Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Homing via Guided Locality Preserving Matching

This study proposes a simple yet surprisingly effective feature matching approach, termed as guided locality preserving matching (GLPM), for visual homing of panoramic images. The key idea of our approach is merely to preserve the neighborhood structures of potential true matches between two panoramic images. We formulate it into a mathematical model, and derive a simple closed-form solution with linearithmic time and linear space complexities. This enables our method to accomplish the mismatch removal from hundreds of putative correspondences in only a few milliseconds. To handle extremely large proportions of outliers, we further design a guided matching strategy based on the proposed method, using the matching result on a small putative set with a high inlier ratio to guide the matching on a large putative set. This strategy can also significantly boost true matches without sacrifice in accuracy. To apply our GLPM to the visual homing problem, we develop a method for dense motion flow estimation from sparse feature matches based on Tikhonov regularization. Moreover, the focus-of-contraction/focus-of-expansion is derived to determine homing directions. The effectiveness of our method is demonstrated on a panoramic database in both feature matching and visual homing.
Design and Implementation of a Shared Multi-tiered Storage System

In recent decades, cloud-based shared storage services create a promising solution for data store. In addition, flash-based solid-state drives (SSDs) have been widely deployed in the storage systems, especially as caches, to boost I/O performance. In a shared storage, performance isolation is critical since misbehaving or high-demand tenants can overuse the storage resource, leading to unfair performance to other well-behaving tenants. However, how to achieve performance isolation in multi-tiered storage systems has not been well addressed. Thus, in this paper, we address the interference problems in shared multi-tiered storage environments, and propose a framework to realize performance isolation in shared multi-tiered storage systems. The experimental results show our proposed framework is effective in realizing the multi-tiered-based shared storage systems in cloud computing environments. With PIMS, interference in a multi-tiered storage can be eliminated and performance isolation can be ensured.
Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals

Automatic emotion recognition based on electroencephalo-graphic (EEG) signals has received increasing attention in recent years. The Deep Residual Networks (ResNets) can solve vanishing gradient problem and exploding gradient problem well in computer vision and can learn more profound semantic information. And for traditional methods, frequency features often play important role in signal processing area. Thus, in this paper, we use the pre-trained ResNets to extract deep semantic information and the linear-frequency cepstral coefficients (LFCC) as features from raw EEG signals. Then the two features are fused to improve the emotion classification performance of our approach. Moreover, several classifiers are used for our fused features to evaluate the performance and it shows that the proposed approach is effective for emotion classification. We find that the best performance is achieved when use k-nearst neighbor (KNN) as classifier, and we provide a detailed discussion for the reason.
A Cumulative Quality Model for HTTP Adaptive Streaming

The quality of adaptive streaming may fluctuate strongly during a session. Therefore, a challenge is how to evaluate the quality of a session over time. In this study, a cumulative quality model is proposed for HTTP adaptive streaming. The model is based on the concept of a sliding window of video segments over a session. It is found that the minimum window quality, the last window quality, and the average window quality are key components of the cumulative quality model. In addition, the proposed model is applicable to real-time quality monitoring, which is especially important for cost-effective evaluation of streaming technologies and as basis for adaptive quality mechanisms.
A review on the current situation and challenges of colistin resistance in poultry production

