SoK: Blockchain Decentralization

Blockchain empowers a decentralized economy by enabling distributed trust in a peer-to-peer network. However, surprisingly, a widely accepted definition or measurement of decentralization is still lacking. We explore a systematization of knowledge (SoK) on blockchain decentralization by comprehensively analyzing existing studies in various aspects. First, we establish a taxonomy for analyzing blockchain decentralization in the five facets of consensus, network, governance, wealth, and transaction. We find a lack of research on the transaction aspects that closely characterize user behavior. Second, we apply Shannon entropy in information theory to propose a decentralization index for blockchain transactions. We show that our index intuitively measures levels of decentralization in peer-to-peer transactions by simulating blockchain token transfers. Third, we apply our index to empirically analyze the dynamics of DeFi token transfers by three methods of description, prediction, and causal inference. In the descriptive analysis, we observe that levels of decentralization converge inter-temporally, regardless of the initial levels. A comparative study across DeFi applications shows that exchange and lending are more decentralized than payment and derivatives across DeFi applications. Second, in the predictive analysis, we also discover that a greater return of Ether, the native coin of the Ethereum blockchain, predicts a greater transaction decentralization in stablecoin that include Ether as collateral. Third, in an event study of causal inference, we find the change of Ethereum Transaction Fee Mechanism to EIP-1559 significantly changes the decentralization level of DeFi transactions. Finally, we identify future research directions

Understanding Deep Architectures with Reasoning Layer

Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks. In many cases, a reasoning task can be solved by an iterative algorithm. This algorithm is often unrolled, and used as a specialized layer in the deep architecture, which can be trained end-to-end with other neural components. Although such hybrid deep architectures have led to many empirical successes, the theoretical foundation of such architectures, especially the interplay between algorithm layers and other neural layers, remains largely unexplored. In this paper, we take an initial step towards an understanding of such hybrid deep architectures by showing that properties of the algorithm layers, such as convergence, stability, and sensitivity, are intimately related to the approximation and generalization abilities of the end-to-end model. Furthermore, our analysis matches closely our experimental observations under various conditions, suggesting that our theory can provide useful guidelines for designing deep architectures with reasoning layers.Comment: 34th Conference on Neural Information Processing Systems (NeurIPS 2020

CoLight: Learning Network-level Cooperation for Traffic Signal Control

Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Such pre-calculated offsets are not suitable for dynamic traffic environments. To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication. Specifically, for a target intersection in a network, CoLight can not only incorporate the temporal and spatial influences of neighboring intersections to the target intersection, but also build up index-free modeling of neighboring intersections. To the best of our knowledge, we are the first to use graph attentional networks in the setting of reinforcement learning for traffic signal control and to conduct experiments on the large-scale road network with hundreds of traffic signals. In experiments, we demonstrate that by learning the communication, the proposed model can achieve superior performance against the state-of-the-art methods.Comment: 10 pages. Proceedings of the 28th ACM International on Conference on Information and Knowledge Management. ACM, 201

Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension

The conversational machine reading comprehension (CMRC) task aims to answer questions in conversations, which has been a hot research topic in recent years because of its wide applications. However, existing CMRC benchmarks in which each conversation is assigned a static passage are inconsistent with real scenarios. Thus, model's comprehension ability towards real scenarios are hard to evaluate reasonably. To this end, we propose the first Chinese CMRC benchmark Orca and further provide zero-shot/few-shot settings to evaluate model's generalization ability towards diverse domains. We collect 831 hot-topic driven conversations with 4,742 turns in total. Each turn of a conversation is assigned with a response-related passage, aiming to evaluate model's comprehension ability more reasonably. The topics of conversations are collected from social media platform and cover 33 domains, trying to be consistent with real scenarios. Importantly, answers in Orca are all well-annotated natural responses rather than the specific spans or short phrase in previous datasets. Besides, we implement three strong baselines to tackle the challenge in Orca. The results indicate the great challenge of our CMRC benchmark. Our datatset and checkpoints are available at https://github.com/nuochenpku/Orca.Comment: 14 page

1D supercapacitors based on graphene hybrids

In recent years, fiber-shaped supercapacitors (FSCs) have emerged as a promising candidate in the field of power electronics due to their unique advantages in energy storage, such as high power density, good rate capability, excellent cycle life, high flexibility, and manufacturing compatibility for integration into woven textiles. Next, graphene has high specific surface area and excellent electrical conductivity, so it has been the attractive electrode material for preparing FSCs. However, the energy density of graphene-based fiber supercapacitors reported at present is generally low (generally <15 MWh cm-3), which cannot meet the requirements for further practical application. Therefore, it is very necessary to develop new kind of FSCs with both high energy density and high power density. To address these issues, first of all, 2D MXene materials exhibit excellent electrical and electrochemical properties due to their inherent two-dimensional atom-thick topology. So integrating MXene materials into graphene-based fibers is one of the efficient strategies to solve the problem of low energy density. However, their low oxygen resistance results in the loss of a large amountr of electronic performance and surface reactivity, therefore, by using a simple carbon nanoplating strategy to stablize MXenes nanosheets, which can effectively prevent MXene from spontaneous oxidation degradation and maintain its structure, and we used a closed interval hydrothermal synthesis method to insert the high quality of MXene nansheets (up to 65 wt. %) evenly into the graphene oxide (GO) hybrid fibers, which show a new type of composite fiber electrodes .This method provides a material platform for the development of MXene-graphene materials with superior structure and performances. Second, supercapacitors based on ion adsorption or fast surface redox reactions usually have high power density but low energy density. In contrast, batteries based on diffusion-controlled Faradic reactions have much higher energy storage capacity and lower power density. Thus, hybrid capacitors comprising of a battery-type negative electrode and a capacitor-type positive electrode have been explored to combine the merits of both batteries and supercapacitors. Miniaturizing such hybrid capacitors is expected to deliver FSCs with high energy densities. So based on these consideration I designed two different electrodes .The positive electrodes are composite carbon fibers assembled hydrothermally in capillary columns using carbon nanotubes (CNTs) and graphene oxide (GO). The negative electrodes are carbon (graphite) fibers plated with a thin layer of Zn metal. A new neutral ZnSO4-filled polyacrylic acid hydrogel act as the quasi-solid-state electrolyte, which offers high ionic conductivity and excellent stretchability. The assembled FSCs delivers a high energy density of 48.5 mWh cm–3 at a power density of 179.9 mW cm-3, which is one of the highest among all Zn-ion hybrid capacitors reported so far. In conclusion, this paper provides a new basic understanding for the fabrication of graphene hybrid materials. Some innovative methods have been demonstrated to synthesis high electrochemical performances of graphene-based fiber electrodes for 1D SCs.These results will benefit to realize the furture practical applications of 1D SCs based on graphene hybrid material