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Low-resolution coarse-grained (CG) models provide significant computational and conceptual advantages for simulating soft materials. However, the properties of CG models depend quite sensitively upon the mapping, M, that maps each atomic configuration, r, to a CG configuration, R. In particular, M determines how the configurational information of the atomic model is partitioned between the mapped ensemble of CG configurations and the lost ensemble of atomic configurations that map to each R. In this work, we investigate how the mapping partitions the atomic configuration space into CG and intra-site components. We demonstrate that the corresponding coordinate transformation introduces a nontrivial Jacobian factor. This Jacobian factor defines a labeling entropy that corresponds to the uncertainty in the atoms that are associated with each CG site. Consequently, the labeling entropy effectively transfers configurational information from the lost ensemble into the mapped ensemble. Moreover, our analysis highlights the possibility of resonant mappings that separate the atomic potential into CG and intra-site contributions. We numerically illustrate these considerations with a Gaussian network model for the equilibrium fluctuations of actin. We demonstrate that the spectral quality, Q , provides a simple metric for identifying high quality representations for actin. Conversely, we find that neither maximizing nor minimizing the information content of the mapped ensemble results in high quality representations. However, if one accounts for the labeling uncertainty, Q (M) correlates quite well with the adjusted configurational information loss, I ̂ map (M) , that results from the mapping. [ABSTRACT FROM AUTHOR]

Knowledge transfer in engineering design activity involves effectively applying prior practical experience in novel contexts. While related works fostering knowledge transfer focus on educational practices and experience generalisation, recent advancements enable us to analyze its complex cognitive processes using neuroimaging technology. This paper proposes a multimodal approach for facilitating knowledge transfer in engineering design, utilising functional near-infrared spectroscopy (fNIRS) and eye-tracking technologies based on neurocognitive profiling. We conducted knowledge transfer experiments using mixed and extended design tasks. Meanwhile, The analysis focused on gaze duration, gaze counts, brain region activation levels, functional connectivity, and effective connectivity of the participants. Finally, we employed stepwise linear regression to identify significant and potential associated factors of knowledge transfer. By combining eye-tracking predictors and brain network predictors, a multivariate linear discriminant analysis (LDA) significantly differentiated between participants in near-transferred and far-transferred states, achieving an accuracy of 91.16% using leave-one-out cross-validation. The 'basic-supplementary-target task' method effectively improved the knowledge transfer performance by 27.6%. Our findings demonstrate the efficacy of the proposed multimodal approach in facilitating knowledge transfer in engineering design activities. [ABSTRACT FROM AUTHOR]

This study seeks to find out the main achievements of a cross-border cooperation project (INESPO III) in terms of innovation and university-firm knowledge transfer in a region situated in the central region of Portugal and Castile and León, Spain. To achieve this aim, research of a qualitative and exploratory nature was chosen., that is, case study method. Data collection was based on seven interviews with several project actors, and content analysis was also used. Through interviews with project beneficiaries, empirical evidence suggests that this type of cooperation allows access to new sources of external knowledge, with the network benefiting globally from each partner's type of specialization. This study makes an important contribution to the literature on cross-border cooperation by extending its field of research to knowledge transfer and innovation, analysing a project that has produced good results. It can be determinant for government institutions responsible for administering cross-border incentive systems, and for partner networks in drawing process of similar projects for benefit of their own regions, besides the clear academic interest in business cooperation networks. Final results and main contributions of the project are also presented. [ABSTRACT FROM AUTHOR]

Spatial transcriptomics (ST) enables the visualization of gene expression within the context of tissue morphology. This emerging discipline has the potential to serve as a foundation for developing tools to design precision medicines. However, due to the higher costs and expertise required for such experiments, its translation into a regular clinical practice might be challenging. Despite implementing modern deep learning to enhance information obtained from histological images using AI, efforts have been constrained by limitations in the diversity of information. In this paper, we developed a model, HistoSPACE, that explores the diversity of histological images available with ST data to extract molecular insights from tissue images. Further, our approach allows us to link the predicted expression with disease pathology. Our proposed study built an image encoder derived from a universal image autoencoder. This image encoder was connected to convolution blocks to build the final model. It was further fine-tuned with the help of ST-Data. The number of model parameters is small and requires lesser system memory and relatively lesser training time. Making it lightweight in comparison to traditional histological models. Our developed model demonstrates significant efficiency compared to contemporary algorithms, revealing a correlation of 0.56 in leave-one-out cross-validation. Finally, its robustness was validated through an independent dataset, showing similar prediction with predefined disease pathology. Our code is available at https://github.com/samrat-lab/HistoSPACE. • Built model to predict spatial transcriptomics (ST) profile from histology images. • The predicted transcriptomics profile correlates with disease pathology. • HistoSPACE shows better correlation in compare to similar ST prediction models. [ABSTRACT FROM AUTHOR]

Conservation faces a chronic shortage of resources, including time, funding, mental capacity, and human capital. Efforts to make the expenditure of these resources more efficient should, therefore, support more equitable and effective conservation prioritization. To achieve this, it is necessary to ensure the integration of the knowledge and perceptions of local stakeholders into larger scale conservation decisions. We used fuzzy cognitive mental modeling to assess the perceptions of mangroves and the prioritization of ecosystem services across 3 groups of stakeholders: representatives from 3 coastal Fijian villages, the national office of an international nongovernmental organization (NGO), and the US office of that same NGO. We found different topologies and valuations among the resultant mental models, with the US NGO office having the most terms. However, when comparing models from local villages with the US NGO office, scale‐dependent perceptions shifted, including the relative devaluation of locally important cultural valuations of mangroves. Despite these variations in perceptions, however, 3 key components of the mental models—women's livelihoods, men's livelihoods, and fisheries supplementation—all appeared as consistently important in multiple models, suggesting areas around which potential collaboration among stakeholders could be forged. By focusing on system‐wide, rather than stakeholder‐specific, optimal solutions within the system, new opportunities for collaboration may emerge. In doing so, these system‐wide solutions may increase efficiency and collaboration. Moreover, we found that boundary‐spanning organizations, such as the national‐level conservation organizations, played a role in facilitating information transfer and mediating conservation goals in a culturally appropriate fashion. Finally, although the specific example used here is mangrove conservation, our methodologies and findings are broadly applicable across a variety of conservation scenarios. [ABSTRACT FROM AUTHOR]

