bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transfer learning from T1‐weighted to T2‐weighted Magnetic resonance sequences for brain image segmentation
Authors: Mecheter Imene , Abbod Maysam , Zaidi Habib , Amira Abbes 
Published: October 2023

AbstractMagnetic resonance (MR) imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body. The segmentation of MR images plays a crucial role in medical image analysis, as it enables accurate diagnosis, treatment planning, and monitoring of various diseases and conditions. Due to the lack of sufficient medical images, it is challenging to achieve an accurate segmentation, especially with the application of deep learning networks. The aim of this work is to study transfer learning from T1‐weighted (T1‐w) to T2‐weighted (T2‐w) MR sequences to enhance bone segmentation with minimal required computation resources. With the use of an excitation‐based convolutional neural networks, four transfer learning mechanisms are proposed: transfer learning without fine tuning, open fine tuning, conservative fine tuning, and hybrid transfer learning. Moreover, a multi‐parametric segmentation model is proposed using T2‐w MR as an intensity‐based augmentation technique. The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources. The segmentation results are evaluated using 14 clinical 3D brain MR and CT images. The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393 ± 0.0007. Although T2‐w‐based augmentation has no significant impact on the performance of T1‐w MR segmentation, it helps in improving T2‐w MR segmentation and developing a multi‐sequences segmentation model.
GAMNet: Global attention via multi‐scale context for depth estimation algorithm and application
Authors: Yang Huitong , Lei Liang , Sang Haiwei 
Published: October 2023

AbstractDeep neural networks significantly enhance the accuracy of the stereo‐based disparity estimation. Some current methods suffer from inefficient use of the global context information, which will lead to the loss of structural details in ill‐posed areas. To this end, a novel stereo network GAMNet is designed, composed of three core components (GDA, MPF, DCA) for estimating the depth prediction in challenging real‐world environments. First, a lightweight attention module is presented, integrating the global semantic cues for every feature position across the channel and spatial dimensions. Next, the MPF module is constructed to fuse the diverse semantic and contextual information from different levels of the feature pyramid. Finally, cost volume is aggregated with a stacked encoder‐decoder composed of the DCA module and 3D convolutions, filtering the transmission of matching clues and capturing the rich global contexts. Substantial experiments conducted on KITTI 2012, KITTI 2015, SceneFlow, and Middlebury‐v3 datasets manifest that GAMNet surpasses preceding methods with contour‐preserving disparity predictions. In addition, the first 3D scene reconstruction linear evaluation strategy on spatial grasping points for the end‐to‐end stereo networks in an unsupervised mode is proposed, and it is deployed on the designed robot vision‐guided system. In application experiments, the method can produce densely high‐precision 3D reconstructions to implement the grasping task in complex real‐world scenes, and achieve excellent robust performance with competitive inference efficiency.
DeepRoadNet: A deep residual based segmentation network for road map detection from remote aerial image
Authors: Ahmed Md. Imteaz , Foysal Md. , Chaity Manisha Das , Hossain A. B. M. Aowlad 
Published: October 2023

AbstractThe extraction of road networks is a critical activity in contemporary transportation networks. Deep neural networks have recently demonstrated excellent performance in the field of road segmentation. However, most of the convolutional neural network (CNN) based architectures could not verify their effectiveness in remote sensing images due to a smaller ratio of the targeted pixels, simple design, and fewer layers. In this study, a practical approach is assessed for road segmentation. The investigation was begun with basic encoder–decoder based segmentation models. Different state‐of‐the‐art segmentation models like U‐Net, V‐Net, ResUNet and SegNet were used for road network detection experiments in this research. A robust model named DeepRoadNet, a more complicated alternative, is proposed by utilizing a pre‐trained EfficientNetB7 architecture in the encoder and residual blocks as the decoder which mostly resembles the U‐Net segmentation process. The proposed model has been trained, validated as well as tested using the high‐resolution aerial image datasets and yielded good segmentation results with a mean intersection over union (mIoU) of 76%, a mean dice coefficient (mDC) of 73.18%, and an accuracy of 97.64% using Massachusetts road dataset. The proposed DeepRoadNet architecture overcomes the issues of lower mIoU, lower mDC, limited flexibility and interpretability already faced by existing models in the road segmentation field. The code is available at https://github.com/Imteaz1998/DeepRoadNet.
A CRNN‐based method for Chinese ship license plate recognition
Authors: Xu Fan , Chen Chuibin , Shang Zhigao , Peng Yuqing , Li Xinbao 
Published: October 2023

