Identification of Outlying Observations with Quantile Regression for Censored Data

Outlying observations, which significantly deviate from other measurements, may distort the conclusions of data analysis. Therefore, identifying outliers is one of the important problems that should be solved to obtain reliable results. While there are many statistical outlier detection algorithms and software programs for uncensored data, few are available for censored data. In this article, we propose three outlier detection algorithms based on censored quantile regression, two of which are modified versions of existing algorithms for uncensored or censored data, while the third is a newly developed algorithm to overcome the demerits of previous approaches. The performance of the three algorithms was investigated in simulation studies. In addition, real data from SEER database, which contains a variety of data sets related to various cancers, is illustrated to show the usefulness of our methodology. The algorithms are implemented into an R package OutlierDC which can be conveniently employed in the \proglang{R} environment and freely obtained from CRAN

Robust Likelihood-Based Survival Modeling with Microarray Data

Gene expression data can be associated with various clinical outcomes. In particular, these data can be of importance in discovering survival-associated genes for medical applications. As alternatives to traditional statistical methods, sophisticated methods and software programs have been developed to overcome the high-dimensional difficulty of microarray data. Nevertheless, new algorithms and software programs are needed to include practical functions such as the discovery of multiple sets of survival-associated genes and the incorporation of risk factors, and to use in the R environment which many statisticians are familiar with. For survival modeling with microarray data, we have developed a software program (called rbsurv) which can be used conveniently and interactively in the R environment. This program selects survival-associated genes based on the partial likelihood of the Cox model and separates training and validation sets of samples for robustness. It can discover multiple sets of genes by iterative forward selection rather than one large set of genes. It can also allow adjustment for risk factors in microarray survival modeling. This software package, the rbsurv package, can be used to discover survival-associated genes with microarray data conveniently.

Robust Likelihood-Based Survival Modeling with Microarray Data

Gene expression data can be associated with various clinical outcomes. In particular, these data can be of importance in discovering survival-associated genes for medical applications. As alternatives to traditional statistical methods, sophisticated methods and software programs have been developed to overcome the high-dimensional difficulty of microarray data. Nevertheless, new algorithms and software programs are needed to include practical functions such as the discovery of multiple sets of survival-associated genes and the incorporation of risk factors, and to use in the R environment which many statisticians are familiar with. For survival modeling with microarray data, we have developed a software program (called rbsurv) which can be used conveniently and interactively in the R environment. This program selects survival-associated genes based on the partial likelihood of the Cox model and separates training and validation sets of samples for robustness. It can discover multiple sets of genes by iterative forward selection rather than one large set of genes. It can also allow adjustment for risk factors in microarray survival modeling. This software package, the rbsurv package, can be used to discover survival-associated genes with microarray data conveniently

Ga-doped Pt-Ni Octahedral Nanoparticles as a Highly Active and Durable Electrocatalyst for Oxygen Reduction Reaction

Bimetallic PtNi nanoparticles have been considered as a promising electrocatalyst for oxygen reduction reaction (ORR) in polymer electrolyte membrane fuel cells (PEMFCs) owing to their high catalytic activity. However, under typical fuel cell operating conditions, Ni atoms easily dissolve into the electrolyte, resulting in degradation of the catalyst and the membrane-electrode assembly (MEA). Here, we report gallium-doped PtNi octahedral nanoparticles on a carbon support (Ga-PtNi/C). The Ga-PtNi/C shows high ORR activity, marking an 11.7-fold improvement in the mass activity (1.24 A mgPt-1) and a 17.3-fold improvement in the specific activity (2.53 mA cm-2) compare to the commercial Pt/C (0.106 A mgPt-1 and 0.146 mA cm-2). Density functional theory calculations demonstrate that addition of Ga to octahedral PtNi can cause an increase in the oxygen intermediate binding energy, leading to the enhanced catalytic activity toward ORR. In a voltage-cycling test, the Ga-PtNi/C exhibits superior stability to PtNi/C and the commercial Pt/C, maintaining the initial Ni concentration and octahedral shape of the nanoparticles. Single cell using the Ga-PtNi/C exhibits higher initial performance and durability than those using the PtNi/C and the commercial Pt/C. The majority of the Ga-PtNi nanoparticles well maintain the octahedral shape without agglomeration after the single cell durability test (30,000 cycles). This work demonstrates that the octahedral Ga-PtNi/C can be utilized as a highly active and durable ORR catalyst in practical fuel cell applications

Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation

This paper investigates Cross-Domain Sequential Recommendation (CDSR), a promising method that uses information from multiple domains (more than three) to generate accurate and diverse recommendations, and takes into account the sequential nature of user interactions. The effectiveness of these systems often depends on the complex interplay among the multiple domains. In this dynamic landscape, the problem of negative transfer arises, where heterogeneous knowledge between dissimilar domains leads to performance degradation due to differences in user preferences across these domains. As a remedy, we propose a new CDSR framework that addresses the problem of negative transfer by assessing the extent of negative transfer from one domain to another and adaptively assigning low weight values to the corresponding prediction losses. To this end, the amount of negative transfer is estimated by measuring the marginal contribution of each domain to model performance based on a cooperative game theory. In addition, a hierarchical contrastive learning approach that incorporates information from the sequence of coarse-level categories into that of fine-level categories (e.g., item level) when implementing contrastive learning was developed to mitigate negative transfer. Despite the potentially low relevance between domains at the fine-level, there may be higher relevance at the category level due to its generalised and broader preferences. We show that our model is superior to prior works in terms of model performance on two real-world datasets across ten different domains.Comment: Accepted at 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023

Ga-doped Pt-Ni Octahedral Nanoparticles as a Highly Active and Durable Electrocatalyst for Oxygen Reduction Reaction

Bimetallic PtNi nanoparticles have been considered as a promising electrocatalyst for oxygen reduction reaction (ORR) in polymer electrolyte membrane fuel cells (PEMFCs) owing to their high catalytic activity. However, under typical fuel cell operating conditions, Ni atoms easily dissolve into the electrolyte, resulting in degradation of the catalyst and the membrane-electrode assembly (MEA). Here, we report gallium-doped PtNi octahedral nanoparticles on a carbon support (Ga-PtNi/C). The Ga-PtNi/C shows high ORR activity, marking an 11.7-fold improvement in the mass activity (1.24 A mgPt-1) and a 17.3-fold improvement in the specific activity (2.53 mA cm-2) compare to the commercial Pt/C (0.106 A mgPt-1 and 0.146 mA cm-2). Density functional theory calculations demonstrate that addition of Ga to octahedral PtNi can cause an increase in the oxygen intermediate binding energy, leading to the enhanced catalytic activity toward ORR. In a voltage-cycling test, the Ga-PtNi/C exhibits superior stability to PtNi/C and the commercial Pt/C, maintaining the initial Ni concentration and octahedral shape of the nanoparticles. Single cell using the Ga-PtNi/C exhibits higher initial performance and durability than those using the PtNi/C and the commercial Pt/C. The majority of the Ga-PtNi nanoparticles well maintain the octahedral shape without agglomeration after the single cell durability test (30,000 cycles). This work demonstrates that the octahedral Ga-PtNi/C can be utilized as a highly active and durable ORR catalyst in practical fuel cell applications