Search

Density functional theory has been widely used in quantum mechanical simulations, but the search for a universal exchange-correlation (XC) functional has been elusive. Over the last two decades, machine-learning techniques have been introduced to approximate the XC functional or potential, and recent advances in deep learning have renewed interest in this approach. In this article, we review early efforts to use machine learning to approximate the XC functional, with a focus on the challenge of transferring knowledge from small molecules to larger systems. Recently, the transferability problem has been addressed through the use of quasi-local density-based descriptors, which are rooted in the holographic electron density theorem. We also discuss recent developments using deep-learning techniques that target high-level ab initio molecular energy and electron density for training. These efforts can be unified under a general framework, which will also be discussed from this perspective. Additionally, we explore the use of auxiliary machine-learning models for van der Waals interactions. [ABSTRACT FROM AUTHOR]

The positive relationship between knowledge transfer and knowledge innovation is recognized by most scholars, while knowledge heterogeneity, as a knowledge situation of the enterprise organization, affects knowledge transfer within enterprise organizations. Therefore, as a state, knowledge transfer can be regarded as a result of knowledge heterogeneity, and it becomes the intermediary state between knowledge heterogeneity and knowledge innovation. The organizational structure of individuals within an enterprise organization may affect these three elements and their interrelationships. In this paper, data are collected by means of on-the-spot investigation and a network questionnaire for analysis. The results show that the microstructure state of the individual in the enterprise organization plays a moderating role for the first three elements. The lower that the micro-restriction of the individual is, the more favorable the knowledge heterogeneity is for the inverted U-shaped impact of knowledge transfer and knowledge innovation and the more favorable the occurrence of knowledge innovation. [ABSTRACT FROM AUTHOR]

Dissociative identity disorder (DID) is characterised by, among others, subjectively reported inter-identity amnesia, reflecting compromised information transfer between dissociative identity states. Studies have found conflicting results regarding memory transfer between dissociative identity states. Here, we investigated inter-identity amnesia in individuals with DID using self-relevant, subject specific stimuli, and behavioural and neural measures. Data of 46 matched participants were included; 14 individuals with DID in a trauma-avoidant state, 16 trauma-avoiding DID simulators, and 16 healthy controls. Reaction times and neural activation patterns related to three types of subject specific words were acquired and statistically analysed, namely non-self-relevant trauma-related words (NSt), self-relevant trauma-related words from a trauma-avoidant identity state (St), and trauma-related words from a trauma-related identity state (XSt). We found no differences in reaction times between XSt and St words and faster reaction times for XSt over NSt. Reaction times of the diagnosed DID group were the longest. Increased brain activation to XSt words was found in the frontal and parietal regions, while decreased brain activity was found in the anterior cingulate cortex in the diagnosed DID group. The current study reproduces and amalgamates previous behavioural reports as well as brain activation patterns. Our finding of increased cognitive control over self-relevant trauma-related knowledge processing has important clinical implications and calls for the redefinition of "inter-identity amnesia" to "inter-identity avoidance". [ABSTRACT FROM AUTHOR]

In many animal species, including humans, producer arousal state is considered a key modifier of vocal production and structure. Encoding of affective arousal state in vocalizations provides a rapid means of information transfer about an individual's internal state, potentially reflecting its response to external stimuli. Meerkats, Suricata suricatta , are a highly vocal species. They use close calls to maintain group cohesion while foraging. Due to their patchily distributed prey, motivation for neighbour proximity varies; being too close results in competition (increased arousal–aggression), while too far results in risks of losing the group and predation threats (increased arousal–fear). We investigated how neighbour proximity and behavioural, social and environmental context influence the acoustic structure of wild meerkats' close calls. We found little effect of neighbour distance on the majority of the acoustic parameters measured, although close calls were longer and had a higher fundamental frequency when in very close proximity. However, there was a consistent effect of the behavioural context in which the call was given across several acoustic parameters. Overall, meerkat close calls potentially convey information on current behaviour, highlighting a potential mechanism in the diversification of acoustic signals. • Vocalizations play an important role in managing group cohesion. • We investigated how meerkats' close call acoustic structure varies with context. • Close call acoustic structure varied slightly with neighbour proximity. • Behavioural context had the main influence on close call acoustic structure. • Close call acoustic structure potentially conveys current behavioural state. [ABSTRACT FROM AUTHOR]

Developing an accurate deep model for facial emotion recognition is a long-term challenge. It is because the uncertainty of emotions, stemming from the ambiguity of different emotional categories and the difference of subjective annotations, can ruin the ability of model to achieve the desired optimization. This paper constructs two distinct datasets, namely original sample set and ambiguous sample set, to explore an effective ambiguous knowledge transfer method to realize the adaptive awareness of uncertainty in facial emotion recognition. The original sample set is the weakly-augmented data with relatively low uncertainty, as most emotions are clean in reality. Meanwhile, the ambiguous sample set is strongly-augmented data that introduces feature and label bias with regard to emotion, which are with relatively high uncertainty. The proposed framework consists of two sub-nets, which are trained using the original set and the ambiguous set respectively. To achieve uncertainty-adaptive learning for two sub-nets, we introduce two modules. One is the cross-space attention consistency learning module that performs attention coupling across original and ambiguous feature spaces, achieving uncertainty-aware representation learning in feature granularity. The other is the soft-label learning module that models and utilizes uncertainty in label granularity, through aligning the posterior distributions between original label space and ambiguous label space. Experimental studies on public datasets indicate that our method is competitive with the state-of-the-art. • We establish an uncertainty-adaptive framework via exploring the bias between two kinds of sample sets. • We custom two modules namely cross-space attention consistency learning module and soft-label learning module. • The experimental results on public datasets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) is widely used in multi‐media real‐time applications for understanding human interactions through computer aided‐analysis. NLP is common in auto‐filling, voice recognition, typo‐checking applications, and so forth. Multilingual NLP requires vast data processing and interaction recognition features for leveraging content retrieval precision. To strengthen this concept, a predictive typological content retrieval method is introduced in this article. The proposed method maximizes and relies on distributed transfer learning for training multilingual interactions with pitch and tone features. The phonetic pronunciation and the previous content‐based predictions are forwarded using knowledge transfer. This knowledge is modelled using the training data and precise contents identified in the previous processing instances. For this purpose, the auto‐fill and error correction data are augmented with the training and multilingual processing databases. Depending on the current prediction and previous content, the knowledge base is updated, and further training relies on this feature. Therefore, the proposed method accurately identifies the content across multilingual NLP models. [ABSTRACT FROM AUTHOR]

