A Novel Multiple Classifier Generation and Combination Framework Based on Fuzzy Clustering and Individualized Ensemble Construction

Multiple classifier system (MCS) has become a successful alternative for improving classification performance. However, studies have shown inconsistent results for different MCSs, and it is often difficult to predict which MCS algorithm works the best on a particular problem. We believe that the two crucial steps of MCS - base classifier generation and multiple classifier combination, need to be designed coordinately to produce robust results. In this work, we show that for different testing instances, better classifiers may be trained from different subdomains of training instances including, for example, neighboring instances of the testing instance, or even instances far away from the testing instance. To utilize this intuition, we propose Individualized Classifier Ensemble (ICE). ICE groups training data into overlapping clusters, builds a classifier for each cluster, and then associates each training instance to the top-performing models while taking into account model types and frequency. In testing, ICE finds the k most similar training instances for a testing instance, then predicts class label of the testing instance by averaging the prediction from models associated with these training instances. Evaluation results on 49 benchmarks show that ICE has a stable improvement on a significant proportion of datasets over existing MCS methods. ICE provides a novel choice of utilizing internal patterns among instances to improve classification, and can be easily combined with various classification models and applied to many application domains

How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction

In view of the propagation environment directly determining the channel fading, the application tasks can also be solved with the aid of the environment information. Inspired by task-oriented semantic communication and machine learning (ML) powered environment-channel mapping methods, this work aims to provide a new view of the environment from the semantic level, which defines the propagation environment semantics (PES) as a limited set of propagation environment semantic symbols (PESS) for diverse application tasks. The PESS is extracted oriented to the tasks with channel properties as a foundation. For method validation, the PES-aided beam prediction (PESaBP) is presented in non-line-of-sight (NLOS). The PESS of environment features and graphs are given for the semantic actions of channel quality evaluation and target scatterer detection of maximum power, which can obtain 0.92 and 0.9 precision, respectively, and save over 87% of time cost.Comment: 5 pages, 5 figure

Structural Insights of Non-canonical U*U Pair and Hoogsteen Interaction Probed with Se Atom

Unlike DNA, in addition to the 20 -OH group, uracil nucleobase and its modifications play essential roles in structure and function diversities of non- coding RNAs. Non-canonical U.U base pair is ubiquitous in non-coding RNAs, which are highly diversified. However, it is not completely clear how uracil plays the diversifing roles. To investigate and compare the uracil in U-A and U.U base pairs, we have decided to probe them with a selenium atom by synthesizing the novel 4-Se-uridine (SeU) phosphoramidite and Se-nucleobase-modified RNAs (SeU-RNAs), where the exo-4-oxygen of uracil is replaced by selenium. Our crystal structure studies of U-A and U.U pairs reveal that the native and Se-derivatized structures are virtually identical, and both U-A and U.U pairs can accommodate large Se atoms. Our thermostability and crystal structure studies indicate that the weakened H-bonding in U-A pair may be compensated by the base stacking, and that the stacking of the trans- Hoogsteen U.U pairs may stabilize RNA duplex and its junction. Our result confirms that the hydrogen bond (O4.. .H-C5) of the Hoogsteen pair is weak. Using the Se atom probe, our Se- functionalization studies reveal more insights into the U.U interaction and U-participation in structure and function diversification of nucleic acids

DataAI-6G: A System Parameters Configurable Channel Dataset for AI-6G Research

With the acceleration of the commercialization of fifth generation (5G) mobile communication technology and the research for 6G communication systems, the communication system has the characteristics of high frequency, multi-band, high speed movement of users and large antenna array. These bring many difficulties to obtain accurate channel state information (CSI), which makes the performance of traditional communication methods be greatly restricted. Therefore, there has been a lot of interest in using artificial intelligence (AI) instead of traditional methods to improve performance. A common and accurate dataset is essential for the research of AI communication. However, the common datasets nowadays still lack some important features, such as mobile features, spatial non-stationary features etc. To address these issues, we give a dataset for future 6G communication. In this dataset, we address these issues with specific simulation methods and accompanying code processing

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, the current communication systems, which were designed on the basis of conventional communication theories, signficantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learningbased communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We vision that the appealing deep learning-based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.Comment: Submitted a possible publication to IEEE Wireless Communications Magazin

Bis[μ-bis­(diphenyl­phosphino)methane-κ2 P:P′]bis­[(4-toluene­sulfonato-κO)silver(I)] monohydrate

The title complex, [Ag2(C7H7O3S)2(C25H22P2)2]·H2O, was obtained by the reaction of silver toluene­sulfonate with diphenyl­phosphinomethane (dppm) in acetonitrile. There are two unique half-mol­ecules of the complex in the asymmetric unit, together with one water mol­ecule, which is disordered over two positions with site occupancy factors of 0.6 and 0.4. In each centrosymmetric neutral dimeric mol­ecule, two Ag atoms are bridged by a pair of dppm ligands to give an eight-membered Ag2P4C2 ring with a distorted AgOP2 trigonal–planar environment. The Ag—Ag distances of 2.9215 (9) and 3.027 (1) Å indicate a direct bonding inter­action

Knowledge-guided multi-scale independent component analysis for biomarker identification

<p>Abstract</p> <p>Background</p> <p>Many statistical methods have been proposed to identify disease biomarkers from gene expression profiles. However, from gene expression profile data alone, statistical methods often fail to identify biologically meaningful biomarkers related to a specific disease under study. In this paper, we develop a novel strategy, namely knowledge-guided multi-scale independent component analysis (ICA), to first infer regulatory signals and then identify biologically relevant biomarkers from microarray data.</p> <p>Results</p> <p>Since gene expression levels reflect the joint effect of several underlying biological functions, disease-specific biomarkers may be involved in several distinct biological functions. To identify disease-specific biomarkers that provide unique mechanistic insights, a meta-data "knowledge gene pool" (KGP) is first constructed from multiple data sources to provide important information on the likely functions (such as gene ontology information) and regulatory events (such as promoter responsive elements) associated with potential genes of interest. The gene expression and biological meta data associated with the members of the KGP can then be used to guide subsequent analysis. ICA is then applied to multi-scale gene clusters to reveal regulatory modes reflecting the underlying biological mechanisms. Finally disease-specific biomarkers are extracted by their weighted connectivity scores associated with the extracted regulatory modes. A statistical significance test is used to evaluate the significance of transcription factor enrichment for the extracted gene set based on motif information. We applied the proposed method to yeast cell cycle microarray data and Rsf-1-induced ovarian cancer microarray data. The results show that our knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification.</p> <p>Conclusion</p> <p>We have proposed a novel method, namely knowledge-guided multi-scale ICA, to identify disease-specific biomarkers. The goal is to infer knowledge-relevant regulatory signals and then identify corresponding biomarkers through a multi-scale strategy. The approach has been successfully applied to two expression profiling experiments to demonstrate its improved performance in extracting biologically meaningful and disease-related biomarkers. More importantly, the proposed approach shows promising results to infer novel biomarkers for ovarian cancer and extend current knowledge.</p