pwc-archive/papers-with-abstracts huggingface dataset:
A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning
Authors: Ruoyu Sun, Yue Xi, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su
Date: 2025-01-12 00:00:00
Tasks: Deep Reinforcement Learning, Portfolio Optimization

Deep Reinforcement Learning (DRL) has been extensively used to address portfolio optimization problems. The DRL agents acquire knowledge and make decisions through unsupervised interactions with their environment without requiring explicit knowledge of the joint dynamics of portfolio assets. Among these DRL algorithms, the combination of actor-critic algorithms and deep function approximators is the most widely used DRL algorithm. Here, we find that training the DRL agent using the actor-critic algorithm and deep function approximators may lead to scenarios where the improvement in the DRL agent's risk-adjusted profitability is not significant. We propose that such situations primarily arise from the following two problems: sparsity in positive reward and the curse of dimensionality. These limitations prevent DRL agents from comprehensively learning asset price change patterns in the training environment. As a result, the DRL agents cannot explore the dynamic portfolio optimization policy to improve the risk-adjusted profitability in the training process. To address these problems, we propose a novel multi-agent Hierarchical Deep Reinforcement Learning (HDRL) algorithmic framework in this research. Under this framework, the agents work together as a learning system for portfolio optimization. Specifically, by designing an auxiliary agent that works together with the executive agent for optimal policy exploration, the learning system can focus on exploring the policy with higher risk-adjusted return in the action space with positive return and low variance. In this way, we can overcome the issue of the curse of dimensionality and improve the training efficiency in the positive reward sparse environment.
A Pan-cancer Classification Model using Multi-view Feature Selection Method and Ensemble Classifier
Authors: Tareque Mohmud Chowdhury, Farzana Tabassum, Sabrina Islam, Abu Raihan Mostofa Kamal
Date: 2025-01-12 00:00:00
Tasks: Cancer Classification, feature selection

Accurately identifying cancer samples is crucial for precise diagnosis and effective patient treatment. Traditional methods falter with high-dimensional and high feature-to-sample count ratios, which are critical for classifying cancer samples. This study aims to develop a novel feature selection framework specifically for transcriptome data and propose two ensemble classifiers. For feature selection, we partition the transcriptome dataset vertically based on feature types. Then apply the Boruta feature selection process on each of the partitions, combine the results, and apply Boruta again on the combined result. We repeat the process with different parameters of Boruta and prepare the final feature set. Finally, we constructed two ensemble ML models based on LR, SVM and XGBoost classifiers with max voting and averaging probability approach. We used 10-fold cross-validation to ensure robust and reliable classification performance. With 97.11\% accuracy and 0.9996 AUC value, our approach performs better compared to existing state-of-the-art methods to classify 33 types of cancers. A set of 12 types of cancer is traditionally challenging to differentiate between each other due to their similarity in tissue of origin. Our method accurately identifies over 90\% of samples from these 12 types of cancers, which outperforms all known methods presented in existing literature. The gene set enrichment analysis reveals that our framework's selected features have enriched the pathways highly related to cancers. This study develops a feature selection framework to select features highly related to cancer development and leads to identifying different types of cancer samples with higher accuracy.
COMPASS: A Compiler Framework for Resource-Constrained Crossbar-Array Based In-Memory Deep Learning Accelerators
Authors: Jihoon Park, Jeongin Choe, Dohyun Kim, Jae-Joon Kim
Date: 2025-01-12 00:00:00

