Miracle-dz/UniFAR-FTUs huggingface dataset:
Imaging an extended object through a thin scattering layer via various point spread functions

Deconvolution-based techniques have been widely used for imaging through scattering medium due to the optical memory effect (OME) in speckles. Once the point spread function (PSF) of a scattering system is measured, a smallscale object within the OME region can be easily recovered. However, an extended object larger than the OME region can only be partially reconstructed due to the limited field of view (FOV). Here, we find a way to get an integrated PSF by exploiting a point source with different locations in object plane. Thereafter, an extended object, within the FOV but exceeding the OME region, could be recovered by the integrated PSF without knowing any other system parameters even the locations of the point source.
Maternal High-Sucrose Diet Affects Phenotype Outcome in Adult Male Offspring: Role of Zbtb16

Overnutrition in pregnancy and lactation affects fetal and early postnatal development, which can result in metabolic disorders in adulthood. We tested a hypothesis that variation of the Zbtb16 gene, a significant energy metabolism regulator, modulates the effect of maternal high-sucrose diet (HSD) on metabolic and transcriptomic profiles of the offspring. We used the spontaneously hypertensive rat (SHR) strain and a minimal congenic rat strain SHR-Zbtb16, carrying the Zbtb16 gene allele originating from the PD/Cub rat, a metabolic syndrome model. Sixteen-week-old SHR and SHR-Zbtb16 rat dams were fed either standard diet (control groups) or a high-sucrose diet (HSD, 70% calories as sucrose) during pregnancy and 4 weeks of lactation. In dams of both strains, we observed an HSD-induced increase of cholesterol and triacylglycerol concentrations in VLDL particles and a decrease of cholesterol and triacylglycerols content in medium to very small LDL particles. In male offspring, exposure to maternal HSD substantially increased brown fat weight in both strains, decreased triglycerides in LDL particles, and impaired glucose tolerance exclusively in SHR. The transcriptome assessment revealed networks of transcripts reflecting the shifts induced by maternal HSD with major nodes including mir-126, Hsd11b1 in the brown adipose tissue, Pcsk9, Nr0b2 in the liver and Hsd11b1, Slc2a4 in white adipose tissue. In summary, maternal HSD feeding during pregnancy and lactation affected brown fat deposition and lipid metabolism in adult male offspring and induced major transcriptome shifts in liver, white, and brown adipose tissues. The Zbtb16 variation present in the SHR-Zbtb16 led to several strain-specific effects of the maternal HSD, particularly the transcriptomic profile shifts of the adult male offspring.
DeepFocus: a deep learning model for focusing microscope systems

Conventional microscopy focusing methods perform a time consuming sweep through the Z-axis in order to estimate the focal plane. As an alternative, we developed a deep learning model that predicts in one shot the distance offset to the focal plane from any initial position using an input of only two images taken a set distance apart. The difference of these two images is processed through a regression CNN model, which was trained to learn a direct mapping between the amount of defocus aberration and the distance from the focal plane. A training dataset was acquired from a semiconductor sample at different surface locations on the sample and at different distances from focus. The ground truth focal plane was determined using a parabolic autofocus algorithm with the Tenengrad scoring metric. The CNN model was tested on bare semiconductor sample using the projected shape of the F-stop. The model was able to determine the in-focus position with high reliability, and was also significantly faster than conventional methods that rely on classical computer vision. Furthermore, the rare cases where our algorithm does not find the focal plane can be detected, and a fine-focus algorithm can be applied to correct the result. With the collection of sufficient training data, our deep learning focusing model provides a significantly faster alternative to conventional focusing methods.
Changes in gene expression and histological injuries as a result of exposure of Crassostrea gigas to the toxic dinoflagellate Gymnodinium catenatum

The harmful alga Gymnodinium catenatum is one of the species that produces the neurotoxic, hydrophilic, tetrahydropurine derivatives known as paralytic shellfish toxins (PST). Studies have shown that several classes of marine toxins have genotoxic effects on bivalves. However, there are few studies on PST and their genotoxic effects on bivalves such as oysters. Experiments were performed to assess the effects of the toxic dinoflagellate G. catenatum on changes in the transcription level of the genes involved in cellcycle regulation (p21, p53, cafp55) and initial inflammatory (casp1) processes and injuries in tissues of juvenile Crassostrea gigas. Our results reveal an effect at the level of transcription. The high transcription levels of the p21 and p53 genes found in oysters exposed to dinoflagellate diets suggest an activation of the cell-cycle checkpoints by DNA damage induction. At the histological level, the oysters exposed to the toxic dinoflagellate showed exfoliation, epithelial rupture and inflammation, which correlate with the observed changes in expression level of casp1 that is known to play a role in initial inflammatory responses. Our results provide molecular data suggesting probable DNA damage through expression modulation of the analysed key genes, in addition to histological evidence of tissue injuries due to PST dinoflagellate exposure.
An Analysis of BERT FAQ Retrieval Models for COVID-19 Infobot

