The increasing presence of mobile robot emphasizes the crucial need of robot interaction skills. In this paper, we introduce eight prosody-only utterances as nonverbal communication sounds representing various speech acts (greeting, farewell, gratitude, apology, warning, prohibition, indirect request, direct request) with two subjective evaluations: i) an audio evaluation test and ii) a video evaluation test. The results from the first study show that nearly all prosody-only utterances were correctly understood as intended speech act. A subset was selected for a second study, where the overall results showed a general tendency of understanding that the robot was requesting human to yield the navigation passage regardless of the actual intent. This paper aims to advance robots' nonverbal sounds towards human language, highlighting prosody's role in signaling request intent in human-robot interaction.

Better quality management in manufacturing systems usually means preventing defects, reducing carbon emissions, and making systems greener. Modeling stress-induced processes is challenging and extremely critical in the quality management of advanced manufacturing systems. While residual stresses may be beneficial in some situations, in composite aircraft assembly, high residual stresses and extreme deformations are crucial and must be accounted for to prevent future catastrophic failures. Currently, conventional approaches to the optimal placement of actuators on composite structures are non-optimal, require ten actuators heuristically, and do not consider residual stresses. To overcome these limitations, we propose a Stress-Aware Optimal Actuator Placement framework. We provide theoretical investigations that demonstrate the convergence to global optimum, computational complexity, and mean prediction error of the proposed optimization algorithm. The stress-aware optimal actuator placement framework is able to achieve significant reductions of at least 39.3% in mean root mean squared deviations (RMSD) and 52% in maximum forces (MF), and only requires eight actuators on average while satisfying the safety threshold of residual stresses. Note to Practitioners—In aerospace manufacturing, about 80% of defects are associated with the assembly process. Defects may result in large errors and waste, high energy costs, low product quality, or even endanger human lives. The actuator placement usually significantly impacts the final quality of the assembled airplanes. Existing actuator placement strategies are not sufficient for composite aircraft assembly due to the complex nonlinear properties, ultra-high precision requirement, and residual stress requirement. The proposed method can improve the dimensional quality by optimizing actuator placement, as well as lower the residual stress and ensure the safety of products. Although our optimization framework was applied to the placement of actuators on composite fuselages, it could also be applied to the engineering design of other actuating systems in which both dimensional quality and stresses are sensitive. The proposed approach can reduce carbon emissions by preventing defects and improving quality management, ultimately aiming at zero-defect green manufacturing.

Complex constrained global optimization problems such as optimal actuators placement are extremely challenging. Such challenges, including nonlinearity and nonstationarity of engineering response surfaces, hinder the use of ordinary constrained Bayesian optimization (CBO) techniques with standard Gaussian processes as surrogate models. To overcome those challenges, we propose a physics-constrained Bayesian optimization with multi-layer deep structured Gaussian processes, MGP-CBO. Specifically, we introduce a surrogate model with a multi-layer deep Gaussian process (MGP) mean function. The hierarchical structure of our model enables the applicability of constrained Bayesian optimization to complex nonlinear and nonstationary processes. The deep Gaussian process regression model, MGP, can efficiently and effectively represent the response surface function between actuators and dimensional deformations, thus yielding a better estimated global optimum in a shorter computational time. The proposed MGP-CBO model can realize faster convergence to the global optimum with lower constraint violations. Through extensive evaluations carried out on synthetic problems and a real-world engineering design problem, we show that MGP-CBO outperforms existing benchmarks. Although we use the optimal actuators placement as a demonstration example, the proposed MGP-CBO model can be applied to other complex nonstationary engineering optimization problems. Note to Practitioners—Bayesian optimization is a widely used sequential design strategy for engineering optimization because it does not rely on functional forms of response surfaces. This paper helps address two questions in practice: (i) how to incorporate physics constraints into Bayesian optimization. (ii) How to do Bayesian optimization when the systems have hierarchical structures. In practice, the hierarchical system structure is ubiquitous, and the engineering optimization is constrained by physical laws or special requirements. Therefore, the proposed physics-constrained Bayesian optimization with a multi-layer Gaussian process could provide a new tool for engineering design optimization problems. The computational convergence and complexity have been investigated. The proposed method is applicable to broad complex and nonstationary engineering optimization problems.

