278 research outputs found
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
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