Partial Domain Research Articles

Ferroelectricity induced double-direction conductance modulation in Hf x Zr1−x O2 capacitors

The artificial synapses are basic units in the hardware implementation of neuromorphic computing, whose performances should be gradually modulated under external stimuli. The underlying mechanism of the increasing and decreasing device conductance is still unclear in the Hf0.5Zr0.5O2 based synapses. In this study, the Hf0.5Zr0.5O2 capacitors with different stack orders are fabricated in atomic layer deposition, whose ferroelectric properties are investigated by analyzing the capacitance–voltage and polarization-voltage curves. The enhanced ferroelectricity is found after the rapid thermal annealing treatment for all the TiN/Hf0.5Zr0.5O2/TiN, TiN/HfO2-ZrO2/TiN and TiN/ZrO2-HfO2/TiN devices. In the device with poor ferroelectricity, the conductance gradually decreases under both positive and negative identical pulse schemes, which corresponds to the gradual dissolution process of the conductive filaments established in the initial pulse. For the capacitors with strong ferroelectricity, dual-direction conductance modulation can be observed due to the partial domain switching process, which can emulate the potentiation and depression process of biological synapses.

In-situ measurement method of material ratio and chemical uniformity in sintering–pelleting operation using laser-induced breakdown spectroscopy and partial least squares regression

The testing cycle, analyzing accuracy and precision of material ratio and chemical uniformity of sintering–pelleting operation directly affect the return fines ratio and economic benefits in the steel industry. In this work, we present an in-situ measurement method, which mainly includes sample preparation, laser-induced breakdown spectroscopy (LIBS) measurement and data processing. Twelve mixture samples composed of Fe2O3, CaO and C powder materials with different concentrations were prepared and measured. The raw spectra were pre-processed with baseline, de-noise, peak finding, sum normalized, line matchning, and feature selection. Important variables of Fe, Ca and C elements were acquired and selected based on partial least squares regression coefficients (PLS-RC) and domain knowledge, and the relationship between the spectral intensity and the certified concentration information was modeled using partial least squares regression (PLSR). After data-processing, the relative standard deviation (RSD) values of Fe I (403.05 nm), Ca I (430.77 nm) and C I (247.85 nm) were optimized to be 4.20 %, 5.29 % and 7.64 %, respectively. Moreover, the coefficient of determination (R2) values of Fe, Ca and C are 98.02 %, 98.43 % and 97.18 %, respectively; the root-mean-square error (RMSE) values are 0.024, 0.010, and 0.014, respectively; and the mean absolute error (MAE) values are 0.019, 0.008, and 0.011, respectively. The evaluation demonstrated that the presented LIBS system can basically satisfy the requirement the industry standards for in-situ material ratio and uniformity analysis in sintering–pelleting operation. Therefore, the proposed in-situ LIBS measuring method has great development potential and is widely applied in sintering–pelleting operations and similar processes.

Confidence based class weight and embedding discrepancy constraint network for partial domain adaptation

Partial domain adaptation (PDA) is a special domain adaptation task where the label space of the target domain is a subset of the source domain. In this work, we present a novel adversarial PDA method named Confidence Based Class Weight and Embedding Discrepancy Constraint Network (CEN). Specifically, we design a robust weighting scheme that takes sample confidence and class information into account. It can automatically distinguish outlier samples in the source domain and reduce their importance. Besides, we consider the relationship between feature norm and domain shift. We limit the expectation of the feature norms of both domains to an adaptive value. By this means, we can align the feature distributions and help the deep model learn domain-invariant representations. Comprehensive experiments on three domain adaptation datasets Office-31, Office-home, and Visda2017 show that our approach surpasses state-of-the-art methods on various PDA tasks.

Bearing fault diagnosis based on partial domain adaptation adversarial network

The existing fault diagnosis algorithm based on domain adaptation solves the problem of degradation of model diagnosis performance due to different data distributions under variable working conditions and cross-machine conditions, and its excellent fault diagnosis performance relies on the assumption that the fault category space of source and target domains is the same; however, it is difficult to meet the above assumption in practical application scenarios. For this reason, focusing on the matter of imbalance within the fault category, this paper proposes a novel unsupervised partial domain adaptational fault diagnosis method—a partial domain adaptation adversarial network (PDAAN). On the one hand, it uses the source domain fault samples to expand the target domain and promotes the effective alignment of the fault feature area of the source domain and the target domain, in order for the model to effectively extract domain invariant features; on the other hand, class-level weights and weighted entropy weights are introduced into the loss function to suppress the uncertainty within the transfer process and avoid negative transfer of the model. Finally, experiments are conducted in the case of variable working conditions and cross-mechanical devices, and it is confirmed that the PDAAN model has high recognition accuracy in the case of class space asymmetry.

