The diet of helmeted guineafowl (Numida meleagris) in the Riemland of the north-eastern Free State, South Africa

This study was conducted to determine the diet of helmeted guineafowl (Numida meleagris) in the Riemland and to establish the effects that these gamebirds may be having on cash crop yield. In the Riemland farming community many farmers complain of harvest losses suffered to guineafowl. It was found that the main dietary items during all seasons are corms of weed plants, primarily Cyperus spp. Helmeted guineafowl rely to a large degree on waste maize and germinating wheat during winter when natural food is difficult to find. Although they do not pose any problems with regard to maize farming, this is not necessarily true for wheat farming

Dynamic Control Flow in Large-Scale Machine Learning

Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditional execution, and other features that call for dynamic control flow. These applications benefit from the ability to make rapid control-flow decisions across a set of computing devices in a distributed system. For performance, scalability, and expressiveness, a machine learning system must support dynamic control flow in distributed and heterogeneous environments. This paper presents a programming model for distributed machine learning that supports dynamic control flow. We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system. Our approach extends the use of dataflow graphs to represent machine learning models, offering several distinctive features. First, the branches of conditionals and bodies of loops can be partitioned across many machines to run on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs. Second, programs written in our model support automatic differentiation and distributed gradient computations, which are necessary for training machine learning models that use control flow. Third, our choice of non-strict semantics enables multiple loop iterations to execute in parallel across machines, and to overlap compute and I/O operations. We have done our work in the context of TensorFlow, and it has been used extensively in research and production. We evaluate it using several real-world applications, and demonstrate its performance and scalability.Comment: Appeared in EuroSys 2018. 14 pages, 16 figure

The Grizzly, December 7, 1993

UC Area Code to Change • Greetings, from Skye • Progress Made on Trade Agreement • UC Mourns Loss of Senior • Silenced by Propaganda • Exam Schedule • The Steps of Bomberger • Four One-Act Plays to be Performedhttps://digitalcommons.ursinus.edu/grizzlynews/1327/thumbnail.jp

The Grizzly, September 15, 2005

Gas Prices Continue to Rise • Campus and Local Community Begin Relief Efforts • Students Share Study Abroad Experiences • The Deal with the Meal Deal • One of Ursinus\u27 Own Performs Professionally • Watch Out, Employers: You Could be Next! • How Much is Too Much? Your Guide to Avoiding Portion Distortion • Excitement Building in Kaleidoscope • Beyond the Condom: Guide to Safe Sex • Opinions: New Price of Driving; Ursinus, U are Worth it • Irony of Work Study • Things They Didn\u27t Teach You at Freshman Orientation • Who Says Division III Players Can\u27t Go Pro?https://digitalcommons.ursinus.edu/grizzlynews/1692/thumbnail.jp

A future workforce of food-system analysts

A programme developed across five UK universities aims to equip graduate professionals with the skills, tools and capabilities to better understand and manage food-system complexity for food security, for the environment and for enterprise.<br/