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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]

Information technology (IT) innovation development within non-IT firms has been a key interest, but it is fraught with challenges because these firms lack sufficient IT knowledge. This study takes a resource dependence perspective to examine how engaging interlocking directorates with IT firms, or IT interlocks, affects non-IT firms' innovation outcomes. Despite the acknowledged role of board interlocks in knowledge transfer, the role of IT interlocks in transferring IT knowledge has not been studied. Using a large-scale panel dataset of Chinese public firms between 2000 and 2020, our findings reveal that IT interlocks of non-IT firms positively impact their IT innovation by transferring IT knowledge, particularly when the interlocked IT firms are knowledge-intensive. Our research contributes to the information systems literature by affirming IT interlocks' positive impact on innovation outcomes and highlighting the value of specific board relational capital in transferring external knowledge in need. It also offers practical implications for non-IT firms overcoming innovation challenges by establishing directorate connections with IT firms. [ABSTRACT FROM AUTHOR]

This paper evaluates the productivity impacts and the subsequent adjustment costs associated with hiring different knowledge workers. I focus on the difference between hiring former entrepreneurs, employees who change jobs, and unemployed individuals. I am the first to evaluate the direct impact that hiring former entrepreneurs has on firm productivity and the heterogenous adjustment costs associated with the different types of new hires. I find no difference between the first-year adjustment costs of entrepreneurs and those of regular-wage employees. Hiring former entrepreneurs is a way to increase productivity after the first year of employment only if the former entrepreneurs are from the highest end of the ability distribution. [ABSTRACT FROM AUTHOR]

Interorganizational partnerships can spur innovation, but their value may be diminished by friction in knowledge flows between firms. We consider how a partner's organizational structure may influence the knowledge that is accessible via partnerships. We focus on how a partner's structure trades off localized autonomy for its managers, which facilitates timelier decision making, and unified control, which facilitates integration. By shaping this balance, centralization of decision rights within the partner organization shapes access to its knowledge. Centralized structures generate wide-ranging internal knowledge pathways that enable access to a broader array of a partner's knowledge. However, the reduced managerial autonomy afforded by centralization makes decision making more cumbersome, which constricts the rate of access to a partner's knowledge. We find evidence of this tradeoff in the context of corporate venture capital relationships between incumbents and startups in the pharmaceutical industry. An increase in the incumbent's diversity of knowledge or in the knowledge required by the startup enhances the value of a greater breadth of access, whereas the degree to which the startup can leverage social ties (affinity) or hierarchical fiat (authority) alleviates the costs of a reduced access rate. Each of these features makes an incumbent organization's centralization more valuable to the startup. By highlighting this tension related to centralization, our findings suggest that new firms striving to maximize their partnership benefits may need to carefully consider their partners' internal structures. [ABSTRACT FROM AUTHOR]

We examine the relation between within-firm office changes and audit quality in the United States. Our primary analysis documents a reduction in audit quality, measured using abnormal discretionary accruals and restatements, when the client is transferred to a smaller within-firm office (downsize effect). We are unable to find evidence that clients experience significant improvement in audit quality among transfers to a larger within-firm office (upsize effect). We then condition our sample on the change in the number of public clients of the receiving office to better understand the source of the underlying association. We find that our downsize effect is driven by offices experiencing a decrease in the number of public clients, suggesting that our main association is not entirely the result of resource constraints for the receiving office. We posit that this finding is consistent with audit quality deterioration among within-firm office changes to smaller offices driven, in part, by the receiving office's inability to adequately overcome the knowledge transfer frictions that accompany a move to a new office. Our findings offer empirical evidence on consequences of within-firm office changes and are particularly relevant to regulators and preparers. [ABSTRACT FROM AUTHOR]

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]

Collective motion, such as milling, flocking, and collective turning, is a common and captivating phenomenon in nature, which arises in a group of many self-propelled individuals using local interaction mechanisms. Recently, vision-based mechanisms, which establish the relationship between visual inputs and motion decisions, have been applied to model and better understand the emergence of collective motion. However, previous studies often characterize the visual input as a transient Boolean-like sensory stream, which makes it challenging to capture the salient movements of neighbors. This further hinders the onset of the collective response in vision-based mechanisms and increases demands on visual sensing devices in robotic swarms. An explicit and context-related visual cue serving as the sensory input for decision-making in vision-based mechanisms is still lacking. Here, we hypothesize that body orientation change (BOC) is a significant visual cue characterizing the motion salience of neighbors, facilitating the emergence of the collective response. To test our hypothesis, we reveal the significant role of BOC during collective U-turn behaviors in fish schools by reconstructing scenes from the view of individual fish. We find that an individual with the larger BOC often takes on the leading role during U-turns. To further explore this empirical finding, we build a pairwise interaction mechanism on the basis of the BOC. Then, we conduct experiments of collective spin and collective turn with a real-time physics simulator to investigate the dynamics of information transfer in BOC-based interaction and further validate its effectiveness on 50 real miniature swarm robots. The experimental results show that BOC-based interaction not only facilitates the directional information transfer within the group but also leads to scale-free correlation within the swarm. Our study highlights the practicability of interaction governed by the neighbor's body orientation change in swarm robotics and the effect of scale-free correlation in enhancing collective response. Collective motion in nature, such as flocking or turning, arises from local interactions between individuals, but vision-based mechanisms often struggle to capture critical neighbor movements. This study demonstrates that body orientation change (BOC) as a visual cue enhances coordination in fish schools and robotic swarms, improving collective responses. [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]