Unsupervised domain adaptation with progressive adaptation of subspaces

Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source domain with domain shift. Most of the existing UDA methods try to mitigate the adverse impact induced by the shift via reducing domain discrepancy. However, such approaches easily suffer a notorious mode collapse issue due to the lack of labels in target domain. Naturally, one of the effective ways to mitigate this issue is to reliably estimate the pseudo labels for target domain, which itself is hard. To overcome this, we propose a novel UDA method named Progressive Adaptation of Subspaces approach (PAS) in which we utilize such an intuition that appears much reasonable to gradually obtain reliable pseudo labels. Specifically, we progressively and steadily refine the shared subspaces as bridge of knowledge transfer by adaptively anchoring/selecting and leveraging those target samples with reliable pseudo labels. Subsequently, the refined subspaces can in turn provide more reliable pseudo-labels of the target domain, making the mode collapse highly mitigated. Our thorough evaluation demonstrates that PAS is not only effective for common UDA, but also outperforms the state-of-the arts for more challenging Partial Domain Adaptation (PDA) situation, where the source label set subsumes the target one.

Transferable regularization and normalization: Towards transferable feature learning for unsupervised domain adaptation

Unsupervised domain adaptation aims at alleviating distribution discrepancy when transferring knowledge from a large-scale labeled source domain to a new unlabeled target domain. A prevailing method is adversarial feature adaptation, which generates transferable features via aligning feature distributions across domains to bound domain discrepancy. However, such approach renders suboptimal performances and vulnerable to negative transfer when target domain has less number of classes compared to source domain as in partial domain adaptation. To address this issue, previous methods adopt a transferable feature norm strategy to adapting feature norms of two domains achieving significant transfer gains. Nonetheless, these works do not consider the intrinsic limitation of the architecture design of deep neural networks which greatly influences the loss function of transferability. In this paper, we propose Transferable Regularization and Normalization (TRN), which simultaneously avoids negative transfer via adapting feature norms of both domains and facilitates positive transfer via replacing the existing normalization techniques in mainstream deep backbones. As a general approach, TRN can be simply embedded into deep transfer learning approaches. After a thorough evaluation of proposed method utilizing several benchmark datasets (Office-31, ImageCLEF-DA, Office-Home and VisDA-2017). TRN yielded the state-of-the-art results and outperformed other approaches (IFAN, PADA) by a large margin (0.5%, 7.74%) for various domain adaptation tasks.

Clustering-Guided Novel Unsupervised Domain Adversarial Network for Partial Transfer Fault Diagnosis of Rotating Machinery

Unsupervised partial transfer fault diagnosis studies of rotating machinery have practical significance, which still exists some challenges, for example, the learned domain-specific statistics and parameters usually influence the learning effect of target-domain features to some degree, and the relatively scattered target-domain features will lead to negative transfer. To overcome those limitations and further improve partial transfer fault diagnosis performance, a clustering-guided novel unsupervised domain adversarial network is proposed in this paper. Firstly, a novel unsupervised domain adversarial network is constructed using domain-specific batch normalization to remove domain-specific information to enhance alignment between source and target domains. Secondly, embedded clustering strategy is designed to learn tightly clustered target-domain features to suppress negative transfer in partial domain adaptation process. Finally, a joint optimization objective function is defined to balance different losses to improve the training and diagnosis performance. Two experimental cases of bevel gearbox and bearing are used to validate the effectiveness and superiority of the proposed method in solving unsupervised partial transfer fault diagnosis problems.

