Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Fracture and Normal Shoulder Bone X-Ray Images Using Ensemble and Transfer Learning With Deep Learning Models Based on Convolutional Neural Networks

Various reasons cause shoulder fractures to occur, an area with wider and more varied range of movement than other joints in body. Firstly, images in 'digital imaging and communications in medicine' (DICOM) format are generated for shoulder via X-radiation (X-ray), magnetic resonance imaging (MRI) and / or computed tomography (CT) devices to diagnose and treat such fractures. Shoulder bone X-ray images were classified and compared via deep learning models based on convolutional neural network (CNN) using transfer learning and ensemble learning in this study to help physicians diagnose and apply required treatment for shoulder fractures. There are a total of 8379 -4211 normal (negative, non-fracture) and 4168 abnormal (positive, fracture)3-channel shoulder bone X-ray images with png format for train data set, and a total of 563 -285 normal and 278 abnormal 3-channel shoulder bone X-ray images with png format for validation and test data in classification conducted using all shoulder images in musculoskeletal radiographs (MURA) dataset, one of the largest public radiographic image datasets. CNN-based built deep learning models herein are; ResNet, ResNeXt, DenseNet, VGG, Inception and MobileNet. Moreover, a classification was also performed by Spinal fully connected (Spinal FC) adaptations of all models. Transfer learning was applied for all these classification procedures. Two different ensemble learning (EL) models were established based on performance of classification results obtained herein. The highest Cohen’s Kappa score of 0.6942 and highest classification test accuracy of 84.72% were achieved in EL2 model, and the highest AUC score of 0.8862 in EL1. INDEX TERMS Biomedical image classification, bone fractures, deep learning, ensemble learning, shoulder, transfer learning, X-ray.
Gaussian Process for Functional Data Analysis: The GPFDA Package for R

We present and describe the GPFDA package for R. The package provides flexible functionalities for dealing with Gaussian process regression (GPR) models for functional data. Multivariate functional data, functional data with multidimensional inputs, and nonseparable and/or nonstationary covariance structures can be modeled. In addition, the package fits functional regression models where the mean function depends on scalar and/or functional covariates and the covariance structure is modeled by a GPR model. In this paper, we present the versatility of GPFDA with respect to mean function and covariance function specifications and illustrate the implementation of estimation and prediction of some models through reproducible numerical examples.
How to Measure Cyber Resilience of an Autonomous Agent: Approaches and Challenges

Several approaches have been used to assess the performance of cyberphysical systems and their exposure to various types of risks. Such assessments have become increasingly important as autonomous attackers ramp up the frequency, duration and intensity of threats while autonomous agents have the potential to respond to cyber-attacks with unprecedented speed and scale. However, most assessment approaches have limitations with respect to measuring cyber resilience, or the ability of systems to absorb, recover from, and adapt to cyberattacks. In this paper, we provide an overview of several common approaches, discuss practical challenges and propose research directions for the development of effective cyber resilience measures.
Quantum cluster equilibrium theory of liquids: molecular clusters and thermodynamics of liquid ethanol

Quantum cluster equilibrium (QCE) theory is presented for liquid ethanol. The cluster equilibria that dictate phase composition are determined by the rigorous techniques of quantum statistical thermodynamics in the canonical ensemble, based on the ab initio partition function. The characteristic features of the supramolecular clusters which comprise the QCE model are discussed in terms of binding energies, geometries, cooperativity and charge transfer. The validity of the resulting QCE model is demonstrated by comparison with experimental ther-modynamic data: Clausius—Clapeyron pressure/temperature dependence and the specific heat. At room temperature, neat, liquid ethanol consists of approximately equal parts of monomer, cyclic tetramer and cyclic pentamer clusters.
Enhancing the Intelligibility of Cleft Lip and Palate Speech Using Cycle-Consistent Adversarial Networks

Cleft lip and palate (CLP) refer to a congenital craniofacial condition that causes various speech-related disorders. As a result of structural and functional deformities, the affected subjects’ speech intelligibility is significantly degraded, limiting the accessibility and usability of speech-controlled devices. Towards addressing this problem, it is desirable to improve the CLP speech intelligibility. Moreover, it would be useful during speech therapy. In this study, the cycle-consistent adversarial network (CycleGAN) method is exploited for improving CLP speech intelligibility. The model is trained on native Kannada-speaking childrens’ speech data. The effectiveness of the proposed approach is also measured using automatic speech recognition performance. Further, subjective evaluation is performed, and those results also confirm the intelligibility improvement in the enhanced speech over the original.
Self-Supervised Equivariant Scene Synthesis from Video

