Miracle-dz/UniFAR-FTUs huggingface dataset:
Wideband Horizontally Polarized Omnidirectional Cylindrical Dielectric Resonator Antenna for Polarization Reconfigurable Design

A compact horizontally polarized omnidirectional cylindrical dielectric resonator antenna (DRA) excited in the TE<inline-formula> <tex-math notation="LaTeX">$_{011+\delta }$ </tex-math></inline-formula> mode is presented. It deploys a planar feed which comprises a cross-shaped feed line, four coupled strips, and four curved arms with end-shorted stubs. The TE<inline-formula> <tex-math notation="LaTeX">$_{011+\delta }$ </tex-math></inline-formula> mode of the DRA is excited by the curved arms. Its intrinsically narrow impedance bandwidth is enhanced by using the coupled strips, and the cross-polar field is suppressed by the end-shorted stubs. The antenna has a measured −10 dB impedance bandwidth of 18.1%, which is much wider than the conventional bandwidths of 4.5%–7.4%. Its cross-polar level is lower than the co-polar level by 15 dB. This basic DRA is used to design the first polarization reconfigurable omnidirectional DRA with diodes. By controlling the states of the diodes, the DRA can be switched between TE<inline-formula> <tex-math notation="LaTeX">$_{011+\delta }$ </tex-math></inline-formula> and TM<inline-formula> <tex-math notation="LaTeX">$_{01\delta }$ </tex-math></inline-formula> modes to obtain horizontal and vertical polarizations, respectively. To verify the idea, a prototype was fabricated and tested. Reasonable agreement between the measured and simulated results is obtained. The measured impedance bandwidths of the TE<inline-formula> <tex-math notation="LaTeX">$_{011+\delta }$ </tex-math></inline-formula> and TM<inline-formula> <tex-math notation="LaTeX">$_{01\delta }$ </tex-math></inline-formula> modes are 16.8% (2.18–2.58 GHz) and 16.0% (2.25–2.64 GHz), respectively. The antenna has stable omnidirectional radiation patterns in both states
Hybrid Cross Coupled Differential Pair and Colpitts Quadrature Digitally Controlled Oscillator Architecture

Growing importance of wireless communication systems forces reduction of power consumption of the designed integrated circuits. The paper focuses on minimization of power consumption in a digitally controlled oscillator (DCO) that can be employed as oscillator in GPS/Galileo receiver. The new hybrid architecture of DCO combines good phase noise performance of a Colpitts oscillator and relaxed startup conditions of a cross-coupled differential pair oscillator. The proposed new DCO generates a quadrature signal in a current reused frequency divider. Such solution allows of the dissipated power to be reduced. The DCO has been implemented in 110 nm CMOS technology. It generates output signal in frequency range from 1.52 GHz to 1.6 GHz and consumes 1.1 mW from 1.5 V supply voltage. The measured phase noise equals −116 dBc/Hz at 1 MHz offset from 1.575 GHz output signal.
Asynchronous Control of P300-Based Brain–Computer Interfaces Using Sample Entropy

Brain–computer interfaces (BCI) have traditionally worked using synchronous paradigms. In recent years, much effort has been put into reaching asynchronous management, providing users with the ability to decide when a command should be selected. However, to the best of our knowledge, entropy metrics have not yet been explored. The present study has a twofold purpose: (i) to characterize both control and non-control states by examining the regularity of electroencephalography (EEG) signals; and (ii) to assess the efficacy of a scaled version of the sample entropy algorithm to provide asynchronous control for BCI systems. Ten healthy subjects participated in the study, who were asked to spell words through a visual oddball-based paradigm, attending (i.e., control) and ignoring (i.e., non-control) the stimuli. An optimization stage was performed for determining a common combination of hyperparameters for all subjects. Afterwards, these values were used to discern between both states using a linear classifier. Results show that control signals are more complex and irregular than non-control ones, reaching an average accuracy of 94.40% in classification. In conclusion, the present study demonstrates that the proposed framework is useful in monitoring the attention of a user, and granting the asynchrony of the BCI system.
Mixed Random Sampling of Frames method for counting number of motifs

The problem of calculating the frequencies of network motifs on three and four nodes in large networks is considered. Telecommunications networks, cell molecular networks are investigated. The sizes of the investigated networks are hundreds of thousands of nodes and connections. These networks are represented in the form of directed and undirected simple graphs. Exact calculating requires huge computational resources for such large graphs. A method for calculating the frequencies of network motifs using the Monte Carlo method with control of an accuracy of calculations is proposed. The proposed effective method minimizes the value of the coefficient of variation.
Distinguishing primordial black holes from astrophysical black holes by Einstein Telescope and Cosmic Explorer

