pwc-archive/papers-with-abstracts huggingface dataset:
SINDyG: Sparse Identification of Nonlinear Dynamical Systems from Graph-Structured Data
Authors: Mohammad Amin Basiri, Sina Khanmohammadi
Date: 2024-09-02 00:00:00
Tasks: Epidemiology

The combination of machine learning (ML) and sparsity-promoting techniques is enabling direct extraction of governing equations from data, revolutionizing computational modeling in diverse fields of science and engineering. The discovered dynamical models could be used to address challenges in climate science, neuroscience, ecology, finance, epidemiology, and beyond. However, most existing sparse identification methods for discovering dynamical systems treat the whole system as one without considering the interactions between subsystems. As a result, such models are not able to capture small changes in the emergent system behavior. To address this issue, we developed a new method called Sparse Identification of Nonlinear Dynamical Systems from Graph-structured data (SINDyG), which incorporates the network structure into sparse regression to identify model parameters that explain the underlying network dynamics. We showcase the application of our proposed method using several case studies of neuronal dynamics, where we model the macroscopic oscillation of a population of neurons using the extended Stuart-Landau (SL) equation and utilize the SINDyG method to identify the underlying nonlinear dynamics. Our extensive computational experiments validate the improved accuracy and simplicity of discovered network dynamics when compared to the original SINDy approach.
MVX-ViT: Multimodal Collaborative Perception for 6G V2X Network Management Decisions Using Vision Transformer
Authors: Ghazi Gharsalla, Georges Kaddoum
Date: 2024-08-30 00:00:00
Proceeding: IEEE Open Journal of the Communications Society 2024 8

Advancements in sixth-generation (6G) networks, coupled with the evolution of multimodal sensing in vehicle-to-everything (V2X) networks, have opened avenues for transformative research into multimodal-based artificial intelligence (AI) applications for wireless communication and network management. However, this promising research direction is often constrained by the limited availability of suitable datasets. In response, this paper introduces a comprehensive configurable co-simulation framework that integrates the state-of-the-art CARLA and Sionna simulators to generate a multimodal multi-view V2X (MVX) dataset. We present novel AI-based models to predict future line-of-sight (LoS) blockages and optimal beam direction as well as an innovative antenna position optimization (APO) solution, all of which are underpinned by the multimodal dataset MVX. Our framework capitalizes on collaborative perception and significantly enhances V2X communication by integrating LiDAR and wireless data. Thorough evaluations demonstrate that our collaborative perception approach outperforms traditional methods of both beam and blockage prediction in terms of accuracy and efficiency. Additionally, we evaluate the importance of infrastructural elements in V2X systems and conduct a computational study to illustrate that our framework is suitable for various operational scenarios and can be used as a digital twin solution. This work not only contributes to the field of V2X wireless communications by providing a versatile framework for network management but also sets the stage for future research on multi-sensor fusion in AI applications for V2X wireless communication environments to enhance the efficiency and resilience of future 6G networks.
HULLMI: Human vs LLM identification with explainability
Authors: Prathamesh Dinesh Joshi, Sahil Pocker, Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat
Date: 2024-09-07 00:00:00
Tasks: Text Detection

As LLMs become increasingly proficient at producing human-like responses, there has been a rise of academic and industrial pursuits dedicated to flagging a given piece of text as "human" or "AI". Most of these pursuits involve modern NLP detectors like T5-Sentinel and RoBERTa-Sentinel, without paying too much attention to issues of interpretability and explainability of these models. In our study, we provide a comprehensive analysis that shows that traditional ML models (Naive-Bayes,MLP, Random Forests, XGBoost) perform as well as modern NLP detectors, in human vs AI text detection. We achieve this by implementing a robust testing procedure on diverse datasets, including curated corpora and real-world samples. Subsequently, by employing the explainable AI technique LIME, we uncover parts of the input that contribute most to the prediction of each model, providing insights into the detection process. Our study contributes to the growing need for developing production-level LLM detection tools, which can leverage a wide range of traditional as well as modern NLP detectors we propose. Finally, the LIME techniques we demonstrate also have the potential to equip these detection tools with interpretability analysis features, making them more reliable and trustworthy in various domains like education, healthcare, and media.
Action is the primary key: a categorical framework for episode description and logical reasoning
Authors: Yoshiki Fukada
Date: 2024-09-07 00:00:00
Tasks: Logical Reasoning

