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1. Fitting Very Flexible Models: Linear Regression With Large Numbers of Parameters

   https://doi.org/10.1088/1538-3873/ac20ac  

   Hogg, David W. ; Villar, Soledad ( September 2021 , Publications of the Astronomical Society of the Pacific)

   null (Ed.)
2. Model-Based Manipulation of Linear Flexible Objects: Task Automation in Simulation and Real World

   https://doi.org/10.3390/machines8030046  

   Chang, Peng ; Padır, Taşkın ( September 2020 , Machines)

   Manipulation of deformable objects is a desired skill in making robots ubiquitous in manufacturing, service, healthcare, and security. Common deformable objects (e.g., wires, clothes, bed sheets, etc.) are significantly more difficult to model than rigid objects. In this research, we contribute to the model-based manipulation of linear flexible objects such as cables. We propose a 3D geometric model of the linear flexible object that is subject to gravity and a physical model with multiple links connected by revolute joints and identified model parameters. These models enable task automation in manipulating linear flexible objects both in simulation and real world. To bridge the gap between simulation and real world and build a close-to-reality simulation of flexible objects, we propose a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim). We demonstrate the feasibility of our approach by completing the Plug Task used in the 2015 DARPA Robotics Challenge Finals both in simulation and real world, which involves unplugging a power cable from one socket and plugging it into another. Numerical experiments are implemented to validate our approach. 

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3. A Flexible-Time Iterative Learning Control Framework for Linear, Time-Based Performance Objectives

   https://doi.org/10.23919/ACC45564.2020.9147962  

   Wu, Maxwell J. ; Cobb, Mitchell ; Vermillion, Chris ; Barton, Kira ( July 2020 , 2020 American Control Conference)
4. Amber: A 16-nm System-on-Chip With a Coarse- Grained Reconfigurable Array for Flexible Acceleration of Dense Linear Algebra

   https://doi.org/10.1109/JSSC.2023.3313116  

   Feng, Kathleen ; Kong, Taeyoung ; Koul, Kalhan ; Melchert, Jackson ; Carsello, Alex ; Liu, Qiaoyi ; Nyengele, Gedeon ; Strange, Maxwell ; Zhang, Keyi ; Nayak, Ankita ; et al ( March 2024 , IEEE Journal of Solid-State Circuits)

   Amber is a system-on-chip (SoC) with a coarse-grained reconfigurable array (CGRA) for acceleration of dense linear algebra applications, such as machine learning (ML), image processing, and computer vision. It is designed using an agile accelerator-compiler co-design flow; the compiler updates automatically with hardware changes, enabling continuous application-level evaluation of the hardware-software system. To increase hardware utilization and minimize reconfigurability overhead, Amber features the following: 1) dynamic partial reconfiguration (DPR) of the CGRA for higher resource utilization by allowing fast switching between applications and partitioning resources between simultaneous applications; 2) streaming memory controllers supporting affine access patterns for efficient mapping of dense linear algebra; and 3) low-overhead transcendental and complex arithmetic operations. The physical design of Amber features a unique clock distribution method and timing methodology to efficiently layout its hierarchical and tile-based design. Amber achieves a peak energy efficiency of 538 INT16 GOPS/W and 483 BFloat16 GFLOPS/W. Compared with a CPU, a GPU, and a field-programmable gate array (FPGA), Amber has up to 3902x, 152x, and 107x better energy-delay product (EDP), respectively. 

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5. Heavy Ball Flexible GMRES Method for Nonsymmetric Linear Systems

   https://doi.org/10.4208/jcm.2101-m2019-0243  

   Yang, Mei ; Li, Ren-Cang ( July 2022 , Journal of Computational Mathematics)