We propose a self-supervised framework to learn scene representations from video that are automatically delineated into background, characters, and their animations. Our method capitalizes on moving characters being equivariant with respect to their transformation across frames and the background being constant with respect to that same transformation. After training, we can manipulate image encodings in real time to create unseen combinations of the delineated components. As far as we know, we are the first method to perform unsupervised extraction and synthesis of interpretable background, character, and animation. We demonstrate results on three datasets: Moving MNIST with backgrounds, 2D video game sprites, and Fashion Modeling.
Joint Beamforming and Phase Optimization in a Multi-user Communication System Composed of Dual Reconfigurable Intelligent Surfaces

Reconfigurable Intelligent Surface (RIS) has becoming a useful tool in future wireless communication systems for close-distance communication network. This paper we use Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication designed to improve energy collection performance while satisfying wireless information and Power Transfer (WIPT). The designed system consists of an IRS-assisted system consists of a multi-antenna assisted base station (BS) and two opposite multi-antenna assisted information receiver cooperated (RIS) as energy receiver (ERs) that meets energy collection requirements. Based on the electromagnetic property of Reconfigurable Intelligent Surface (RIS), like two mirrors that are opposite each other, setting two Reconfigurable Intelligent Surface (RIS) attached to the city buildings to reflect the sending signals. The transmitting precoding of the Multi-antenna Auxiliary Base Station (BS) and the angular phase transfer matrix of the multi-antenna Auxiliary Information Receiver (IRs) need to be optimized together to maximize the energy harvesting of IoT devices for energy efficiency (EE) of the IRs system and to provide users with the efficiency of the received signal. In order to solve the joint optimization problem effectively, we turn the non-convex maximize problem into the equivalent formal error method based on the mean square, and finally use the iterative algorithm for optimization. As for algorithm, we respectively use MSE method, semidefinite relaxation techniques to simplify transmitting beamforming matrix and the matrix phase shift. Through the observation of simulation data, it can be concluded that the performance optimization method of SDR based on RIS is effective.
Text-to-hashtag Generation using Seq2seq Learning

In this paper, we studied whether models based on BiLSTM and BERT can predict hashtags in Brazilian Portuguese for Ecommerce websites. Hashtags have a sizable financial impact on Ecommerce. We processed a corpus of Ecommerce reviews as inputs, and predicted hashtags as outputs. We evaluated the results using four quantitative metrics: NIST, BLEU, METEOR and a crowdsourced score. A word cloud was used as a qualitative metric. While all computer-generated metrics (NIST, BLEU and METEOR) indicated bad results, the crowdsourced results produced amazing scores. We concluded that the texts predicted by the neural networks are very promising for use as hashtags for products on Ecommerce websites. The code for this work is available at https://github.com/augustocamargo/text-to-hashtag.
Online Markov Decision Processes with Aggregate Bandit Feedback

We study a novel variant of online finite-horizon Markov Decision Processes with adversarially changing loss functions and initially unknown dynamics. In each episode, the learner suffers the loss accumulated along the trajectory realized by the policy chosen for the episode, and observes aggregate bandit feedback: the trajectory is revealed along with the cumulative loss suffered, rather than the individual losses encountered along the trajectory. Our main result is a computationally efficient algorithm with $ ( √ ) regret for this setting, where is the number of episodes. We establish this result via an efficient reduction to a novel bandit learning setting we call Distorted Linear Bandits (DLB), which is a variant of bandit linear optimization where actions chosen by the learner are adversarially distorted before they are committed. We then develop a computationally-efficient online algorithm for DLB for which we prove an $ ( √ )) regret bound, where ) is the number of time steps. Our algorithm is based on online mirror descent with a self-concordant barrier regularization that employs a novel increasing learning rate schedule.
Stochastic High Fidelity Simulation and Scenarios for Testing of Fixed Wing Autonomous GNSS-Denied Navigation Algorithms

Autonomous unmanned aerial vehicle (UAV) inertial navigation exhibits an extreme dependency on the availability of global navigation satellite systems (GNSS) signals, without which it incurs in a slow but unavoidable position drift that may ultimately lead to the loss of the platform if the GNSS signals are not restored or the aircraft does not reach a location from which it can be recovered by remote control. This article describes an stochastic high fidelity simulation of the flight of a fixed wing low SWaP (size, weight, and power) autonomous UAV in turbulent and varying weather intended to test and validate the GNSS-Denied performance of different navigation algorithms. Its open-source \nm{\CC} implementation has been released and is publicly available. Onboard sensors include accelerometers, gyroscopes, magnetometers, a Pitot tube, an air data system, a GNSS receiver, and a digital camera, so the simulation is valid for inertial, visual, and visual inertial navigation systems. Two scenarios involving the loss of GNSS signals are considered: the first represents the challenges involved in aborting the mission and heading towards a remote recovery location while experiencing varying weather, and the second models the continuation of the mission based on a series of closely spaced bearing changes. All simulation modules have been modeled with as few simplifications as possible to increase the realism of the results. While the implementation of the aircraft performances and its control system is deterministic, that of all other modules, including the mission, sensors, weather, wind, turbulence, and initial estimations, is fully stochastic. This enables a robust evaluation of each proposed navigation system by means of Monte-Carlo simulations that rely on a high number of executions of both scenarios.
Unified approach for solving exit problems for additive-increase and multiplicative-decrease processes

