Miracle-dz/UniFAR-FTUs huggingface dataset:
Fire segmentation using a DeepLabv3+ architecture

In the last decade, the number of forest fires events is growing due to the fast change of earth’s climate. Hence, more automatized fire fighting actions had become necessary. Deep learning had drawn interesting results for pixel level classification for smoke detection, but few systems are proposed for fire flame detection. In this paper, a semantic segmentation approach using Deeplabv3+ architecture for wildfire detection is proposed. The network uses Deeplabv3 architecture as encoder and Atrous Spatial Pyramid Pooling (ASPP) which allows to encode multi-scale information and boost the network performance. In fact, the ASPP block concatenates a stack of parallel Atrous convolutions with graduating rates, which produces multi-scale feature map that is further resized. The tests were performed on a public dataset, Corsican fire dataset, which contains 1135 RGB images and 640 infrared pictures. The experiments were conducted on two customized datasets, one using the whole dataset within a single channel information (red and infra-red), and another using only the RGB images set that contains information coded in 3 channels. The used dataset is unbalanced, which could induces high precision with very low sensitivity. Therefore, to measure the performance Dice similarity and Tversky loss functions with cross-entropy are adopted. The capability of the Deeplabv3+ was tested with two different backbones, ResNet18 and ResNet-50, and compared to a very simple Convolutional Neural Network (CNN) architecture with dilated convolution. Four different metrics were used to evaluate the segmentation capability: Accuracy, mean Intersection over Union (IoU), Mean Boundary F1 (BF) Score, and Mean Dice coefficient. The experimental results demonstrate that the Deeplabv3+ with ResNet-50 backbone and a loss function type Dice or Tversky can accurately detect the fire flame, the given results are very encouraging for further study using deep learning approaches.
A novel deep learning data structure for multispectral remote sensing images

Standard deep-learning (DL) architectures do not optimize the use of the spatial and spectral information in the multi-spectral images but often consider only one of the two components. Two-stream DL architectures split and process them separately. However, the fusing of the output of the two streams is a challenging task. 3D-CNN processes spatial and spectral information together at the cost of a large number of parameters. To overcome these limitations, we propose a novel DL data structure that re-organizes the spectral and spatial information in remote-sensing (RS) images and process them together. Representing a RS image I as a data cube, we handle the spatial and spectral information by reducing the spectral bands from N to M, where M can drop out to one. The spectral information is projected in the spatial dimensions and re-organized in 2-dimensional B blocks. The proposed approach analyzes the spectral information of each block by using 2-dimensional convolutional kernels of appropriate size and stride. The output represents the relationship between the spectral bands of the input image and preserves the spatial relationship between its neighboring pixels. The spatial relationships are analyzed by processing the output of the previous layer with standard 2D-CNNs. Experiments by using images acquired by Sentinel-2 and Landsat-8 data and the labels of the LUCAS database released in 2018 provide promising results.
Fluorescence spectral shape analysis for fast COVID-19 virus identification: machine learning approach

Fast and reliable tests for the new coronavirus are urgently needed. Current Polymerase Chain Reaction based virus detection approaches are typically time-consuming and expensive. Technologies capable of providing a fast, real-time and non-contact detection of virus contamination and real-time virus classification are not yet available. Here, we demonstrate the potential of a fluorescence detection technique along with machine-learning based classification for virus detection. The ultraviolet (UV) light irradiated virus emits a fluorescent signal with a characteristic spectrum, which is regarded as a fingerprint for the virus. We analyzed eight virus samples including a heat-inactivated SARS-CoV-2 (virus causing COVID-19) and collected a number of emission spectra. Machine learning techniques are applied to discriminate among the candidate viruses via classifying a number of spectra data collected. First, Principle Component Analysis (PCA) was applied to reduce spectra data dimensionality. Then support vector machine (SVM) with various kernel functions (kernelSVM), k-nearest-neighbor (k-NN) and Artificial Neural Networks (ANN) methods were used to classify these viruses with dimension-reduced data from PCA. We found that dimension-reduced data in 3 principal components (PCs) space performs better than that in 2 PCs space in the machine learning algorithms mentioned above. Variance ratio analysis is able to explain nearly 95% of variance which allows nearly 100% accuracy of predictions for 25% data test set randomly chosen from the whole dataset. Finally, cross validation (CV) analysis is applied to kernel-SVM and k-NN methods.
LightTouch: Passive Gadgets for Extending Interactions on Capacitive Touchscreens by Automating Touch Inputs

