Miracle-dz/UniFAR-FTUs huggingface dataset:
On 3d dipolar Bose-Einstein condensates involving quantum fluctuations and three-body interactions

We study the following nonlocal mixed order Gross-Pitaevskii equation $$i\,\partial_t \psi=-\frac{1}{2}\,\Delta \psi+V_{ext}\,\psi+\lambda_1\,|\psi|^2\,\psi+\lambda_2\,(K*|\psi|^2)\,\psi+\lambda_3\,|\psi|^{p-2}\,\psi,$$ where $K$ is the classical dipole-dipole interaction kernel, $\lambda_3>0$ and $p\in(4,6]$; the case $p=6$ being energy critical. For $p=5$ the equation is considered currently as the state-of-the-art model for describing the dynamics of dipolar Bose-Einstein condensates (Lee-Huang-Yang corrected dipolar GPE). We prove existence and nonexistence of standing waves in different parameter regimes; for $p\neq 6$ we prove global well-posedness and small data scattering.
Optical Quality and Intraocular Scattering in the Diabetic Eye without Diabetic Retinopathy

SIGNIFICANCE This study suggests that despite having comparable best-corrected visual acuity and normal fundus appearance, objective measurements of optical quality showed that patients with diabetes but without overt retinopathy may have impaired visual function. Screening using the Optical Quality Analysis System might help identify those patients. PURPOSE Visual impairments are common in diabetes, but the status of the problem is unclear before the development of diabetic retinopathy. The aim of this pilot study was to investigate the optical quality and intraocular scattering in the diabetic eye without diabetic retinopathy. METHODS Twenty-seven patients with diabetes without diabetic retinopathy were enrolled. Twenty-seven age- and sex-matched healthy volunteers served as a control group. Optical quality parameters included modulation transfer function cutoff frequency, Strehl (two-dimensional) ratio, and Optical Quality Analysis System values at 100, 20, and 9 contrast levels. The objective scatter index was assessed using the Optical Quality Analysis System. Correlations were analyzed between the modulation transfer function cutoff, Strehl ratio, objective scatter index, and Optical Quality Analysis System value, and the age of the patient and the duration of diabetes mellitus. RESULTS The diabetic group exhibited lower modulation transfer function cutoff, Strehl ratio, and Optical Quality Analysis System values at 100, 20, and 9% contrast levels and higher objective scatter index than did the controls (all, P < .01). There were no associations between the optical quality parameters and age or the duration of diabetes mellitus in the diabetic participants (all, P > .05). Moderate associations were found between all parameters obtained from the Optical Quality Analysis System and age in the control group (all, P < .01). CONCLUSIONS This pilot study suggests that optical quality was reduced, and intraocular scattering increased in the diabetic eye without diabetic retinopathy compared with controls.
Automated Diabetic Retinopathy Detection Based on Binocular Siamese-Like Convolutional Neural Network

Diabetic retinopathy (DR) is an important cause of blindness worldwide. However, DR is hard to be detected in the early stages, and the diagnostic procedure can be time-consuming even for the experienced experts. Therefore, a computer-aided diagnosis method based on deep learning algorithms is proposed to automatedly diagnose the referable diabetic retinopathy by classifying color retinal fundus photographs into two grades. In this paper, a novel convolutional neural network model with the Siamese-like architecture is trained with a transfer learning technique. Different from the previous works, the proposed model accepts binocular fundus images as inputs and learns their correlation to help to make a prediction. In the case with a training set of only 28 104 images and a test set of 7024 images, an area under the receiver operating curve of 0.951 is obtained by the proposed binocular model, which is 0.011 higher than that obtained by the existing monocular model. To further verify the effectiveness of the binocular design, a binocular model for five-class DR detection is also trained and evaluated on a 10% validation set. The result shows that it achieves a kappa score of 0.829 which is higher than that of the existing non-ensemble model.
Reviews

