Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning

Deep Learning shows very good performance when trained on large labeled data sets. The problem of training a deep net on a few or one sample per class requires a different learning approach which can generalize to unseen classes using only a few representatives of these classes. This problem has previously been approached by meta-learning. Here we propose a novel meta-learner which shows state-of-the-art performance on common benchmarks for one/few shot classification. Our model features three novel components: First is a feed-forward embedding that takes random class support samples (after a customary CNN embedding) and transfers them to a better class representation in terms of a classification problem. Second is a novel attention mechanism, inspired by competitive learning, which causes class representatives to compete with each other to become a temporary class prototype with respect to the query point. This mechanism allows switching between representatives depending on the position of the query point. Once a prototype is chosen for each class, the predicated label is computed using a simple attention mechanism over prototypes of all considered classes. The third feature is the ability of our meta-learner to incorporate deeper CNN embedding, enabling larger capacity. Finally, to ease the training procedure and reduce overfitting, we averages the top $t$ models (evaluated on the validation) over the optimization trajectory. We show that this approach can be viewed as an approximation to an ensemble, which saves the factor of $t$ in training and test times and the factor of of $t$ in the storage of the final model.
Improving Super Resolution Methods Via Incremental Residual Learning

Recently, Convolutional Neural Networks (CNNs) have shown promising performance in super-resolution (SR). However, these methods operate primarily on Low Resolution (LR) inputs for memory efficiency but this limits, as we demonstrate, their ability to (i) model high frequency information; and (ii) smoothly translate from LR to High Resolution (HR) space. To this end, we propose a novel Incremental Residual Learning (IRL) framework to address these mentioned issues. In IRL, first we select a typical SR pre-trained network as a master branch. Next we sequentially train and add residual branches to the main branch, where each residual branch is learned to model accumulated residuals of all previous branches. We plug state of the art methods in IRL framework and demonstrate consistent performance improvement on public benchmark datasets to set a new state of the art for SR at only ≈ 20% increase in training time.1
Differential Program Analysis with Fuzzing and Symbolic Execution

Differential program analysis means to identify the behavioral divergences in one or multiple programs, and it can be classified into two categories: identify the behavioral divergences (1) between two program versions for the same input (aka regression analysis), and (2) for the same program with two different inputs (e.g, side-channel analysis). Most of the existent approaches for both subproblems try to solve it with single techniques, which suffer from its weaknesses like scalability issues or imprecision. This research proposes to combine two very strong techniques, namely fuzzing and symbolic execution to tackle these problems and provide scalable solutions for real-world applications. The proposed approaches will be implemented on top of state-of-the-art tools like AFL and Symbolic Pathfinder to evaluate them against existent work.
Current controversies in urinary tract infections: ICI‐RS 2017

The current definition of urinary tract infection (UTI) relies on laboratory and clinical findings, which may or may not be relevant, depending upon the patient group under consideration. This report considers the utility of current definitions for UTI in adults with and without underlying neurological conditions in order to identify gaps in current understanding and to recommend directions for research.
Dynamic Self-Attention : Computing Attention over Words Dynamically for Sentence Embedding

In this paper, we propose Dynamic Self-Attention (DSA), a new self-attention mechanism for sentence embedding. We design DSA by modifying dynamic routing in capsule network (Sabouretal.,2017) for natural language processing. DSA attends to informative words with a dynamic weight vector. We achieve new state-of-the-art results among sentence encoding methods in Stanford Natural Language Inference (SNLI) dataset with the least number of parameters, while showing comparative results in Stanford Sentiment Treebank (SST) dataset.
Reliability and Accuracy of Peri-Interventional Stenosis Grading in Peripheral Artery Disease Using Color-Coded Quantitative Fluoroscopy: A Phantom Study Comparing a Clinical and Scientific Postprocessing Software

