bluuebunny/crossref_metadata_2025 huggingface dataset:
‘With/for Plants’
Authors: Brandstetter Gabriele
Published: October 2024

This article examines the intricate interplay between humans and plants within the context of contemporary dialogues surrounding the climate crisis and the evolving dynamics of human and more-than-human relationships in the Anthropocene. With a particular focus on dance and performance, the article explores how networks of plants manifest in and influence artistic expressions. It scrutinises anthropocentric perspectives embedded in both scholarly discourses and artistic practices related to plant interactions. By delving into the nuanced variations in these relationships, as expressed through the use of prepositions such as ‘about’ plants, ‘as’ plants, ‘with’ plants and ‘for’ plants, the article aims to unveil how dance and performance have grappled with dismantling the hierarchical structures and colonising practices that often define the connections between humans and plants over the course of the twentieth century and up to the present day. The discussion extends to an analysis of whether there exists a potential for degrowth and an ethical alignment encapsulated in collaborative performances, exemplified by Ruth Geiersberger’s ‘For Plants’ (2020). Additionally, the article explores the transformative potential of ‘minor performances’ in reshaping and decolonising hierarchical dynamics in the intricate tapestry of human-plant relationships. Through this examination, the article seeks to contribute to the ongoing discourse on fostering languages of resonance and envisioning a more harmonious coexistence in the face of environmental challenges.
Fatigue Life Improvement by Reducing Welding Residual Stress with Local Heating for Steel Bridge Members
Authors: Hirohata Mikihito , Nakatani Hiroto , Chang Kyong-Ho , Suzuki Toshimitsu , Konishi Hideaki , Tominaga Shusuke
Published: October 2024

This study investigated the possibility of fatigue life improvement by reducing the weld residual stress with local heating for the steel bridge member. A crossing welded part of longitudinal and transverse stiffeners of a steel plate deck was chosen as the target of this study because it is one of the parts prone to fatigue damage in steel bridge structures. To reduce weld residual stresses and improve the fatigue life of this part, the authors applied a local heating method using a high-frequency induction heating (i.e., IH) device. The welded part was heated up to 250 °C for 9 seconds and 550 °C for 30 seconds. This quick and local heating provided the newly generated compressive stress to the welding tensile stress originally existed. Eventually, the tensile residual stress around 200 N/mm2 near the weld toe was reduced to less than –30 N/mm2. The reduction effect of weld residual stress by local heating on the fatigue life was examined through the experimental approach. The locally heated group had higher durability than that of the non-locally heated group in certain stress ranges.
Numerical Simulation to Assess Cutting Tool Wear During Sheet Metal Blanking
Authors: Rizk Jean , Rachik Mohamed , Maillard Andre
Published: October 2024

Sheet metal blanking is a manufacturing process widely used in many industries. It consists in shearing thin sheets metal using two sharp tools called punch and die. During a blanking operation, these cutting tools are subjected to extreme stress, which leads to their progressive wear. Wear of blanking tools is an inevitable phenomenon during the blanking process. It leads to significant press shutdowns and can have a significant impact on the quality of the blanked parts, particularly the quality of the cut edge. Additionally, punch wear noticeably affects punch force, making the punch force/penetration curve a good wear indicator that can be combined with cut edge quality to quantify wear. In this context, this work focuses on the study of the effect of punch wear on the cutting force curve and on the quality of the cut edge with the aim of establishing a correlation between these two indicators and the degree of punch wear. To achieve that, realistic wear profiles of the punch based on wear profile measurements were implemented in a finite element model to predict the force on the punch during the entire punching process (i.e. cutting phase, phase of punch penetration into the die and stripping phase). As high-fidelity predictions are required, particular attention is paid to the sheet metal constitutive model. In this work the sheet metal behavior is described using a J2 plasticity model combined with Modified Mohr-Coulomb (MMC) fracture criterion. The procedure thus developed was used to link the state of the punch wear to the cutting force curve and the shape of the cut edge. These results are intended to enrich a database of physical test measurements for machine learning training purposes.
Truck Load Positions Effect on Dynamic Behavior of Fractured Steel Box Girder Bridge
Authors: Brinissat Marame , Ray Richard Paul , Kuti Rajmund
Published: October 2024

The harsh climate and environmental hazards contribute to the structural damage of steel bridges. Substantial dynamic loading from heavy trucks can worsen existing cracks. This paper investigates the dynamic behavior of a steel box girder bridge, the Szapáry bridge, with a fractured girder subjected to moving truck loads. Initially, a finite element model simulates the seven-span continuous bridge behavior during static load testing. The model also accurately simulated the dynamic load tests performed. A series of hypothetical damage (fractured girder) and dynamic loading scenarios reveal the effects of truck positions on the damaged bridge’s dynamic response. Dynamic displacement induced due to traffic loading helps evaluate a bridge’s structural health. Results of the parametric analysis highlight that several factors, including truck velocity and position, bridge span length, and truck lateral spacing, significantly affect the dynamic vibration of the fractured bridge. The results offer insight into the effectiveness of dynamic response analysis for conditioned-based maintenance and damage detection.
Mechanical Properties of Acid Activated Metakaolin
Authors: Keppert Martin , Pommer Vojtěch , Krejsová Jitka , Gill Lukáš , Breníková Michala , Urbanová Martina , Vejmelková Eva , Koňáková Dana
Published: October 2024

