Miracle-dz/UniFAR-FTUs huggingface dataset:
Drop freezing: Fine detection of contaminants by measuring the tip angle

In this Letter, we show that the shape of a freezing drop of water can be sensitive to the presence of impurities. We measure the tip angle of water drops frozen on a cold plate. The fine changes in tip angle are robustly captured by our image analysis method, which shows a deviation from that of pure water in solutions with salt (NaCl), polymer (PEG) and surfactant (TTAB) starting at concentrations of $10^{-6}$, $10^{-4}$ and $10^{-6}$ M respectively. The method could be adapted into a portable water purity tester, but the work also highlights the complexity of water freezing as it is influenced by trace concentrations of impurities.
How Much Knowledge Can You Pack into the Parameters of a Language Model?

It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales surprisingly well with model size and outperforms models that explicitly look up knowledge on the open-domain variants of Natural Questions and WebQuestions. To facilitate reproducibility and future work, we release our code and trained models.
Intrinsic characterization of Sobolev spaces with boundary conditions

We investigate fractional Sobolev spaces of order $s \in (0,1)$ with mixed boundary conditions. We provide an extension operator for these spaces that requires the usual measure density condition only on the Neumann boundary part, and our condition is sharp at the interface. We also investigate the interpolation behavior of the considered spaces and provide a new Hardy's inequality in the case $s = 1$.
Maximum Edge-Colorable Subgraph and Strong Triadic Closure Parameterized by Distance to Low-Degree Graphs

Given an undirected graph $G$ and integers $c$ and $k$, the Maximum Edge-Colorable Subgraph problem asks whether we can delete at most $k$ edges in $G$ to obtain a graph that has a proper edge coloring with at most $c$ colors. We show that Maximum Edge-Colorable Subgraph admits, for every fixed $c$, a linear-size problem kernel when parameterized by the edge deletion distance of $G$ to a graph with maximum degree $c-1$. This parameterization measures the distance to instances that, due to Vizing's famous theorem, are trivial yes-instances. For $c\le 4$, we also provide a linear-size kernel for the same parameterization for Multi Strong Triadic Closure, a related edge coloring problem with applications in social network analysis. We provide further results for Maximum Edge-Colorable Subgraph parameterized by the vertex deletion distance to graphs where every component has order at most $c$ and for the list-colored versions of both problems.
A Unified Framework for Gaussian Mixture Reduction with Composite Transportation Distance

Gaussian mixture reduction (GMR) is the problem of approximating a finite Gaussian mixture by one with fewer components. It is widely used in density estimation, nonparametric belief propagation, and Bayesian recursive filtering. Although optimization and clustering-based algorithms have been proposed for GMR, they are either computationally expensive or lacking in theoretical supports. In this work, we propose to perform GMR by minimizing the entropic regularized composite transportation distance between two mixtures. We show our approach provides a unified framework for GMR that is both interpretable and computationally efficient. Our work also bridges the gap between optimization and clustering-based approaches for GMR. A Majorization-Minimization algorithm is developed for our optimization problem and its theoretical convergence is also established in this paper. Empirical experiments are also conducted to show the effectiveness of GMR. The effect of the choice of transportation cost on the performance of GMR is also investigated.
A Link Prediction Model using Similarity and Centrality based Features

With the recent development of internet, many users have started interacting and creating their relationship in online social network. These relationship and interaction between users can be represented as links in social network graph. At present, most of the link prediction methods used similarity based features only and few have used centrality based features for prediction. In this paper, we develop a link prediction model by combining similarity and centrality-based features of social network to improve the accuracy. The experimental result shows that by combing similarity and centrality based features we can improve the prediction accuracy of link prediction model.
Compact Dual-Band Bandpass Filter Using Overlapped Helical Resonators

In this paper, we propose a compact dual-band bandpass filter using overlapped helical resonators. One helical resonator is placed inside of the other one to generate the two resonant frequencies for the two passbands. Thus, the circuit size of the two overlapped helical resonators is most the same as one resonator, resulting in significant size reduction. The filter topologies at the two operating frequencies are analyzed to obtain the desired filtering responses of the two passbands. Since the two overlapped helical resonators have no influence on the resonant frequencies to each other, the center frequencies of the two passbands can be controlled independently. To validate the proposed ideas, a dual-band bandpass filter is designed and the results are presented.
3D Path Tracking Controller Based on Fuzzy PID Optimized by PSO for Quadrotor

