Click-aware purchase prediction with push at the top

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user-item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see https://doi.org/10.1016/j.ins.2020.02.06

Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach

In this paper, we explore incremental few-shot object detection (iFSD), which incrementally learns novel classes using only a few examples without revisiting base classes. Previous iFSD works achieved the desired results by applying meta-learning. However, meta-learning approaches show insufficient performance that is difficult to apply to practical problems. In this light, we propose a simple fine-tuning-based approach, the Incremental Two-stage Fine-tuning Approach (iTFA) for iFSD, which contains three steps: 1) base training using abundant base classes with the class-agnostic box regressor, 2) separation of the RoI feature extractor and classifier into the base and novel class branches for preserving base knowledge, and 3) fine-tuning the novel branch using only a few novel class examples. We evaluate our iTFA on the real-world datasets PASCAL VOC, COCO, and LVIS. iTFA achieves competitive performance in COCO and shows a 30% higher AP accuracy than meta-learning methods in the LVIS dataset. Experimental results show the effectiveness and applicability of our proposed method.Comment: Accepted to ICRA 202

Molecular methods for genomic analyses of variant PML-RARA or other RARA-related chromosomal translocations in acute promyelocytic leukemia

TO THE EDITOR: We read an interesting paper by Palta et al. in a recent issue of the Korean Journal of Hematology titled, "ZBTB16-RARA variant of acute promyelocytic leukemia with tuberculosis: a case report and review of literature" [1]. We would like to add some comments to their article and suggest additional molecular methods to confirm variant translocations in acute promyelocytic leukemia (APL)...

Effect of Angelica gigas Nakai extract on hepatic damage in rats

Purpose: To determine the antioxidant and hepatoprotective effects of decursin and decursinol angelate (D/DA) isolated from Angelica gigas Nakai (AGN).Methods: The 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity of D/DA was assessed in a rat model using blood tests, western blotting, and histopathological analyses to identify the pharmaceutical effects of D/DA on liver enzymes and liver morphology.Results: The DPPH scavenging activity of D/DA was 47.11 μg/mL. Administration of D/DA to carbon tetrachloride (CCl4)-treated rats led to a decrease (13.59 %) in the total liver mass of control rats. Decursin and decursinol angelate also lowered the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST), but increased the concentrations of antioxidant enzymes in the liver, including catalase (CAT) and glutathione peroxidase (GPx). Histological examination revealed that D/DA also reduced hepatocellular damage in the rats.Conclusion: D/DA from AGN has significant anti-hepatotoxic and antioxidant activities, and thus, is a potential herbal drug for treating liver damage. Keywords: Decursin, Decursinol angelate, Antihepatotoxicity, Antioxidant, Angelica gigas Naka