We present LightTouch, a passive gadget to enhance touch interactions on unmodified capacitive touchscreens. It simulates finger operations such as tapping, swiping, or multi-touch gestures using conductive materials and photoresistors embedded inside the objects. The touchscreen emits visible light and the photoresistor senses the level of this light, which changes its resistance value. By controlling the screen brightness, it connects or disconnects the path between the GND and the touchscreen, thus allowing the touch inputs to be controlled. In contrast to conventional physical extensions for touchscreens, our technique does not require continuous finger contact on the conductive part nor the use of batteries. Our technique opens up new possibilities for touch interaction such as for enhancing the trackability of tangibles beyond the simple automation of touch inputs.
Comparison of learning-based and maximum-likelihood estimators of image noise variance for real-life and synthetic anisotropic textures

Processing of remote sensing data is often based on an assumption that noise parameters in component images are a priori known. If this assumption is not valid, it is desired to perform estimation of noise parameters directly from noisy image patches. In this paper, two estimators - model- and learning-based ones possessing the ability to evaluate noise standard deviation (SD) or variance and to predict estimation accuracy for each image patch are considered. The former approach is the representative of maximum likelihood estimator (MLE) of parameters for anisotropic fractional Brownian motion (afBm) field whilst the learning-based one is the representative of convolutional neural networks (CNN) that employs training on real-life images. Our goal is to compare the performance for two cases: for pure afBm data and for real-life images. It is shown that the learning-based approach occurs to be less effective for pure afBm data since it produces a certain bias whilst the model-based approach runs into problems for complex image patches in reallife images. Based on this analysis, we propose to use synthetic afBm data as additional source of training data for learning-based methods of noise parameters estimation. By mixing real and synthetic data for training of the NoiseNet CNN, we were able to improve its performance in both domains. For afBm data, NoiseNet bias was significantly reduced and ability to predict noise SD estimates confidence improved. On NED2012 database of real images, the modified NoiseNet reduces signal-independent noise SD component estimation error by about 40% as compared to the original CNN version.
Change detection in UWB VHF SAR images exploiting flight heading diversity through robust principal component analysis

Change detection methods are frequently associated with wavelength-resolution synthetic aperture radar (SAR) images for foliage-penetrating (FOPEN) applications (e.g., the detection of concealed targets in forestry areas), being a research topic of interest over the last decades. The challenge associated with the design of automated change detection techniques goes beyond performing the target detection. It is also related to clutter suppression aiming at a low false alarm rate (FAR). The problem of detecting targets and removing content in SAR data can be treated as an unsupervised signal separation problem, usually referred to as blind source separation (BSS). Additionally, low frequency wavelength-resolution SAR images can be considered to follow an additive separation model due to their backscatter characteristics. In this context, it is possible to explore robust principal component analysis (RPCA) as a source-separation method for problems in which the mixing model is additive and two-dimensional, as the interest SAR images. This paper presents a change detection method for wavelengthresolution SAR images based on the RPCA via principal component pursuit (PCP), considering the use of small image stacks to explore the data diversity from measurements of different flight headings. The proposed method is evaluated using real data obtained from measurements of the ultrawideband (UWB) very high frequency (VHF) SAR system CARABAS II. The experimental results show that the proposed method can achieve a high probability of detection (PD) values for a low FAR (i.e., PD of 0.98 for a FAR of 0.41 objects per square kilometer). Finally, discussions regarding the use of the RPCA in change detection methods and the diversity gains are provided in the paper.
A study of the robustness of the long short-term memory classifier to cloudy time series of multispectral images

Sentinel-2 and Landsat satellites provide huge amount of optical images with high spatial and temporal resolution. These dense Time Series (TS) of multispectral data are used for a wide range of applications enabling multi-temporal monitoring of physical phenomena. Nevertheless, one of the main challenges in their usage is related to missing information caused by cloud occlusions. In the literature, many cloud restoration approaches have been proposed. However, to properly recover missing information, sophisticated and usually computationally intensive techniques should be used. In this work, we consider the deep Long Short Term Memory (LSTM) classifier which is very promising for classification of dense time series of images, and investigate its robustness to the cloud presence without any cloud restoration. Indeed, this classifier has proven to be able to handle the presence of clouds. However, no work which extensively analyzes the robustness of LSTM to clouds can be found in the literature. In this study, we aim to quantitatively asses the capability of the network of handling different amount of cloud coverage under different lengths of the TS. In greater detail, we analyze the effect of the cloud coverage on the classification maps produced by the LSTM by considering: (i) simulated cloud values, (ii) detected clouds represented by zeros values, and (iii) restored images by simple linear temporal gap filling (i.e., average of the spectral values acquired in the previous and following cloud-free images in the TS). The obtained results demonstrate that the capability of the LSTM to handle the cloud cover depends on: (i) the length of the TS, (ii) the position of the cloudy images in the TS, and (iii) the cloud representation values. For example, when clouds are restored with very simple and fast linear temporal gap filling, the map agreement between the cloud-free and the cloudy map is 96% even when the 40% of images in the TS are covered with clouds, regardless of their position.
Neuromorphic technologies for defence and security

