Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Label Prior: Pre-Training-Free Salient Object Detection Network Based on Label Learning

Due to the excellent semantics extraction capabilities, deep learning methods have significantly progressed in salient object detection (SOD). However, these methods often require time-consuming pre-training and large training datasets with ground truth. To address these issues, by referring to the framework known as “deep image prior (DIP),” we propose a SOD method called deep label prior network (DLPNet), which consists of <inline-formula><tex-math notation="LaTeX">$\mathcal A$</tex-math></inline-formula>-stream and <inline-formula><tex-math notation="LaTeX">$\mathcal B$</tex-math></inline-formula>-stream. The <inline-formula><tex-math notation="LaTeX">$\mathcal A$</tex-math></inline-formula>-stream includes two cascaded UNets and a simple CNNs module to extract the initial saliency map, while the <inline-formula><tex-math notation="LaTeX">$\mathcal B$</tex-math></inline-formula>-stream contains only two cascaded UNets, which refines the extracted initial saliency map. Unlike most of the current deep learning methods, DLPNet views the SOD task as a conditional image generation problem, relying on only the internal prior of the input itself to generate the saliency map. Hence, our DLPNet does not require pre-training or large annotated / unannotated datasets. Furthermore, we propose a morphology operation scheme, which creates rich pseudo-labels for facilitating the updating of network weights. Extensive experiments demonstrate that our method outperforms state-of-the-art unsupervised techniques and is even comparable to state-of-the-art supervised and weakly supervised methods on different evaluation metrics.
Factors Influencing the Adaption of Online Pharmacy in Oman: Pilot Study

When it comes to consumer convenience, value-added information, and communication with their pharmacist, online pharmacies are held up as one of the most cutting-edge personal developments in the physical health sector. While online pharmacies have seen tremendous success in rich nations, there is still a long way to go before they are commonly used in developing nations like Oman. There is a dearth of data on what influences people in Oman to use internet pharmacies. By fusing the Unified Theory of Acceptance and Use of Technology (UTUAT-2) with the Technology Acceptance Model (TAM), this study presents a conceptual model for understanding what drives people in Oman to use online pharmacies. The two theories were then used to generate 12 research hypotheses. In order to perform appropriate statistical testing methods and examine the reliability of the acquired data, a survey questionnaire was provided to 35 interested respondents in this pilot project. In order to account for all of the variables, the covariance matrix method was utilized to compute the descriptive function. According to the findings, the identified criteria significantly impacted Oman’s adoption of online pharmacies. As this study demonstrates, top management and politicians in Oman need to work toward raising health literacy, consumer knowledge, and understanding so that people may make educated decisions about their healthcare. By doing so, they may reap the most advantages of consumer acceptance while minimizing the risks associated with online pharmacies.
Identification of Papilionidae Species in Yunnan Province Based on Deep Learning

Yunnan is known as the "Hometown of Butterflies" in China. The colorful and morphological diversity of the Papilioidae in Yunnan province is the subject of insect ecology and evolution research. At the same time, the Yunnan Papilio has great ornamental value. It is of great significance to accurately identify the species of Papilionidae in Yunnan Province. At present, Yunnan Papilio has not been classified in the related research on butterfly identification using deep learning methods, and there is a situation that the sample data set between species is small and the number is unbalanced, which may cause the model to fail to learn the morphological characteristics of butterflies. In response to the above problems, this study established a data set consisting of 12,956 original images of papilionidae from Yunnan Province, including two subfamilies, 12 genera and 80 species. Five deep learning network models (VGG-19, ResNet-34, ResNet-50, ResNet-101 and DenseNet-121) were explored from the perspective of prediction accuracy and loss value by transfer learning method. And modeling effects of SGD, Adam, Adamax and RMsprop optimization algorithms. The final data set adopts balanced sampling and 11 data enhancement methods for data fusion to expand the data set to 16,000 images. The ResNet-50 network structure optimized by Adamax algorithm is selected to achieve the optimal effect. The experimental results show that the recognition accuracy of ResNet-50 in the constructed model reaches 87.47%. The study provides a basis for constructing a visual recognition model of Papilioidae in Yunnan and applying it to the mobile terminal, and provides a fast and efficient new method for species identification of Papillidae in Yunnan. (Abstract)
Evaluation of Sodium Relaxation Times and Concentrations in the Achilles Tendon Using MRI

