Miracle-dz/UniFAR-FTUs huggingface dataset:
Point-Cloud-Based Place Recognition Using CNN Feature Extraction

This paper proposes a novel point-cloud-based place recognition system that adopts a deep learning approach for feature extraction. By using a convolutional neural network pre-trained on color images to extract features from a range image without fine-tuning on extra range images, significant improvement has been observed when compared to using hand-crafted features. The resulting system is illumination invariant, rotation invariant and robust against moving objects that are unrelated to the place identity. Apart from the system itself, we also bring to the community a new place recognition dataset (dataset is available at https://drive.google.com/file/d/1TYga-55gvyMKAaUqX_lxJQr2A5ZAOC00/view? usp=sharing) contai- ning both point cloud and grayscale images covering a full 360° environmental view. In addition, the dataset is organized in such a way that it facilitates experimental validation with respect to rotation invariance or robustness against unrelated moving objects separately.
Gabapentin regulates dopaminergic neuron firing and theta oscillation in the ventral tegmental area to reverse depression-like behavior in chronic neuropathic pain state

Purpose Ventral tegmental area (VTA) dopamine system plays an important role in depression and is also involved in pain experience. In this study, we investigated the VTA dopaminergic (DA) neuron firing and local field potential (LFP) in pain-related depression, and we try to explore the underlying relationship between pain and depression. Materials and methods We used neuropathic pain model [spare nerve injury (SNI)] to induce pain-related depression. The Dixon up–down method was used to test mechanical hypersensitivity. Behavioral changes like open field test, sucrose preference test, and forced swim test were used to test depression-like behaviors. Gabapentin (GBP) was used to explore the chronic analgesic treatment that could reverse pain-related depression. To investigate the in vivo variations of VTA DA neuron firing and LFP, multichannel acquisition processor system was used. Results We used SNI to induce depression-like behaviors. Repeated GBP treatment reversed these behaviors after 14 days of injection. An in vivo electrophysiological analysis of the firing characteristics of VTA DA neurons and LFP revealed that SNI increased the firing rate of DA neurons, but not the burst firing activity. Surprisingly, chronic GBP reversed the firing rate of DA neurons and reduced the burst firing activity. Moreover, SNI increased the LFP power in delta and theta oscillation and decreased it in beta oscillation. Repeated administration of GBP significantly suppressed theta oscillation. Above all, chronic GBP altered these characteristics to reverse depression-like behaviors. Conclusion The present study confirmed that the tonic firing activity of VTA DA neurons, but not the burst firing activity, was the key factor in peripheral neuropathy–induced depression. Chronic GBP regulated the firing pattern of DA neurons and decreased theta oscillation in VTA to treat pain-related depression. This variation tendency of electrophysiological characteristics of VTA DA neurons and theta oscillation in VTA might represent an attempt to cope with pain-related negative mood disorder.
The effects of a balloon-blowing exercise in a 90/90 bridge position using a ball on the pulmonary function of females in their twenties

[Purpose] This study was conducted to investigate pulmonary function before and after the performance of a balloon-blowing exercise in a 90/90 bridge position using a ball among female university students in their twenties. [Participants and Methods] Participants were randomly assigned to the control group, which performed the bridge position using hip and knee flexion on a ball with a diaphragm respiratory exercise, or the experimental group, which performed a 90/90 bridge using a ball with a balloon exercise. The exercise programs were conducted 30 minutes a day, five times a week for four weeks. Pulmonary function was measured with a digital equipment before and after the exercise program period. [Results] The findings suggest that the training group resulted in significant changes in forced vital capacity, vital capacity, peak expiratory flow, and forced expiratory volume at one second. There was no significant increase in maximal voluntary capacity or vital capacity, but the pre- and post-test values improved. To compare the two groups, an independent t-test was conducted to determine vital capacity, and the results showed statistically significant differences between the experimental and control groups. [Conclusion] This study showed that a balloon-blowing exercise in a 90/90 bridge position using a ball can be used to improve pulmonary function.
Application of Foreground Detection Technology in Intelligent Video Monitoring System of Substation

