Miracle-dz/UniFAR-FTUs huggingface dataset:
P-wave characteristics and histological atrial abnormality

Introduction: Fibro-fatty transformation of atrial walls is believed to be the leading cause of deteriorated atrial conduction, however any direct assessment of fibrosis extent in the major atrial conduction routes in relation to P-wave characteristics is lacking. We aimed at assessing P-wave morphology and duration in relation to histology of atrial myocardium. Method: Atrial specimens from 11 patients died from cardiovascular causes (7 men; median age 73 years, range 54-82) were collected. Tissue samples were taken at the level of superior and inferior pulmonary veins, center of posterior left atrial wall, Terminal Crest (CT) and Bachmann's Bundle (BB) and stained with Masson's trichrome for assessment of fibrosis and fatty tissue extent at all locations. Standard 12-lead ECGs in sinus rhythm recorded during hospital stay were retrieved from medical records, scanned for manual assessment of PWD and P-wave morphology. Partial Interatrial Block (pIAB) was defined as prolonged (≥120 ms) and bimodal P-wave in any lead on a standard 12-lead ECG. Results: The median PWD was 160 (range 120-200) ms. Fibrosis extent in CT highly correlated to PWD (r=0.914, p<0.001). The combination of fibrosis extent and fatty tissue in BB (16%, range 1-41%), CT (18%, range 3-47%) or SPV (15%, range 6-24%) correlated to PWD (r=0.627, p=0.039; r=0.795, p=0.003; and r=0.668, p=0.025, respectively). pIAB pattern was observed in 6 patients, however it was not associated with increased fibrosis or fatty tissue content at any sampling location. Conclusions: Our findings provide direct evidence of strong association between PWD and the extent of structural abnormalities in the atrial myocardium and the major atrial conduction routes, but do not support presumed association between fibrosis and ECG pattern of pIAB.
Asymptotic behavior at infinity of solutions of Lagrangian mean curvature equations

We studied the asymptotic behavior of solutions with quadratic growth condition of a class of Lagrangian mean curvature equations $F_{\tau}(\lambda(D^2u))=f(x)$ in exterior domain, where $f$ satisfies a given asymptotic behavior at infinity. When f(x) is a constant near infinity, it is not necessary to demand the quadratic growth condition anymore. These results are a kind of exterior Liouville theorem, and can also be regarded as an extension of theorems of Pogorelov, Flanders and Yuan.
Housing Investment, Stock Market Participation and Household Portfolio choice: Evidence from China's Urban Areas

This paper employs the survey data of CHFS (2013) to investigate the impact of housing investment on household stock market participation and portfolio choice. The results show that larger housing investment encourages the household participation in the stock market, but reduces the proportion of their stockholding. The above conclusion remains true even when the endogeneity problem is controlled with risk attitude classification, Heckman model test and subsample regression. This study shows that the growth in the housing market will not lead to stock market development because of lack of household financial literacy and the low expected yield on stock market.
Deep Snake for Real-Time Instance Segmentation

This paper introduces a novel contour-based approach named deep snake for real-time instance segmentation. Unlike some recent methods that directly regress the coordinates of the object boundary points from an image, deep snake uses a neural network to iteratively deform an initial contour to match the object boundary, which implements the classic idea of snake algorithms with a learning-based approach. For structured feature learning on the contour, we propose to use circular convolution in deep snake, which better exploits the cycle-graph structure of a contour compared against generic graph convolution. Based on deep snake, we develop a two-stage pipeline for instance segmentation: initial contour proposal and contour deformation, which can handle errors in object localization. Experiments show that the proposed approach achieves competitive performances on the Cityscapes, KINS, SBD and COCO datasets while being efficient for real-time applications with a speed of 32.3 fps for 512 x 512 images on a 1080Ti GPU. The code is available at https://github.com/zju3dv/snake/.
Correlations of Immunopathologic Types with Clinical Manifestations and Histological Classifications in Children with Henoch Schonlein Purpura Nephritis