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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]

Characterizing multipartite quantum systems is crucial for quantum computing and many-body physics. The problem, however, becomes challenging when the system size is large and the properties of interest involve correlations among a large number of particles. Here we introduce a neural network model that can predict various quantum properties of many-body quantum states with constant correlation length, using only measurement data from a small number of neighboring sites. The model is based on the technique of multi-task learning, which we show to offer several advantages over traditional single-task approaches. Through numerical experiments, we show that multi-task learning can be applied to sufficiently regular states to predict global properties, like string order parameters, from the observation of short-range correlations, and to distinguish between quantum phases that cannot be distinguished by single-task networks. Remarkably, our model appears to be able to transfer information learnt from lower dimensional quantum systems to higher dimensional ones, and to make accurate predictions for Hamiltonians that were not seen in the training. Inferring quantum properties of many-body systems is both essential and challenging. Here the authors develop a neural network model that infers these properties from short-range measurements using a quantum-adapted multi-task learning approach. [ABSTRACT FROM AUTHOR]

Leaf diseases are hazardous to the yield and quality of food crops. There are many deep learning algorithms developed for their detection that may require large computational resources to train a single model with voluminous amounts of data. In the agriculture domain, various plant diseases are found across the country. Accumulating such a huge dataset from various regions is a tedious task, and training them with a single model can also be challenging. This paper proposes Federated Deep Learning (FDL) for Plant Leaf Disease Detection. This concept allows multiple local models to get trained with their region-based datasets and share their knowledge with siblings through the parent, instead of sharing complete datasets. Knowledge transfer significantly reduces the computational costs. This paper formulates the federated dataset using PlantVillage, to simulate the configuration of the FDL. A lightweight and efficient Hierarchical Convolutional Neural Network (H-CNN) is proposed for parent model and child models with 0.09 million parameters and 0.35MB model size. The simulation results show that the proposed FDL attains 93% testing accuracy, outperforming state-of-the-art methods namely, FedAdam and FedAvg with 86.8% and 87.7% respectively. In addition to this, the proposed FDL achieves 95.7%, 95.4% and 95.3% of weighted precision, weighted recall and weighted F1-score respectively for first local model and 92.1%, 90.8% and 91.2% respectively of weighted precision, weighted recall and weighted F1-score respectively for second local model. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to the spatiotemporal design of groundwater quality monitoring networks for coastal aquifers. A fusion model combines the outputs of several developed simulation models to make estimates more accurate. A modified GALDIT method is used to incorporate the aquifer vulnerability to saltwater intrusion. The value of information (VOI) theory is applied to determine sufficient monitoring wells. The groundwater quality monitoring network is designed by employing a robust decision-making (RDM) approach under different management strategies and economic considerations. This approach incorporates the deep uncertainties of some critical variables, including water level and total dissolved solids (TDS) concentration at the coastline and pumping flow rates of agricultural wells. The new methodology is implemented in the coastal Qom-Kahak aquifer, Iran. The results illustrate that the combination model has significantly improved evaluation criteria compared to individual prediction models. The fusion model results indicate that thirty monitoring wells would be ideal. The RDM-based analyses in the Qom-Kahak aquifer showed that an optimal network with 30 monitoring wells outperforms the current network regarding various criteria, such as VOI and variance of estimation error. The new well configuration also demonstrates a suitable spatial distribution. Given that the current sampling frequencies are unsuitable for areas with varying vulnerabilities, we recommend sampling every 3 months in areas with moderate vulnerabilities and once every three seasons in areas with low vulnerabilities, based on the information transfer index. Finally, a management strategy in which the pumping rate should be less than 60% of the current rate is suggested to prevent saltwater intrusion into the aquifer. [ABSTRACT FROM AUTHOR]