Abstract We analyse an additive-increase and multiplicative-decrease (also known as growth–collapse) process that grows linearly in time and that, at Poisson epochs, experiences downward jumps that are (deterministically) proportional to its present position. For this process, and also for its reflected versions, we consider one- and two-sided exit problems that concern the identification of the laws of exit times from fixed intervals and half-lines. All proofs are based on a unified first-step analysis approach at the first jump epoch, which allows us to give explicit, yet involved, formulas for their Laplace transforms. All eight Laplace transforms can be described in terms of two so-called scale functions associated with the upward one-sided exit time and with the upward two-sided exit time. All other Laplace transforms can be obtained from the above scale functions by taking limits, derivatives, integrals, and combinations of these.
Monotone Additive Statistics

The expectation is an example of a descriptive statistic that is monotone with respect to stochastic dominance, and additive for sums of independent random variables. We provide a complete characterization of such statistics, and explore a number of applications to models of individual and group decision-making. These include a representation of stationary, monotone time preferences, extending the work of Fishburn and Rubinstein (1982) to time lotteries, as well as a characterization of risk-averse preferences over monetary gambles that are invariant to mean-zero background risks. The full paper is available at https://arxiv.org/abs/2102.00618.
Example Abstract

In this communication we present millimetreand THz-waves experimental confirmation of enhanced transmission through subwavelength hole arrays with rectangular lattice when the incident electric field is parallel to the short periodicity. Extraordinary optical transmission (EOT) [1] has become one of the hot topics of this century because of its potential to a wide range of application, such as polarization control, biosensing, non-linear optics and absorption spectroscopy to name a few [2]. This phenomenon is founded, roughly speaking, on complex surface waves (surface plasmon polariton at optics, leaky or Sommerfeld-Zeneck waves at microwaves-to-THz) [3] supported by the texturized metal and the evanescent coupling though the holes [1]. Given the impact of EOT in science and industry, alternative routes to enhance the transmission through subwavelength apertures are being exploring. One that has attracted little attention up to day is the enhancement for s-polarized waves through dielectric sandwiched hole [4] and slit arrays [5]. Although at a first sight it looks counterintuitive, its underlying physics is also based on a complex surface waves as the case of regular EOT. However, in this case, the complex surface wave is a waveguide mode supported by the metallic-backed dielectric slab [3, 4]. Here, we show the TE nature of this waveguide mode (see figure below) and we demonstrate experimentally enhanced transmission through dielectric loaded subwavelength hole as well as slit arrays at millimetre-waves and THz [6, 7]. Fig. 1. Electric and magnetic field distribution for the anomalous EOT.
Estimating adsorption isotherm parameters in chromatography via a virtual injection promoting double feed-forward neural network

Abstract The means to obtain the adsorption isotherms is a fundamental open problem in competitive chromatography. A modern technique of estimating adsorption isotherms is to solve a nonlinear inverse problem in a partial differential equation so that the simulated batch separation coincides with actual experimental results. However, this identification process is usually ill-posed in the sense that the uniqueness of adsorption isotherms cannot be guaranteed, and moreover, the small noise in the measured response can lead to a large fluctuation in the traditional estimation of adsorption isotherms. The conventional mathematical method of solving this problem is the variational regularization, which is formulated as a non-convex minimization problem with a regularized objective functional. However, in this method, the choice of regularization parameter and the design of a convergent solution algorithm are quite difficult in practice. Moreover, due to the restricted number of injection profiles in experiments, the types of measured data are extremely limited, which may lead to a biased estimation. In order to overcome these difficulties, in this paper, we develop a new inversion method – the virtual injection promoting double feed-forward neural network (VIP-DFNN). In this approach, the training data contain various types of artificial injections and synthetic noisy measurement at outlet, generated by a conventional physics model – a time-dependent convection-diffusion system. Numerical experiments with both artificial and real data from laboratory experiments show that the proposed VIP-DFNN is an efficient and robust algorithm.
Stochastic Geometry Analysis of Spatial-Temporal Performance in Wireless Networks: A Tutorial