Higher Education Institutions are expected to stimulate the innovation system and to contribute to the development of societies in a global perspective. Within this context, there is an increasing focus on internationalization. HEIs are developing institutional mechanisms to support internationalization while academics are expected to carry specific practices that allow them to improve the internationalization of knowledge production. At the same time, they are encouraged to engage with non-academic partners to co-produce and transfer knowledge, contributing, in this way, to economic and social development. Although there is some research on the impact of internationalization on academic entrepreneurship, studies on the effects in other dimensions of academic engagement with society are almost absent. This paper aims to fill this gap by analyzing the relationship between academics' internationalization practices and perceptions and the development of different types of academic engagement. Our analysis draws on quantitative analysis with data provided by an international survey (APIKS) and explores the Portuguese context. Findings show that the different practices and perspectives on internationalization impact differently on the way scientists engage. While there are positive effects of internationalization of research on engagement, the time academics stay abroad and the high focus on publishing in internationally high-rated journals may negatively affect academics' orientation towards the local community. This paper contributes to the debate on the role of the internationalization in the context of a knowledge-based society, considering different dimensions and a broad spectrum of knowledge-based engagement activities. [ABSTRACT FROM AUTHOR]

End-to-end speech translation (ST) has attracted substantial attention due to its less error accumulation and lower latency. Based on triplet ST data ⟨ speech-transcription-translation ⟩ , multitask learning (MTL) that utilizes machine translation ⟨ transcription-translation ⟩ or automatic speech recognition ⟨ speech-transcription ⟩ task to assist in training ST model is widely employed. However, current MTL methods often suffer from subnet role mismatch, semantic inconsistency, or usually focus only on transferring knowledge from automatic speech recognition (ASR) or machine translation (MT) task, leading to insufficient transferring of cross-task knowledge. To solve these problems, we propose the multitask co-training network (MCTN) to jointly model ST, MT, and ASR tasks. Specifically, the ASR task enables the acoustic encoder to better capture local information of speech frames, and the MT task enhances the translation capability of the model. MCTN benefits from three key aspects: a well-designed multitask framework to fully exploit the association between tasks, a model decoupling and parameter sharing method to maintain consistency in subnet roles, and a co-training strategy to utilize task information in triplet ST data. Our experiments show that MCTN achieves state-of-the-art results, when using only MuST-C dataset, and significantly outperforms strong end-to-end ST baselines and cascaded systems when external data are available. [ABSTRACT FROM AUTHOR]

Single image raindrop removal aims at recovering high-resolution images from degraded ones. However, existing methods primarily employ pixel-level supervision between image pairs to learn spatial features, thus ignoring the more discriminative frequency information. This drawback results in the loss of high-frequency structures and the generation of diverse artifacts in the restored image. To ameliorate this deficiency, we propose a novel frequency-oriented Hierarchical Fusion Network (HFNet) for raindrop image restoration. Specifically, to compensate for spatial representation deficiencies, we design a dynamic adaptive frequency loss (DAFL), which allows the model to adaptively handle the high-frequency components that are difficult to recover. To handle spatially diverse raindrops, we propose a hierarchical fusion network to efficiently learn both contextual information and spatial features. Meanwhile, a calibrated attention mechanism is proposed to facilitate the transfer of valuable information. Comparative experiments with existing methods indicate the advantages of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Aim/objective: Given an escalated interest in fostering environmental protection, scholars have associated green entrepreneurial behavior as a stimulating factor and the cornerstone of green entrepreneurial performance. Nevertheless, the underlying mechanism that nurtures university students' green entrepreneurial behavior is yet to be explored in the extant literature. Our study proposes the antecedent effects of institutional support and green knowledge transfer to enhance university students' green entrepreneurial behavior. Moreover, we also expand the boundary conditions of these relationships and suggest the mediating effect of university students' absorptive capacity and the moderating effect of environmental responsibility. Methodology: The study samples university graduates in Chinese universities (N = 434) by adopting a lagged research design spanning over three months. We assessed the proposed model through the multivariate analytical technique. Findings: The findings indicate that institutional support and green knowledge transfer significantly elevate university students' green entrepreneurial behavior. Further, these relationships are intervened considerably through absorptive capacity's mediating effect and environmental responsibility's moderating effect. Implications: By investigating the crucial roles of institutional support and green knowledge transfer in culminating university students' green entrepreneurial behavior, our study extends the boundary conditions of these relationships and investigates the hitherto unexplored moderated mediation model. [ABSTRACT FROM AUTHOR]

The capitalisation on and transfer of technological, engineering and scientific knowledge associated with empirical know-how is an important issue for the sustainability and development of manufacturing. Indeed, certain sectors of industry are facing the increasing ageing of the labour force, recruitment difficulties and high staff turnover, leading to a loss of knowledge and know-how. In a context of numerical and digital transition and the migration of processes to industry 4.0, one of major challenges manufacturers face today is their capacity to build intelligent platforms for acquiring, storing and transferring their know-how and knowledge. It is crucial to create new media and tools for staff training and development capable of capturing knowledge and reusing it to create a project history through expertise and data collection. This paper presents the methodology and guidelines for implementing electronic knowledge books (eK-Books), along with their uses. The eK-Book is a semantic web-based hypertext medium (channel) allowing stakeholders to capitalise on, structure and transfer knowledge by using concept maps, process maps, influence graphs, downloadable documents, web pages and hypermedia knowledge sheets. They are intended for engineers, expert or novice technicians, manufacturers, sector coordinators and plant managers, as well as trainers and learners. They are usable and manageable in all types of environments and with different levels of accessibility. This paper highlights (1) the transfer knowledge capacity of eK-Books and (2) their usability in two agri-food sectors namely (1) the cheese sector with protected designation of origin (PDO) and protected geographical indication (PGI), and (2) the butchery and cold meat sectors. [ABSTRACT FROM AUTHOR]

This work is dedicated to the design of flexible composite elements, specifically leaf springs. The design of these flexible composite elements took in consideration the technologies, materials and intermediate goods that are available and useable in laboratory manufacturing and the possibility for the transfer of gained knowledge to industrial practice. This work deals with individual types of materials and their processability and usability for the manufacturing of composite products exposed to cyclic stress. The impact of the designed lay-up diagrams and cyclic load boundary on the fatigue behaviour of manufactured specimens was used to evaluate the effect of cyclic stressing. Based on this assessment, a conclusion and recommendation were formulated for the serial manufacturing of flexible composite elements. [ABSTRACT FROM AUTHOR]

