Brain substrates for automatic retrieval of value memory in the primate basal ganglia

Our behavior is often carried out automatically. Automatic behavior can be guided by past experiences, such as learned values associated with objects. Passive-viewing and free-viewing tasks with no immediate outcomes provide a testable condition in which monkeys and humans automatically retrieve value memories and perform habitual searching. Interestingly, in these tasks, caudal regions of the basal ganglia structures are involved in automatic retrieval of learned object values and habitual gaze. In contrast, rostral regions do not participate in these activities but instead monitor the changes in outcomes. These findings indicate that automatic behaviors based on the value memories are processed selectively by the caudal regions of the primate basal ganglia system. Understanding the distinct roles of the caudal basal ganglia may provide insight into finding selective causes of behavioral disorders in basal ganglia disease.

Automatic Behavior and Posture Detection of Sows in Loose Farrowing Pens Based on 2D-Video Images

The monitoring of farm animals and the automatic recognition of deviant behavior have recently become increasingly important in farm animal science research and in practical agriculture. The aim of this study was to develop an approach to automatically predict behavior and posture of sows by using a 2D image-based deep neural network (DNN) for the detection and localization of relevant sow and pen features, followed by a hierarchical conditional statement based on human expert knowledge for behavior/posture classification. The automatic detection of sow body parts and pen equipment was trained using an object detection algorithm (YOLO V3). The algorithm achieved an Average Precision (AP) of 0.97 (straw rack), 0.97 (head), 0.95 (feeding trough), 0.86 (jute bag), 0.78 (tail), 0.75 (legs) and 0.66 (teats). The conditional statement, which classifies and automatically generates a posture or behavior of the sow under consideration of context, temporal and geometric values of the detected features, classified 59.6% of the postures (lying lateral, lying ventral, standing, sitting) and behaviors (interaction with pen equipment) correctly. In conclusion, the results indicate the potential of DNN toward automatic behavior classification from 2D videos as potential basis for an automatic farrowing monitoring system.

Automatic Monitoring of Relevant Behaviors for Crustacean Production in Aquaculture: A Review

Crustacean farming is a fast-growing sector and has contributed to improving incomes. Many studies have focused on how to improve crustacean production. Information about crustacean behavior is important in this respect. Manual methods of detecting crustacean behavior are usually infectible, time-consuming, and imprecise. Therefore, automatic growth situation monitoring according to changes in behavior has gained more attention, including acoustic technology, machine vision, and sensors. This article reviews the development of these automatic behavior monitoring methods over the past three decades and summarizes their domains of application, as well as their advantages and disadvantages. Furthermore, the challenges of individual sensitivity and aquaculture environment for future research on the behavior of crustaceans are also highlighted. Studies show that feeding behavior, movement rhythms, and reproduction behavior are the three most important behaviors of crustaceans, and the applications of information technology such as advanced machine vision technology have great significance to accelerate the development of new means and techniques for more effective automatic monitoring. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Our purpose is to provide researchers and practitioners with a better understanding of the state of the art of automatic monitoring of crustacean behaviors, pursuant of supporting the implementation of smart crustacean farming applications.

AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System

Recently, deep learning models have been widely explored in recommender systems. Though having achieved remarkable success, the design of task-aware recommendation models usually requires manual feature engineering and architecture engineering from domain experts. To relieve those efforts, we explore the potential of neural architecture search (NAS) and introduce AMEIR for Automatic behavior Modeling, interaction Exploration and multi-layer perceptron (MLP) Investigation in the Recommender system. Specifically, AMEIR divides the complete recommendation models into three stages of behavior modeling, interaction exploration, MLP aggregation, and introduces a novel search space containing three tailored subspaces that cover most of the existing methods and thus allow for searching better models. To find the ideal architecture efficiently and effectively, AMEIR realizes the one-shot random search in recommendation progressively on the three stages and assembles the search results as the final outcome. The experiment over various scenarios reveals that AMEIR outperforms competitive baselines of elaborate manual design and leading algorithmic complex NAS methods with lower model complexity and comparable time cost, indicating efficacy, efficiency, and robustness of the proposed method.

No Pyro, No Party: Social factors, deliberate choices, and shared fan culture determine the use of illegal fireworks in a soccer stadium

Lighting illegal fireworks inside soccer stadiums is a worldwide and persistent problem. Despite rules and regulations as well as rigorous enforcement, the use of illegal fireworks in football stadium is increasing rather than decreasing. Little is yet known about the causes and predictors of this behavior, preventing the development of effective interventions or communication strategies. We therefore conducted a qualitative study, using semistructured interviews with both supporters of a large Dutch soccer club who participated in lighting fireworks, and with professionals who dealt with illegal fireworks in daily practice. Semi-structures interviews were based on hooliganism literature as well as the COM-B model. We concluded that hooliganism and lighting illegal are distinctly different phenomena, although they share some underlying constructs. From a behavioral perspective, using the COM-B model as a framework, reflective motivation was identified as the strongest facilitator of lighting fireworks, which appeared to be an important part of supporter life and shared culture. Quick interventions that target automatic behavior, such as nudges, will probably thus not be successful in changing this behavior. Supporters suggest compromise between supporters and professionals as preferred future direction. Reported feelings of stigmatization by and feeling unappreciated by professionals, could interfere with successful implementation of this direction. Professionals however contradict negative to have judgements of supporters. Building a bridge between supporters and professionals should be a first step towards a solution.

An Infrared Touch System for Automatic Behavior Monitoring

Key requirements of successful animal behavior research in the laboratory are robustness, objectivity, and high throughput, which apply to both the recording and analysis of behavior. Many automatic methods of monitoring animal behavior meet these requirements. However, they usually depend on high-performing hardware and sophisticated software, which may be expensive. Here, we describe an automatic infrared behavior-monitor (AIBM) system based on an infrared touchscreen frame. Using this, animal positions can be recorded and used for further behavioral analysis by any PC supporting touch events. This system detects animal behavior in real time and gives closed-loop feedback using relatively low computing resources and simple algorithms. The AIBM system automatically records and analyzes multiple types of animal behavior in a highly efficient, unbiased, and low-cost manner.

A Framework for Automatic Behavior Generation in Multi-Function Swarms

Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of Radio Frequency (RF) emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire enables the swarm to online transition between behaviors featuring different trade-offs of applications depending on the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study investigates the importance of individual sensor or controller inputs. This is done through ablation, where individual inputs are disabled and their impact on the performance of the swarm controllers is assessed and analyzed.