pwc-archive/papers-with-abstracts huggingface dataset:
Transformers for Complex Query Answering over Knowledge Hypergraphs
Authors: Hong Ting Tsang, ZiHao Wang, Yangqiu Song
Date: 2025-04-23 00:00:00
Tasks: Complex Query Answering, Knowledge Graphs, Negation

Complex Query Answering (CQA) has been extensively studied in recent years. In order to model data that is closer to real-world distribution, knowledge graphs with different modalities have been introduced. Triple KGs, as the classic KGs composed of entities and relations of arity 2, have limited representation of real-world facts. Real-world data is more sophisticated. While hyper-relational graphs have been introduced, there are limitations in representing relationships of varying arity that contain entities with equal contributions. To address this gap, we sampled new CQA datasets: JF17k-HCQA and M-FB15k-HCQA. Each dataset contains various query types that include logical operations such as projection, negation, conjunction, and disjunction. In order to answer knowledge hypergraph (KHG) existential first-order queries, we propose a two-stage transformer model, the Logical Knowledge Hypergraph Transformer (LKHGT), which consists of a Projection Encoder for atomic projection and a Logical Encoder for complex logical operations. Both encoders are equipped with Type Aware Bias (TAB) for capturing token interactions. Experimental results on CQA datasets show that LKHGT is a state-of-the-art CQA method over KHG and is able to generalize to out-of-distribution query types.
Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control
Authors: Haochen Wang, Zhiwei Shi, Chengxi Zhu, Yafei Qiao, Cheng Zhang, Fan Yang, Pengjie Ren, Lan Lu, Dong Xuan
Date: 2025-04-24 00:00:00
Tasks: Imitation Learning, Reinforcement Learning (RL)

Learning-based methods, such as imitation learning (IL) and reinforcement learning (RL), can produce excel control policies over challenging agile robot tasks, such as sports robot. However, no existing work has harmonized learning-based policy with model-based methods to reduce training complexity and ensure the safety and stability for agile badminton robot control. In this paper, we introduce Hamlet, a novel hybrid control system for agile badminton robots. Specifically, we propose a model-based strategy for chassis locomotion which provides a base for arm policy. We introduce a physics-informed "IL+RL" training framework for learning-based arm policy. In this train framework, a model-based strategy with privileged information is used to guide arm policy training during both IL and RL phases. In addition, we train the critic model during IL phase to alleviate the performance drop issue when transitioning from IL to RL. We present results on our self-engineered badminton robot, achieving 94.5% success rate against the serving machine and 90.7% success rate against human players. Our system can be easily generalized to other agile mobile manipulation tasks such as agile catching and table tennis. Our project website: https://dreamstarring.github.io/HAMLET/.
ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation
Authors: Ruobing Yao, Yifei Zhang, Shuang Song, Yuhua Liu, Neng Gao, Chenyang Tu
Date: 2025-02-12 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation, Sentence

While Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by incorporating external knowledge, they still face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information. We present ParetoRAG, an unsupervised framework that optimizes RAG systems through sentence-level refinement guided by the Pareto principle. By decomposing paragraphs into sentences and dynamically re-weighting core content while preserving contextual coherence, ParetoRAG achieves dual improvements in both retrieval precision and generation quality without requiring additional training or API resources. This framework has been empirically validated across various datasets, LLMs, and retrievers.
Machine learning-aided engineering of hydrolases for PET depolymerization
Authors: Hongyuan Lu, Daniel J. Diaz, Natalie J. Czarnecki, Congzhi Zhu, Wantae Kim, Raghav Shroff, Daniel J. Acosta, Bradley R. Alexander, Hannah O. Cole, Yan Zhang, Nathaniel A. Lynd, Andrew D. Ellington & Hal S. Alper
Date: 2022-04-27 00:00:00
Proceeding: Nature 2022 4

