Recently, wideband microwave spectroscopy (WBMS) has been applied for material characterization. Blood glucose sensing through microwave spectroscopy is usually done with resonant frequency-domain methods. Time-domain (TD) WBMS is a low-cost and conv...
Domain adaptation is a learning strategy that aims to improve the performance of models in the current field by leveraging similar domain information. In order to analyze the effects of feature disentangling on domain adaptation and evaluate a model&...
Emotion Recognition in Conversation (ERC) aims to recognize the emotion for each utterance in a conversation automatically. Due to the difficulty of collecting and labeling, this task lacks the dataset corpora available on a large scale. This increas...
Unsupervised domain adaptation (UDA) effectively transfers knowledge learned from a labeled source domain to an unlabeled target domain. The teacher–student framework, which generates pseudo-labels for target domain samples and uses them for ps...
Most mainstream statistical models will achieve poor performance in Out-Of-Distribution (OOD) generalization. This is because these models tend to learn the spurious correlation between data and will collapse when the domain shift exists. If we want...
Cross-domain scene classification aims to mitigate the distribution discrepancy between domains through domain adaptation techniques. With the rapid advancement of Vision–Language Models (VLMs), utilizing them for cross-domain scene classificat...
Cross-domain sentiment analysis remains challenging due to distributional shifts and heterogeneous sentiment expressions across platforms. Existing domain adaptation approaches primarily focus on enforcing domain-invariant representations. However, s...
Traditional machine learning methods rely on the training data and target data having the same feature space and data distribution. The performance may be unacceptable if there is a difference in data distribution between the training and target data...
Contemporary software systems focus on usability and accessibility from the point of view of effectiveness and ergonomics. However, the correct usage of the domain dictionary and the description of domain relations and properties via their user inter...
Certain progress has been made in fault diagnosis under cross-domain scenarios recently. Most researchers have paid almost all their attention to promoting domain adaptation in a common space. However, several challenges that will cause negative tran...
Although a large number of databases are available for regulatory elements, a bottleneck has been created by the lack of bioinformatics tools to predict the interaction modes of regulatory elements. To reduce this gap, we developed the Arabidopsis Tr...
Industry 5.0 requires resilient, adaptive, and trustworthy manufacturing systems capable of maintaining reliable diagnostic performance across heterogeneous industrial environments. However, data-driven fault diagnosis models often experience substan...
Information service platforms or management information systems of various institutions or sectors of enterprises are gradually interconnected to form a multi-domain environment. A multi-domain environment is convenient for managers to supervise and...
Few-shot domain adaptation (FSDA) has become an important approach for cross-domain intrusion detection by enabling models to leverage limited labeled target data under distribution shifts. Although numerous adaptation methods have been proposed, the...
To minimize dependency on the availability of data labels, some WiFi-CSI based-gesture recognition solutions utilize an unsupervised representation learning phase prior to fine-tuning downstream task classifiers. In this case, however, the overall pe...
Tim-4 promotes the engulfment of apoptotic cells or exogenous particles by securing them on phagocytes. It is unable to transduce signals by itself but helps other engulfment receptors sense and internalize them. However, the identity of the engulfme...
Faces and words are ever-present stimuli in social environments that require fine-grained, efficient discrimination of their constituents in order to acquire meaning. Provided that these stimuli share multiple characteristics, while simultaneously be...
Recent breakthroughs in artificial intelligence are accelerating the intelligent transformation of vehicles. Vehicle electronic and electrical architectures are converging toward centralized domain controllers. Deep learning, reinforcement learning,...
Lipid domains less than 200 nm in size may form a scaffold, enabling the concerted function of plasma membrane proteins. The size-regulating mechanism is under debate. We tested the hypotheses that large values of spontaneous monolayer curvature are...
Facial expression recognition (FER) is a key task in affective computing and human–computer interaction, aiming to decode facial muscle movements into emotional categories. Although deep learning-based FER has achieved remarkable progress, robu...
Domain adaptation is an effective approach to improve the generalization ability of deep learning methods, which makes a deep model more stable and robust. However, these methods often suffer from a deployment problem when deep models are deployed on...
Conventional deep learning methods for fault detection often assume that the training and the testing sets share the same fault domain spaces. However, some fault patterns are rare, and many real-world faults have not appeared in the training set. As...
Day–night image classification under low-light conditions faces substantial domain shift, uncertain target-domain predictions, and unreliable pseudo-label supervision. To address these challenges, this paper proposes an unsupervised day–n...
In this era of big data, Multi-source Domain Adaptation (MDA) becomes more and more popular and is employed to make full use of available source data collected from several different, but related domains. Although multiple source domains provide much...
Entity-based information extraction is one of the main applications of Natural Language Processing (NLP). Recently, deep transfer-learning utilizing contextualized word embedding from pre-trained language models has shown remarkable results for many...
