attitude tracking of quad-rotor system in the presence of disturbances, an integral active finite-time disturbance rejection control method is presented in this paper. Specifically, first, based on a second-order attitude model of quad-rotor in the absence of disturbances, a finite-time attitude control algorithm under is proposed. At the second step, considering the existence of disturbances including constant disturbances or time-varying disturbances, a disturbance observer is designed to estimate the disturbances. Finally, using the idea of active disturbance rejection, the disturbance is directly compensated through the control input channel in the case of constant disturbance. In the case of time-varying disturbance, an integral sliding mode control method is employed here to handle the disturbance estimating error. Rigorous theoretical analysis is provided to prove that the the desired attitude can be tracked in a finite time no matter what kinds of disturbances. Simulation results are given to verify the effectiveness and advantages of the the proposed integral active finite-time control method.
A 1.65 GHz Lithium Niobate A1 Resonator with Electromechanical Coupling of 14% and Q of 3112

This paper reports on the demonstration of a 1.65 GHz A1 mode lithium niobate resonator with a high electromechanical coupling (${k_{t}}^{2}$) of 14%, a high-quality factor ($Q$) of 3112, and a near spurious-free response. The 1.65 GHz resonance has been achieved by exploiting the high phase velocity of the first-order antisymmetric (A1) Lamb wave mode in a Y-cut lithium niobate (LiNbO3) thin film, while spurious mode suppression is accomplished with electrode optimization. The performance demonstrated herein marks the first time that a new resonator technology outperforms surface acoustic wave (SAW) resonators and thin-film bulk acoustic resonators (FBARs) regarding the figure of merit (FoM) in the 1–6 GHz frequency range. Thus, the A1 mode lithium niobate resonator in this work promises a new platform for the fifth-generation (5G) front-end filtering.
Design Approach of Inverse Magnetron Injection Gun for 240 GHz Gyrotron for Plasma Fusion

Gyrotron is a high power microwave source widely used for electron cyclotron resonance heating (ECRH) and plasma stabilization through the localized current drive (CD) in the magnetically confined plasma. The magnetron injection gun is the most decisive part of any gyrotron as the RF power and efficiency of the device directly depend on the quality of gyrating electron beam. An inverse magnetron injection gun (IMIG) is designed for 240 GHz, 1MW gyrotron operated at TE46,17 mode and presented here. Selection of the inverse magnetron injection gun refers the possibility to insert the large emitter ring (for large beam current and beam radius) without increasing the bore radius of the magnet while this is not possible with conventional MIG (Magnetron Injection Gun), which further reduces the overall cost of the device. The electron beam parameters and various electrical and geometrical parameters are optimized as per the requirements at the interaction cavity. 2.5 D electron beam optics code EGUN is used to simulate the electrode geometry.
Secure Health Record Sharing for Mobile Healthcare in Privacy Preserving Cloud Environment

using mobile healthcare (mHealth) application, a patient can receive consultation from healthcare providers anywhere without visiting hospital. Thus mHealth helps in extending the healthcare services outside the hospitals. Cloud computing is proving itself crucial in healthcare by helping in storing and maintaining the patient's health record which can be accessed from any location. But patient's health record is highly vulnerable and has to be secured by encrypting before storing it in the cloud. Hence this paper encompasses the study and proposal of certain approaches applicable to mHealth application which lets patients send health records to doctors and doctors to further share the record and negotiate with specialists and also helps patients in finding friends suffering from common symptoms in privacy preserving environment ensuring the health record's integrity.
Luminescent Solar Power — Quantum Separation between Free-Energy and Heat for Cost-Effective Base-Load Solar Energy Generation

The challenge in solar energy today is not the cost of photovoltaics (PVs) electricity generation, already competing with fossil fuel prices, but rather utility-scale energy storage costs. Alternatively, low cost thermal energy storage (TES) exists, but relies on expensive concentrated solar power (CSP). A photovoltaic/thermal (PV/T) technology, able to efficiently unify PV conversion and TES, may usher in the era of efficient base-load renewable power plants. Spectral splitting, one PV/T option where inefficient photons for PV conversion are redirected and thermally utilized, is economically limited by the low yield of each generator. Operating PVs at high temperatures while utilizing the thermalization induced heat for the thermal cycle is another possibility; yet, while conceptually supporting full utilization of solar thermal and free energy, it too is limited by PV efficiency reduction with temperature increase. Here we introduce the concept of luminescence solar power (LSP), where sunlight is absorbed in a photoluminescent (PL) absorber, followed by red-shifted PL emission matched to an adjacent PV band-edge. The PL absorber temperature rises due to thermalization, allowing spatial separation between heat and free-energy, for maximal harvesting of both. We solve the material challenge by experimentally demonstrating tailored luminescence with PL efficiency of up to 90% while operating at 600°C. At such high temperatures, LSP efficiency offers a 50% enhancement over conventional side-by-side PV/CSP efficiency under real-world conditions, leading to a potential reduction in solar energy storage levelized cost of electricity (LCOE) to below 3C/kWh. Such a low LCOE complies with the 2030 SunShot goal, enabling future US solar energy production to reach 50%.
PAPR reduction of BF-OFDM waveform using DFT-Spread technique

