pwc-archive/papers-with-abstracts huggingface dataset:
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
Authors: Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei zhang, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang
Date: 2023-11-07 00:00:00
Tasks: Decision Making

Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as "thoughts". An effective thought design should consider three key perspectives: performance, efficiency, and flexibility. However, existing thought can at most exhibit two of these attributes. To address these limitations, we introduce a novel thought prompting approach called "Everything of Thoughts" (XoT) to defy the law of "Penrose triangle of existing thought paradigms. XoT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge into thoughts, thereby enhancing LLMs' capabilities and enabling them to generalize to unseen problems efficiently. Through the utilization of the MCTS-LLM collaborative thought revision framework, this approach autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XoT empowers LLMs to engage in unconstrained thinking, allowing for flexible cognitive mappings for problems with multiple solutions. We evaluate XoT on several challenging multi-solution problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XoT significantly outperforms existing approaches. Notably, XoT can yield multiple solutions with just one LLM call, showcasing its remarkable proficiency in addressing complex problems across diverse domains.
MixtureGrowth: Growing Neural Networks by Recombining Learned Parameters
Authors: Chau Pham, Piotr Teterwak, Soren Nelson, Bryan A. Plummer
Date: 2023-11-07 00:00:00

Most deep neural networks are trained under fixed network architectures and require retraining when the architecture changes. If expanding the network's size is needed, it is necessary to retrain from scratch, which is expensive. To avoid this, one can grow from a small network by adding random weights over time to gradually achieve the target network size. However, this naive approach falls short in practice as it brings too much noise to the growing process. Prior work tackled this issue by leveraging the already learned weights and training data for generating new weights through conducting a computationally expensive analysis step. In this paper, we introduce MixtureGrowth, a new approach to growing networks that circumvents the initialization overhead in prior work. Before growing, each layer in our model is generated with a linear combination of parameter templates. Newly grown layer weights are generated by using a new linear combination of existing templates for a layer. On one hand, these templates are already trained for the task, providing a strong initialization. On the other, the new coefficients provide flexibility for the added layer weights to learn something new. We show that our approach boosts top-1 accuracy over the state-of-the-art by 2-2.5% on CIFAR-100 and ImageNet datasets, while achieving comparable performance with fewer FLOPs to a larger network trained from scratch. Code is available at https://github.com/chaudatascience/mixturegrowth.
Unifying Structure and Language Semantic for Efficient Contrastive Knowledge Graph Completion with Structured Entity Anchors
Authors: Sang-hyun Je, Wontae Choi, Kwangjin Oh
Date: 2023-11-07 00:00:00
Tasks: Contrastive Learning, Knowledge Graph Completion, Language Modelling, Link Prediction

The goal of knowledge graph completion (KGC) is to predict missing links in a KG using trained facts that are already known. In recent, pre-trained language model (PLM) based methods that utilize both textual and structural information are emerging, but their performances lag behind state-of-the-art (SOTA) structure-based methods or some methods lose their inductive inference capabilities in the process of fusing structure embedding to text encoder. In this paper, we propose a novel method to effectively unify structure information and language semantics without losing the power of inductive reasoning. We adopt entity anchors and these anchors and textual description of KG elements are fed together into the PLM-based encoder to learn unified representations. In addition, the proposed method utilizes additional random negative samples which can be reused in the each mini-batch during contrastive learning to learn a generalized entity representations. We verify the effectiveness of the our proposed method through various experiments and analysis. The experimental results on standard benchmark widely used in link prediction task show that the proposed model outperforms existing the SOTA KGC models. Especially, our method show the largest performance improvement on FB15K-237, which is competitive to the SOTA of structure-based KGC methods.
Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation
Authors: Chenwei Tang, Wenqiang Zhou, Dong Wang, Caiyang Yu, Zhenan He, Jizhe Zhou, Shudong Huang, Yi Gao, Jianming Chen, Wentao Feng, Jiancheng Lv
Date: 2023-11-07 00:00:00
Tasks: Few-Shot Learning, Open Set Learning, Zero-Shot Learning

