Miracle-dz/UniFAR-FTUs huggingface dataset:
U-Patch Antenna for RFID and Wireless Applications

in this paper, we propose a new multi-band patch antenna structure for embedded RFID (Radio Frequency Identification) readers and wireless communications. The proposed antenna is a dual band microstrip patch antenna using U-slot geometry. The operating frequencies of the proposed antenna are chosen as 2.4 and 0.9 (GHz), obtained by optimizing the physical dimensions of the U-slot. Several parameters have been investigated using Ansoft Designer software. The antenna is fed through a quarter wavelength transformer for impedance matching. An additional layer of alumina is added above the surface of the conductors to increase the performance of the antenna.
Closed-open morphisms on periodic Floer homology

In this note, we investigate homomorphisms from the periodic Floer homology (PFH) to the quantitative Heegaard Floer homology. We call the homomorphisms closed-open morphisms. Under certain assumptions on the Lagrangian link, we first follow R. Lipshitz's idea to give a cylindrical formulation of the quantitative Heegaard Floer homology. Then we construct the closed-open morphisms from the PFH to the quantitative Heegaard Floer homology. Moreover, we show that the morphisms are non-vanishing. As an application, we deduce a relation between the PFH-spectral invariants and the HF-spectral invariants.
CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning

Motivation: In recent years, image-based biological assays have steadily become high-throughput, sparking a need for fast automated methods to extract biologically-meaningful information from hundreds of thousands of images. Taking inspiration from the success of ImageNet, we curate CytoImageNet, a large-scale dataset of openly-sourced and weakly-labeled microscopy images (890K images, 894 classes). Pretraining on CytoImageNet yields features that are competitive to ImageNet features on downstream microscopy classification tasks. We show evidence that CytoImageNet features capture information not available in ImageNet-trained features. The dataset is made available at https://www.kaggle. com/stanleyhua/cytoimagenet.
Gait Identification under Surveillance Environment based on Human Skeleton

As an emerging biological identification technology, vision-based gait identification is an important research content in biometrics. Most existing gait identification methods extract features from gait videos and identify a probe sample by a query in the gallery. However, video data contains redundant information and can be easily influenced by bagging (BG) and clothing (CL). Since human body skeletons convey essential information about human gaits, a skeleton-based gait identification network is proposed in our project. First, extract skeleton sequences from the video and map them into a gait graph. Then a feature extraction network based on Spatio-Temporal Graph Convolutional Network (ST-GCN) is constructed to learn gait representations. Finally, the probe sample is identified by matching with the most similar piece in the gallery. We tested our method on the CASIA-B dataset. The result shows that our approach is highly adaptive and gets the advanced result in BG, CL conditions, and average.
Gaussian random fields: with and without covariances

We begin with isotropic Gaussian random fields, and show how the Bochner–Godement theorem gives a natural way to describe their covariance structure. We continue with a study of Matérn processes on Euclidean space, spheres, manifolds and graphs, using Bessel potentials and stochastic partial differential equations (SPDEs). We then turn from this continuous setting to approximating discrete settings, Gaussian Markov random fields (GMRFs), and the computational advantages they bring in handling large data sets, by exploiting the sparseness properties of the relevant precision (concentration) matrices.
VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles

Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios. However, the poor photorealism and lack of diverse sensor modalities of existing simulation engines remain key hurdles towards realizing this potential. Here, we present VISTA††Full code release for the VISTA data-driven simulation engine is available here: vista.csail. mit.edu. , an open source, data-driven simulator that integrates multiple types of sensors for autonomous vehicles. Using high fidelity, real-world datasets, VISTA represents and simulates RGB cameras, 3D LiDAR, and event-based cameras, enabling the rapid generation of novel viewpoints in simulation and thereby enriching the data available for policy learning with corner cases that are difficult to capture in the physical world. Using VISTA, we demonstrate the ability to train and test perception-to-control policies across each of the sensor types and showcase the power of this approach via deployment on a full scale autonomous vehicle. The policies learned in VISTA exhibit sim-to-real transfer without modification and greater robustness than those trained exclusively on real-world data.
Deep Residual Fourier Transformation for Single Image Deblurring

