Learning to Act Properly: Predicting and Explaining Affordances from Images

We address the problem of affordance reasoning in diverse scenes that appear in the real world. Affordances relate the agent's actions to their effects when taken on the surrounding objects. In our work, we take the egocentric view of the scene, and aim to reason about action-object affordances that respect both the physical world as well as the social norms imposed by the society. We also aim to teach artificial agents why some actions should not be taken in certain situations, and what would likely happen if these actions would be taken. We collect a new dataset that builds upon ADE20k, referred to as ADE-Affordance, which contains annotations enabling such rich visual reasoning. We propose a model that exploits Graph Neural Networks to propagate contextual information from the scene in order to perform detailed affordance reasoning about each object. Our model is showcased through various ablation studies, pointing to successes and challenges in this complex task

InfoOT: Information Maximizing Optimal Transport

Optimal transport aligns samples across distributions by minimizing the transportation cost between them, e.g., the geometric distances. Yet, it ignores coherence structure in the data such as clusters, does not handle outliers well, and cannot integrate new data points. To address these drawbacks, we propose InfoOT, an information-theoretic extension of optimal transport that maximizes the mutual information between domains while minimizing geometric distances. The resulting objective can still be formulated as a (generalized) optimal transport problem, and can be efficiently solved by projected gradient descent. This formulation yields a new projection method that is robust to outliers and generalizes to unseen samples. Empirically, InfoOT improves the quality of alignments across benchmarks in domain adaptation, cross-domain retrieval, and single-cell alignment

Histone deacetylase 3 binds to and regulates the GCMa transcription factor

Human GCMa transcription factor regulates expression of syncytin, a placental fusogenic protein mediating trophoblastic fusion. Recently, we have demonstrated that CBP-mediated GCMa acetylation underlies the activated cAMP/PKA signaling pathway that stimulates trophoblastic fusion. Because protein acetylation is a reversible modification governed by histone acetyltransferases (HATs) and histone deacetylase (HDACs), in this study we investigated the key HDACs responsible for deacetylation of GCMa and thus the reduction in GCMa activity to avoid unwanted fusion events that may have adverse effects on placental morphogenesis. We herein demonstrate that the HDAC inhibitor, trichostatin A (TSA), increases the level of acetylated GCMa and that HDAC1, 3, 4 and 5 interact with and deacetylate GCMa. Glutathione S-transferase (GST) pull-down assays further verified direct interaction between GCMa and HDAC3 or CBP and HDAC3. HDAC3 counteracts the transcriptional coactivator activity of CBP and the enhancement effect of CBP on GCMa-mediated transcriptional activation. Correlatively, we found in placental cells that HDAC3 associates with the proximal GCMa-binding site (pGBS) in the syncytin promoter and dissociates from pGBS in the presence of forskolin, which stimulates the association of CBP and GCMa with pGBS. Our studies support that trophoblastic fusion in placental morphogenesis depends on the regulation of GCMa activity by HAT and HDAC

Co-management and Labor Stickiness in Fishing Communities: Determination of the Optimal Number of Vessels

In the recent years, resource depletion of inshore and coastal fisheries has seriously impacted Taiwan. Local fishing communities’ economic profits in these fisheries have declined and resulted in lower earned incomes for the fishermen. These phenomena have lead many scholars, government agencies and fishing communities to evaluate the optimal number of operating vessels in these fisheries. This study has explicitly applied the concepts of community-based co-management, fish market concentration and labor stickiness to an economic model that can be used to determine the optimum number of fishing vessels in a fishing community. One corollary of this approach is that we modify the traditional assumption regarding labor mobility in a fishing community and explore here how labor stickiness to the extent that it exists in Taiwan’s fishing communities might bias traditional fishing management policies and influence the determination of optimal number of vessels. In addition, the Herfindahl index (H), which measures the degree of concentration in the structure of a fishery market, will also affect the final determination of the optimal number of vessels. Results suggest that when there are no labor mobility barriers, then with flexible fishing operation costs, the optimal number of vessels and the fish stock would be smaller. Larger values of H (i.e., Herfindahl index) and greater differentials in the fishing efficiency index in the fishing community also result in relatively fewer vessels and fish stock. Finally, as to the impacts of changing fish stock growth rate and fish price on the optimal vessel number and fish stock are also discussed.Keywords: community-based co-management, labor stickiness, market concentratio

The Inductive Bias of Flatness Regularization for Deep Matrix Factorization

Recent works on over-parameterized neural networks have shown that the stochasticity in optimizers has the implicit regularization effect of minimizing the sharpness of the loss function (in particular, the trace of its Hessian) over the family zero-loss solutions. More explicit forms of flatness regularization also empirically improve the generalization performance. However, it remains unclear why and when flatness regularization leads to better generalization. This work takes the first step toward understanding the inductive bias of the minimum trace of the Hessian solutions in an important setting: learning deep linear networks from linear measurements, also known as \emph{deep matrix factorization}. We show that for all depth greater than one, with the standard Restricted Isometry Property (RIP) on the measurements, minimizing the trace of Hessian is approximately equivalent to minimizing the Schatten 1-norm of the corresponding end-to-end matrix parameters (i.e., the product of all layer matrices), which in turn leads to better generalization. We empirically verify our theoretical findings on synthetic datasets