The advent of Industry 4.0 has precipitated the incorporation of Artificial Intelligence (AI) methods within industrial contexts, aiming to realize intelligent manufacturing, operation as well as maintenance, also known as industrial intelligence. However, intricate industrial milieus, particularly those relating to energy exploration and production, frequently encompass data characterized by long-tailed class distribution, sample imbalance, and domain shift. These attributes pose noteworthy challenges to data-centric Deep Learning (DL) techniques, crucial for the realization of industrial intelligence. The present study centers on the intricate and distinctive industrial scenarios of Nuclear Power Generation (NPG), meticulously scrutinizing the application of DL techniques under the constraints of finite data samples. Initially, the paper expounds on potential employment scenarios for AI across the full life-cycle of NPG. Subsequently, we delve into an evaluative exposition of DL's advancement, grounded in the finite sample perspective. This encompasses aspects such as small-sample learning, few-shot learning, zero-shot learning, and open-set recognition, also referring to the unique data characteristics of NPG. The paper then proceeds to present two specific case studies. The first revolves around the automatic recognition of zirconium alloy metallography, while the second pertains to open-set recognition for signal diagnosis of machinery sensors. These cases, spanning the entirety of NPG's life-cycle, are accompanied by constructive outcomes and insightful deliberations. By exploring and applying DL methodologies within the constraints of finite sample availability, this paper not only furnishes a robust technical foundation but also introduces a fresh perspective toward the secure and efficient advancement and exploitation of this advanced energy source.
HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment
Authors: Zhanting Zhou, Kejun Bi, Yuyanzhen Zhong, Chao Tang, Dongfen Li, Shi Ying, Ruijin Wang
Date: 2023-11-07 00:00:00
Tasks: Graph Embedding, Graph Neural Network

The strength of a supply chain is an important measure of a country's or region's technical advancement and overall competitiveness. Establishing supply chain risk assessment models for effective management and mitigation of potential risks has become increasingly crucial. As the number of businesses grows, the important relationships become more complicated and difficult to measure. This emphasizes the need of extracting relevant information from graph data. Previously, academics mostly employed knowledge inference to increase the visibility of links between nodes in the supply chain. However, they have not solved the data hunger problem of single node feature characteristics. We propose a hierarchical knowledge transferable graph neural network-based (HKTGNN) supply chain risk assessment model to address these issues. Our approach is based on current graph embedding methods for assessing corporate investment risk assessment. We embed the supply chain network corresponding to individual goods in the supply chain using the graph embedding module, resulting in a directed homogeneous graph with just product nodes. This reduces the complicated supply chain network into a basic product network. It addresses difficulties using the domain difference knowledge transferable module based on centrality, which is presented by the premise that supply chain feature characteristics may be biased in the actual world. Meanwhile, the feature complement and message passing will alleviate the data hunger problem, which is driven by domain differences. Our model outperforms in experiments on a real-world supply chain dataset. We will give an equation to prove that our comparative experiment is both effective and fair.
AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent Surfaces: Deployment, Prototyping, Experiments, and Opportunities
Authors: Li-Hsiang Shen, Kai-Ten Feng, Ta-Sung Lee, Yuan-Chun Lin, Shih-Cheng Lin, Chia-Chan Chang, Sheng-Fuh Chang
Date: 2023-11-06 00:00:00
Tasks: Multi-agent Reinforcement Learning

The requirement of wireless data demands is increasingly high as the sixth-generation (6G) technology evolves. Reconfigurable intelligent surface (RIS) is promisingly deemed to be one of 6G techniques for extending service coverage, reducing power consumption, and enhancing spectral efficiency. In this article, we have provided some fundamentals of RIS deployment in theory and hardware perspectives as well as utilization of artificial intelligence (AI) and machine learning. We conducted an intelligent deployment of RIS (i-Dris) prototype, including dual-band auto-guided vehicle (AGV) assisted RISs associated with an mmWave base station (BS) and a receiver. The RISs are deployed on the AGV with configured incident/reflection angles. While, both the mmWave BS and receiver are associated with an edge server monitoring downlink packets for obtaining system throughput. We have designed a federated multi-agent reinforcement learning scheme associated with several AGV-RIS agents and sub-agents per AGV-RIS consisting of the deployment of position, height, orientation and elevation angles. The experimental results presented the stationary measurement in different aspects and scenarios. The i-Dris can reach up to 980 Mbps transmission throughput under a bandwidth of 100 MHz with comparably low complexity as well as rapid deployment, which outperforms the other existing works. At last, we highlight some opportunities and future issues in leveraging RIS-empowered wireless communication networks.
Environmental-Impact Based Multi-Agent Reinforcement Learning
Authors: Farinaz Alamiyan-Harandi, Pouria Ramazi
Date: 2023-11-06 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