Sensory neurons reconstruct the world from action potentials (spikes) impinging on them. To effectively transfer information about the stimulus to the next processing level, a neuron needs to be able to adapt its working range to the properties of the stimulus. Here, we focus on the intrinsic neural properties that influence information transfer in cortical neurons and how tightly their properties need to be tuned to the stimulus statistics for them to be effective. We start by measuring the intrinsic information encoding properties of putative excitatory and inhibitory neurons in L2/3 of the mouse barrel cortex. Excitatory neurons show high thresholds and strong adaptation, making them fire sparsely and resulting in a strong compression of information, whereas inhibitory neurons that favour fast spiking transfer more information. Next, we turn to computational modelling and ask how two properties influence information transfer: 1) spike-frequency adaptation and 2) the shape of the IV-curve. We find that a subthreshold (but not threshold) adaptation, the 'h-current', and a properly tuned leak conductance can increase the information transfer of a neuron, whereas threshold adaptation can increase its working range. Finally, we verify the effect of the IV-curve slope in our experimental recordings and show that excitatory neurons form a more heterogeneous population than inhibitory neurons. These relationships between intrinsic neural features and neural coding that had not been quantified before will aid computational, theoretical and systems neuroscientists in understanding how neuronal populations can alter their coding properties, such as through the impact of neuromodulators. Why the variability of intrinsic properties of excitatory neurons is larger than that of inhibitory ones is an exciting question, for which future research is needed. Author summary: Intracellular information transfer from synaptic input to output spike train is necessarily lossy. Here, we explicitly measure the mutual information between a neuron's input and spike output and show that information transfer is more lossy and heterogeneous for excitatory than for inhibitory neurons. By using computational modelling we show that the shape of the input-output curve as well as how fast a neuron adapts to its input collectively determine the rate of information loss. These insights will help both experimentalists and modellers in designing and simulating experiments that investigate how network coding properties can adapt to the environment, for instance through the effects of neuromodulators. [ABSTRACT FROM AUTHOR]

Spin waves, known for their ability to propagate without the involvement of moving charges, hold immense promise for on-chip information transfer and processing, offering a path toward post-CMOS computing technologies. This study investigates the potential synergy between propagating Damon–Eshbach spin waves and voltage-controlled magnetization in the pursuit of environmentally sustainable computing solutions. Employing micromagnetic simulations, we assess the feasibility of utilizing spin waves in DE mode in conjunction with localized voltage-induced alterations in surface anisotropy to enable low-energy logic operations. Our findings underscore the critical importance of selecting an optimal excitation frequency and gate width, which significantly influence the efficiency of the phase shift induced in propagating spin waves. Notably, we demonstrate that a realistic phase shift of 2.5 [ π mrad ] can be achieved at a Co(5 nm)/MgO material system via the voltage-controlled magnetic anisotropy effect. Moreover, by tuning the excitation frequency, Co layer thickness, gate width, and carefully selecting the dielectric layer, we extrapolate the potential to enhance the phase shift by a factor of 200 when compared to MgO dielectrics. This research contributes valuable insights toward developing next-generation computing technologies with reduced energy consumption. [ABSTRACT FROM AUTHOR]

The detection of cracks is extremely important for maintenance of concrete structures. Deep learning-based segmentation models have achieved high accuracy in crack segmentation. However, mainstream crack segmentation models have very high computational complexity, and therefore cannot be used in portable crack detection equipment. To address this problem, a knowledge distilling structure is designed by us. In this structure, a large teacher model named TBUNet is proposed to transfer crack knowledge to a student model with symmetry structure named ULNet. In the TBUNet, stacked transformer modules are used to capture dependency relationships between different crack positions in feature maps and achieve contextual awareness. In the ULNet, only a tiny U-Net with light-weighted parameters is used to maintain very low computational complexity. In addition, a mixed loss function is designed to ensure detail and global features extracted by the teacher model are consistent with those of the student model. Our designed experiments demonstrate that the ULNet can achieve accuracies of 96.2%, 87.6%, and 75.3%, and recall of 97.1%, 88.5%, and 76.2% on the Cracktree200, CRACK500, and MICrack datasets, respectively, which is 4–6% higher than most crack segmentation models. However, the ULNet only has a model size of 1 M, which is suitable for use in portable crack detection equipment. [ABSTRACT FROM AUTHOR]

It is important to have an awareness of the factors that may impact DNA transfer. Here we perform a preliminary study on the impact of airflow on the transfer of DNA from biological materials. Common airflows at different distances from their source were determined. Biological materials on different substrates were exposed to relatively high airflows. Tests showed that airflow can transfer detectable levels of DNA away from where they were deposited, especially blood, less so saliva and touch DNA. Information on indirect transfer of biological materials due to airflow can assist activity level evaluations and considerations towards improvements in laboratory design and procedures. [ABSTRACT FROM AUTHOR]

For some years now, information organisations have been increasingly struggling with the retirement of employees who have a great deal of experience and knowledge. There is a great danger that these professional experts will take their knowledge with them into retirement or to other institutions and thus irrevocably lose it for the organisations. This article deals with the knowledge chain method, which makes it possible to effectively externalise experiential knowledge in particular. In order to apply this method effectively, it is important to understand the difference between tacit knowledge and explicit knowledge. To this end, the characteristics of tacit and explicit knowledge are analysed in more detail. [ABSTRACT FROM AUTHOR]

Promoting media literacy among senior citizens is an important step in eliminating discrimination and unequal treatment. Libraries play an important role as places for knowledge transfer. There are already many counseling and training programs offered by organizations and volunteer initiatives that cater to the needs of older people. It is important to create educational offerings that meet the specific limitations of older individuals. Various aspects of course design and learning environments are mentioned as important for successful learning. The author emphasizes the importance of tailored offerings and the need to reduce discrimination and promote the participation of senior citizens in society. [Extracted from the article]

