Miracle-dz/UniFAR-FTUs huggingface dataset:
A fast multi-target detection method based on improved YOLO

Detection of sea surface targets in large-scale remote sensing images is one of the important research topics of ocean remote sensing technology. Ocean remote sensing images have the characteristics of wide format, strong interference and small target. This paper adopts the spinning target detection method, and proposes a ship detection model based on YOLO to output the real length, width and axial information. The model can accurately output the position, length and width and axial information of a ship target by predicting the minimum external rectangular area of the ship target, so as to realize multi-target detection and improve the detection performance significantly. To improve the recall rate of the target detection algorithm, this paper adopts the spinning target detection method, and proposes a ship detection model based on YOLO. Through redefining the representation of the rotation matrix and redesigning a new network loss function and the rotated IOU computing method, this model accurately outputs the real length, width and axial information, increases the output feature dimensions, and effectively raises the recall rate and speed of multi-target detection. Lastly, to improve the practicability of the algorithm on mobile devices, the model is processed in a lightweight way. Its parameters are significantly reduced while the detection accuracy is ensured.
Optimizing quasi-adiabaticity and its application in photonic lantern devices

Photonic lanterns are novel photonic devices initially developed for an astro-photonics application, but later found immense potential in the field of space-division multiplexing and novel optical communication interconnects. They are adiabatically tapered structures, thus having large device lengths which scale quadratically with the number of cores. Adiabaticity is exploited to design a mode-selective photonic lantern. The Shortcuts to adiabaticity (STA) protocol is used to design an optimum quasi-adiabatic taper profile corresponding to a certain device length and a measure of adiabaticity. By homogenizing adiabaticity, a framework was developed to obtain an optimum taper profile for any required device length, which will have a corresponding measure of quasi-adiabaticity. This measure relates to the coupling losses suffered by the system digressing from absolute adibaticity. We have shown that the optimum taper profile significantly reduces the device length without compromising on adiabaticity. Photonic lanterns have also been examined for the generation of Orbital angular momentum modes in fibers. Here results for a three core photonic lantern structure for the generation of OAM1 mode have been discussed.
Blind deblurring with image layer separation using relative smoothness

Blind image deblurring is a challenging problem which has drawn a lot of attention in recent years. Previous work states shows that image details caused by blur could adversely affect the kernel estimation, especially when the blur kernel is large. In this paper, we focus on how to extract the suitable salient structure for kernel estimation from a single blurred image. A fast method for estimating the salient structure of an image is proposed in the paper. The image is divided into two layers with different smoothness, and the local relative smoothness layer eliminates the image structure that adversely affects the kernel estimation. Further kernel estimation using the layer can obtain more accurate results. Substantial experiment shows that our method is effective on some challenging examples.
Synthetic dataset generation for text recognition with generative adversarial networks

Automated text recognition is used in autonomous driving systems, search engines, document analysis, and many other applications. There are many techniques to extract text information from scanned documents, but text recognition from arbitrary images is a much harder task. Recently suggested deep learning approaches have demonstrated highquality results, but they require a huge amount of data to achieve them. The process of collecting and labelling training data to train a deep learning network is costly. In this paper, we suggest an approach for automatic dataset generation for text recognition for arbitrary languages. We use a generative adversarial network structure, which is adapted to generate readable and clear text looking naturally on the image background. We evaluate our approach using SegLink and Textboxes++ text localization models, which were trained on examples generated by SynthText and by variations of our method. The comparison showed the superiority of our method on a subset of the ICDAR 2017 dataset for English and Arabic languages.
Segmentation-based orbiting satellite tracking

To obtained the trajectory of an orbiting satellite is a fundamental and vital step for space rendezvous and manipulation by space robots. Due to the freely and rapidly motion of on-orbiting satellites, the sudden change in appearance, orbiting satellite tracking is difficult for traditional tracker, which usually relies on a single bounding box of the target object. However, more information should be provided by visual tracking such as binary mask. In this paper, we proposed a SOST (Segmentation-based Orbiting Satellite Tracking) algorithm that improves the performance of tracking. Our method, SOST, improves the tracking performance by generating a mask map obtained from segmentation within the initial bounding box. The final bounding box will be refined using the segmentation result. Experiment using real on-orbit rendezvous and docking video from NASA (Nation Aeronautics and Space Administration), simulated satellite animation sequence from ESA (European Space Agency) and image sequences of 3D printed satellite model took in our laboratory demonstrate the robustness, versatility and fast speed of our method compared to state-of-the-art tracking methods. Our dataset will be released for academic use in future.
Research of express box defect detection based on machine vision

