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

A New Extragradient-Type Algorithm for the Split Feasibility Problem

We consider the split feasibility problem (SFP) in Hilbert spaces, inspired by extragradient method presented by Ceng, Ansari for split feasibility problem, subgradient extragradient method proposed by Censor, and variant extragradient-type method presented by Yao for variational inequalities; we suggest an extragradient-type algorithm for the SFP. We prove the strong convergence under some suitable conditions in infinite-dimensional Hilbert spaces

Hybrid CQ projection algorithm with line-search process for the split feasibility problem

Abstract In this paper, we propose a hybrid CQ projection algorithm with two projection steps and one Armijo-type line-search step for the split feasibility problem. The line-search technique is intended to construct a hyperplane that strictly separates the current point from the solution set. The next iteration is obtained by the projection of the initial point on a regress region (the intersection of three sets). Hence, algorithm converges faster than some other algorithms. Under some mild conditions, we show the convergence. Preliminary numerical experiments show that our algorithm is efficient

A new 3D multi-scroll chaotic system generated with three types of hidden attractors

In this paper, A new 3D multi-scroll hidden attractor chaotic system is proposed. The proposed system has chaotic attractors with no equilibrium point, one stable equilibrium point and several stable equilibria. And these three types of hidden attractors can be obtained just through varying a parameter of the system. In the other hand, multi-scroll attractors are generated by a piecewise linear function. The phase diagrams and basins of attraction are respectively used to prove that this system has multi-scroll attractors and hidden attractors. There are also some other powerful tools to analyze the dynamical characteristics of this system like Lyapunov spectrums, bifurcation diagrams and Poincaré maps. This system has great application prospects in communication encryption due to the complex dynamic behaviors of the multi-scroll chaotic attractors and the security of the hidden attractors. Moreover, we accomplish the circuit experiment, and verify the feasibility of each case of the multi-scroll hidden attractor chaotic system

Liver-specific glucocorticoid action in alcoholic liver disease: Study of glucocorticoid receptor knockout and knockin mice

Background and aim: Glucocorticoids are the only first-line drugs for severe alcoholic hepatitis (AH), with limited efficacy and various side effects on extrahepatic tissues. Liver-targeting glucocorticoid therapy may have multiple advantages over systemic glucocorticoid for AH. The aim of this study was to determine the role of hepatocellular glucocorticoid receptor (GR) in alcohol-associated steatosis (AS) and AH. Materials and methods: AS was induced by a high-fat diet plus binge alcohol in adult male and female mice with liver-specific knockout (LKO) and heterozygote of GR. AH was induced by chronic-plus-binge in middle-aged male mice with liver-specific knockin of GR. Changes in hepatic mRNA and protein expression were determined by quantitative real-time polymerase chain reaction and Western blot. Results: GR-LKO aggravated steatosis and decreased hepatic expression and circulating levels of albumin in both genders of AS mice but only increased markers of liver injury in male AS mice. Marked steatosis in GR-LKO mice was associated with induction of lipogenic genes and down-regulation of bile acid synthetic genes. Hepatic protein levels of GR, hepatocyte nuclear factor 4 alpha, and phosphorylated signal transducer and activator of transcription 3 were gene-dosage-dependently decreased, whereas that of lipogenic ATP citrate lyase was increased in male GR heterozygote and LKO mice. Interestingly, hepatic expression of estrogen receptor alpha (ERα) was induced, and the essential estrogen-inactivating enzyme sulfotransferase 1e1 was gene-dosage-dependently down-regulated in GR heterozygote and knockout AS mice, which was associated with induction of ERα-target genes. Liver-specific knockin of GR protected against liver injury and steatohepatitis in middle-aged AH mice. Conclusions: Hepatic GR deficiency plays a crucial role in the pathogenesis of AS induced by high-fat diet plus binge, and liver-specific overexpression/activation of GR protects against chronic-plus-binge-induced AH in middle-aged mice. Hepatocellular GR is important for protection against AS and AH

Analytical Solution on Ground Deformation Caused by Parallel Construction of Rectangular Pipe Jacking

Pipe jacking has been widely used in urban underground engineering construction in recent years. Prediction of ground deformation caused by pipe jacking is particularly important for the safety of construction. With regard to the densely arranged pipes used in the pipe roof structure method, an analytical model of stratum disturbance caused by jacking of parallel rectangular pipes is proposed on the basis of Mindlin’s displacement solution and the stochastic medium theory. The influencing factors such as soil loss, additional thrust on the excavation face, friction between pipe jacking machine and soil, friction between subsequent pipes and soil, and the grouting pressure were comprehensively considered. Then, a 3D numerical simulation and a case study were conducted. The results showed consistent agreement with the analytical solution, and the proposed method can take into account the asymmetry of surface settlement curve induced by construction. A discussion of the ground deformation law shows that the proposed approach can reasonably predict the ground deformation and provide a reference for relevant pipe jacking construction

Using i-vectors from voice features to identify major depressive disorder

Background: Machine-learning methods using acoustic features in the diagnosis of major depressive disorder (MDD) have insufficient evidence from large-scale samples and clinical trials. This study aimed to evaluate the effectiveness of the promising i-vector method on a large sample of women with recurrent MDD diagnosed clinically, examine its robustness, and provide an explicit acoustic explanation of the i-vectors. Methods: We collected utterances edited from clinical interview speech records of 785 depressed and 1,023 healthy individuals. Then, we extracted Mel-frequency cepstral coefficient (MFCC) features and MFCC i-vectors from their utterances. To examine the effectiveness of i-vectors, we compared the performance of binary logistic regression between MFCC i-vectors and MFCC features and tested its robustness on different utterance durations. We also determined the correlation between MFCC features and MFCC i-vectors to analyze the acoustic meaning of i-vectors. Results: The i-vectors improved 7% and 14% of area under the curve (AUC) for MFCC features using different utterances. When the duration is &gt; 40 s, the classification results are stabilized. The i-vectors are consistently correlated to the maximum, minimum, and deviations of MFCC features (either positively or negatively). Limitations: This study included only women. Conclusions: The i-vectors can improve 14% of the AUC on a large-scale clinical sample. This system is robust to utterance duration &gt; 40 s. This study provides a foundation for exploring the clinical application of voice features in the diagnosis of MDD.</p