Sodium magnetic resonance imaging (MRI) can be used to evaluate the change in the proteoglycan content in Achilles tendons (ATs) of patients with different AT pathologies by measuring the 23Na signal-to-noise ratio (SNR). As 23Na SNR alone is difficult to compare between different studies, because of the high influence of hardware configurations and sequence settings on the SNR, we further set out to measure the apparent tissue sodium content (aTSC) in the AT as a better comparable parameter. Ten healthy controls and one patient with tendinopathy in the AT were examined using a clinical 3 Tesla (T) MRI scanner in conjunction with a dual tuned 1H/23Na surface coil to measure 23Na SNR and aTSC in their ATs. 23Na T1 and T2* of the AT were also measured for three controls to correct for different relaxation behavior. The results were as follows: 23Na SNR = 11.7 ± 2.2, aTSC = 82.2 ± 13.9 mM, 23Na T1 = 20.4 ± 2.4 ms, 23Na T2s* = 1.4 ± 0.4 ms, and 23Na T2l* = 13.9 ± 0.8 ms for the whole AT of healthy controls with significant regional differences. These are the first reported aTSCs and 23Na relaxation times for the AT using sodium MRI and may serve for future comparability in different studies regarding examinations of diseased ATs with sodium MRI.
Free, Conjugated, and Bound Phenolics in Peel and Pulp from Four Wampee Varieties: Relationship between Phenolic Composition and Bio-Activities by Multivariate Analysis

Free, conjugated, and bound phenolic fractions of peel and pulp in four wampee varieties from South China were analyzed for their contents, composition, antioxidant capacities, and inhibitory activities against α-glucosidase. We found that there were significant differences in phenolic/flavonoid contents among diverse varieties and different parts (peel and pulp), and the contents were highest in the peel’s bound form. The results of UHPL-Q-Exactive HF-X and HPLC showed that chlorogenic acid, gentisic acid, and rutin were abundantly distributed over the three phenolic fractions in peel and pulp of all wampee samples, while isoquercitrin was the most abundant in the conjugated form of peel/pulp and myricetin had the richest content in the free form of peel/pulp. Wampee peel had stronger antioxidant capacities of ABTS+, DPPH, ·OH, and FRAP than the pulp, and the bound phenolic fraction of the peel/pulp had much higher antioxidant activities than FP and CP fractions. It is interesting that the same phenolic fraction of the wampee peel displayed roughly close IC50 values of α-glucosidase inhibition to those from the pulp samples. The relationship between individual phenolic and TPC/TFC/the bio-activities and the similarity among the free, conjugated, and bound phenolic fractions in peel and pulp samples were explored by using Pearson correlation analysis, principal component analysis, and hierarchical cluster analysis. This work provides a systematic and comprehensive comparison of the three phenolic fractions of diverse wampee varieties and different parts, and a rationale for applying phenolics from wampee fruits.
A Comprehensive Assessment on Predictive Analytics using Data Mining Techniques

Educational Data Mining is a fast emerging multidisciplinary field that reveals unexpected patterns in educational organizations. Considering the issues and demanding situations faced through instructional establishments in studying people overall performance and enhancing their instructional management, the diverse automated studying strategies have been examined. In this work, more Research contribution were reviewed and evaluated in revealing alternative ways for evaluating students' educational attainment to improve quality and support students in attaining remarkable achievements in their academics. The most typical applications of educational data mining encompass: enhancing the study process, intensifying course completion, assisting students in curriculum selection, student profiling, classifying factors that predispose to dropout, student targeting, curriculum planning, predicting student performance, and as a support for decision-making at the time of student enrolment. Predictive analytics is a machine learning and data mining method that is commonly utilized in educational data mining, to foresee expected future trends, events, and behaviors based on prior data. As a result of data mining algorithms, academic establishments will be able to make people academic outcomes accessible in aims to enhance the quality of learning they deliver.
Future Land Use/Land Cover Change Has Nontrivial and Potentially Dominant Impact on Global Gross Primary Productivity

