Miracle-dz/UniFAR-FTUs huggingface dataset:
eXpOS: A Simple Pedagogical Operating System for Undergraduate Instruction

An operating system project suitable for undergraduate computing/electrical sciences students is presented. The project can be used as a course project in a one semester course, or as a self-study project for motivated students. The course is organized such that a student with a basic background in programming and computer organization can follow the implementation road map available online, and build the OS from scratch on her personal machine/laptop, with minimal instructional supervision. The student is provided with a simulated abstract machine, an application interface specification, specification and design of the OS, and a step by step project implementation road map. The functionalities of the OS include multitasking, virtual memory, semaphores, shared memory, an elementary file system, interrupt driven disk and console I/O, and a limited multi-user support. The final stage of the project involves porting the OS to a two-core machine. An independent one semester compiler design project, where the student builds a compiler for a tiny object oriented programming language that generates target code that can be loaded and executed by the OS is also briefly discussed.
Evidence of a genotype by feeding level interaction in grazing Holstein-Friesian dairy cattle at different stocking rates

Lactation yields and estimated breeding values (EBVs) for protein, fat and milk were obtained for individual cows in a farmlet trial carried out at Dexcel in Hamilton, using Holstein-Friesian cows, to test for the existence of a genotype by environment interaction across the range of feeding levels, and EBVs typically found on New Zealand pasturebased dairy farms. At very low levels of feeding, with yields of 267 – 307 kg milksolids per cow (kg MS/cow), regression coefficients of lactation yield on EBVs were not significantly different from the expected value of 1. At high levels of feeding, with yields of 383 – 411 kg MS/cow, most of the regression coefficients for yields of protein, fat and milk on EBVs were significantly greater than 1. The results of this study show that at low levels of feeding the actual differences in production between cows, which differ in EBVs correspond to the actual differences in EBVs, whereas at high levels of feeding the difference in milk yields between genetic groups are greater than the difference in EBVs. This constitutes a form of genotype x environment interaction, in which genotype is measured as breeding values, and has important practical and economic implications for dairy farms, and for the expected value of genetic improvements.
Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment

Currently, most image quality assessment (IQA) models are supervised by the MAE or MSE loss with empirically slow convergence. It is well-known that normalization can facilitate fast convergence. Therefore, we explore normalization in the design of loss functions for IQA. Specifically, we first normalize the predicted quality scores and the corresponding subjective quality scores. Then, the loss is defined based on the norm of the differences between these normalized values. The resulting "Norm-in-Norm" loss encourages the IQA model to make linear predictions with respect to subjective quality scores. After training, the least squares regression is applied to determine the linear mapping from the predicted quality to the subjective quality. It is shown that the new loss is closely connected with two common IQA performance criteria (PLCC and RMSE). Through theoretical analysis, it is proved that the embedded normalization makes the gradients of the loss function more stable and more predictable, which is conducive to the faster convergence of the IQA model. Furthermore, to experimentally verify the effectiveness of the proposed loss, it is applied to solve a challenging problem: quality assessment of in-the-wild images. Experiments on two relevant datasets (KonIQ-10k and CLIVE) show that, compared to MAE or MSE loss, the new loss enables the IQA model to converge about 10 times faster and the final model achieves better performance. The proposed model also achieves state-of-the-art prediction performance on this challenging problem. For reproducible scientific research, our code is publicly available at \urlhttps://github.com/lidq92/LinearityIQA.
SplitNN-driven Vertical Partitioning

In this work, we introduce SplitNN-driven Vertical Partitioning, a configuration of a distributed deep learning method called SplitNN to facilitate learning from vertically distributed features. SplitNN does not share raw data or model details with collaborating institutions. The proposed configuration allows training among institutions holding diverse sources of data without the need of complex encryption algorithms or secure computation protocols. We evaluate several configurations to merge the outputs of the split models, and compare performance and resource efficiency. The method is flexible and allows many different configurations to tackle the specific challenges posed by vertically split datasets.
Depth-Quality-Aware Salient Object Detection

