Complex Data Objects Research Articles

Deep and wide nonnegative matrix factorization with embedded regularization

End-to-end learning is an advanced framework in deep learning. It combines feature extraction, followed by pattern recognition (classification, clustering, etc.) in a unified learning structure. However, these deep networks face several challenges such as overfitting, vanishing gradient, computational complexity, information loss in layers, and weak robustness to noisy data/features. To address these challenges, this paper presents Deep and Wide Nonnegative Matrix Factorization (DWNMF) with embedded regularization for the feature extraction stage of the end-to-end models. DWNMF aims to identify more robust features while preventing overfitting via embedding regularization. For this purpose, DWNMF integrates input data with its noisy versions as diverse augmented channels. Then, the features in all channels are extracted in parallel using distinct network branches. The parameters of this model learn the intrinsic hierarchical features in the channels of complex data objects. Finally, the extracted features in different channels are aggregated in a single feature space to perform the classification task. To embed regularization in the DWNMF model, some NMF neurons in the layers are substituted by random neurons to increase the stability and robustness of the extracted features. Experimental results confirm that the DWNMF model extracts more robust features, prevents overfitting, and achieves better classification accuracy compared to state-of-the-art methods.

Error‐controlled feature selection for ultrahigh‐dimensional and highly correlated feature space using deep learning

AbstractDeep learning has been at the center of analytics in recent years due to its impressive empirical success in analyzing complex data objects. Despite this success, most existing tools behave like black‐box machines, thus the increasing interest in interpretable, reliable, and robust deep learning models applicable to a broad class of applications. Feature‐selected deep learning has emerged as a promising tool in this realm. However, the recent developments do not accommodate ultrahigh‐dimensional and highly correlated features or high noise levels. In this article, we propose a novel screening and cleaning method with the aid of deep learning for a data‐adaptive multi‐resolutional discovery of highly correlated predictors with a controlled error rate. Extensive empirical evaluations over a wide range of simulated scenarios and several real datasets demonstrate the effectiveness of the proposed method in achieving high power while keeping the false discovery rate at a minimum.

Robust DTW-based entropy fuzzy clustering of time series

AbstractTime series are complex data objects whose partitioning into homogeneous groups is still a challenging task, especially in the presence of outliers or noisy data. To address the problem of robustness against outliers in clustering techniques, this paper proposes a robust fuzzy C-medoids method based on entropy regularization. In-depth, we use an appropriate exponential transformation of the dissimilarity based on Dynamic Time Warping, which can be computed also for time series of different length. In addition, the fuzzy framework provides the necessary flexibility to cope with the complexity of the features space. It allows a time series to be assigned to more than one group, considering potential switching behaviours. Moreover, the use of a medoids-based approach enables the identification of observed representative objects within the dataset, thus enhancing interpretability for practical applications. Through an extensive simulation study, we successfully demonstrate the effectiveness of our proposal, comparing and emphasizing its strengths. Finally, our proposed methodology is applied to the daily mean concentrations of three air pollutants in 2022 in the Province of Rome. This application highlights its potential, namely the capability to intercept outliers and switching time series while preserving group structures.

Analyzing microbial evolution through gene and genome phylogenies.

Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary histories. To address this challenge, we propose to treat estimated gene-level phylogenies as data objects, and present an interactive method for the analysis of a collection of gene phylogenies. We use a local linear approximation of phylogenetic tree space to visualize estimated gene trees as points in low-dimensional Euclidean space, and address important practical limitations of existing related approaches, allowing an intuitive visualization of complex data objects. We demonstrate the utility of our proposed approach through microbial data analyses, including by identifying outlying gene histories in strains of Prevotella, and by contrasting Streptococcus phylogenies estimated using different gene sets. Our method is available as an open-source R package, and assists with estimating, visualizing, and interacting with a collection of bacterial gene phylogenies.

Three-Dimensional Reconstruction Based on Multiple Views of Structured Light Projectors and Point Cloud Registration Noise Removal for Fusion.

