OpenRaiser/Intern-Atlas huggingface dataset:
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI
Venue: arXiv cs.CV 2024
Year: 2024

Recent advancements in Multi-modal Large Language Models (MLLMs) have opened new avenues for applications in Embodied AI. Building on previous work, EgoThink, we introduce VidEgoThink, a comprehensive benchmark for evaluating egocentric video understanding capabilities. To bridge the gap between MLLMs and low-level control in Embodied AI, we design four key interrelated tasks: video question-answering, hierarchy planning, visual grounding and reward modeling. To minimize manual annotation costs, we develop an automatic data generation pipeline based on the Ego4D dataset, leveraging the prior knowledge and multimodal capabilities of GPT-4o. Three human annotators then filter the generated data to ensure diversity and quality, resulting in the VidEgoThink benchmark. We conduct extensive experiments with three types of models: API-based MLLMs, open-source image-based MLLMs, and open-source video-based MLLMs. Experimental results indicate that all MLLMs, including GPT-4o, perform poorly across all tasks related to egocentric video understanding. These findings suggest that foundation models still require significant advancements to be effectively applied to first-person scenarios in Embodied AI. In conclusion, VidEgoThink reflects a research trend towards employing MLLMs for egocentric vision, akin to human capabilities, enabling active observation and interaction in the complex real-world environments.
Fast Local Neural Regression for Low-Cost, Path Traced Lambertian Global Illumination
Venue: arXiv cs.CV 2024
Year: 2024

Despite recent advances in hardware acceleration of ray tracing, real-time ray budgets remain stubbornly limited at a handful of samples per pixel (spp) on commodity hardware, placing the onus on denoising algorithms to achieve high visual quality for path traced global illumination. Neural network-based solutions give excellent result quality at the cost of increased execution time relative to hand-engineered methods, making them less suitable for deployment on resource-constrained systems. We therefore propose incorporating a neural network into a computationally-efficient local linear model-based denoiser, and demonstrate faithful single-frame reconstruction of global illumination for Lambertian scenes at very low sample counts (1spp) and for low computational cost. Other contributions include improving the quality and performance of local linear model-based denoising through a simplified mathematical treatment, and demonstration of the surprising usefulness of ambient occlusion as a guide channel. We also show how our technique is straightforwardly extensible to joint denoising and upsampling of path traced renders with reference to low-cost, rasterized guide channels.
Fast and Robust Hexahedral Mesh Optimization via Augmented Lagrangian, L-BFGS, and Line Search
Venue: arXiv cs.CG 2024
Year: 2024

We present a new software package, ``HexOpt,'' for improving the quality of all-hexahedral (all-hex) meshes by maximizing the minimum mixed scaled Jacobian-Jacobian energy functional, and projecting the surface points of the all-hex meshes onto the input triangular mesh. The proposed HexOpt method takes as input a surface triangular mesh and a volumetric all-hex mesh. A constrained optimization problem is formulated to improve mesh quality using a novel function that combines Jacobian and scaled Jacobian metrics which are rectified and scaled to quadratic measures, while preserving the surface geometry. This optimization problem is solved using the augmented Lagrangian (AL) method, where the Lagrangian terms enforce the constraint that surface points must remain on the triangular mesh. Specifically, corner points stay exactly at the corner, edge points are confined to the edges, and face points are free to move across the surface. To take the advantage of the Quasi-Newton method while tackling the high-dimensional variable problem, the Limited-Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm is employed. The step size for each iteration is determined by the Armijo line search. Coupled with smart Laplacian smoothing, HexOpt has demonstrated robustness and efficiency, successfully applying to 3D models and hex meshes generated by different methods without requiring any manual intervention or parameter adjustment.
Are UFOs Driving Innovation? The Illusion of Causality in Large Language Models
Venue: arXiv cs.AI 2024
Year: 2024

