Marginalized average attentional network for weakly-supervised learning

© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved. In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner. The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion. MAA samples multiple subsets from the video snippet features according to a set of latent discriminative probabilities and takes the expectation over all the averaged subset features. Theoretically, we prove that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others. Therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos. Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from O(2T) to O(T2). Extensive experiments on two large-scale video datasets show that our MAAN achieves a superior performance on weakly-supervised temporal action localization

Marginalized average attentional network for weakly-supervised learning

© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved. In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner. The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion. MAA samples multiple subsets from the video snippet features according to a set of latent discriminative probabilities and takes the expectation over all the averaged subset features. Theoretically, we prove that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others. Therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos. Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from O(2T) to O(T2). Extensive experiments on two large-scale video datasets show that our MAAN achieves a superior performance on weakly-supervised temporal action localization

SparCL: Sparse Continual Learning on the Edge

Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, i.e., model performance deterioration on past tasks when learning a new task. However, the training efficiency of a CL system is under-investigated, which limits the real-world application of CL systems under resource-limited scenarios. In this work, we propose a novel framework called Sparse Continual Learning(SparCL), which is the first study that leverages sparsity to enable cost-effective continual learning on edge devices. SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity. Specifically, we propose task-aware dynamic masking (TDM) to learn a sparse network throughout the entire CL process, dynamic data removal (DDR) to remove less informative training data, and dynamic gradient masking (DGM) to sparsify the gradient updates. Each of them not only improves efficiency, but also further mitigates catastrophic forgetting. SparCL consistently improves the training efficiency of existing state-of-the-art (SOTA) CL methods by at most 23X less training FLOPs, and, surprisingly, further improves the SOTA accuracy by at most 1.7%. SparCL also outperforms competitive baselines obtained from adapting SOTA sparse training methods to the CL setting in both efficiency and accuracy. We also evaluate the effectiveness of SparCL on a real mobile phone, further indicating the practical potential of our method.Comment: Published at NeurIPS 2022 as a conference pape

Undifferentiated liver sarcoma – rare entity: a case report and review of the literature

<p>Abstract</p> <p>Introduction</p> <p>Undifferentiated Liver Sarcoma, also known as Undifferentiated Embryonal Sarcoma of the Liver, is a rare, highly malignant neoplasm which affects mostly the pediatric population, although a few cases have been reported in adults. It accounts for about 13% of pediatric hepatic malignancies.</p> <p>Case presentation</p> <p>We report a case of undifferentiated liver sarcoma in a 14-year-old Chinese boy who presented with non-specific right hypochondriac pain. Exploratory laparotomy with tumor resection was performed, followed by adjuvant chemotherapy.</p> <p>Conclusion</p> <p>Undifferentiated Liver Sarcoma is a rare, highly malignant hepatic neoplasm affecting almost exclusively the pediatric population. The prognosis is poor but recent evidence shows that long-term survival is possible after complete surgical resection and postoperative chemotherapy.</p

Genetic Structure of the Tree Peony (Paeonia rockii) and the Qinling Mountains as a Geographic Barrier Driving the Fragmentation of a Large Population

Tree peonies are great ornamental plants associated with a rich ethnobotanical history in Chinese culture and have recently been used as an evolutionary model. The Qinling Mountains represent a significant geographic barrier in Asia, dividing mainland China into northern (temperate) and southern (semi-tropical) regions; however, their flora has not been well analyzed. In this study, the genetic differentiation and genetic structure of Paeonia rockii and the role of the Qinling Mountains as a barrier that has driven intraspecific fragmentation were evaluated using 14 microsatellite markers.Twenty wild populations were sampled from the distributional range of P. rockii. Significant population differentiation was suggested (F(ST) value of 0.302). Moderate genetic diversity at the population level (H(S) of 0.516) and high population diversity at the species level (H(T) of 0.749) were detected. Significant excess homozygosity (F(IS) of 0.076) and recent population bottlenecks were detected in three populations. Bayesian clusters, population genetic trees and principal coordinate analysis all classified the P. rockii populations into three genetic groups and one admixed Wenxian population. An isolation-by-distance model for P. rockii was suggested by Mantel tests (r = 0.6074, P<0.001) and supported by AMOVA (P<0.001), revealing a significant molecular variance among the groups (11.32%) and their populations (21.22%). These data support the five geographic boundaries surrounding the Qinling Mountains and adjacent areas that were detected with Monmonier's maximum-difference algorithm.Our data suggest that the current genetic structure of P. rockii has resulted from the fragmentation of a formerly continuously distributed large population following the restriction of gene flow between populations of this species by the Qinling Mountains. This study provides a fundamental genetic profile for the conservation and responsible exploitation of the extant germplasm of this species and for improving the genetic basis for breeding its cultivars