Acid (usually by phosphoric acid) activation of aluminosilicate precursors provides a new group of inorganic materials with promising chemical and thermal stability. The three mixtures in system metakaolin – phosphoric acid were prepared; they differed in Al/P molar ratio (1, 1.5, 2). Compressive and bending strength were determined in the age of 1 week; the 1 day 60 °C curing was applied. The highest compressive strength reached sample with Al/P ratio 1.5, what falls within the range found in literature. The porosity of the samples increased with the increasing Al/P ratio, thus there was not any simple relationship between strength and porosity. It implies that Al/P ratio influenced not only porosity and pore size distribution, but also the nature of activated cementing product.
The Effect of Rectangle Shaped Cutouts of Corrugated Paperboard Packages on Compressive Strength of Boxes
Authors: Fehér Lajos , Böröcz Péter
Published: October 2024

Corrugated paperboard boxes are widely used as packaging in logistics processes. Corrugated boxes must have adequate strength to ensure proper protection of the product. In many cases, the product will require cutouts in the side walls of the boxes. These significantly affect the strength behavior of the box which can be determined by measurements and various models. The aim of this study is to determine the compressive strength of corrugated cardboard boxes of different sizes and different rectangle shaped cutouts formed on them. Five different box dimensions and five different cutout rates were investigated. Box compression tests were carried out to obtain the compression force result of the boxes. The results of these measurements were compared with the box compression force estimation formulas. Two types of McKee formula were used for the comparison that are commonly used in industry. The complete and the simplified McKee formulas are the used models in this study. The constant parameters for both McKee formulas was calculated for each cutout rates. For higher cutout ratios the accuracy of both the simplified and the complete McKee formula increases. In three groups at 0%, 4% and 16% cutouts the fitted complete and simplified McKee formulas predict the BCT results with significant differences. It can be observed that neither the complete nor the simplified McKee formula is able to account for changes in compression force due to variations in box size for any cutout category.
Predicting Natural Frequencies of a Cantilever Using Machine Learning
Authors: Horváth Krisztián , Zelei Ambrus
Published: October 2024

In the context of structural analysis and design, natural frequencies play a vital role, and their prediction is essential in machine and vehicle design processes. The simulations related to the modal parameters are computationally intensive for systems with large complexity. This paper demonstrates on an illustrative academic example that natural frequencies can be successfully predicted using ML models. This paper aims to develop a model based on machine learning (ML) to predict a simple cantilever’s natural frequencies based on the physical parameters of the beam. The independent variables X are the geometric parameters including width, length, and thickness, while the dependent variable Y is the natural frequency. The study is framed using a systematic methodology that covers the stages of data collection, ML model selection, model training and validation. The validation process proves the effectiveness of ML as a computationally cheap replacement for traditional methods of prediction. The current research contributes to the investigation of the usage of commercially available ML tools in structural engineering. We report that the ODYSSEE A-Eye software is capable of natural frequency prediction with a varying geometry structure with less than 4% error for an 80-member training set of cantilever beam with various dimensions. Further developments will include considerations of noise, vibration, and harshness (NVH) to enhance system performance and improve user comfort.
Fatigue Performance of Aluminum Alloy as a Biomaterial for Schanz Screws in Intertrochanteric Femoral Fractures: An Investigation Using Finite Element Analysis
Authors: Gök Kadir , Gök Arif
Published: October 2024

This study explores the relation between corrosion behavior and fatigue performance of Aluminum alloy as a biomaterial for intertrochanteric femoral fracture fixation using Schanz screws. Finite element analysis via ANSYS Workbench software revealed a fatigue life of approximately 6 months for the screws, with a safety factor of 1.66. The dynamic fatigue test results indicated favorable fatigue performance of the aluminum alloy. Despite this, biocompatibility and corrosion concerns remain regarding aluminum alloys. Hence, it is suggested to adopt a biomaterial with superior fatigue performance for Schanz screws to ensure fixator safety during patient mobilization. Additionally, proposing a real-time monitoring Digital Twin Model for aluminum alloy-based Schanz screws is recommended. Equipped with sensors, this model enables continuous data collection on corrosion, stress, and temperature, aiding healthcare professionals in early issue detection and predictive maintenance, thus enhancing long-term reliability and safety of orthopedic implant systems.
Application Analysis of a Novel Reduction Method for Spurious Oscillations in 2D Impact Problems
Authors: Serfőző Dániel , Pere Balázs
Published: October 2024