We investigate how the next generation gravitational-wave (GW) detectors, such as Einstein Telescope (ET) and Cosmic Explorer (CE), can be used to distinguish primordial black holes (PBHs) from astrophysical black holes (ABHs). Since a direct detection of sub-solar mass black holes can be taken as the smoking gun for PBHs, we estimate the detectable limits of the abundance of sub-solar mass PBHs in cold dark matter by the targeted search for sub-solar mass PBH binaries and binaries containing a sub-solar mass PBH and a super-solar mass PBH, respectively. On the other hand, according to the different redshift evolutions of the merger rate for PBH binaries and ABH binaries, we forecast the detectable event rate distributions for the PBH binaries and ABH binaries by ET and CE respectively, which can serve as a method to distinguish super-solar mass PBHs from ABHs.
Evaluating the Transient Energy Dissipation in a Centrifugal Impeller under Rotor-Stator Interaction

In fluid machineries, the flow energy dissipates by transforming into internal energy which performs as the temperature changes. The flow-induced noise is another form that flow energy turns into. These energy dissipations are related to the local flow regime but this is not quantitatively clear. In turbomachineries, the flow regime becomes pulsating and much more complex due to rotor-stator interaction. To quantitatively understand the energy dissipations during rotor-stator interaction, the centrifugal air pump with a vaned diffuser is studied based on total energy modeling, turbulence modeling and acoustic analogy method. The numerical method is verified based on experimental data and applied to further simulation and analysis. The diffuser blade leading-edge site is under the influence of impeller trailing-edge wake. The diffuser channel flow is found periodically fluctuating with separations from the blade convex side. Stall vortex is found on the diffuser blade trailing-edge near outlet. High energy loss coefficient sites are found in the undesirable flow regions above. Flow-induced noise is also high in these sites except in the stall vortex. Frequency analyses show that the impeller blade frequency dominates in the diffuser channel flow except in the outlet stall vortexes. These stall vortices keep their own stall frequency which is about 1/5 impeller frequency with high energy loss coefficient but low noise level. Results comparatively prove the energy dissipation mechanism in the centrifugal air pump under rotor-stator interaction. Results also provide the quantitative basis for turbomachinery’s loss reduction design.
Spatial analysis for political scientists

Abstract How does space matter in our analyses? How can we evaluate diffusion of phenomena or interdependence among units? How biased can our analysis be if we do not consider spatial relationships? All the above questions are critical theoretical and empirical issues for political scientists belonging to several subfields from Electoral Studies to Comparative Politics, and also for International Relations. In this special issue on methods, our paper introduces political scientists to conceptualizing interdependence between units and how to empirically model these interdependencies using spatial regression. First, the paper presents the building blocks of any feature of spatial data (points, polygons, and raster) and the task of georeferencing. Second, the paper discusses what a spatial matrix (W) is, its varieties and the assumptions we make when choosing one. Third, the paper introduces how to investigate spatial clustering through visualizations (e.g. maps) as well as statistical tests (e.g. Moran's index). Fourth and finally, the paper explains how to model spatial relationships that are of substantive interest to some of our research questions. We conclude by inviting researchers to carefully consider space in their analysis and to reflect on the need, or the lack thereof, to use spatial models.
AoI-Energy-Aware UAV-Assisted Data Collection for IoT Networks: A Deep Reinforcement Learning Method

Thanks to the inherent characteristics of flexible mobility and autonomous operation, unmanned aerial vehicles (UAVs) will inevitably be integrated into 5G/B5G cellular networks to assist remote sensing for real-time assessment and monitoring applications. Most existing UAV-assisted data collection schemes focus on optimizing energy consumption and data collection throughput, which overlook the temporal value of collected data. In this article, we employ Age of Information (AoI) as a performance metric to quantify the temporal correlation among data packets consecutively sampled by the Internet of Things (IoT) devices, and investigate an AoI-energy-aware data collection scheme for UAV-assisted IoT networks. We aim to minimize the weighted sum of expected average AoI, propulsion energy of UAV, and the transmission energy at IoT devices, by jointly optimizing the UAV flight speed, hovering locations, and bandwidth allocation for data collection. Considering the system dynamics, the optimization problem is modeled as a Markov decision process. To cope with the multidimensional action space, we develop a twin-delayed deep deterministic (TD3) policy gradient-based UAV trajectory planning algorithm (TD3-AUTP) by introducing the deep neural network (DNN) for feature extraction. Through simulation results, we demonstrate that our proposed scheme outperforms the deep $Q$ -network and actor–critic-based algorithms in terms of achievable AoI and energy efficiency.
Learning When Not to Answer: a Ternary Reward Structure for Reinforcement Learning Based Question Answering

