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Open and permissionless blockchains are distributed systems with thousands to tens of thousands of nodes, establishing novel platforms for decentralized applications. When realizing such an application, data might be stored and retrieved from one or more blockchains by distributed network nodes without relying on centralized coordination and trusted third parties. Data access could be provided through a query language such as SQL at the application level, establishing a unified view on application-level data that is verifiably stored. However, when accessing multiple blockchains through their node software and APIs, interoperability cannot be assumed today, resulting in challenges of inhomogeneous data access. In addition, different feature sets and trade-offs exist, e.g., regarding smart contract functionality, availability, distribution, scalability, and security. For increasing interoperability, the paper at hand suggests pursuing the development of a cross-chain query language at the application level. The language abstracts from implementation by providing a standardized syntax, an integrated data model, and a processing architecture for data queries. This research is an extended and updated paper demonstrating the language syntax, data model, and architecture with an evaluation of compatibility against the largest open and permissionless blockchains today.

Research on the theoretical expressiveness of Graph Neural Networks (GNNs) has developed rapidly, and many methods have been proposed to enhance the expressiveness. However, most methods do not have a uniform expressiveness measure except for a few that strictly follow the $k$-dimensional Weisfeiler-Lehman ($k$-WL) test hierarchy. Their theoretical analyses are often limited to distinguishing certain families of non-isomorphic graphs, leading to difficulties in quantitatively comparing their expressiveness. In contrast to theoretical analysis, another way to measure expressiveness is by evaluating model performance on certain datasets containing 1-WL-indistinguishable graphs. Previous datasets specifically designed for this purpose, however, face problems with difficulty (any model surpassing 1-WL has nearly 100% accuracy), granularity (models tend to be either 100% correct or near random guess), and scale (only a few essentially different graphs in each dataset). To address these limitations, we propose a new expressiveness dataset, $\textbf{BREC}$, which includes 400 pairs of non-isomorphic graphs carefully selected from four primary categories (Basic, Regular, Extension, and CFI). These graphs have higher difficulty (up to 4-WL-indistinguishable), finer granularity (able to compare models between 1-WL and 3-WL), and a larger scale (400 pairs). Further, we synthetically test 23 models with higher-than-1-WL expressiveness on our BREC dataset. Our experiment gives the first thorough comparison of the expressiveness of those state-of-the-art beyond-1-WL GNN models. We expect this dataset to serve as a benchmark for testing the expressiveness of future GNNs. Our dataset and evaluation code are released at: //github.com/GraphPKU/BREC.

Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially accelerate autonomous driving research, especially for perception tasks such as 3D detection and trajectory forecasting. Since the driving logs in these datasets contain HD maps and detailed object annotations which accurately reflect the real-world complexity of traffic behaviors, we can harvest a massive number of complex traffic scenarios and recreate their digital twins in simulation. Compared to the hand-crafted scenarios often used in existing simulators, data-driven scenarios collected from the real world can facilitate many research opportunities in machine learning and autonomous driving. In this work, we present ScenarioNet, an open-source platform for large-scale traffic scenario modeling and simulation. ScenarioNet defines a unified scenario description format and collects a large-scale repository of real-world traffic scenarios from the heterogeneous data in various driving datasets including Waymo, nuScenes, Lyft L5, and nuPlan datasets. These scenarios can be further replayed and interacted with in multiple views from Bird-Eye-View layout to realistic 3D rendering in MetaDrive simulator. This provides a benchmark for evaluating the safety of autonomous driving stacks in simulation before their real-world deployment. We further demonstrate the strengths of ScenarioNet on large-scale scenario generation, imitation learning, and reinforcement learning in both single-agent and multi-agent settings. Code, demo videos, and website are available at //metadriverse.github.io/scenarionet.

