Complete Matrix Research Articles

Randomized Approximate Channel Estimator in Massive-MIMO Communication

Massive MIMO is considered as one key enabling technology in 5G communications. Although the high-quality channel state information (CSI) estimation is essential to improve the energy/spectrum efficiency of massive MIMO systems, the optimal MMSE estimator unfortunately creates one major challenge, due to its formidable computation complexity. In this letter, we propose a rank-restrained low-complexity MMSE channel estimator for massive MIMO communications, leveraging a novel concept of randomized low-rank approximation. To accomplish this, we first design a two-stage pilot training scheme. Rather than directly estimating the large channel matrix as usual, we then acquire two low-dimensional subsets of it, which respectively gives the row and column sampling version of unknown channel matrix. Finally, we reconstruct the complete channel matrix via such two estimated low-dimensional sketches, by exploiting the low-rank structure of channel. As demonstrated, our scheme significantly reduces the time complexity as well as the energy consumption in CSI acquisition, which yet attains the near-optimal estimation accuracy. It thus provides great promises to the practical deployment of massive MIMO communications.

An implementation for 2nd-order M-N coupling and geometric stiffness adaptation in tapered beam-column elements

The present work outlines a computational approach, based on the so-called Finite Analytic Method (FAM), to deal with second-order effects in the structural analysis of tapered members accounting for geometrical nonlinearity by using only one element per physical member. The proposed modelization considers the equilibrium in the deformed configuration of a “beam-column” element, by addressing the resulting mechanical coupling between bending moment and axial force (M-N interaction). A flexibility-based method is developed, to derive the force-displacement relations of the finite element, by obtaining its flexibility and stiffness matrices. Through Gauss-Lobatto integration, the stiffness coefficients capturing second-order geometrical effects are consistently evaluated, to achieve the complete stiffness matrix. Examples and comparisons with reference studies are provided, in order to prove the effectiveness of the proposed methodology. Results on the critical load evaluation of sample tapered members are delivered.

Inferring linear feasible regions using inverse optimization

Consider a problem where a set of feasible observations are provided by an expert, and a cost function exists that characterizes which of the observations dominate the others and are hence, preferred. Assume the expert has an implicit optimization model in mind to identify the feasible observations, but the explicit constraints of this underlying model are unknown. Our goal is to infer the feasible region of such an optimization model that would render these observations feasible while making the best ones optimal for the cost (objective) function. Such feasible regions (i) build a baseline for a systematic categorization of future observations, and (ii) allow for using sensitivity analysis to discern changes in optimal solutions if the objective function changes in the future.In this paper, we propose a general inverse optimization methodology that recovers the complete constraint matrix of a linear model and then introduce a tractable equivalent reformulation. Furthermore, we provide and discuss several generalized loss functions to inform the desirable properties of the feasible region based on user preference and historical data. We demonstrate our approach using numerical examples and a realistic diet recommendation case study for imputing personalized diets. Our numerical examples verify the validity of our approach and emphasize the differences among the proposed loss functions. The diet recommendation case study shows that the proposed models can improve the palatability of the recommended diets for each user, and it provides further intuition for large-scale implementations of the proposed methodology.

Rapid diversification rates in Amazonian Chrysobalanaceae inferred from plastid genome phylogenetics

AbstractWe studied the evolutionary history of Chrysobalanaceae with phylogenetic analyses of complete plastid genomes from 156 species to assess the tempo of diversification in the Neotropics and help to unravel the causes of Amazonian plant diversification. These plastid genomes had a mean length of 162 204 base pairs, and the nearly complete DNA sequence matrix, with reliable fossils, was used to estimate a phylogenetic tree. Chrysobalanaceae diversified from 38.9 Mya (95% highest posterior density, 95% HPD: 34.2–43.9 Mya). A single clade containing almost all Neotropical species arose after a single dispersal event from the Palaeotropics into the Amazonian biome c. 29.1 Mya (95% HPD: 25.5–32.6 Mya), with subsequent dispersals into other Neotropical biomes. All Neotropical genera diversified from 10 to 14 Mya, lending clear support to the role of Andean orogeny as a major cause of diversification in Chrysobalanaceae. In particular, the understory genus Hirtella diversified extremely rapidly, producing > 100 species in the last 6 Myr (95% HPD: 4.9–7.4 Myr). Our study suggests that a large fraction of the Amazonian tree flora has been assembled in situ in the last 15 Myr.

