OpenRaiser/Intern-Atlas huggingface dataset:
Generative midtended cognition and Artificial Intelligence. Thinging with thinging things
Venue: arXiv cs.AI 2024
Year: 2024

This paper introduces the concept of ``generative midtended cognition'', exploring the integration of generative AI with human cognition. The term "generative" reflects AI's ability to iteratively produce structured outputs, while "midtended" captures the potential hybrid (human-AI) nature of the process. It stands between traditional conceptions of intended creation, understood directed from within, and extended processes that bring exo-biological processes into the creative process. We examine current generative technologies (based on multimodal transformer architectures typical of large language models like ChatGPT), to explain how they can transform human cognitive agency beyond what standard theories of extended cognition can capture. We suggest that the type of cognitive activity typical of the coupling between a human and generative technologies is closer (but not equivalent) to social cognition than to classical extended cognitive paradigms. Yet, it deserves a specific treatment. We provide an explicit definition of generative midtended cognition in which we treat interventions by AI systems as constitutive of the agent's intentional creative processes. Furthermore, we distinguish two dimensions of generative hybrid creativity: 1. Width: captures the sensitivity of the context of the generative process (from the single letter to the whole historical and surrounding data), 2. Depth: captures the granularity of iteration loops involved in the process. Generative midtended cognition stands in the middle depth between conversational forms of cognition in which complete utterances or creative units are exchanged, and micro-cognitive (e.g. neural) subpersonal processes. Finally, the paper discusses the potential risks and benefits of widespread generative AI adoption, including the challenges of authenticity, generative power asymmetry, and creative boost or atrophy.
OPTIMA: Design-Space Exploration of Discharge-Based In-SRAM Computing: Quantifying Energy-Accuracy Trade-Offs
Venue: arXiv cs.AR 2024
Year: 2024

In-SRAM computing promises energy efficiency, but circuit nonlinearities and PVT variations pose major challenges in designing robust accelerators. To address this, we introduce OPTIMA, a modeling framework that aids in analyzing bit-line discharge and power consumption in 6T-SRAM-based accelerators. It provides insights into limiting factors and enables fast design-space exploration of circuit configurations. Leveraging OPTIMA for in-SRAM multiplications exhibits ~100x simulation speed-up while maintaining an RMS modeling error of 0.88mV. Exploration yields an optimized multiplier with 1.05pJ energy consumption per 4-bit operation and classification accuracies of 71.8% (top-1) and 90.4% (top-5) for ImageNet and 92.5% for CIFAR-10 datasets respectively when applied in quantized DNNs. To further support research and development, we made our tool flow available open source at https://github.com/sevjaeg/optima.
Fast and Efficient Transformer-based Method for Bird's Eye View Instance Prediction
Venue: arXiv cs.CV 2024
Year: 2024

Accurate object detection and prediction are critical to ensure the safety and efficiency of self-driving architectures. Predicting object trajectories and occupancy enables autonomous vehicles to anticipate movements and make decisions with future information, increasing their adaptability and reducing the risk of accidents. Current State-Of-The-Art (SOTA) approaches often isolate the detection, tracking, and prediction stages, which can lead to significant prediction errors due to accumulated inaccuracies between stages. Recent advances have improved the feature representation of multi-camera perception systems through Bird's-Eye View (BEV) transformations, boosting the development of end-to-end systems capable of predicting environmental elements directly from vehicle sensor data. These systems, however, often suffer from high processing times and number of parameters, creating challenges for real-world deployment. To address these issues, this paper introduces a novel BEV instance prediction architecture based on a simplified paradigm that relies only on instance segmentation and flow prediction. The proposed system prioritizes speed, aiming at reduced parameter counts and inference times compared to existing SOTA architectures, thanks to the incorporation of an efficient transformer-based architecture. Furthermore, the implementation of the proposed architecture is optimized for performance improvements in PyTorch version 2.1. Code and trained models are available at https://github.com/miguelag99/Efficient-Instance-Prediction
Evaluating Large Language Models on Financial Report Summarization: An Empirical Study
Venue: arXiv cs.CL 2024
Year: 2024

