Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrastive learning-based Adenoid Hypertrophy Grading Network Using Nasoendoscopic Image

Adenoid hypertrophy is a common disease in children with otolaryngology diseases. Otolaryngologists usually use nasoendoscopy for adenoid hypertrophy screening, which is however tedious and time-consuming for the grading. So far, artificial intelligence technology has not been applied to the grading of nasoendoscopic adenoid. In this work, we firstly propose a novel multi-scale grading network, MIB-ANet, for adenoid hypertrophy classification. And we further propose a contrastive learning-based network to alleviate the overfitting problem of the model caused by lacking of nasoendoscopic adenoid images with high-quality annotations. The experimental results show that MIB-ANet shows the best grading performance compared to four classic CNNs, i.e., AlexNet, VGG16, ResNet50 and GoogleNet. Take $F_{1}$ score as an example, MIB-ANet achieves 1.38% higher $F_{1}$ score than the best baseline CNN - AlexNet. Due to the capability of the contrastive learning-based pre-training strategy in exploring unannotated data, the pre-training using SimCLR pretext task can consistently improve the performance of MIB-ANet when different ratios of the labeled training data are employed. The MIB-ANet pre-trained by SimCLR pretext task achieves 4.41%, 2.64%, 3.10%, and 1.71% higher $F_{1}$ score when 25%, 50%, 75% and 100% of the training data are labeled, respectively.
A novel CNN model with dense connectivity and attention mechanism for arrhythmia classification

Cardiac arrhythmia is a common cardiovascular disease that can cause sudden death in severe cases. Electro-cardiography (ECG) is the most well-known and widely applied method for heart diseases detection. Computer-aided diagnosis of ECG can help improve physician efficiency and reduce the rate of misdiagnosis of ECG. In this paper, we propose a method for arrhythmia classification based on the dense convolutional network (DenseNet) and efficient channel attention (ECA). Evaluation experiments were performed using the ECG records from the MIT-BIH database. The accuracy, sensitivity, specificity, and F1 values of 99.69%, 97.55%, 99.81%, and 97.72% were achieved for the six types of heartbeats classification, respectively. The experimental results demonstrate the validity and feasibility of the method, which can be used for ECG screening.
Problems of knowledge : a critical introduction to epistemology

Introduction: The Very Idea of a Theory of Knowledge 1. The Standard Analysis 2. Knowledge without Evidence 3. Two Ideals 4. Unstable Knowledge 5. Agrippa's Trilemma 6. Experience and Reality 7. Foundations 8. The Problem of the Basis 9. Reduction and Inference 10. Coherence 11. The Myth of the System 12. Realism and Truth 13. Evidence and Entitlement 14. Knowledge in Context 15. Seeing and Knowing 16. Scepticism and Epistemic Priority 17. Induction 18. Projection and Conjecture 19. Relativism 20. Objectivity and Progress Conclusion: Epistemology After Scepticism?
SOT-MRAM Based Main Memory: An Alternative to DRAM

Data is generated enormously today and to process this we need large memories with high speed and optimized power consumption. Conventional DRAM based system which were used as memory in computing systems for several decades are facing challenges in scalability and power consumption. In this regard non-volatile memories with excellent scalability and minimal power consumption are studied extensively. This paper analyzes the impact of introducing SOT-MRAM a non-volatile main memory device as an alternative to DRAM. Experimental results show an average power reduction of 46.09% with an increase in performance up to 30% when DRAM is replaced with SOT-MRAM for embedded system workloads.
CNN optimization using surrogate evolutionary algorithm for breast cancer detection using infrared images

Convolutional neural networks (CNNs) have shown great potential in different real word application. Defining a suitable CNN architecture is vital for obtaining good performance. In this work we propose a random forest surrogate combined with two bio-inspired optimization algorithm, genetic algorithms (GA) and particle swarm optimization (PSO) used to find good CNN fully connected layer architecture and hyperparameters for three state of the art CNNs: VGG-16, Resnet-50 and Densenet-201. The proposed model is used to classify breast thermography images from the DMR-IR database in order to find whether or not the patient has cancer. The proposed model improved F1-score from 0.92 to 1 for the Densenet using the GA and also Resnet from 0.85 of F1-score to 0.92 using the PSO. Moreover, the surrogate model also helped reducing training time.
Predicting the Onset of Delirium on Hourly Basis in an Intensive Care Unit Following Cardiac Surgery