Purpose To assess quantitative stenosis grading by color-coded fluoroscopy using an in vitro pulsatile flow phantom. Methods Three different stenotic tubes (80%, 60%, and 40% diameter restriction) and a nonstenotic reference tube were compared regarding their different flow behavior by using contrast-enhanced fluoroscopy with a flat-detector system for visualisation purposes. Time-density curves (TDC), area under the curve (AUC), time-to-peak (TTP), and different ROI sizes were analyzed in three independent measurements using two different postprocessing software solutions. In addition, exemplary TDCs of a patient with a high-grade stenosis before and after stent angioplasty were acquired. Results Color-coded fluoroscopy enabled depiction of differences in AUC and TDC between high-grade (80%), middle (60%), low-grade (40%), and nonstenotic tubes. The best correlation between high-, middle-, and low-grade stenosis was appreciated in ROIs behind the stenosis. This effect was enhanced by using longer integration times (5s, 7s) and a maximum frame rate of image acquisition for analysis (correlation coefficient rho=0.9284 at 5s). TTP showed no significant differences between high- and low-grade stenosis. Conclusions Various clinical studies in the literature already demonstrated reproducible and reliable stenosis grading by analyzing TDCs acquired with color-coded fluoroscopy. In contrast to TTP, AUC values derived in ROIs behind the stenosis proved to be reliable parameters for stenosis grading. However, our results also demonstrate that several factors are able to significantly impact the evaluation of AUC values. More precisely, accuracy of acquired AUC values can be improved by choosing longer integration times, a large ROI size adapted to the vessel diameter, and a higher frame rate of image acquisition.
HCN Channels: New Therapeutic Targets for Pain Treatment

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are highly regulated proteins which respond to different cellular stimuli. The HCN currents (Ih) mediated by HCN1 and HCN2 drive the repetitive firing in nociceptive neurons. The role of HCN channels in pain has been widely investigated as targets for the development of new therapeutic drugs, but the comprehensive design of HCN channel modulators has been restricted due to the lack of crystallographic data. The three-dimensional structure of the human HCN1 channel was recently reported, opening new possibilities for the rational design of highly-selective HCN modulators. In this review, we discuss the structural and functional properties of HCN channels, their pharmacological inhibitors, and the potential strategies for designing new drugs to block the HCN channel function associated with pain perception.
ATN-161 as an Integrin α5β1 Antagonist Depresses Ocular Neovascularization by Promoting New Vascular Endothelial Cell Apoptosis

Background ATN-161 (Ac-PHSCN-NH2), an antagonist of integrin α5β1, has shown an important influence in inhibiting tumor angiogenesis and metastasis of other tumor types. However, the mechanism of action of ATN-161 and whether it can inhibit ocular neovascularization (NV) are unclear. This study investigated the role of ATN-161 in regulating ocular angiogenesis in mouse models and explored the underlying signaling pathway. Material/Methods An oxygen-induced retinopathy (OIR) mouse model and a laser-induced choroidal neovascularization (CNV) mouse model were used to test integrin α5β1 expression and the effect of ATN-161 on ocular NV by immunofluorescence staining, Western blot analysis, and flat-mount analysis. The activation of nuclear factor-κB (NF-κB), matrix metalloproteinase-2/9 (MMP-2/9), and cell apoptosis were detected by immunofluorescence staining, Western blot, real-time RT-PCR, and terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL). The cell proliferation was detected by BrdU labeling. Results In OIR and CNV mice, the protein expression level of integrin α5β1 increased compared with that in age-matched controls. The mice given ATN-161 had significantly reduced retinal neovascularization (RNV) and CNV. Blocking integrin α5β1 by ATN-161 strongly inhibited nuclear factor-κB (NF-κB) activation and matrix metalloproteinase-2/9 (MMP-2/9) expression and promoted cell apoptosis, but the effect of ATN-161 on proliferation in CNV mice was indirect and required the inhibition of neovascularization. Inhibiting NF-κB activation by ammonium pyrrolidinedithiocarbamate (PDTC) reduced RNV and promoted cell apoptosis in ocular NV. Conclusions Blocking integrin α5β1 by ATN-161 reduced ocular NV by inhibiting MMP-2/MMP-9 expression and promoting the cell apoptosis of ocular NV.
Patient-specific anatomical model for deep brain stimulation based on 7 Tesla MRI

Objective Deep brain stimulation (DBS) requires accurate localization of the anatomical target structure, and the precise placement of the DBS electrode within it. Ultra-high field 7 Tesla (T) MR images can be utilized to create patient-specific anatomical 3D models of the subthalamic nuclei (STN) to enhance pre-surgical DBS targeting as well as post-surgical visualization of the DBS lead position and orientation. We validated the accuracy of the 7T imaging-based patient-specific model of the STN and measured the variability of the location and dimensions across movement disorder patients. Methods 72 patients who underwent DBS surgery were scanned preoperatively on 7T MRI. Segmentations and 3D volume rendering of the STN were generated for all patients. For 21 STN-DBS cases, microelectrode recording (MER) was used to validate the segmentation. For 12 cases, we computed the correlation between the overlap of the STN and volume of tissue activated (VTA) and the monopolar review for a further validation of the model’s accuracy and its clinical relevancy. Results We successfully reconstructed and visualized the STN in all patients. Significant variability was found across individuals regarding the location of the STN center of mass as well as its volume, length, depth and width. Significant correlations were found between MER and the 7T imaging-based model of the STN (r = 0.86) and VTA-STN overlap and the monopolar review outcome (r = 0.61). Conclusion The results suggest that an accurate visualization and localization of a patient-specific 3D model of the STN can be generated based on 7T MRI. The imaging-based 7T MRI STN model was validated using MER and patient’s clinical outcomes. The significant variability observed in the STN location and shape based on a large number of patients emphasizes the importance of an accurate direct visualization of the STN for DBS targeting. An accurate STN localization can facilitate postoperative stimulation parameters for optimized patient outcome.
Unified Vision‐Based Methodology for Simultaneous Concrete Defect Detection and Geolocalization

