Drought-Damaged Corn Silage for Growing Beef Calves

One hundred thirty-six steer calves averaging 440 lb. were used in a 56-day trial to study the value of supplementing drought-damaged corn silage with protein and energy. The silage contained only about 8 bushels of grain per acre and was harvested in mid-August at 30% dry matter. It was stored in a 71-ton stack, covered with a plastic cover and sealed with earth around the bottom. The stack was opened with a plastic cover and sealed with earth around the bottom. The nitrogen (dry basis) were 11.7 and 0.24 at ensiling. The four treatments (34 steers each) were 1 or 2 lb. of supplement (32% protein) per head daily each with 2 levels of corn grain. The supplements supplied 350 mg. each of chlortetracycline and sulfamethazine and 30,000 I.U. vitamin A per steer daily

Value of Shelter for Growing and Finishing Cattle

In an experiment which involved a study of sulfur additions to supplements with high levels of urea fed with corn silage and with ground ear corn silage and with ground ear corn, four diet treatments were replicated with inside or outside feeding. Results for this aspect of the experiment are summarized for this report

Sulfur Supplementation With Urea as the Supplemental Protein With Corn Silage or Ear Corn Diets for Beef Steers

This experiment was conducted to determined the need for a sulfur supplement with urea used as the primary supplemental protein in corn silage or ear corn diets for growing and finishing steers. Supplements which contained one part of sulfur to 10 or 20 parts nitrogen from the urea were compared to a urea supplement without added sulfur and to a low-protein corn supplement fed at the same level. The experiment consisted of a corn silage feeding phase of about 4 months and a ground ear corn phase of about 6 months

Effect of Cooked Soybeans and Type of Housing on Growth Performance and Carcass Chracteristics of Swine

Previous research conducted at the Southeast South Dakota Experiment Farm had shown very little difference in rate of gain of growing pigs housed in to tally confined or in open-front buildings . However, approximately 9% less feed was required when pigs were housed in the confinement buildings . The present experiment was conducted to obtain further information on the effect of cooked soybeans in diets for growing pigs wh en housed in a controlled environment confinement building or an open-front building with waterers and feeders in an outside concrete lot

Cooked and Raw Soybeans as Supplemental Protein Sources for Growing-Finishing Swine

The objective of the experiment reported herein was to evaluate the use of cooked and raw soybeans in diets for finishing swine

Machine Learning Models that Remember Too Much

Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data. We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of them very similar to standard ML techniques such as regularization and data augmentation, that "memorize" information about the training dataset in the model yet the model is as accurate and predictive as a conventionally trained model. We then explain how the adversary can extract memorized information from the model. We evaluate our techniques on standard ML tasks for image classification (CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20 Newsgroups and IMDB). In all cases, we show how our algorithms create models that have high predictive power yet allow accurate extraction of subsets of their training data