5,484 research outputs found

    Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks

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    Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs, can mislead a DNN to classify them as any target labels. This work provides a solution to hardening DNNs under adversarial attacks through defensive dropout. Besides using dropout during training for the best test accuracy, we propose to use dropout also at test time to achieve strong defense effects. We consider the problem of building robust DNNs as an attacker-defender two-player game, where the attacker and the defender know each others' strategies and try to optimize their own strategies towards an equilibrium. Based on the observations of the effect of test dropout rate on test accuracy and attack success rate, we propose a defensive dropout algorithm to determine an optimal test dropout rate given the neural network model and the attacker's strategy for generating adversarial examples.We also investigate the mechanism behind the outstanding defense effects achieved by the proposed defensive dropout. Comparing with stochastic activation pruning (SAP), another defense method through introducing randomness into the DNN model, we find that our defensive dropout achieves much larger variances of the gradients, which is the key for the improved defense effects (much lower attack success rate). For example, our defensive dropout can reduce the attack success rate from 100% to 13.89% under the currently strongest attack i.e., C&W attack on MNIST dataset.Comment: Accepted as conference paper on ICCAD 201

    Emerging Strategies in TCR-Engineered T Cells

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    Immunotherapy of cancer has made tremendous progress in recent years, as demonstrated by the remarkable clinical responses obtained from adoptive cell transfer (ACT) of patient-derived tumor infiltrating lymphocytes, chimeric antigen receptor (CAR)-modified T cells (CAR-T) and T cell receptor (TCR)-engineered T cells (TCR-T). TCR-T uses specific TCRS optimized for tumor engagement and can recognize epitopes derived from both cell-surface and intracellular targets, including tumor-associated antigens, cancer germline antigens, viral oncoproteins, and tumor-specific neoantigens (neoAgs) that are largely sequestered in the cytoplasm and nucleus of tumor cells. Moreover, as TCRS are naturally developed for sensitive antigen detection, they are able to recognize epitopes at far lower concentrations than required for CAR-T activation. Therefore, TCR-T holds great promise for the treatment of human cancers. In this focused review, we summarize basic, translational, and clinical insights into the challenges and opportunities of TCR-T. We review emerging strategies used in current ACT, point out limitations, and propose possible solutions. We highlight the importance of targeting tumor-specific neoAgs and outline a strategy of combining neoAg vaccines, checkpoint blockade therapy, and adoptive transfer of neoAg-specific TCR-T to produce a truly tumor-specific therapy, which is able to penetrate into solid tumors and resist the immunosuppressive tumor microenvironment. We believe such a combination approach should lead to a significant improvement in cancer immunotherapies, especially for solid tumors, and may provide a general strategy for the eradication of multiple cancers

    Time-dependent global simulations of a thin accretion disc: the effects of magnetically-driven winds on thermal instability

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    According to the standard thin disc theory, it is predicted that the radiation-pressure-dominated inner region of a thin disc is thermally unstable, while observations suggest that it is common for a thin disc of more than 0.01 Eddington luminosity to be in a thermally stable state. Previous studies have suggested that magnetically driven winds have the potential to suppress instability. In this work, we implement one-dimensional global simulations of the thin accretion disc to study the effects of magnetically driven winds on thermal instability. The winds play a role in transferring the angular momentum of the disc and cooling the disc. When the mass outflow rate of winds is low, the important role of winds is to transfer the angular momentum and then shorten the outburst period. When the winds have a high mass outflow rate, they can calm down the thermal instability. We also explore the parameter space of the magnetic field strength and the mass loading parameter.Comment: 9 pages, 6 figures, accepted for publication in MNRA

    Coordinated Damping Control Design for Power System With Multiple Virtual Synchronous Generators Based on Prony Method

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    With more renewables integrated into power grids, the systems are being transformed into low inertia power electronic dominated systems. In this situation, the virtual synchronous generator (VSG) control strategy was proposed to deal with insufficient inertia challenge caused by the reduction of synchronous generation. However, as the VSG control method emulates the dynamic behavior of traditional synchronous machines, the interaction between multiple VSG controllers and synchronous generators (SGs) may cause low-frequency oscillation similar to that caused by the interaction between multiple SGs. This paper reveals that the system low-frequency oscillatory modes are affected by multiple VSGs. Then Prony analysis is utilized to extract the system mode information which will be subsequently used for VSG controller design, and a decentralized sequential coordinated method is proposed to design the supplementary damping controller (SDC) for multiple VSGs. The system low-frequency oscillation is first analyzed based on a modified two-area system with a linearized state-space model, and a further case study based on a revised New England 10-machine 39-bus system is used to demonstrate the effectiveness of the proposed coordinated method for multiple VSGs
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