The numerical solution of impact problems are oftentimes problematic due to the presence of spurious oscillations in the resulting functions. Eliminating these oscillations is complicated and requires a damping effect to be exerted in the solution. In the proposed method, viscous damping has been applied with a special damping character. Using this scheme, the numerical solution of 2D impact problems contains significantly less oscillations compared to other widely applied methods. In quantitative terms, an improvement of 2.42% and 9.29% or more can be achieved in the time evolution of the velocity and the pressure, respectively.
Analysis and Measurement of Bending Stiffness of Wound String According to Higher Order Frequencies
Authors: Szalai Peter , Antali Mate
Published: October 2024

Bass strings of piano are manufactured by winding a wire around a core string. At these strings, the bending stiffness is not negligible. This effect modifies the higher harmonics of the string compared to the harmonic spectrum of the ideal string. This inharmonicity is even more significant in upright pianos. The effect has recently received renewed attention in the literature. In the present paper, the authors present a dynamic measurement for determining the bending stiffness of the piano strings. By using the so-called inharmonicity coefficient, the spectrum of the string is predicted, and the deviation from the ideal string is determined.
Calculation of Thermal Stresses of Cast Iron Tubbings Under Fire Effect
Authors: Major Zoltán , Szép János , Lublóy Éva
Published: October 2024

This article builds on our existing knowledge of the heating of cast iron tunnel linings and deals with the structural analysis of tunnel linings under fire exposure. Due to space constraints, we do not address the issue of sizing for earth pressure and surface loads. Since the analysis of thermal stresses due to restrained deformations is insufficient in the available literature, we will try to complement the existing theoretical knowledge with the knowledge provided by the relevant standards for cast iron lining of tunnels. In addition, we will try to add our own individual reflections to the theory where we have identified gaps. The theoretical summary is compiled in such a way that it is easily transferable and applicable to everyday practice. Our finite element analysis shows that the value of the embedding factor has a small effect on the development of the axial constraint stresses in the tunnel direction, while in the ring direction has a significant effect. In all cases, stiffer embedding results in higher stress values. In all cases, the ring direction compressive stresses are lower than the longitudinal stresses due to the deformation of the tunnel ring. There is no literature data available on the value of the compressive stresses, so we have tried to provide some indicative data in tabular form for the profession. The solution adopted and the values proposed are based on the authors’ individual ideas and are not the result of an accepted professional consensus. In all cases where more precise data are required, it is recommended that a more detailed study be carried out. Finite element modelling can provide the necessary support for designers and experts.
Customized 3D-Printed Insoles for Diabetic Foot Care: Finite Element Analysis and Machine Learning Approach
Authors: Lu Zhenghui , Li Xin , Gao Zixiang , Chen Hairong , Zhang Qiaolin , Xu Yining , Fekete Gusztáv , Gu Yaodong
Published: October 2024

Diabetic foot is a common complication in patients with diabetes, which can lead to plantar ulcers and even necessitate amputation. This study aims to utilize finite element analysis to simulate the offloading effects of 3D-printed insoles with various structures on plantar pressure and to explore the use of machine learning in providing optimal plantar pressure offloading solutions for patients with diabetic foot. The results demonstrated that negative Poisson’s ratio structured insoles were more effective in reducing plantar pressure (reducing pressure by an average of 39.2%) than barefoot and conventional structures. This was achieved through a unique lateral contraction deformation, which increased the contact area with the foot. The pressure-reducing effect of insoles may be weight-related, suggesting that heavier patients may require stiffer insoles. However, the machine learning algorithm demonstrated a poor fit (only 60.75%) in the task of recommending suitable insoles. In conclusion, this study demonstrated the significant effect of negative Poisson’s ratio structured insoles in reducing plantar pressure in diabetic patients, providing new ideas for diabetic foot protection. With the development of data analysis technology in the future, the feasibility and application of personalised insole design will be more promising.
Experimental Study on Ground Reaction Force Parameters with Regard to Novice and Recreational Runners
Authors: Jiang Xinyan , Bíró István , Wang Hai-Qiao , Dilna Natalia , Nemoga Karol , Fekete Gusztáv
Published: October 2024

The connection between running experience and running-related injuries is still unclear, and the underlying mechanisms are yet to be fully investigated. Therefore, this study aimed to investigate differences in ground reaction forces (GRFs) between novice runners and recreational runners. 15 novice and 15 recreational runners participated in this study. An independent samples T-test was applied using SPSS 25.0 and SPM1D via Matlab. The results showed that recreational runners exhibited a significantly larger peak vertical impact force and peak medial force than the novice group, while the peak propulsive force was smaller than the novice group. The SPM1D results also showed that recreational runners and novice runners exhibited significant differences in medial-lateral force, anterior-posterior force and vertical force. The differences between the groups may reveal differences in running kinetics, which could be related to superior running performance or ability. Valuable insights may be gained from this study to guide future research on injury risks and performance benefits from running.
Back to the Future: Symbolic Reasoning to Combat Malicious Use of Social Media
Authors: Martinez Maria Vanina
Published: October 2024