Objective To explore the relationships among immunopathologic types,clinical manifestations and histological classifications in children with henoch-schonlein purpura nephritis(HSPN). Methods Sixty-two children with HSPN performed renal biopsy in children′s hospital affiliated to Soochow university from Feb.2003 to Dec.2008 were analyzed retrospectively in terms of immunopathologic types,clinical manifestations and histological classifications.Five immunopathologic types had been categorized based on the result of immunofluorescence test,the respective characteristic of clinical manifestations,histological classifications in 5 immunopathologic types were analyzed. Results Among the 62 patients,hematuria associated with proteinunia was the most common type in clinical manifestation,grade Ⅲb and IgA plus IgG plus IgM deposit was the most common type in histological and immunopathologic changes respectively.There was no statistically correlation between the clinical manifestations and immunopathologic types(P 0.05). Compared with other immunopathologic types,IgA plus IgG plus IgM deposition type had higher proportion of histological grade Ⅲb-Ⅵ(P 0.05),and patients with IgA plus IgM or IgA plus IgG plus IgM deposit had higher rate of histological grade as Ⅲb-Ⅵ than those with pure IgA or IgA plus IgG(P 0.05). Contrasted to the patients without C 3 deposition,those with C 3 deposition were inclined to histological grade Ⅲb-Ⅵ(P 0.05). Conclusions For children with HSPN,immunopathologic types have some relationships with histological changes,but no correlation is found between immunopathologic types and clinical manifestations in this paper.
Covert Association of Applications on Edge Devices by Processor Workload

The scheme of application (app) distribution systems involving incentivized third-party app vendors is a desirable option for the emerging edge computing systems. However, such a scheme also brings various security challenges as faced by the current mobile app distribution systems. In this paper, we study a threat named covert device association, in which the vendors of two apps collude to figure out which of their app installations run on the same edge device. If the two colluding apps are popular, the threat can be used to launch various types of further attacks at scale. For example, the user of the compromised edge device, who wishes to remain anonymous to one of the two apps, will be de-anonymized if the user is not anonymous to the other app.
Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

We investigate learning a ConvNet classifier with class-imbalanced data. We found that a ConvNet over-fits significantly to the minor classes that do not have sufficient training instances, even if it is trained using vanilla empirical risk minimization (ERM). We conduct a series of analysis and argue that feature deviation between the training and test instances serves as the main cause. We propose to incorporate class-dependent temperatures (CDT) in learning a ConvNet: CDT forces the minor-class instances to have larger decision values in training, so as to compensate for the effect of feature deviation in testing. We validate our approach on several benchmark datasets and achieve promising results. Our studies further suggest that class-imbalanced data affects traditional machine learning and recent deep learning in very different ways. We hope that our insights can inspire new ways of thinking in resolving class-imbalanced deep learning.
Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification

Person re-identification (re-ID) aims at identifying the same persons' images across different cameras. However, domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to another one. State-of-the-art unsupervised domain adaptation methods for person re-ID transferred the learned knowledge from the source domain by optimizing with pseudo labels created by clustering algorithms on the target domain. Although they achieved state-of-the-art performances, the inevitable label noise caused by the clustering procedure was ignored. Such noisy pseudo labels substantially hinders the model's capability on further improving feature representations on the target domain. In order to mitigate the effects of noisy pseudo labels, we propose to softly refine the pseudo labels in the target domain by proposing an unsupervised framework, Mutual Mean-Teaching (MMT), to learn better features from the target domain via off-line refined hard pseudo labels and on-line refined soft pseudo labels in an alternative training manner. In addition, the common practice is to adopt both the classification loss and the triplet loss jointly for achieving optimal performances in person re-ID models. However, conventional triplet loss cannot work with softly refined labels. To solve this problem, a novel soft softmax-triplet loss is proposed to support learning with soft pseudo triplet labels for achieving the optimal domain adaptation performance. The proposed MMT framework achieves considerable improvements of 14.4%, 18.2%, 13.1% and 16.4% mAP on Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT unsupervised domain adaptation tasks.
Recognizing Images with at most one Spike per Neuron