A matrix completion algorithm to recover modes orthogonal to the minimum energy path in chemical reactions.

Structured statistical methods are promising for recovering or completing information from noisy and incomplete data with high fidelity. In particular, matrix completion exploits underlying structural properties such as rank or sparsity. Our objective is to employ matrix completion to reduce computational effort associated with the calculation of multiple quantum chemical Hessians, which are necessary for identification of temperature-dependent free energy maxima under canonical variational transition state theory (VTST). We demonstrate proof-of-principle of an algebraic variety-based matrix completion method for recovering missing elements in a matrix of transverse Hessian eigenvalues constituting the minimum energy path (MEP) of a reaction. The algorithm, named harmonic variety-based matrix completion (HVMC), utilizes the fact that the points lying on the MEP of a reaction step constitute an algebraic variety in the reaction path Hamiltonian representation. We demonstrate that, with as low as 30% random sampling of matrix elements for the largest system in our test set (46 atoms), the complete matrix of eigenvalues can be recovered. We further establish algorithm performance for VTST rate calculations by quantifying errors in zero-point energies and vibrational free energies. Motivated by this success, we outline next steps toward developing a practical HVMC algorithm, which utilizes a gradient-based sampling protocol for low-cost VTST rate computations.

A blood-based biomarker panel (NIS4) for non-invasive diagnosis of non-alcoholic steatohepatitis and liver fibrosis: a prospective derivation and global validation study

Non-invasive tests that can identify patients with non-alcoholic steatohepatitis (NASH) at higher risk of disease progression are lacking. We report the development and validation of a blood-based diagnostic test to non-invasively rule in and rule out at-risk NASH (defined as non-alcoholic fatty liver disease [NAFLD] activity score [NAS] ≥4 and fibrosis stage ≥2). In this prospective derivation and global validation study, blood samples, clinical data, and liver biopsy results from three independent cohorts with suspected NAFLD were used to develop and validate a non-invasive blood-based diagnostic test, called NIS4. Derivation was done in the discovery cohort, which comprised 239 prospectively recruited patients with biopsy-confirmed NASH (NAFLD NAS ≥3; fibrosis stage 0-3) from the international GOLDEN-505 phase 2b clinical trial. A complete matrix based on 23 variables selected for univariate association with the presence of at-risk NASH and avoiding high multi-collinearity was used to derive the model in a bootstrap-based process that minimised the Akaike information criterion. The overall diagnostic performance of NIS4 was externally validated in two independent cohorts: RESOLVE-IT diag and Angers. The RESOLVE-IT diag cohort comprised the first 475 patients screened for potential inclusion into the RESOLVE-IT phase 3 clinical trial. Angers was a retrospective cohort of 227 prospectively recruited patients with suspected NAFLD and clinical risk factors for NASH or fibrosis stage 2 or more according to abnormal elastography results or abnormal liver biochemistry. Both external validation cohorts were independently analysed and were combined into a pooled validation cohort (n=702) to assess clinical performance of NIS4 and other non-invasive tests. The derived NIS4 algorithm comprised four independent NASH-associated biomarkers (miR-34a-5p, alpha-2 macroglobulin, YKL-40, and glycated haemoglobin; area under the receiver operating characteristics curve [AUROC] 0·80, 95% CI 0·73-0·85), and did not require adjustment for age, sex, body-mass index (BMI), or aminotransferase concentrations. Clinical cutoffs were established within the discovery cohort to optimise both rule out and rule in clinical performance while minimising indeterminate results. NIS4 was validated in the RESOLVE-IT diag cohort (AUROC 0·83, 95% CI 0·79-0·86) and the Angers cohort (0·76, 0·69-0·82). In the pooled validation cohort, patients with a NIS4 value less than 0·36 were classified as not having at-risk NASH (ruled out) with 81·5% (95% CI 76·9-85·3) sensitivity, 63·0% (57·8-68·0) specificity, and a negative predictive value of 77·9% (72·5-82·4), whereas those with a NIS4 value of more than 0·63 were classified as having at-risk NASH (ruled in) with 87·1% (83·1-90·3) specificity, 50·7% (45·3-56·1) sensitivity, and a positive predictive value of 79·2% (73·1-84·2). The diagnostic performance of NIS4 within the external validation cohorts was not influenced by age, sex, BMI, or aminotransferase concentrations. NIS4 is a novel blood-based diagnostic that provides an effective way to non-invasively rule in or rule out at-risk NASH in patients with metabolic risk factors and suspected disease. Use of NIS4 in clinical trials or in the clinic has the potential to greatly reduce unnecessary liver biopsies in patients with lower risk of disease progression. Genfit.