The outbreak of the COVID-19 pandemic has caused tremendous amounts of suffering and deaths around the world and greatly affected the lives of humanity. As the world sees more infected cases every day, the need and demand for reliable and up-to-date information on COVID-19 have never been higher. While recent pre-trained language models show successes on many other NLP tasks, we did not have COVID-19 related dataset to help us evaluate the performance of QA systems and infobots based on these models. After the creation of a COVID-19 question similarity dataset by public health experts from the Johns Hopkins Bloomberg School of Public Health (JHSPH), we create models sufﬁcient for application. We also analyze the amount of supervised data required.
A Review on Recent Advancements in FOREX Currency Prediction

In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX prices within the stipulated time. Our research shows that in recent years, researchers have been interested mostly in neural networks models, pattern-based approaches, and optimization techniques. Our review also shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM), have been fully explored and show huge potential in time series prediction.
Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies

The Reed-Solomon algorithm is well known in different fields of computer science. The novelty of this study lies in the different interpretation of the algorithm itself and its scope of application for remote sensing, especially at the preparatory stage, i.e., data fusion. A short review of the attempts to use different data fusion approaches in geospatial technologies explains the possible usage of the algorithm. The rationale behind its application for data fusion is to include all possible information from all acquired spectral bands, assuming that complete composite information in the form of one compound image will improve both the quality of visualization and some aspects of further quantitative and qualitative analyses. The concept arose from an empirical, heuristic combination of geographic information systems (GIS), map algebra, and two-dimensional cellular automata. The challenges are related to handling big quantitative data sets and the awareness that these numbers are in fact descriptors of a real-world multidimensional view. An empirical case study makes it easier to understand the operationalization of the Reed-Solomon algorithm for land use studies.
Effect of partially decomposed guar gum on high-cholesterol-fed rats and non-dietary fiber-fed rats.

本研究では, 難消化性多糖類の分子量と生理作用の関係を解明する目的で, グアガムおよびそれを酵素で低分子化したグアガム部分分解物が, ラットの脂質代謝あるいは糞便排泄に与える作用について検討した。 その結果, グアガムに比べて著しく粘度の低いグアガム部分分解物が, 血漿コレステロール上昇抑制作用と糞排泄量および水分含量の増加作用を保持していることを明らかにした。
Growth Responses to Wind Differ among Papaya Roots, Leaves, and Stems

Sunrise' and 'Tainung 2' papaya seedlings were subjected to 3 weeks of ambient winds in Guam during five experiments, and growth responses of roots, leaves, and stems were quantified to compare speed and extent of the plasticity among the organs. The cultivars responded similarly with 1 week eliciting stem growth responses and 2 weeks eliciting root responses. The timeframe of these studies was sufficient to enable adaptive responses in all three organs. Wind reduced stem and leaf expansion rate but not root extension rate, providing one example of how the form of response differed among the organs. A dose-effect was evident among the experiments with magnitude of response increasing with mean ambient wind speed. Asymmetric stem diameter and root tip density were examples of adaptive responses to directional wind load. These data on young papaya plants may be used to inform field experiments aiming to determine how chronic winds influence long-term growth and fitness.
Invitro Evaluation of Native Rice Specific Isolates of Trichoderma against Rice Sheath Blight caused by Rhizoctonia solani

Rice (Oryza sativa L.) is apre-eminent crop of India as it is the staple food for most of the people of the country. It is one of the major food crops of India. More than 90% of the world’s rice is grown and consumed in Asia where 60% of the earth’s people live (Mahajan et al., 2017). China produces largest amount of rice (142.3 million tonnes) followed by India (110.4 million tonnes) (According to FAO: Rice Market Monitor 2018). Rice is the staple food crop of Manipur. It is widely cultivated in both hill and valley areas of Manipur occupying nearly 1.80 lakh ha of the total cropped area in the state (Goud et al., 2018). Rice is found to suffer from many fungal and bacterial diseases which results in heavy grain yield losses. Its productivity is affected by several International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 7 (2020) Journal homepage: http://www.ijcmas. com
Fake News Detection from Data Streams

