Week Ending 11.10.2024
RESEARCH WATCH: 11.10.2024
Interdisciplinary Translations: Sensory Perception as a Universal Language
This groundbreaking research explores how sensory perception serves as a fundamental bridge across disciplines, particularly in media art and human-computer interaction. By examining how our senses interpret and convey meaning, the study demonstrates how this universal "language" can improve interactive experiences and AI system design. This work has significant implications for creating more intuitive user interfaces, developing cross-cultural communication tools, and designing more human-centered AI systems that better understand and respond to human sensory experiences.
Authors: Xindi Kang, Xuanyang Huang, Mingdong Song, Varvara Guljajeva, JoAnn Kuchera-Morin
Link: https://arxiv.org/abs/2411.05374v1
Date: 2024-11-08
Summary:
This paper investigates sensory perception's pivotal role as a universal communicative bridge across varied cultures and disciplines, and how it manifests its value in the study of media art, human computer interaction and artificial intelligence. By analyzing its function in non-verbal communication through interactive systems, and drawing on the interpretive model in translation studies where "sense" acts as a mediation between two languages, this paper illustrates how interdisciplinary communication in media art and human-computer interaction is afforded by the abstract language of human sensory perception. Specific examples from traditional art, interactive media art, HCI, communication, and translation studies demonstrate how sensory feedback translates and conveys meaning across diverse modalities of expression and how it fosters connections between humans, art, and technology. Pertaining to this topic, this paper analyzes the impact of sensory feedback systems in designing interactive experiences, and reveals the guiding role of sensory perception in the design philosophy of AI systems. Overall, the study aims to broaden the understanding of sensory perception's role in communication, highlighting its significance in the evolution of interactive experiences and its capacity to unify art, science, and the human experience.
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Minimal Conditions for Beneficial Neighbourhood Search and Local Descent
This mathematical study provides the first formal proof of what makes neighborhood search effective in optimization problems. The research focuses on two key properties: neighborhood locality and the probability of finding better solutions near the optimum. These findings have practical applications in solving complex optimization problems like satisfiability and traveling salesman problems. The work also introduces "local blind descent," a hybrid approach combining random and local search, which could improve optimization algorithms in various fields from logistics to circuit design.
Authors: Mark G. Wallace
Link: https://arxiv.org/abs/2411.05263v1
Date: 2024-11-08
Summary:
This paper investigates what properties a neighbourhood requires to support beneficial local search. We show that neighbourhood locality, and a reduction in cost probability towards the optimum, support a proof that search among neighbours is more likely to find an improving solution in a single search step than blind search. This is the first paper to introduce such a proof. The concepts underlying these properties are illustrated on a satisfiability problem class, and on travelling salesman problems. Secondly, for a given cost target t, we investigate a combination of blind search and local descent termed local blind descent, and present various conditions under which the expected number of steps to reach a cost better than t using local blind descent, is proven to be smaller than with blind search. Experiments indicate that local blind descent, given target cost t, should switch to local descent at a starting cost that reduces as t approaches the optimum.
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HourVideo: 1-Hour Video-Language Understanding
This research introduces a groundbreaking benchmark for testing AI systems' ability to understand and process long-form video content. With 500 egocentric videos and nearly 13,000 questions, HourVideo tests various capabilities including summarization, visual reasoning, and navigation. The study reveals a significant gap between current AI capabilities and human performance, with even advanced models like GPT-4 struggling to match human expertise. This benchmark could drive improvements in AI applications for video surveillance, content moderation, and automated video analysis.
