Miracle-dz/UniFAR-FTUs huggingface dataset:
Canvas GAN: Bootstrapped Image-Conditional Models

Generative Adversarial Networks (GANs) learn generating functions that map a random noise distribution $Z$ to a target data distribution. Usually, little attention is paid to the form of $Z$, resulting in nearly all models assuming $Z$ follows a convenient parametric form (almost always $Z\ \sim \ Normal (0,1)$ or $Z\ \sim\ Uniform(-1,1))$. However, we observe that image-conditional generators, such as those used in the CycleGAN, produce better quality generation than comparable single domain GANs can currently achieve. This is true even when the images being conditioned upon are substantially different from the target images. We hypothesize these models benefit from input which already has the general structure of images, even if their semantic content is different. As a result, we propose the Canvas GAN: using just a small handful of real images (“canvases”), we create random input for an image-conditional generator by randomly cropping, flipping along either or both axis, coloring, and resizing the canvas. These diverse samples allow the generator to edit an input that already exhibits natural image structure, as opposed to having to generate it from scratch from independent white noise.
Adaptive Control for a Quadrotor Transporting a Cable-Suspended Payload With Unknown Mass in the Presence of Rotor Downwash

This paper investigates the control problem for a quadrotor with a cable-suspended payload (QCSP). A new adaptive control strategy is designed to control the velocity of a volumetric payload with unknown mass in the presence of rotor downwash. The asymptotic stability of the resulting closed-loop system is analysed via the Lyapunov method. Furthermore, a novel structure is designed to suspend the payload on the quadrotor by a cable, and a gyroscope is installed on this structure to measure the payload's swing angle. The designed controller's performance is demonstrated by simulations implemented in a Simulink/SimMechanics environment. The effectiveness of the proposed control scheme is validated in real experiments.
Automated detection of COVID-19 on a small dataset of chest CT images using metric learning

Coronavirus disease has caused unprecedented chaos across the globe causing potentially fatal pneumonia, since the beginning of 2020. Researchers from different communities are working in conjunction with front-line doctors and policymakers to better understand the disease. The key to prevent the spread is a rapid diagnosis, prioritized isolation, and fastidious contact tracing. Recent studies have confirmed the presence of underlying patterns on chest CT for patients with COVID-19. We present a completely automated framework to detect COVID-19 using chest CT scans, only needing a small number of training samples. We present a few-shot learning technique based on the Triplet network in comparison to the conventional deep learning techniques which require a substantial amount of training examples. We used 140 chest CT images for training and the rest for testing from a total of 2482 images for both COVID-19 and non-COVID-19 cases from a publicly available dataset. The model trained with chest CT images achieves an AUC of 0.94, separates the two classes into distinct clusters; thereby giving correct prediction accuracy on the evaluation dataset.
A Hybrid Voltage/Current Control Scheme With Low-Communication Burden for Grid-Connected Series-Type Inverters in Decentralized Manner

In this article, a novel hybrid voltage/current control scheme with low-communication burden is proposed for series-type inverters in a decentralized manner. All the inverter units are controlled by their individual local controllers without a real-time central global-communication. Among them, a few inverters serve as the current controlled inverters (CCIs) to regulate the grid current, and the rest inverters work as the voltage controlled inverters (VCIs). Under this control architecture, five main control targets are achieved: 1) frequency self-synchronization of each VCI inverter based on local current phase-angle signals; 2) fault-tolerant capability of all inverters without one-to-all-failure; 3) high disturbance-rejection-capability to grid variations; 4) adjustable grid power factor; and 5) independent power control for inverters with unbalanced power sources. As the central high-bandwidth communication is avoided, it has the features of decreased cost, increased scalability, and reduced geographic restrictions of inverters. Compared with conventional decentralized approaches, only a few CCI units acquire the grid phase-angle signal to achieve grid synchronization by communication, and the most VCI units are local self-synchronization via the local line-current-phase signal rather than real-time grid-voltage-phase communication signal. Thus, the proposed control can be realized with a very low-communication burden, which is more cost-effective and higher reliability in terms of communication faults. The feasibility of the proposed method is verified by experimental results of eight different cases, such as source power change, large power gap, grid voltage/frequency deviation, grid harmonics, and one-fault redundancy.
Theoretical Analysis of Beam-Steerable, Broadside-Radiating Huygens Dipole Antenna Arrays and Experimental Verification of an Ultrathin Prototype for Wirelessly Powered IoT Applications

