Convex Optimization for Binary Classifier Aggregation in Multiclass Problems

Multiclass problems are often decomposed into multiple binary problems that are solved by individual binary classifiers whose results are integrated into a final answer. Various methods, including all-pairs (APs), one-versus-all (OVA), and error correcting output code (ECOC), have been studied, to decompose multiclass problems into binary problems. However, little study has been made to optimally aggregate binary problems to determine a final answer to the multiclass problem. In this paper we present a convex optimization method for an optimal aggregation of binary classifiers to estimate class membership probabilities in multiclass problems. We model the class membership probability as a softmax function which takes a conic combination of discrepancies induced by individual binary classifiers, as an input. With this model, we formulate the regularized maximum likelihood estimation as a convex optimization problem, which is solved by the primal-dual interior point method. Connections of our method to large margin classifiers are presented, showing that the large margin formulation can be considered as a limiting case of our convex formulation. Numerical experiments on synthetic and real-world data sets demonstrate that our method outperforms existing aggregation methods as well as direct methods, in terms of the classification accuracy and the quality of class membership probability estimates.Comment: Appeared in Proceedings of the 2014 SIAM International Conference on Data Mining (SDM 2014

The Impact of Job Retention on Continuous Growth of Engineering and Informational Technology SMEs in South Korea

This study aims to explore what factors are critically associated with job retention in Engineering and Information Technology small- and medium-sized enterprises (SMEs) in South Korea, and how employers think about sta retention policy in relation to business growth. This contrasts with previous studies that mainly focus on employee motivation, job retention, and turnover. Qualitative semi-structured interviews were conducted face-to-face with founder Chief Executive O cers (CEOs). The results suggest that an important factor influencing job retention policies of these SMEs was to motivate employees to make greater voluntary e ort, including through developing a collaborative organizational culture, rather than solely o ering them additional financial rewards or using other Human Resource Management (HRM) practices to improve individual performances. Interviewees believed that job retention and business growth were closely related, and they discussed various ways of eliciting emotional commitment from employees. Unlike research on larger firms, these suggestions did not involve immediate financial rewards. How employers thought that the roles played by employees strongly influenced their firm’s productivity and competitiveness. This study suggests SME employers adjust their retention policy specifically to improve their firm’s survival and long-term growth

Zero-Permutation Jet-Parton Assignment using a Self-Attention Network

In high-energy particle physics events it can be useful to find the jets correlated with the decay of intermediate states, for example the three jets produced by the hadronic decay of the top quark. Typically, a goodness-of-association measure, such as a $\chi^2$ related to the mass of the associated jets, is constructed, and the best jet combination is found by minimising this $\chi^2$. As this process suffers from combinatorial explosion with the number of jets, the number of permutations is limited by using only the $n$ highest $p_T$ jets. The self-attention block is a neural network unit used for the machine translation problem, which can highlight relationships between any number of inputs in a single iteration without permutations. In this paper, we introduce the self-attention for jet assignment (SaJa) network. SaJa can take any number of jets for input, and outputs probabilities of jet-parton assignment for all jets in a single step. We apply SaJa to find jet-parton assignments of fully-hadronic $t\bar{t}$ events to test the performance.Comment: Code available from https://github.com/CPLUOS/SaJ

Coagulation-Ceramic Membrane Filtrati on for U.S. Surface Water Treatment Summary

The objective of this project was to conduct a systematic pilot-scale investigation of a hybrid coagulation-ceramic membrane treatment system to gain fundamental insights about necessary pretreatment conditions, fouling mechanisms, and contaminant removal capabilities, using two U.S. surface water sources. A two-phase plan was implemented for each of the three coagulants considered in this study: aluminum sulfate, aluminum chlorohydrate, and ferric chloride. The first phase involved the optimization of the coagulation pretreatment conditions that provided the best performance in terms of particle removal, organics removal, and membrane fouling. The second phase involved a comprehensive performance evaluation of the optimized system. The removal of precursors of selected regulated and emerging disinfection by-products as well as selected microorganisms and surrogates from the two U.S. surface waters was determined

High-throughput peptide quantification using mTRAQ reagent triplex

<p>Abstract</p> <p>Background</p> <p>Protein quantification is an essential step in many proteomics experiments. A number of labeling approaches have been proposed and adopted in mass spectrometry (MS) based relative quantification. The mTRAQ, one of the stable isotope labeling methods, is amine-specific and available in triplex format, so that the sample throughput could be doubled when compared with duplex reagents.</p> <p>Methods and results</p> <p>Here we propose a novel data analysis algorithm for peptide quantification in triplex mTRAQ experiments. It improved the accuracy of quantification in two features. First, it identified and separated triplex isotopic clusters of a peptide in each full MS scan. We designed a schematic model of triplex overlapping isotopic clusters, and separated triplex isotopic clusters by solving cubic equations, which are deduced from the schematic model. Second, it automatically determined the elution areas of peptides. Some peptides have similar atomic masses and elution times, so their elution areas can have overlaps. Our algorithm successfully identified the overlaps and found accurate elution areas. We validated our algorithm using standard protein mixture experiments.</p> <p>Conclusions</p> <p>We showed that our algorithm was able to accurately quantify peptides in triplex mTRAQ experiments. Its software implementation is compatible with Trans-Proteomic Pipeline (TPP), and thus enables high-throughput analysis of proteomics data.</p

Manipulation of Rat Movement via Nigrostriatal Stimulation Controlled by Human Visually Evoked Potentials

Here, we report that the development of a brain-to-brain interface (BBI) system that enables a human user to manipulate rat movement without any previous training. In our model, the remotely-guided rats (known as ratbots) successfully navigated a T-maze via contralateral turning behaviour induced by electrical stimulation of the nigrostriatal (NS) pathway by a brain-computer interface (BCI) based on the human controller&apos;s steady-state visually evoked potentials (SSVEPs). The system allowed human participants to manipulate rat movement with an average success rate of 82.2% and at an average rat speed of approximately 1.9 m/min. The ratbots had no directional preference, showing average success rates of 81.1% and 83.3% for the left-and right-turning task, respectively. This is the first study to demonstrate the use of NS stimulation for developing a highly stable ratbot that does not require previous training, and is the first instance of a training-free BBI for rat navigation. The results of this study will facilitate the development of borderless communication between human and untrained animals, which could not only improve the understanding of animals in humans, but also allow untrained animals to more effectively provide humans with information obtained with their superior perception.11Ysciescopu