Books reviewed: William Coleman and Alf Hagger, The Rage Over Economic Rationalism and the Campaign Against Australian Economists Hian Teck Hoon, Trade, Jobs and Wages Yoram Amiel and Frank A. Cowell, Thinking About Inequality Paul Downward and Alistair Dawson, The Economics of Professional Team Sports George Fane, Capital Mobility, Exchange Rates and Economic Crises Ben S. Bernanke, Thomas Laubach, Frederic S. Mishkin and Adam S. Posen, Inflation Targeting: Lessons from the International Experience Martin F.J. Prachowny, The Kennedy-Johnson Tax Cut William D. Nordhaus and Joseph Boyer, 'Warming the World - Economic Models of Global Warming' David S. Evans and Richard Schmalensee, Paying with Plastic: The Digital Revolution in Buying and Borrowing
Yosys+nextpnr: An Open Source Framework from Verilog to Bitstream for Commercial FPGAs

This paper introduces a fully free and open source software (FOSS) architecture-neutral FPGA framework comprising of Yosys for Verilog synthesis, and nextpnr for placement, routing, and bitstream generation. Currently, this flow supports two commercially available FPGA families, Lattice iCE40 (up to 8K logic elements) and Lattice ECP5 (up to 85K elements) and has been hardware-proven for custom-computing machines including a low-power neural-network accelerator and an OpenRISC system-on-chip capable of booting Linux. Both Yosys and nextpnr have been engineered in a highly flexible manner to support many of the features present in modern FPGAs by separating architecture-specific details from the common mapping algorithms.This framework is demonstrated on a longest-path case study to find an atypical single source-sink path occupying up to 45% of all on-chip wiring.
Fisher Discriminant Analysis Random Forest for Online Class Incremental Learning

Class incremental learning needs to deal with a dynamic environment where data class appears incrementally, it is a challenge to learn new knowledge while preserving what has already been learned. On the other hand, due to the limited storage of the online scenario, algorithm is usually obstructed to frequently scan or simply store all historical data, it is another challenge to reduce the historical data storage for algorithm. Few existing work have addressed above challenges simultaneously. In this paper, we propose Fisher Discriminant Analysis Random Forest (FDARF), which consists of two parts, GHS (Generate Hierarchical Split) and RRS (Random Reform Subtree), that cooperatively operate. GHS combines FDA (Fisher Discriminant Analysis) with tree hierarchy to learn a hierarchical split of data space that provides strong ability for classification. The statistics in leaves (i.e. historical data) can be described by covariance matrix and further optimized by matrix sketching algorithm to reduce storage; for every tree initialized by GHS, RRS randomly reforms certain state subtree, which creates diversity that can be ensemble for ensuring effectiveness of class incremental learning. Extensive experiments on diverse datasets validate that FDARF can well adapt to the online class incremental learning.
The Genetic-Evolutionary Random Support Vector Machine Cluster Analysis in Autism Spectrum Disorder

Previous researches have produced a number of conclusions on the functional magnetic resonance imaging (fMRI) study for autism spectrum disorder (ASD) patients, but there are different opinions about the brain regions of the lesions. In order to study ASD more deeply, an advanced framework, i.e., genetic-evolutionary random support vector machine (SVM) cluster, was proposed in this paper. In our method, an initial cluster of multiple SVMs was first built by randomly picking samples and features. Then, these SVMs were selected to recombine and mutate the aim of genetic evolution until the number of genetic evolution which reached the threshold or the classification accuracy was stable. We evaluated the proposed method by using the resting state fMRI data (103 ASD patients and 106 healthy controls), which achieved a 96.8% accuracy. Based on the classification results, the abnormal brain regions were found out. This study suggests the pathogenesis of ASD to a certain extent and offers great assistance for the diagnosis of potential patients with ASD.
A Wideband CP Dielectric Resonator Antenna Design with QLQC Structure