Process knowledge reuse technology can improve the computer numerical control (CNC) programming efficiency and shorten the production cycle for similar parts. However, current process knowledge reuse methods focus on retrieving similar machining cases based on the machining features or topological structures of a part, which failed to deeply consider the close relationship between the 3D part geometry and CNC machining process, can result in incomplete matches between the reused knowledge and CNC process. It is more obvious for the similar complex surface parts that the most existing reuse methods are hardly to integrate 3D part geometry with CNC machining process. To overcome the problem, a reuse method for similar ruled surface blades is proposed through dual mapping mechanism in this paper, which is based on the 3D solid mapping model and spatial transformation relationship. First of all, a dual mapping mechanism is constructed to transfer process knowledge from the parameter domain to physical domain for similar ruled surface blades. Secondly, the acquired process knowledge of the ruled surface blade is characterized to the process knowledge template by the multi-vector function, followed by its inheritability is analyzed for the process knowledge reuse. Finally, two similar ruled surface blades are machined in an experiment, the machined surfaces of both blades are smooth without obvious pits in no violent vibration machining, furthermore, the surface roughness value is 4.314 µm and 4.933 µm of the two similar blades, which can meet the requirement of semi-fishing, this result indicates the correctness and validity of the process knowledge reuse method presented in this paper. [ABSTRACT FROM AUTHOR]

It is usually hard to obtain adequate annotated data for delivering satisfactory scene classification results. Semi-supervised scene classification approaches can transfer the knowledge learned from previously annotated data to remote sensing images with scarce samples for satisfactory classification results. However, due to the differences between sensors, environments, seasons, and geographical locations, cross-domain remote sensing images exhibit feature distribution deviations. Therefore, semi-supervised scene classification methods may not achieve satisfactory classification accuracy. To address this problem, a novel semi-supervised subcategory centroid alignment (SSCA)-based scene classification approach is proposed. The SSCA framework is made up of two components, namely the rotation-robust convolutional feature extractor (RCFE) and the neighbor-based subcategory centroid alignment (NSCA). The RCFE aims to suppress the impact of rotation changes on remote sensing image representation, while the NSCA aims to decrease the impact of intra-category variety across domains on cross-domain scene classification. The SSCA algorithm and several competitive approaches are validated on two datasets to demonstrate its effectiveness. The results prove that the proposed SSCA approach performs better than most competitive approaches by no less than 2% overall accuracy. [ABSTRACT FROM AUTHOR]

In real-world networks, the structure and connections are constantly evolving over time. Detecting community changes within dynamic networks has always been an important research topic. When such changes are significant, it leads to difficulty for community detection algorithms to effectively utilize valuable information from the previous network snapshot, resulting in negative transfer in the next time step. To address the issue of poor algorithm adaptability to network mutations, this paper proposed a dynamic community detection algorithm based on genetic evolution ideas and higher-order knowledge transfer strategies. Firstly, it used the adjacency matrix similarity of adjacent snapshots to determine the use of first-order or higherorder information. Then, it employed the spider Web model for population initialization, followed by the non-dominated sorting genetic algorithm NSGA-II to iteratively obtain multi-objective optimal solutions on the Pareto front. It designed a novel gene crossover method to enhance population diversity. Finally, experimental results on multiple real and simulated datasets demonstrate that, compared to existing algorithms, the proposed method achieves higher temporal smoothness in community detection results during network upheavals while maintaining a good community modularity level. [ABSTRACT FROM AUTHOR]

Multiplex network community detection methods based on graph neural network face two main challenges. Firstly, how to effectively utilize the node content information of multiplex network; and secondly, how to effectively utilize the interlayer relationships in multiplex networks. Therefore, this paper proposed the multiplex network community detection model AGCFN. Firstly, the autoencoder independently extracted the node content information of each network layer and passed the extracted node content information to the graph autoencoder for fusing the node content information of the current network layer with the topology information through the transfer operator to obtain the representation of each node of the current network layer, which made full use of the node content information of the network and the topology information of the network. The modularity maximization module and graph decoder optimized the obtained node representation. Secondly, the multilayer information fusion module fused the node representations extracted from each network layer to obtain a comprehensive representation of each node. Finally, the model under went training, and it achieved community detection results through a self-training mechanism. Comparison with six models on three datasets demonstrate improvements in both ACC and NMI evaluation metrics, thereby validating the effectiveness of AGCFN. [ABSTRACT FROM AUTHOR]

