Platform Pricing Structure and Moral Hazard

We study pricing by a two-sided platform when it faces moral hazard on the sellers? side. In doing so, we introduce an equilibrium notion of platform reputation in an infinite horizon model. We find that with transaction fees only, the platform cannot eliminate the loss of reputation induced by moral hazard. If registration fees can be levied, moral hazard can be overcome. The registration fee determines the participation threshold of sellers and extracts them, while (lower) transaction fees provide incentives for good behavior. This provides a motivation for platforms to use registration fees in addition to transaction fees

Non-Exclusive Financial Advice

We propose a simple model of non-exclusive financial advice in which two households rely on a self-interested (common) expert to make their investment choices. There is only one source of risk, and the expert is privately informed about the risky asset's volatility. When monetary transfers are unenforceable, we show that investors may delegate their investment decisions to the expert. When doing so, however, they impose restrictions on her choices which crucially depend on whether the expert perceives investors' asset allocations as complements or as substitutes. Finally, we analyze the implications of non-exclusivity in financial advice on investment behavior and welfare, and highlight a set of novel testable implications

Behavior of argon gas release from manganese oxide minerals as revealed by Ar-40/Ar-39 laser incremental heating analysis

Manganese oxides in association with paleo-weathering may provide significant insights into the multiple factors affecting the formation and evolution of weathering profiles, such as temperature, precipitation, and biodiversity. Laser probe step-heating analysis of supergene hollandite and cryptomelane samples collected from central Queensland, Australia, yield well-defined plateaus and consistent isochron ages, confirming the feasibility, dating very-fined supergene manganese oxides by Ar-40/(39) Ar technique. Two distinct structural sites hosting Ar isotopes can be identified in light of their degassing behaviors obtained by incremental heating analyses. The first site, releasing its gas fraction at the laser power 0.2-0.4 W, yields primarily Ar-40(atm), Ar-38(atm), and Ar-36(atm), (atmospheric Ar isotopes). The second sites yield predominantly Ar-40* (radiogenic Ar-40), Ar-39(K), and Ar-38(K) (nucleogenic components), at similar to0.5-1.0 W. There is no significant Ar gas released at the laser power higher than 1.0 W, indicating the breakdown of the tunnel sites hosting the radiogenic and nucleogenic components. The excellent match between the degassing behaviors of Ar-40*, Ar-39(K), and Ar-38(K) suggests that these isotopes occupy the same crystallographic sites and that Ar-39(K) loss from the tunnel site by recoil during neutron irradiation and/or bake-out procedure preceding isotopic analysis does not occur. Present investigation supports that neither the overwhelming atmospheric Ar-40 nor the very-fined nature of the supergene manganese oxides poses problems in extracting meaningful weathering geo-chronological information by analyzing supergene manganese oxides minerals

SViTT: Temporal Learning of Sparse Video-Text Transformers

Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has revealed the strong tendency of video-text models towards frame-based spatial representations, while temporal reasoning remains largely unsolved. In this work, we identify several key challenges in temporal learning of video-text transformers: the spatiotemporal trade-off from limited network size; the curse of dimensionality for multi-frame modeling; and the diminishing returns of semantic information by extending clip length. Guided by these findings, we propose SViTT, a sparse video-text architecture that performs multi-frame reasoning with significantly lower cost than naive transformers with dense attention. Analogous to graph-based networks, SViTT employs two forms of sparsity: edge sparsity that limits the query-key communications between tokens in self-attention, and node sparsity that discards uninformative visual tokens. Trained with a curriculum which increases model sparsity with the clip length, SViTT outperforms dense transformer baselines on multiple video-text retrieval and question answering benchmarks, with a fraction of computational cost. Project page: http://svcl.ucsd.edu/projects/svitt.Comment: CVPR 202