Delirium, affecting up to 52% of cardiac surgery patients, can have serious long-term effects on patients by damaging cognitive ability and causing subsequent functional decline. This study reports on the development and evaluation of predictive models aimed at identifying the likely onset of delirium on an hourly basis in intensive care unit following cardiac surgery. Most models achieved a mean AUC > 0.900 across all lead times. A support vector machine achieved the highest performance across all lead times of AUC = 0.941 and Sensitivity = 0.907, and BARTm, where missing values were replaced with missForest imputation, achieved the highest Specificity of 0.892. Being able to predict delirium hours in advance gives clinicians the ability to intervene and optimize treatments for patients who are at risk and avert potentially serious and life-threatening consequences.
Measuring the Influence of Gamification Features to Needs Satisfaction

Following the era of digital retail strategy, gamification has become an essential marketing technique used by e-commerce companies. Furthermore, studies have shown that gamification contributes to leverage brand equity. This impact can be met by satisfying the user’s intrinsic motivation needs. However, negative gamification consequences may occur, one of which is the boredom phenomenon that leads to brand equity obstruction. Therefore, this study examines the relationship between gamification features, intrinsic need satisfaction, and user boredom at e-commerce businesses with 399 respondents across Indonesia. The results showed immersion-related gamification features are positively related to autonomy need satisfaction. Furthermore, achievement related gamification features have a significant positive relationship with satisfaction of competence needs. The social related gamification features positively influenced relatedness need satisfaction. Also, boredom is negatively associated with satisfaction of the three psychological needs. However, autonomy and relatedness needs have little influence on boredom. Therefore, gamification interaction positively impacts intrinsic motivation and satisfaction, while this satisfaction has negatively influenced user boredom.
Angle of arrival estimation for all‐sky interferometric meteor radar systems

Simulations have been used to investigate angle of arrival (AOA) estimation for all‐sky interferometric meteor radars using the Jones et al. (1998) antenna configuration. The results suggest that the use of 2λ spacing phase differences significantly improves AOA accuracy, while use of wraparound and differencing the sines of the zenith angles lead to small improvements in AOA accuracy. The combined use of 2λ spacing, wraparound, and differencing the sines of the zenith angles reduces the percentage of estimates outside ±1° of the actual AOA by a factor of 2.5. The results suggest that the use of 2.5, 3, and 5. 5λ spacing produces more accurate AOA estimates than the standard 2, 2.5, and 4. 5λ spacing and that the optimal spacing for the addition of a fourth antenna to each antenna arm is around 20λ.
MartiNet: An Efficient Approach For River Segmentation In SAR Images

The ubiquity of deep learning models in applications involving satellite imagery has seen steady progress particularly with Synthetic Aperture Radar (SAR) images. Known for its quality of image data regardless of the weather or time of the day, SAR images capturing water bodies has been discussed and extensively studied. With the focus shifting from inefficient image processing techniques to more accurate and bigger neural networks, the need for a computationally inexpensive model arises. In this paper we propose an end-to-end methodology to obtain higher segmentation accuracies, while also keeping the number of trainable parameters low. We recommend the method of gamma correcting and thresholding SAR images to obtain a cleaner dataset. We invert images to bring focus to thin streams and employ a triple learning strategy in the model architecture to improve outputs from the final layer. Finally, we compare our methodology with existing architectures and discuss the results. A quantitative analysis shows that our approach achieves an mIoU of 0.9903 and an F1 score of 0.9947.
Photonic-assisted Wideband RF Beamformer on InP Membrane on Silicon Platform

We report the experimental performance of a compact beamformer fabricated on the novel InP membrane on silicon platform exhibiting large steering angle $(\pm 90 ^{\circ}$ at 26 GHz), high throughput (12 Gbps), and fast reconfiguration time $(\sim 250$ ns). © 2021 The Author(s)
A Bayesian approach to variable selection when the number of variables is very large