Plastic waste poses an ecological challenge1,2,3 and enzymatic degradation offers one, potentially green and scalable, route for polyesters waste recycling4. Poly(ethylene terephthalate) (PET) accounts for 12% of global solid waste5, and a circular carbon economy for PET is theoretically attainable through rapid enzymatic depolymerization followed by repolymerization or conversion/valorization into other products6,7,8,9,10. Application of PET hydrolases, however, has been hampered by their lack of robustness to pH and temperature ranges, slow reaction rates and inability to directly use untreated postconsumer plastics11. Here, we use a structure-based, machine learning algorithm to engineer a robust and active PET hydrolase. Our mutant and scaffold combination (FAST-PETase: functional, active, stable and tolerant PETase) contains five mutations compared to wild-type PETase (N233K/R224Q/S121E from prediction and D186H/R280A from scaffold) and shows superior PET-hydrolytic activity relative to both wild-type and engineered alternatives12 between 30 and 50 °C and a range of pH levels. We demonstrate that untreated, postconsumer-PET from 51 different thermoformed products can all be almost completely degraded by FAST-PETase in 1 week. FAST-PETase can also depolymerize untreated, amorphous portions of a commercial water bottle and an entire thermally pretreated water bottle at 50 ºC. Finally, we demonstrate a closed-loop PET recycling process by using FAST-PETase and resynthesizing PET from the recovered monomers. Collectively, our results demonstrate a viable route for enzymatic plastic recycling at the industrial scale.
VoTranhOmniLearner - A Self-Evolving Digital Universe for Simulating Consciousness and Societal Dynamics
Authors: Vi Nhat Son
Date: 2025-04-26 00:00:00
Proceeding: Independent publication 2025 4
Tasks: Management

VoTranhOmniLearner is an innovative simulation framework designed to model a dynamic digital universe with thousands of entities, intricate social interactions, and self-reflective processes. By integrating machine learning, database management, and real-time monitoring, it creates a self-evolving system that explores computational consciousness, societal evolution, and knowledge archival. This whitepaper presents the system’s architecture, key features, technical implementation, and potential applications in artificial intelligence, social simulation, and philosophical inquiry.
The economic value of transport infrastructure in the UK: an input output analysis
Authors: Nikolaos Kalyviotis, Christopher D. F. Rogers, Geoffrey J. D. Hewings
Date: 2025-04-18 00:00:00

Transport infrastructure systems operate in, and are shaped by, the specific context in which they are expected to perform and contribute to the system-of-systems that support civilised life; they must strive to be sustainable and resilient. However, transport, one of the economic infrastructures, is viewed narrowly in political circles as a vehicle for economic prosperity: political focus falls on the economic pillar of sustainability. One perennial challenge concerns the inclusion of social and environmental value into such performance judgements. A deep understanding of system interdependencies is essential when comparing input output tables (a top down approach) with cost benefit analysis (CBA, a bottom up approach). CBA works best when there is adequate information to calculate a monetary value for all economic, social, and environmental outcomes, whereas input output tables are most effective when environmental and social value are kept distinct from economic value and where the beneficial economic impacts extend across multiple sectors (CBA is inadequate in this regard). This research uses the World Input Output Database and principal component analysis to develop a model to capture this complex accounting process.In recognising how past interdependencies inform future development, this study s model provides new insights into indirect economic value creation within infrastructure systems.
On Multivariate Financial Time Series Classification
Authors: Grégory Bournassenko
Date: 2025-04-24 00:00:00
Tasks: Classification, Time Series, Time Series Analysis, Time Series Classification

This article investigates the use of Machine Learning and Deep Learning models in multivariate time series analysis within financial markets. It compares small and big data approaches, focusing on their distinct challenges and the benefits of scaling. Traditional methods such as SVMs are contrasted with modern architectures like ConvTimeNet. The results show the importance of using and understanding Big Data in depth in the analysis and prediction of financial time series.
A Doubly Decoupled Network for edge detection
Authors: Yachuan Li, Xavier Soria Poma, Yongke Xi, Guanlin Li, Chaozhi Yang, Qian Xiao, Yun Bai, Zongmin Li
Date: 2025-04-01 00:00:00
Proceeding: Neurocomputing 2025 4
Tasks: Diversity, Edge Detection