Recently, crossbar array based in-memory accelerators have been gaining interest due to their high throughput and energy efficiency. While software and compiler support for the in-memory accelerators has also been introduced, they are currently limited to the case where all weights are assumed to be on-chip. This limitation becomes apparent with the significantly increasing network sizes compared to the in-memory footprint. Weight replacement schemes are essential to address this issue. We propose COMPASS, a compiler framework for resource-constrained crossbar-based processing-in-memory (PIM) deep neural network (DNN) accelerators. COMPASS is specially targeted for networks that exceed the capacity of PIM crossbar arrays, necessitating access to external memories. We propose an algorithm to determine the optimal partitioning that divides the layers so that each partition can be accelerated on chip. Our scheme takes into account the data dependence between layers, core utilization, and the number of write instructions to minimize latency, memory accesses, and improve energy efficiency. Simulation results demonstrate that COMPASS can accommodate much more networks using a minimal memory footprint, while improving throughput by 1.78X and providing 1.28X savings in energy-delay product (EDP) over baseline partitioning methods.
Hierarchy-Boosted Funnel Learning for Identifying Semiconductors with Ultralow Lattice Thermal Conductivity
Authors: Mengfan Wu, Shenshen Yan, Jie Ren
Date: 2025-01-12 00:00:00

Data-driven machine learning (ML) has demonstrated tremendous potential in material property predictions. However, the scarcity of materials data with costly property labels in the vast chemical space presents a significant challenge for ML in efficiently predicting properties and uncovering structure-property relationships. Here, we propose a novel hierarchy-boosted funnel learning (HiBoFL) framework, which is successfully applied to identify semiconductors with ultralow lattice thermal conductivity ($\kappa_\mathrm{L}$). By training on only a few hundred materials targeted by unsupervised learning from a pool of hundreds of thousands, we achieve efficient and interpretable supervised predictions of ultralow $\kappa_\mathrm{L}$, thereby circumventing large-scale brute-force \textit{ab initio} calculations without clear objectives. As a result, we provide a list of candidates with ultralow $\kappa_\mathrm{L}$ for potential thermoelectric applications and discover a new factor that significantly influences structural anharmonicity. This HiBoFL framework offers a novel practical pathway for accelerating the discovery of functional materials.
Pareto Set Learning for Multi-Objective Reinforcement Learning
Authors: Erlong Liu, Yu-Chang Wu, Xiaobin Huang, Chengrui Gao, Ren-Jian Wang, Ke Xue, Chao Qian
Date: 2025-01-12 00:00:00
Tasks: Decision Making, Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multi-objective decision-making problems have emerged in numerous real-world scenarios, such as video games, navigation and robotics. Considering the clear advantages of Reinforcement Learning (RL) in optimizing decision-making processes, researchers have delved into the development of Multi-Objective RL (MORL) methods for solving multi-objective decision problems. However, previous methods either cannot obtain the entire Pareto front, or employ only a single policy network for all the preferences over multiple objectives, which may not produce personalized solutions for each preference. To address these limitations, we propose a novel decomposition-based framework for MORL, Pareto Set Learning for MORL (PSL-MORL), that harnesses the generation capability of hypernetwork to produce the parameters of the policy network for each decomposition weight, generating relatively distinct policies for various scalarized subproblems with high efficiency. PSL-MORL is a general framework, which is compatible for any RL algorithm. The theoretical result guarantees the superiority of the model capacity of PSL-MORL and the optimality of the obtained policy network. Through extensive experiments on diverse benchmarks, we demonstrate the effectiveness of PSL-MORL in achieving dense coverage of the Pareto front, significantly outperforming state-of-the-art MORL methods in the hypervolume and sparsity indicators.
MTPareto: A MultiModal Targeted Pareto Framework for Fake News Detection
Authors: Kaiying Yan, Moyang Liu, Yukun Liu, Ruibo Fu, Zhengqi Wen, JianHua Tao, Xuefei Liu, Guanjun Li
Date: 2025-01-12 00:00:00
Tasks: Fake News Detection