Structured light technology is typical for capturing 3D point cloud data. This paper proposes a 3D reconstruction system to obtain point cloud data of complex objects based on nine-order Gray code and an eight-step structured light projection combined with a phase shift and phase unwrapping method. In this system, two projectors serve as bilateral projectors for structured light, along with a camera and rotating platforms. These components were used to obtain point cloud data from multiple perspectives, which helps avoid the shadow areas caused by a single projection angle and provides complementary point cloud data. The point clusters scanned under each perspective were transformed into the same coordinate system. Furthermore, a distance-based point cloud noise removal algorithm was proposed to optimize platform noise and facilitate point cloud data fusion. The experimental results proved that the system effectively captures 3D point cloud data for complex objects. The dimensional quantitative analysis of an aero engine blade was also performed.

Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic.

Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex data sets that capture the 3D spatial positions, dynamics, and interactions of thousands of molecules. Analyzing MD data sets is key for understanding and predicting emergent phenomena and in identifying key drivers and tuning design knobs of such phenomena. In this work, we show that the Euler characteristic (EC) provides an effective topological descriptor that facilitates MD analysis. The EC is a versatile, low-dimensional, and easy-to-interpret descriptor that can be used to reduce, analyze, and quantify complex data objects that are represented as graphs/networks, manifolds/functions, and point clouds. Specifically, we show that the EC is an informative descriptor that can be used for machine learning and data analysis tasks such as classification, visualization, and regression. We demonstrate the benefits of the proposed approach through case studies that aim to understand and predict the hydrophobicity of self-assembled monolayers and the reactivity of complex solvent environments.

TWO-SAMPLE TESTS FOR MULTIVARIATE REPEATED MEASUREMENTS OF HISTOGRAM OBJECTS WITH APPLICATIONS TO WEARABLE DEVICE DATA.

Repeated observations have become increasingly common in biomedical research and longitudinal studies. For instance, wearable sensor devices are deployed to continuously track physiological and biological signals from each individual over multiple days. It remains of great interest to appropriately evaluate how the daily distribution of biosignals might differ across disease groups and demographics. Hence, these data could be formulated as multivariate complex object data, such as probability densities, histograms, and observations on a tree. Traditional statistical methods would often fail to apply, as they are sampled from an arbitrary non-Euclidean metric space. In this paper we propose novel, nonparametric, graph-based two-sample tests for object data with the same structure of repeated measures. We treat the repeatedly measured object data as multivariate object data, which requires the same number of repeated observations per individual but eliminates any assumptions on the errors of the repeated observations. A set of test statistics are proposed to capture various possible alternatives. We derive their asymptotic null distributions under the permutation null. These tests exhibit substantial power improvements over the existing methods while controlling the type I errors under finite samples as shown through simulation studies. The proposed tests are demonstrated to provide additional insights on the location, inter- and intra-individual variability of the daily physical activity distributions in a sample of studies for mood disorders.

Analysis the Innovation Path on Psychological Ideological with Political Teaching in Universities by Big Data in New Era.

The increasing pressure of life and the rapid development of the economy have caused huge mental health situation problems for people. Mental health situation has an important relationship with one's own life values. The cognition Ideological with political is to improve people's optimistic attitude and values. The students of college will face enormous pressure from their studies, which can easily causes the mental health situation problems. However, the psychological and ideological education in universities still adopts the traditional teaching method, which reduces students' learning hobbies and learning efficiency. This also reduces students' understanding of the content of psychological ideological with political teaching. Big data theory can process complex research object data and relationships. It can help researchers discover characteristic factors related to psychological ideological with political teaching. This study uses the hole convolution in big data theory and GRU technology to analyze the three factors of student behavior characteristics, mental health situation content characteristics, and the cognition Ideological with political content characteristics in college psychological the cognition Ideological with political. The research results show that the atrous convolution and GRU methods can more accurately predict the three characteristics of psychological ideological with political teaching in universities. This is helpful for educators to discover more appropriate psychological and ideological with political teaching methods.