The L‐tryptophan–derived purple pigment violacein (VIO) is produced in recombinant bacteria and studied for its versatile applications. Microbial synthetic co‐cultures are gaining more importance as efficient factories for synthesizing high‐value compounds. In this work, a mutualistic and cross‐feeding Escherichia coli co‐culture is metabolically engineered to produce VIO. The strains are genetically modified by auxotrophies in the tryptophan (TRP) pathway to enable a metabolic division of labor. Therein, one strain produces anthranilate (ANT) and the other transforms it into TRP and further to VIO. Population dynamics and stability depend on the choice of carbon source, impacting the presence and thus exchange of metabolites as well as overall VIO productivity. Four carbon sources (D‐glucose, glycerol, D‐galactose, and D‐xylose) were compared. D‐Xylose led to co‐cultures which showed stable growth and VIO production, ANT‐TRP exchange, and enhanced VIO production. Best titers were ∼126 mg L–1 in shake flasks. The study demonstrates the importance and advantages of a mutualistic approach in VIO synthesis and highlights the carbon source's role in co‐culture stability and productivity. Transferring this knowledge into an up‐scaled bioreactor system has great potential in improving the overall VIO production. [ABSTRACT FROM AUTHOR]

In contrast to the other papers in this special issue, this paper reflects a specific case of co-agency between states and individual migrants, particularly students and alumni networks. Based on a qualitative case study with 29 Georgian students and 14 institutions and organizations, this paper explores the impact of Georgian alumni on polity building in Georgia. Here we can observe two phases: Firstly, the states of Georgia and of Germany act as co-agents by facilitating migration to young Georgian students by financing their studies in Germany (student scholarships) with the objective that the students return and transfer their knowledge to the country of origin. Subsequently, the Georgian alumni who studied law in Germany act as co-agents between different institutions in both countries. In doing so, they transform state institutions by themselves and contribute to the development of the legal and political system in Georgia. They do that particularly in such important legal fields like constitutional law, civic law, and criminal law, but also in the creation of parliamentarism based on a bicameral system or the promotion of separation of church and state in Georgia. That takes place after return migration as well as from Germany through transnational networks and mobility. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we design a transfer sparse identification algorithm under the Bayesian framework through introducing other system knowledge into the system to be identified. This method provides a new identification solution for a nonlinear autoregressive model with exogenous inputs (NARX). The estimates of the transferred parameters are calculated by adding the transfer correction term to the un‐transferred estimates. To achieve this, a joint prior distribution is devised for the parameters, ultimately enhancing the efficient utilization of existing data, reducing the reliance on new data, and achieving more accurate identification. The maximized marginal likelihood method is used to find the transfer gain and the transfer information matrix in the transfer correction term. Meanwhile, in order to make the algorithm automatically adapt to different data, we design an automatic structure detection method based on the transfer framework. The method automatically determines the sparsity threshold based on the maximum inter‐class variance. Two examples are provided to demonstrate the advantages of our algorithm. [ABSTRACT FROM AUTHOR]

The integration of hippocampal oscillations during non-rapid eye movement (NREM) sleep is crucial for memory consolidation. However, how cardinal sleep oscillations bind across various subfields of the human hippocampus to promote information transfer and synaptic plasticity remains unclear. Using human intracranial recordings from 25 epilepsy patients, we find that hippocampal subfields, including DG/CA3, CA1, and SUB, all exhibit significant delta and spindle power during NREM sleep. The DG/CA3 displays strong coupling between delta and ripple oscillations with all the other hippocampal subfields. In contrast, the regions of CA1 and SUB exhibit more precise coordination, characterized by event-level triple coupling between delta, spindle, and ripple oscillations. Furthermore, we demonstrate that the synaptic plasticity within the hippocampal circuit, as indexed by delta-wave slope, is linearly modulated by spindle power. In contrast, ripples act as a binary switch that triggers a sudden increase in delta-wave slope. Overall, these results suggest that different subfields of the hippocampus regulate one another through diverse layers of sleep oscillation synchronization, collectively facilitating information processing and synaptic plasticity during NREM sleep. Different subfields of the hippocampus complement and regulate each other, collectively facilitating information processing and synaptic plasticity during NREM sleep. [ABSTRACT FROM AUTHOR]

Galvanic Vestibular Stimulation (GVS) has been proposed as an alternative display modality to relay information without increasing demands on the visual or auditory sensory modalities of the wearer or in environments where those modalities cannot be used (e.g., covert night operations). We further investigated this concept with four experiments designed to test: (1) thresholds at which subjects could distinguish between different GVS current amplitudes and polarities, (2) thresholds at which different bipolar (i.e., sinusoidal waveform with current oscillating between left and right directions) current frequencies were distinguishable among room temperature, hot, cold, and windy environments, (3) effects of unipolar (i.e., sinusoidal waveform with current occurring in only the left or right direction) currents on balance performance, and (4) dual-task performance among frequency and polarity modulated GVS conditions during a concordant visual search task. Subjects reliably distinguished between current amplitudes that varied from a pedestal of ± 0.6 mA by a median of 0.03 mA (range of 0.02–0.32 mA) and between unipolar currents at a median amplitude of 0.55 mA (range of 0.32–0.83 mA). GVS frequency thresholds were robust to the environment conditions tested, with no statistical differences found. Sway and balance errors were increased with unipolar currents. GVS thresholds were not impacted by the dual-task paradigm, but the visual search scores were slightly elevated when congruently performing a polarity thresholding task. Overall findings continue to support GVS use as a display modality, but some limitations are noted, such as the use of unipolar currents under scenarios where postural control is important. [ABSTRACT FROM AUTHOR]

