Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast and Accurate Graph Stream Summarization

A graph stream is a continuous sequence of data items, in which each item indicates an edge, including its two endpoints and edge weight. It forms a dynamic graph that changes with every item. Graph streams play important roles in cyber security, social networks, cloud troubleshooting systems and more. Due to the vast volume and high update speed of graph streams, traditional data structures for graph storage such as the adjacency matrix and the adjacency list are no longer sufficient. However, prior art of graph stream summarization, like CM sketches, gSketches, TCM and gMatrix, either supports limited kinds of queries or suffers from poor accuracy of query results. In this paper, we propose a novel Graph Stream Sketch (GSS for short) to summarize the graph streams, which has linear space cost O(|E|) (E is the edge set of the graph) and constant update time cost (O(1)) and supports most kinds of queries over graph streams with the controllable errors. Both theoretical analysis and experiment results confirm the superiority of our solution with regard to the time/space complexity and query results’ precision compared with the state-of-the-art.
A Theoretical Model for Resonant Frequency and Radiation Pattern on Rectangular Microstrip Patch Antenna on Liquid Crystal Substrate

This paper presents a theoretical model of the rectangular-shaped microstrip patch antennas on liquid crystal (LC) substrates in order to accurately derive the resonant frequencies and radiation performances. The model is based on the solution of the mode matching about an LC loaded cavity. The calculation results of the theoretical model are compared with the experiment and simulation results. The discrepancy between calculation and simulation results is merely 2.7%. In addition, a tuning mechanism of the patch antenna on the LC substrate is verified depending on the strength of a biasing voltage. The calculated results of the theoretical model indicate that the resonant frequency is shifted toward lower direction and 3-dB beamwidth of radiation pattern increases as the bias voltage increases. Although the calculated maximum directivity decreases with the increment of voltage, the realized gain increases due to the radiation efficiency of the antenna.
Variational Inference-Based Automatic Relevance Determination Kernel for Embedded Feature Selection of Noisy Industrial Data

In this paper, an embedded feature selection based on variational relevance vector machines is proposed to simultaneously perform feature selection and model construction. With the settings of specific hierarchical priors over the parameters of an automatic relevance determination kernel (ARDK) function, an approximate posterior distribution over these parameters is here derived and expressed as a multivariate Gaussian distribution, in which a first-order Taylor expansion-based Laplace approximation with respect to the parameters is introduced into the variational inference procedure. The posterior distributions, rather than generic pointwise estimates, over the rest of parameters of the model are also derived. The proposed method can simultaneously select relevant features and samples by adjusting the parameters of ARDK and the weighting vector, respectively. To verify the effectiveness of the proposed method, a synthetic dataset and a number of benchmark datasets, as well as a practical industrial dataset, are employed to solve the regression and classification problems. These experimental results indicate that the proposed method supports the mechanisms of feature selection and model construction while maintaining prediction performance, particularly in an industrial environment.
Modeling and Optimizing the Coverage Performance of the LIDAR Sensor Network

This paper presents a coverage model for a class of mechanical LIDAR (Light Detection and Ranging) sensors based on a new performance measure characterizing the difference between the sensor and the target configurations in the 3D space. In particular, this measure is developed on those parameters of the sensor carrying significant impact on the LIDAR coverage performance, within a geometric constraint derived from the physical principles of the LIDAR sensor. The validity of the proposed performance characterization is examined by the simulation carried out for the deployment optimization of the LIDAR sensor network for covering a 3D environment using a gradient-based control law.
Experimental Validation of a Phase Control Concept for Photonically Steered Terahertz Phased Array Transmitters at Microwave Frequencies