The theoretical analysis of beam-steerable, broadside-radiating Huygens dipole antenna arrays (HDAAs) is presented. Linear HDAAs with different numbers of elements are investigated and compared with full-wave simulations. Their attractive performance characteristics for wirelessly powered IoT applications are emphasized. Each Huygens dipole antenna (HDA) is an electrically small, linearly polarized, efficient, unidirectional radiating element. Linear HDAAs are confirmed to achieve high directivity beams in one principle plane and significantly broad beamwidths in the orthogonal principle plane. Very stable gain variation when their main beam is steered is demonstrated. A practical beam-steerable, broadside-radiating, linear HDAA is developed that employs an experimentally-verified HDA and is facilitated by a microstrip power-divider feed network. The entire HDAA design is ultrathin (<inline-formula> <tex-math notation="LaTeX">$\lambda _{0}/240.87$ </tex-math></inline-formula>), lying only on a single piece Rogers Duroid<inline-formula> <tex-math notation="LaTeX">$^{TM}~5880$ </tex-math></inline-formula> copper-clad substrate. A proof-of-concept 3-element HDAA prototype excited with a <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> Butler matrix centered at 2.45 GHz was designed, fabricated and measured. The measured results, in very good agreement with their simulated values, demonstrate the efficacy of the linear HDAA designs and their potential usefulness for wireless power transfer (WPT) systems dedicated to emerging IoT applications that require power be directed towards terminals in multiple specified directions with broad area coverage at each one.
Recursive DFT-Based Method for Fast and Accurate Estimation of Three-Phase Grid Frequency

This letter proposes a method for fast and accurate estimation of three-phase grid frequency under unbalanced and distorted conditions. It includes a recursive discrete Fourier transform-based frequency adaptive bandpass filter for α–β signals generated by the Clarke transform and a two-point-based algorithm for frequency estimation. The proposed method is relatively simple to implement and can eliminate adverse influences created by the imbalances, dc offset and both odd and even harmonics. It can also provide fast estimation with a response time of almost one fundamental cycle under grid disturbances. Moreover, it can present improved performance when compared to several methods reported in the technical literature. The advantages of the proposed method are confirmed by both simulated and real-time experimental results.
Learning Online for Unified Segmentation and Tracking Models

Tracking requires building a discriminative model for the target in the inference stage. An effective way to achieve this is online learning, which can comfortably outperform models that are only trained offline. Recent research shows that visual tracking benefits significantly from the unification of visual tracking and segmentation due to its pixel-level discrimination. However, it imposes a great challenge to perform online learning for such a unified model. A segmentation model cannot easily learn from prior information given in the visual tracking scenario. In this paper, we propose TrackMLP: a novel meta-learning method optimized to learn from only partial information to resolve the imposed challenge. Our model is capable of extensively exploiting limited prior information hence possesses much stronger target-background discriminability than other online learning methods. Empirically, we show that our model achieves state-of-the-art performance and tangible improvement over competing models. Our model achieves improved average overlaps of 66.0%,67.1%, and 68.5% in VOT2019, VOT2018, and VOT2016 datasets, which are 6.4%, 7.3%, and 6.4% higher than our baseline. Code will be made publicly available.
Local-Global based Deep Registration Neural Network for Rigid Alignment

Three-dimensional registration is a well-known topic in computer vision that aims to align two datasets (e.g. point clouds). Recent approaches to this problem are based on learning techniques. In this paper, we present an improved solution to the problem of registration with a novel architecture that, given two 3D point clouds as input, estimates the rotation to map one into the other. The network architecture is conceptually divided into two parts, the first part is a feature selection based on PointNet and PointNet++. The second part estimates the rotation with Euler angles by calculating the correspondences with a FlowNet-based network, and finally the rotations in yaw, pitch, and roll. The generalization capability of the proposal allows mapping two point clouds in a wide range of angles with a stable error over the whole range. Experiments have been carried out using the ModelNet10 objects dataset, varying the axis and the angle of rotation to provide a sufficiently complete evaluation of the architecture. The results show an average distance Mean Square Error of 4.94 × 10−5within a unit sphere and a rotation error of 1.18 degrees. The results with noisy point clouds are sufficiently accurate providing the network was trained only with noise-free data, demonstrating good generalization of the approach.
Current-Source Solid-State DC Transformer Integrating LVDC Microgrid, Energy Storage, and Renewable Energy Into MVDC Grid

