Miracle-dz/UniFAR-FTUs huggingface dataset:
False Discovery Rate Control via Data Splitting

Abstract Selecting relevant features associated with a given response variable is an important problem in many scientific fields. Quantifying quality and uncertainty of a selection result via false discovery rate (FDR) control has been of recent interest. This article introduces a data-splitting method (referred to as “DS”) to asymptotically control the FDR while maintaining a high power. For each feature, DS constructs a test statistic by estimating two independent regression coefficients via data splitting. FDR control is achieved by taking advantage of the statistic’s property that, for any null feature, its sampling distribution is symmetric about zero; whereas for a relevant feature, its sampling distribution has a positive mean. Furthermore, a Multiple Data Splitting (MDS) method is proposed to stabilize the selection result and boost the power. Surprisingly, with the FDR under control, MDS not only helps overcome the power loss caused by data splitting, but also results in a lower variance of the false discovery proportion (FDP) compared with all other methods in consideration. Extensive simulation studies and a real-data application show that the proposed methods are robust to the unknown distribution of features, easy to implement and computationally efficient, and are often the most powerful ones among competitors especially when the signals are weak and correlations or partial correlations among features are high. Supplementary materials for this article are available online.
Precise neural network computation with imprecise analog devices.

The operations used for neural network computation map favorably onto simple analog circuits, which outshine their digital counterparts in terms of compactness and efficiency. Nevertheless, such implementations have been largely supplanted by digital designs, partly because of device mismatch effects due to material and fabrication imperfections. We propose a framework that exploits the power of deep learning to compensate for this mismatch by incorporating the measured device variations as constraints in the neural network training process. This eliminates the need for mismatch minimization strategies and allows circuit complexity and power-consumption to be reduced to a minimum. Our results, based on large-scale simulations as well as a prototype VLSI chip implementation indicate a processing efficiency comparable to current state-of-art digital implementations. This method is suitable for future technology based on nanodevices with large variability, such as memristive arrays.
The Symbolic Power of the Modern Personal Physician: The Placebo Response under Challenge

The change in a patient's condition that results from the meaning or symbolism of the encounter with the physician, rather than from the pharmacologic properties of the interventions used, constitutes the placebo response. Placebo research suggests that the placebo response forms a part of virtually all healing encounters, and is not limited to circumstances in which a “dummy” pill is used. This suggests, in turn, that the placebo effect has been important in medicine throughout history, and that the modern physician has important elements in common with her “pre-scientific” predecessors. Historical analysis of the meaning of the healing encounter for physician and patient suggests that the personal physician, as symbolizing a human intermediary between the impersonal forces of science and the suffering patient, remains an important ingredient even in modern technological medicine. Contemporary forces toward depersonalization of medical care may threaten this dimension of treatment, but appropriate research may measure those elements of the physician encounter that most effectively promote a positive placebo response.
Role of the triangle mechanism in the 
Λb→Λcπ−f0(980)
 reaction

We investigate the $\Lambda_b\to\Lambda_c\pi^-f_0(980)$ production with a $f_0(980)$ decay into $\pi^+\pi^-$ via the $K^{*0}K^-K^+$ and $K^{*-}K^0\bar{K}^0$ triangle loops. These loops produce a peak around 1.42 GeV in the $\pi^-f_0(980)$ invariant mass distribution, which is the same mechanism as the one considered to explain the $a_1(1420)$ peak. In the $\pi^+\pi^-$ distribution obtained by fixing the $\pi^-f_0(980)$ invariant mass to some values, a clear peak of $f_0(980)$ is seen, and the $\pi^-f_0(980)$ distribution has a peak around $M_{\pi^-f_0}=1.42$ GeV, which is caused by the triangle mechanism of the $K^*\bar KK$ loop. The branching ratio of $\Lambda_b\rightarrow\Lambda_c\pi^-f_0(980)$ with $f_0(980)\rightarrow\pi^+\pi^-$ by the triangle mechanism, obtained by integrating the $\pi^-f_0(980)$ distribution from 1 to 1.6 GeV, is estimated to be the order $10^{-4}$. Future measurements of the $\Lambda_b\to\Lambda_c\pi^-f_0(980)$ branching ratio and the $\pi^-f_0(980)$ invariant mass distribution predicted in this work would give further clues to clarify the nature of the $a_1(1420)$ peak.
A Multi-step Traffic Speed Forecasting Model Based on Graph Convolutional LSTM

