In recent times, there has been a growing focus on end-to-end autonomous driving technologies. This technology involves the replacement of the entire driving pipeline with a single neural network, which has a simpler structure and faster inference time. However, while this approach reduces the number of components in the driving pipeline, it also presents challenges related to interpretability and safety. For instance, the trained policy may not always comply with traffic rules, and it is difficult to determine the reason for such misbehavior due to the lack of intermediate outputs. Additionally, the successful implementation of autonomous driving technology heavily depends on the reliable and expedient processing of sensory data to accurately perceive the surrounding environment. In this paper, we provide penalty-based imitation learning approach combined with cross semantics generation sensor fusion technologies (P-CSG) to efficiently integrate multiple modalities of information and enable the autonomous agent to effectively adhere to traffic regulations. Our model undergoes evaluation within the Town 05 Long benchmark, where we observe a remarkable increase in the driving score by more than 12% when compared to the state-of-the-art (SOTA) model, InterFuser. Notably, our model achieves this performance enhancement while achieving a 7-fold increase in inference speed and reducing the model size by approximately 30%. For more detailed information, including code-based resources, they can be found at //hk-zh.github.io/p-csg/
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Current technological advancements of quantum computers highlight the need for application-driven, practical and well-defined methods of benchmarking their performance. As the existing NISQ device's quality of two-qubit gate errors rate is even around one percent and the number of qubits is still limited to a few or several dozen, naturally, we need to propose rather small algorithms instances taken from key promising application areas, such as quantum chemistry, combinatorial optimisation or machine learning. While many techniques for assessing the performance of logical components, such as gate fidelity and qubit coherence exist, it is still challenging to extrapolate those values onto the performance of different quantum algorithms and subroutines. This work aims to introduce a series of initial quantum application benchmarks together with a methodology of execution for measuring the performance and fidelity of the results. The proposed suite refers to several variational algorithms widely used on available NISQ devices but also includes examples of quantum circuits designed for a fault-tolerant quantum computer.
The rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e.g., network optimization and management by allowing users to input task requirements to LLMs by nature language. However, directly applying native LLMs in 6G encounters various challenges, such as a lack of private communication data and knowledge, limited logical reasoning, evaluation, and refinement abilities. Integrating LLMs with the capabilities of retrieval, planning, memory, evaluation and reflection in agents can greatly enhance the potential of LLMs for 6G communications. To this end, we propose a multi-agent system with customized communication knowledge and tools for solving communication related tasks using natural language, comprising three components: (1) Multi-agent Data Retrieval (MDR), which employs the condensate and inference agents to refine and summarize communication knowledge from the knowledge base, expanding the knowledge boundaries of LLMs in 6G communications; (2) Multi-agent Collaborative Planning (MCP), which utilizes multiple planning agents to generate feasible solutions for the communication related task from different perspectives based on the retrieved knowledge; (3) Multi-agent Evaluation and Reflecxion (MER), which utilizes the evaluation agent to assess the solutions, and applies the reflexion agent and refinement agent to provide improvement suggestions for current solutions. Finally, we validate the effectiveness of the proposed multi-agent system by designing a semantic communication system, as a case study of 6G communications.
Quantum Internet signifies a remarkable advancement in communication technology, harnessing the principles of quantum entanglement and superposition to facilitate unparalleled levels of security and efficient computations. Quantum communication can be achieved through the utilization of quantum entanglement. Through the exchange of entangled pairs between two entities, quantum communication becomes feasible, enabled by the process of quantum teleportation. Given the lossy nature of the channels and the exponential decoherence of the transmitted photons, a set of intermediate nodes can serve as quantum repeaters to perform entanglement swapping and directly entangle two distant nodes. Such quantum repeaters may be malicious and by setting up malicious entanglements, intermediate nodes can jeopardize the confidentiality of the quantum information exchanged between the two communication nodes. Hence, this paper proposes a quantum identity authentication protocol that protects quantum networks from malicious entanglements. Unlike the existing protocols, the proposed quantum authentication protocol does not require periodic refreshments of the shared secret keys. Simulation results demonstrate that the proposed protocol can detect malicious entanglements with a 100% probability after an average of 4 authentication rounds.
The advancements in Artificial Intelligence (AI) technologies such as ChatGPT have gained popularity in recent days. The integration of ChatGPT in educational contexts has already created attractions due to a wide range of applications. However, the automatic generation of human-like texts also poses potential risks to academic integrity, especially when faced with writing-intensive language courses. Considering the ongoing debates, this study aims to investigate the academic policies and guidelines established by US universities regarding the use of ChatGPT in teaching and learning. The data sources include academic policies, statements, guidelines as well as relevant resources that were provided by the top 50 universities in the United States, according to U.S. News. Thematic analysis and qualitative analysis were employed in the analysis and showed that most top 50 universities were open but cautious towards the integration of generative AI in teaching and learning and also expressed their concerns on ethical usage, accuracy, and data privacy. Most universities also provided a variety of resources and guidelines, including syllabus templates/samples, workshops and discussions, shared articles, and one-on-one consultations, with focuses on general technical introduction, ethical concerns, pedagogical applications, preventive strategies, data privacy, limitations, and detective tools. The findings will inform future policy-making regarding the integration of ChatGPT in college-level education and influence the provision of supportive resources by universities for the appropriate application of ChatGPT in education.