The Interaction of Fluorinated Glycomimetics with DC-SIGN: Multiple Binding Modes Disentangled by the Combination of NMR Methods and MD Simulations.

Fluorinated glycomimetics are frequently employed to study and eventually modulate protein–glycan interactions. However, complex glycans and their glycomimetics may display multiple binding epitopes that enormously complicate the access to a complete picture of the protein–ligand complexes. We herein present a new methodology based on the synergic combination of experimental 19F-based saturation transfer difference (STD) NMR data with computational protocols, applied to analyze the interaction between DC-SIGN, a key lectin involved in inflammation and infection events with the trifluorinated glycomimetic of the trimannoside core, ubiquitous in human glycoproteins. A novel 2D-STD-TOCSYreF NMR experiment was employed to obtain the experimental STD NMR intensities, while the Complete Relaxation Matrix Analysis (CORCEMA-ST) was used to predict that expected for an ensemble of geometries extracted from extensive MD simulations. Then, an in-house built computer program was devised to find the ensemble of structures that provide the best fit between the theoretical and the observed STD data. Remarkably, the experimental STD profiles obtained for the ligand/DC-SIGN complex could not be satisfactorily explained by a single binding mode, but rather with a combination of different modes coexisting in solution. Therefore, the method provides a precise view of those ligand–receptor complexes present in solution.

Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces

Integrating large intelligent reflecting surfaces (IRS) into millimeter-wave (mmWave) massive multi-input-multi-ouput (MIMO) has been a promising approach for improved coverage and throughput. Most existing work assumes the ideal channel estimation, which can be challenging due to the high-dimensional cascaded MIMO channels and passive reflecting elements. Therefore, this paper proposes a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems to reduce the training overhead. Specifically, we first introduce a hybrid passive/active IRS architecture, where very few receive chains are employed to estimate the uplink user-to-IRS channels. At the channel training stage, only a small proportion of elements will be successively activated to sound the partial channels. Moreover, the complete channel matrix can be reconstructed from the limited measurements based on compressive sensing, whereby the common sparsity of angular domain mmWave MIMO channels among different subcarriers is leveraged for improved accuracy. Besides, a complex-valued denoising convolution neural network (CV-DnCNN) is further proposed for enhanced performance. Simulation results demonstrate the superiority of the proposed solution over state-of-the-art solutions.

