Miracle-dz/UniFAR-FTUs huggingface dataset:
Canine Distemper Outbreak in a Population of Eastern Spotted Skunks

Abstract Canine distemper is an epizootic disease that can cause high mortality rates in carnivores, such as mustelids, and can have important deleterious effects on vulnerable, often small, carnivore populations due to its highly transmissible nature. In April and May 2020, we recovered 5 Spilogale putorius (Eastern Spotted Skunk) carcasses over a 15-day period that showed no visible signs of trauma. Clinical examination of the carcasses indicated that the skunks died due to canine distemper. Four of these skunks were radio-collared as part of our research at the time of their deaths, meaning that 50% of our study sample succumbed to distemper. Additionally, 2 other skunks we had radio-collared went missing during this time and might have contracted and died of distemper as well. To our knowledge, this is the first documented outbreak of distemper in Eastern Spotted Skunks. Our observation underscores the need to further study the effects of disease on the declining Eastern Spotted Skunk across its range.
Risk-aware Fine-grained Access Control in Cyber-physical Contexts

Access to resources by users may need to be granted only upon certain conditions and contexts, perhaps particularly in cyber-physical settings. Unfortunately, creating and modifying context-sensitive access control solutions in dynamic environments creates ongoing challenges to manage the authorization contexts. This article proposes RASA, a context-sensitive access authorization approach and mechanism leveraging unsupervised machine learning to automatically infer risk-based authorization decision boundaries. We explore RASA in a healthcare usage environment, wherein cyber and physical conditions create context-specific risks for protecting private health information. The risk levels are associated with access control decisions recommended by a security policy. A coupling method is introduced to track coexistence of the objects within context using frequency and duration of coexistence, and these are clustered to reveal sets of actions with common risk levels; these are used to create authorization decision boundaries. In addition, we propose a method for assessing the risk level and labelling the clusters with respect to their corresponding risk levels. We evaluate the promise of RASA-generated policies against a heuristic rule-based policy. By employing three different coupling features (frequency-based, duration-based, and combined features), the decisions of the unsupervised method and that of the policy are more than 99% consistent.
Longitudinal rates of hospital adverse events that contributed to death in Norway and Sweden from 2013 to 2018

Objectives In this paper, we explore and compare types and longitudinal trends of hospital adverse events in Norway and Sweden in the years 2013–2018 with special reference to AEs that contributed to death. Design Acute care hospitals in both countries performed medical record reviews on randomly selected medical records from all eligible admissions. Analysis: Comparison between Norway and Sweden of linear trends from 2013–2018, and percentage rates of admissions with at least one AE according to types and severities. Setting Norway and Sweden have similar socio-economic and demographic characteristics, which constitutes a relevant context for cooperation, comparison and mutual learning. This setting has promoted the use of GTT to monitor national rates of AEs in hospital care in the two countries. Participants 53 367 medical records in Norway and 88 637 medical records in Sweden were reviewed. Results 13.2% of hospital admissions in Norway and 13.1% in Sweden were associated with an AE of all severities (E-I). 0.23% of hospital admissions in Norway and 0.26% in Sweden were associated with an AE that contributed to death (I). The differences between the two countries were not statistically significant. Conclusions There were no significant differences in overall rates (E-I) of AEs in Norway and Sweden, nor in rates of AEs that contributed to death (I). There was no significant change in AEs or fatal AEs in either country over the six-year time period.
Air Fraction Correction Optimisation in PET Imaging of Lung Disease