Quadrotors have developed rapidly due to low-cost, precise hovering and the ability of vertical take-off and landing (VTOL). As the crucial part of quadrotors, the control system has been researched extensively. In this paper, we present a PID controller for path tracking based on a double-loop controller where the inner loop is for attitude control and the outer loop is for position control, and then combine fuzzy logic and the PID controller to improve the controlling effect. However, the fuzzy rules based on experience cannot guarantee excellent tracking effect and it is difficult to adjust the fuzzy rules manually. So we employ particle swarm optimization (PSO) to optimize fuzzy rules. Finally, we testify the effectiveness of the PID controller by simulation, and show that the fuzzy PID controller with PSO optimization achieves better effect compared with basic PID controller and the fuzzy PID controller without the optimization of fuzzy rules.
The Design of AR-based Virtual Educational Robotics Learning System

In this study, we propose an AR (Augmented Reality)-based virtual educational robotics learning system, called "AR Bot", which aims to support the learning activities of computational thinking and STEM (science technology engineering and mathematics) education. The system consists of two major elements: AR Bot card and AR Bot app. The AR Bot card is a AR trigger for displaying learning materials and quizzes. Smartphones with AR Bot app can be used to scan the AR Bot cards, and then enter the learning units that uses visual blocky programming editor to practice various robot tasks. The motions of virtual robot can be demonstrated in the authentic scenes with AR technology via mobile devices. The AR Bot system is designed to facilitate computational thinking and STEM education in K-12 schools. In the current state of this study, the core programming editor engine of AR Bot has been developed, and the researcher team will prepare to conduct the experiments into STEM courses in a primary school in Taiwan. The AR Bot system is expected to help strengthen the interaction between teachers and students in the classroom, leverage the learning effectiveness, and reduce the cost of robot equipment for schools.
Image classification method based on improved deep residual networks

In order to solve the problem of image classification, a novel image classification method based on Residual Networks(ResNet) is proposed. Firstly, the 7*7 convolutional layer of the first layer of the network is replaced by a consequent three layer 3*3 convolutional layer, which reduces the number of model parameters without changing the receptive field. Secondly, the pooling layer of the network and the fully connected layer are replaced by the global average pooling layer, makes the model easier to train. Thirdly, the RelU function replaced by the better activation function Leaky ReLU. Finally, the model is verified by using crop disease images, and the experimental results show that the improved algorithm proposed in this study can effectively solve the problem of overfitting, and the classification of crop disease images reaches more than 98.3%, which is 1% higher than that of the original network.
Beyond Electrolytic Capacitor: High Frequency On-chip Micro Supercapacitor with Large Capacitance Density

We report on, for the first time, an on-chip high frequency micro supercapacitor (MSC) featured by ultra-high capacitance density and chip integratability. High-aspect-ratio 3D mesoporous gold (MPG) electrodes, which guarantee wide frequency response and large material loading, are developed by metal co-sputtering technique and selective etching technique. MXene quantum dots, a novel 1D material with high pseudocapacitance, are engineered on MPG through in-situ electrochemical deposition. Consequently, the device demonstrates a 9.7 times higher areal capacitance (14.5 mF/cm2) and 34.2 times volume capacitance (270 mF/cm3) than electrolytic capacitors. The MSC is applied in a low-pass filtering circuit, showing great advantage in circuit functionalization and size reduction.
Overview of planar spiral inductance design and modeling

In this paper, the geometric parameters and parasitic effects of planar spiral inductance are introduced theoretically. Various physical models of helical inductance are analyzed, the general situation of various modeling methods of planar helical inductance is summarized while their advantages and disadvantages are compared. This paper is of guiding significance for the design and modeling of on-chip spiral inductance.
MemDefender: An Allocation Monitoring and Memory Leak Injection Tool for Java

Memory leaks are one of the major causes of software aging. Despite of Garbage Collection, such leaks are also prevalent in Java applications. Consequently, a lot of sophisticated approaches for memory leak detection in Java have been proposed in the last two decades. Unfortunately, it is difficult to compare and evaluate them as it is challenging and cumbersome to find suitable datasets for evaluation purposes. A further handicap for effective research is a high effort of collecting certain types of statistics on memory usage which have proved to be effective for memory leak detection. In this paper we propose MemDefender, a tool for facilitating studies on automated memory leak detection in Java. Our tool serves two purposes: it can inject leaks into arbitrary Java applications in flexible ways, facilitating evaluation and comparison of new approaches. As a second function it provides statistics on number and size of allocated objects during application execution, also grouped by GC generations. These statistics can serve as building blocks for developing new leak detection techniques.
A Compact Dual-Band Circularly-Polarized Antenna with Wide Axial-Ratio Beamwidth