In recent years, Large Language Models (LLMs) have demonstrated remarkable versatility across various applications, including natural language understanding, domain-specific knowledge tasks, etc. However, applying LLMs to complex, high-stakes domains like finance requires rigorous evaluation to ensure reliability, accuracy, and compliance with industry standards. To address this need, we conduct a comprehensive and comparative study on three state-of-the-art LLMs, GLM-4, Mistral-NeMo, and LLaMA3.1, focusing on their effectiveness in generating automated financial reports. Our primary motivation is to explore how these models can be harnessed within finance, a field demanding precision, contextual relevance, and robustness against erroneous or misleading information. By examining each model's capabilities, we aim to provide an insightful assessment of their strengths and limitations. Our paper offers benchmarks for financial report analysis, encompassing proposed metrics such as ROUGE-1, BERT Score, and LLM Score. We introduce an innovative evaluation framework that integrates both quantitative metrics (e.g., precision, recall) and qualitative analyses (e.g., contextual fit, consistency) to provide a holistic view of each model's output quality. Additionally, we make our financial dataset publicly available, inviting researchers and practitioners to leverage, scrutinize, and enhance our findings through broader community engagement and collaborative improvement. Our dataset is available on huggingface.
Timing and Dynamics of the Rosanna Shuffle
Venue: arXiv cs.SD 2024
Year: 2024

The Rosanna shuffle, the drum pattern from Toto's 1982 hit "Rosanna", is one of the most recognized drum beats in popular music. Recorded by Jeff Porcaro, this drum beat features a half-time shuffle with rapid triplets on the hi-hat and snare drum. In this analysis, we examine the timing and dynamics of the original drum track, focusing on rhythmic variations such as swing factor, microtiming deviations, tempo drift, and the overall dynamics of the hi-hat pattern. Our findings reveal that "Rosanna" exhibits a surprisingly pronounced swing for its genre, along with notable tempo drift typical of tracks recorded without a metronome. Additionally, we observe clear long-range correlations in the microtiming deviations, consistent with previous studies. Notably, the two-bar phrases of the song feature a distinctive repeating pattern in the timing and dynamics of the hi-hat beats, which enhances the song's phrasing. Overall, the Rosanna shuffle boasts a rich array of rhythmic characteristics that solidify its significant place in music history.
Finite Variable Counting Logics with Restricted Requantification
Venue: arXiv cs.LO 2024
Year: 2024

Counting logics with a bounded number of variables form one of the central concepts in descriptive complexity theory. Although they restrict the number of variables that a formula can contain, the variables can be nested within scopes of quantified occurrences of themselves. In other words, the variables can be requantified. We study the fragments obtained from counting logics by restricting requantification for some but not necessarily all the variables. Similar to the logics without limitation on requantification, we develop tools to investigate the restricted variants. Specifically, we introduce a bijective pebble game in which certain pebbles can only be placed once and for all, and a corresponding two-parametric family of Weisfeiler-Leman algorithms. We show close correspondences between the three concepts. By using a suitable cops-and-robber game and adaptations of the Cai-Fürer-Immerman construction, we completely clarify the relative expressive power of the new logics. We show that the restriction of requantification has beneficial algorithmic implications in terms of graph identification. Indeed, we argue that with regard to space complexity, non-requantifiable variables only incur an additive polynomial factor when testing for equivalence. In contrast, for all we know, requantifiable variables incur a multiplicative linear factor. Finally, we observe that graphs of bounded tree-depth and 3-connected planar graphs can be identified using no, respectively, only a very limited number of requantifiable variables.
Bipedal walking with continuously compliant robotic legs
Venue: arXiv cs.RO 2024
Year: 2024