Accurate quantification of radiopharmaceutical uptake from lung PET/CT is challenging due to large variations in fractions of tissue, air, blood and water. Air fraction correction (AFC) uses voxel-wise air fractions, which can be determined from the CT acquired for attenuation correction (AC). However, resolution effects can cause artefacts in either of these corrections. In this work, we hypothesise that the resolution of the CT image used for AC should match that of the intrinsic resolution of the PET scanner but should approximate the reconstructed PET image resolution for AFC. Simulations and reconstructions were performed with the Synergistic Image Reconstruction Framework (SIRF) using phantoms with inhomogeneous attenuation (mu) maps, mimicking the densities observed in lung pathologies. Poisson noise was added to the projection data prior to OSEM reconstruction. AC was performed with a smoothed mu-map, the full-width-half-maximum (FWHM) of the 3D Gaussian kernel was varied (0 - 10 mm). Post-filters were applied to the reconstructed AC images (FWHM: 0 - 8 mm). The simulated mu-map was independently convolved with another set of 3D Gaussian kernels, of varying FWHM (0 - 12 mm), for AFC. The coefficient of variation (CV) in the lung region, designed to be homogeneous post-AFC with optimised kernels, and the mean AFC-standardized uptake value (AFC-SUV) in the regions of simulated pathologies were determined. The spatial resolution of each post-filtered image was determined via a point-source insertion-and-subtraction method on noiseless data. Results showed that the CV was minimised when the kernel applied to the mu-map for AC matched that for the simulated PET scanner and the kernel applied to the mu-map for AFC matched the spatial resolution of the reconstructed PET image. This was observed for all post-reconstruction filters and supports the hypothesis. Initial results from Monte Carlo simulations validate these findings.
Dynamic Cardiac SPECT for diagnostic and theranostics applications: latest results

To cost-effectively satisfy the high spatial resolution, high sensitivity, and SNR needs for cardiac SPECT imaging that has not been met with the conventional gamma camera, we are developing Dynamic Cardiac SPECT (DC-SPECT) for diagnostic and theranostics applications featuring: static body-contouring geometry with large detector coverage (~225 deg), large number of views using 80 detector modules each with a dedicated loft-hole collimator, and laser-processed CsI:Tl with converging pixels to avoid depth of interaction (DOI) blurring. The DC-SPECT design is based on using 10 mm thick CsI:Tl scintillators that can provide high stopping power for gamma-ray energies beyond the 140 keV. Given the DOI blurring issue inherent with pinhole collimators, the CsI:Tl pixels in our detectors have converging pixel volumes with a focal length equal to the detector-pinhole distance. Geometry optimization show that at 15 mm fixed resolution, DC-SPECT's sensitivity is 15x larger than that of a gamma camera. At 0.01% sensitivity, 4.1 mm resolution can be achieved which is close to whole-body PET. Also, at 10 mm resolution, sensitivity of ~0.07%can be achieved. Note that the resolution values are without employing resolution recovery techniques. We have laser-processed multiple CsI:Tl crystals using a programmable femtosecond high power laser. Flood map of laser processed 17.4x17. 4x6 mm3 detector coupled to Hamamatsu MPPC array shows that all pixel-like volumes can be resolved even with the unfavorable geometry imposed by the converging pixels. Another laser processed CsI:Tl crystal (50x50x10 mm3) coupled to SensL SiPM show that while most pixels are resolved, the 20% QE of the SiPM at 550nm (peak wavelength of CsI:Tl) compared with 35% of the MPPC array results in deteriorated flood map quality and lower energy resolution (~250/0 at 122 keV). We are now optimizing the laser process and readout electronics to improve flood map quality before implementing detector and collimator integration for phantom studies
Large-Scale Exploration of Cave Environments by Unmanned Aerial Vehicles

This letter presents a self-contained system for the robust utilization of aerial robots in the autonomous exploration of cave environments to help human explorers, first responders, and speleologists. The proposed system is generally applicable to an arbitrary exploration task within an unknown and unstructured subterranean environment and interconnects crucial robotic subsystems to provide full autonomy of the robots. Such subsystems primarily include mapping, path and trajectory planning, localization, control, and decision making. Due to the diversity, complexity, and structural uncertainty of natural cave environments, the proposed system allows for the possible use of any arbitrary exploration strategy for a single robot, as well as for a cooperating team. A multi-robot cooperation strategy that maximizes the limited flight time of each aerial robot is proposed for exploration and search & rescue scenarios where the homing of all deployed robots back to an initial location is not required. The entire system is validated in a comprehensive experimental analysis comprising of hours of flight time in a real-world cave environment, as well as by hundreds of hours within a state-of-the-art virtual testbed that was developed for the DARPA Subterranean Challenge robotic competition. Among others, experimental results include multiple real-world exploration flights traveling over 470 m on a single battery in a demanding unknown cave environment.
Coupling Field Analysis of Transverse Flux Linear Switched Reluctance Motor