Authors: Keshigeyan Chandrasegaran, Agrim Gupta, Lea M. Hadzic, Taran Kota, Jimming He, Cristóbal Eyzaguirre, Zane Durante, Manling Li, Jiajun Wu, Li Fei-Fei
Link: https://arxiv.org/abs/2411.04998v1
Date: 2024-11-07
Summary:
We present HourVideo, a benchmark dataset for hour-long video-language understanding. Our dataset consists of a novel task suite comprising summarization, perception (recall, tracking), visual reasoning (spatial, temporal, predictive, causal, counterfactual), and navigation (room-to-room, object retrieval) tasks. HourVideo includes 500 manually curated egocentric videos from the Ego4D dataset, spanning durations of 20 to 120 minutes, and features 12,976 high-quality, five-way multiple-choice questions. Benchmarking results reveal that multimodal models, including GPT-4 and LLaVA-NeXT, achieve marginal improvements over random chance. In stark contrast, human experts significantly outperform the state-of-the-art long-context multimodal model, Gemini Pro 1.5 (85.0% vs. 37.3%), highlighting a substantial gap in multimodal capabilities. Our benchmark, evaluation toolkit, prompts, and documentation are available at https://hourvideo.stanford.edu
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FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI
This ambitious project presents a collection of extremely challenging mathematics problems designed to test the limits of AI mathematical reasoning. Created by expert mathematicians, these problems span various mathematical branches and require multiple hours or days for experts to solve. With current AI models solving less than 2% of problems, this benchmark sets a new standard for measuring progress in mathematical AI capabilities. The research has implications for advancing AI's role in mathematical research and education.
Authors: Elliot Glazer, Ege Erdil, Tamay Besiroglu, Diego Chicharro, Evan Chen, Alex Gunning, Caroline Falkman Olsson, Jean-Stanislas Denain, Anson Ho, Emily de Oliveira Santos, Olli Järviniemi, Matthew Barnett, Robert Sandler, Jaime Sevilla, Qiuyu Ren, Elizabeth Pratt, Lionel Levine, Grant Barkley, Natalie Stewart, Bogdan Grechuk, Tetiana Grechuk, Shreepranav Varma Enugandla
Link: https://arxiv.org/abs/2411.04872v1
Date: 2024-11-07
Summary:
We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most major branches of modern mathematics -- from computationally intensive problems in number theory and real analysis to abstract questions in algebraic geometry and category theory. Solving a typical problem requires multiple hours of effort from a researcher in the relevant branch of mathematics, and for the upper end questions, multiple days. FrontierMath uses new, unpublished problems and automated verification to reliably evaluate models while minimizing risk of data contamination. Current state-of-the-art AI models solve under 2% of problems, revealing a vast gap between AI capabilities and the prowess of the mathematical community. As AI systems advance toward expert-level mathematical abilities, FrontierMath offers a rigorous testbed that quantifies their progress.
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Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research
This innovative study proposes a multi-agent AI system for financial analysis, focusing on analyzing SEC 10-K forms of Dow Jones companies. The system employs multiple AI agents working collaboratively on different aspects of financial analysis, including fundamentals, market sentiment, and risk assessment. Results show superior performance compared to single-agent approaches, suggesting potential applications in investment decision-making, portfolio management, and automated financial analysis.
Authors: Xuewen Han, Neng Wang, Shangkun Che, Hongyang Yang, Kunpeng Zhang, Sean Xin Xu
Link: https://arxiv.org/abs/2411.04788v1
Date: 2024-11-07
Summary:
In recent years, the application of generative artificial intelligence (GenAI) in financial analysis and investment decision-making has gained significant attention. However, most existing approaches rely on single-agent systems, which fail to fully utilize the collaborative potential of multiple AI agents. In this paper, we propose a novel multi-agent collaboration system designed to enhance decision-making in financial investment research. The system incorporates agent groups with both configurable group sizes and collaboration structures to leverage the strengths of each agent group type. By utilizing a sub-optimal combination strategy, the system dynamically adapts to varying market conditions and investment scenarios, optimizing performance across different tasks. We focus on three sub-tasks: fundamentals, market sentiment, and risk analysis, by analyzing the 2023 SEC 10-K forms of 30 companies listed on the Dow Jones Index. Our findings reveal significant performance variations based on the configurations of AI agents for different tasks. The results demonstrate that our multi-agent collaboration system outperforms traditional single-agent models, offering improved accuracy, efficiency, and adaptability in complex financial environments. This study highlights the potential of multi-agent systems in transforming financial analysis and investment decision-making by integrating diverse analytical perspectives.
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Scaling Laws for Pre-training Agents and World Models
This research investigates how increasing model parameters, dataset size, and compute affects the performance of AI agents in tasks like robotics and gaming. The study reveals that these systems follow power laws similar to language models, but with variations based on specific factors like tokenizers and architectures. These findings have practical implications for optimizing AI system design and resource allocation in robotics, gaming, and simulation applications.