Deep learning-based edge detectors are increasingly scaled up to achieve higher performance. However, this growth comes at the cost of higher computational demands and a greater risk of model overfitting. In this paper, a Doubly Decoupled Network (DDN) is proposed to alleviate these problems by decoupling the data and features separately. Firstly, we decouple image-edge pairs and build learnable Gaussian distributions. The images are mapped to learnable Gaussian distributions, and the corresponding edges are mapped to samples of the distributions. More robust features are generated by normalizing the latent variables and sampling from the Gaussian distributions. Secondly, we compress the redundant calculations by decoupling image features. Shallow features need not only high resolution to provide accurate spatial information, but also diversity to provide sufficient cues for deep features. We decouple image features into spatial and semantic features and encode them separately. Shallow features no longer provide cues for semantic features and their diversity can be drastically compressed. The proposed DDN achieves the state-of-the-art accuracy across multiple edge detection benchmarks, while the computational cost is similar to that of VGG-based methods. The code is available at https://github.com/Li-yachuan/DDN.
TriLiteNet: Lightweight Model for Multi-Task Visual Perception
Authors: Quang-Huy Che Duc-Khai Lam
Date: 2025-03-17 00:00:00
Proceeding: IEEE Access 2025 3
Tasks: Autonomous Driving, Computational Efficiency, Drivable Area Detection, Lane Detection, model, Segmentation, Traffic Object Detection, vehicle detection

Efficient perception models are essential for Advanced Driver Assistance Systems (ADAS), as these applications require rapid processing and response to ensure safety and effectiveness in real-world environments. To address the real-time execution needs of such perception models, this study introduces the TriLiteNet model. This model can simultaneously manage multiple tasks related to panoramic driving perception. TriLiteNet is designed to optimize performance while maintaining low computational costs. Experimental results on the BDD100k dataset demonstrate that the model achieves competitive performance across three key tasks: vehicle detection, drivable area segmentation, and lane line segmentation. Specifically, the TriLiteNetbase demonstrated a recall of 85.6% for vehicle detection, a mean Intersection over Union (mIoU) of 92.4% for drivable area segmentation, and an Acc of 82.3% for lane line segmentation with only 2.35M parameters and a computational cost of 7.72 GFLOPs. Our proposed model includes a tiny configuration with just 0.14M parameters, which provides a multi-task solution with minimal computational demand. Evaluated for latency and power consumption on embedded devices, TriLiteNet in both configurations shows low latency and reasonable power during inference. By balancing performance, computational efficiency, and scalability, TriLiteNet offers a practical and deployable solution for real-world autonomous driving applications. Code is available at https://github.com/chequanghuy/TriLiteNet.
Augmented and Programmatically Optimized LLM Prompts Reduce Chemical Hallucinations
Authors: Scott Reed
Date: 2025-04-22 00:00:00
Proceeding: Journal of Chemical Information and Modeling 2025 4

Utilizing large Language models (LLMs) for handling scientific information comes with risk of the outputs not matching expectations, commonly called hallucinations. To fully utilize LLMs in research requires improving their accuracy, avoiding hallucinations, and extending their scope to research topics outside their direct training. There is also a benefit to getting the most accurate information from an LLM at the time of inference without having to create and train custom new models for each application. Here, augmented generation and machine learning-driven prompt optimization are combined to extract performance improvements over base LLM function on a common chemical research task. Specifically, an LLM was used to predict the topological polar surface area (TPSA) of molecules. By using augmented generation and machine learning-optimized prompts, the error in the prediction was reduced to 11.76 root-mean-squared error (RMSE) from 62.34 RMSE with direct calls to the same LLM.
Vision-Language Models Are Not Pragmatically Competent in Referring Expression Generation
Authors: Ziqiao Ma, Jing Ding, Xuejun Zhang, Dezhi Luo, Jiahe Ding, Sihan Xu, Yuchen Huang, Run Peng, Joyce Chai
Date: 2025-04-22 00:00:00
Tasks: Referring Expression, Referring expression generation