Effective conservation requires a variety of perspectives that center on different ways of knowing. Disciplinary diversity and inclusion (DDI) offers an important means of integrating different ways of knowing into pressing conservation challenges. However, DDI means more than multiple disciplinary approaches to conservation; cognitive diversity and epistemic justice are key. In 2020, the Disciplinary Inclusion Task Force was formed via a grassroots movement of the Society for Conservation Biology (SCB) to assess the extent of DDI and to chart a path to increase DDI. First, we assessed past and present SCB governance documents. Next, we surveyed current SCB members (n = 577). Finally, we surveyed nonmember conservationists (n = 213). Members who were not biological scientists perceived SCB as less diverse (21.4% vs. 16%) and not equitable (21.8% vs. 161%), and, although the majority (44) of nonmembers reported that their work aligned reasonably well with the mission of the SCB, they thought the organization focused on biological sciences. Despite SCB's mission to be diverse and inclusive, realizing this mission will likely require diverse epistemological perspectives and shifting from top‐down models of knowledge transfer. In centering on DDI, SCB can achieve its aspirations of connecting members across disciplines and ways of knowing to foster diverse perspectives and practices. We recommend that SCB and other organizations develop mechanisms to increase recruitment and retention of diverse members and leadership as well as expand strategic partnerships to flatten disciplinary hierarchies and promote inclusivity. [ABSTRACT FROM AUTHOR]

Universities are increasingly considered as crucial actors for innovation and regional development through university-industry collaborations. Extant research suggests that the impact of knowledge transfer is largely related to the multi-dimensional proximities between universities and knowledge recipients. However, majority of them approached the issue of proximity by treating collaborations between universities and their industrial partners as geographically static. Our case study looks at one Chinese research university which adopted an innovative outreach solution by setting up off-campus institutional arrangements to mitigate the problem caused by the geographical constraint, exploring how such entrepreneurial effort can pose an impact on the various dimensions of proximity. By doing so, this paper aims to enrich the knowledge of university entrepreneurship, elucidate the dynamic nature of proximity in the context of university-industry collaborations and draw practical implications for the promotion of such collaborations at large. [ABSTRACT FROM AUTHOR]

Individuals' movements are conditioned by the acquisition of information from personal interactions with the environment or from social sources. Despite the importance of social information in movement decision making, little is known about how individuals proceed when social information comes from multiple sources. Here, we specifically tackled this issue by experimentally testing (1) how social information from multiple sources is used to make relocation decisions and (2) whether a contrast in this information enables individuals to orient themselves in space. Using the common lizard as a model species, we conducted repeated experiments in which a focal neonate received information from two other neonates coming from distinct peripheral environments before being given the opportunity to relocate to either peripheral environment. Specifically, we focused on information on resource availability and intraspecific competition by considering informants' body mass (BM) at birth and their subsequent food intake. Our analyses revealed that the amount of resources in the informants' environments affected relocation decisions, depending on the focal individual's phenotype. We found the probability of relocation increased when both the informants' food intake and the focal individual's BM increased or decreased. We also found the relocation increased when both the informants' and focal individual's BM increased or decreased. Our findings suggest that focal individuals adjust their relocation response to social information according to their needs and/or physical abilities. Conversely, we found no significant effect of differences in BM between informants or spatial variability in resource availability on spatial orientation. This study highlights that multiple sources of social information that reflect the quality of the surrounding environment (competition or resource availability) can be used for movement decision making. • Common lizard neonates can use multiple social information for relocation decisions. • Social information is processed based on the information receiver's condition. • Relocation increased when both informants' food intake and receiver's body mass did. • Relocation increased when both informants' body mass and receiver's body mass did. [ABSTRACT FROM AUTHOR]

As a vital problem in pattern analysis and machine intelligence, Unsupervised Domain Adaptation (UDA) attempts to transfer an effective feature learner from a labeled source domain to an unlabeled target domain. Inspired by the success of the Transformer, several advances in UDA are achieved by adopting pure transformers as network architectures, but such a simple application can only capture patch-level information and lacks interpretability. To address these issues, we propose the Domain-Transformer (DoT) with domain-level attention mechanism to capture the long-range correspondence between the cross-domain samples. On the theoretical side, we provide a mathematical understanding of DoT: (1) We connect the domain-level attention with optimal transport theory, which provides interpretability from Wasserstein geometry; (2) From the perspective of learning theory, Wasserstein distance-based generalization bounds are derived, which explains the effectiveness of DoT for knowledge transfer. On the methodological side, DoT integrates the domain-level attention and manifold structure regularization, which characterize the sample-level information and locality consistency for cross-domain cluster structures. Besides, the domain-level attention mechanism can be used as a plug-and-play module, so DoT can be implemented under different neural network architectures. Instead of explicitly modeling the distribution discrepancy at domain-level or class-level, DoT learns transferable features under the guidance of long-range correspondence, so it is free of pseudo-labels and explicit domain discrepancy optimization. Extensive experiment results on several benchmark datasets validate the effectiveness of DoT. [ABSTRACT FROM AUTHOR]

Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each image, may greatly reduce the cost of annotation and thus facilitate large-scale MLR. We find that strong semantic correlations exist within each image and across different images, and these correlations can help transfer the knowledge possessed by the known labels to retrieve the unknown labels and thus improve the performance of the MLR-PL task. In this work, we propose a novel heterogeneous semantic transfer (HST) framework that consists of two complementary transfer modules that explore both within-image and cross-image semantic correlations to transfer the knowledge possessed by known labels to generate pseudo labels for the unknown labels. Specifically, an intra-image semantic transfer (IST) module learns an image-specific label co-occurrence matrix for each image and maps the known labels to complement the unknown labels based on these matrices. Additionally, a cross-image transfer (CST) module learns category-specific feature-prototype similarities and then helps complement the unknown labels that have high degrees of similarity with the corresponding prototypes. It is worthy-noting that the HST framework requires searching appropriate thresholds to determine the co-occurrence and similarity scores to generate pseudo labels for the IST and CST modules, respectively. To avoid highly time-consuming and resource-intensive manual tuning, we introduce a differential threshold learning algorithm that adjusts the nondifferential indication function to a differential formulation to automatically learn the appropriate thresholds. Finally, both the known and generated pseudo labels are used to train MLR models. Extensive experiments conducted on the Microsoft COCO, Visual Genome, and Pascal VOC 2007 datasets show that the proposed HST framework achieves superior performance to that of current state-of-the-art algorithms. Specifically, it obtains mean average precision (mAP) improvements of 1.4, 3.3, and 0.4% on the three datasets over the results of the best-performing previously developed algorithm. [ABSTRACT FROM AUTHOR]