Illusions of causality occur when people develop the belief that there is a causal connection between two variables with no supporting evidence. This cognitive bias has been proposed to underlie many societal problems including social prejudice, stereotype formation, misinformation and superstitious thinking. In this research we investigate whether large language models develop the illusion of causality in real-world settings. We evaluated and compared news headlines generated by GPT-4o-Mini, Claude-3.5-Sonnet, and Gemini-1.5-Pro to determine whether the models incorrectly framed correlations as causal relationships. In order to also measure sycophantic behavior, which occurs when a model aligns with a user's beliefs in order to look favorable even if it is not objectively correct, we additionally incorporated the bias into the prompts, observing if this manipulation increases the likelihood of the models exhibiting the illusion of causality. We found that Claude-3.5-Sonnet is the model that presents the lowest degree of causal illusion aligned with experiments on Correlation-to-Causation Exaggeration in human-written press releases. On the other hand, our findings suggest that while mimicry sycophancy increases the likelihood of causal illusions in these models, especially in GPT-4o-Mini, Claude-3.5-Sonnet remains the most robust against this cognitive bias.
MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have displayed massive improvements in reasoning and decision-making skills and can hold natural conversations with users. Recently, many tool-use benchmark datasets have been proposed. However, existing datasets have the following limitations: (1). Insufficient evaluation scenarios (e.g., only cover limited tool-use scenes). (2). Extensive evaluation costs (e.g., GPT API costs). To address these limitations, in this work, we propose a multi-granularity tool-use benchmark for large language models called MTU-Bench. For the "multi-granularity" property, our MTU-Bench covers five tool usage scenes (i.e., single-turn and single-tool, single-turn and multiple-tool, multiple-turn and single-tool, multiple-turn and multiple-tool, and out-of-distribution tasks). Besides, all evaluation metrics of our MTU-Bench are based on the prediction results and the ground truth without using any GPT or human evaluation metrics. Moreover, our MTU-Bench is collected by transforming existing high-quality datasets to simulate real-world tool usage scenarios, and we also propose an instruction dataset called MTU-Instruct data to enhance the tool-use abilities of existing LLMs. Comprehensive experimental results demonstrate the effectiveness of our MTU-Bench. Code and data will be released at https: //github.com/MTU-Bench-Team/MTU-Bench.git.
YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection
Venue: arXiv cs.CV 2024
Year: 2024

Existing detection methods for insulator defect identification from unmanned aerial vehicles (UAV) struggle with complex background scenes and small objects, leading to suboptimal accuracy and a high number of false positives detection. Using the concept of local attention modeling, this paper proposes a new attention-based foundation architecture, YOLO-ELA, to address this issue. The Efficient Local Attention (ELA) blocks were added into the neck part of the one-stage YOLOv8 architecture to shift the model's attention from background features towards features of insulators with defects. The SCYLLA Intersection-Over-Union (SIoU) criterion function was used to reduce detection loss, accelerate model convergence, and increase the model's sensitivity towards small insulator defects, yielding higher true positive outcomes. Due to a limited dataset, data augmentation techniques were utilized to increase the diversity of the dataset. In addition, we leveraged the transfer learning strategy to improve the model's performance. Experimental results on high-resolution UAV images show that our method achieved a state-of-the-art performance of 96.9% mAP0.5 and a real-time detection speed of 74.63 frames per second, outperforming the baseline model. This further demonstrates the effectiveness of attention-based convolutional neural networks (CNN) in object detection tasks.
Near-Field Communications for Extremely Large-Scale MIMO: A Beamspace Perspective
Venue: arXiv cs.IT 2024
Year: 2024

Extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as one of the key techniques to enhance the performance of future wireless communications. Different from regular MIMO, the XL-MIMO shifts part of the communication region from the far field to the near field, where the spherical-wave channel model cannot be accurately approximated by the commonly-adopted planar-wave channel model. As a result, the well-explored far-field beamspace is unsuitable for near-field communications, thereby requiring the exploration of specialized near-field beamspace. In this article, we investigate the near-field communications for XL-MIMO from the perspective of beamspace. Given the spherical wavefront characteristics of the near-field channels, we first map the antenna space to the near-field beamspace with the fractional Fourier transform. Then, we divide the near-field beamspace into three parts, including high mainlobe, low mainlobe, and sidelobe, and provide a comprehensive analysis of these components. Based on the analysis, we demonstrate the advantages of the near-field beamspace over the existing methods. Finally, we point out several applications of the near-field beamspace and highlight some potential directions for future study in the near-field beamspace.
Octopus-Swimming-Like Robot with Soft Asymmetric Arms
Venue: arXiv cs.RO 2024
Year: 2024

Underwater vehicles have seen significant development over the past seventy years. However, bio-inspired propulsion robots are still in their early stages and require greater interdisciplinary collaboration between biologists and roboticists. The octopus, one of the most intelligent marine animals, exhibits remarkable abilities such as camouflaging, exploring, and hunting while swimming with its arms. Although bio-inspired robotics researchers have aimed to replicate these abilities, the complexity of designing an eight-arm bionic swimming platform has posed challenges from the beginning. In this work, we propose a novel bionic robot swimming platform that combines asymmetric passive morphing arms with an umbrella-like quick-return mechanism. Using only two simple constant-speed motors, this design achieves efficient swimming by replicating octopus-like arm movements and stroke time ratios. The robot reached a peak speed of 314 mm/s during its second power stroke. This design reduces the complexity of traditional octopus-like swimming robot actuation systems while maintaining good swimming performance. It offers a more achievable and efficient platform for biologists and roboticists conducting more profound octopus-inspired robotic and biological studies.
Encoding architecture algebra
Venue: arXiv cs.LG 2024
Year: 2024

