OpenRaiser/Intern-Atlas huggingface dataset:
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
Venue: arXiv cs.LG 2024
Year: 2024

Graph Neural Networks (GNNs) have emerged as powerful representation learning tools for capturing complex dependencies within diverse graph-structured data. Despite their success in a wide range of graph mining tasks, GNNs have raised serious concerns regarding their trustworthiness, including susceptibility to distribution shift, biases towards certain populations, and lack of explainability. Recently, integrating causal learning techniques into GNNs has sparked numerous ground-breaking studies since many GNN trustworthiness issues can be alleviated by capturing the underlying data causality rather than superficial correlations. In this survey, we comprehensively review recent research efforts on Causality-Inspired GNNs (CIGNNs). Specifically, we first employ causal tools to analyze the primary trustworthiness risks of existing GNNs, underscoring the necessity for GNNs to comprehend the causal mechanisms within graph data. Moreover, we introduce a taxonomy of CIGNNs based on the type of causal learning capability they are equipped with, i.e., causal reasoning and causal representation learning. Besides, we systematically introduce typical methods within each category and discuss how they mitigate trustworthiness risks. Finally, we summarize useful resources and discuss several future directions, hoping to shed light on new research opportunities in this emerging field. The representative papers, along with open-source data and codes, are available in https://github.com/usail-hkust/Causality-Inspired-GNNs.
Scaling Opponent Shaping to High Dimensional Games
Venue: arXiv cs.AI 2024
Year: 2024

In multi-agent settings with mixed incentives, methods developed for zero-sum games have been shown to lead to detrimental outcomes. To address this issue, opponent shaping (OS) methods explicitly learn to influence the learning dynamics of co-players and empirically lead to improved individual and collective outcomes. However, OS methods have only been evaluated in low-dimensional environments due to the challenges associated with estimating higher-order derivatives or scaling model-free meta-learning. Alternative methods that scale to more complex settings either converge to undesirable solutions or rely on unrealistic assumptions about the environment or co-players. In this paper, we successfully scale an OS-based approach to general-sum games with temporally-extended actions and long-time horizons for the first time. After analysing the representations of the meta-state and history used by previous algorithms, we propose a simplified version called Shaper. We show empirically that Shaper leads to improved individual and collective outcomes in a range of challenging settings from literature. We further formalize a technique previously implicit in the literature, and analyse its contribution to opponent shaping. We show empirically that this technique is helpful for the functioning of prior methods in certain environments. Lastly, we show that previous environments, such as the CoinGame, are inadequate for analysing temporally-extended general-sum interactions.
Comparison of Waymo Rider-Only Crash Data to Human Benchmarks at 7.1 Million Miles
Venue: arXiv cs.RO 2024
Year: 2024

This paper examines the safety performance of the Waymo Driver, an SAE level 4 automated driving system (ADS) used in a rider-only (RO) ride-hailing application without a human driver, either in the vehicle or remotely. ADS crash data was derived from NHTSA's Standing General Order (SGO) reporting over 7.14 million RO miles through the end of October 2023 in Phoenix, AZ, San Francisco, CA, and Los Angeles, CA. When considering all locations together, the any-injury-reported crashed vehicle rate was 0.6 incidents per million miles (IPMM) for the ADS vs 2.80 IPMM for the human benchmark, an 80% reduction or a human crash rate that is 5 times higher than the ADS rate. Police-reported crashed vehicle rates for all locations together were 2.1 IPMM for the ADS vs. 4.68 IPMM for the human benchmark, a 55% reduction or a human crash rate that was 2.2 times higher than the ADS rate. Police-reported and any-injury-reported crashed vehicle rate reductions for the ADS were statistically significant when compared in San Francisco and Phoenix, as well as combined across all locations (except for any-injury-reported in Phoenix). The any property damage or injury comparison had statistically significant decrease in 3 comparisons, but also non-significant results in 3 other benchmarks. Given imprecision in the benchmark estimate and multiple potential sources of underreporting biasing the benchmarks, caution should be taken when interpreting the results of the any property damage or injury comparison. Together, these crash-rate results should be interpreted as a directional and continuous confidence growth indicator, together with other methodologies, in a safety case approach.
Synchronous Consensus in Partial Synchrony
Venue: arXiv cs.DC 2024
Year: 2024

We demonstrate a deterministic Byzantine consensus algorithm with synchronous operation in partial synchrony. It is naturally leaderless, tolerates any number of $ f<n/2 $ Byzantine processes with 2 rounds of exchange of originator-only signed messages, and terminates within a bounded interval of time. The algorithm is resilient to transient faults and asynchrony in a fraction of links with known size per number of faulty processes. It circumvents asynchronous and faulty links with 3-hop epidemic dissemination. Key finding: the resilience to asynchrony of links and the enabled by it leaderless consensus in partial synchrony ensure algorithm operation with simultaneous validity, safety, and bounded liveness.
FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise
Venue: arXiv cs.LG 2024
Year: 2024

