Three-level algorithm to control COVID-19 spreading

As the main ways of the coronavirus (COVID-19) spreading are airand physical contact, actions to mitigate and suppress its spreading must bedeveloped in order to change population behavior, providing efficient controlstrategies. Here, these actions are described as a simple heuristic frameworkto establish public policies. The possible effects of these actions are modeledas a stratified control algorithm applied to an extension of the Susceptible-Infected-Removed (SIR) compartmental model. The model dynamics is an-alyzed and validated with simulations considering parameters obtained fromepidemiological data from Brazil and Uruguay. Model simulations allow theproposition of a three-level control strategy that, as the numerical experimentsshow, presents promising results for the development of strategies to reducethe spread of COVID-19.

Exploiting Long-Term Temporal Cache Access Patterns for LRU Insertion Prioritization

In a CPU cache utilizing least recently used (LRU) replacement, cache sets manage a buffer which orders all cache lines in the set from LRU to most recently used (MRU). When a cache line is brought into cache, it is placed at the MRU and the LRU line is evicted. When re-accessed, a line is promoted to the MRU position. LRU replacement provides a simple heuristic to predict the optimal cache line to evict. However, LRU utilizes only simple, short-term access patterns. In this paper, we propose a method that uses a buffer called the history queue to record longer-term access-eviction patterns than the LRU buffer can capture. Using this information, we make a simple modification to LRU insertion policy such that recently-recalled blocks have priority over others. As lines are evicted, their addresses are recorded in a FIFO history queue. Incoming lines that have recently been evicted and now recalled (those in the history queue at recall time) remain in the MRU for an extended period of time as non-recalled lines entering the cache thereafter are placed below the MRU. We show that the proposed LRU insertion prioritization increases performance in single-threaded and multi-threaded workloads in simulations with simple adjustments to baseline LRU.

A Simple Heuristic Policy for Stochastic Distribution Inventory Systems with Fixed Shipment Costs

Inventory Management in Stochastic Distribution Systems

Follow the straggler: zebrafish use a simple heuristic for collective decision-making

Animal groups often make decisions sequentially, from the front to the back of the group. In such cases, individuals can use the choices made by earlier ranks, a form of social information, to inform their own choice. The optimal strategy for such decisions has been explored in models which differ on, for example, whether or not agents take into account the sequence of observed choices. The models demonstrate that choices made later in a sequence are more informative, but it is not clear if animals use this information or rely instead on simpler heuristics, such as quorum rules. We show that a simple rule ‘copy the last observed choice', gives similar predictions to those of optimal models for most likely sequences. We trained groups of zebrafish to choose one arm of a Y-maze and used them to demonstrate various sequences to naive fish. We show that the naive fish appear to use a simple rule, most often copying the choice of the last demonstrator, which results in near-optimal choices at a fraction of the computational cost.