Miracle-dz/UniFAR-FTUs huggingface dataset:
Increasing Physical Exam Teaching on Family-Centered Rounds Utilizing a Web-Based Tool

Introduction: Millennial trainees prefer innovative, multimodal education on topics including the physical exam (PE). Attendings inconsistently meet these needs on family-centered rounds. To enhance PE teaching, a Web site (PEToolkit) was created, but its use was infrequent. We aimed to increase PEToolkit use from 2 to 5 page counts per week in 7 months. Methods: This quality improvement project took place at a large academic center in 1 Hospital Medicine team. Key drivers informed interventions, and an annotated run chart tracked progress. We tracked secondary measures, including changes in perception of teaching skill among attendings and resident-observed methods of PE teaching, through survey methodology. Results: Median page counts increased to 5 counts per week in 7 months. The most impactful interventions included training senior residents to teach with the PEToolkit Web site and team feedback on Web site usage midweek. Survey responses from 37 attendings showed that those with more exposure to PEToolkit had increased self-perceived skill of PE teaching (P = 0.02). Survey responses from 52 residents showed that those on the intervention team reported more use of video for PE teaching (P < 0.001) and higher frequency of PE teaching (P = 0.02), compared with those on the nonintervention team. Conclusions: We increased PEToolkit Web site use during family-centered rounds, thereby emphasizing the importance of PE teaching in this setting in an innovative way. Engagement of learners, frequent feedback, and coaching should be considered when incorporating technology in teaching.
Resistance is futile: lack of predator switching and a preference for native prey predict the success of an invasive prey species

Invasive species continue to severely impact biodiversity, yet predicting the success or failure of introduced species has remained elusive. In particular, the relationship between community invasibility and native species diversity remains obscure. Here, we apply two traditional ecological concepts that inform prey population stability and hence invasibility. We first show that the native predatory crustacean Gammarus duebeni celticus exhibited similar type II (destabilizing) functional responses (FRs) towards native mayfly prey and invasive amphipod prey, when these prey species were presented separately. However, when the two prey species were presented simultaneously, the predator did not exhibit prey switching, instead consuming disproportionately more native prey than expected from the relative abundance of native and invasive species. These consumptive propensities foster reductions of native prey, while simultaneously limiting biotic resistance against the invasive species by the native predator. Since our theoretical considerations and laboratory results match known field invasion patterns, we advocate the increased consideration of FR and prey switching studies to understand and predict the success of invasive species.
Lesion Segmentation Using Entropy Based Membership

The challenges faced in medical image processing makes image processing domain one of the active research areas nowadays. Brain tumors are aberrant growth formed by cells reproducing in an abnormal manner. Detecting the location of brain lesion (tumor) is essential as it helps in the diagnosis and cure of tumor. This paper proposes a novel clustering approach for the brain lesions in BRATS training 2017. This work is an advancement of a well-known Fuzzy C-Means (FCM) method to separate out different tissues of MR Brain images. Here, a novel membership function has been proposed. The evaluation of the adopted approach is compared using several validity functions with other state-of-the-art methods. The computational result on brain MR image shows that the proposed technique is powerful and precise than the standard FCM process. This proposed approach, based on entropy based variation of fuzzy membership function is able to handle uncertainty and vagueness in images by adopting new distance modification to calculate the optimum cluster for even mixed pixels. The method has been tested on BRATS 2017 training dataset. Compared to K-Means, K-Medoids and Fuzzy C-Means the proposed method significantly reduces the segmentation errors.
QTL and QTL networks for cold tolerance at the reproductive stage detected using selective introgression in rice

