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13. Fast and Robust Bin-picking System for Densely Piled Industrial Objects

Author

Guo, Jiaxin, Fu, Lian, Jia, Mingkai, Wang, Kaijun, and Liu, Shan

Subjects
Computer Science - Robotics
Abstract

Objects grasping, also known as the bin-picking, is one of the most common tasks faced by industrial robots. While much work has been done in related topics, grasping randomly piled objects still remains a challenge because much of the existing work either lack robustness or costs too much resource. In this paper, we develop a fast and robust bin-picking system for grasping densely piled objects adaptively and safely. The proposed system starts with point cloud segmentation using improved density-based spatial clustering of application with noise (DBSCAN) algorithm, which is improved by combining the region growing algorithm and using Octree to speed up the calculation. The system then uses principle component analysis (PCA) for coarse registration and iterative closest point (ICP) for fine registration. We propose a grasp risk score (GRS) to evaluate each object by the collision probability, the stability of the object, and the whole pile's stability. Through real tests with the Anno robot, our method is verified to be advanced in speed and robustness.

Published
2020

16. Predicting the EQ-5D utilities from the Kansas City Cardiomyopathy Questionnaire in patients with heart failure

Author

Thomas, Merrill, Jones, Philip G, Cohen, David J, Suzanne, Arnold V, Magnuson, Elizabeth A, Wang, Kaijun, Thourani, Vinod H, Fonarow, Gregg C, Sandhu, Alexander T, and Spertus, John A

Subjects
Cardiovascular, Clinical Research, Heart Disease, Good Health and Well Being, Cardiomyopathies, Heart Failure, Humans, Kansas, Quality of Life, Stroke Volume, Surveys and Questionnaires, Health economics, Health status, Utilities
Abstract

IntroductionEvaluation of health status benefits, cost-effectiveness, and value of new heart failure therapies is critical for supporting their use. The Kansas City Cardiomyopathy Questionnaire (KCCQ) measures patients' heart failure-specific health status but does not provide utilities needed for cost-effectiveness analyses. We mapped the KCCQ scores to EQ-5D scores so that estimates of societal-based utilities can be generated to support economic analyses.MethodsUsing data from two US cohort studies, we developed models for predicting EQ-5D utilities (3L and 5L versions) from the KCCQ (23- and 12-item versions). In addition to predicting scores directly, we considered predicting the five EQ-5D health state items and deriving utilities from the predicted responses, allowing different countries' health state valuations to be used. Model validation was performed internally via bootstrap and externally using data from two clinical trials. Model performance was assessed using R2, mean prediction error, mean absolute prediction error, and calibration of observed vs. predicted values.ResultsThe EQ-5D-3L models were developed from 1000 health status assessments in 547 patients with heart failure and reduced ejection fraction (HFrEF), while the EQ-5D-5L model was developed from 3925 patients with HFrEF. For both versions, models predicting individual EQ-5D items performed as well as those predicting utilities directly. The selected models for the 3L had internally validated R2 of 48.4-50.5% and 33.7-45.6% on external validation. The 5L version had validated R2 of 57.7%.ConclusionMappings from the KCCQ to the EQ-5D can yield the estimates of societal-based utilities to support cost-effectiveness analyses when EQ-5D data are not available.

Published
2021

17. Breiman's 'Two Cultures' Revisited and Reconciled

Author

Subhadeep, Mukhopadhyay, and Wang, Kaijun

Subjects
Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Economics - Econometrics, Statistics - Methodology
Abstract

In a landmark paper published in 2001, Leo Breiman described the tense standoff between two cultures of data modeling: parametric statistical and algorithmic machine learning. The cultural division between these two statistical learning frameworks has been growing at a steady pace in recent years. What is the way forward? It has become blatantly obvious that this widening gap between "the two cultures" cannot be averted unless we find a way to blend them into a coherent whole. This article presents a solution by establishing a link between the two cultures. Through examples, we describe the challenges and potential gains of this new integrated statistical thinking., Comment: This paper celebrates the 70th anniversary of Statistical Machine Learning--- how far we've come, and how far we have to go. Keywords: Integrated statistical learning theory, Exploratory machine learning, Uncertainty prediction machine, ML-powered modern applied statistics, Information theory

Published
2020

18. On The Problem of Relevance in Statistical Inference

Author

Mukhopadhyay, Subhadeep and Wang, Kaijun

Subjects
Statistics - Methodology, Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract

This paper is dedicated to the "50 Years of the Relevance Problem" - a long-neglected topic that begs attention from practical statisticians who are concerned with the problem of drawing inference from large-scale heterogeneous data., Comment: Revised (much-improved) version. The procedure (including all the datasets) is implemented in the R-package LPRelevance

Published
2020

30. Spectral Graph Analysis: A Unified Explanation and Modern Perspectives

Author

Mukhopadhyay, Subhadeep and Wang, Kaijun

Subjects
Mathematics - Statistics Theory, Statistics - Computation
Abstract

Complex networks or graphs are ubiquitous in sciences and engineering: biological networks, brain networks, transportation networks, social networks, and the World Wide Web, to name a few. Spectral graph theory provides a set of useful techniques and models for understanding `patterns of interconnectedness' in a graph. Our prime focus in this paper is on the following question: Is there a unified explanation and description of the fundamental spectral graph methods? There are at least two reasons to be interested in this question. Firstly, to gain a much deeper and refined understanding of the basic foundational principles, and secondly, to derive rich consequences with practical significance for algorithm design. However, despite half a century of research, this question remains one of the most formidable open issues, if not the core problem in modern network science. The achievement of this paper is to take a step towards answering this question by discovering a simple, yet universal statistical logic of spectral graph analysis. The prescribed viewpoint appears to be good enough to accommodate almost all existing spectral graph techniques as a consequence of just one single formalism and algorithm., Comment: The first draft of the paper was written in June 2015

