UniTabE: Pretraining a Unified Tabular Encoder for Heterogeneous Tabular Data

Recent advancements in Natural Language Processing (NLP) have witnessed the groundbreaking impact of pretrained models, yielding impressive outcomes across various tasks. This study seeks to extend the power of pretraining methodologies to tabular data, a domain traditionally overlooked, yet inherently challenging due to the plethora of table schemas intrinsic to different tasks. The primary research questions underpinning this work revolve around the adaptation to heterogeneous table structures, the establishment of a universal pretraining protocol for tabular data, the generalizability and transferability of learned knowledge across tasks, the adaptation to diverse downstream applications, and the incorporation of incremental columns over time. In response to these challenges, we introduce UniTabE, a pioneering method designed to process tables in a uniform manner, devoid of constraints imposed by specific table structures. UniTabE's core concept relies on representing each basic table element with a module, termed TabUnit. This is subsequently followed by a Transformer encoder to refine the representation. Moreover, our model is designed to facilitate pretraining and finetuning through the utilization of free-form prompts. In order to implement the pretraining phase, we curated an expansive tabular dataset comprising approximately 13 billion samples, meticulously gathered from the Kaggle platform. Rigorous experimental testing and analyses were performed under a myriad of scenarios to validate the effectiveness of our methodology. The experimental results demonstrate UniTabE's superior performance against several baseline models across a multitude of benchmark datasets. This, therefore, underscores UniTabE's potential to significantly enhance the semantic representation of tabular data, thereby marking a significant stride in the field of tabular data analysis.Comment: 9 page

Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection

Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though quite a few rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent semantics between images and texts, and rarely spot the inconsistency among the post contents and background knowledge. In addition, they commonly assume the completeness of multiple modalities and thus are incapable of handling handle missing modalities in real-life scenarios. Motivated by the intuition that rumors in social media are more likely to have inconsistent semantics, a novel Knowledge-guided Dual-consistency Network is proposed to detect rumors with multimedia contents. It uses two consistency detection subnetworks to capture the inconsistency at the cross-modal level and the content-knowledge level simultaneously. It also enables robust multi-modal representation learning under different missing visual modality conditions, using a special token to discriminate between posts with visual modality and posts without visual modality. Extensive experiments on three public real-world multimedia datasets demonstrate that our framework can outperform the state-of-the-art baselines under both complete and incomplete modality conditions. Our codes are available at https://github.com/MengzSun/KDCN

Load shifting of a supplier-based demand response of multi-class subscribers in smart grid

We propose a demand response (DR) solution approach for a real-time pricing model with multi-class users to determine the electricity supply mix. The model aims to address the problem of power consumption overloading in peak hours using the real-time information obtained from the interaction between suppliers and users in a smart grid. The proposed DR algorithm allocates the overloaded demand assigned to a supplier to other electricity suppliers in order to satisfy all users’ demand while the supplier ensures to maximize the utility or reserved demand of users. Furthermore, a priority approach based on different user groups is developed for allocating the extra demand to other suppliers. Numerical experiments have been conducted to analyze the performance of the algorithm and compare the real-time electricity price with the fixed price

Coffee constituents and modulation of antioxidant status in Caco-2 cells

Coffee contains biologically active components which may affect chronic disease risk. These biologically active components include caffeine, cafestol and kahweol, and antioxidants such as chlorogenic acids and Maillard reaction products (MRPs) that are generated during roasting. Although MRPs are regarded as being the most abundant group of antioxidants present in coffee, the mechanism underlying the antioxidant effects of coffee MRPs in both in vitro and in biological systems has yet to be elucidated. In this study, the in vitro antioxidant properties of roasted and non-roasted coffee extracts (Coffea arabica L.) were tested using oxygen radical absorbance capacity (ORAC), Trolox equivalent antioxidant capacity (TEAC) and reducing power assays. MRPs were shown to be the prevailing antioxidants in roasted coffee extracts. The mechanisms of the antioxidant action associated with coffee MRPs involve the hydrogen atom transfer (HAT) mechanism and the single electron transfer (SET) mechanism. The biological effects of MRPs derived from coffee extracts on the enzymatic antioxidant defense in human colon adenocarcinoma Caco-2 cells were also investigated. No induction of antioxidant enzyme activities of catalase, glutathione peroxidase, glutathione reductase and superoxide dismutase were observed in Caco-2 cells after exposure to coffee MRPs, except for an increased glutathione peroxidase activity after 24 h exposure. In contrast, significantly decreased activities of catalase and glutathione peroxidase, and a reduced glutathione content were observed in Caco-2 cells after treatment with coffee MRPs (p<0.05). The antioxidant gene expression profile in Caco-2 cells after coffee treatment was further investigated using a Real-Time Polymerase Chain Reaction (PCR) array technology. Results demonstrated that roasted coffee extracts induced the expression of specific antioxidant response element (ARE)-driven genes in Caco-2 cells, thus enhancing cellular endogenous defense systems. This is the first report of the molecular mechanism underlying the antioxidant effect of coffee in Caco-2 cells. Hydrogen peroxide generated in the cell culture system as a consequence of coffee exposure, may serve as a signaling molecule that is involved in the gene regulatory effect associated with coffee extracts.Land and Food Systems, Faculty ofGraduat