To promote cooperation and strengthen the individual impact on the collective outcome in social dilemmas, we propose the Environmental-impact Multi-Agent Reinforcement Learning (EMuReL) method where each agent estimates the "environmental impact" of every other agent, that is, the difference in the current environment state compared to the hypothetical environment in the absence of that other agent. Inspired by the Inequity Aversion model, the agent then compares its own reward with those of its fellows multiplied by their environmental impacts. If its reward exceeds the scaled reward of one of its fellows, the agent takes "social responsibility" toward that fellow by reducing its own reward. Therefore, the less influential an agent is in reaching the current state, the more social responsibility is taken by other agents. Experiments in the Cleanup (resp. Harvest) test environment demonstrate that agents trained based on EMuReL learn to cooperate more effectively and obtain $54\%$ ($39\%$) and $20\%$ ($44\%$) more total rewards while preserving the same cooperation levels compared to when they are trained based on the two state-of-the-art reward reshaping methods inequity aversion and social influence.
Kindness in Multi-Agent Reinforcement Learning
Authors: Farinaz Alamiyan-Harandi, Mersad Hassanjani, Pouria Ramazi
Date: 2023-11-06 00:00:00
Tasks: counterfactual, Counterfactual Reasoning, Fairness, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

In human societies, people often incorporate fairness in their decisions and treat reciprocally by being kind to those who act kindly. They evaluate the kindness of others' actions not only by monitoring the outcomes but also by considering the intentions. This behavioral concept can be adapted to train cooperative agents in Multi-Agent Reinforcement Learning (MARL). We propose the KindMARL method, where agents' intentions are measured by counterfactual reasoning over the environmental impact of the actions that were available to the agents. More specifically, the current environment state is compared with the estimation of the current environment state provided that the agent had chosen another action. The difference between each agent's reward, as the outcome of its action, with that of its fellow, multiplied by the intention of the fellow is then taken as the fellow's "kindness". If the result of each reward-comparison confirms the agent's superiority, it perceives the fellow's kindness and reduces its own reward. Experimental results in the Cleanup and Harvest environments show that training based on the KindMARL method enabled the agents to earn 89\% (resp. 37\%) and 44% (resp. 43\%) more total rewards than training based on the Inequity Aversion and Social Influence methods. The effectiveness of KindMARL is further supported by experiments in a traffic light control problem.
Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition
Authors: Ahmad Esmaeili, Zahra Ghorrati, Eric T. Matson
Date: 2023-11-06 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

The rapid growth of wearable sensor technologies holds substantial promise for the field of personalized and context-aware Human Activity Recognition. Given the inherently decentralized nature of data sources within this domain, the utilization of multi-agent systems with their inherent decentralization capabilities presents an opportunity to facilitate the development of scalable, adaptable, and privacy-conscious methodologies. This paper introduces a collaborative distributed learning approach rooted in multi-agent principles, wherein individual users of sensor-equipped devices function as agents within a distributed network, collectively contributing to the comprehensive process of learning and classifying human activities. In this proposed methodology, not only is the privacy of activity monitoring data upheld for each individual, eliminating the need for an external server to oversee the learning process, but the system also exhibits the potential to surmount the limitations of conventional centralized models and adapt to the unique attributes of each user. The proposed approach has been empirically tested on two publicly accessible human activity recognition datasets, specifically PAMAP2 and HARTH, across varying settings. The provided empirical results conclusively highlight the efficacy of inter-individual collaborative learning when contrasted with centralized configurations, both in terms of local and global generalization.
Can LLMs Follow Simple Rules?
Authors: Norman Mu, Sarah Chen, Zifan Wang, Sizhe Chen, David Karamardian, Lulwa Aljeraisy, Basel Alomair, Dan Hendrycks, David Wagner
Date: 2023-11-06 00:00:00

As Large Language Models (LLMs) are deployed with increasing real-world responsibilities, it is important to be able to specify and constrain the behavior of these systems in a reliable manner. Model developers may wish to set explicit rules for the model, such as "do not generate abusive content", but these may be circumvented by jailbreaking techniques. Existing evaluations of adversarial attacks and defenses on LLMs generally require either expensive manual review or unreliable heuristic checks. To address this issue, we propose Rule-following Language Evaluation Scenarios (RuLES), a programmatic framework for measuring rule-following ability in LLMs. RuLES consists of 14 simple text scenarios in which the model is instructed to obey various rules while interacting with the user. Each scenario has a programmatic evaluation function to determine whether the model has broken any rules in a conversation. Our evaluations of proprietary and open models show that almost all current models struggle to follow scenario rules, even on straightforward test cases. We also demonstrate that simple optimization attacks suffice to significantly increase failure rates on test cases. We conclude by exploring two potential avenues for improvement: test-time steering and supervised fine-tuning.
A Practical Large-Scale Roadside Multi-View Multi-Sensor Spatial Synchronization Framework for Intelligent Transportation Systems
Authors: Yong Li, Zhiguo Zhao, Yunli Chen, Rui Tian
Date: 2023-11-04 00:00:00
Tasks: Camera Calibration

