A strategic niche management approach for shaping bio-based economy in Europe

The goal of this paper is to investigate the transition towards a bio-based economy as part of a broader sustainable transition in Europe. To analyse the challenges and opportunities associated with the bio-based economy, we applied the Strategic Niche Management approach to investigate the drivers that boost the emergence of the bio-based economy, the factors hindering it, as well as institutional changes which are at the base of the socio-technological transition. Although considered as just one piece of the sustainability puzzle, the bio-based economy behaves as a socio-technical system on its own, providing valuable hints on systemic transitions

Poverty Law 101: The Law and History of the U.S. Welfare State

Poverty law will remain marginalized so long as we confine it to a population that we and our students understand as marginal. Tani discusses Professor Wax’s characterization of the “old welfare law framework,” as well as her account of what happened to it, and would not advocate a return to a court-centered, advocacy-oriented approach

An Administrative Right To Be Free from Sexual Violence? Title IX Enforcement in Historical and Institutional Perspective

One of the most controversial administrative actions in recent years is the U.S. Department of Education’s campaign against sexual assault on college campuses. Using its authority under Title IX of the Education Amendments of 1972 (mandating nondiscrimination on the basis of sex in all educational programs and activities receiving federal funds), the Department’s Office for Civil Rights (OCR) has launched an enforcement effort that critics denounce as aggressive, manipulative, and corrosive of individual liberties. Missing from the commentary is a historically informed understanding of why this administrative campaign unfolded as it did. This Article offers crucial context by reminding readers that freedom from sexual violence was once celebrated as a national civil right—upon the enactment of the Violence Against Women Act of 1994—but then lost that status in a 5–4 decision by the U.S. Supreme Court. OCR’s recent campaign reflects a legal and political landscape in which at least some potential victims of sexual violence had come to feel rightfully connected to the institutions of the federal government, and then became righteously outraged by the endurance of such violence in their communities. OCR’s campaign also reflects the unique role of federal administrative agencies in this landscape. Thanks to the power of the purse and the conditions that Congress has attached to funding streams, agencies enjoy a powerful form of jurisdiction over particular spaces and institutions. Attempts to harness this jurisdiction in service of aspirational rights claims should not surprise us; indeed, we should expect such efforts to continue. Building on this insight, the Article concludes with a research agenda for other scholars seeking to understand and evaluate OCR’s handiwork

A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition

This study introduces PV-RNN, a novel variational RNN inspired by the predictive-coding ideas. The model learns to extract the probabilistic structures hidden in fluctuating temporal patterns by dynamically changing the stochasticity of its latent states. Its architecture attempts to address two major concerns of variational Bayes RNNs: how can latent variables learn meaningful representations and how can the inference model transfer future observations to the latent variables. PV-RNN does both by introducing adaptive vectors mirroring the training data, whose values can then be adapted differently during evaluation. Moreover, prediction errors during backpropagation, rather than external inputs during the forward computation, are used to convey information to the network about the external data. For testing, we introduce error regression for predicting unseen sequences as inspired by predictive coding that leverages those mechanisms. The model introduces a weighting parameter, the meta-prior, to balance the optimization pressure placed on two terms of a lower bound on the marginal likelihood of the sequential data. We test the model on two datasets with probabilistic structures and show that with high values of the meta-prior the network develops deterministic chaos through which the data's randomness is imitated. For low values, the model behaves as a random process. The network performs best on intermediate values, and is able to capture the latent probabilistic structure with good generalization. Analyzing the meta-prior's impact on the network allows to precisely study the theoretical value and practical benefits of incorporating stochastic dynamics in our model. We demonstrate better prediction performance on a robot imitation task with our model using error regression compared to a standard variational Bayes model lacking such a procedure.Comment: The paper is accepted in Neural Computatio

Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network

It is crucial to ask how agents can achieve goals by generating action plans using only partial models of the world acquired through habituated sensory-motor experiences. Although many existing robotics studies use a forward model framework, there are generalization issues with high degrees of freedom. The current study shows that the predictive coding (PC) and active inference (AIF) frameworks, which employ a generative model, can develop better generalization by learning a prior distribution in a low dimensional latent state space representing probabilistic structures extracted from well habituated sensory-motor trajectories. In our proposed model, learning is carried out by inferring optimal latent variables as well as synaptic weights for maximizing the evidence lower bound, while goal-directed planning is accomplished by inferring latent variables for maximizing the estimated lower bound. Our proposed model was evaluated with both simple and complex robotic tasks in simulation, which demonstrated sufficient generalization in learning with limited training data by setting an intermediate value for a regularization coefficient. Furthermore, comparative simulation results show that the proposed model outperforms a conventional forward model in goal-directed planning, due to the learned prior confining the search of motor plans within the range of habituated trajectories.Comment: 30 pages, 19 figure