bluuebunny/crossref_metadata_2025 huggingface dataset:
Becoming Media
Authors: Schneider Birgit
Published: October 2024

Plants communicate with their fellow species, but also with other species. The communication of humans with plants, in turn, represents an old phantasm that is intended to lead to contact with the seemingly mute creatures. This article revisits the history of plant cognition from a media theory perspective. The article questions the extent to which media and electricity have historically been and are currently leitmotifs for entering into resonance with plants. The focus is on media-historical and science-historical approaches that have conferred to plants the ability to communicate and on current sensor interfaces through which plants are turned into sound in media art. The article argues that, whenever media technology makes plants ‘speak’, what the human listener actually hears is the medium, not the plant, because it is a human construct.
Resonance and Vegetal Citizenship in Aldo Leopold’s A Sand County Almanac
Authors: Bergthaller Hannes
Published: October 2024

The problem of ethical obligations to plants has rarely been raised in Western traditions of thought. However, it is central to Aldo Leopold’s A Sand County Almanac, one of the key texts of modern American environmentalism. For Leopold, the effects of a civilisation on the vegetal base of the trophic pyramid are the ultimate measure of its value, and the principal failure of modern civilisation is that it blinds people to this fact. For humans to become proper citizens of the land community, they must build reciprocal relationships with other members of the community, which can be understood as relationships of resonance (H. Rosa). Crucially, this also includes plants, who are thus recognised as vegetal citizens. Leopold’s descriptions of his own relationships to the native flora, built over the course of a decade of ecological restoration work on his property in Sauk County, Wisconsin, must be viewed in this context.
Dangerous Resonances in Charlotte McConaghy’s Once There Were Wolves
Authors: Keller Claudia
Published: October 2024

Charlotte McConaghy’s novel Once There Were Wolves tells the story of the reintroduction of wolves in the Scottish Highlands, thereby bringing the concept of rewilding – an innovative form of biodiversity conservation – into fiction. This novel shows how, within the paradigm of biodiversity, ‘plant perspectives’ and the search for a language of resonance must be considered in the broader ecological context, alongside the wolves. The article analyses how the novel highlights the importance of resonance through the protagonist Inti Flynn’s special relationship with plants, while also critically reflecting on this concept by depicting a resonance catastrophe – arising from Inti’s overly symbiotic relationship with her sister and the wolves. The conservation effort only succeeds, and the trees begin to grow over the bare hills, when Inti and the initially opposed local sheep farmers come together. Thus, this article argues, the novel demonstrates that contemporary conservation issues require a critical discussion of both the lack and excess of resonance in our relations to nature, fostering a mutual transformation of both ecologists and the local population in their relationships with each other and with nature.
A Novel Approach to Estimate Creep Rupture Life of the Co-Ni-Cr-W-Fe-Mn Superalloy by Multi-Station Compressive Creep Tests
Authors: Xu Zhen , Zhu Qiang
Published: October 2024

Creep rupture life prediction of superalloys is very important to ensure the safety of aerospace and power stations. However, the prediction is challenging due to high cost of time, materials and creep test. Here, we propose a multi-station compressive creep test method to predict and evaluate the long-term creep life efficiently and cheaply. Experiments were demonstrated on Co-based superalloy Co-Ni-Cr-W-Fe-Mn. Multi-station compressive creep tests were performed at 650 °C, 750 °C and 850 °C, tensile creep tests were also carried out under the same conditions. P parameter method is proposed to quantitatively establish the relationship between tensile minimum creep rate and compressive minimum creep rate, and compressive minimum creep rates from multi-station compressive creep tests were used to predict creep rupture life. Results show that predicted creep rupture life is well consistent with test data, and this method shows great potential for the evaluation and assessment of creep properties on high-temperature structural materials faster and cheaper.
Strengthening RC Slabs with CFRP Bars Using the Plastic Limit Method to Control Plastic Deformation
Authors: Sharhan Zahraa Saleem , Cucuzza Raffaele , Domaneschi Marco , Movahedi Rad Majid
Published: October 2024

The objective of this work is to improve the punching strength and control the plastic deformation of two-way reinforced concrete (RC) slabs using carbon fiber reinforced polymer (CFRP) bars. The efficacy of this reinforcement technique was evaluated by constructing four reinforced concrete flat slabs. One specimen was utilized as a reference slab, while the other three specimens were reinforced using the Near Surface Mounted (NSM) CFRP bars approach. The slabs, which had identical dimensions and steel reinforcement, were exposed to patch load, and tested until they reached the point of failure. For evaluating the strength of two-way reinforced concrete (RC) slabs, the Concrete Plastic Damage (CDP) constitutive model was developed and implemented. CFRP bars are inserted into the slab at a depth from the tension face to enhance their strength. The investigation commences with the calibration of a numerical model utilizing data obtained from laboratory experiments. This will be achieved by establishing an advanced analytical method that incorporates the plasticity of concrete damage and the use of CFRP bars, along with a multiplier to determine the plastic limit load. Numerical simulations are employed to investigate shear dynamics by including diverse elements. The results showed that an increase in the ratio of strengthening had a significant effect on shear strength.
Research on the Novel Crack Resistant Concrete Based on Interlayer Water Locking Technology
Authors: Xu Zhonghao , Zhang Feiyue , Shen Tianyou , Tang Zhongmin , Mu Lin , Peng Juwei , Chen Shilin , Li Huangmin , He Bi , Hu Shengwei , Yang Dailiu , Tian Xianzhong
Published: October 2024