Universal domain adaptation aims to transfer the knowledge of common classes from the source domain to the target domain without any prior knowledge on the label set, which requires distinguishing in the target domain the unknown samples from the known ones. Recent methods usually focused on categorizing a target sample into one of the source classes rather than distinguishing known and unknown samples, which ignores the inter-sample affinity between known and unknown samples, and may lead to suboptimal performance. Aiming at this issue, we propose a novel UniDA framework where such inter-sample affinity is exploited. Specifically, we introduce a knowability-based labeling scheme which can be divided into two steps: (1) Knowability-guided detection of known and unknown samples based on the intrinsic structure of the neighborhoods of samples, where we leverage the first singular vectors of the affinity matrix to obtain the knowability of every target sample. (2) Label refinement based on neighborhood consistency to relabel the target samples, where we refine the labels of each target sample based on its neighborhood consistency of predictions. Then, auxiliary losses based on the two steps are used to reduce the inter-sample affinity between the unknown and the known target samples. Finally, experiments on four public datasets demonstrate that our method significantly outperforms existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Animals that feed socially can sometimes better locate prey, often by transferring information about food that is patchy, dense, and temporally and spatially unpredictable. Information transfer is a potential benefit of living in breeding colonies where unsuccessful foragers can more readily locate successful ones and thereby improve feeding efficiency. Most studies on social foraging have been short term, and how long‐term environmental change affects both foraging strategies and the associated benefits of coloniality is generally unknown. In the colonial Cliff Swallow (Petrochelidon pyrrhonota), we examined how social foraging, information transfer, and feeding ecology changed over a 40‐year period in western Nebraska. Relative to the 1980s, Cliff Swallows in 2016–2022 were more likely to forage solitarily or in smaller groups, spent less time foraging, were more successful as solitaries, fed in more variable locations, and engaged less in information transfer at the colony site. The total mass of insects brought back to nestlings per parental visit declined over the study. The diversity of insect families captured increased over time, and some insect taxa dropped out of the diet, although the three most common insect families remained the same over the decades. Nestling Cliff Swallow body mass at 10 days of age and the number of nestlings surviving per nest declined more sharply with colony size in 2015–2022 than in 1984–1991 at sites where the confounding effects of ectoparasites were removed. Adult body mass during the provisioning of nestlings was lower in more recent years, but the change did not vary with colony size. The reason(s) for the reduction in social foraging and information transfer over time is unclear, but the consequence is that colonial nesting may no longer offer the same fitness advantages for Cliff Swallows as in the 1980s. The results illustrate the flexibility of foraging behavior and dynamic shifts in the potential selective pressures for group living. [ABSTRACT FROM AUTHOR]

The author, a Red River Métis beadwork artist and researcher, offers a brief introduction to the history and purpose of octopus/fire bags, a traditional Métis garment. She explores both her experience of reclaiming the garment design following disruption of intergenerational knowledge transfer as a result of colonialism, and details textile and garment construction processes. Reflecting on the finer points of selecting materials, choosing lining, and binding of the piece, the author takes an organic detour into the sphere of Métis fingerweaving and beadwork, and ultimately explores the meaning and visual language of the beadwork featured on Ob-La-Di, Oc-To-Pus (Life Goes On): A Modern Métis Fire Bag. [ABSTRACT FROM AUTHOR]

The limited of texture details information in low‐resolution facial or eye images presents a challenge for gaze estimation. To address this, FSKT‐GE (feature maps similarity knowledge transfer for low‐resolution gaze estimation) is proposed, a gaze estimation framework consisting of both a high resolution (HR) network and low resolution (LR) network with the identical structure. Rather than mere feature imitation, this issue is addressed by assessing the cosine similarity of feature layers, emphasizing the distribution similarity between the HR and LR networks. This enables the LR network to acquire richer knowledge. This framework utilizes a combination loss function, incorporating cosine similarity measurement, soft loss based on probability distribution difference and gaze direction output, along with a hard loss from the LR network output layer. This approach on low‐resolution datasets derived from Gaze360 and RT‐Gene datasets is validated, demonstrating excellent performance in low‐resolution gaze estimation. Evaluations on low‐resolution images obtained through 2×, 4×, and 8× down‐sampling are conducted on two datasets. On the Gaze360 dataset, the lowest mean angular errors of 10.97°, 11.22°, and 13.61° were achieved, while on the RT‐Gene dataset, the lowest mean angular errors of 6.73°, 6.83°, and 7.75° were obtained. [ABSTRACT FROM AUTHOR]

It is important to have an awareness of the factors that may impact DNA transfer. Here we perform a preliminary study on the impact of airflow on the transfer of DNA from biological materials. Common airflows at different distances from their source were determined. Biological materials on different substrates were exposed to relatively high airflows. Tests showed that airflow can transfer detectable levels of DNA away from where they were deposited, especially blood, less so saliva and touch DNA. Information on indirect transfer of biological materials due to airflow can assist activity level evaluations and considerations towards improvements in laboratory design and procedures. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis is a fine-grained task that involves jointly extracting aspect terms and their corresponding sentiment polarities. However, due to the high cost of data labeling, obtaining annotated corpora for a specific target domain is often challenging. To address this issue, previous studies have attempted to transfer knowledge from a labeled source domain to the target domain by optimizing with pseudo-labels generated for the target domain data. These approaches allow both domains to be used to learn domain-invariant features. Nevertheless, such methods are susceptible to label noise, which hinders the extraction of domain-invariant features for the target domain. To mitigate the impact of error-prone pseudo-labels, we propose a mutual mean teacher framework for cross-domain aspect-based sentiment analysis. This framework generates pseudo-labels using a peer teacher network, thereby providing more reliable and robust pseudo-labels. Additionally, to develop a robust task classifier that performs well on both the source and target domains, we maximize the mutual information between the input token representations and the probability distributions of output labels, which helps prevent the model from making overly confident and incorrect predictions. Experiments conducted on ten different domain dataset pairs demonstrate that our proposed model exhibits competitive performance improvements compared to the current state-of-the-art model. [ABSTRACT FROM AUTHOR]

