Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions

Feature transformation aims to generate new pattern-discriminative feature space from original features to improve downstream machine learning (ML) task performances. However, the discrete search space for the optimal feature explosively grows on the basis of combinations of features and operations from low-order forms to high-order forms. Existing methods, such as exhaustive search, expansion reduction, evolutionary algorithms, reinforcement learning, and iterative greedy, suffer from large search space. Overly emphasizing efficiency in algorithm design usually sacrifices stability or robustness. To fundamentally fill this gap, we reformulate discrete feature transformation as a continuous space optimization task and develop an embedding-optimization-reconstruction framework. This framework includes four steps: 1) reinforcement-enhanced data preparation, aiming to prepare high-quality transformation-accuracy training data; 2) feature transformation operation sequence embedding, intending to encapsulate the knowledge of prepared training data within a continuous space; 3) gradient-steered optimal embedding search, dedicating to uncover potentially superior embeddings within the learned space; 4) transformation operation sequence reconstruction, striving to reproduce the feature transformation solution to pinpoint the optimal feature space.Comment: Accepted by NeurIPS 202

Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective

Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. It serves as a pivotal approach to combat the curse of dimensionality, enhance model generalization, mitigate data sparsity, and extend the applicability of classical models. Existing research predominantly focuses on domain knowledge-based feature engineering or learning latent representations. However, these methods, while insightful, lack full automation and fail to yield a traceable and optimal representation space. An indispensable question arises: Can we concurrently address these limitations when reconstructing a feature space for a machine-learning task? Our initial work took a pioneering step towards this challenge by introducing a novel self-optimizing framework. This framework leverages the power of three cascading reinforced agents to automatically select candidate features and operations for generating improved feature transformation combinations. Despite the impressive strides made, there was room for enhancing its effectiveness and generalization capability. In this extended journal version, we advance our initial work from two distinct yet interconnected perspectives: 1) We propose a refinement of the original framework, which integrates a graph-based state representation method to capture the feature interactions more effectively and develop different Q-learning strategies to alleviate Q-value overestimation further. 2) We utilize a new optimization technique (actor-critic) to train the entire self-optimizing framework in order to accelerate the model convergence and improve the feature transformation performance. Finally, to validate the improved effectiveness and generalization capability of our framework, we perform extensive experiments and conduct comprehensive analyses.Comment: 21 pages, submitted to TKDD. arXiv admin note: text overlap with arXiv:2209.08044, arXiv:2205.1452

Self-Optimizing Feature Transformation

Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features. It is crucial to address the curse of dimensionality, enhance model generalization, overcome data sparsity, and expand the availability of classic models. Current research focuses on domain knowledge-based feature engineering or learning latent representations; nevertheless, these methods are not entirely automated and cannot produce a traceable and optimal representation space. When rebuilding a feature space for a machine learning task, can these limitations be addressed concurrently? In this extension study, we present a self-optimizing framework for feature transformation. To achieve a better performance, we improved the preliminary work by (1) obtaining an advanced state representation for enabling reinforced agents to comprehend the current feature set better; and (2) resolving Q-value overestimation in reinforced agents for learning unbiased and effective policies. Finally, to make experiments more convincing than the preliminary work, we conclude by adding the outlier detection task with five datasets, evaluating various state representation approaches, and comparing different training strategies. Extensive experiments and case studies show that our work is more effective and superior.Comment: Under review of TKDE. arXiv admin note: substantial text overlap with arXiv:2205.1452

Knock-down of YME1L1 induces mitochondrial dysfunction during early porcine embryonic development

YME1L1, a mitochondrial metalloproteinase, is an Adenosine triphosphate (ATP)-dependent metalloproteinase and locates in the mitochondrial inner membrane. The protease domain of YME1L1 is oriented towards the mitochondrial intermembrane space, which modulates the mitochondrial GTPase optic atrophy type 1 (OPA1) processing. However, during embryonic development, there is no report yet about the role of YME1L1 on mitochondrial biogenesis and function in pigs. In the current study, the mRNA level of YME1L1 was knocked down by double strand RNA microinjection to the 1-cell stage embryos. The expression patterns of YME1L1 and its related proteins were performed by immunofluorescence and western blotting. To access the biological function of YME1L1, we first counted the preimplantation development rate, diameter, and total cell number of blastocyst on day-7. First, the localization of endogenous YME1L1 was found in the punctate structures of the mitochondria, and the expression level of YME1L1 is highly expressed from the 4-cell stage. Following significant knock-down of YME1L1, blastocyst rate and quality were decreased, and mitochondrial fragmentation was induced. YME1L1 knockdown induced excessive ROS production, lower mitochondrial membrane potential, and lower ATP levels. The OPA1 cleavage induced by YME1L1 knockdown was prevented by double knock-down of YME1L1 and OMA1. Moreover, cytochrome c, a pro-apoptotic signal, was released from the mitochondria after the knock-down of YME1L1. Taken together, these results indicate that YME1L1 is essential for regulating mitochondrial fission, function, and apoptosis during porcine embryo preimplantation development

