Minimal Loss Of Information Research Articles

Managing consistency and consensus measures and adjustment strategies in group decision making with probabilistic linguistic preference relations

The use of probabilistic linguistic preference relations (PLPRs) in pairwise comparisons enhances the flexibility of quantitative decision making. To promote the application of probabilistic linguistic term sets (PLTSs) and PLPRs, this paper introduces the consistency and consensus measures and adjustment strategies to guarantee the rationality of preference information utilized in the group decision making process. First of all, a novel entropy-based similarity measure is developed with PLTSs. Hereafter an improved consistency measure is defined on the basis of the proposed similarity measure, and a convergent algorithm is constructed to deal with the consistency improving process. Furthermore, a similarity-based consensus measure is developed in a given PLPR, and the consensus reaching process is presented to deal with the unacceptable consensus degree. The proposed consistency improving and consensus reaching processes follow a principle of minimum information loss, called a local adjustment strategy. In particular, the presented methods not only overcome the deficiencies in existing studies but also enhance the interpretation and reduce the complexity of the group decision making process. Finally, the proposed consistency measure and improving process, as well as consensus measure and reaching process are verified through a numerical example for the medical plan selection issue. The result and in-depth comparison analysis validate the feasibility and effectiveness of the proposed methods.

In-Depth Comparison of Matrigel Dissolving Methods on Proteomic Profiling of Organoids

Patient-derived organoids recently emerged as promising ex vivo 3D culture models recapitulating histological and molecular characteristics of original tissues, thus proteomic profiling of organoids could be valuable for function investigation and clinical translation. However, organoids are usually cultured in murine Matrigel (served as scaffolds and matrix), which brings an issue to separate organoids from Matrigel. Because of the complex compositions of Matrigel and thousands of identical peptides shared between Matrigel and organoids, insufficiently dissolved Matrigel could influence proteomic analysis of organoids in multiple ways. Thus, how to dissolve Matrigel matrix and recovery organoid cells efficiently is vital for sample preparation. Here, we comprehensively compared three popular Matrigel dissolving methods (cell recovery solution, dispase, and PBS–EDTA buffer) and investigated the effect of undissolved Matrigel proteins on proteomic profiles of organoids. By integrative analysis of label-free proteomes of Matrigel and stable isotope labeling by amino acids in cell culture proteomes of organoids collected by three methods, respectively, we found that dispase showed an optimal efficiency, with the highest peptide yield and the highest incorporation ratio of stable isotope labeling by amino acids in cell culture labels (97.1%), as well as with the least potential Matrigel contaminants. To help analysis of proteomic profiles of organoids collected by the other two methods, we identified 312 high-confidence Matrigel contaminants, which could be filtered out to attenuate Matrigel interference with minimal loss of biological information. Together, our study identifies bioinformatics and experimental approaches to eliminate interference of Matrigel contaminants efficiently, which will be valuable for basic and translational proteomic research using organoid models.

Right-Censored Time Series Modeling by Modified Semi-Parametric A-Spline Estimator.

This paper focuses on the adaptive spline (A-spline) fitting of the semiparametric regression model to time series data with right-censored observations. Typically, there are two main problems that need to be solved in such a case: dealing with censored data and obtaining a proper A-spline estimator for the components of the semiparametric model. The first problem is traditionally solved by the synthetic data approach based on the Kaplan–Meier estimator. In practice, although the synthetic data technique is one of the most widely used solutions for right-censored observations, the transformed data’s structure is distorted, especially for heavily censored datasets, due to the nature of the approach. In this paper, we introduced a modified semiparametric estimator based on the A-spline approach to overcome data irregularity with minimum information loss and to resolve the second problem described above. In addition, the semiparametric B-spline estimator was used as a benchmark method to gauge the success of the A-spline estimator. To this end, a detailed Monte Carlo simulation study and a real data sample were carried out to evaluate the performance of the proposed estimator and to make a practical comparison.

Compressing atmospheric data into its real information content

Hundreds of petabytes are produced annually at weather and climate forecast centers worldwide. Compression is essential to reduce storage and to facilitate data sharing. Current techniques do not distinguish the real from the false information in data, leaving the level of meaningful precision unassessed. Here we define the bitwise real information content from information theory for the Copernicus Atmospheric Monitoring Service (CAMS). Most variables contain fewer than 7 bits of real information per value and are highly compressible due to spatio-temporal correlation. Rounding bits without real information to zero facilitates lossless compression algorithms and encodes the uncertainty within the data itself. All CAMS data are 17× compressed relative to 64-bit floats, while preserving 99% of real information. Combined with four-dimensional compression, factors beyond 60× are achieved. A data compression Turing test is proposed to optimize compressibility while minimizing information loss for the end use of weather and climate forecast data.