Authors: Tim Pearce, Tabish Rashid, Dave Bignell, Raluca Georgescu, Sam Devlin, Katja Hofmann
Link: https://arxiv.org/abs/2411.04434v1
Date: 2024-11-07
Summary:
The performance of embodied agents has been shown to improve by increasing model parameters, dataset size, and compute. This has been demonstrated in domains from robotics to video games, when generative learning objectives on offline datasets (pre-training) are used to model an agent's behavior (imitation learning) or their environment (world modeling). This paper characterizes the role of scale in these tasks more precisely. Going beyond the simple intuition that `bigger is better', we show that the same types of power laws found in language modeling (e.g. between loss and optimal model size), also arise in world modeling and imitation learning. However, the coefficients of these laws are heavily influenced by the tokenizer, task \& architecture -- this has important implications on the optimal sizing of models and data.
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This technical study explores a new advancement in reconfigurable intelligent surfaces (RIS) for wireless communications. The research demonstrates how non-reciprocal BD-RIS can enhance full-duplex communication systems, allowing for better simultaneous two-way communication. This breakthrough has potential applications in improving wireless network performance, 5G/6G communications, and creating more efficient mobile communication systems.
Authors: Hongyu Li, Bruno Clerckx
Link: https://arxiv.org/abs/2411.04370v1
Date: 2024-11-07
Summary:
Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) is a new advance in RIS techniques that introduces reconfigurable inter-element connections to generate scattering matrices not limited to being diagonal. BD-RIS has been recently proposed and proven to have benefits in enhancing channel gain and enlarging coverage in wireless communications. Uniquely, BD-RIS enables reciprocal and non-reciprocal architectures characterized by symmetric and non-symmetric scattering matrices. However, the performance benefits and new use cases enabled by non-reciprocal BD-RIS for wireless systems remain unexplored. This work takes a first step toward closing this knowledge gap and studies the non-reciprocal BD-RIS in full-duplex systems and its performance benefits over reciprocal counterparts. We start by deriving a general RIS aided full-duplex system model using a multiport circuit theory, followed by a simplified channel model based on physically consistent assumptions. With the considered channel model, we investigate the effect of BD-RIS non-reciprocity and identify the theoretical conditions for reciprocal and non-reciprocal BD-RISs to simultaneously achieve the maximum received power of the signal of interest in the uplink and the downlink. Simulation results validate the theories and highlight the significant benefits offered by non-reciprocal BD-RIS in full-duplex systems. The significant gains are achieved because of the non-reciprocity principle which implies that if a wave hits the non-reciprocal BD-RIS from one direction, the surface behaves differently than if it hits from the opposite direction. This enables an uplink user and a downlink user at different locations to optimally communicate with the same full-duplex base station via a non-reciprocal BD-RIS, which would not be possible with reciprocal surfaces.
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Price Prediction Using Machine Learning
This study examines various machine learning models for predicting Dollar/TL exchange rates, evaluating different approaches' effectiveness in volatile markets. The research finds that multilayer perceptron and linear regression models perform best, even during periods of sharp currency fluctuation. These findings have practical applications in currency trading, financial risk management, and economic forecasting.
Authors: Asef Yelghi, Aref Yelghi, Shirmohammad Tavangari
Link: https://arxiv.org/abs/2411.04259v1
Date: 2024-11-06
Summary:
The development of artificial intelligence has made significant contributions to the financial sector. One of the main interests of investors is price predictions. Technical and fundamental analyses, as well as econometric analyses, are conducted for price predictions; recently, the use of AI-based methods has become more prevalent. This study examines daily Dollar/TL exchange rates from January 1, 2020, to October 4, 2024. It has been observed that among artificial intelligence models, random forest, support vector machines, k-nearest neighbors, decision trees, and gradient boosting models were not suitable; however, multilayer perceptron and linear regression models showed appropriate suitability and despite the sharp increase in Dollar/TL rates in Turkey as of 2019, the suitability of valid models has been maintained.
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Two-Stage Pretraining for Molecular Property Prediction in the Wild
This research introduces MoleVers, an innovative model for predicting molecular properties with limited experimental data. The two-stage pretraining approach first learns from unlabeled data, then uses computational methods for further training. This advancement has potential applications in drug discovery, materials science, and chemical engineering, particularly where experimental data is scarce or expensive to obtain.
