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  5. Deep models are known to be vulnerable to data adversarial attacks, and many adversarial training techniques have been developed to improve their adversarial robustness. While data adversaries attack model predictions through modifying data, little is known about their impact on the neuron activations produced by the model, which play a crucial role in determining the model’s predictions and interpretability. In this work, we aim to develop a topological understanding of adversarial training to enhance its interpretability. We analyze the topological structure—in particular, mapper graphs—of neuron activations of data samples produced by deep adversarial training. Each node of a mapper graph represents a cluster of activations, and two nodes are connected by an edge if their corresponding clusters have a nonempty intersection. We provide an interactive visualization tool that demonstrates the utility of our topological framework in exploring the activation space. We found that stronger attacks make the data samples more indistinguishable in the neuron activation space that leads to a lower accuracy. Our tool also provides a natural way to identify the vulnerable data samples that may be useful in improving model robustness. 
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    Free, publicly-accessible full text available July 1, 2024
  6. The two-stream instability (Buneman instability) is traditionally derived as a collisionless instability with the presumption that collisions inhibit this instability. We show here via a combination of a collisional two-fluid model and associated experimental observations made in the Caltech plasma jet experiment, that in fact, a low-frequency mode of the two-stream instability is indifferent to collisions. Despite the collision frequency greatly exceeding the growth rate of the instability, the instability can still cause an exponential growth of electron velocity and a rapid depletion of particle density. Nevertheless, high collisionality has an important effect as it enables the development of a double layer when the cross section of the plasma jet is constricted by a kink-instigated Rayleigh–Taylor instability. 
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    Free, publicly-accessible full text available May 1, 2024
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  8. We present IronSync, an automated verification framework for concurrent code with shared memory. IronSync scales to complex systems by splitting system-wide proofs into isolated concerns such that each can be substantially automated. As a starting point, IronSync’s ownership type system allows a developer to straightforwardly prove both data safety and the logical correctness of thread-local operations. IronSync then introduces the concept of a Localized Transition System, which connects the correctness of local actions to the correctness of the entire system. We demonstrate IronSync by verifying two state-of-the-art concurrent systems comprising thousands of lines: a library for black-box replication on NUMA architectures, and a highly concurrent page cache. 
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    Free, publicly-accessible full text available July 10, 2024
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