The existing fusion-based RGB-D salient object detection methods usually adopt the bistream structure to strike a balance in the fusion trade-off between RGB and depth (D). While the D quality usually varies among the scenes, the state-of-the-art bistream approaches are depth-quality-unaware, resulting in substantial difficulties in achieving complementary fusion status between RGB and D and leading to poor fusion results for low-quality D. Thus, this paper attempts to integrate a novel depth-quality-aware subnet into the classic bistream structure in order to assess the depth quality prior to conducting the selective RGB-D fusion. Compared to the SOTA bistream methods, the major advantage of our method is its ability to lessen the importance of the low-quality, no-contribution, or even negative-contribution D regions during RGB-D fusion, achieving a much improved complementary status between RGB and D. Our source code and data are available online at https://github.com/qdu1995/DQSD.
Diversity and identification of fungi associated with needles of Pinus radiata in Tasmania

Needle fungi have been extensively studied in conifers but rarely in Pinus radiata. Previous studies of P. radiata have been based on fungal isolation and not direct PCR detection from needles. This research was a component of a study examining factors linked to spring needle cast (SNC) in Tasmanian P. radiata plantations and aimed to identify as many as possible of the fungal species commonly associated with P. radiata needles in Tasmanian plantations. Needle samples were collected from 13 sites representative of the range of sites in which P. radiata is grown in Tasmania. Fungi were detected by a direct PCR approach and identified using barcode sequences from a reference collection of isolates from Tasmania, mainland Australia and New Zealand as well as sequences from the international nucleotide sequence databases. The total number of molecular operational taxonomic units (MOTUs) was 152, with 127 detected by direct PCR and sequencing, and only 35 operational taxonomic units (OTUs) isolated by culture-based methods. Dothideomycetes was the most diverse class detected in this study, with many MOTUs detected by direct PCR and not isolated. Leotiomycetes was the second-most diverse class and Sordariomycetes third, with several OTUs frequently isolated but rarely or not at all detected by direct PCR. DNA sequence data facilitated the discrimination and identification of OTUs, but some effort was required to avoid confusion caused by poorly identified isolates in public DNA databases. This is the most comprehensive study yet of fungi associated with pine needles in Australia and highlights the hitherto unrecognised diversity of Teratosphaeriaceae in P. radiata.
PAN: Towards Fast Action Recognition via Learning Persistence of Appearance

Efficiently modeling dynamic motion information in videos is crucial for action recognition task. Most state-of-the-art methods heavily rely on dense optical flow as motion representation. Although combining optical flow with RGB frames as input can achieve excellent recognition performance, the optical flow extraction is very time-consuming. This undoubtably will count against real-time action recognition. In this paper, we shed light on fast action recognition by lifting the reliance on optical flow. Our motivation lies in the observation that small displacements of motion boundaries are the most critical ingredients for distinguishing actions, so we design a novel motion cue called Persistence of Appearance (PA). In contrast to optical flow, our PA focuses more on distilling the motion information at boundaries. Also, it is more efficient by only accumulating pixel-wise differences in feature space, instead of using exhaustive patch-wise search of all the possible motion vectors. Our PA is over 1000x faster (8196fps vs. 8fps) than conventional optical flow in terms of motion modeling speed. To further aggregate the short-term dynamics in PA to long-term dynamics, we also devise a global temporal fusion strategy called Various-timescale Aggregation Pooling (VAP) that can adaptively model long-range temporal relationships across various timescales. We finally incorporate the proposed PA and VAP to form a unified framework called Persistent Appearance Network (PAN) with strong temporal modeling ability. Extensive experiments on six challenging action recognition benchmarks verify that our PAN outperforms recent state-of-the-art methods at low FLOPs. Codes and models are available at: this https URL.
FrUITeR - A Framework for Evaluating UI Test Reuse