Solid-state dc transformer to integrate low-voltage dc (LVdc) microgrid, wind turbine (WT) generator, photovoltaic (PV), and energy storage (ES) into medium-voltage (MV) direct-current (MVdc) distribution grids is attractive. This article proposes current-source dc solid-state transformer (SST) for MVdc collection system in WT, PV, and ES farms or as an interface between the MVdc grid and the LVdc microgrid. Compared to conventional current-source converter (CSC) based SSTs, a switch reduction scheme on reverse-blocking device bridges is proposed to reduce device count and the number of devices on the dc-link current path. Importantly, the proposed switch reduction scheme is generic and can be applied to the dc ports of dc–ac, ac–dc, or dc–dc hard-switching or soft-switching CSC-based SSTs. Based on this scheme, the proposed current-source dc SSTs are derived, which have reduced electrolytic-capacitor-less dc-link. The proposed dc SSTs also achieve single-stage isolated dc–dc or dc–ac conversion, full-range zero-voltage switching (ZVS) for main switches, zero-current switching (ZCS) for resonant switches, and controlled $ dv/dt $. The proposed dc SSTs, operating principles, predictive control method, the ZVS, and the controlled $ dv/dt $ under voltage buck-boost ranges are verified with MV simulations and an experimental prototype based on SiC mosfets, diodes, and a nanocrystalline transformer.
Case Selection in Public Management Research: Problems and Solutions

Case selection is ubiquitous in public management research. Rarely do scholars have access to entire populations of interest. Yet, the manner by which scholars select samples to conduct their analyses can have profound consequences on their ability both to draw valid causal inferences and to estimate accurate relationships. In this article, we review the basic threats to inference that are likely to emerge in the presence of non-random case selection, with specific attention to their manifestation in empirical public management research. The article first reviews the threats to causal inference presented by case selection, focusing on their implications for internal and external validity. We then summarize a standard set of solutions to address potential problems for empirical models caused by non-random case selection. As part of this discussion, we review recent articles published in this journal to illustrate the prevalence of selection issues in contemporary public management studies, and then illustrate several techniques that have been developed to overcome specific problems to show their utility for public management research.
NMR Studies of Membrane Associating Peptides and Implications in Autotransporter Function

Membrane associating peptides such as antimicrobial peptides and viral fusion peptides are involved in a diverse set of physiological processes. Their functions often require a change in the structure of the peptide, caused by interactions between the peptide and the biological membrane. This change in structure can be investigated in vitro, by performing circular dichroism (CD) and nuclear magnetic resonance (NMR) spectroscopy experiments with peptide solutions in membrane mimetic such as detergent micelles and in organic solvents. NalP is an outer membrane autotransporter protein from N. meningitidis that transports its serine protease passenger domain across the outer membrane of the cell. The secondary structure of a linker peptide from this protein was determined in an aqueous medium, in sodium dodecyl sulfate (SDS) detergent micelles and in trifluoroethanol (TFE). The peptide exhibits a random coiled secondary structure in the aqueous medium and has a partial helical character in SDS detergent micelles. In TFE, the peptide has an α-helical secondary structure, and this structure was determined by NMR spectroscopy. The difference in structure of the peptide in the detergent micelle and in the hydrophobic organic solvent, when compared to the aqueous medium, suggests that the linker might interact with the biological membrane during the protein transport event. The stability of the α-helix formed by this peptide in TFE was determined by investigating the overall and residue specific effects of temperature on the secondary structure of the peptide. Partial loss of secondary structure is observed when the peptide is heated to a temperature of 348 K. Nuclear Overhauser Effect (NOE) crosspeaks that had high relative intensities at elevated temperatures were observed on a stretch of residues located in the middle of the α-helix, suggesting that this region of the α-helix is comparatively more stable and that the unfolding is initiated at both termini of the helix.
A Soft-Switching Interleaved Buck–Boost LED Driver With Coupled Inductor

In this article, a novel dc–dc light-emitting diode driver employing an interleaved converter is proposed and analyzed. The circuit topology mainly consists of two parallel buck–boost converters. A coupled inductor, which is composed of a magnetic core and two windings, is used to replace the energy storage inductors of the two buck–boost converters. This not only does not add any components, but also saves a magnetic core. The buck–boost converters are designed to operate near boundary conduction mode. Due to the characteristics of magnetic flux balance, the magnetic-excited current can be converted between the windings of the coupled inductor. By using the magnetic-excited current to release the charge stored in the parasitic capacitors of the active switches, these switches can fulfill zero-voltage switching on (ZVS) without the use of any auxiliary switches, active clamping circuits, or snubber circuits. Moreover, the freewheel diodes of both buck–boost converters can achieve zero-current switching off (ZCS). The steady-state analyses for different operation modes are provided, and the mathematical equations for designing circuit components are conducted. Finally, a 200-W prototype circuit was built and tested to verify the analytical predictions. According to the experimental results, all the semiconductor devices are operated at either ZVS or ZCS, and the circuit efficiency as high as 95.0% is measured. Satisfactory performance has verified the feasibility of the proposed converter.
Emotion Recognition and Understanding Using EEG Data in A Brain-Inspired Spiking Neural Network Architecture

