pwc-archive/papers-with-abstracts huggingface dataset:
Multi-View Fusion Neural Network for Traffic Demand Prediction
Authors: Dongran Zhang, Jun Li
Date: 2024-12-24 00:00:00
Tasks: Prediction

The extraction of spatial-temporal features is a crucial research in transportation studies, and current studies typically use a unified temporal modeling mechanism and fixed spatial graph for this purpose. However, the fixed spatial graph restricts the extraction of spatial features for similar but not directly connected nodes, while the unified temporal modeling mechanism overlooks the heterogeneity of temporal variation of different nodes. To address these challenges, a multi-view fusion neural network (MVFN) approach is proposed. In this approach, spatial local features are extracted through the use of a graph convolutional network (GCN), and spatial global features are extracted using a cosine re-weighting linear attention mechanism (CLA). The GCN and CLA are combined to create a graph-cosine module (GCM) for the extraction of overall spatial features. Additionally, the multi-channel separable temporal convolutional network (MSTCN) makes use of a multi-channel temporal convolutional network (MTCN) at each layer to extract unified temporal features, and a separable temporal convolutional network (STCN) to extract independent temporal features. Finally, the spatial-temporal feature data is input into the prediction layer to obtain the final result. The model has been validated on two traffic demand datasets and achieved the best prediction accuracy.
Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network
Authors: Dongran Zhang, Jiangnan Yan, Kemal Polat, Adi Alhudhaif, Jun Li
Date: 2024-12-24 00:00:00
Tasks: Feature Correlation, Prediction

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spatial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance.
Generative Landmarks Guided Eyeglasses Removal 3D Face Reconstruction
Authors: Dapeng Zhao, Yue Qi
Date: 2024-12-25 00:00:00
Tasks: 3D Face Reconstruction, Face Parsing, Face Reconstruction

Single-view 3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the input is unobstructed faces which makes their method not suitable for in-the-wild conditions. We present a method for performing a 3D face that removes eyeglasses from a single image. Existing facial reconstruction methods fail to remove eyeglasses automatically for generating a photo-realistic 3D face "in-the-wild".The innovation of our method lies in a process for identifying the eyeglasses area robustly and remove it intelligently. In this work, we estimate the 2D face structure of the reasonable position of the eyeglasses area, which is used for the construction of 3D texture. An excellent anti-eyeglasses face reconstruction method should ensure the authenticity of the output, including the topological structure between the eyes, nose, and mouth. We achieve this via a deep learning architecture that performs direct regression of a 3DMM representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related face parsing task can be incorporated into the proposed framework and help improve reconstruction quality. We conduct extensive experiments on existing 3D face reconstruction tasks as concrete examples to demonstrate the method's superior regulation ability over existing methods often break down.
3D Face Reconstruction With Geometry Details From a Single Color Image Under Occluded Scenes
Authors: Dapeng Zhao, Yue Qi
Date: 2024-12-25 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction

3D face reconstruction technology aims to generate a face stereo model naturally and realistically. Previous deep face reconstruction approaches are typically designed to generate convincing textures and cannot generalize well to multiple occluded scenarios simultaneously. By introducing bump mapping, we successfully added mid-level details to coarse 3D faces. More innovatively, our method takes into account occlusion scenarios. Thus on top of common 3D face reconstruction approaches, we in this paper propose a unified framework to handle multiple types of obstruction simultaneously (e.g., hair, palms and glasses et al.).Extensive experiments and comparisons demonstrate that our method can generate high-quality reconstruction results with geometry details from captured facial images under occluded scenes.
AI-driven Automation as a Pre-condition for Eudaimonia
Authors: Anastasia Siapka
Date: 2024-10-31 00:00:00
Tasks: Jurisprudence

The debate surrounding the 'future of work' is saturated with alarmist warnings about the loss of work as an intrinsically valuable activity. Instead, the present doctoral research approaches this debate from the perspective of human flourishing (eudaimonia). It articulates a neo-Aristotelian interpretation according to which the prospect of mass AI-driven automation, far from being a threat, is rather desirable insofar as it facilitates humans' flourishing and, subsequently, their engagement in leisure. Drawing on virtue jurisprudence, this research further explores what this desirability may imply for the current legal order.
QCQA: Quality and Capacity-aware grouped Query Attention
Authors: Vinay Joshi, Prashant Laddha, Shambhavi Sinha, Om Ji Omer, Sreenivas Subramoney
Date: 2024-06-08 00:00:00
Tasks: Text Generation

