Safe Control Under Input Limits with Neural Control Barrier Functions

We propose new methods to synthesize control barrier function (CBF)-based safe controllers that avoid input saturation, which can cause safety violations. In particular, our method is created for high-dimensional, general nonlinear systems, for which such tools are scarce. We leverage techniques from machine learning, like neural networks and deep learning, to simplify this challenging problem in nonlinear control design. The method consists of a learner-critic architecture, in which the critic gives counterexamples of input saturation and the learner optimizes a neural CBF to eliminate those counterexamples. We provide empirical results on a 10D state, 4D input quadcopter-pendulum system. Our learned CBF avoids input saturation and maintains safety over nearly 100% of trials.Comment: CORL 202

DESIGNING AN AI-BASED ADVISORY PLATFORM FOR DESIGN TECHNIQUES

The usage of design techniques in design processes is an important driver for the success of digital services. However, before using design techniques, suitable techniques need to be selected. With the continuous growth of the number of design techniques, the selection of appropriate ones becomes more difficult, especially for design novices with limited knowledge and expertise. In order to support the selection process, we propose design principles for the development of an advisory platform that interacts with design novices to suggest design techniques for different design situations using artificial intelligence (AI) techniques. Specifically, we leverage conversational agents, recommender techniques, and taxonomic background knowledge to conceptualize and implement an AI-based advisory platform. Following a design science research methodology, we contribute design knowledge for the class of advanced advisory platforms. Furthermore, from a practical point of view, we help design novices with our implemented advisory platform in the contextualized selection process of design techniques

Safety Index Synthesis via Sum-of-Squares Programming

Control systems often need to satisfy strict safety requirements. Safety index provides a handy way to evaluate the safety level of the system and derive the resulting safe control policies. However, designing safety index functions under control limits is difficult and requires a great amount of expert knowledge. This paper proposes a framework for synthesizing the safety index for general control systems using sum-of-squares programming. Our approach is to show that ensuring the non-emptiness of safe control on the safe set boundary is equivalent to a local manifold positiveness problem. We then prove that this problem is equivalent to sum-of-squares programming via the Positivstellensatz of algebraic geometry. We validate the proposed method on robot arms with different degrees of freedom and ground vehicles. The results show that the synthesized safety index guarantees safety and our method is effective even in high-dimensional robot systems

DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization

DL compiler's primary function is to translate DNN programs written in high-level DL frameworks such as PyTorch and TensorFlow into portable executables. These executables can then be flexibly executed by the deployed host programs. However, existing DL compilers rely on a tracing mechanism, which involves feeding a runtime input to a neural network program and tracing the program execution paths to generate the computational graph necessary for compilation. Unfortunately, this mechanism falls short when dealing with modern dynamic neural networks (DyNNs) that possess varying computational graphs depending on the inputs. Consequently, conventional DL compilers struggle to accurately compile DyNNs into executable code. To address this limitation, we propose \tool, a general approach that enables any existing DL compiler to successfully compile DyNNs. \tool tackles the dynamic nature of DyNNs by introducing a compilation mechanism that redistributes the control and data flow of the original DNN programs during the compilation process. Specifically, \tool develops program analysis and program transformation techniques to convert a dynamic neural network into multiple sub-neural networks. Each sub-neural network is devoid of conditional statements and is compiled independently. Furthermore, \tool synthesizes a host module that models the control flow of the DyNNs and facilitates the invocation of the sub-neural networks. Our evaluation demonstrates the effectiveness of \tool, achieving a 100\% success rate in compiling all dynamic neural networks. Moreover, the compiled executables generated by \tool exhibit significantly improved performance, running between $1.12\times$ and $20.21\times$ faster than the original DyNNs executed on general-purpose DL frameworks.Comment: This paper has been accepted to ISSTA 202

Ruscogenin alleviates palmitic acid-induced endothelial cell inflammation by suppressing TXNIP/NLRP3 pathway