This paper is in the scope of emotion recognition by employing a recurrent spiking neural network (BI-SNN) architecture for modelling, mapping, learning, classifying, visualising, and understanding of spatio-temporal Electroencephalogram (EEG) data related to different emotional states. It further explores, develops, and applies a methodology based on the NeuCube BI-SNN, that includes methods for EEG data encoding, data mapping into a 3-dimensional BI-SNN model, unsupervised learning using spike-timing dependent plasticity (STDP) rule, spike-driven supervised learning, output classification, network analysis, and model visualisation and interpretation. The research conducted to model different emotional subtypes through mapping both space (brain regions) and time (brain dynamics) components of EEG brain data into SNN architecture. Here, a benchmark EEG dataset was used to design an empirical study that consisted of different experiments for classification of emotions. The obtained accuracy of 94.83% for EEG classification of four types of emotions was superior when compared with traditional machine learning techniques. The BI-SNN models not only detected the brain activity patterns related to positive and negative emotions with a high accuracy, but also revealed new knowledge about the brain areas activated in relation to different emotions. The research confirmed that neural activation increased in the frontal sites of brain (F7, F3, AF4) associated with positive emotions, while in the case of the negative emotions, connectivity strength was concentrated in the frontal (F4, AF3, F7, F8) and parietal sites of the brain (P7, P8).
A new stock price prediction model based on improved KNN

With the rapid development of economy, how to accurately analyze and forecast the stock price has become a key problem. There are many methods and models about stock price prediction, such as KNN algorithm. However, the traditional KNN algorithm only uses the data of latest one day to predict the change trend of the next day, which is of little significance for reference. Therefore, this paper proposes an improved KNN algorithm, that is, the share price information group of the first N days is synthesized into a sample, which is input to the KNN model for learning. Experiments show that the improved KNN algorithm has better predictive performance than the traditional KNN algorithm.
BCQ4DCA: Budget Constrained Deep Q-Network for Dynamic Campaign Allocation in Computational Advertising

Digital advertising companies typically conduct several advertising campaigns in parallel while being constrained by a fixed overall advertising budget. This gives rise to the problem of distributing the budget across the different campaigns dynamically so as to optimize the overall return on investment (ROI) (or some other metric) within a specified time duration. In this paper, we propose an RL formulation called BCQ4DCA for dynamic optimization of budget-constrained campaign allocation. The formulation is model-free and uses a novel cumulative reward model that is learned alongside a Deep Q-Network. We utilize a real-world Criteo user interaction dataset to evaluate BCQ4DCA in terms of conversion rate, budget utilization, cost per conversion, and ROI, finding that it outperforms current heuristic, attribution based approaches like DARNN, DNAMTA across a common time window.
Unified Control Scheme of Grid-Connected Inverters for Autonomous and Smooth Transfer to Stand-Alone Mode

As one of the approaches for a grid-sustaining inverter, the inverter should cover not only grid-connected (GC) mode but also stand-alone (SA) mode for power supply to local loads; therefore, there are separate control loops for each mode. In order for an uninterruptible power supply to the local load, it should be seamless to change from GC mode to SA mode. This carries two types of issues. One is a transient state caused by switching the control loops. The other is an uncontrolled state during the time interval between a grid failure and its detection, called clearing time. These can lead to unstable voltage for the local load. Hence, a smooth and autonomous mode switching method is required even if the fault detection is late. Existing mode transfer methods considering both the issues have been accompanied by a slow dynamic response, additional sensors, restriction of adopting filter, or controller complexity due to third-order plant. To overcome them, in this article, a control scheme realized by a unified control loop is proposed for smooth and autonomous mode switching with a novel antiderailing control. Furthermore, the proposed control scheme can achieve a high bandwidth for the output power control in GC mode because it is based on controlling the current flowing the inverter-side inductor, and both filter types, LC and LCL, can be adopted. To validate the proposed concept, simulations and experiments were conducted. The results show that the proposed control scheme can be used to achieve autonomous and smooth mode transition even under reactive power reference and reactive load conditions.
Fault Management in DC Microgrids: A Review of Challenges, Countermeasures, and Future Research Trends

The significant benefits of DC microgrids have instigated extensive efforts to be an alternative network as compared to conventional AC power networks. Although their deployment is ever-growing, multiple challenges still occurred for the protection of DC microgrids to efficiently design, control, and operate the system for the islanded mode and grid-tied mode. Therefore, there are extensive research activities underway to tackle these issues. The challenge arises from the sudden exponential increase in DC fault current, which must be extinguished in the absence of the naturally occurring zero crossings, potentially leading to sustained arcs. This paper presents cut-age and state-of-the-art issues concerning the fault management of DC microgrids. It provides an account of research in areas related to fault management of DC microgrids, including fault detection, location, identification, isolation, and reconfiguration. In each area, a comprehensive review has been carried out to identify the fault management of DC microgrids. Finally, future trends and challenges regarding fault management in DC-microgrids are also discussed.