Excessive memory requirements of key and value features (KV-cache) present significant challenges in the autoregressive inference of large language models (LLMs), restricting both the speed and length of text generation. Approaches such as Multi-Query Attention (MQA) and Grouped Query Attention (GQA) mitigate these challenges by grouping query heads and consequently reducing the number of corresponding key and value heads. However, MQA and GQA decrease the KV-cache size requirements at the expense of LLM accuracy (quality of text generation). These methods do not ensure an optimal tradeoff between KV-cache size and text generation quality due to the absence of quality-aware grouping of query heads. To address this issue, we propose Quality and Capacity-Aware Grouped Query Attention (QCQA), which identifies optimal query head groupings using an evolutionary algorithm with a computationally efficient and inexpensive fitness function. We demonstrate that QCQA achieves a significantly better tradeoff between KV-cache capacity and LLM accuracy compared to GQA. For the Llama2 $7\,$B model, QCQA achieves $\mathbf{20}$\% higher accuracy than GQA with similar KV-cache size requirements in the absence of fine-tuning. After fine-tuning both QCQA and GQA, for a similar KV-cache size, QCQA provides $\mathbf{10.55}\,$\% higher accuracy than GQA. Furthermore, QCQA requires $40\,$\% less KV-cache size than GQA to attain similar accuracy. The proposed quality and capacity-aware grouping of query heads can serve as a new paradigm for KV-cache optimization in autoregressive LLM inference.
A novel deep learning approach for facial emotion recognition: application to detecting emotional responses in elderly individuals with Alzheimer’s disease
Authors: Amine Bohi, Yassine El Boudouri, Imad Sfeir
Date: 2024-12-30 00:00:00
Proceeding: Neural Computing and Applications 2024 12
Tasks: Emotion Recognition, Facial Emotion Recognition, Facial Expression Recognition (FER)

Facial expressions are a critical form of nonverbal communication, conveying a wide range of emotions. Recent advancements in artificial intelligence and computer vision have led to the development of deep learning methods, particularly convolutional neural networks, which are highly effective in facial emotion recognition. This paper presents EmoNeXt, an advanced deep learning framework for FER that builds upon a modified ConvNeXt architecture and incorporates several key innovations. EmoNeXt integrates spatial transformer networks to enable the model to focus on the most expressive regions of the face, squeeze-and-excitation blocks to enhance channel dependencies, and a self-attention regularization term that encourages the learning of compact and discriminative feature vectors. Initially evaluated on the FER2013 dataset, EmoNeXt is now further validated on two other widely used benchmark datasets, AffectNet and CK+, to demonstrate its robustness and generalizability across various real-world and posed scenarios. Additionally, we conduct an extensive ablation study to analyze and quantify the contribution of each enhancement, confirming their positive impact on model performance. Finally, this paper explores the application of EmoNeXt in emotion recognition for elderly individuals with Alzheimer’s disease, highlighting the urgent need for accurate emotion recognition to improve patient care. Our results underscore the potential of EmoNeXt as a valuable tool for enhancing emotional communication in healthcare settings, particularly for patients with neurodegenerative disorders.
Toward a Generalizable Machine-Learned Potential for Metal–Organic Frameworks
Authors: Yifei YueSaad Aldin MohamedN. Duane Loh*Jianwen Jiang*
Date: 2024-12-31 00:00:00
Proceeding: ACS Nano 2024 12

Machine-learned potentials (MLPs) have transformed the field of molecular simulations by scaling “quantum-accurate” potentials to linear time complexity. While they provide more accurate reproduction of physical properties as compared to empirical force fields, it is still computationally costly to generate their training data sets from ab initio calculations. Despite the emergence of foundational or general MLPs for organic molecules and dense materials, it is unexplored if one general MLP can be effectively developed for a wide variety of nanoporous metal–organic frameworks (MOFs) with different chemical moieties and geometric properties. Herein, by leveraging upon data-efficient equivariant MLPs, we demonstrate the possibility of developing a general MLP for nearly 3000 Zn-based MOFs. After curating a training data set comprising augmented MOF structures generated from density functional theory optimization, we validate the reliability of the general MLP in predicting accurate forces and energies when evaluated on a test set with chemically distinct MOF structures. Despite incurring slightly higher errors on structures containing rare chemical moieties, the general MLP can reliably reproduce physical (e.g., vibrational, thermodynamic, and mechanical) properties for a large sample of Zn-based MOFs. Crucially, by developing one MLP for many MOFs, the computational cost of high-throughput screening is potentially reduced by a few orders of magnitude. This enables us to predict quantum-accurate properties for notable Zn-MOFs that were previously uninvestigated via expensive theoretical calculations. To facilitate computational discovery among other families of complex chemical structures, we provide our data set and codes in the public Zenodo repository.
Action Recognition for Privacy-Preserving Ambient Assisted Living
Authors: Vincent Gbouna Zakka, Zhuangzhuang Dai, Luis J. Manso
Date: 2024-08-15 00:00:00
Proceeding: International Conference on AI in Healthcare 2024 8
Tasks: Action Recognition, Activity Recognition, Data Augmentation, Privacy Preserving, Skeleton Based Action Recognition

