Dune Formation and Sand Transportation on Titan

Nearly 20% of Titan’s surface is covered with equatorial linear dunes similar to sand dunes on Earth. However, the sand on Titan is not made of silicates, as on Earth, but mainly of organic materials produced by photochemistry in the atmosphere. The combination of low gravity, high atmospheric density, low temperature, and the unique composition of sand means that additional theoretical and experimental studies are needed to improve our understanding of aeolian sediment transport on Titan. An improved understanding of the minimum wind speed to initiate sand particle movements (threshold wind speed) will better constrain models of the global circulation of Titan’s atmosphere. The formation of dune particles on Titan is also not well understood. The particles may be formed by transforming small aerosol particles (∼1 μm) in Titan’s atmosphere into large sand-size saltating particles on the surface (100–300 μm). Alternatively, deposits of organics on the surface may be eroded into sand-sized particles. Even though several mechanisms have been proposed for this transformation, there has been no experimental data to help determine which, if any, of the formation mechanisms are occurring. Here, I experimentally measured the liquid adsorption properties of materials used in the Titan Wind Tunnel (TWT), and modeled the effect of adsorbed liquid on threshold wind speed for materials frequently used in the TWT, silicate sand on Earth, and organic sand on Titan. I demonstrated that the effect of methane humidity on tholin is similar to the effect of water on silicate sand, but different from the effect of water on the low density wind tunnel materials. I also found that it may be easier to transport “wet" (methane-saturated) sand than “dry" sand on Titan, because the methane capillary force may be smaller than the “dry" adhesion forces between the organic sand particles. I have also used atomic force microscopy to study the interparticle interactions between Titan aerosol analogues, or ‘tholin’. I found that the interparticle cohesion forces are much larger for tholin and presumably Titan sand particles than for silicate sand and other materials used in the TWT. This suggests that we should increase the interparticle forces in both analog experiments (TWT) and threshold models to correctly translate the results to real Titan conditions. The strong cohesion of tholin also indicates that Titan’s sand could be formed by effective agglomeration of small aerosol particles in the atmosphere. I have also used nanoindentation to study the mechanical properties of a few Titan sand candidates to understand the mobility of Titan sand. I measured the elastic modulus, hardness, and fracture toughness of these materials. The elastic modulus and hardness of tholin are both an order of magnitude smaller than silicate sand and are smaller than mechanically weak sand like white gypsum sand. With an order of magnitude smaller fracture toughness, tholin is also much more brittle than silicate sand. Other possible Titan sand candidates are also mechanically weaker than sand on Earth. This indicates that Titan sand should be derived close to the equatorial regions where the current dunes are located, because tholin and other organics are too soft and brittle to be transported for long distances. The above results suggest that it is more favorable for the Titan sand to be formed by “dry" agglomeration of small aerosol particles. Since the organics have higher cohesion and are less likely to be formed in the polar liquid reservoirs on Titan by “wet" agglomeration, they are not mechanically strong enough to transport long distances to form the equatorial dunes

The Fate of Simple Organics on Titan's Surface: A Theoretical Perspective

Atmospheric photochemistry on Titan continuously transforms methane and nitrogen gases into various organic compounds. This study explores the fate of these molecules when they land on Titan's surface. Our analytical exploration reveals that most simple organics found in Titan's atmosphere, including all nitriles, triple-bonded hydrocarbons, and benzene, land as solids. Only a few compounds are in the liquid phase, while only ethylene remains gaseous. For the simple organics that land as solids, we further examine their interactions with Titan's lake liquids. Utilizing principles of buoyancy, we found that flotation can be achieved via porosity-induced (25-60% porosity) or capillary force-induced buoyancy for HCN ices on ethane-rich lakes. Otherwise, these ices would sink and become lakebed sediments. By evaluating the timescale of flotation, our findings suggest that porosity-induced flotation of millimeter-sized and larger sediments is the only plausible mechanism for floating solids to explain the transient "magic islands" phenomena on Titan's lakes.Comment: 11 pages, 4 figure

MALA: Cross-Domain Dialogue Generation with Action Learning

Response generation for task-oriented dialogues involves two basic components: dialogue planning and surface realization. These two components, however, have a discrepancy in their objectives, i.e., task completion and language quality. To deal with such discrepancy, conditioned response generation has been introduced where the generation process is factorized into action decision and language generation via explicit action representations. To obtain action representations, recent studies learn latent actions in an unsupervised manner based on the utterance lexical similarity. Such an action learning approach is prone to diversities of language surfaces, which may impinge task completion and language quality. To address this issue, we propose multi-stage adaptive latent action learning (MALA) that learns semantic latent actions by distinguishing the effects of utterances on dialogue progress. We model the utterance effect using the transition of dialogue states caused by the utterance and develop a semantic similarity measurement that estimates whether utterances have similar effects. For learning semantic actions on domains without dialogue states, MsALA extends the semantic similarity measurement across domains progressively, i.e., from aligning shared actions to learning domain-specific actions. Experiments using multi-domain datasets, SMD and MultiWOZ, show that our proposed model achieves consistent improvements over the baselines models in terms of both task completion and language quality.Comment: 9 pages, 3 figure

MASP: Scalable GNN-based Planning for Multi-Agent Navigation

We investigate the problem of decentralized multi-agent navigation tasks, where multiple agents need to reach initially unassigned targets in a limited time. Classical planning-based methods suffer from expensive computation overhead at each step and offer limited expressiveness for complex cooperation strategies. In contrast, reinforcement learning (RL) has recently become a popular paradigm for addressing this issue. However, RL struggles with low data efficiency and cooperation when directly exploring (nearly) optimal policies in the large search space, especially with an increased agent number (e.g., 10+ agents) or in complex environments (e.g., 3D simulators). In this paper, we propose Multi-Agent Scalable GNN-based P lanner (MASP), a goal-conditioned hierarchical planner for navigation tasks with a substantial number of agents. MASP adopts a hierarchical framework to divide a large search space into multiple smaller spaces, thereby reducing the space complexity and accelerating training convergence. We also leverage graph neural networks (GNN) to model the interaction between agents and goals, improving goal achievement. Besides, to enhance generalization capabilities in scenarios with unseen team sizes, we divide agents into multiple groups, each with a previously trained number of agents. The results demonstrate that MASP outperforms classical planning-based competitors and RL baselines, achieving a nearly 100% success rate with minimal training data in both multi-agent particle environments (MPE) with 50 agents and a quadrotor 3-dimensional environment (OmniDrones) with 20 agents. Furthermore, the learned policy showcases zero-shot generalization across unseen team sizes.Comment: Submitted to IEEE RA-