Cracks are a common phenomenon in large-volume concrete and are an important issue of concern in the engineering field. To address the so-called problem of “no dam without cracks,” engineering technicians have adopted a series of technical measures, such as mix proportion optimization, high fly ash content, and expansive agents, which have played a certain role in controlling the generation and propagation of cracks. This paper introduces the proposed layered water-locking technology and a new type of excellent anti-cracking material developed using this technology, which maximizes the reduction or even prevention of cracks in large-volume concrete, enhancing the concrete’s own crack resistance.
Short Fatigue Crack Behavior Under Various Level of Mixed-Mode
Authors: Malíková Lucie , Miarka Petr , Seitl Stanislav
Published: October 2024

In this work, results of a numerical parametric analysis are presented in order to compare them with experimentally observed concrete cone failure features. Such a kind of failure is typical for cast-in steel anchors embedded in concrete substrates. Thus, numerical simulations via finite element method have been performed in order to investigate basic fracture parameters for specimens with a short fatigue crack and stress distribution for specimens without any crack. Several parameters were varied and their effect on the stress intensity factors or distribution of stress tensor components around the anchor corner were studied and discussed. Slight differences between the average angle typical for concrete cone failure observed experimentally and numerical results were found out and thus, more complex numerical simulations shall be recommended.
Dynamics of Rolling Wheels with Elliptical Tread Profiles
Authors: Kupi Gabor , Antali Mate
Published: October 2024

In this paper, the dynamics of a loose wheel is considered rolling on a horizontal plane, where the tread profile of the wheel is ellipsoidal. From the equations of motion, a two-parametric family of steady motions are obtained, corresponding to the rolling motion on a circular or straight line with a uniform rotational speed and tilt angle. It is shown that compared to the geometries analysed in the literature, the elliptical tread profile leads to a significant change in the qualitative behaviour of the wheel.
Energy Storage Capacity of Shape Memory Natural Rubber Nanocomposite Films
Authors: Tan Yi Wei , Chai Ai Bao , Tshai Kim Yeow , Ho Jee Hou , Kamaruddin Shamsul , Andriyana Andri
Published: October 2024

Shape memory polymers (SMPs) are smart and responsive materials that hold great promise for various emerging applications. They possess the capability to perform reversible changes when exposed to external stimuli such as temperature, pH, light and vapor. Demand for SMPs derived from sustainable sources is significant due to the increasing scarcity of fossil resources. Shape memory natural rubber (SMNR) has shown shape memory behaviour achieving shape fixity (Sf) of 76.7% and shape recovery (Sr) of 100% with relatively poor mechanical properties. In this present study, the shape memory behaviour of starch nanocrystals (SNC) reinforced rubber composite films is investigated. To this end, the emphasis is largely on evaluating the energy stored during the shape memory cycles and quantifying the energy storing efficiency of the rubber nanocomposite films. The sustainable SMNR is developed based on adopting natural rubber latex (NRL) and SNC as the raw materials. The shape memory effect (SME) of the rubber nanocomposite films is effectively improved with an increasing amount of SNC. Excellent SME of the rubber nanocomposite films is evidenced by high Sf of 95.8% and Sr of 100%. On the other hand, SMNR is deemed to be an excellent material for energy storage application as they are capable of storing energy up to 19.7 MJ/m3 with storing efficiency of 79%.
Evaluating the Reliability of Hinge Definitions in Pushover Analyses: A Comparative Study of SAP2000, AxisVM
Authors: Kuria Kevin Karanja , Kegyes-Brassai Orsolya
Published: October 2024