Multimodal fake news detection is essential for maintaining the authenticity of Internet multimedia information. Significant differences in form and content of multimodal information lead to intensified optimization conflicts, hindering effective model training as well as reducing the effectiveness of existing fusion methods for bimodal. To address this problem, we propose the MTPareto framework to optimize multimodal fusion, using a Targeted Pareto(TPareto) optimization algorithm for fusion-level-specific objective learning with a certain focus. Based on the designed hierarchical fusion network, the algorithm defines three fusion levels with corresponding losses and implements all-modal-oriented Pareto gradient integration for each. This approach accomplishes superior multimodal fusion by utilizing the information obtained from intermediate fusion to provide positive effects to the entire process. Experiment results on FakeSV and FVC datasets show that the proposed framework outperforms baselines and the TPareto optimization algorithm achieves 2.40% and 1.89% accuracy improvement respectively.
Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
Authors: ZiMing Wang, Changwu Huang, Ke Tang, Xin Yao
Date: 2025-01-12 00:00:00
Tasks: Decision Making, Fairness

Fairness in machine learning (ML) has garnered significant attention in recent years. While existing research has predominantly focused on the distributive fairness of ML models, there has been limited exploration of procedural fairness. This paper proposes a novel method to achieve procedural fairness during the model training phase. The effectiveness of the proposed method is validated through experiments conducted on one synthetic and six real-world datasets. Additionally, this work studies the relationship between procedural fairness and distributive fairness in ML models. On one hand, the impact of dataset bias and the procedural fairness of ML model on its distributive fairness is examined. The results highlight a significant influence of both dataset bias and procedural fairness on distributive fairness. On the other hand, the distinctions between optimizing procedural and distributive fairness metrics are analyzed. Experimental results demonstrate that optimizing procedural fairness metrics mitigates biases introduced or amplified by the decision-making process, thereby ensuring fairness in the decision-making process itself, as well as improving distributive fairness. In contrast, optimizing distributive fairness metrics encourages the ML model's decision-making process to favor disadvantaged groups, counterbalancing the inherent preferences for advantaged groups present in the dataset and ultimately achieving distributive fairness.
DRDT3: Diffusion-Refined Decision Test-Time Training Model
Authors: Xingshuai Huang, Di wu, Benoit Boulet
Date: 2025-01-12 00:00:00
Tasks: D4RL, Offline RL, Reinforcement Learning (RL), Trajectory Modeling

Decision Transformer (DT), a trajectory modeling method, has shown competitive performance compared to traditional offline reinforcement learning (RL) approaches on various classic control tasks. However, it struggles to learn optimal policies from suboptimal, reward-labeled trajectories. In this study, we explore the use of conditional generative modeling to facilitate trajectory stitching given its high-quality data generation ability. Additionally, recent advancements in Recurrent Neural Networks (RNNs) have shown their linear complexity and competitive sequence modeling performance over Transformers. We leverage the Test-Time Training (TTT) layer, an RNN that updates hidden states during testing, to model trajectories in the form of DT. We introduce a unified framework, called Diffusion-Refined Decision TTT (DRDT3), to achieve performance beyond DT models. Specifically, we propose the Decision TTT (DT3) module, which harnesses the sequence modeling strengths of both self-attention and the TTT layer to capture recent contextual information and make coarse action predictions. We further integrate DT3 with the diffusion model using a unified optimization objective. With experiments on multiple tasks of Gym and AntMaze in the D4RL benchmark, our DT3 model without diffusion refinement demonstrates improved performance over standard DT, while DRDT3 further achieves superior results compared to state-of-the-art conventional offline RL and DT-based methods.
Sequential Portfolio Selection under Latent Side Information-Dependence Structure: Optimality and Universal Learning Algorithms
Authors: Duy Khanh Lam
Date: 2025-01-12 00:00:00

