pwc-archive/papers-with-abstracts huggingface dataset:
Cypher4BIM: Releasing the Power of Graph for Building Knowledge Discovery
Authors: Junxiang Zhu, Nicholas Nisbet, Mengtian Yin, Ran Wei, Ioannis Brilakis
Date: 2024-05-25 00:00:00

Graph is considered a promising way for managing building information. A new graphic form of IFC (Industry Foundation Classes) data has just been developed, referred to as IFC-Graph. However, understanding of IFC-Graph is insufficient, especially for information query. This study aims to explore graphic building information query and develop a graph query language tailored for IFC-Graph. A series of tasks were carried out, including a) investigating the structure of IFC data and the main types of information in IFC, b) investigating the graph query language Cypher, and c) developing a set of tailored functional query patterns. The developed language is referred to as Cypher4BIM. Five IFC models were used for validation, and the result shows that Cypher4BIM can query individual instances and complex relations from IFC, such as spatial structure, space boundary, and space accessibility. This study contributes to applications that require effective building information query, such as digital twin.
Guaranteeing Accuracy and Fairness under Fluctuating User Traffic: A Bankruptcy-Inspired Re-ranking Approach
Authors: Xiaopeng Ye, Chen Xu, Jun Xu, Xuyang Xie, Gang Wang, Zhenhua Dong
Date: 2024-05-25 00:00:00
Tasks: Fairness, Recommendation Systems, Re-Ranking

Out of sustainable and economical considerations, two-sided recommendation platforms must satisfy the needs of both users and providers. Previous studies often show that the two sides' needs show different urgency: providers need a relatively long-term exposure demand while users want more short-term and accurate service. However, our empirical study reveals that previous methods for trading off fairness-accuracy often fail to guarantee long-term fairness and short-term accuracy simultaneously in real applications of fluctuating user traffic. Especially, when user traffic is low, the user experience often drops a lot. Our theoretical analysis also confirms that user traffic is a key factor in such a trade-off problem. How to guarantee accuracy and fairness under fluctuating user traffic remains a problem. Inspired by the bankruptcy problem in economics, we propose a novel fairness-aware re-ranking approach named BankFair. Intuitively, BankFair employs the Talmud rule to leverage periods of abundant user traffic to offset periods of user traffic scarcity, ensuring consistent user service at every period while upholding long-term fairness. Specifically, BankFair consists of two modules: (1) employing the Talmud rule to determine the required fairness degree under varying periods of user traffic; and (2) conducting an online re-ranking algorithm based on the fairness degree determined by the Talmud rule. Experiments on two real-world recommendation datasets show that BankFair outperforms all baselines regarding accuracy and provider fairness.
LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation
Authors: Ngoc-Du Tran, Thi-Thao Tran, Quang-Huy Nguyen, Manh-Hung Vu, Van-Truong Pham
Date: 2024-04-04 00:00:00
Tasks: Data Augmentation, Decoder, Image Segmentation, Medical Image Segmentation, Segmentation, Self-Learning, Semantic Segmentation

The emergence of deep learning techniques has advanced the image segmentation task, especially for medical images. Many neural network models have been introduced in the last decade bringing the automated segmentation accuracy close to manual segmentation. However, cutting-edge models like Transformer-based architectures rely on large scale annotated training data, and are generally designed with densely consecutive layers in the encoder, decoder, and skip connections resulting in large number of parameters. Additionally, for better performance, they often be pretrained on a larger data, thus requiring large memory size and increasing resource expenses. In this study, we propose a new lightweight but efficient model, namely LiteNeXt, based on convolutions and mixing modules with simplified decoder, for medical image segmentation. The model is trained from scratch with small amount of parameters (0.71M) and Giga Floating Point Operations Per Second (0.42). To handle boundary fuzzy as well as occlusion or clutter in objects especially in medical image regions, we propose the Marginal Weight Loss that can help effectively determine the marginal boundary between object and background. Additionally, the Self-embedding Representation Parallel technique is proposed as an innovative data augmentation strategy that utilizes the network architecture itself for self-learning augmentation, enhancing feature extraction robustness without external data. Experiments on public datasets including Data Science Bowls, GlaS, ISIC2018, PH2, Sunnybrook, and Lung X-ray data show promising results compared to other state-of-the-art CNN-based and Transformer-based architectures. Our code is released at: https://github.com/tranngocduvnvp/LiteNeXt.
Impact of satellites streaks for observational astronomy: a study on data captured during one year from Luxembourg Greater Region
Authors: Olivier Parisot, Mahmoud Jaziri
Date: 2024-07-09 00:00:00
Proceeding: 13th International Conference on Data Science, Technology and Applications (DATA) 2024 7
Tasks: Astronomy, Explainable artificial intelligence

