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Building on the cross-linguistic variability in the meaning of vague quantifiers, this study explores the potential for negative transfer in Italian-Slovenian bilinguals concerning the use of quantificational determiners, specifically the translational equivalents of the English "many", that is the Slovenian "precej" and "veliko". The aim is to identify relevant aspects of pragmatic knowledge for cross-linguistic influence. The study presents the results of a sentence-picture verification task in which Slovenian native speakers and Italian-Slovenian bilinguals evaluated sentences of the form "Quantifier X are Y" in relation to visual contexts. The results suggest that Italian learners of Slovenian, unlike Slovenian native speakers, fail to distinguish between "precej" and "veliko". This finding aligns with the negative transfer hypothesis. The study highlights the potential role of pragmatic knowledge in cross-linguistic transfer, particularly in the context of vague quantifiers.

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Experience with instances that vary in their surface features helps individuals to form abstract task knowledge, leading to transfer of that knowledge to novel contexts. The current study sought to examine the role of this variability effect in how adults and school-aged children learn to engage cognitive control. We focused on the engagement of cognitive control in advance (proactive control) and in response to conflicts (reactive control) in a cued task-switching paradigm, and conducted four preregistered online experiments with adults (Experiment 1A: N = 100, Experiment 1B: N = 105) and 9- to 10-year-olds (Experiment 2A: N = 98, Experiment 2B: N = 97). It was shown that prior task experience of engaging reactive control makes both adults and 9- to 10-year-olds respond more slowly in a subsequent similar-structured condition with different stimuli in which proactive control could have been engaged. 9- to 10-year-olds (Experiment 2B) exhibited more negative transfer of a reactive control mode when uninformative cue and pre-target stimuli, which do not convey task-relevant information, were changed in each block, compared with when they were fixed. Furthermore, adults showed suggestive evidence of the variability effect both when cue and target stimuli were varied (Experiment 1A) and when uninformative cue and pre-target stimuli were varied (Experiment 1B). The collective findings of these experiments provide important insights into the contribution of stimulus variability to the engagement of cognitive control.

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Currently, most models rarely consider the negative transfer problem in the research field of cross-subject EEG emotion recognition. To solve this problem, this paper proposes a semi-supervised domain adaptive algorithm based on few labeled samples of target subject, which called multi-domain geodesic flow kernel dynamic distribution alignment (MGFKD). It consists of three modules: 1) GFK common feature extractor: projects the feature distribution of source and target subjects to the Grassmann manifold space, and obtains the latent common features of the two feature distributions through GFK method. 2) Source domain selector: obtains pseudo-labels of the target subject through weak classifier, finds "golden source subjects" by using few known labels of target subjects. 3) Label corrector: uses a dynamic distribution balance strategy to correct the pseudo-labels of the target subject. We conducted comparison experiments on the SEED and SEED-IV datasets, and the results show that MGFKD outperforms unsupervised and semi-supervised domain adaptation algorithms, achieving an average accuracy of 87.51±7.68% and 68.79±8.25% on the SEED and SEED-IV datasets with only one labeled sample per video for target subject. Especially when the number of source domains is set as 6 and the number of known labels is set as 5, the accuracy increase to 90.20±7.57% and 69.99±7.38%, respectively. The above results prove that our proposed algorithm can efficiently and quickly improve the cross-subject EEG emotion classification performance. Since it only need a small number of labeled samples of new subjects, making it has strong application value in future EEG-based emotion recognition applications.