Intelligent 1 video monitoring technology is an integration of image processing, pattern recognition and computer vision technology, which has developed rapidly in recent years. Due to the special environment of the substation, the video processing capacity of the traditional substation monitoring system to the complex environment is weak. In this paper, an embedded image processing board based on gaussian model hybrid foreground detection algorithm is applied to video monitoring system of substation. This monitoring system can realize different real-time monitoring tasks, such as intrusion detection, smoke detection and legacy object detection. The experimental results show that the designed system has good anti-shadow performance, and the false alarm rate is significantly improved compared with the traditional video monitoring system, which has practical promotion value in the video monitoring system of substation.
Legal Article-Aware End-To-End Memory Network for Charge Prediction

The1 task of charge prediction is to decide accurate charges for a given case of crime. The relevant law texts play an important role in the work of charge prediction, and therefore we used an end-to-end memory network to perform the charge prediction. We combine the supportive law articles from the statutory laws and regulations with the large external memory, which can be trained without requiring significantly more supervision. Several experimental results demostrate that neural networks taking the Position Embeddings (pF), Part-of-speech tag Embeddings (POSF), the WordNet (WN) and bigram information as input can get better result than previous other methods.
Fully automatic segmentation on prostate MR images based on cascaded fully convolution network

Computer‐aided diagnosis (CAD) can aid radiologists in quantifying prostate cancer, and MRI segmentation plays an essential role in CAD applications. Clinical experience shows that prostate cancer occurs predominantly in the peripheral zone (PZ) and there exist different evaluation criteria for different regions in the Prostate Imaging Reporting and Data System (PI‐RADS).
The association between AB blood group and neonatal disease.

BACKGROUND Numerous studies have examined the association between ABO blood groups and adult disease states, but very few have studied the neonatal population. The objective of this study was to determine the relationship between AB blood group and the occurrence of common neonatal disorders such as neutropenia at birth, sepsis, respiratory distress syndrome (RDS), intraventricular hemorrhage (IVH), retinopathy of prematurity (ROP), and patent ductus arteriosus (PDA) compared to all other blood groups. METHODS We performed a retrospective review on 3,981 infants born at 22 0/7 to 42 6/7 weeks' gestational age and compared the relative risk of neonatal diseases in infants with AB blood group to that of infants with all other blood groups (A, B, and O). RESULTS When compared to all other blood groups, AB infants demonstrated an increased risk for developing negative clinical outcomes. AB blood group was significantly associated with a 14-89% increased risk of neutropenia at birth, sepsis, RDS, and ROP. Risks for IVH and PDA were not significant. CONCLUSION We hypothesize that the phenotypic expression of A and B antigens, rather than the antigens themselves, in the AB group may reveal an enhanced susceptibility to injury at the endothelial level resulting in an increased risk for disease development.
Two-Dimensional Nanomaterials for Gas Sensing Applications: The Role of Theoretical Calculations

Two-dimensional (2D) nanomaterials have attracted a large amount of attention regarding gas sensing applications, because of their high surface-to-volume ratio and unique chemical or physical gas adsorption capabilities. As an important research method, theoretical calculations have been massively applied in predicting the potentially excellent gas sensing properties of these 2D nanomaterials. In this review, we discuss the contributions of theoretical calculations in the study of the gas sensing properties of 2D nanomaterials. Firstly, we elaborate on the gas sensing mechanisms of 2D layered nanomaterials, such as the traditional charge transfer mechanism, and a standard for distinguishing between physical and chemical adsorption, from the perspective of theoretical calculations. Then, we describe how to conduct a theoretical analysis to explain or predict the gas sensing properties of 2D nanomaterials. Thirdly, we discuss three important methods that have been applied in order to improve the gas sensing properties, that is, defect functionalization (vacancy, edge, grain boundary, and doping), heterojunctions, and electric fields. Among these strategies, theoretical calculations play a very important role in explaining the mechanisms underlying the enhanced gas sensing properties. Finally, we summarize both the advantages and limitations of the theoretical calculations, and present perspectives for further research on the 2D nanomaterials-based gas sensors.
Genetically coating oncolytic herpes simplex virus with CD47 allows efficient systemic delivery and prolongs virus persistence at tumor site