Traffic speed is an indicator reflecting the traffic state and it is important for traffic control and optimization. Traffic speed forecasting is a typical application of spatial-temporal forecasting problems. However, it is very difficult to accurately predict the traffic speed especially when we need a long-term forecasting result. And how to capture the spatial features is also a huge challenge. In this paper, a sequence to sequence (Seq2Seq) model based on Graph Convolutional LSTM (GC-LSTM) was proposed, which is an end-to-end deep learning model that uses the network topology and historical speed data. The Graph Convolutional Networks (GCN) is used to deal with spatial relationship features while LSTM for temporal relationship features. The Seq2Seq model is a structure that can predict the multi-step traffic speed. Then an experiment on a real-world dataset shows that our model can get a good result for the long-term traffic forecasting problem.
A Control Method for Tracking Large Position Deviation Signal Fast and Smoothly

Classic servo systems typically use Dual Closed-loop Feedback (DCF) control as the tracking control method. When the system tracks the large position deviation signal, the DCF often has too much overshoot problem due to the integral failure (Windup), which is unacceptable for some precision equipment. Based on this, this paper proposes a control method for large position deviation signals (CLPS) by adding transition signals based on DCF, in which the transition signal is used to smooth the input and makes the jumping large position deviation signal become to a slowly changing one. And the transition signal is time optimal, this reduces the settling time while decreasing the overshoot. Finally, the control algorithm is verified in the electro-optical tracking (EOT) system and the experimental results show that the fast and smoothness of the system reach the expected goal, and the control method has obvious tracking effect improvement compared with the DCF.
A New Resource Scheduler For LTE Systems

LTE systems provide high data rates. For excellent customer pleasure, it is important to provide high data rates fairly. Scheduling algorithms have a key role to allocate radio resources among users efficiently. Best CQI algorithm is one of the well-known schedulers which aims to maximize cell throughput but compromises fairness. On the other hand, Round Robin endeavor to enhance fairness but not sufficient for cell throughput performance. PF algorithm offers a balance between throughput and fairness. In this paper, a new scheduling algorithm that balances the performance between fairness and cell throughput is proposed. The proposed algorithm is better than the Best CQI algorithm in terms of fairness. At the same time, peak and average throughput remain higher than PF, it has been achieved to allocate resources to users who have never reached the resources, unlike Best CQI. The criteria of throughput fairness increased together with distribution fairness.
MCA-GAN: Text-to-Image Generation Adversarial Network Based on Multi-Channel Attention

It is an extremely challenging task to text-to-image generation based on generative adversarial network(GAN). Although some researchers have made remarkable progress in the field of image generation in recent years, it is still a problem to be solved in the aspect of texture detail in image generation. An attention generation algorithm based on multi-channel is presented in this paper. The network gradually refine by three branches to produce high-resolution images that conform to the text description. By the iterative training of GAN, the multi-channel feature attention mechanism is used to guide the pixel value deviation and loss in the process of image generation. Through a lot of experiments and people’s visual perception of the generated images, the algorithm based on multi-channel attention mechanism proposed in this paper has been significantly improved in the processing of details.
End-to-End Slicing With Optimized Communication and Computing Resource Allocation in Multi-Tenant 5G Systems