Over the last decade, the THz gap is slowly filled in with the rapid improvement of THz sources and detectors. However, low radiation power at higher THz frequencies still limits the variety of applications and remains as a challenge. A phased array transmitter offers a solution to overcome that issue. It can be realized in the optical domain by employing photomixing for terahertz generation and implementing the mature concept of photonic beamforming. However, the fiber based photonic beamforming system may suffer from phase drift that cannot be easily determined due to the lack of phase detection circuitry at terahertz frequencies. In this paper a 4-element phased array with phase control using a lower frequency reference tone is demonstrated and preliminary measurement results at microwave frequencies are presented.
A Crowd-Sensing Procrustes-Based Method for Indoor Positioning in a Common Frame of Reference

Indoor positioning techniques have been employed in a wide number of applications and have become attractive tools for emerging technologies like Internet of Things (IoT). Numerous indoor localization methods are highly constrained since they take into account the knowledge of building information, inter-node distances, some absolute position information and/or WiFi fingerprints. Here, we propose a less constrained crowd-sensing positioning method based only on dead reckoning information, and on relative positions of detected access points (APs). Our method comprises selection of APs, choice of a common frame of reference, a partial Procrustes transformation, and rotational and translational transformations applied to the trajectories of the users. By performing Monte Carlo simulations, the results show that the estimation of trajectories and APs performed by our crowd-sensing method is statistically accurate.
Cost Efficient Scheduling for Delay-Sensitive Tasks in Edge Computing System

Edge computing, as an emerging computing model, can offload delay-sensitive computing tasks from Internet of Thing (IoT) devices with limited computing resources and energy to the edge cloud. In the edge computing system, several servers are placed on the network edge near the IoT devices to process the offloaded tasks. A key issue in edge computing system is how to reduce the system cost while completing the offloaded tasks. In this paper, we study the task scheduling problem to reduce the cost of edge computing system. We model the task scheduling problem as an optimization problem, where the objective is to minimize the system cost while satisfying the delay requirements of all the tasks. Then, we prove that the proposed optimization problem is NP-hard. To solve this optimization problem effectively, we propose a task scheduling algorithm, called Two-stage Task Scheduling Cost Optimization (TTSCO). We validate the effectiveness of our algorithm by comparing with optimal solutions. The results show that the approximate ratio is less than 1.2 for 95% of the data sets we use. Performance evaluation shows that our algorithm can effectively reduce the cost of edge computing system while satisfying the delay requirements of all the tasks.
A Reconfigurable Inductor Based on Vanadium Dioxide Insulator-to-Metal Transition

This letter introduces a reconfigurable planar square-coil-shaped inductor exploiting as the tuning mechanism the insulator-to-metal transition (IMT) of a vanadium dioxide (VO2) switch placed in the interwinding space in an unprecedented manner. The VO2 thin-film bar-shaped switch is electrically connected to provide a temperature-selective current path that effectively short-circuits a part of the inductor coil changing the inductance of the device. The inductor is fabricated on a high-resistivity silicon substrate using a CMOS-compatible 2-D planar low-cost technology (four photolithography steps). The design, optimized to work in the 4–10-GHz range, provides measured inductances at 5 GHz of 2.1 nH at 20 °C and 1.35 nH at 100 °C with good stability in the entire frequency band (4–10 GHz) resulting in a reconfiguration ratio of 55%. The quality factor (Q-factor) at 7 GHz is about 8 at 20 °C (off state) and 3 at 100 °C (on state), outperforming tunable inductors employing VO2 with 2 orders of magnitude higher Q-factor and a smaller footprint. This represents an advancement for the state of the art of 2-D CMOS-compatible inductors in the considered frequency range.
Adverse effects of fillers

Filler injections belong to the most frequently performed noninvasive beautifying procedures. When done correctly they are generally well tolerated. However, a number of factors, such as poor filler quality, and particularly host as well as user dependent filler reactions may lead to unwanted effects. These may be early, late, or delayed events with characteristics for each of them. Temporary fillers almost invariably cause temporary side effects whereas those of permanent fillers may last forever. Some fillers are notorious for their poor safety profile; for example, silicone is banned in the European Union and the United States but nevertheless used by many practitioners and beauticians. Many fillers can be identified in histopathologic sections allowing specific measures to be instituted.
Association between Cigarette Smoking Frequency and Health Factors among Korean Adults