Determining whether a molecule can be synthesized is crucial in chemistry and drug discovery, as it guides experimental prioritization and molecule ranking in de novo design tasks. Existing scoring approaches to assess synthetic feasibility struggle to extrapolate to new chemical spaces or fail to discriminate based on subtle differences such as chirality. This work addresses these limitations by introducing the Focused Synthesizability score~(FSscore), which uses machine learning to rank structures based on their relative ease of synthesis. First, a baseline trained on an extensive set of reactant-product pairs is established, which is then refined with expert human feedback tailored to specific chemical spaces. This targeted fine-tuning improves performance on these chemical scopes, enabling more accurate differentiation between molecules that are hard and easy to synthesize. The FSscore showcases how a human-in-the-loop framework can be utilized to optimize the assessment of synthetic feasibility for various chemical applications.
Bandit Sequential Posted Pricing via Half-Concavity
Venue: arXiv cs.LG 2024
Year: 2024

Sequential posted pricing auctions are popular because of their simplicity in practice and their tractability in theory. A usual assumption in their study is that the Bayesian prior distributions of the buyers are known to the seller, while in reality these priors can only be accessed from historical data. To overcome this assumption, we study sequential posted pricing in the bandit learning model, where the seller interacts with $n$ buyers over $T$ rounds: In each round the seller posts $n$ prices for the $n$ buyers and the first buyer with a valuation higher than the price takes the item. The only feedback that the seller receives in each round is the revenue. Our main results obtain nearly-optimal regret bounds for single-item sequential posted pricing in the bandit learning model. In particular, we achieve an $\tilde{O}(\mathsf{poly}(n)\sqrt{T})$ regret for buyers with (Myerson's) regular distributions and an $\tilde{O}(\mathsf{poly}(n)T^{{2}/{3}})$ regret for buyers with general distributions, both of which are tight in the number of rounds $T$. Our result for regular distributions was previously not known even for the single-buyer setting and relies on a new half-concavity property of the revenue function in the value space. For $n$ sequential buyers, our technique is to run a generalized single-buyer algorithm for all the buyers and to carefully bound the regret from the sub-optimal pricing of the suffix buyers.
Comparing Machine Learning Algorithms by Union-Free Generic Depth
Venue: arXiv cs.LG 2024
Year: 2024

We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies in linear and metric spaces, there is very little discussion on depth functions for non-standard data types such as partial orders. We introduce an adaptation of the well-known simplicial depth to the set of all partial orders, the union-free generic (ufg) depth. Moreover, we utilize our ufg depth for a comparison of machine learning algorithms based on multidimensional performance measures. Concretely, we provide two examples of classifier comparisons on samples of standard benchmark data sets. Our results demonstrate promisingly the wide variety of different analysis approaches based on ufg methods. Furthermore, the examples outline that our approach differs substantially from existing benchmarking approaches, and thus adds a new perspective to the vivid debate on classifier comparison.
Deep-Unfolded Joint Activity and Data Detection for Grant-Free Transmission in Cell-Free Systems
Venue: arXiv cs.IT 2024
Year: 2024

Massive grant-free transmission and cell-free wireless communication systems have emerged as pivotal enablers for massive machine-type communication. This paper proposes a deep-unfolding-based joint activity and data detection (DU-JAD) algorithm for massive grant-free transmission in cell-free systems. We first formulate a joint activity and data detection optimization problem, which we solve approximately using forward-backward splitting (FBS). We then apply deep unfolding to FBS to optimize algorithm parameters using machine learning. In order to improve data detection (DD) performance, reduce algorithm complexity, and enhance active user detection (AUD), we employ a momentum strategy, an approximate posterior mean estimator, and a novel soft-output AUD module, respectively. Simulation results confirm the efficacy of DU-JAD for AUD and DD.
Sparse Mean Field Load Balancing in Large Localized Queueing Systems
Venue: arXiv cs.DC 2024
Year: 2024

Scalable load balancing algorithms are of great interest in cloud networks and data centers, necessitating the use of tractable techniques to compute optimal load balancing policies for good performance. However, most existing scalable techniques, especially asymptotically scaling methods based on mean field theory, have not been able to model large queueing networks with strong locality. Meanwhile, general multi-agent reinforcement learning techniques can be hard to scale and usually lack a theoretical foundation. In this work, we address this challenge by leveraging recent advances in sparse mean field theory to learn a near-optimal load balancing policy in sparsely connected queueing networks in a tractable manner, which may be preferable to global approaches in terms of wireless communication overhead. Importantly, we obtain a general load balancing framework for a large class of sparse bounded-degree wireless topologies. By formulating a novel mean field control problem in the context of graphs with bounded degree, we reduce the otherwise difficult multi-agent problem to a single-agent problem. Theoretically, the approach is justified by approximation guarantees. Empirically, the proposed methodology performs well on several realistic and scalable wireless network topologies as compared to a number of well-known load balancing heuristics and existing scalable multi-agent reinforcement learning methods.
AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation
Venue: arXiv cs.CL 2024
Year: 2024

