Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers

We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation learning problem, which we call "IB learning". We show that IB learning is, in fact, equivalent to a special class of the quantization problem. The classical results in rate-distortion theory then suggest that IB learning can benefit from a "vector quantization" approach, namely, simultaneously learning the representations of multiple input objects. Such an approach assisted with some variational techniques, result in a novel learning framework, "Aggregated Learning", for classification with neural network models. In this framework, several objects are jointly classified by a single neural network. The effectiveness of this framework is verified through extensive experiments on standard image recognition and text classification tasks.Comment: Proof of theoretical results are provide

Association between diabetes mellitus and rs2868371; a polymorphism of HSPB1

Introduction: Diabetes (DM) is a type of metabolic disorder that its types are generated by collectingof genetic and environmental risk agents. Here, the association between HSPB1 polymorphism as a genetic risk factor and DM was investigated. Methods: Total 690 participants from MASHAD cohort study population were recruited into the study.Anti-HSP27-level was assessed followed by genotyping using Taqman®-probes-based assay. Anthropometric, demographic and hematological/biochemical characteristics were evaluated. Kaplan-Meier curves were utilized, while logistic regression models were used to assess the association of the genetic variant with clinical characteristics of population. Results: Finds was shown there are meaningful differences among groups of age, height, waist circumference, systolic blood pressure, FBG,TG, HDL-C, and hs-CRP, and was no big -significant difference between theexists in different HSP27 SNP in the two studied groups (with and without DM), also was no remarkable relation between genetic forms of HSPB1and T2DM. This investigation was the first research that analyzed the relationship between the genetic type of the HSPB1 gene (rs2868371) and Type 2 diabetes (DM2). In our population, the CC genotype (68.1%) had a higher prevalence versus GC (26.6%) and GG (5.3%) genotypes and the data shown that no genetic difference of HSPB1 gene polymorphism (rs2868371) was related with DM2. Conclusion: HSPB1 polymorphism, rs2868371, was not associated with type 2 diabetes mellitus

Adaptive Equalizer Using Selective Partial Update Algorithm and Selective Regressor Affine Projection Algorithm over Shallow Water Acoustic Channels

One of the most important problems of reliable communications in shallow water channels is intersymbol interference (ISI) which is due to scattering from surface and reflecting from bottom. Using adaptive equalizers in receiver is one of the best suggested ways for overcoming this problem. In this paper, we apply the family of selective regressor affine projection algorithms (SR-APA) and the family of selective partial update APA (SPU-APA) which have low computational complexity that is one of the important factors that influences adaptive equalizer performance. We apply experimental data from Strait of Hormuz for examining the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE) of SR-APA and SPU-APA decrease by 5.8 (dB) and 5.5 (dB), respectively, in comparison with least mean square (LMS) algorithm. Also the families of SPU-APA and SR-APA have better convergence speed than LMS type algorithm