UI testing is tedious and time-consuming due to the manual effort required. Recent research has explored opportunities for reusing existing UI tests for an app to automatically generate new tests for other apps. However, the evaluation of such techniques currently remains manual, unscalable, and unreproducible, which can waste effort and impede progress in this emerging area. We introduce FrUITeR, a framework that automatically evaluates UI test reuse in a reproducible way. We apply FrUITeR to existing test-reuse techniques on a uniform benchmark we established, resulting in 11,917 test reuse cases from 20 apps. We report several key findings aimed at improving UI test reuse that are missed by existing work.
Bounded Scale Measure and Property A

We introduce a generalization for bounded geometry that we call bounded scale measure. From there we show that it is a coarse invariant, equivalent definitions for bounded scale measures, and what properties it satisfies. We lastly look at generalizations of property A for uniformly discrete metric spaces to large scale spaces with bounded geometry, and finally for a space that satisfies bounded scale measure. We show these generalizations are coarse invariants among other things.
Advancing environmentally explicit structured population models of plants

The relationship between the performance of individuals and the surrounding environment is fundamental in ecology and evolutionary biology. Assessing how abiotic and biotic environmental factors influence demographic processes is necessary to understand and predict population dynamics, as well as species distributions and abundances. We searched the literature for studies that have linked abiotic and biotic environmental factors to vital rates and, using structured demographic models, population growth rates of plants. We found 136 studies that had examined the environmental drivers of plant demography. The number of studies has been increasing rapidly in recent years. Based on the reviewed studies, we identify and discuss several major gaps in our knowledge of environmentally driven demography of plants. We argue that some drivers may have been underexplored and that the full potential of spatially and temporally replicated studies may not have been realized. We also stress the need to employ relevant statistical methods and experiments to correctly identify drivers. Moreover, assessments of the relationship between drivers and vital rates need to consider interactive, nonlinear and indirect effects, as well as effects of intraspecific density dependence. Synthesis. Much progress has already been made by using structured population models to link the performance of individuals to the surrounding environment. However, by improving the design and analyses of future studies, we can substantially increase our ability to predict changes in plant population dynamics, abundances and distributions in response to changes in specific environmental drivers. Future environmentally explicit demographic models should also address how genetic changes prompted by selection imposed by environmental changes will alter population trajectories in the face of continued environmental change and investigate the reciprocal feedback between plants and their biotic drivers.
hsa-microRNA-411-5p regulates proliferation, migration and invasion by targeting the hyaluronan mediated motility receptor in ovarian cancer

The mortality rate of ovarian cancer is the highest out of all gynecological malignancies worldwide. Therefore, it is important to understand the mechanisms of ovarian cancer, identify new biomarkers and develop targeted drugs. The role and molecular mechanisms of hsa-microRNA (miR)-411-5p in ovarian cancer have not been fully elucidated. The present study investigated the ovarian cancer cell lines OVCAR-8 and SKOV3. After transfection with miRNA mimics, cell proliferation was monitored by a proliferation assay. Furthermore, cell migration was measured by a cell wound healing assay and cell invasion was measured by Matrigel invasion assays. A miRNA luciferase reporter assay was used to analyze the relationship between miRNAs and the target gene HMMR, which was then further evaluated by gene differential analysis. In the current study, hsa-mir-411-5p was identified as a miRNA regulator of the hyaluronan mediated motility receptor, which negatively regulated the activity of ERK1/2 and ultimately inhibited ovarian cancer cell proliferation and motility. Although hsa-mir-411-5p may have different roles in other types of cancer, the present study suggested that miR-411-5p functions as a negative tumor regulator in ovarian cancer cells, displaying the potential of miR-411-5p as a biomarker for ovarian cancer.
Thoracic Imaging Findings of Multisystem Inflammatory Syndrome in Children Associated with COVID-19: What Radiologists Need to Know Now