In this paper, we present a rapid Bayesian variable selection technique which can be used when the number of variables is much greater than the number of samples. The method can handle tens of thousands of variables, such as might be measured using biological array technologies. A general formulation is first given, followed by specific details for the class of generalised linear models.
Finite-Resistivity Instabilities of a Sheet Pinch

An algebraic but general procedure which is the direct application of semi-group representation is used to obtain the criteria for instabilities and a simple evaluation of their growth rates is demonstrated for the finite resistive magnetohydrodynamical case. The results are compared qualitatively with other works and the parameter dependence of the gravitational mode agrees fairly well with the computer experiment.
High-resolution global atmospheric evaporation to precipitation

A key Earth system process is the circulation of evaporated moisture through the atmosphere. Spatial connections between evaporation and precipitation affect the global and regional climates by redistributing water and latent heat. Through this atmospheric moisture recycling, land-cover changes influence regional precipitation patterns, with potentially far-reaching 10 effects on human livelihoods and biome distributions across the globe. However, a globally complete dataset of atmospheric moisture flows from evaporation to precipitation has been lacking so far. Here we present a dataset of global atmospheric moisture recycling on both 0.5° and 1.0° spatial resolution. We simulated the moisture flows between each pair of cells across all land and oceans for 2008−2017 and present their monthly climatological means. We applied the Lagrangian moisture tracking model UTrack, which is forced with ERA5 reanalysis data on 25 atmospheric layers and hourly wind speeds and 15 directions. Due to the global coverage of the simulations, a complete picture of both the upwind source areas of precipitation and downwind target areas of evaporation can be obtained. We show a number of statistics of global atmospheric moisture flows: land recycling, basin recycling, mean latitudinal and longitudinal flows, absolute latitudinal and longitudinal flows, and basin recycling for the 26 largest river basins. We find that, on average, 70% of global land evaporation rains down over land, varying between 62% and 74% across the year; 51% of global land precipitation has evaporated from land, varying between 20 36% and 57% across the year. Highest basin recycling occurs in the Amazon and Congo basins, with evaporation and precipitation recycling of 63% and 36% for the Amazon basin and 60% and 47% for the Congo basin. These statistics are examples of the potential usage of the dataset, which allows users to identify and quantify the moisture flows from and to any area on Earth, from local to global scales. The dataset is available at https://doi.pangaea. de/10.1594/PANGAEA.912710 (Tuinenburg et al., 2020).
OPTIMIZATION OF SURFACE ROUGHNESS OF ALUMINIUM 6013-T6 ALLOY IN THE TURNING PROCESS

: One of the most common methods of machining is turning. Cutting speed, depth of cut, and feed rate are the most effective cutting parameters on the surface roughness. In addition to cutting parameters, the use of cooling type, the cutting tool is also essential on the surface roughness of materials. In this study, the surface roughness properties of Al 6013-T6 material were investigated depending on feed rate and cutting speed in turning process. Experiments were planned according to L9 orthogonal array. Optimum conditions were found via Taguchi’s Signal/Noise analysis. Variance analysis (ANOVA) was performed to determine the parameters that affect the turning process. As a result of experimental studies surface roughness values increased as feed rate increased and decreased as cutting speed increased. The analysis results showed that feed rate is a dominant parameter on surface roughness. It was also observed that the cutting parameters had a significant effect on the machining time. As the machining time decreases, the surface roughness increases.
Exploring the use of multi-source high-resolution satellite data for snow water equivalent reconstruction over mountainous catchments

