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  1. Free, publicly-accessible full text available February 22, 2025
  2. We consider the question of Gaussian mean testing, a fundamental task in high-dimensional distribution testing and signal processing, subject to adversarial corruptions of the samples. We focus on the relative power of different adversaries, and show that, in contrast to the common wisdom in robust statistics, there exists a strict separation between adaptive adversaries (strong contamination) and oblivious ones (weak contamination) for this task. Specifically, we resolve both the information-theoretic and computational landscapes for robust mean testing. In the exponential-time setting, we establish the tight sample complexity of testing N(0,I) against N(αv,I), where ∥v∥2=1, with an ε-fraction of adversarial corruptions, to be Θ~(max(d√α2,dε3α4,min(d2/3ε2/3α8/3,dεα2))) while the complexity against adaptive adversaries is Θ~(max(d√α2,dε2α4)) which is strictly worse for a large range of vanishing ε,α. To the best of our knowledge, ours is the first separation in sample complexity between the strong and weak contamination models. In the polynomial-time setting, we close a gap in the literature by providing a polynomial-time algorithm against adaptive adversaries achieving the above sample complexity Θ~(max(d−−√/α2,dε2/α4)), and a low-degree lower bound (which complements an existing reduction from planted clique) suggesting that all efficient algorithms require this many samples, even in the oblivious-adversary setting. 
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    Free, publicly-accessible full text available November 1, 2024
  3. Federated learning (FL) has found many important applications in smart-phone-APP based machine learning applications. Although many algorithms have been studied for FL, to the best of our knowledge, algorithms for FL with nonconvex constraints have not been studied. This paper studies FL over Riemannian manifolds, which finds important applications such as federated PCA and federated kPCA. We propose a Riemannian federated SVRG (RFedSVRG) method to solve federated optimization over Riemannian manifolds. We analyze its convergence rate under different scenarios. Numerical experiments are conducted to compare RFedSVRG with the Riemannian counterparts of FedAvg and FedProx. We observed from the numerical experiments that the advantages of RFedSVRG are significant. 
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    Free, publicly-accessible full text available August 1, 2024
  4. Free, publicly-accessible full text available July 1, 2024
  5. We propose a method to measure anisotropic stiffness of microtissues and cells by two indentations in orthogonal directions using our novel toroidal probe. Our preliminary results indicate that this approach is applicable in measuring anisotropic stiffness of aligned tissues and cells. This method will provide researchers with a simple and cost-effective means for measuring mechanical anisotropy of micro-scale samples. 
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    Free, publicly-accessible full text available July 1, 2024
  6. Lin, MT. ; Furlong, C. ; Hwang, CH. ; Naraghi, M. ; DelRio, F. (Ed.)
    Tissue engineering is an active field and one of its aims is to produce tissues to repair the human body. The Advanced Regenerative Medicine Initiative (ARMI) currently seeks to help increase the manufacturability of tissue engineering products (TEMPs). One of the critical components of large-scale manufacturing is the sensing of information for quality control and critical feedback of tissue growth patterns. Modern sensors that provide information about physical qualities of tissues, however, are invasive or destructive. The goal of this project is to develop noninvasive methodologies to measure the mechanical properties of TEMPs. Our approach is to utilize acoustic waves to induce nano-scale level vibrations in the enginineered tissues in which corresponding displacements are measured in full-field with quantitative optical techniques. In our work, a digital holographic system images the tissue’s vibration at significant modes and provides the displacement patterns of the tissue at various points along the sinusoidal excitation curve. These data are applied to a neural network to compare the experimental vibrational modes to the ones obtained by FEA simulation to estimate the physical properties of the tissue. This methodology has the promise of yielding critical control parameters that would allow technicians to noninvasively and consistently determine when samples are ready to be packaged or if their growth deviates from expected time frames or if there are defects in the tissue. It is expected that this approach will streamline several components of the quality control and production process. 
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    Free, publicly-accessible full text available July 1, 2024
  7. Free, publicly-accessible full text available June 18, 2024
  8. Safety-guaranteed motion planning is critical for self-driving cars to generate collision-free trajectories. A layered motion planning approach with decoupled path and speed planning is widely used for this purpose. This approach is prone to be suboptimal in the presence of dynamic obstacles. Spatial-temporal approaches deal with path planning and speed planning simultaneously; however, the existing methods only support simple-shaped corridors like cuboids, which restrict the search space for optimization in complex scenarios. We propose to use trapezoidal prism-shaped corridors for optimization, which significantly enlarges the solution space compared to the existing cuboidal corridors-based method. Finally, a piecewise Bezier curve optimization is conducted in our proposed ´ corridors. This formulation theoretically guarantees the safety of the continuous-time trajectory. We validate the efficiency and effectiveness of the proposed approach in numerical and CommonRoad simulations 
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    Free, publicly-accessible full text available May 1, 2024
  9. Free, publicly-accessible full text available June 25, 2024