Image classification and segmentation are common applications of deep learning to radiology. While many tasks can be framed using either classification or segmentation, classification has historically been cheaper to label and more widely used. However, recent work has drastically reduced the cost of training segmentation networks. In light of this recent work, we reexamine the choice of training classification vs. segmentation models. First, we use an information theoretic approach to analyze why segmentation vs. classification models may achieve different performance on the same dataset and overarching task. We then implement multiple methods for using segmentation models to classify medical images, which we call *segmentation-for-classification*, and compare these methods against traditional classification on three retrospective datasets. We use our analysis and experiments to summarize the benefits of switching from segmentation to classification, including: improved sample efficiency, enabling improved performance with fewer labeled images (up to an order of magnitude lower), on low-prevalence classes, and on certain rare subgroups (up to 161.1\% improved recall); improved robustness to spurious correlations (up to 44.8\% improved robust AUROC); and improved model interpretability, evaluation, and error analysis.

Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency. We introduce a simple sequence model inspired by control systems that generalizes these approaches while addressing their shortcomings. The Linear State-Space Layer (LSSL) maps a sequence $u \mapsto y$ by simply simulating a linear continuous-time state-space representation $\dot{x} = Ax + Bu, y = Cx + Du$. Theoretically, we show that LSSL models are closely related to the three aforementioned families of models and inherit their strengths. For example, they generalize convolutions to continuous-time, explain common RNN heuristics, and share features of NDEs such as time-scale adaptation. We then incorporate and generalize recent theory on continuous-time memorization to introduce a trainable subset of structured matrices $A$ that endow LSSLs with long-range memory. Empirically, stacking LSSL layers into a simple deep neural network obtains state-of-the-art results across time series benchmarks for long dependencies in sequential image classification, real-world healthcare regression tasks, and speech. On a difficult speech classification task with length-16000 sequences, LSSL outperforms prior approaches by 24 accuracy points, and even outperforms baselines that use hand-crafted features on 100x shorter sequences.

Embedded systems are ubiquitous in every aspect of modern life. As the Internet of Thing expands, our dependence on these systems increases. Many of these interconnected systems are and will be low cost bare-metal systems, executing without an operating system. Bare-metal systems rarely employ any security protection mechanisms and their development assumptions (unrestricted access to all memory and instructions), and constraints(runtime, energy, and memory) makes applying protections challenging. To address these challenges we present EPOXY, an LLVM-based embedded compiler. We apply a novel technique, called privilege overlaying, wherein operations requiring privileged execution are identified and only these operations execute in privileged mode. This provides the foundation on which code-integrity, adapted control-flow hijacking defenses, and protections for sensitive IO are applied. We also design fine-grained randomization schemes, that work within the constraints of bare-metal systems to provide further protection against control-flow and data corruption attacks. These defenses prevent code injection attacks and ROP attacks from scaling across large sets of devices. We evaluate the performance of our combined defense mechanisms for a suite of 75 benchmarks and 3 real-world IoT applications. Our results for the application case studies show that EPOXY has, on average, a 1.8% increase in execution time and a 0.5% increase in energy usage.

This letter shows that traditional Newton-Raphson (NR) method cannot achieve zero-convergence in presence of additive noise without adding a multiplicative gain. Furthermore, this gain needs to converge to zero. This article proposes a novel recursive algorithm providing optimal iterative-varying gains associated with the NR method. The development of the proposed optimal algorithm is based on minimizing a stochastic performance index. The estimation error covariance matrix is shown to converge to zero for linearized functions while considering additive zero-mean white noise. In addition, the proposed approach is capable of overcoming common drawbacks associated with the traditional NR method. Simulation results are included to illustrate the performance capabilities of the proposed algorithm. We show that the proposed recursive algorithm provides significant improvement over the traditional NR method.