Probabilistic Clustering Using Maximal Matrix Norm Couplings

In this paper, we present a local information theoretic approach to explicitly learn probabilistic clustering of a discrete random variable. Our formulation yields a convex maximization problem for which it is NP-hard to find the global optimum. In order to algorithmically solve this optimization problem, we propose two relaxations that are solved via gradient ascent and alternating maximization. Experiments on the MSR Sentence Completion Challenge, MovieLens 100K, and Reuters21578 datasets demonstrate that our approach is competitive with existing techniques and worthy of further investigation.Comment: Presented at 56th Annual Allerton Conference on Communication, Control, and Computing, 201

Dynamic changes in connexin expression correlate with key events in the wound healing process.

Wound healing is a complex process requiring communication for the precise co-ordination of different cell types. The role of extracellular communication through growth factors in the wound healing process has been extensively documented, but the role of direct intercellular communication via gap junctions has scarcely been investigated. We have examined the dynamics of gap junction protein (Connexins 26, 30, 31.1 and 43) expression in the murine epidermis and dermis during wound healing, and we show that connexin expression is extremely plastic between 6 hours and 12 days post-wounding. The immediate response (6 h) to wounding is to downregulate all connexins in the epidermis, but thereafter the expression profile of each connexin changes dramatically. Here, we correlate the changing patterns of connexin expression with key events in the wound healing process

Inferring Occluded Agent Behavior in Dynamic Games with Noise-Corrupted Observations

Robots and autonomous vehicles must rely on sensor observations, e.g., from lidars and cameras, to comprehend their environment and provide safe, efficient services. In multi-agent scenarios, they must additionally account for other agents' intrinsic motivations, which ultimately determine the observed and future behaviors. Dynamic game theory provides a theoretical framework for modeling the behavior of agents with different objectives who interact with each other over time. Previous works employing dynamic game theory often overlook occluded agents, which can lead to risky navigation decisions. To tackle this issue, this paper presents an inverse dynamic game technique which optimizes the game model itself to infer unobserved, occluded agents' behavior that best explains the observations of visible agents. Our framework concurrently predicts agents' future behavior based on the reconstructed game model. Furthermore, we introduce and apply a novel receding horizon planning pipeline in several simulated scenarios. Results demonstrate that our approach offers 1) robust estimation of agents' objectives and 2) precise trajectory predictions for both visible and occluded agents from observations of only visible agents. Experimental findings also indicate that our planning pipeline leads to safer navigation decisions compared to existing baseline methods