Ju Mei,†,‡,∥ Nelson L. C. Leung,†,‡,∥ Ryan T. K. Kwok,†,‡ Jacky W. Y. Lam,†,‡ and Ben Zhong Tang*,†,‡,§ †HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China ‡Department of Chemistry, HKUST Jockey Club Institute for Advanced Study, Institute of Molecular Functional Materials, Division of Biomedical Engineering, State Key Laboratory of Molecular Neuroscience, Division of Life Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China Guangdong Innovative Research Team, SCUT-HKUST Joint Research Laboratory, State Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou 510640, China

Aggregation-Induced Emission: Together We Shine, United We Soar!

Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a simple subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data.

Williamson, estimating the support of a high-dimensional distribution

Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data.

/pdf/estimating-the-support-of-a-high-dimensional-distribution-1tg8d7chbx.pdf

Estimating the Support of a High-Dimensional Distribution

The dip test measures multimodality in a sample by the maximum difference, over all sample points, between the empirical distribution function, and the unimodal distribution function that minimizes that maximum difference. The uniform distribution is the asymptotically least favorable unimodal distribution, and the distribution of the test statistic is determined asymptotically and empirically when sampling from the uniform.

/pdf/the-dip-test-of-unimodality-35m8rstuzx.pdf

The Dip Test of Unimodality

Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate the problem: In order to manipulate large sets of complex objects as efficiently as today's database systems manipulate simple records, query-processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software. This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and postrelational database systems, including iterative execution of complex query evaluation plans, the duality of sort- and hash-based set-matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.

/pdf/query-evaluation-techniques-for-large-databases-45td1anbls.pdf

Query evaluation techniques for large databases

This paper analyzes the relation between the patenting behavior of startup firms and the progress of those firms through the venture capital cycle. Linking data relating to venture capital financing of software startup firms with data concerning the patents obtained by those firms, we find significant and robust positive correlations between patenting and several variables measuring the firm's performance (including number of rounds, total investment, exit status, receipt of late stage financing, and longevity). The data also show that (1) only about one in four venture-backed software firms acquired even one patent during the period of the study; (2) patenting practices very considerably among the sub-sectors of the software industry; and (3) the relationship between patent metrics and firm performance depends less on the size of the patent portfolio than on the firm's receipt of at least one patent.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID970078_code1234.pdf?abstractid=802806&mirid=1

Patents, Venture Capital, and Software Start-Ups

Ranked-set sampling has been shown to provide improved estimators of the mean and variance when actual measurement of the observations is difficult but ranking of the elements in a sample is relatively easy. This result holds even if the ranking is imperfect. In this article, we provide a characterization of a ranked-set sample that makes the source of this additional information intuitively clear. It is applied to show that the empirical distribution function of a ranked-set sample is unbiased and has greater precision than that from a random sample. The null distribution of a Kolmogorov-Smirnov statistic based on this empirical distribution function is derived for the case in which perfect ranking is possible. It is seen to be stochastically smaller than the usual Kolmogorov-Smirnov statistic based on a simple random sample, resulting in a smaller simultaneous confidence interval for the cumulative distribution function.

Characterization of a Ranked-Set Sample with Application to Estimating Distribution Functions

A statistical profile summarizes the instances of a database. It describes aspects such as the number of tuples, the number of values, the distribution of values, the correlation between value sets, and the distribution of tuples among secondary storage units. Estimation of database profiles is critical in the problems of query optimization, physical database design, and database performance prediction. This paper describes a model of a database of profile, relates this model to estimating the cost of database operations, and surveys methods of estimating profiles. The operators and objects in the model include build profile, estimate profile, and update profile. The estimate operator is classified by the relational algebra operator (select, project, join), the property to be estimated (cardinality, distribution of values, and other parameters), and the underlying method (parametric, nonparametric, and ad-hoc). The accuracy, overhead, and assumptions of methods are discussed in detail. Relevant research in both the database and the statistics disciplines is incorporated in the detailed discussion.

/pdf/statistical-profile-estimation-in-database-systems-4igvhgj6h2.pdf

Statistical profile estimation in database systems

http://www.columbia.edu/~mr2651/Data/57.pdf

Patents, venture capital, and software start-ups

The advent of the alternative sweeteners market has signaled a demand for chemosensors which target multiple saccharides and saccharide derivatives, in aqueous media at physiological pH. This demand has largely been unmet as existing molecular receptors for saccharides have generally not shown sufficient degrees of affinity and selectivity in aqueous media. A chemosensor array for saccharides and saccharide derivatives, fully operational in aqueous media at physiological pH, has been developed and is reported herein. Boronic acid based peptidic receptors, derived from a combinatorial library, served as the cross-reactive sensor elements in this array. The binding of saccharides to these receptors was assessed colorimetrically using an indicator uptake protocol in the taste-chip platform. The differential indicator uptake rates of these receptors in the presence of saccharides were exploited in order to identify patterns within the data set using linear discriminant analysis. This chemosensor array is capa...

Thomas W. Sager

Papers

Patents, Venture Capital, and Software Start-Ups

Characterization of a Ranked-Set Sample with Application to Estimating Distribution Functions

Statistical profile estimation in database systems

Patents, venture capital, and software start-ups

Boronic acid based peptidic receptors for pattern-based saccharide sensing in neutral aqueous media, an application in real-life samples