Social decisions and fairness change when people’s interests are represented by autonomous agents

There has been growing interest on agents that represent people’s interests or act on their behalf such as automated negotiators, self-driving cars, or drones. Even though people will interact often with others via these agent representatives, little is known about whether people’s behavior changes when acting through these agents, when compared to direct interaction with others. Here we show that people’s decisions will change in important ways because of these agents; specifically, we showed that interacting via agents is likely to lead people to behave more fairly, when compared to direct interaction with others. We argue this occurs because programming an agent leads people to adopt a broader perspective, consider the other side’s position, and rely on social norms—such as fairness—to guide their decision making. To support this argument, we present four experiments: in Experiment 1 we show that people made fairer offers in the ultimatum and impunity games when interacting via agent representatives, when compared to direct interaction; in Experiment 2, participants were less likely to accept unfair offers in these games when agent representatives were involved; in Experiment 3, we show that the act of thinking about the decisions ahead of time—i.e., under the so-called “strategy method”—can also lead to increased fairness, even when no agents are involved; and, finally, in Experiment 4 we show that participants were less likely to reach an agreement with unfair counterparts in a negotiation setting. We discuss theoretical implications for our understanding of the nature of people’s social behavior with agent representatives, as well as practical implications for the design of agents that have the potential to increase fairness in society

Human cooperation when acting through autonomous machines

Recent times have seen an emergence of intelligent machines that act autonomously on our behalf, such as autonomous vehicles. Despite promises of increased efficiency, it is not clear whether this paradigm shift will change how we decide when our self-interest (e.g., comfort) is pitted against the collective interest (e.g., environment). Here we show that acting through machines changes the way people solve these social dilemmas and we present experimental evidence showing that participants program their autonomous vehicles to act more cooperatively than if they were driving themselves. We show that this happens because programming causes selfish short-term rewards to become less salient, leading to considerations of broader societal goals. We also show that the programmed behavior is influenced by past experience. Finally, we report evidence that the effect generalizes beyond the domain of autonomous vehicles. We discuss implications for designing autonomous machines that contribute to a more cooperative society

STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition

We study the problem of human action recognition using motion capture (MoCap) sequences. Unlike existing techniques that take multiple manual steps to derive standardized skeleton representations as model input, we propose a novel Spatial-Temporal Mesh Transformer (STMT) to directly model the mesh sequences. The model uses a hierarchical transformer with intra-frame off-set attention and inter-frame self-attention. The attention mechanism allows the model to freely attend between any two vertex patches to learn non-local relationships in the spatial-temporal domain. Masked vertex modeling and future frame prediction are used as two self-supervised tasks to fully activate the bi-directional and auto-regressive attention in our hierarchical transformer. The proposed method achieves state-of-the-art performance compared to skeleton-based and point-cloud-based models on common MoCap benchmarks. Code is available at https://github.com/zgzxy001/STMT.Comment: CVPR 202

Digital sensor based on timer for embedded systems / Sensor digital baseado em temporizador para sistemas embarcados

Embedded instrumentation needs analog sensors relating to a physical measurement that must be converted to a digital signal (ADC). Most microprocessors feature built-in AD converters, but there are some that do not. In this work, we analyze a digital sensor based on a timer circuit. The timer circuit functions as a signal conditioner using resistive and / or capacitive elements, where an element becomes the sensor, causing a pulse proportional to a physical quantity to be measured. The sampling signal is sent, initiating the process. In order to check the performance of the circuit, some experiments were performed and the results showed that it is a very simple solution for an unavailability of an AD-Converter. 

Estudo da poluição pontual e difusa na bacia de contribuição do reservatório da usina hidrelétrica de Funil utilizando modelagem espacialmente distribuída em Sistema de Informação Geográfica

RESUMO Este estudo avaliou o potencial poluidor da bacia de contribuição do reservatório de Funil (BCRF), localizado na bacia hidrográfica do rio Paraíba do Sul, considerando a geração da carga de nutrientes, nitrogênio (N) e fósforo (P), por fontes pontuais e difusas, a partir de uma modelagem distribuída utilizando Sistema de Informação Geográfica (SIG). As cargas e concentrações médias anuais desses nutrientes foram geradas a partir do acoplamento de equações empíricas, em SIG, considerando informações espaciais de uso e cobertura do solo, população residente na bacia e vazão média anual de longo período, obtida por equações do tipo chuva vazão. Os resultados indicaram que 80% da carga total de nitrogênio foram provenientes de fontes pontuais e 20% de fontes difusas, enquanto que, da carga total de fósforo, 89,1% foram originadas de fontes pontuais e 10,9% de fontes difusas. As concentrações de nutrientes estimadas pelo modelo empírico apresentaram bons ajustes em relação aos valores observados de fósforo e de nitrogênio no rio Paraíba do Sul, com R²=0,96 (p<0,01) e R²=0,70 (p<0,01), respectivamente. Dessa forma, o modelo foi capaz de detectar, de forma significativa, a tendência das variações nas concentrações de nutrientes ao longo de diferentes trechos da BCRF