This study evaluates the performance of SAP2000, and AxisVM in conducting pushover analyses, with a focus on the manual (user) definition of plastic hinges versus the automatic definitions provided by the software. Three structures, namely a single column subjected to a point load, a 2D reinforced concrete (RC) frame model, and a 3D RC model, are considered. It explores the performance of the three models in different conditions, including, application of the different software, the use of user defined (manual) hinges, and automatically generated hinges to assess their response to various analytical excitations. The need for engineers to focus on understanding the application of defining the hinge properties according to existing guidelines, including FEMA-356, ASCE 41-13/17, and Eurocodes, is highly emphasized, in addition to the existing methods as found in the literature. This is achieved by considering and comparing the capacity curves generated by the software. The results vary in some instances, especially when comparing results from user-defined and automatic hinges. It is also found that when used with precision, the results are almost similar, it is also important to use software that allows ultimate precision for better results.
Non-Linear Time History and Pushover Analysis of a Steel Silo Behavior
Authors: Shahidi Farhad , Shahidi Farhood , Ghodousian Oveys , Shafaie Vahid , Movahedi Rad Majid
Published: October 2024

Earthquakes, among the most destructive natural hazards, result in substantial economic and demographic losses. An effective strategy to mitigate future structural damage involves investigating past collapses. Numerical modeling proves instrumental in analyzing and identifying deficiencies in collapsed structures. This study numerically evaluates a steel silo damaged during the 2011 Van earthquake. Employing non-linear time history and pushover analyses, the research assesses the silo’s performance. Findings highlight inadequate welding dimensions and incomplete fusion with the base metal in fillet welds between columns and the silo tank as primary causes of collapse. Numerical simulations with varied column removal scenarios underscore the importance of robust silo tank-column connections in reducing earthquake-induced damage.
Finite Element Analysis of Microtunneling Under a Railway Track
Authors: Koch Edina , Alsamia Shaymaa , Major Zoltán
Published: October 2024

Microtunneling is a trenchless construction method used to install pipelines beneath highways, railroads, runways, harbors, rivers, and environmentally sensitive areas. For railway lines, the primary objective of the method is to address the challenges posed by the installation of utilities without disrupting rail operations. The aim is to minimize the impact on railway services, ensuring the uninterrupted flow of transportation while facilitating essential infrastructure development. Traditional excavation methods often involve significant ground disturbance and pose risks to the stability of the railway track, leading to service interruptions and safety hazards. Microtunneling, on the other hand, offers a non-disruptive alternative by utilizing advanced tunneling equipment that minimizes ground settlement and vibration, reducing the risk of damage to the railway structure. The technique involves the use of remotely controlled boring machines to excavate tunnels with precision, allowing for the installation of pipelines or other utilities with minimal impact on the railway infrastructure above. The aim is to achieve a seamless integration of new underground utilities while maintaining the structural integrity and operational functionality of the railway line. Furthermore, microtunneling under a railway line contributes to sustainable development by minimizing the environmental footprint associated with construction activities. The reduced excavation and disturbance to the surrounding environment lead to lower levels of noise, dust, and disruption, aligning with modern principles of environmentally conscious infrastructure development. In this study, the installation of a sewage pipeline constructed by microtunneling under an existing railway track is investigated using geotechnical and structural FEM.
Calculation of Heating of Reinforced Concrete Tunnel Wall During Fire
Authors: Major Zoltán , Szép János , Lublóy Éva
Published: October 2024

In this article, we present the thermal parameters of reinforced concrete tunnel lining materials and their changes during fire exposure. After describing the material properties, we present a test method to investigate the heating of reinforced concrete tunnel linings. As the presented method can only be considered as partially standard, we validate it on the basis of the available literature and our Excel program based on the presented theory. During the validation, it has been demonstrated that the method is suitable for solving practical professional tasks and that it is able to provide sufficiently accurate results. Since the results presented can be used not only for design purposes but also as an initial step in fire diagnostics to determine the extent of damage in fire-loaded tunnel walls, we also construct novel curves for the analysis of reinforced concrete walls, which can be used effectively for fire curves with cooling phase, where the accumulated temperature inside the wall further heats the zones inside and further residual strength loss may occur due to chemical processes in the zones. Based on the results of the model presented in this paper, designers can take into account the changes in the temperature distribution of the reinforced concrete tunnel wall, which have a decisive influence on the evolution of the internal forces due to external influences, and can calculate the magnitude of the stresses due to inhibited thermal expansion by using approximate models.
Model Calibration of High Damping Rubber Bearings: A Preliminary Mass Production Reliability Study
Authors: Londoño López Santiago , Cucuzza Raffaele , Movahedi Rad Majid , Domaneschi Marco , Marano Giuseppe Carlo
Published: October 2024

Over the past decade, Building Isolation Systems (BIS) have gain significant relevance due to their ability to reduce horizontal acceleration and inter-story drifts in structures. Since the 1950s, researchers have focused on developing numerical models to simulate the dissipative behavior of High Damping Rubber Bearings (HDRB) in parallel major earthquake events have highlighted the need for BIS devices in medium and large-scale infrastructure, accentuating the need for further research into accurate models and adding the pressing interest in variability of mass-produced HDRB parameters. This study presents initial results from an identification process using two numerical models, validated using experimental tests at the SISMALAB laboratory. The experimental data involved eight samples subjected to compression forces and horizontal displacement. Optimal values were obtained through a Genetic Algorithm optimization process, minimizing discrepancies between experimental and numerical response. Preliminary variability analysis was conducted on data from 20 independent iterations over the eight samples.
Stiffness Ratio Evaluation of Steel Exoskeletons Through Performance-Based Optimal Design
Authors: Olivo Jana , Cucuzza Raffaele , Movahedi Rad Majid , Domaneschi Marco , Ferro Giuseppe Andrea , Marano Giuseppe Carlo
Published: October 2024