This research presents a computational framework for describing and recognizing episodes and for logical reasoning. This framework, named cognitive-logs, consists of a set of relational and graph databases. Cognitive-logs record knowledge, particularly in episodes that consist of "actions" represented by verbs in natural languages and "participants" who perform the actions. These objects are connected by arrows (morphisms) that link each action to its participant and link cause to effect. Operations based on category theory enable comparisons between episodes and deductive inferences, including abstractions of stories. One of the goals of this study is to develop a database-driven artificial intelligence. This artificial intelligence thinks like a human but possesses the accuracy and rigour of a machine. The vast capacities of databases (up to petabyte scales in current technologies) enable the artificial intelligence to store a greater volume of knowledge than neural-network based artificial intelligences. Cognitive-logs serve as a model of human cognition and designed with references to cognitive linguistics. Cognitive-logs also have the potential to model various human mind activities.
Scalable Task Planning via Large Language Models and Structured World Representations
Authors: Rodrigo Pérez-Dattari, Zhaoting Li, Robert Babuška, Jens Kober, Cosimo Della Santina
Date: 2024-09-07 00:00:00
Tasks: Task Planning

Planning methods struggle with computational intractability in solving task-level problems in large-scale environments. This work explores leveraging the commonsense knowledge encoded in LLMs to empower planning techniques to deal with these complex scenarios. We achieve this by efficiently using LLMs to prune irrelevant components from the planning problem's state space, substantially simplifying its complexity. We demonstrate the efficacy of this system through extensive experiments within a household simulation environment, alongside real-world validation using a 7-DoF manipulator (video https://youtu.be/6ro2UOtOQS4).
Algorithmic Scenario Generation as Quality Diversity Optimization
Authors: Stefanos Nikolaidis
Date: 2024-09-07 00:00:00
Tasks: Diversity

The increasing complexity of robots and autonomous agents that interact with people highlights the critical need for approaches that systematically test them before deployment. This review paper presents a general framework for solving this problem, describes the insights that we have gained from working on each component of the framework, and shows how integrating these components leads to the discovery of a diverse range of realistic and challenging scenarios that reveal previously unknown failures in deployed robotic systems interacting with people.
MuAP: Multi-step Adaptive Prompt Learning for Vision-Language Model with Missing Modality
Authors: Ruiting Dai, Yuqiao Tan, Lisi Mo, Tao He, Ke Qin, Shuang Liang
Date: 2024-09-07 00:00:00
Tasks: Language Modeling, Language Modelling, Prompt Learning

Recently, prompt learning has garnered considerable attention for its success in various Vision-Language (VL) tasks. However, existing prompt-based models are primarily focused on studying prompt generation and prompt strategies with complete modality settings, which does not accurately reflect real-world scenarios where partial modality information may be missing. In this paper, we present the first comprehensive investigation into prompt learning behavior when modalities are incomplete, revealing the high sensitivity of prompt-based models to missing modalities. To this end, we propose a novel Multi-step Adaptive Prompt Learning (MuAP) framework, aiming to generate multimodal prompts and perform multi-step prompt tuning, which adaptively learns knowledge by iteratively aligning modalities. Specifically, we generate multimodal prompts for each modality and devise prompt strategies to integrate them into the Transformer model. Subsequently, we sequentially perform prompt tuning from single-stage and alignment-stage, allowing each modality-prompt to be autonomously and adaptively learned, thereby mitigating the imbalance issue caused by only textual prompts that are learnable in previous works. Extensive experiments demonstrate the effectiveness of our MuAP and this model achieves significant improvements compared to the state-of-the-art on all benchmark datasets
Solving Stochastic Orienteering Problems with Chance Constraints Using a GNN Powered Monte Carlo Tree Search
Authors: Marcos Abel Zuzuárregui, Stefano Carpin
Date: 2024-09-06 00:00:00
Tasks: Graph Neural Network

