Optimal subsampling for large scale Elastic-net regression

Datasets with sheer volume have been generated from fields including computer vision, medical imageology, and astronomy whose large-scale and high-dimensional properties hamper the implementation of classical statistical models. To tackle the computational challenges, one of the efficient approaches is subsampling which draws subsamples from the original large datasets according to a carefully-design task-specific probability distribution to form an informative sketch. The computation cost is reduced by applying the original algorithm to the substantially smaller sketch. Previous studies associated with subsampling focused on non-regularized regression from the computational efficiency and theoretical guarantee perspectives, such as ordinary least square regression and logistic regression. In this article, we introduce a randomized algorithm under the subsampling scheme for the Elastic-net regression which gives novel insights into L1-norm regularized regression problem. To effectively conduct consistency analysis, a smooth approximation technique based on alpha absolute function is firstly employed and theoretically verified. The concentration bounds and asymptotic normality for the proposed randomized algorithm are then established under mild conditions. Moreover, an optimal subsampling probability is constructed according to A-optimality. The effectiveness of the proposed algorithm is demonstrated upon synthetic and real data datasets.Comment: 28 pages, 7 figure

Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk

Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observation. Most of the existing methods for safe reinforcement learning can only handle transition disturbance or observation disturbance since these two kinds of disturbance affect different parts of the agent; besides, the popular worst-case return may lead to overly pessimistic policies. To address these issues, we first theoretically prove that the performance degradation under transition disturbance and observation disturbance depends on a novel metric of Value Function Range (VFR), which corresponds to the gap in the value function between the best state and the worst state. Based on the analysis, we adopt conditional value-at-risk (CVaR) as an assessment of risk and propose a novel reinforcement learning algorithm of CVaR-Proximal-Policy-Optimization (CPPO) which formalizes the risk-sensitive constrained optimization problem by keeping its CVaR under a given threshold. Experimental results show that CPPO achieves a higher cumulative reward and is more robust against both observation and transition disturbances on a series of continuous control tasks in MuJoCo

Source attack of decoy-state quantum key distribution using phase information

Quantum key distribution (QKD) utilizes the laws of quantum mechanics to achieve information-theoretically secure key generation. This field is now approaching the stage of commercialization, but many practical QKD systems still suffer from security loopholes due to imperfect devices. In fact, practical attacks have successfully been demonstrated. Fortunately, most of them only exploit detection-side loopholes which are now closed by the recent idea of measurement-device-independent QKD. On the other hand, little attention is paid to the source which may still leave QKD systems insecure. In this work, we propose and demonstrate an attack that exploits a source-side loophole existing in qubit-based QKD systems using a weak coherent state source and decoy states. Specifically, by implementing a linear-optics unambiguous-state-discrimination measurement, we show that the security of a system without phase randomization --- which is a step assumed in conventional security analyses but sometimes neglected in practice --- can be compromised. We conclude that implementing phase randomization is essential to the security of decoy-state QKD systems under current security analyses.Comment: 12 pages, 5 figure

Vagus Nerve Stimulation for Depression: A Systematic Review

Background: Depression is a common mental disorder worldwide. Psychological treatments and antidepressant medication are the usual treatments for depression. However, a large proportion of patients with depression do not respond to the treatments. In 2005, Vagus nerve stimulation was approved for the adjunctive long-term treatment of chronic or recurrent depression in adult patients experiencing a major depressive episode who had failed to respond to four or more adequate antidepressant treatments. However, the efficacy of VNS for treating depression remains unclear. Accordingly, we performed a systematic review to evaluate the efficacy and safety of VNS.Methods: We conducted a systematic review in accordance with the Cochrane Handbook for Systematic Reviews of Interventions. Systematic search was performed in the database of Pubmed, Embase, CENTRAL, and Web of science for identifying the suitable trials. Suicidal rate was considered as the primary outcome in this review.Result: Only two randomized sham controlled add-on studies including 255 cases (134 with VNS treatment and 121 control cases) were included in this review. None of the studies reported suicidal rate. We performed a qualitative analysis and it is suggested that there was no significant statistic difference between VNS and sham VNS on the score of 24-item Hamilton Rating Scale for Depression (HAMD24) (MD: −2.40, 95% CI: −7.90 to 3.10). Similar findings were also reported on improvement percentage of HAMD24 (MD: 1.00, 95%CI: −6.06 to 8.06), Montgomery-Asberg Depression Rating Scale (MADRS) (MD: 4.70, 95%CI: −2.98 to 12.38) and 30 item Inventory of Depressive Symptomalogy-Self-Report (IDS-SR30) (MD: 4.9, 95%CI: −1.89 to 11.69). However, a marginal difference of Beck Depression Inventory self-rating score was detected between the real and sham treatment (MD: 7.80, 95% CI: 0.34 to 15.26). Aminor effect of IDS-SR30was also found in real VNS group (RR: 2.33, 95% CI: 1.07 to 5.10).Conclusion: The efficacy and safety of VNS for depression is still unclear. Further randomized controlled trials are needed to confirm the efficacy and safety of VNS