The care challenges posed by an increasing elderly population have made ambient assisted living a significant research focus. Computer vision-based technologies can monitor older adults’ daily activities in their homes, providing insights into their health and prolonging their capacity to live independently. However, despite the benefits of these technologies, their widespread adoption has been hampered due to privacy concerns. These concerns frequently stem from the need to stream user data to cloud servers for computation, posing a risk to user privacy. This study proposes a privacy-preserving method for activity recognition that enhances the accuracy of activity recognition locally, eliminating the need to stream user data to the cloud. The paper’s contributions are twofold: a Temporal Decoupling Graph Depthwise Separable Convolution Network (TD-GDSCN) to address the challenges of real-time performance and a data augmentation technique to prevent accuracy degradation in real-world environmental conditions. The experimental results show that the TD-GDSCN and data augmentation techniques outperform existing methods in addressing real-time performance and degradation challenges on the NTU-RGB+D 60 and NW-UCLA datasets.
New frequency stability assessment based on contribution rates of wind power plants
Authors: SungHoon Lim a, KangWon Seo b, Jung-Wook Park b, *, Kwang Y. Lee c
Date: 2024-05-24 00:00:00
Proceeding: Electrical Power and Energy Systems 2024 5

In future power systems, various generation units are expected to contribute to frequency stability. Depending on
their characteristics, these units will support the frequency stability in diverse ways. Traditionally, power systems have relied on synchronous generators (SGs), ensuring sufficient inertia and providing ancillary services for
frequency stability. However, with the integration of multiple wind power plants (WPPs), which have substantially different characteristics, many countries are restructuring their ancillary service markets to accommodate WPPs.
This study proposes a novel frequency stability contribution rate index to analyze and compare the contribution of SGs and WPPs to frequency stability. To do so, this paper first analyzes the factors from SGs and WPPs,
such as inertia, power reserves, resource conditions, and available frequency responses, and compares their roles
in supporting frequency stability. The proposed index is then calculated using a multiple linear regression model
that incorporates these factors. Therefore, this index provides a comprehensive evaluation of SGs and WPPs in
terms of their contribution to frequency stability, offering valuable insights into their roles in supporting three
key frequency stability indicators, the rate of change of frequency, frequency nadir, and settling frequency. The
effectiveness of the proposed index is verified through case studies on the IEEE 39-bus system. In all cases, the
coefficient of determination exceeds 95%, indicating a highly predictive regression model. Furthermore, as the
penetration level of WPPs rises from 20% to 60%, the contribution weight of factors related to WPPs increases
while that of SGs decreases.
EDG-Based Question Decomposition for Complex Question Answering over Knowledge Bases
Authors: Xixin Hu, Yiheng Shu, Xiang Huang, Yuzhong Qu
Date: 2021-10-24 00:00:00
Proceeding: ISWC 2021 10
Tasks: Knowledge Base Question Answering, Question Answering

Knowledge base question answering (KBQA) aims at automatically answering factoid questions over knowledge bases (KBs). For complex questions that require multiple KB relations or constraints, KBQA faces many challenges including question understanding, component linking (e.g., entity, relation, and type linking), and query composition. In this paper, we propose a novel graph structure called Entity Description Graph (EDG) to represent the structure of complex questions, which can help alleviate the above issues. By leveraging the EDG structure of given questions, we implement a QA system over DBpedia, called EDGQA. Extensive experiments demonstrate that EDGQA outperforms state-of-the-art results on both LC-QuAD and QALD-9, and that EDG-based decomposition is a feasible way for complex question answering over KBs.
FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting
Authors: Ben-Ao Dai, Nengchao Lyu, Yongchao Miao
Date: 2025-01-01 00:00:00
Tasks: Prediction, Traffic Prediction

Accurate traffic flow prediction heavily relies on the spatio-temporal correlation of traffic flow data. Most current studies separately capture correlations in spatial and temporal dimensions, making it difficult to capture complex spatio-temporal heterogeneity, and often at the expense of increasing model complexity to improve prediction accuracy. Although there have been groundbreaking attempts in the field of spatio-temporal synchronous modeling, significant limitations remain in terms of performance and complexity control.This study proposes a quicker and more effective spatio-temporal synchronous traffic flow forecast model to address these issues.
KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model
Authors: Xinshuo Hu, Zifei Shan, Xinping Zhao, Zetian Sun, Zhenyu Liu, Dongfang Li, Shaolin Ye, Xinyuan Wei, Qian Chen, Baotian Hu, Haofen Wang, Jun Yu, Min Zhang
Date: 2025-01-02 00:00:00
Tasks: MTEB Benchmark, Retrieval-augmented Generation

