OpenRaiser/Intern-Atlas huggingface dataset:
Smart Fleet Solutions: Simulating Electric AGV Performance in Industrial Settings
Venue: arXiv cs.RO 2024
Year: 2024

This paper explores the potential benefits and challenges of integrating Electric Vehicles (EVs) and Autonomous Ground Vehicles (AGVs) in industrial settings to improve sustainability and operational efficiency. While EVs offer environmental advantages, barriers like high costs and limited range hinder their widespread use. Similarly, AGVs, despite their autonomous capabilities, face challenges in technology integration and reliability. To address these issues, the paper develops a fleet management tool tailored for coordinating electric AGVs in industrial environments. The study focuses on simulating electric AGV performance in a primary aluminum plant to provide insights into their effectiveness and offer recommendations for optimizing fleet performance.
Beyond Shortsighted Navigation: Merging Best View Trajectory Planning with Robot Navigation
Venue: arXiv cs.RO 2024
Year: 2024

Gathering visual information effectively to monitor known environments is a key challenge in robotics. To be as efficient as human surveyors, robotic systems must continuously collect observational data required to complete their survey task. Inspection personnel instinctively know to look at relevant equipment that happens to be ``along the way.'' In this paper, we introduce a novel framework for continuous long-horizon viewpoint planning, for ground robots, applied to tasks involving patrolling, monitoring or visual data gathering in known environments. Our approach to Long Horizon Viewpoint Planning (LHVP), enables the robot to autonomously navigate and collect environmental data optimizing for coverage over the horizon of the patrol. Leveraging a quadruped's mobility and sensory capabilities, our LHVP framework plans patrol paths that account for coupling the viewpoint planner for the arm camera with the mobile base's navigation planner. The viewpath optimization algorithm seeks a balance between comprehensive environmental coverage and dynamically feasible movements, thus ensuring prolonged and effective operation in scenarios including monitoring, security surveillance, and disaster response. We validate our approach through simulations and in the real world and show that our LHVP significantly outperforms naive patrolling methods in terms of area coverage generating information-gathering trajectories for the robot arm. Our results indicate a promising direction for the deployment of mobile robots in long-term, autonomous surveying, and environmental data collection tasks, highlighting the potential of intelligent robotic systems in challenging real-world applications.
Towards Evaluating and Building Versatile Large Language Models for Medicine
Venue: arXiv cs.CL 2024
Year: 2024

In this study, we present MedS-Bench, a comprehensive benchmark designed to evaluate the performance of large language models (LLMs) in clinical contexts. Unlike existing benchmarks that focus on multiple-choice question answering, MedS-Bench spans 11 high-level clinical tasks, including clinical report summarization, treatment recommendations, diagnosis, named entity recognition, and medical concept explanation, among others. We evaluated six leading LLMs, e.g., MEDITRON, Mistral, InternLM 2, Llama 3, GPT-4, and Claude-3.5 using few-shot prompting, and found that even the most sophisticated models struggle with these complex tasks. To address these limitations, we developed MedS-Ins, a large-scale instruction tuning dataset for medicine. MedS-Ins comprises 58 medically oriented language corpora, totaling 13.5 million samples across 122 tasks. To demonstrate the dataset's utility, we conducted a proof-of-concept experiment by performing instruction tuning on a lightweight, open-source medical language model. The resulting model, MMedIns-Llama 3, significantly outperformed existing models across nearly all clinical tasks. To promote further advancements in the application of LLMs to clinical challenges, we have made the MedS-Ins dataset fully accessible and invite the research community to contribute to its expansion.Additionally, we have launched a dynamic leaderboard for MedS-Bench, which we plan to regularly update the test set to track progress and enhance the adaptation of general LLMs to the medical domain. Leaderboard: https://henrychur.github.io/MedS-Bench/. Github: https://github.com/MAGIC-AI4Med/MedS-Ins.
Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysis
Venue: arXiv cs.CV 2024
Year: 2024

Vehicle detection is an important task in the management of traffic and automatic vehicles. This study provides a comparative analysis of five YOLOv5 variants, YOLOv5n6s, YOLOv5s6s, YOLOv5m6s, YOLOv5l6s, and YOLOv5x6s, for vehicle detection in various environments. The research focuses on evaluating the effectiveness of these models in detecting different types of vehicles, such as Car, Bus, Truck, Bicycle, and Motorcycle, under varying conditions including lighting, occlusion, and weather. Performance metrics such as precision, recall, F1-score, and mean Average Precision are utilized to assess the accuracy and reliability of each model. YOLOv5n6s demonstrated a strong balance between precision and recall, particularly in detecting Cars. YOLOv5s6s and YOLOv5m6s showed improvements in recall, enhancing their ability to detect all relevant objects. YOLOv5l6s, with its larger capacity, provided robust performance, especially in detecting Cars, but not good with identifying Motorcycles and Bicycles. YOLOv5x6s was effective in recognizing Buses and Cars but faced challenges with Motorcycle class.
Greybox Learning of Languages Recognizable by Event-Recording Automata
Venue: arXiv cs.FL 2024
Year: 2024