Spatial synchronization in roadside scenarios is essential for integrating data from multiple sensors at different locations. Current methods using cascading spatial transformation (CST) often lead to cumulative errors in large-scale deployments. Manual camera calibration is insufficient and requires extensive manual work, and existing methods are limited to controlled or single-view scenarios. To address these challenges, our research introduces a parallel spatial transformation (PST)-based framework for large-scale, multi-view, multi-sensor scenarios. PST parallelizes sensor coordinate system transformation, reducing cumulative errors. We incorporate deep learning for precise roadside monocular global localization, reducing manual work. Additionally, we use geolocation cues and an optimization algorithm for improved synchronization accuracy. Our framework has been tested in real-world scenarios, outperforming CST-based methods. It significantly enhances large-scale roadside multi-perspective, multi-sensor spatial synchronization, reducing deployment costs.
GENOME: GenerativE Neuro-symbOlic visual reasoning by growing and reusing ModulEs
Authors: Zhenfang Chen, Rui Sun, Wenjun Liu, Yining Hong, Chuang Gan
Date: 2023-11-08 00:00:00
Tasks: Question Answering, Referring Expression, Referring Expression Comprehension, Visual Question Answering, Visual Reasoning

Recent works have shown that Large Language Models (LLMs) could empower traditional neuro-symbolic models via programming capabilities to translate language into module descriptions, thus achieving strong visual reasoning results while maintaining the model's transparency and efficiency. However, these models usually exhaustively generate the entire code snippet given each new instance of a task, which is extremely ineffective. We propose generative neuro-symbolic visual reasoning by growing and reusing modules. Specifically, our model consists of three unique stages, module initialization, module generation, and module execution. First, given a vision-language task, we adopt LLMs to examine whether we could reuse and grow over established modules to handle this new task. If not, we initialize a new module needed by the task and specify the inputs and outputs of this new module. After that, the new module is created by querying LLMs to generate corresponding code snippets that match the requirements. In order to get a better sense of the new module's ability, we treat few-shot training examples as test cases to see if our new module could pass these cases. If yes, the new module is added to the module library for future reuse. Finally, we evaluate the performance of our model on the testing set by executing the parsed programs with the newly made visual modules to get the results. We find the proposed model possesses several advantages. First, it performs competitively on standard tasks like visual question answering and referring expression comprehension; Second, the modules learned from one task can be seamlessly transferred to new tasks; Last but not least, it is able to adapt to new visual reasoning tasks by observing a few training examples and reusing modules.
Are foundation models efficient for medical image segmentation?
Authors: Danielle Ferreira, Rima Arnaout
Date: 2023-11-08 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Self-Supervised Learning, Semantic Segmentation

Foundation models are experiencing a surge in popularity. The Segment Anything model (SAM) asserts an ability to segment a wide spectrum of objects but required supervised training at unprecedented scale. We compared SAM's performance (against clinical ground truth) and resources (labeling time, compute) to a modality-specific, label-free self-supervised learning (SSL) method on 25 measurements for 100 cardiac ultrasounds. SAM performed poorly and required significantly more labeling and computing resources, demonstrating worse efficiency than SSL.
Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box Reconstruction
Authors: Zacharias Anastasakis, Dimitrios Mallis, Markos Diomataris, George Alexandridis, Stefanos Kollias, Vassilis Pitsikalis
Date: 2023-11-08 00:00:00
Tasks: Predicate Detection, Relationship Detection, Representation Learning, Self-Supervised Learning, Visual Relationship Detection

We present a novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD). Motivated by the effectiveness of Masked Image Modeling (MIM), we propose Masked Bounding Box Reconstruction (MBBR), a variation of MIM where a percentage of the entities/objects within a scene are masked and subsequently reconstructed based on the unmasked objects. The core idea is that, through object-level masked modeling, the network learns context-aware representations that capture the interaction of objects within a scene and thus are highly predictive of visual object relationships. We extensively evaluate learned representations, both qualitatively and quantitatively, in a few-shot setting and demonstrate the efficacy of MBBR for learning robust visual representations, particularly tailored for VRD. The proposed method is able to surpass state-of-the-art VRD methods on the Predicate Detection (PredDet) evaluation setting, using only a few annotated samples. We make our code available at https://github.com/deeplab-ai/SelfSupervisedVRD.