Among the various seismic retrofitting techniques, steel exoskeletons are distinguished by their non-invasive nature. However, only a few consolidated methodologies have been proposed for their design. The approach of several standard codes is based on the classification of elements according to their relative stiffness. In this way, a ratio between the stiffness of the exoskeletons and that of the building is taken as the main design parameter. In this study, a performance-based design approach was employed, with the inter-story drift of the building as the performance target. A sensitivity analysis was conducted to assess the impact of different inter-story drift thresholds on the structural behavior of the building-exoskeleton system. For each threshold, an optimization process was conducted to identify the optimal number of exoskeletons, their placement around the building, and the dimensions of their elements. Finally, the stiffness ratios were determined for each optimal configuration and were compared to the threshold provided by the regulations. This comparison yielded interesting insights into the differences in the approaches.
Effects of Different Longitudinal Bending Stiffness Shoes on Distal Joint Kinematics and Muscular Mechanics in Adolescent Amateur Runners
Authors: Chen Hairong , Zhang Qiaolin , Bíró István , Fekete Gusztáv
Published: October 2024

Research has indicated that modifying shoes’ longitudinal bending stiffness (LBS) could potentially influence running biomechanics and performance among amateur runners. Nevertheless, scant attention has been given to adolescent runners in previous studies, leaving the impact of various LBS shoes on distal joint kinematics and muscular mechanics unclear. Given the distinctive musculoskeletal attributes of adolescents, delving into this matter holds significant importance. Thirteen adolescent amateur runners with rear foot strikes were recruited for the study. Each participant performed running tasks along a 10-meter runway at a speed of 3.3±5% m/s while wearing two types of LBS shoes, randomized for each trial. The specific LBS values of the shoes were 2.7 Nm/rad (low) and 8.6 Nm/rad (high). Lower limb joint biomechanical data were collected using a Vicon motion analysis system and AMTI force platform. Lower limb joint kinematics and muscular mechanics were analyzed using Opensim software. Paired t-tests were employed to identify differences in distal joint kinematics and muscular mechanics during stance phases. We found that there was a significant increase in contact time, while the range of motion (ROM) of the metatarsophalangeal (MTP) joint in the sagittal plane significantly decreased in the high LBS shoe condition. Additionally, the impulse of flexor digitorum brevis and flexor hallucis longus significantly increased under the high LBS shoe condition. The results show that high LBS shoes impose a greater load on the distal muscles, potentially elevating the risk of running-related injuries. The low LBS shoes are more suitable for adolescent runners.
Biomechanical Characteristics of Adolescent Cervical Forward Flexion Analyses Based on the Finite Element Method
Authors: Zhang Qiaolin , Chen Hairong , Bíró István , Fekete Gusztáv
Published: October 2024

Adolescents’ poor sitting posture is hazardous, and long-term poor sitting posture can lead to elevated incidence of neck pain. This study investigated the biomechanical attributes of the adolescent neck to mitigate the hazards of poor sitting posture and to provide recommendations for adolescent neck health; The C1-T3 images of the cervical region were acquired from the same subject in normal posture as well as in cervical forward flexion posture with a gap between scans of 0.50mm, and the CT images were transformed into DOCM format in Mimics for subsequent 3D modelling. A finite element (FE) model of the C1-T3 normal posture as well as the cervical forward flexion posture was established. In order to investigate the differences between the two models’ cervical vertebrae and intervertebral disc stress, the stress and intervertebral disc strain of the two models were compared. A standard cervical spine model and a FE model for cervical forward flexion were created and validated. The range of motion, vertebral body, and intervertebral disc stresses were examined for both models. Comparison with previous literature confirmed the accuracy of the forward flexion model, showing consistent results with the normal cervical spine model. In the forward flexion direction, the model demonstrated increased stresses in the vertebral body, particularly in the anterior side, surpassing those in the normal model. The maximum stress in the vertebral body reached 5.99 MPa, and in the intervertebral disc, it was 1.02 MPa. Overall, stresses in the anterior cervical flexion model exceeded those in the normal model. Poor neck posture leads to more pronounced stress concentration phenomena in the vertebral body, increasing peak pressure in the vertebral body, in addition increasing com-pression on the intervertebral discs, leading to an increased risk of neck pain risk as well as cervical dysplasia, and therefore excessive forward flexion of the cervical spine in adolescents should be avoided.
Mechanical Fatigue Test of Individual Dental Implants
Authors: Talabérné Klaudia Kulcsár , Vörös Ádám , Zsoldos Ibolya
Published: October 2024