Automatic Treatment Regimen Design

As a data-driven healthcare service, automatic treatment regimen design has great potential to improve healthcare efficiency and quality. However, it is a nontrivial endeavor to develop such a healthcare service due to two major challenges: 1) the treatment records are complex data objects consisting of various semantic and temporal information, and 2) the treatment outcome usually depends on a large number of internal and external factors. Because of these difficulties, automatic treatment regimen design is still an open research problem nowadays. To fill this research gap, this paper first formulates a treatment sequence as temporal sets, then provides a novel Extended Jaccard Similarity (EJS) measure to quantify the similarities between treatment sequences. We show that the proposed EJS is a general and effective measure to capture the similarity between two complex temporal sets. Further, we develop an efficient clustering algorithm which can achieve reasonable clustering results with only a portion of the pairwise similarities between treatment sequences and then extract a semantic prototype of the treatment regimen from each cluster of treatment sequences. Finally, we adopt a matrix factorization framework to predict the treatment outcomes by integrating multiple internal and external factors. We conduct comprehensive experiments on Electronic Medical Records (EMRs) of more than 28,000 patients from 14 hospitals. The results demonstrate the effectiveness of our approach and its superiority over the state-of-the-art ones.

DaQL 2.0: Measure Data Quality based on Entity Models

Abstract In order to make good decisions, the data used for decision-making needs to be of high quality. As the volume of data continually increases, ensuring high data quality is a big challenge nowadays and needs to be automated with tools. The goal of the Data Quality Library (DaQL) is to provide a tool to continuously ensure and measure data quality as proposed in [5]. In this paper, we present the current status of the development of the new DaQL version 2.0. The main contribution of DaQL 2.0 is the possibility to define data quality rules for complex data objects (called entities), which represent business objects. In contrast to existing tools, a user does not require detailed knowledge about the database schema that is observed.

Interpretable and effective hashing via Bernoulli variational auto-encoders

Due to the rapid increase in the amount of data generated in many fields of science and engineering, information retrieval methods tailored to large-scale datasets have become increasingly important in the last years. Semantic hashing is an emerging technique for this purpose that works on the idea of representing complex data objects, like images and text, using similarity-preserving binary codes that are then used for indexing and search. In this paper, we investigate a hashing algorithm that uses a deep variational auto-encoder to learn and predict the codes. Unlike previous approaches of this type, that learn a continuous (Gaussian) representation and then project the embedding to obtain hash codes, our method employs Bernoulli latent variables in both the training and the prediction stage. Constraining the model to use a binary encoding allow us to obtain a more interpretable representation for hashing: each factor in the generative model represents a bit that should help to reconstruct and thus identify the input pattern. Interestingly, we found that the binary constraint does not lead to a loss but an increase of search accuracy. We argue that continuous formulations learn a representation that can significantly differ from the code used for search. Minding this gap in the design of the auto-encoder can translate into more accurate retrieval results. Extensive experiments on seven datasets involving image data and text data illustrate these findings and demonstrate the advantages of our approach.

A New Approach for Functional Connectivity via Alignment of Blood Oxygen Level-Dependent Signals.

Due to technological advances, spatially indexed objects, such as blood oxygen level-dependent time series or electroencephalography data, are commonly observed across different scientific disciplines. Such object data are typically high dimensional and therefore challenging to handle. We propose a new approach for spatially indexed object data by mapping their spatial locations to a targeted one-dimensional interval so objects that are similar are placed near each other on the new target space. The proposed alignment not only provides a visualization tool for such complex object data but also facilitates a new way to study brain functional connectivity. Specifically, we introduce a new concept of path length to quantify the functional connectivity and a new community detection method. The advantages of the proposed methods are illustrated by simulations and in a study of functional connectivity for Alzheimer's disease.

Ball Covariance: A Generic Measure of Dependence in Banach Space

ABSTRACTTechnological advances in science and engineering have led to the routine collection of large and complex data objects, where the dependence structure among those objects is often of great interest. Those complex objects (e.g., different brain subcortical structures) often reside in some Banach spaces, and hence their relationship cannot be well characterized by most of the existing measures of dependence such as correlation coefficients developed in Hilbert spaces. To overcome the limitations of the existing measures, we propose Ball Covariance as a generic measure of dependence between two random objects in two possibly different Banach spaces. Our Ball Covariance possesses the following attractive properties: (i) It is nonparametric and model-free, which make the proposed measure robust to model mis-specification; (ii) It is nonnegative and equal to zero if and only if two random objects in two separable Banach spaces are independent; (iii) Empirical Ball Covariance is easy to compute and can be used as a test statistic of independence. We present both theoretical and numerical results to reveal the potential power of the Ball Covariance in detecting dependence. Also importantly, we analyze two real datasets to demonstrate the usefulness of Ball Covariance in the complex dependence detection. Supplementary materials for this article are avaiable online.