In order to port the performance of trained artificial neural networks (ANNs) to spiking neural networks (SNNs), which can be implemented in neuromorphic hardware with a drastically reduced energy consumption, an efficient ANN to SNN conversion is needed. Previous conversion schemes focused on the representation of the analog output of a rectified linear (ReLU) gate in the ANN by the firing rate of a spiking neuron. But this is not possible for other commonly used ANN gates, and it reduces the throughput even for ReLU gates. We introduce a new conversion method where a gate in the ANN, which can basically be of any type, is emulated by a small circuit of spiking neurons, with At Most One Spike (AMOS) per neuron. We show that this AMOS conversion improves the accuracy of SNNs for ImageNet from 74.60% to 80.97%, thereby bringing it within reach of the best available ANN accuracy (85.0%). The Top5 accuracy of SNNs is raised to 95.82%, getting even closer to the best Top5 performance of 97.2% for ANNs. In addition, AMOS conversion improves latency and throughput of spike-based image classification by several orders of magnitude. Hence these results suggest that SNNs provide a viable direction for developing highly energy efficient hardware for AI that combines high performance with versatility of applications.
The mean-field limit of quantum Bose gases at positive temperature

We prove that the grand canonical Gibbs state of an interacting quantum Bose gas converges to the Gibbs measure of a nonlinear Schr\"odinger equation in the mean-field limit, where the density of the gas becomes large and the interaction strength is proportional to the inverse density. Our results hold in dimensions $d \leq 3$. For $d > 1$ the Gibbs measure is supported on distributions of negative regularity and we have to renormalize the interaction. More precisely, we prove the convergence of the relative partition function and of the reduced density matrices in the $L^r$-norm with optimal exponent $r$. Moreover, we prove the convergence in the $L^\infty$-norm of Wick-ordered reduced density matrices, which allows us to control correlations of Wick-ordered particle densities as well as the asymptotic distribution of the particle number. Our proof is based on a functional integral representation of the grand canonical Gibbs state, in which convergence to the mean-field limit follows formally from an infinite-dimensional stationary phase argument for ill-defined non-Gaussian measures. We make this argument rigorous by introducing a white-noise-type auxiliary field, through which the functional integral is expressed in terms of propagators of heat equations driven by time-dependent periodic random potentials and can, in turn, be expressed as a gas of interacting Brownian loops and paths. When the gas is confined by an external trapping potential, we control the decay of the reduced density matrices using excursion probabilities of Brownian bridges.
The properties of prompt emission in short gamma-ray bursts with extended emission observed by Fermi/GBM

Short gamma-ray bursts (GRB) with extended emission (EE) that are composed of an initial short hard spike followed by a long-lasting EE are thought to comprise a sucategory of short GRBs. The narrow energy band available during the Swift era, combined with a lack of spectral information, prevented the discovery of the intrinsic properties of these events. In this paper, we perform a systematic search of short GRBs with EE using all available Fermi/GBM data. The search identified 26 GBM-detected short GRBs with EE that are similar to GRB 060614 observed by Swift/BAT. We focus on investigating the spectral and temporal properties of both the hard spike and the EE component of all 26 GRBs, and explore differences and possible correlations between them. We find that while the peak energy (Ep) of the hard spikes is slightly harder than that of the EE, their fluences are comparable. The harder Ep seems to correspond to a larger fluence and peak flux, with a large scatter for both the hard spike and the EE component. Moreover, the Ep of both the hard spike and the EE are compared with other short GRBs. Finally, we also compare the properties of GRB 170817A with those of short GRBs with EE and find no significant statistical differences between them. We find that GRB 170817A has the lowest Ep, probably because it is off-axis.
Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images