Since subperiosteal dental implants are custom made implants, design and manufacturing must be carried out with great care, solving comprehensive mechanical analyses. In this work, we performed a 3-day fatigue test on a 3D-printed titanium implant in a custom-designed mechanical testing device. After three days of average chewing loading, no residual deformation was observed. Moreover, no cracks or inclusions were observed when examining the CT scans. Nor did the printing defects on the implant cause any problems. The tested implant showed satisfactory results and was considered to be suitable for implantation. With the case study, we also showed that the new experimental methodology developed for the mechanical examination of subperiosteal implants is suitable for fatigue examinations of implants.
The Impact of Shoe Heel-Toe Drop on Plantar Pressure During the Third Trimester of Pregnancy
Authors: Li Xin , Lu Zhenghui , Gao Zixiang , Shen Siqin , He Yuqi , Fekete Gusztáv , Kovács András , Gu Yaodong
Published: October 2024

Pregnancy induces various physiological adaptations to accommodate the growing fetus. Pregnant women commonly experience changes in gait, balance, and center of gravity, which may increase the risk of falls. This study investigates the effects of negative heel shoes on plantar pressure distribution during walking in third-trimester pregnant women. Twelve healthy primigravidas participated, wearing both flat shoes and negative heel shoes while walking. Plantar pressure data were collected using the Pedar-X® insole system. Results revealed that negative heel shoes significantly reduced maximum force in the medial forefoot regions compared to flat shoes, and the force-time integral only significantly decreased in the medial forefoot region. Wearing negative-heeled shoes resulted in an increase in peak force in the hallux region. The study suggests that modifying heel-toe drop in shoes can effectively mitigate plantar pressure during third-trimester pregnancy, reducing the risk of forefoot discomfort and potential injuries. Negative heel shoes could be beneficial for pregnant women, offering a solution to alleviate forefoot pressure and promote foot blood circulation during walking. However, further optimization is needed in the hallux region for negative heel shoes.
Biomechanical Analysis of Gymnastics Movements Using Wearable Motion Capture Systems and Linear Sensors: A Case Study of the Kipping Bar Muscle-Up
Authors: Liu Wei , Lu Zhenghui , Li Xin , Gao Zixiang , Xu Yining , Mei Qichang , Fekete Gusztáv , Gu Yaodong
Published: October 2024

Gymnastics moves are complex and varied, needing precise technique and body coordination, which traditional biomechanics methods struggle to capture in detail. This study aims to look at and judge how well new motion capture and analysis technology works in gymnastics biomechanics. This study picks the kipping bar muscle up move and uses the IMU-based Xsens system and the GymAware RS unit power test system to finely look at how athletes do the move in terms of body position, power, work done by the body, and main upper limb joint movements. The study tested 8 male elite collegiate gymnasts, collecting movement data with Xsens and power data with GymAware RS unit. Results show the kipping bar muscle up takes 1.42 seconds, with a 1.13-meter shift of the body’s center and a peak speed of 3.40m/s. In terms of power, the peak output was 2772.96J/s, showing the need for explosive power and fast strength. Also, the total work done was 889.70J, showing the move’s efficiency and energy level. This study shows that new motion capture and analysis tech is effective in capturing complex gymnastics moves. The use of these techs not only expands the ways biomechanics can be studied but also helps in making training better and improving how efficiently moves are done.
Caveats and Solutions for Characterising General-Purpose AI
Authors: Hernandez-Orallo Jose
Published: October 2024

The concept of General-Purpose AI (GPAI) has recently been permeating research papers, policy reports and legal regulations, as a way of referring to current and future models with high levels of capability and generality. Yet precisely characterising GPAI models remains elusive. Current definitions often describe GPAI models as those that ‘competently perform a wide range of distinct tasks’. To properly characterise GPAI we need well-grounded definitions of capability and generality. In this paper, I will briefly introduce –or revisit– the concept of capability, going well beyond aggregate performance on benchmarks, and discuss practical procedures to evaluate the capability profile of AI systems, and derive generality metrics from them.
Proportional Representation for Artificial Intelligence
Authors: Peters Dominik
Published: October 2024

In recent years, the computational social choice community has increasingly studied the topic of proportional representation. This topic is particularly relevant for political elections, with many countries basing their voting systems on this principle, especially in Europe. However, the ideas behind proportional representation are also relevant in many other domains, including applications in artificial intelligence. We discuss a model of sequential decision making with proportional rules, and how it can be used for three applications: for merging the outputs of several large language models, for improving reinforcement learning from human feedback (RLHF), and for virtual democracy.
TEOcc: Radar-Camera Multi-Modal Occupancy Prediction via Temporal Enhancement
Authors: Lin Zhiwei , Jin Hongbo , Wang Yongtao , Wei Yufei , Dong Nan
Published: October 2024