ABSTRACT Colistin has been re-assessed as a critically important antimicrobial in humans due to its efficacy against multi-drug resistant Gram-negative bacteria, in particular P. aeruginosa, A. baumannii, and K. pneumoniae. The recent discovery of mobile colistin resistance (mcr) determinants in humans and animals has brought concerns regarding the future of this antimicrobial. In this paper, we aim to highlight the current challenges with colistin resistant bacteria and to summarize reliable global data on colistin resistance in poultry production. In addition, we present and compare data from a screening for colistin resistance carried out on a collection of clinical Escherichia coli isolated from poultry in Italy. In Europe, resistance rates for Salmonella and E. coli are in general low with sporadic incidence of high colistin resistance levels. Absence of resistance or very low rates have been recorded in countries where colistin is either not employed (e.g. Norway) or used in minimal amounts (e.g. Denmark) in food-producing animals. In large poultry meat producing countries, such as China and Brazil, the widespread use of colistin has resulted in the dissemination of resistance determinants in diverse bacterial species. Worryingly, these bacteria are often co-resistant to other critically important antimicrobials, such as extended-spectrum cephalosporins. The data gap for many countries and for zoonotic bacteria, the role of the “phantom resistome” and the circulation of mcr-carriers expressing resistance phenotypes close or below the current ECOFF values, should be considered in future investigations. The importance of poultry as a cheap protein source and the global effort to mitigate colistin resistance and preserve this essential antimicrobial require a thorough re-assessment of colistin use in poultry.
Expression analysis of limb element markers during mouse embryonic development

Background: While data regarding expression of limb element and tissue markers during normal mouse limb development exist, few studies show expression patterns in upper and lower limbs throughout key limb development stages. A comparison to normal developmental events is essential when analyzing development of the limb in mutant mice models. Results: Expression patterns of the joint marker Gdf5, tendon and ligament marker Scleraxis, early muscle marker MyoD1, and blood vessel marker Cadherin5 (Cdh5) are presented during the most active phases of embryonic mouse limb patterning. Anti‐neurofilament staining of developing nerves in the fore‐ and hindlimbs and cartilage formation and progression also are described. Conclusions: This study demonstrates and describes a range of key morphological markers and methods that together can be used to assess normal and abnormal limb development. Developmental Dynamics 247:1217–1226, 2018. © 2018 The Authors. Developmental Dynamics published by Wiley Periodicals, Inc. on behalf of American Association of Anatomists
Stiffness Modulator: A Novel Actuator for Human Augmentation

Stiffness modulators are devices that promote a novel means of actuation; they provide stiffness modulation without deliberately doing mechanical work. These type of compliant actuators may be used for human augmentation to complement co-contracted antagonistic muscles and as such reduce muscle activity and metabolic energy cost. Despite the theoretical appeal of this concept, its implementation remains elusive in practical applications. This is particularly true for human augmentation which requires a portable stiffness modulator. In this paper, we present a compact, lightweight, and self-contained stiffness modulator. Using this device, we demonstrate stiffness augmentation of the human knee joint in a sit to stand task. The experimental results indicate that the proposed device is able to assist a human by reducing muscle activity while drawing minimal battery power.
Automatic Music Transcription Leveraging Generalized Cepstral Features and Deep Learning

Spectral features are limited in modeling musical signals with multiple concurrent pitches due to the challenge to suppress the interference of the harmonic peaks from one pitch to another. In this paper, we show that using multiple features represented in both the frequency and time domains with deep learning modeling can reduce such interference. These features are derived systematically from conventional pitch detection functions that relate to one another through the discrete Fourier transform and a nonlinear scaling function. Neural networks modeled with these features outperform state-of-the-art methods while using less training data.
Eleutheroside B1 mediates its anti-influenza activity through POLR2A and N-glycosylation

Influenza viruses represent a serious threat to human health. Although our research group has previously demonstrated the antiviral and anti-inflammatory activities of eleutheroside B1, a detailed explanation of the mechanism by which it is effective against the influenza virus remains to be elucidated. In the present study, the transcriptomic responses of influenza A virus-infected lung epithelial cells (A549) treated with eleutheroside B1 were investigated using high-throughput RNA sequencing, and potential targets were identified using a molecular docking technique, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assay, and DNA methylation analysis. The transcriptomic data revealed that there are 1,871 differentially expressed genes (DEGs) between the cells infected with the influenza virus strain variant PR8, and the cells infected with PR8 and treated with eleutheroside B1. Among the DEGs, RNA polymerase II subunit A (POLR2A; encoding the largest subunit of RNA polymerase II) and mannosidase α class II member 1 (MAN2A1) were selected from the molecular docking analysis with eleutheroside B1. The docking score of Drosophila melanogaster MAN2A1 (3BVT) was 11.3029, whereas that of POLR2A was 9.0133. The RT-qPCR results demonstrated that the expression levels of host genes (MAN2A2, POLR2A) and viral genes (PA, PB1, PB2, HA) were downregulated following eleutheroside B1 treatment. Bisulfite-sequencing PCR was performed to investigate whether eleutheroside B1 was able to modify the DNA methylation of POLR2A, and the results suggested that the average proportion of methylated CpGs (-222-72 bp) increased significantly following treatment with eleutheroside B1. Taken together, these findings suggested that eleutheroside B1 may affect N-glycan biosynthesis, the chemokine signaling pathway, cytokine-cytokine receptor interaction and, in particular, may target the POLR2A to inhibit the production of influenza virus genes.
Early Targeting of L-Selectin on Leukocytes Promotes Recovery after Spinal Cord Injury, Implicating Novel Mechanisms of Pathogenesis