Low temperature stress is one of the major abiotic stresses limiting the productivity of Geng (japonica) rice grown the temperate regions as well as in tropical high lands worldwide. To develop rice varieties with improved cold tolerance (CT) at the reproductive stage, 84 BC2 CT introgression lines (ILs) were developed from five populations through backcross breeding. These CT ILs plus 310 random ILs from the same BC populations were used for dissecting genetic networks underlying CT in rice by detecting QTLs and functional genetic units (FGUs) contributing to CT. Seventeen major QTLs for CT were identified using five selective introgression populations and the method of segregation distortion. Of them, three QTLs were confirmed using the random populations and seven others locate in the regions with previously reported CT QTLs/genes. Using multi-locus probability tests and linkage disequilibrium (LD) analyses, 46 functional genetic units (FGUs) (37 single loci and 9 association groups or AGs) distributed in 37 bins (~20%) across the rice genome for CT were detected. Together, each of the CT loci (bins) was detected in 1.7 populations, including 18 loci detected in two or more populations. Putative genetic networks (multi-locus structures) underlying CT were constructed based on strong non-random associations between or among donor alleles at the unlinked CT loci/FGUs identified in the CT ILs, suggesting the presence of strong epistasis among the detected CT loci. Our results demonstrated the power and usefulness of using selective introgression for simultaneous improvement and genetic dissection of complex traits such as CT in rice.
Curriculum-Based Neighborhood Sampling For Sequence Prediction

The task of multi-step ahead prediction in language models is challenging considering the discrepancy between training and testing. At test time, a language model is required to make predictions given past predictions as input, instead of the past targets that are provided during training. This difference, known as exposure bias, can lead to the compounding of errors along a generated sequence at test time. In order to improve generalization in neural language models and address compounding errors, we propose a curriculum learning based method that gradually changes an initially deterministic teacher policy to a gradually more stochastic policy, which we refer to as \textit{Nearest-Neighbor Replacement Sampling}. A chosen input at a given timestep is replaced with a sampled nearest neighbor of the past target with a truncated probability proportional to the cosine similarity between the original word and its top $k$ most similar words. This allows the teacher to explore alternatives when the teacher provides a sub-optimal policy or when the initial policy is difficult for the learner to model. The proposed strategy is straightforward, online and requires little additional memory requirements. We report our main findings on two language modelling benchmarks and find that the proposed approach performs particularly well when used in conjunction with scheduled sampling, that too attempts to mitigate compounding errors in language models.
Augmented Data and Improved Noise Residual-Based CNN for Printer Source Identification

With the classification revolution driven by convolutional neural networks (CNN), plenty of suitable CNN architectures are readily available. However, a limiting aspect of CNN is their hunger for data. Training data can be scarce in many forensics applications, particularly for printer forensics where hard copy pages need to be printed and then digitized using scanners. This paper aims to tackle this problem by using: 1) complementary representations of data and 2) augmented data variations. We derive a simple yet effective noise residual representation of a character image that complements the information contained in the original image. Further, rotated character variations and their monotonic grayscale transformations are used as augmented data. Since such variations are directly linked to printer mechanism, they help improve the overall classification accuracy. Finally, spatial pyramid pooling is used to accommodate characters of all sizes without compromising on a character's spatial information. The proposed method outperforms state-of-the-art CNN based method on a publicly available dataset, and its generic nature allows it to be combined with any other CNN architecture as well. Experiments indicate that for the case of very limited training data availability, the proposed method can achieve 2.5% increase in printer classification accuracy.
Design of Industrial Robot Sorting System with Visual Guidance Based on Webots

Aiming at the low efficiency of artificial detection in industrial production line, this paper builds a simulation platform of visual guide sorting system based on Webots. For static target work pieces, image is captured by the camera, use Hu invariant moments quick recognition, and get the help of chain code to further identify, so that the sorting task can be done by robots. For dynamic target work pieces, it takes much time to process the image. There will be a tracking lag phenomenon. Using Kalman filter to predict the position, we can track the target. Experimental results showed that, with the feature in the centroid of shape and the tracking error in two pixels or less, the system could achieve target-detecting and tracking at the 0.2Sm/s speed of conveyor, as well as have a better robustness.
The Robust Exact Differentiator Toolbox: Improved Discrete-Time Realization