Ship Detection From PolSAR Imagery Using the Hybrid Polarimetric Covariance Matrix

In this letter, we first investigate the relationship between polarimetric covariance matrix [C] and complete polarimetric covariance difference matrix [CP], and then construct a scattering difference parameter SDP. Subsequently, a hybrid polarimetric covariance matrix [HC] is developed based on SDP for curing the disadvantage of [CP], that is the scattering difference information of small ships cannot be well contained in [CP]. By fusing the feature “1-SDP” and the power detector SPAN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HC</sub> derived from [HC] together, a novel ship detection method SPAN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SDP</sub> is finally proposed to detect ships. Experiments performed on the airborne SAR (AIRSAR) L-Band and GF-3 C-Band data verify that 1) SPAN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SDP</sub> can detect small ships more accurately than other state-of-the-art methods and 2) [HC] is more effective in improving ship detectors' detection performances in comparison with [CP].

Preparation of a Unique Bioavailable Bacoside Formulation (Cognique®) Using Polar-Nonpolar-Sandwich (PNS) Technology and Its Characterization, In Vitro Release Study, and Proposed Mechanism of Action

Bacopa monnieri Linn. (B. monnieri) is a medicinal herb that has been utilized as a traditional medicine to improve memory and learning. Bacosides, the important components of B. monnieri, are used in Ayurvedic herbal formulation to improve cognitive and neuroprotective action and as therapeutics for the management of many central nervous disorders. A novel bioavailable bacoside formulation, Cognique®, was prepared through the preservation of bacosides with complete multiple natural matrix including gingerols and rosemary extracts containing rosemary essential oil, rosmarinic acid, and carnosic acid using Polar-Nonpolar-Sandwich (PNS) technology. The successful PNS formulation of Cognique® was confirmed by different instrumental methods. The SEM images clearly indicated that Cognique® was almost well dispersed and has hollow core nest-like spherical structure with porous nature and rough surface due to the interaction between the polar-bacosides-nonpolar matrixes. IR, XRD, and DSC studies also confirmed the existence of bacosides in Cognique® with high stability due to the PNS formulation. The stability and in vitro drug release studies revealed that the PNS technology is an advanced technology to preserve the bioactive components, particularly bacosides, and they are capable of maintaining the sustained release of bacosides at target sites. The existence of synergistic effects of bacosides, extracts of rosemary, and gingerols in Cognique® can be utilized as a natural nutritional supplement for supporting neuroprotective effect and cognition improvement through maintenance of gut-brain axis. This study demonstrated a novel bioavailable bacoside formulation—Cognique®—through the preservation of bacosides with multiple natural matrix. Future study will investigate the efficacy, safety, and improvement of normal cognitive function in human volunteers.

Highly efficient general method for sensitivity analysis of eigenvectors with repeated eigenvalues without passing through adjacent eigenvectors

SummaryIt is well known that the sensitivity analysis of the eigenvectors corresponding to multiple eigenvalues is a difficult problem. The main difficulty is that for given multiple eigenvalues, the eigenvector derivatives can be computed for a specific eigenvector basis, the so‐called adjacent eigenvector basis. These adjacent eigenvectors depend on individual variables, which makes the eigenvector derivative calculation elaborate and expensive from a computational perspective. This research presents a method that avoids passing through adjacent eigenvectors in the calculation of the partial derivatives of any prescribed eigenvector basis. As our method fits into the adjoint sensitivity analysis , it is efficient for computing the complete Jacobian matrix because the adjoint variables are independent of each variable. Thus our method clarifies and unifies existing theories on eigenvector sensitivity analysis. Moreover, it provides a highly efficient computational method with a significant saving of the computational cost. Additional benefits of our approach are that one does not have to solve a deficient linear system and that the method is independent of the existence of repeated eigenvalue derivatives of the multiple eigenvalues. Our method covers the case of eigenvectors associated to a single eigenvalue. Some examples are provided to validate the present approach.

Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted KNN Algorithms

Electrocardiogram (ECG) signal is critical to the classification of cardiac arrhythmia using some machine learning methods. In practice, the ECG datasets are usually with multiple missing values due to faults or distortion. Unfortunately, many established algorithms for classification require a fully complete matrix as input. Thus it is necessary to impute the missing data to increase the effectiveness of classification for datasets with a few missing values. In this paper, we compare the main methods for estimating the missing values in electrocardiogram data, e.g., the “Zero method”, “Mean method”, “PCA-based method”, and “RPCA-based method” and then propose a novel KNN-based classification algorithm, i.e., a modified kernel Difference-Weighted KNN classifier (MKDF-WKNN), which is fit for the classification of imbalance datasets. The experimental results on the UCI database indicate that the “RPCA-based method” can successfully handle missing values in arrhythmia dataset no matter how many values in it are missing and our proposed classification algorithm, MKDF-WKNN, is superior to other state-of-the-art algorithms like KNN, DS-WKNN, DF-WKNN, and KDF-WKNN for uneven datasets which impacts the accuracy of classification.

Consensus in networks of uncertain euler-lagrange agents using adaptive gravity compensation

The leaderless and the leader-follower consensus are the most basic synchronization behaviors for multi-agent systems. For networks of Euler-Lagrange (EL) agents different controllers have been proposed to achieve consensus, requiring in all cases, either the cancellation or the estimation of the gravity forces. While, in the first case, it is shown that a simple Proportional plus damping (P+d) scheme with exact gravity cancellation can achieve consensus, in the latter case, it is necessary to estimate, not just the gravity forces, but the parameters of the whole dynamics. This requires the computation of a complicated regressor matrix, that grows in complexity as the degrees-of-freedom of the EL-agents increase. To simplify the controller implementation we propose in this paper a simple P+d scheme with only adaptive gravity compensation. In particular, two adaptive controllers that solve both consensus problems by only estimating the gravitational term of the agents and hence without requiring the complete regressor matrix are reported. The first controller is a simple P+d scheme that does not require to exchange velocity information between the agents but requires centralized information. The second controller is a Proportional-Derivative plus damping (PD+d) scheme that is fully decentralized but requires exchanges of speed information between the agents. Simulation results demonstrate the performance of the proposed controllers.

Performance analysis and optimization of a rigid-flexible parallel manipulator

In order to meet the design requirements of friction stir welding equipment, a three-degree-of-freedom rigid-flexible 3UPS-UP-3Cable-Driven (3UPS-UP-3CD) parallel manipulator was applied to the main feed mechanism of the friction stir welding robot. The kinematics analysis of the new parallel manipulator was carried out, and the complete homogeneous dimensional Jacobian matrix of the 3UPS-UP-3CD manipulator was solved. Through kinematic performance analysis, the carrying capacity, dexterity and stiffness of 3UPS-UP-3CD manipulator are obviously better than 3UPS-UP mechanism, and the impact of cables on the workspace can also be eliminated by adjusting the dimension parameter. The composite quality workspace map of the manipulator was drawn, and the composite kinematic performance index was proposed. Multiobjective optimization was completed for 3UPS-UP-3CD manipulator, and its performance was improved. The dynamic analysis of the manipulator was carried out, and the driving forces of two mechanisms were calculated under two trajectories. The results showed that the maximum driving force of 3UPS-UP-3CD manipulator was lower than 3UPS-UP mechanism, which proved that 3UPS-UP-3CD manipulator was more suitable for heavy-duty conditions.

Dataset for off-grid micro-hydroelectric community system under drought conditions.

This submission contains the complete balanced process matrix of an off-grid community system primarily powered by a micro-hydroelectric powerplant. The system is meant to provide the needs of the community for electricity, potable water and ice. The system also considers the provision of a diesel engine generator set as a back-up to provide electricity. The data serves as inputs to simulate the performance of the system under different drought scenarios. The data provided here is in support of the co-submitted article of Aviso et al. [1].