As technology widens our possibilities for communication and knowledge discovery, it can also leave us vulnerable to misinformation and toxic, abusive, manipulative content, which can cause substantial harm both individually and collectively. Combating the malicious use of social media implies solving very complex and subjective tasks that require a synergy between automated tools and humans. Existing ML models, trained on vast datasets, excel in many tasks like summarization, translation, and human-like content generation. However, we argue that in order for those tools to be effective, they need to be able to form the types of representations and semantic inferences that humans use and create, have the ability to combine automatically acquired data and insights with the domain-specific expertise of the users, and involve high-level reasoning, all three features in which knowledge-based and symbolic approaches to artificial intelligence seem to be well-fitted for.
Dissecting Scorpion: Ablation Study of an Optimal Classical Planner
Authors: Seipp Jendrik
Published: October 2024

Currently, one of the predominant approaches for optimal classical planning is A* search with heuristics that partition action costs among several abstractions of the input planning task. One example of this approach is the Scorpion planner, which computes saturated cost partitionings over projections and Cartesian abstractions. Scorpion participated in the International Planning Competition 2023 and achieved the second place in the optimal track. It was only outperformed by the Ragnarok portfolio planner, which includes Scorpion as a component. In this invited paper for the ECAI Frontiers in AI series, we present the components of Scorpion and analyze their contributions to the overall performance in an ablation study. As a result, the paper serves as a short introduction to many of the techniques that are vital for state-of-the-art performance in optimal classical planning.
FMViT: A Multiple-Frequency Mixing Vision Transformer
Authors: Tan Wei , Geng Yifeng , Xie Xuansong
Published: October 2024

The transformer model has gained widespread adoption in computer vision tasks in recent times. However, due to the quadratic time and memory complexity of self-attention, which is proportional to the number of input tokens, most existing Vision Transformers (ViTs) encounter challenges in achieving efficient performance in practical industrial deployment scenarios, such as TensorRT and CoreML, where traditional CNNs excel. Although some recent attempts have been made to design CNN-Transformer hybrid architectures to tackle this problem, their overall performance has not met expectations. To tackle these challenges, we propose an efficient hybrid ViT architecture named FMViT. This approach enhances the model’s expressive power by blending high-frequency features and low-frequency features with varying frequencies, enabling it to capture both local and global information effectively. Additionally, we introduce deploy-friendly mechanisms such as Multiple groups of MLP (gMLP) Reparameterization, Lightweight Multi-head Self-Attention (RLMHSA), and Convolutional Fusion Block (CFB) to further improve the model’s performance and reduce computational overhead. Our experiments demonstrate that FMViT surpasses existing CNNs, ViTs, and CNN-Transformer hybrid architectures in terms of latency/accuracy trade-offs for various vision tasks. On the TensorRT platform, FMViT outperforms Resnet101 by 2.5% (83.3% vs. 80.8%) in top-1 accuracy on the ImageNet dataset while maintaining similar inference latency. Moreover, FMViT achieves comparable performance with EfficientNet-B5 but with a 43% improvement in inference speed. On CoreML, FMViT outperforms MobileOne by 2.6% in top-1 accuracy on the ImageNet dataset, with inference latency comparable to MobileOne (78.5% vs. 75.9%).
Convolutional Bypasses Are Better Vision Transformer Adapters
Authors: Jie Shibo , Deng Zhi-Hong , Chen Shixuan , Jin Zhijuan
Published: October 2024

The pretrain-then-finetune paradigm has been widely adopted in computer vision. But as the size of Vision Transformer (ViT) grows exponentially, the full finetuning becomes prohibitive in view of the heavier storage overhead. Motivated by parameter-efficient transfer learning (PETL) on language transformers, recent studies attempt to insert lightweight adaptation modules (e.g., adapter layers or prompt tokens) to pretrained ViT and only finetune these modules while the pretrained weights are frozen. However, these modules were originally proposed to finetune language models and did not take into account the prior knowledge specifically for visual tasks. In this paper, we propose to construct Convolutional Bypasses (Convpass) in ViT as adaptation modules, introducing only a small amount (less than 0.5% of model parameters) of trainable parameters to adapt the large ViT. Different from other PETL methods, Convpass benefits from the hard-coded inductive bias of convolutional layers and thus is more suitable for visual tasks, especially in the low-data regime. Experimental results on VTAB-1K benchmark and few-shot learning datasets show that Convpass outperforms current language-oriented adaptation modules, demonstrating the necessity to tailor vision-oriented adaptation modules for adapting vision models.
MultiCounter: Multiple Action Agnostic Repetition Counting in Untrimmed Videos
Authors: Tang Yin , Luo Wei , Zhang Jinrui , Huang Wei , Jing Ruihai , Zhang Deyu
Published: October 2024