The visible and significant presence of satellites in the night sky has an impact on astronomy and astrophotography activities for both amateurs and professionals, by perturbing observations sessions with undesired streaks in captured images, and the number of spacecrafts orbiting the Earth is expected to increase steadily in the coming years. In this article, we test an existing method and we propose a dedicated approach based on eXplainable Artificial Intelligence to detect streaks in astronomical data captured between March 2022 and February 2023 with a smart telescope in the Greater Luxembourg Region. To speed up the calculation, we also propose a detection approach based on Generative Adversarial Networks.
Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans
Authors: Rodrigo Marcuzzi, Lucas Nunes, Louis Wiesmann, Ignacio Vizzo, Jens Behley, Cyrill Stachniss
Date: 2021-12-01 00:00:00
Proceeding: IRAL 2021 12
Tasks: 4D Panoptic Segmentation, Autonomous Navigation, Contrastive Learning, Metric Learning, Panoptic Segmentation, Scene Understanding, Segmentation

Scene understanding is critical for autonomous navigation in dynamic environments. Perception tasks in this domain like segmentation and tracking are usually tackled individually. In this paper, we address the problem of 4D panoptic segmentation using LiDAR scans, which requires to assign to each 3D point in a temporal sequence of scans a semantic class and for each object a temporally consistent instance ID. We propose a novel approach that builds on top of an arbitrary single-scan panoptic segmentation network and extends it to the temporal domain by associating instances across time. We propose a contrastive aggregation network that leverages the point-wise features from the panoptic network. It generates an embedding space in which encodings of the same instance at different timesteps lie close together and far from encodings belonging to other instances. The training is inspired by contrastive learning techniques for self-supervised metric learning. Our association module combines appearance and motion cues to associate instances across scans, allowing us to perform temporal perception. We evaluate our proposed method on the SemanticKITTI benchmark and achieve state-of-the-art results even without relying on pose information.
Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR Sequences
Authors: Rodrigo Marcuzzi, Lucas Nunes, Louis Wiesmann, Elias Marks, Jens Behley, Cyrill Stachniss
Date: 2023-09-18 00:00:00
Proceeding: IRAL 2023 9
Tasks: 3D Panoptic Segmentation, 4D Panoptic Segmentation, Navigate, Panoptic Segmentation, Scene Understanding, Segmentation

Scene understanding is crucial for autonomous systems to reliably navigate in the real world. Panoptic segmentation of 3D LiDAR scans allows us to semantically describe a vehicle’s environment by predicting semantic classes for each 3D point and to identify individual instances through different instance IDs. To describe the dynamics of the surroundings, 4D panoptic segmentation further extends this information with temporarily consistent instance IDs to identify the different instances in the scans consistently over whole sequences. Previous approaches for 4D panoptic segmentation rely on post-processing steps and are often not end-to-end trainable. In this paper, we propose a novel approach that can be trained end-to-end and directly predicts a set of non-overlapping masks along with their semantic classes and instance IDs that are consistent over time without any post-processing like clustering or associations between predictions. We extend a mask-based 3D panoptic segmentation model to 4D by reusing queries that decoded instances in previous scans. This way, each query decodes the same instance over time, carries its ID and the tracking is performed implicitly. This enables us to jointly optimize segmentation and tracking and directly supervise for 4D panoptic segmentation.
Do We Really Need Imputation in AutoML Predictive Modeling?
Authors: GEORGE PATERAKIS, STEFANOS FAFALIOS, Paulos Charonyktakis, Vassilis Christophides, Ioannis Tsamardinos
Date: 2024-04-12 00:00:00
Proceeding: TKDD Volume 18, Issue 6 2024 4
Tasks: Automatic Machine Learning Model Selection, AutoML, Binary Classification, feature selection, Imputation, Missing Values

