Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks

Adversarial attacks on a convolutional neural network (CNN) -- injecting human-imperceptible perturbations into an input image -- could fool a high-performance CNN into making incorrect predictions. The success of adversarial attacks raises serious concerns about the robustness of CNNs, and prevents them from being used in safety-critical applications, such as medical diagnosis and autonomous driving. Our work introduces a visual analytics approach to understanding adversarial attacks by answering two questions: (1) which neurons are more vulnerable to attacks and (2) which image features do these vulnerable neurons capture during the prediction? For the first question, we introduce multiple perturbation-based measures to break down the attacking magnitude into individual CNN neurons and rank the neurons by their vulnerability levels. For the second, we identify image features (e.g., cat ears) that highly stimulate a user-selected neuron to augment and validate the neuron's responsibility. Furthermore, we support an interactive exploration of a large number of neurons by aiding with hierarchical clustering based on the neurons' roles in the prediction. To this end, a visual analytics system is designed to incorporate visual reasoning for interpreting adversarial attacks. We validate the effectiveness of our system through multiple case studies as well as feedback from domain experts.Comment: Accepted by the Special Issue on Human-Centered Explainable AI, ACM Transactions on Interactive Intelligent System

Seismic Risk Assessment and Rehabilitation Method of Existing RCC Structures Using Micro Concrete

Aging reinforced concrete (RC) building structures typically experience more severe damage and are prone to collapse during earthquakes, constituting a primary factor in casualties and direct economic losses. To enhance the seismic performance of these old structures, this paper proposes a seismic risk assessment and a micro-concrete restoration method. It applies the process to an existing three-story reinforced concrete structure. A practical framework for mitigating structural vulnerabilities in seismic-prone regions was proposed. Then an as-built survey was conducted to create as-built architectural and structural drawings. Concrete core tests, ferroscans, and rebar tests were also performed. Based on field surveys and test data, nonlinear static and dynamic analyses have been used to evaluate structural safety. Concrete column jacketing was used to strengthen weak existing columns with micro-concrete. In assessing the structural response of retrofitted buildings, a comparison was made to their initial state. The comparison shows that applying concrete column jacketing with micro concrete can reduce other structural elements' demand capacity ratio (DCR), minimize maximum displacements, and enhance overall stiffness. The results indicate that the proposed method effectively evaluates the seismic risk of aging structures and enhances seismic resilience in existing buildings. Moreover, the application to the actual structure demonstrates that micro-concrete is highly durable and compatible with parent-concrete. Doi: 10.28991/CEJ-2023-09-12-04 Full Text: PD

The emerging role of deubiquitylating enzymes as therapeutic targets in cancer metabolism.

Cancer cells must rewire cellular metabolism to satisfy the unbridled proliferation, and metabolic reprogramming provides not only the advantage for cancer cell proliferation but also new targets for cancer treatment. However, the plasticity of the metabolic pathways makes them very difficult to target. Deubiquitylating enzymes (DUBs) are proteases that cleave ubiquitin from the substrate proteins and process ubiquitin precursors. While the molecular mechanisms are not fully understood, many DUBs have been shown to be involved in tumorigenesis and progression via controlling the dysregulated cancer metabolism, and consequently recognized as potential drug targets for cancer treatment. In this article, we summarized the significant progress in understanding the key roles of DUBs in cancer cell metabolic rewiring and the opportunities for the application of DUBs inhibitors in cancer treatment, intending to provide potential implications for both research purpose and clinical applications

Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning

Recent advancements in pre-trained large-scale language-image models have ushered in a new era of visual comprehension, offering a significant leap forward. These breakthroughs have proven particularly instrumental in addressing long-standing challenges that were previously daunting. Leveraging these innovative techniques, this paper tackles two well-known issues within the realm of visual analytics: (1) the efficient exploration of large-scale image datasets and identification of potential data biases within them; (2) the evaluation of image captions and steering of their generation process. On the one hand, by visually examining the captions automatically generated from language-image models for an image dataset, we gain deeper insights into the semantic underpinnings of the visual contents, unearthing data biases that may be entrenched within the dataset. On the other hand, by depicting the association between visual contents and textual captions, we expose the weaknesses of pre-trained language-image models in their captioning capability and propose an interactive interface to steer caption generation. The two parts have been coalesced into a coordinated visual analytics system, fostering mutual enrichment of visual and textual elements. We validate the effectiveness of the system with domain practitioners through concrete case studies with large-scale image datasets

A causal effects of gut microbiota in the development of migraine

Background: The causal association between the gut microbiome and the development of migraine and its subtypes remains unclear. Methods: The single nucleotide polymorphisms concerning gut microbiome were retrieved from the gene-wide association study (GWAS) of the MiBioGen consortium. The summary statistics datasets of migraine, migraine with aura (MA), and migraine without aura (MO) were obtained from the GWAS meta-analysis of the International Headache Genetics Consortium (IHGC) and FinnGen consortium. Inverse variance weighting (IVW) was used as the primary method, complemented by sensitivity analyses for pleiotropy and increasing robustness. Results: In IHGC datasets, ten, five, and nine bacterial taxa were found to have a causal association with migraine, MA, and MO, respectively, (IVW, all P < 0.05). Genus.Coprococcus3 and genus.Anaerotruncus were validated in FinnGen datasets. Nine, twelve, and seven bacterial entities were identified for migraine, MA, and MO, respectively. The causal association still exists in family.Bifidobacteriaceae and order.Bifidobacteriales for migraine and MO after FDR correction. The heterogeneity and pleiotropy analyses confirmed the robustness of IVW results. Conclusion: Our study demonstrates that gut microbiomes may exert causal effects on migraine, MA, and MO. We provide novel evidence for the dysfunction of the gut-brain axis on migraine. Future study is required to verify the relationship between gut microbiome and the risk of migraine and its subtypes and illustrate the underlying mechanism between them