A Nonmonotone Projection Method for Constrained System of Nonlinear Equations

This paper deals with the nonmonotone projection algorithm for constrained nonlinear equations. For some starting points, the previous projection algorithms for the problem may encounter slow convergence which is related to the monotone behavior of the iterative sequence as well as the iterative direction. To circumvent this situation, we adopt the nonmonotone technique introduced by Dang to develop a nonmonotone projection algorithm. After constructing the nonmonotone projection algorithm, we show its convergence under some suitable condition. Preliminary numerical experiment is reported at the end of this paper, from which we can see that the algorithm we propose converges more quickly than that of the usual projection algorithm for some starting points

A Nonmonotone Projection Method for Constrained System of Nonlinear Equations

This paper deals with the nonmonotone projection algorithm for constrained nonlinear equations. For some starting points, the previous projection algorithms for the problem may encounter slow convergence which is related to the monotone behavior of the iterative sequence as well as the iterative direction. To circumvent this situation, we adopt the nonmonotone technique introduced by Dang to develop a nonmonotone projection algorithm. After constructing the nonmonotone projection algorithm, we show its convergence under some suitable condition. Preliminary numerical experiment is reported at the end of this paper, from which we can see that the algorithm we propose converges more quickly than that of the usual projection algorithm for some starting points

An Inertial Iterative Algorithm with Strong Convergence for Solving Modified Split Feasibility Problem in Banach Spaces

In this paper, we propose an iterative scheme for a special split feasibility problem with the maximal monotone operator and fixed-point problem in Banach spaces. The algorithm implements Halpern’s iteration with an inertial technique for the problem. Under some mild assumption of the monotonicity of the related mapping, we establish the strong convergence of the sequence generated by the algorithm which does not require the spectral radius of ATA. Finally, the numerical example is presented to demonstrate the efficiency of the algorithm

Influence of Stress on Kinetics and Transformation Plasticity of Ferrite Transformation Based on Hysteresis Effects

Transformation plasticity and kinetics play an essential role in the prediction of residual stresses resulting from transformation. This paper is devoted to the investigation of the influence of stress on the kinetics and transformation plasticity of ferrite for H420LA steel. It has been shown that under small external stresses, lower than the yield stress of the weaker phase, the ferrite transformation is inhibited at the beginning of the transformation in the continuous cooling process and the mechanical stabilization of austenite is observed, due to transformation hysteresis effects. This phenomenon affects the metallurgical and mechanical behaviors of the transformation progress. However, most existing models ignore these effects, leading to deviations in the description of transformation plasticity during the transformation progress. Considering the hysteresis effects, the micromechanical model for kinetics and transformation plasticity is reexamined. A general formulation of austenite decomposition kinetics accounting for these effects is developed to better describe the phase transformation under a continuous cooling process. In addition, the influence of hysteresis effects on the evolution of transformation plasticity is analyzed. Consideration of the hysteresis effects decreases the discrepancy between the calculated and experimental values. This will allow better prediction of residual stresses in the thermomechanically controlled processes

Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders

Background: The application of polygenic risk scores (PRSs) in major depressive disorder (MDD) detection is constrained by its simplicity and uncertainty. One promising way to further extend its usability is fusion with other biomarkers. This study constructed an MDD biomarker by combining the PRS and voice features and evaluated their ability based on large clinical samples.Methods: We collected genome-wide sequences and utterances edited from clinical interview speech records from 3,580 women with recurrent MDD and 4,016 healthy people. Then, we constructed PRS as a gene biomarker by p value-based clumping and thresholding and extracted voice features using the i-vector method. Using logistic regression, we compared the ability of gene or voice biomarkers with the ability of both in combination for MDD detection. We also tested more machine learning models to further improve the detection capability.Results: With a p-value threshold of 0.005, the combined biomarker improved the area under the receiver operating characteristic curve (AUC) by 9.09% compared to that of genes only and 6.73% compared to that of voice only. Multilayer perceptron can further heighten the AUC by 3.6% compared to logistic regression, while support vector machine and random forests showed no better performance.Conclusion: The addition of voice biomarkers to genes can effectively improve the ability to detect MDD. The combination of PRS and voice biomarkers in MDD detection is feasible. This study provides a foundation for exploring the clinical application of genetic and voice biomarkers in the diagnosis of MDD