MOTIVATION: Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex. RESULTS: To model complex effects including non-linear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models non-linear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies. AVAILABILITY AND IMPLEMENTATION: The HisCoM-Kernel software is freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/. The RNA-seq data underlying this article are available at https://xena.ucsc.edu/, and the others will be shared on reasonable request to the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Some response surface functions in complex engineering systems are usually highly nonlinear, unformed, and expensive to evaluate. To tackle this challenge, Bayesian optimization (BO), which conducts sequential design via a posterior distribution over the objective function, is a critical method used to find the global optimum of black-box functions. Kernel functions play an important role in shaping the posterior distribution of the estimated function. The widely used kernel function, e.g., radial basis function (RBF), is very vulnerable and susceptible to outliers; the existence of outliers is causing its Gaussian process (GP) surrogate model to be sporadic. In this article, we propose a robust kernel function, asymmetric elastic net radial basis function (AEN-RBF). Its validity as a kernel function and computational complexity are evaluated. When compared with the baseline RBF kernel, we prove theoretically that AEN-RBF can realize smaller mean squared prediction error under mild conditions. The proposed AEN-RBF kernel function can also realize faster convergence to the global optimum. We also show that the AEN-RBF kernel function is less sensitive to outliers, and hence improves the robustness of the corresponding BO with GPs. Through extensive evaluations carried out on synthetic and real-world optimization problems, we show that AEN-RBF outperforms the existing benchmark kernel functions. Note to Practitioners—Some industrial systems cannot be accurately represented by physical models. In this situation, data-driven black-box optimization is necessary for advancing the system automation and intelligence. BO is one of the widely used strategies for learning the global optimum of black-box functions. BO has been applied to robotics, anomaly detection, automatic learning algorithm configuration, reinforcement learning, and deep learning. This article proposes one new kernel function, named after AEN-RBF. The new kernel function will make BO with GPs more robust to outliers and lower the data quality barrier of model training. This article was motivated by the hyperparameter tuning problem of deep learning models for image defect detection in advanced manufacturing, but the method can be easily extended to other applications where kernel functions are needed. Our proposed method is verified by synthetic and real-world optimization problems.

Breast cancer is classified into five intrinsic subtypes, with differing treatment methods and prognoses. Therefore, accurate identification of subtypes from patient transcriptome data is essential. Many gene signatures, including PAM50, have been developed to classify breast cancer subtypes. However, existing gene selection methods do not utilize biological pathways. Gene signature selection using biological pathways can explain signature genes in terms of biological functions. Thus, we propose a probabilistic model for pathway-guided gene set selection using gene expression data. First, we defined gene and pathway factors based on gene expression and pathway activation levels, and calculated the posterior probability. Second, we adopted the prediction strength to guide gene set selection. Third, the gene set was selected using the posterior probability and prediction strength values. Finally, on evaluating the selected gene set, it was experimentally confirmed that our gene set performed better on classification tasks than the PAM50 gene set, a gene set produced by the XGBoost classifier, and a random gene set. Among the genes selected by our method, it was confirmed that the genes included in the cell cycle and circadian rhythm pathways showed different expression patterns for each breast cancer subtype. Our selected gene set exhibited biological significance in terms of pathway activation.

Hierarchical clustering, a traditional clustering method, has been getting attention again. Among several reasons, a credit goes to a recent paper by Dasgupta in 2016 that proposed a cost function that quantitatively evaluates hierarchical clustering trees. An important question is how to combine this recent advance with existing successful clustering methods. In this paper, we propose a hierarchical clustering method to minimize the cost function of clustering tree by incorporating existing clustering techniques. First, we developed an ensemble tree-search method that finds an integrated tree with reduced cost by integrating multiple existing hierarchical clustering methods. Second, to operate on large and arbitrary shape data, we designed an efficient hierarchical clustering framework, called integrating divisive and ensemble-agglomerate (IDEA) by combining it with advanced clustering techniques such as nearest neighbor graph construction, divisive-agglomerate hybridization, and dynamic cut tree. The IDEA clustering method showed better performance in minimizing Dasgupta's cost and improving accuracy (adjusted rand index) over existing cost-minimization-based, and density-based hierarchical clustering methods in experiments using arbitrary shape datasets and complex biology-domain datasets.