Cross-Domain Few-Shot Learning (CD-FSL) aims at recognizing unseen classes from target domains that vastly differ from training classes from source domains, utilizing only a few labeled samples. However, the substantial domain disparities between target and source domains pose huge challenges to few-shot generalization. To resolve domain disparities, we propose HybridPrompt, a novel architecture for Domain-Aware Prompting that integrates a variety of cross-domain learned prompts as knowledge experts for CD-FSL. The proposed method enjoys several merits. First, to encode knowledge from diverse source domains, several Domain Prompts are introduced to capture domain-specific knowledge. Subsequently, to facilitate the cross-domain transfer of valuable knowledge, a Transferred Prompt is specifically tailored for each target task by retrieving highly relevant Domain Prompts based on domain properties. Finally, to complement insufficient transferred information, an Adaptive Prompt is learned to incorporate additional target characteristics for model adaptation. Consequently, the collaboration of these three types of prompts contributes to a hybridly prompted model that achieves domain-aware encoding, transfer, and adaptation, thereby enhancing adaptability on unseen domains. Extensive experimental results on the Meta-Dataset benchmark demonstrate that our method achieves superior performance against state-of-the-art methods. The source code is available at https://github.com/Jamine-W/HybridPrompt. [ABSTRACT FROM AUTHOR]

Employing the Foucauldian term 'conduct', this article explores how social resilience and morale became a target of state intervention in Denmark during the Cold War. 'Psychological defence' was a Cold War phenomenon designed to bring an imagined future war into a space of control as well as a tool for the authorities' exercise of power in case another world war became a reality. Advocating a methodological internationalism, the article analyses how the concept of psychological defence travelled from Sweden to Denmark via Norway and NATO, and in a complex process of translation, mixing and hybridization was adapted and appropriated to Danish security policy conditions, preparedness culture, and historical experiences. Ultimately, psychological defence was replaced with a more practical or even cynical approach to public information and media preparedness, even if the objectives remained the same. The article employs source material from Danish, Swedish, and NATO archives and combines Scandinavian Cold War history with media history and the history of knowledge. [ABSTRACT FROM AUTHOR]

The weight initialization technique for transfer learning refers to the practice of using pretrained models that can be modified to solve new problems, instead of starting the training process from scratch. In this study, six different transfer learning weight initialization strategies were proposed for plant disease detection: scratch (i.e., random initialization), pretrained model on cross‐domain (ImageNet), model trained on related domain (ISIC 2019), model trained on related domain (ISIC 2019) with cross‐domain (ImageNet) weights, model trained on same domain (PlantVillage), and model trained on same domain (PlantVillage) with cross‐domain weights (ImageNet). Weights from each strategy were transferred to a target dataset (Plant Pathology 2021). These strategies were implemented using eight deep learning architectures. It was observed that transferring from any strategy led to an average acceleration of convergence ranging from 33.88% to 73.16% in mean loss and an improvement of 8.72%–42.12% in mean F1‐score compared to the scratch strategy. Moreover, although smaller and less comprehensive than ImageNet, transferring information from the same domain or related domain proved to be competitive compared to transferring from ImageNet. This indicates that ImageNet, which is widely favoured in the literature, may not necessarily represent the most optimal transfer source for the given context. In addition, to identify which strategies have significant differences, a post hoc analysis using Tukey's HSD test was conducted. Finally, the classifications made by the proposed models were visualized using Grad‐CAM to provide a qualitative understanding of how different weight initialization strategies affect the focus areas of the models. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) aims to reduce the distribution discrepancy across domains, enabling the transfer of knowledge from the labeled source domain to the unlabeled target domain. The main focus of most current UDA methods lies on extracting domain invariant representations to reduce the gap between domains. However, this singular emphasis on domain invariance may inadvertently ignore domain relevant information, which may lead to negative transfer effects. Moreover, current adversarial DA methods give uniform weight to all samples, overlooking the distinct influence that variations within source domain data or noise may exert on the adversarial performance. To address these challenges, we propose a novel method called Mutual-weighted Feature Disentanglement for Unsupervised Domain Adaptation (MWFD). Specifically, we decouple domain invariant features from domain specific features, and then use the entropy of the classifier to rebalance the weights of the domain discriminator, and simultaneously adjust the weights of the classifier using the domain entropy of the domain discriminator to reduce domain discrepancies. Finally, to obtain more discriminative features, we adopt a self-supervised contrastive learning framework to bring positive sample pairs closer together while pushing negative sample pairs apart, enhancing the discriminability of the model on the target domain. Extensive experiments on five benchmark datasets demonstrate that our model outperforms several state-of-the-art domain adaptation methods. [ABSTRACT FROM AUTHOR]

Cloud application programming interface (API) is a software intermediary that enables applications to communicate and transfer information to one another in the cloud. As the number of cloud APIs continues to increase, developers are inundated with a plethora of cloud API choices, so researchers have proposed many cloud API recommendation methods. Existing cloud API recommendation methods can be divided into two types: content-based (CB) cloud API recommendation and collaborative filtering-based (CF) cloud API recommendation. CF methods mainly consider the historical information of cloud APIs invoked by mashups. Generally, CF methods have better recommendation performances on head cloud APIs due to more interaction records, and poor recommendation performances on tail cloud APIs. Meanwhile, CB methods can improve the recommendation performances of tail cloud APIs by leveraging the content information of cloud APIs and mashups, but their overall performances are not as good as those of CF methods. Moreover, traditional cloud API recommendation methods ignore the complementarity relationship between mashups and cloud APIs. To address the above issues, this paper first proposes the complementary function vector (CV) based on tag co-occurrence and graph convolutional networks, in order to characterize the complementarity relationship between cloud APIs and mashups. Then we utilize the attention mechanism to systematically integrate CF, CB, and CV methods, and propose a model named Content and Complementarity enhanced Attentional Collaborative Filtering (CCeACF). Finally, the experimental results show that the proposed approach outperforms the state-of-the-art cloud API recommendation methods, can effectively alleviate the long tail problem in the cloud API ecosystem, and is interpretable. [ABSTRACT FROM AUTHOR]

This study investigates the propagation characteristics of spin waves in an yttrium iron garnet waveguide using a vector network analyzer and a real-time oscilloscope. We confirm the propagation of backward volume magnetostatic spin waves in the linear regime. Solitary spin-wave formation was observed, and the transition from linear to nonlinear response was verified by establishing a threshold power. In the nonlinear regime, collision experiments between two spin waves were conducted, revealing a dependence of attenuation on the input carrier frequency. A comparison with the transmission loss curve confirms the correlation between attenuation and the position of "frequency regions with strong dispersion." Notably, only within a specific frequency range among these regions do the colliding spin waves maintain their shapes and momenta, passing through each other without dissipation. This remarkable phenomenon is crucial for dissipation-free information transfer. Our findings offer valuable insights into spin-wave behavior, particularly for developing spin-wave-based logic and long-distance magnonic soliton information transfer. [ABSTRACT FROM AUTHOR]