A Multi-Modal Fusion Method Based on Higher-Order Orthogonal Iteration Decomposition.

Multi-modal fusion can achieve better predictions through the amalgamation of information from different modalities. To improve the performance of accuracy, a method based on Higher-order Orthogonal Iteration Decomposition and Projection (HOIDP) is proposed, in the fusion process, higher-order orthogonal iteration decomposition algorithm and factor matrix projection are used to remove redundant information duplicated inter-modal and produce fewer parameters with minimal information loss. The performance of the proposed method is verified by three different multi-modal datasets. The numerical results validate the accuracy of the performance of the proposed method having 0.4% to 4% improvement in sentiment analysis, 0.3% to 8% improvement in personality trait recognition, and 0.2% to 25% improvement in emotion recognition at three different multi-modal datasets compared with other 5 methods.

Most-intersection of countable sets

We introduce a novel set-intersection operator called ‘most-intersection’ based on the logical quantifier ‘most’, via natural density of countable sets, to be used in determining the majority characteristic of a given countable (possibly infinite) collection of systems. The new operator determines, based on the natural density, the elements which are in ‘most’ sets in a given collection. This notion allows one to define a majority set-membership characteristic of an infinite/finite collection with minimal information loss, compared to the standard intersection operator, when used in statistical ensembles. We also give some applications of the most-intersection operator in formal language theory and hypergraphs. The introduction of the most-intersection operator leads to a large number of applications in pure and applied mathematics some of which we leave open for further study.

Adaptive Privacy Preservation Approach for Big Data Publishing in Cloud using k-anonymization

Background: Big data is an emerging technology that has numerous applications in the fields, like hospitals, government records, social sites, and so on. As the cloud computing can transfer large amount of data through servers it has found its importance in big data. Hence, it is important in cloud computing to protect the data so that the third party users cannot access the information from the users. Methods: This paper develops an anonymization model and adaptive Dragon Particle Swarm Optimization (adaptive Dragon-PSO) algorithm for privacy preservation in cloud environment. The development of proposed adaptive DragonPSO incorporates the integration of adaptive idea in the dragon-PSO algorithm. The dragon-PSO is the integration of Dragonfly Algorithm (DA) and Particle Swarm Optimization (PSO) algorithm. The proposed method derives the fitness function for the proposed adaptive Dragon-PSO algorithm to attain the higher value of privacy and utility. The performance of the proposed method was evaluated using the metrics, such as information loss and classification accuracy for different anonymization constant values. Conclusion: The proposed method provided a minimal information loss and maximal classification accuracy of 0.0110 and 0.7415, respectively when compared with the existing methods.

Knowledge Preserving and Distribution Alignment for Heterogeneous Domain Adaptation

Domain adaptation aims at improving the performance of learning tasks in a target domain by leveraging the knowledge extracted from a source domain. To this end, one can perform knowledge transfer between these two domains. However, this problem becomes extremely challenging when the data of these two domains are characterized by different types of features, i.e., the feature spaces of the source and target domains are different, which is referred to as heterogeneous domain adaptation (HDA). To solve this problem, we propose a novel model called Knowledge Preserving and Distribution Alignment (KPDA), which learns an augmented target space by jointly minimizing information loss and maximizing domain distribution alignment. Specifically, we seek to discover a latent space, where the knowledge is preserved by exploiting the Laplacian graph terms and reconstruction regularizations. Moreover, we adopt the Maximum Mean Discrepancy to align the distributions of the source and target domains in the latent space. Mathematically, KPDA is formulated as a minimization problem with orthogonal constraints, which involves two projection variables. Then, we develop an algorithm based on the Gauss–Seidel iteration scheme and split the problem into two subproblems, which are solved by searching algorithms based on the Barzilai–Borwein (BB) stepsize. Promising results demonstrate the effectiveness of the proposed method.

Lossless compression method for ultraspectral sounder data based on key information extraction and spectral–spatial prediction

Given the unprecedented size of ultraspectral sounder data, there is a special process of radiance thinning in assimilating this data to reduce the data volume with minimal loss of atmospheric information. Considering the potential correlation between the selected data by radiance thinning and the unselected data, a lossless compression method for ultraspectral sounder data is proposed based on key information extraction and spatial–spectral prediction. Sensitive channels are first selected by stepwise iteration based on information entropy to maintain critical atmospheric information, and then auxiliary channels are further selected based on information content and correlation constraints to facilitate prediction. All of the selected channels are spatially thinned to generate key information, which is then used to predict original ultaspectral sounder data by spatially bicubic interpolation and spectrally sparse reconstruction. The residual errors are processed by the least-squares linear prediction to further reduce data redundancy. Together with the key information, the final residual errors are then fed into a range coder after positive mapping and histogram packing. Experimental results with IASI-L1C data show that the proposed method achieves an average compression ratio of 2.68, which is 4.7% higher than that of the typical methods, including JPEG-LS, JPEG-2000, M-CALIC, CCSDS-122.0, CCDS-123.0, and HEVC.

Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

PurposeThe purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information. Putting a stop to privacy divulgence and bestowing relevant information to legitimate users are at the same time said to be of differing goals. Also, the swift evolution of big data has put forward considerable ease to all chores of life. As far as the big data era is concerned, propagation and information sharing are said to be the two main facets. Despite several research works performed on these aspects, with the incremental nature of data, the likelihood of privacy leakage is also substantially expanded through various benefits availed of big data. Hence, safeguarding data privacy in a complicated environment has become a major setback.Design/methodology/approachIn this study, a method called deep restricted additive homomorphic ElGamal privacy preservation (DR-AHEPP) to preserve the privacy of data even in case of incremental data is proposed. An entropy-based differential privacy quasi identification and DR-AHEPP algorithms are designed, respectively, for obtaining privacy-preserved minimum falsified quasi-identifier set and computationally efficient privacy-preserved data.FindingsAnalysis results using Diabetes 130-US hospitals illustrate that the proposed DR-AHEPP method is more significant in preserving privacy on incremental data than existing methods. A comparative analysis of state-of-the-art works with the objective to minimize information loss, false positive rate and execution time with higher accuracy is calibrated.Originality/valueThe paper provides better performance using Diabetes 130-US hospitals for achieving high accuracy, low information loss and false positive rate. The result illustrates that the proposed method increases the accuracy by 4% and reduces the false positive rate and information loss by 25 and 35%, respectively, as compared to state-of-the-art works.

Transformation of Electronic Health Records and Questionnaire Data to OMOP CDM: A Feasibility Study Using SG_T2DM Dataset.

Background Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats. Objective The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept. Methods We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard. Results The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks. Conclusion Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.

Linear Integration of Sensory Evidence over Space and Time Underlies Face Categorization.

Visual object recognition relies on elaborate sensory processes that transform retinal inputs to object representations, but it also requires decision-making processes that read out object representations and function over prolonged time scales. The computational properties of these decision-making processes remain underexplored for object recognition. Here, we study these computations by developing a stochastic multifeature face categorization task. Using quantitative models and tight control of spatiotemporal visual information, we demonstrate that human subjects (five males, eight females) categorize faces through an integration process that first linearly adds the evidence conferred by task-relevant features over space to create aggregated momentary evidence and then linearly integrates it over time with minimum information loss. Discrimination of stimuli along different category boundaries (e.g., identity or expression of a face) is implemented by adjusting feature weights of spatial integration. This linear but flexible integration process over space and time bridges past studies on simple perceptual decisions to complex object recognition behavior.SIGNIFICANCE STATEMENT Although simple perceptual decision-making such as discrimination of random dot motion has been successfully explained as accumulation of sensory evidence, we lack rigorous experimental paradigms to study the mechanisms underlying complex perceptual decision-making such as discrimination of naturalistic faces. We develop a stochastic multifeature face categorization task as a systematic approach to quantify the properties and potential limitations of the decision-making processes during object recognition. We show that human face categorization could be modeled as a linear integration of sensory evidence over space and time. Our framework to study object recognition as a spatiotemporal integration process is broadly applicable to other object categories and bridges past studies of object recognition and perceptual decision-making.

Adaptive partial scanning transmission electron microscopy with reinforcement learning

Compressed sensing can decrease scanning transmission electron microscopy electron dose and scan time with minimal information loss. Traditionally, sparse scans used in compressed sensing sample a static set of probing locations. However, dynamic scans that adapt to specimens are expected to be able to match or surpass the performance of static scans as static scans are a subset of possible dynamic scans. Thus, we present a prototype for a contiguous sparse scan system that piecewise adapts scan paths to specimens as they are scanned. Sampling directions for scan segments are chosen by a recurrent neural network (RNN) based on previously observed scan segments. The RNN is trained by reinforcement learning to cooperate with a feedforward convolutional neural network that completes the sparse scans. This paper presents our learning policy, experiments, and example partial scans, and discusses future research directions. Source code, pretrained models, and training data is openly accessible at https://github.com/Jeffrey-Ede/adaptive-scans.

