Background: Cardiovascular disease is a leading cause of death among kidney transplant recipients... more Background: Cardiovascular disease is a leading cause of death among kidney transplant recipients. Metabolic syndrome increases the risk for cardiovascular events and decreases graft survival. Lately, guidelines for management of the metabolic syndrome, primarily hypertension, diabetes mellitus (DM) and hypercholesterolemia have dramatically changed in an attempt to decrease cardiovascular risks among kidney transplant recipients. In the present study we examined whether these guideline changes had impact on our management of post-transplantation patients and the subsequent treatment outcomes for these diseases.
The Israel Medical Association journal : IMAJ, 2013
Vitamin D deficiency was shown to be prevalent among renal transplant recipients in northern coun... more Vitamin D deficiency was shown to be prevalent among renal transplant recipients in northern countries, but little is known regarding risk factors. To test vitamin D levels in kidney transplant recipients residing closer to the equator, compare them to levels in liver transplant recipients and hemodialysis patients, and identify possible risk factors. In a cross-sectional study 103 kidney transplant recipients, 27 liver transplant recipients and 50 hemodialysis patients followed at our institute were tested for vitamin D levels. Demographic data, medical history and current treatment were recorded from the medical files. Inadequate vitamin D levels (< 30 ng/ml) were found in 75% of all patients and 75% of all kidney transplant recipients. Vitamin D levels were higher among dialysis patients than transplant recipients, though deficiency rates were similar. No association was found between kidney function and vitamin deficiency. Deficiency was associated with higher prednisone dose...
Proceedings of the sixth ACM conference on Recommender systems - RecSys '12, 2012
Media Websites often solicit users&#39; comments on content items such as videos, news storie... more Media Websites often solicit users&#39; comments on content items such as videos, news stories, blog posts, etc. Commenting activity increases user engagement with the sites, by both comment writers and readers, and so sites are looking for ways to increase the volume of comments. This work develops a recommender system aiming to present users with items -- news stories, in
2010 IEEE International Conference on Data Mining Workshops, 2010
We introduce a new method for discovering latent topics in sets of objects, such as documents. Ou... more We introduce a new method for discovering latent topics in sets of objects, such as documents. Our method, which we call PARIS (for Principal Atoms Recognition In Sets), aims to detect principal sets of elements, representing latent topics in the data, that tend to appear frequently together. These latent topics, which we refer to as 'atoms', are used as the basis for clustering, classification, collaborative filtering, and more. We develop a target function which balances compression and low error of representation, and the algorithm which minimizes the function. Optimization of the target function enables an automatic discovery of the number of atoms, representing the dimensionality of the data, and the atoms themselves, all in a single iterative procedure. We demonstrate PARIS's ability to discover latent topics, even when those are arranged hierarchically, on synthetic, documents and movie ranking data, showing improved performance compared to existing algorithms, such as LDA, on text analysis and collaborative filtering tasks.
International Symposium on Technologies for Digital Photo Fulfillment, 2009
a) (b) Figure 1. Image enhancement by the HP Smartstream Photo Enhancement Server. (a) Original i... more a) (b) Figure 1. Image enhancement by the HP Smartstream Photo Enhancement Server. (a) Original image, (b) after processing.
Proceedings of the 7th ACM conference on Recommender systems - RecSys '13, 2013
ABSTRACT One of the most challenging recommendation tasks is recommending to a new, previously un... more ABSTRACT One of the most challenging recommendation tasks is recommending to a new, previously unseen user. This is known as the &#39;user cold start&#39; problem. Assuming certain features or attributes of users are known, one approach for handling new users is to initially model them based on their features. Motivated by an ad targeting application, this paper describes an extreme online recommendation setting where the cold start problem is perpetual. Every user is encountered by the system just once, receives a recommendation, and either consumes or ignores it, registering a binary reward. We introduce One-pass Factorization of Feature Sets, OFF-Set, a novel recommendation algorithm based on Latent Factor analysis, which models users by mapping their features to a latent space. Furthermore, OFF-Set is able to model non-linear interactions between pairs of features. OFF-Set is designed for purely online recommendation, performing lightweight updates of its model per each recommendation-reward observation. We evaluate OFF-Set against several state of the art baselines, and demonstrate its superiority on real ad-targeting data.
We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive n... more We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.