We are also grateful to our

BACKGROUND: Bioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omics data has become huge, e.g., TCGA, and the data types to be analyzed have come in many varieties, including mutations, copy number variations, and transcriptome. We also need to consider a complex relationship with regulators of genes, particularly Transcription Factors(TF). However, there has not been a system for pathway based exploration and analysis of TCGA multi-omics data. In this reason, We have developed a web based system BRCA-Pathway to fulfill the need for pathway based analysis of TCGA multi-omics data. RESULTS: BRCA-Pathway is a structured integration and visual exploration system of TCGA breast cancer data on KEGG pathways. For data integration, a relational database is designed and used to integrate multi-omics data of TCGA-BRCA, KEGG pathway data, Hallmark gene sets, transcription factors, driver genes, and PAM50 subtypes. For data exploration, multi-omics data such as SNV, CNV and gene expression can be visualized simultaneously in KEGG pathway maps, together with transcription factors-target genes (TF-TG) correlation and relationships among cancer driver genes. In addition, 'Pathways summary' and 'Oncoprint' with mutual exclusivity sort can be generated dynamically with a request by the user. Data in BRCA-Pathway can be downloaded by REST API for further analysis. CONCLUSIONS: BRCA-Pathway helps researchers navigate omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative perspective on the breast cancer omics data, which can help researchers discover new insights on the biological mechanisms of breast cancer.

We study the secondary user power allocation in OFDM-based cognitive radio networks with underlay mode where secondary user is allowed to share the spectrum licensed to primary user, provided that limited interference is generated to primary user. While this problem has been studied extensively in the literature, most of the previous works in this context assume that secondary user has the exact information of how much interference it generates to primary user, which may not be the case in practice. We assume that the interference channel gains are known to secondary user with uncertainty, and develop a power allocation algorithm that can keep interference under a desired level. In particular, we formulate the power allocation problem as a chance-constrained program that finds a power allocation that guarantees limited interference with high probability. The formulation is non-convex in general, and we apply techniques from robust optimization theory to convexify the formulation. We show through simulations that our approach finds a power allocation that remains feasible under time-varying channel conditions.

A Physical Unclonable Function (PUF) is a promising solution to many security issues due its ability to generate a die unique identifier that can resist cloning attempts as well as physical tampering. However, the efficiency of a PUF depends on its implementation cost, its reliability, its resiliency to attacks, and the amount of entropy in it. PUF entropy is used to construct crypto graphic keys, chip identifiers, or challenge-response pairs (CRPs) in a chip authentication mechanism. The amount of entropy in a PUF is limited by the circuit resources available to build a PUF. As a result, generating longer keys or larger sets of CRPs may increase PUF circuit cost. We address this limitation in a PUF by proposing an identity-mapping function that expands the set of CRPs of a ring-oscillator PUF (RO-PUF) with low area cost. The CRPs generated through this function exhibit strong PUF qualities in terms of uniqueness and reliability. To introduce the identity-mapping function, we formulate a novel PUF system model that uncouples PUF measurement from PUF identifier formation. We show the enhanced CRP generation capability of the new function using a statistical hypothesis test. An implementation of our technique on a low-cost FPGA platform shows at least 2 times savings in area compared to the traditional RO-PUF. The proposed technique is validated using a population of 125 chips, and its reliability over varying environmental conditions is shown.

In this paper, we propose to apply non-uniform weighs to the genes in the pathway based analysis and present two weighting schemes for the genes. Specifically, we incorporate our weighting schemes into the global test pathway based analysis approach and investigate the effects of our weighting schemes. We observe that when non-uniform weights are applied, some originally lower ranked pathways are elevated to the high ranks, prediction performances of the selected genes are improved, and some genes associated with the related phenotype are identified, which are missed by the uniform weighting approach.

MOTIVATION: It is a common practice in cancer microarray experiments that a normal tissue is collected from the same individual from whom the tumor tissue was taken. The indirect design is usually adopted for the experiment that uses a common reference RNA hybridized both to normal and tumor tissues. However, it is often the case that the test material is not large enough for the experimenter to extract enough RNA to conduct the microarray experiment. Hence, collecting n cases does not necessarily end up with a matched pair sample of size n. Instead we usually have a matched pair sample of size n1, and two independent samples of sizes n2 and n3, respectively, for 'reference versus normal tissue only' and 'reference versus tumor tissue only' hybridizations (n=n1 + n2 + n3). Standard statistical methods need to be modified and new statistical procedures are developed for analyzing this mixed dataset. RESULTS: We propose a new test statistic, t3, as a means of combining all the information in the mixed dataset for detecting differentially expressed (DE) genes between normal and tumor tissues. We employed the extended receiver operating characteristic approach to the mixed dataset. We devised a measure of disagreement between a RT-PCR experiment and a microarray experiment. Hotelling's T2 statistic is employed to detect a set of DE genes and its prediction rate is compared with the prediction rate of a univariate procedure. We observe that Hotelling's T2 statistic detects DE genes more efficiently than a univariate procedure and that further research is warranted on the formal test procedure using Hotelling's T2 statistic. CONTACT: [email protected].