Deep depth profiling using gas cluster secondary ion mass spectrometry: Micrometer topography development and effects on depth resolution.

Secondary ion mass spectrometry using the argon cluster primary ion beam enables molecular compositional depth profiling of organic thin films with minimal loss of chemical information or changes in sputter rate. However, for depth profiles of thicker organic films (> 10 μm of sputtered depth) we have observed the rapid formation of micron-scale topography in the shape of pillars that significantly affect both the linearity of the sputter yield and depth resolution. To minimize distortions in the 3D reconstruction of the sample due to this topography, a step-wise, staggered sample rotation was employed. By using polymer spheres embedded in an organic film, it was possible to measure the depth resolution at the film-sphere interface as a function of sputtered depth and observe when possible distortions in the 3D image occurred. In this way, it was possible to quantitatively measure the effect of micron-scale topography and sample rotation on the quality of the depth profile.

Privacy and Utility-Assisted Data Protection Strategy for Secure Data Sharing and Retrieval in Cloud System

ABSTRACT The outsourcing of Electronic Health Records (EHR) on cloud infrastructures has enabled medical data sharing among several healthcare applications. The blockchain offers security by authenticating users with encryption methods. The collaboration with the cloud provides better management but poses threats to the privacy of the patient. This paper devises a novel blockchain-assisted framework for effective data sharing and retrieval using cloud platforms. Here, the data protection model is devised in EHR application for secure transmission. The entities in the cloud platform include data user, data owner, smart agreement, transactional blockchain, and Inter-Planetary File System (IPFS). Here, the data owner includes a data protection model to secure EHR in which secured EHR is transferred to IPFS before sharing with the data user. The data protection is done by preserving data privacy using Tracy-Singh product and proposed Conditional Autoregressive Value at risk (CAViaR)-based Bird swarm algorithm (CAViaR-based BSA) combination of BSA and CAViaR for generating optimal privacy-preserving coefficients. The objective function is newly devised considering privacy and utility. The proposed CAViaR-based BSA outperformed other methods with minimal responsiveness of 251.339 s, maximal genuine user detection of 32.451%, maximal privacy of 96.5%, and minimal information loss of 3.5%.

INTELLIGENT UPPER-LIMB PROSTHETIC CONTROL (iULP) WITH NOVEL FEATURE EXTRACTION METHOD FOR PATTERN RECOGNITION USING EMG

EMG signal-based pattern recognition (EMG-PR) techniques have gained lots of focus to develop myoelectric prosthesis. The performance of the prosthesis control-based applications mainly depends on extraction of eminent features with minimum neural information loss. The machine learning algorithms have a significant role to play for the development of Intelligent upper-limb prosthetic control (iULP) using EMG signal. This paper proposes a new technique of extracting the features known as advanced time derivative moments (ATDM) for effective pattern recognition of amputees. Four heterogeneous datasets have been used for testing and validation of the proposed technique. Out of the four datasets, three datasets have been taken from the standard NinaPro database and the fourth dataset comprises data collected from three amputees. The efficiency of ATDM features is examined with the help of Davies–Bouldin (DB) index for separability, classification accuracy and computational complexity. Further, it has been compared with similar work and the results reveal that ATDM features have excellent classification accuracy of 98.32% with relatively lower time complexity. The lower values of DB criteria prove the good separation of features belonging to various classes. The results are carried out on 2.6[Formula: see text]GHz Intel core i7 processor with MATLAB 2015a platform.

Psychometric properties of the brief version of the questionnaire of olfactory disorders in patients with chronic rhinosinusitis.

The Questionnaire of Olfactory Disorders-Negative Statements (QOD-NS) is a 17-item instrument measuring olfactory-specific quality of life (QOL). However, in clinical research patients can be overwhelmed with multiple questionnaires. We recently developed the 7-item brief QOD-NS (B-QOD). Our objective was to evaluate the psychometric properties of the B-QOD in both the development (D) sample, and in a separate replication (R) sample. Testing on D (n=203) and R (n=281) samples included initial exploratory factor analysis (EFA), followed by internal reliability, information loss, and confirmatory factor analysis (CFA). Finally, incremental predictive utility analysis (IPUA) was performed by correlating the B-QOD with the 22-item Sino-Nasal Outcome Test (SNOT-22) survey. EFAs of both D and R demonstrated an underlying single-factor structure (eigenvalue=4.17 and 3.57, respectively) with comparable loading factors (R>0.30 for both). B-QOD also had good internal reliability in both D and R (Cronbach's alpha=0.88 and 0.83, respectively). Also, there is minimal information loss with B-QOD compared to QOD-NS in both D and R (R=0.98 and 0.96, respectively). CFA indicates that the B-QOD single-factor model has good overall fit as measured by the Comparative Fit Index (CFI) and the Standardized Root Mean Squared Residuals (SRMSR) in the D and R samples (CFI=0.99 and 0.97; SRMSR=0.035 and 0.053). IPUA shows that the QOD-NS offers no additional predictive benefit of SNOT-22 scores when compared with B-QOD. The 7-item B-QOD captures a structurally coherent and reliable single dimension, with minimal information loss and excellent external predictive utility when compared to the QOD-NS.