AbstractExisting deep learning methods cannot achieve satisfactory ship license plate (SLP) recognition due to the harsh marine environment, such as foggy weather, unstable ship state and small targets. Therefore, a convolutional recurrent neural network (CRNN)‐based method is proposed for accurate SLP image recognition. Overall, the suggested method improves a CRNN recognition model by SLP image enhancement and data augmentation. The SLP image enhancement employs dark channel prior and Hough transform line detector to address the fog/blurriness and tilt issues existing in SLP images. As separate and joint operations, the two enhancements contribute to data augmentation for CRNN recognition. Preprocessing algorithms, including adaptive histogram equalization and image edge padding, are used to improve and unify the enlarged dataset for augmenting the CRNN model. As a final step, correction of the CRNN recognition results is made according to the character rule of SLPs, using an edit‐distance algorithm to match against a pre‐established SLP dictionary. A variety of real SLP images were collected to build an SLP image dataset for verification. The experimental results indicate that our method can reach an SLP recognition accuracy of , which is significantly superior to other text‐based deep learning methods.
Entropy‐guided contrastive learning for semi‐supervised medical image segmentation
Authors: Xie Junsong , Wu Qian , Zhu Renju 
Published: October 2023

AbstractAccurately segmenting medical images is a critical step in clinical diagnosis and developing patient‐specific treatment plans. While supervised learning algorithms have achieved excellent performance in this area, they require a large amount of annotated data, which is often time‐consuming and difficult to obtain. As a result, semi‐supervised learning (SSL) has gained attention as it has the potential to alleviate this challenge by using not only limited labelled data but also a large amount of unlabelled data. A common approach in SSL is to filter out high‐entropy features and use the low‐entropy part to compute unsupervised loss. However, it is believed that the high‐entropy part is also beneficial for model training, and discarding it can lead to information loss. To address this issue, a simple yet efficient contrastive learning approach is proposed in this work for semi‐supervised medical image segmentation, called Entropy‐Guided Contrastive Learning Segmentation Network (EGCL‐Net). The proposed method separates the low‐entropy and high‐entropy features via the average of predictions, using contrastive loss to pull the intra‐class entropy representation distance close and push the inter‐class entropy representation distance away. Extensive experiments on the automated cardiac diagnosis challenge dataset, COVID‐19, and BraTS2019 datasets showed that: (1) EGCL‐Net can significantly improve performance by utilizing high‐entropy representation, and (2) the authors’ EGCL‐Net outperforms recent state‐of‐the‐art semi‐supervised methods in both qualitative and quantitative evaluations.
Power limitations of magneto‐dielectric substrate microstrip antennas
Authors: Blosser Clayton , Sigmarsson Hjalti , Ruyle Jessica 
Published: October 2023

AbstractMicrostrip patch antennas are broadly deployed low‐profile antennas. Unfortunately, there are some common issues with standard patch antennas, such as narrow bandwidth, low radiation efficiency, and large dimensions for low‐frequency applications. Patch antennas can simultaneously be greatly miniaturized while increasing bandwidth through the use of magneto‐dielectric substrates. However, this too could come with complications—the potential for radiation at unintended frequencies and distortion in the passband. Two patch antennas, one with a standard dielectric substrate and one with a magneto‐dielectric substrate, were fabricated and tested up to 10 dBW input power. The performance of the magneto‐dielectric substrate was compared to that of the standard substrate to test passive intermodulation and harmonic distortion. The magneto‐dielectric substrate proved to have some resilience to harmonic distortion, but not to passive intermodulation distortion.
To trust or not to trust? ‘Exit’ approaches in EU citizens’ attitudes after Brexit
Authors: De Luca Marino 
Published: October 2023

AbstractThe European Union (EU) is a unique international alliance project, successful in maintaining political stability through its institutional model and decision-making procedures. However, recent economic and border crises, and COVID-19, have shown the institution’s fragility and undermined citizens’ trust. The UK’s 2016 vote to exit the EU demonstrated this. Indeed, Brexit was the first real example of the consequences of intensifying Eurosceptic sentiments. Since then, the fear of a likely ‘contagion’ in other countries has persistently spread. This paper investigates whether and how the Brexit issue impacts on EU citizens. In particular, it aims to understand how European citizens would react to a hypothetical referendum, along UK lines, on exiting the EU. Analysing the European Election Studies (EES) Voter Study 2019, this article tries to identify clusters of European citizens according to their attitudes to European policies, their trust in the EU, and their thoughts on an exit referendum.
Changes in the ways authors refer to themselves: a diachronic study of self-mention in English research articles
Authors: Wang Hua , Hu Zhiqing 
Published: October 2023