Group-living organisms commonly exhibit collective escape responses, yet how information flows among group members in these events remains an open question. Here, we study the collective responses of a sheep flock (Ovis aries) to a shepherd dog (border collie) in a driving task between two well-defined target points. We collected high-resolution spatiotemporal data from 14 sheep and the dog, using Ultra-Wide-Band tags attached to each individual. We find that the spatial positions of sheep along the front-back axis of the group's velocity strongly correlate with their impact on the collective movement. Our analyses reveal that, even though the dog chases the sheep flock from behind, directional information on shorter time scales propagates from the front of the group towards the rear; further, the dog adjusts its movement in response to the flock's dynamics. We introduce an agent-based model that captures key data features. Specifically, in response to chasing, the sheep change their spatial relative positions less frequently and exhibit a transfer of directional information flow from front to back; this pattern disappears in the absence of chasing. Our study reveals some general insights into how directional information propagates in escaping animal groups. Experimental analysis and computational modeling of collective responses of flocking sheep to a herding dog reveal how directional information spreads in escaping animal groups. [ABSTRACT FROM AUTHOR]

Classification of epileptic electroencephalograms can provide strong technical support for early warning and monitoring of epilepsy. However, Electroencephalogram (EEG) has characteristics of specificity, multi-feature, and susceptibility to noise interference. Therefore, this paper proposes a joint low-rank representation and transfer multi-view learning (LRR-TML) approach for EEG epilepsy classification. The LRR-TML approach inherits the advantages of low-rank representation, filters redundant features and noise information, and focuses on the main feature and significant feature of EEG. The LRR-TML approach introduces the ideas of transfer learning and multi-view learning on the framework of low-rank representation, mines the linear correlation between different domains in the subspace through low-rank representation, and effectively transfers the discriminative knowledge of the source domain to the target domain. According to the principles of complementarity and consistency of views, the LRR-TML approach comprehensively describes EEG by integrating various features, maximizing the consistency between different views and minimizing the divergence between views. Experimental results on the Bonn dataset show that the LRR-TML approach achieves the best classification results among all comparison approaches. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 pandemic caused dashboards to become widely used by the public and decision-makers. Nevertheless, dashboard interfaces have been related to business intelligence since their origins, and the search for improvements in their design is not new. This article's objective is to conduct a user evaluation of COVID-19 dashboards that contain geospatial information. This is done through a formative study to identify problematic aspects of user/dashboard interaction. This is enhanced by comparing two self-developed dashboards that, according to previous tests, have functionalities with different appearances. User evaluation is performed through mixed research that combines objective (eye-tracking) and subjective (a questionnaire and an interview) methods. The results generate recommendations for better-designed dashboard interfaces that can transfer information appropriately. The vital elements needed to achieve this are interactivity, the option to choose the metrics, and the distribution of the elements in the layout, all playing a role in a more user-friendly interaction between the user and the dashboard. [ABSTRACT FROM AUTHOR]

A striking feature of human cognition is an exceptional ability to rapidly adapt to novel situations. It is proposed this relies on abstracting and generalizing past experiences. While previous research has explored how humans detect and generalize single sequential processes, we have a limited understanding of how humans adapt to more naturalistic scenarios, for example, complex, multisubprocess environments. Here, we propose a candidate computational mechanism that posits compositional generalization of knowledge about subprocess dynamics. In two samples (N = 238 and N = 137), we combined a novel sequence learning task and computational modeling to ask whether humans extract and generalize subprocesses compositionally to solve new problems. In prior learning, participants experienced sequences of compound images formed from two graphs' product spaces (group 1: G1 and G2, group 2: G3 and G4). In transfer learning, both groups encountered compound images from the product of G1 and G3, composed entirely of new images. We show that subprocess knowledge transferred between task phases, such that in a new task environment each group had enhanced accuracy in predicting subprocess dynamics they had experienced during prior learning. Computational models utilizing predictive representations, based solely on the temporal contiguity of experienced task states, without an ability to transfer knowledge, failed to explain these data. Instead, behavior was consistent with a predictive representation model that maps task states between prior and transfer learning. These results help advance a mechanistic understanding of how humans discover and abstract subprocesses composing their experiences and compositionally reuse prior knowledge as a scaffolding for new experiences. [ABSTRACT FROM AUTHOR]

Based on the study of the professional careers of 439 parliamentary assistants of the 7th parliamentary term (2009–2014), we identify their different career patterns, especially after leaving the European parliament. We show which factors (social, biographical) influence mobility within the field of Eurocracy. We assess the importance of public/private circulation. Beyond the notorious ‘revolving doors’ and the question of the transfer of information from EU institutions to private interests, we show that parliamentary assistants form a group of specialised intermediaries of EU affairs who, after having acquired specific knowledge and know-how at the heart of European policymaking, are able to convert these assets in different sectors (public bureaucracies, private companies, or NGOs), at the EU level or in the member states. Such mobility illustrates the blurring of the public/private divide, and the hybridisation of EU governance. We thus shed new light on the structuring of the field of Eurocracy and the circulation of professionals within it. [ABSTRACT FROM AUTHOR]

Timely detection of hard exudates in fundus images can effectively avoid the severity of the disease, but the labelling of small and numerous lesion areas requires a lot of labour costs. This paper proposes a semi‐supervised segmentation network, which integrates pseudo‐labels and multi‐level features consistency correction. It achieves accurate segmentation of hard exudates by making full use of a small amount of labelled data and a large amount of unlabelled data. The network effectively extracts features from the unlabelled data through knowledge transfer of the teacher‐student model, and incorporates a Transformer network for auxiliary training to promote the quality of transfer. In addition, three unsupervised losses are introduced to improve the performance: the perturbation loss improves the robustness of the model to noise by adding different noises to the same input; the multi‐level feature consistency correction loss ensures the consistency of features of the student model at different scales; and the pseudo‐labelling cross‐supervision loss utilizes the generated pseudo‐labels for supervision between CNN and Transformer. By comparing the segmentation results with different proportion of the labelled data, it has better segmentation performance compared to other methods. The proposed methods can totally increase dice by 16.56% and mean intersection over union (MIoU) by 25.11%. [ABSTRACT FROM AUTHOR]

Social network–based data collection methods that rely on third‐party reporting have emerged as a promising approach for measuring abortion in restrictive settings. In order for these methods to accurately measure abortion incidence, they must also assess the visibility of abortions within social networks. Failure to do so may result in estimates affected by transmission bias, caused by imperfect knowledge of all abortions within one's social network. In this paper, we present exploratory research that uses respondent‐driven sampling (RDS) and the game of contacts method to measure abortion visibility in four sites in Uganda and Ethiopia. We assess the existence of potential biases in the game of contacts estimate of abortion visibility in each site by conducting several internal and external validity tests. While these tests provided some promising results, other factors such as the representativeness of the RDS samples, direct versus indirect abortion knowledge transfers, and the generalizability of the study sites may have introduced biases into the final estimates of abortion visibility in this study. We conclude by making recommendations on how applications of this methodology could be improved to better estimate abortion‐related transmission bias. [ABSTRACT FROM AUTHOR]