As retrieval-augmented generation prevails in large language models, embedding models are becoming increasingly crucial. Despite the growing number of general embedding models, prior work often overlooks the critical role of training data quality. In this work, we introduce KaLM-Embedding, a general multilingual embedding model that leverages a large quantity of cleaner, more diverse, and domain-specific training data. Our model has been trained with key techniques proven to enhance performance: (1) persona-based synthetic data to create diversified examples distilled from LLMs, (2) ranking consistency filtering to remove less informative samples, and (3) semi-homogeneous task batch sampling to improve training efficacy. Departing from traditional BERT-like architectures, we adopt Qwen2-0.5B as the pre-trained model, facilitating the adaptation of auto-regressive language models for general embedding tasks. Extensive evaluations of the MTEB benchmark across multiple languages show that our model outperforms others of comparable size, setting a new standard for multilingual embedding models with <1B parameters.
BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference
Authors: Wonsuk Jang, Thierry Tambe
Date: 2025-01-02 00:00:00
Tasks: Quantization

The rapidly increasing size of large language models (LLMs) presents significant challenges in memory usage and computational costs. Quantizing both weights and activations can address these issues, with hardware-supported fine-grained scaling emerging as a promising solution to mitigate outliers. However, existing methods struggle to capture nuanced block data distributions. We propose BlockDialect, a block-wise fine-grained mixed format technique that assigns a per-block optimal number format from a formatbook for better data representation. Additionally, we introduce DialectFP4, a formatbook of FP4 variants (akin to dialects) that adapt to diverse data distributions. To leverage this efficiently, we propose a two-stage approach for online DialectFP4 activation quantization. Importantly, DialectFP4 ensures energy efficiency by selecting representable values as scaled integers compatible with low-precision integer arithmetic. BlockDialect achieves 10.78% (7.48%) accuracy gain on the LLaMA3-8B (LLaMA2-7B) model compared to MXFP4 format with lower bit usage per data, while being only 5.45% (2.69%) below full precision even when quantizing full-path matrix multiplication. Focusing on how to represent over how to scale, our work presents a promising path for energy-efficient LLM inference.
VoxVietnam: a Large-Scale Multi-Genre Dataset for Vietnamese Speaker Recognition
Authors: Hoang Long Vu, Phuong Tuan Dat, Pham Thao Nhi, Nguyen Song Hao, Nguyen Thi Thu Trang
Date: 2024-12-31 00:00:00
Tasks: Diversity, Speaker Recognition

Recent research in speaker recognition aims to address vulnerabilities due to variations between enrolment and test utterances, particularly in the multi-genre phenomenon where the utterances are in different speech genres. Previous resources for Vietnamese speaker recognition are either limited in size or do not focus on genre diversity, leaving studies in multi-genre effects unexplored. This paper introduces VoxVietnam, the first multi-genre dataset for Vietnamese speaker recognition with over 187,000 utterances from 1,406 speakers and an automated pipeline to construct a dataset on a large scale from public sources. Our experiments show the challenges posed by the multi-genre phenomenon to models trained on a single-genre dataset, and demonstrate a significant increase in performance upon incorporating the VoxVietnam into the training process. Our experiments are conducted to study the challenges of the multi-genre phenomenon in speaker recognition and the performance gain when the proposed dataset is used for multi-genre training.
Population Aware Diffusion for Time Series Generation
Authors: Yang Li, Han Meng, Zhenyu Bi, Ingolv T. Urnes, Haipeng Chen
Date: 2025-01-01 00:00:00
Tasks: Time Series, Time Series Generation

Diffusion models have shown promising ability in generating high-quality time series (TS) data. Despite the initial success, existing works mostly focus on the authenticity of data at the individual level, but pay less attention to preserving the population-level properties on the entire dataset. Such population-level properties include value distributions for each dimension and distributions of certain functional dependencies (e.g., cross-correlation, CC) between different dimensions. For instance, when generating house energy consumption TS data, the value distributions of the outside temperature and the kitchen temperature should be preserved, as well as the distribution of CC between them. Preserving such TS population-level properties is critical in maintaining the statistical insights of the datasets, mitigating model bias, and augmenting downstream tasks like TS prediction. Yet, it is often overlooked by existing models. Hence, data generated by existing models often bear distribution shifts from the original data. We propose Population-aware Diffusion for Time Series (PaD-TS), a new TS generation model that better preserves the population-level properties. The key novelties of PaD-TS include 1) a new training method explicitly incorporating TS population-level property preservation, and 2) a new dual-channel encoder model architecture that better captures the TS data structure. Empirical results in major benchmark datasets show that PaD-TS can improve the average CC distribution shift score between real and synthetic data by 5.9x while maintaining a performance comparable to state-of-the-art models on individual-level authenticity.