Abstract. The hydrological cycle is strongly influenced by the accumulation and melting of seasonal snow. For this reason, mountains are often claimed to be the “water towers” of the world. In this context, a key variable is the snow water equivalent (SWE). However, the complex processes of snow accumulation, redistribution, and ablation make its quantification and prediction very challenging. In this work, we explore the use of multi-source data to reconstruct SWE at a high spatial resolution (HR) of 25 m. To this purpose, we propose a novel approach based on (i) in situ snow depth or SWE observations, temperature data and synthetic aperture radar (SAR) images to determine the pixel state, i.e., whether it is undergoing an SWE increase (accumulation) or decrease (ablation), (ii) a daily HR time series of snow cover area (SCA) maps derived by high- and low-resolution multispectral optical satellite images to define the days of snow presence, and (iii) a degree-day model driven by in situ temperature to determine the potential melting. Given the typical high spatial heterogeneity of snow in mountainous areas, the use of HR images represents an important novelty that allows us to sample its distribution more adequately, thus resulting in highly detailed spatialized information. The proposed SWE reconstruction approach also foresees a novel SCA time series regularization technique that models impossible transitions based on the pixel state, i.e., the erroneous change in the pixel class from snow to snow-free when it is expected to be in accumulation or equilibrium and, vice versa, from snow-free to snow when it is expected to be in ablation or equilibrium. Furthermore, it reconstructs the SWE for the entire hydrological season, including late snowfall. The approach does not require spatialized precipitation information as input, which is usually affected by uncertainty. The method provided good results in two different test catchments: the South Fork of the San Joaquin River, California, and the Schnals catchment, Italy. It obtained good agreement when evaluated against HR spatialized reference maps (showing an average bias of −22 mm, a root mean square error – RMSE – of 212 mm, and a correlation of 0.74), against a daily dataset at coarser resolution (showing an average bias of −44 mm, an RMSE of 127 mm, and a correlation of 0.66), and against manual measurements (showing an average bias of −5 mm, an RMSE of 191 mm, and a correlation of 0.35). The main sources of error are discussed to provide insights into the main advantages and disadvantages of the method that may be of interest for several hydrological and ecological applications.
CAR T-Cell Therapy for Patients with Multiple Myeloma: Current Evidence and Challenges

: The therapeutic landscape of multiple myeloma (MM) has benefited from an emergence of novel therapies over the last decade. By inducing T-cell kill of target cancer cells, chimeric antigen receptor (CAR) T-cell therapies have improved outcomes of patients with hematologic malignancies. B-cell maturation antigen (BCMA) is the current target antigen of choice for most CAR T-cell products under investigation for MM. However, their shortcomings deal with logistical and clinical challenges, including limited availability, manufacturing times, and toxicities. This article provides an overview of recently developed and investigational CAR T-cell therapies for MM, highlighting current evidence and challenges.
Impact of Family Caregiver Training on Care of Burn Patients

The purpose of this study was to investigate the impact of a family caregiver training program on care provision to burn patients. A quasi-experimental study was conducted with 96 family caregivers of burn patients admitted to a burn hospital in Iran. The caregivers' knowledge and training needs were measured at baseline. Four training sessions were provided for the experimental group and caregivers' practices were measured 6 weeks after discharge. Comparing the scores of knowledge and total care with its subdomains (i.e., diet and medication, wound care, itch decrease, wound scar healing, pain management, use of mobility assistive devices, and follow-up considerations) revealed a significant difference between the two groups of caregivers after the intervention (p < 0.01). The caregivers' practices regarding provision of care to burn patients were improved after attending the training program. Receiving sufficient and continuous information from admission to discharge can empower family members.
The Role of miR-29s in Human Cancers—An Update

MicroRNAs (miRNAs) are small non-coding RNAs that directly bind to the 3’ untranslated region (3’-UTR) of the target mRNAs to inhibit their expression. The miRNA-29s (miR-29s) are suggested to be either tumor suppressors or oncogenic miRNAs that are strongly dysregulated in various types of cancer. Their dysregulation alters the expression of their target genes, thereby exerting influence on different cellular pathways including cell proliferation, apoptosis, migration, and invasion, thereby contributing to carcinogenesis. In the present review, we aimed to provide an overview of the current knowledge on the miR-29s biological network and its functions in cancer, as well as its current and potential applications as a diagnostic and prognostic biomarker and/or a therapeutic target in major types of human cancer.
Deciphering the CircRNA-Regulated Response of Western Honey Bee (Apis mellifera) Workers to Microsporidian Invasion