Referring Expression Generation (REG) is a core task for evaluating the pragmatic competence of vision-language systems, requiring not only accurate semantic grounding but also adherence to principles of cooperative communication (Grice, 1975). However, current evaluations of vision-language models (VLMs) often overlook the pragmatic dimension, reducing REG to a region-based captioning task and neglecting Gricean maxims. In this work, we revisit REG from a pragmatic perspective, introducing a new dataset (RefOI) of 1.5k images annotated with both written and spoken referring expressions. Through a systematic evaluation of state-of-the-art VLMs, we identify three key failures of pragmatic competence: (1) failure to uniquely identify the referent, (2) inclusion of excessive or irrelevant information, and (3) misalignment with human pragmatic preference, such as the underuse of minimal spatial cues. We also show that standard automatic evaluations fail to capture these pragmatic violations, reinforcing superficial cues rather than genuine referential success. Our findings call for a renewed focus on pragmatically informed models and evaluation frameworks that align with real human communication.
An Interpolation-Based Blind CFO Estimator for Faster-Than-Nyquist Signaling Over LEO Satellite Channel
Authors: Xiaohu Liang; Hehao Niu; Hao Liang; Aijun Liu; Zhixiang Gao; Xin Lin
Date: 2024-09-16 00:00:00
Proceeding: IEEE 2024 9

In this article, an interpolation-based blind carrier frequency-offset (CFO) estimator is proposed for faster-than-Nyquist (FTN) system in low-Earth-orbit satellite channel. According to maximum-likelihood criterion, we can obtain the CFO estimation problem formulation from the joint probability density function of output samples of matched filter. After simplifying, the CFO estimation problem can be equivalently solved by nonlinear least-square approach. Based on this, we have designed the objective function for CFO estimation of FTN. The physical meaning of designed objective function can be seen as the periodogram of the data samples. The peak location of the periodogram of the data samples is corresponding to the CFO estimate of FTN. Then, the objective function is proved to be approximated as a parabolic curve. By maximizing the designed objective function, an interpolation-based blind CFO estimator is proposed without iterated procedure. Finally, simulation results demonstrated that our proposed interpolation-based CFO estimator has lower computation complexity, and outperforms traditional estimators.
PixelWeb: The First Web GUI Dataset with Pixel-Wise Labels
Authors: Qi Yang, Weichen Bi, Haiyang Shen, Yaoqi Guo, Yun Ma
Date: 2025-04-23 00:00:00
Tasks: GUI Element Detection

Graphical User Interface (GUI) datasets are crucial for various downstream tasks. However, GUI datasets often generate annotation information through automatic labeling, which commonly results in inaccurate GUI element BBox annotations, including missing, duplicate, or meaningless BBoxes. These issues can degrade the performance of models trained on these datasets, limiting their effectiveness in real-world applications. Additionally, existing GUI datasets only provide BBox annotations visually, which restricts the development of visually related GUI downstream tasks. To address these issues, we introduce PixelWeb, a large-scale GUI dataset containing over 100,000 annotated web pages. PixelWeb is constructed using a novel automatic annotation approach that integrates visual feature extraction and Document Object Model (DOM) structure analysis through two core modules: channel derivation and layer analysis. Channel derivation ensures accurate localization of GUI elements in cases of occlusion and overlapping elements by extracting BGRA four-channel bitmap annotations. Layer analysis uses the DOM to determine the visibility and stacking order of elements, providing precise BBox annotations. Additionally, PixelWeb includes comprehensive metadata such as element images, contours, and mask annotations. Manual verification by three independent annotators confirms the high quality and accuracy of PixelWeb annotations. Experimental results on GUI element detection tasks show that PixelWeb achieves performance on the mAP95 metric that is 3-7 times better than existing datasets. We believe that PixelWeb has great potential for performance improvement in downstream tasks such as GUI generation and automated user interaction.
A New Fuzzy MARCOS Method for Road Traffic Risk Analysis
Authors: Miomir Stanković, Željko Stević, Dillip Kumar Das, Marko Subotić, Dragan Pamučar
Date: 2020-03-24 00:00:00
Proceeding: Mathematics 2020 3

