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In its Sixth Edition, this book has been updated and revised to incorporate new information on technology, economic, and political issues that have impacted the use of fluids to drill and complete oil and gas wells. With updated content on completion fluids and reservoir drilling fluids; health, safety and environment; drilling fluid systems and products; new fluid systems and additives from both chemical and engineering perspectives; wellbore stability, adding the new R&D on water-based muds; and equipment and procedures for evaluating drilling fluid performance in light of the advent of digital technology and better manufacturing techniques, this book has been thoroughly updated to meet the drilling and completion engineer's needs.

Matrix completion has attracted significant recent attention in many fields including statistics, applied mathematics, and electrical engineering. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. We provide theoretical justification for the proposed SMC method and derive lower bound for the estimation errors, which together establish the optimal rate of recovery over certain classes of approximately low-rank matrices. Simulation studies show that the method performs well in finite sample under a variety of configurations. The method is applied to integrate several ovarian cancer genomic studies with different extent of genomic measurements, which enables us to construct more accurate prediction rules for ovarian cancer survival. Supplementary materials for this article are available online.

Point cloud completion from partial observations remains challenging due to the trade-off between preserving global structural consistency and recovering fine-grained local details, especially under severe incompleteness. We propose a symmetry-guided progressive refinement network to address this problem by learning flexible structural correspondences and progressively refining incomplete shapes. First, a Symmetry Graph Inference Network (SymGraphNet) constructs a feature-space graph over sampled keypoints and predicts symmetry-guided structural counterparts for robust coarse shape recovery, without explicitly estimating a rigid symmetry plane or axis. Second, a confidence-weighted Cross-Aware Decoder adaptively fuses partial-observation features and symmetry-guided features to balance visible-region fidelity and missing-region completion. Third, a multi-stage residual refinement strategy progressively improves geometric fidelity, local continuity, and point distribution uniformity. Experiments on PCN, MVP, and KITTI datasets demonstrate consistent improvements over representative state-of-the-art methods under both synthetic and real-world incomplete point cloud settings.

Intensive research in matrix completions, moments, and sums of Hermitian squares has yielded a multitude of results in recent decades. This book provides a comprehensive account of this quickly developing area of mathematics and applications and gives complete proofs of many recently solved problems. With MATLAB codes and more than 200 exercises, the book is ideal for a special topics course for graduate or advanced undergraduate students in mathematics or engineering, and will also be a valuable resource for researchers. Often driven by questions from signal processing, control theory, and quantum information, the subject of this book has inspired mathematicians from many subdisciplines, including linear algebra, operator theory, measure theory, and complex function theory. In turn, the applications are being pursued by researchers in areas such as electrical engineering, computer science, and physics. The book is self-contained, has many examples, and for the most part requires only a basic background in undergraduate mathematics, primarily linear algebra and some complex analysis. The book also includes an extensive discussion of the literature, with close to 600 references from books and journals from a wide variety of disciplines.

We develop a method for subdividing polyhedral complexes in a way that restricts the possible recession cones and allows one to work with a fixed class of polyhedron. We use these results to construct locally finite completions of rational polyhedral complexes whose recession cones lie in a fixed fan, locally finite polytopal completions of polytopal complexes, and locally finite zonotopal completions of zonotopal complexes.

[This corrects the article DOI: 10.3389/fnins.2024.1375225.].

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open nature of KGs often implies that they are incomplete, having self-defects. This creates the need to build a more complete knowledge graph for enhancing the practical utilization of KGs. Link prediction is a fundamental task in knowledge graph completion that utilizes existing relations to infer new relations so as to build a more complete knowledge graph. Numerous methods have been proposed to perform the link-prediction task based on various representation techniques. Among them, KG-embedding models have significantly advanced the state of the art in the past few years. In this paper, we provide a comprehensive survey on KG-embedding models for link prediction in knowledge graphs. We first provide a theoretical analysis and comparison of existing methods proposed to date for generating KG embedding. Then, we investigate several representative models that are classified into five categories. Finally, we conducted experiments on two benchmark datasets to report comprehensive findings and provide some new insights into the strengths and weaknesses of existing models.

This paper considers regularized block multiconvex optimization, where the feasible set and objective function are generally nonconvex but convex in each block of variables. It also accepts nonconvex blocks and requires these blocks to be updated by proximal minimization. We review some interesting applications and propose a generalized block coordinate descent method. Under certain conditions, we show that any limit point satisfies the Nash equilibrium conditions. Furthermore, we establish global convergence and estimate the asymptotic convergence rate of the method by assuming a property based on the Kurdyka--Lojasiewicz inequality. The proposed algorithms are tested on nonnegative matrix and tensor factorization, as well as matrix and tensor recovery from incomplete observations. The tests include synthetic data and hyperspectral data, as well as image sets from the CBCL and ORL databases. Compared to the existing state-of-the-art algorithms, the proposed algorithms demonstrate superior performance in both speed and solution quality. The MATLAB code of nonnegative matrix/tensor decomposition and completion, along with a few demos, are accessible from the authors' homepages. [PUBLICATION ABSTRACT]

Bachelor’s degree (BA) completion is lower among black students than among white students. In this study, we use data from the Education Longitudinal Study of 2002 and the Integrated Postsecondary Education Data System, together with regression-based analytical techniques, to identify the primary sources of the BA completion gap. We find that black students’ lower academic and socioeconomic resources are the biggest drivers of the gap. However, we also find that black students are more likely to enroll in four-year colleges than are white students, given pre-college resources. We describe this dynamic as “paradoxical persistence” because it challenges Boudon’s well-known assertion that the secondary effect of educational decision-making should reinforce the primary effect of resource discrepancies. Instead, our results indicate that black students’ paradoxical persistence widens the race gap in BA completion while also narrowing the race gap in BA attainment, or the proportion of high school graduates to receive a BA. This narrowing effect on the BA attainment gap is as large or larger than the narrowing effect of black students’ “overmatch” to high-quality colleges, facilitated in part by affirmative action. Paradoxical persistence refocuses attention on black students’ individual agency as an important source of existing educational gains.

Episodic memory capacity requires several processes, including mnemonic discrimination of similar experiences, termed pattern separation , and holistic retrieval of multidimensional experiences given a cue, termed pattern completion . Both computations seem to rely on the hippocampus proper, but they also seem to be instantiated by distinct hippocampal subfields. Thus, we investigated whether individual differences in behavioral expressions of pattern separation and pattern completion were correlated after accounting for general mnemonic ability. Young adult participants learned events comprised of a scene-animal-object triad. In the pattern separation task, we estimated mnemonic discrimination using lure classification for events that contained a similar lure element. In the pattern completion task, we estimated holistic recollection using dependency in retrieval success for different associations from the same event. Although overall accuracies for the two tasks correlated as expected, specific measures of individual variation in holistic retrieval and mnemonic discrimination did not correlate, suggesting that these two processes involve distinguishable properties of episodic memory.