Visual Abstract L-selectin, a lectin-like receptor on all leukocyte classes, functions in adhesive and signaling roles in the recruitment of myeloid cells from the blood to sites of inflammation. Here, we consider L-selectin as a determinant of neurological recovery in a murine model of spinal cord injury (SCI). Spinal cord-injured, L-selectin knock-out (KO) mice (male) showed improved long-term recovery with greater white matter sparing relative to wild-type (WT) mice and reduced oxidative stress in the injured cord at 72 h post-SCI. There was a partial and transient reduction in accumulation of neutrophils in the injured spinal cords of KOs at 24 h post-injury. To complement these findings with KO mice, we sought a pharmacologic means for lowering L-selectin levels. We found that diclofenac, a nonsteroidal anti-inflammatory drug (NSAID), induced the shedding of L-selectin from the cell surface of myeloid subsets, specifically neutrophils and non-classical monocytes, in the blood and the injured spinal cord. Diclofenac administration to injured WT mice enhanced neurological recovery to a level comparable to that of KOs but did not improve recovery in KOs. While diclofenac treatment had no effect on myeloid cell accumulation, there was a reduction in oxidative stress at 72 h post-SCI. These findings implicate L-selectin in secondary pathogenesis beyond a role in leukocyte recruitment and raise the possibility of repurposing diclofenac for the treatment of SCI.
Duo Binary Turbo Coded 256 QAM with Constellation Shaping

Bit interleaved coded modulation (BICM) is a widely used technique which associates powerful error correction codes with high order modulations in order to achieve a robust system with high spectral efficiency. The use of non-equiprobable modulations (constellation shaping) makes it possible to approach the capacity limit and a shaping gain is obtained. Contrary to what is used traditionally to improve the performances of BICM systems, in this paper we use only one shaping code to control the two components (I and Q) of the duo binary turbo coded 256 QAM (Quadrature Amplitude Modulation) modulation. Furthermore, no iterative process is used between decoding and demapping. This leads to a reduction in complexity with an interesting gain. Simulation results over Gaussian channel show a shaping gain of 1.2 dB compared to the uniform 256 QAM constellation. Moreover, a gain in terms of computation time of more than 45% is observed. In Rayleigh channel a shaping gain of 0.6 dB is obtained. To improve this gain a $\pi/\mathbf{8}$ rotated 256 QAM constellation is used with iterative processes between decoding and demapping. This gives a gain of 1.1 dB compared to the classical BICM systems.
The Sensitivity, Specificity and Accuracy of Warning Signs in Predicting Severe Dengue, the Severe Dengue Prevalence and Its Associated Factors