In biomechanics and robotics, elasticity plays a crucial role in enhancing locomotion efficiency and stability. Traditional approaches in legged robots often employ series elastic actuators (SEA) with discrete rigid components, which, while effective, add weight and complexity. This paper presents an innovative alternative by integrating continuously compliant structures into the lower legs of a bipedal robot, fundamentally transforming the SEA concept. Our approach replaces traditional rigid segments with lightweight, deformable materials, reducing overall mass and simplifying the actuation design. This novel design introduces unique challenges in modeling, sensing, and control, due to the infinite dimensionality of continuously compliant elements. We address these challenges through effective approximations and control strategies. The paper details the design and modeling of the compliant leg structure, presents low-level force and kinematics controllers, and introduces a high-level posture controller with a gait scheduler. Experimental results demonstrate successful bipedal walking using this new design.
Enhancing Accessibility in Special Libraries: A Study on AI-Powered Assistive Technologies for Patrons with Disabilities
Venue: arXiv cs.DL 2024
Year: 2024

This study seeks to identify the potential role of AI-driven assistive technologies in enhancing access to libraries for persons with varying degrees of challenges. Traditional libraries pose a problem to many users with vision and mobility, among other conditions related to physical and infirmities. This mixed-methods research approach will examine ways in which AI-powered assistive tools and applications associated with text-to-speech, navigation systems, and personalized assistants are revolutionizing library services through a literature review, survey methods, interviews, and case studies. Our findings suggest that these technologies greatly increase the autonomy and participation of people with physical disabilities, providing personalized support and access to a wide range of resources. From this, some key findings have been deduced from the research, showing a strong impact on user experience and efficiency in services, while at the same time bringing out important considerations related to privacy and ethical implementation. This study highlights the central role of AI in making library settings more inclusive, thereby allowing equal access to knowledge and participation in the community. Such insight thus serves professionals working in libraries, policymakers, and technology developers for innovations to occur uninterruptedly, with future research directions proposed that would refine such technologies, especially toward the special needs of diverse populations. By adopting AI, libraries could uphold their mission of providing equal access to knowledge through full and equal participation of all persons, regardless of any type of physical ability, in the learning and community activities carried out by the library. This study paves the way for future innovations in creating more accessible and inclusive library spaces.
The Singular Optimality of Distributed Computation in LOCAL
Venue: arXiv cs.DC 2024
Year: 2024

It has been shown that one can design distributed algorithms that are (nearly) singularly optimal, meaning they simultaneously achieve optimal time and message complexity (within polylogarithmic factors), for several fundamental global problems such as broadcast, leader election, and spanning tree construction, under the $\text{KT}_0$ assumption. With this assumption, nodes have initial knowledge only of themselves, not their neighbors. In this case the time and message lower bounds are $Ω(D)$ and $Ω(m)$, respectively, where $D$ is the diameter of the network and $m$ is the number of edges, and there exist (even) deterministic algorithms that simultaneously match these bounds. On the other hand, under the $\text{KT}_1$ assumption, whereby each node has initial knowledge of itself and the identifiers of its neighbors, the situation is not clear. For the $\text{KT}_1$ CONGEST model (where messages are of small size), King, Kutten, and Thorup (KKT) showed that one can solve several fundamental global problems (with the notable exception of BFS tree construction) such as broadcast, leader election, and spanning tree construction with $\tilde{O}(n)$ message complexity ($n$ is the network size), which can be significantly smaller than $m$. Randomization is crucial in obtaining this result. While the message complexity of the KKT result is near-optimal, its time complexity is $\tilde{O}(n)$ rounds, which is far from the standard lower bound of $Ω(D)$. In this paper, we show that in the $\text{KT}_1$ LOCAL model (where message sizes are not restricted), singular optimality is achievable. Our main result is that all global problems, including BFS tree construction, can be solved in $\tilde{O}(D)$ rounds and $\tilde{O}(n)$ messages, where both bounds are optimal up to polylogarithmic factors. Moreover, we show that this can be achieved deterministically.
A neural-network based anomaly detection system and a safety protocol to protect vehicular network
Venue: arXiv cs.LG 2024
Year: 2024