Authors: Kevin Tirta Wijaya, Minghao Guo, Michael Sun, Hans-Peter Seidel, Wojciech Matusik, Vahid Babaei
Link: https://arxiv.org/abs/2411.03537v1
Date: 2024-11-05
Summary:
Accurate property prediction is crucial for accelerating the discovery of new molecules. Although deep learning models have achieved remarkable success, their performance often relies on large amounts of labeled data that are expensive and time-consuming to obtain. Thus, there is a growing need for models that can perform well with limited experimentally-validated data. In this work, we introduce MoleVers, a versatile pretrained model designed for various types of molecular property prediction in the wild, i.e., where experimentally-validated molecular property labels are scarce. MoleVers adopts a two-stage pretraining strategy. In the first stage, the model learns molecular representations from large unlabeled datasets via masked atom prediction and dynamic denoising, a novel task enabled by a new branching encoder architecture. In the second stage, MoleVers is further pretrained using auxiliary labels obtained with inexpensive computational methods, enabling supervised learning without the need for costly experimental data. This two-stage framework allows MoleVers to learn representations that generalize effectively across various downstream datasets. We evaluate MoleVers on a new benchmark comprising 22 molecular datasets with diverse types of properties, the majority of which contain 50 or fewer training labels reflecting real-world conditions. MoleVers achieves state-of-the-art results on 20 out of the 22 datasets, and ranks second among the remaining two, highlighting its ability to bridge the gap between data-hungry models and real-world conditions where practically-useful labels are scarce.
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STEER: Flexible Robotic Manipulation via Dense Language Grounding
This research presents a framework for making robots more adaptable to real-world situations through language-grounded policies. STEER bridges high-level reasoning with precise control, allowing robots to understand and execute complex tasks through natural language instructions. This has potential applications in manufacturing, household robotics, and automated assistance systems where flexibility and adaptability are crucial.
Authors: Laura Smith, Alex Irpan, Montserrat Gonzalez Arenas, Sean Kirmani, Dmitry Kalashnikov, Dhruv Shah, Ted Xiao
Link: https://arxiv.org/abs/2411.03409v1
Date: 2024-11-05
Summary:
The complexity of the real world demands robotic systems that can intelligently adapt to unseen situations. We present STEER, a robot learning framework that bridges high-level, commonsense reasoning with precise, flexible low-level control. Our approach translates complex situational awareness into actionable low-level behavior through training language-grounded policies with dense annotation. By structuring policy training around fundamental, modular manipulation skills expressed in natural language, STEER exposes an expressive interface for humans or Vision-Language Models (VLMs) to intelligently orchestrate the robot's behavior by reasoning about the task and context. Our experiments demonstrate the skills learned via STEER can be combined to synthesize novel behaviors to adapt to new situations or perform completely new tasks without additional data collection or training.
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This study addresses a critical challenge in robotic learning: handling unexpected situations not covered in training data. The proposed framework uses object keypoints to help robots recover from out-of-distribution scenarios, improving task success rates by 77.7%. This advancement has practical applications in industrial robotics, autonomous systems, and any situation where robots need to handle unexpected circumstances.
Authors: George Jiayuan Gao, Tianyu Li, Nadia Figueroa
Link: https://arxiv.org/abs/2411.03294v2
Date: 2024-11-06
Summary:
We propose an object-centric recovery policy framework to address the challenges of out-of-distribution (OOD) scenarios in visuomotor policy learning. Previous behavior cloning (BC) methods rely heavily on a large amount of labeled data coverage, failing in unfamiliar spatial states. Without relying on extra data collection, our approach learns a recovery policy constructed by an inverse policy inferred from object keypoint manifold gradient in the original training data. The recovery policy serves as a simple add-on to any base visuomotor BC policy, agnostic to a specific method, guiding the system back towards the training distribution to ensure task success even in OOD situations. We demonstrate the effectiveness of our object-centric framework in both simulation and real robot experiments, achieving an improvement of 77.7% over the base policy in OOD. Project Website: https://sites.google.com/view/ocr-penn
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Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: A Survey
This survey examines how video generation and world models can be integrated to improve autonomous driving systems. It analyzes various approaches and their effectiveness in creating more accurate simulations of driving scenarios. The research has implications for developing safer autonomous vehicles, improving driver assistance systems, and creating more realistic driving simulators.