Background: Recently, there has been a trend that cigarette smoking rate in Asian and Africa adults has increased while the age group to start smoking has decreased gradually. This study aimed to investigate the relationships between lifetime smoking and hypertension, diabetes, obesity, waist measure, fasting blood pressure and food consumption, in order to look into health status depending on smoking status in Koreans. Methods: Totally, 1075 men and 697 women with no disease participated in this study, in which one-way ANOVA was conducted by using SPSS version 18.0 for statistical process. The level of statistical significance was 0.05. Results: As a result of analysis on relationship between lifetime smoking and hypertension, obesity and diabetes, statistically significant differences were revealed. Lifetime smoking was found to be significantly associated with increased waist measure, higher level of fasting blood sugar, and more ingestion of nutrients (carbohydrate, fat, and protein). Conclusion: Increased amount of lifetime cigarette smoking was shown to negatively influence various health factors, which might become to be a drive to cause diseases. Therefore, method to improve health factors must be sought for via education and campaign to control an amount of cigarette smoking in Korean adults.
A Unified System With Integrated Generation of High-Speed Communication and High-Resolution Sensing Signals Based on THz Photonics

Multifunctional convergence is one of the key physical features in future generation networks and Internet of things architectures. In this paper, we propose and experimentally demonstrate a unified terahertz (THz) system operating in the 300 GHz band, with a potential of simultaneously enabling high-speed communication and high-resolution ranging over a common optical infrastructure. Both THz communication and THz sensing signals are generated based on THz photonics and cutting-edge terahertz transceiver technologies. In the experiment, 16-quadrature amplitude modulation modulated THz signal is generated by photo-mixing two free running lasers for the communication, and linear frequency modulated (LFM) THz pulses are generated based on optical interferometer-based frequency-to-time mapping (FTM) for sensing. The experimental results show that up to 56 Gbit/s net rate is successfully transmitted over a 2 m free-space line-of-sight link, and the THz LFM pulses with a time-bandwidth product of up to 207 are successfully generated, which is potentially able to enable a cm-scale range resolution. We also investigate the frequency multiplexing schemes for two signals by changing the channel gap at the transmitter side. To the best of our knowledge, such a system represents the first demonstration of integrated generation system in the THz region above 300 GHz, which has great potential in prospective applications of future converged networks.
Equivalent Circuit Model of Crosstalk Reduction Parallel Transmission Lines with Defected Microstrip Structures

A defected microstrip structure (DMS) method is used for crosstalk reduction between parallel coupled pair transmission lines (CPTLs). To reduce the couplings from the adjacent transmission line, a new DMS is designed and etched on one of the transmission line. Additionally, equivalent circuit model is also studied to provide an effective analysis method for analyzing the crosstalk of DMS-CPTLs by considering the S-parameters, namely, S11, S12, S13 and S14. The proposed designs are verified by using HFSS based on FEM and the equivalent circuit is studied by using ADS. The results show that the far-end crosstalk and near-end crosstalk are reduced by about 30dB and the equivalent circuit simulation agrees well with the HFSS simulation.
60 GHz Substrate-Integrated Waveguide-Based Monopulse Slot Antenna Arrays

This communication presents a new kind of substrate-integrated waveguide (SIW) slot antenna array with 1-D and 2-D monopulse radiation patterns. First, a new SIW magic-tee is developed, which exhibits a wide phase/amplitude balance bandwidth. Then, the magic-tee is extended to a new comparator which is used to feed the antenna with 2-D monopulse radiation pattern. Based on the proposed magic-tee and the comparator, 1-D and 2-D monopulse slot antenna arrays with $4\times 4$ elements are designed and measured, respectively. The experimental results show that the arrays exhibit good monopulse performance. More importantly, compared with their former counterparts, the monopulse performance of the arrays exhibits merely slight degradation over a much wider bandwidth due to the wide phase/ amplitude balance bandwidth that the comparators achieved.
Enabling High-Performance Onboard Computing with Virtualization for Unmanned Aerial Systems