Multi-instance Repetitive Action Counting (MRAC) aims to estimate the number of repetitive actions performed by multiple instances in untrimmed videos, commonly found in human-centric domains like sports and exercise. In this paper, we propose MultiCounter, a fully end-to-end deep learning framework that enables simultaneous detection, tracking, and counting of repetitive actions of multiple human instances. Specifically, MultiCounter incorporates two novel modules: 1) mixed spatiotemporal interaction for efficient context correlation across consecutive frames, and 2) task-specific heads for accurate perception of periodic boundaries and generalization for action-agnostic human instances. We train MultiCounter on a synthetic dataset called MultiRep that is generated from annotated real-world videos. Experiments on the MultiRep dataset validate the fundamental challenge of MRAC tasks and showcase the superiority of our proposed model. Compared to ByteTrack+RepNet, a solution that combines an advanced tracker with a single repetition counter, MultiCounter substantially improves Period-mAP by 41.0%, reduces AvgMAE by 58.6%, and increases AvgOBO 1.48 times. This sets a new benchmark in the field of MRAC. Moreover, MultiCounter runs in real-time on a commodity GPU server and is insensitive to the number of human instances in a video.
Modality-Aware and Shift Mixer for Multi-Modal Brain Tumor Segmentation
Authors: Huang Zhongzhen , Wei Linda , Zhang Shaoting , Zhang Xiaofan
Published: October 2024

Combining images from multi-modalities is beneficial for exploring various information in computer vision, especially in the medical domain. As an essential part of clinical diagnosis, multi-modal brain tumor segmentation presents a set of distinct challenges for accurately delineating both the normal anatomy and the pathologic deviations caused by the tumor. In this paper, we aim to fuse information on different imaging modalities with the medical domain knowledge to segment tumors. We present MASM, a novel Modality Aware and Shift Mixer that integrates intra-modality and inter-modality dependencies of multi-modal images for effective and robust brain tumor segmentation. Specifically, we introduce a Modality-Aware (MA) module according to neuroimaging studies for modeling the specific modality pair relationships at low levels, and a Modality-Shift (MS) module with specific mosaic patterns is developed to explore the complex relationships that are not addressed by the MA module across modalities efficiently. Experimentally, we outperform previous state-of-the-art approaches on the public Brain Tumor Segmentation dataset. Further qualitative experiments demonstrate the effectiveness and robustness of MASM.
CBM: Curriculum by Masking
Authors: Jarcă Andrei , Croitoru Florinel-Alin , Ionescu Radu Tudor
Published: October 2024

We propose Curriculum by Masking (CBM), a novel state-of-the-art curriculum learning strategy that effectively creates an easy-to-hard training schedule via patch (token) masking, offering significant accuracy improvements over the conventional training regime and previous curriculum learning (CL) methods. CBM leverages gradient magnitudes to prioritize the masking of salient image regions via a novel masking algorithm and a novel masking block. Our approach enables controlling sample difficulty via the patch masking ratio, generating an effective easy-to-hard curriculum by gradually introducing harder samples as training progresses. CBM operates with two easily configurable parameters, i.e. the number of patches and the curriculum schedule, making it a versatile curriculum learning approach for object recognition and detection. We conduct experiments with various neural architectures, ranging from convolutional networks to vision transformers, on five benchmark data sets (CIFAR-10, CIFAR-100, ImageNet, Food-101 and PASCAL VOC), to compare CBM with conventional as well as curriculum-based training regimes. Our results reveal the superiority of our strategy compared with the state-of-the-art curriculum learning regimes. We also observe improvements in transfer learning contexts, where CBM surpasses previous work by considerable margins in terms of accuracy. We release our code for free non-commercial use at https://github.com/CroitoruAlin/CBM.
MPT4LM: Multi-Modal Prompt Tuning Makes Pre-Trained Large Language Models Better Vision-Language Learners
Authors: Miao Yongzhu , Tang Jintao , Li Shasha , Wang Ting
Published: October 2024

Pre-trained Large Language Models (LLMs) have demonstrated prominent generalization to various linguistic tasks. However, due to the inherent modality and task discrepancy, parameter-efficient transfer learning for adapting LLMs to vision-language (VL) tasks remains challenging, which may struggle with excessive extra computation and data expenditure for VL pre-training and disconnection between multi-modal representations. This paper concentrates on the parameter-efficient adaptation of LLMs to VL tasks without inflexible multi-modal alignment pre-training on additional image-text pairs. Inspired by Instruction Tuning and the nature of multi-modal representation learning, we propose Multi-modal Prompt Tuning for Language Models (MPT4LM). This method provides text-relevant visual prompts via a plug-and-play Cross-Attention module and integrates them with textual Learnable Instruction as multi-modal prompts into LLMs. We further assemble MPT4LM with the currently prevalent Adapter approach to alleviate the trainable parameter scale and facilitate the collaboration of multi-modal prompts. We evaluate MPT4LM upon two representative LLMs: LLAMA-2 and Flan-T5, over two VL tasks: Visual Question Answering (VQAv2.0, GQA) and Visual Entailment (SNLI-VE). Extensive experimental results reveal that MPT4LM achieves state-of-the-art performance among prompting methods with only fine-tuning about 0.65% of the parameters of backbones, indicating a better trade-off between computation and data overhead and model performance. Our code is available at: https://github.com/YzM1a0/MPT4LM.
SFDFusion: An Efficient Spatial-Frequency Domain Fusion Network for Infrared and Visible Image Fusion
Authors: Hu Kun , Zhang Qingle , Yuan Maoxun , Zhang Yitian
Published: October 2024