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

A finite-strain model for a superelastic NiTi shape memory alloy

A finite-strain constitutive model of a superelastic NiTi shape memory alloy is proposed in this paper. Via backward Euler implicit integration scheme and the incorporation of material softening, the model is implemented into finite element code to reproduce a Lüders like deformation of a superelastic NiTi. The simulation results are in agreement with the experimental results, indicating that the constitutive model can reasonably predict the mechanical behavior of a superelastic NiTi. A parametric study further verifies that the magnitude of softening modulus has a significant effect on the stress-strain response and Lüders-like deformation of a superelastic NiTi

Thermomechanical modeling on the crack initiation of NiTi shape memory alloy

The fracture of shape memory alloys is distinct from that of conventional metals, owing to the coexistence and interaction of multiple special features such as martensitic transformation, dislocation-induced plasticity, thermomechanical coupling and others. In this paper, the impact of thermomechanical behavior upon the crack initiation of a NiTi shape memory alloy under Mode I loading is investigated numerically and verified experimentally. A constitutive model incorporating phase transformation, plasticity and thermomechanical coupling is established. Via backward Euler integration and finite-element implementation, the longitudinal strain, martensite volume fraction and temperature field in the vicinity of the crack tip are furnished. The effects of grain size and loading rate on J-integral are revealed. The grain size dependence of crack initiation is non-monotonic. For the samples with grain sizes of 1500 nm, 18 nm and 10 nm, the shielding effect takes place in front of the crack. Additionally, the anti-shielding effect is detected for samples with grain sizes of 80 nm and 42 nm. The parametric study shows that loading rate imposes limited influence on J-integral, which is attributed to a small scale transformation. The decrement of yield stress and the increment of transformation hardening modulus can alleviate the anti-shielding effect and arouse the shielding effect upon crack initiation. The presented results shed light on the design and fabrication of high toughness phase transformable materials

Will Greenwashing Result in Brand Avoidance? A Moderated Mediation Model

Greenwashing has become a widespread phenomenon and obstructs green products, but literature on how consumers react to misbehaving brands is still scarce. This study aims to investigate the effect of greenwashing on consumers’ brand avoidance, integrating the mediating effect of brand hypocrisy and the moderating effect of CSR–CA belief. Data were acquired from a questionnaire survey of 317 consumers. Hypotheses were tested in a first-stage moderated mediation model with a bootstrapping method using the PROCESS program in SPSS. The empirical results demonstrated that greenwashing has a positive effect on brand avoidance, which is partially mediated by brand hypocrisy. Meanwhile, the positive effects of greenwashing on brand hypocrisy and brand avoidance are both weaker at higher levels of CSR–CA belief. Furthermore, the mediating effect of brand hypocrisy is also weaker at higher levels of CSR–CA belief. Based on these findings, we recommend that brands fulfill their environmental claims and balance their quality control, manufacturing costs and environment protection. Moreover, the government and environmental protection organizations should educate the public that there is not necessarily a tradeoff between corporate social responsibility (CSR) and corporate capability (CA)

Spatial–Temporal Evolution and Analysis of the Driving Force of Oil Palm Patterns in Malaysia from 2000 to 2018

Oil palm is the main cash crop grown in Malaysia, and palm oil plays an important role in the world oil market. A number of studies have used multisource remote sensing data to conduct research on oil palms in Malaysia, but there are a lack of long-term oil palm mapping studies, especially when the percentage of oil palm tree cover was higher than other plantations in Malaysia during the period of 2000–2012. To overcome this limitation, we used the Google Earth Engine platform to perform oil palm classification based on Landsat reflectance data. The spatial distribution of oil palms in Malaysia in five periods from 2000 to 2018 was obtained. Then, the planting center of gravity transfer method was applied to analyze the expansion of oil palms in Malaysia from 2000 to 2018 using Landsat data, elevation data, oil palm planting area, crude palm oil price, and other statistical data. Meanwhile, the driving factors affecting the change in oil palm planting area were also analyzed. The results showed that: (1) During 2000–2018, the oil palm planted area in Malaysia increased by 5.06 Mha (million ha), with a growth rate of 83.50%. Specifically, the increased area and growth rate for West Malaysia were 2.05 Mha and 62.05% and for East Malaysia were 3.01 Mha and 109.45%, respectively. (2) Three expansion patterns of oil palms were observed: (i) from a fragmented pattern to a connected area, (ii) expansion along a river, and (iii) from a plain to a gently sloped area. (3) The maximum shift of the center of gravity of the oil palms in West Malaysia was 10 km, while in East Malaysia, it reached 100 km. The East Malaysia oil palm planting potential was greater than that of West Malaysia and showed a trend of shifting from coastal areas to inland areas. (4) Malaysia’s oil palms are mainly planted in areas below 100 m above sea level; although a trend of expansion into high altitudes is visible, oil palm plantings extend to areas below 300 m above sea level. (5) Topography, crude palm oil prices, and deforestation are closely related to changes in oil palm planted area