Automatic Singing Label Calibration (ASLC) aims to enhance the labeling accuracy of coarse singing labels through the analysis of raw audio. However, the ASLC model faces limitations due to the challenges and costs associated with generating or augmenting real‐world songs. To address this problem, we propose a novel approach to strengthen limited singing audio using easily available musical instrument audio. Directly using the musical instrument audio as a data augmentation for the singing audio is unreliable due to the distinct differences between vocal and instrumental sounds. Therefore, we employ transfer learning, which allows relevant knowledge to be transferred from one domain to another. In the pre‐training stage, the ASLC model learns to predict the accurate labels from the musical instrument audio. We then consider the vocal as a special musical instrument and fine‐tune the pretrained ASLC model using a singing annotation data set. Experimental results demonstrate that our transfer learning‐based approach outperforms the original ASLC model. By leveraging the readily available musical instrument audio, our method achieves improved performance in enhancing the labeling accuracy of singing audio. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Purpose: Knowledge transfer which refers to the communication of knowledge from a source so that it is learned and applied by a recipient has long been a challenge for knowledge management. The purpose of this study is to understand influencing factors of transactive memory system (TMS) and knowledge transfer. Design/methodology/approach: Drawing on the theories of communication visibility, social distance and flow, this study develops a research model. Then, data are collected from users of the social media mobile App. Partial least squares-structural equation modeling (PLS-SEM) is employed to analyze data. Findings: TMS is a valid second-order construct in the social media mobile app context, which is more reflected by credibility. Meanwhile, communication visibility and social distance each have positive effects on TMS which further has a positive effect on knowledge transfer. Flow has a positive effect on knowledge transfer. Practical implications: Developers of the mobile App should carefully consider the role of information and communication technology (ICT) in supporting TMS and knowledge transfer. They should consider recommendation algorithm so that the benefit of communication visibility can be retained. They should design the feature to classify users based on similarity so as to stimulate users' feeling of close social distance. They should keep on improving features based on users' holistic experience. Originality/value: This study incorporates the perspectives of communication visibility, social distance and flow to understand TMS and knowledge transfer, presenting a new lens for research. [ABSTRACT FROM AUTHOR]

Universities are widely recognized as key players in entrepreneurial ecosystems that support entrepreneurial activities to create economic and social value in the region. Previous research has increased our understanding about the role of universities contributing to regional economic development and entrepreneurship through knowledge transfer mechanisms, increasing human capital and innovation activity. However, little is known about the contribution of universities to the actual entrepreneurial processes in spatial context. Utilizing the case study approach, this study examines university as an ecosystem actor, focusing particularly on the enabling role of universities in entrepreneurial opportunity formation. As a result, this study provides a conceptualization of four different entrepreneurial opportunity spaces, bounded by the varying degrees of knowledge applicability and opportunity proximity. This article contributes to research on regional entrepreneurial ecosystems and on the role of universities as ecosystem actors contributing to research knowledge-based entrepreneurial opportunity formation for innovative entrepreneurship. [ABSTRACT FROM AUTHOR]

Fault detection is an important part of aeroengine health management. Intelligent fault detection methods represented by machine learning have been widely studied. However, most studies assume that training and test data follow the same distribution, which is unrealistic. Due to the degradation of engine performance or change of engine operating environment, the historical operation data of aeroengines are different from the current operation data of the engine. If the engine history operation data are directly used to train a fault detection model, the fault detection of the current engine may lead to low efficiency and affect the reliability of fault detection. In order to overcome this problem, transfer learning is introduced into aircraft engine fault detection in this paper. This paper combines transfer learning with support vector data description (SVDD), a common fault detection algorithm, and proposes SVDD-based transfer learning (SVDD-TL). This algorithm takes the spherical center of the SVDD as the knowledge structure to transfer from the source domain to the target domain, which can improve the detection accuracy of the model in the target domain. A fault detection experiment for an aeroengine was designed. Single and mixed fault data were used in the experiment, and the variation of fault data quantity was considered. Experimental results showed that the proposed method can improve the fault detection accuracy of the model in the target domain and still have good detection performance when the amount of fault data changes. [ABSTRACT FROM AUTHOR]

A novel coupled approach to modeling various stages of waves generated by different types of landslides (e.g., subaerial to submerged) through divergence-free incompressible smoothed particle hydrodynamics (ISPH) was developed in this study. In this approach, coexisting fluid and granular modules interact with each other through an interaction force pair, which necessitates the accurate transfer of information related to the fluid stress tensor and velocity vectors of the complementary module. A support domain centering each particle of both phases along with specific corrective treatments caters to the averaging of information from the complementary module. The model was tested against three scenarios (with limiting criteria such as suspended load transport and hindrance to porosity conceptualization) ranging from subaerial to submerged granular slides and resulting water surface fluctuations. [ABSTRACT FROM AUTHOR]

In the simulation of two-phase flow dominated by surface tension, accurate surface tension modeling is beneficial to better reproduce and understand the mechanism of interphase flow. In this paper, based on an ISPH-FVM coupling framework, three different surface tension models are implemented and tested respectively, including the generally used continuum surface force (CSF) model, the continuous surface stress (CSS) model and the height function (HF) model. In the present ISPH-FVM coupling framework, the ISPH particle approximate interpolation technique combined with a volume fraction correction scheme is employed to ensure the volume conservation in the computational domain during the information transfer between particles and grids. Meanwhile, the three surface tension models are discretized and calculated by the volume fraction defined on the FVM grid. The volume fraction of the FVM grid is obtained by approximate interpolation of ISPH particles within the grid support domain. Several benchmark cases are tested to verify the performance of three surface tension models in the ISPH-FVM coupling method. The results show that the CSF model and CSS model have less spurious currents and better robustness than HF model under the present coupling method. In addition, the CSF model and CSS model can simulate the flow regime involving complex interface topology changes more accurately than HF model. • An ISPH-FVM coupling method is developed to simulate two-phase flows. • The volume conservation is guaranteed during the information transfer. • Three surface tension models are implemented and compared under the present coupling method. • The CSF and CSS model can get relatively small spurious currents than HF model. • The CSF and CSS model perform well in predicting the complex interface topological changes. [ABSTRACT FROM AUTHOR]

Knowledge Translation (KT) aims to convey novel ideas to relevant stakeholders, motivating their response or action to improve people's health. Initially, the KT literature focused on evidence-based medicine, applying findings from laboratory and clinical research to disease diagnosis and treatment. Since the early 2000s, the scope of KT has expanded to include decision-making with health policy implications. This systematic scoping review aims to assess the evolving knowledge-to-policy concepts, that is, macro-level KT theories, models and frameworks (KT TMFs). While significant attention has been devoted to transferring knowledge to healthcare settings (i.e. implementing health policies, programmes or measures at the meso-level), the definition of 'context' in the realm of health policymaking at the macro-level remains underexplored in the KT literature. This study aims to close the gap. A total of 32 macro-level KT TMFs were identified, with only a limited subset of them offering detailed insights into contextual factors that matter in health policymaking. Notably, the majority of these studies prompt policy changes in low- and middle-income countries and received support from international organisations, the European Union, development agencies or philanthropic entities. [ABSTRACT FROM AUTHOR]