Data visualization is a complex matter and so is teaching it in the context of engineering and data science. Even more challenging is teaching in a midst of once in a century pandemic. In this paper, we show the results of a workshop conducted with second-year students on a university degree in data science, which took place in the context of pandemic restrictions in a hybrid learning environment. About half of the students were in the classroom and the other half at home. They learned to use STMaps, a tool developed in the scope of the SILKNOW project. We transferred research outcomes to the classroom; similarly, the results of evaluating the tool were transferred to the project. But more importantly, we found insights that the learning process of students that were online was not the same quality as of those that were physically in the classroom. While both groups performed well in terms of correctly fulfilling the tasks proposed during the workshop, the students at home were less motivated and perceived the tasks more cumbersome to achieve. From a research perspective, this activity was perceived as very useful, and similar activities are being planned for each academic year. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion The American Speech–Language–Hearing Association (ASHA) promotes the use of evidence‐based practice (EBP) in the provision of clinical services by speech–language pathologists (SLPs). Therefore, training in the EBP process is vital in the education of future SLPs. Case‐based learning (CBL) has been found effective in facilitating transfer of knowledge and skills in various fields; however, its utilisation to teach EBP to SLP students is understudied.This phenomenological investigation explores the use of CBL, an active learning strategy that gives students the opportunity to practice application of knowledge to realistic scenarios, to teach EBP to graduate SLP students. Graduate SLP students worked in small groups to complete an article review activity centred around an assigned hypothetical clinical case. Students submitted article review papers and written reflections on their learning process. The written reflections and review papers were analysed using qualitative content analysis to understand students' approach to clinical cases and their perspectives on the CBL approach.Analysis of the papers and reflections indicated that students tended to rely heavily on scientific evidence for clinical decision making, often to the exclusion of clinical expertise or client perspectives. Students reported that the project was applicable to their future clinical practice. Further, they reported learning from multiple sources, including the instructor, class discussion and their peers.Results indicate that graduate SLP students found CBL activities beneficial for learning the principles of EBP. Students specifically indicated an appreciation for the realistic nature of the activities. Future applications are examined. [ABSTRACT FROM AUTHOR]

In 1492, a Lübeckian printer Stephan Arndes released an updated edition of a popular late medieval hagiographic collection Der Heiligen Leben in Middle Low German. From a few dozens of newly added legends, nine of them are about saints who were venerated only in Northern Germany and Scandinavia – or both. By adding these ‘Northern’ hagiographies, Arndes attempted to domesticate an otherwise rather universal collection – and not only among a Northern German but also a Scandinavian audience. By analysing these hagiographies, their possible Latin sources, and how Stephan Arndes got hold of these texts, the article shows that the edition in question is a testimony to transregional hagiographic knowledge transfer between Northern Germany and Scandinavia. Such an exchange was realized through specific editorial decisions, printing networks, and business connections, enabled by the wide usage of Middle Low German in the Nordic countries not only among foreign merchants but also locals, especially in legal and religious milieux. This case study of Stephan Arndes’ hagiographic print reveals that late medieval transregional devotional networks were bi-directional, unlike what is suggested by the centre-periphery paradigm, often applied when discussing medieval German influences on Scandinavian religious culture and institutions. [ABSTRACT FROM AUTHOR]

The tree of blobs of a species network shows only the tree-like aspects of relationships of taxa on a network, omitting information on network substructures where hybridization or other types of lateral transfer of genetic information occur. By isolating such regions of a network, inference of the tree of blobs can serve as a starting point for a more detailed investigation, or indicate the limit of what may be inferrable without additional assumptions. Building on our theoretical work on the identifiability of the tree of blobs from gene quartet distributions under the Network Multispecies Coalescent model, we develop an algorithm, TINNiK, for statistically consistent tree of blobs inference. We provide examples of its application to both simulated and empirical datasets, utilizing an implementation in the MSCquartets 2.0 R package. [ABSTRACT FROM AUTHOR]

This paper takes the experiment of “preparation of manganese carbonate” as an example, and applies the teaching mode of problem-based leaning (“PBL”) in the inorganic chemistry laboratory course. Through the design of a series of problems, students are guided to engage in independent learning, cooperative communication, problem analysis and experimental verification, thereby achieving an organic integration of knowledge transfer and value guidance. The application of “PBL” teaching mode in inorganic chemistry experimental education not only provides valuable exploration and attempt for the reform and innovation of the teaching, but also provides an excellent platform for the integration of course ideology and politics. [ABSTRACT FROM AUTHOR]

The increasing single-cell RNA sequencing (scRNA-seq) data enable researchers to explore cellular heterogeneity and gene expression profiles, offering a high-resolution view of the transcriptome at the single-cell level. However, the dropout events, which are often present in scRNA-seq data, remaining challenges for downstream analysis. Although a number of studies have been developed to recover single-cell expression profiles, their performance may be hindered due to not fully exploring the inherent relations between genes. To address the issue, we propose scDTL, a deep transfer learning based approach for scRNA-seq data imputation by harnessing the bulk RNA-sequencing information. We firstly employ a denoising autoencoder trained on bulk RNA-seq data as the initial imputation model, and then leverage a domain adaptation framework that transfers the knowledge learned by the bulk imputation model to scRNA-seq learning task. In addition, scDTL employs a parallel operation with a 1D U-Net denoising model to provide gene representations of varying granularity, capturing both coarse and fine features of the scRNA-seq data. Finally, we utilize a cross-channel attention mechanism to fuse the features learned from the transferred bulk imputation model and U-Net model. In the evaluation, we conduct extensive experiments to demonstrate that scDTL could outperform other state-of-the-art methods in the quantitative comparison and downstream analyses. [ABSTRACT FROM AUTHOR]

The integration of data from multiple modalities generated by single-cell omics technologies is crucial for accurately identifying cell states. One challenge in comprehending multi-omics data resides in mosaic integration, in which different data modalities are profiled in different subsets of cells, as it requires simultaneous batch effect removal and modality alignment. Here, we develop Multi-omics Mosaic Auto-scaling Attention Variational Inference (mmAAVI), a scalable deep generative model for single-cell mosaic integration. Leveraging auto-scaling self-attention mechanisms, mmAAVI can map arbitrary combinations of omics to the common embedding space. If existing well-annotated cell states, the model can perform semisupervised learning to utilize existing these annotations. We validated the performance of mmAAVI and five other commonly used methods on four benchmark datasets, which vary in cell numbers, omics types, and missing patterns. mmAAVI consistently demonstrated its superiority. We also validated mmAAVI's ability for cell state knowledge transfer, achieving balanced accuracies of 0.82 and 0.97 with less 1% labeled cells between batches with completely different omics. The full package is available at https://github.com/luyiyun/mmAAVI. [ABSTRACT FROM AUTHOR]