Current oncolytic virotherapy is primarily administered by intratumoral injection. However, systemic delivery is desirable for treating patients, particularly for those who have developed metastatic diseases. Several components are impeding the systemic delivery efficiency of oncolytic viruses. Chief among them is the rapid clearance of viral particles by the host’s mononuclear phagocyte system (MPS). We explored the possibility of genetically engrafting CD47, a “don’t eat me” signal molecule, to the membrane envelop of an oncolytic herpes simplex virus (HSV) to enable it to escape from the MPS for systemic delivery. Our results show that this modification indeed allows the virus to be more efficiently delivered to local tumors by the systemic route. Moreover, this modification also prolongs the virus persistence in local tumors after it arrives there. Consequently, systemic delivery of the modified virus produced a measurable antitumor effect against a murine tumor model that is otherwise resistant to the parental virus delivered by the same route. Our data thus suggest that engrafting enveloped oncolytic viruses such as those derived from HSV with CD47 molecule represents a conceivable strategy to enhance the efficiency of systemic delivery.
Exploring Temporal Communities in Mass Media Archives

One task key to the analysis of large multimedia archive over time is to dynamically monitor the activity of concepts and entities with their interactions. This is helpful to analyze threads of topics over news archives (how stories unfold), or to monitor evolutions and development of social groups. Dynamic graph modeling is a powerful tool to capture these interactions over time, while visualization and finding communities still remain difficult, especially with a high density of links. We propose to extract the backbone of dynamic graphs in order to ease community detection and guide the exploration of trends evolution. Through the graph structure, we interactively coordinate node-link diagrams, Sankey diagrams, time series, and animations in order to extract patterns and follow community behavior. We illustrate our system with the exploration of the role of soccer in 6 years of TV/radio magazines in France, and the role of North Korea in about 10 years of Japanese news.
An Improved Fast Visual Odometry Based on Semi-probabilistic Trimmed ICP

This paper presents an improved method for Fast Visual Odometry estimation from a Kinect-style RGB-D camera. In order to improve the accuracy and robustness of Fast Visual Odometry, we propose a modified ICP algorithm called Semi-probabilistic Trimmed ICP and a transform strategy between frame-to-model approach and frame-to-frame approach. An overlap parameter is computed to reject outlier before the registration. And if it comes to a occasional large camera motion, we skip the current frame and compute a coarse initial guess by the RANSAC algorithm between the next frame and the previous frame, finally refine the pose of camera by the original ICP. The evaluation on TUM RGB-D benchmark shows that Our Visual Odometry outperforms state-of-the-art in certain scenarios like a small-scale camera motion and it's capable of dealing with an occasional large camera motion.
Propagation of moments in Hawkes networks

The present paper provides a mathematical description of high-order moments of spiking activity in a recurrently-connected network of Hawkes processes. It extends previous studies that have explored the case of a (linear) Hawkes network driven by deterministic rate functions to the case of a stimulation by external inputs (rate functions or spike trains) with arbitrary correlation structure. Our approach describes the spatio-temporal filtering induced by the afferent and recurrent connectivities using operators of the input moments. This algebraic viewpoint provides intuition about how the network ingredients shape the input-output mapping for moments, as well as cumulants.
Multi-modal Preference Modeling for Product Search

The visual preference of users for products has been largely ignored by the existing product search methods. In this work, we propose a multi-modal personalized product search method, which aims to search products which not only are relevant to the submitted textual query, but also match the user preferences from both textual and visual modalities. To achieve the goal, we first leverage the also_view and buy_after_viewing products to construct the visual and textual latent spaces, which are expected to preserve the visual similarity and semantic similarity of products, respectively. We then propose a translation-based search model (TranSearch ) to 1) learn a multi-modal latent space based on the pre-trained visual and textual latent spaces; and 2) map the users, queries and products into this space for direct matching. The TranSearch model is trained based on a comparative learning strategy, such that the multi-modal latent space is oriented to personalized ranking in the training stage. Experiments have been conducted on real-world datasets to validate the effectiveness of our method. The results demonstrate that our method outperforms the state-of-the-art method by a large margin.
Fast Discrete Cross-modal Hashing With Regressing From Semantic Labels