The coronavirus disease 2019 (COVID-19) global pandemic is an ongoing public health emergency, with over 4 million confirmed cases worldwide. Due to the novel nature of this coronavirus and our evolving understanding of its pathophysiology, there is continued uncertainty surrounding diagnosis and management of COVID-19, especially in pediatric patients. In addition, a new febrile hyperinflammatory Kawasaki-like syndrome (also known as multisystem inflammatory syndrome in children, or MIS-C) has emerged in pediatric patients with temporal association to COVID-19 infection. This review article aims to provide an up-to-date review of the clinical and imaging findings of pediatric MIS-C associated with COVID-19, compared with typical acute pediatric COVID-19 infection, with an emphasis on thoracic imaging findings. Supplemental material is available for this article. © RSNA, 2020
Diagnostic and treatment delays among patients with primary bladder cancer in Poland: a survey study

Introduction The aim of this study was to determine and quantify the mechanisms responsible for the delays in bladder cancer diagnosis and initial treatment. Material and methods Patients referred to two academic hospitals in Poland with a primary bladder tumor were prospectively identified and structurally interviewed. For all patients, time intervals between symptom onset, diagnostic and therapeutic interventions were assessed. Results A total of 144 patients diagnosed with bladder cancer were included in the analysis. The median time from symptom onset to treatment was 112 days. This comprised of the following median waiting times: 1) patient waiting time of 13 days, 2) assessment waiting time of 14 days and 3) treatment waiting time of 42 days. In the multivariate analysis, large city residence (OR 0.2, 95% CI 0.1–0.6) and comorbidity (OR 0.3, 95% CI 0.1–0.8) reduced the risk of delay, whereas medium-sized city residence (OR 1.4, 95% CI 0.4–5.1) and general practitioner as the first medical professional contact (OR 5.3, 95% CI 0.6–50.0) increased the risk of delay. Conclusions Diagnostic and treatment waiting times for bladder cancer in Poland are unsatisfactory. Potential solutions for shortening these delays include healthcare policy changes such as utilization of the oncological priority programs, primary care education and public health campaigns.
Role of microRNA-33a in malignant cells

Cancer causes most of the mortality and morbidity worldwide, with a significant increase in incidence during recent years. MicroRNAs (miRNAs/miRs) are non-coding small RNAs capable of regulating gene expression. They regulate crucial cellular processes, including proliferation, differentiation, metastasis and apoptosis. Therefore, abnormal miRNA expression is associated with multiple diseases, including cancer. There are two types of cancer-associated miRNAs, oncogenic and tumor suppressor miRNAs, depending on their roles and expression patterns in cancer. Accordingly, miRNAs are considered to be targets for cancer prevention and treatment. miR-33a controls cellular cholesterol uptake and synthesis, which are both closely associated with carcinogenesis. The present review thoroughly describes the roles of miR-33a in more than a dozen types of cancer and the underlying mechanisms. Accordingly, the present review may serve as a guide for researchers studying the involvement of miR-33a in diverse cancer settings.
Relationship between the immunohistological examination and fluorescence visualization of oral squamous cell carcinoma

Disorders of the oral mucosa are considered easy to diagnose since they can be visualized and examined directly. A change in the color of the oral mucosa reflects histopathological changes and is an important diagnostic parameter. However, the subjective perception of color varies. To determine the extent of resection for oral mucosa conditions, it is necessary to digitize the color and perform objective assessments. In recent years, fluorescence visualization devices and analysis software that measure tissue luminance G have been employed for the identification of oral mucosa diseases. Fluorescence visualization is presumably based on the decrease in epithelial flavin adenine dinucleotide content and luminance G values due to the destruction of collagen cross-links [fluorescence visualization loss (FVL)]. However, cases with differences between luminance values and histopathological presentation exist. Therefore, additional factors may affect fluorescence visualization. The present study used a portable, non-contact oral mucosa fluorescence visualization device for luminance measurements in seven patients with oral squamous cell carcinoma. Furthermore, Picro-Sirius Red and immunohistochemical staining were performed for CK13, CK17, Ki67, p53 and E-cadherin in the FVL(+) (lesion) and FVL(−) (resection stump) areas to elucidate the principle of fluorescence visualization. Fluorescence was significantly lower in the FVL(+) than in the FVL(−) areas, and the mean luminance G value was 56. The Picro-Sirius Red stain revealed collagen destruction in the FVL(+) areas but no collagen disruption in the FVL(−) areas. CK13 was negative in the FVL(+) and positive in the FVL(−) areas, whereas the opposite pattern was observed for CK17. In the FVL(+) area, p53 staining was positive. E-cadherin expression was enhanced in the FVL(−) areas and reduced in the FVL(+) areas. Furthermore, the luminance G value tended to be lower in cases with weaker E-cadherin staining. The aforementioned results suggest that decreased E-cadherin expression may be a factor that regulates fluorescence visualization.
Sodium valproate combined with levetiracetam in pediatric epilepsy and its influence on NSE, IL-6, hs-CRP and electroencephalogram improvement