Authors: Ao Fu, Yi Zhou, Tao Zhou, Yi Yang, Bojun Gao, Qun Li, Guobin Wu, Ling Shao
Link: https://arxiv.org/abs/2411.02914v1
Date: 2024-11-05
Summary:
World models and video generation are pivotal technologies in the domain of autonomous driving, each playing a critical role in enhancing the robustness and reliability of autonomous systems. World models, which simulate the dynamics of real-world environments, and video generation models, which produce realistic video sequences, are increasingly being integrated to improve situational awareness and decision-making capabilities in autonomous vehicles. This paper investigates the relationship between these two technologies, focusing on how their structural parallels, particularly in diffusion-based models, contribute to more accurate and coherent simulations of driving scenarios. We examine leading works such as JEPA, Genie, and Sora, which exemplify different approaches to world model design, thereby highlighting the lack of a universally accepted definition of world models. These diverse interpretations underscore the field's evolving understanding of how world models can be optimized for various autonomous driving tasks. Furthermore, this paper discusses the key evaluation metrics employed in this domain, such as Chamfer distance for 3D scene reconstruction and Fr\'echet Inception Distance (FID) for assessing the quality of generated video content. By analyzing the interplay between video generation and world models, this survey identifies critical challenges and future research directions, emphasizing the potential of these technologies to jointly advance the performance of autonomous driving systems. The findings presented in this paper aim to provide a comprehensive understanding of how the integration of video generation and world models can drive innovation in the development of safer and more reliable autonomous vehicles.
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Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM Safety Alignment
This security research reveals how simple random input modifications can bypass safety alignments in large language models. The study shows that even unsophisticated attackers can potentially circumvent AI safety measures with minimal resources. These findings have important implications for AI safety, security protocol development, and the need for more robust safety mechanisms in AI systems.
Authors: Jason Vega, Junsheng Huang, Gaokai Zhang, Hangoo Kang, Minjia Zhang, Gagandeep Singh
Link: https://arxiv.org/abs/2411.02785v1
Date: 2024-11-05
Summary:
Safety alignment of Large Language Models (LLMs) has recently become a critical objective of model developers. In response, a growing body of work has been investigating how safety alignment can be bypassed through various jailbreaking methods, such as adversarial attacks. However, these jailbreak methods can be rather costly or involve a non-trivial amount of creativity and effort, introducing the assumption that malicious users are high-resource or sophisticated. In this paper, we study how simple random augmentations to the input prompt affect safety alignment effectiveness in state-of-the-art LLMs, such as Llama 3 and Qwen 2. We perform an in-depth evaluation of 17 different models and investigate the intersection of safety under random augmentations with multiple dimensions: augmentation type, model size, quantization, fine-tuning-based defenses, and decoding strategies (e.g., sampling temperature). We show that low-resource and unsophisticated attackers, i.e. $\textit{stochastic monkeys}$, can significantly improve their chances of bypassing alignment with just 25 random augmentations per prompt.
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Evaluating the quality of published medical research with ChatGPT
This study assesses ChatGPT's ability to evaluate medical research quality, comparing its assessments with established quality indicators. While showing overall positive correlations, the research identifies limitations in evaluating prestigious medical journals and health-impact research. These findings have implications for academic evaluation systems, research assessment methodologies, and the use of AI in academic quality control.
Authors: Mike Thelwall, Xiaorui Jiang, Peter A. Bath
Link: https://arxiv.org/abs/2411.01952v1
Date: 2024-11-04
Summary:
Evaluating the quality of published research is time-consuming but important for departmental evaluations, appointments, and promotions. Previous research has shown that ChatGPT can score articles for research quality, with the results correlating positively with an indicator of quality in all fields except Clinical Medicine. This article investigates this anomaly with the largest dataset yet and a more detailed analysis. The results showed that ChatGPT 4o-mini scores for articles submitted to the UK's Research Excellence Framework (REF) 2021 Unit of Assessment (UoA) 1 Clinical Medicine correlated positively (r=0.134, n=9872) with departmental mean REF scores, against a theoretical maximum correlation of r=0.226 (due to the departmental averaging involved). At the departmental level, mean ChatGPT scores correlated more strongly with departmental mean REF scores (r=0.395, n=31). For the 100 journals with the most articles in UoA 1, their mean ChatGPT score correlated strongly with their REF score (r=0.495) but negatively with their citation rate (r=-0.148). Journal and departmental anomalies in these results point to ChatGPT being ineffective at assessing the quality of research in prestigious medical journals or research directly affecting human health, or both. Nevertheless, the results give evidence of ChatGPT's ability to assess research quality overall for Clinical Medicine, so now there is evidence of its ability in all academic fields.