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Engaging cognitive control is essential to flexibly adapt to constantly changing environments. However, relatively little is known about how prior task experience impacts on the engagement of cognitive control in novel task environments. We aimed to clarify how individuals learn and transfer the engagement of cognitive control with a focus on the hierarchical and temporal aspects of task knowledge. Highlighting two distinct cognitive control processes, the engagement of cognitive control in advance (proactive control) and in response to conflicts (reactive control), we conducted six preregistered online experiments with both adults (Experiment 1, 3, and 5: N = 71, N = 108, and N = 70) and 9- to 10-year-olds (Experiment 2, 4, 6: N = 69, N = 108, and N = 70). Using two different experimental paradigms, we demonstrated that prior task experience of engaging reactive control makes adults and 9-to 10-year-olds respond in a reactive way in a subsequent similar-structured condition with different stimuli in which proactive control could have been engaged. This indicates that individuals do learn knowledge about the temporal structure of task goal activation and, on occasion, negatively transfer this knowledge. Furthermore, individuals exhibited these negative transfer effects in a similar-structured condition with different task goals and stimuli, indicating that they learn hierarchically-structured task knowledge. The collective findings suggest a new way of understanding how hierarchical and temporal task knowledge influences the engagement of cognitive control and highlight potential mechanisms underlying the near transfer effects observed in cognitive control training.

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This study aimed to investigate the acquisition of liaison in English by Chinese-speaking learners. Ten second-year postgraduate students of non-English majors in Tongji University, China, were invited to take part in an experiment. They were asked to prepare recordings of a set of English materials, including phrases, dialogues, and a talking topic, before and after a self-study training on liaison. To examine every type of liaison in their speech, the study analysed the recordings using the speech analysis software Praat. The results showed that before the training, the students negatively transferred the native language (L1) pattern to the target second language (L2). This kind of negative transfer of L1 Chinese to the acquisition of liaison in L2 English could be explained by the differences between English and Chinese syllables. After the training, the students showed substantial improvement in phrase and dialogue reading. The findings are expected to help both teachers and students gain a better understanding of liaison and the differences between English and Chinese syllables, thus contributing to English teaching and learning.

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Introduction: Boxing as a sport is growing on Chinese campuses, resulting in a coaching shortage. The human pose estimation technology can be employed to estimate boxing poses and teach interns to relieve the shortage. Currently, 3D cameras can provide more depth information than 2D cameras. It can potentially improve the estimation. However, the input channels are inconsistent between 2D and 3D images, and there is a lack of detailed analysis about the key point location, which indicates the network design for improving the human pose estimation technology. Method: Therefore, a model transfer with channel patching was implemented to solve the problems of channel inconsistency. The differences between the key points were analyzed. Three popular and highly structured 2D models of OpenPose (OP), stacked Hourglass (HG), and High Resolution (HR) networks were employed. Ways of reusing RGB channels were investigated to fill up the depth channel. Then, their performances were investigated to find out the limitations of each network structure. Results and discussion: The results show that model transfer learning by the mean way of RGB channels patching the lacking channel can improve the average accuracies of pose key points from 1 to 20% than without transfer. 3D accuracies are 0.3 to 0.5% higher than 2D baselines. The stacked structure of the network shows better on hip and knee points than the parallel structure, although the parallel design shows much better on the residue points. As a result, the model transfer can practically fulfill boxing pose estimation from 2D to 3D.

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In this work, we study the transfer learning problem under highdimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information from useful source data. Given which sources to transfer, we propose a transfer learning algorithm on GLM, and derive its ℓ1 / ℓ2-estimation error bounds as well as a bound for a prediction error measure. The theoretical analysis shows that when the target and source are sufficiently close to each other, these bounds could be improved over those of the classical penalized estimator using only target data under mild conditions. When we don't know which sources to transfer, an algorithm-free transferable source detection approach is introduced to detect informative sources. The detection consistency is proved under the high-dimensional GLM transfer learning setting. We also propose an algorithm to construct confidence intervals of each coefficient component, and the corresponding theories are provided. Extensive simulations and a real-data experiment verify the effectiveness of our algorithms. We implement the proposed GLM transfer learning algorithms in a new R package glmtrans, which is available on CRAN.