Slicing is a key technology in 5G networks to provide scalability and flexibility in allocating computing and communication resources among multiple tenants. Typically, 5G networks have a 2-tier architecture consisting of a central office and transport network in the upper tier and a multi-access edge and radio access network in the lower tier. The tenants which share the 2-tier architecture typically have different service-dependent resource requirements. This study proposes an algorithm, designated as Upper-tier First with Latency-bounded Over-provisioning Prevention (UFLOP), to adjust the capacity and traffic allocation in such a way as to minimize the “over-provisioning ratio” while still satisfying the latency constraints and Service Level Agreements (SLAs) of the tenants. The performance of UFLOP is evaluated experimentally with a real testbed on an end-to-end slicing framework using three typical 5G services, namely Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency (URLLC), and massive Machine Type Connection (mMTC). It is shown that UFLOP successfully determines the critical traffic allocation ratio between the central office and the edge which achieves an over-provisioning ratio close to zero while still meeting the latency requirements. The results suggest optimal resource allocation ratios of 10:0, 1.5:8.5 and 7.8:2.2 for the eMBB, URLLC and mMTC applications, respectively. Furthermore, it is shown that the computing resource behaves as a bottleneck for the eMBB and mMTC services, while the communication resource serves as a bottleneck for the URLLC service.
Surface Waveguide Leaky-wave Antenna for Single-Side Beam Scanning

A surface waveguide (SWG) leaky-wave antenna for directional single-side beam scanning is presented in this paper. A novelty of this design is periodically removing one-sided unit cells of the SWG to obtain $n=-1$ space harmonic mode, which can transform the guided surface wave into radiated leaky wave and realize single side beam scanning. In addtion, An efficient spoof surface plasmon (SSP)-based transition is utilized to excite the surface wave from conventional coplanar waveguide (CPW). The whole transition consists of one ground-tapered CPW and SSP structures. Because SSP and SWG have the similar transmission mode, SSP structures work as an intermedia to improve the whole transition performance from CPW to SWG. The simulated results show that the transition exhibits low loss and broadband properties. And the average gain of proposed antena is up to 10.5 dB with overall 0.82 radiation efficiency in the range from 10 GHz to 15 GHz. The one-sided directional bean scans from $\varphi=135^{\circ}$ to $\varphi=85^{\circ}$. The total scanning angle is 50°.
Circularly Polarized Patch Antenna with Miniaturized Quarter-Mode Slotted SIW Cavity

This paper presents a compact planar WIFI antenna on FR4 substrate with circular polarization (CP). It is based on a quarter mode substrate integrated waveguide (SIW) cavity with a slot near diagonal direction. CP radiation is achieved by the slot mode and a quarter SIW patch mode which are orthogonal to each other. The antenna has a compact size of 0.14 $\lambda \times 0.14 \lambda \times 0.024\ \lambda$, where $\lambda$ is the resonance wavelength in free space. The measured $\vert {S}_{11}\vert < -10\ \text{dB}$ frequency band is from 2.38 to 2.51 GHz, and the measured axial ratio ($\text{AR} < 3\text{dB}$) frequency range is from 2.445-2.475 GHz. The antenna is amid for directional CP radiation, low cost, low profile Internet of Things (IOT) device applications.
An Improved LCL-L Compensation Topology for Capacitive Power Transfer in Electric Vehicle Charging

This paper proposes an LCL-L compensation circuit for high power capacitive power transfer (CPT) aiming at minimizing number of resonant components and improving system performance. The proposed topology adopts only four external resonant components at both sides of the capacitive coupler. The output power is proportional to the capacitive coupling coefficient, therefore, it simplifies the design procedure and abolishes protection circuit requirements under coupler’s misalignment. Moreover, optimizing efficiency at full-load conditions of compensation network can be easily achieved in this system by designing resonant components at the highest value of the mutual capacitance. Theoretical analysis of the proposed system is conducted alongside comparison to lasted CPT compensation circuits. Simulation and experimental results of a 1.5-kW CPT prototype with an air gap distance of 150 mm are provided to verify the feasibility and the effectiveness of the proposed system. System performances under different coupler’s misalignment conditions and output power levels are also examined and discussed as well.
DC-SRGM: Deep Cross-Project Software Reliability Growth Model