Purpose: To investigate the involvement of ruscogenin in palmitic acid (PA)-induced endothelial cell inflammation. Method: Cultured human umbilical vein endothelial cells (HUVECs) were divided into five groups: control (normal untreated cells), PA (cell treated with palmitic acid), and PA + ruscogenin (1, 10, or 30 μM). Cell viability and apoptosis rate were determined using MTT (3-(4,5)-dimethylthiahiazo(-z-y1)-3,5- di-phenytetrazolium bromide) and flow cytometry assays, respectively. The levels of cytokines, including interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), intercellular adhesion molecule-1 (ICAM-1), and monocyte chemo-attractant protein-1 (MCP-1) were determined by an enzyme-linked immunosorbent assay. Western blotting and real-time polymerase chain reaction (RT-PCR) were used to evaluate the underlying mechanisms of action. Results: PA treatment decreased the viability of HUVECs and induced apoptosis (p < 0.05). Ruscogenin attenuated PA-induced cell death in a dose-dependent manner (p < 0.05). On the other hand, PA induced an increase in IL-1β, TNF-α, ICAM-1, MCP-1, TXNIP (thioredoxin-interacting protein),as well as NLRP3 (nucleotide oligomerization domain-, leucine-rich repeat- and pyrin domain-containing protein 3), all of which were attenuated by ruscogenin (p < 0.05). Conclusion: Ruscogenin alleviates PA-induced endothelial cell inflammation via TXNIP/NLRP3 pathway, thereby providing an insight into new therapeutic strategies to treat cardiovascular diseases. Keywords: Ruscogenin, Palmitic acid, Endothelial cells, Inflammation, TXNIP, NLRP3, Cardiovascular disease

Planning Reliability Assurance Tests for Autonomous Vehicles

Artificial intelligence (AI) technology has become increasingly prevalent and transforms our everyday life. One important application of AI technology is the development of autonomous vehicles (AV). However, the reliability of an AV needs to be carefully demonstrated via an assurance test so that the product can be used with confidence in the field. To plan for an assurance test, one needs to determine how many AVs need to be tested for how many miles and the standard for passing the test. Existing research has made great efforts in developing reliability demonstration tests in the other fields of applications for product development and assessment. However, statistical methods have not been utilized in AV test planning. This paper aims to fill in this gap by developing statistical methods for planning AV reliability assurance tests based on recurrent events data. We explore the relationship between multiple criteria of interest in the context of planning AV reliability assurance tests. Specifically, we develop two test planning strategies based on homogeneous and non-homogeneous Poisson processes while balancing multiple objectives with the Pareto front approach. We also offer recommendations for practical use. The disengagement events data from the California Department of Motor Vehicles AV testing program is used to illustrate the proposed assurance test planning methods.Comment: 29 pages, 5 figure

SNPHunter: a bioinformatic software for single nucleotide polymorphism data acquisition and management

BACKGROUND: Single nucleotide polymorphisms (SNPs) provide an important tool in pinpointing susceptibility genes for complex diseases and in unveiling human molecular evolution. Selection and retrieval of an optimal SNP set from publicly available databases have emerged as the foremost bottlenecks in designing large-scale linkage disequilibrium studies, particularly in case-control settings. RESULTS: We describe the architectural structure and implementations of a novel software program, SNPHunter, which allows for both ad hoc-mode and batch-mode SNP search, automatic SNP filtering, and retrieval of SNP data, including physical position, function class, flanking sequences at user-defined lengths, and heterozygosity from NCBI dbSNP. The SNP data extracted from dbSNP via SNPHunter can be exported and saved in plain text format for further down-stream analyses. As an illustration, we applied SNPHunter for selecting SNPs for 10 major candidate genes for type 2 diabetes, including CAPN10, FABP4, IL6, NOS3, PPARG, TNF, UCP2, CRP, ESR1, and AR. CONCLUSION: SNPHunter constitutes an efficient and user-friendly tool for SNP screening, selection, and acquisition. The executable and user's manual are available at