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Electrocardiogram classification is crucial for various applications such as the medical diagnosis of cardiovascular diseases, the level of heart damage, and stress. One of the typical challenges of electrocardiogram classification problems is the small size of the datasets, which may lead to limitation in the performance of the classification models, particularly for models based on deep-learning algorithms. Transfer learning has demonstrated effectiveness in transferring knowledge from a source model with a similar domain and can enhance the performance of the target model. Nevertheless, the consideration of datasets with similar domains restricts the selection of source domains. In this paper, electrocardiogram classification was enhanced by distant transfer learning where a generative-adversarial-network-based auxiliary domain with a domain-feature-classifier negative-transfer-avoidance (GANAD-DFCNTA) algorithm was proposed to bridge the knowledge transfer from distant sources to target domains. To evaluate the performance of the proposed algorithm, eight benchmark datasets were chosen, with four from electrocardiogram datasets and four from the following distant domains: ImageNet, COCO, WordNet, and Sentiment140. The results showed an average accuracy improvement of 3.67 to 4.89%. The proposed algorithm was also compared with existing works using traditional transfer learning, revealing an average accuracy improvement of 0.303-5.19%. Ablation studies confirmed the effectiveness of the components of GANAD-DFCNTA.

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DNNs trained for predicting cellular events from DNA sequence have become emerging tools to help elucidate biological mechanisms underlying associations identified in genome-wide association studies. To enhance the training, multi-task learning (MTL) has been commonly exploited in previous works where trained networks were needed for multiple profiles differing in either event modality or cell type. All existing works adopted a simple MTL framework where all tasks share a single feature extraction network. Such a strategy even though effective to a certain extent leads to substantial negative transfer, meaning the existence of a large portion of tasks for which models obtained through MTL perform worse than those by single-task learning. There have been methods developed to address such negative transfer in other domains, such as computer vision. However, these methods are generally with limited scalability. In this paper, we propose a highly scalable task grouping framework to address negative transfer by only jointly training tasks that are potentially beneficial to each other. The proposed method exploits the network weights associated with task-specific classification heads that can be cheaply obtained by one-time joint training of all tasks. Our results using a dataset consisting of 367 epigenetic profiles demonstrate the effectiveness of the proposed approach and its superiority over baseline methods.

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The objective of this study is to develop an adaptive software sensor technique that can predict objective process variables for a target grade in a plant while also considering information related to various other grades. We use a dataset of the target grade as the target domain and those of the other grades as source domains to perform transfer learning. Multiple models or sub-models are constructed by setting a source domain for each grade and changing the number of samples used as the source domain. Furthermore, to prevent the negative transfer, the use of a source domain is automatically judged. In this study, we constructed sub-models using the locally weighted partial least squares approach as an adaptive soft sensor technique. The values of an objective variable were predicted with ensemble learning using sub-models. The effectiveness of the proposed method was verified using a dataset measured in an actual incineration plant, and the proposed method was able to accurately predict the product quality even when the plant was operated in five grades and when a new grade was produced.

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To improve the classification results of high-resolution remote sensing images (RSIs), it is necessary to use feature transfer methods to mine the relevant information between high-resolution RSIs and low-resolution RSIs to train the classifiers together. Most of the existing feature transfer methods can only handle homogeneous data (i.e., data with the same dimension) and are susceptible to the quality of the RSIs, while RSIs with different resolutions present different feature dimensions and samples obtained from illumination conditions. To obtain effective classification results, unlike existing methods that focus only on the projection transformation in feature space, a joint feature-space and sample-space heterogeneous feature transfer (JFSSS-HFT) method is proposed to simultaneously process heterogeneous multi-resolution images in feature space using projection matrices of different dimensions and reduce the impact of outliers by adaptive weight factors in the sample space simultaneously to reduce the occurrence of negative transfer. Moreover, the maximum interclass variance term is embedded to improve the discriminant ability of the transferred features. To solve the optimization problem of JFSSS-HFT, the alternating-direction method of multipliers (ADMM) is introduced to alternatively optimize the parameters of JFSSS-HFT. Using different types of ship patches and airplane patches with different resolutions, the experimental results show that the proposed JFSSS-HFT obtains better classification results than the typical feature transferred methods.

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