MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods.Comment: Accepted by IJCAI 201

OPTIMIZING AND UNDERSTANDING CHECKPOINT INHIBITION THERAPY AND CHIMERIC ANTIGEN RECEPTOR THERAPY AGAINST BREAST CANCER

Cancer immunotherapies, which include chimeric antigen receptor therapy (CAR-T) and immune checkpoint inhibition therapy (ICI), have revolutionized the field of cancer therapy. Those therapies modulate the immune system to recognize and target cancer cells. Despite the success of immunotherapy, a large majority of cancer patients do not respond to these immunotherapies. The goal of my research is to understand the mechanisms that cause resistance to immunotherapy and utilize novel combinatorial approaches to enhance current immunotherapy. We developed strategies to enhance the activity of CAR T cells against solid tumors by utilizing a mouse model of breast cancer. We found that CAR T cells generated from Th/Tc17 cells had improved persistence in the TME. Administration of the STING agonist DMXAA greatly enhanced tumor control and was associated with Th/Tc17 CAR T cell persistence and recruitment into the TME. Additionally, DMXAA strongly modulated the immunosuppressive TME through alterations in the balance of immune-stimulatory and suppressive myeloid cells. Sustained long term tumor regression was accomplished with the addition of anti-PD-1 and anti-GR-1 mAb to Th/Tc17 CAR T cell therapy. This study provides a new understanding of the approaches needed to enhance adoptive T cell therapy in solid tumors. Another focus of my research is to further understand the mechanism by which ICI therapy boosts the anti-tumor response. To do this, we engineered a novel mammary mouse tumor that is sensitive to immune checkpoint therapy. Using this model, we uncovered that ICI therapy induced T follicular helper cell activation of B cells to facilitate the anti-tumor response. We also showed that B cell activation of T cells and the generation of antibody are key to the immunotherapy response. This work uncovers new components of the response to immune checkpoint inhibitors. In conclusion, this work has provided insight into mechanisms that can enhance the anti-tumor response of immunotherapies in breast cancer. These strategies, either through harnessing the activity of B cells or by providing STING agonists, have the potential to translate into the clinic to enhance the efficacy of current ICI therapy and CAR-T therapy.Doctor of Philosoph

GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model

Central to robot exploration and mapping is the task of persistent localization in environmental fields characterized by spatially correlated measurements. This paper presents a Gaussian process localization (GP-Localize) algorithm that, in contrast to existing works, can exploit the spatially correlated field measurements taken during a robot's exploration (instead of relying on prior training data) for efficiently and scalably learning the GP observation model online through our proposed novel online sparse GP. As a result, GP-Localize is capable of achieving constant time and memory (i.e., independent of the size of the data) per filtering step, which demonstrates the practical feasibility of using GPs for persistent robot localization and autonomy. Empirical evaluation via simulated experiments with real-world datasets and a real robot experiment shows that GP-Localize outperforms existing GP localization algorithms.Comment: 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Extended version with proofs, 10 page

Disordered hyperuniformity signals functioning and resilience of self-organized vegetation patterns

In harsh environments, organisms may self-organize into spatially patterned systems in various ways. So far, studies of ecosystem spatial self-organization have primarily focused on apparent orders reflected by regular patterns. However, self-organized ecosystems may also have cryptic orders that can be unveiled only through certain quantitative analyses. Here we show that disordered hyperuniformity as a striking class of hidden orders can exist in spatially self-organized vegetation landscapes. By analyzing the high-resolution remotely sensed images across the American drylands, we demonstrate that it is not uncommon to find disordered hyperuniform vegetation states characterized by suppressed density fluctuations at long range. Such long-range hyperuniformity has been documented in a wide range of microscopic systems. Our finding contributes to expanding this domain to accommodate natural landscape ecological systems. We use theoretical modeling to propose that disordered hyperuniform vegetation patterning can arise from three generalized mechanisms prevalent in dryland ecosystems, including (1) critical absorbing states driven by an ecological legacy effect, (2) scale-dependent feedbacks driven by plant-plant facilitation and competition, and (3) density-dependent aggregation driven by plant-sediment feedbacks. Our modeling results also show that disordered hyperuniform patterns can help ecosystems cope with arid conditions with enhanced functioning of soil moisture acquisition. However, this advantage may come at the cost of slower recovery of ecosystem structure upon perturbations. Our work highlights that disordered hyperuniformity as a distinguishable but underexplored ecosystem self-organization state merits systematic studies to better understand its underlying mechanisms, functioning, and resilience.Comment: 34 pages, 6 figures; Supplementary Materials, 19 pages, 10 figures, 2 table

NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks

Membership inference attacks (MIAs) against machine learning models can lead to serious privacy risks for the training dataset used in the model training. In this paper, we propose a novel and effective Neuron-Guided Defense method named NeuGuard against membership inference attacks (MIAs). We identify a key weakness in existing defense mechanisms against MIAs wherein they cannot simultaneously defend against two commonly used neural network based MIAs, indicating that these two attacks should be separately evaluated to assure the defense effectiveness. We propose NeuGuard, a new defense approach that jointly controls the output and inner neurons' activation with the object to guide the model output of training set and testing set to have close distributions. NeuGuard consists of class-wise variance minimization targeting restricting the final output neurons and layer-wise balanced output control aiming to constrain the inner neurons in each layer. We evaluate NeuGuard and compare it with state-of-the-art defenses against two neural network based MIAs, five strongest metric based MIAs including the newly proposed label-only MIA on three benchmark datasets. Results show that NeuGuard outperforms the state-of-the-art defenses by offering much improved utility-privacy trade-off, generality, and overhead