Infrared and visible image fusion aims to utilize the complementary information from two modalities to generate fused images with prominent targets and rich texture details. Most existing algorithms only perform pixel-level or feature-level fusion from different modalities in the spatial domain. They usually overlook the information in the frequency domain, and some of them suffer from inefficiency due to excessively complex structures. To tackle these challenges, this paper proposes an efficient Spatial-Frequency Domain Fusion (SFDFusion) network for infrared and visible image fusion. First, we propose a Dual-Modality Refinement Module (DMRM) to extract complementary information. This module extracts useful information from both the infrared and visible modalities in the spatial domain and enhances fine-grained spatial details. Next, to introduce frequency domain information, we construct a Frequency Domain Fusion Module (FDFM) that transforms the spatial domain to the frequency domain through Fast Fourier Transform (FFT) and then integrates frequency domain information. Additionally, we design a frequency domain fusion loss to provide guidance for the fusion process. Extensive experiments on public datasets demonstrate that our method produces fused images with significant advantages in various fusion metrics and visual effects. Furthermore, our method demonstrates high efficiency in image fusion and good performance on downstream detection tasks, thereby satisfying the real-time demands of advanced visual tasks. The code is available at https://github.com/lqz2/SFDFusion.
Crafting Lifelike Avatars: Model Compression and Advanced Rendering Techniques
Authors: Zhang Shengjia
Published: October 2024

In order to integrate digital avatars into people’s lives, efficiently generating complete, realistic, and animatable avatars is crucial. However, increasing parameter counts and model sizes challenge the efficiency of training and deployment on devices. Additionally, traditional graphical rule-based micro-renderers, which simplify real-world photorealistic mechanisms such as illumination and reflections, fail to generate truly photorealistic images. To address these issues, we propose a two-stage model compression optimization architecture. In the first stage, our proposed distillation architecture compresses the model, and in the second stage, our generative adversarial renderer enhances the realism of digital avatars by customizing its inverse version to the student network. Specifically, during the knowledge distillation process, we achieve multi-scale feature fusion by concatenating the output features of RandLA-Net and GCN, combining global and local information to better capture the details and contextual information of the point cloud. We construct assisted supervision, enabling point-level supervision by building the graph topology of the entire point cloud. Furthermore, we propose feeding the extracted point cloud features as latent codes into our well-designed neural renderer to produce more realistic facial images. Experiments demonstrate that our method not only improves network performance but also significantly reduces the parameters and computation compared to existing state-of-the-art methods. Specifically, our method reduces the number of parameters of the teacher model by about 95% and the computation in knowledge distillation by 90%.
High-Quality Human Motion Prediction Using Size Invariant Motion Space
Authors: Zhao Haichuan , Ru Xudong , Du Peng , Liu Shaolong , Liu Na , Wang Xingce , Wu Zhongke
Published: October 2024

3D human motion prediction is a challenging task due to the highly non-linear nature of movements. Existing deep learning-based action prediction methods emphasize the design of sophisticated network architectures to achieve state-of-the-art performance on datasets. However, in real-life scenarios, changes in skeletal size lead to a shift in data distribution, which presents challenges for accurate motion prediction. Additionally, the presence of stretching artifacts in predicted bone sequences significantly impacts the quality of the predictions. To address these issues, we propose a framework that combines geometric encoding with neural networks to achieve size-invariant and high-quality motion prediction without stretching artifacts. We consider the constraint of bone length and construct a motion space using Riemannian manifold theory, which remains unaffected by changes in skeletal size and can fully represent human motion. Furthermore, we propose the trajectory transport square-root velocity function to encode motion sequences into a flattened space. This transformation simplifies the distance calculation and linearizes the optimization problem in non-flattened space. Experiments on the Human 3.6M and CMU MoCap datasets demonstrated that the proposed method has achieved competitive performance without any stretching artifacts and exhibits robustness to changes in skeletal size.
Detecting Objects as Cascade Corners
Authors: Liu Chenglong , Liu Jintao , Wei Haorao , Yang Jinze , Xu Liangyu , Guo Yuchen , Fang Lu
Published: October 2024

