Evaluation of Soilless Media Used in Tobacco Float Systems

A wide range of soilless media is currently available to tobacco growers utilizing the float system for transplant production. Most of the media are predominantly made up of peat moss with varying amounts of perlite, vermiculite,and coconut fibers (coir). One of the most difficult problems for growers has been inconsistency in the media from year to year. Because peat is a natural product, some year to year variability is unavoidable. However, many manufacturers of soilless media have procedures in place to ensure that the final product is as consistent as possible

“Burn Down” Management of Winter Cereal Cover Crops for No-tillage Burley Tobacco Production

Recent developments in the design of no-till transplanters and significant improvements in weed control have made no-till tobacco production a feasible option for burley tobacco growers. No-till production reduces soil erosion when tobacco is grown on sloping land. This helps maintain the long term productivity of the soil and may provide the grower with more options for crop rotation, by allowing sloping land to be utilized for tobacco production

DeepFacePencil: Creating Face Images from Freehand Sketches

In this paper, we explore the task of generating photo-realistic face images from hand-drawn sketches. Existing image-to-image translation methods require a large-scale dataset of paired sketches and images for supervision. They typically utilize synthesized edge maps of face images as training data. However, these synthesized edge maps strictly align with the edges of the corresponding face images, which limit their generalization ability to real hand-drawn sketches with vast stroke diversity. To address this problem, we propose DeepFacePencil, an effective tool that is able to generate photo-realistic face images from hand-drawn sketches, based on a novel dual generator image translation network during training. A novel spatial attention pooling (SAP) is designed to adaptively handle stroke distortions which are spatially varying to support various stroke styles and different levels of details. We conduct extensive experiments and the results demonstrate the superiority of our model over existing methods on both image quality and model generalization to hand-drawn sketches.Comment: ACM MM 2020 (oral

Misguided Transcriptional Elongation Causes Mixed Lineage Leukemia

Investigation of the activity of a family of fusion proteins that cause aggressive leukemia suggests transcriptional elongation as a new mechanism for oncogenic transformation

A Practical Approach For Writer-Dependent Symbol Recognition Using A Writer-Independent Symbol Recognizer

We present a practical technique for using a writer-independent recognition engine to improve the accuracy and speed while reducing the training requirements of a writerdependent symbol recognizer. Our writer-dependent recognizer uses a set of binary classifiers based on the AdaBoost learning algorithm, one for each possible pairwise symbol comparison. Each classifier consists of a set of weak learners, one of which is based on a writer-independent handwriting recognizer. During online recognition, we also use the n-best list of the writer-independent recognizer to prune the set of possible symbols and thus reduce the number of required binary classifications. In this paper, we describe the geometric and statistical features used in our recognizer and our all-pairs classification algorithm. We also present the results of experiments that quantify the effect incorporating a writer-independent recognition engine into a writer-dependent recognizer has on accuracy, speed, and user training time. © 2007 IEEE

C.: A practical approach for writer-dependent symbol recognition using a writerindependent symbol recognizer

Abstract—We present a practical technique for using a writer-independent recognition engine to improve the accuracy and speed while reducing the training requirements of a writer-dependent symbol recognizer. Our writer-dependent recognizer uses a set of binary classifiers based on the AdaBoost learning algorithm, one for each possible pairwise symbol comparison. Each classifier consists of a set of weak learners, one of which is based on a writer-independent handwriting recognizer. During online recognition, we also use the n-best list of the writer-independent recognizer to prune the set of possible symbols and, thus, reduce the number of required binary classifications. In this paper, we describe the geometric and statistical features used in our recognizer and our all-pairs classification algorithm. We also present the results of experiments that quantify the effect incorporating a writer-independent recognition engine into a writer-dependent recognizer has on accuracy, speed, and user training time. Index Terms—Handwriting recognition, AdaBoost, writer dependence, writer independence, pairwise classification, real-time systems. Ç