In an era when uncertainty and complexity are more tangible than ever, decision-makers are under pressure to turn from short-term politics and departmentalization to safeguard not only current wellbeing but also the wellbeing of future generations via longer term planning and visioning. In regions, as multilevel governance systems, no actor has the power to determine the decisions of others. Place leadership demands the mobilization of different actors to define an effective strategy for future development, by combining institutional, interpretive and network power in a dynamic manner. This paper reflects on how leadership and power shape shared vision in regional foresight processes, and the role of academic knowledge. We conduct an in-depth single case study analysis of 'Euskadi 2040', the visioning initiative launched by the Basque regional government in collaboration with the research institute Orkestra. We articulate five main contributions of research institutes: facilitating the systemic character of the vision; incorporating academic action-oriented knowledge; supporting the divergence from traditional linear planning paradigms towards flexible learning-centred approaches to policy; promoting an interinstitutional and multi-agent shared discourse; and lastly, leveraging knowledge transfer within international networks. [ABSTRACT FROM AUTHOR]

Inter-species emotional relationships, particularly the symbiotic interaction between humans and dogs, are complex and intriguing. Humans and dogs share fundamental mammalian neural mechanisms including mirror neurons, crucial to empathy and social behavior. Mirror neurons are activated during the execution and observation of actions, indicating inherent connections in social dynamics across species despite variations in emotional expression. This study explores the feasibility of using deep-learning Artificial Intelligence systems to accurately recognize canine emotions in general environments, to assist individuals without specialized knowledge or skills in discerning dog behavior, particularly related to aggression or friendliness. Starting with identifying key challenges in classifying pleasant and unpleasant emotions in dogs, we tested advanced deep-learning techniques and aggregated results to distinguish potentially dangerous human--dog interactions. Knowledge transfer is used to fine-tune different networks, and results are compared on original and transformed sets of frames from the Dog Clips dataset to investigate whether DogFACS action codes detailing relevant dog movements can aid the emotion recognition task. Elaborating on challenges and biases, we emphasize the need for bias mitigation to optimize performance, including different image preprocessing strategies for noise mitigation in dog recognition (i.e., face bounding boxes, segmentation of the face or body, isolating the dog on a white background, blurring the original background). Systematic experimental results demonstrate the system's capability to accurately detect emotions and effectively identify dangerous situations or signs of discomfort in the presence of humans. [ABSTRACT FROM AUTHOR]

In recent years, the exploration of knowledge in large-scale human mobility has gained significant attention. In order to achieve a semantic understanding of human behavior and uncover patterns in large-scale human mobility, Named Entity Recognition (NER) is a crucial technology. The rapid advancements in IoT and CPS technologies have led to the collection of massive human mobility data from various sources. Therefore, there's a need for Cross-domain NER which can transfer entity information from the source domain to automatically identify and classify entities in different target domain texts. In the situation of the data-poor, how could we transfer human mobility knowledge over time and space is particularly significant, therefore this paper proposes an Adaptive Text Sequence Enhancement Module (at-SAM) to help the model enhance the association between entities in sentences in the data-poor target domains. This paper also proposes a Predicted Label-Guided Dual Sequence Aware Information Module (Dual-SAM) to improve the transferability of label information. Experiments were conducted in domains that contain hidden knowledge about human mobility, the results show that this method can transfer task knowledge between multiple different domains in the data-poor scenarios and achieve SOTA performance. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT), a large number of devices generate data. Federated learning can take advantage of the distributional nature of these devices to parallelize model training, thus speeding up model training. The data used for model training in IoT can be heterogeneous, which can greatly reduce the inference accuracy of the global model. To improve the accuracy of models, we propose a new scheme: for the knowledge transfer process, we utilize soft lables to transfer knowledge between clients, and for the parameter aggregation process, we group all the clients based on the similarity of their soft lables and select one client from each group for aggregation in each round of aggregation, and design its weight when participating in the aggregation based on the reliability of its soft lables. We demonstrate through experimentation that our approach is effective and outperforms previous algorithms, resulting in improved model accuracy. [ABSTRACT FROM AUTHOR]