The performance of wireless networks is fundamentally limited by the aggregate interference, which depends on the spatial distributions of the interferers, channel conditions, and user traffic patterns (or queueing dynamics). These factors usually exhibit spatial and temporal correlations and thus make the performance of large-scale networks environment-dependent (i.e., dependent on network topology, locations of the blockages, etc.). The correlation can be exploited in protocol designs (e.g., spectrum-, load-, location-, energy-aware resource allocations) to provide efficient wireless services. For this, accurate system-level performance characterization and evaluation with spatial-temporal correlation are required. In this context, stochastic geometry models and random graph techniques have been used to develop analytical frameworks to capture the spatial-temporal interference correlation in large-scale wireless networks. The objective of this article is to provide a tutorial on the stochastic geometry analysis of large-scale wireless networks that captures the spatial-temporal interference correlation (and hence the signal-to-interference ratio (SIR) correlation). We first discuss the importance of spatial-temporal performance analysis, different parameters affecting the spatial-temporal correlation in the SIR, and the different performance metrics for spatial-temporal analysis. Then we describe the methodologies to characterize spatial-temporal SIR correlations for different network configurations (independent, attractive, repulsive configurations), shadowing scenarios, user locations, queueing behavior, relaying, retransmission, and mobility. We conclude by outlining future research directions in the context of spatial-temporal analysis of emerging wireless communications scenarios.
CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning

Cycling is a promising sustainable mode for commuting and leisure in cities, however, the fear of getting hit or fall reduces its wide expansion as a commuting mode. In this paper, we introduce a novel method called CyclingNet for detecting cycling near misses from video streams generated by a mounted frontal camera on a bike regardless of the camera position, the conditions of the built, the visual conditions and without any restrictions on the riding behaviour. CyclingNet is a deep computer vision model based on convolutional structure embedded with self-attention bidirectional long-short term memory (LSTM) blocks that aim to understand near misses from both sequential images of scenes and their optical flows. The model is trained on scenes of both safe rides and near misses. After 42 hours of training on a single GPU, the model shows high accuracy on the training, testing and validation sets. The model is intended to be used for generating information that can draw significant conclusions regarding cycling behaviour in cities and elsewhere, which could help planners and policy-makers to better understand the requirement of safety measures when designing infrastructure or drawing policies. As for future work, the model can be pipelined with other state-of-the-art classifiers and object detectors simultaneously to understand the causality of near misses based on factors related to interactions of road-users, the built and the natural environments.
Triple M: A Practical Neural Text-to-speech System With Multi-guidance Attention And Multi-band Multi-time Lpcnet

In this work, a robust and efficient text-to-speech system, named Triple M, is proposed for large-scale online application. The key components of Triple M are: 1) A seq2seq model with multi-guidance attention which obtains stable feature generation and robust long sentence synthesis ability by learning from the guidance attention mechanisms. Multi-guidance attention improves the robustness and naturalness of long sentence synthesis without any in-domain performance loss or online service modification. Compared with the our best result obtained by using single attention mechanism (GMM-based attention), the word error rate of long sentence synthesis decreases by 23.5% when multi-guidance attention mechanism is applied. 2) A efficient multi-band multi-time LPCNet, which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies (from 2.8 to 1.0 GFLOP). Due to these strategies, the vocoder speed is increased by 2.75x on a single CPU without much MOS degradatiaon (4.57 vs. 4.45).
Optimized Energy Efficient Virtualization and Content Caching in 5G Networks

Network function virtualization (NFV) and content caching are two promising technologies that hold great potential for network operators and designers. This paper optimizes the deployment of NFV and content caching in 5G networks and focuses on the associated power consumption savings. In addition, it introduces an approach to combine content caching with NFV in one integrated architecture for energy aware 5G networks. A mixed integer linear programming (MILP) model has been developed to minimize the total power consumption by jointly optimizing the cache size, virtual machine (VM) workload, and the locations of both cache nodes and VMs. The results were investigated under the impact of core network virtual machines (CNVMs) inter-traffic. The result show that the optical line terminal (OLT) access network nodes are the optimum location for content caching and for hosting VMs during busy times of the day whilst IP over WDM core network nodes are the optimum locations for caching and VM placement during off-peak time. Furthermore, the results reveal that a virtualization-only approach is better than a caching-only approach for video streaming services where the virtualization-only approach compared to caching-only approach, achieves a maximum power saving of 7% (average 5%) when no CNVMs intertraffic is considered and 6% (average 4%) with CNVMs inter-traffic at 10% of the total backhaul traffic. On the other hand, the integrated approach has a maximum power saving of 15% (average 9%) with and without CNVMs inter-traffic compared to the virtualization-only approach, and it achieves a maximum power saving of 21% (average 13%) without CNVMs inter-traffic and 20% (average 12%) when CNVMs inter-traffic is considered compared with the caching-only approach. In order to validate the MILP models and achieve real-time operation in our approaches, a heuristic was developed. The heuristic results are comparable to the MILP model results with and without CNVMs inter-traffic. INDEX TERMS 5G networks, Backhaul, BBU, Energy Efficiency, Fronthaul, IP over WDM, Network Function Virtualization, NFV.