The corner-based detection paradigm enjoys the potential to produce high-quality boxes. But the development is constrained by three factors: 1) Hard to match corners. Heuristic corner matching algorithms can lead to incorrect boxes, especially when similar-looking objects co-occur. 2) Poor instance context. Two separate corners preserve few instance semantics, so it is difficult to guarantee getting both two class-specific corners on the same heatmap channel. 3) Unfriendly backbone. The training cost of the hourglass network is high. Accordingly, we build a novel corner-based framework, named Corner2Net. To achieve the corner-matching-free manner, we devise the cascade corner pipeline which progressively predicts the associated corner pair in two steps instead of synchronously searching two independent corners via parallel heads. Corner2Net decouples corner localization and object classification. Both two corners are class-agnostic and the instance-specific bottom-right corner further simplifies its search space. Meanwhile, RoI features with rich semantics are extracted for classification. Popular backbones (e.g., ResNeXt) can be easily connected to Corner2Net. Experimental results on COCO show Corner2Net surpasses all existing corner-based detectors by a large margin in accuracy and speed.
Boundary-Enhanced Instance Segmentation
Authors: Zhang Fangyuan , Pan Tianxiang , Tai Yu-Wing , Wang Bin
Published: October 2024

Despite significant progress in instance segmentation, recent solutions still fall short of boundary accuracy especially for overlapping instances of the same category. In this paper, we propose a novel boundary-enhanced instance segmentation (BEIS) framework that explicitly models the feature relationships across object boundaries for high-quality instance segmentation. Specifically, BEIS generates boundary-enhanced features using both intra-mask and cross-image boundary discrimination learning. The intra-mask boundary discrimination learning (IBDL) employs pixel-level discrimination learning to disentangle pixel representations along boundaries. The cross-image boundary discrimination learning (CBDL) learns a boundary-aware feature bank from training data to further boost the performance. Thus, CBDL can take advantage of boundary relations across images to enhance the quality of segmented boundaries. To focus on hard-to-segment boundaries, we propose an adaptive sampling strategy to automatically construct discriminative pairs in regions with high possibilities of confusion. Extensive experiments show BEIS outperforms on various datasets.
Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule
Authors: Zhao Xuan , Broelemann Klaus , Ruggieri Salvatore , Kasneci Gjergji
Published: October 2024

We introduce an innovative approach to enhancing the empirical risk minimization (ERM) process in model training through a refined reweighting scheme of the training data to enhance fairness. This scheme aims to uphold the sufficiency rule in fairness by ensuring that optimal predictors maintain consistency across diverse sub-groups. We employ a bilevel formulation to address this challenge, wherein we explore sample reweighting strategies. Unlike conventional methods that hinge on model size, our formulation bases generalization complexity on the space of sample weights. We discretize the weights to improve training speed. Empirical validation of our method showcases its effectiveness and robustness, revealing a consistent improvement in the balance between prediction performance and fairness metrics across various experiments. Code is available at https://github.com/zhaoxuan00707/Reweighting_for_sufficiency.
Making Fair Classification via Correlation Alignment
Authors: Yang Jingran , Zhang Lingfeng , Zhang Min
Published: October 2024

Machine learning learns patterns from data to improve the performance of the decision-making systems through computing, and gradually affects people’s lives. However, it shows that in current research machine learning algorithms may reinforce human discrimination, and exacerbate negative impacts on unprivileged groups. To mitigate potential unfairness in machine learning classifiers, we propose a fair classification approach by quantifying the difference in the prediction distribution with the idea of correlation alignment in transfer learning, which improves fairness efficiently by minimizing the second-order statistical distance of the prediction distribution. We evaluate the validity of our approach on four real-world datasets. It demonstrates that our approach significantly mitigates bias w.r.t demographic parity, equality of opportunity, and equalized odds across different groups in a classification setting, and achieves better trade-off between accuracy and fairness than previous work. In addition, our approach can further improve fairness and mitigate the fair conflict problem in debiased networks.
PAGE: Parametric Generative Explainer for Graph Neural Network
Authors: Qiu Yang , Liu Wei , Wang Jun , Li Ruixuan
Published: October 2024

This article introduces PAGE, a parameterized generative interpretive framework. PAGE is capable of providing faithful explanations for any graph neural network without necessitating prior knowledge or internal details. Specifically, we train the autoencoder to generate explanatory substructures by designing appropriate training strategy. Due to the dimensionality reduction of features in the latent space of the autoencoder, it becomes easier to extract causal features leading to the model’s output, which can be easily employed to generate explanations. To accomplish this, we introduce an additional discriminator to capture the causality between latent causal features and the model’s output. By designing appropriate optimization objectives, the well-trained discriminator can be employed to constrain the encoder in generating enhanced causal features. Finally, these features are mapped to substructures of the input graph through the decoder to serve as explanations. Compared to existing methods, PAGE operates at the sample scale rather than nodes or edges, eliminating the need for perturbation or encoding processes as seen in previous methods. Experimental results on both artificially synthesized and real-world datasets demonstrate that our approach not only exhibits the highest faithfulness and accuracy but also significantly outperforms baseline models in terms of efficiency.
Explaining Text Classifiers with Counterfactual Representations
Authors: Lemberger Pirmin , Saillenfest Antoine
Published: October 2024