The advancement of natural language processing (NLP) has been significantly boosted by the development of transformer-based large language models (LLMs). These models have revolutionized NLP tasks, particularly in code generation, aiding developers in creating software with enhanced efficiency. Despite their advancements, challenges in balancing code snippet generation with effective test case generation and execution persist. To address these issues, this paper introduces Multi-Agent Assistant Code Generation (AgentCoder), a novel solution comprising a multi-agent framework with specialized agents: the programmer agent, the test designer agent, and the test executor agent. During the coding procedure, the programmer agent will focus on the code generation and refinement based on the test executor agent's feedback. The test designer agent will generate test cases for the generated code, and the test executor agent will run the code with the test cases and write the feedback to the programmer. This collaborative system ensures robust code generation, surpassing the limitations of single-agent models and traditional methodologies. Our extensive experiments on 9 code generation models and 12 enhancement approaches showcase AgentCoder's superior performance over existing code generation models and prompt engineering techniques across various benchmarks. For example, AgentCoder (GPT-4) achieves 96.3\% and 91.8\% pass@1 in HumanEval and MBPP datasets with an overall token overhead of 56.9K and 66.3K, while state-of-the-art obtains only 90.2\% and 78.9\% pass@1 with an overall token overhead of 138.2K and 206.5K.
ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automation
Venue: arXiv cs.CV 2024
Year: 2024

Graphical User Interface (GUI) automation holds significant promise for assisting users with complex tasks, thereby boosting human productivity. Existing works leveraging Large Language Model (LLM) or LLM-based AI agents have shown capabilities in automating tasks on Android and Web platforms. However, these tasks are primarily aimed at simple device usage and entertainment operations. This paper presents a novel benchmark, AssistGUI, to evaluate whether models are capable of manipulating the mouse and keyboard on the Windows platform in response to user-requested tasks. We carefully collected a set of 100 tasks from nine widely-used software applications, such as, After Effects and MS Word, each accompanied by the necessary project files for better evaluation. Moreover, we propose an advanced Actor-Critic Embodied Agent framework, which incorporates a sophisticated GUI parser driven by an LLM-agent and an enhanced reasoning mechanism adept at handling lengthy procedural tasks. Our experimental results reveal that our GUI Parser and Reasoning mechanism outshine existing methods in performance. Nevertheless, the potential remains substantial, with the best model attaining only a 46% success rate on our benchmark. We conclude with a thorough analysis of the current methods' limitations, setting the stage for future breakthroughs in this domain.
RIShield: Enabling Electromagnetic Blackout in Radiation-Sensitive Environments
Venue: arXiv cs.IT 2024
Year: 2024

Reconfigurable Intelligent Surfaces (RIS) have emerged as a disruptive technology with the potential to revolutionize wireless communication systems. In this paper, we present RIShield, a novel application of RIS technology specifically designed for radiation-sensitive environments. The aim of RIShield is to enable electromagnetic blackouts, preventing radiation leakage from target areas. We propose a comprehensive framework for RIShield deployment, considering the unique challenges and requirements of radiation-sensitive environments. By strategically positioning RIS panels, we create an intelligent shielding mechanism that selectively absorbs and reflects electromagnetic waves, effectively blocking radiation transmission. To achieve optimal performance, we model the corresponding channel and design a dynamic control that adjusts the RIS configuration based on real-time radiation monitoring. By leveraging the principles of reconfiguration and intelligent control, RIShield ensures adaptive and efficient protection while minimizing signal degradation. Through full-wave and ray-tracing simulations, we demonstrate the effectiveness of RIShield in achieving significant electromagnetic attenuation. Our results highlight the potential of RIS technology to address critical concerns in radiation-sensitive environments, paving the way for safer and more secure operations in industries such as healthcare, nuclear facilities, and defense.
SOVC: Subject-Oriented Video Captioning
Venue: arXiv cs.CV 2024
Year: 2024