This paper investigates the investment problem of constructing an optimal no-short sequential portfolio strategy in a market with a latent dependence structure between asset prices and partly unobservable side information, which is often high-dimensional. The results demonstrate that a dynamic strategy, which forms a portfolio based on perfect knowledge of the dependence structure and full market information over time, may not grow at a higher rate infinitely often than a constant strategy, which remains invariant over time. Specifically, if the market is stationary, implying that the dependence structure is statistically stable, the growth rate of an optimal dynamic strategy, utilizing the maximum capacity of the entire market information, almost surely decays over time into an equilibrium state, asymptotically converging to the growth rate of a constant strategy. Technically, this work reassesses the common belief that a constant strategy only attains the optimal limiting growth rate of dynamic strategies when the market process is identically and independently distributed. By analyzing the dynamic log-optimal portfolio strategy as the optimal benchmark in a stationary market with side information, we show that a random optimal constant strategy almost surely exists, even when a limiting growth rate for the dynamic strategy does not. Consequently, two approaches to learning algorithms for portfolio construction are discussed, demonstrating the safety of removing side information from the learning process while still guaranteeing an asymptotic growth rate comparable to that of the optimal dynamic strategy.
Average Reward Reinforcement Learning for Wireless Radio Resource Management
Authors: Kun Yang, Jing Yang, Cong Shen
Date: 2025-01-12 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we address a crucial but often overlooked issue in applying reinforcement learning (RL) to radio resource management (RRM) in wireless communications: the mismatch between the discounted reward RL formulation and the undiscounted goal of wireless network optimization. To the best of our knowledge, we are the first to systematically investigate this discrepancy, starting with a discussion of the problem formulation followed by simulations that quantify the extent of the gap. To bridge this gap, we introduce the use of average reward RL, a method that aligns more closely with the long-term objectives of RRM. We propose a new method called the Average Reward Off policy Soft Actor Critic (ARO SAC) is an adaptation of the well known Soft Actor Critic algorithm in the average reward framework. This new method achieves significant performance improvement our simulation results demonstrate a 15% gain in the system performance over the traditional discounted reward RL approach, underscoring the potential of average reward RL in enhancing the efficiency and effectiveness of wireless network optimization.
Predicting House Rental Prices in Ghana Using Machine Learning
Authors: Philip Adzanoukpe
Date: 2025-01-08 00:00:00
Tasks: Feature Importance

This study investigates the efficacy of machine learning models for predicting house rental prices in Ghana, addressing the need for accurate and accessible housing market information. Utilising a comprehensive dataset of rental listings, we trained and evaluated various models, including CatBoost, XGBoost, and Random Forest. CatBoost emerged as the best-performing model, achieving an $R^2$ of 0.876, demonstrating its ability to effectively capture complex relationships within the housing market. Feature importance analysis revealed that location-based features, number of bedrooms, bathrooms, and furnishing status are key drivers of rental prices. Our findings provide valuable insights for stakeholders, including real estate professionals, investors, and policymakers, while also highlighting opportunities for future research, such as incorporating temporal data and exploring regional variations.
Generative AI for Cel-Animation: A Survey
Authors: Yunlong Tang, Junjia Guo, Pinxin Liu, Zhiyuan Wang, Hang Hua, Jia-Xing Zhong, Yunzhong Xiao, Chao Huang, Luchuan Song, Susan Liang, Yizhi Song, Liu He, Jing Bi, Mingqian Feng, Xinyang Li, Zeliang Zhang, Chenliang Xu
Date: 2025-01-08 00:00:00
Tasks: Colorization, Layout Design, Survey

Traditional Celluloid (Cel) Animation production pipeline encompasses multiple essential steps, including storyboarding, layout design, keyframe animation, inbetweening, and colorization, which demand substantial manual effort, technical expertise, and significant time investment. These challenges have historically impeded the efficiency and scalability of Cel-Animation production. The rise of generative artificial intelligence (GenAI), encompassing large language models, multimodal models, and diffusion models, offers innovative solutions by automating tasks such as inbetween frame generation, colorization, and storyboard creation. This survey explores how GenAI integration is revolutionizing traditional animation workflows by lowering technical barriers, broadening accessibility for a wider range of creators through tools like AniDoc, ToonCrafter, and AniSora, and enabling artists to focus more on creative expression and artistic innovation. Despite its potential, issues such as maintaining visual consistency, ensuring stylistic coherence, and addressing ethical considerations continue to pose challenges. Furthermore, this paper discusses future directions and explores potential advancements in AI-assisted animation. For further exploration and resources, please visit our GitHub repository: https://github.com/yunlong10/Awesome-AI4Animation
SAR Images Clustering Based on Modified Nonlinear Orthogonal Nonnegative Matrix Factorization (NMF)
Authors: Mahdi Jowkar Dehouei, Soolmaz Khazandi, Yaser Norouzi
Date: 2023-12-11 00:00:00
Proceeding: 31st International Conference on Electrical Engineering (ICEE) 2023 12
Tasks: Clustering