In order to accurately analysis the transverse flux linear switched reluctance motor temperature distribution, and prevent the motor from overheating risk, a multi-physical field analysis method based on magnetic-thermal coupling is proposed in this paper. Firstly, a 3D calculation model is established by FEA. Secondly, considering the temperature characteristics of the iron, PM and winding materials of the motor, the loss at different temperatures is calculated, and the change law of the loss with the temperature is studied. Furthermore, establish the temperature boundary conditions and heat transfer coefficients, through the iterative feedback of the two fields information, construct magnetic-thermal coupling analysis model to calculate the motor temperature rise. This new analysis method proposed would be helpful for the optimization design of the motor.
Self-Powered Photovoltaic Bluetooth® Low Energy Temperature Sensor Node

A wireless self-powered temperature sensor node is presented. This sensor node is powered by a commercially available indoor photovoltaic energy harvester module. The sensor node uses Bluetooth® Low Energy as wireless connection. Both the hardware and firmware have been optimized to obtain a very low power consumption. An auto adaptive energy management routine has been implemented to extend the autonomy of the sensor node. The system consumption and photovoltaic generation have been characterized at different conditions, obtaining a tool to estimate the autonomy of the system. The system has also been tested in a climatic chamber to study the effect of the temperature, a key factor that affects the power consumption, and hence the autonomy of the sensor.
An AMDOCT-NET for Automated AMD Detection under Evaluations of Different Image Size, Denoising and Cropping

This paper proposed a novel deep learning architecture, called the AMDOCT-NET architecture, to accurately detect age-related macular degeneration (AMD) on optical coherence tomography (OCT) images. Using the AMDOCT-NET architecture, the performance of various image processing, such as resizing, denoising, and cropping has been evaluated. The simulation results show that the AMDOCT-NET architecture with an input size of 224×224 pixels, no cropping, and no denoising achieves the accuracy of 99.09% to automatically detect the AMD. Compared with the well-known deep learning architecture, VGG16, the AMDOCT-NET improves accuracy by 2.09% and reduces the model size by 53.7%.
Two Dimensional Analytical Modeling and Electromagnetic Force Optimization of Permanent Magnet Electrodynamic Suspension System

In this paper, a system analytical approach is developed to optimize the 2-D permanent magnet electrodynamic suspension system which mainly consists of a Halbach array and a conductive plate. Firstly, a 2-D analytical model was built in ideal conditions which accuracy was confirmed by comparing the results of analytic method and finite element method. Secondly, based on the 2-D analytical model the impact of structure parameters on the optimization objectives, lift to weight and power loss, was studied through optimization analysis of PM-EDS, and a system optimization approach was proposed.
A Real-Time Non-Contact Heart Rate Measurement based on Imaging Photoplethysmography (iPPG)-Power Spectral Density (PSD)

The demands of accessible physiological information have opened an interest among researchers to integrate contact to non-contact techniques to assess health status. This paper presents a real-time and non-contact heart rate (HR) assessment based on imaging photoplethysmography (iPPG) – Power Spectral Density (PSD) to quantify dynamic changes of blood volume at the forehead due to cardiac activity. The HR assessment was implemented on ten healthy subjects using a built-in camera laptop with an executable algorithm (Python-OpenCV) in PyCharm environment. A commercial pulse amped sensor was attached to the subject’s right index finger to extract contact photoplethysmography (cPPG) signal simultaneously. The cPPG signal was processed using the post-processing techniques in the MATLAB environment. The statistical analysis was demonstrated on HRcPPG and HRiPPG to determine the correlation between the measurement. The results show correlation between the measurement with correlation coefficient, r = 0.67, and the linear regression, r2 = 0.49, p-value = 0.024. The 95% of the Limit of Agreement (LOA) from the Bland-Altman plot was 12.77 to -22.37 (BPM). The outcomes from the analysis are significant to improve measurement and post-processing techniques.
Intelligent Locomotion Planning With Enhanced Postural Stability for Lower-Limb Exoskeletons