The traditional box appearance defect detection adopts manual detection which is low accuracy and time consuming. In order to overcome the drawbacks of manual detection and improve transportation automation, an effective and convenient defect detection algorithm of express boxes is proposed. The central idea of the proposed algorithm is to solve defect identification problems during transportation. This technique only needs to observe defects through the binocular camera system, thus the parameters of shape and size can be obtained subsequently. Finally, experiments are carried out. Results show that the algorithm can accurately identify different shaped defects and the highest reconstruction correct rate is up to 95.83%. The results verify the applicability of the proposed approach.
Adaptive frequency saliency model based on convolutional neural networks: a case study for prostate cancer MRI

This article introduces a novel adaptive frequency saliency model (AFSM) that selects relevant information by filtering an image with a set of band pass filters optimally placed in the frequency space using an auto- encoder CNN. The obtained images show a higher signal-to-noise ratio and therefore they improve a classifier performance. The proposed method is challenged by a classification task: prostate magnetic resonance imaging (MRI) to be labeled as cancerous or non-cancerous tissue. Evaluation in this case was carried out by training a convolutional neural network (CNN) with a prostate dataset but at the testing phase, the trained model is assessed with non-filtered and filtered images. The classifier tried with filtered images outperformed the results obtained with the non filtered ones (classification accuracy scores of 0.792± 0.016 and 0.776± 0.036 respectively), demonstrating better overall performance and the importance of using filtering processes.
A restoration method for the turbulent degraded images based on the salient edge selection and the L0 norm constraint

At present, the restoration of turbulent degraded images is a worldwide problem in the fields of astronomical imaging. Atmospheric turbulence is the reason why images will be blurred, which gravely interferes the object of recognition and the detection of images. This paper presents a restoration method for the turbulent degraded images based on the image saliency edge selection and the L0 norm constraint, which aims to recover sharp images from the turbulent degraded images. The proposed method imposes the L0 norm sparse constraint on the latent image, and uses the method of split Bregman to solve the problem of optimization. To avoid the influence of the tiny details on the point spread function (PSF) , we use the image saliency algorithm to build a weighted model to select salient edges from the latent images. Based on the salient edge in the gradient domain, the proposed method establishes an estimation model of the point spread function. The calculation part of the point spread function is solved accurately by using the fast Fourier transform (FFT) in the frequency domain. The proposed method uses the multi-scale pyramid strategy to alternatively solve the point spread function and the latent images, which can obtain the final accuracy of the point spread function.
Where to drive: free space detection with one fisheye camera

The development in the field of autonomous driving goes hand in hand with ever new developments in the field of image processing and machine learning methods. In order to fully exploit the advantages of deep learning, it is necessary to have sufficient labeled training data available. This is especially not the case for omnidirectional fisheye cameras. As a solution, we propose in this paper to use synthetic training data based on Unity3D. A five-pass algorithm is used to create a virtual fisheye camera. This synthetic training data is evaluated for the application of free space detection for different deep learning network architectures. The results indicate that synthetic fisheye images can be used in deep learning context.
Depth perception tendencies on a widescreen display: an experimental study

There are numerous cues which influence human visual attention. Some of the cues cannot be explored by the conventional eye-tracking studies which makes use of a pictorial data presented to the observers on common displays. Depth perception occurs naturally in the real three-dimensional environment and, therefore, the depth cues are one of them. However, the eye-tracking studies in the real environment and their evaluation are complicated to carry out with a relevant number of participants while maintaining the laboratory conditions. We propose an experimental study methodology for exploring the depth perception tendencies during the free-viewing task on a widescreen display in a laboratory. This method is beyond the current hardware capabilities of the static eye-trackers mounted on the displays. Therefore, the eye-tracking glasses were used in the study to measure the attention data. We carried out the proposed study on a sample of 25 participants and created a novel dataset suitable for further visual attention research. The depth perception tendencies on a widescreen display were evaluated from the acquired data and the results were discussed in the context of the previous similar studies. Our results revealed some differences in the depth perception tendencies in comparison to the previous studies with the two-dimensional pictorial data and resembled some depth perception tendencies observed in the real environment.
Borrelia miyamotoi: 43 Cases Diagnosed in France by Real-Time PCR in Patients With Persistent Polymorphic Signs and Symptoms

