etiologic factors for recurrent miscarriage (rm) include autoimmune diseases, the most frequently antiphospholipid syndrome and thyroiditis. some women who suffer from rm might also have an altered immune system. we aimed to evaluate possible associations between anti-thyroid and anti-phospholipid antibodies in women with rm. in a retrospective case series 1 on 156 women with rm, major outcome ...

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) ...

We suggest a recurrent neural network (RNN) model with a recurrent part corresponding to iterative function systems (IFS) introduced by Barnsley [1] as a fractal image compression mechanism. The key idea is that 1) in our model we avoid learning the RNN state part by having non-trainable connections between the context and recurrent layers (this makes the training process less problematic and f...

One major role of primary visual cortex (V1) in vision is the encoding of the orientation of lines and contours. The role of the local recurrent network in these computations is, however, still a matter of debate. To address this issue, we analyze intracellular recording data of cat V1, which combine measuring the tuning of a range of neuronal properties with a precise localization of the recor...

The fully connected recurrent network (FRN) using the on-line training method, Real Time Recurrent Learning (RTRL), is computationally expensive. It has a computational complexity of O(N 4) and storage complexity of O(N 3), where N is the number of non-input units. We have devised a locally connected recurrent model which has a much lower complexity in both computational time and storage space....

Image Associative Memory by Recurrent Neural Subnetworks W ladys law S k arbek y3 and Andrzej Cichocki y33 , Members SUMMARY Gray scale images are represented by recurrent neural subnetworks which together with a competition layer create an associative memory. The single recurrent subnetwork N i implements a stochastic nonlinear fractal operator F i , constructed for the given image f i. W e sh...

Recurrent perceptron classifiers generalize the usual perceptron model. They correspond to linear transformations of input vectors obtained by means of “autoregressive movingaverage schemes”, or infinite impulse response filters, and allow taking into account those correlations and dependences among input coordinates which arise from linear digital filtering. This paper provides tight bounds on...

We consider problems of sequence processing and we propose a solution based on a discrete state model. We introduce a recurrent architecture having a modular structure that allocates subnetworks to discrete states. Di erent subnetworks are model the dynamics (state transition) and the output of the model, conditional on the previous state and an external input. The model has a statistical inter...

Learning to recognize or predict sequences using long-term context has many applications. However, practical and theoretical problems are found in training recurrent neural networks to perform tasks in which input/output dependencies span long intervals. Starting from a mathematical analysis of the problem, we consider and compare alternative algorithms and architectures on tasks for which the ...

We introduce the new Wave model for exposing the temporal relationship among the queries in data-intensive distributed computing. The model defines the notion of query series to capture the recurrent nature of batched computation on periodically updated input streams. This seemingly simple concept captures a significant portion of the queries we observed in a production system. The recurring na...