This study presents the results of unsupervised learning (clustering) on Synthetic Aperture Radar (SAR) images. Fast and accurate clustering allows us to perform Automatic Target Recognition (ATR) in SAR images. Data can be automatically clustered based on similar properties. Nonnegative matrix factorization (NMF) is usually applied linearly. We employed a nonlinear NMF with orthogonal properties in this presentation. The outcomes of spectral clustering for subspaces and NMF matrix factorization are equivalent, as demonstrated by mathematical and empirical experiments. The results of kernel-based orthogonal in subspace clustering with updates in this method have been superior to those in other methods. We have been using the stationary target dataset to study our method's accuracy in acquisition and recognition.
CAMs as Shapley Value-based Explainers
Authors: Huaiguang Cai
Date: 2025-01-09 00:00:00

Class Activation Mapping (CAM) methods are widely used to visualize neural network decisions, yet their underlying mechanisms remain incompletely understood. To enhance the understanding of CAM methods and improve their explainability, we introduce the Content Reserved Game-theoretic (CRG) Explainer. This theoretical framework clarifies the theoretical foundations of GradCAM and HiResCAM by modeling the neural network prediction process as a cooperative game. Within this framework, we develop ShapleyCAM, a new method that leverages gradients and the Hessian matrix to provide more precise and theoretically grounded visual explanations. Due to the computational infeasibility of exact Shapley value calculation, ShapleyCAM employs a second-order Taylor expansion of the cooperative game's utility function to derive a closed-form expression. Additionally, we propose the Residual Softmax Target-Class (ReST) utility function to address the limitations of pre-softmax and post-softmax scores. Extensive experiments across 12 popular networks on the ImageNet validation set demonstrate the effectiveness of ShapleyCAM and its variants. Our findings not only advance CAM explainability but also bridge the gap between heuristic-driven CAM methods and compute-intensive Shapley value-based methods. The code is available at \url{https://github.com/caihuaiguang/pytorch-shapley-cam}.
AI Driven Water Segmentation with deep learning models for Enhanced Flood Monitoring
Authors: Sanjida Afrin Mou, Tasfia Noor Chowdhury, Adib Ibn Mannan, Sadia Nourin Mim, Lubana Tarannum, Tasrin Noman, Jamal Uddin Ahamed
Date: 2025-01-14 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Flooding is a major natural hazard causing significant fatalities and economic losses annually, with increasing frequency due to climate change. Rapid and accurate flood detection and monitoring are crucial for mitigating these impacts. This study compares the performance of three deep learning models UNet, ResNet, and DeepLabv3 for pixelwise water segmentation to aid in flood detection, utilizing images from drones, in field observations, and social media. This study involves creating a new dataset that augments wellknown benchmark datasets with flood-specific images, enhancing the robustness of the models. The UNet, ResNet, and DeepLab v3 architectures are tested to determine their effectiveness in various environmental conditions and geographical locations, and the strengths and limitations of each model are also discussed here, providing insights into their applicability in different scenarios by predicting image segmentation masks. This fully automated approach allows these models to isolate flooded areas in images, significantly reducing processing time compared to traditional semi-automated methods. The outcome of this study is to predict segmented masks for each image effected by a flood disaster and the validation accuracy of these models. This methodology facilitates timely and continuous flood monitoring, providing vital data for emergency response teams to reduce loss of life and economic damages. It offers a significant reduction in the time required to generate flood maps, cutting down the manual processing time. Additionally, we present avenues for future research, including the integration of multimodal data sources and the development of robust deep learning architectures tailored specifically for flood detection tasks. Overall, our work contributes to the advancement of flood management strategies through innovative use of deep learning technologies.