Hashing has recently received great attention in cross-modal retrieval. Cross-modal retrieval aims at retrieving information across heterogeneous modalities (e.g., texts vs. images). Cross-modal hashing compresses heterogeneous high-dimensional data into compact binary codes with similarity preserving, which provides efficiency and facility in both retrieval and storage. In this study, we propose a novel fast discrete cross-modal hashing (FDCH) method with regressing from semantic labels to take advantage of supervised labels to improve retrieval performance. In contrast to existing methods that learn the projection from hash codes to semantic labels, the proposed FDCH regresses the semantic labels of training examples to the corresponding hash codes with a drift. It not only accelerates the hash learning process, but also helps generate stable hash codes. Furthermore, the drift can adjust the regression and enhance the discriminative capability of hash codes. Especially in the case of training efficiency, FDCH is much faster than existing methods. Comparisons with several state-of-the-art techniques on three benchmark datasets have demonstrated the superiority of FDCH under various cross-modal retrieval scenarios.
Feature Constrained by Pixel: Hierarchical Adversarial Deep Domain Adaptation

In multimedia analysis, one objective of unsupervised visual domain adaptation is to train a classifier that works well on a target domain given labeled source samples and unlabeled target samples. Feature alignment of two domains is the key issue which should be addressed to achieve this objective. Inspired by the recent study of Generative Adversarial Networks (GAN) in domain adaptation, this paper proposes a new model based on Generative Adversarial Network, named Hierarchical Adversarial Deep Network (HADN), which jointly optimizes the feature-level and pixel-level adversarial adaptation within a hierarchical network structure. Specifically, the hierarchical network structure ensures that the knowledge from pixel-level adversarial adaptation can be back propagated to facilitate the feature-level adaptation, which achieves a better feature alignment under the constraint of pixel-level adversarial adaptation. Extensive experiments on various visual recognition tasks show that the proposed method performs favorably against or better than competitive state-of-the-art methods.
A dynamic, modifiable model for estimating cost‐effectiveness of smoking cessation interventions in pregnancy: application to an RCT of self‐help delivered by text message

Abstract Background and Aims Previous evaluations of smoking cessation interventions in pregnancy have several limitations. Our solution to these limitations is the Economics of Smoking in Pregnancy (ESIP) model, which estimates the life‐time cost‐effectiveness of smoking cessation interventions in pregnancy from a National Health Service (NHS) and personal social services perspective. We aim to (1) describe how ESIP has been constructed and (2) illustrate its use with trial data. Methods ESIP links mothers’ and offspring pregnancy outcomes to estimate the burdens of smoking‐related disease they experience with different rates of smoking in pregnancy, both in pregnancy and throughout their life‐times. Smoking rates are inputted by model users. ESIP then estimates the costs of treating disease burdens and also mothers’ and offspring life‐years and quality‐adjusted life years (QALYs). By comparing costs incurred and healthy life following different smoking rates, ESIP estimates incremental cost‐effectiveness and benefit–cost ratios for mothers or offspring or both combined. We illustrate ESIP use using data from a pragmatic randomized controlled trial that tested a smoking cessation intervention in pregnancy. Results Throughout women's and offspring life‐times, the intervention proved cheaper than usual care, having a negative incremental cost of £38.37 (interquartile range = £21.46–56.96) and it improved health, demonstrating a 0.04 increase in incremental QALYs for mothers and offspring, implying that it is ‘dominant’ over usual care. Benefit–cost ratios suggested that every £1 spent would generate a median of £14 (interquartile range = £8–20) in health‐care savings. Conclusions Economics of Smoking in Pregnancy is the first economic model to link mothers’ and infants’ costs and benefits while reporting cost‐effectiveness in readily‐comparable units. Using ESIP with data from a trial which reported only short‐term economic analysis showed that the intervention was very likely to be cost‐effective in the longer term and to generate health‐care savings.
Response of patients with locally advanced pancreatic adenocarcinoma to high-intensity focused ultrasound treatment: a single-center, prospective, case series in China

