T-Crowd: Effective Crowdsourcing for Tabular Data

Crowdsourcing employs human workers to solve computer-hard problems, such as data cleaning, entity resolution, and sentiment analysis. When crowdsourcing tabular data, e.g., the attribute values of an entity set, a worker's answers on the different attributes (e.g., the nationality and age of a celebrity star) are often treated independently. This assumption is not always true and can lead to suboptimal crowdsourcing performance. In this paper, we present the T-Crowd system, which takes into consideration the intricate relationships among tasks, in order to converge faster to their true values. Particularly, T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. The attribute relationship information is also used to guide task allocation to workers. Finally, T-Crowd seamlessly supports categorical and continuous attributes, which are the two main datatypes found in typical databases. Our extensive experiments on real and synthetic datasets show that T-Crowd outperforms state-of-the-art methods in terms of truth inference and reducing the cost of crowdsourcing

Truth Inference in Crowdsourcing: Is the Problem Solved?

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Bis(2-amino­pyrazine-κN 1)tetra­aqua­cadmium(II) bis­(perchlorate)–2-amino­pyrazine (1/4)

In the title compound, [Cd(C4H5N3)2(H2O)4](ClO4)2·4C4H5N3, the CdII atom (site symmetry ) is coordinated by two N-heterocycles and four water mol­ecules, resulting in a distorted trans-CdN2O4 octa­hedral geometry for the metal. In the crystal, the cation, anion and free N-heterocycle mol­ecules are linked by N—H⋯N, N—H⋯O, O—H⋯N and O—H⋯O hydrogen bonds, forming a three-dimensional network

Entanglement dynamics of two-qubit system in different types of noisy channels

In this paper, we study entanglement dynamics of a two-qubit extended Werner-like state locally interacting with independent noisy channels, i.e., amplitude damping, phase damping and depolarizing channels. We show that the purity of initial entangled state has direct impacts on the entanglement robustness in each noisy channel. That is, if the initial entangled state is prepared in mixed instead of pure form, the state may exhibit entanglement sudden death (ESD) and/or be decreased for the critical probability at which the entanglement disappear.Comment: 11 pages, 6 figure

Label Propagation for Graph Label Noise

Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels in computer vision; however, graph models encompass both node features and graph topology as input, and become more susceptible to label noise through message-passing mechanisms. Recently, only a few works have been proposed to tackle the label noise on graphs. One major limitation is that they assume the graph is homophilous and the labels are smoothly distributed. Nevertheless, real-world graphs may contain varying degrees of heterophily or even be heterophily-dominated, leading to the inadequacy of current methods. In this paper, we study graph label noise in the context of arbitrary heterophily, with the aim of rectifying noisy labels and assigning labels to previously unlabeled nodes. We begin by conducting two empirical analyses to explore the impact of graph homophily on graph label noise. Following observations, we propose a simple yet efficient algorithm, denoted as LP4GLN. Specifically, LP4GLN is an iterative algorithm with three steps: (1) reconstruct the graph to recover the homophily property, (2) utilize label propagation to rectify the noisy labels, (3) select high-confidence labels to retain for the next iteration. By iterating these steps, we obtain a set of correct labels, ultimately achieving high accuracy in the node classification task. The theoretical analysis is also provided to demonstrate its remarkable denoising "effect". Finally, we conduct experiments on 10 benchmark datasets under varying graph heterophily levels and noise types, comparing the performance of LP4GLN with 7 typical baselines. Our results illustrate the superior performance of the proposed LP4GLN

AAA ATPases as therapeutic targets: Structure, functions, and small-molecule inhibitors

ATPases Associated with Diverse Cellular Activity (AAA ATPase) are essential enzymes found in all organisms. They are involved in various processes such as DNA replication, protein degradation, membrane fusion, microtubule serving, peroxisome biogenesis, signal transduction, and the regulation of gene expression. Due to the importance of AAA ATPases, several researchers identified and developed small-molecule inhibitors against these enzymes. We discuss six AAA ATPases that are potential drug targets and have well-developed inhibitors. We compare available structures that suggest significant differences of the ATP binding pockets among the AAA ATPases with or without ligand. The distances from ADP to the His20 in the His-Ser-His motif and the Arg finger (Arg353 or Arg378) in both RUVBL1/2 complex structures bound with or without ADP have significant differences, suggesting dramatically different interactions of the binding site with ADP. Taken together, the inhibitors of six well-studied AAA ATPases and their structural information suggest further development of specific AAA ATPase inhibitors due to difference in their structures. Future chemical biology coupled with proteomic approaches could be employed to develop variant specific, complex specific, and pathway specific inhibitors or activators for AAA ATPase proteins