As a novel 3D scene representation, semantic occupancy has gained much attention in autonomous driving. However, existing occupancy prediction methods mainly focus on designing better occupancy representations, such as tri-perspective view or neural radiance fields, while ignoring the advantages of using long-temporal information. In this paper, we propose a radar-camera multi-modal temporal enhanced occupancy prediction network, dubbed TEOcc. Our method is inspired by the success of utilizing temporal information in 3D object detection. Specifically, we introduce a temporal enhancement branch to learn temporal occupancy prediction. In this branch, we randomly discard the t−k input frame of the multi-view camera and predict its 3D occupancy by long-term and short-term temporal decoders separately with the information from other adjacent frames and multi-modal inputs. Besides, to reduce computational costs and incorporate multi-modal inputs, we specially designed 3D convolutional layers for long-term and short-term temporal decoders. Furthermore, since the lightweight occupancy prediction head is a dense classification head, we propose to use a shared occupancy prediction head for the temporal enhancement and main branches. It is worth noting that the temporal enhancement branch is only performed during training and is discarded during inference. Experiment results demonstrate that TEOcc achieves state-of-the-art occupancy prediction on nuScenes benchmarks. In addition, the proposed temporal enhancement branch is a plug-and-play module that can be easily integrated into existing occupancy prediction methods to improve the performance of occupancy prediction. The source code and models will be released at https://github.com/VDIGPKU/TEOcc.
SinLane: Siamese Visual Transformer via Pyramid Feature Integration for Lane Detection
Authors: Lv Zinan , Han Dong , Wang Wenzhe , Chen Danny Z.
Published: October 2024

Lane detection is an important yet challenging task in autonomous driving systems. Based on the development of the Visual Transformer, early Transformer-based lane detection studies have achieved promising results in some scenarios. However, for complex road conditions such as uneven illumination intensity and heavy traffic, the performance of these methods remains limited and may even be worse than that of contemporaneous CNN-based methods. In this paper, we propose a novel Transformer-based end-to-end network, called SinLane, that attains the attention weights focusing on the sparse yet meaningful locations and improves the accuracy of lane detection in complex environments. SinLane is composed of a novel Siamese Visual Transformer structure and a novel Feature Pyramid Network (FPN) structure called Pyramid Feature Integration (PFI). We utilize the proposed PFI to better integrate global semantics and finer-scale features and to promote the optimization of the Transformer. Moreover, the designed Siamese Visual Transformer is combined with multiple levels of the PFI and is employed to refine the multi-scale lane line features output from the PFI. Extensive experiments on three benchmark datasets of lane detection demonstrate that our SinLane achieves state-of-the-art results with high accuracy and efficiency. Specifically, our SinLane improves the accuracy by over 3% compared to the current best-performing Transformer-based method for lane detection on CULane.
Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution
Authors: Xu Tianyi , Zhou Yijie , Hu Xiaotao , Zhang Kai , Zhang Anran , Qiu Xingye , Xu Jun
Published: October 2024

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code is available at https://github.com/Tillyhere/TADT.
GRIF-DM: Generation of Rich Impression Fonts Using Diffusion Models
Authors: Kang Lei , Yang Fei , Wang Kai , Souibgui Mohamed Ali , Gomez Lluis , Fornés Alicia , Valveny Ernest , Karatzas Dimosthenis
Published: October 2024

Fonts are integral to creative endeavors, design processes, and artistic productions. The appropriate selection of a font can significantly enhance artwork and endow advertisements with a higher level of expressivity. Despite the availability of numerous diverse font designs online, traditional retrieval-based methods for font selection are increasingly being supplanted by generation-based approaches. These newer methods offer enhanced flexibility, catering to specific user preferences and capturing unique stylistic impressions. However, current impression font techniques based on Generative Adversarial Networks (GANs) necessitate the utilization of multiple auxiliary losses to provide guidance during generation. Furthermore, these methods commonly employ weighted summation for the fusion of impression-related keywords. This leads to generic vectors with the addition of more impression keywords, ultimately lacking in detail generation capacity. In this paper, we introduce a diffusion-based method, termed GRIF-DM, to generate fonts that vividly embody specific impressions, utilizing an input consisting of a single letter and a set of descriptive impression keywords. The core innovation of GRIF-DM lies in the development of dual cross-attention modules, which process the characteristics of the letters and impression keywords independently but synergistically, ensuring effective integration of both types of information. Our experimental results, conducted on the MyFonts dataset, affirm that this method is capable of producing realistic, vibrant, and high-fidelity fonts that are closely aligned with user specifications. This confirms the potential of our approach to revolutionize font generation by accommodating a broad spectrum of user-driven design requirements. Our code is publicly available at https://github.com/leitro/GRIF-DM.
MAMO: Multi-Task Architecture Learning via Multi-Objective and Gradients Mediative Kernel
Authors: Tan Yuzheng , Hu Guangneng , Zhang Shuxin
Published: October 2024