Purpose Patients with unresectable locally advanced pancreatic cancer (LAPC) are still in dire need of effective therapies. We performed this cohort study in order to assess the efficacy and safety of high-intensity focused ultrasound (HIFU) ablation in treating patients with unresectable LAPC. Patients and methods Eighty-seven cases with unresectable LAPC from January 2014 to December 2016 were finally recruited according to the inclusion criteria. The primary end point of our study was OS of all the cases, and the secondary end points included 6-month and 12-month survival rate, tumor response rate, carbohydrate antigen (CA) 19-9 response rate, VAS, quality of life, and safety. Results All the 87 patients received HIFU ablation successfully, and were included in the efficacy and safety analysis. With a median follow-up of 16 months, median OS was estimated to be 12.2 months, with 95 % CI of 11.1–12.7 months. The 6-month and 12-month survival rates were 94.25% (95% CI =86.74–97.57) and 50.85% (95% CI =38.17–62.21), respectively. Multivariate analysis revealed that patients with VAS <4, Karnofsky performance status ≥80, and tumor size <3 cm have a significant improvement in their OS (adjusted HR [aHR] =0.26 [95% CI =0.12–0.57], P=0.001; aHR =0.34 [95% CI =0.17–0.68], P=0.02; and aHR =0.39 [95% CI =0.20–0.78], P=0.007; respectively). Tumor responses were observed in 32 (36.8%) of 87 patients and CA 19-9 response rate was 56.2%. Global health status, physical function, emotional function, and cognitive function of patients were significantly improved after HIFU treatment, and symptoms of fatigue and pain were significantly reduced. A total of 28.7% (25/87) of patients reported adverse events (AEs), mainly including fatigue (14/87), abdominal pain (7/87), fever (7/87), nausea (5/87), and rash (4/87). No severe AEs and HIFU-related deaths were reported. Conclusion HIFU ablation might be a potentially effective and safe therapeutic option for the patients with unresectable LAPC.
An Effector, BxSapB1, Induces Cell Death and Contributes to Virulence in the Pine Wood Nematode Bursaphelenchus xylophilus.

The pine wood nematode (PWN) Bursaphelenchus xylophilus has caused serious damage to pine forests in China. Effectors secreted by phytonematodes play a role in host infection. We identified and characterized an effector, BxSapB1, based on the B. xylophilus transcriptome at the early stages of infection and the transient expression of proteins in Nicotiana benthamiana. BxSapB1 triggered cell death in N. benthamiana when secreted into the apoplast, and this effect was independent of N. benthamiana brassinosteroid-insensitive 1-associated kinase 1 (NbBAK1) and suppressor of BIR1-1 (NbSOBIR1). The signal peptide of BxSapB1 was proven to be functional in yeast using the yeast signal sequence trap system and BxSapB1 was strongly expressed in the subventral gland cells of B. xylophilus, as revealed by in-situ hybridization. In addition, based on local BLAST analysis, the BxSapB1 showed 100% identity to BUX.s00139. 62, which was identified from the B. xylophilus secretome during Pinus thunbergii infection. BxSapB1 was upregulated in a highly virulent strain and downregulated in a weakly virulent strain of PWN at the early stages of infection. RNA interference assays showed that silencing BxSapB1 resulted in decreased expression of pathogenesis-related genes (PtPR-1b, PtPR-3, and PtPR-5) as well as delayed onset of symptoms in P. thunbergii infected by B. xylophilus. The combined data suggest that BxSapB1 can trigger cell death in N. benthamiana and that it contributes to the virulence in B. xylophilus during parasitic interaction.