Published
2019

32. A Nonparametric Approach to High-dimensional k-sample Comparison Problems

Author

Subhadeep, Mukhopadhyay, and Wang, Kaijun

Subjects
Statistics - Methodology, Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract

High-dimensional k-sample comparison is a common applied problem. We construct a class of easy-to-implement nonparametric distribution-free tests based on new tools and unexplored connections with spectral graph theory. The test is shown to possess various desirable properties along with a characteristic exploratory flavor that has practical consequences. The numerical examples show that our method works surprisingly well under a broad range of realistic situations., Comment: Biometrika (in press)

Published
2018

34. Long-Term Outcomes in IgA Nephropathy

Author

Pitcher, David, Braddon, Fiona, Hendry, Bruce, Mercer, Alex, Osmaston, Kate, Saleem, Moin A., Steenkamp, Retha, Wong, Katie, Turner, A. Neil, Wang, Kaijun, Gale, Daniel P., and Barratt, Jonathan

Published
2023
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35. Cardiovascular, Kidney Failure, and All-Cause Mortality Events in Patients with Focal Segmental Glomerulosclerosis in a U.S. Real-World Database

Author

Velez, Juan Carlos Q., primary, Thakker, Kamlesh M., additional, Bensink, Mark E., additional, Lerma, Edgar V., additional, Lieblich, Richard, additional, Bunke, C. Martin, additional, Gong, Wu, additional, Wang, Kaijun, additional, Rava, Andrew R., additional, Amari, Diana T., additional, Oliveri, David, additional, Murphy, Michael V., additional, and Cork, David M.W., additional

Published
2024
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38. Economic Outcomes of Transcatheter Versus Surgical Aortic Valve Replacement in Patients with Severe Aortic Stenosis and Low Surgical Risk: Results from the PARTNER 3 Trial

Author

Galper, Benjamin Z., Chinnakondepalli, Khaja M., Wang, Kaijun, Magnuson, Elizabeth A., Lu, Michael, Thourani, Vinod H., Kodali, Susheel, Makkar, Raj, Herrmann, Howard C., Kapadia, Samir, Williams, Mathew, Webb, John, Smith, Craig R., Mack, Michael J., Leon, Martin B., and Cohen, David J.

Published
2023
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41. Alkaline Pre-Fermentation Promotes Anaerobic Digestion of Enhanced Membrane Coagulation (EMC) Sludge: Performance and Microbial Community Response.

Author

Kou, Qingshuang, Yuan, Quan, Chen, Song, Xu, Heng, Wei, Shanghui, and Wang, Kaijun

Subjects
ANAEROBIC digestion, WASTE recycling, SEWAGE, ORGANIC acids, BIOCONVERSION, UPFLOW anaerobic sludge blanket reactors, METHANE as fuel
Abstract

Concentrating organic matter in sludge and converting it into methane through anaerobic bioconversion can improve resource recovery from domestic wastewater. Enhanced membrane coagulation (EMC) is highly efficient at concentrating organic matter, but residual coagulants (aluminum salts) can obstruct bioconversion by blocking microbial access. Limited research exists on evaluating EMC sludge bioconversion performance and addressing coagulant inhibition. This study proposes alkaline pre-fermentation to break down HO-Al-P backbones in coagulated sludge flocs, thereby improving hydrolysis and organic acid production for anaerobic digestion. Among the tested alkaline conditions (pH 9, pH 10, pH 11), pre-fermentation at pH 11 released the most organic matter (4710.0 mg/L SCOD), 20.4 times higher than without alkaline treatment. At pH 11, phosphate (61 mg/L PO43−–P) and organic acid production (2728.1 mg COD/L, with nearly 50% acetic acid) peaked, resulting in superior volatile solids removal (65.2%) and methane production (185.8 mL/g VS) during anaerobic digestion. Alkaline pre-fermentation favored alkali-tolerant bacteria such as Firmicutes and Actinobacteria, especially at pH 11, while neutrophilic Proteobacteria were suppressed. Trichococcus and Bifidobacterium, known acid producers, dominated under all conditions, with their abundance increasing at higher pH levels. Anaerobic digestion enriched fermentative bacteria like Chloroflexi and Synergistota (e.g., Thermovirga), especially in high pH reactors. Methanothrix, an acetoclastic methanogen, became the dominant methanogenic archaeon, indicating that methane production from EMC sludge primarily followed the acetoclastic methanogenesis pathway. Our findings demonstrate that alkaline pre-fermentation at pH 11 significantly enhances the hydrolysis efficiency of EMC sludge for methane recovery. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Kidney Failure Events, Cardiovascular Disease Events, and All-Cause Mortality in Patients with IgA Nephropathy in a Real-World Database

Author

Lerma, Edgar V., primary, Thakker, Kamlesh M., additional, Bensink, Mark E., additional, Lieblich, Richard, additional, Bunke, C. Martin, additional, Gong, Wu, additional, Rava, Andrew R., additional, Wang, Kaijun, additional, Amari, Diana T., additional, Oliveri, David, additional, Murphy, Michael V., additional, Cork, David M.W., additional, and Velez, Juan Carlos Q., additional

Published
2024
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