We propose a new method for cancer subtype classification from histopathological images, which can automatically detect tumor-specific features in a given whole slide image (WSI). The cancer subtype should be classified by referring to a WSI, i.e., a large-sized image (typically 40,000x40,000 pixels) of an entire pathological tissue slide, which consists of cancer and non-cancer portions. One difficulty arises from the high cost associated with annotating tumor regions in WSIs. Furthermore, both global and local image features must be extracted from the WSI by changing the magnifications of the image. In addition, the image features should be stably detected against the differences of staining conditions among the hospitals/specimens. In this paper, we develop a new CNN-based cancer subtype classification method by effectively combining multiple-instance, domain adversarial, and multi-scale learning frameworks in order to overcome these practical difficulties. When the proposed method was applied to malignant lymphoma subtype classifications of 196 cases collected from multiple hospitals, the classification performance was significantly better than the standard CNN or other conventional methods, and the accuracy compared favorably with that of standard pathologists.
Deceiving Image-to-Image Translation Networks for Autonomous Driving With Adversarial Perturbations

Deep neural networks (DNNs) have achieved impressive performance on handling computer vision problems. However, it has been found that DNNs are vulnerable to adversarial examples. For such reason, adversarial perturbations have been recently studied in several respects. However, most previous works have focused on image classification tasks, and it has never been studied regarding adversarial perturbations on Image-to-image (Im2Im) translation tasks, showing great success in handling paired and/or unpaired mapping problems in the field of autonomous driving and robotics. This letter examines different types of adversarial perturbations that can fool Im2Im frameworks for autonomous driving purposes. We propose both quasi-physical and digital adversarial perturbations that can make Im2Im models yield unexpected results. We then empirically analyze these perturbations and show that they generalize well under both paired for image synthesis and unpaired settings for style transfer. We also validate that there exist some perturbation thresholds over which the Im2Im mapping is disrupted or impossible. The existence of these perturbations reveals that there exist crucial weaknesses in Im2Im models. Lastly, we show that our methods illustrate how perturbations affect the quality of outputs, pioneering the improvement of the robustness of current SOTA networks for autonomous driving.
Measurement of the absolute branching fraction of D∗s0(2317)±→π0D±s

The process e(+)e(-) -> D*D-+(s)*(s0) (2317)(-) + c.c. is observed for the first time with the data sample of 567 pb(-1) collected with the BESIII detector operating at the BEPCII collider at a center-of-mass energy root s = 4.6 GeV. The statistical significance of the D*(s0) (2317)(+/-) signal is 5.8 sigma and the mass is measured to be (2318.3 +/- 1.2 +/- 1.2) MeV/c(2). The absolute branching fraction B(D*(s0) (2317)(+/-) -> pi D-0(s)+/-) is measured as 1.00(-0.14)(+0.00) (stat)(-0.14)(+0.00) (syst) for the first time. The uncertainties are statistical and systematic, respectively.
Effects of electric acupuncture on expression of cerebral ischemic vascular endothelial growth factor and angiopoietin 1 in hypertensive rats

Objective To observe the effects of electric acupuncture on the expression of vascular endothelial growth factor,angiopoietin 1 and angiogenesis in stroke-prone renovascular hypertensive rat(RHRSP). Methods 120 RHRSP models were established by bilaterally narrowing the renal arteries with silver loop clips.The RHRSP were randomly divided into 4 groups:naive group,sham-operated group,model group and electric acupuncture group.The models of rat MCAO were created by stringing middle cerebral artery in the two later groups.The electric acupuncture group was treated with electric acupuncture stimulating "BaiHui"and "DaZhui" point once a day for 14 days.VEGF,Ang 1 and microvessel density(MVD)in the rat cerebral ischemic tissue were detected by immunohistochemistry,light microscope,etc.at the different time points after 2 h ischemia and 1,7,14 and 28-day reperfusion.Results The VEGF and Ang 1 positive cells of the ischemic area increased markedly in model group compared with the naive and sham-operated groups (P0,05). The number of VEGF and Ang 1 positive cells in electric acupuncture group was increased significantly compared with the model group (P 0.05). MVD in electric acupuncture group was markedly increased compared with model group after 14-day and 28- day reperfusion (P 0.05). Conclusions The electric acupuncture can promote the neurofunctional recovery in cerebral ischemic area.The mechanism may be related with the regulation of the expression of VEGF and Ang 1 and activating angiogenesis.
Bacterial Isolation and Their Antibiotic Susceptibility Pattern from Post-Operative Wound Infected Patients