Multi-task learning (MTL) is effective in solving multiple related tasks simultaneously by sharing knowledge. However, a key challenge hindering its applications is the task interference problem where different tasks compete with each other, leading to the gradients conflicts during optimization and suffering from negative transfer. One thread is to manipulate task gradients by adjusting conflicting directions, ignoring architecture learning. Another thread is to learn architectures by generating task-exclusive modules, ignoring all-task balances. We address the problem by proposing a novel Multi-task Architecture learning model via Multi-Objective (MAMO) optimization. It achieves the goal in two steps. First, for the competing tasks detected during architecture learning, MAMO automatically generates a new module of gradient mediative kernel (GMK). Second, MAMO finds a Pareto optimal solution that balances all tasks during model parameter learning. MAMO outperforms various MTL baselines on benchmarks with an effective model size. It is model-agnostic and can be integrated into other SOTA methods to promote their performance. Extensive ablation study is conducted to understand the working of MAMO.
First Creating Backgrounds Then Rendering Texts: A New Paradigm for Visual Text Blending
Authors: Li Zhenhang , Shu Yan , Zeng Weichao , Yang Dongbao , Zhou Yu
Published: October 2024

Diffusion models, known for their impressive image generation abilities, have played a pivotal role in the rise of visual text generation. Nevertheless, existing visual text generation methods often focus on generating entire images with text prompts, leading to imprecise control and limited practicality. A more promising direction is visual text blending, which focuses on seamlessly merging texts onto text-free backgrounds. However, existing visual text blending methods often struggle to generate high-fidelity and diverse images due to a shortage of backgrounds for synthesis and limited generalization capabilities. To overcome these challenges, we propose a new visual text blending paradigm including both creating backgrounds and rendering texts. Specifically, a background generator is developed to produce high-fidelity and text-free natural images. Moreover, a text renderer named GlyphOnly is designed for achieving visually plausible text-background integration. GlyphOnly, built on a Stable Diffusion framework, utilizes glyphs and backgrounds as conditions for accurate rendering and consistency control, as well as equipped with an adaptive text block exploration strategy for small-scale text rendering. We also explore several downstream applications based on our method, including scene text dataset synthesis for boosting scene text detectors, as well as text image customization and editing. Code and model will be available at https://github.com/Zhenhang-Li/GlyphOnly.
Domain Generalised Cell Nuclei Segmentation in Histopathology Images Using Domain-Aware Curriculum Learning and Colour-Perceived Meta Learning
Authors: Xie Kunzi , Guo Ruoyu , Cong Cong , Pagnucco Maurice , Song Yang
Published: October 2024

Cell nuclei segmentation in histopathology images is critical in computer-aided diagnosis and treatment planning. However, this task is challenging due to inherent heterogeneity in histopathology images especially when originating from different domains, caused by variations in imaging protocols, staining techniques, and tissue preparation methods. Such domain shifts can significantly affect segmentation performance when the segmentation model is trained and tested on different domains. In this work, we present a novel gradient-based meta-learning approach for domain generalisation in histopathology cell nuclei segmentation. Specifically, we propose a domain-aware regularisation to correct each pixel’s classification based on the specific domain. We also embed a novel network module to preserve the colour features in histopathology images via an enhanced feature extraction procedure. We demonstrate that our proposed framework can achieve consistent and accurate segmentation performance across domains through extensive experiments on multiple histopathology datasets from diverse sources. Our code is available at: https://github.com/winnie172026/DG.
LGAD: Local and Global Attention Distillation for Efficient Semantic Segmentation
Authors: Wang Chen , Qi Yafei , Li Qi , Liu Huawen
Published: October 2024

Efficient semantic segmentation is essential for a wide array of computer vision applications, and knowledge distillation has emerged as a promising methodology for model compression and efficiency. However, we observed that an excess of positive pixels can dilute attention weights, hindering the student model’s learning process. To tackle this significant challenge, we introduce the Local and Global Attention Distillation (LGAD) framework, a pioneering block-based technique that distills both local and global attention. The LGAD framework segments feature maps and output probabilities into well-defined local and global blocks, effectively mitigating the dilution of attention weights. By doing so, it enhances the distinction between positive and negative pixels, particularly amplifying the focus on salient regions within each local and global block. We have conducted comprehensive experiments on three benchmark datasets, Cityscapes, CamVid, and Pascal VOC 2012. The experiment results demonstrate the effectiveness of our proposed LGAD and confirm its superiority over several state-of-the-art distillation methods for semantic segmentation.
Exemplar-Free Incremental Deepfake Detection
Authors: Xiong Wuti , Zhao Guoying , Li Xiaobai
Published: October 2024