Arctic curves of the 6V model with partial DWBC and double Aztec rectangles

Previous numerical studies have shown that in the disordered and anti-ferroelectric phases the six-vertex (6V) model with partial domain wall boundary conditions (DWBC) exhibits an arctic curve whose exact shape is unknown. The model is defined on a s × n square lattice (s ⩽ n). In this paper, we derive the analytic expression of the arctic curve, for a = b = 1 and (Δ = 0), while keeping the ratio s/n ∈ [0, 1] as a free parameter. The computation relies on the tangent method. We also consider domino tilings of double Aztec rectangles and show via the tangent method that, for particular parameters, the arctic curve is identical to that of the 6V model with partial DWBC. Our results are confirmed by extensive numerical simulations.

Mathematical Models and Nonlinear Optimization in Continuous Maximum Coverage Location Problem

This paper considers the maximum coverage location problem (MCLP) in a continuous formulation. It is assumed that the coverage domain and the family of geometric objects of arbitrary shape are specified. It is necessary to find such a location of geometric objects to cover the greatest possible amount of the domain. A mathematical model of MCLP is proposed in the form of an unconstrained nonlinear optimization problem. Python computational geometry packages were used to calculate the area of partial coverage domain. Many experiments were carried out which made it possible to describe the statistical dependence of the area calculation time of coverage domain on the number of covering objects. To obtain a local solution, the BFGS method with first-order differences was used. An approach to the numerical estimation of the objective function gradient is proposed, which significantly reduces computational costs, which is confirmed experimentally. The proposed approach is shown to solve the maximum coverage problem of a rectangular area by a family of ellipses.

Manifold discrimination partial adversarial domain adaptation

Most traditional domain adaptations promote knowledge transfer with the assumption that source domain and target domain have the same label space. In a big data environment, we usually obtain the source dataset with a larger label space than the target dataset. Under this background, partial domain adaptation (PDA) comes into being naturally. In PDA, when the target domain label space is a subset of the source domain label space, cross-domain adaptation can be achieved by matching shared class samples in cross-domain. Many researchers make good progress in this field and propose different kinds of PDA methods. But in the existing PDA methods, the samples of the unknown class are still used for distribution alignment, and this makes the model suffer from the risk of negative transfer. In order to solve these problems, we propose manifold discrimination partial adversarial domain adaptation (MDPDA) to align the relevant classes across two domains and obtain effective domain adaptation. MDPDA combines manifold learning and adversarial learning. In MDPDA, we design the manifold discrimination module to map high-dimensional data into low-dimensional space and learn low-dimensional data information of source domain and target domain. We construct discriminative source domain feature space for model discrimination in MDPDA. MDPDA distinguishes and confuses the two domains through manifold learning and similarity structure, and generates discriminant feature space to promote the auxiliary network to better recognize the source domain samples. The comprehensive experiments on several benchmark datasets evaluate the superior performance of the proposed method compared to the state-of-the-art methods.

Multi-attention representation network partial domain adaptation for COVID-19 diagnosis

The outbreak of COVID-19 threatens the safety of all human beings. Rapid and accurate diagnosis of patients is the effective way to prevent the rapid spread of COVID-19. The current computer-aided diagnosis of COVID-19 requires extensive labeled data for training, and this undoubtedly increases human and material resources costs. Domain adaptation (DA), an existing promising approach, can transfer knowledge from rich labeled pneumonia datasets for COVID-19 diagnosis and classification. However, due to the differences in feature distribution and task semantic between pneumonia and COVID-19, negative transfer may reduce the performance in diagnosis COVID-19 and pneumonia. Furthermore, the training data is usually mixed with many noise samples in practice, and this also poses new challenges for domain adaptation. As a kind of domain adaptation, partial domain adaptation (PDA) can well avoid outlier samples in the source domain and achieve good classification performance in the target domain. However, the existing PDA methods all learn a single feature representation; this can only learn local information about the inputs and ignore other important information in the samples. Therefore multi-attention representation network partial domain adaptation (MARPDA) is proposed in this paper to overcome the above shortcomings of PDA. In MARPDA, we construct the multiple representation networks with attention to acquire the image representation and effectively learn knowledge from different feature spaces. We design the sample-weighted strategy to achieve partial data transfer and address the negative transfer of noise data during training. MARPDA adapts to complex application scenarios and learns fine-grained features of the image from multiple representations. We apply the model to classify pneumonia and COVID-19 respectively, and evaluate it in qualitative and quantitative manners. The experimental results show that our classification accuracy is higher than that of the existing state-of-the-art methods. The stability and reliability of the proposed method are validated by the confusion matrix and the performance curves experiments. In summary, our method has better performance for diagnosis COVID-19 compared to the existing state-of-the-art methods.