In this paper, we revisit the active learning of timed languages recognizable by event-recording automata. Our framework employs a method known as greybox learning, which enables the learning of event-recording automata with a minimal number of control states. This approach avoids learning the region automaton associated with the language, contrasting with existing methods. We have implemented our greybox learning algorithm with various heuristics to maintain low computational complexity. The efficacy of our approach is demonstrated through several examples.
ssProp: Energy-Efficient Training for Convolutional Neural Networks with Scheduled Sparse Back Propagation
Venue: arXiv cs.LG 2024
Year: 2024

Recently, deep learning has made remarkable strides, especially with generative modeling, such as large language models and probabilistic diffusion models. However, training these models often involves significant computational resources, requiring billions of petaFLOPs. This high resource consumption results in substantial energy usage and a large carbon footprint, raising critical environmental concerns. Back-propagation (BP) is a major source of computational expense during training deep learning models. To advance research on energy-efficient training and allow for sparse learning on any machine and device, we propose a general, energy-efficient convolution module that can be seamlessly integrated into any deep learning architecture. Specifically, we introduce channel-wise sparsity with additional gradient selection schedulers during backward based on the assumption that BP is often dense and inefficient, which can lead to over-fitting and high computational consumption. Our experiments demonstrate that our approach reduces 40\% computations while potentially improving model performance, validated on image classification and generation tasks. This reduction can lead to significant energy savings and a lower carbon footprint during the research and development phases of large-scale AI systems. Additionally, our method mitigates over-fitting in a manner distinct from Dropout, allowing it to be combined with Dropout to further enhance model performance and reduce computational resource usage. Extensive experiments validate that our method generalizes to a variety of datasets and tasks and is compatible with a wide range of deep learning architectures and modules. Code is publicly available at https://github.com/lujiazho/ssProp.
Automating Deformable Gasket Assembly
Venue: arXiv cs.RO 2024
Year: 2024

In Gasket Assembly, a deformable gasket must be aligned and pressed into a narrow channel. This task is common for sealing surfaces in the manufacturing of automobiles, appliances, electronics, and other products. Gasket Assembly is a long-horizon, high-precision task and the gasket must align with the channel and be fully pressed in to achieve a secure fit. To compare approaches, we present 4 methods for Gasket Assembly: one policy from deep imitation learning and three procedural algorithms. We evaluate these methods with 100 physical trials. Results suggest that the Binary+ algorithm succeeds in 10/10 on the straight channel whereas the learned policy based on 250 human teleoperated demonstrations succeeds in 8/10 trials and is significantly slower. Code, CAD models, videos, and data can be found at https://berkeleyautomation.github.io/robot-gasket/
Improving Radiography Machine Learning Workflows via Metadata Management for Training Data Selection
Venue: arXiv cs.LG 2024
Year: 2024

Most machine learning models require many iterations of hyper-parameter tuning, feature engineering, and debugging to produce effective results. As machine learning models become more complicated, this pipeline becomes more difficult to manage effectively. In the physical sciences, there is an ever-increasing pool of metadata that is generated by the scientific research cycle. Tracking this metadata can reduce redundant work, improve reproducibility, and aid in the feature and training dataset engineering process. In this case study, we present a tool for machine learning metadata management in dynamic radiography. We evaluate the efficacy of this tool against the initial research workflow and discuss extensions to general machine learning pipelines in the physical sciences.
Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation
Venue: arXiv cs.LG 2024
Year: 2024

With the emergence of data marketplaces, the demand for methods to assess the value of data has increased significantly. While numerous techniques have been proposed for this purpose, none have specifically addressed graphs as the main data modality. Graphs are widely used across various fields, ranging from chemical molecules to social networks. In this study, we break down graphs into two main components: structural and featural, and we focus on evaluating data without relying on specific task-related metrics, making it applicable in practical scenarios where validation requirements may be lacking. We introduce a novel framework called blind message passing, which aligns the seller's and buyer's graphs using a shared node permutation based on graph matching. This allows us to utilize the graph Wasserstein distance to quantify the differences in the structural distribution of graph datasets, called the structural disparities. We then consider featural aspects of buyers' and sellers' graphs for data valuation and capture their statistical similarities and differences, referred to as relevance and diversity, respectively. Our approach ensures that buyers and sellers remain unaware of each other's datasets. Our experiments on real datasets demonstrate the effectiveness of our approach in capturing the relevance, diversity, and structural disparities of seller data for buyers, particularly in graph-based data valuation scenarios.
GSFusion: Online RGB-D Mapping Where Gaussian Splatting Meets TSDF Fusion
Venue: arXiv cs.CV 2024
Year: 2024