Leveraging the power of a graph neural network (GNN) with message passing, we present a Monte Carlo Tree Search (MCTS) method to solve stochastic orienteering problems with chance constraints. While adhering to an assigned travel budget the algorithm seeks to maximize collected reward while incurring stochastic travel costs. In this context, the acceptable probability of exceeding the assigned budget is expressed as a chance constraint. Our MCTS solution is an online and anytime algorithm alternating planning and execution that determines the next vertex to visit by continuously monitoring the remaining travel budget. The novelty of our work is that the rollout phase in the MCTS framework is implemented using a message passing GNN, predicting both the utility and failure probability of each available action. This allows to enormously expedite the search process. Our experimental evaluation shows that with the proposed method and architecture we manage to efficiently solve complex problem instances while incurring in moderate losses in terms of collected reward. Moreover, we demonstrate how the approach is capable of generalizing beyond the characteristics of the training dataset. The paper's website, open-source code, and supplementary documentation can be found at ucmercedrobotics.github.io/gnn-sop.
Convergence of Decentralized Actor-Critic Algorithm in General-sum Markov Games
Authors: Chinmay Maheshwari, Manxi Wu, Shankar Sastry
Date: 2024-09-06 00:00:00

Markov games provide a powerful framework for modeling strategic multi-agent interactions in dynamic environments. Traditionally, convergence properties of decentralized learning algorithms in these settings have been established only for special cases, such as Markov zero-sum and potential games, which do not fully capture real-world interactions. In this paper, we address this gap by studying the asymptotic properties of learning algorithms in general-sum Markov games. In particular, we focus on a decentralized algorithm where each agent adopts an actor-critic learning dynamic with asynchronous step sizes. This decentralized approach enables agents to operate independently, without requiring knowledge of others' strategies or payoffs. We introduce the concept of a Markov Near-Potential Function (MNPF) and demonstrate that it serves as an approximate Lyapunov function for the policy updates in the decentralized learning dynamics, which allows us to characterize the convergent set of strategies. We further strengthen our result under specific regularity conditions and with finite Nash equilibria.
The emergence of Large Language Models (LLM) as a tool in literature reviews: an LLM automated systematic review
Authors: Dmitry Scherbakov, Nina Hubig, Vinita Jansari, Alexander Bakumenko, Leslie A. Lenert
Date: 2024-09-06 00:00:00
Tasks: Articles

Objective: This study aims to summarize the usage of Large Language Models (LLMs) in the process of creating a scientific review. We look at the range of stages in a review that can be automated and assess the current state-of-the-art research projects in the field. Materials and Methods: The search was conducted in June 2024 in PubMed, Scopus, Dimensions, and Google Scholar databases by human reviewers. Screening and extraction process took place in Covidence with the help of LLM add-on which uses OpenAI gpt-4o model. ChatGPT was used to clean extracted data and generate code for figures in this manuscript, ChatGPT and Scite.ai were used in drafting all components of the manuscript, except the methods and discussion sections. Results: 3,788 articles were retrieved, and 172 studies were deemed eligible for the final review. ChatGPT and GPT-based LLM emerged as the most dominant architecture for review automation (n=126, 73.2%). A significant number of review automation projects were found, but only a limited number of papers (n=26, 15.1%) were actual reviews that used LLM during their creation. Most citations focused on automation of a particular stage of review, such as Searching for publications (n=60, 34.9%), and Data extraction (n=54, 31.4%). When comparing pooled performance of GPT-based and BERT-based models, the former were better in data extraction with mean precision 83.0% (SD=10.4), and recall 86.0% (SD=9.8), while being slightly less accurate in title and abstract screening stage (Maccuracy=77.3%, SD=13.0). Discussion/Conclusion: Our LLM-assisted systematic review revealed a significant number of research projects related to review automation using LLMs. The results looked promising, and we anticipate that LLMs will change in the near future the way the scientific reviews are conducted.
ActionFlow: Equivariant, Accurate, and Efficient Policies with Spatially Symmetric Flow Matching
Authors: Niklas Funk, Julen Urain, Joao Carvalho, Vignesh Prasad, Georgia Chalvatzaki, Jan Peters
Date: 2024-09-06 00:00:00
Tasks: Action Generation, Spatial Reasoning

