A Game-Theoretic Framework for AI Governance

As a transformative general-purpose technology, AI has empowered various industries and will continue to shape our lives through ubiquitous applications. Despite the enormous benefits from wide-spread AI deployment, it is crucial to address associated downside risks and therefore ensure AI advances are safe, fair, responsible, and aligned with human values. To do so, we need to establish effective AI governance. In this work, we show that the strategic interaction between the regulatory agencies and AI firms has an intrinsic structure reminiscent of a Stackelberg game, which motivates us to propose a game-theoretic modeling framework for AI governance. In particular, we formulate such interaction as a Stackelberg game composed of a leader and a follower, which captures the underlying game structure compared to its simultaneous play counterparts. Furthermore, the choice of the leader naturally gives rise to two settings. And we demonstrate that our proposed model can serves as a unified AI governance framework from two aspects: firstly we can map one setting to the AI governance of civil domains and the other to the safety-critical and military domains, secondly, the two settings of governance could be chosen contingent on the capability of the intelligent systems. To the best of our knowledge, this work is the first to use game theory for analyzing and structuring AI governance. We also discuss promising directions and hope this can help stimulate research interest in this interdisciplinary area. On a high, we hope this work would contribute to develop a new paradigm for technology policy: the quantitative and AI-driven methods for the technology policy field, which holds significant promise for overcoming many shortcomings of existing qualitative approaches

Deep Interest Evolution Network for Click-Through Rate Prediction

Click-through rate~(CTR) prediction, whose goal is to estimate the probability of the user clicks, has become one of the core tasks in advertising systems. For CTR prediction model, it is necessary to capture the latent user interest behind the user behavior data. Besides, considering the changing of the external environment and the internal cognition, user interest evolves over time dynamically. There are several CTR prediction methods for interest modeling, while most of them regard the representation of behavior as the interest directly, and lack specially modeling for latent interest behind the concrete behavior. Moreover, few work consider the changing trend of interest. In this paper, we propose a novel model, named Deep Interest Evolution Network~(DIEN), for CTR prediction. Specifically, we design interest extractor layer to capture temporal interests from history behavior sequence. At this layer, we introduce an auxiliary loss to supervise interest extracting at each step. As user interests are diverse, especially in the e-commerce system, we propose interest evolving layer to capture interest evolving process that is relative to the target item. At interest evolving layer, attention mechanism is embedded into the sequential structure novelly, and the effects of relative interests are strengthened during interest evolution. In the experiments on both public and industrial datasets, DIEN significantly outperforms the state-of-the-art solutions. Notably, DIEN has been deployed in the display advertisement system of Taobao, and obtained 20.7\% improvement on CTR.Comment: 9 pages. Accepted by AAAI 201

Relationship between plant diversity and spatial stability of aboveground net primary productivity (ANPP) across different grassland ecosystems

Theory predicts that greater biodiversity is expected to enhance stability of ecosystem. In field experiment, we created some diversity-level assemblages by removing functional groups across two grassland ecosystems and evaluated the responses of spatial stability of aboveground net primary productivity (ANPP) to varying functional trait diversity. The results revealed that higher diversity promoted greater spatial stability in the semi-shrub grassland ecosystem except SGB, whereas the similar pattern in diversity-stability relationship had been scarcely found in the typical steppe ecosystem. Additionally, we found that factors-influencing spatial stability varied across different grassland types. In the typical steppe ecosystem, spatial stability was only accounted for by positive sampling effect induced by high dispersal rate of rhizomatous grass. By contrast, in the semi-shrub grassland ecosystem, diversity level together with positive sampling effect commonly contributed to spatial stability, moreover, effect of particular trait overshadowed that of diversity. We also found that the positive diversity-stability relationship did not exist when compared with two grassland types. Research provides new insights into understanding the relationship between biodiversity and ecosystem functioning in varying environments. This relationship is not consistent across different ecosystems and is often system-dependent. Critical trait of species is particularly an important determinant for ecosystem functioning.Key words: Biodiversity experiment, spatial variability, functional trait diversity, ecosystem type

Identifying Material Parameters for a Micro-Polar Plasticity Model Via X-Ray Micro-Computed Tomographic (Ct) Images: Lessons Learned from the Curve-Fitting Exercises

Abstract: Unlike a conventional first-order continuum model, the material parameters of which can be identified via an inverse problem conducted at material point that exhibits homogeneous deformation, a higher-order continuum model requires information from the derivative of the deformation gradient. This study concerns an integrated experimental-numerical procedure designed to identify material parameters for higher-order continuum models. Using a combination of microCT images and macroscopic stress–strain curves as the database, we construct a new finite element inverse problem which identifies the optimal value of material parameters that matches both the macroscopic constitutive responses and the meso-scale micropolar kinematics. Our results indicate that the optimal characteristic length predicted by the constrained optimization procedure is highly sensitive to the types and weights of constraints used to define the objective function of the inverse problems. This sensitivity may in return affect the resultant failure modes (localized vs. diffuse), and the coupled stress responses. This result signals that using the mean grain diameter alone to calibrate the characteristic length may not be sufficient to yield reliable forward predictions. Key words: micro-CT imaging, micro-polar plasticity, critical state, higher-order continuum, Hostun San

Mesenchymal Stem Cell-Based Tissue Engineering for Chondrogenesis

In tissue engineering fields, recent interest has been focused on stem cell therapy to replace or repair damaged or worn-out tissues due to congenital abnormalities, disease, or injury. In particular, the repair of articular cartilage degeneration by stem cell-based tissue engineering could be of enormous therapeutic and economic benefit for an aging population. Bone marrow-derived mesenchymal stem cells (MSCs) that can induce chondrogenic differentiation would provide an appropriate cell source to repair damaged cartilage tissues; however, we must first understand the optimal environmental conditions for chondrogenic differentiation. In this review, we will focus on identifying the best combination of MSCs and functional extracellular matrices that provides the most successful chondrogenesis