Multi-target tracking via space transformations using a single frequency continuous wave radar

This paper investigates the location and velocity estimation problem involving multiple targets using the phase difference and frequency shift of the returned Doppler modulated signal. The minimal receiver configuration that addresses the data association and missing information problem is presented for the case of linear arrays. Non-linearly modeled Doppler radar measurements are used to obtain an accurate estimate of the target dynamics progressively in a linear framework utilizing a recently developed robust state estimation approach.

The e-learning cycle and continuous improvement for e-entrepreneurs

The purpose of this paper is to explore the integration of learning, continuous improvement and innovation theories as a basis for enhancing the education of e-entrepreneurs. Conceptual development of emerging interdisciplinary literature is combined with example analysis to develop the Circle of E-learning uniquely augmented by hermeneutics, action research and the creative destruction cycle of innovation using applied examples of e-entrepreneurship. Four R’s are discussed in the Circle of E-learning; Review, Revise, Reconstruct, and Reveal. Observations for each of the 4R’s are made regarding continuous improvement of the education of e-entrepreneurs. Findings are that the procedural pivot points indicated by the 4R’s can be helpful for administrators and educators to improve operations and outcomes in management and professional development situations.

Phase II trial of continuous low-dose temozolomide for patients with recurrent malignant glioma

Background In this phase II trial, we investigated the efficacy of a metronomic temozolomide schedule in the treatment of recurrent malignant gliomas (MGs).

Methods Eligible patients received daily temozolomide (50 mg/m2) continuously until progression. The primary endpoint was progression-free survival rate at 6 months in the glioblastoma cohort (N = 37). In an exploratory analysis, 10 additional recurrent grade III MG patients were enrolled. Correlative studies included evaluation of 76 frequent mutations in glioblastoma (iPLEX assay, Sequenom) aiming at establishing the frequency of potentially “drugable” mutations in patients entering recurrent MG clinical trials.

Results Among glioblastoma patients, median age was 56 y; median Karnofsky Performance Score (KPS) was 80; 62% of patients had been treated for ≥2 recurrences, including 49% of patients having failed bevacizumab. Treatment was well tolerated; clinical benefit (complete response + partial response + stable disease) was seen in 10 (36%) patients. Progression-free survival rate at 6 months was 19% and median overall survival was 7 months. Patients with previous bevacizumab exposure survived significantly less than bevacizumab-naive patients (median overall survival: 4.3 mo vs 13 mo; hazard ratio = 3.2; P = .001), but those patients had lower KPS (P = .04) and higher number of recurrences (P < .0001). Mutations were found in 13 of the 38 MGs tested, including mutations of EGFR (N = 10), IDH1 (N = 5), and ERBB2 (N = 1).

Conclusions In spite of a heavily pretreated population, including nearly half of patients having failed bevacizumab, the primary endpoint was met, suggesting that this regimen deserves further investigation. Results in bevacizumab-naive patients seemed particularly favorable, while results in bevacizumab-failing patients highlight the need to develop further treatment strategies for advanced MG.

Comparative evaluation of multi-label classification methods

This paper presents a comparative evaluation of popular multi-label classification methods on several multi-label problems from different domains. The methods include multi-label k-nearest neighbor, binary relevance, label power set, random k-label set ensemble learning, calibrated label ranking, hierarchy of multi-label classifiers and triple random ensemble multi-label classification algorithms. These multi-label learning algorithms are evaluated using several widely used MLC evaluation metrics. The evaluation results show that for each multi-label classification problem a particular MLC method can be recommended. The multi-label evaluation datasets used in this study are related to scene images, multimedia video frames, diagnostic medical report, email messages, emotional music data, biological genes and multi-structural proteins categorization.