Overtraining, or staleness, is a state when standard training regimes become extremely difficult and previous performance results are no longer possible. The American Medical Association defines staleness (slump) as a psychological or physiological state of overtraining which manifests itself as deteriorated athletic readiness. Various experts in the field define overtraining as; a special neur...

this research studied the addition of hull-less barley and wheat malt flour on the qualitative parameters of semi-voluminous bread (form, softness, porosity, upper and under surface characters, chewing capability, aroma, taste, hardness). a completely randomized design was used with 3 treatments (control, use of malted wheat, use of barley flour) in 4 replications. a sensory evaluation and the ...

Allowing read operations to return stale data with low probability has been proposed as a means to increase availability in quorums systems. Existing solutions that allow stale reads cannot tolerate an adversarial scheduler that can maliciously delay messages between servers and clients in the system and for such a scheduler existing solutions cannot enforce a bound on the staleness of data rea...

A key challenge to optimising marathon running performance is to train with adequate frequency, duration and intensity as well as get enough recovery to optimise biological adaptations underlying performance. Some marathon runners train inadequately and underperform, while others perform poorly because they become injured or develop staleness in response to overtraining. Staleness, a depression...

This paper presents an analysis technique for estimating maximum data staleness in a data warehouse that collects “near-real-time” data streams. Data is pushed to the warehouse from a variety of external sources with a wide range of inter-arrival times (e.g., once a minute to once a day). In prior work, ad hoc heuristic algorithms have been proposed for scheduling warehouse updates. In this pap...

This work addresses the instability in asynchronous data parallel optimization. It does so by introducing a novel distributed optimizer which is able to efficiently optimize a centralized model under communication constraints. The optimizer achieves this by pushing a normalized sequence of first-order gradients to a parameter server. This implies that the magnitude of a worker delta is smaller ...

Consistency in data storage systems requires any read operation to return the most recent written version of the content. In replicated storage systems, consistency comes at the price of delay due to large-scale write and read operations. Many applications with low latency requirements tolerate data staleness in order to provide high availability and low operation latency. Using age of informat...

Ad-hoc routing protocols in mobile environments face a fundamental tradeoff between delay and communication overhead. Since routes become stale as nodes move, nodes need to frequently recompute routes, leading to an increase in control traffic overhead. In dealing with this tradeoff, protocols often adopt a fixed stance: some protocols assume frequent node movement, regularly recompute routes a...

This paper investigates the effect of stale (delayed) gradient updates within the context of asynchronous stochastic gradient descent (Async-SGD) optimization for distributed training of deep neural networks. We demonstrate that our implementation of Async-SGD on a HPC cluster can achieve a tight bound on the gradient staleness while providing near-linear speedup. We propose a variant of the SG...