In this letter, an integrated control strategy is developed for both locomotion trajectory planning and postural stability, enabling shared autonomy between the human and lower-limb exoskeleton. Divergent component of motion (DCM) analysis was employed previously based on the linear inverted pendulum flywheel (LIPF) model to regulate the position of the center of mass (CoM) for humanoid robots. In this study, a new extended model is investigated for the DCM analysis by replacing the previous LIPF model, which is tailored for multi-degree-of-freedom (DOF) exoskeletons. This new model is designed to be personalized for each specific user's body by relaxing the assumption of having the total CoM at the hip joint in the previous LIPF model. Accordingly, the exoskeleton has the authority to ensure the postural stability and viability of locomotion in this human-robot interaction (HRI) by adjusting the upper body position using a DCM-based hip correction strategy. Integrating adaptive central pattern generators (CPGs), the human has enough authority to modify the gait trajectories in real-time, while the amplitude and frequency of walking are constrained to their feasible ranges. The effectiveness of this intelligent controller for safe and stable locomotion is investigated through experimental studies on a lower-limb exoskeleton.
Assessment of mmWave Exposure From Antenna Based on Transformation of Spherical Wave Expansion to Plane Wave Expansion

To protect human beings from overexposure to mmWave electromagnetic fields, international guidelines and standard specified the (exposure) reference level in terms of the peak spatial-average incident power density (psPD). In this study, we proposed a method to effectively assess the psPD from a mmWave antenna using its spherical near-field measurement data. This method employed the transformation of spherical wave expansion to plane wave expansion to reconstruct the electric and magnetic field components on an evaluation plane in front of the antenna aperture. The psPD was subsequently obtained based on the time-average Poynting vector. The accuracy of the proposed method was validated by comparing it with full-wave simulation results using 12 array antennas at frequencies ranging from 10 to 90 GHz. It was revealed that at 30 GHz, the reconstruction errors for psPD4cm2 were less than 0.8 dB and 0.2 dB on evaluation planes 2 and 5 mm, respectively, in front of the antennas. The robustness of the proposed method against the noise, scan range, and number of spherical modes were evaluated. The proposal may eliminate conventional compliance assessment procedures once antenna performance is evaluated in spherical near-field measurement.
Fast and Portable Concurrent FIFO Queues With Deterministic Memory Reclamation

In this article we present an algorithm for a high performance, unbounded, portable, multi-producer/multi-consumer, lock-free FIFO (first-in first-out) queue. Aside from its competitive performance on current hardware, it is further characterized by its integrated memory reclamation mechanism, which is able to reliably and deterministically de-allocate nodes as soon as the final operation with a reference has concluded, similar to reference counting. This differentiates our approach from most other lock-free data structures, which usually require external (generic) memory reclamation or garbage collection mechanisms such as hazard pointers. Our deterministic memory reclamation mechanism completely prevents the build up of memory awaiting reclamation and is hence very memory efficient, yet it does not introduce any substantial performance overhead. By utilizing concrete knowledge about the internal structure and access patterns of our queue, we are able to construct and constrain the reclamation mechanism in such a way that keeps the overhead for memory management almost entirely out of the common fast path. The presented algorithm is portable to all modern 64-bit processor architectures, as it only relies on the commonly available and lock-free atomic synchronization primitives compare-and-swap and fetch-and-add.
High Spatial Resolution Remote Sensing Image Scene Classification using CNN with Transfer Learning