Numerous real-world data contain missing values, while in contrast, most Machine Learning (ML) algorithms assume complete datasets. For this reason, several imputation algorithms have been proposed to predict and fill in the missing values. Given the advances in predictive modeling algorithms tuned in an Automated Machine Learning context (AutoML) setting, a question that naturally arises is to what extent sophisticated imputation algorithms (e.g., Neural Network based) are really needed, or we can obtain a descent performance using simple methods like Mean/Mode (MM). In this article, we experimentally compare six state-of-the-art representatives of different imputation algorithmic families from an AutoML predictive modeling perspective, including a feature selection step and combined algorithm and hyper-parameter selection. We used a commercial AutoML tool for our experiments, in which we included the selected imputation methods. Experiments ran on 25 binary classification real-world incomplete datasets with missing values and 10 binary classification complete datasets in which synthetic missing values are introduced according to different missingness mechanisms, at varying missing frequencies. The main conclusion drawn from our experiments is that the best method on average is the Denoise AutoEncoder on real-world datasets and the MissForest in simulated datasets, followed closely by MM. In addition, binary indicator variables encoding missingness patterns actually improve predictive performance, on average. Last, although there are cases where Neural-Network-based imputation significantly improves predictive performance, this comes at a great computational cost and requires measuring all feature values to impute new samples.
You Only Read Once: Constituency-Oriented Relational Graph Convolutional Network for Multi-Aspect Multi-Sentiment Classification
Authors: Yongqiang Zheng, Xia Li
Date: 2024-03-24 00:00:00
Proceeding: AAAI 2024 3
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis, Sentiment Classification

Most of the existing aspect-based sentiment analysis (ABSA) models only predict the sentiment polarity of a single aspect at a time, focusing primarily on enhancing the representation of this single aspect based on the other contexts or aspects. This one-to-one paradigm ignores the fact that multi-aspect, multi-sentiment sentences contain not only distinct specific descriptions for distinct specific aspects, but also shared global context information for multiple aspects. To fully consider these issues, we propose a one-to-many ABSA framework, called You Only Read Once (YORO), that can simultaneously model representations of all aspects based on their specific descriptions and better fuse their relationships using globally shared contextual information in the sentence. Predicting the sentiment polarity of multiple aspects simultaneously is beneficial to improving the efficacy of calculation and prediction. Extensive experiments are conducted on three public datasets (MAMS, Rest14, and Lap14). Experimental results demonstrate the effectiveness of YORO in handling multi-aspect, multi-sentiment scenarios and highlight the promise of one-to-many ABSA in balancing efficiency and accuracy.
Multimodal Object Detection by Channel Switching and Spatial Attention
Authors: Yue Cao, Junchi Bin, Jozsef Hamari, Erik Blasch, Zheng Liu
Date: 2023-06-18 00:00:00
Proceeding: Conference on Computer Vision and Pattern Recognition (CVPR) 2023 6
Tasks: Multispectral Object Detection, object-detection, Object Detection, Pedestrian Detection

Multimodal object detection has attracted great attention in recent years since the information specific to different modalities can complement each other and effectively improve the accuracy and stability of the detection model. However, compared to processing the inputs from a single modality, fusing information from multiple modalities can significantly increase the computational complexity of the model, thus impairing its efficiency. Therefore the multi-modal fusion module needs to be carefully designed to enhance the performance of the detection model while keeping the computational consumption low. In this paper, we propose a novel lightweight fusion module that can efficiently fuse the inputs from different modalities using channel switching and spatial attention (CSSA). The effectiveness and generalizability of the module are tested using two public multimodal datasets LLVIP and FLIR, both of which comprise paired infrared (IR) and visible (RGB) images. The experiments demonstrate that the proposed CSSA module can substantially improve the accuracy of multimodal object detection without consuming excessive computing resources.
Soft Multipath Information-Based UWB Tracking in Cluttered Scenarios: Preliminaries and Validations
Authors: Chenglong Li, Zukun Lu, Long Huang, Shaojie Ni, Guangfu Sun, Emmeric Tanghe, Wout Joseph
Date: 2024-05-28 00:00:00

