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Active learning has received great interests from researchers due to its ability to reduce the amount of supervision required for effective learning. As the core component of active learning algorithms, query synthesis and pool-based sampling are two main scenarios of querying considered in the literature. Query synthesis features low querying time, but only has limited applications as the synthesized query might be unrecognizable to human oracle. As a result, most efforts have focused on pool-based sampling in recent years, although it is much more time-consuming. In this paper, we propose new strategies for a novel querying framework that combines query synthesis and pool-based sampling. It overcomes the limitation of query synthesis, and has the advantage of fast querying. The basic idea is to synthesize an instance close to the decision boundary using labeled data, and then select the real instance closest to the synthesized one as a query. For this purpose, we propose a synthesis strategy, which can synthesize instances close to the decision boundary and spreading along the decision boundary. Since the synthesis only depends on the relatively small labelled set, instead of evaluating the entire unlabeled set as many other active learning algorithms do, our method has the advantage of efficiency. In order to handle more complicated data and make our framework compatible with powerful kernel-based learners, we also extend our method to kernel version. Experiments on several real-world data sets show that our method has significant advantage on time complexity and similar performance compared to pool-based uncertainty sampling methods.

Therapeutic agents are urgently needed for treating metastatic castration‐refractory prostate cancer (mCRPC) that is unresponsive to androgen deprivation and chemotherapy. Our screening assays demonstrated that chemotherapy‐resistant prostate cancer (PCa) cells are more sensitive to HDAC inhibitors than paired sensitive PCa cells, as demonstrated by cell proliferation and apoptosis in vitro and in vivo. Kinetic study revealed that TSA‐induced apoptosis was significantly dependent on enhanced transcription and protein synthesis in an early stage, which subsequently caused ER stress and apoptosis. ChIP analysis indicated that TSA increased H4K16 acetylation, promoting ER stress gene transcription. The changes in Ac‐H4K16, ATF3 and ATF4 were also validated in TSA‐treated animals. Further study revealed the higher enzyme activity of HDACs and an increase in acetylated proteins in resistant cells. The higher nucleocytoplasmic acetyl‐CoA in resistant cells was responsible for elevated acetylation status of protein and a more vigorous growth state. These results strongly support the pre‐clinical application of HDAC inhibitors for treating chemotherapy‐resistant mCRPC.

Abstract. With uncorrelated Gaussian factors extended to mutually independent factors beyond Gaussian, the conventional factor analysis is extended to what is recently called independent factor analysis. Typically, it is called binary factor analysis (BFA) when the factors are binary and called non-Gaussian factor analysis (NFA) when the factors are from real non-Gaussian distributions. A crucial issue in both BFA and NFA is the determination of the number of factors. In the literature of statistics, there are a number of model selection criteria that can be used for this purpose. Also, the Bayesian Ying-Yang (BYY) harmony learning provides a new principle for this purpose. This paper further investigates BYY harmony learning in comparison with existing typical criteria, including Akaik_s information criterion (AIC), the consistent Akaike_s information criterion (CAIC), the Bayesian inference criterion (BIC), and the cross-validation (CV) criterion on selection of the number of factors. This comparative study is made via experiments on the data sets with different sample sizes, data space dimensions, noise variances, and hidden factors numbers. Experiments have shown that for both BFA and NFA, in most cases BIC outperforms AIC, CAIC, and CV while the BYY criterion is either comparable with or better than BIC. In consideration of the fact that the selection by these criteria has to be implemented at the second stage based on a set of candidate models which have to be obtained at the first stage of parameter learning, while BYY harmony learning can provide not only a new class of criteria implemented in a similar way but also a new family of algorithms that perform parameter learning at the first stage with automated model selection, BYY harmony learning is more preferred since computing costs can be saved significantly. (2000): 68Q10, 62H25, 68T05, 65C10, 68Q32. Mathematical Subject Classifications

Abstract. The selection of the number of clusters is an important and challenging issue in cluster analysis. In this paper we perform an experimental comparison of several criteria for determining the number of clusters based on Gaussian mixture model. The criteria that we consider include Akaike's information criterion (AIC), the consistent Akaike's information criterion (CAIC), the minimum description length (MDL) criterion which formally coincides with the Bayesian inference criterion (BIC), and two model selection methods driven from Bayesian YingYang (BYY) harmony learning: harmony empirical learning criterion (BYY-HEC) and harmony data smoothing criterion (BYY-HDS). We investigate these methods on synthetic data sets of different sample size and the iris data set. The results of experiments illustrate that BYY-HDS has the best overall success rate and obviously outperforms other methods for small sample size. CAIC and MDL tend to underestimate the number of clusters, while AIC and BYY-HEC tend to overestimate the number of clusters especially in the case of small sample size.

In this paper, we aim at irregular-shape object localization under weak supervision. With over-segmentation, this task can be transformed into multiple-instance context. However, most multiple-instance learning methods only emphasize single most positive instance in a positive bag to optimize bag-level classification, and leads to imprecise or incomplete localization. To address this issue, we propose a scheme for instance annotation, where all of the positive instances are detected by labeling each instance in each positive bag. Inspired by the successful application of bag-of-words (BoW) to feature representation, we leverage it at instance-level to model the distributions of the positive class and negative class, and then incorporate the BoW learning and instance labeling in a single optimization formulation. We also demonstrate that the scheme is well suited to weakly supervised object localization of irregular-shape. Experimental results validate the effectiveness both for the problem of generic instance annotation and for the application of weakly supervised object localization compared to some existing methods.

BackgroundThe evaluation of the eighth edition of ypTNM staging system for patients with esophageal cancer was limited in the setting of neoadjuvant therapy.MethodsA total of 2324 patients with esophageal cancer receiving radio(chemo)therapy prior to surgery from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2013 were eligible for the analysis. Kaplan‐Meier method and Cox proportional hazards models were used to estimate overall survivals.ResultsAmong patients with preoperative therapy, both the seventh edition TNM grouping and the eighth edition ypTNM grouping could significantly stratify the overall survival (both log‐rank P < .001). There was not significant difference in the C‐index of the seventh edition TNM grouping (0.575; 95%CI, 0.558‐0.593) and the eighth edition ypTNM grouping (0.569; 95%CI, 0.551‐0.587) (P = .098). In multivariable Cox analysis, ypN category was the strongest predictor of overall survival (P < .001), followed by tumor grade (HR, 1.33; 95%CI, 1.12‐1.56; P = .001). The combination of ypT, ypN, and ypG categories yielded significantly higher C‐index (0.591; 95%CI, 0.573‐0.609) than that of the seventh edition TNM staging (P = .024).ConclusionTumor grade remained an independent predictor of overall survival in the setting of neoadjuvant therapy, and could improve the performance of ypTNM staging system.

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