A compact circularly-polarized patch antenna with wide axial-ratio beamwidth (ARBW) operating at dual bands is proposed in this paper. The antenna consists of two stacked patches, which account for GPS L1 band ($1575 \pm 2\ \text{MHz}$) and L2 band ($1228 \pm 2\ \text{MHz}$). In the proposed antenna, four pillars are introduced to realize miniaturization. To adjust the beamwidth response of the far-field components, the antenna is backed by a modified metallic cavity, which in turn widen the 3-dB axial ratio beamwidth (ARBW) at two operating bands effectively. Finally, the proposed design with excellent performance is realized in a size of $0.368\ {\lambda}_{0}$ (diameter) and $0.105\ {\lambda}_{0}$ (height). Simulated and measured results show that the ARBWs at L1 and L2 bands are as wide as 173° and 133°, respectively.
A Novel Mechanism for Integrated Design of Guidance and Fuze System Based on the Digital Array Radar Seeker

The separated design of the traditional missile seeker and fuze makes it occupy more space, efficiency and resources, which is not permitted in limited missile guidance and control hardware systems. In this article, an integrated design mechanism that consists of guidance and fuze simultaneously was introduced based on array antennas, digital transceiver components and digital signal processing systems of the digital array seekers, with the improvement of digital beamforming and related hardware technologies. The integrated signal processing block diagram is suggested, and a new fuze beam design scheme based on digital array antenna is presented in this paper. The integrated design can realize the functions of guidance and fuze detection in different stages of missile operation, which can not only realize the complete sharing of the hardware of the guidance signal, but also provide information for the fuze detection stage to achieve more accurate fuze detection. Moreover, the fuze can realize the adaptation of the beam tilt angle and cope with different situations where the missile performing the target interception tasks.
A Dual-Polarized Microstrip Antenna with 2D Beam-Scanning Capability: Invited paper

A reconfigurable microstrip antenna with a dual-polarization beam-scanning feature is proposed in this paper. The proposed antenna is based on microstrip Yagi-Uda antenna comprised of a driven square patch and four parasitic square patches. Two different feed ports are utilized to separately excite two orthogonal modes for dual-polarization operation. The capability of beam-scanning is realized by loading a narrow loop slot and four varactors within each parasitic patch. By tuning the capacitance value of the varactors, the parasitic patch can act as a director or a reflector for the driven patch. Simulation results demonstrate that the beam can be scanned in 2D plane. The simulated cross-polarization discriminations are higher than 20 dB in all seven states presented in this paper. The proposed antenna is in fabrication and experimental results will be provided in the final submission.
Loop Closure Detection for Visual SLAM Systems Using Various CNN algorithms Contrasts

In the Visual Simultaneous Localization and Mapping (V-SLAM) system, loop closure detection is playing a decisive role in the accurate construction of maps. Traditional loop closure detection mainly use the Bag-of-Words (BoW) model to extract features and judge the similarity of images. Recently, more related researches tend to combine machine learning with loop closure detection, but so far no breakthrough results have been achieved yet, and even it is still in the stage of finding suitable machine learning algorithms. In this paper, by improving the loop closure detection process, a variety of CNN modified algorithms (including VGG, Inception 1-4, ResNet, Inception Resnet, etc.) are used in the loop closure detection, and are further conducted for comparison and analysis. Through a number of experiments and data analysis, the advantages and disadvantages of these machine learning algorithms in V-SLAM loop closure detection are finally obtained, which would provide a solid foundation for the research work in the future.
Salient Textural Anomaly Proposals and Classification for Metal Surface Anomalies

With a drastic growth in the development of Automated Visual Inspection (AVI) systems in the industries, the capabilities of such applications to aid human inspectors for anomaly localization and identification have also increased. However, some issues with anomaly detection and classification in AVI systems are that such anomalies are rare in occurrence and exhibit behaviours that are unique to the application. Hence, these anomaly datasets are small and imbalanced, and a robust framework is required for such datasets. This paper proposes a Salient Textural Anomaly Proposal (STAP) framework to generate and classify salient textural proposals of regions of anomalies on metal surfaces. These anomalies have both salient and texture characteristics that are dependent on the properties of the metal surface. Furthermore, when observed across different lighting conditions, the anomalies in this AVI anomaly dataset have a small inter-class variance and large intraclass variance. The proposed STAP framework uses a Fourier transformation based technique to generate proposals of salient and textural anomaly regions. Transfer learning of Convolutional Neural Network (CNN) learned from a large dataset is used to train a linear Support Vector Machine (SVM) to classify the generated proposals. The proposed STAP framework performs the best when compared to state-of-the-art object recognition techniques on an AVI anomaly industrial dataset that has salient and texture anomalies on metal surfaces.
Smart Card Based Remote User Authentication Scheme for Cloud Computing