Sensory data are the basis for the intelligent health state awareness of planetary gearboxes, which are the critical components of electromechanical systems. Despite the advantages of intelligent diagnostic techniques for detecting intricate fault patterns and improving diagnostic speed, challenges still persist, which include the limited availability of fault data, the lack of labeling information and the discrepancies in features across different signals. Targeting this issue, a subdomain distribution adversarial adaptation diagnosis method (SDAA) is proposed for faults diagnosis of planetary gearboxes across different conditions. Firstly, nonstationary vibration signals are converted into a two-dimensional time–frequency representation to extract intrinsic information and avoid frequency overlapping. Secondly, an adversarial training mechanism is designed to evaluate subclass feature distribution differences between the source and target domain. A conditional distribution adaptation is employed to account for correlations among data from different subclasses. Finally, the proposed method is validated through experiments on planetary gearboxes, and the results demonstrate that SDAA can effectively diagnose faults under crossing conditions with an accuracy of 96.7% in diagnosing gear faults and 95.2% in diagnosing planet bearing faults. It outperforms other methods in both accuracy and model robustness. This confirms that this approach can refine domain-invariant information for transfer learning with less information loss from the sub-class level of fault data instead of the overall class level. [ABSTRACT FROM AUTHOR]

This study used a quasirandomized within-classroom design to investigate whether prior knowledge about physics gained in elementary school prepares students for future learning in related content areas in secondary school. A total of 433 children (intervention group) received four basic curriculum units on physics from their elementary school teachers. The units dealt with floating and sinking, air and atmospheric pressure, the stability of bridges, and sound and the spreading of sound. These children entered 60 newly composed classes in early secondary school that completed an advanced curriculum unit on hydrostatic pressure and buoyancy force with their secondary school teachers. A total of 942 students (control group) in these classes had not received the four basic physics curriculum units. On a conceptual knowledge test about hydrostatic pressure and buoyancy force, the intervention group outperformed the control group in the pretest (d = 0.28) and in the posttest (d = 0.25). Students in the intervention group showed similar learning gains as those in the control group, but when controlling for pretest performance, they achieved higher learning outcomes. Regression analyses within the intervention group revealed that this advantage resulted from the content-specific transfer of conceptual knowledge from topically related basic curriculum units. The basic physics instruction also prepared male and female students equally for future learning. Educational Impact and Implications Statement: Our study shows that students who have received early physics instruction in elementary school benefit slightly more from later, more advanced physics instruction in secondary school. This finding provides first evidence that the idea of a spiral curriculum, in which learners first build basic knowledge that is later on expanded in more demanding instruction, can work, although it is yet to be further examined how this process can be optimized in school instruction. [ABSTRACT FROM AUTHOR]

Social foraging is commonly used across taxa to increase animal foraging success in uncertain environments and is believed to be a driver of social group formation. In temperate zones, females of many bat species form seasonal colonies, whereas males are usually solitary. Males of only a few bat species form temporary colonies during sperm production, likely to benefit from social foraging, social thermoregulation, or both. We radiotracked a group of reproductive male particoloured bats, Vespertilio murinus , to test the hypothesis that they use social foraging. Foraging bats overlapped in time and space significantly more than expected by chance, suggesting that they used social information to increase detection of insect swarms. Dyads also sometimes switched foraging patches together, suggesting part-time use of the more coordinated group facilitation social foraging strategy. Our results support the hypothesis that male particoloured bats use local enhancement mixed with group facilitation during sperm production and that improved foraging success through information transfer is a likely driver of seasonal sociality in these and other male bats. • Male particoloured bats foraged socially. • Bats often changed their direction of flight at the same time. • Males did not forage together during all nocturnal activity. • Bats probably used local enhancement mixed with group facilitation. [ABSTRACT FROM AUTHOR]

Immersive virtual reality (VR) is anticipated to peak in development this decade bringing new opportunities for 3D multimodal designing across all levels of education. The need for students to gain capabilities with multimodal texts—texts that combine two or more modes, such as spoken, written, and visual—is emphasised at all levels of education from P-12 in the Australian Curriculum. Likewise, the use of technology-supported pedagogies is increasing worldwide, rendering multimodal texts ubiquitous across all knowledge domains. This original, qualitative classroom research investigated students' 3D designing of multimodal texts using an immersive VR head-mounted display. Upper primary students (ages 10–12 years, n = 48) transferred their knowledge of ancient Rome through 2D drawing, writing, speaking, and 3D multimodal designing with VR. The application of multimodal analysis to video data, screen recordings, and think-aloud protocols, and the thematic coding of student and teacher interviews yielded four key findings: (i) VR gaming supported 3D multimodal designing through haptic and embodied experience, (ii) VR improved performance through creative redesigning, (iii) VR-supported knowledge application, consolidation, and transfer, and (iv) pedagogical strengths of VR were situated and transformed practice. This research is timely and significant given the increasing accessibility and affordability of VR and the need to connect research and pedagogical practice to support students' advanced knowledge and capabilities with multimodal learning across the curriculum. [ABSTRACT FROM AUTHOR]

Approximately 70%–75% of people worldwide have no formally registered land rights. Fit-For-Purpose Land Administration was introduced to address this problem and focuses on delineating visible cadastral boundaries from earth observation imagery. Recent studies have shown the potential of deep learning models to extract these visible cadastral boundaries automatically. However, studies are limited by the small size and geographical coverage of available datasets and by the lack of information about which cadastral boundaries are visible, i.e., associated with a physical object boundary. To overcome these problems, we present CadastreVision , a benchmark dataset containing cadastral reference data and corresponding multi-resolution earth observation imagery from The Netherlands, with a spatial resolution ranging from 0.1 m to 10 m. The ratio between visible and non-visible cadastral boundaries is essential to evaluate the potential automation level in cadastral boundary extraction from earth observation images and interpret results obtained by deep learning models. We investigate this ratio using a novel analysis pipeline that overlays cadastral reference data with visible topographic object boundaries. Our results show that approximately 72% of the total length of cadastral boundaries in The Netherlands are visible. CadastreVision will enable new developments in cadastral boundary delineation and future endeavours to investigate knowledge transfer to data-scarce areas. Our data and code is available at https://github.com/jeroengrift/cadastrevision. [Display omitted] [ABSTRACT FROM AUTHOR]

Usually, high-accuracy target detection algorithms have many model parameters, which require much storage space and a long testing time and are difficult to deploy on intelligent inspection robots with limited storage capacity and computational resource capability. Although lightweight target detection algorithms have lower model parameters, they cannot meet the demand for high-precision detection. To solve this problem, we design a lightweight target detection method SSD-KDGAN using knowledge distillation and generative adversarial networks. SSD-KDGAN transfers the feature maps generated by deeper and more complex teacher networks as shared knowledge to a student model with a small number of parameters without increasing testing costs, to improve the detection accuracy of the lightweight student model. First, we align the feature maps generated by the student model and the teacher model to avoid information loss caused by forced training between feature maps of different scales. Second, we use the knowledge of the teacher network to guide the learning of the student network through distillation loss. Finally, we make the feature maps output from the student network have similar spatial distributions as the feature maps output from the teacher network through adversarial loss. Extensive experiments on the PASCAL VOC and images in real environments validate the effectiveness of our framework. The experimental results indicate that transferring the knowledge of the teacher network to the student network can significantly improve the detection accuracy of the student network without increasing the testing cost. [ABSTRACT FROM AUTHOR]