Traditional volumetric fusion algorithms preserve the spatial structure of 3D scenes, which is beneficial for many tasks in computer vision and robotics. However, they often lack realism in terms of visualization. Emerging 3D Gaussian splatting bridges this gap, but existing Gaussian-based reconstruction methods often suffer from artifacts and inconsistencies with the underlying 3D structure, and struggle with real-time optimization, unable to provide users with immediate feedback in high quality. One of the bottlenecks arises from the massive amount of Gaussian parameters that need to be updated during optimization. Instead of using 3D Gaussian as a standalone map representation, we incorporate it into a volumetric mapping system to take advantage of geometric information and propose to use a quadtree data structure on images to drastically reduce the number of splats initialized. In this way, we simultaneously generate a compact 3D Gaussian map with fewer artifacts and a volumetric map on the fly. Our method, GSFusion, significantly enhances computational efficiency without sacrificing rendering quality, as demonstrated on both synthetic and real datasets. Code will be available at https://github.com/goldoak/GSFusion.
Can LLMs Understand Social Norms in Autonomous Driving Games?
Venue: arXiv cs.AI 2024
Year: 2024

Social norm is defined as a shared standard of acceptable behavior in a society. The emergence of social norms fosters coordination among agents without any hard-coded rules, which is crucial for the large-scale deployment of AVs in an intelligent transportation system. This paper explores the application of LLMs in understanding and modeling social norms in autonomous driving games. We introduce LLMs into autonomous driving games as intelligent agents who make decisions according to text prompts. These agents are referred to as LLM-based agents. Our framework involves LLM-based agents playing Markov games in a multi-agent system (MAS), allowing us to investigate the emergence of social norms among individual agents. We aim to identify social norms by designing prompts and utilizing LLMs on textual information related to the environment setup and the observations of LLM-based agents. Using the OpenAI Chat API powered by GPT-4.0, we conduct experiments to simulate interactions and evaluate the performance of LLM-based agents in two driving scenarios: unsignalized intersection and highway platoon. The results show that LLM-based agents can handle dynamically changing environments in Markov games, and social norms evolve among LLM-based agents in both scenarios. In the intersection game, LLM-based agents tend to adopt a conservative driving policy when facing a potential car crash. The advantage of LLM-based agents in games lies in their strong operability and analyzability, which facilitate experimental design.
SonarWatch: Field sensing technique for smartwatches based on ultrasound and motion
Venue: arXiv cs.HC 2024
Year: 2024

A smartwatch worn continuously on the wrist has the potential to perceive rich interactive gestures and natural behaviors of the user. Unfortunately, the current interaction functionality of smartwatches is primarily limited by the small touch screen. This paper proposes SonarWatch, a novel sensing technique that uses the acoustic field generated by the transceiver on the opposite sides of the watch to detect the presence of nearby objects and their shapes. This enables a range of gesture interactions and natural behavior perception. We designed an algorithm combining IMU and acoustic fields to identify these actions and optimize power consumption. We tested the performance of SonarWatch in different noise environments, achieving an overall accuracy of 93.7%. Its power consumption is close to that of physiological sensors. SonarWatch can achieve the above capabilities by utilizing the existing built-in sensors, making it a technology with solid practical value.
The Matrix Reloaded: A Mechanized Formal Analysis of the Matrix Cryptographic Suite
Venue: arXiv cs.CR 2024
Year: 2024

Secure instant group messaging applications such as WhatsApp, Facebook Messenger, Matrix, and the Signal Application have become ubiquitous in today's internet, cumulatively serving billions of users. Unlike WhatsApp, for example, Matrix can be deployed in a federated manner, allowing users to choose which server manages their chats. To account for this difference in architecture, Matrix employs two novel cryptographic protocols: Olm, which secures pairwise communications, and Megolm, which relies on Olm and secures group communications. Olm and Megolm are similar to and share security goals with Signal and Sender Keys, which are widely deployed in practice to secure group communications. While Olm, Megolm, and Sender Keys have been manually analyzed in the computational model, no symbolic analysis nor mechanized proofs of correctness exist. Using mechanized proofs and computer-aided analysis is important for cryptographic protocols, as hand-written proofs and analysis are error-prone and often carry subtle mistakes. Using Verifpal, we construct formal models of Olm and Megolm, as well as their composition. We prove various properties of interest about Olm and Megolm, including authentication, confidentiality, forward secrecy, and post-compromise security. We also mechanize known limitations, previously discovered attacks, and trivial attacker wins from the specifications and previous literature. Finally, we model Sender Keys and the composition of Signal with Sender Keys in order to draw a comparison with Olm, Megolm, and their composition. From our analysis we conclude the composition of Olm and Megolm has comparable security to the composition of Signal and Sender Keys if Olm pre-keys are signed, and provably worse post-compromise security if Olm pre-keys are not signed.
Contrastive Representation Learning for Dynamic Link Prediction in Temporal Networks
Venue: arXiv cs.LG 2024
Year: 2024

