Technion – Israel Institute of Technology

About: Technion – Israel Institute of Technology is a education organization based out in Haifa, Israel. It is known for research contribution in the topics: Population & Upper and lower bounds. The organization has 31714 authors who have published 79377 publications receiving 2603976 citations. The organization is also known as: Technion Israel Institute of Technology & Ṭekhniyon, Makhon ṭekhnologi le-Yiśraʼel.

Prognostic relevance of integrated genetic profiling in acute myeloid leukemia

Abstract: We identified at least one somatic alteration in 97.3% of the patients. We found that internal tandem duplication in FLT3 (FLT3-ITD), partial tandem duplication in MLL (MLL-PTD), and mutations in ASXL1 and PHF6 were associated with reduced overall survival (P = 0.001 for FLT3-ITD, P = 0.009 for MLL-PTD, P = 0.05 for ASXL1, and P = 0.006 for PHF6); CEBPA and IDH2 mutations were associated with improved overall survival (P = 0.05 for CEBPA and P = 0.01 for IDH2). The favorable effect of NPM1 mutations was restricted to patients with co-occurring NPM1 and IDH1 or IDH2 mutations. We identified genetic predictors of outcome that improved risk stratification among patients with AML, independently of age, white-cell count, induction dose, and post-remission therapy, and validated the significance of these predictors in an independent cohort. High-dose daunorubicin, as compared with standarddose daunorubicin, improved the rate of survival among patients with DNMT3A or NPM1 mutations or MLL translocations (P = 0.001) but not among patients with wild-type DNMT3A, NPM1, and MLL (P = 0.67). Conclusions We found that DNMT3A and NPM1 mutations and MLL translocations predicted an improved outcome with high-dose induction chemotherapy in patients with AML. These findings suggest that mutational profiling could potentially be used for risk stratification and to inform prognostic and therapeutic decisions regarding patients with AML. (Funded by the National Cancer Institute and others.)

A fine is a price

Abstract: The deterrence hypothesis predicts that the introduction of a penalty that leaves everything else unchanged will reduce the occurrence of the behavior subject to the fine. We present the result of a field study in a group of day‐care centers that contradicts this prediction. Parents used to arrive late to collect their children, forcing a teacher to stay after closing time. We introduced a monetary fine for late‐coming parents. As a result, the number of late‐coming parents increased significantly. After the fine was removed no reduction occurred. We argue that penalties are usually introduced into an incomplete contract, social or private. They may change the information that agents have, and therefore the effect on behavior may be opposite of that expected. If this is true, the deterrence hypothesis loses its predictive strength, since the clause “everything else is left unchanged” might be hard to satisfy.

Context-Aware Saliency Detection

Abstract: We propose a new type of saliency—context-aware saliency—which aims at detecting the image regions that represent the scene. This definition differs from previous definitions whose goal is to either identify fixation points or detect the dominant object. In accordance with our saliency definition, we present a detection algorithm which is based on four principles observed in the psychological literature. The benefits of the proposed approach are evaluated in two applications where the context of the dominant objects is just as essential as the objects themselves. In image retargeting, we demonstrate that using our saliency prevents distortions in the important regions. In summarization, we show that our saliency helps to produce compact, appealing, and informative summaries.

Unsupervised optimal fuzzy clustering

Abstract: This study reports on a method for carrying out fuzzy classification without a priori assumptions on the number of clusters in the data set. Assessment of cluster validity is based on performance measures using hypervolume and density criteria. An algorithm is derived from a combination of the fuzzy K-means algorithm and fuzzy maximum-likelihood estimation. The unsupervised fuzzy partition-optimal number of classes algorithm performs well in situations of large variability of cluster shapes, densities, and number of data points in each cluster. The algorithm was tested on different classes of simulated data, and on a real data set derived from sleep EEG signal. >