One well motivated explanation method for classifiers leverages counterfactuals which are hypothetical events identical to real observations in all aspects except for one feature. Constructing such counterfactual poses specific challenges for texts, however, as some attribute values may not necessarily align with plausible real-world events. In this paper we propose a simple method for generating counterfactuals by intervening in the space of text representations which bypasses this limitation. We argue that our interventions are minimally disruptive and that they are theoretically sound as they align with counterfactuals as defined in Pearl’s causal inference framework. To validate our method, we conducted experiments first on a synthetic dataset and then on a realistic dataset of counterfactuals. This allows for a direct comparison between classifier predictions based on ground truth counterfactuals—obtained through explicit text interventions—and our counterfactuals, derived through interventions in the representation space. Eventually, we study a real world scenario where our counterfactuals can be leveraged both for explaining a classifier and for bias mitigation.
On the Cultural Gap in Text-to-Image Generation
Authors: Liu Bingshuai , Wang Longyue , Lyu Chenyang , Zhang Yong , Su Jinsong , Shi Shuming , Tu Zhaopeng
Published: October 2024

One challenge in text-to-image (T2I) generation is the inadvertent reflection of culture gaps present in the training data, which signifies the disparity in generated image quality when the cultural elements of the input text are rarely collected in the training set. Although various T2I models have shown impressive but arbitrary examples, there is no benchmark to systematically evaluate a T2I model’s ability to generate cross-cultural images. To bridge the gap, we propose a Challenging Cross-Cultural (C3) benchmark with comprehensive evaluation criteria, which can assess how well-suited a model is to a target culture. By analyzing the flawed images generated by the Stable Diffusion model on the C3 benchmark, we find that the model often fails to generate certain cultural objects. Accordingly, we propose a novel multi-modal metric that considers object-text alignment to filter the fine-tuning data in the target culture, which is used to fine-tune a T2I model to improve cross-cultural generation. Experimental results show that our multi-modal metric provides stronger data selection performance on the C3 benchmark than existing metrics, in which the object-text alignment is crucial. We release the benchmark, data, code, and generated images to facilitate future research on culturally diverse T2I generation.
Analogical Classifier as a Surrogate for Explanations
Authors: Lim Suryani , Prade Henri , Richard Gilles
Published: October 2024

This paper offers a unified, model-agnostic, analogy-based framework for extracting relevant attributes and building contrastive explanations for a given classification. Analogical reasoning relies on the idea of comparing items by pairs to discern their similarities and differences, based on an observable sample S of data. First, adhering to this principle, we propose a pair-based approach to extract relevant attributes via an analogical relevance index (ARI) from Boolean or nominal datasets. We prove properties establishing the behavior of ARI with respect to available samples S. When comparing ARI against established methods such as χ2, mutual information, Shapley, or Sobol indices, empirical evidence highlights the appropriateness of ARI for synthetic datasets, where the classes are generated using specific Boolean functions. ARI also demonstrates robust performance across a diverse range of functions commonly encountered in real datasets. However, some synthetic datasets created from certain functions pose challenges for all indices. Second, we use analogical classification as a surrogate for explanation provision. Analogical classifiers are rooted on analogical proportions, involving the comparison of two pairs of items (a, b) and (c, d) through statements like “a differs from b as c differs from d”, denoted as a:b::c:d. To explain the classification of an item d, we search for an item c close to d but in a different class, forming a pair (c, d). Then, the analogical classifier extracts pairs (a, b) satisfying a:b::c:d leveraging the relevant features identified earlier. Finally we explain why d belongs to a class distinct from that of c by computing the frequency of pairs (a,b), mirroring the difference between c and d, that lead to the same class change. Standard local methods typically capitalize on examples within the item’s neighborhood for explanation. In contrast, our approach operates beyond the neighborhood, broadening the scope of explanatory power. Experiments on categorical synthetic and real datasets indicate that the proposed framework provides plausible and essentially human-understandable explanations.
DeepDFA: Automata Learning through Neural Probabilistic Relaxations
Authors: Umili Elena , Capobianco Roberto
Published: October 2024

In this work, we introduce DeepDFA, a novel approach to identifying Deterministic Finite Automata (DFAs) from traces, harnessing a differentiable yet discrete model. Inspired by both the probabilistic relaxation of DFAs and Recurrent Neural Networks (RNNs), our model offers interpretability post-training, alongside reduced complexity and enhanced training efficiency compared to traditional RNNs. Moreover, by leveraging gradient-based optimization, our method surpasses combinatorial approaches in both scalability and noise resilience. Validation experiments conducted on target regular languages of varying size and complexity demonstrate that our approach is accurate, fast, and robust to noise in both the input symbols and the output labels of training data, integrating the strengths of both logical grammar induction and deep learning.