Propagation Networks for Model-Based Control Under Partial Observation

There has been an increasing interest in learning dynamics simulators for model-based control. Compared with off-the-shelf physics engines, a learnable simulator can quickly adapt to unseen objects, scenes, and tasks. However, existing models like interaction networks only work for fully observable systems; they also only consider pairwise interactions within a single time step, both restricting their use in practical systems. We introduce Propagation Networks (PropNet), a differentiable, learnable dynamics model that handles partially observable scenarios and enables instantaneous propagation of signals beyond pairwise interactions. Experiments show that our propagation networks not only outperform current learnable physics engines in forward simulation, but also achieve superior performance on various control tasks. Compared with existing model-free deep reinforcement learning algorithms, model-based control with propagation networks is more accurate, efficient, and generalizable to new, partially observable scenes and tasks.Comment: Accepted to ICRA 2019. Project Page: http://propnet.csail.mit.edu Video: https://youtu.be/ZAxHXegkz4

The maturity-lengthening role of national development banks

We analyze why national development banks (NDBs) may provide longer-term loans to firms than private commercial banks (PCBs). If NDB bonds have higher collateral value than PCB bonds, then NDBs may lend longer-term than PCBs. NDBs may enjoy higher recapitalization willingness and capacity by the state and hence greater collateral value than PCBs. Moreover, NDBs may have advantages over state-owned commercial banks if NDB bonds enjoy higher market liquidity. However, NDBs may suffer from poor monitoring quality owing to undue political intervention, thus undermining collateral value. Our study implies that NDBs are not substitutes for but complements to PCBs.Fil: Schclarek Curutchet, Alfredo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Xu, Jiajun. Peking University; ChinaFil: Yan, Jianye. Beijing Institute Of Technology; Chin

Visual Object Networks: Image Generation with Disentangled 3D Representation

Recent progress in deep generative models has led to tremendous breakthroughs in image generation. However, while existing models can synthesize photorealistic images, they lack an understanding of our underlying 3D world. We present a new generative model, Visual Object Networks (VON), synthesizing natural images of objects with a disentangled 3D representation. Inspired by classic graphics rendering pipelines, we unravel our image formation process into three conditionally independent factors---shape, viewpoint, and texture---and present an end-to-end adversarial learning framework that jointly models 3D shapes and 2D images. Our model first learns to synthesize 3D shapes that are indistinguishable from real shapes. It then renders the object's 2.5D sketches (i.e., silhouette and depth map) from its shape under a sampled viewpoint. Finally, it learns to add realistic texture to these 2.5D sketches to generate natural images. The VON not only generates images that are more realistic than state-of-the-art 2D image synthesis methods, but also enables many 3D operations such as changing the viewpoint of a generated image, editing of shape and texture, linear interpolation in texture and shape space, and transferring appearance across different objects and viewpoints.Comment: NeurIPS 2018. Code: https://github.com/junyanz/VON Website: http://von.csail.mit.edu

Long-term finance provision: National development banks vs commercial banks

Despite its practical significance in promoting long-term economic growth, long-term finance is often in short supply, especially in developing countries. Governments in both developed and developing countries have established national development banks (NDBs) to provide much-needed long-term loans. We have built the first database on NDBs worldwide to systematically examine whether NDBs lend longer than commercial banks in deciding the maturity of their loans. We find that long-term loans constitute a larger proportion of the total loan portfolio in NDBs than that in commercial banks in general and privately owned commercial banks in particular. This result is statistically significant after controlling for country- and bank-level factors. Our study contributes to the literature on loan maturity because we are the first to use a comprehensive panel data to systematically examine whether NDBs—an understudied but important financial intermediary—play a maturity-lengthening role in filling the financing gap.Fil: Hu, Bo. Peking University; ChinaFil: Schclarek Curutchet, Alfredo. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Xu, Jiajun. Peking University; ChinaFil: Yan, Jianye. China Agricultural University; Chin

BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training

Automatic metrics play a crucial role in machine translation. Despite the widespread use of n-gram-based metrics, there has been a recent surge in the development of pre-trained model-based metrics that focus on measuring sentence semantics. However, these neural metrics, while achieving higher correlations with human evaluations, are often considered to be black boxes with potential biases that are difficult to detect. In this study, we systematically analyze and compare various mainstream and cutting-edge automatic metrics from the perspective of their guidance for training machine translation systems. Through Minimum Risk Training (MRT), we find that certain metrics exhibit robustness defects, such as the presence of universal adversarial translations in BLEURT and BARTScore. In-depth analysis suggests two main causes of these robustness deficits: distribution biases in the training datasets, and the tendency of the metric paradigm. By incorporating token-level constraints, we enhance the robustness of evaluation metrics, which in turn leads to an improvement in the performance of machine translation systems. Codes are available at \url{https://github.com/powerpuffpomelo/fairseq_mrt}.Comment: Accepted to ACL 2023 main conferenc

Transfer function based input impedance determination of triple active bridge converter

The concept of multiport dc-dc converter was proposed to reduce the conversion stages of dc microgrid on more electric aircraft (MEA). The structure of multiport dc-dc converter is basically developed from the dual active bridge (DAB) converter because of its galvanic isolation and bidirectional power flow. A power electronics converter as a key element of the electrical power distribution system may cause stability issues. To address these challenges, the impedance characteristic of the multiport converter will be analyzed. In this paper, a transfer function based small signal model is developed and validated with a switching model, to figure out the characteristic of input impedance of triple active bridge (TAB) converter. Preliminary experimental results are presented to be as a support

Molecular Mechanism Study on the Effect of Nonionic Surfactants with Different Degrees of Ethoxylation on the Wettability of Anthracite

A serious risk to the production safety of coal mines is coal dust. The wettability of coal may be successfully changed by adding surfactants to water. However, the creation of very effective dust suppressants is constrained by the lack of knowledge about the microscopic interaction mechanism between coal dust and surfactants. In this investigation, we explained macroscopic experimental phenomena from a molecular perspective. The lauryl polyoxyethylene ethers (C12 (EO)n, n = 7,15,23) were selected. The macromolecular model of anthracite with 55 different components was constructed. Surface tension experiments and hydrophilic lipophilic balance (HLB) calculations showed that the ability of surface hydrophilicization followed the order of C12 (EO)712 \u3e(EO)1512 \u3e(EO)23. Contact angle experiment, XPS and FTIR experiments proved that after the surfactants were adsorbed on the surface of anthracite, the content of carbon element decreased and the content of oxygen element increased, indicating the enhanced surface hydrophilicity. The simulation results showed that with the degree of ethoxylation increases, the adsorption strength of surfactants becomes stronger, and the hydrophilic head group of surfactant on anthracite surface is more uniformly distributed. The greater the degree of ethoxylation, the more powerfully the modified coal surface can bind to water molecules