In this paper, we investigate ultra-wideband (UWB) localization and tracking in cluttered environments. Instead of mitigating the multipath, we exploit the specular reflections to enhance the localizability and improve the positioning accuracy. With the assistance of the multipath, it is also possible to achieve localization purposes using fewer anchors or when the line-of-sight propagations are blocked. Rather than using single-value distance, angle, or Doppler estimates for the localization, we model the likelihoods of both the line-of-sight and specular multipath components, namely soft multipath information, and propose the multipath-assisted probabilistic UWB tracking algorithm. Experimental results in a cluttered industrial scenario show that the proposed algorithm achieves 46.4 cm and 33.1 cm 90th percentile errors in the cases of 3 and 4 anchors, respectively, which outperforms conventional methods with more than 61.8% improvement given fewer anchors and strong multipath effect.
DuanzAI: Slang-Enhanced LLM with Prompt for Humor Understanding
Authors: Yesian Rohn
Date: 2024-05-23 00:00:00
Tasks: Chatbot

Language's complexity is evident in the rich tapestry of slang expressions, often laden with humor and cultural nuances. This linguistic phenomenon has become increasingly prevalent, especially in digital communication. However, existing AI models, including ChatGPT-3.5, face challenges in comprehending these nuances, particularly in Chinese slang. In this study, we present DuanzAI, an innovative approach enhancing Large Language Models (LLMs) with deep Chinese slang comprehension. Leveraging curated datasets and advanced techniques, DuanzAI bridges the gap between human expression and AI comprehension, enabling contextually relevant responses. Our experiments contrast LLMs' performance with a custom Punchline Entity Recognition (PER) system, integrating phonetic matching and pinyin2hanzi techniques. Applying these insights, we developed ChatDAI, an advanced chatbot and released our code at \url{https://github.com/YesianRohn/DuanzAI}.
Active Use of Latent Constituency Representation in both Humans and Large Language Models
Authors: Wei Liu, Ming Xiang, Nai Ding
Date: 2024-05-28 00:00:00
Tasks: One-Shot Learning, Sentence

Understanding how sentences are internally represented in the human brain, as well as in large language models (LLMs) such as ChatGPT, is a major challenge for cognitive science. Classic linguistic theories propose that the brain represents a sentence by parsing it into hierarchically organized constituents. In contrast, LLMs do not explicitly parse linguistic constituents and their latent representations remains poorly explained. Here, we demonstrate that humans and LLMs construct similar latent representations of hierarchical linguistic constituents by analyzing their behaviors during a novel one-shot learning task, in which they infer which words should be deleted from a sentence. Both humans and LLMs tend to delete a constituent, instead of a nonconstituent word string. In contrast, a naive sequence processing model that has access to word properties and ordinal positions does not show this property. Based on the word deletion behaviors, we can reconstruct the latent constituency tree representation of a sentence for both humans and LLMs. These results demonstrate that a latent tree-structured constituency representation can emerge in both the human brain and LLMs.
MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations
Authors: Zhangyu Wang, Gengchen Mai, Krzysztof Janowicz, Ni Lao
Date: 2024-05-28 00:00:00
Tasks: Clustering, Constrained Clustering

A wide range of (multivariate) temporal (1D) and spatial (2D) data analysis tasks, such as grouping vehicle sensor trajectories, can be formulated as clustering with given metric constraints. Existing metric-constrained clustering algorithms overlook the rich correlation between feature similarity and metric distance, i.e., metric autocorrelation. The model-based variations of these clustering algorithms (e.g. TICC and STICC) achieve SOTA performance, yet suffer from computational instability and complexity by using a metric-constrained Expectation-Maximization procedure. In order to address these two problems, we propose a novel clustering algorithm, MC-GTA (Model-based Clustering via Goodness-of-fit Tests with Autocorrelations). Its objective is only composed of pairwise weighted sums of feature similarity terms (square Wasserstein-2 distance) and metric autocorrelation terms (a novel multivariate generalization of classic semivariogram). We show that MC-GTA is effectively minimizing the total hinge loss for intra-cluster observation pairs not passing goodness-of-fit tests, i.e., statistically not originating from the same distribution. Experiments on 1D/2D synthetic and real-world datasets demonstrate that MC-GTA successfully incorporates metric autocorrelation. It outperforms strong baselines by large margins (up to 14.3% in ARI and 32.1% in NMI) with faster and stabler optimization (>10x speedup).
Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning
Authors: Yixiao Zhang, Yukara Ikemiya, Woosung Choi, Naoki Murata, Marco A. Martínez-Ramírez, Liwei Lin, Gus Xia, Wei-Hsiang Liao, Yuki Mitsufuji, Simon Dixon
Date: 2024-05-28 00:00:00