Background: Postoperative wound infections are found to be the cause of morbidity and mortality in the surgical patients. The wound that drain purulent discharge, with bacteria identified on culture is Postoperative infection. Wound draining pus, wounds that are opened by the surgeon; and wounds that the surgeon considers infected are considered postoperative wound infection. Objectives: Bacterial isolation and antibiotic susceptibility patternof postoperative wound infected patients and to find the correlation between their age, sex and the site of infection. Material and methods: 200post surgical patients with wound infections admitted in different surgical wards wereincluded. Pus and serous fluid samples from the wounds were collected. Gram staining &culture was done via conventional techniques followed by AST by Kirby Bauer disk diffusion technique. Results:Gram negative (27.5%) were predominant over gram positive (11.5%). In gram positive organism maximum isolates were S.Aureus followed by Enterococcus and streptococcus In gram negative organism’s maximum isolate was E.coli followed by Pseudomonas Aeruginosa ,klebsiella& Proteus. Conclusion: E.coli (14.5%) was the commonest pathogen isolated due to more laprotomy surgical procedures followed by equal rate of Staphyalococcusaureus (7%) and Pseudomonas aeruginosa (7%), Klebsiella (3.5%), Proteus (2.5%), Enterococcus (2.5%) and Streptococcus (2%). First generation of Cephalosporins and most of the Aminoglycosides were sensitive against gram positives whereas successive generations of cephalosporins were sensitive against gram negatives including some penicillin drugs like Ampicillin, Amoxicillin, and Pipracillin-Tazobactum which were sensitive for both gram positive and gram
(D* to D + gamma) and (B* to B + gamma) as derived from QCD Sum Rules

The method of QCD sum rules in the presence of the external electromagnetic $F_{\mu\nu}$ field is used to analyze radiative decays of charmed or bottomed mesons such as $D^{\ast}\to D\gamma$ and $B^{\ast}\to B\gamma$, with the susceptibilities obtained previously from the study of baryon magnetic moments. Our predictions on $D^{\ast}$ decays agree very well with the experimental data. There are differences among the various theoretical predictions on $B^{\ast}$ decays but the data are not yet available.
Dynamical masses of early-type galaxies: a comparison to lensing results and implications for the stellar initial mass function and the distribution of dark matter: Luminous and dark matter in early-type galaxies

This work aims to study the distribution of the luminous and dark matter in Coma early-type galaxies. Dynamical masses obtained under the assumption that mass follows light do not match with the masses of strong gravitational lens systems of similar velocity dispersions. Instead, dynamical ﬁts with dark matter haloes are in good agreement with lensing results. We derive mass-to-light ratios of the stellar populations from Lick absorption line indices, reproducing well the observed galaxy colours. Even in dynamical models with dark matter haloes the amount of mass that follows the light increases more rapidly with the galaxy velocity dispersion than expected for a constant stellar initial mass function (IMF). While galaxies around σ eff ≈ 200 km s − 1 are consistent with a Kroupa IMF, the same IMF underpredicts luminous dynamical masses of galaxies with σ eff ≈ 300 km s − 1 by a factor of 2 and more. A systematic variation in the stellar IMF with the galaxy velocity dispersion could explain this trend with a Salpeter IMF for the most massive galaxies. If the IMF is instead constant, then some of the dark matter in high-velocity-dispersion galaxies must follow a spatial distribution very similar to that of the light. A combination of both, a varying IMF and a component of dark matter that follows the light is possible as well. For a subsample of galaxies with old stellar populations, we show that the tilt in the Fundamental Plane can be explained by systematic variations of the total (stellar + dark) mass inside the effective radius. We tested commonly used mass estimator formulae, ﬁnding them accurate at the 20–30 per cent level.