This paper presents a new release of A Robust Exact Differentiator Toolbox for Matlab®/Simulink® proposed in [1]. This release features a new discrete-time realization of the continuous-time robust exact differentiator. The implemented discretization scheme is less sensitive to gain overestimation and does not suffer from the discretization chattering effect. Hence, the single tuning parameter of the new version of the implemented differentiator is more intuitive to tune. Furthermore, it shows superior estimation performance in the case of large sampling times in comparison to the previous release. This is confirmed by the presented results obtained by numerical simulations and a real world application.
Query Expansion with Diffusion On Mutual Rank Graphs

In query expansion for object retrieval, there is substantial danger of query drift, where irrelevant information is inferred from pseudo-relevant images to enrich the query. To address this issue, we propose a query expansion method from the viewpoint of diffusion. It explores the structure of highly ranked images in a topological space, assuming that false positives reside on different manifolds from the query. For this purpose, a mutual rank graph is defined on pseudo-relevant images, and their distribution is learned by diffusing their query similarities through the graph. The relevance of a database image can thus be obtained by marginalizing over the learned distribution. The mutual rank graph accounts for varying local density in the image space, leading to great robustness as regards query drift and high generalization ability. The proposed method experimentally shows a consistent boost in the performance of object retrieval with handcrafted features on standard benchmarks.
Moderate Intensity Intermittent Exercise Modality May Prevent Cardiovascular Drift

Cardiovascular drift (CV-Drift) may occur after the ~10th min of submaximal continuous exercising. The purpose of this study was to examine whether CV-Drift is prevented by an intermittent exercise modality, instead of a continuous exercise. Seven well-trained male cyclists volunteered to take part in the study (V˙O2max: 61.7 ± 6.13 mL·min−1·kg−1). Following familiarization sessions, athletes’ individual maximal O2 consumption (V˙O2max), maximum stroke volume responses (SVmax), and cardiac outputs (Qc) were evaluated by a nitrous-oxide re-breathing system and its gas analyzer. Then, continuous exercises were performed 30 min at cyclists’ 60% V˙O2max, while intermittent exercises consisted of three 10 min with 1:0.5 workout/recovery ratios at the same intensity. Qc measurements were taken at the 5th, 9th, 12nd, 15th, 20th, 25th, and 30th min of continuous exercises versus 5th and 10th min of workout phases of intermittent exercise modality. Greater than a 5% SV decrement, with accompanying HR, increase, while Qc remained stable and was accepted as CV-Drift criterion. It was demonstrated that there were greater SV responses throughout intermittent exercises when compared to continuous exercises (138.9 ± 17.9 vs. 144.5 ± 14.6 mL, respectively; p ≤ 0.05) and less HR responses (140.1 ± 14.8 vs. 135.2 ± 11.6 bpm, respectively; p ≤ 0.05), while mean Qc responses were similar (19.4 ± 2.1 vs. 19.4 ± 1.5 L, respectively; p > 0.05). Moreover, the mean times spent at peak SV scores of exercise sessions were greater during intermittent exercise (1.5 vs. 10 min) (p < 0.001). In conclusion, intermittent exercises reduce CV-Drift risk and increases cardiac adaptation potentials of exercises with less physiological stress.
A Classification of Video Games based on Game Characteristics linked to Video Coding Complexity