A wideband circularly polarized (CP) dielectric resonator antenna (DRA) is presented in this paper. To obtain wideband CP in proposed antenna, the quasi-lumped quadrature coupler (QLQC) feed network has been investigated with compact size. It has been claimed that excitation of right- and left-handed CP could be controlled by changing input ports. The DRA achieves the favorable broadband characteristics in S band, with 10-dB impedance bandwidth of 62.6% (2.04GHz – 3.9GHz) and 3-dB axial ratio (AR) bandwidth of 40.7% (2.42GHz – 3.66GHz). Stable radiation patterns can be realized over the entire operating band while the boresight gain is larger than 4.7dBi.
Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web (Dagstuhl Seminar 18371)

The increasingly pervasive nature of the Web, expanding to devices and things in everyday life, along with new trends in Artificial Intelligence call for new paradigms and a new look on Knowledge Representation and Processing at scale for the Semantic Web. The emerging, but still to be concretely shaped concept of "Knowledge Graphs" provides an excellent unifying metaphor for this current status of Semantic Web research. More than two decades of Semantic Web research provides a solid basis and a promising technology and standards stack to interlink data, ontologies and knowledge on the Web. However, neither are applications for Knowledge Graphs as such limited to Linked Open Data, nor are instantiations of Knowledge Graphs in enterprises – while often inspired by – limited to the core Semantic Web stack. This report documents the program and the outcomes of Dagstuhl Seminar 18371 "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web", where a group of experts from academia and industry discussed fundamental questions around these topics for a week in early September 2018, including the following: what are knowledge graphs? Which applications do we see to emerge? Which open research questions still need be addressed and which technology gaps still need to be closed?
Reconstructing Geostationary Satellite Land Surface Temperature Imagery Based on a Multiscale Feature Connected Convolutional Neural Network

Geostationary satellite land surface temperature (GLST) data are important for various dynamic environmental and natural resource applications for terrestrial ecosystems. Due to clouds, shadows, and other atmospheric conditions, the derived LSTs are often missing a large number of values. Reconstructing the missing values is essential for improving the usability of the geostationary satellite LST data. However, current reconstruction methods mainly aim to fill the values of a small number of invalid pixels with many valid pixels, which can provide useful land surface temperature values. When the missing data extent becomes large, the reconstruction effect will worsen because the relationship between different spatiotemporal geostationary satellite LSTs is complex and highly nonlinear. Inspired by the superiority of the deep convolutional neural network (CNN) in solving highly nonlinear and dynamic problems, a multiscale feature connection CNN model is proposed to fill missing LSTs with large missing regions. The proposed method has been tested on both FengYun-2G and Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager geostationary satellite LST datasets. The results of simulated and actual experiments show that the proposed method is accurate to within about 1 °C, with 70% missing data rates. This is feasible and effective for large regions of LST reconstruction tasks.
Operational Monitoring of Illegal Fishing in Ghana through Exploitation of Satellite Earth Observation and AIS Data

Over the last decade, West African coastal countries, including Ghana, have experienced extensive economic damage due to illegal, unreported and unregulated (IUU) fishing activity, estimated at about USD 100 million in losses each year. Illegal, unreported and unregulated fishing poses an enormous threat to the conservation and management of the dwindling fish stocks, causing multiple adverse consequences for fisheries, coastal and marine ecosystems and for the people who depend on these resources. The Integrated System for Surveillance of Illegal, Unlicensed and Unreported Fishing (INSURE) is an efficient and inexpensive system that has been developed for the monitoring of IUU fishing in Ghanaian waters. It makes use of fast-delivery Earth observation data from the synthetic aperture radar instrument on Sentinel-1 and the Multi Spectral Imager on Sentinel-2, detecting objects that differ markedly from their immediate background using a constant false alarm rate test. Detections are matched to, and verified by, Automatic Identification System (AIS) data, which provide the location and dimensions of ships that are legally operating in the region. Matched and unmatched data are then displayed on a web portal for use by coastal management authorities in Ghana. The system has a detection success rate of 91% for AIS-registered vessels, and a fast throughput, processing and delivering information within 2 h of acquiring the satellite overpass. However, over the 17-month analysis period, 75% of SAR detections have no equivalent in the AIS record, suggesting significant unregulated marine activity, including vessels potentially involved in IUU. The INSURE system demonstrated its efficiency in Ghana’s exclusive economic zone and it can be extended to the neighbouring states in the Gulf of Guinea, or other geographical regions that need to improve fisheries surveillance.
A Non-invasive Investigation of Egyptian Faience Using Long Wavelength Optical Coherence Tomography (OCT) at 2 μm