PolSAR Ship Detection Using the Joint Polarimetric Information

In this article, we investigate the scattering components of ships and find that the surface scattering may be the primary scattering for some ships, especially small ships. Meanwhile, the drawbacks of the complete polarimetric covariance difference matrix [CP] are also pointed out in theory. Based on these analyses, two new methods are then constructed to detect the ships. More specifically, the first one RsP is constructed by directly combining the similarity parameter of surface scattering Rs and the power-maximization synthesis (PMS) detector. The second one RsDVH is designed by taking advantage of four different features (i.e., Rs, double-bounce scattering, volume scattering, and helix scattering), which are all derived from the joint polarimetric information that is developed by combing the information of the polarimetric covariance matrix [ $C$ ] and [CP]. Subsequently, the generalized Gamma distribution ( $\text{G}\Gamma \text{D}$ ) is found suitable for characterizing the RsDVH values of the sea clutter. At last, an adaptive constant false-alarm-rate (CFAR) detector developed from RsDVH is proposed for ship detection. To verify the effectiveness of RsP and RsDVH, four polarization synthetic aperture radar (PolSAR) imageries are tested, including one L-band UAVSAR imagery with 19 ships, two L-band AIRSAR imageries with 22 and 53 ships, respectively, and one C-band GF-3 imagery with ten ships. The experimental results show that: 1) the surface scattering is beneficial to detecting ships, especially the ships with prominent surface scatterings; 2) compared with other state-of-the-art methods, RsDVH can more effectively enhance the target-to-clutter ratio (TCR) values of small ships in the case of rough sea surface; and 3) the joint polarimetric information that is put forward and exploited for the first time in this article has a greater potential to help ship detectors improve their detection performances than the traditional polarimetric information included in [ $C$ ].

Restoration Method of Hadamard Coding Spectral Imager

Hadamard coding spectral imaging technology is a computational spectral imaging technology, which modulates the target's spectral information and recovers the original spectrum by inverse transformation. Because it has the advantage of multichannel detection, it is being studied by more researchers. For the engineering realization of push-broom coding spectral imaging instrument, it will inevitably be subjected to push-broom error, template error and detection noise, the redundant sampling problem caused by detector. Therefore, three restoration methods are presented in this paper: firstly, the one is the least squares solution, the two is the zero-filling inverse solution by extending the coding matrix in the redundant coding state to a complete higher order Hadamard matrix, the three is sparse method. Secondly, the numerical and principle analysis shows that the inverse solution of zero-compensation has better robustness and is more suitable for engineering application; its conditional number, error expectation and covariance are better and more stable because it directly uses Hadamard matrix, which has good generalized orthogonality. Then, a real-time spectral reconstruction method is presented, which is based on inverse solution of zero-compensation. Finally, simulation analysis shows that spectral data could be destructed relative accuracy in the error condition; however, the effect of template noise and push error on reconstruction is much greater than that of detection error. Therefore, in addition to reducing the detection noise as much as possible, lower template noise and more accurate push controlling should be guaranteed specifically in engineering realization.

Abstract B35: The effect of patient demographics and polyp histologic characteristics on immune gene expression in microenvironment is mediated by infiltrating microbiome