Neural processes (NPs) are models for meta-learning which output uncertainty estimates. So far, most studies of NPs have focused on low-dimensional datasets of highly-correlated tasks. While these homogeneous datasets are useful for benchmarking, they may not be representative of realistic transfer learning. In particular, applications in scientific research may prove especially challenging due to the potential novelty of meta-testing tasks. Molecular property prediction is one such research area that is characterized by sparse datasets of many functions on a shared molecular space. In this paper, we study the application of graph NPs to molecular property prediction with DOCKSTRING, a diverse dataset of docking scores. Graph NPs show competitive performance in few-shot learning tasks relative to supervised learning baselines common in chemoinformatics, as well as alternative techniques for transfer learning and meta-learning. In order to increase meta-generalization to divergent test functions, we propose fine-tuning strategies that adapt the parameters of NPs. We find that adaptation can substantially increase NPs' regression performance while maintaining good calibration of uncertainty estimates. Finally, we present a Bayesian optimization experiment which showcases the potential advantages of NPs over Gaussian processes in iterative screening. Overall, our results suggest that NPs on molecular graphs hold great potential for molecular property prediction in the low-data setting. Scientific contribution: Neural processes are a family of meta-learning algorithms which deal with data scarcity by transferring information across tasks and making probabilistic predictions. We evaluate their performance on regression and optimization molecular tasks using docking scores, finding them to outperform classical single-task and transfer-learning models. We examine the issue of generalization to divergent test tasks, which is a general concern of meta-learning algorithms in science, and propose strategies to alleviate it. [ABSTRACT FROM AUTHOR]

Living simultaneously in worlds of subsurface darkness and cosmic light, plants transfer knowledge across time and place thresholds. They communicate through their interactions in plain sight and beyond view, often escaping the reaches of human imagination. Medicinal plants, in particular, offer the intimacy of embodied sensory knowledge. As we consume them, ecosystem functions are reenacted inside our bodies through physiological actions. Drawing on botanically centered practices incorporating field work, herbalism, and writing, this project expands creative methods within geography. Through botanical stories, I build on established knowledge adding insights and interpretations derived from ecological observations and botanical medicine practices. These stories feature ecologically and culturally important species that serve as guides for navigating emerging and ongoing environmental and social issues. Through interconnectivity with Betony (Pedicularis spp.), Globemallow (Sphaeralcea spp.), and Vervain (Verbena spp.), I explore plant perspectives for living relationally with environmental change. [ABSTRACT FROM AUTHOR]

The traditional passive heat dissipation method has low heat dissipation efficiency, which is not suitable for the heat dissipation of the concentrated heat source inside the long-focal aerial camera, resulting in temperature level changes and temperature gradients in the optical system near the heat source, which seriously affect the imaging performance of the aerial camera. To solve this problem, an active heat dissipation method of liquid cooling cycle is proposed in this paper. To improve the solving efficiency and ensure simulation accuracy, a dynamic boundary information transfer method based on grid area weighting is proposed. The thermal simulation results show that the liquid cooling method reduces the heat source temperature by 70.12%, and the boundary temperature transfer error is 0.015%. The accuracy of thermal simulation is verified by thermal test, and the simulation error is less than 6.44%. In addition, the performance of the optical system is further analyzed, and the results show that the MTF of the optical system is increased from 0.077 to 0.194 under the proposed active liquid cooling cycle heat dissipation method. [ABSTRACT FROM AUTHOR]

Using oral histories of three generations of a Tzotzil Maya Muslim family in Chiapas, Mexico, this paper examines how these members navigate and make meaning of their different identities as their traditional knowledge systems have been disrupted by land dispossession and displacement. This storied process illustrates the knowledge production and transferal processes while underscoring the critical relationship with the land that intertwines with identity formation and knowledge production of three generations of this Maya Muslim community. [ABSTRACT FROM AUTHOR]

We employ the theory of asymptotic homogenization (AH) to study the elasto‐plastic behavior of a composite medium comprising two solid phases, separated by a sharp interface and characterized by mechanical properties, such as elastic coefficients and “initial yield stresses” (i.e., a threshold stress above which remodeling is triggered), that may differ up to several orders of magnitude. We speak of “plastic” behavior because we have in mind a material behavior that, to a certain extent, resembles plasticity, although, for biological systems, it embraces a much wider class of inelastic phenomena. In particular, we are interested in studying the influence of gradient effects in the remodeling variable on the homogenized mechanical properties of the composite. The jump of the mechanical properties from one phase to the other makes the composite highly heterogeneous and calls for the determination of effective properties, that is, properties that are associated with a homogenized “version” of the original composite, and that are obtained through a suitable averaging procedure. The determination of the effective properties results convenient, in particular, when it comes to the multiscale description of inelastic processes, such as remodeling in soft or hard tissues, like bones. To accomplish this task with the aid of AH, we assume that the length scale over which the heterogeneities manifest themselves is several orders of magnitude smaller than the characteristic length scale of the composite as a whole. We identify both a fine‐scale problem and a coarse‐scale problem, each of which characterizes the elasto‐plastic dynamics of the composite at the corresponding scale, and we discuss how they are reciprocally coupled through a transfer of information from one scale to the other. In particular, we highlight how the coarse‐scale plastic distortions influence the fine‐scale problem. Moreover, in the limit of negligible hysteresis effects, we individuate two viscoplastic effective coefficients that encode the information of the two‐scale nature of the composite medium in the upscaled equations. Finally, to deal with a case study tractable semi‐analytically, we consider a multilayered composite material with an initial yield stress that is constant in each phase. Such investigation is meant to contribute to the constitution of a robust framework for devising the effective properties of hierarchical biological media. [ABSTRACT FROM AUTHOR]