Despite the highly promising advances in Machine Learning (ML) and Deep Learning (DL) in recent years, DL requires significant hardware acceleration to be effective, as it is rather computationally expensive. Moreover, miniaturisation of electronic devices requires small form-factor processing units, with reduced SWaP (Size,Weight and Power) profile. Therefore, a completely new processing paradigm is needed to address both issues. In this context, the concept of neuromorphic (NM) engineering provides an attractive alternative, seen as the analog/digital implementation of biologically brain inspired neural networks. NM systems propagate spikes as means of processing data, with the information being encoded in the timing and rate of spikes generated by each neuron of a so-called spiking neural network (SNN). Based on this, the key advantages of SNNs are: less computational power required, more efficient and faster processing, much lower power consumption. This paper reports on the current state of the art in the field of NM systems, and it describes three application scenarios of SNN-based processing for security and defence, namely target detection and tracking, semantic segmentation, and control.
Vehicle target detection in SAR image based on complex data statistics and superpixel characteristics

In this paper, a new kind of feature(AT feature) to measure the possibility of artificial target in SAR image was proposed based on the difference between real part and imaginary part of complex data of single-channel SAR imagery. Under the guidance of this feature, a superpixel-level CFAR detection method (AS-CFAR) was proposed for vehicle targets. The proposed method consists of three steps. Firstly superpixel segmentation algorithm was used to presegment SAR images. Then AT feature was calculated to find out the potential target superpixel and background superpixel using SAR complex data. Finally, CFAR detection was carried out only for the superpixels of potential targets, and the selection of background area was also conducted under AT features, which maximally ensured the uniformity of background area. Experiments on miniSAR data verified that the proposed method could detect more target pixels in less time than other CFAR detectors.
Synonymy of three pestiferous Matsucoccus scale insects (Hemiptera: Coccoidea: Matsucoccidae) based on morphological and molecular evidence

The scale insect genus Matsucoccus Cockerell (Coccoidea: Matsucoccidae) contains several economically important species that cause damage to pine trees, Pinus species, in the United States and elsewhere in the Holarctic Region. Efforts to reconstruct the phylogeny of the group have provided information on genetic variation within and among species. Here, three species of Matsucoccus are synonymized based on newly acquired molecular data and reassessment of morphological data. Matsucoccus resinosae Bean and Godwin, described from the eastern United States, and Matsucoccus thunbergianae Miller and Park, from South Korea, are considered to be new synonyms of Xylococcus (now Matsucoccus) matsumurae Kuwana. The taxonomic confusion surrounding these names is discussed. In addition, we suggest that several other species of Matsucoccus, including M. pini Green, should be investigated as possible synonyms of M. matsumurae.
Endothelin-1 myocardial clearance, production, and effect on capillary permeability in vivo

Myocardial metabolism of endothelin-1 (ET-1) and its effect on coronary microcirculatory exchanges were obtained in anesthetized dogs by combining the indicator-dilution technique with immunoreactive ET-1 measurements. The myocardium extracted 17.7 +/- 4.6% of tracer ET-1 (n = 12). Simultaneously measured ET-1 levels in the aorta (0.97 +/- 0.46 pg/ml) and coronary sinus (0.96 +/- 0.53 pg/ml) were not different, supporting a production of ET-1 by the heart that balances the amount extracted. Intracoronary infusion of ET-1 (5 ng.kg-1.min-1) increased coronary sinus ET-1 levels approximately 50-fold, decreased coronary blood flow per unit of interstitial space by approximately 30% (P = 0.006), and increased myocardial microcirculatory transit times (n = 6). Permeability to albumin was unaffected by ET-1, whereas the permeability-surface area product for sucrose decreased following derecruitment of myocardial capillaries. We conclude that there is a normal myocardial metabolic balance of ET-1 and that the heart marginally contributes to circulating ET-1. Pharmacological doses of ET-1 may adversely affect myocardial metabolism by reducing blood flow and the permeability-surface area product for small circulating substances.
Genetic differentiation of six sympatric species of Isotomurus (Collembola, Isotomidae); is there any difference in their microhabitat preference ?

