Probability Weighted Clustered Coefficients Regression Models in Complex Survey Sampling

Regression analysis is commonly conducted in survey sampling. However, existing methods fail when the relationships vary across different areas or domains. In this paper, we propose a unified framework to study the group-wise covariate effect under complex survey sampling based on pairwise penalties, and the associated objective function is solved by the alternating direction method of multipliers. Theoretical properties of the proposed method are investigated under some generality conditions. Numerical experiments demonstrate the superiority of the proposed method in terms of identifying groups and estimation efficiency for both linear regression models and logistic regression models.Comment: 35 pages,2 figure

Quantum Discord for Investigating Quantum Correlations without Entanglement in Solids

Quantum systems unfold diversified correlations which have no classical counterparts. These quantum correlations have various different facets. Quantum entanglement, as the most well known measure of quantum correlations, plays essential roles in quantum information processing. However, it has recently been pointed out that quantum entanglement cannot describe all the nonclassicality in the correlations. Thus the study of quantum correlations in separable states attracts widely attentions. Herein, we experimentally investigate the quantum correlations of separable thermal states in terms of quantum discord. The sudden change of quantum discord is observed, which captures ambiguously the critical point associated with the behavior of Hamiltonian. Our results display the potential applications of quantum correlations in studying the fundamental properties of quantum system, such as quantum criticality of non-zero temperature.Comment: 4 pages, 4 figure

Liquid phase blockage in micro-nano capillary pores of tight condensate reservoirs

The development of tight condensate gas reservoirs faces complex formation damage mechanisms, seepage characteristics and hydrocarbon phase changes, which are common challenges for both tight gas reservoirs and condensate gas reservoirs. In the near-well area, the liquid phase blockage problem due to water phase retention formed by capillary spontaneous imbibition of invasive water and oil phase accumulation due to retrograde condensation precipitation has become a key obstacle to the efficient development of tight condensate gas reservoirs. Experiments were conducted to evaluate the damage of liquid phase blockage under different conditions near the wellbore area. The results show that when the liquid phase saturation in the near-wellbore area increased to 80.12%, the relative permeability of the gas phase decreased to 0. It is concluded that the mixed wettability of formation rocks, ultra-low water saturation, abundant hydrophilic clay minerals and high capillary resistance of micro-nano pores are the main causes for the easy adsorption and retention of liquid phase. Reduced pressure transmission capacity and irreversible formation damage induced by liquid-phase blockage are the two major controlling factors for the low liquid phase flowback rate. It is suggested that developing a flowback system based on the formation physical properties differentiation to control water phase invasion, and changing wettability or injecting thermochemical fluid to control condensate blocking are feasible methods to relieve liquid phase blockage damage in tight condensate reservoirs.Cited as: Wang, Y., Kang, Y., Wang, D., You, L., Chen, M., Yan, X. Liquid phase blockage in micro-nano capillary pores of tight condensate reservoirs. Capillarity, 2022, 5(1): 12-22. https://doi.org/10.46690/capi.2022.01.0

Sequence-to-point learning with neural networks for nonintrusive load monitoring

Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a single-channel blind source separation problem, aims to decompose the mains which records the whole house electricity consumption into appliance-wise readings. This problem is difficult because it is inherently unidentifiable. Recent approaches have shown that the identifiability problem could be reduced by introducing domain knowledge into the model. Deep neural networks have been shown to be a promising approach for these problems, but sliding windows are necessary to handle the long sequences which arise in signal processing problems, which raises issues about how to combine predictions from different sliding windows. In this paper, we propose sequence-to-point learning, where the input is a window of the mains and the output is a single point of the target appliance. We use convolutional neural networks to train the model. Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem. We applied the proposed neural network approaches to real-world household energy data, and show that the methods achieve state-of-the-art performance, improving two standard error measures by 84% and 92%

CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering

Text clustering, as one of the most fundamental challenges in unsupervised learning, aims at grouping semantically similar text segments without relying on human annotations. With the rapid development of deep learning, deep clustering has achieved significant advantages over traditional clustering methods. Despite the effectiveness, most existing deep text clustering methods rely heavily on representations pre-trained in general domains, which may not be the most suitable solution for clustering in specific target domains. To address this issue, we propose CEIL, a novel Classification-Enhanced Iterative Learning framework for short text clustering, which aims at generally promoting the clustering performance by introducing a classification objective to iteratively improve feature representations. In each iteration, we first adopt a language model to retrieve the initial text representations, from which the clustering results are collected using our proposed Category Disentangled Contrastive Clustering (CDCC) algorithm. After strict data filtering and aggregation processes, samples with clean category labels are retrieved, which serve as supervision information to update the language model with the classification objective via a prompt learning approach. Finally, the updated language model with improved representation ability is used to enhance clustering in the next iteration. Extensive experiments demonstrate that the CEIL framework significantly improves the clustering performance over iterations, and is generally effective on various clustering algorithms. Moreover, by incorporating CEIL on CDCC, we achieve the state-of-the-art clustering performance on a wide range of short text clustering benchmarks outperforming other strong baseline methods.Comment: The Web Conference 202