Energy efficient data correlation aware opportunistic routing protocol for wireless sensor networks

In this paper, we study the design of data correlation aware opportunistic routing protocol for wireless sensor networks (WSNs) constituent of lossy links for improved network performance. We focus on a scenario where only M < N sensor nodes are required to report their readings, where N is the number of sensor nodes in the network. We formulate the problem of selecting M such reporting sensors among the N nodes as a mutual information maximization problem such that minimal information loss is resulted. Due to the NP-hardness of this issue, we present a greedy informative sensor selection algorithm. To reduce the transmission redundancy and improve the network lifetime performance, we design an energy efficient correlation aware hop-by-hop opportunistic routing protocol EECAR, which takes the following factors into account: spatial correlation characteristics among sensor readings, broadcast nature of wireless channels, and residual battery energy of sensor nodes. EECAR requires very limited routing information to be kept at nodes in the network. Extensive simulation results demonstrate the effectiveness of our greedy informative sensor selection algorithm and also show that EECAR outperforms existing work in terms of network lifetime and data gathering.

Removing the influence of group variables in high-dimensional predictive modelling.

In many application areas, predictive models are used to support or make important decisions. There is increasing awareness that these models may contain spurious or otherwise undesirable correlations. Such correlations may arise from a variety of sources, including batch effects, systematic measurement errors, or sampling bias. Without explicit adjustment, machine learning algorithms trained using these data can produce poor out-of-sample predictions which propagate these undesirable correlations. We propose a method to pre-process the training data, producing an adjusted dataset that is statistically independent of the nuisance variables with minimum information loss. We develop a conceptually simple approach for creating an adjusted dataset in high-dimensional settings based on a constrained form of matrix decomposition. The resulting dataset can then be used in any predictive algorithm with the guarantee that predictions will be statistically independent of the group variable. We develop a scalable algorithm for implementing the method, along with theory support in the form of independence guarantees and optimality. The method is illustrated on some simulation examples and applied to two case studies: removing machine-specific correlations from brain scan data, and removing race and ethnicity information from a dataset used to predict recidivism. That the motivation for removing undesirable correlations is quite different in the two applications illustrates the broad applicability of our approach.

Coupling sparse Cox models with clustering of longitudinal transcriptomics data for trauma prognosis

BackgroundLongitudinal gene expression analysis and survival modeling have been proved to add valuable biological and clinical knowledge. This study proposes a novel framework to discover gene signatures and patterns in a high-dimensional time series transcriptomics data and to assess their association with hospital length of stay.MethodsWe investigated a longitudinal and high-dimensional gene expression dataset from 168 blunt-force trauma patients followed during the first 28 days after injury. To model the length of stay, an initial dimensionality reduction step was performed by applying Cox regression with elastic net regularization using gene expression data from the first hospitalization days. Also, a novel methodology to impute missing values to the genes selected previously was proposed. We then applied multivariate time series (MTS) clustering to analyse gene expression over time and to stratify patients with similar trajectories. The validation of the patients’ partitions obtained by MTS clustering was performed using Kaplan-Meier curves and log-rank tests.ResultsWe were able to unravel 22 genes strongly associated with hospital’s discharge. Their expression values in the first days after trauma showed to be good predictors of the length of stay. The proposed mixed imputation method allowed to achieve a complete dataset of short time series with a minimum loss of information for the 28 days of follow-up. MTS clustering enabled to group patients with similar genes trajectories and, notably, with similar discharge days from the hospital. Patients within each cluster have comparable genes’ trajectories and may have an analogous response to injury.ConclusionThe proposed framework was able to tackle the joint analysis of time-to-event information with longitudinal multivariate high-dimensional data. The application to length of stay and transcriptomics data revealed a strong relationship between gene expression trajectory and patients’ recovery, which may improve trauma patient’s management by healthcare systems. The proposed methodology can be easily adapted to other medical data, towards more effective clinical decision support systems for health applications.