Previous studies have suggested that software reliability growth models (SRGMs) for cross-project predictions are more practical for ongoing development projects. Several software reliability growth models (SRGMs) have been proposed based on various factors to measure the reliability and are helpful to indicate the number of remaining defects before release. Software industries want to predict the number of bugs and monitor the situation of projects for new or ongoing development projects. However, the available data is limited for projects in the initial development phases. In this situation, applying SRGMs may incorrectly predict the future number of bugs. This paper proposes a new SRGM method using the features of previous projects to predict the number of bugs for ongoing development projects. Through a case study, we identify similar projects for a target project by k-means clustering and form new training datasets. The Recurrent Neural Network based deep long short-term memory model is built over the obtained new dataset for prediction model. According to experiment results, the prediction by the proposed deep cross-project (DC) SRGM performs better than traditional SRGMs and deep SRGMs for ongoing projects.
Design of Monitoring System for Integrated Vertical Injection Molding Machine

Aiming at the need of the integrated vertical injection molding machine, a monitoring system for integrated vertical injection molding machine is designed in this paper. The system integrates functions like field coordinated control, data collection and transmission, and remote monitoring. According to the processing technology and structure of integrated vertical injection molding machine, the scheme of monitoring system is proposed. Then, the hardware is designed, including Input/Output configuration, hardware configuration and CAN node design. Finally, the software is designed, including the communication strategy and monitoring interface. The practice demonstrates that the system can stably control, effectively improve the production efficiency of injection molding machine and reduce the maintenance cost of enterprises.
Output Current Control of Modular MultiLevel Converter Using BackStepping Controller

This paper presents a control designed structure for output current in Modular Multi-Level Converter (MMC). The MMC has diverse attractive properties e.g. modular structure, power quality, transformerless operation, scalability and reliability and low harmonics in output waveforms. These promising features make MMC a good choice in the power system and increased the interest of researchers in this field. As a result, different control structures, converter models, circuit configurations and modulation strategies have been developed. But design controls in MMC is a challenging and difficult task. A backstepping controller is designed to control the output current. The backstepping controller exhibits good tracking references both in transient and steady-state. The output current is decoupled in αβ components to analysis the designed control structure. The designed controller is implemented for a three-phase MMC model using MATLAB/Simulink.
Evaluation of edge cloud server placement for edge computing environments

In edge computing environments, edge cloud servers are deployed in networks in addition to centralized cloud servers to provide high quality services to users. The performance of the edge computing environments depend on where the edge cloud servers are located. This paper introduces some placement methods of edge cloud servers based on optimization problems, which take into account the network loads and the edge cloud server loads. Through numerical experiments, we compare these placement methods.
Parallel Approach for Document Representation using Dictionary Learning

There has been drastic advancements in data science both in terms of algorithm development and storage is concerned. This work takes into consideration an area which is coming of use in data handling based on sparse representation. The collection of documents which is considered as a dictionary is large with a massive amount of data providing in. So in order to represent an incoming document in sparse mode, we have considered parallelizing the algorithm for dictionary learning and sparse code generation. This work show good improvement in reducing the time for generating sparse representation with help of GPUs. There is a 2x increase in time when compared to CPU usage for generating the same sparse code for an input collection.
Smartphone Mobile-Learning Application for Implementing Analytical Drawing Process in On-site Sketching

Analytical drawing is a method to study the three- dimensional configuration of a subject and to represent the same with a freehand drawing. Further, applying this method to architectural sketching requires imagination, to disassemble complex architectural scenes into geometric forms and shapes and to reassemble these scenes on paper step-by-step with lines drawn freehand. To assist with this process, a smartphone application prototype with interactive illustration of underlying theoretical concepts and stepwise drawing processes of selected architectural examples is presented in this paper. This interactive mobile learning application was tested in an actual class, with promising responses from students.
Fault Diagnosis of Relative Position Sensor for High Speed Maglev Train