This study aims to develop a more comprehensive understanding of design factors as they relate to the role of learning when implementing innovation contests. Through synthesising the principles of knowledge transfer and absorptive capacity, this study provides novel findings on the role of learning when implementing innovation contests. Using a unique data set from a six-year engagement (2015–2020), this paper presents case studies of seven firms implementing innovation contests. Firms ranged in size, industry, and type, which led to four primary data sources including 28 interviews and a range of data from 414 problem-solvers. This research contributes to the literature by demonstrating that learning is a critical design factor in innovation contests and occurs in a bidirectional way with differing levels of intensity for problem-solvers and solution-seeking firms. We find that learning is a salient factor that has direct and indirect effects on innovation contests, differing from what has been previously outlined in the innovation literature which solely focused on learning from a firm's perspective. The study contributes to practice by providing a blueprint for practitioners to make more informed decisions about the role of learning and its importance as a factor in the design and implementation of innovation contests. [ABSTRACT FROM AUTHOR]

Currently, schools are not born inclusive, and teachers report feeling that this approach is alien to their practical possibilities, so they constantly ask for training on the subject. However, this training is hardly transferred to the school. This chapter has therefore set out to analyse how training bodies in Costa Rica are doing, and how much of this effort is permeating the educational culture. The methodologies applied were studied, as well as the background of good practices, with the final intention of guiding more effective training to make inclusion a sine qua non condition of being a school. [ABSTRACT FROM AUTHOR]

Endowing tactile feedback for prosthetic hands is profound for upper‐limb amputees. However, existing prosthetic hands are generally not in possession of the embedded sensory feedback. Herein, a flexible tactile sensor array which can be integrated into an untethered soft prosthetic hand to achieve static and dynamic discrimination tasks is presented. The flexible piezoresistive sensory arrays with 25 sensor units which can be arranged on five fingers of the soft prosthetic hand are fabricated. According to the collected large‐scale tactile dataset (including pressure distribution and pressure magnitude) during different grasping tasks, a learning‐based classification model that can reveal the correspondences between tactile information and object attributes while interacting with touched objects is developed. To transfer tactile information extracted from tactile sensor arrays, a wearable vibrotactile feedback band with a spatial coding feedback strategy is implemented by selectively activating vibrotactile motors located on the skin of the upper arm. In a set of tests performed by an individual with transradial amputation and eight able‐bodied subjects, the soft prosthetic hand integrated with tactile sensor arrays can help the users regain finger tactile sensation, discriminate grasped objects, and achieve real‐time dynamic rolling detection. [ABSTRACT FROM AUTHOR]

Remote-sensing image dehazing (RSID) is crucial for applications such as military surveillance and disaster assessment. However, current methods often rely on complex network architectures, compromising computational efficiency and scalability. Furthermore, the scarcity of annotated remote-sensing-dehazing datasets hinders model development. To address these issues, a Dual-View Knowledge Transfer (DVKT) framework is proposed to generate a lightweight and efficient student network by distilling knowledge from a pre-trained teacher network on natural image dehazing datasets. The DVKT framework includes two novel knowledge-transfer modules: Intra-layer Transfer (Intra-KT) and Inter-layer Knowledge Transfer (Inter-KT) modules. Specifically, the Intra-KT module is designed to correct the learning bias of the student network by distilling and transferring knowledge from a well-trained teacher network. The Inter-KT module is devised to distill and transfer knowledge about cross-layer correlations. This enables the student network to learn hierarchical and cross-layer dehazing knowledge from the teacher network, thereby extracting compact and effective features. Evaluation results on benchmark datasets demonstrate that the proposed DVKT framework achieves superior performance for RSID. In particular, the distilled model achieves a significant speedup with less than 6% of the parameters and computational cost of the original model, while maintaining a state-of-the-art dehazing performance. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANETs) make it easy to transfer information between vehicles, and this feature is utilized to enable collaborative decision-making between vehicles to enhance the safety, economy, and entertainment of vehicle operation. The high mobility of vehicles leads to a time-varying topology between vehicles, which makes inter-vehicle information transfer challenging in terms of delay control and ensuring the stability of collaborative decision-making among vehicles. The clustering algorithm is a method aimed at improving the efficiency of VANET communication. Currently, most of the research based on this method focuses on maintaining the stability of vehicle clustering, and few methods focus on the information interaction and collaborative decision-making of vehicles in the region. In this context, this paper proposes a networking method for intra-regional vehicle information interaction, through an efficient information transmission mechanism, vehicles can quickly obtain the required information and make more accurate decisions. Firstly, this networking method utilizes DBSCAN and the proposed vehicle scoring model to form clusters, ensuring the stability and adaptability of clusters; secondly, in the process of interacting with the information, the cosine similarity is utilized to check the similarity of the information to eliminate the highly similar information, effectively reducing redundant information; and lastly, in the case of a consensus reached by the cluster, the frequency of broadcasting of information between vehicles is reduced as a way to minimize the waste of communication resources. The proposed method is simulated based on Python and Sumo platforms, and several metrics such as cluster clustering situation, information volume, and state change rate are analyzed. The results show that the method maintains better cluster stability with a 60% and 92% reduction in information overhead compared to the FVC and HCAR algorithms, respectively. [ABSTRACT FROM AUTHOR]

