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1. A Comment on and Correction to: Opinion Dynamics in the Presence of Increasing Agreement Pressure

   https://doi.org/10.1109/TCYB.2021.3087710  

   Griffin, Christopher (June 2021, IEEE Transactions on Cybernetics)

   null (Ed.)

   We identify counterexamples to the consensus result given in Semonsen et al. (2018). We resolve the counterexamples by replacing Lemma 5 in the given reference with a novel variation of the Banach fixed-point theorem, which explains both the numerical results in the reference and the counterexample(s) in this note and provides a sufficient condition for consensus in systems with increasing peer-pressure. This work is relevant for other papers that have used the proof technique from Semonsen et al. and establishes the veracity of their claims assuming the new sufficient condition. 

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2. A counterexample to the HK-conjecture that is principal

   https://doi.org/10.1017/etds.2022.25  

   DEELEY, ROBIN J. (June 2023, Ergodic Theory and Dynamical Systems)

   Abstract Scarparo has constructed counterexamples to Matui’s HK-conjecture. These counterexamples and other known counterexamples are essentially principal but not principal. In the present paper, a counterexample to the HK-conjecture that is principal is given. Like Scarparo’s original counterexample, our counterexample is the transformation groupoid associated to a particular odometer. However, the relevant group is the fundamental group of a flat manifold (and hence is torsion-free) and the associated odometer action is free. The examples discussed here do satisfy the rational version of the HK-conjecture. 

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3. Counterexample-guided data augmentation

   Dreossi, Tommaso; Ghosh, Shromona; Yue, Xiangyu; Keutzer, Kurt; Sangiovanni-Vincentelli, Alberto L; Seshia, Sanjit A. (July 2018, Proceedings of the ... International Conference on Artificial Intelligence)

   We present a novel framework for augmenting data sets for machine learning based on counterexamples. Counterexamples are misclassified examples that have important properties for retraining and improving the model. Key components of our framework include a counterexample generator, which produces data items that are misclassified by the model and error tables, a novel data structure that stores information pertaining to misclassifications. Error tables can be used to explain the model's vulnerabilities and are used to efficiently generate counterexamples for augmentation. We show the efficacy of the proposed framework by comparing it to classical augmentation techniques on a case study of object detection in autonomous driving based on deep neural networks. 

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4. When Sentiment Analysis Meets Social Network: A Holistic User Behavior Modeling in Opinionated Data

   https://doi.org/10.1145/3219819.3220120  

   Gong, Lin; Wang, Hongning (August 2018, KDD '18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining)

   User modeling is critical for understanding user intents, while it is also challenging as user intents are so diverse and not directly observable. Most existing works exploit specific types of behavior signals for user modeling, e.g., opinionated data or network structure; but the dependency among different types of user-generated data is neglected. We focus on self-consistence across multiple modalities of user-generated data to model user intents. A probabilistic generative model is developed to integrate two companion learning tasks of opinionated content modeling and social network structure modeling for users. Individual users are modeled as a mixture over the instances of paired learning tasks to realize their behavior heterogeneity, and the tasks are clustered by sharing a global prior distribution to capture the homogeneity among users. Extensive experimental evaluations on large collections of Amazon and Yelp reviews with social network structures confirm the effectiveness of the proposed solution. The learned user models are interpretable and predictive: they enable more accurate sentiment classification and item/friend recommendations than the corresponding baselines that only model a singular type of user behaviors. 

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5. LTLf Synthesis on Probabilistic Systems

   https://doi.org/10.4204/EPTCS.326.11  

   Wells, Andrew; Lahijanian, Morteza; Kavraki, Lydia E; Vardi, Moshe Y (October 2020, Wells, Andrew; Lahijanian Morteza; Kavraki, Lydia E; Vardi, Moshe Y.)

   Many systems are naturally modeled as Markov Decision Processes (MDPs), combining probabilities and strategic actions. Given a model of a system as an MDP and some logical specification of system behavior, the goal of synthesis is to find a policy that maximizes the probability of achieving this behavior. A popular choice for defining behaviors is Linear Temporal Logic (LTL). Policy synthesis on MDPs for properties specified in LTL has been well studied. LTL, however, is defined over infinite traces, while many properties of interest are inherently finite. Linear Temporal Logic over finite traces (LTLf ) has been used to express such properties, but no tools exist to solve policy synthesis for MDP behaviors given finite-trace properties. We present two algorithms for solving this synthesis problem: the first via reduction of LTLf to LTL and the second using native tools for LTLf . We compare the scalability of these two approaches for synthesis and show that the native approach offers better scalability compared to existing automaton generation tools for LTL. 

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