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The generalization ability of machine learning models degrades significantly when the test distribution shifts away from the training distribution. We investigate the problem of training models that are robust to shifts caused by changes in the distribution of class-priors or group-priors. The presence of skewed training priors can often lead to the models overfitting to spurious features. Unlike existing methods, which optimize for either the worst or the average performance over classes or groups, our work is motivated by the need for finer control over the robustness properties of the model. We present an extremely lightweight post-hoc approach that performs scaling adjustments to predictions from a pre-trained model, with the goal of minimizing a distributionally robust loss around a chosen target distribution. These adjustments are computed by solving a constrained optimization problem on a validation set and applied to the model during test time. Our constrained optimization objective is inspired from a natural notion of robustness to controlled distribution shifts. Our method comes with provable guarantees and empirically makes a strong case for distributional robust post-hoc classifiers. An empirical implementation is available at https://github.com/weijiaheng/Drops. 

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs for maintaining an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about the device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device’s detection of its multimedia context characterized by its utilization of different media input/output components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability. 

Composites play progressively significant roles across a spectrum of applications involving high‐performance materials and products within industries such as aerospace, naval, automotive, construction, missiles, and defense technology. Notably, oriented fiber composites have garnered substantial attention due to their advantageous attributes like a high strength‐to‐weight ratio and controlled anisotropy. Nonetheless, challenges persist in uneven fiber alignment, fiber clustering within the matrix material, and constraints on fiber volume, impeding the mass production of oriented fiber‐reinforced composites. In this study, we present a novel approach to 3D printing of uniformly aligned short fiber reinforcement in a composite of heavily loaded carbon and nylon. Capitalizing on the additive manufacturing potential of rapidity and precision, the extrusion process induces carbon fiber (CF) alignments in filaments via shear forces. The 3D‐printed structures that were created displayed impressive potential for customization. They consistently demonstrated improved mechanical and thermal properties when compared to the original nylon structures. Our methodology for producing uniformly dispersed and aligned short fiber reinforcement in polymer composites promises to propel the advancement of design and manufacturing for high‐performance composite materials and components.