A core function of the olfactory system is to determine the valence of odors. In humans, central processing of odor valence perception has been shown to take form already within the olfactory bulb (OB), but the neural mechanisms by which this important information is communicated to, and from, the olfactory cortex (piriform cortex, PC) in humans are not known. To assess communication between the 2 nodes, we simultaneously measured odor-dependent neural activity in the OB and PC from human participants while obtaining trial-by-trial valence ratings. By doing so, we could determine when subjective valence information was communicated, what kind of information was transferred, and how the information was transferred (i.e., in which frequency band). Support vector machine (SVM) learning was used on the coherence spectrum and frequency-resolved Granger causality to identify valence-dependent differences in functional and effective connectivity between the OB and PC. We found that the OB communicates subjective odor valence to the PC in the gamma band shortly after odor onset, while the PC subsequently feeds valence-related information back to the OB in the beta band. Decoding accuracy was better for negative than positive valence, suggesting a focus on negative valence. Critically, we replicated these findings in an independent data set using additional odors across a larger perceived valence range. Combined, these results demonstrate that the OB and PC communicate levels of subjective odor pleasantness across multiple frequencies, at specific time points, in a direction-dependent pattern in accordance with a two-stage model of odor processing. How is information about odor valence communicated from the olfactory bulb to the olfactory cortex in humans? This neuroimaging study reveals when the subjective valence information is communicated, as well the type of information and the frequency band in which it is transferred. [ABSTRACT FROM AUTHOR]

The study explores the clan names and praises of the Mambwe-speaking people of Northern Zambia from a social semiotics perspective. Specifically drawing on the concept of semiotic assemblages, it considers the names and praises as more than just linguistic resources but as meaning-making collections that contribute to the collective self-representation of the Mambwe people. The assembled expressions reflect their worldviews, sociocultural narratives, and identities. The study shows the interconnectedness of gender roles, bioenvironmental factors, community activities, folklore, ethnicity, and self-asserting narratives within the contexts of clan names and praises. Furthermore, it demonstrates the multi-layered meanings resulting from the emotional forces of the interactions among individuals, living and non-living objects, events and bioenvironmental factors in time-spaces. It concludes that clan names and praises are narratives of expressions that represent the psycho-social and historical paths of the Mambwe people, and they play a crucial role in the intergenerational transfer of knowledge and culture. [ABSTRACT FROM AUTHOR]

Urolithiasis is a leading urological disorder where accurate preoperative identification of stone types is critical for effective treatment. Deep learning has shown promise in classifying urolithiasis from CT images, yet faces challenges with model size and computational efficiency in real clinical settings. To address these challenges, we developed a non-invasive prediction approach for determining urinary stone types based on CT images. Through the refinement and improvement of the self-distillation architecture, coupled with the incorporation of feature fusion and the Coordinate Attention Module (CAM), we facilitated a more effective and thorough knowledge transfer. This method circumvents the extra computational expenses and performance reduction linked with model compression and removes the reliance on external teacher models, markedly enhancing the efficacy of lightweight models. achieved a classification accuracy of 74.96% on a proprietary dataset, outperforming current techniques. Furthermore, our method demonstrated superior performance and generalizability on two public datasets. This not only validates the effectiveness of our approach in classifying urinary stones but also showcases its potential in other medical image processing tasks. These results further reinforce the feasibility of our model for actual clinical deployment, potentially assisting healthcare professionals in devising more precise treatment plans and reducing patient discomfort. [ABSTRACT FROM AUTHOR]

We investigate the synchronization behavior and the emergence of chimera states in a system of two interacting populations of maps possessing chaotic neural-like dynamics. We characterize four collective states on the space of coupling parameters of the system: complete synchronization, generalized synchronization, chimera states, and incoherence. We quantify the information exchange between the two neuron populations in chimera states. We have found a well-defined direction of the flow of information in chimera states, from the desynchronized population to the synchronized one. The incoherent population functions as a driver of the coherent neuron population in a chimera state. This feature is independent of the population sizes or population partitions. Our results yield insight into the communication mechanisms arising in brain processes such as unihemispheric sleep and epileptic seizures that have been associated to chimera states. [ABSTRACT FROM AUTHOR]

Existing recommendation algorithms based on heterogeneous graphs often face performance limitations due to the sparsity and nonlinearity of the heterogeneous graph structure and semantic information, which hinders the full exploitation of the association information between users and items. In order to tackle these challenges and improve the quality of user and item feature representations, a Heterogeneous Graph Contrastive Learning Recommendation algorithm based on Attention Mechanism (HAMRec) has been proposed. To enhance the robustness of graph representations, this algorithm introduces an unsupervised contrastive learning approach and utilizes attention mechanisms on top of graph neural networks to extract both local and global information from different heterogeneous graphs. Considering the varying impact of heterogeneous auxiliary information on recommendation results in real-life scenarios, HAMRec employs personalized knowledge transfer to enhance self-supervised learning. Through a large number of experiments, it has been proven that HAMRec surpasses existing baseline models in recommendation tasks, proving its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

The rapid progress in the transfer of information and its availability are the reasons behind the widespread use of the Internet. Document images are the most complicated and challenging category among various forms of digital data to which watermarking can be applied for its security and authentication. Watermarking of the document image is a very difficult task, since these images have very limited redundancy. Because of this factor there has been very little research in this area. A comprehensive research should be carried out to ensure the effective assessment, review as well as implementation of document image watermarking techniques. This paper reviews the existing document image watermarking by considering different evaluation parameters. Based on this review, a variety of challenges and directions have been prescribed for the development of effective watermarking techniques for document images. [ABSTRACT FROM AUTHOR]

The usefulness of wireless sensor networks has grown throughout numerous areas, many of which demand constant monitoring of the event of interest by the sensor nodes. The wireless transfer of information and the connection of the sensors, however, present the network with a number of security risks. A real-world concern is a danger to source location privacy for networks when several events of interest are monitored. In this research, we present the quadrant-based boundary routing (QBR) method for multiple source location privacy protection. In the first phase, the packet is routed arbitrarily away from the source nodes by random routing, and then quadrant routing begins. Before being sent to the base station using a forward random walk, the packet is transmitted to either side of the network border. We present a theoretical analysis of the maximum delay for the proposed technique. Extensive simulations are carried out and results demonstrate that in contrast to Shortest Path Routing, QBR elevates security by 880.95%, entropy by 623.28%, and capture rate by 71.42% for multiple assets, whereas contemporary PRLPRW increases the corresponding metrics by 692%, 390%, and 49.28%, respectively. [ABSTRACT FROM AUTHOR]

This paper presents practical challenges and solutions developed while leveraging ICT tools to address the mobility needs of refugees with physical disabilities in Cox's Bazar Rohingya camps. The study sheds light on the role of ICT tools in working with vulnerable groups in conflict-prone regions. Various ICT collaboration and communication tools were utilized, including audio-visual recordings, 3D printing of customized crutch shoes, and CAD-based production of assistive devices. The research highlights disconnections in communication, design, knowledge transfer, production, and device usage. Despite these challenges, the project succeeded in developing and disseminating AT devices to physically disabled refugees. The paper adds a longitudinal aspect by presenting the results of the follow-up visit that occurred one year after the project. The research emphasizes the importance of local ICT expertise, contextualized cultural knowledge, equitable engagement with local engineers, and collaboration with refugee beneficiaries to achieve successful design, development, and implementation of assistive devices. [ABSTRACT FROM AUTHOR]