Background: Borrelia species are divided into three groups depending on the induced disease and the tick vector. Borrelia miyamotoi is a relapsing fever Borrelia but can induce symptoms related to Lyme disease. Discovered in 1995, it is found in ticks around the world. In France, this species of Borrelia has been isolated in ticks and rodents, but was not yet observed in humans. Objective: The aim of the study was to look for B. miyamotoi in symptomatic patients. Methods: Real-time PCR was performed on 824 blood samples from patients presenting symptoms of persistent polymorphic syndrome possibly due to tick bite, a syndrome recognized by the French Authority for Health, which is close to the post-treatment Lyme disease syndrome. PCR was also performed on 24 healthy control persons. The primers were specifically designed for this particular species of Borrelia. The sequence of interest of 94 bp is located on the glpQ gene. Sequencing of amplification products, randomly chosen, confirmed the amplification specificity. To better investigate cases, a clinical questionnaire was sent to the patients PCR-positive for B. miyamotoi and to their physician. Results: This search revealed a positive PCR for B. miyamotoi in the blood from 43 patients out of 824 (5.22%). PCR was negative in all control persons. A clinical chart was obtained from 31 of the 43 patients. A history of erythema migrans was reported in five of these 31 patients (16%). All patients complained about fatigue, joint pain and neuro-cognitive disorders. Some patients complained about respiratory problems (chest tightness and/or lack of air in 41.9%). Episodes of relapsing fever were reported by 11 of the 31 patients (35.5%). Chilliness, hot flushes and/or sweats were reported by around half of the patients. B. miyamotoi may not cross-react with B. burgdorferi serology. Conclusion: This study is the first to detect B. miyamotoi in human blood in France. This series of human B. miyamotoi infection is the largest in patients with long term persistent syndrome. Our data suggest that this infection may be persistent, even on the long term.
Roles for Countercharge in the Voltage Sensor Domain of Ion Channels

Voltage-gated ion channels share a common structure typified by peripheral, voltage sensor domains. Their S4 segments respond to alteration in membrane potential with translocation coupled to ion permeation through a central pore domain. The mechanisms of gating in these channels have been intensely studied using pioneering methods such as measurement of charge displacement across a membrane, sequencing of genes coding for voltage-gated ion channels, and the development of all-atom molecular dynamics simulations using structural information from prokaryotic and eukaryotic channel proteins. One aspect of this work has been the description of the role of conserved negative countercharges in S1, S2, and S3 transmembrane segments to promote sequential salt-bridge formation with positively charged residues in S4 segments. These interactions facilitate S4 translocation through the lipid bilayer. In this review, we describe functional and computational work investigating the role of these countercharges in S4 translocation, voltage sensor domain hydration, and in diseases resulting from countercharge mutations.
GAIT analysis: 3D pose estimation and prediction in defence applications using pattern recognition

Availability of humungous visual data and increasing in generation of visual data in Security and Surveillance domain made a pathway to Computer Vision algorithms. The existing algorithms are not precise enough for predictive analytics. Sensitive use cases such as action recognition and identifying missing people in huge crowds has thrown a challenging research of drawing accurate and precise results. The existing 2-D plots for action recognition have failed due to unstructured visual data available where the accuracy is around <50%. Due to unstructured visual data, the existing 3-D plots often get overlapped with each other. Although the accuracy is noted >90% which maps it to False Positives. The existing solutions deals with object detection through Boolean logic then Pose Plots are mapped. Our research focus in on reverse engineer the existing solutions by applying smart segmentation to isolate background and then map the pose formula to detect the action. Our proposed solution obliterates the over-lap complications and unravels the False Positives. Our proposed solution achieved accuracy and precision of mAP>0.8 for both images and video feeds.
Primary Metabolite Responses to Oxidative Stress in Early-Senescing and Paraquat Resistant Arabidopsis thaliana rcd1 (Radical-Induced Cell Death1)