This thesis addresses the use of Cooperative Intelligent Transport Systems (CITS) to improve road safety and efficiency by enabling vehicle-to-vehicle communication, highlighting the importance of secure and accurate data exchange. To ensure safety, the thesis proposes a Machine Learning-based Misbehavior Detection System (MDS) using Long Short-Term Memory (LSTM) networks to detect and mitigate incorrect or misleading messages within vehicular networks. Trained offline on the VeReMi dataset, the detection model is tested in real-time within a platooning scenario, demonstrating that it can prevent nearly all accidents caused by misbehavior by triggering a defense protocol that dissolves the platoon if anomalies are detected. The results show that while the system can accurately detect general misbehavior, it struggles to label specific types due to varying traffic conditions, implying the difficulty of creating a universally adaptive protocol. However, the thesis suggests that with more data and further refinement, this MDS could be implemented in real-world CITS, enhancing driving safety by mitigating risks from misbehavior in cooperative driving networks.
Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation
Venue: arXiv cs.IR 2024
Year: 2024

Retrieval-augmented generation (RAG) has shown impressive capability in providing reliable answer predictions and addressing hallucination problems. A typical RAG implementation uses powerful retrieval models to extract external information and large language models (LLMs) to generate answers. In contrast, recent LLM-based retrieval has gained attention for its substantial improvements in information retrieval (IR) due to the LLMs' semantic understanding capability. However, directly applying LLM to RAG systems presents challenges. This may cause feature locality problems as massive parametric knowledge can hinder effective usage of global information across the corpus; for example, an LLM-based retriever often inputs document summaries instead of full documents. Moreover, various pre-trained tasks in LLMs introduce variance, further weakening performance as a retriever. To address these issues, we propose a novel two-stage fine-tuning architecture called Invar-RAG. In the retrieval stage, an LLM-based retriever is constructed by integrating LoRA-based representation learning to tackle feature locality issues. To enhance retrieval performance, we develop two patterns (invariant and variant patterns) and an invariance loss to reduce LLM variance. In the generation stage, a refined fine-tuning method is employed to improve LLM accuracy in generating answers based on retrieved information. Experimental results show that Invar-RAG significantly outperforms existing baselines across three open-domain question answering (ODQA) datasets. Code is available in the Supplementary Material for reproducibility.
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning
Venue: arXiv cs.LG 2024
Year: 2024

The rapid expansion of Internet of Things (IoT) has resulted in vast, heterogeneous graphs that capture complex interactions among devices, sensors, and systems. Efficient analysis of these graphs is critical for deriving insights in IoT scenarios such as smart cities, industrial IoT, and intelligent transportation systems. However, the scale and diversity of IoT-generated data present significant challenges, and existing methods often struggle with preserving the structural integrity and semantic richness of these complex graphs. Many current approaches fail to maintain the balance between computational efficiency and the quality of the insights generated, leading to potential loss of critical information necessary for accurate decision-making in IoT applications. We introduce HeteroSample, a novel sampling method designed to address these challenges by preserving the structural integrity, node and edge type distributions, and semantic patterns of IoT-related graphs. HeteroSample works by incorporating the novel top-leader selection, balanced neighborhood expansion, and meta-path guided sampling strategies. The key idea is to leverage the inherent heterogeneous structure and semantic relationships encoded by meta-paths to guide the sampling process. This approach ensures that the resulting subgraphs are representative of the original data while significantly reducing computational overhead. Extensive experiments demonstrate that HeteroSample outperforms state-of-the-art methods, achieving up to 15% higher F1 scores in tasks such as link prediction and node classification, while reducing runtime by 20%.These advantages make HeteroSample a transformative tool for scalable and accurate IoT applications, enabling more effective and efficient analysis of complex IoT systems, ultimately driving advancements in smart cities, industrial IoT, and beyond.
Scaling Mesh Generation via Compressive Tokenization
Venue: arXiv cs.GR 2024
Year: 2024