We present a hierarchical planning framework for dexterous robotic manipulation (HiDex). This framework exploits in-hand and extrinsic dexterity by actively exploring contacts. It generates rigid-body motions and complex contact sequences. Our framework is based on Monte-Carlo Tree Search (MCTS) and has three levels: 1) planning object motions and environment contact modes; 2) planning robot contacts; 3) path evaluation and control optimization that passes the rewards to the upper levels. This framework offers two main advantages. First, it allows efficient global reasoning over high-dimensional complex space created by contacts. It solves a diverse set of manipulation tasks that require dexterity, both intrinsic (using the fingers) and extrinsic (also using the environment), mostly in seconds. Second, our framework allows the incorporation of expert knowledge and customizable setups in task mechanics and models. It requires minor modifications to accommodate different scenarios and robots. Hence, it could provide a flexible and generalizable solution for various manipulation tasks. As examples, we analyze the results on 7 hand configurations and 15 scenarios. We demonstrate 8 of them on two robot platforms.

Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across labs), and we lack internet-scale robotics data. We take on these challenges via a new benchmark: Train Offline, Test Online (TOTO). TOTO provides remote users with access to shared robotic hardware for evaluating methods on common tasks and an open-source dataset of these tasks for offline training. Its manipulation task suite requires challenging generalization to unseen objects, positions, and lighting. We present initial results on TOTO comparing five pretrained visual representations and four offline policy learning baselines, remotely contributed by five institutions. The real promise of TOTO, however, lies in the future: we release the benchmark for additional submissions from any user, enabling easy, direct comparison to several methods without the need to obtain hardware or collect data.

Semantic communication represents a promising roadmap toward achieving end-to-end communication with reduced communication overhead and an enhanced user experience. The integration of semantic concepts with wireless communications presents novel challenges. This paper proposes a flexible simulation software that automatically transmits semantic segmentation map images over a communication channel. An additive white Gaussian noise (AWGN) channel using binary phase-shift keying (BPSK) modulation is considered as the channel setup. The well-known polar codes are chosen as the channel coding scheme. The popular COCO-Stuff dataset is used as an example to generate semantic map images corresponding to different signal-to-noise ratios (SNRs). To evaluate the proposed software, we have generated four small datasets, each containing a thousand semantic map samples, accompanied by comprehensive information corresponding to each image, including the polar code specifications, detailed image attributes, bit error rate (BER), and frame error rate (FER). The capacity to generate an unlimited number of semantic maps utilizing desired channel coding parameters and preferred SNR, in conjunction with the flexibility of using alternative datasets, renders our simulation software highly adaptable and transferable to a broad range of use cases.

Smart contracts are programs that execute transactions involving independent parties and cryptocurrencies. As programs, smart contracts are susceptible to a wide range of errors and vulnerabilities. Such vulnerabilities can result in significant losses. Furthermore, by design, smart contract transactions are irreversible. This creates a need for methods to ensure the correctness and security of contracts pre-deployment. Recently there has been substantial research into such methods. The sheer volume of this research makes articulating state-of-the-art a substantial undertaking. To address this challenge, we present a systematic review of the literature. A key feature of our presentation is to factor out the relationship between vulnerabilities and methods through properties. Specifically, we enumerate and classify smart contract vulnerabilities and methods by the properties they address. The methods considered include static analysis as well as dynamic analysis methods and machine learning algorithms that analyze smart contracts before deployment. Several patterns about the strengths of different methods emerge through this classification process.

The advent of artificial intelligence technology paved the way of many researches to be made within air combat sector. Academicians and many other researchers did a research on a prominent research direction called autonomous maneuver decision of UAV. Elaborative researches produced some outcomes, but decisions that include Reinforcement Learning(RL) came out to be more efficient. There have been many researches and experiments done to make an agent reach its target in an optimal way, most prominent are Genetic Algorithm(GA) , A star, RRT and other various optimization techniques have been used. But Reinforcement Learning is the well known one for its success. In DARPHA Alpha Dogfight Trials, reinforcement learning prevailed against a real veteran F16 human pilot who was trained by Boeing. This successor model was developed by Heron Systems. After this accomplishment, reinforcement learning bring tremendous attention on itself. In this research we aimed our UAV which has a dubin vehicle dynamic property to move to the target in two dimensional space in an optimal path using Twin Delayed Deep Deterministic Policy Gradients (TD3) and used in experience replay Hindsight Experience Replay(HER).We did tests on two different environments and used simulations.