In recent years, unmanned aerial systems (UAS) have attracted significant attentions because of their broad civilian and commercial applications. Nevertheless, most existing UAS platforms only have limited computing capabilities to perform various delay-sensitive operations. To tackle this issue, in this paper, we develop a high-performance onboard UAS computing platform with the virtualization technique. Specifically, we first discuss the selection of microcomputers that are suitable for UAS onboard computing. We then investigate virtualization schemes that can effectively manage constrained resources in UAS, flexibly support UAS applications, and enable resource sharing among multiple UAS to achieve higher computing power. In our study, we compare the performance (such as computing, network, isolation, etc.) of two representative virtualization techniques including virtual machine (VM) and container, using KVM and Docker, respectively. Extensive experimental results demonstrate the performance trade-offs between VM and container, and validate the benefits of virtualization in supporting real UAS applications.
A Fast Obstacle Collision Avoidance Algorithm for Fixed Wing UAS

This paper presents a novel fast collision avoidance algorithm for navigation in 3D space of fixed-wing Unmanned Aerial Systems (UAS). This algorithm is aimed at increasing the ability of aircraft operations to complete mission goals by enabling fast collision avoidance of multiple obstacles. The new algorithm, named Flexible Geometric Algorithm (FGA), combines geometric avoidance of obstacles and selection of a critical avoidance start time based on kinematic considerations. FGA reduced computational time by 90% when compared to current waypoint generation methods for collision avoidance. The starting point for the avoidance time window is determined by collision likelihood. Using this algorithm, the (Unmanned Air Vehicle) UAV is able to avoid static and dynamic obstacles while still being able to recover its original trajectory after successful collision avoidance. Simulations for different mission scenarios show that this method is much more efficient at avoiding multiple obstacles than other methods. Algorithm effectiveness validation is provided with Monte Carlo simulations and parametric results. In addition, this algorithm does not have specific requirements on the sensor data types and can be applied to cooperative and non-cooperative intruders.
Candida parapsilosis and Candida tropicalis infections in an Okhotsk snailfish (Liparis ochotensis)

An Okhotsk snailfish (Liparis ochotensis) kept at Nagoya aquarium exhibited sudden death. Microscopically, the fish showed multiple granulomatous foci in the gills, liver and kidney. Multiple yeast-like organisms as well as pseudohyphal elements were observed within granulomatous lesions. Immunohistochemically, the hyphae were negative for both Asperigullus and Mucor spp. , and a weak positive for Candida sp. The seminated-PCR product was consistent with Candida parapsilosis and C. tropicalis. This is the first record of disseminated mycotic granulomatous lesion due to C. parapsilosis and C. tropicalis infection in fish.
Tibetan Micro-Blog Sentiment Analysis Based on Mixed Deep Learning

The analysis of micro-blog sentiment tendency can reveal the changes of people's emotions, which has become a hot topic in text research. There is a relative lack of research on Tibetan language. In this paper, deep learning algorithm is introduced into the Tibetan sentiment analysis to study the accuracy of different algorithms on Tibetan micro-blog sentiment classification. Firstly, the Tibetan word micro-blog is trained as a word vector by using the word vector tool; then the trained word vectors and the corresponding sentiment orientation labels are directly introduced into the different deep learning models to classify the Tibetan micro-blogs. The results show that the hybrid deep learning algorithm obtains a good classification effect. Different optimization parameters were studied on this model, and the accuracy was improved by 1.22% compared with the previous one.
Identifying Agile Waveforms with Neural Networks

