Cultural representations of mental illness in contemporary Japan

This paper presents the results of a research project aimed at studying the cultural representations of mental illness and related interventions models in contemporary Japan, and providing the basis for a comparison between Japanese and Italian mental health cultures. The research methodology is based on interviews with scholars and professionals from multiple disciplinary areas and fields of practice, in order to analyze the interactions between medical, social sciences’ and humanities’ discourse on mental illness. The results highlight the significance of home custody within the modernization of the country, between Edo and Meiji periods; the cultural frameworks of contemporary psychiatry’s action; what anti-psychiatry and the ‘critical’ reflection on mental illness represented within the academic debate; the new demands and potentialities connected to the spread of psychology within the mental health sector; remarkably new experiences of social integration with the contribution of arts

Water and Nutrition: Harmonizing actions for the United Nations Decade of Action on Nutrition and the United Nations Water Action Decade

Progress for both SDG 2 and SDG 6 has been unsatisfactory, with several indicators worsening over time, including an increase in the number of undernourished, overweight and obese people, as well as rapid increases in the number of people at risk of severe water shortages. This lack of progress is exacerbated by climate change and growing regional and global inequities in food and water security, including access to good quality diets, leading to increased violation of the human rights to water and food. Reversing these trends will require a much greater effort on the part of water, food security, and nutrition communities, including stronger performances by the United Nations Decade of Action on Nutrition and the United Nations International Decade for Action on Water for Sustainable Development. To date, increased collaboration by these two landmark initiatives is lacking, as neither work program has systematically explored linkages or possibilities for joint interventions. Collaboration is especially imperative given the fundamental challenges that characterize the promotion of one priority over another. Without coordination across the water, food security, and nutrition communities, actions toward achieving SDG2 on zero hunger may contribute to further degradation of the world’s water resources and as such, further derail achievement of the UN Decade of Action on Water and SDG 6 on water and sanitation. Conversely, actions to enhance SDG 6 may well reduce progress on the UN Decade of Action on Nutrition and SDG 2. This paper reviews these challenges as part of a broader analysis of the complex web of pathways that link water, food security and nutrition outcomes. Climate change and the growing demand for water resources are also considered, given their central role in shaping future water and nutrition security. The main conclusions are presented as three recommendations focused on potential avenues to deal with the complexity of the water-nutrition nexus, and to optimize outcomes

Combining the conservation of biodiversity with the provision of ecosystem services in urban green infrastructure planning. Critical features arising from a case study in the metropolitan area of Rome

A large number of green infrastructure (GI) projects have recently been proposed, planned and implemented in European cities following the adoption of the GI strategy by the EU Commission in 2013. Although this policy tool is closely related to biodiversity conservation targets, some doubts have arisen as regards the ability of current urban GI to provide beneficial effects not only for human societies but also for the ecological systems that host them. The aim of this work is to review the features that should be considered critical when searching for solutions that simultaneously support biodiversity and guarantee the provision of ecosystem services (ES) in urban areas. Starting from a case study in the metropolitan area of Rome, we highlight the role of urban trees and forests as proxies for overall biodiversity and as main ecosystem service providers. We look beyond the individual functional features of plant species and vegetation communities to promote the biogeographic representativity, ecological coherence and landscape connectivity of new or restored GI elements

Im2Flow: Motion Hallucination from Static Images for Action Recognition

Existing methods to recognize actions in static images take the images at their face value, learning the appearances---objects, scenes, and body poses---that distinguish each action class. However, such models are deprived of the rich dynamic structure and motions that also define human activity. We propose an approach that hallucinates the unobserved future motion implied by a single snapshot to help static-image action recognition. The key idea is to learn a prior over short-term dynamics from thousands of unlabeled videos, infer the anticipated optical flow on novel static images, and then train discriminative models that exploit both streams of information. Our main contributions are twofold. First, we devise an encoder-decoder convolutional neural network and a novel optical flow encoding that can translate a static image into an accurate flow map. Second, we show the power of hallucinated flow for recognition, successfully transferring the learned motion into a standard two-stream network for activity recognition. On seven datasets, we demonstrate the power of the approach. It not only achieves state-of-the-art accuracy for dense optical flow prediction, but also consistently enhances recognition of actions and dynamic scenes.Comment: Published in CVPR 2018, project page: http://vision.cs.utexas.edu/projects/im2flow

Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding

Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly summarized by a common abstractive sentence. This paper provides a novel approach to this task. We first introduce a neural contextual utterance encoder featuring three types of self-attention mechanisms. We then train it using the siamese and triplet energy-based meta-architectures. Experiments on the AMI corpus show that our system outperforms multiple energy-based and non-energy based baselines from the state-of-the-art. Code and data are publicly available.Comment: Update baseline

Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition

We present a unified framework for understanding human social behaviors in raw image sequences. Our model jointly detects multiple individuals, infers their social actions, and estimates the collective actions with a single feed-forward pass through a neural network. We propose a single architecture that does not rely on external detection algorithms but rather is trained end-to-end to generate dense proposal maps that are refined via a novel inference scheme. The temporal consistency is handled via a person-level matching Recurrent Neural Network. The complete model takes as input a sequence of frames and outputs detections along with the estimates of individual actions and collective activities. We demonstrate state-of-the-art performance of our algorithm on multiple publicly available benchmarks