It has been a common practice to adopt the ResBlock, which learns the difference between blurry and sharp image pairs, in end-to-end image deblurring architectures. Reconstructing a sharp image from its blurry counterpart requires changes regarding both low-and high-frequency information. Although conventional ResBlock may have good abilities in capturing the high-frequency components of images, it tends to overlook the low-frequency information. Moreover, ResBlock usually fails to felicitously model the long-distance information which is non-trivial in reconstructing a sharp image from its blurry counterpart. In this paper, we present a Residual Fast Fourier Transform with Convolution Block (Res FFT-Conv Block), capable of capturing both long-term and short-term interactions, while integrating both low-and high-frequency residual information. Res FFT-Conv Block is a conceptually simple yet computationally efﬁcient, and plug-and-play block, leading to remarkable performance gains in different architectures. With Res FFT-Conv Block, we further propose a Deep Residual Fourier Transformation (DeepRFT) framework, based upon MIMO-UNet, achieving state-of-the-art image deblurring performance on GoPro, HIDE, RealBlur and DPDD datasets. Experiments show our DeepRFT can boost image deblurring performance signiﬁcantly (e.g., with 1.09 dB improvement in PSNR on GoPro dataset compared with MIMO-UNet), and DeepRFT+ even reaches 33.23 dB in PSNR on GoPro dataset.
Is Deep Image Prior in Need of a Good Education?

Deep image prior [55] was recently introduced as an effective prior for image reconstruction. It represents the image to be recovered as the output of a deep convolutional neural network, and learns the network’s parameters such that the output ﬁts the corrupted observation. Despite its impressive reconstructive properties, the approach is slow when compared to learned or traditional reconstruction techniques. Our work develops a two-stage learning paradigm to address the computational challenge: (i) we perform a supervised pretraining of the network on a synthetic dataset; (ii) we ﬁne-tune the network’s parameters to adapt to the target reconstruction. We showcase that pretraining considerably speeds up the subsequent reconstruction from real-measured micro computed tomography data of biological specimens. The code and additional experimental materials are available at educated-dip.github. io/docs. educated_deep_image_prior/.
RepoMiner: a Language-agnostic Python Framework to Mine Software Repositories for Defect Prediction

Data originating from open-source software projects provide valuable information to enhance software quality. In the scope of Software Defect Prediction, one of the most challenging parts is extracting valid data about failure-prone software components from these repositories, which can help develop more robust software. In particular, collecting data, calculating metrics, and synthesizing results from these repositories is a tedious and error-prone task, which often requires understanding the programming languages involved in the mined repositories, eventually leading to a proliferation of language-specific data-mining software. This paper presents RepoMiner, a language-agnostic tool developed to support software engineering researchers in creating datasets to support any study on defect prediction. RepoMiner automatically collects failure data from software components, labels them as failure-prone or neutral, and calculates metrics to be used as ground truth for defect prediction models. We present its implementation and provide examples of its application.
Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances

Iris recognition technology has attracted an increasing interest in the last decades in which we have witnessed a migration from research laboratories to real world applications. The deployment of this technology raises questions about the main vulnerabilities and security threats related to these systems. Among these threats presentation attacks stand out as some of the most relevant and studied. Presentation attacks can be defined as presentation of human characteristics or artifacts directly to the capture device of a biometric system trying to interfere its normal operation. In the case of the iris, these attacks include the use of real irises as well as artifacts with different level of sophistication such as photographs or videos. This chapter introduces iris Presentation Attack Detection (PAD) methods that have been developed to reduce the risk posed by presentation attacks. First, we summarise the most popular types of attacks including the main challenges to address. Secondly, we present a taxonomy of Presentation Attack Detection methods as a brief introduction to this very active research area. Finally, we discuss the integration of these methods into Iris Recognition Systems according to the most important scenarios of practical application.
de Sitter space as a BRST invariant coherent state of gravitons

The S-matrix formulation indicates that a consistent embedding of de Sitter state in quantum gravity is possible exclusively as an excited quantum state constructed on top of a valid S-matrix vacuum such as Minkowski. In the present paper we offer such a construction of de Sitter in the form of a coherent state of gravitons. Unlike previous realizations of this idea, we focus on BRST invariance as the guiding principle for physicality. In order to establish the universal rules of gauge consistency, we study the BRST-invariant construction of coherent states created by classical and quantum sources in QED and in linearized gravity. Introduction of N copies of scalar matter coupled to gravity allows us to take a special double scaling limit, a so-called species limit, in which our construction of de Sitter becomes exact. In this limit, the irrelevant quantum gravitational effects vanish whereas the collective phenomena, such as Gibbons-Hawking radiation, are calculable.
Catastrophic and parametric fault detection by dynamic power supply current test