Recent advances in text-to-music editing, which employ text queries to modify music (e.g.\ by changing its style or adjusting instrumental components), present unique challenges and opportunities for AI-assisted music creation. Previous approaches in this domain have been constrained by the necessity to train specific editing models from scratch, which is both resource-intensive and inefficient; other research uses large language models to predict edited music, resulting in imprecise audio reconstruction. To Combine the strengths and address these limitations, we introduce Instruct-MusicGen, a novel approach that finetunes a pretrained MusicGen model to efficiently follow editing instructions such as adding, removing, or separating stems. Our approach involves a modification of the original MusicGen architecture by incorporating a text fusion module and an audio fusion module, which allow the model to process instruction texts and audio inputs concurrently and yield the desired edited music. Remarkably, Instruct-MusicGen only introduces 8% new parameters to the original MusicGen model and only trains for 5K steps, yet it achieves superior performance across all tasks compared to existing baselines, and demonstrates performance comparable to the models trained for specific tasks. This advancement not only enhances the efficiency of text-to-music editing but also broadens the applicability of music language models in dynamic music production environments.
DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention
Authors: Lianghui Zhu, Zilong Huang, Bencheng Liao, Jun Hao Liew, Hanshu Yan, Jiashi Feng, Xinggang Wang
Date: 2024-05-28 00:00:00
Proceeding: CVPR 2025 1
Tasks: GPU, Mamba

Diffusion models with large-scale pre-training have achieved significant success in the field of visual content generation, particularly exemplified by Diffusion Transformers (DiT). However, DiT models have faced challenges with quadratic complexity efficiency, especially when handling long sequences. In this paper, we aim to incorporate the sub-quadratic modeling capability of Gated Linear Attention (GLA) into the 2D diffusion backbone. Specifically, we introduce Diffusion Gated Linear Attention Transformers (DiG), a simple, adoptable solution with minimal parameter overhead. We offer two variants, i,e, a plain and U-shape architecture, showing superior efficiency and competitive effectiveness. In addition to superior performance to DiT and other sub-quadratic-time diffusion models at $256 \times 256$ resolution, DiG demonstrates greater efficiency than these methods starting from a $512$ resolution. Specifically, DiG-S/2 is $2.5\times$ faster and saves $75.7\%$ GPU memory compared to DiT-S/2 at a $1792$ resolution. Additionally, DiG-XL/2 is $4.2\times$ faster than the Mamba-based model at a $1024$ resolution and $1.8\times$ faster than DiT with FlashAttention-2 at a $2048$ resolution. We will release the code soon. Code is released at https://github.com/hustvl/DiG.
Classifying Overlapping Gaussian Mixtures in High Dimensions: From Optimal Classifiers to Neural Nets
Authors: Khen Cohen, Noam Levi, Yaron Oz
Date: 2024-05-28 00:00:00
Tasks: Binary Classification

We derive closed-form expressions for the Bayes optimal decision boundaries in binary classification of high dimensional overlapping Gaussian mixture model (GMM) data, and show how they depend on the eigenstructure of the class covariances, for particularly interesting structured data. We empirically demonstrate, through experiments on synthetic GMMs inspired by real-world data, that deep neural networks trained for classification, learn predictors which approximate the derived optimal classifiers. We further extend our study to networks trained on authentic data, observing that decision thresholds correlate with the covariance eigenvectors rather than the eigenvalues, mirroring our GMM analysis. This provides theoretical insights regarding neural networks' ability to perform probabilistic inference and distill statistical patterns from intricate distributions.