AbstractSelf-mention refers to the presentation and mention of writers themselves in texts. As there has been limited research on whether authors have changed the ways they refer to themselves in their English research articles, we explored whether and to what extent self-mention and its three sub-categories have changed based on a corpus of 1200 research articles from 4 disciplines spanning the period from 1970 to 2019. The findings demonstrate that self-mention and its three sub-categories in academic writing have undergone changes over time and tend to be shaped by discipline. Writers in soft science use fewer self-mentions and first-person pronouns. They tend to present themselves in a subtle way by diverting their attention from first-person pronouns by increasing the percentages of third-person nouns and abstract subjects. Conversely, writers in hard science strive to present themselves more directly via using self-mention more frequently, especially first-person pronouns. Accordingly, the percentages of third-person nouns and abstract subjects in hard sciences are decreasing. This study provides a diachronic and cross-disciplinary overview of how self-mention has changed in English research articles throughout the 50 years and offers some pedagogical implications for self-mention in English academic writing and teaching.
Tangential extreme ultraviolet and soft x-ray diagnostic system for time-resolved temperature measurement on the High Beta Tokamak-Extended Pulse
Authors: Li Boting , Levesque J. P. , Wei Y. , Saperstein A. , Chandra R. N. , Navratil G. A. , Mauel M. E. , Hansen C. 
Published: October 2023

The High Beta Tokamak-Extended Pulse has recently incorporated a tangential multi-energy extreme ultraviolet and soft x-ray diagnostic system. This system enables measurements of the electron temperature and the examination of mode dynamics within the tokamak. While other systems have been built for poloidal views over similar temperature ranges, this is the first multi-energy tangential-view system designed to work in a temperature range below 200 eV in a tokamak. To facilitate these measurements, a filter wheel comprising five distinct groups of dual-filters has been developed and implemented. By employing a combination of 0.1 μm aluminum and 0.2 μm titanium filters, the system allows estimation of electron temperature profiles through reconstruction of the emission profile using the standard “double-foil” technique. The influence of impurities and filter oxide layers on measurement outcomes is examined. Results reveal that, while the absolute electron temperature values may exhibit some deviations, key characteristics like the electron temperature profile shape and inversion radius during sawtooth events remain consistent. This consistency confirms the system’s suitability for core plasma studies. This system has proven effective in detecting and analyzing internal magnetohydrodynamic phenomena, such as sawteeth.
Measurement of inkjet droplet size based on Fraunhofer diffraction
Authors: Shin Dong Yeol , Moon Yoon Jae , Kim Jun Young , Kang Kyung-Tae 
Published: October 2023

Inkjet printers have started to manufacture OLED/QLED pixel arrays for the display industry, and the precise measurement and control of ink droplet volume during the printing process has become important. We investigated the feasibility of Fraunhofer diffraction analysis as a volume measurement tool for fast-moving inkjet droplets. To confirm the basic idea, two Fraunhofer diffraction-based methods were used to calculate the wire diameters of well-known sized and steady-positioned metal wires. The first method was to curve-fit the whole measured diffraction intensity curve with the extensive Fraunhofer diffraction equation. The second one was to use the simple approximate diameter calculation equation with the measured position data of minimum diffraction intensity. The metal wire diameters calculated by the two methods showed less than 1.17% error. For the size measurement of fast-moving inkjet droplets, the first method showed 24.5 µm diameter and 7.7 pL volume, while the second method showed 25.4 µm diameter and 8.58 pL volume. We found that the second method was more suitable for real-time inkjet monitoring because its average computer calculation time was 33 ms, and the first method took an average of 34 ms, about 1000 times more CPU time. Hence, Fraunhofer diffraction analysis as an inkjet droplet volume measurement tool was feasible with a good balance of measurement time and measurement accuracy.
Cultivation of sustainability in a discourse of change
Authors: Weder Franzisca 
Published: October 2023

Abstract
Sustainability has been well used (and abused) as “buzz-word”, label or language token for certain behavior and action in political, organizational and increasingly in individual communication. Based on critical approaches in language, discourse and communication studies, the paper explores potential processes of normalization of sustainability as a new norm, discusses new theories and methodological variations that can be applied to better understand sustainable development, and offers a theoretical concept for cultivation of sustainability as a dialectic process of questioning and stabilization in transformation and change processes. Complemented by a communication for development and social change perspective, the paper lays the theoretical foundation for an understanding of sustainability as organizing principle in socio-ecological change processes, which is further elaborated in the contributions of this Special Issue, which are introduced at the end.
The Gravitational Pull Hypothesis and imperfective/perfective aspect in Catalan translation
Authors: Borillo Josep Marco , Martínez Gemma Peña 
Published: October 2023