In this paper, a new fuzzy multi-criteria decision-making model for traffic risk assessment was developed. A part of a main road network of 7.4 km with a total of 38 Sections was analyzed with the aim of determining the degree of risk on them. For that purpose, a fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (fuzzy MARCOS) method was developed. In addition, a new fuzzy linguistic scale quantified into triangular fuzzy numbers (TFNs) was developed. The fuzzy PIvot Pairwise RElative Criteria Importance Assessment—fuzzy PIPRECIA method—was used to determine the criteria weights on the basis of which the road network sections were evaluated. The results clearly show that there is a dominant section with the highest risk for all road participants, which requires corrective actions. In order to validate the results, a comprehensive validity test was created consisting of variations in the significance of model input parameters, testing the influence of dynamic factors—of reverse rank, and applying the fuzzy Simple Additive Weighing (fuzzy SAW) method and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS). The validation test show the stability of the results obtained and the justification for the development of the proposed model.
Wanda++: Pruning Large Language Models via Regional Gradients
Authors: Yifan Yang, Kai Zhen, Bhavana Ganesh, Aram Galstyan, Goeric Huybrechts, Markus Müller, Jonas M. Kübler, Rupak Vignesh Swaminathan, Athanasios Mouchtaris, Sravan Babu Bodapati, Nathan Susanj, Zheng Zhang, Jack FitzGerald, Abhishek Kumar
Date: 2025-03-06 00:00:00
Proceeding: ICLR Workshop on Sparsity in LLMs 2025 3
Tasks: Decoder, GPU, Language Modeling, Language Modelling

Large Language Models (LLMs) pruning seeks to remove unimportant weights for inference speedup with minimal performance impact. However, existing methods often suffer from performance loss without full-model sparsity-aware fine-tuning. This paper presents Wanda++, a novel pruning framework that outperforms the state-of-the-art methods by utilizing decoder-block-level \textbf{regional} gradients. Specifically, Wanda++ improves the pruning score with regional gradients for the first time and proposes an efficient regional optimization method to minimize pruning-induced output discrepancies between the dense and sparse decoder output. Notably, Wanda++ improves perplexity by up to 32\% over Wanda in the language modeling task and generalizes effectively to downstream tasks. Further experiments indicate our proposed method is orthogonal to sparsity-aware fine-tuning, where Wanda++ can be combined with LoRA fine-tuning to achieve a similar perplexity improvement as the Wanda method. The proposed method is lightweight, pruning a 7B LLaMA model in under 10 minutes on a single NVIDIA H100 GPU.
Learning Underwater Active Perception in Simulation
Authors: Alexandre Cardaillac, Donald G. Dansereau
Date: 2025-04-23 00:00:00

When employing underwater vehicles for the autonomous inspection of assets, it is crucial to consider and assess the water conditions. Indeed, they have a significant impact on the visibility, which also affects robotic operations. Turbidity can jeopardise the whole mission as it may prevent correct visual documentation of the inspected structures. Previous works have introduced methods to adapt to turbidity and backscattering, however, they also include manoeuvring and setup constraints. We propose a simple yet efficient approach to enable high-quality image acquisition of assets in a broad range of water conditions. This active perception framework includes a multi-layer perceptron (MLP) trained to predict image quality given a distance to a target and artificial light intensity. We generated a large synthetic dataset including ten water types with different levels of turbidity and backscattering. For this, we modified the modelling software Blender to better account for the underwater light propagation properties. We validated the approach in simulation and showed significant improvements in visual coverage and quality of imagery compared to traditional approaches. The project code is available on our project page at https://roboticimaging.org/Projects/ActiveUW/.