Applications used for video streaming of gaming content have seen tremendous growth over the recent years as evident with the increasing popularity of services such as Twitch.tv and YouTubeGaming. Gaming video streaming encoding needs to be performed in real-time and thus has a strict set of encoding constraints. Therefore, many traditional encoding optimization methods such as multiple-pass encoding are not suitable for live gaming video streaming applications. The video quality of streaming services is highly content dependent. While this holds true also for conventional contents, there exist many characteristics of games that do not vary much over time. Therefore, such game-specific information can be exploited to optimize the encoding process. In this paper, we present a classification of games using characteristics such as the type of camera movement, texture details, and static areas of a scene. Using a database of gaming videos from different genres and complexity, we obtain clusters corresponding to the calculated quality values (VMAF). The derived gaming characteristics are then mapped to the quality classes to obtain a decision tree based game classification. We illustrate how the classification can be used for encoding bitrate selection and quality prediction.
Timing and Order of the Molecular Events Marking the Onset of Berry Ripening in Grapevine1[OPEN]

A transcriptomic and metabolomic map of grape berry development elucidates timing and order of the key events of the global reprogramming featuring the ripening process. Grapevine (Vitis vinifera) is a model for the investigation of physiological and biochemical changes during the formation and ripening of nonclimacteric fleshy fruits. However, the order and complexity of the molecular events during fruit development remain poorly understood. To identify the key molecular events controlling berry formation and ripening, we created a highly detailed transcriptomic and metabolomic map of berry development, based on samples collected every week from fruit set to maturity in two grapevine genotypes for three consecutive years, resulting in 219 samples. Major transcriptomic changes were represented by coordinated waves of gene expression associated with early development, veraison (onset of ripening)/midripening, and late-ripening and were consistent across vintages. The two genotypes were clearly distinguished by metabolite profiles and transcriptional changes occurring primarily at the veraison/midripening phase. Coexpression analysis identified a core network of transcripts as well as variations in the within-module connections representing varietal differences. By focusing on transcriptome rearrangements close to veraison, we identified two rapid and successive shared transitions involving genes whose expression profiles precisely locate the timing of the molecular reprogramming of berry development. Functional analyses of two transcription factors, markers of the first transition, suggested that they participate in a hierarchical cascade of gene activation at the onset of ripening. This study defined the initial transcriptional events that mark and trigger the onset of ripening and the molecular network that characterizes the whole process of berry development, providing a framework to model fruit development and maturation in grapevine.
Synthetic View Generation for Absolute Pose Regression and Image Synthesis

Image based localization is one of the important problems in computer vision due to its wide applicability in robotics, augmented reality, and autonomous systems. There is a rich set of methods described in the literature on how to geometrically register a 2D image w.r. t. a 3D model. In particular, data augmentation methods such as synthetic image generation have been shown to be useful for this task. In this work, we propose a synthetic data augmentation technique and design a deep neural network, that can be trained to estimate the absolute pose of an image from synthesized sparse feature descriptors. Our choice of using sparse feature descriptors has two major advantages: ﬁrst, our network is signiﬁcantly smaller than the CNNs proposed in the literature for this task—thereby making our approach more efﬁcient and scalable. Second—and more importantly—, usage of sparse features allows to augment the training data with synthetic viewpoints, which leads to substantial improvements in the generalization performance to unseen poses. The synthetic views are further employed to augment realistic RGB images which again surpasses recent deep learning based synthetic image generation technique. A detailed analysis of the proposed networks and a rigorous evaluation on the existing datasets are provided to support our method.
Exploring the Effect of Politeness on User Contribution in Q&A sites: A Case Study of Stack Overflow

Online question and answer (Q&A) sites are platforms allowing users to seek information or social support, which is sometimes socially uncomfortable. A major challenge in Q&A sites is how to encourage user contribution. Theory of linguistic politeness informs us that politeness is able to ease the tension resulting from face-threatening acts (FTA), and it is applicable to Q&A sites. However, Stack Overflow (SO), one of the most popular Q&A site for programmers, denies the use of linguistic politeness (e.g., greetings, salutations, and gratitude expressions), aiming at building a nonsocial platform. This paper focuses on a specific politeness strategy-expressing gratitude-in Q&A sites, and examines its effect on answerers' future contributions from the aspects of quality. Results indicate that gratitude expressions in comments can motivate answerers to generate higher quality content.
Camouflage Moving Object Detection: A Review