Background: Cardiovascular disease is a leading cause of death among kidney transplant recipients... more Background: Cardiovascular disease is a leading cause of death among kidney transplant recipients. Metabolic syndrome increases the risk for cardiovascular events and decreases graft survival. Lately, guidelines for management of the metabolic syndrome, primarily hypertension, diabetes mellitus (DM) and hypercholesterolemia have dramatically changed in an attempt to decrease cardiovascular risks among kidney transplant recipients. In the present study we examined whether these guideline changes had impact on our management of post-transplantation patients and the subsequent treatment outcomes for these diseases.
The Israel Medical Association journal : IMAJ, 2013
Vitamin D deficiency was shown to be prevalent among renal transplant recipients in northern coun... more Vitamin D deficiency was shown to be prevalent among renal transplant recipients in northern countries, but little is known regarding risk factors. To test vitamin D levels in kidney transplant recipients residing closer to the equator, compare them to levels in liver transplant recipients and hemodialysis patients, and identify possible risk factors. In a cross-sectional study 103 kidney transplant recipients, 27 liver transplant recipients and 50 hemodialysis patients followed at our institute were tested for vitamin D levels. Demographic data, medical history and current treatment were recorded from the medical files. Inadequate vitamin D levels (< 30 ng/ml) were found in 75% of all patients and 75% of all kidney transplant recipients. Vitamin D levels were higher among dialysis patients than transplant recipients, though deficiency rates were similar. No association was found between kidney function and vitamin deficiency. Deficiency was associated with higher prednisone dose...
Proceedings of the sixth ACM conference on Recommender systems - RecSys '12, 2012
Media Websites often solicit users&#39; comments on content items such as videos, news storie... more Media Websites often solicit users&#39; comments on content items such as videos, news stories, blog posts, etc. Commenting activity increases user engagement with the sites, by both comment writers and readers, and so sites are looking for ways to increase the volume of comments. This work develops a recommender system aiming to present users with items -- news stories, in
2010 IEEE International Conference on Data Mining Workshops, 2010
We introduce a new method for discovering latent topics in sets of objects, such as documents. Ou... more We introduce a new method for discovering latent topics in sets of objects, such as documents. Our method, which we call PARIS (for Principal Atoms Recognition In Sets), aims to detect principal sets of elements, representing latent topics in the data, that tend to appear frequently together. These latent topics, which we refer to as 'atoms', are used as the basis for clustering, classification, collaborative filtering, and more. We develop a target function which balances compression and low error of representation, and the algorithm which minimizes the function. Optimization of the target function enables an automatic discovery of the number of atoms, representing the dimensionality of the data, and the atoms themselves, all in a single iterative procedure. We demonstrate PARIS's ability to discover latent topics, even when those are arranged hierarchically, on synthetic, documents and movie ranking data, showing improved performance compared to existing algorithms, such as LDA, on text analysis and collaborative filtering tasks.
International Symposium on Technologies for Digital Photo Fulfillment, 2009
a) (b) Figure 1. Image enhancement by the HP Smartstream Photo Enhancement Server. (a) Original i... more a) (b) Figure 1. Image enhancement by the HP Smartstream Photo Enhancement Server. (a) Original image, (b) after processing.
Proceedings of the 7th ACM conference on Recommender systems - RecSys '13, 2013
ABSTRACT One of the most challenging recommendation tasks is recommending to a new, previously un... more ABSTRACT One of the most challenging recommendation tasks is recommending to a new, previously unseen user. This is known as the &#39;user cold start&#39; problem. Assuming certain features or attributes of users are known, one approach for handling new users is to initially model them based on their features. Motivated by an ad targeting application, this paper describes an extreme online recommendation setting where the cold start problem is perpetual. Every user is encountered by the system just once, receives a recommendation, and either consumes or ignores it, registering a binary reward. We introduce One-pass Factorization of Feature Sets, OFF-Set, a novel recommendation algorithm based on Latent Factor analysis, which models users by mapping their features to a latent space. Furthermore, OFF-Set is able to model non-linear interactions between pairs of features. OFF-Set is designed for purely online recommendation, performing lightweight updates of its model per each recommendation-reward observation. We evaluate OFF-Set against several state of the art baselines, and demonstrate its superiority on real ad-targeting data.
We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive n... more We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.
Uploads
Papers by Michal Aharon