Multi-scale segmentation algorithm for pattern-based partitioning of large categorical rasters

Analyzing large Earth Observation (EO) data on the broad spatial scales frequently involves regionalization of patterns. To automate this process we present a segmentation algorithm designed specifically to delineate segments containing quasi-stationary patterns. The algorithm is designed to work with patterns of a categorical variable. This makes it possible to analyze very large spatial datasets (for example, a global land cover) in their entirety. An input categorical raster is first tessellated into small square tiles to form a new, coarser, grid of tiles. A mosaic of categories within each tile forms a local pattern, and the segmentation algorithm partitions the grid of tiles while maintaining the cohesion of pattern in each segment. The algorithm is based on the principle of seeded region growing (SRG) but it also includes segment merging and other enhancements to segmentation quality. Our key contribution is an extension of the concept of segmentation to grids in which each cell has a non-negligible size and contains a complex data structure (histograms of pattern features). Specific modification of a standard SRG algorithm include: working in a distance space with complex data objects, introducing six-connected “brick wall” topology of the grid to decrease artifacts associated with tessellation of geographical space, constructing the SRG priority queue of seeds on the basis of local homogeneity of patterns, and using a content-dependent value of segment-growing threshold. The detailed description of the algorithm is given followed by an assessment of its performance on test datasets representing three pertinent themes of land cover, topography, and a high-resolution image. Pattern-based segmentation algorithm will find application in ecology, forestry, geomorphology, land management, and agriculture. The algorithm is implemented as a module of GeoPAT – an already existing, open source toolbox for performing pattern-based analysis of categorical rasters.

Enabling Autonomous Navigation for Affordable Scooters.

Despite the technical success of existing assistive technologies, for example, electric wheelchairs and scooters, they are still far from effective enough in helping those in need navigate to their destinations in a hassle-free manner. In this paper, we propose to improve the safety and autonomy of navigation by designing a cutting-edge autonomous scooter, thus allowing people with mobility challenges to ambulate independently and safely in possibly unfamiliar surroundings. We focus on indoor navigation scenarios for the autonomous scooter where the current location, maps, and nearby obstacles are unknown. To achieve semi-LiDAR functionality, we leverage the gyros-based pose data to compensate the laser motion in real time and create synthetic mapping of simple environments with regular shapes and deep hallways. Laser range finders are suitable for long ranges with limited resolution. Stereo vision, on the other hand, provides 3D structural data of nearby complex objects. To achieve simultaneous fine-grained resolution and long range coverage in the mapping of cluttered and complex environments, we dynamically fuse the measurements from the stereo vision camera system, the synthetic laser scanner, and the LiDAR. We propose solutions to self-correct errors in data fusion and create a hybrid map to assist the scooter in achieving collision-free navigation in an indoor environment.

The Application of Database Technology in Information Society and its Existing Problems

With the development of computer technology and network communication technology and multimedia technology,database technology has become an important technical means and software technology for organizing and managinglarge amount of data in information society. It is the foundation of network information management system. Itnot only has a complete theoretical basis, and with the rapid development of hardware technology and softwaretechnology, its application is more and more widely. However, the rapid development of information technology isthat people face such a situation: CAD / CAM, CIMS, CASE, GIS and other applications require the managementof increasingly complex information, such as multimedia data, such as multimedia data, spatial data, scientifi c data,Data and other data types, these data compared with the traditional type, not only the type of complex, diverse forms,and storage, transmission, operation and retrieval methods also have many new features, new requirements, on theone hand, the need for database technology and database systems To manage these complex data in order to obtainthe database system has many of the data management functions, such as query, retrieval, recovery, concurrencycontrol, integrity, storage management. On the other hand, due to the vigorous development of network technology,information processing has long been from a single user environment to the development of the network environment,this environment for resources, information sharing provides a great convenience, and because of multi-machine worktogether to greatly improve the work , The combination of the two requires that the database management system beable to manage complex data objects in a networked environment or heterogeneous environment. Traditional DBMSscannot eff ectively handle complex multimedia data, thus requiring the use of new multimedia indexing and retrievaltechniques. Multimedia database technology is an important part of computer technology, it can simultaneously acquire,process, edit, store and display text, sound, video, graphics and other diff erent media, while it has diversity, integrationand interactivity and so on. Only to solve the key technical issues of multimedia, can be widely used to promote itsfaster development.