The relative position sensor provides phase angle information for the traction system. The failure of the sensors directly affects whether the train can operate normally. Based on the analysis of the sensor, the common faults of the sensor are analyzed, and the corresponding fault features are extracted. The fault characteristics of the sensor are presented. Based on the research of the decision tree fault diagnosis method, the improved decision tree based on support vector machine is proposed to realize the fault diagnosis and improve the effect of the diagnosis. Finally, the fault diagnosis platform is designed, and the tests show that the improved decision tree has high diagnosis correct rate.
Injection Lock Frequency Divider with Class-C Coupled VCO for Nanoelectronics Harmonic Demodulator of Fluxgate Sensing

This article presents a multi-band divide-by-3 injection locked frequency divider (ILFD) and three output phases class-C voltage-controlled oscillator (VCO) to generate signal by current reused and it uses three identical single-ended current reused VCOs in a ring configuration. The proposed fluxgate magnetometer is proposed dynamic bias circuit is used to reduce the power consumption, and the back-gate BJTs in PMOSFETs are used as a phase coupling device, the circuit oscillates for nanoelectronics.
$X-BAND$ : Expiration Band for Anonymizing Varied Data Streams

The Internet of Things (IoT) has formed a whole new layer of the world built on the Internet, reaching every connected device, actuator, and sensor. Many organizations utilize IoT data streams for research and development purposes. To make value out of these data streams, the data handling party must ensure the privacy of the individuals. The most common approach to provide privacy preservation is anonymization. IoT data provide varied data streams due to the nature of the individual’s preference and versatile devices pool. The conventional single-tuple expiration-driven sliding window method is not adequate to provide efficient anonymization. Furthermore, the minimization of missingness has to be considered for the varied data stream anonymization. Therefore, we propose the X-BAND algorithm that utilizes the new expiration-band mechanism for handling varied data streams to achieve efficient anonymization, and we introduce weighted distance function for X-BAND to reduce missingness of published data. Our experiment on real data sets shows that X-BAND is effective and efficient compared to the famous conventional anonymization algorithm FADS. X-BAND demonstrated 5%–11% and 1%–3% less information loss on real data sets Adult and PM2.5, respectively, while performing similar on clustering, comparable to reusing suppression and runtime. Also, the new weighted distance function is effective for reducing missingness for anonymization.
Loop Closure Detection for Visual SLAM Based on SuperPoint Network

Loop closure detection is one of the important components of Visual SLAM. It can reduce the accumulating drift of visual odometer if the loops are detected correctly. Traditional loop closure detection mainly relies on the traditional algorithms to extract feature points. However, these algorithms are difficult to adapt to the complex and dynamic environment, e.g. illumination changes. In this paper, a new approach of loop closure detection is proposed. It learns inner structures from raw data using neural networks named SuperPoint. SuperPoint is designed to detect interest points and corresponding descriptors at the same time. The method clusters the feature descriptors as words using K-means and converts descriptors of each tested image into a vector by comparing the similarity between the descriptors and words. It measures the similarity of the images by calculating the cosine similarity of the corresponding vectors. A series of experiments on New College and City Center are demonstrated to evaluate the performance of the proposed method. The average precision of loop closure detection based on SuperPoint increases by 28% and 122% respectively compared with that based on SIFT and ORB under the same conditions. It is proved that the method based on SuperPoint is possible to detecti loops at a satisfactory precision compared with the traditional method based on ORB or SIFT.
Wideband Coupler Based on Wide-Wall Coupling Half-Mode Ridge Substrate Integrated Waveguide

A wideband coupler based on half-mode ridge substrate integrated waveguide (HMRSIW) in Ku-band is designed in this paper. The coupler utilizes a gradual slot on wide-wall to couple. The measured coupling bandwidth is 49.8%. The return loss and isolation are better than 20dB from 11.76GHz to 17.88GHz.