Ahstract- With the advent of widespread digital technology, modern radar and communication systems have grown more complex and agile, rendering them difficult to adequately document and identify. The traditional solution of comparing incoming signals to a library of known waveforms is therefore becoming unworkable. The authors present two solutions to the problem of modern radio frequency (RF) waveform identification: a deep neural network and a recurrent neural network using GRUs. Both networks are designed to fuse together an arbitrary number of agile RF pulses and identify the emitter that produced them. Compared to a naïve DNN approach that simply averaged together pulses before classification, our solution adds a pre-projection step, which preserves information about sequential agility, even after averaging across pulses. After being trained against a set of 15 highly ambiguous emitters, the naïve DNN identified 52.2 % of test waveforms, our DNN with projection identified 72.3 %, and our RNN solution identified 84.8%.
Dropframe Scheme in Recurrent Neural Networks for Time Series Modeling

Recurrent neural networks have been proven to be powerful models for many time series modeling applications, but are still limited by their tendency to overfit. The widely adopted dropout method, which is applied to feed-forward architectures, is shown to fail when applied to recurrent layers. We propose a novel dropframe scheme to allow arbitrary rearrangement of training data in RNNs. This scheme extends existing RNN models to skip frames randomly during the training phase, with the approximative behavior of data augmentation for time series. We apply this dropframe technique in long short-term memory (LSTM) and gated recurrent unit (GRU) models, assessing it on acoustic scene classification and video classification tasks. Our LSTM approach with dropframe scheme can perform significantly better than the vanilla LSTM architecture on LITIS Rouen dataset and DCASE2016 dataset, obtaining relative reductions of 4.7% and 3.6% on classification error.
Crowd-Based Learning of Spatial Fields for the Internet of Things: From Harvesting of Data to Inference

The knowledge of spatial distributions of physical quantities, such as radio-frequency (RF) interference, pollution, geomagnetic field magnitude, temperature, humidity, audio, and light intensity, will foster the development of new context-aware applications. For example, knowing the distribution of RF interference might significantly improve cognitive radio systems [1], [2]. Similarly, knowing the spatial variations of the geomagnetic field could support autonomous navigation of robots (including drones) in factories and/or hazardous scenarios [3]. Other examples are related to the estimation of temperature gradients, detection of sources of RF signals, or percentages of certain chemical components. As a result, people could get personalized health-related information based on their exposure to sources of risks (e.g., chemical or pollution). We refer to these spatial distributions of physical quantities as spatial fields. All of the aforementioned examples have in common that learning the spatial fields requires a large number of sensors (agents) surveying the area [4], [5].
Crypto-Mining Application Fingerprinting Method

Computing power has increased exponentially in the last decades, and more and more consumer devices are now permanently connected to the internet, to form the so called “Internet of Things” (IoT), Following this trend, security threats and vulnerabilities have increased drastically over a short period of time, as more and more devices are inter-connected and not properly secured. Lately, the growing popularity of Bitcoin and other cryptocurrencies are generating a lot of hype and concern, among security specialists, as unwanted mining applications on an end-user device can potentially pose both security risks and increased operating costs. There are multiple ways of detecting unwanted applications at the user-level, by means of URL blocking and application fingerprinting, and this paper will cover a detection method proposed by our group.
A Simple Prediction Fusion Improves Data-driven Full-Reference Video Quality Assessment Models

When developing data-driven video quality assessment algorithms, the size of the available ground truth subjective data may hamper the generalization capabilities of the trained models. Nevertheless, if the application context is known a priori, leveraging data-driven approaches for video quality prediction can deliver promising results. Towards achieving highperforming video quality prediction for compression and scaling artifacts, Netflix developed the Video Multi-method Assessment Fusion (VMAF) Framework, a full-reference prediction system which uses a regression scheme to integrate multiple perceptionmotivated features to predict video quality. However, the current version of VMAF does not fully capture temporal video features relevant to temporal video distortions. To achieve this goal, we developed Ensemble VMAF (E-VMAF): a video quality predictor that combines two models: VMAF and predictions based on entropic differencing features calculated on video frames and frame differences. We demonstrate the improved performance of E-VMAF on various subjective video databases. The proposed model will become available as part of the open source package in https://github. com/Netflix/vmaf.