Anthropogenic land use/land cover (LULC) change alters terrestrial gross primary productivity (GPP), the largest land‐atmosphere carbon exchanges. Identifying the impacts of LULC changes on future terrestrial GPP has been challenging due to the scarcity of standardized future LULC for ecosystem models. Here, we present eight scenario‐based projections of global spatially explicit LULC at 1‐km resolution over the period 2015–2100 with a Future Land Use Simulation model—consistent with the Shared Socioeconomic Pathways and Representative Concentration Pathways. Twenty computational experiments with different LULC patterns, climate forcing, and CO2 concentrations were conducted to quantify their contributions to future GPP dynamics. Results show that the global terrestrial GPP would decline in the 21st century in most LULC scenarios due to urbanization, agricultural expansion, and deforestation. Moreover, the contribution of LULC changes to global GPP dynamics ranges from 3.43% to 10.78% when CO2 fertilization effect (CFE) is not modeled during 2000–2100 (7%–9% of the terrestrial area is dominated by LULC change). However, this value may range from 10.92% to 16.16% during 2000–2050 and 1.41%–14.57% during 2050–2100. The contribution of LULC even reached 56.08% during 2050–2100 in Southeast Asia due to deforestation. Despite the relatively important role of LULC to GPP dynamics, it becomes trivial globally when incorporating CFE into the model (i.e., LULC accounts for 1.24%–2.51% during 2000–2100). Our findings emphasize the strategic role of CFE in enhancing global GPP and highlight the quantitatively nontrivial role of LULC at the regional scale.
Using the QR Factorization to swiftly update least squares problems

In this paper we study how to update the solution of the linear system Ax = b after the matrix A is changed by addition or deletion of rows or columns. Studying the QR Factorization of the system, more specifically, the factorization created by the Householder reflection algorithm, we find that we can split the algorithm in two parts. The result from the first part is trivial to update and is the only dependency for calculating the second part. We find that not only can this save a considerable amount of time when solving least squares problems but the algorithm is also very easy to implement.
Baby Step Giant Step in Real Quadratic Function Fields

The principal topic of this article is to extend Shanks' infrastructure ideas in real quadratic number elds to the case of real quadratic congruence function elds. We apply these techniques to the problem of computing the regulator R of a real quadratic congruence function eld. By making use of symmetries and optimized formulas we obtain a considerable improvement in the complexity of calculating R. We also apply the ideas of Lenstra and Schoof to produce an algorithm for determining the regulator unconditionally in O(q 1 5 deg(D)+") operations.
Solar wind forcing at Mercury: WSA‐ENLIL model results

Analysis and interpretation of observations from the MESSENGER spacecraft in orbit about Mercury require knowledge of solar wind “forcing” parameters. We have utilized the Wang‐Sheeley‐Arge (WSA)‐ENLIL solar wind modeling tool in order to calculate the values of interplanetary magnetic field (IMF) strength (B), solar wind velocity (V) and density (n), ram pressure (~nV2), cross‐magnetosphere electric field (V × B), Alfvén Mach number (MA), and other derived quantities of relevance for solar wind‐magnetosphere interactions. We have compared upstream MESSENGER IMF and solar wind measurements to see how well the ENLIL model results compare. Such parameters as solar wind dynamic pressure are key for determining the Mercury magnetopause standoff distance, for example. We also use the relatively high‐time‐resolution B‐field data from MESSENGER to estimate the strength of the product of the solar wind speed and southward IMF strength (Bs) at Mercury. This product VBs is the electric field that drives many magnetospheric dynamical processes and can be compared with the occurrence of energetic particle bursts within the Mercury magnetosphere. This quantity also serves as input to the global magnetohydrodynamic and kinetic magnetosphere models that are being used to explore magnetospheric and exospheric processes at Mercury. Moreover, this modeling can help assess near‐real‐time magnetospheric behavior for MESSENGER or other mission analysis and/or ground‐based observational campaigns. We demonstrate that this solar wind forcing tool is a crucial step toward bringing heliospheric science expertise to bear on planetary exploration programs.
EFFECTS OF ROTATION AND HELICAL FORCE ON THE ONSET OF RAYLEIGH-BENARD CONVECTION WITH FREE-FREE BOUNDARIES