Vision‐based autonomous inspection of concrete surface defects is crucial for efficient maintenance and rehabilitation of infrastructures and has become a research hot spot. However, most existing vision‐based inspection methods mainly focus on detecting one kind of defect in nearly uniform testing background where defects are relatively large and easily recognizable. But in the real‐world scenarios, multiple types of defects often occur simultaneously. And most of them occupy only small fractions of inspection images and are swamped in cluttered background, which easily leads to missed and false detections. In addition, the majority of the previous researches only focus on detecting defects but few of them pay attention to the geolocalization problem, which is indispensable for timely performing repair, protection, or reinforcement works. And most of them rely heavily on GPS for tracking the locations of the defects. However, this method is sometimes unreliable within infrastructures where the GPS signals are easily blocked, which causes a dramatic increase in searching costs. To address these limitations, we present a unified and purely vision‐based method denoted as defects detection and localization network, which can detect and classify various typical types of defects under challenging conditions while simultaneously geolocating the defects without requiring external localization sensors. We design a supervised deep convolutional neural network and propose novel training methods to optimize its performance on specific tasks. Extensive experiments show that the proposed method is effective with a detection accuracy of 80.7% and a localization accuracy of 86% at 0.41 s per image (at a scale of 1,200 pixels in the field test experiment), which is ideal for integration within intelligent autonomous inspection systems to provide support for practical applications.
Surgical techniques for reconstruction of the hypoplastic thumb

The reconstruction of a congenital hypoplastic thumb usually involves release of a tight first web space, metacarpophalangeal joint stabilization, reconstruction of intrinsic muscle function, and extrinsic tendon reconstruction, as appropriate. Numerous surgical options and combinations are available, but the approaches vary among surgeons who work in the field of congenital hand surgery and the empirical evidence that allows for evaluation of the results of techniques is scarce. Both the pre-operative assessment and intra-operative findings of all thumb elements – bone, joints, and soft tissues – should be considered in the surgical decision-making and eventually define the methods of reconstruction. This article summarizes the different reconstructive options.
Right- and left-sided stage III colon cancers present different prognostic outcomes of oxaliplatin-based adjuvant chemotherapy after curative resection

Background Growing evidence has suggested that right-sided colon cancer (RCC) and left-sided colon cancer (LCC) should be considered as different tumor entities. However, stage III colon cancer is currently treated as the same entity with uniform therapy. This study was aimed at investigating the prognostic influence of tumor location in patients with stage III colon cancer receiving adjuvant chemotherapy after curative resection. Patients and methods We retrospectively analyzed 274 eligible patients with stage III colon cancer undergoing curative tumor resection followed by adjuvant chemotherapy with oxaliplatin and capecitabine between December 2007 and December 2013. Disease-free survival (DFS) and overall survival (OS) were analyzed using Kaplan–Meier and log-rank tests, and prognostic factors were identified by Cox regression methods. Results Patients with RCC exhibited lower hemoglobin levels (23.6% vs. 9.8%; P = 0.002), larger tumor size (60.6% vs. 40.9%; P = 0.001), and a higher proportion of 12 or more resected lymph nodes (86.4% vs. 64.6%; P < 0.001) than patients with LCC. Grade 1 neurotoxicity was more common in patients with RCC than in those with LCC (53.6% vs. 40.9%; P = 0.037). RCC was significantly associated with a shorter 3-year OS than LCC, whereas a difference was noted only for stage IIIC and not stage IIIA or stage IIIB colon cancer. Multivariate analyses revealed that RCC was independently associated with a worse 3-year OS (hazard ratio: 2.213; 95% CI: 1.063–4.606; P = 0.002). In addition, an increase in 3-year OS and DFS after 6–8 cycles of adjuvant chemotherapy was only observed in patients with RCC and not in those with LCC. Conclusion This study indicated that RCC has a worse prognostic outcome for stage III colon cancer, and a full course of adjuvant chemotherapy should be suggested for patients with RCC.
The Novel Activity of Carbamazepine as an Activation Modulator Extends from NaV1.7 Mutations to the NaV1.8-S242T Mutant Channel from a Patient with Painful Diabetic Neuropathy