Efficacy of sodium valproate combined with levetiracetam (LEV) in pediatric epilepsy and its influence on neuron-specific enolase (NSE), interleukin-6 (IL-6) and high-sensitivity C-reactive protein (hs-CRP) as well as electroencephalogram (EEG) improvement were studied. Patients (n=100) with pediatric epilepsy admitted to and treated in Xiantao First People's Hospital Affiliated to Yangtze University from December 2015 to 2018 were enrolled in this study and randomly divided into observation group (n=50) and control group (n=50). Sodium valproate was administered in the control group, and the treatment with LEV was combined with sodium valproate in the observation group. After 12 weeks the cognitive function of patients was assessed using the Mini-Mental State Examination (MMSE) scale, Montreal cognitive assessment (MoCA) scale and Wechsler Memory Scale-Revised in China (WMS-RC). The quality of life (QOL) of patients was evaluated with the QOL in epilepsy-31 inventory (QOLIE-31) scale and Barthel Index, and blood was drawn from the patients to detect the neurological function indicators [NSE and glial fibrillary acidic protein (GFAP)] and inflammatory indicators (IL-6, IL-2 and hs-CRP). After treatment, the incidence rates of adverse reactions notably declined in the observation group (P<0.05), and the improvement in the cognitive function in the observation group were both superior to those in the control group (P<0.05). Observation group had lowered content of NSE, GFAP, IL-6, hs-CRP and IL-2 (P<0.05), and α wave was markedly decreased, but θ and δ waves were notably increased in the observation group (P<0.05). In the treatment of pediatric epilepsy, sodium valproate combined with LEV produces better efficacy, fewer adverse reactions, significantly improves patients' QOL and notably lowers the content of NSE, IL-6 and hs-CRP with notable EEG improvement, so it is a safe and reliable treatment that is worth popularization.
A meta-analysis of the added value of diffusion weighted imaging in combination with contrast-enhanced magnetic resonance imaging for the diagnosis of small hepatocellular carcinoma lesser or equal to 2 cm

Diffusion weighted imaging (DWI) has been found to increase the sensitivity in the diagnosis of small hepatocellular carcinoma (HCC), although additional studies are required to confirm its value. The aim of the present study was to explore the diagnostic performance of DWI combined with contrast-enhanced magnetic resonance imaging (MRI) for small HCC by performing a meta-analysis. Literature databases (PubMed, Embase, Web of Science and Cochrane Library databases) were searched to identify studies reporting the sensitivity and specificity of MRI with DWI for the diagnosis of small HCCs. Pooled sensitivity and specificity were generated using a bivariate random effect model. Multilevel mixed-effects logistic regression analysis was used to examine the value of DWI combined with conventional MRI. A total of 837 small HCCs and 545 benign liver lesions from 10 studies were included. The overall sensitivity and specificity of DWI combined with contrast-enhanced MRI was 0.88 (95% CI, 0.80–0.93) and 0.90 (95% CI, 0.81–0.95), respectively. Compared with that in contrast-enhanced MRI, DWI with contrast-enhanced MRI had a significantly higher sensitivity for the diagnosis of small HCC (P=0.01) while there was no significant difference in the specificity (P=0.603). The present meta-analysis suggests that DWI combined with contrast-enhanced MRI may increase the sensitivity, whilst maintaining high specificity for the diagnosis of small HCCs with a diameter ≤2 cm.