The increase in importance of mixed signal ICs (MSICs) has led to the need to provide reliable test and fault detection methods for these circuits. The problem of successful testing of MSICs is complex. Many tools and techniques have been proposed to optimise the testability of these circuits, most of them considering separately the analogue and the digital parts of the mixed signal circuits. A different approach to the unification of mixed-mode testing is to analyse the power-supply current through the MSIC. We propose techniques for fault detection, based on power supply dynamic current testing in mixed circuits which take into account catastrophic and parametric faults. The inclusion of parametric faults made it necessary to use multivariate statistics to analyse the power supply current signal. We have developed dynamic current sensors for the digital and analogue parts of the MSIC which depend on of the fault type, catastrophic or parametric. Furthermore, the evaluation methods of the built-in dynamic current sensors output has also been considered through digital processing or by means of a multivariate statistical analysis. The differences between the test methods applied to both fault models, catastrophic and parametric, obliged the adjustment of the dynamic power supply current signal so it can be analysed by the same test equipment. These adjustment may be included in the signal processing measured by the sensors. The application of the dynamic power supply current test methods to some benchmark circuits with injected catastrophic and parametric faults allow us to evaluate the efficiency of these methods. (6 pages)
On the Capacity of MISO Optical Intensity Channels With Per-Antenna Intensity Constraints

This paper investigates the capacity of general multiple-input single-output (MISO) optical intensity channels (OICs) under per-antenna peak- and average-intensity constraints. We first consider the MISO equal-cost constrained OIC (EC-OIC), where, apart from the peak-intensity constraint, average intensities of inputs are equal to arbitrarily preassigned constants. The second model of our interest is the MISO bounded-cost constrained OIC (BC-OIC), where, as compared with the EC-OIC, average intensities of inputs are no larger than arbitrarily preassigned constants. By leveraging tools from quantile functions, stop-loss transform and convex ordering of nonnegative random variables, we prove two decomposition theorems for bounded and nonnegative random variables, based on which we equivalently transform both the EC-OIC and the BC-OIC into respective single-input single-output channels under a peak-intensity and several stop-loss mean constraints. Capacity lower and upper bounds for both channels are established, based on which the asymptotic capacity at high and low signal-to-noise-ratio are determined.
Explainable Deep Image Classifiers for Skin Lesion Diagnosis

A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems. Research in eXplainable Artificial Intelligence (XAI) is trying to solve this issue. However, often XAI approaches are only tested on generalist classifier and do not represent realistic problems such as those of medical diagnosis. In this paper, we analyze a case study on skin lesion images where we customize an existing XAI approach for explaining a deep learning model able to recognize different types of skin lesions. The explanation is formed by synthetic exemplar and counter-exemplar images of skin lesion and offers the practitioner a way to highlight the crucial traits responsible for the classification decision. A survey conducted with domain experts, beginners and unskilled people proof that the usage of explanations increases the trust and confidence in the automatic decision system. Also, an analysis of the latent space adopted by the explainer unveils that some of the most frequent skin lesion classes are distinctly separated. This phenomenon could derive from the intrinsic characteristics of each class and, hopefully, can provide support in the resolution of the most frequent misclassifications by human experts.
Effects of Gauss-Bonnet gravity on thermodynamics of (3+1)$-D$ AdS black holes

We studied the thermodynamics of the black holes with Gauss-Bonnet correction term in $3+1-$ dimensional AdS spacetime. It is known that the term has no effect on the equation of motion, however, it modifies the entropy formula of Wald as it has an effect of the Gauss-Bonnet parameter term. We studied charged black holes, namely, Reissner-Nordstr\"{o}m and Born-Infeld under this regime. We encountered divergences in heat capacity. After eliminating the possibility of first order phase transition, we applied two well trusted methods from standard thermodynamics, namely, Ehrenfest scheme and Ruppeiner's state space geometry analysis to ensure the second order nature of phase transition points. Effects of Gauss-Bonnet and Born-Infeld parameter are also discussed.
Variational and parquet-diagram calculations for neutron matter. IV. Spin-orbit interactions and linear response

We develop the parquet-diagram summation method for neutron matter interacting via potentials that include spin, tensor, and spin-orbit components. For that purpose, we derive an exact expression for the sum of all ring-diagrams in terms effective local particle-hole interactions involving the above four operators. The parquet equations are closed by deriving the spin-orbit contribution to that particle-hole interaction. We show that many-body correlations screen the bare spin-orbit potential considerably, and the corrections of that screened spin-orbit potential to the other three interaction channels are quite small. We apply our method to the calculation of the response of neutron matter to density and both longitudinal and transverse spin-dependent external fields.
Reduced-order observer design for a robotic manipulator