Describing video content according to users' needs is a long-held goal. Although existing video captioning methods have made significant progress, the generated captions may not focus on the entity that users are particularly interested in. To address this problem, we propose a new video captioning task, Subject-Oriented Video Captioning (SOVC), which aims to allow users to specify the describing target via a bounding box. To support this task, we construct two subject-oriented video captioning datasets based on two widely used video captioning datasets: MSVD and MSRVTT, by annotating subjects in each video for each caption. These datasets pave the way for describing users' interested targets. To tackle this task, we introduce a method tailored to this task, named SOVCNet. It consists of two key components: a subject-oriented sampling module that samples frames related to the subject to minimize irrelevant information; and a subject-oriented encoding module that utilizes the subject areas as hard prompts and integrates learnable soft prompts, enhancing the model's focus on the subject's activities and facilitating adaptation to the downstream generation task. Extensive experimental results demonstrate the effectiveness of our method on this new task.
Investigating Assumptions and Proposals for Blockchain Integration in the Circular Economy. A Delphi Study
Venue: arXiv cs.CY 2024
Year: 2024

Given the rising interest in the circular economy and blockchain hype, numerous integrations were proposed. However, studies on the practical feasibility were scarce, and the assumptions of blockchain potential in the circular economy were rarely questioned. With the help of eleven of the most prominent blockchain experts, the present study critically analyzed technology integration in many areas of the circular economy to forecast their possible outcomes. Delphi's technique is leveraged to reach a consensus among experts' visions and opinions. Results support the view that some circular economy integrations are unlikely to succeed, while others if specific conditions are met, may prove to be successful in the long run.
Benchmarking Evolutionary Community Detection Algorithms in Dynamic Networks
Venue: arXiv cs.NE 2024
Year: 2024

In dynamic complex networks, entities interact and form network communities that evolve over time. Among the many static Community Detection (CD) solutions, the modularity-based Louvain, or Greedy Modularity Algorithm (GMA), is widely employed in real-world applications due to its intuitiveness and scalability. Nevertheless, addressing CD in dynamic graphs remains an open problem, since the evolution of the network connections may poison the identification of communities, which may be evolving at a slower pace. Hence, naively applying GMA to successive network snapshots may lead to temporal inconsistencies in the communities. Two evolutionary adaptations of GMA, sGMA and $α$GMA, have been proposed to tackle this problem. Yet, evaluating the performance of these methods and understanding to which scenarios each one is better suited is challenging because of the lack of a comprehensive set of metrics and a consistent ground truth. To address these challenges, we propose (i) a benchmarking framework for evolutionary CD algorithms in dynamic networks and (ii) a generalised modularity-based approach (NeGMA). Our framework allows us to generate synthetic community-structured graphs and design evolving scenarios with nine basic graph transformations occurring at different rates. We evaluate performance through three metrics we define, i.e. Correctness, Delay, and Stability. Our findings reveal that $α$GMA is well-suited for detecting intermittent transformations, but struggles with abrupt changes; sGMA achieves superior stability, but fails to detect emerging communities; and NeGMA appears a well-balanced solution, excelling in responsiveness and instantaneous transformations detection.
Image Clustering using Restricted Boltzman Machine
Venue: arXiv cs.CV 2024
Year: 2024

In various verification systems, Restricted Boltzmann Machines (RBMs) have demonstrated their efficacy in both front-end and back-end processes. In this work, we propose the use of RBMs to the image clustering tasks. RBMs are trained to convert images into image embeddings. We employ the conventional bottom-up Agglomerative Hierarchical Clustering (AHC) technique. To address the challenge of limited test face image data, we introduce Agglomerative Hierarchical Clustering based Method for Image Clustering using Restricted Boltzmann Machine (AHC-RBM) with two major steps. Initially, a universal RBM model is trained using all available training dataset. Subsequently, we train an adapted RBM model using the data from each test image. Finally, RBM vectors which is the embedding vector is generated by concatenating the visible-to-hidden weight matrices of these adapted models, and the bias vectors. These vectors effectively preserve class-specific information and are utilized in image clustering tasks. Our experimental results, conducted on two benchmark image datasets (MS-Celeb-1M and DeepFashion), demonstrate that our proposed approach surpasses well-known clustering algorithms such as k-means, spectral clustering, and approximate Rank-order.
GVE-Leiden: Fast Leiden Algorithm for Community Detection in Shared Memory Setting
Venue: arXiv cs.DC 2024
Year: 2024

Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions is critical in a number of applications, where the size of datasets have reached significant scales. This technical report presents one of the most efficient implementations of the Leiden algorithm, a high quality community detection method. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our Leiden implementation, which we term as GVE-Leiden, outperforms the original Leiden, igraph Leiden, NetworKit Leiden, and cuGraph Leiden (running on NVIDIA A100 GPU) by 436x, 104x, 8.2x, and 3.0x respectively - achieving a processing rate of 403M edges/s on a 3.8B edge graph. In addition, GVE-Leiden improves performance at an average rate of 1.6x for every doubling of threads.