Not All \u3ci\u3eAgua\u3c/i\u3e Is \u3ci\u3eCaliente\u3c/i\u3e: Proposing the \u3ci\u3eWinters\u3c/i\u3e Groundwater Test

I. Introduction II. The Winters Doctrine ... A. Winters v. United States ... B. Development of the Winters Doctrine III. The Winters Doctrine Applied to Groundwater ... A. Growing Trend of Recognizing Federal Reserved Groundwater Rights ... B. Agua Caliente ... C. Agua Caliente’s Always-Never Approach IV. Proposing the Winters Groundwater Test ... A. Primary Purpose Prong ... B. Actual Need Prong ... C. Sensitivity Prong V. Conclusio

The Global Genome Biodiversity Network (GGBN) Data Standard specification

© The Author(s) 2016. Published by Oxford University Press. Page 1 of 11 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. The article attached is the publisher's pdf

Governing Artificial Intelligence to benefit the UN Sustainable Development Goals

Big Tech's unregulated roll-out out of experimental AI poses risks to the achievement of the UN Sustainable Development Goals (SDGs), with particular vulnerability for developing countries. The goal of financial inclusion is threatened by the imperfect and ungoverned design and implementation of AI decision-making software making important financial decisions affecting customers. Automated decision-making algorithms have displayed evidence of bias, lack ethical governance, and limit transparency in the basis for their decisions, causing unfair outcomes and amplify unequal access to finance. Poverty reduction and sustainable development targets are risked by Big Tech's potential exploitation of developing countries by using AI to harvest data and profits. Stakeholder progress toward preventing financial crime and corruption is further threatened by potential misuse of AI. In the light of such risks, Big Tech's unscrupulous history means it cannot be trusted to operate without regulatory oversight. The article proposes effective pre-emptive regulatory options to minimize scenarios of AI damaging the SDGs. It explores internationally accepted principles of AI governance, and argues for their implementation as regulatory requirements governing AI developers and coders, with compliance verified through algorithmic auditing. Furthermore, it argues that AI governance frameworks must require a benefit to the SDGs. The article argues that proactively predicting such problems can enable continued AI innovation through well-designed regulations adhering to international principles. It highlights risks of unregulated AI causing harm to human interests, where a public and regulatory backlash may result in over-regulation that could damage the otherwise beneficial development of AI.Qatar National Research Fund, Grant/Award Number: NPRP 11S-1119-17001

Constrained Dynamic Tree Networks

We generalise Constrained Dynamic Pushdown Networks, introduced by Bouajjani\et al, to Constrained Dynamic Tree Networks.<br>In this model, we have trees of processes which may monitor their children.<br>We allow the processes to be defined by any computation model for which the alternating reachability problem is decidable.<br>We address the problem of symbolic reachability analysis for this model. More precisely, we consider the problem of computing an effective representation of their reachability<br>sets using finite state automata. <div>We show that backwards reachability sets starting from regular sets of configurations are always regular. </div><div>We provide an algorithm for computing backwards reachability sets using tree automata.<br><br></div

Combining Feature Selection and Integration—A Neural Model for MT Motion Selectivity

Background: The computation of pattern motion in visual area MT based on motion input from area V1 has been investigated in many experiments and models attempting to replicate the main mechanisms. Two different core conceptual approaches were developed to explain the findings. In integrationist models the key mechanism to achieve pattern selectivity is the nonlinear integration of V1 motion activity. In contrast, selectionist models focus on the motion computation at positions with 2D features. Methodology/Principal Findings: Recent experiments revealed that neither of the two concepts alone is sufficient to explain all experimental data and that most of the existing models cannot account for the complex behaviour found. MT pattern selectivity changes over time for stimuli like type II plaids from vector average to the direction computed with an intersection of constraint rule or by feature tracking. Also, the spatial arrangement of the stimulus within the receptive field of a MT cell plays a crucial role. We propose a recurrent neural model showing how feature integration and selection can be combined into one common architecture to explain these findings. The key features of the model are the computation of 1D and 2D motion in model area V1 subpopulations that are integrated in model MT cells using feedforward and feedback processing. Our results are also in line with findings concerning the solution of the aperture problem. Conclusions/Significance: We propose a new neural model for MT pattern computation and motion disambiguation that i

Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics

Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex (V1). However, the computational and ecological principles underlying contextual effects are incompletely understood. We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics, and we interpret the firing rates of V1 neurons as performing optimal recognition in this model. We show that this leads to a substantial generalization of divisive normalization, a computation that is ubiquitous in many neural areas and systems. A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence. We optimized the parameters of the model on natural image patches, and then simulated neural and perceptual responses on stimuli used in classical experiments. The model reproduces some rich and complex response patterns observed in V1, such as the contrast dependence, orientation tuning and spatial asymmetry of surround suppression, while also allowing for surround facilitation under conditions of weak stimulation. It also mimics the perceptual salience produced by simple displays, and leads to readily testable predictions. Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs, and lends statistical support to the theory that V1 computes visual salience

Not All \u3ci\u3eAgua\u3c/i\u3e Is \u3ci\u3eCaliente\u3c/i\u3e: Proposing the \u3ci\u3eWinters\u3c/i\u3e Groundwater Test

I. Introduction II. The Winters Doctrine ... A. Winters v. United States ... B. Development of the Winters Doctrine III. The Winters Doctrine Applied to Groundwater ... A. Growing Trend of Recognizing Federal Reserved Groundwater Rights ... B. Agua Caliente ... C. Agua Caliente’s Always-Never Approach IV. Proposing the Winters Groundwater Test ... A. Primary Purpose Prong ... B. Actual Need Prong ... C. Sensitivity Prong V. Conclusio