Grid-based storage systems with uniformly shaped loads (e.g., containers, pallets, totes) are commonplace in logistics, industrial, and transportation domains. A key performance metric for such systems is the maximization of space utilization, which requires some loads to be placed behind or below others, preventing direct access to them. Consequently, dense storage settings bring up the challenge of determining how to place loads while minimizing costly rearrangement efforts necessary during retrieval. This paper considers the setting involving an inbound phase, during which loads arrive, followed by an outbound phase, during which loads depart. The setting is prevalent in distribution centers, automated parking garages, and container ports. In both phases, minimizing the number of rearrangement actions results in more optimal (e.g., fast, energy-efficient, etc.) operations. In contrast to previous work focusing on stack-based systems, this effort examines the case where loads can be freely moved along the grid, e.g., by a mobile robot, expanding the range of possible motions. We establish that for a range of scenarios, such as having limited prior knowledge of the loads' arrival sequences or grids with a narrow opening, a (best possible) rearrangement-free solution always exists, including when the loads fill the grid to its capacity. In particular, when the sequences are fully known, we establish an intriguing characterization showing that rearrangement can always be avoided if and only if the open side of the grid (used to access the storage) is at least 3 cells wide. We further discuss useful practical implications of our solutions.

Based on anatomical and physiological data, we have developed a computer simulation of piri(cid:173) form (olfactory) cortex which is capable of reproducing spatial and temporal patterns of actual cortical activity under a variety of conditions. Using a simple Hebb-type learning rule in conjunc(cid:173) tion with the cortical dynamics which emerge from the anatomical and physiological organiza(cid:173) tion of the model, the simulations are capable of establishing cortical representations for differ(cid:173) ent input patterns. The basis of these representations lies in the interaction of sparsely distribut(cid:173) ed, highly divergent/convergent interconnections between modeled neurons. We have shown that different representations can be stored with minimal interference. Further, we have demonstrated that the degree of overlap of cortical representations for different stimuli can also be modulated.

In an era of AI adoption in industry, stark contrasts in our thinking begin to show about how we leverage computing, data, and inference. This article considers graph technologies in the context of business: enhancing human thinking and enabling data exploration, especially among teams of domain experts augmented by AI applications. Specifically, let's develop and deconstruct the notion of graph thinking. Suppose you have an errand to run, such as shopping for groceries: "Remember to buy eggs and more rice on the way home from work today." The needs are clear, and your approach is well understood. People use phrases such as "It's not rocket science" to describe the level of competency required here. Or perhaps still count on your fingers? In any case, let's call this a "Simple" context.

What happens when a serial entrepreneur with a Master's degree in robotics goes out looking for a problem to solve? For InVia Robotics founder and CEO Lior Elazary, it leads to a new army of self-learning, self-guided warehouse robots. Elazary had already helped found web hosting company HostPro Inc., and Edgecast Networks Inc., a content delivery network. Then he set out to apply the lessons from his graduate study and decided at first to focus on home robotics. "I thought there might be an opportunity in in-home elderly care," he said.

An experimental system that uses LISP to make a conceptual dictionary is described. The dictionary associates with each English word the syntactic information, definitional material, and references to the contexts in which it has been used to define other words. Such relations as class inclusion, possession, and active or passive actions are used as definitional material. The resulting structure serves as a powerful vehicle for research on the logic of question answering. Examples of methods of inputting information and answering simple English questions are given.