Traditional HSR remote sensing imagery understanding is based on recognizing pixel-based or object-based ground elements, but this cannot describe the whole content of the scene images and cannot bridge the "semantic gap" between the low-level features and the high-level semantics. Deep learning algorithms attempt to learn hierarchical features, corresponding to different levels of abstraction. Current progress in deep-learning models, specifically d eep c onvolutional n eural network (CNN) architectures, have improved the state-of-the-art in many fields of study i ncluding r emote s ensing scene c lassification. The choice of a proper network architecture for making strong and correct assumptions about the nature of the input data is still a big challenge. This work presents an implementation of the Convolutional Neural Network (CNN) AlexNet trained on the well-known datasets UC Merced Land-Use and WHU- RS by using Transfer Learning for the High Spatial Resolution Image Scene Classification p roblem. A l ayer c orresponding to the Spatial Pyramid Pooling (SPP) is incorporated in order to consider different image sizes in the input of the network and multi-scale spatial information of the scenes to be classified. Results are comparable to literature, improving some published approaches.
Exploring Neural Architecture Search Space via Deep Deterministic Sampling

Recent developments in Neural Architecture Search (NAS) resort to training the supernet of a predefined search space with weight sharing to speed up architecture evaluation. These include random search schemes, as well as various schemes based on optimization or reinforcement learning, in particular policy gradient, that aim to optimize a parametric architecture distribution and the shared model weights simultaneously. In this paper, we focus on efficiently exploring the important region of a neural architecture search space with reinforcement learning. We propose Deep Deterministic Architecture Sampling (DDAS) based on deep deterministic policy gradient and the actor-critic framework, to selectively sample important architectures in the supernet for training. Through balancing exploitation and exploration, DDAS is designed to combat the disadvantages of prior random supernet warm-up schemes and optimization schemes. Gradient-based NAS approaches require the execution of multiple short experiments in order to combat the random stochastic nature of gradient descent, while still only producing a single architecture. Contrary to this approach, DDAS employs a reinforcement learning-based agent and focuses on discovering a Pareto frontier containing many architectures over the course of a single experiment requiring 1 GPU day. Experimental results for CIFAR-10 and CIFAR-100 on the DARTS search space show that DDAS can depict in a single search, the accuracy-FLOPs (or model size) Pareto frontier, which outperforms random sampling and search. With a test accuracy of 97.27%, the best architecture found on CIFAR-10 outperforms the original second-order DARTS while using 600M fewer parameters. Additionally, DDAS finds an architecture capable of achieving 82.00% test accuracy on CIFAR-100 while using only 3.14M parameters and outperforming GDAS.
A review of silicon subwavelength gratings: building break-through devices with anisotropic metamaterials

Abstract Silicon photonics is playing a key role in areas as diverse as high-speed optical communications, neural networks, supercomputing, quantum photonics, and sensing, which demand the development of highly efficient and compact light-processing devices. The lithographic segmentation of silicon waveguides at the subwavelength scale enables the synthesis of artificial materials that significantly expand the design space in silicon photonics. The optical properties of these metamaterials can be controlled by a judicious design of the subwavelength grating geometry, enhancing the performance of nanostructured devices without jeopardizing ease of fabrication and dense integration. Recently, the anisotropic nature of subwavelength gratings has begun to be exploited, yielding unprecedented capabilities and performance such as ultrabroadband behavior, engineered modal confinement, and sophisticated polarization management. Here we provide a comprehensive review of the field of subwavelength metamaterials and their applications in silicon photonics. We first provide an in-depth analysis of how the subwavelength geometry synthesizes the metamaterial and give insight into how properties like refractive index or anisotropy can be tailored. The latest applications are then reviewed in detail, with a clear focus on how subwavelength structures improve device performance. Finally, we illustrate the design of two ground-breaking devices in more detail and discuss the prospects of subwavelength gratings as a tool for the advancement of silicon photonics.