Cloud computing is an emerging technology in modern society. In recent years mobile users and cloud service providers are increased rapidly. In the cloud or any other distributed environment, if the users increased then security risks also increase parallelly. Many security problems are still unclear even after the advanced security measures have been taken. Security of stored data in the server is one of the most challenging issues in the cloud-based environment. Only authorized users should access the stored data of the server. Authentication plays a significant role in providing authorization. Therefore, in cloud computing, authentication is the primary measure to protect the data and the server from an unauthorized user. In this paper, an efficient authentication scheme using the smart card in the cloud environment is proposed. In the proposed scheme, with the help of a single private key, the user can access many cloud services. Also, the proposed scheme resists all security attacks. Through the simulation using the AVISPA tool, it is proved that the proposed scheme is robust and suitable for practical implementation.
A High-Speed and High-Reliability TRNG Based on Analog RRAM for IoT Security Application

A novel True Random Number Generator (TRNG) based on analog RRAM is developed. For the first time, the proposed TRNG harvests the variation of the pulse number in programming the analog states as the random source, and utilizes the parity of the pulse number to generate random bits. The TRNG throughput reaches >1 Mbit/sec for a single cell. The feasibility for chip-level parallel operation on multiple cells is verified experimentally to further improve the throughput. Excellent stability against temperature variation is also demonstrated. All the generated random bit streams pass the NIST tests across –40 to 125 °C. With an optimized small analog switching window, the endurance problem is significantly relieved as the functionality of TRNG is retained after 1011 incremental switching cycles. Our high-speed and high-reliability TRNG design is compatible with the existing memory block and convenient for circuit implementation, making it suitable for future IoT applications.
Computation-Efficient Distributed Algorithm for Convex Optimization Over Time-Varying Networks With Limited Bandwidth Communication

A novel computation-efficient quantized distributed optimization algorithm is presented in this article for solving a class of convex optimization problems over time-varying undirected networks with limited communication capacity. These convex optimization problems are usually relevant to the minimization of a sum of local convex objective functions using only local communication and local computation. In most of the existing distributed optimization algorithms, each agent needs to calculate the subgradient of its local convex objective function at each time step, which leads to extremely heavy computation. The proposed algorithm incorporates random sleep scheme into procedures of agents’ updates in a probabilistic form to reduce the computation load, and further allows for uncoordinated step-sizes of all agents. The quantized strategy is also applied, which overcomes the limitation of communication capacity. Theoretical analysis indicates that the convex optimization problems can be solved and numerical analysis shows that the computation load of subgradient can be significantly reduced by the proposed algorithm. The boundedness of the quantization levels at each time step has been explicitly characterized. Simulation examples are presented to demonstrate the effectiveness of the algorithm and the correctness of the theoretical results.
Performance Analysis of a Residential Wind-Turbine Dual-Stator Winding Synchronous Reluctance Generator with Armature Reaction Effect

The electric generators in residential wind-turbine drivetrains do not only require high ratio power per mass, high efficiency and good overload capabilities, but they also require less volume and quick dynamic responses during severe loading, start-ups, shut-downs and during emergency stops. This paper deals with the analysis of a three-phase dual stator-winding synchronous reluctance generator (DSWSynRG) for residential wind-turbine drivetrains. The performance of the DSWSynRG is evaluated by means of 2D Finite Element Analysis (FEA), and the armature reaction effects are accounted for in the analysis of the generator performance parameters. The stator of the DSWSynRG has two magnetically coupled three-phase windings (main and auxiliary), distributed in 36 slots. The auxiliary (excitation) winding provides the most needed q-axis magnetic flux for voltage generation in the main (armature) winding. The DSWSynRG has rated and peak powers of 5.5 kW and 7 kW respectively. From the FEA results, it is evident that the armature reaction has a magnetizing effect for both resistive and inductive loads. The results also evidenced that the effect of armature reaction is more distortional for loads such as washing machine, dish washer, hand drier, refrigerator etc., which are highly inductive than light bulbs, geysers, stoves, irons etc., which are more resistive.