Discovery of a Distinct Superfamily of Kunitz-Type Toxin (KTT) from Tarantulas

BACKGROUND: Kuntiz-type toxins (KTTs) have been found in the venom of animals such as snake, cone snail and sea anemone. The main ancestral function of Kunitz-type proteins was the inhibition of a diverse array of serine proteases, while toxic activities (such as ion-channel blocking) were developed under a variety of Darwinian selection pressures. How new functions were grafted onto an old protein scaffold and what effect Darwinian selection pressures had on KTT evolution remains a puzzle. PRINCIPAL FINDINGS: Here we report the presence of a new superfamily of ktts in spiders (TARANTULAS: Ornithoctonus huwena and Ornithoctonus hainana), which share low sequence similarity to known KTTs and is clustered in a distinct clade in the phylogenetic tree of KTT evolution. The representative molecule of spider KTTs, HWTX-XI, purified from the venom of O. huwena, is a bi-functional protein which is a very potent trypsin inhibitor (about 30-fold more strong than BPTI) as well as a weak Kv1.1 potassium channel blocker. Structural analysis of HWTX-XI in 3-D by NMR together with comparative function analysis of 18 expressed mutants of this toxin revealed two separate sites, corresponding to these two activities, located on the two ends of the cone-shape molecule of HWTX-XI. Comparison of non-synonymous/synonymous mutation ratios (omega) for each site in spider and snake KTTs, as well as PBTI like body Kunitz proteins revealed high Darwinian selection pressure on the binding sites for Kv channels and serine proteases in snake, while only on the proteases in spider and none detected in body proteins, suggesting different rates and patterns of evolution among them. The results also revealed a series of key events in the history of spider KTT evolution, including the formation of a novel KTT family (named sub-Kuntiz-type toxins) derived from the ancestral native KTTs with the loss of the second disulfide bridge accompanied by several dramatic sequence modifications. CONCLUSIONS/SIGNIFICANCE: These finding illustrate that the two activity sites of Kunitz-type toxins are functionally and evolutionally independent and provide new insights into effects of Darwinian selection pressures on KTT evolution, and mechanisms by which new functions can be grafted onto old protein scaffolds

Disparities and risks of sexually transmissible infections among men who have sex with men in China: a meta-analysis and data synthesis.

BACKGROUND: Sexually transmitted infections (STIs), including Hepatitis B and C virus, are emerging public health risks in China, especially among men who have sex with men (MSM). This study aims to assess the magnitude and risks of STIs among Chinese MSM. METHODS: Chinese and English peer-reviewed articles were searched in five electronic databases from January 2000 to February 2013. Pooled prevalence estimates for each STI infection were calculated using meta-analysis. Infection risks of STIs in MSM, HIV-positive MSM and male sex workers (MSW) were obtained. This review followed the PRISMA guidelines and was registered in PROSPERO. RESULTS: Eighty-eight articles (11 in English and 77 in Chinese) investigating 35,203 MSM in 28 provinces were included in this review. The prevalence levels of STIs among MSM were 6.3% (95% CI: 3.5-11.0%) for chlamydia, 1.5% (0.7-2.9%) for genital wart, 1.9% (1.3-2.7%) for gonorrhoea, 8.9% (7.8-10.2%) for hepatitis B (HBV), 1.2% (1.0-1.6%) for hepatitis C (HCV), 66.3% (57.4-74.1%) for human papillomavirus (HPV), 10.6% (6.2-17.6%) for herpes simplex virus (HSV-2) and 4.3% (3.2-5.8%) for Ureaplasma urealyticum. HIV-positive MSM have consistently higher odds of all these infections than the broader MSM population. As a subgroup of MSM, MSW were 2.5 (1.4-4.7), 5.7 (2.7-12.3), and 2.2 (1.4-3.7) times more likely to be infected with chlamydia, gonorrhoea and HCV than the broader MSM population, respectively. CONCLUSION: Prevalence levels of STIs among MSW were significantly higher than the broader MSM population. Co-infection of HIV and STIs were prevalent among Chinese MSM. Integration of HIV and STIs healthcare and surveillance systems is essential in providing effective HIV/STIs preventive measures and treatments. TRIAL REGISTRATION: PROSPERO NO: CRD42013003721

Assessing the clinical utility of cancer genomic and proteomic data across tumor types

Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, miRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We found that incorporating molecular data with clinical variables yielded statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2–23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data