In this paper, we investigate the challenges of using reinforcement learning agents for question-answering over knowledge graphs for real-world applications. We examine the performance metrics used by state-of-the-art systems and determine that they are inadequate for such settings. More specifically, they do not evaluate the systems correctly for situations when there is no answer available and thus agents optimized for these metrics are poor at modeling confidence. We introduce a simple new performance metric for evaluating question-answering agents that is more representative of practical usage conditions, and optimize for this metric by extending the binary reward structure used in prior work to a ternary reward structure which also rewards an agent for not answering a question rather than giving an incorrect answer. We show that this can drastically improve the precision of answered questions while only not answering a limited number of previously correctly answered questions. Employing a supervised learning strategy using depth-first-search paths to bootstrap the reinforcement learning algorithm further improves performance.
The investigation of social networks based on multi-component random graphs

The methods of non-homogeneous random graphs calibration are developed for social networks simulation. The graphs are calibrated by the degree distributions of the vertices and the edges. The mathematical foundation of the methods is formed by the theory of random graphs with the nonlinear preferential attachment rule and the theory of Erdôs-Rényi random graphs. In fact, well-calibrated network graph models and computer experiments with these models would help developers (owners) of the networks to predict their development correctly and to choose effective strategies for controlling network projects.
Pyramid Feature Attention Network for Saliency Detection

Saliency detection is one of the basic challenges in computer vision. Recently, CNNs are the most widely used and powerful techniques for saliency detection, in which feature maps from different layers are always integrated without distinction. However, instinctively, the different feature maps of CNNs and the different features in the same maps should play different roles in saliency detection. To address this problem, a novel CNN named pyramid feature attention network (PFAN) is proposed to enhance the high-level context features and the low-level spatial structural features. In the proposed PFAN, a context-aware pyramid feature extraction (CPFE) module is designed for multi-scale high-level feature maps to capture the rich context features. A channel-wise attention (CA) model and a spatial attention (SA) model are respectively applied to the CPFE feature maps and the low-level feature maps, and then fused to detect salient regions. Finally, an edge preservation loss is proposed to get the accurate boundaries of salient regions. The proposed PFAN is extensively evaluated on five benchmark datasets and the experimental results demonstrate that the proposed network outperforms the state-of-the-art approaches under different evaluation metrics.
Text Generation from Knowledge Graphs with Graph Transformers

Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce. In this work, we address the problem of generating coherent multi-sentence texts from the output of an information extraction system, and in particular a knowledge graph. Graphical knowledge representations are ubiquitous in computing, but pose a significant challenge for text generation techniques due to their non-hierarchical nature, collapsing of long-distance dependencies, and structural variety. We introduce a novel graph transforming encoder which can leverage the relational structure of such knowledge graphs without imposing linearization or hierarchical constraints. Incorporated into an encoder-decoder setup, we provide an end-to-end trainable system for graph-to-text generation that we apply to the domain of scientific text. Automatic and human evaluations show that our technique produces more informative texts which exhibit better document structure than competitive encoder-decoder methods.
Perceptual Relational Attributes: Navigating and Discovering Shared Perspectives from User-Generated Reviews

Effectively modelling and querying experience items like movies, books, or games in databases is challenging because these items are better described by their resulting user experience or perceived properties than by factual attributes. However, such information is often subjective, disputed, or unclear. Thus, social judgments like comments, reviews, discussions, or ratings have become a ubiquitous component of most Web applications dealing with such items, especially in the e-commerce domain. However, they usually do not play major role in the query process, and are typically just shown to the user. In this paper, we will discuss how to use unstructured user reviews to build a structured semantic representation of database items such that these perceptual attributes are (at least implicitly) represented and usable for navigational queries. Especially, we argue that a central challenge when extracting perceptual attributes from social judgments is respecting the subjectivity of expressed opinions. We claim that no representation consisting of only a single tuple will be sufficient. Instead, such systems should aim at discovering shared perspectives, representing dominant perceptions and opinions, and exploiting those perspectives for query processing.
UU-Nets Connecting Discriminator and Generator for Image to Image Translation

Adversarial generative model have successfully manifest itself in image synthesis. However, the performance deteriorate and unstable, because discriminator is far stable than generator, and it is hard to control the game between the two modules. Various methods have been introduced to tackle the problem such as WGAN, Relativistic GAN and their successors by adding or restricting the loss function, which certainly help balance the min-max game, but they all focused on the loss function ignoring the intrinsic structure limitation. We present a UU-Net architecture inspired by U-net bridging the encoder and the decoder, UU-Net composed by two U-Net liked modules respectively served as generator and discriminator. Because the modules in U-net are symmetrical, therefore it shares weights easily between all four components. Thanks to UU-net's modules identical and symmetric property, we could not only carried the features from inner generator's encoder to its decoder, but also to the discriminator's encoder and decoder. By this design, it give us more control and condition flexibility to intervene the process between the generator and the discriminator.
Band engineering of a magnetic thin film rare-earth monopnictide: A platform for high Chern number

