Scanning tunneling microscopy characterization and metallic nanocontacts for atomically precise graphene nanoribbons

As a potential candidate for replacing silicon (Si) as a next-generation semiconducting material, atomically precise graphene nanoribbons (GNRs) have been predicted to show very interesting electronic properties based on their geometries and their underlying substrates. Once the ribbons are synthesized, confirmation of their geometries and investigating their electronic properties are essential for further implementation in devices. This dissertation addresses investigations of three different solution-synthesized atomically precise GNRs by scanning tunneling microscopy (STM) and scanning tunneling spectroscopy (STS). A dry contact transfer (DCT) technique was implemented for depositing GNRs onto various semiconducting substrates. Detailed STM and STS measurements of doublewide GNRs on InAs(110) and InSb(110) confirmed their geometries and revealed a 2 eV bandgap as well as the 3-D distribution of the local density of states. Computational modeling of the ribbon´s electronic structure showed good agreement with our experimental results, indicating a weak coupling between the InAs substrate and the GNR. STM studies of two additional types of GNRs, the extended chevron GNRs and the nitrogen-doped GNRs on InAs, demonstrate how structural modifications affect the properties of the ribbons including their bandgaps and interactions with the substrate. We also proposed a scheme of writing metallic hafnium diboride nanocontacts onto isolated GNRs using STM tip-assisted deposition for conducting transport measurements. In order to perform transport measurement in situ through sample biasing, we prefabricated an array of large metallic electrodes on Si and loaded it into the STM system. The material chosen, structural design and e-beam fabrication process are described in detail. The effect on thermal treatment to the formation of metal-silicide compounds was explored.Ope

Case-Aware Adversarial Training

The neural network (NN) becomes one of the most heated type of models in various signal processing applications. However, NNs are extremely vulnerable to adversarial examples (AEs). To defend AEs, adversarial training (AT) is believed to be the most effective method while due to the intensive computation, AT is limited to be applied in most applications. In this paper, to resolve the problem, we design a generic and efficient AT improvement scheme, namely case-aware adversarial training (CAT). Specifically, the intuition stems from the fact that a very limited part of informative samples can contribute to most of model performance. Alternatively, if only the most informative AEs are used in AT, we can lower the computation complexity of AT significantly as maintaining the defense effect. To achieve this, CAT achieves two breakthroughs. First, a method to estimate the information degree of adversarial examples is proposed for AE filtering. Second, to further enrich the information that the NN can obtain from AEs, CAT involves a weight estimation and class-level balancing based sampling strategy to increase the diversity of AT at each iteration. Extensive experiments show that CAT is faster than vanilla AT by up to 3x while achieving competitive defense effect

A secure IoT cloud storage system with fine-grained access control and decryption key exposure resistance

Internet of Things (IoT) cloud provides a practical and scalable solution to accommodate the data management in large-scale IoT systems by migrating the data storage and management tasks to cloud service providers (CSPs). However, there also exist many data security and privacy issues that must be well addressed in order to allow the wide adoption of the approach. To protect data confidentiality, attribute-based cryptosystems have been proposed to provide fine-grained access control over encrypted data in IoT cloud. Unfortunately, the existing attributed-based solutions are still insufficient in addressing some challenging security problems, especially when dealing with compromised or leaked user secret keys due to different reasons. In this paper, we present a practical attribute-based access control system for IoT cloud by introducing an efficient revocable attribute-based encryption scheme that permits the data owner to efficiently manage the credentials of data users. Our proposed system can efficiently deal with both secret key revocation for corrupted users and accidental decryption key exposure for honest users. We analyze the security of our scheme with formal proofs, and demonstrate the high performance of the proposed system via experiments

Lightning-fast and privacy-preserving outsourced computation in the cloud

AXA Research Fun

SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation

As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target dataset, is gaining increasing popularity. While extensive studies have been devoted to improving the model accuracy on target domain, an important issue of model robustness is neglected. To make things worse, conventional adversarial training (AT) methods for improving model robustness are inapplicable under UDA scenario since they train models on adversarial examples that are generated by supervised loss function. In this paper, we present a new meta self-training pipeline, named SRoUDA, for improving adversarial robustness of UDA models. Based on self-training paradigm, SRoUDA starts with pre-training a source model by applying UDA baseline on source labeled data and taraget unlabeled data with a developed random masked augmentation (RMA), and then alternates between adversarial target model training on pseudo-labeled target data and finetuning source model by a meta step. While self-training allows the direct incorporation of AT in UDA, the meta step in SRoUDA further helps in mitigating error propagation from noisy pseudo labels. Extensive experiments on various benchmark datasets demonstrate the state-of-the-art performance of SRoUDA where it achieves significant model robustness improvement without harming clean accuracy. Code is available at https://github.com/Vision.Comment: This paper has been accepted for presentation at the AAAI202