Partial Domain Adaptation on Semantic Segmentation

The research of semantic segmentation based on unsupervised domain adaptation greatly alleviates the high-cost bottleneck of manual annotation in deep learning. Inevitably domain gap limits the ability of target domain to learn knowledge from source domain. Previous domain alignment strategies aim to explore maximum domain-invariant space to enlarge knowledge that target domain learns. They based on the assumption that images are symmetrical in categories covering all predefined categories. However, in practical training, there is class asymmetry in image samples inter domains. This will lead to outlier categories and negative transfer, because it lacks the guidance of the corresponding category of the source domain. To tackle this issue, we propose a partial domain adaptive method on semantic segmentation to guide target model to learn category-level knowledge selectively. PAM (Partial Adaptive Map) module is introduced to motivate the target model to obtain more knowledge from non-outliers and less knowledge from outliers to avoid negative transfer. We further analyze the effectiveness of this method from the perspective of JS divergence. Our method has achieved significant improvement without additional discriminators. Experiments on general datasets GTA5 and SYNTHIA can compare with SOTA methods.

Estimation of Class Probability through Adversarial Training for Partial Domain Adaptation

Estimation of Class Probability through Adversarial Training for Partial Domain Adaptation

An ensemble and shared selective adversarial network for partial domain fault diagnosis of machinery

Recently, some adversarial transfer learning (TL) approaches have been developed for addressing the partial domain adaptation (DA) problems in machinery fault diagnosis. However, these existing methods generally follow the partial DA framework of multiple sub-domain discriminators, which causes the overly complex model in dealing with many source classes. Moreover, the classifier mostly based on a single intelligent model with limited generalization, it may predict the wrong class-probability for unlabeled samples, which will cause the negative transfer when used in the model training. In response to the challenges, an ensemble and shared selective adversarial network (ES-SAN) is proposed in this paper. In this network, by introducing a correlation layer to correlate both class and domain informations for each sample, a single-intelligent model based shared module is constructed, which can transform between the classifier and the discriminator capable of multi sub-domain discrimination, so as to form a simplified partial DA framework. Moreover, multiple shared modules based on different intelligent models are integrated into an ensemble module, which can output reliable probability weights to promote positive transfer. Experimental investigations on two diagnosis datasets demonstrate that the proposed ES-SAN outperforms the existing methods in the partial DA diagnosis.

Wasserstein Uncertainty Estimation for Adversarial Domain Matching.

Domain adaptation aims at reducing the domain shift between a labeled source domain and an unlabeled target domain, so that the source model can be generalized to target domains without fine tuning. In this paper, we propose to evaluate the cross-domain transferability between source and target samples by domain prediction uncertainty, which is quantified via Wasserstein gradient flows. Further, we exploit it for reweighting the training samples to alleviate the issue of domain shift. The proposed mechanism provides a meaningful curriculum for cross-domain transfer and adaptively rules out samples that contain too much domain specific information during domain adaptation. Experiments on several benchmark datasets demonstrate that our reweighting mechanism can achieve improved results in both balanced and partial domain adaptation.

Tight Source Domain Match for Partial Domain Adaptation Based on Maximum Density

Unsupervised domain adaptation (UDA) aims at transferring knowledge between a well-labelled ’source domain’ and an unlabelled ’target domain’ by decreasing distribution discrepancy. In real scenario, partial domain adaptation (PDA), where target domain only includes part of the classes of source domain, is adopted as fully-shared label space is often unavailable. Non-identical label spaces across domains lead to performance degradation due to source-unique classes being mis-matched to the target domain, i.e. negative transfer of the target domain. Although existing PDA approaches have produced promising results, they still confront with negative transfer problem without rigorous generalization bounds. In our work, a novel PDA model has been proposed based on margin disparity and maximum source intra-class density divergence (MDSD). It matches the feature distributions with shared labels and congregates source samples in the source with affirmative labels. Removing maximum target density with pseudo labels, it effectively avoids over-fitting and accelerates learning speed. In addition, we construct a new garbage classification dataset, which is comprised of source domain - Product (Pr) and target domain - Garbage (Ga) for PDA validation. Experiments show that our proposed unsupervised partial domain adaptation method has a good performance on Office-31, Office-Home and Pr-Ga datasets.