Environmental changes require resources that are able to adapt. This condition requires a special strategy to remain competitive. This can be realized from real resources and employee skills that match market needs (Dubey et al., 2019). Therefore, continuous training is the “way” to face business scenarios (Santana-Domínguez et al., 2022). So, employee training is the best solution to maintain company sustainability. This study aims to explore the importance of training to maintain the stability of the company’s future. This research is qualitative research with in-depth interviews as the main data collection method. The analytical method uses Foucault’s (1980) power-knowledge with three main focuses as a tool for data reduction, namely sharing, generating and using knowledge. The results of the study show that training is important to be carried out by companies due to three main reasons, namely training as a place to transfer knowledge, training to produce employee/company performance, and training as an inseparable part of the company’s sustainability process. In addition, the results show that continuous learning is a necessity that must be carried out to deal with environmental change. It is important to know that everything will change except the change itself. The next research approach is to examine the training section with a quantitative approach. [ABSTRACT FROM AUTHOR]

Universities play a crucial role in social, economic, and technological development. Over the last decades, higher education systems have experimented with multiple transformations due to social demands, socioeconomic paradigms, and external shakeouts. Even though teaching and research are still the core functions of universities, other activities are emerging within/beyond the universities' scope and boundaries to configure the "third mission." Despite the increasing importance of universities' third mission, little is known about the role of dynamic capabilities underpinning the configuration of the third mission across higher education systems. Using a unique longitudinal dataset that captures the German higher education landscape from 2000 to 2016, we investigate the effect of dynamic teaching/research capabilities for achieving the third university mission (knowledge transfer and technology commercialization). Our results reveal tensions between complementary and substitution effects when pursuing universities' three missions (teaching, research, and knowledge transfer and technology commercialization), requiring university managers' and policymakers' strategic decisions. We provide implications for university managers and the university community as well as policymakers during the re-configuration process of becoming more entrepreneurial and innovative, highlighting the relevance of effectively managing universities' dynamic capabilities. Plain English Summary: Universities have undergone significant transformations in recent decades, responding to societal demands, economic shifts, and external pressures. The third mission of universities thereby serves as a driving force and encompasses endeavors that go beyond traditional academic functions, such as knowledge transfer and technology commercialization. Despite its increasing importance, little is known about the underlying mechanisms that lead to third mission outcomes. To shed light on this crucial topic, this paper delves into the impact of dynamic teaching and research capabilities on achieving the third mission's goals. Our findings reveal goal conflicts that universities face in balancing their three missions, requiring university managers and policymakers to make strategic decisions to navigate these tensions effectively. As universities aim to become more entrepreneurial and innovative, effectively managing dynamic capabilities and making strategic decisions becomes paramount during reconfiguration processes, enabling universities to unlock their full potential for economic, technological, and societal impacts. [ABSTRACT FROM AUTHOR]

This study adopts a competitive dynamics perspective to illuminate how and when different types of knowledge transferred by a spin-out trigger parent hostility. Specifically, I propose based on the awareness-motivation-capability framework that (1) transfers of market-related and technology-related knowledge will yield hostility and that (2) market commonality, resource similarity, and competitive intensity will exacerbate this effect. Findings from 207 spin-outs support several of my hypotheses. The study contributes to the extant literature by providing a more nuanced view on knowledge legacies, in particular regarding their interplay with a spin-out's competitive positioning. Plain English Summary: In a popular view, spin-outs spawn with 'silver spoons', i.e., performance boosting knowledge transferred from their parent firms. This study shows that such 'knowledge legacies' can turn into liabilities by spurring potentially harmful parent hostility. This was more likely for transfers of market-related knowledge than for transfers of technological knowledge. Moreover, the hostility impact was also contingent on the spin-out's competitive positioning vis-à-vis the parent. Entering parents' markets amplified the hostility effect of transferred market-related knowledge. Transferred technological knowledge only posed hostility problems if a spin-out attempted to imitate the resource base of their 'home' business unit or operated in a highly competitive industry. This study adds to prior work by demonstrating the flipside of knowledge legacies (thereby suggesting a more nuanced view) and by bridging the relatively disconnected literatures on knowledge-related advantages of spin-outs and on competitive dynamics. [ABSTRACT FROM AUTHOR]