*The linear stability analysis is applied to investi gate the effects of rotation and helical force on t he onset of stationary and oscillatory convection in a horizontal fluid layer heated from below with free-free boundary condition s. From this analysis, the condition for the formation of a single large-s cale structure is obtained. We have shown, moreover , how the helical force affects the Pr-Ta parameter space that is divided i nto stationary and oscillatory convection regions.
Data-Driven Virtual Metrology and Retraining Systems for Color Filter Processes of TFT-LCD Manufacturing

With the innovations in commercial-electronics products and fierce competition in the global panel industry, most panel manufacturers have adopted the small batch production mode to deal with a wide range of customization. With small batch sizes, productive yield and troubleshooting are considered the top priority, so monitoring capability conducted via virtual metrology (VM) is essentially necessary to satisfy the requirement of process quality. In this article, the real-world dataset involving the photoresist (PR) spacer heights of the color filter (CF) in the array sector of thin-film transistor liquid crystal display (TFT-LCD) manufacturing is investigated. In practice, the PR spacer heights can only be measured in an infrequent manner due to the scheduling restriction. Without taking additional sample measurements, how to design a high-accuracy VM model based on small batch sizes warrants urgent research for the TFT-LCD industry. The proposed framework can be divided into two parts. First, two novel distance-measuring methods are proposed, direct (fully connected) neural network (DNN) and ${K}$ -means, to allocate the most affiliated positions so that the VM system can utilize these positions to execute metrology prediction on the same product. Next, a modified random forests (RFs) regression model is integrated into the VM system to create an ensemble VM predictor that can handle multiple products of different small batch sizes. Last, the VM model is repeatedly evaluated to check the performance of the retraining procedure when the model needs to be renewed between production maintenance periods.
Crop Water Stress Identification and Estimation: A Review

Water stress in plants is due to water deficiency probably caused by soil salinity and drought. Water stress in crops leads to loss in agricultural production that results in food shortage. Crop water stress index (CWSI) changes with respect to temperature, humidity, soil moisture, vapour pressure deficit(VPD), wind speed etc., Various environmental factors also need to be considered for crop water stress identification. Our paper reviews, on various effects, responses of water stress and methods used for Crop Water Stress Index identification for sustainable agriculture.
Research on Sleep Staging Method Based on DeepStageNet

In order to solve the problem of inaccurate results in the prediction of sleep stages by traditional networks, a DeepStageNet network was proposed. The network firstly express that deep characteristics through three-layer parallel CNN. Secondly, the extracted features are respectively transferred to the density at the next layer and further backward to the BiLSTM, which continues to transfer information and mine the dependency relationship between the various stages of sleep in the same way, thus realizing the automatic learning of the staging features of EEG signals and the determination of sleep periods. Finally, the experimental results on Sleep-EDF Database show that, after removing the influence of the data in the half-hour awake period before and after sleep, the Staging Accuracy of DeepStageNet Network on the traditiona130s data set and the new 210s data set after secondary processing are 86.5% and 87.7%. This effectively improves the Staging Accuracy of Sleep Model, and has certain practical value.
Potential Candidates for Emergency Department Initiated Extracorporeal Cardiopulmonary Resuscitation (ECPR) in a Canadian Institution

