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With the computational resources becoming available, data-driven methods have emerged as powerful means for equation discovery and model construction. Sparse regression methods such as SINDy (Sparse Identification for Nonlinear Dynamical Systems) can be used for developing reduced-order models of nonlinear systems. In this study, the authors examine how SINDy can be used for developing low-dimensional models for airfoil systems, which experience unsteady aerodynamic loads and flutter instabilities. For a system of multiple closely spaced airfoil oscillators, analytical models are not readily available to determine flutter instabilities, and one has to take recourse to experimental and numerical means. In this work, as a starting point, data collected through simulations of unsteady aerodynamics of a single airfoil oscillator system are considered and a reduced-order model is constructed based on this data.
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System 1 + System 2 = Better World: Neural-Symbolic Chain of Logic Reasoning
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Nowadays, the data collected in physical/engineering systems allows various machine learning methods to conduct system monitoring and control, when the physical knowledge on the system edge is limited and challenging to recover completely. Solving such problems typically requires identifying forward system mapping rules, from system states to the output measurements. However, the forward system identification based on digital twin can hardly provide complete monitoring functions, such as state estimation, e.g., to infer the states from measurements. While one can directly learn the inverse mapping rule, it is more desirable to re-utilize the forward digital twin since it is relatively easy to embed physical law there to regularize the inverse process and avoid overfitting. For this purpose, this paper proposes an invertible learning structure based on designing parallel paths in structural neural networks with basis functionals and embedding virtual storage variables for information preservation. For such a two-way digital twin modeling, there is an additional challenge of multiple solutions for system inverse, which contradict the reality of one feasible solution for the current system. To avoid ambiguous inverse, the proposed model maximizes the physical likelihood to contract the original solution space, leading to the unique system operation status of interest. We validate the proposed method on various physical system monitoring tasks and scenarios, such as inverse kinematics problems, power system state estimation, etc. Furthermore, by building a perfect match of a forward-inverse pair, the proposed method obtains accurate and computation-efficient inverse predictions, given observations. Finally, the forward physical interpretation and small prediction errors guarantee the explainability of the invertible structure, compared to standard learning methods.more » « less
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null (Ed.)Abstract—System call checking is extensively used to protect the operating system kernel from user attacks. However, existing solutions such as Seccomp execute lengthy rule-based checking programs against system calls and their arguments, leading to substantial execution overhead. To minimize checking overhead, this paper proposes Draco, a new architecture that caches system call IDs and argument values after they have been checked and validated. System calls are first looked-up in a special cache and, on a hit, skip all checks. We present both a software and a hardware implementation of Draco. The latter introduces a System Call Lookaside Buffer (SLB) to keep recently-validated system calls, and a System Call Target Buffer to preload the SLB in advance. In our evaluation, we find that the average execution time of macro and micro benchmarks with conventional Seccomp checking is 1.14_ and 1.25_ higher, respectively, than on an insecure baseline that performs no security checks. With our software Draco, the average execution time reduces to 1.10_ and 1.18_ higher, respectively, than on the insecure baseline. With our hardware Draco, the execution time is within 1% of the insecure baseline.more » « less
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Abstract BackgroundEfficient cell-free protein expression from linear DNA templates has remained a challenge primarily due to template degradation. In addition, the yields of transcription in cell-free systems lag behind transcriptional efficiency of live cells. Most commonly used in vitro translation systems utilize T7 RNA polymerase, which is also the enzyme included in many commercial kits. ResultsHere we present characterization of a variant of T7 RNA polymerase promoter that acts to significantly increase the yields of gene expression withinin vitrosystems. We have demonstrated that T7Max increases the yield of translation in many types of commonly used in vitro protein expression systems. We also demonstrated increased protein expression yields from linear templates, allowing the use of T7Max driven expression from linear templates. ConclusionsThe modified promoter, termed T7Max, recruits standard T7 RNA polymerase, so no protein engineering is needed to take advantage of this method. This technique could be used with any T7 RNA polymerase- basedin vitroprotein expression system.more » « less
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