Despite the wide variety of input types in machine learning, this diversity is often not fully reflected in their representations or model architectures, leading to inefficiencies throughout a model's lifecycle. This paper introduces an algebraic approach to constructing input-encoding architectures that properly account for the data's structure, providing a step toward achieving more typeful machine learning.
Majorized Bayesian Persuasion and Fair Selection
Venue: arXiv cs.GT 2024
Year: 2024

We address the fundamental problem of selection under uncertainty by modeling it from the perspective of Bayesian persuasion. In our model, a decision maker with imperfect information always selects the option with the highest expected value. We seek to achieve fairness among the options by revealing additional information to the decision maker and hence influencing its subsequent selection. To measure fairness, we adopt the notion of majorization, aiming at simultaneously approximately maximizing all symmetric, monotone, concave functions over the utilities of the options. As our main result, we design a novel information revelation policy that achieves a logarithmic-approximation to majorization in polynomial time. On the other hand, no policy, regardless of its running time, can achieve a constant-approximation to majorization. Our work is the first non-trivial majorization result in the Bayesian persuasion literature with multi-dimensional information sets.
Practices and Challenges of Online Love-seeking Among Deaf or Hard of Hearing People: A Case Study in China
Venue: arXiv cs.HC 2024
Year: 2024

People who are deaf or hard of hearing (DHH) in China are increasingly exploring online platforms to connect with potential partners. This research explores the online dating experiences of DHH communities in China, an area that has not been extensively researched. We interviewed sixteen participants who have varying levels of hearing ability and love-seeking statuses to understand how they manage their identities and communicate with potential partners online. We find that DHH individuals made great efforts to navigate the rich modality features to seek love online. Participants used both algorithm-based dating apps and community-based platforms like forums and WeChat to facilitate initial encounters through text-based functions that minimized the need for auditory interaction, thus fostering a more equitable starting point. Community-based platforms were found to facilitate more in-depth communication and excelled in fostering trust and authenticity, providing a more secure environment for genuine relationships. Design recommendations are proposed to enhance the accessibility and inclusiveness of online dating platforms for DHH individuals in China. This research sheds light on the benefits and challenges of online dating for DHH individuals in China and provides guidance for platform developers and researchers to enhance user experience in this area.
Learning Smooth Humanoid Locomotion through Lipschitz-Constrained Policies
Venue: arXiv cs.RO 2024
Year: 2024

Reinforcement learning combined with sim-to-real transfer offers a general framework for developing locomotion controllers for legged robots. To facilitate successful deployment in the real world, smoothing techniques, such as low-pass filters and smoothness rewards, are often employed to develop policies with smooth behaviors. However, because these techniques are non-differentiable and usually require tedious tuning of a large set of hyperparameters, they tend to require extensive manual tuning for each robotic platform. To address this challenge and establish a general technique for enforcing smooth behaviors, we propose a simple and effective method that imposes a Lipschitz constraint on a learned policy, which we refer to as Lipschitz-Constrained Policies (LCP). We show that the Lipschitz constraint can be implemented in the form of a gradient penalty, which provides a differentiable objective that can be easily incorporated with automatic differentiation frameworks. We demonstrate that LCP effectively replaces the need for smoothing rewards or low-pass filters and can be easily integrated into training frameworks for many distinct humanoid robots. We extensively evaluate LCP in both simulation and real-world humanoid robots, producing smooth and robust locomotion controllers. All simulation and deployment code, along with complete checkpoints, is available on our project page: https://lipschitz-constrained-policy.github.io.
GeoLife+: Large-Scale Simulated Trajectory Datasets Calibrated to the GeoLife Dataset
Venue: arXiv cs.DB 2024
Year: 2024

Analyzing individual human trajectory data helps our understanding of human mobility and finds many commercial and academic applications. There are two main approaches to accessing trajectory data for research: one involves using real-world datasets like GeoLife, while the other employs simulations to synthesize data. Real-world data provides insights from real human activities, but such data is generally sparse due to voluntary participation. Conversely, simulated data can be more comprehensive but may capture unrealistic human behavior. In this Data and Resource paper, we combine the benefit of both by leveraging the statistical features of real-world data and the comprehensiveness of simulated data. Specifically, we extract features from the real-world GeoLife dataset such as the average number of individual daily trips, average radius of gyration, and maximum and minimum trip distances. We calibrate the Pattern of Life Simulation, a realistic simulation of human mobility, to reproduce these features. Therefore, we use a genetic algorithm to calibrate the parameters of the simulation to mimic the GeoLife features. For this calibration, we simulated numerous random simulation settings, measured the similarity of generated trajectories to GeoLife, and iteratively (over many generations) combined parameter settings of trajectory datasets most similar to GeoLife. Using the calibrated simulation, we simulate large trajectory datasets that we call GeoLife+, where + denotes the Kleene Plus, indicating unlimited replication with at least one occurrence. We provide simulated GeoLife+ data with 182, 1k, and 5k over 5 years, 10k, and 50k over a year and 100k users over 6 months of simulation lifetime.
An Innovative Solution: AI-Based Digital Screen-Integrated Tables for Educational Settings
Venue: arXiv cs.HC 2024
Year: 2024

