Pressure dependent friction on granular slopes close to avalanche

We investigate the sliding of objects on an inclined granular surface close to the avalanche threshold. Our experiments show that the stability is driven by the surface deformations. Heavy objects generate footprint-like deformations which stabilize the objects on the slopes. Light objects do not disturb the sandy surfaces and are also stable. For intermediate weights, the deformations of the surface destabilize the objects and generate sliding. A characteristic pressure for which the solid friction is minimal is evidenced. Applications to the locomotion of devices and animals on sandy slopes as a function of their mass are proposed

A Finite Presentation of Graphs of Treewidth at Most Three

We provide a finite equational presentation of graphs of treewidth at most three, solving an instance of an open problem by Courcelle and Engelfriet. We use a syntax generalising series-parallel expressions, denoting graphs with a small interface. We introduce appropriate notions of connectivity for such graphs (components, cutvertices, separation pairs). We use those concepts to analyse the structure of graphs of treewidth at most three, showing how they can be decomposed recursively, first canonically into connected parallel components, and then non-deterministically. The main difficulty consists in showing that all non-deterministic choices can be related using only finitely many equational axioms

Learning to Speak and Act in a Fantasy Text Adventure Game

We introduce a large scale crowdsourced text adventure game as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dialogue with other agents. Models and humans can both act as characters within the game. We describe the results of training state-of-the-art generative and retrieval models in this setting. We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions. In particular, we show that grounding on the details of the local environment, including location descriptions, and the objects (and their affordances) and characters (and their previous actions) present within it allows better predictions of agent behavior and dialogue. We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully

Cluster-based Aggregate Forecasting for Residential Electricity Demand using Smart Meter Data

While electricity demand forecasting literature has focused on large, industrial, and national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Since electricity consumption behavior may vary between households, we first build a feature universe, and then apply Correlation-based Feature Selection to select features relevant to each household. Additionally, smart meter data can be used to obtain aggregate forecasts with higher accuracy using the so-called Cluster-based Aggregate Forecasting (CBAF) strategy, i.e., by first clustering the households, forecasting the clusters' energy consumption separately, and finally aggregating the forecasts. We found that the improvement provided by CBAF depends not only on the number of clusters, but also more importantly on the size of the customer base

Individual, Aggregate, and Cluster-based Aggregate Forecasting of Residential Demand

Pervasive installation of smart meters opens new possibilities for advanced analytics of electricity consumption at the level of the individual household. One of the important tasks in various Smart Grid applications, from demand-response to emergency management, is the short-term electricity load forecasting at different scales, from an individual customer to a whole portfolio of customers. In this work we perform a quantitative evaluation of different machine learning methods for short-term (1 hour ahead and 24 hour ahead) electricity load forecasting at the individual and aggregate level. We discuss the relevant features that best help to improve forecasting accuracy, as well as the effectiveness of exploiting correlations between different customers. Furthermore, for aggregate forecast, we explore different clustering techniques that can be used to segment the whole customer portfolio and show that forecasting each cluster separately and then aggregating the forecast produces better accuracy compared to the traditional approach (which forecast directly the aggregate load). We also found that the improvement provided by this strategy changes as a function of total customers