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A Bayesian explanation of machine learning models based on modes and functional ANOVA
This research proposes a new approach to explaining AI decisions using Bayesian frameworks and functional ANOVA. Unlike traditional explainable AI methods, it focuses on understanding why specific outcomes occur by identifying influential features. This method has potential applications in improving AI transparency, regulatory compliance, and building more trustworthy AI systems.
Authors: Quan Long
Link: https://arxiv.org/abs/2411.02746v1
Date: 2024-11-05
Summary:
Most methods in explainable AI (XAI) focus on providing reasons for the prediction of a given set of features. However, we solve an inverse explanation problem, i.e., given the deviation of a label, find the reasons of this deviation. We use a Bayesian framework to recover the ``true'' features, conditioned on the observed label value. We efficiently explain the deviation of a label value from the mode, by identifying and ranking the influential features using the ``distances'' in the ANOVA functional decomposition. We show that the new method is more human-intuitive and robust than methods based on mean values, e.g., SHapley Additive exPlanations (SHAP values). The extra costs of solving a Bayesian inverse problem are dimension-independent.
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Tangled Program Graphs as an alternative to DRL-based control algorithms for UAVs
This study presents Tangled Program Graphs as a more efficient and explainable alternative to deep reinforcement learning for controlling autonomous vehicles. The research demonstrates TPGs' effectiveness in UAV navigation using LiDAR sensors, offering lower computational requirements and better explainability. This has applications in autonomous vehicle control, especially in safety-critical situations where decision transparency is crucial.
Authors: Hubert Szolc, Karol Desnos, Tomasz Kryjak
Link: https://arxiv.org/abs/2411.05586v1
Date: 2024-11-08
Summary:
Deep reinforcement learning (DRL) is currently the most popular AI-based approach to autonomous vehicle control. An agent, trained for this purpose in simulation, can interact with the real environment with a human-level performance. Despite very good results in terms of selected metrics, this approach has some significant drawbacks: high computational requirements and low explainability. Because of that, a DRL-based agent cannot be used in some control tasks, especially when safety is the key issue. Therefore we propose to use Tangled Program Graphs (TPGs) as an alternative for deep reinforcement learning in control-related tasks. In this approach, input signals are processed by simple programs that are combined in a graph structure. As a result, TPGs are less computationally demanding and their actions can be explained based on the graph structure. In this paper, we present our studies on the use of TPGs as an alternative for DRL in control-related tasks. In particular, we consider the problem of navigating an unmanned aerial vehicle (UAV) through the unknown environment based solely on the on-board LiDAR sensor. The results of our work show promising prospects for the use of TPGs in control related-tasks.
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This perspective paper examines the potential integration of large language models with healthcare robots. It identifies system requirements for implementing LLM-based robots in clinical settings and discusses ethical considerations. The research has implications for addressing healthcare workforce shortages, improving patient care, and developing next-generation medical assistance systems.
Authors: Souren Pashangpour, Goldie Nejat
Link: https://arxiv.org/abs/2411.03287v1
Date: 2024-11-05
Summary:
The potential use of large language models (LLMs) in healthcare robotics can help address the significant demand put on healthcare systems around the world with respect to an aging demographic and a shortage of healthcare professionals. Even though LLMs have already been integrated into medicine to assist both clinicians and patients, the integration of LLMs within healthcare robots has not yet been explored for clinical settings. In this perspective paper, we investigate the groundbreaking developments in robotics and LLMs to uniquely identify the needed system requirements for designing health specific LLM based robots in terms of multi modal communication through human robot interactions (HRIs), semantic reasoning, and task planning. Furthermore, we discuss the ethical issues, open challenges, and potential future research directions for this emerging innovative field.