ABSTRACT Egyptian faience is a non-clay ceramic semi-transparent material formed of a quartz core and alkali-lime glaze. Previous investigations have identified production techniques by using microstructure images obtained from invasive methods. Optical coherence tomography (OCT) is a non-invasive 3D imaging technique that produces virtual cross-sections of transparent and semi-transparent materials. A previous study by one of the authors demonstrated the feasibility of non-invasive investigation of microstructures of Egyptian faience using 930 nm OCT, but the limited probing depth prevented viewing down to the quartz core of the objects. This paper shows that an in-house developed OCT system using a longer wavelength (2 µm) was able to image the full microstructure from the top glaze layer down to the core, allowing rapid and non-invasive studies of intact objects and demonstrating the potential for surveying large museum collections. OCT virtual cross-section images at 5 wavelengths, 550, 810, 930, 1300 and 1960nm, were compared and the optimum wavelength for OCT investigation of Egyptian faience microstructure was found to be 2 μm.
An End-to-End Framework for Integrated Pulmonary Nodule Detection and False Positive Reduction

Pulmonary nodule detection using low-dose Computed Tomography (CT) is often the first step in lung disease screening and diagnosis. Recently, algorithms based on deep convolutional neural nets have shown great promise for automated nodule detection. Most of the existing deep learning nodule detection systems are constructed in two steps: a) nodule candidates screening and b) false positive reduction, using two different models trained separately. Although it is commonly adopted, the two-step approach not only imposes significant resource overhead on training two independent deep learning models, but also is sub-optimal because it prevents crosstalk between the two. In this work, we present an end-to-end framework for nodule detection, integrating nodule candidate screening and false positive reduction into one model, trained jointly. We demonstrate that the end-to-end system improves the performance by 3.88% over the two-step approach, while at the same time reducing model complexity by one third and cutting inference time by 3.6 fold. Code will be made publicly available.
Compact High-Isolation Multiplexer With Wide Stopband Using Spiral Defected Ground Resonator

In this paper, the compact wide-stopband diplexer/triplexer is presented. The spiral defected ground resonator (SDGR) is analyzed by using the equivalent model. Based on the symmetry of the SDGR, a diplexer and a triplexer are implemented with a miniaturized size and a wide stopband. Each filter of the channels in the diplexer or triplexer can be independently controlled and exhibits a constant fractional bandwidth. To validate the design concept, the diplexer and the triplexer are designed, fabricated, and measured. The isolation between the channels is greater than 30 dB, and the measured out-of-band suppression is greater than 10 dB in the very wide frequency range. The measured results agree well with the simulation ones.
A Planar Flexible Quad-band Antenna for WLAN/WiMAX/LTE Applications

In this work a quad-band, planar, low-profile and compact antenna envisioned for incorporation into portable wireless devices is presented. The antenna is modeled on flexible Rogers RT/Duroid 5880 substrate of 0.127mm thickness. The proposed antenna structure consists of symmetrically placed F-shaped slits and a curved rectangular shaped ground plane with a CPW feed line. The four bands obtained for the radiator operates at the resonant frequencies 2.8, 3.9, 5.45 and 6.2 GHz with impedance bandwidths of 14%, 14.5%, 5.7%, and 5% respectively. Thus the proposed antenna supports WLAN, LTE, WiMAX, and C-band applications. The peak gain achieved for the antenna is 3.4 dB.
Roboethics as a Research Puzzle