Rcd1 (radical-induced cell death1) is an Arabidopsis thaliana mutant, which exhibits high tolerance to paraquat [methyl viologen (MV)], herbicide that interrupts photosynthetic electron transport chain causing the formation of superoxide and inhibiting NADPH production in the chloroplast. To understand the biochemical mechanisms of MV-resistance and the role of RCD1 in oxidative stress responses, we performed metabolite profiling of wild type (Col-0) and rcd1 plants in light, after MV exposure and after prolonged darkness. The function of RCD1 has been extensively studied at transcriptomic and biochemical level, but comprehensive metabolite profiling of rcd1 mutant has not been conducted until now. The mutant plants exhibited very different metabolic features from the wild type under light conditions implying enhanced glycolytic activity, altered nitrogen and nucleotide metabolism. In light conditions, superoxide production was elevated in rcd1, but no metabolic markers of oxidative stress were detected. Elevated senescence-associated metabolite marker levels in rcd1 at early developmental stage were in line with its early-senescing phenotype and possible mitochondrial dysfunction. After MV exposure, a marked decline in the levels of glycolytic and TCA cycle intermediates in Col-0 suggested severe plastidic oxidative stress and inhibition of photosynthesis and respiration, whereas in rcd1 the results indicated sustained photosynthesis and respiration and induction of energy salvaging pathways. The accumulation of oxidative stress markers in both plant lines indicated that MV-resistance in rcd1 derived from the altered regulation of cellular metabolism and not from the restricted delivery of MV into the cells or chloroplasts. Considering the evidence from metabolomic, transcriptomic and biochemical studies, we propose that RCD1 has a negative effect on reductive metabolism and rerouting of the energy production pathways. Thus, the altered, highly active reductive metabolism, energy salvaging pathways and redox transfer between cellular compartments in rcd1 could be sufficient to avoid the negative effects of MV-induced toxicity.
A convolution neural network for reconstructing microgrid polarimeter imagery

Light vector polarization as a fundamental property of lightwave, can be used to effectively distinguish objects in complicated circumstance including surface shape and materials type and transmission medium. As shown, polarization imaging is an advanced information acquirement method which combines the light intensity image and light vector vibration behaviors, which is the direction of electric field of incident lightwaves. A typical microgrid polarimeter with a minimum repeat unit is composed of four pixelated linear polarizer demonstrating different vibration directions. Compared with full polarization information, the polarization image obtained has only one quarter polarization information in each direction. Thus, it will influence the accuracy of other information such as Stokes components, the degree of linear polarization (DoLP), and the angle of polarization (AoP). In this paper we propose a polarization demosaicing network to address the poloarized image demosaicing issue, which are then recovered into the original polarized image. This network aims to improve the accuracy of DoLP and AoP of the targets by adjusting three Stokes components of the network output. We already remove the batch normalization (BN) commonly used in CNN, and thus use a customized loss function to make it suitable for polarization image demosaicing. The experimental results show that network has demonstrated a best peak signal-to-noise ratio (PSNR) and then richer image detail and polarization target information than that of the original image.
Diversity of Naturalized Hairy Vetch (Vicia villosa Roth) Populations in Central Argentina as a Source of Potential Adaptive Traits for Breeding

Hairy vetch (Vicia villosa ssp. villosa Roth) is native of Europe and Western Asia and it is the second most cultivated vetch worldwide. Hairy vetch is used as forage species in semiarid environments and as a legume cover crop in sub-humid and humid regions. Being an incompletely domesticated species, hairy vetch can form spontaneous populations in a new environment. These populations might contain novel and adaptive traits valuable for breeding. Niche occupancy based on geographic occurrence and environmental data of naturalized populations in central Argentina showed that these populations were distributed mainly on disturbed areas with coarse soil texture and alkaline-type soils. Low rainfall and warm temperatures during pre- and post-seed dispersal explained the potential distribution under sub-humid and semiarid conditions from Pampa and Espinal ecoregions. Conversely, local adaptation along environmental gradients did not drive the divergence among recently established Argentinian (AR) populations. The highest genetic diversity revealed by microsatellite analysis was observed within accessions (72%) while no clear separation was detected between AR and European (EU) genotypes, although naturalized AR populations showed strong differentiation with the wild EU accessions. Common garden experiments were conducted in 2014–16 in order to evaluate populations’ germination, flowering, and biomass traits. European cultivars were characterized by low physical seed dormancy (PY), while naturalized AR accessions showed higher winter biomass production. Detected variation in the quantitative assessment of populations could be useful for selection in breeding for traits that convey favorable functions within specific contexts.
Integrated-optic spectrum synthesis circuit for manipulating 64 frequency components

We report on an integrated-optic spectrum synthesis circuit that can individually control amplitude and phase of 40 GHzspaced and 64 frequency components. This synthesis circuit is fabricated with silica waveguide technology, and is composed of two arrayed-waveguide gratings (AWGs), and an array of variable optical attenuators (VOAs) and phase shifters. The AWGs function as frequency multi/demultiplexers, and the VOA and phase shifter are used to adjust amplitude and phase of each spectral component, respectively. The size of the circuit is 80 mm x 80 mm, and the manipulation accuracy relating to the VOA and phase shifter is in the order of 0.1 dB and 0.01, respectively. The VOA extinction ratio, namely, the dynamic range is about 30 dB.