Abstract
This article aims to test the Gravitational Pull Hypothesis on the imperfective/perfective aspect distinction in
the language pairs English-Catalan and French-Catalan. It draws on the corresponding corpora in COVALT. The GPH posits three
cognitive causes of translational effects: source or target language salience and connectivity. Different configurations of these
causes, or factors, are expected to result in over- or under-representation of target language features. The
imperfective/perfective aspect distinction was chosen as a testing ground for the GPH because it is morphologically marked in
Catalan and French but not in English. That may give rise to different configurations of factors and, therefore, to different
translational effects. It is predicted that the preterite, which conveys perfective aspect in Catalan, will be over-represented in
Catalan translations from English as compared to translations from French and to Catalan non-translations. On the other hand, the
imperfect, which conveys imperfective aspect, will be under-represented. Results confirm these predictions. For translations from
French, both adherence to the patterns observed in Catalan non-translations and over-representation of the preterite are possible
outcomes. Results lend support to the second alternative ― over-representation of the preterite. These results highlight the
importance of relying on frequency and other sources of evidence when formulating hypotheses in the framework of
the GPH. Research from the field of second language acquisition proved particularly significant in this respect.
Spanish heritage speakers as pre-departure conversation partners
Authors: Marijuan Silvia 
Published: October 2023

Abstract
Drawing on previous research showing the centrality of pre-departure interventions in the success of a study
abroad (SA) program and on a translingual practice framework (Canagarajah, 2013), the
current study addresses the implementation of a pre-departure conversation exchange between peers from diverse
racial/ethnic/cultural/linguistic background as an integral part of a four-week SA program in Spain. Reflections and survey data
were collected from a mixed group of SA participants, both second language learners (n = 14) and Spanish heritage
speakers (SHSs; n = 4), as well as from their SHS conversation partners (n = 5) to determine:
(a) the perceived goals and challenges of the mixed SA cohort for studying abroad; (b) the perceived benefits of the pre-departure
conversation exchange with SHSs on SA learners’ performative competence; and (c) the perceived benefits for SHS conversation
partners on their identity formation, heritage language maintenance, and agency.
“A mí no me gusta España mucho” vs.“Yo quiero ir a toda España, me encanta
España”
Authors: Peace Meghann M. 
Published: October 2023

Abstract
Research on heritage speakers abroad constitutes a growing field. Of particular interest is linguistic
accommodation by heritage speakers in Spain. Previous work suggests that such accommodation may be affected by participants’
social networks and Spanish fluency. Most work, however, has not directly examined interactions with local peers, nor has it
considered participants’ receptiveness to Spain as a possible factor. This study analyzes the linguistic choices of four heritage
speakers in interaction with Spanish peers at the midpoint of a semester-long program in central Spain. Spain was the desired
destination of two participants, while the other two would have preferred Latin America. However, all showed minimal
accommodation. Receptiveness to Spain does not seem to explain their choices; rather, their language use in interaction was likely
driven by their identity stances and their desire to create relationships of either closeness or distance with their particular
Spanish interlocutors.
“Being myself in Spanish”
Authors: Pozzi Rebecca , Escalante Chelsea , Quan Tracy 
Published: October 2023

AbstractThis case study examines the pragmatic development of address forms of a US-based Spanish heritage speaker of Mexican descent, Juan, during an 11-week abroad program in Argentina. Instruments included a background questionnaire, a pre/post-written elicitation task, four interviews, and 16 naturalistic recordings during host family dinners and service encounters. Findings indicate that Juan decreased his use ofvoson elicitation tasks and did not usevosat all in naturalistic recordings. There was an increase, however, in his metapragmatic awareness, or his understanding of the ways variable forms index social meaning, specifically regarding address forms. These results were related to Juan’s bicultural identity construction, investment, and evolving withdrawal from or participation in the host community. This study highlights the importance of triangulating elicited and naturalistic data with qualitative information and moving away from appropriate-based models that compare heritage speakers’ pragmatic choices to those of monolingual native speakers.
The changing landscape of heritage language learners abroad
Authors: George Angela 
Published: October 2023

Abstract
This special issue highlights current research on topics related to linguistic and identity development of
heritage language learners (HLLs) who participate in study abroad programs. With the steady increase of study abroad, it is
becoming more common for HLLs to study in the country of their heritage or in one where the heritage language is spoken. The
issues and challenges faced by HLLs often differ from their second language counterparts. While research on the linguistic
development of HLLs abroad is increasing, much work remains. This introduction will highlight the importance of researching HLLs
abroad, the growing amount of research conducted on HLLs, and identity work on HLLs abroad. In addition to these topics, several
ideas are provided for future research on HLLs abroad. Following this, the articles in the special issue are presented,
emphasizing how they contribute to the topics presented in this introductory section.
On the connection between Lenz’s law and relativity
Authors: Rodrigues Thales B S F , Rizzuti Bruno F 
Published: October 2023

Abstract
In this work, we demonstrate explicitly the unified nature of electric and magnetic fields, from the principles of special relativity and Lorentz transformations of the electromagnetic field tensor. Using an operational approach we construct the tensor and its corresponding transformation law, based on the principle of relativity. Our work helps to elucidate concepts of advanced courses on electromagnetism for primary-level learners and shows an alternative path to derive Lenz’s law based on the connection between relativity arguments and a standard electromagnetism experiment.