We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, reducing their length by approximately 75\% compared to the original sequences. This compression milestone unlocks the potential to utilize mesh data with significantly more faces, thereby enhancing detail richness and improving generation robustness. Empowered with the BPT, we have built a foundation mesh generative model training on scaled mesh data to support flexible control for point clouds and images. Our model demonstrates the capability to generate meshes with intricate details and accurate topology, achieving SoTA performance on mesh generation and reaching the level for direct product usage.
Scaling Long-Horizon Online POMDP Planning via Rapid State Space Sampling
Venue: arXiv cs.RO 2024
Year: 2024

Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of POMDP solvers, long-horizon POMDPs (e.g., $\geq15$ steps) remain difficult to solve. This paper proposes a new approximate online POMDP solver, called Reference-Based Online POMDP Planning via Rapid State Space Sampling (ROP-RaS3). ROP-RaS3 uses novel extremely fast sampling-based motion planning techniques to sample the state space and generate a diverse set of macro actions online which are then used to bias belief-space sampling and infer high-quality policies without requiring exhaustive enumeration of the action space -- a fundamental constraint for modern online POMDP solvers. ROP-RaS3 is evaluated on various long-horizon POMDPs, including on a problem with a planning horizon of more than 100 steps and a problem with a 15-dimensional state space that requires more than 20 look ahead steps. In all of these problems, ROP-RaS3 substantially outperforms other state-of-the-art methods by up to multiple folds.
ProP: Efficient Backdoor Detection via Propagation Perturbation for Overparametrized Models
Venue: arXiv cs.CR 2024
Year: 2024

Backdoor attacks pose significant challenges to the security of machine learning models, particularly for overparameterized models like deep neural networks. In this paper, we propose ProP (Propagation Perturbation), a novel and scalable backdoor detection method that leverages statistical output distributions to identify backdoored models and their target classes without relying on exhausive optimization strategies. ProP introduces a new metric, the benign score, to quantify output distributions and effectively distinguish between benign and backdoored models. Unlike existing approaches, ProP operates with minimal assumptions, requiring no prior knowledge of triggers or malicious samples, making it highly applicable to real-world scenarios. Extensive experimental validation across multiple popular backdoor attacks demonstrates that ProP achieves high detection accuracy and computational efficiency, outperforming existing methods. These results highlight ProP's potential as a robust and practical solution for backdoor detection.
Automatic Contact-Based 3D Scanning Using Articulated Robotic Arm
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents an open-loop articulated 6-degree-of-freedom (DoF) robotic system for three-dimensional (3D) scanning of objects by contact-based method. A digitizer probe was used to detect contact with the object. Inverse kinematics (IK) was used to determine the joint angles of the robot corresponding to the probe position and orientation, and straight-line trajectory planning was implemented for motion. The system can take single-point measurements and 3D scans of freeform surfaces. Specifying the scanning area's size, position, and density, the system automatically scans the designated volume. The system produces 3D scans in Standard Triangle Language (STL) format, ensuring compatibility with commonly used 3D software. Tests based on ASME B89.4.22 standards were conducted to quantify accuracy and repeatability. The point cloud from the scans was compared to the original 3D model of the object.
Distributed Spatial Awareness for Robot Swarms
Venue: arXiv cs.RO 2024
Year: 2024

Building a distributed spatial awareness within a swarm of locally sensing and communicating robots enables new swarm algorithms. We use local observations by robots of each other and Gaussian Belief Propagation message passing combined with continuous swarm movement to build a global and distributed swarm-centric frame of reference. With low bandwidth and computation requirements, this shared reference frame allows new swarm algorithms. We characterise the system in simulation and demonstrate two example algorithms.