Spatial understanding is a critical aspect of most robotic tasks, particularly when generalization is important. Despite the impressive results of deep generative models in complex manipulation tasks, the absence of a representation that encodes intricate spatial relationships between observations and actions often limits spatial generalization, necessitating large amounts of demonstrations. To tackle this problem, we introduce a novel policy class, ActionFlow. ActionFlow integrates spatial symmetry inductive biases while generating expressive action sequences. On the representation level, ActionFlow introduces an SE(3) Invariant Transformer architecture, which enables informed spatial reasoning based on the relative SE(3) poses between observations and actions. For action generation, ActionFlow leverages Flow Matching, a state-of-the-art deep generative model known for generating high-quality samples with fast inference - an essential property for feedback control. In combination, ActionFlow policies exhibit strong spatial and locality biases and SE(3)-equivariant action generation. Our experiments demonstrate the effectiveness of ActionFlow and its two main components on several simulated and real-world robotic manipulation tasks and confirm that we can obtain equivariant, accurate, and efficient policies with spatially symmetric flow matching. Project website: https://flowbasedpolicies.github.io/
Neurosymbolic Methods for Dynamic Knowledge Graphs
Authors: Mehwish Alam, Genet Asefa Gesese, Pierre-Henri Paris
Date: 2024-09-06 00:00:00
Tasks: Entity Alignment, Knowledge Graphs

Knowledge graphs (KGs) have recently been used for many tools and applications, making them rich resources in structured format. However, in the real world, KGs grow due to the additions of new knowledge in the form of entities and relations, making these KGs dynamic. This chapter formally defines several types of dynamic KGs and summarizes how these KGs can be represented. Additionally, many neurosymbolic methods have been proposed for learning representations over static KGs for several tasks such as KG completion and entity alignment. This chapter further focuses on neurosymbolic methods for dynamic KGs with or without temporal information. More specifically, it provides an insight into neurosymbolic methods for dynamic (temporal or non-temporal) KG completion and entity alignment tasks. It further discusses the challenges of current approaches and provides some future directions.
Revolutionizing Database Q&A with Large Language Models: Comprehensive Benchmark and Evaluation
Authors: Yihang Zheng, Bo Li, Zhenghao Lin, Yi Luo, Xuanhe Zhou, Chen Lin, Jinsong Su, Guoliang Li, Shifu Li
Date: 2024-09-05 00:00:00
Tasks: Fairness, RAG, Retrieval-augmented Generation

The development of Large Language Models (LLMs) has revolutionized QA across various industries, including the database domain. However, there is still a lack of a comprehensive benchmark to evaluate the capabilities of different LLMs and their modular components in database QA. To this end, we introduce DQABench, the first comprehensive database QA benchmark for LLMs. DQABench features an innovative LLM-based method to automate the generation, cleaning, and rewriting of evaluation dataset, resulting in over 200,000 QA pairs in English and Chinese, separately. These QA pairs cover a wide range of database-related knowledge extracted from manuals, online communities, and database instances. This inclusion allows for an additional assessment of LLMs' Retrieval-Augmented Generation (RAG) and Tool Invocation Generation (TIG) capabilities in the database QA task. Furthermore, we propose a comprehensive LLM-based database QA testbed DQATestbed. This testbed is highly modular and scalable, with basic and advanced components such as Question Classification Routing (QCR), RAG, TIG, and Prompt Template Engineering (PTE). Moreover, DQABench provides a comprehensive evaluation pipeline that computes various metrics throughout a standardized evaluation process to ensure the accuracy and fairness of the evaluation. We use DQABench to evaluate the database QA capabilities under the proposed testbed comprehensively. The evaluation reveals findings like (i) the strengths and limitations of nine LLM-based QA bots and (ii) the performance impact and potential improvements of various service components (e.g., QCR, RAG, TIG). Our benchmark and findings will guide the future development of LLM-based database QA research.
Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysis
Authors: Xianbing Zhao, Lizhen Qu, Tao Feng, Jianfei Cai, Buzhou Tang
Date: 2024-09-05 00:00:00
Tasks: feature selection, Multimodal Sentiment Analysis, Sentiment Analysis