Incremental Deepfake Detection (IDD) aims to continuously update models with new domain data, adapting to evolving forgery techniques. Existing works require extra buffers to store old exemplars for maintaining previously learned knowledge. However, it is infeasible when previous data is unavailable due to storage and privacy issues. This paper focuses on a more challenging but practical exemplar-free IDD problem that requests zero old exemplars when updating the model. To address this problem, we design a domain-adaptive module that uses independent adapters to learn domain-specific knowledge for each domain, avoiding using old exemplars. Besides, we introduce an uncertainty optimization strategy to optimize the adapters more efficiently. With excellent scalability, our method can be easily deployed to various models. To simulate the practical scenarios, we designed two new protocols based on diverse deepfake datasets. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods by a large margin. The code is available at https://github.com/woody-panda/EF-IDD.
EdgeNAT: Transformer for Efficient Edge Detection
Authors: Jie Jinghuai , Guo Yan , Wu Guixing , Wu Junmin , Hua Baojian
Published: October 2024

Transformers, renowned for their powerful feature extraction capabilities, have played an increasingly prominent role in various vision tasks. Especially, recent advancements present transformer with hierarchical structures such as Dilated Neighborhood Attention Transformer (DiNAT), demonstrating outstanding ability to efficiently capture both global and local features. However, transformers’ application in edge detection has not been fully exploited. In this paper, we propose EdgeNAT, a one-stage transformer-based edge detector with DiNAT as the encoder, capable of extracting object boundaries and meaningful edges both accurately and efficiently. On the one hand, EdgeNAT captures global contextual information and detailed local cues with DiNAT, on the other hand, it enhances feature representation with a novel SCAF-MLA decoder by utilizing both inter-spatial and inter-channel relationships of feature maps. Extensive experiments on multiple datasets show that our method achieves state-of-the-art performance on both RGB and depth images. Notably, on the widely used BSDS500 dataset, our L model achieves impressive performances, with ODS F-measure and OIS F-measure of 86.0%, 87.6% for multi-scale input,and 84.9%, and 86.3% for single-scale input, surpassing the current state-of-the-art EDTER by 1.2%, 1.1%, 1.7%, and 1.6%, respectively. Moreover, as for throughput, our approach runs at 20.87 FPS on RTX 4090 GPU with single-scale input. Code: https://github.com/jhjie/EdgeNAT.
TDCL: Dense Semantic Contrastive Learning for Vision-Language Tracking
Authors: Wang Zheng , He Xiankang , Lan Kaiyang , Cui Ying , Guo Dongyan
Published: October 2024

Traditional single-object tracking tasks are undergoing a new wave of transformation, especially with the emergence of the lack of semantics, which has led to the rise of the vision-language tracking task. However, previous approaches that combine the visual tracker with natural language descriptions tend to rely on a global representation of the text description, considering less about the fine-grained connections between the text description and the visual appearance. This paper proposes to utilize a bi-directional cross-attention module to capture the connections between language and visual features, which are further projected as dense semantic representations for alignment. In order to keep the semantic consistency between the search region and the coupled natural language and align the fused feature, this paper proposes a novel dense semantic contrastive learning loss to bridge the semantic gap between text and visual modalities and align them in a dense form. The proposed framework achieves promising results in tracking datasets that contain natural language descriptions, such as TNL2K, and OTB99-LANG. Our approach provides a novel solution for representing and aligning cross-modal information for the single object tracking task and may inspire further research in this field.
MFF-YOLO: Multi-scale Feature Fusion Network for Small Ship Detection in Night Scenes
Authors: Li Guangyu , Li Jun , Cao Hai , Wang Houjun , Guo Weili , Gong Chen
Published: October 2024

Ship detection plays a critical role in intelligent maritime applications including port management, marine monitoring and so on. Most existing ship detectors are trained on high-quality conventional-sized ship images under normal lighting conditions. However,low-quality images with poor lighting conditions often exist, where the features are difficult to be distinguished. Additionally, small ships with fewer pixels exhibit minimal appearance information and weak contour characteristics in night scenes, which are harmful to the multi-scale feature fusion. To address the above challenges, we propose an effective multi-scale feature fusion network for small ship detection in night scenes named MFF-YOLO. Specifically, we first design a night-friendly enhanced channel attention module, to better represent channel-dimensional features of small ships. In addition, we construct a multi-scale feature fusion architecture based on space and channel, to obtain richer semantic information of small ships in poor lighting conditions and further enhance the feature distinguishability. Finally, a series of experiments are implemented and corresponding results demonstrate the effectiveness and feasibility of our proposed method.