Abstract We apply new panomic distance mediation analysis with feature selection to understand the effect of polyp-infiltrating microbes on host microenvironment immune response. Multivariate omnibus distance mediation analysis (MODIMA) allows for testing for mediation in the context of multivariate exposure-mediator-response triples, such as polyp characteristics, microbiome composition, and expression, respectively. Here we extend MODIMA to allow for selection of individual components of the response and mediator responsible for mediation effects. Next we apply the method to new data from 30 colorectal polyps with simultaneous measurements of expression of 594 immune-related genes ascertained via the NanoString Immunology panel and microbiome composition via a 16S rRNA gene amplicon sequencing assay. The polyp specimens are derived from a cohort consisting of 11 female and 19 male subjects, of whom 17 are black and 25 are older than 55. The polyp biopsies come from proximal (17), distal (6), and rectal (7) locations of the colon. According to pathology evaluation, half of the polyps have evidence of high-grade dysplasia. The distribution across tubular, tubular villous, and villous histology is likewise approximately even (10, 9, and 11 specimens, respectively). Microbial DNA has been extracted from formalin-fixed, paraffin-embedded polyp tissues and analyzed using Illumina MiSeq 16S rRNA gene amplicon sequencing assay. After preprocessing, the species and genus level data have been normalized using central log ratio transformation with pseudo-counts. Species and genera with literature evidence for association with colorectal polyps and cancers have been selected for further analysis. Using these data, we evaluate the hypothesis that infiltrating microbiome mediates the immune gene expression in response to histodemographic characteristics. We expand the histodemographic data into a complete 6-way interaction design matrix. Euclidean distances have been used for all data types in all analyses. After feature selection, the joint expression of 140 immune genes is associated with polyp characteristics (dCor t-test, T = 2.9, bias corrected dCor = 0.14, p = 0.002). Likewise, 4 microbial features are jointly associated (dCor t-test, T = 2.1, bias corrected dCor = 0.10, p = 0.02). Within these data 9 genes (BCL2, CD19, CLEC6A, IRAK3, IRF3, NLRP3, PSMC2, TBX21, POLR2A) and 2 taxa (Bifidobacterium and Alistipes sp.) are jointly associated to each other (dCor t-test, T = 4.7, bias corrected dCor = 0.23, p &amp;lt; 10−5). MODIMA analysis after feature selection suggests a significant mediation effect of these microbes on the association between polyp characteristics and the expression of the 9 genes (MODIMA stat = 0.03, p-value = 0.01). Further univariate analysis corroborates these associations and suggests a role for the infiltrating microbiome in Th1 signaling, apoptosis, and interferon and expression regulation. Citation Format: Alexander V. Alekseyenko, Chentha Vasu, Brianna Bronsky, David N. Lewin, Shaoli Sun, Christine Bookout, John N. Baron, Kristin Wallace. The effect of patient demographics and polyp histologic characteristics on immune gene expression in microenvironment is mediated by infiltrating microbiome [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr B35.

On the Parameterized Complexity of Clustering Incomplete Data into Subspaces of Small Rank

We consider a fundamental matrix completion problem where we are given an incomplete matrix and a set of constraints modeled as a CSP instance. The goal is to complete the matrix subject to the input constraints and in such a way that the complete matrix can be clustered into few subspaces with low rank. This problem generalizes several problems in data mining and machine learning, including the problem of completing a matrix into one with minimum rank. In addition to its ubiquitous applications in machine learning, the problem has strong connections to information theory, related to binary linear codes, and variants of it have been extensively studied from that perspective. We formalize the problem mentioned above and study its classical and parameterized complexity. We draw a detailed landscape of the complexity and parameterized complexity of the problem with respect to several natural parameters that are desirably small and with respect to several well-studied CSP fragments.

A Novel MIMO T–S Fuzzy Modeling for Prediction of Blast Furnace Molten Iron Quality With Missing Outputs

For complex and difficult-to-control blast furnace systems with hour-level delay, accurate prediction of molten iron quality plays a very important role in guaranteeing the stable and smooth operation. Recently, some data-driven multi-input multi-output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature, silicon content ([Si]), phosphorus content ([P]), and sulfur content ([S]). However, those data-driven MIMO models ignore the interindicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. Moreover, the above methods do not pay attention to the molten iron quality indicators missing issue, which often occurs on blast furnace. To address the above two issues, this article proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model by utilizing an output transfer matrix. In the novel method, the interindicator correlation was explicitly modeled by a low-rank learning of the correlation matrix that overcame the great challenge of jointly determining the fuzzy rules of the MIMO T-S model and the interindicator correlation. Moreover, a new complete complementary matrix can be obtained by the output transfer from the original incomplete matrix resulting from molten iron quality indicators missing issues. For the corresponding optimization problem, an effective alternating optimization algorithm is presented, and the convergence of the optimization algorithm is also rigorously proved. The validity of the proposed method is verified by comparison with some related methods on real blast furnace data.