For actual (4G) and future (5G) wireless systems, multicarrier modulations (MCM) have been widely used because of their low implementation complexity and their simple equalization in frequency selective channels. For 4G and 5G, Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) has been chosen. Nevertheless, because of the poor spectral containment of CP-OFDM, many MCM schemes with better frequency localizations have been developed in the past few years. These waveforms (WF) are classically named as post-OFDM WFs. In this paper, we will focus on Block-Filtered OFDM (BF-OFDM) waveform which has a very good frequency localization because of its prototype filter, which allows sharp filtering of subcarrier blocks and has the same classical receiver as CP-OFDM. Nevertheless, like any Multicarrier modulation, BF-OFDM has a high peak to average power ratio (PAPR) of the same order as that of the OFDM. In this paper, we propose a DFT spread technique to BF-OFDM for the uplink and compare the performances in terms of PAPR reduction with DFT spread CP-OFDM named SC-OFDM or SC-FDMA. We show that the performances obtained in PAPR reduction with DFT spread BF-OFDM are very close to those obtained with SC-FDMA.
SLM 3D-Printed Horn Antenna for Satellite Communications at X-band

This paper presents a SLM (Selective Laser Melting) 3D-printed, circularly polarised horn antenna suited for satellite communications at X-band. As described in this paper, SLM 3D-printing is used to directly 3D-print the horn antenna in aluminium from a powdered metal bed as opposed to more indirect and imprecise methods found in literature, such as metallising plastic 3D-printed parts. Using this novel manufacturing technique, the entire antenna with stepped septum polariser is printed as one solid component. This ensures perfect alignment and electrical continuity between subcomponents and negates the need for any post-processing work before the antenna could be used. The unibody design of the antenna results in exceptional performance over the satellite communications receive band of 7.9 - 8.4 GHz, and maintains excellent performance over a much larger bandwidth of 7.5 - 11.5 GHz.
Radars for Probing the Terrestrial Ionosphere: an Overview of Old and New Techniques

The first instrument that exploited the radar principle was used to investigate the properties of the ionospheric reflecting layers. Over the decades since then, the techniques have improved and many other types of probing have been developed. The presentation summarises various methodologies, with particular focus on the vertical sounding, and describes the recent improvements, taking into account that in the last years the classic vertical soundings have been joined by new measures of the attenuation undergone by the signal, that are useful to improve the knowledge of the characteristics of the medium.
Enabling Container-Based Fog Computing with OpenStack

Operating system-level virtualization using containerization technologies have changed the world of application development and software by bringing flexibility, efficiency and new methods for managing and distributing software. Edge/Fog computing complements nowadays powerful centralized approach leveraging datacenters resources with a number of distributed fog nodes with relatively capable resources in order to provide advanced services in proximity to end users and data sources. In fact, this emerging paradigm provides ubiquitous processing abilities through scattered heterogeneous hardware with different energy availability and computational capabilities. This paper aims at presenting an extension of an IoT centric infrastructure Cloud framework, named Stack4Things, towards the edge through an integration with two of the OpenStack subsystems (i.e., Zun and Kuryr) that deal with containers management.
Application Topology-Aware Virtual Network Mapping and Service Provisioning in Programmable Networks

In this paper, an application topology-aware virtual network mapping and service provisioning framework dubbed “APTASP” is introduced. The framework is presented as a candidate to deal with the expected increase in the complexity of network management originating from the evolution of IoT and Industrial Internet. Envisaged under the programmable network paradigm, it infers the communication relationships between interacting applications from packet headers. Using this information, it recognizes the communication patterns to build up communication relations. The communication relations are then mapped to template network topologies that enable optimized resource allocation while at the same time ensuring QoS adherence. The service provisioning aspect of the framework introduces as well service function constructs which represent virtualized network function chains required for dynamic and automated service setup.
Interface Circuit Design to Enable Miniaturization of Thermal-Piezoresistive Oscillators for Mass Sensing Applications

This work focuses on the integrated circuit design for CMOS-MEMS-based thermal-piezoresistive oscillators (TPOs). Conventional TPOs necessitate bulky discrete circuit components, which impedes integration and miniaturization of the microsystems. The proposed circuit implemented using TSMC $0.18\ \mu\mathrm{m}$ CMOS technology serves as sustaining amplifier and biasing circuit for our previously developed CMOS-MEMS thermal-piezoresistive resonators (TPRs), thus achieving TPOs with small form factor, targeted for mass/particle sensing applications. The previous version using commercial PCB electronics consumes 2.73 W while the proposed design greatly reduces the power consumption down to 15 mW. In addition, no harmonics have been found in oscillator output power spectrum while reaching 0 dBm (i.e., 1 mW). The far-from-carrier phase noise is demonstrated with −73.85 dBc/Hz and −80.06 dBc/Hz at 1 kHz and 100 kHz offsets respectively at a carrier frequency of 814.49 kHz in ambient pressure; as a result it is suitable for sensor applications. The miniaturized CMOS-MEMS TPO system features a frequency resolution of 65.9 Hz, thus corresponding to a mass resolution of 33.9 pg, which would potentially serve as aerosol (PM2.5) sensors.
Personality and psychopathology

Personality and psychopathology can relate to one another in three different ways: personality and psychopathology can influence the presentation or appearance of one another (pathoplastic relationships); they can share a common, underlying etiology (spectrum relationships); and they can have a causal role in the development or etiology of one another. Each of these possible forms of inter-relationship is considered in this paper.