In this paper, we present a Unified Framework for cross-domain Space drone Pose Estimation (UF-SPE), addressing the simulation-to-reality gap that limits the deployment of deep learning models in real space missions. The proposed UF-SPE framework int...
Plant disease recognition models trained under laboratory conditions often degrade markedly after cross-domain transfer because of the pronounced distribution gap between source and target domains and the scarcity of labeled target-domain samples. To...
Rapid and accurate acquisition of landslide inventories is essential for effective disaster relief. Deep learning-based pixel-wise semantic segmentation of remote sensing imagery has greatly advanced in landslide mapping. However, the heavy dependanc...
The accurate detection of relevant vehicles, pedestrians, and other targets on the road plays a crucial role in ensuring the safety of autonomous driving. In recent years, object detectors based on Transformers or CNNs have achieved excellent perform...
Unsupervised domain adaptation (UDA) methods, based on adversarial learning, employ the means of implicit global and class-aware domain alignment to learn the symmetry between source and target domains and facilitate the transfer of knowledge from a...
Deep neural networks suffer performance drops when source and target domains differ in distribution, motivating research into domain adaptation (DA). Traditional DA approaches presume source samples come from a single domain and can be available duri...
Recent years have witnessed outstanding success in supervised domain adaptive person re-identification (ReID). However, the model often suffers serious performance drops when transferring to another domain in real-world applications. To address the d...
Inter-domain communication in time-sensitive networks (TSN) has been identified as a requirement for various use cases. The TSN Toolbox provides standards for dynamic stream reservations, which is needed for, e.g., batch size 1 production, but these...
In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method...
The transfer learning method, based on unsupervised domain adaptation (UDA), has been broadly utilized in research on fault diagnosis under variable working conditions with certain results. However, traditional UDA methods pay more attention to extra...
Domain boundary engineering endeavors to develop materials that contain localized functionalities inside domain walls, which do not exist in the bulk. Here we review multiferroic devices that are based on ferroelectricity inside ferroelastic domain b...
Cross-domain relation extraction has become an essential approach when target domain lacking labeled data. Most existing works adapted relation extraction models from the source domain to target domain through aligning sequential features, but failed...
This paper presents a detailed analysis of a convolutional neural network based on multi-valued neurons (CNNMVN) and a fully connected multilayer neural network based on multi-valued neurons (MLMVN), employed here as a convolutional neural network in...
A special type of Streptococcus mutans expressing collagen-binding proteins (CBPs), Cnm, and Cbm, on the cell surface has been shown to be highly pathogenic. It is believed that S. mutans with CBPs that has entered the blood vessel attaches to collag...
This paper deals with the extension of principal component analysis, canonical correlation analysis, the Cramer–Rao inequality, and a few other statistical concepts in the real domain to the corresponding complex domain. Optimizations of Hermit...
To alleviate the performance degradation caused by domain shift, domain adaptive object detection (DAOD) has achieved compelling success in recent years. DAOD aims to improve the model’s detection performance on the target domain by reducing th...
High benchmark performance in fake news detection may reflect interpolation within familiar semantic distributions rather than robustness to latent semantic domain shift. However, datasets containing veracity labels without explicit semantic domain a...
Rapid and reliable tea classification is valuable for routine product screening, yet conventional sensory or physicochemical methods are subjective or time-consuming. Electronic nose (E-nose) sensing provides a fast alternative, but performance often...
Enhancing target domain discriminability is a key focus in Unsupervised Domain Adaptation (UDA) for HyperSpectral Image (HSI) classification. However, existing methods overlook bringing similar cross-domain samples closer together in the feature spac...
The specified domain patterns were written by AFM-tip voltages in LiNbO3 films composing LNOI (LiNbO3-on-insulator). The domain wall conductivity (DWC) was estimated in the written patterns. This estimate was based on the effects of load resistors RL...
Applying pre-trained medical deep learning segmentation models to out-of-domain images often yields predictions of insufficient quality. In this study, we propose using a robust generalizing descriptor, along with augmentation, to enable domain-gener...
An as-grown domain structure in nominally pure and Mn-doped calcium orthovanadate (CVO) crystals was studied by several methods of domain imaging: optical microscopy, piezoelectric force microscopy, and Cherenkov-type second harmonic generation. The...
Regulatory adenine nucleotide-binding cystathionine β-synthase (CBS) domains are widespread in proteins; however, information on the mechanism of their modulating effects on protein function is scarce. The difficulty in obtaining structural data...
Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks. Recently, methods based on domain generalization have yielded pr...
Predicting biological properties of unseen proteins is shown to be improved by the use of protein sequence embeddings. However, these sequence embeddings have the caveat that biological metadata do not exist for each amino acid, in order to measure t...
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