This position paper discusses the question of incorporating roboethics into the roboticists' thinking about their research. On the one hand, there has been a growing recognition of the need to develop and advance the field of roboethics. On the other hand, for different reasons, a large part of the robotics community has still been reluctant to explicitly address ethical considerations in robotics research. We argue here that in order to facilitate and foster ethical reflection in roboticists' work, roboethics should be seen as a research puzzle. This implies studying rather than only applying specific ethical principles, as well as taking highly creative and pioneering approaches towards emerging ethical challenges.
Transferred Knowledge Aided Positioning via Global and Local Structural Consistency Constraints

The fluctuation of received signal strength (RSS) induced by changing environment is the main hindrance from practical applications of the fingerprint-based indoor positioning methods. Transfer learning can mitigate the fluctuation of RSS by transferring knowledge from a source domain (off-line RSS data) to a target domain (online RSS data). However, the existing transfer learning approaches do not fully take into account the full constraints in Global and LOcal Structural conSistency (GLOSS), thus resulting in insufficient knowledge transfer. To overcome the above drawback, we propose a Transferred knowlEdge-Aided POsiTioning (TEAPOT) approach via GLOSS constraints in this paper. TEAPOT imposes the global structural consistency by minimizing the differences between the marginal and conditional distributions of the source and target domains and maximizing the samples variance in a latent subspace. Simultaneously, it also imposes the local structural consistency by minimizing within class variance and maximizing between class variance to retain the source discriminative information and preserving the local neighborhood relationship by using manifold regularization. Furthermore, a nonlinear TEAPOT is derived to improve the ability of TEAPOT to alleviate the limitation of linear projection. Compared with the existing methods, two of our proposed TEAPOT approaches via GLOSS constraints show higher accuracy and better ability in handling out-of-sample generalization. The experimental results verify that the proposed method significantly outperforms the existing methods.
1D CNN Based Human Respiration Pattern Recognition using Ultra Wideband Radar

The respiration status of a person is one of the vital signs that can be used to check the health condition of the person. The respiration status has been measured in various ways in the medical and healthcare sectors. Contact type sensors were conventionally used to measure respiration. The contact type sensors have been used primarily in the medical sector, because they can be only used in a limited environment. Recent studies have evaluated the ways of detecting human respiration patterns using Ultra-Wideband (UWB) Radar, which relies on non-contact type sensors. Previous studies evaluated the apnea pattern during sleep by analyzing the respiration signals acquired by UWB Radar using a principal component analysis (PCA). However, it is necessary to measure various respiration patterns in addition to apnea in order to accurately analyze the health condition of an individual in the healthcare sector. Therefore, this study proposed a method to recognize four respiration patterns based on the 1D convolutional neural network from the respiration signals acquired from UWB Radar. The proposed method extracts the eupnea, bradypnea, tachypnea, and apnea respiration patterns from UWB Radar and composes a learning dataset. The proposed method learned data through 1D CNN and the recognition accuracy was measured. The results of this study revealed that the accuracy of the proposed method was up to 15% higher than that of the conventional classification algorithms (i.e., PCA and Support Vector Machine (SVM)).
BICSF: Bio-Inspired Clustering Scheme for FANETs

Flying ad hoc networks (FANETs) have dynamic topology because of the mobile unmanned aerial vehicles (UAVs). The limited battery resource and mobility of UAVs cause unstable routing in the FANET. In this paper, we try to minimize this issue with the help of an efficient clustering scheme. We propose a bio-inspired clustering scheme for FANETs (BICSF), which uses the hybrid mechanism of glowworm swarm optimization (GSO) and krill herd (KH). The proposed scheme uses energy aware cluster formation and cluster head election on the basis of the GSO algorithm. Furthermore, we propose an efficient cluster management algorithm using the behavioral study of KH. We also use genetic operators such as mutation and crossover for the optimal position of the UAV. For route selection, we propose a path detection function based on the weighted residual energy, number of neighbors, and distance between the UAVs for efficient communication. The performance of BICSF is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with grey wolf optimization and ant colony optimization-based clustering algorithms.