Moving object detection is an important and basic step in any video surveillance application. Detected object has used an input to higher level tasks such as tracking, event detection, and behavioral analysis. There are several challenges in moving object detection such as illumination changes, occlusion, shadow, bootstrap, dynamic background etc. Detection of moving object having similar color as background structure is called as camouflage, which is another big challenge. The method of detecting camouflage moving object is known as Decamouflaging. In recent years, the researchers are attracted towards the camouflage moving object detection due to its application in the military. In this review paper, different techniques using various features for camouflaged moving obj ect detection were studied.
Catheter-Associated Urinary Tract Infection and Obstinate Biofilm Producers

Background Biofilms, or colonies of uropathogen growing on the surface of indwelling medical devices, can inflict obstinate or recurring infection, thought-provoking antimicrobial therapy. Methods This prospective analysis included 105 urine samples from catheterized patients receiving intensive care. Ensuing phenotypic identification, antibiotic sensitivity test was performed by modified Kirby–Bauer disc diffusion method following CLSI guidelines; MDR isolates were identified according to the combined guidelines of the European Centre for Disease Prevention and Control (ECDC) and the Centers for Disease Control and Prevention (CDC). Biofilm-forming uropathogens were detected by the tissue culture plate (TCA) method. Results The predominant uropathogen in catheter-associated UTIs (CAUTIs) was Escherichia coli 57%, followed by Klebsiella pneumonia 15%, Pseudomonas aeruginosa 12%, Staphylococcus aureus 8%, Enterobacter spp. 3%, Enterococcus faecalis, Acinetobacter spp. , and Proteus mirabilis 1.5%, of which 46% isolates were biofilm producers. Prime biofilm producers were Escherichia coli 33%, followed by Klebsiella pneumoniae 30%, Pseudomonas aeruginosa 20%, Staphylococcus aureus 10%, Acinetobacter, and Enterobacter 3.33%. Multidrug resistance associated with biofilm producers were greater than biofilm nonproducers. The Gram-negative biofilm producers found 96.15%, 80.76%, 73.07%, 53.84%, 53.84%, 46.15%, 19.23%, and 11.5% resistant to amoxyclave, ceftazidime, tetracycline, gentamicin, meropenem, nitrofurantoin, amikacin, imipenem, and fosfomycin, respectively. Gram-positive biofilm producers, however, were found 100% resistant to tetracycline, cloxacillin, and amoxyclave: 66.67% resistant to ampicillin while 33.33% resistant to gentamicin, ciprofloxacin, and nitrofurantoin. Conclusion High antimicrobial resistance was observed in biofilm producers than non-biofilm producers. Of recommended antimicrobial therapies for CAUTIs, ampicillin and amoxicillin-clavulanate were the least active antibiotics, whereas piperacillin/tazobactam and imipenem were found as the most effectual for gram-negative biofilm producer. Likewise, amoxicillin-clavulanate and tetracycline were the least active antibiotics, whereas vancomycin, fosfomycin, piperacillin-tazobactam, and meropenem were found as the most effective antibiotic for Gram-positive biofilm producer. In the limelight, the activity fosfomycin was commendable against both Gram-positive and Gram-negative biofilm producers.
The Brain Dynamics Toolbox for Matlab

The Brain Dynamics Toolbox provides a graphical tool for simulating user-defined dynamical systems in matlab. It supports three classes of differential equations commonly used in computational neuroscience: Ordinary Differential Equations, Delay Differential Equations and Stochastic Differential Equations. The design of the graphical interface fosters intuitive exploration of the dynamics, yet there is no barrier to scripting large-scale simulations and parameter explorations. System variables and parameters may range in size from simple scalars to large vectors or matrices. The toolbox is intended for dynamical models in computational neuroscience but it can be applied to continuous dynamical systems from any domain.