Neuropathic pain in patients carrying sodium channel gain-of-function mutations is generally refractory to pharmacotherapy. However, we have shown that pretreatment of cells with clinically achievable concentration of carbamazepine (CBZ; 30 μM) depolarizes the voltage dependence of activation in some NaV1.7 mutations such as S241T, a novel CBZ mode of action of this drug. CBZ reduces the excitability of dorsal root ganglion (DRG) neurons expressing NaV1.7-S241T mutant channels, and individuals carrying the S241T mutation respond to treatment with CBZ. Whether the novel activation-modulating activity of CBZ is specific to NaV1.7, and whether this pharmacogenomic approach can be extended to other sodium channel subtypes, are not known. We report here the novel NaV1.8-S242T mutation, which corresponds to the NaV1.7-S241T mutation, in a patient with neuropathic pain and diabetic peripheral neuropathy. Voltage-clamp recordings demonstrated hyperpolarized and accelerated activation of NaV1.8-S242T. Current-clamp recordings showed that NaV1.8-S242T channels render DRG neurons hyperexcitable. Structural modeling shows that despite a substantial difference in the primary amino acid sequence of NaV1.7 and NaV1.8, the S242 (NaV1.8) and S241 (NaV1.7) residues have similar position and orientation in the domain I S4-S5 linker of the channel. Pretreatment with a clinically achievable concentration of CBZ corrected the voltage dependence of activation of NaV1.8-S242T channels and reduced DRG neuron excitability as predicted from our pharmacogenomic model. These findings extend the novel activation modulation mode of action of CBZ to a second sodium channel subtype, NaV1.8.
Evidence-Based Cutoff Threshold Values from Receiver Operating Characteristic Curve Analysis for Knee Osteoarthritis in the 50-Year-Old Korean Population: Analysis of Big Data from the National Health Insurance Sharing Service

We aimed to investigate the characteristics of patients with osteoarthritis (OA), using the data of all Koreans registered in the National Health Insurance Sharing Service Database (NHISS DB), and to provide ideal alternative cutoff thresholds for alleviating OA symptoms. Patients with OA (codes M17 and M17.1–M17.9 in the Korean Standard Classification of Disease and Causes of Death) were analyzed using SAS software. Optimal cutoff thresholds were determined using receiver operating characteristic curve analysis. The 50-year age group was the most OA pathogenic group (among 40~70 years, n = 2088). All exercise types affected the change of body mass index (p < 0.05) and the sex difference in blood pressure (BP) (p < 0.01). All types of exercise positively affected the loss of waist circumference and the balance test (standing time on one leg in seconds) (p < 0.01). The cutoff threshold for the time in seconds from standing up from a chair to walking 3 m and returning to the same chair was 8.25 (80% sensitivity and 100% specificity). By using the exercise modalities, categorized multiple variables, and the cutoff threshold, an optimal alternative exercise program can be designed for alleviating OA symptoms in the 50-year age group.
A Survey on Food Computing

Food is essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding human behavior, improving human health, and understanding the culinary culture. With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries, leading to large-scale food data. Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing. Food computing acquires and analyzes heterogenous food data from different sources for perception, recognition, retrieval, recommendation, and monitoring of food. In food computing, computational approaches are applied to address food-related issues in medicine, biology, gastronomy, and agronomy. Both large-scale food data and recent breakthroughs in computer science are transforming the way we analyze food data. Therefore, a series of works has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews that shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this article, we formalize food computing and present such a comprehensive overview of various emerging concepts, methods, and tasks. We summarize key challenges and future directions ahead for food computing. This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.
A Survey of Modern Object Detection Literature using Deep Learning

Object detection is the identification of an object in the image along with its localisation and classification. It has wide spread applications and is a critical component for vision based software systems. This paper seeks to perform a rigorous survey of modern object detection algorithms that use deep learning. As part of the survey, the topics explored include various algorithms, quality metrics, speed/size trade offs and training methodologies. This paper focuses on the two types of object detection algorithms- the SSD class of single step detectors and the Faster R-CNN class of two step detectors. Techniques to construct detectors that are portable and fast on low powered devices are also addressed by exploring new lightweight convolutional base architectures. Ultimately, a rigorous review of the strengths and weaknesses of each detector leads us to the present state of the art.