In today's society, we are becoming increasingly dependent on software systems. However, we also constantly witness the negative impacts of buggy software. Program synthesis aims to improve software correctness by automatically generating the program given an outline of the expected behavior. For decades, program synthesis has been an active research field, with recent approaches looking to incorporate Large Language Models to help generate code. This paper explores the concept of LLM4TDD, where we guide Large Language Models to generate code iteratively using a test-driven development methodology. We conduct an empirical evaluation using ChatGPT and coding problems from LeetCode to investigate the impact of different test, prompt and problem attributes on the efficacy of LLM4TDD.
Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently. This paper contributes such an environment based on ideas inspired by elementary Microeconomics. Agents learn to produce resources in a spatially complex world, trade them with one another, and consume those that they prefer. We show that the emergent production, consumption, and pricing behaviors respond to environmental conditions in the directions predicted by supply and demand shifts in Microeconomics. We also demonstrate settings where the agents' emergent prices for goods vary over space, reflecting the local abundance of goods. After the price disparities emerge, some agents then discover a niche of transporting goods between regions with different prevailing prices -- a profitable strategy because they can buy goods where they are cheap and sell them where they are expensive. Finally, in a series of ablation experiments, we investigate how choices in the environmental rewards, bartering actions, agent architecture, and ability to consume tradable goods can either aid or inhibit the emergence of this economic behavior. This work is part of the environment development branch of a research program that aims to build human-like artificial general intelligence through multi-agent interactions in simulated societies. By exploring which environment features are needed for the basic phenomena of elementary microeconomics to emerge automatically from learning, we arrive at an environment that differs from those studied in prior multi-agent reinforcement learning work along several dimensions. For example, the model incorporates heterogeneous tastes and physical abilities, and agents negotiate with one another as a grounded form of communication.
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way. With the joint utilization of EO data, much research on multimodal RS data fusion has made tremendous progress in recent years, yet these developed traditional algorithms inevitably meet the performance bottleneck due to the lack of the ability to comprehensively analyse and interpret these strongly heterogeneous data. Hence, this non-negligible limitation further arouses an intense demand for an alternative tool with powerful processing competence. Deep learning (DL), as a cutting-edge technology, has witnessed remarkable breakthroughs in numerous computer vision tasks owing to its impressive ability in data representation and reconstruction. Naturally, it has been successfully applied to the field of multimodal RS data fusion, yielding great improvement compared with traditional methods. This survey aims to present a systematic overview in DL-based multimodal RS data fusion. More specifically, some essential knowledge about this topic is first given. Subsequently, a literature survey is conducted to analyse the trends of this field. Some prevalent sub-fields in the multimodal RS data fusion are then reviewed in terms of the to-be-fused data modalities, i.e., spatiospectral, spatiotemporal, light detection and ranging-optical, synthetic aperture radar-optical, and RS-Geospatial Big Data fusion. Furthermore, We collect and summarize some valuable resources for the sake of the development in multimodal RS data fusion. Finally, the remaining challenges and potential future directions are highlighted.
The development of unmanned aerial vehicles (UAVs) has been gaining momentum in recent years owing to technological advances and a significant reduction in their cost. UAV technology can be used in a wide range of domains, including communication, agriculture, security, and transportation. It may be useful to group the UAVs into clusters/flocks in certain domains, and various challenges associated with UAV usage can be alleviated by clustering. Several computational challenges arise in UAV flock management, which can be solved by using machine learning (ML) methods. In this survey, we describe the basic terms relating to UAVS and modern ML methods, and we provide an overview of related tutorials and surveys. We subsequently consider the different challenges that appear in UAV flocks. For each issue, we survey several machine learning-based methods that have been suggested in the literature to handle the associated challenges. Thereafter, we describe various open issues in which ML can be applied to solve the different challenges of flocks, and we suggest means of using ML methods for this purpose. This comprehensive review may be useful for both researchers and developers in providing a wide view of various aspects of state-of-the-art ML technologies that are applicable to flock management.
Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i.e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries. This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations. Specifically, entities are modeled by an adaptive neighbor encoder to discern their task-oriented roles, while references are modeled by an adaptive query-aware aggregator to differentiate their contributions. Through the attention mechanism, both entities and references can capture their fine-grained semantic meanings, and thus render more expressive representations. This will be more predictive for knowledge acquisition in the few-shot scenario. Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-the-art results with different few-shot sizes.
For better user experience and business effectiveness, Click-Through Rate (CTR) prediction has been one of the most important tasks in E-commerce. Although extensive CTR prediction models have been proposed, learning good representation of items from multimodal features is still less investigated, considering an item in E-commerce usually contains multiple heterogeneous modalities. Previous works either concatenate the multiple modality features, that is equivalent to giving a fixed importance weight to each modality; or learn dynamic weights of different modalities for different items through technique like attention mechanism. However, a problem is that there usually exists common redundant information across multiple modalities. The dynamic weights of different modalities computed by using the redundant information may not correctly reflect the different importance of each modality. To address this, we explore the complementarity and redundancy of modalities by considering modality-specific and modality-invariant features differently. We propose a novel Multimodal Adversarial Representation Network (MARN) for the CTR prediction task. A multimodal attention network first calculates the weights of multiple modalities for each item according to its modality-specific features. Then a multimodal adversarial network learns modality-invariant representations where a double-discriminators strategy is introduced. Finally, we achieve the multimodal item representations by combining both modality-specific and modality-invariant representations. We conduct extensive experiments on both public and industrial datasets, and the proposed method consistently achieves remarkable improvements to the state-of-the-art methods. Moreover, the approach has been deployed in an operational E-commerce system and online A/B testing further demonstrates the effectiveness.
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