In this paper, we have gone through different AI-Based frameworks used for various educational tasks like digital customized assignment allotment and performance monitoring, identifying slow-learners and fast-learners, etc. application describes a novel invention, digital screen-integrated tables, designed specifically for educational settings. The tables feature integrated digital screens controlled by a central processing unit (CPU), enabling synchronized display of educational content such as textbooks, presentations, exam questions, and interactive learning materials. Additionally, the invention facilitates the collection of student performance data during classroom activities and assessments. The gathered data is utilized for analysis using machine learning models to identify patterns and trends in student learning behaviours. By leveraging machine learning algorithms, educators can ascertain whether a student is a fast learner or a slow learner, based on which, the teacher can allocate more resources to the slow learners. This innovative approach aims to address the evolving needs of modern classrooms by providing a dynamic and data-driven learning environment. The unique integration of digital screens into traditional classroom furniture represents a significant advancement in educational technology. This patent filing encompasses the design, functionality, and method of operation of the digital screen-integrated tables, emphasizing their innovative features and applications in educational institutions.
A Framework for SLO, Carbon, and Wastewater-Aware Sustainable FaaS Cloud Platform Management
Venue: arXiv cs.DC 2024
Year: 2024

Function-as-a-Service (FaaS) is a growing cloud computing paradigm that is expected to reduce the user cost of service over traditional serverful approaches. However, the environmental impact of FaaS has not received much attention. We investigate FaaS scheduling and scaling from a sustainability perspective in this work. We find that the service-level objectives (SLOs) of FaaS and carbon emissions conflict with each other. We also find that SLO-focused FaaS scheduling can exacerbate water use in a datacenter. We propose a novel sustainability-focused FaaS scheduling and scaling framework to co-optimize SLO performance, carbon emissions, and wastewater generation.
Neural Metamorphosis
Venue: arXiv cs.CV 2024
Year: 2024

This paper introduces a new learning paradigm termed Neural Metamorphosis (NeuMeta), which aims to build self-morphable neural networks. Contrary to crafting separate models for different architectures or sizes, NeuMeta directly learns the continuous weight manifold of neural networks. Once trained, we can sample weights for any-sized network directly from the manifold, even for previously unseen configurations, without retraining. To achieve this ambitious goal, NeuMeta trains neural implicit functions as hypernetworks. They accept coordinates within the model space as input, and generate corresponding weight values on the manifold. In other words, the implicit function is learned in a way, that the predicted weights is well-performed across various models sizes. In training those models, we notice that, the final performance closely relates on smoothness of the learned manifold. In pursuit of enhancing this smoothness, we employ two strategies. First, we permute weight matrices to achieve intra-model smoothness, by solving the Shortest Hamiltonian Path problem. Besides, we add a noise on the input coordinates when training the implicit function, ensuring models with various sizes shows consistent outputs. As such, NeuMeta shows promising results in synthesizing parameters for various network configurations. Our extensive tests in image classification, semantic segmentation, and image generation reveal that NeuMeta sustains full-size performance even at a 75% compression rate.
Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents
Venue: arXiv cs.HC 2024
Year: 2024

This paper presents a novel application of large language models (LLMs) to enhance user comprehension of privacy policies through an interactive dialogue agent. We demonstrate that LLMs significantly outperform traditional models in tasks like Data Practice Identification, Choice Identification, Policy Summarization, and Privacy Question Answering, setting new benchmarks in privacy policy analysis. Building on these findings, we introduce an innovative LLM-based agent that functions as an expert system for processing website privacy policies, guiding users through complex legal language without requiring them to pose specific questions. A user study with 100 participants showed that users assisted by the agent had higher comprehension levels (mean score of 2.6 out of 3 vs. 1.8 in the control group), reduced cognitive load (task difficulty ratings of 3.2 out of 10 vs. 7.8), increased confidence in managing privacy, and completed tasks in less time (5.5 minutes vs. 15.8 minutes). This work highlights the potential of LLM-based agents to transform user interaction with privacy policies, leading to more informed consent and empowering users in the digital services landscape.