TY - CONF AB - The decentralized nature of multi-agent systems requires continuous data exchange to achieve global objectives. In such scenarios, Age of Information (AoI) has become an important metric of the freshness of exchanged data due to the error-proneness and delays of communication systems. Communication systems usually possess dependencies: the process describing the success or failure of communication is highly correlated when these attempts are ``close'' in some domain (e.g. in time, frequency, space or code as in wireless communication) and is, in general, non-stationary. To study AoI in such scenarios, we consider an abstract event-based AoI process $\Delta(n)$, expressing time since the last update: If, at time $n$, a monitoring node receives a status update from a source node (event $A(n-1)$ occurs), then $\Delta(n)$ is reset to one; otherwise, $\Delta(n)$ grows linearly in time. This AoI process can thus be viewed as a special random walk with resets. The event process $A(n)$ may be nonstationary and we merely assume that its temporal dependencies decay sufficiently, described by $\alpha$-mixing. We calculate moment bounds for the resulting AoI process as a function of the mixing rate of $A(n)$. Furthermore, we prove that the AoI process $\Delta(n)$ is itself $\alpha$-mixing from which we conclude a strong law of large numbers for $\Delta(n)$. These results are new, since AoI processes have not been studied so far in this general strongly mixing setting. This opens up future work on renewal processes with non-independent interarrival times. AU - Redder, Adrian AU - Ramaswamy, Arunselvan AU - Karl, Holger ID - 32811 T2 - Proceedings of the 58th Allerton Conference on Communication, Control, and Computing TI - Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law ER - TY - CONF AU - Redder, Adrian AU - Ramaswamy, Arunselvan AU - Karl, Holger ID - 30793 T2 - Proceedings of the 14th International Conference on Agents and Artificial Intelligence TI - Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication ER - TY - CONF AB - Gender bias may emerge from an unequal representation of agency and power, for example, by portraying women frequently as passive and powerless ("She accepted her future'') and men as proactive and powerful ("He chose his future''). When language models learn from respective texts, they may reproduce or even amplify the bias. An effective way to mitigate bias is to generate counterfactual sentences with opposite agency and power to the training. Recent work targeted agency-specific verbs from a lexicon to this end. We argue that this is insufficient, due to the interaction of agency and power and their dependence on context. In this paper, we thus develop a new rewriting model that identifies verbs with the desired agency and power in the context of the given sentence. The verbs' probability is then boosted to encourage the model to rewrite both connotations jointly. According to automatic metrics, our model effectively controls for power while being competitive in agency to the state of the art. In our evaluation, human annotators favored its counterfactuals in terms of both connotations, also deeming its meaning preservation better. AU - Stahl, Maja AU - Spliethöver, Maximilian AU - Wachsmuth, Henning ID - 34082 T2 - Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science TI - To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation ER - TY - GEN AB - Iterative distributed optimization algorithms involve multiple agents that communicate with each other, over time, in order to minimize/maximize a global objective. In the presence of unreliable communication networks, the Age-of-Information (AoI), which measures the freshness of data received, may be large and hence hinder algorithmic convergence. In this paper, we study the convergence of general distributed gradient-based optimization algorithms in the presence of communication that neither happens periodically nor at stochastically independent points in time. We show that convergence is guaranteed provided the random variables associated with the AoI processes are stochastically dominated by a random variable with finite first moment. This improves on previous requirements of boundedness of more than the first moment. We then introduce stochastically strongly connected (SSC) networks, a new stochastic form of strong connectedness for time-varying networks. We show: If for any $p \ge0$ the processes that describe the success of communication between agents in a SSC network are $\alpha$-mixing with $n^{p-1}\alpha(n)$ summable, then the associated AoI processes are stochastically dominated by a random variable with finite $p$-th moment. In combination with our first contribution, this implies that distributed stochastic gradient descend converges in the presence of AoI, if $\alpha(n)$ is summable. AU - Redder, Adrian AU - Ramaswamy, Arunselvan AU - Karl, Holger ID - 30790 T2 - arXiv:2201.11343 TI - Distributed gradient-based optimization in the presence of dependent aperiodic communication ER - TY - JOUR AU - Hagengruber, Ruth Edith ID - 34109 JF - La Lettre clandestine n° 30: Émilie Du Châtelet et la littérature clandestine. Moreau, P.F. & Seguin, M.S. (Eds.). Paris TI - Du Châtelet et la tradition critique de la Bible. De la ‘philofolie’ aux Examens ER - TY - CONF AU - Clausing, Lennart AU - Platzner, Marco ID - 32855 T2 - 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) TI - ReconOS64: A Hardware Operating System for Modern Platform FPGAs with 64-Bit Support ER - TY - GEN AB - We present sufficient conditions that ensure convergence of the multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm. It is an example of one of the most popular paradigms of Deep Reinforcement Learning (DeepRL) for tackling continuous action spaces: the actor-critic paradigm. In the setting considered herein, each agent observes a part of the global state space in order to take local actions, for which it receives local rewards. For every agent, DDPG trains a local actor (policy) and a local critic (Q-function). The analysis shows that multi-agent DDPG using neural networks to approximate the local policies and critics converge to limits with the following properties: The critic limits minimize the average squared Bellman loss; the actor limits parameterize a policy that maximizes the local critic's approximation of $Q_i^*$, where $i$ is the agent index. The averaging is with respect to a probability distribution over the global state-action space. It captures the asymptotics of all local training processes. Finally, we extend the analysis to a fully decentralized setting where agents communicate over a wireless network prone to delays and losses; a typical scenario in, e.g., robotic applications. AU - Redder, Adrian AU - Ramaswamy, Arunselvan AU - Karl, Holger ID - 30791 T2 - arXiv:2201.00570 TI - Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms ER - TY - CONF AU - Richter, Cedric AU - Wehrheim, Heike ID - 32590 T2 - 2022 IEEE Conference on Software Testing, Verification and Validation (ICST) TI - Learning Realistic Mutations: Bug Creation for Neural Bug Detectors ER - TY - CONF AU - Richter, Cedric AU - Wehrheim, Heike ID - 32591 T2 - 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR) TI - TSSB-3M: Mining single statement bugs at massive scale ER - TY - GEN AU - Siek, Hanna ID - 32398 TI - Bringing Structure to Structure-Preserving Signatures: Overview, Implementation and Comparison of Selected SPS Schemes ER -