Dynamic Control of Local Field Emission Current from Carbon Nanowalls

We report on a systematic study of modulation of the field emission current from carbon nanowalls using a sharp probe as the anode in an ultrahigh vacuum system. Modulation of the local emission current was achieved by either varying the anode-cathode distance (d) with the aid of an AC magnetic field or superimposing a small AC bias on a DC bias during the field emission measurement. Current modulation ratio of over two orders of magnitude was achieved with the modulation becoming more efficient at a smaller d. The experimental results are discussed using the Fowler-Nordheim theory in combination with a simple cantilever model to account for the modulation effect. The experimental results demonstrated good static stability and dynamic controllability of local field emission current from the carbon nanowalls

Global Triggers for Attacking and Analyzing Ranking Models

Text ranking models based on BERT are now well established for a wide range of pas- sage and document ranking tasks. However, the robustness of BERT-based ranking models under adversarial attack is under-explored. In this work, we argue that BERT- rankers are vulnerable to adversarial attacks targeting retrieved documents given a query. We propose algorithms for generating adversarial perturbation of documents locally to individual queries or globally across the dataset using gradient-based optimization methods. The aim of our algorithms is to add a small number of tokens to a highly relevant or non-relevant document to cause a significant rank demotion or promotion. Our experiments show that a few number of tokens can already change the document rank by a large margin. Besides, we find that BERT-rankers heavily rely on the docu- ment start/head for relevance prediction, making the initial part of the document more susceptible to adversarial attacks. More interestingly, our statistical analysis finds a small set of recurring adversar- ial tokens that when concatenated to documents result in successful rank demo- tion/promotion of any relevant/non-relevant document respectively. Finally, our ad- versarial tokens also show particular topic preferences within and across datasets, exposing potential biases from BERT pre-training or downstream datasets

Targeting Endothelial SIRT1 for the Prevention of Arterial Aging

Cardiovascular diseases are the leading cause of morbidity and mortality in the elderly population all over the world. Arterial aging is the earliest manifestation and a key risk factor for age-induced cardiovascular abnormalities. The longevity regulator Sirtuin 1 (SIRT1) is abundantly expressed in the endothelium of the arteries and elicits potent protective functions against arterial aging. Targeting endothelial SIRT1 represents a promising approach for the prevention and treatment of cardiovascular diseases. This chapter provides an overview of SIRT1’s regulation and function in endothelial cells and discusses the potential applications of targeting endothelial SIRT1 for arterial aging-related cardiovascular diseases

ReBoot: Distributed statistical learning via refitting Bootstrap samples

In this paper, we study a one-shot distributed learning algorithm via refitting Bootstrap samples, which we refer to as ReBoot. Given the local models that are fit on multiple independent subsamples, ReBoot refits a new model on the union of the Bootstrap samples drawn from these local models. The whole procedure requires only one round of communication of model parameters. Theoretically, we analyze the statistical rate of ReBoot for generalized linear models (GLM) and noisy phase retrieval, which represent convex and non-convex problems respectively. In both cases, ReBoot provably achieves the full-sample statistical rate whenever the subsample size is not too small. In particular, we show that the systematic bias of ReBoot, the error that is independent of the number of subsamples, is $O(n ^ {-2})$ in GLM, where n is the subsample size. This rate is sharper than that of model parameter averaging and its variants, implying the higher tolerance of ReBoot with respect to data splits to maintain the full-sample rate. Simulation study exhibits the statistical advantage of ReBoot over competing methods including averaging and CSL (Communication-efficient Surrogate Likelihood) with up to two rounds of gradient communication. Finally, we propose FedReBoot, an iterative version of ReBoot, to aggregate convolutional neural networks for image classification, which exhibits substantial superiority over FedAve within early rounds of communication