Prediction of Pediatric Sepsis Using a Deep Encoding Network with Cross Features

Abstract

In the present paper we shall investigate the Waring's problem for upper triangular matrix algebras. The main result is the following: Let n ≥ 2 and m ≥ 1 be integers. Let p(x 1 , . . . , xm) be a noncommutative polynomial with zero constant term over an infinite field K. Let Tn(K) be the set of all n × n upper triangular matrices over K. Suppose 1 < r < n − 1, where r is the order of p. We have that p(Tn(K)) + p(Tn(K)) = J r , where J is the Jacobson radical of Tn(K). If r = n − 2, then p(Tn(K)) = J n−2 . This gives a definitive solution of a conjecture proposed by Panja and Prasad.

Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis

“…The end result is precision intervention. For example, this type of intervention is being applied very successfully in oncological treatment [26] and in paediatric sepsis [27].…”

CAGNet: A Multi-Scale Convolutional Attention Method for Glass Detection Based on Transformer

“…Each unit comprises the following four components: a transformer head (TH), cross-modal atrous spatial pyramid pooling module (C-ASPP) [20], transformer conversion head (TCH) [20], and feature fusion block (FFB). Given that the MCA progressively integrates and independently outputs features from each receptive field, features from different levels might exhibit discrepancies, as highlighted in references [42,43]. Directly processing these features could potentially limit the model's performance.…”

“…The proposed BFA can fuse features across different levels in the absence of a GC module, thereby amplifying the information from the feature backbone. Simultaneously, BFA can filter incorrect features owing to the disparities between different levels [42,43]. BFA enhances the model's performance to some extent; however, instances of misprediction because of overfitting are observed.…”