Objectives: To study Malaysian dengue clinical practice guideline (CPG) warning signs (WS) in predicting severe dengue (SD) and its associated factors among confirmed cases presented to a teaching hospital in north-eastern Malaysia in 2014. Methods: A cross-sectional study was performed in February 2015 using secondary data acquired from the hospital records. There were 2607 confirmed dengue cases presented to Hospital Universiti Sains Malaysia (HUSM) in 2014. Seven hundred patients were selected after proportionate stratified random sampling conducted according to the number of cases in 12 different months in 2014. Data were collected and analysed using SPSS version 22.0. Results: Severe dengue outcomes represented 4.9% of cases. The prevalence of any of WS in SD was 91.2%. The most common WSs prior to SD were persistent vomiting (55.9%), and abdominal pain/tenderness (52.9%). The most sensitive warning sign in detecting SD was abdominal pain (59%). Specificity of individual WS were generally good, especially of clinical fluid accumulation (99%), hepatomegaly (98%) and mucosal bleeding (93%). Factors associated with SD were persistent vomiting (Adjusted odds ratio (aOR)): 2.41), mucosal bleeding (aOR: 4.73) and haematocrit rise with rapid platelet drop (aOR: 2.74). Conclusion: A focus on sensitivity, specificity, predictive values and association of a number of particular WS should be emphasized in order to better predict severe dengue outcomes.
High-Precision Depth Estimation with the 3D LiDAR and Stereo Fusion

We present a deep convolutional neural network (CNN) architecture for high-precision depth estimation by jointly utilizing sparse 3D LiDAR and dense stereo depth information. In this network, the complementary characteristics of sparse 3D LiDAR and dense stereo depth are simultaneously encoded in a boosting manner. Tailored to the LiDAR and stereo fusion problem, the proposed network differs from previous CNNs in the incorporation of a compact convolution module, which can be deployed with the constraints of mobile devices. As training data for the LiDAR and stereo fusion is rather limited, we introduce a simple yet effective approach for reproducing the raw KITTI dataset. The raw LiDAR scans are augmented by adapting an off-the-shelf stereo algorithm and a confidence measure. We evaluate the proposed network on the KITTI benchmark and data collected by our multi-sensor acquisition system. Experiments demonstrate that the proposed network generalizes across datasets and is significantly more accurate than various baseline approaches.
Distal End Force Sensing with Optical Fiber Bragg Gratings for Tendon-Sheath Mechanisms in Flexible Endoscopic Robots

Accurate haptic feedback is a critical challenge for surgical robots, especially for flexible endoscopic surgical robots whose transmission systems are Tendon-Sheath Mechanisms (TSMs) with highly nonlinear friction profiles and force hysteresis. For distal end haptic sensing of TSMs, this paper, for the first time, proposes to measure the compression force on the sheath at the distal end so that the tension force on the tendon, which equals the compression force on the sheath, can be obtained. A new force sensor, i.e., a nitinol tube attached with an optical Fiber Bragg Grating (FBG) fiber, is proposed to measure the compression force on the sheath. This sensor, with similar diameter and configuration (hollow) as the sheath, can be compactly integrated with TSMs and surgical end-effectors. In this paper, mechanics analysis and verification tests are presented to reveal the relationship between the tension force on the tendon and the compression force on the sheath. The proposed force sensor was calibrated in tests with a sensitivity of 24.28 pm/N and integrated with a tendon-sheath driven grasper to demonstrate the effectiveness of the proposed approach and sensor. The proposed approach and sensor can also be applied for a variety of TSMs-driven systems, such as robotic fingers/hands, wearable devices, and rehabilitation devices.
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation

Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots. Most semantic segmentation research focuses on improving estimation accuracy with little consideration on efficiency. Several previous studies that emphasize high-speed inference often fail to produce high-accuracy segmentation results. In this paper, we propose a novel convolutional network named Efficient Dense modules with Asymmetric convolution (EDANet), which employs an asymmetric convolution structure and incorporates dilated convolution and dense connectivity to achieve high efficiency at low computational cost and model size. EDANet is 2.7 times faster than the existing fast segmentation network, ICNet, while it achieves a similar mIoU score without any additional context module, post-processing scheme, and pretrained model. We evaluate EDANet on Cityscapes and CamVid datasets, and compare it with the other state-of-art systems. Our network can run with the high-resolution inputs at the speed of 108 FPS on one GTX 1080Ti.
Implementing a Standardized Clinical Pathway Leads to Reduced Asthma Admissions and Health Care Costs

