A fluid EOQ model of perishable items with intermittent high and low demand rates

We consider a stochastic fluid EOQ-type model with demand rates that operate in a two-state random environment. This environment alternates between exponentially distributed periods of high demand and generally distributed periods of low demand. The inventory level starts at some level q, and decreases linearly at rate a during the periods of high demand, and at rate b <a at periods of low demand. The inventory level is refilled to level q when level 0 is hit or when an expiration date is reached, whichever comes first. We determine the steady-state distribution of the inventory level, as well as other quantities of interest like the distribution of the time between successive refills. Finally, for a given cost/revenue structure, we determine the long-run average profit, and we consider the problem of choosing q such that the profit is optimized

A compound Poisson EOQ model for perishable items with intermittent high and low demand periods

We consider a stochastic EOQ-type model, with demand operating in a two-state random environment. This environment alternates between exponentially distributed periods of high demand and generally distributed periods of low demand. The inventory level starts at some level q, and decreases according to different compound Poisson processes during the periods of high demand and of low demand. The inventory level is refilled to level q when level 0 is hit or when an expiration date is reached, whichever comes first. We determine various performance measures of interest, like the distribution of the time until refill, the expected amount of discarded material and of material held (inventory), and the expected values of various kinds of shortages. For a given cost/revenue structure, we can thus determine the long-run average profit

ZERO-SHOT SUPERPIXEL LEARNING FOR NETWORK CONFIGURATION OPTIMIZATION

Proposed herein are novel techniques that utilize zero-shot learning and superpixels in order to efficiently predict an optimized configuration profile for a given network. The techniques may be able to not only efficiently handle large amounts of data for the prediction, but also account for any dynamic network changes without the need of manual intervention. The techniques may perform well with dynamic unseen and/or out-of-training sample changes. Such techniques may be useful for efficiently creating network configurations that can improve network performance even if the network experiences structural changes

Rapid Transfer of Abstract Rules to Novel Contexts in Human Lateral Prefrontal Cortex

Flexible, adaptive behavior is thought to rely on abstract rule representations within lateral prefrontal cortex (LPFC), yet it remains unclear how these representations provide such flexibility. We recently demonstrated that humans can learn complex novel tasks in seconds. Here we hypothesized that this impressive mental flexibility may be possible due to rapid transfer of practiced rule representations within LPFC to novel task contexts. We tested this hypothesis using functional MRI and multivariate pattern analysis, classifying LPFC activity patterns across 64 tasks. Classifiers trained to identify abstract rules based on practiced task activity patterns successfully generalized to novel tasks. This suggests humans can transfer practiced rule representations within LPFC to rapidly learn new tasks, facilitating cognitive performance in novel circumstances

Higher-level goals in the processing of human action events

The concept of a goal critically separates dynamic events involving humans from other events. Human behaviours are motivated by goals, which are known to the actor but typically inferred on the part of the observer. Goals can be hierarchical in nature, such that a collection of sub-goals (e.g., getting a mug, boiling water) can be nested under a higher-level goal (e.g., making tea), which can be further nested under an even higher-level goal (e.g., making breakfast). The diverse set of talks in this symposia all highlight the foundational role that goals play in action processing and representation. Eisenberg et al. detail how online prediction of others’ goals shapes observers’ sampling of information during action observation. Howard and Woodward provide evidence that children’s memory for non-human events can be facilitated by priming children with their own goal-directed actions. Loucks and Meltzoff highlight the importance of goal structure in children’s memory for complex action sequences. Finally, Cooper presents a computational model to explain the emergence of goal-directed action hierarchies