Predicting the Future with a Scale-Invariant Temporal Memory for the Past

In recent years, it has become clear that the brain maintains a temporal memory of recent events stretching far into the past. This letter presents a neutrally inspired algorithm to use a scale-invariant temporal representation of the past to predict a scale-invariant future. The result is a scale-invariant estimate of future events as a function of the time at which they are expected to occur. The algorithm is time-local, with credit assigned to the present event by observing how it affects the prediction of the future. To illustrate the potential utility of this approach, we test the model on simultaneous renewal processes with different timescales. The algorithm scales well on these problems despite the fact that the number of states needed to describe them as a Markov process grows exponentially.

Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes

Intermittency are a common and challenging problem in demand forecasting. We introduce a new, unified framework for building probabilistic forecasting models for intermittent demand time series, which incorporates and allows to generalize existing methods in several directions. Our framework is based on extensions of well-established model-based methods to discrete-time renewal processes, which can parsimoniously account for patterns such as aging, clustering and quasi-periodicity in demand arrivals. The connection to discrete-time renewal processes allows not only for a principled extension of Croston-type models, but additionally for a natural inclusion of neural network based models—by replacing exponential smoothing with a recurrent neural network. We also demonstrate that modeling continuous-time demand arrivals, i.e., with a temporal point process, is possible via a trivial extension of our framework. This leads to more flexible modeling in scenarios where data of individual purchase orders are directly available with granular timestamps. Complementing this theoretical advancement, we demonstrate the efficacy of our framework for forecasting practice via an extensive empirical study on standard intermittent demand data sets, in which we report predictive accuracy in a variety of scenarios.

Digital Urban Regeneration and Its Impact on Urban Renewal Processes and Development

Urban renewal is one of the main motivations of city regeneration. Urban renewal strategies mainly relate to demolishing old buildings and redeveloping new buildings instead, improving buildings and deteriorated areas, infilling new buildings within existing urban fabric, integrating new communities into old and rolling-down areas, and so on. In parallel to this situation, the modern world is in the wake of the 4th Industrial Revolution, which is characterized by a merger of physical and digital spaces and is consequently affecting cities and their quality of life. Therefore, urban regeneration must take into consideration these digital innovations and harness the emerging technological changes into new development of urban renewal processes and decision-making approaches. This editorial introduces the topic of digital urban regeneration, by discussing possible methodologies and decision-making approaches and presents the thematic issue on “embedding digital technologies into urban renewal processes and development.”

Semi-Markov Model of the System of Repairs and Preventive Replacements by Age of City Buses

The paper presents a mathematical model of the system of repairs and preventive replacements by age of city buses. The mathematical model was developed using the theory of semi-Markov processes. In the model developed, four types of city bus renewal processes are considered and three types of corrective repairs and preventive replacement. Corrective repairs are considered in two types: minimal repairs (repairs carried out by the Technical Service units) and perfect repairs (repairs carried out at the stations of the Service Station). The models of restoration systems that use semi-Markov processes in which minimal repairs, perfect repairs, and preventive replacements by age, have been examined in the literature to a limited extent. The system under consideration is analysed from the point of view of two criteria: profit per time unit and availability of city buses to carry out the assigned transport tasks. Conditions of criterion functions’ extremum (maximum) existence were formulated for the adopted assumptions. The considerations presented in the paper are illustrated by exemplary results of calculations.

Serous Glands in the Skin of the Tungara Frog, Engystomops pustulosus (Cope, 1864) (Anura, Leptodactylidae): Degenerated Secretory Units are Selectively Removed by Macrophages

The cutaneous apparatus of Engystomops pustulosus (Cope, 1864) (the Tungara frog) includes serous glands that show impressive patterns of degeneration in their syncytial secretory units, and thus represent suitable organ models to investigate the role of macrophages in renewal processes of multicellular structures. The present case report exploits this chance and highlights that: (a) degenerating glands pertain to the Ia line of the polymorphic serous gland assortment in Tungara skin; (b) resident macrophages migrate from spongy dermis and remove syncytium debris; (c) secretory syncytium collapse results from impairment of the equilibrium between serous product manufacturing/storage and merocrine release into the dermal environment; (d) Intercalated tract (or gland neck) and myoepithelium (included its ortho-sympathetic nerve supply), are neither involved in degeneration nor affected by macrophage response. According to present evidence and current literature, it is concluded that the scavenger activity of macrophages prepares secretory unit renewal, performed by stem cells from the neck. In addition, gland functional rehabilitation may rely on effectiveness of the preexisting neuromuscular apparatus to achieve secretory bulk release onto the cutaneous surface.