Evolving networks are complex data structures that emerge in a wide range of systems in science and engineering. Learning expressive representations for such networks that encode their structural connectivity and temporal evolution is essential for downstream data analytics and machine learning applications. In this study, we introduce a self-supervised method for learning representations of temporal networks and employ these representations in the dynamic link prediction task. While temporal networks are typically characterized as a sequence of interactions over the continuous time domain, our study focuses on their discrete-time versions. This enables us to balance the trade-off between computational complexity and precise modeling of the interactions. We propose a recurrent message-passing neural network architecture for modeling the information flow over time-respecting paths of temporal networks. The key feature of our method is the contrastive training objective of the model, which is a combination of three loss functions: link prediction, graph reconstruction, and contrastive predictive coding losses. The contrastive predictive coding objective is implemented using infoNCE losses at both local and global scales of the input graphs. We empirically show that the additional self-supervised losses enhance the training and improve the model's performance in the dynamic link prediction task. The proposed method is tested on Enron, COLAB, and Facebook datasets and exhibits superior results compared to existing models.
Enhancing Vehicle Environmental Awareness via Federated Learning and Automatic Labeling
Venue: arXiv cs.CV 2024
Year: 2024

Vehicle environmental awareness is a crucial issue in improving road safety. Through a variety of sensors and vehicle-to-vehicle communication, vehicles can collect a wealth of data. However, to make these data useful, sensor data must be integrated effectively. This paper focuses on the integration of image data and vehicle-to-vehicle communication data. More specifically, our goal is to identify the locations of vehicles sending messages within images, a challenge termed the vehicle identification problem. In this paper, we employ a supervised learning model to tackle the vehicle identification problem. However, we face two practical issues: first, drivers are typically unwilling to share privacy-sensitive image data, and second, drivers usually do not engage in data labeling. To address these challenges, this paper introduces a comprehensive solution to the vehicle identification problem, which leverages federated learning and automatic labeling techniques in combination with the aforementioned supervised learning model. We have validated the feasibility of our proposed approach through experiments.
Symmetric masking strategy enhances the performance of Masked Image Modeling
Venue: arXiv cs.CV 2024
Year: 2024

Masked Image Modeling (MIM) is a technique in self-supervised learning that focuses on acquiring detailed visual representations from unlabeled images by estimating the missing pixels in randomly masked sections. It has proven to be a powerful tool for the preliminary training of Vision Transformers (ViTs), yielding impressive results across various tasks. Nevertheless, most MIM methods heavily depend on the random masking strategy to formulate the pretext task. This strategy necessitates numerous trials to ascertain the optimal dropping ratio, which can be resource-intensive, requiring the model to be pre-trained for anywhere between 800 to 1600 epochs. Furthermore, this approach may not be suitable for all datasets. In this work, we propose a new masking strategy that effectively helps the model capture global and local features. Based on this masking strategy, SymMIM, our proposed training pipeline for MIM is introduced. SymMIM achieves a new SOTA accuracy of 85.9\% on ImageNet using ViT-Large and surpasses previous SOTA across downstream tasks such as image classification, semantic segmentation, object detection, instance segmentation tasks, and so on.
Environment-Centric Active Inference
Venue: arXiv cs.RO 2024
Year: 2024

To handle unintended changes in the environment by agents, we propose an environment-centric active inference EC-AIF in which the Markov Blanket of active inference is defined starting from the environment. In normal active inference, the Markov Blanket is defined starting from the agent. That is, first the agent was defined as the entity that performs the "action" such as a robot or a person, then the environment was defined as other people or objects that are directly affected by the agent's "action," and the boundary between the agent and the environment was defined as the Markov Blanket. This agent-centric definition does not allow the agent to respond to unintended changes in the environment caused by factors outside of the defined environment. In the proposed EC-AIF, there is no entity corresponding to an agent. The environment includes all observable things, including people and things conventionally considered to be the environment, as well as entities that perform "actions" such as robots and people. Accordingly, all states, including robots and people, are included in inference targets, eliminating unintended changes in the environment. The EC-AIF was applied to a robot arm and validated with an object transport task by the robot arm. The results showed that the robot arm successfully transported objects while responding to changes in the target position of the object and to changes in the orientation of another robot arm.