Simple Summary The western honey bee (Apis mellifera) is commercially kept for crop pollination and production of bee products in China and many other countries. Currently, the circRNA-regulated response of A. mellifera to Vairimorpha ceranae infection is completely unknown. Here, characterization of circRNAs engaged in the response of A. mellifera workers to V. ceranae infection was conducted for the first time, followed by an in-depth investigation of the regulatory role of the hosts’ differentially expressed circRNAs (DEcircRNAs). Our findings not only revealed the underlying molecular mechanism of host response, but also offered novel insights into honey bee–microsporidian interaction. Abstract Vairimorpha ceranae is a widespread fungal parasite of adult honey bees that leads to a serious disease called nosemosis. Circular RNAs (circRNAs) are newly discovered non-coding RNAs (ncRNAs) that regulate biological processes such as immune defense and development. Here, 8199 and 8711 circRNAs were predicted from the midguts of Apis mellifera ligustica workers at 7 d (Am7T) and 10 d (Am10T) after inoculation (dpi) with V. ceranae spores. In combination with transcriptome data from corresponding uninoculated midguts (Am7CK and Am10CK), 4464 circRNAs were found to be shared by these four groups. Additionally, 16 circRNAs were highly conserved among A. m. ligustica, Apis cerana cerana, and Homo sapiens. In the Am7CK vs. Am7T (Am10CK vs. Am10T) comparison group, 168 (306) differentially expressed circRNAs (DEcircRNAs) were identified. RT-qPCR results showed that the expression trend of eight DEcircRNAs was consistent with that in the transcriptome datasets. The source genes of DEcircRNAs in Am7CK vs. Am7T (Am10CK vs. Am10T) were engaged in 27 (35) GO functional terms, including 1 (1) immunity-associated terms. Moreover, the aforementioned source genes were involved in three cellular immune-related pathways. Moreover, 86 (178) DEcircRNAs in workers’ midguts at 7 (10) dpi could interact with 75 (103) miRNAs, further targeting 215 (305) mRNAs. These targets were associated with cellular renewal, cellular structure, carbohydrate and energy metabolism, and cellular and humoral immunity. Findings in the present study unraveled the mechanism underlying circRNA-mediated immune responses of western honey bee workers to V. ceranae invasion, but also provided new insights into host–microsporidian interaction during nosemosis.
A mathematical analysis of a belt system with a low and time-varying velocity

In this thesis a mathematical analysis has been given for model which describes the transversal vibrations of belt systems. The belt speed is assumed to be time-varying and to be small compared to the wave speed. Not only linear string-like or beam-like models but also nonlinear models have been studied. In all cases initial-boundary value problems are formulated, and are investigated by using multiple time-scales perturbation methods. Formal approximations of the solutions are constructed and it is shown whether or not mode interactions between vibration modes occur for specific values of the belt parameters. For some linear models instabilities in the solution occur, which disappear when nonlinear terms are included in the model. It is also shown for what parameter values in the nonlinear models a simplification in the formulation of the problem (based on Kirchhoff's assumption) can (or can not) be used.
The Importance of Institutional Capacity and Negotiation Capacity in Affordable Housing Agreements: The Potential for Collective Action in Melbourne, Australia

ABSTRACT New legislation was introduced in 2018 in Victoria, Australia to encourage the negotiation of affordable housing agreements. This change resulted in the expansion of formal and informal mechanisms for cross-sectoral affordable housing delivery. In this paper we draw on 20 interviews with housing stakeholders, focusing on the process of negotiation. We propose a novel theoretical framework to interpret capacity for collective action in a loosely regulated policy area, combining insights from negotiation theory and Institutional Capacity Development (ICD) literature. We find widespread concerns about the opaque, inefficient and potentially exploitative nature of outcomes. We also find that agreements varied across projects based on levels of trust; access to information; political capital; capacity for mutual gain; and the presence of shared rules for interacting. We conclude that competition-based negotiations may lead to increased institutional capacity while also highlighting the challenges of housing delivery in the context of institutional uncertainty.