Introduction Out-of-hospital cardiac arrest (OHCA) patients experience poor survival. The use of extracorporeal membrane oxygenation (ECMO), a form of heart-lung bypass, in the setting of cardiac arrest, termed extracorporeal cardiopulmonary resuscitation (ECPR), has promise in improving survival with good neurologic outcomes. The study objective was to determine the number of potential annual ECPR candidates among the OHCA population in a health region within the Atlantic Canadian province of Nova Scotia. Methods A retrospective chart review was conducted over a five-year period: January 1st, 2012 to December 31st, 2016. Consecutive non-traumatic OHCA and emergency department (ED) cardiac arrests occurring in a pre-determined catchment area (20-minute transport to ECMO center) defined by a geographic bounding box were identified. Criteria for ECPR were developed to identify candidates for activation of a “Code ECPR”: (1) age 16-70, (2) witnessed arrest, (3) no flow duration (time to CPR, including bystander) <10 minutes, (4) resuscitation >10 minutes without return of spontaneous circulation (ROSC), (5) emergency medical service (EMS) transport to hospital <20 minutes, (6) no patient factors precluding ongoing resuscitation (do not resuscitate status (DNR), palliative care involvement, or metastatic cancer), and (7) initial rhythm not asystole. Candidates were stratified by initial rhythm. Candidates were considered ultimately ED ECPR eligible if they failed conventional treatment, defined by death or resuscitation >30 minutes. Clinical data related to candidacy was extracted by an electronic query from prehospital and ED electronic records and manual chart review by three researchers. Results Our search yielded 561 cases of EMS-treated OHCA or in-ED arrests. Of those 204/561 (36%; 95% CI 33-40%) met the criteria for activation of a “Code ECPR”. Ultimately 79/204 (34%; 95% CI 28-41%) of those who met activation criteria were considered ED ECPR eligible; which is 14% (95% CI 11-17%) of the total number of arrests-of the total number of arrests, the initial rhythms were pulseless electrical activity (PEA) 33/79 (42%; 95% CI 32-53%) and shockable 46/79 (58%; 95% CI 47-69%). Conclusion Of all cardiac arrests in the area surrounding our ECMO center, approximately 41 per year met the criteria for a Code ECPR activation, with 16 per year ultimately being eligible for ED ECPR. This annual estimate varies based on the inclusion of initial rhythm. This provides insight into both prehospital and hospital implications of an ED ECPR program and will help guide the establishment of a program within our Nova Scotian health region. This study also provides a framework for similar investigation at other institutions contemplating ED ECPR program implementation.
Phenolics from Defatted Black Cumin Seeds (Nigella sativa L.): Ultrasound-Assisted Extraction Optimization, Comparison, and Antioxidant Activity

An ultrasound-assisted method was used for the extraction of phenolics from defatted black cumin seeds (Nigella sativa L.), and the effects of several extraction factors on the total phenolic content and DPPH radical scavenging activity were investigated. To improve the extraction efficiency of phenolics from black cumin seed by ultrasonic-assisted extraction, the optimal extraction conditions were determined as follows: ethanol concentration of 59.1%, extraction temperature of 44.6 °C and extraction time of 32.5 min. Under these conditions, the total phenolic content and DPPH radical scavenging activity increased by about 70% and 38%, respectively, compared with conventional extraction. Furthermore, a complementary quantitative analysis of individual phenolic compounds was carried out using the HPLC-UV technique. The phenolic composition revealed high amounts of epicatechin (1.88–2.37 mg/g) and rutin (0.96–1.21 mg/g) in the black cumin seed extracts. Ultrasonic-assisted extraction can be a useful extraction method for the recovery of polyphenols from defatted black cumin seeds.
Computing representations of the structure of written discourse

R scASTA (Rhetorical Structure Theory Analyzer), a discourse analysis component within the Microsoft English Grammar, efficiently computes representations of the structure of written discourse using cue phrases and additional information available in syntactic and logical form analyses of a text. R scASTA heuristically scores the rhetorical relations that it hypothesizes, using those scores to guide it in producing more plausible discourse representations before less plausible ones. The heuristic scores also provide a genre-independent method for evaluating competing discourse analyses: the best discourse analyses are those constructed from the strongest hypotheses. This dissertation describes in detail a set of linguistic cues that can be identified in a text as evidence of discourse relations, and gives complete and explicit algorithms for identifying the terminal nodes of a discourse analysis and for efficiently combining those terminal nodes to form hierarchical representations of discourse structure.