Natural image matting focuses on accurately estimating the opacity of the foreground object in an arbitrary background. Recently, deep learning-based approaches made significant progress in the matting task benefit from their powerful learning ability for semantic features. However, artifacts, blurry structures, and miscalculated pixels still often appear in some difficult regions with background interference and complex details. To address the above issues, we propose a cross-layer contextual information propagation mechanism (CCIP) that can explicitly model the long-range correlations between global and unknown regions. Specifically, we first calculate region affinity at high-level features with rich structure and semantic information; then reconstruct the adjacent low-level features by propagating information from the global region to the unknown region under the guidance of the affinity matrix; finally, transfer the reconstructed information to the corresponding decoder stage to further improve the feature distinctiveness. In addition, we design a simple and effective supervision strategy in a deep-to-shallow manner to gradually optimize the edges and details of the foreground object. We conducted extensive experiments on the common dataset Composition-1k, the alphamatting.com benchmark, and some real-world images. Compared with previous methods, the proposed method achieves competitive performance on the Composition-1k dataset (30.3 on SAD, 6.8 on MSE, 13.3 on Grad, and 26.7 on Con) and alphamatting.com benchmark (17 on average SAD rank and 16.8 on average Grad rank), while simultaneously yielding high-quality matting results on real-world images. [ABSTRACT FROM AUTHOR]

Organizations are vulnerable to cyber attacks as they rely on computer networks and the internet for communication and data storage. While Reinforcement Learning (RL) is a widely used strategy to simulate and learn from these attacks, RL-guided offensives against unknown scenarios often lead to early exposure due to low stealth resulting from mistakes during the training phase. To address this issue, this work evaluates if the use of Knowledge Transfer Techniques (KTT), such as Transfer Learning and Imitation Learning, reduces the probability of early exposure by smoothing mistakes during training. This study developed a laboratory platform and a method to compare RL-based cyber attacks using KTT for unknown scenarios. The experiments simulated 2 unknown scenarios using 4 traditional RL algorithms and 4 KTT. In the results, although some algorithms using KTT obtained superior results, they were not so significant for stealth during the initial epochs of training. Nevertheless, experiments also revealed that throughout the entire learning cycle, Trust Region Policy Optimization (TRPO) is a promising algorithm for conducting cyber offensives based on Reinforcement Learning. [ABSTRACT FROM AUTHOR]

Graph domain adaptation (GDA) aims to address the challenge of limited label data in the target graph domain. Existing methods such as UDAGCN, GRADE, DEAL, and COCO for different-level (node-level, graph-level) adaptation tasks exhibit variations in domain feature extraction, and most of them solely rely on representation alignment to transfer label information from a labeled source domain to an unlabeled target domain. However, this approach can be influenced by irrelevant information and usually ignores the conditional shift of the downstream predictor. To effectively address this issue, we introduce a target-oriented unsupervised graph domain adaptive framework for graph adaptation called TO-UGDA. Particularly, domain-invariant feature representations are extracted using graph information bottleneck. The discrepancy between two domains is minimized using an adversarial alignment strategy to obtain a unified feature distribution. Additionally, the meta pseudo-label is introduced to enhance downstream adaptation and improve the model's generalizability. Through extensive experimentation on real-world graph datasets, it is proved that the proposed framework achieves excellent performance across various node-level and graph-level adaptation tasks. [ABSTRACT FROM AUTHOR]

Background: Informativeness, in the context of clinical trials, defines whether a study's results definitively answer its research questions with meaningful next steps. Many clinical trials end uninformatively. Clinical trial protocols are required to go through reviews in regulatory and ethical domains: areas that focus on specifics outside of trial design, biostatistics, and research methods. Private foundations and government funders rarely require focused scientific design reviews for these areas. There are no documented standards and processes, or even best practices, toward a capability for funders to perform scientific design reviews after their peer review process prior to a funding commitment. Main body: Considering the investment in and standardization of ethical and regulatory reviews, and the prevalence of studies never finishing or failing to provide definitive results, it may be that scientific reviews of trial designs with a focus on informativeness offer the best chance for improved outcomes and return-on-investment in clinical trials. A maturity model is a helpful tool for knowledge transfer to help grow capabilities in a new area or for those looking to perform a self-assessment in an existing area. Such a model is offered for scientific design reviews of clinical trial protocols. This maturity model includes 11 process areas and 5 maturity levels. Each of the 55 process area levels is populated with descriptions on a continuum toward an optimal state to improve trial protocols in the areas of risk of failure or uninformativeness. Conclusion: This tool allows for prescriptive guidance on next investments to improve attributes of post-funding reviews of trials, with a focus on informativeness. Traditional pre-funding peer review has limited capacity for trial design review, especially for detailed biostatistical and methodological review. Select non-industry funders have begun to explore or invest in post-funding review programs of grantee protocols, based on exemplars of such programs. Funders with a desire to meet fiduciary responsibilities and mission goals can use the described model to enhance efforts supporting trial participant commitment and faster cures. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has highlighted the importance of local evidence ecosystems in which academia and practice in the Public Health Service (PHS) are interconnected. However, appropriate organizational structures and well-trained staff are lacking and evidence use in local public health decision-making has to be integrated into training programs in Germany. To address this issue, we developed a framework incorporating a toolbox to conceptualize training programs designed to qualify public health professionals for working at the interface between academia and practice. We conducted a scoping review of training programs, key-informant interviews with public health experts, and a multi-professional stakeholder workshop and triangulated their output. The resulting toolbox consists of four core elements, encompassing 15 parameters: (1) content-related aspects, (2) context-related aspects, (3) aspects relevant for determining the training format, and (4) aspects relevant for consolidation and further development. Guiding questions with examples supports the application of the toolbox. Additionally, we introduced a how-to-use guidance to streamline the creation of new training programs, fostering knowledge transfer at the academia-practice interface, equipping public health researchers and practitioners with relevant skills for needs-based PHS research. By promoting collaborative training development across institutions, our approach encourages cross-institutional cooperation, enhances evidence utilization, and enables efficient resource allocation. This collaborative effort in developing training programs within local evidence ecosystems not only strengthens the scientific and practical impact but also lays a foundation for implementing complex public health measures effectively at the local level. [ABSTRACT FROM AUTHOR]