Add Data into Business Process Verification: Bridging the Gap between Theory and Practice

The need to extend business process languages with the capability to model complex data objects along with the control flow perspective has lead to significant practical and theoretical advances in the field of Business Process Modeling (BPM).On the practical side, there are several suites for control flow and data modeling; nonetheless, when it comes to formal verification, the data perspective is abstracted away due to the intrinsic difficulty of handling unbounded data. On the theoretical side, there is significant literature providing decidability results for expressive data-aware processes. However, they struggle to produce a concrete impact as being far from real BPM architectures and, most of all, not providing actual verification tools. In this paper we aim at bridging such a gap: we provide a concrete framework which, on the one hand, being based on Petri Nets and relational models, is close to the widely used BPM suites, and on the other is grounded on solid formal basis which allow to perform formal verification tasks. Moreover, we show how to encode our framework in an action language so as to perform reachability analysis using virtually any state-of-the-art planner.

REFINED MODELING OF COMPLEX GEOLOGICAL BODY BASED ON THERE-DIMENSIONAL LASER SCANNING TECHNIQUE

In the platform of FLAC3D software, how to build the three-dimensional models of complex geological bodies is always a difficulty encountered by many numerical simulation operators. On the other hand, the three-dimensional laser scanning technique presents a unique advantage in obtaining the fine spatial data of complex objects. Therefore, by the aid of reverse engineering, a new modeling method of complex geological bodies in FLAC3D platform is proposed. The accurate spatial and geometric information of complex geological bodies is firstly obtained by employing three-dimensional laser scanning technique. The point cloud data describing surface characteristics of complex geological bodies is processed and its surface model is then reconstructed in CAD format. Using the formidable geometry cleanup and mesh generation function of Hypermesh software, the surface model is transformed into three-dimensional finite element model. The node and element information can be translated into the format which can be received by FLAC3D. According to the above processes, the model data for FLAC3D is generated and the refined model of complex geological bodies is created in the pre-processing of FLAC3D.the feasibility and practicability of this approach is verified by combining practical project.

Inherent Fusion

The rapid growth of unstructured data, commonly denoted as the Big Data challenge, requires new technologies that are capable of dealing with complex data objects such as multimedia. In this work, the authors focus on the content-based retrieval approach, which is able to organize such data by exploiting the similarity of data content. In particular, they focus on solutions that are able to combine multiple similarity measures during the query evaluation. The authors introduce a classification of existing approaches and analyze their performance in terms of effectiveness, efficiency, and scalability. Further, they present a novel technique of inherent fusion that combines the efficiency of fast indexed retrieval with the effectiveness of ranking methods. The performance of all discussed methods is evaluated by extensive experiments with user participation.

A Survey of Big Data Issues in Electronic Health Record Analysis

ABSTRACTThe Electronic Health Record (EHR) groups all digital documents related to a given patient such as anamnesis, results of the laboratory tests, prescriptions, recorded medical signals as ECG or images, etc. Dealing with such data representation incurs a plethora of problems, such as different data types, even unstructured data (i.e., doctor’s notes), huge and fast-growing volume, etc. Therefore. EHR should be considered as one of the most complex data objects in the information processing industry. Accordingly, taking into consideration its complexity, heterogeneity, fast growth, and size, the analysis of EHR data increasingly needs big data tools. Such tools should be able to analyze datasets characterized by the so-called 4Vs (volume, velocity, variety, and veracity). These notwithstanding, we should also add the fifth V—value—because analytics tool deployment makes sense only if it leads to health-care improvement (as personalized patient care, decreasing unnecessary hospitalization, or reducing patient readmissions). In this study, we focus on the selected aspects of EHR analysis from the big data perspective.