Le systeme gene-enzyme et la sequence d'ADN ont ete utilises pour etablir l'importance des details morphologiques ainsi que de la pigmentation dans la systematique du genre Isatomurus. Les systemes enzymatiques ont permis de mettre en evidence l'existence de six especes bien caracterisees du point de vue genetique et vivant dans le meme endroit. La presence de loci fixes pour differents alleles interdit la possibilite de formation d'hybrides et confere aux taxons le statut d'especes. La methode donne des resultats significatifs dans la diagnose des especes lorsqu'elles ne sont pas bien differenciees morphologiquement. Les marqueurs genetiques ont ete egalement utilises pour evaluer l'affinite phylogenetique des differentes especes. Les reconstructions obtenues utilisant differentes methodes ne conduisent pas toujours aux memes resultats, mais elles suggerent toutefois une affinite tres forte entre Isotomurus palustris, I. ghibellinus n. sp. et I. indipendente n. sp. Bien que des differences ecologiques bien definies n'aient pas ete mises en evidence, chaque espece etudiee montre une specialisation microgeographique et une preference pour un biotope particulier. Cette preference pourrait etre a l'origine (et/ou l'effet) du processus de speciation.
The influence of chronotype on the academic achievement of children and adolescents – evidence from Russian Karelia

Abstract The inter-individual differences of human time-of-day preferences could divide population into “morning”, “intermediate” and “evening” types. This variety of sleep patterns is accompanied by differences in the timing of peak cognitive performance. The morningness–eveningness trait, also known as chronotype, is distributed on a continuum, with unequivocal early morning and night owl types at the opposite extremes of the distribution while most of the population shows an intermediate or neither type weak sleep pattern. Obviously, the discrepancy between biological and the social clocks has a distinct impact on physiological processes in humans with different chronotype. It is known that natural light regime in circumpolar regions influences human performance. Most work focused on the relationship between academic achievement and chronotype in students from middle latitudes. The primary aim of our study is to investigate the relationship between chronotype and academic achievement in schoolchildren (6th–11th grade) from north-west Russia (Russian Karelia). The Munich chronotype questionnaire was used in the study, and all participants were required to answer a question about their school achievement. Early midpoint of sleep and longer average sleep duration were associated with better grades. Large social jetlag was associated with worse grades. In a linear regression, gender was the most important predictor of grades, followed by midpoint of sleep and age. This is the first study that has been carried out among school pupils from Russian Karelia, and it shows that evening orientation linked with poorer academic grades at this high latitude.
Gamma Radiation Sensitivity of Listeria monocytogenes.

Seven strains of Listeria monocytogenes were irradiated in culture media or in mechanically deboned chicken meat. The survivor plots were quadratic curves when cultures were in the log phase of growth or when they were irradiated in chicken meat; cultures in the senescent phase of growth showed linear responses to irradiation. Cultures from cells surviving an irradiation dose of 1.5 kGy were no more radiation resistant that those which had had no previous exposure to irradiation. Cultures centrifuged and resuspended in water were more sensitive to radiation than those resuspended in solutions containing organic materials. These studies indicated that a dose of 2 kGy was sufficient to destroy 1 × 104 cells of L. monocytogenes .
Specificities to cow milk proteins of human serum antibodies from clinically allergic patients

In the present study, reactivities of human IgE and IgG antibodies from clinically allergic children to various peptide fragments derived from cow milk α-CN, β-CN and β-LG were analyzed by enzyme-linked immunosorbent assay (ELISA), and compared with those of rabbit IgG antibodies to α-CN, β-CN and β-LG
Dual structure model for the population history of the Japanese

この論文は, 沖縄人およびアイヌを含む日本人集団の形成史を単一の仮説で説明する二重構造モデルを提唱するものである。 このモデルは次の点を想定する。 すなわち, 日本列島の最初の居住者は後期旧石器時代に移動してきた東南アジア系の集団で, 縄文人はその子孫である。 弥生時代になって第2の移動の波が北アジアから押し寄せたため, これら2系統の集団は列島内で徐々に混血した。 この混血の過程は現在も続いており, 日本人集団の二重構造性は今もなお解消されていない。 したがって身体•文化の両面にみられる日本の地域性-たとえば東西日本の差など-は, 混血または文化の混合の程度が地域によって異なるために生じたと説明することができる。 またこのモデルは, 日本人の形質•文化にみられるさまざまな現象を説明するのみならず, イヌやハツカネズミなど, 人間以外の動物を対象とする研究結果にも適合する。 同時に, このモデルによって日本の本土, 沖縄およびアイヌ系各集団の系統関係も矛盾なく説明することができる。
Within-patient genetic diversity of SARS-CoV-2

SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity. Author Summary Since it arose in late 2019, the new coronavirus (SARS-CoV-2) behind the COVID-19 pandemic has mutated and evolved during its global spread. Individual patients may host different versions, or variants, of the virus, hallmarked by different mutations. We examine the diversity of genetic variants coexisting within patients across a cohort of 3939 publicly accessible samples and 749 recently sequenced samples from Switzerland. We find that a small number of patients carry most of the diversity, and that patients with more diversity tend to be older. We also find that most of the diversity is concentrated in certain regions and positions of the virus genome. In particular, we find that a variant reported to increase infectivity is among the most diverse positions. Our study provides a large-scale survey of within-patient diversity of the SARS-CoV-2 genome.