Using fake news as a political or economic tool is not new, but the scale of their use is currently alarming, especially on social media. The authors of misinformation try to influence the users’ decisions, both in the economic and political sphere. The facts of using disinformation during elections are well known. Currently, two fake news detection approaches dominate. The first approach, so-called fact or news checker, is based on the knowledge and work of volunteers, the second approach employs artificial intelligence algorithms for news analysis and manipulation detection. In this work, we will focus on using machine learning methods to detect fake news. However, unlike most approaches, we will treat incoming messages as stream data, taking into account the possibility of concept drift occurring, i.e., appearing changes in the probabilistic characteristics of the classification model during the exploitation of the classifier. The developed methods have been evaluated based on computer experiments on benchmark data, and the obtained results prove their usefulness for the problem under consideration. The proposed solutions are part of the distributed platform developed by the H2020 SocialTruth project consortium.
Starch content and activities of starch biosynthetic enzymes in wheat, rice and millets

The aim of the study was to correlate the accumulation of amylose and total starch with activities of starch biosynthetic enzymes (AGPase, SSS and GBSS) in cereals like rice (PB-2, PD-19), wheat (UP-262, PBW-343) and millets (Barnyard millet, Finger millet and Foxtail millet) at different stages of endosperm development. ADP-glucose pyrophosphorylase (AGPase) activity was found higher in all varieties during developmental stage followed by granule bound starch synthase (GBSS) and soluble starch synthase (SSS). Accumulation of amylose was positively influenced by activity of AGPase and GBSS. Total starch accumulation was positively correlated with activity of AGPase and SSS. In all these varieties rate of enzymes activity was found higher at S3 stage. Higher ratio of AGPase and GBSS contributes lesser resistant starch, amylose content and am/ap ratio. Ratio of AGPase and GBSS can be used as an index for amylose content when it was studied in large sample size of similar varieties.
Trajectory Clustering and k-NN for Robust Privacy Preserving k-NN Query Processing in GeoSpark

Privacy Preserving and Anonymity have gained significant concern from the big data perspective. We have the view that the forthcoming frameworks and theories will establish several solutions for privacy protection. The k-anonymity is considered a key solution that has been widely employed to prevent data re-identifcation and concerns us in the context of this work. Data modeling has also gained significant attention from the big data perspective. It is believed that the advancing distributed environments will provide users with several solutions for efficient spatio-temporal data management. GeoSpark will be utilized in the current work as it is a key solution that has been widely employed for spatial data. Specifically, it works on the top of Apache Spark, the main framework leveraged from the research community and organizations for big data transformation, processing and visualization. To this end, we focused on trajectory data representation so as to be applicable to the GeoSpark environment, and a GeoSpark-based approach is designed for the efficient management of real spatio-temporal data. Th next step is to gain deeper understanding of the data through the application of k nearest neighbor (k-NN) queries either using indexing methods or otherwise. The k-anonymity set computation, which is the main component for privacy preservation evaluation and the main issue of our previous works, is evaluated in the GeoSpark environment. More to the point, the focus here is on the time cost of k-anonymity set computation along with vulnerability measurement. The extracted results are presented into tables and figures for visual inspection.
Pollinator visitation patterns, floral rewards and the probability of transmission of Microbotryum violaceum, a venereal disease of plants

1 We investigated patterns of pollinator visitation to plants of the white campion, Silene alba, and related these to floral nectar rewards in healthy female and male plants and in plants diseased with the anther smut disease Microbotryum violaceum (= Ustilago violacea). 2 Pollinators preferred plants with large floral displays, and also preferred males to females and healthy to diseased plants. Male plants consistently produced nectar with higher sugar concentration, thereby offering higher quality floral rewards than either females or diseased plants. 3 Variation in nectar production was also found among individual plants and different plant families, suggesting that more attractive plants may be predisposed to infection since pollinating insects also serve as vectors for this fungal disease. 4 Such patterns of pollinator preference could affect the dynamics of disease transmission within populations by influencing the probability that insect visitors make transitions between diseased and healthy plants. This vector transmitted disease may therefore modify the course of selection on floral traits by imparting a cost to pollinator attraction.
Escherichia coli e Staphylococcus aureus produttori di beta ‐ lattamasi isolati da polli in Nigeria