Tissue development occurs through a complex interplay between many individual cells. Yet, the fundamental question of how collective tissue behavior emerges from heterogeneous and noisy information processing and transfer at the single-cell level remains unknown. Here, we reveal that tissue scale signaling regulation can arise from local gap-junction mediated cell-cell signaling through the spatiotemporal establishment of an intermediate-scale of transient multicellular communication communities over the course of tissue development. We demonstrated this intermediate scale of emergent signaling using Ca2+ signaling in the intact, ex vivo cultured, live developing Drosophila hematopoietic organ, the lymph gland. Recurrent activation of these transient signaling communities defined self-organized signaling "hotspots" that gradually formed over the course of larva development. These hotspots receive and transmit information to facilitate repetitive interactions with nonhotspot neighbors. Overall, this work bridges the scales between single-cell and emergent group behavior providing key mechanistic insight into how cells establish tissue-scale communication networks. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify and characterize the actors involved in river restoration in Romania by untangling the complex networks that they are part of. Actors were considered nodes in a social network, tied by a common action or project. The nodes were characterized through the centrality metrics of a network. The network of actors in river restoration in Romania has a low density and high average path length and diameter for such a spare network, which suggests that knowledge transfer and collaboration are difficult. In the context of a highly centralized water governance system in Romania, the National Authority for Water exerts power and influence in river restoration as well. It has numerous partners, both disconnected and well connected in the network, therefore, it tends to be connected to everybody, as well as gets and diffuses knowledge. Yet, other actors are willing to get involved in river restoration in Romania. NGOs and research organizations have a central position in the network and play various roles (e.g., unconcerned influencers, gate-keepers, pulse-takers). As a recommendation for governing river restoration in Romania, we urge the creation of a national programme focused on river restoration, where the call for projects would be coordinated by the National Authority for Water and open to its territorial subordinates in agreement with stakeholders from various sectors and domains. [ABSTRACT FROM AUTHOR]

In January 1940, the Wehrmacht had only a two-month supply of torpedo oil, an irreplaceable lubricant derived solely from the feet and shinbones of slaughtered cattle. The Wehrmacht resolved this shortage by appointing the man responsible for its supply during the First World War: Alfons Knetsch. This case study shows not only the importance of knowledge transfer from one war to the other but also how competing industrial interests shaped Nazi recycling policy and how an efficient, vigorously managed recycling organization could buy time for the research and development of substitutes, overcoming seemingly impossible raw material bottlenecks. [ABSTRACT FROM AUTHOR]

With the development of modern society, business organizations have higher and higher requirements for the efficiency of cloud computing services. In order to improve the comprehensive computing capability of cloud computing network, it is very important to optimize its end-side computing power. This research takes the Hadoop platform as the computing end-side cloud computing network structure as the research object, and designs a Hadoop end-side multi-granularity and multi-level multi-level network that integrates the Graphics processing unit (GPU) and the information transfer interface (Multi Point Interface, MPI). Hierarchical computing power optimization scheduling model and improved microservice deployment s11trategy that integrates multi-level resources. The performance verification experiment results show that the mean value of all node balance ratios of the original strategy and the improved strategy on computing resource-oriented, memory resource-oriented, and disk resource-oriented microservices are 0.13 and 0.12, 0.21 and 0.17, and 0.22 and 0.19, respectively. The value of the service instance cost in the scheme using the critical path optimization scheduling strategy is always at a low level, while the instance cost value of the native strategy is significantly higher than the former. It can be seen that the end-side computing power optimization scheduling model designed in this study can indeed play a role in improving the computing performance of the end-side computing power network. [ABSTRACT FROM AUTHOR]

Cross-lingual event retrieval is an information retrieval task aimed at cross-lingual event retrieval among multiple languages to find text or documents related to a specific event. Specific to Chinese-Vietnamese cross-language event retrieval, it involves using Chinese as a query to retrieve Vietnamese documents related to the query event. The critical issue is how to efficiently align query and document representations with limited resources. Existing cross-language pre-training models are trained on large-scale multilingual corpora, but their training goals do not include explicit language alignment tasks. Due to the uneven distribution of training corpora between different languages, these models have The problem of language bias. Therefore, this linguistic bias is also inherited in cross-lingual retrieval based on these models. To solve this problem, this paper proposes a Chinese-Vietnamese cross-lingual event retrieval method based on knowledge distillation. This approach enables the model to learn good query-document matching features from monolingual retrieval by transferring knowledge from high-resource to low-resource languages. By enhancing the alignment between queries and documents in different languages in a shared semantic space, the method improves the performance of Chinese-Vietnamese cross-lingual event retrieval. [ABSTRACT FROM AUTHOR]

Whisper speaker recognition (WSR) has received extensive attention from researchers in recent years, and it plays an important role in medical, judicial, and other fields. Among them, the establishment of a whisper dataset is very important for the study of WSR. However, the existing whisper dataset suffers from the problems of a small number of speakers, short speech duration, and lack of neutral speech with the same-text as the whispered speech in the same dataset. To address this issue, we present Whisper40, a multi-person Chinese WSR dataset containing same-text neutral speech spanning around 655.90 min sourced from volunteers. In addition, we use the current state-of-the-art speaker recognition model to build a WSR baseline system and combine the idea of transfer learning for pre-training the speaker recognition model using neutral speech datasets and transfer the empirical knowledge of specific network layers to the WSR system. The Whisper40 and CHAINs datasets are then used to fine-tune the model with transferred specific layers. The experimental results show that the Whisper40 dataset is practical, and the time delay neural network (TDNN) model performs well in both the same/cross-scene experiments. The equal error rate (EER) of Chinese WSR after transfer learning is reduced by 27.62% in comparison. [ABSTRACT FROM AUTHOR]

Transfer learning, focusing on information borrowing to address limited sample size issues, has gained increasing attention in recent years. Our method aims to utilize data from other population groups as a complement to enhance risk factor discernment and failure time prediction among underrepresented subgroups. However, a literature gap exists in effective knowledge transfer from the source to the target for risk assessment with interval‐censored data while accommodating population incomparability and privacy constraints. Our objective is to bridge this gap by developing a transfer learning approach under the Cox proportional hazards model. We introduce the tuning‐free Trans‐Cox‐MIC algorithm, enabling adaptable information sharing in regression coefficients and baseline hazards, while ensuring computational efficiency. Our approach accommodates covariate distribution shifts, coefficient variations, and baseline hazard discrepancies. Extensive simulations showcase the method's accuracy, robustness, and efficiency. Application to the prostate cancer screening data demonstrates enhanced risk estimation precision and predictive performance in the African American population. [ABSTRACT FROM AUTHOR]

