More Like this

1. Assume(), Capture(), Verify(), Establish(): A Vocabulary for Static Program Analysis

   https://doi.org/10.1109/QRS-C60940.2023.00067  

   Mohammadi, Hessamaldin; Ghardallou, Wided; Brick, Elijah; Mili, Ali (October 2023, IEEE)

   We propose a set of functions that a user can invoke to analyze a program written in a C-like language: Assume() refers to a label in the source code or to a program part, and enables the user to make an assumption about the state of the program at some label or the function of some program part; Capture() refers to a label or a program part and returns an assertion about the state of the program at the label or the function of the program part; Verify() refers to a label or a program part and tests a unary assertion about the state of the program at the label or a binary assertion about the function of the program part; Establish() refers to a label or a program part and modifies the program code to make Verify() return TRUE at that label or program part, if it did not originally. We discuss the foundations of this tool as well as a preliminary implementation. 

   more » « less
2. Utility of Missing Concepts in Query-biased Summarization

   https://doi.org/10.1145/3404835.3463121  

   Sarwar, Sheikh Muhammad; Moraes, Felipe; Jiang, Jiepu; Allan, James (July 2021, Proceedings of The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 21))

   null (Ed.)

   Query Biased Summarization (QBS) aims to produce a summary of the documents retrieved against a query to reduce the human effort for inspecting the full-text content of a document. Typical summarization approaches extract a document text snippet that has term overlap with the query and show that to a searcher. While snippets show relevant information in a document, to the best of our knowledge, there does not exist a summarization system that shows what relevant concepts is missing in a document. Our study focuses on the reduction of user effort in finding relevant documents by exposing them to omitted relevant information. To this end, we use a classical approach, DSPApprox, to find terms or phrases relevant to a query. Then we identify which terms or phrases are missing in a document, present them in a search interface, and ask crowd workers to judge document relevance based on snippets and missing information. Experimental results show both benefits and limitations of this approach. 

   more » « less
3. Cycle and Commute: Rare-Event Probability Verification for Chemical Reaction Networks

   Taylor, Landon; Israelsen, Bryant; Zhang, Zhen (October 2023, Formal Methods in ComputerAided Design)

   Nadel, Alexander; Rozier, Kristin_Yvonne (Ed.)

   In synthetic biological systems, rare events can cause undesirable behavior leading to pathological effects. Due to their low observability, rare events are challenging to analyze using existing stochastic simulation methods. Chemical Reaction Networks (CRNs) are a general-purpose formal language for modeling chemical kinetics. This paper presents a fully automated approach to efficiently construct a large number of concurrent traces by expanding a sample of known traces. These traces constitute a partial state space containing only traces leading to a rare event of interest. This state space is then used to compute a lower bound for the rare event’s probability. We propose a novel approach for the analysis of highly concurrent CRNs, including a CRN reaction independence analysis and an algorithm that exploits CRN concurrency to rapidly enumerate parallel traces. We then present a novel algorithm to add cycles to a partial state space to further increase the rare event’s probability lower bound to its actual value. The resulting prototype tool, RAGTIMER, demonstrates improvement over stochastic simulation and probabilistic model checking. 

   more » « less
4. Navigating mobile robots to target in near shortest time using reinforcement learning with spiking neural networks

   Mahadevuni, A; Li, P (May 2017, Proceedings of ... International Joint Conference on Neural Networks)

   The autonomous navigation of mobile robots in unknown environments is of great interest in mobile robotics. This article discusses a new strategy to navigate to a known target location in an unknown environment using a combination of the “go-to-goal” approach and reinforcement learning with biologically realistic spiking neural networks. While the “goto-goal” approach itself might lead to a solution for most environments, the added neural reinforcement learning in this work results in a strategy that takes the robot from a starting position to a target location in a near shortest possible time. To achieve the goal, we propose a reinforcement learning approach based on spiking neural networks. The presented biologically motivated delayed reward mechanism using eligibility traces results in a greedy approach that leads the robot to the target in a close to shortest possible time. 

   more » « less
5. Using Data-Driven Domain Randomization to Transfer Robust Control Policies to Mobile Robots

   https://doi.org/10.1109/ICRA.2019.8794343  

   Sheckells, Matthew; Garimella, Gowtham; Mishra, Subhransu; Kobilarov, Marin (May 2019, ICRA 2019)

   This work develops a technique for using robot motion trajectories to create a high quality stochastic dynamics model that is then leveraged in simulation to train control policies with associated performance guarantees. We demonstrate the idea by collecting dynamics data from a 1/5 scale agile ground vehicle, fitting a stochastic dynamics model, and training a policy in simulation to drive around an oval track at up to 6.5 m/s while avoiding obstacles. We show that the control policy can be transferred back to the real vehicle with little loss in predicted performance. We compare this to an approach that uses a simple analytic car model to train a policy in simulation and show that using a model with stochasticity learned from data leads to higher performance in terms of trajectory tracking accuracy and collision probability. Furthermore, we show empirically that simulation-derived performance guarantees transfer to the actual vehicle when executing a policy optimized using a deep stochastic dynamics model fit to vehicle data. 

   more » « less