Balanced Sparsity for Efficient DNN Inference on GPU

In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage cost. However, it requires the customization of hardwares to speed up practical inference. Another trend accelerates sparse model inference on general-purpose hardwares by adopting coarse-grained sparsity to prune or regularize consecutive weights for efficient computation. But this method often sacrifices model accuracy. In this paper, we propose a novel fine-grained sparsity approach, balanced sparsity, to achieve high model accuracy with commercial hardwares efficiently. Our approach adapts to high parallelism property of GPU, showing incredible potential for sparsity in the widely deployment of deep learning services. Experiment results show that balanced sparsity achieves up to 3.1x practical speedup for model inference on GPU, while retains the same high model accuracy as fine-grained sparsity

Towards Trustworthy Dataset Distillation

Efficiency and trustworthiness are two eternal pursuits when applying deep learning in real-world applications. With regard to efficiency, dataset distillation (DD) endeavors to reduce training costs by distilling the large dataset into a tiny synthetic dataset. However, existing methods merely concentrate on in-distribution (InD) classification in a closed-world setting, disregarding out-of-distribution (OOD) samples. On the other hand, OOD detection aims to enhance models' trustworthiness, which is always inefficiently achieved in full-data settings. For the first time, we simultaneously consider both issues and propose a novel paradigm called Trustworthy Dataset Distillation (TrustDD). By distilling both InD samples and outliers, the condensed datasets are capable to train models competent in both InD classification and OOD detection. To alleviate the requirement of real outlier data and make OOD detection more practical, we further propose to corrupt InD samples to generate pseudo-outliers and introduce Pseudo-Outlier Exposure (POE). Comprehensive experiments on various settings demonstrate the effectiveness of TrustDD, and the proposed POE surpasses state-of-the-art method Outlier Exposure (OE). Compared with the preceding DD, TrustDD is more trustworthy and applicable to real open-world scenarios. Our code will be publicly available.Comment: 20 pages, 20 figure

Wide-Area Damping Controller of FACTS Devices for Inter-Area Oscillations Considering Communication Time Delays

The usage of remote signals obtained from a wide-area measurement system (WAMS) introduces time delays to a wide-area damping controller (WADC), which would degrade system damping and even cause system instability. The time-delay margin is defined as the maximum time delay under which a closed-loop system can remain stable. In this paper, the delay margin is introduced as an additional performance index for the synthesis of classical WADCs for flexible ac transmission systems (FACTS) devices to damp inter-area oscillations. The proposed approach includes three parts: a geometric measure approach for selecting feedback remote signals, a residue method for designing phase-compensation parameters, and a Lyapunov stability criterion and linear matrix inequalities (LMI) for calculating the delay margin and determining the gain of the WADC based on a tradeoff between damping performance and delay margin. Three case studies are undertaken based on a four-machine two-area power system for demonstrating the design principle of the proposed approach, a New England ten-machine 39-bus power system and a 16-machine 68-bus power system for verifying the feasibility on larger and more complex power systems. The simulation results verify the effectiveness of the proposed approach on providing a balance between the delay margin and the damping performance

Active Generalized Category Discovery

Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some labeled data of old classes. Given that knowledge learned from old classes is not fully transferable to new classes, and that novel categories are fully unlabeled, GCD inherently faces intractable problems, including imbalanced classification performance and inconsistent confidence between old and new classes, especially in the low-labeling regime. Hence, some annotations of new classes are deemed necessary. However, labeling new classes is extremely costly. To address this issue, we take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD). The goal is to improve the performance of GCD by actively selecting a limited amount of valuable samples for labeling from the oracle. To solve this problem, we devise an adaptive sampling strategy, which jointly considers novelty, informativeness and diversity to adaptively select novel samples with proper uncertainty. However, owing to the varied orderings of label indices caused by the clustering of novel classes, the queried labels are not directly applicable to subsequent training. To overcome this issue, we further propose a stable label mapping algorithm that transforms ground truth labels to the label space of the classifier, thereby ensuring consistent training across different active selection stages. Our method achieves state-of-the-art performance on both generic and fine-grained datasets. Our code is available at https://github.com/mashijie1028/ActiveGCDComment: Accepted to CVPR 202

microRNA-33a-5p increases radiosensitivity by inhibiting glycolysis in melanoma.