The occurrence of beta‐lactamase‐producing Escherichia coli and Staphylococcus aureus in chickens was investigated. Specimens (n = 1 300) were collected from 400 chickens and were streaked on MacConkey agar plates. From each plate, presumptive growths of organisms were picked and streaked on eosin methylene blue and Baird‐Parker agars, respectively. Typical colonies of E. coli and S. aureus with similar morphologies were identified by biochemical tests. Isolates were tested for beta‐ lactamase production and antimicrobial susceptibilities. Results indicated that 805 E. coli isolates from which 89 (11%) were beta‐lactamase‐positive and 660 S. aureus from which 58 (8.8%) were beta‐lactamase‐positive. Both isolates showed a high level of resistance to all twelve antibiotics screened. The increased prevalence of antibiotic resistance amongst bacterial organisms is undoubtedly correlated with the discovery and characteris‐ ation of multiple, transferrable resistance determinants, such as beta‐lactamases, corresponding to their respective phenotypes. The implications of this for humans when handling and/or consuming chickens and chicken products contaminated with strains of such isolates, is a risk of transferrable multi‐ drug resistance and a failure of treatment. The results of our study indicated that beta‐ lactamase‐producing E. coli and S. aureus are prevalent in chickens in Nigeria.
Analyzing the influential factors of industry 4.0 in precision machinery industry

Abstract. Nowadays the science and technology progresses not only create the change to have a big impact on various industries, but also stimulate Industry 4.0 being applied in the manufacturing industry to achieve manufacturing efficiency and to reduce its cost to increase additional values. This study uses the Analytical Hierarchical Process (AHP) evaluation method, which considers four criteria layers: Internet of things factors, Automationfactors, Intelligent factors, Big data factors, and twelve influence factors in sub-layer are: perceived layer, network layer, application layer, field layer, management layer, control layer, process control visualization, system supervisory and control omni bearing, green energy manufacturing production, variety, volume, and velocity. Then, the relative risk indicator (RRI) is obtained by the Analytical Hierarchical Process method, and the overall risk indicator (ORI) can be obtained after introducing the evaluation value of each impact factor through the case. The research results confirm that the risk assessment values obtained the hierarchical analysis method are consistent. This research through the Analytic Hierarchy Process, then discusses Industry 4.0 pair of Taiwan's precision machinery industry management pattern institute emphatically face with target, expected will provide the existing machine manufacture industry as well as the future wants to invest the precision machine industry the management policy-maker reference value, also might take the government policy consideration factors and the machine manufacture industry scholars study the academic for reference. Keywords. Industry 4.0, Precision machine industry, Analytic Hierarchy Process. JEL. L22, M11, O14.
Supplementary Material for Multi-way Encoding for Robustness

We generate FGSM attacks with = 0.2 from substitute models A1ofK and C1ofK on MNIST to evaluate the models of Section A.1. 1 and Section A.1. 2. We also measure a correlation coefficient of the sign of the input gradients between target and substitute models as explained in Section 4.1. 1. Tables A and B demonstrate that RO, among the different target models, achieves the highest accuracy and the lowest input gradient correlation with the substitute model. It should be noted that the two alternative models have 10 neurons at the last layer while RO has 2000 neurons. In addition, ROsoftmax has a softmax layer so that the gradients at the final layer are determined by a ground-truth class of an example. B. Transferability
Two-tiered face verification with low-memory footprint for mobile devices

Mobile devices have their popularity and affordability greatly increased in recent years. As a consequence of their ubiquity, these devices now carry all sorts of personal data that should be accessed only by their owner. Even though knowledge-based procedures are still the main methods to secure the owner's identity, recently biometric traits have been employed for more secure and effortless authentication. In this work, the authors propose a facial verification method optimised to the mobile environment. It consists of a two-tiered procedure that combines hand-crafted features and a convolutional neural network (CNN) to verify if the person depicted in a photograph corresponds to the device owner. To train a CNN for the verification task, the authors propose a hybrid-image input, which allows the network to process encoded information of a pair of face images. The proposed experiments show that the solution outperforms state of the art face verification methods, providing a 4× speedup when processing an image in recent smartphone models. Additionally, the authors show that the two-tiered procedure can be coupled with existing face verification CNNs improving their accuracy and efficiency. They also present a new data set of selfie pictures – RECOD Selfie data set – that hopefully will support future research in this scenario.
Freeze preservation of rose pollen in liquid nitrogen: Feasibility, viability and fertility status after long-term storage

SummaryThe pollen cryobehaviour of four rose cultivars was studied. Pollen retained viability as indexed by in vitro germination after 48 h and one year (three cultivars) in liquid nitrogen. Cryostored pollen retained its ability to fertilize and produce seeds. The availability of cryostored pollen would enable rose breeders to pollinate at any time of the year at any location. Cryogenic preservation also offers the possibility of conserving the haploid gene pool in rose species through a pollen cryobank facility.