Augmentative and alternative communication (AAC) refers to methods and tools that facilitate communication for individuals with limited functional speech. This study aims to present and analyse the research status of AAC interventions for children with special needs in mainland China using a bibliometric analysis approach. The researcher conducted a literature search in the China National Knowledge Infrastructure (CNKI) database to identify relevant studies. Based on the literature search, 80 articles met the inclusion criteria. The research literature spans from 2007 to 2022, with a notable increase in publications after 2015. The majority of studies focus on children with autism, while other disabilities, such as intellectual disabilities and cerebral palsy, receive less attention. AAC interventions primarily utilize low‐technology communication aids, with limited use of high‐technology tools. Research on AAC interventions for children with special needs in mainland China has grown recently. However, it still falls short of meeting the potential demand to promote the application and development of AAC. More diverse research is needed, including various disabilities and higher‐technology AAC tools. The effectiveness of AAC in impacting users and their communication partners will increase their quality of life and social interactions. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present a case study on how the strong relationships with multiple stakeholder groups can benefit educational programs through the curriculum redesign process by using the experience of Marshall University and Intuit. Design/methodology/approach: This paper highlights how Marshall's faculty used Intuit's Design Thinking process to engage with stakeholders to innovate a curriculum that is sustainable and socially responsible and transfers workforce skills and concepts. This case describes how to: involve stakeholders to discover their pain points and desired outcomes, develop prototype curriculums from stakeholders' feedback and test and iterate prototype curriculums on stakeholders until their expectations (e.g. quality, innovation and value) of the curriculum are exceeded. Findings: Faculty applied Design Thinking to engage with stakeholders to infuse contemporary knowledge and skills that positively impact their workforce development and societal goals into the curriculum. This process promoted critical thinking and a "stakeholder-centered" orientation with various groups including AACSB and employers. These curriculum changes ultimately provide greater experiences to students while providing relevant skills of interest to employers and society. Originality/value: Stakeholders are often consulted on educational programs; however, it is unusual for an industry stakeholder to provide their best innovative practices to assist colleges with the transformation of academic curriculums. Moreover, it is unusual for higher education institutions to fully embrace these industry processes and integrate these experiences within their collective culture. Finally, the detailing of this case allows for other colleges to apply these concepts to their curriculums. [ABSTRACT FROM AUTHOR]

Human-object interaction detection can be mainly categorized into two core problems, namely human-object association detection and interaction understanding. Firstly, for association detection, previous methods tend to directly detect obvious human-object interaction pairs, while ignoring some interaction pairs that may have potential interaction relationships, which is contrary to the actual situation. Secondly, for the interaction understanding problem, traditional methods face the challenges of long-tailed distribution and zero-shot detection, which cannot flexibly deal with complex and changing real-world scenarios. To this end, adaptive Query Learning for HOI Detection via vision-language knowledge Transfer(QLDT) is proposed. Specifically, a two-stage dynamic matching scoring algorithm based on dynamically changing thresholds and scores is designed to explore obscure H-O pairs and labeling to enlarge the sample size. Secondly, a visual-language pre-trained model GLIP (Grounded Language-Image Pre-training), is introduced to enhance the model's interactive comprehension ability, extract the visual and linguistic features of the images through GLIP, minimize the gap with the predicted values using cross-entropy loss, and take the maximum value of the score with the obscure H-O pairs as the final prediction, which ensures the model's positivity. The proposed method shows excellent performance on both HICO-DET and V-COCO datasets, for HICO-DET, QLDT achieved 35.37% mAP on the full category, 30.15% mAP on the rare category, and also improved on all five zero-shot metrics. For V-COCO, 62.74% mAP and 67.71% mAP were achieved under Scenario 1 and 2, respectively. [ABSTRACT FROM AUTHOR]

In unsupervised domain adaptation (UDA), knowledge is transferred from label-rich source domains to relevant but unlabeled target domains. Current most popular state-of-the-art works suggest that performing domain alignment from the class perspective can alleviate domain shift. However, most of them based on domain adversarial which is hard to train and converge. In this paper, we propose a novel contrastive learning to improve diversity and discriminability for domain adaptation, dubbed as IDD_ICL, which improve the discriminativeness of the model while increasing the sample diversity. To be precise, we first design a novel implicits contrastive learning loss at sample-level by implicit augment sample of the source domain. While augmenting the diversity of the source domain, we can cluster the samples of the same category in the source domain together, and disperse the samples of different categories, thereby improving the discriminative ability of the model. Furthermore, we show that our algorithm is effective by implicitly learning an infinite number of similar samples. Our results demonstrate that our method doesn't require complex technologies or specialized equipment, making it readily adoptable and applicable in practical scenarios. [ABSTRACT FROM AUTHOR]