QEBA: Query-Efficient Boundary-Based Blackbox Attack

Machine learning (ML), especially deep neural networks (DNNs) have been widely used in various applications, including several safety-critical ones (e.g. autonomous driving). As a result, recent research about adversarial examples has raised great concerns. Such adversarial attacks can be achieved by adding a small magnitude of perturbation to the input to mislead model prediction. While several whitebox attacks have demonstrated their effectiveness, which assume that the attackers have full access to the machine learning models; blackbox attacks are more realistic in practice. In this paper, we propose a Query-Efficient Boundary-based blackbox Attack (QEBA) based only on model's final prediction labels. We theoretically show why previous boundary-based attack with gradient estimation on the whole gradient space is not efficient in terms of query numbers, and provide optimality analysis for our dimension reduction-based gradient estimation. On the other hand, we conducted extensive experiments on ImageNet and CelebA datasets to evaluate QEBA. We show that compared with the state-of-the-art blackbox attacks, QEBA is able to use a smaller number of queries to achieve a lower magnitude of perturbation with 100% attack success rate. We also show case studies of attacks on real-world APIs including MEGVII Face++ and Microsoft Azure.Comment: Accepted by CVPR 202

Deep integrative information extraction from scientific literature

Doctor of PhilosophyDepartment of Computer ScienceWilliam H HsuThis dissertation presents deep integrative methods from both visual and textual perspectives to address the challenges of extracting information from documents, particularly scientific literature. The number of publications in the academic literature has soared. Published literature includes large amounts of valuable information that can help scientists and researchers develop new directions in their fields of interest. Moreover, this information can be used in many applications, among them scholar search engines, relevant paper recommendations, and citation analysis. However, the increased production of scientific literature makes the process of literature review laborious and time-consuming, especially when large amounts of data are stored in heterogeneous unstructured formats, both numerical and image-based text, both of which are challenging to read and analyze. Thus, the ability to automatically extract information from the scientific literature is necessary. In this dissertation, we present integrative information extraction from scientific literature using deep learning approaches. We first investigated a vision-based approach for understanding layout and extracting metadata from scanned scientific literature images. We tried convolutional neural network and transformer-based approaches to document layout. Furthermore, for vision-based metadata information extraction, we proposed a trainable recurrent convolutional neural network that integrated scientific document layout detection and character recognition to extract metadata information from the scientific literature. In doing so, we addressed the problem of existing methods that cannot combine the techniques of layout extraction and text recognition efficiently because different publishers use different formats to present information. This framework requires no additional text features added into the network during the training process and will generate text content and appropriate labels of major sections of scientific documents. We then extracted key-information from unstructured texts in the scientific literature using technologies based on Natural Language Processing (NLP). Key-information could include the named entity and the relationship between pairs of entities in the scientific literature. This information can help provide researchers with key insights into the scientific literature. We proposed the attention-based deep learning method to extract key-information with limited annotated data sets. This method enhances contextualized word representations using pre-trained language models like a Bidirectional Encoder Representations from Transformers (BERT) that, unlike conventional machine learning approaches, does not require hand-crafted features or training with massive data. The dissertation concludes by identifying additional challenges and future work in extracting information from the scientific literature

Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science

This paper describes a machine learning and data science pipeline for structured information extraction from documents, implemented as a suite of open-source tools and extensions to existing tools. It centers around a methodology for extracting procedural information in the form of recipes, stepwise procedures for creating an artifact (in this case synthesizing a nanomaterial), from published scientific literature. From our overall goal of producing recipes from free text, we derive the technical objectives of a system consisting of pipeline stages: document acquisition and filtering, payload extraction, recipe step extraction as a relationship extraction task, recipe assembly, and presentation through an information retrieval interface with question answering (QA) functionality. This system meets computational information and knowledge management (CIKM) requirements of metadata-driven payload extraction, named entity extraction, and relationship extraction from text. Functional contributions described in this paper include semi-supervised machine learning methods for PDF filtering and payload extraction tasks, followed by structured extraction and data transformation tasks beginning with section extraction, recipe steps as information tuples, and finally assembled recipes. Measurable objective criteria for extraction quality include precision and recall of recipe steps, ordering constraints, and QA accuracy, precision, and recall. Results, key novel contributions, and significant open problems derived from this work center around the attribution of these holistic quality measures to specific machine learning and inference stages of the pipeline, each with their performance measures. The desired recipes contain identified preconditions, material inputs, and operations, and constitute the overall output generated by our computational information and knowledge management (CIKM) system.Comment: 15th International Conference on Document Analysis and Recognition Workshops (ICDARW 2019

PARP-1 and Ku compete for repair of DNA double strand breaks by distinct NHEJ pathways

Poly(ADP-ribose)polymerase 1 (PARP-1) recognizes DNA strand interruptions in vivo and triggers its own modification as well as that of other proteins by the sequential addition of ADP-ribose to form polymers. This modification causes a release of PARP-1 from DNA ends and initiates a variety of responses including DNA repair. While PARP-1 has been firmly implicated in base excision and single strand break repair, its role in the repair of DNA double strand breaks (DSBs) remains unclear. Here, we show that PARP-1, probably together with DNA ligase III, operates in an alternative pathway of non-homologous end joining (NHEJ) that functions as backup to the classical pathway of NHEJ that utilizes DNA-PKcs, Ku, DNA ligase IV, XRCC4, XLF/Cernunnos and Artemis. PARP-1 binds to DNA ends in direct competition with Ku. However, in irradiated cells the higher affinity of Ku for DSBs and an excessive number of other forms of competing DNA lesions limit its contribution to DSB repair. When essential components of the classical pathway of NHEJ are absent, PARP-1 is recruited for DSB repair, particularly in the absence of Ku and non-DSB lesions. This form of DSB repair is sensitive to PARP-1 inhibitors. The results define the function of PARP-1 in DSB repair and characterize a candidate pathway responsible for joining errors causing genomic instability and cancer

Hollow mesoporous silica nanoparticles for intracellular delivery of fluorescent dye

In this study, hollow mesoporous silica nanoparticles (HMSNs) were synthesized using the sol-gel/emulsion approach and its potential application in drug delivery was assessed. The HMSNs were characterized, by transmission electron microscopy (TEM), Scanning Electron Microscopy (SEM), nitrogen adsorption/desorption and Brunauer-Emmett-Teller (BET), to have a mesoporous layer on its surface, with an average pore diameter of about 2 nm and a surface area of 880 m2/g. Fluorescein isothiocyanate (FITC) loaded into these HMSNs was used as a model platform to assess its efficacy as a drug delivery tool. Its release kinetic study revealed a sequential release of FITC from the HMSNs for over a period of one week when soaked in inorganic solution, while a burst release kinetic of the dye was observed just within a few hours of soaking in organic solution. These FITC-loaded HMSNs was also found capable to be internalized by live human cervical cancer cells (HeLa), wherein it was quickly released into the cytoplasm within a short period of time after intracellular uptake. We envision that these HMSNs, with large pores and high efficacy to adsorb chemicals such as the fluorescent dye FITC, could serve as a delivery vehicle for controlled release of chemicals administered into live cells, opening potential to a diverse range of applications including drug storage and release as well as metabolic manipulation of cells