This work proposes a novel and simple sequential learning strategy to train models on videos and texts for multimodal sentiment analysis. To estimate sentiment polarities on unseen out-of-distribution data, we introduce a multimodal model that is trained either in a single source domain or multiple source domains using our learning strategy. This strategy starts with learning domain invariant features from text, followed by learning sparse domain-agnostic features from videos, assisted by the selected features learned in text. Our experimental results demonstrate that our model achieves significantly better performance than the state-of-the-art approaches on average in both single-source and multi-source settings. Our feature selection procedure favors the features that are independent to each other and are strongly correlated with their polarity labels. To facilitate research on this topic, the source code of this work will be publicly available upon acceptance.
Diff-INR: Generative Regularization for Electrical Impedance Tomography
Authors: Bowen Tong, Junwu Wang, Dong Liu
Date: 2024-09-06 00:00:00

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique that reconstructs conductivity distributions within a body from boundary measurements. However, EIT reconstruction is hindered by its ill-posed nonlinear inverse problem, which complicates accurate results. To tackle this, we propose Diff-INR, a novel method that combines generative regularization with Implicit Neural Representations (INR) through a diffusion model. Diff-INR introduces geometric priors to guide the reconstruction, effectively addressing the shortcomings of traditional regularization methods. By integrating a pre-trained diffusion regularizer with INR, our approach achieves state-of-the-art reconstruction accuracy in both simulation and experimental data. The method demonstrates robust performance across various mesh densities and hyperparameter settings, highlighting its flexibility and efficiency. This advancement represents a significant improvement in managing the ill-posed nature of EIT. Furthermore, the method's principles are applicable to other imaging modalities facing similar challenges with ill-posed inverse problems.
SCARF: Scalable Continual Learning Framework for Memory-efficient Multiple Neural Radiance Fields
Authors: Yuze Wang, Junyi Wang, Chen Wang, Wantong Duan, Yongtang Bao, Yue Qi
Date: 2024-09-06 00:00:00
Tasks: Continual Learning, Knowledge Distillation, NeRF

This paper introduces a novel continual learning framework for synthesising novel views of multiple scenes, learning multiple 3D scenes incrementally, and updating the network parameters only with the training data of the upcoming new scene. We build on Neural Radiance Fields (NeRF), which uses multi-layer perceptron to model the density and radiance field of a scene as the implicit function. While NeRF and its extensions have shown a powerful capability of rendering photo-realistic novel views in a single 3D scene, managing these growing 3D NeRF assets efficiently is a new scientific problem. Very few works focus on the efficient representation or continuous learning capability of multiple scenes, which is crucial for the practical applications of NeRF. To achieve these goals, our key idea is to represent multiple scenes as the linear combination of a cross-scene weight matrix and a set of scene-specific weight matrices generated from a global parameter generator. Furthermore, we propose an uncertain surface knowledge distillation strategy to transfer the radiance field knowledge of previous scenes to the new model. Representing multiple 3D scenes with such weight matrices significantly reduces memory requirements. At the same time, the uncertain surface distillation strategy greatly overcomes the catastrophic forgetting problem and maintains the photo-realistic rendering quality of previous scenes. Experiments show that the proposed approach achieves state-of-the-art rendering quality of continual learning NeRF on NeRF-Synthetic, LLFF, and TanksAndTemples datasets while preserving extra low storage cost.