This paper investigates the design of reduced-order observers for robotic manipulators. Observer stability conditions are obtained based on a Lyapunov analysis and the proposed observer is enhanced with a hybrid scheme that may adjust the gains to cope with possible unbounded velocities of the robot joints. Thanks to such hybrid strategy, the observer works accurately both for robots driven by open-loop controllers and by output feedback controllers. Numerical simulations illustrate the efficacy of the reduced-order observer in several scenarios, including a comparison with the performances of a classical full-order observer.
Magnetic cations doped into a Double Perovskite Semiconductor

We report two solid solutions based on magnetic ion doping of the double perovskite oxide Sr2GaSbO6: Sr2Ga1-xCrxSbO6 (0.1 ≤ x ≤ 0.4) and Sr2Ga1-xFexSbO6 (0.1 ≤ x ≤ 0.4). All.
Semi-Supervised Learning with Taxonomic Labels

We propose techniques to incorporate coarse taxonomic labels to train image classifiers in fine-grained domains. Such labels can often be obtained with a smaller effort for fine-grained domains such as the natural world where categories are organized according to a biological taxonomy. On the Semi-iNat dataset consisting of 810 species across three Kingdoms, incorporating Phylum labels improves the Species level classification accuracy by 6% in a transfer learning setting using ImageNet pre-trained models. Incorporating the hierarchical label structure with a state-of-the-art semi-supervised learning algorithm called FixMatch improves the performance further by 1.3%. The relative gains are larger when detailed labels such as Class or Order are provided, or when models are trained from scratch. However, we find that most methods are not robust to the presence of out-of-domain data from novel classes. We propose a technique to select relevant data from a large collection of unlabeled images guided by the hierarchy which improves the robustness. Overall, our experiments show that semi-supervised learning with coarse taxonomic labels are practical for training classifiers in fine-grained domains.
Largest component of subcritical random graphs with given degree sequence

We study the size of the largest component of two models of random graphs with prescribed degree sequence, the configuration model (CM) and the uniform model (UM), in the (barely) subcritical regime. For the CM, we give upper bounds that are asymptotically tight for certain degree sequences. These bounds hold under mild conditions on the sequence and improve previous results of Hatami and Molloy on the barely subcritical regime. For the UM, we give weaker upper bounds that are tight up to logarithmic terms but require no assumptions on the degree sequence. In particular, the latter result applies to degree sequences with infinite variance in the subcritical regime.
Strongly isospectral hyperbolic 3-manifolds with nonisomorphic rational cohomology rings

This paper shows that one cannot “hear” the rational cohomology ring of a hyperbolic 3manifold. More precisely, while it is well-known that strongly isospectral manifolds have the same cohomology as vector spaces, we give an example of compact hyperbolic 3-manifolds that are strongly isospectral but have nonisomorphic rational cohomology rings. Along the way we implement a computer program which finds the nullity of the cup product map H(M ;Q) ∧ H(M ;Q) → H(M ;Q) for any aspherical space in terms of the presentation of the fundamental group.
Triple Classification for Scholarly Knowledge Graph Completion

structured information representing knowledge encoded in scientific publications. With the sheer volume of published scientific literature comprising a plethora of inhomogeneous entities and relations to describe scientific concepts, these KGs are inherently incomplete. We present exBERT, a method for leveraging pre-trained transformer language models to perform scholarly knowledge graph completion. We model triples of a knowledge graph as text and perform triple classification (i.e., belongs to KG or not). The evaluation shows that exBERT outperforms other baselines on three scholarly KG completion datasets in the tasks of triple classification, link prediction, and relation prediction. Furthermore, we present two scholarly datasets as resources for the research community, collected from public KGs and online resources.
Reconstruction of People in Loose Clothing

3D human body reconstruction from monocular images is an interesting and ill-posed problem in computer vision with wider applications in multiple domains. In this paper, we propose SHARP, a novel end-to-end trainable network that accurately recovers the detailed geometry and appearance of 3D people in loose clothing from a monocular image. We propose a sparse and efficient fusion of a parametric body prior with a non-parametric peeled depth map representation of clothed models. The parametric body prior constraints our model in two ways: first, the network retains geometrically consistent body parts that are not occluded by clothing, and second, it provides a body shape context that improves prediction of the peeled depth maps. This enables SHARP to recover fine-grained 3D geometrical details with just L1 losses on the 2D maps, given an input image. We evaluate SHARP on publicly available Cloth3D and THuman datasets and report superior performance to state-of-the-art approaches.