Introduction: Asthma exacerbations are 1 of the leading causes of hospital admissions in children in the United States. High volumes in the emergency department can lead to delayed treatment. Several studies have shown that implementation of a standardized clinical pathway can improve adherence to evidence-based standards. The purpose of our quality improvement project was to develop a standardized pathway of care for children with asthma exacerbations to improve time to treatment and reduce admissions. Methods: The team used process mapping to review the current process of care for patients with asthma exacerbations presenting to the Emergency Department. After identification of several barriers, the team used plan-do-study-act cycles to develop a standardized clinical pathway of care for children based on their respiratory clinical score. Further interventions occurred after data collection and analyzation through run charts. Results: Implementation of a standardized clinical pathway for children with asthma presenting to the Emergency Department resulted in treatment with steroids in less than 60 minutes. Overall admissions were decreased from an average of 24% to 17% throughout the intervention period. We estimated cost savings for the institution at over $230,000 for the 2 years after implementation of the pathway. Conclusions: Using a multidisciplinary team approach to develop a standardized clinical pathway for a common childhood illness like asthma can result in reduced time to treatment and admissions.
A Low-Cost Navigation Strategy for Yield Estimation in Vineyards

Accurate yield estimation is very important for improving the vineyard management, the quality of the grapes and the health of the vines. The most common systems use RGB image processing for achieving a good estimation. In order to collect images, robots or farming vehicles can be equipped with a RGB camera. In this paper, we propose a low-cost autonomous system which can navigate through a vineyard while collecting grape pictures in order to provide a yield estimation. Our system uses only a laser scanner to detect the row and follows it until its end, then it navigates towards the next one, exploiting the knowledge of the vineyard. The navigation algorithm was tested both in simulation and in a real environment with good results. Furthermore, a yield estimation of two different grape varieties is presented.
Tolerability of iobitridol in patients with non‐immediate hypersensitivity reactions to iodinated contrast media

Youngwoo Choi Dong-Hyun Lee Ji-Ho Lee Yoo Seob Shin Seung-Hyun Kim Hae-Sim Park Department of Allergy and Clinical Immunology, Ajou University School of Medicine, Suwon, Korea Clinical Trial Center, Ajou University Medical Center, Suwon, Korea Correspondence: Hae-Sim Park, Department of Allergy and Clinical Immunology, Ajou University School of Medicine, 164 World cup-ro, Yeongtong-gu, Suwon, Korea (hspark@ajou.ac.kr).
Performance Analysis of the Idle Mode Capability in a Dense Heterogeneous Cellular Network

In this paper, we study the impact of the base station (BS) idle mode capacity (IMC) on the network performance of multi-tier and dense heterogeneous cellular networks (HCNs) with both line-of-sight (LoS) and non-line-of-sight transmissions. Different from most existing works that investigated network scenarios with an infinite number of user equipments (UEs), we consider a more practical set-up with a finite number of UEs in our analysis. Moreover, in our model, the small BSs (SBSs) apply a positive power bias in the cell association procedure, so that macrocell UEs are actively encouraged to use the more lightly loaded SBSs. In addition, to address the severe interference that these cell range expanded UEs may suffer, the macro BSs (MBSs) apply enhanced inter-cell interference coordination, in the form of almost blank subframe (ABS) mechanism. For this model, we derive the coverage probability and the rate of a typical UE in the whole network or a certain tier. The impact of the IMC on the performance of the network is shown to be significant. In particular, it is important to note that there will be a surplus of BSs when the BS density exceeds the UE density, and thus a large number of BSs switch off. As a result, the overall coverage probability, as well as the area spectral efficiency, will continuously increase with the BS density, addressing the network outage that occurs when all BSs are active and the interference becomes LoS dominated. Finally, the optimal ABS factors are investigated in different BS density regions. One of major findings is that MBSs should give up all resources in favor of the SBSs when the small cell networks go ultra-dense. This reinforces the need for orthogonal deployments, shedding new light on the design and deployment of the future 5G dense HCNs.