Glycolysis was reported to have a positive correlation with radioresistance. Our previous study found that the miR-33a functioned as a tumor suppressor in malignant melanoma by targeting hypoxia-inducible factor1-alpha (HIF-1α), a gene known to promote glycolysis. However, the role of miR-33a-5p in radiosensitivity remains to be elucidated. We found that miR-33a-5p was downregulated in melanoma tissues and cells. Cell proliferation was downregulated after overexpression of miR-33a-5p in WM451 cells, accompanied by a decreased level of glycolysis. In contrast, cell proliferation was upregulated after inhibition of miR-33a-5p in WM35 cells, accompanied by increased glycolysis. Overexpression of miR-33a-5p enhanced the sensitivity of melanoma cells to X-radiation by MTT assay, while downregulation of miR-33a-5p had the opposite effects. Finally, in vivo experiments with xenografts in nude mice confirmed that high expression of miR-33a-5p in tumor cells increased radiosensitivity via inhibiting glycolysis. In conclusions, miR-33a-5p promotes radiosensitivity by negatively regulating glycolysis in melanoma

Second generation Dirac cones and inversion symmetry breaking induced gaps in graphene/hexagonal boron nitride

Graphene/h-BN has emerged as a model van der Waals heterostructure, and the band structure engineering by the superlattice potential has led to various novel quantum phenomena including the self-similar Hofstadter butterfly states. Although newly generated second generation Dirac cones (SDCs) are believed to be crucial for understanding such intriguing phenomena, so far fundamental knowledge of SDCs in such heterostructure, e.g. locations and dispersion of SDCs, the effect of inversion symmetry breaking on the gap opening, still remains highly debated due to the lack of direct experimental results. Here we report first direct experimental results on the dispersion of SDCs in 0$^\circ$ aligned graphene/h-BN heterostructure using angle-resolved photoemission spectroscopy. Our data reveal unambiguously SDCs at the corners of the superlattice Brillouin zone, and at only one of the two superlattice valleys. Moreover, gaps of $\approx$ 100 meV and $\approx$ 160 meV are observed at the SDCs and the original graphene Dirac cone respectively. Our work highlights the important role of a strong inversion symmetry breaking perturbation potential in the physics of graphene/h-BN, and fills critical knowledge gaps in the band structure engineering of Dirac fermions by a superlattice potential.Comment: Nature Physics 2016, In press, Supplementary Information include

H.pylori Infection inhibits Inflammatory Bowel Disease(IBD) by affecting the intestinal flora: A systematic Review

Background: Inflammatory bowel diseases (IBD) are chronic, relapsing-remitting diseases of the gastrointestinal tract, including Crohn’s disease (CD), Ulcerative Colitis (UC), and Unclassified IBD (IBDU). Their pathogenesis involves genes and the environment as cofactors in inducing autoimmunity; mainly, the interactions between enteric pathogens and immunity are studied. For example, Helicobacter pylori (HP) is a common pathogen causing gastric inflammation. However, studies found that the number of people with HP was lower than those with IBD. Therefore, it suggests that HP might protect against IBD. Methods: The search terms "helicobacter pylori," "inflammatory bowel disease," "Crohn's disease," and "ulcerative colitis" were entered into the PubMed database. Embase, Medline, Web of Science, Scopus, PubMed publisher, Cochrane, and Google Scholar were also searched. The HP prevalence rates in IBD patients, CD patients, UC patients, and IBDU patients were calculated. So its to prove that there is an inverse relationship between HP and IBD, each group was compared to a control group. Results: Even when the comparison was made separately between each group of newly diagnosed patients and controls to rule out the possibility of pharmacologic bias, the data showed an inverse relationship between the IBD group and the controls. Conclusion: The results of this review demonstrate a striking inverse association between HP infection and the prevalence of inflammatory bowel disease (IBD), regardless of the type of IBD considered across different geographic regions. Anyway, data should be interpreted with care because more research is needed on this topic that is broader, more prospective, and more consistent. This could lead to new ideas about how the environment could cause IBD. Keywords: Inflammatory bowel disease; Helicobacter pylori; Crohn’s disease; Ulcerative colitis; Colorectal cancer DOI: 10.7176/JMPB/72-04 Publication date: May 31st 202

Impact of Power Grid Strength and PLL Parameters on Stability of Grid-Connected DFIG Wind Farm

This paper investigates the impact of power grid strength and phase-locked loop (PLL) parameters on small signal stability of grid-connected doubly fed induction generator (DFIG)-based wind farm. Modal analysis of the grid-connected DFIG wind turbine under different operating conditions and various power grid strengths are investigated at first. Modal analysis results reveal that the DFIG connected to a weak grid may easily lose stability under the heavy-duty operating conditions due to PLL oscillation. The object of this paper is to identify the PLL oscillation mechanism as well as influence factors and propose a damping solution for this oscillation mode. A simplified linear system model of the grid-connected DFIG wind turbine is proposed for analyzing the PLL oscillation. Through the complex torque coefficients method and using this model, the oscillation mechanism and influence factors including the power grid strength and the PLL parameters are identified. To suppress this PLL oscillation, a mixed H2/H∞ robust damping controller is proposed and designed for the DFIG. Electromagnetic transient simulation results of both single-DFIG system and multiply-DFIG system verify the correctness of the analysis results and effectiveness of the proposed damping controller