Nanoscale Doping and Its Impact on the Ferroelectric and Piezoelectric Properties of Hf0.5Zr0.5O2.

Ferroelectric hafnium oxide thin films—the most promising materials in microelectronics’ non-volatile memory—exhibit both unconventional ferroelectricity and unconventional piezoelectricity. Their exact origin remains controversial, and the relationship between ferroelectric and piezoelectric properties remains unclear. We introduce a new method to investigate this issue, which consists in a local controlled modification of the ferroelectric and piezoelectric properties within a single Hf0.5Zr0.5O2 capacitor device through local doping and a further comparative nanoscopic analysis of the modified regions. By comparing the ferroelectric properties of Ga-doped Hf0.5Zr0.5O2 thin films with the results of piezoresponse force microscopy and their simulation, as well as with the results of in situ synchrotron X-ray microdiffractometry, we demonstrate that, depending on the doping concentration, ferroelectric Hf0.5Zr0.5O2 has either a negative or a positive longitudinal piezoelectric coefficient, and its maximal value is −0.3 pm/V. This is several hundreds or thousands of times less than those of classical ferroelectrics. These changes in piezoelectric properties are accompanied by either improved or decreased remnant polarization, as well as partial or complete domain switching. We conclude that various ferroelectric and piezoelectric properties, and the relationships between them, can be designed for Hf0.5Zr0.5O2 via oxygen vacancies and mechanical-strain engineering, e.g., by doping ferroelectric films.

Partial Domain Adaptation by Progressive Sample Learning of Shared Classes

Partial Domain Adaptation by Progressive Sample Learning of Shared Classes

Partial domain adaption based prediction calibration methodology for fault detection and diagnosis of chillers under variable operational condition scenarios

Data-driven fault detection and diagnosis (FDD) for building energy system has drawn much attention from both industry and academy due to their ability to learn complex relationships from big data. Most existing FDD studies only focus on the model diagnosis accuracy where the operational conditions of test dataset are similar with training dataset. However, it is rare for online operational data to be similar with offline training data. To better simulate the online environment in the aspect of operational conditions, a novel online data simulation method, which is named as variable operational conditions test, is proposed in this study. Furthermore, in order to improve the poor diagnosis performance of FDD models under variable operational condition scenarios, an unsupervised partial domain adaption algorithm is employed to calibrate the predictions of the FDD model within a short period of time. A comprehensive data experiment was conduct based on ASHARE RP-1043. The experimental results indicate the prediction calibration method yields decent performance improvement compared with three traditional data-driven-based FDD models, the highest and overall improvements are 55.07% and 14.5%, respectively. The research outcomes provide practical guidelines for the online application of FDD models when the training data cannot cover the whole sample space.

Isometric Stratified Ensembles: A Partial and Incremental Adaptive Applicability Domain and Consensus-Based Classification Strategy for Highly Imbalanced Data Sets with Application to Colloidal Aggregation.

Partial and incremental stratification analysis of a quantitative structure-interference relationship (QSIR) is a novel strategy intended to categorize classification provided by machine learning techniques. It is based on a 2D mapping of classification statistics onto two categorical axes: the degree of consensus and level of applicability domain. An internal cross-validation set allows to determine the statistical performance of the ensemble at every 2D map stratum and hence to define isometric local performance regions with the aim of better hit ranking and selection. During training, isometric stratified ensembles (ISE) applies a recursive decorrelated variable selection and considers the cardinal ratio of classes to balance training sets and thus avoid bias due to possible class imbalance. To exemplify the interest of this strategy, three different highly imbalanced PubChem pairs of AmpC β-lactamase and cruzain inhibition assay campaigns of colloidal aggregators and complementary aggregators data set available at the AGGREGATOR ADVISOR predictor web page were employed. Statistics obtained using this new strategy show outperforming results compared to former published tools, with and without a classical applicability domain. ISE performance on classifying colloidal aggregators shows from a global AUC of 0.82, when the whole test data set is considered, up to a maximum AUC of 0.88, when its highest confidence isometric stratum is retained.