Redundancy Removal Adversarial Active Learning Based on Norm Online Uncertainty Indicator

Active learning aims to select the most valuable unlabelled samples for annotation. In this paper, we propose a redundancy removal adversarial active learning (RRAAL) method based on norm online uncertainty indicator, which selects samples based on their distribution, uncertainty, and redundancy. RRAAL includes a representation generator, state discriminator, and redundancy removal module (RRM). The purpose of the representation generator is to learn the feature representation of a sample, and the state discriminator predicts the state of the feature vector after concatenation. We added a sample discriminator to the representation generator to improve the representation learning ability of the generator and designed a norm online uncertainty indicator (Norm-OUI) to provide a more accurate uncertainty score for the state discriminator. In addition, we designed an RRM based on a greedy algorithm to reduce the number of redundant samples in the labelled pool. The experimental results on four datasets show that the state discriminator, Norm-OUI, and RRM can improve the performance of RRAAL, and RRAAL outperforms the previous state-of-the-art active learning methods.

A Novel Query Strategy-Based Rank Batch-Mode Active Learning Method for High-Resolution Remote Sensing Image Classification

An informative training set is necessary for ensuring the robust performance of the classification of very-high-resolution remote sensing (VHRRS) images, but labeling work is often difficult, expensive, and time-consuming. This makes active learning (AL) an important part of an image analysis framework. AL aims to efficiently build a representative and efficient library of training samples that are most informative for the underlying classification task, thereby minimizing the cost of obtaining labeled data. Based on ranked batch-mode active learning (RBMAL), this paper proposes a novel combined query strategy of spectral information divergence lowest confidence uncertainty sampling (SIDLC), called RBSIDLC. The base classifier of random forest (RF) is initialized by using a small initial training set, and each unlabeled sample is analyzed to obtain the classification uncertainty score. A spectral information divergence (SID) function is then used to calculate the similarity score, and according to the final score, the unlabeled samples are ranked in descending lists. The most “valuable” samples are selected according to ranked lists and then labeled by the analyst/expert (also called the oracle). Finally, these samples are added to the training set, and the RF is retrained for the next iteration. The whole procedure is iteratively implemented until a stopping criterion is met. The results indicate that RBSIDLC achieves high-precision extraction of urban land use information based on VHRRS; the accuracy of extraction for each land-use type is greater than 90%, and the overall accuracy (OA) is greater than 96%. After the SID replaces the Euclidean distance in the RBMAL algorithm, the RBSIDLC method greatly reduces the misclassification rate among different land types. Therefore, the similarity function based on SID performs better than that based on the Euclidean distance. In addition, the OA of RF classification is greater than 90%, suggesting that it is feasible to use RF to estimate the uncertainty score. Compared with the three single query strategies of other AL methods, sample labeling with the SIDLC combined query strategy yields a lower cost and higher quality, thus effectively reducing the misclassification rate of different land use types. For example, compared with the Batch_Based_Entropy (BBE) algorithm, RBSIDLC improves the precision of barren land extraction by 37% and that of vegetation by 14%. The 25 characteristics of different land use types screened by RF cross-validation (RFCV) combined with the permutation method exhibit an excellent separation degree, and the results provide the basis for VHRRS information extraction in urban land use settings based on RBSIDLC.

The association between reflective function and global functionality in patients with borderline personality disorder

Objective: To examine the association between reflective function and global functionality in borderline personality disorder (BPD) patients, controlling for symptomatology and defensive style. Method: Thirty-nine female inpatients were evaluated employing a sociodemographic questionnaire, the Structured Clinical Interview for Personality Disorders-II (SCID-II), the Self-Reporting Questionnaire (SRQ-20), the Reflective Functioning Questionnaire (RFQ), the Defence Style Questionnaire-40 (DSQ-40) and the Global Assessment of Functioning (GAF). Results: Functionality was inversely associated with the reflective function uncertainty score (–.458; p < .01) and neurotic defences (–.335; p < .05). Symptom severity (SRQ-20) was associated with the use of immature defences (–.445; p < .01). The association between functionality and the reflective function uncertainty score remained significant, even when controlled for symptoms and defensive style ( p = .002). Conclusion: The ability to mentalise seems to play a central and somehow independent role in BPD psychopathology.

Illness Uncertainty in Patients Awaiting Liver Transplant

Illness uncertainty is prevalent in patients awaiting liver transplant. We described high levels of illness uncertainty in these patients and examined relationships between uncertainty and person factors and the antecedents of uncertainty. Mishel uncertainty in illness scale was used to measure illness uncertainty. We used modes and interquartile range (IQR) to describe illness uncertainty levels in 115 patients. Multiple logistic and linear regression models estimated the associations of uncertainty with hypothesized antecedents. High total illness uncertainty score was reported by 15.6% of the patients. After adjusting for all variables, illness uncertainty was associated with two antecedents of uncertainty, low social well-being (OR = 0.816; p = .025) and low self-efficacy (OR = 0.931; p = .013). Complexity was negatively associated with social well-being; ambiguity and inconsistency were negatively associated with self-efficacy. One in seven patients experienced high illness uncertainty. Social well-being and self-efficacy were negatively related to illness uncertainty.

Molecular Transformer – A Model for Uncertainty-Calibrated Chemical Reaction Prediction

<div><div><div><p>Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: given reactants and reagents, predict the products. Similar to other work, we treat reaction prediction as a machine translation problem between SMILES strings of reactants-reagents and the products. We show that a multi-head attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark dataset. Our algorithm requires no handcrafted rules, and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. Furthermore, we show that the model is able to handle inputs without reactant-reagent split and including stereochemistry, which makes our method universally applicable.</p></div></div></div>

Molecular Transformer – A Model for Uncertainty-Calibrated Chemical Reaction Prediction

<div><div><div><p>Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: given reactants and reagents, predict the products. Similar to other work, we treat reaction prediction as a machine translation problem between SMILES strings of reactants-reagents and the products. We show that a multi-head attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark dataset. Our algorithm requires no handcrafted rules, and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. Furthermore, we show that the model is able to handle inputs without reactant-reagent split and including stereochemistry, which makes our method universally applicable.</p></div></div></div>

Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation

<div><div><div><p>Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: given reactants and reagents, predict the products. Similar to other works, we treat reaction prediction as a machine translation problem between SMILES strings of reactants-reagents and the products. We show that a multi-head attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark dataset. Our algorithm requires no handcrafted rules, and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. Furthermore, we show that the model is able to handle inputs without reactant-reagent split and including stereochemistry, which makes our method universally applicable.</p></div></div></div>