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Foundation AI Model for Medical Image Segmentation
This paper explores the development of foundation models for medical image segmentation, discussing two potential approaches: adapting existing models or building new ones specifically for medical images. This research has implications for streamlining medical image analysis, improving diagnostic accuracy, and reducing the need for multiple task-specific AI models in healthcare settings.
Authors: Rina Bao, Erfan Darzi, Sheng He, Chuan-Heng Hsiao, Mohammad Arafat Hussain, Jingpeng Li, Atle Bjornerud, Ellen Grant, Yangming Ou
Link: https://arxiv.org/abs/2411.02745v1
Date: 2024-11-05
Summary:
Foundation models refer to artificial intelligence (AI) models that are trained on massive amounts of data and demonstrate broad generalizability across various tasks with high accuracy. These models offer versatile, one-for-many or one-for-all solutions, eliminating the need for developing task-specific AI models. Examples of such foundation models include the Chat Generative Pre-trained Transformer (ChatGPT) and the Segment Anything Model (SAM). These models have been trained on millions to billions of samples and have shown wide-ranging and accurate applications in numerous tasks such as text processing (using ChatGPT) and natural image segmentation (using SAM). In medical image segmentation - finding target regions in medical images - there is a growing need for these one-for-many or one-for-all foundation models. Such models could obviate the need to develop thousands of task-specific AI models, which is currently standard practice in the field. They can also be adapted to tasks with datasets too small for effective training. We discuss two paths to achieve foundation models for medical image segmentation and comment on progress, challenges, and opportunities. One path is to adapt or fine-tune existing models, originally developed for natural images, for use with medical images. The second path entails building models from scratch, exclusively training on medical images.
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Behavioral Sequence Modeling with Ensemble Learning
This research introduces a framework for analyzing sequential human behavior using ensembles of Hidden Markov Models. The approach emphasizes the importance of sequential context over aggregate features and offers solutions for handling fragmented data. This has applications in healthcare monitoring, financial fraud detection, and e-commerce behavior analysis.
Authors: Maxime Kawawa-Beaudan, Srijan Sood, Soham Palande, Ganapathy Mani, Tucker Balch, Manuela Veloso
Link: https://arxiv.org/abs/2411.02174v1
Date: 2024-11-04
Summary:
We investigate the use of sequence analysis for behavior modeling, emphasizing that sequential context often outweighs the value of aggregate features in understanding human behavior. We discuss framing common problems in fields like healthcare, finance, and e-commerce as sequence modeling tasks, and address challenges related to constructing coherent sequences from fragmented data and disentangling complex behavior patterns. We present a framework for sequence modeling using Ensembles of Hidden Markov Models, which are lightweight, interpretable, and efficient. Our ensemble-based scoring method enables robust comparison across sequences of different lengths and enhances performance in scenarios with imbalanced or scarce data. The framework scales in real-world scenarios, is compatible with downstream feature-based modeling, and is applicable in both supervised and unsupervised learning settings. We demonstrate the effectiveness of our method with results on a longitudinal human behavior dataset.
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This study investigates how different types of noise affect neural networks' classification accuracy, particularly in physical implementations. The research proposes and evaluates various noise reduction techniques, demonstrating their effectiveness in maintaining network performance. These findings have practical applications in developing more robust hardware-based neural networks and improving the reliability of physical AI systems.
Authors: Nadezhda Semenova, Daniel Brunner
Link: https://arxiv.org/abs/2411.04354v1
Date: 2024-11-07
Summary:
In recent years, the hardware implementation of neural networks, leveraging physical coupling and analog neurons has substantially increased in relevance. Such nonlinear and complex physical networks provide significant advantages in speed and energy efficiency, but are potentially susceptible to internal noise when compared to digital emulations of such networks. In this work, we consider how additive and multiplicative Gaussian white noise on the neuronal level can affect the accuracy of the network when applied for specific tasks and including a softmax function in the readout layer. We adapt several noise reduction techniques to the essential setting of classification tasks, which represent a large fraction of neural network computing. We find that these adjusted concepts are highly effective in mitigating the detrimental impact of noise.
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