Deep models may have disappointing performance in real applications due to the domain shifts in data distributions between the source and target domain. Although a few unsupervised domain adaptation methods have been proposed to make the pre-train models effective on target domain datasets, constraints like data privacy, security, and transmission limits restrict access to VHR remote sensing images, making existing unsupervised domain adaptation methods almost ineffective in specific change detection areas. Therefore, we propose a source-free unsupervised domain adaptation change detection structure to complete specific change detection tasks, using only the pre-trained source model and unlabelled target data. The GAN-based source generation component is designed to generate synthetic source data, which, to some extent, reflects the distribution of the source domain. Moreover, these data can be utilised in model knowledge transfer. The model adaptation component facilitates knowledge transfer between models by minimising the differences between deep features, using AAM (Attention Adaptation Module) to extract the difference between high-level features, meanwhile we proposed ISM (Intra-domain Self-supervised Module) to train target model in a self-supervised strategy in order to improve the knowledge adaptation. Our SFDA-CD framework demonstrates superior accuracy over existing unsupervised domain adaptation change detection methods, which has 0.6% cIoU and 1.5% F1 score up in cross-regional tasks and 1.4% cIoU and 1.9% F1 score up in cross-scenario tasks, proving that it can effectively reduce the domain shift between the source and target domains even without access to source data. Additionally, it can facilitate knowledge transfer from the source model to the target model. [ABSTRACT FROM AUTHOR]

Permeable surface mapping, which mainly is the identification of surface materials that will percolate, is essential for various environmental and civil engineering applications, such as urban planning, stormwater management, and groundwater modeling. Traditionally, this task involves labor-intensive manual classification, but deep learning offers an efficient alternative. Although several studies have tackled aerial image segmentation, the challenges in permeable surface mapping arid environments remain largely unexplored because of the difficulties in distinguishing pixel values of the input data and due to the unbalanced distribution of its classes. To address these issues, this research introduces a novel approach using a parallel U-Net model for the fine-grained semantic segmentation of permeable surfaces. The process involves binary classification to distinguish between entirely and partially permeable surfaces, followed by fine-grained classification into four distinct permeability levels. Results show that this novel method enhances accuracy, particularly when working with small, unbalanced datasets dominated by a single category. Furthermore, the proposed model is capable of generalizing across different geographical domains. Domain adaptation is explored to transfer knowledge from one location to another, addressing the challenges posed by varying environmental characteristics. Experiments demonstrate that the parallel U-Net model outperforms the baseline methods when applied across domains. To support this research and inspire future research, a novel permeable surface dataset is introduced, with pixel-wise fine-grained labeling for five distinct permeable surface classes. In summary, in this work, we offer a novel solution to permeable surface mapping, extend the boundaries of arid environment mapping, introduce a large-scale permeable surface dataset, and explore cross-area applications of the proposed model. The three contributions are enhancing the efficiency and accuracy of permeable surface mapping while progressing in this field. [ABSTRACT FROM AUTHOR]

With the growing significance of data privacy protection, Source‐Free Domain Adaptation (SFDA) has gained attention as a research topic that aims to transfer knowledge from a labeled source domain to an unlabeled target domain without accessing source data. However, the absence of source data often leads to model collapse or restricts the performance improvements of SFDA methods, as there is insufficient true‐labeled knowledge for each category. To tackle this, Source‐Free Active Domain Adaptation (SFADA) has emerged as a new task that aims to improve SFDA by selecting a small set of informative target samples labeled by experts. Nevertheless, existing SFADA methods impose a significant burden on human labelers, requiring them to continuously label a substantial number of samples throughout the training period. In this paper, a novel approach is proposed to alleviate the labeling burden in SFADA by only necessitating the labeling of an extremely small number of samples on a one‐time basis. Moreover, considering the inherent sparsity of these selected samples in the target domain, a Self‐adaptive Clustering‐based Active Learning (SCAL) method is proposed that propagates the labels of selected samples to other datapoints within the same cluster. To further enhance the accuracy of SCAL, a self‐adaptive scale search method is devised that automatically determines the optimal clustering scale, using the entropy of the entire target dataset as a guiding criterion. The experimental evaluation presents compelling evidence of our method's supremacy. Specifically, it outstrips previous SFDA methods, delivering state‐of‐the‐art (SOTA) results on standard benchmarks. Remarkably, it accomplishes this with less than 0.5% annotation cost, in stark contrast to the approximate 5% required by earlier techniques. The approach thus not only sets new performance benchmarks but also offers a markedly more practical and cost‐effective solution for SFADA, making it an attractive choice for real‐world applications where labeling resources are limited. [ABSTRACT FROM AUTHOR]

This article examines the English translations of works by Johann August Neander (the reputed father of modern church history) in order to consider his transatlantic influence. American editions of Neander's work supported the development of church historiography in nineteenth-century America, and influenced the direction of mediating theology at American academic and religious institutions. Besides identifying the agents who championed these translation efforts, the article explores how translations of church history texts supported knowledge transfer from Germany to America. Neander's books received positive attention in the translation culture of the 1830s, and he gained a reputation as a model scholar of church history. [ABSTRACT FROM AUTHOR]

System knowledge and reasoning mechanisms are essential means for intellectualization of cyber-physical systems (CPSs). As enablers of system intelligence, they make such systems able to solve application problems and to maintain their efficient operation. Normally, system intelligence has a human-created initial part and a system-produced (extending) part, called synthetic system intelligence (SSI). This position paper claims that SSI can be converted to a new industrial asset and utilized as such. Unfortunately, no overall theory of SSI exists and its conceptual framework, management strategy, and computational methodologies are still in a premature stage. This is the main reason why no significant progress has been achieved in this field, contrary to the latent potentials. This paper intends to contribute to: (i) understanding the nature and fundamentals of SSI, (ii) systematizing the elicitation and transfer of SSI, (iii) exploration of analogical approaches to utilization of SSI, and (iv) road-mapping and scenario development for the exploitation of SSI as an industrial asset. First, the state of the art is surveyed and the major findings are presented. Then, four families of analogical approaches to SSI transfer are analyzed. These are: (i) knowledge transfer based on repositories, (ii) transfer among agents, (iii) transfer of learning resources, and (iv) transfer by emerging approaches. A procedural framework is proposed that identifies the generic functionalities needed for a quasi-autonomous handling of SSI as an industrial asset. The last section casts light on some important open issues and necessary follow-up research and development activities. [ABSTRACT FROM AUTHOR]