Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166282/1/itr2bf00873.pd

Effect of abiraterone combined with prednisone on serum CgA and NSE in metastatic castration-resistant prostate cancer without previous chemotherapy

Purpose: To investigate the influence of a combination of abiraterone and prednisone on serum chromogranin A (CgA) and neuron-specific enolase (NSE) in patients with metastatic castrationresistant prostate cancer (mCRPC) without previous chemotherapy, so as to provide reference data for drug therapy of prostate cancer. Methods: A total of 103 mCRPC patients without chemotherapy from January 2013 to March 2017 were included in this retrospective study. Seventy-one (71) patients received prednisone combined with abiraterone (study group), while 32 patients accepted prednisone (control group). The CgA, NSE and prostate-specific antigen (PSA) in the two groups were monitored, while PSA progression-free survival (PSA-PFS), radiographic PFS (rPFS), and overall survival (OS) were determined during follow-up. Results: PSA-PFS, rPFS and OS in the study group were significantly higher than those in the control group (p < 0.05). The increased proportion of CgA or NSE in the study group was significantly lower than that in the control group at 6 months of treatment (p < 0.05). The occurrences of NED before treatment and 6 months after treatment were both independent predictors of PSA and radiographic progression in the study group (p < 0.05). Conclusion: The combination of prednisone and abiraterone is helpful for prognosis in mCRPC patients that are not on chemotherapy. The occurrence of NED predicts mostly poor prognosis of mCRPC patients on a combination of abiraterone and prednison

Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin

In the absence of prior knowledge, ordinal embedding methods obtain new representation for items in a low-dimensional Euclidean space via a set of quadruple-wise comparisons. These ordinal comparisons often come from human annotators, and sufficient comparisons induce the success of classical approaches. However, collecting a large number of labeled data is known as a hard task, and most of the existing work pay little attention to the generalization ability with insufficient samples. Meanwhile, recent progress in large margin theory discloses that rather than just maximizing the minimum margin, both the margin mean and variance, which characterize the margin distribution, are more crucial to the overall generalization performance. To address the issue of insufficient training samples, we propose a margin distribution learning paradigm for ordinal embedding, entitled Distributional Margin based Ordinal Embedding (\textit{DMOE}). Precisely, we first define the margin for ordinal embedding problem. Secondly, we formulate a concise objective function which avoids maximizing margin mean and minimizing margin variance directly but exhibits the similar effect. Moreover, an Augmented Lagrange Multiplier based algorithm is customized to seek the optimal solution of \textit{DMOE} effectively. Experimental studies on both simulated and real-world datasets are provided to show the effectiveness of the proposed algorithm.Comment: Accepted by AAAI 201