Realizing quantum materials in few atomic layer morphologies is a key to both observing and controlling a wide variety of exotic quantum phenomena. This includes topological electronic materials, where the tunability and dimensionality of few layer materials have enabled the detection of $Z_2$, Chern, and Majorana phases. Here, we report the development of a platform for thin film correlated, topological states in the magnetic rare-earth monopnictide ($RX$) system GdBi synthesized by molecular beam epitaxy. This material is known from bulk single crystal studies to be semimetallic antiferromagnets with Neel temperature $T_N =$ 28 K and is the magnetic analog of the non-$f$-electron containing system LaBi proposed to have topological surface states. Our transport and magnetization studies of thin films grown epitaxially on BaF$_2$ reveal that semimetallicity is lifted below approximately 8 crystallographic unit cells while magnetic order is maintained down to our minimum thickness of 5 crystallographic unit cells. First-principles calculations show that the non-trivial topology is preserved down to the monolayer limit, where quantum confinement and the lattice symmetry give rise to a $C=2$ Chern insulator phase. We further demonstrate the stabilization of these films against atmospheric degradation using a combination of air-free buffer and capping procedures. These results together identify thin film $RX$ materials as potential platforms for engineering topological electronic bands in correlated magnetic materials.
PEPITO: atmospheric Profiling from short-Exposure focal Plane Images in seeing-limiTed mOde

Atmospheric profiling is a requirement for controlling wide-field Adaptive Optics (AO) instruments, analyzing the AO performance with respect to the observing conditions and predicting the Point Spread Function (PSF) spatial variations. We present PEPITO, a new concept for profiling atmospheric turbulence from post facto tip-tilt (TT) corrected short-exposure images. PEPITO utilizes the anisokinetism effect in the images between several stars separated from a reference star, and then produces the profile estimation using a model-fitting methodology, by fitting to the long exposure TT-corrected PSF. PEPITO has a high sensitivity to both C2n(h) and L0(h) by relying on the full telescope aperture and a large field of view. It then obtains a high vertical resolution (1 m-400 m) configurable by the camera pixel scale, taking advantage of fast statistical convergence (of order of tens of seconds). With only a short exposure-capable large format detector and a numerical complexity independent of the telescope diameter, PEPITO perfectly suits accurate profiling for night optical turbulence site characterization or adaptive optics instruments operations. We demonstrate, in simulation, that the C2n(h) and L0(h) can be estimated to better than 1% accuracy, from fitted PSFs of magnitude V=11 on a D=0.5 m telescope with a 10 arcmin field of view.
Comparison Network for One-Shot Conditional Object Detection

The current advances in object detection depend on large-scale datasets to get good performance. However, there may not always be sufficient samples in many scenarios, which leads to the research on few-shot detection as well as its extreme variation one-shot detection. In this paper, the one-shot detection has been formulated as a conditional probability problem. With this insight, a novel one-shot conditional object detection (OSCD) framework, referred as Comparison Network (ComparisonNet), has been proposed. Specifically, query and target image features are extracted through a Siamese network as mapped metrics of marginal probabilities. A two-stage detector for OSCD is introduced to compare the extracted query and target features with the learnable metric to approach the optimized non-linear conditional probability. Once trained, ComparisonNet can detect objects of both seen and unseen classes without further training, which also has the advantages including class-agnostic, training-free for unseen classes, and without catastrophic forgetting. Experiments show that the proposed approach achieves state-of-the-art performance on the proposed datasets of Fashion-MNIST and PASCAL VOC.
Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images †

In recent years, large datasets of high-resolution mammalian neural images have become available, which has prompted active research on the analysis of gene expression data. Traditional image processing methods are typically applied for learning functional representations of genes, based on their expressions in these brain images. In this paper, we describe a novel end-to-end deep learning-based method for generating compact representations of in situ hybridization (ISH) images, which are invariant-to-translation. In contrast to traditional image processing methods, our method relies, instead, on deep convolutional denoising autoencoders (CDAE) for processing raw pixel inputs, and generating the desired compact image representations. We provide an in-depth description of our deep learning-based approach, and present extensive experimental results, demonstrating that representations extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Our methods improve the previous state-of-the-art classification rate (Liscovitch, et al.) from an average AUC of 0.92 to 0.997, i.e., it achieves 96% reduction in error rate. Furthermore, the representation vectors generated due to our method are more compact in comparison to previous state-of-the-art methods, allowing for a more efficient high-level representation of images. These results are obtained with significantly downsampled images in comparison to the original high-resolution ones, further underscoring the robustness of our proposed method.